diff --git a/multi_participants 2/.idea/inspectionProfiles/profiles_settings.xml b/multi_participants 2/.idea/inspectionProfiles/profiles_settings.xml new file mode 100644 index 0000000..105ce2d --- /dev/null +++ b/multi_participants 2/.idea/inspectionProfiles/profiles_settings.xml @@ -0,0 +1,6 @@ + + + + \ No newline at end of file diff --git a/multi_participants 2/.idea/misc.xml b/multi_participants 2/.idea/misc.xml new file mode 100644 index 0000000..ee3f7c7 --- /dev/null +++ b/multi_participants 2/.idea/misc.xml @@ -0,0 +1,7 @@ + + + + + + \ No newline at end of file diff --git a/multi_participants 2/.idea/modules.xml b/multi_participants 2/.idea/modules.xml new file mode 100644 index 0000000..43176e6 --- /dev/null +++ b/multi_participants 2/.idea/modules.xml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/multi_participants 2/.idea/multi_participants.iml b/multi_participants 2/.idea/multi_participants.iml new file mode 100644 index 0000000..5ce20fc --- /dev/null +++ b/multi_participants 2/.idea/multi_participants.iml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/multi_participants 2/.idea/workspace.xml b/multi_participants 2/.idea/workspace.xml new file mode 100644 index 0000000..ea2ce9b --- /dev/null +++ b/multi_participants 2/.idea/workspace.xml @@ -0,0 +1,52 @@ + + + + + + + + + + + + + + + + + + + + + + + + 1611897976422 + + + + + + + + + + + + \ No newline at end of file diff --git a/multi_participants 2/case_3/case_3_accuracy.py b/multi_participants 2/case_3/case_3_accuracy.py new file mode 100644 index 0000000..ba58406 --- /dev/null +++ b/multi_participants 2/case_3/case_3_accuracy.py @@ -0,0 +1,65 @@ +import os +from mind_reading_package import mind_reading as mr +import pandas as pd + +# list all folders' name +participants = os.listdir('path') + +# remove the 'cha' folder we don't need +participants = participants.remove('cha') + +# create the initial dataframe +df = pd.DataFrame(index = ['SVC', 'DTC', 'NB', 'NN']) + +for participant in participants: + # iterate all the folders + + for file in os.listdir(participant): + # iterate all files in every folder, find out the one end with 'Cong.csv' and 'Incong.csv' as input data + + if file.endswith('Cong.csv'): file1 = f"{participant}/{file}" + if file.endswith('Incong.csv'): file2 = f"{participant}/{file}" + + # load in cong and incong data for them + df1 = mr.load_data(file1) + df2 = mr.load_data(file2) + + # concatenate such data + data = mr.concatenate_data(df1, df2) + + # find trials to later separate + trials_index = mr.find_trials(data) + + # separate trials + trials = mr.separate_trials(data, trials_index) + + # create the label column + labels = mr.create_binary_labels(data) + + # Go through each trial, reset the columns, we split from 100-300ms ((308th sample to 513th sample)) + pro_trials = mr.process_trials(trials) + + # Find the mean across channels + avg_trials = mr.average_trials(pro_trials) + + # concatenates the average trials dataframe with labels + ml_df = mr.create_ml_df(avg_trials, labels) + + # train models + X_train, X_test, y_train, y_test = mr.prepare_ml_df(ml_df) + + acc_svc, precision_svc = mr.train_svc(X_train, X_test, y_train, y_test) + + acc_dtc, precision_dtc = mr.train_dtc(X_train, X_test, y_train, y_test) + + acc_nb, precision_nb = mr.train_nb(X_train, X_test, y_train, y_test) + + acc_nn, precision_nn = mr.train_nn(64, X_train, X_test, y_train, y_test) + + # add every participant's accuracy together + acc_list = [f"{acc_svc:.2f}", f"{acc_dtc:.2f}", f"{acc_nb:.2f}", f"{acc_nn:.2f}"] + + df = mr.res_df(df, acc_list, participant) + +# generate result .csv file +df.to_csv('case_3_accuracy.csv') \ No newline at end of file diff --git a/multi_participants 2/case_3/case_3_precision.py b/multi_participants 2/case_3/case_3_precision.py new file mode 100644 index 0000000..78103db --- /dev/null +++ b/multi_participants 2/case_3/case_3_precision.py @@ -0,0 +1,66 @@ +import os +from mind_reading_package import mind_reading as mr +import pandas as pd + +# list all folders' name +participants = os.listdir('path') + +# remove the 'cha' folder we don't need +participants = participants.remove('cha') + +# create the initial dataframe +df = pd.DataFrame(index = ['SVC', 'DTC', 'NB', 'NN']) + +for participant in participants: + # iterate all the folders + + for file in os.listdir(participant): + # iterate all files in every folder, find out the one end with 'Cong.csv' and 'Incong.csv' as input data + + if file.endswith('Cong.csv'): file1 = f"{participant}/{file}" + if file.endswith('Incong.csv'): file2 = f"{participant}/{file}" + + # load in cong and incong data for them + df1 = mr.load_data(file1) + df2 = mr.load_data(file2) + + # concatenate such data + data = mr.concatenate_data(df1, df2) + + # find trials to later separate + trials_index = mr.find_trials(data) + + # separate trials + trials = mr.separate_trials(data, trials_index) + + # create the label column + labels = mr.create_binary_labels(data) + + # Go through each trial, reset the columns, we split from 100-300ms ((308th sample to 513th sample)) + pro_trials = mr.process_trials(trials) + + # Find the mean across channels + avg_trials = mr.average_trials(pro_trials) + + # concatenates the average trials dataframe with labels + ml_df = mr.create_ml_df(avg_trials, labels) + + # train models + X_train, X_test, y_train, y_test = mr.prepare_ml_df(ml_df) + + acc_svc, precision_svc = mr.train_svc(X_train, X_test, y_train, y_test) + + acc_dtc, precision_dtc = mr.train_dtc(X_train, X_test, y_train, y_test) + + acc_nb, precision_nb = mr.train_nb(X_train, X_test, y_train, y_test) + + acc_nn, precision_nn = mr.train_nn(64, X_train, X_test, y_train, y_test) + + # add every participant's precision together + precision_list = [f"{precision_svc:.2f}", f"{precision_dtc:.2f}", f"{precision_nb:.2f}", f"{precision_nn:.2f}"] + + df = mr.res_df(df, precision_list, participant) + +# generate result .csv file +df.to_csv('case_3_accuracy.csv') + diff --git a/multi_participants 2/case_4/case_4_accuracy.py b/multi_participants 2/case_4/case_4_accuracy.py new file mode 100644 index 0000000..f807dd0 --- /dev/null +++ b/multi_participants 2/case_4/case_4_accuracy.py @@ -0,0 +1,65 @@ +import os +from mind_reading_package import mind_reading as mr +import pandas as pd + +# list all folders' name +participants = os.listdir('path') + +# remove the 'cha' folder we don't need +participants = participants.remove('cha') + +# create the initial dataframe +df = pd.DataFrame(index = ['SVC', 'DTC', 'NB', 'NN']) + +for participant in participants: + # iterate all the folders + + for file in os.listdir(participant): + # iterate all files in every folder, find out the one end with 'Cong.csv' and 'Incong.csv' as input data + + if file.endswith('Cong.csv'): file1 = f"{participant}/{file}" + if file.endswith('Incong.csv'): file2 = f"{participant}/{file}" + + # load in cong and incong data for them + df1 = mr.load_data(file1) + df2 = mr.load_data(file2) + + # concatenate such data + data = mr.concatenate_data(df1, df2) + + # find trials to later separate + trials_index = mr.find_trials(data) + + # separate trials + trials = mr.separate_trials(data, trials_index) + + # create the label column + labels = mr.create_multi_labels(data) + + # Go through each trial, reset the columns, we split from 100-300ms ((308th sample to 513th sample)) + pro_trials = mr.process_trials(trials) + + # Find the mean across channels + avg_trials = mr.average_trials(pro_trials) + + # concatenates the average trials dataframe with labels + ml_df = mr.create_ml_df(avg_trials, labels) + + # train models + X_train, X_test, y_train, y_test = mr.prepare_ml_df(ml_df) + + acc_svc, precision_svc = mr.train_svc_multi(X_train, X_test, y_train, y_test) + + acc_dtc, precision_dtc = mr.train_dtc_multi(X_train, X_test, y_train, y_test) + + acc_nb, precision_nb = mr.train_nb_multi(X_train, X_test, y_train, y_test) + + acc_nn, precision_nn = mr.train_nn_multi(64, X_train, X_test, y_train, y_test) + + # add every participant's accuracy together + acc_list = [f"{acc_svc:.2f}", f"{acc_dtc:.2f}", f"{acc_nb:.2f}", f"{acc_nn:.2f}"] + + df = mr.res_df(df, acc_list, participant) + +# generate result .csv file +df.to_csv('case_4_accuracy.csv') diff --git a/multi_participants 2/case_4/case_4_precision.py b/multi_participants 2/case_4/case_4_precision.py new file mode 100644 index 0000000..ca07645 --- /dev/null +++ b/multi_participants 2/case_4/case_4_precision.py @@ -0,0 +1,65 @@ +import os +from mind_reading_package import mind_reading as mr +import pandas as pd + +# list all folders' name +participants = os.listdir('path') + +# remove the 'cha' folder we don't need +participants = participants.remove('cha') + +# create the initial dataframe +df = pd.DataFrame(index = ['SVC', 'DTC', 'NB', 'NN']) + +for participant in participants: + # iterate all the folders + + for file in os.listdir(participant): + # iterate all files in every folder, find out the one end with 'Cong.csv' and 'Incong.csv' as input data + + if file.endswith('Cong.csv'): file1 = f"{participant}/{file}" + if file.endswith('Incong.csv'): file2 = f"{participant}/{file}" + + # load in cong and incong data for them + df1 = mr.load_data(file1) + df2 = mr.load_data(file2) + + # concatenate such data + data = mr.concatenate_data(df1, df2) + + # find trials to later separate + trials_index = mr.find_trials(data) + + # separate trials + trials = mr.separate_trials(data, trials_index) + + # create the label column + labels = mr.create_multi_labels(data) + + # Go through each trial, reset the columns, we split from 100-300ms ((308th sample to 513th sample)) + pro_trials = mr.process_trials(trials) + + # Find the mean across channels + avg_trials = mr.average_trials(pro_trials) + + # concatenates the average trials dataframe with labels + ml_df = mr.create_ml_df(avg_trials, labels) + + # train models + X_train, X_test, y_train, y_test = mr.prepare_ml_df(ml_df) + + acc_svc, precision_svc = mr.train_svc_multi(X_train, X_test, y_train, y_test) + + acc_dtc, precision_dtc = mr.train_dtc_multi(X_train, X_test, y_train, y_test) + + acc_nb, precision_nb = mr.train_nb_multi(X_train, X_test, y_train, y_test) + + acc_nn, precision_nn = mr.train_nn_multi(64, X_train, X_test, y_train, y_test) + + # add every participant's precision together + precision_list = [f"{precision_svc:.2f}", f"{precision_dtc:.2f}", f"{precision_nb:.2f}", f"{precision_nn:.2f}"] + + df = mr.res_df(df, precision_list, participant) + +# generate result .csv file +df.to_csv('case_4_precision.csv') diff --git a/multi_participants/mind-reading package/mind_reading.py b/multi_participants 2/mind_reading_package/mind_reading.py similarity index 95% rename from multi_participants/mind-reading package/mind_reading.py rename to multi_participants 2/mind_reading_package/mind_reading.py index 73270d6..6125b04 100644 --- a/multi_participants/mind-reading package/mind_reading.py +++ b/multi_participants 2/mind_reading_package/mind_reading.py @@ -310,3 +310,18 @@ def create_metric_df(acc_list, prec_list, model_list): metric_df.columns = ['acc', 'prec'] return metric_df + +def res_df(df, column, participant): + ''' + Add precision/accuracy for every participant to the whole results + Args: + df: the dataframe of all results we have had + column: the dataframe of result we want to add + participant: participant number + returns: + all precision/accuracy results + ''' + + data = pd.DataFrame({f"Participant {participant}": column}) + df[f"Participant {participant}"] = data[f"Participant {participant}"].values + return df \ No newline at end of file diff --git a/multi_participants/mind-reading package/mind_reading_v2.py b/multi_participants 2/mind_reading_package/mind_reading_v2.py similarity index 97% rename from multi_participants/mind-reading package/mind_reading_v2.py rename to multi_participants 2/mind_reading_package/mind_reading_v2.py index 0cb25f2..989e8ca 100644 --- a/multi_participants/mind-reading package/mind_reading_v2.py +++ b/multi_participants 2/mind_reading_package/mind_reading_v2.py @@ -512,3 +512,18 @@ def train_nn_multi(n_inputs, X_train, X_test, y_train, y_test): _, accuracy, precision = model.evaluate(X_test, y_test, verbose=0) return accuracy, precision + +def res_df(df, column, participant): + ''' + Add precision/accuracy for every participant to the whole results + Args: + df: the dataframe of all results we have had + column: the dataframe of result we want to add + participant: participant number + returns: + all precision/accuracy results + ''' + + data = pd.DataFrame({f"Participant {participant}": column}) + df[f"Participant {participant}"] = data[f"Participant {participant}"].values + return df \ No newline at end of file diff --git a/multi_participants/case_3/case_3_accuracy.csv b/multi_participants 2/res_01:2020/case_3_accuracy.csv similarity index 100% rename from multi_participants/case_3/case_3_accuracy.csv rename to multi_participants 2/res_01:2020/case_3_accuracy.csv diff --git a/multi_participants/case_4/case_4_accuracy.csv b/multi_participants 2/res_01:2020/case_4_accuracy.csv similarity index 100% rename from multi_participants/case_4/case_4_accuracy.csv rename to multi_participants 2/res_01:2020/case_4_accuracy.csv diff --git a/multi_participants/case_3/multi_participants_case_3 (accuracy).ipynb b/multi_participants/case_3/multi_participants_case_3 (accuracy).ipynb deleted file mode 100644 index c6b1a40..0000000 --- a/multi_participants/case_3/multi_participants_case_3 (accuracy).ipynb +++ /dev/null @@ -1 +0,0 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"multi_participants_case_3 (acc).ipynb","provenance":[],"collapsed_sections":[],"authorship_tag":"ABX9TyM55xNp+h45SFwHHN0gE0EH"},"kernelspec":{"display_name":"Python 3","name":"python3"}},"cells":[{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sOg2lGMlThR2","executionInfo":{"status":"ok","timestamp":1610549441540,"user_tz":300,"elapsed":23171,"user":{"displayName":"Changhong Ma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg4a7esTogCkibNImqhE9gCGWTIpBdm_K1v1bWc=s64","userId":"04324339310204701212"}},"outputId":"7a4ca863-1547-454b-cbdd-9e7fd0b4838f"},"source":["from google.colab import drive\n","drive.mount('/content/gdrive')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Mounted at /content/gdrive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hV5sT-YaYhdN","executionInfo":{"status":"ok","timestamp":1610549444238,"user_tz":300,"elapsed":806,"user":{"displayName":"Changhong Ma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg4a7esTogCkibNImqhE9gCGWTIpBdm_K1v1bWc=s64","userId":"04324339310204701212"}},"outputId":"1b329ec1-f3a5-468f-e2af-6c15012884cc"},"source":["import os\n","os.chdir(\"/content/gdrive/MyDrive\")\n","!ls"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" 2020fall-ml\n","'CBT test Diagram.drawio'\n","'CEN 5011 '\n","'Changhong Ma.pdf'\n","'Colab Notebooks'\n","'CPT_Changhong Ma.pdf'\n"," CV.pdf\n"," Data_by_Participant\n","'Getting started.pdf'\n"," mind_reading.py\n","'New Panther Virtual Check In Module CERTIFICATE OF COMPLETION-Quiz Passed.pdf'\n"," oa\n"," __pycache__\n"," testing.ipynb\n","'UCD-User Account Home Page.drawio'\n","'Untitled Diagram.drawio'\n","'Untitled spreadsheet.gsheet'\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"KokWiR8kannV"},"source":["import mind_reading as mr\n","import pandas as pd\n","import re"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5Es9ZTM-TqT9","executionInfo":{"status":"ok","timestamp":1610549462118,"user_tz":300,"elapsed":333,"user":{"displayName":"Changhong Ma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg4a7esTogCkibNImqhE9gCGWTIpBdm_K1v1bWc=s64","userId":"04324339310204701212"}},"outputId":"29b2417a-bc3e-4798-b38a-d126f9abdf64"},"source":["%cd \"/content/gdrive/MyDrive/Data_by_Participant\""],"execution_count":null,"outputs":[{"output_type":"stream","text":["/content/gdrive/MyDrive/Data_by_Participant\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PJtXxGsTV6Fm","executionInfo":{"status":"ok","timestamp":1610549464039,"user_tz":300,"elapsed":736,"user":{"displayName":"Changhong Ma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg4a7esTogCkibNImqhE9gCGWTIpBdm_K1v1bWc=s64","userId":"04324339310204701212"}},"outputId":"02b2151b-ad14-4aef-e6ec-5980edf7edb8"},"source":["directory = os.fsencode(\"/content/gdrive/MyDrive/Data_by_Participant\")\n","os.listdir(directory)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[b'001',\n"," b'007',\n"," b'004',\n"," b'003',\n"," b'006',\n"," b'010',\n"," b'012',\n"," b'011',\n"," b'009',\n"," b'016',\n"," b'020',\n"," b'023',\n"," b'017',\n"," b'013',\n"," b'021',\n"," b'019',\n"," b'015',\n"," b'014',\n"," b'018',\n"," b'033',\n"," b'026',\n"," b'027',\n"," b'029',\n"," b'031',\n"," b'030',\n"," b'025',\n"," b'034',\n"," b'024',\n"," b'032',\n"," b'044',\n"," b'038',\n"," b'040',\n"," b'043',\n"," b'041',\n"," b'036',\n"," b'039',\n"," b'042',\n"," b'037',\n"," b'035',\n"," b'048',\n"," b'049',\n"," b'055',\n"," b'053',\n"," b'054',\n"," b'046',\n"," b'047',\n"," b'050',\n"," b'051',\n"," b'052',\n"," b'cha',\n"," b'059',\n"," b'061',\n"," b'063',\n"," b'060',\n"," b'058',\n"," b'057',\n"," b'056']"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"DATvRI_ZzO9S","executionInfo":{"status":"ok","timestamp":1610549467201,"user_tz":300,"elapsed":361,"user":{"displayName":"Changhong Ma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg4a7esTogCkibNImqhE9gCGWTIpBdm_K1v1bWc=s64","userId":"04324339310204701212"}},"outputId":"28332618-89aa-4ecb-8d2e-a976af886df2"},"source":["orig_participants = os.listdir(directory)\n","participants = []\n","\n","for participant in orig_participants:\n"," # decode byte, make sure use the string type\n"," participant = participant.decode('utf-8')\n"," participants.append(participant)\n","\n","# remove the 'cha' folder\n","participants.remove('cha')\n","participants"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['001',\n"," '007',\n"," '004',\n"," '003',\n"," '006',\n"," '010',\n"," '012',\n"," '011',\n"," '009',\n"," '016',\n"," '020',\n"," '023',\n"," '017',\n"," '013',\n"," '021',\n"," '019',\n"," '015',\n"," '014',\n"," '018',\n"," '033',\n"," '026',\n"," '027',\n"," '029',\n"," '031',\n"," '030',\n"," '025',\n"," '034',\n"," '024',\n"," '032',\n"," '044',\n"," '038',\n"," '040',\n"," '043',\n"," '041',\n"," '036',\n"," '039',\n"," '042',\n"," '037',\n"," '035',\n"," '048',\n"," '049',\n"," '055',\n"," '053',\n"," '054',\n"," '046',\n"," '047',\n"," '050',\n"," '051',\n"," '052',\n"," '059',\n"," '061',\n"," '063',\n"," '060',\n"," '058',\n"," '057',\n"," '056']"]},"metadata":{"tags":[]},"execution_count":8}]},{"cell_type":"code","metadata":{"id":"Yi5ZlWGc71ts"},"source":["df = pd.DataFrame(index = ['SVC', 'DTC', 'NB', 'NN'])\n","\n","def acc_df(df, acc_column, participant):\n"," '''\n"," Add accuracy for every participant to the whole results\n"," Args: \n"," df: the dataframe of all results we have had\n"," acc_column: the dataframe of result we want to add \n"," participant: participant number\n"," returns: \n"," all accuracy results\n"," '''\n","\n"," data = pd.DataFrame({f\"Participant {participant}\": acc_column})\n"," df[f\"Participant {participant}\"] = data[f\"Participant {participant}\"].values\n"," return df"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_sLrZfHhcDHi","executionInfo":{"status":"ok","timestamp":1610571296790,"user_tz":300,"elapsed":1205404,"user":{"displayName":"Changhong Ma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg4a7esTogCkibNImqhE9gCGWTIpBdm_K1v1bWc=s64","userId":"04324339310204701212"}},"outputId":"2b62ad90-61e5-430a-c102-c40ddad2935c"},"source":["for participant in participants:\n"," # iterate all the folders\n","\n"," for file in os.listdir(participant):\n"," # iterate all files in every folder, find out the one end with 'Cong.csv' and 'Incong.csv' as input data\n","\n"," if file.endswith('Cong.csv'): file1 = f\"{participant}/{file}\" \n"," if file.endswith('Incong.csv'): file2 = f\"{participant}/{file}\"\n","\n"," # load in cong and incong data for them\n"," df1 = mr.load_data(file1)\n"," df2 = mr.load_data(file2)\n","\n"," # concatenate such data \n"," data = mr.concatenate_data(df1, df2)\n","\n"," # find trials to later separate\n"," trials_index = mr.find_trials(data)\n","\n"," # separate trials\n"," trials = mr.separate_trials(data, trials_index)\n","\n"," # create the label column \n"," labels = mr.create_binary_labels(data)\n","\n"," # Go through each trial, reset the columns, we split from 100-300ms ((308th sample to 513th sample))\n"," pro_trials = mr.process_trials(trials)\n","\n"," # Find the mean across channels\n"," avg_trials = mr.average_trials(pro_trials)\n","\n"," # concatenates the average trials dataframe with labels\n"," ml_df = mr.create_ml_df(avg_trials, labels)\n","\n"," # train models\n"," X_train, X_test, y_train, y_test = mr.prepare_ml_df(ml_df)\n","\n"," acc_svc, precision_svc = mr.train_svc(X_train, X_test, y_train, y_test)\n","\n"," acc_dtc, precision_dtc = mr.train_dtc(X_train, X_test, y_train, y_test)\n","\n"," acc_nb, precision_nb = mr.train_nb(X_train, X_test, y_train, y_test)\n","\n"," acc_nn, precision_nn = mr.train_nn(64, X_train, X_test, y_train, y_test)\n","\n"," # add every participant's accuracy together\n"," acc_list = [f\"{acc_svc:.2f}\", f\"{acc_dtc:.2f}\", f\"{acc_nb:.2f}\", f\"{acc_nn:.2f}\"]\n","\n"," df = acc_df(df, acc_list, participant) \n","\n"," \n","\n"," "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":210},"id":"A8mdzpzIFHCd","executionInfo":{"status":"ok","timestamp":1610571298383,"user_tz":300,"elapsed":77,"user":{"displayName":"Changhong Ma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg4a7esTogCkibNImqhE9gCGWTIpBdm_K1v1bWc=s64","userId":"04324339310204701212"}},"outputId":"b7806164-96dd-4994-f60d-cd1df17f1039"},"source":["df"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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Participant 001Participant 007Participant 004Participant 003Participant 006Participant 010Participant 012Participant 011Participant 009Participant 016Participant 020Participant 023Participant 017Participant 013Participant 021Participant 019Participant 015Participant 014Participant 018Participant 033Participant 026Participant 027Participant 029Participant 031Participant 030Participant 025Participant 034Participant 024Participant 032Participant 044Participant 038Participant 040Participant 043Participant 041Participant 036Participant 039Participant 042Participant 037Participant 035Participant 048Participant 049Participant 055Participant 053Participant 054Participant 046Participant 047Participant 050Participant 051Participant 052Participant 059Participant 061Participant 063Participant 060Participant 058Participant 057Participant 056
SVC0.630.540.470.570.580.450.540.520.630.530.520.560.520.470.540.500.550.620.560.550.630.500.490.460.510.500.490.540.460.530.540.460.560.560.500.600.500.450.480.470.450.470.460.520.520.560.510.490.580.560.560.490.460.470.460.54
DTC0.550.540.480.540.610.520.530.420.560.460.530.630.460.560.580.500.530.530.500.470.500.530.510.490.490.490.430.570.510.520.490.460.440.480.500.420.500.500.540.500.560.480.490.460.460.490.490.500.440.480.580.490.500.550.470.48
NB0.470.540.440.530.600.530.470.510.450.470.510.640.450.510.470.440.460.510.460.500.460.510.450.390.520.450.480.500.500.440.590.530.520.530.450.530.540.530.460.400.560.540.550.520.480.530.490.470.490.470.520.510.510.530.590.41
NN0.470.550.490.510.520.530.500.460.450.560.520.450.520.540.530.480.530.570.580.500.590.520.490.500.500.550.490.490.490.520.440.570.590.490.430.540.500.470.480.480.550.460.500.450.440.520.510.520.480.510.500.510.560.480.470.51
\n","
"],"text/plain":[" Participant 001 Participant 007 ... Participant 057 Participant 056\n","SVC 0.63 0.54 ... 0.46 0.54\n","DTC 0.55 0.54 ... 0.47 0.48\n","NB 0.47 0.54 ... 0.59 0.41\n","NN 0.47 0.55 ... 0.47 0.51\n","\n","[4 rows x 56 columns]"]},"metadata":{"tags":[]},"execution_count":15}]},{"cell_type":"code","metadata":{"id":"Hx54gxyR4cjc"},"source":["df.to_csv('accuracy.csv') "],"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/multi_participants/case_3/multi_participants_case_3 (precision).ipynb b/multi_participants/case_3/multi_participants_case_3 (precision).ipynb deleted file mode 100644 index e1a0425..0000000 --- a/multi_participants/case_3/multi_participants_case_3 (precision).ipynb +++ /dev/null @@ -1 +0,0 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"multi_participants_case_3 (precision).ipynb","provenance":[],"collapsed_sections":[],"authorship_tag":"ABX9TyOyFXyU1opTIAibzNtGdMxM"},"kernelspec":{"display_name":"Python 3","name":"python3"}},"cells":[{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sOg2lGMlThR2","executionInfo":{"status":"ok","timestamp":1610549441540,"user_tz":300,"elapsed":23171,"user":{"displayName":"Changhong Ma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg4a7esTogCkibNImqhE9gCGWTIpBdm_K1v1bWc=s64","userId":"04324339310204701212"}},"outputId":"7a4ca863-1547-454b-cbdd-9e7fd0b4838f"},"source":["from google.colab import drive\n","drive.mount('/content/gdrive')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Mounted at /content/gdrive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hV5sT-YaYhdN","executionInfo":{"status":"ok","timestamp":1610549444238,"user_tz":300,"elapsed":806,"user":{"displayName":"Changhong Ma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg4a7esTogCkibNImqhE9gCGWTIpBdm_K1v1bWc=s64","userId":"04324339310204701212"}},"outputId":"1b329ec1-f3a5-468f-e2af-6c15012884cc"},"source":["import os\n","os.chdir(\"/content/gdrive/MyDrive\")\n","!ls"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" 2020fall-ml\n","'CBT test Diagram.drawio'\n","'CEN 5011 '\n","'Changhong Ma.pdf'\n","'Colab Notebooks'\n","'CPT_Changhong Ma.pdf'\n"," CV.pdf\n"," Data_by_Participant\n","'Getting started.pdf'\n"," mind_reading.py\n","'New Panther Virtual Check In Module CERTIFICATE OF COMPLETION-Quiz Passed.pdf'\n"," oa\n"," __pycache__\n"," testing.ipynb\n","'UCD-User Account Home Page.drawio'\n","'Untitled Diagram.drawio'\n","'Untitled spreadsheet.gsheet'\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"KokWiR8kannV"},"source":["import mind_reading as mr\n","import pandas as pd\n","import re"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5Es9ZTM-TqT9","executionInfo":{"status":"ok","timestamp":1610549462118,"user_tz":300,"elapsed":333,"user":{"displayName":"Changhong Ma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg4a7esTogCkibNImqhE9gCGWTIpBdm_K1v1bWc=s64","userId":"04324339310204701212"}},"outputId":"29b2417a-bc3e-4798-b38a-d126f9abdf64"},"source":["%cd \"/content/gdrive/MyDrive/Data_by_Participant\""],"execution_count":null,"outputs":[{"output_type":"stream","text":["/content/gdrive/MyDrive/Data_by_Participant\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PJtXxGsTV6Fm","executionInfo":{"status":"ok","timestamp":1610549464039,"user_tz":300,"elapsed":736,"user":{"displayName":"Changhong Ma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg4a7esTogCkibNImqhE9gCGWTIpBdm_K1v1bWc=s64","userId":"04324339310204701212"}},"outputId":"02b2151b-ad14-4aef-e6ec-5980edf7edb8"},"source":["directory = os.fsencode(\"/content/gdrive/MyDrive/Data_by_Participant\")\n","os.listdir(directory)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[b'001',\n"," b'007',\n"," b'004',\n"," b'003',\n"," b'006',\n"," b'010',\n"," b'012',\n"," b'011',\n"," b'009',\n"," b'016',\n"," b'020',\n"," b'023',\n"," b'017',\n"," b'013',\n"," b'021',\n"," b'019',\n"," b'015',\n"," b'014',\n"," b'018',\n"," b'033',\n"," b'026',\n"," b'027',\n"," b'029',\n"," b'031',\n"," b'030',\n"," b'025',\n"," b'034',\n"," b'024',\n"," b'032',\n"," b'044',\n"," b'038',\n"," b'040',\n"," b'043',\n"," b'041',\n"," b'036',\n"," b'039',\n"," b'042',\n"," b'037',\n"," b'035',\n"," b'048',\n"," b'049',\n"," b'055',\n"," b'053',\n"," b'054',\n"," b'046',\n"," b'047',\n"," b'050',\n"," b'051',\n"," b'052',\n"," b'cha',\n"," b'059',\n"," b'061',\n"," b'063',\n"," b'060',\n"," b'058',\n"," b'057',\n"," b'056']"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"DATvRI_ZzO9S","executionInfo":{"status":"ok","timestamp":1610549467201,"user_tz":300,"elapsed":361,"user":{"displayName":"Changhong Ma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg4a7esTogCkibNImqhE9gCGWTIpBdm_K1v1bWc=s64","userId":"04324339310204701212"}},"outputId":"28332618-89aa-4ecb-8d2e-a976af886df2"},"source":["orig_participants = os.listdir(directory)\n","participants = []\n","\n","for participant in orig_participants:\n"," # decode byte, make sure use the string type\n"," participant = participant.decode('utf-8')\n"," participants.append(participant)\n","\n","# remove the 'cha' folder\n","participants.remove('cha')\n","participants"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['001',\n"," '007',\n"," '004',\n"," '003',\n"," '006',\n"," '010',\n"," '012',\n"," '011',\n"," '009',\n"," '016',\n"," '020',\n"," '023',\n"," '017',\n"," '013',\n"," '021',\n"," '019',\n"," '015',\n"," '014',\n"," '018',\n"," '033',\n"," '026',\n"," '027',\n"," '029',\n"," '031',\n"," '030',\n"," '025',\n"," '034',\n"," '024',\n"," '032',\n"," '044',\n"," '038',\n"," '040',\n"," '043',\n"," '041',\n"," '036',\n"," '039',\n"," '042',\n"," '037',\n"," '035',\n"," '048',\n"," '049',\n"," '055',\n"," '053',\n"," '054',\n"," '046',\n"," '047',\n"," '050',\n"," '051',\n"," '052',\n"," '059',\n"," '061',\n"," '063',\n"," '060',\n"," '058',\n"," '057',\n"," '056']"]},"metadata":{"tags":[]},"execution_count":8}]},{"cell_type":"code","metadata":{"id":"Yi5ZlWGc71ts"},"source":["df = pd.DataFrame(index = ['SVC', 'DTC', 'NB', 'NN'])\n","\n","def precision_df(df, precision_column, participant):\n"," '''\n"," Add precision for every participant to the whole results\n"," Args: \n"," df: the dataframe of all results we have had\n"," precision_column: the dataframe of result we want to add \n"," participant: participant number\n"," returns: \n"," all precision results\n"," '''\n","\n"," data = pd.DataFrame({f\"Participant {participant}\": precision_column})\n"," df[f\"Participant {participant}\"] = data[f\"Participant {participant}\"].values\n"," return df"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"_sLrZfHhcDHi"},"source":["for participant in participants:\n"," # iterate all the folders\n","\n"," for file in os.listdir(participant):\n"," # iterate all files in every folder, find out the one end with 'Cong.csv' and 'Incong.csv' as input data\n","\n"," if file.endswith('Cong.csv'): file1 = f\"{participant}/{file}\" \n"," if file.endswith('Incong.csv'): file2 = f\"{participant}/{file}\"\n","\n"," # load in cong and incong data for them\n"," df1 = mr.load_data(file1)\n"," df2 = mr.load_data(file2)\n","\n"," # concatenate such data \n"," data = mr.concatenate_data(df1, df2)\n","\n"," # find trials to later separate\n"," trials_index = mr.find_trials(data)\n","\n"," # separate trials\n"," trials = mr.separate_trials(data, trials_index)\n","\n"," # create the label column \n"," labels = mr.create_binary_labels(data)\n","\n"," # Go through each trial, reset the columns, we split from 100-300ms ((308th sample to 513th sample))\n"," pro_trials = mr.process_trials(trials)\n","\n"," # Find the mean across channels\n"," avg_trials = mr.average_trials(pro_trials)\n","\n"," # concatenates the average trials dataframe with labels\n"," ml_df = mr.create_ml_df(avg_trials, labels)\n","\n"," # train models\n"," X_train, X_test, y_train, y_test = mr.prepare_ml_df(ml_df)\n","\n"," acc_svc, precision_svc = mr.train_svc(X_train, X_test, y_train, y_test)\n","\n"," acc_dtc, precision_dtc = mr.train_dtc(X_train, X_test, y_train, y_test)\n","\n"," acc_nb, precision_nb = mr.train_nb(X_train, X_test, y_train, y_test)\n","\n"," acc_nn, precision_nn = mr.train_nn(64, X_train, X_test, y_train, y_test)\n","\n"," # add every participant's precision together\n"," precision_list = [f\"{precision_svc:.2f}\", f\"{precision_dtc:.2f}\", f\"{precision_nb:.2f}\", f\"{precision_nn:.2f}\"]\n","\n"," df = precision_df(df, precision_list, participant) \n","\n"," \n","\n"," "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"A8mdzpzIFHCd"},"source":["df"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Hx54gxyR4cjc"},"source":["df.to_csv('case_3_precision.csv') "],"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/multi_participants/case_4/multi_participants_case_4 (accuracy).ipynb b/multi_participants/case_4/multi_participants_case_4 (accuracy).ipynb deleted file mode 100644 index 0493077..0000000 --- a/multi_participants/case_4/multi_participants_case_4 (accuracy).ipynb +++ /dev/null @@ -1 +0,0 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"multi_participants_case_4 (acc).ipynb","provenance":[],"collapsed_sections":[],"authorship_tag":"ABX9TyO/pdWioa2EElevYwLS/0rL"},"kernelspec":{"display_name":"Python 3","name":"python3"}},"cells":[{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sOg2lGMlThR2","executionInfo":{"elapsed":21298,"status":"ok","timestamp":1610636456526,"user":{"displayName":"Changhong Ma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg4a7esTogCkibNImqhE9gCGWTIpBdm_K1v1bWc=s64","userId":"04324339310204701212"},"user_tz":300},"outputId":"61ea6628-3b0e-4575-d8aa-de4d644892b7"},"source":["from google.colab import drive\n","drive.mount('/content/gdrive')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Mounted at /content/gdrive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hV5sT-YaYhdN","executionInfo":{"elapsed":3941,"status":"ok","timestamp":1610636456527,"user":{"displayName":"Changhong Ma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg4a7esTogCkibNImqhE9gCGWTIpBdm_K1v1bWc=s64","userId":"04324339310204701212"},"user_tz":300},"outputId":"a7be0149-c263-4ac7-e4c1-a021637f7ade"},"source":["import os\n","os.chdir(\"/content/gdrive/MyDrive\")\n","!ls"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" 2020fall-ml\n","'CBT test Diagram.drawio'\n","'CEN 5011 '\n","'Changhong Ma.pdf'\n","'Colab Notebooks'\n","'Copy of multi_participants_case_3 (acc).ipynb'\n","'CPT_Changhong Ma.pdf'\n"," CV.pdf\n"," Data_by_Participant\n","'Getting started.pdf'\n"," mind_reading.py\n"," mind_reading_v2.py\n","'New Panther Virtual Check In Module CERTIFICATE OF COMPLETION-Quiz Passed.pdf'\n"," oa\n"," __pycache__\n"," testing.ipynb\n","'UCD-User Account Home Page.drawio'\n","'Untitled Diagram.drawio'\n","'Untitled spreadsheet.gsheet'\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"KokWiR8kannV"},"source":["import mind_reading_v2 as mr\n","import pandas as pd\n","import re"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5Es9ZTM-TqT9","executionInfo":{"elapsed":493,"status":"ok","timestamp":1610636477121,"user":{"displayName":"Changhong Ma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg4a7esTogCkibNImqhE9gCGWTIpBdm_K1v1bWc=s64","userId":"04324339310204701212"},"user_tz":300},"outputId":"267dd1e9-8781-4b4f-ead1-c9ea0d5f81c5"},"source":["%cd \"/content/gdrive/MyDrive/Data_by_Participant\""],"execution_count":null,"outputs":[{"output_type":"stream","text":["/content/gdrive/MyDrive/Data_by_Participant\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PJtXxGsTV6Fm","executionInfo":{"elapsed":481,"status":"ok","timestamp":1610636479041,"user":{"displayName":"Changhong Ma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg4a7esTogCkibNImqhE9gCGWTIpBdm_K1v1bWc=s64","userId":"04324339310204701212"},"user_tz":300},"outputId":"e2d946d8-6a18-4989-d715-f64a32b8d287"},"source":["directory = os.fsencode(\"/content/gdrive/MyDrive/Data_by_Participant\")\n","os.listdir(directory)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[b'001',\n"," b'007',\n"," b'004',\n"," b'003',\n"," b'006',\n"," b'010',\n"," b'012',\n"," b'011',\n"," b'009',\n"," b'016',\n"," b'020',\n"," b'023',\n"," b'017',\n"," b'013',\n"," b'021',\n"," b'019',\n"," b'015',\n"," b'014',\n"," b'018',\n"," b'033',\n"," b'026',\n"," b'027',\n"," b'029',\n"," b'031',\n"," b'030',\n"," b'025',\n"," b'034',\n"," b'024',\n"," b'032',\n"," b'044',\n"," b'038',\n"," b'040',\n"," b'043',\n"," b'041',\n"," b'036',\n"," b'039',\n"," b'042',\n"," b'037',\n"," b'035',\n"," b'048',\n"," b'049',\n"," b'055',\n"," b'053',\n"," b'054',\n"," b'046',\n"," b'047',\n"," b'050',\n"," b'051',\n"," b'052',\n"," b'cha',\n"," b'059',\n"," b'061',\n"," b'063',\n"," b'060',\n"," b'058',\n"," b'057',\n"," b'056',\n"," b'output',\n"," b'accuracy.csv']"]},"metadata":{"tags":[]},"execution_count":6}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"DATvRI_ZzO9S","executionInfo":{"elapsed":408,"status":"ok","timestamp":1610636483138,"user":{"displayName":"Changhong Ma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg4a7esTogCkibNImqhE9gCGWTIpBdm_K1v1bWc=s64","userId":"04324339310204701212"},"user_tz":300},"outputId":"fc0d0e6c-2e3f-49d7-e146-d1452c6a52d2"},"source":["orig_participants = os.listdir(directory)\n","participants = []\n","\n","for participant in orig_participants:\n"," # decode byte, make sure use the string type\n"," participant = participant.decode('utf-8')\n"," participants.append(participant)\n","\n","# remove the 'cha' folder\n","participants.remove('cha')\n","participants"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['001',\n"," '007',\n"," '004',\n"," '003',\n"," '006',\n"," '010',\n"," '012',\n"," '011',\n"," '009',\n"," '016',\n"," '020',\n"," '023',\n"," '017',\n"," '013',\n"," '021',\n"," '019',\n"," '015',\n"," '014',\n"," '018',\n"," '033',\n"," '026',\n"," '027',\n"," '029',\n"," '031',\n"," '030',\n"," '025',\n"," '034',\n"," '024',\n"," '032',\n"," '044',\n"," '038',\n"," '040',\n"," '043',\n"," '041',\n"," '036',\n"," '039',\n"," '042',\n"," '037',\n"," '035',\n"," '048',\n"," '049',\n"," '055',\n"," '053',\n"," '054',\n"," '046',\n"," '047',\n"," '050',\n"," '051',\n"," '052',\n"," '059',\n"," '061',\n"," '063',\n"," '060',\n"," '058',\n"," '057',\n"," '056',\n"," 'output',\n"," 'accuracy.csv']"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"code","metadata":{"id":"Yi5ZlWGc71ts"},"source":["df = pd.DataFrame(index = ['SVC', 'DTC', 'NB', 'NN'])\n","\n","def acc_df(df, acc_column, participant):\n"," '''\n"," Add accuracy for every participant to the whole results\n"," Args: \n"," df: the dataframe of all results we have had\n"," acc_column: the dataframe of result we want to add \n"," participant: participant number\n"," returns: \n"," all accuracy results\n"," '''\n","\n"," data = pd.DataFrame({f\"Participant {participant}\": acc_column})\n"," df[f\"Participant {participant}\"] = data[f\"Participant {participant}\"].values\n"," return df"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"background_save":true,"base_uri":"https://localhost:8080/"},"id":"_sLrZfHhcDHi","outputId":"d5722979-cc88-4c81-bc54-e840bfd83329"},"source":["for participant in participants:\n"," # iterate all the folders\n","\n"," for file in os.listdir(participant):\n"," # iterate all files in every folder, find out the one end with 'Cong.csv' and 'Incong.csv' as input data\n","\n"," if file.endswith('Cong.csv'): file1 = f\"{participant}/{file}\" \n"," if file.endswith('Incong.csv'): file2 = f\"{participant}/{file}\"\n","\n"," # load in cong and incong data for them\n"," df1 = mr.load_data(file1)\n"," df2 = mr.load_data(file2)\n","\n"," # concatenate such data \n"," data = mr.concatenate_data(df1, df2)\n","\n"," # find trials to later separate\n"," trials_index = mr.find_trials(data)\n","\n"," # separate trials\n"," trials = mr.separate_trials(data, trials_index)\n","\n"," # create the label column \n"," labels = mr.create_multi_labels(data)\n","\n"," # Go through each trial, reset the columns, we split from 100-300ms ((308th sample to 513th sample))\n"," pro_trials = mr.process_trials(trials)\n","\n"," # Find the mean across channels\n"," avg_trials = mr.average_trials(pro_trials)\n","\n"," # concatenates the average trials dataframe with labels\n"," ml_df = mr.create_ml_df(avg_trials, labels)\n","\n"," # train models\n"," X_train, X_test, y_train, y_test = mr.prepare_ml_df(ml_df)\n","\n"," acc_svc, precision_svc = mr.train_svc_multi(X_train, X_test, y_train, y_test)\n","\n"," acc_dtc, precision_dtc = mr.train_dtc_multi(X_train, X_test, y_train, y_test)\n","\n"," acc_nb, precision_nb = mr.train_nb_multi(X_train, X_test, y_train, y_test)\n","\n"," acc_nn, precision_nn = mr.train_nn_multi(64, X_train, X_test, y_train, y_test)\n","\n"," # add every participant's accuracy together\n"," acc_list = [f\"{acc_svc:.2f}\", f\"{acc_dtc:.2f}\", f\"{acc_nb:.2f}\", f\"{acc_nn:.2f}\"]\n","\n"," df = acc_df(df, acc_list, participant) \n","\n"," \n","\n"," "],"execution_count":null,"outputs":[{"output_type":"stream","text":["Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 20.1s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","168/168 [==============================] - 1s 3ms/step - loss: 1.4169 - acc: 0.3015 - precision_m: 0.0446 - val_loss: 1.3908 - val_acc: 0.2569 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.4083 - acc: 0.2249 - precision_m: 0.0000e+00 - val_loss: 1.3888 - val_acc: 0.2431 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3961 - acc: 0.2800 - precision_m: 0.0000e+00 - val_loss: 1.3913 - val_acc: 0.2014 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3811 - acc: 0.2890 - precision_m: 0.0000e+00 - val_loss: 1.3978 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3899 - acc: 0.2844 - precision_m: 0.0000e+00 - val_loss: 1.3939 - val_acc: 0.2500 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3867 - acc: 0.1879 - precision_m: 0.0000e+00 - val_loss: 1.3911 - val_acc: 0.2153 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3936 - acc: 0.2266 - precision_m: 0.0000e+00 - val_loss: 1.3913 - val_acc: 0.2569 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3929 - acc: 0.2415 - precision_m: 0.0000e+00 - val_loss: 1.3911 - val_acc: 0.2569 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3867 - acc: 0.2587 - precision_m: 0.0000e+00 - val_loss: 1.3887 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3862 - acc: 0.2355 - precision_m: 0.0000e+00 - val_loss: 1.3912 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3820 - acc: 0.2431 - precision_m: 0.0000e+00 - val_loss: 1.3903 - val_acc: 0.2014 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3870 - acc: 0.2448 - precision_m: 0.0000e+00 - val_loss: 1.3891 - val_acc: 0.2153 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3870 - acc: 0.2672 - precision_m: 0.0000e+00 - val_loss: 1.3882 - val_acc: 0.2153 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3873 - acc: 0.2244 - precision_m: 0.0000e+00 - val_loss: 1.3885 - val_acc: 0.2153 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3854 - acc: 0.2502 - precision_m: 0.0000e+00 - val_loss: 1.3893 - val_acc: 0.2153 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3846 - acc: 0.3005 - precision_m: 0.0000e+00 - val_loss: 1.3905 - val_acc: 0.2083 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3827 - acc: 0.2422 - precision_m: 0.0000e+00 - val_loss: 1.3910 - val_acc: 0.2014 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3832 - acc: 0.2544 - precision_m: 0.0000e+00 - val_loss: 1.3899 - val_acc: 0.2014 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3872 - acc: 0.2364 - precision_m: 0.0000e+00 - val_loss: 1.3898 - val_acc: 0.2153 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3851 - acc: 0.2783 - precision_m: 0.0000e+00 - val_loss: 1.3936 - val_acc: 0.2222 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3850 - acc: 0.2889 - precision_m: 0.0000e+00 - val_loss: 1.3935 - val_acc: 0.1944 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3727 - acc: 0.2676 - precision_m: 0.0063 - val_loss: 1.3900 - val_acc: 0.2153 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3837 - acc: 0.2435 - precision_m: 0.0000e+00 - val_loss: 1.3929 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3846 - acc: 0.2529 - precision_m: 0.0000e+00 - val_loss: 1.3897 - val_acc: 0.2153 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3821 - acc: 0.2376 - precision_m: 0.0000e+00 - val_loss: 1.3922 - val_acc: 0.1944 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3919 - acc: 0.2320 - precision_m: 0.0000e+00 - val_loss: 1.3933 - val_acc: 0.1944 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3856 - acc: 0.2380 - precision_m: 0.0000e+00 - val_loss: 1.3930 - val_acc: 0.1806 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3860 - acc: 0.2621 - precision_m: 0.0000e+00 - val_loss: 1.3908 - val_acc: 0.2153 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3821 - acc: 0.3241 - precision_m: 0.0000e+00 - val_loss: 1.3939 - val_acc: 0.1806 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3857 - acc: 0.2217 - precision_m: 0.0000e+00 - val_loss: 1.3916 - val_acc: 0.2014 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3812 - acc: 0.2606 - precision_m: 0.0000e+00 - val_loss: 1.3922 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3816 - acc: 0.2151 - precision_m: 0.0000e+00 - val_loss: 1.3910 - val_acc: 0.2083 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3824 - acc: 0.3158 - precision_m: 0.0000e+00 - val_loss: 1.3917 - val_acc: 0.2153 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3821 - acc: 0.2695 - precision_m: 0.0000e+00 - val_loss: 1.3967 - val_acc: 0.2083 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3840 - acc: 0.2939 - precision_m: 0.0000e+00 - val_loss: 1.3940 - val_acc: 0.1597 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3837 - acc: 0.2737 - precision_m: 0.0000e+00 - val_loss: 1.3960 - val_acc: 0.2292 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3813 - acc: 0.2645 - precision_m: 1.7738e-04 - val_loss: 1.3929 - val_acc: 0.2153 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3847 - acc: 0.2897 - precision_m: 0.0000e+00 - val_loss: 1.3925 - val_acc: 0.2083 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3861 - acc: 0.2454 - precision_m: 0.0000e+00 - val_loss: 1.3971 - val_acc: 0.2083 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3823 - acc: 0.2869 - precision_m: 0.0021 - val_loss: 1.3959 - val_acc: 0.2083 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3695 - acc: 0.3161 - precision_m: 0.0134 - val_loss: 1.3994 - val_acc: 0.2292 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3873 - acc: 0.2733 - precision_m: 0.0000e+00 - val_loss: 1.3940 - val_acc: 0.1806 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3836 - acc: 0.2735 - precision_m: 0.0000e+00 - val_loss: 1.3913 - val_acc: 0.2153 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3823 - acc: 0.2845 - precision_m: 0.0051 - val_loss: 1.3930 - val_acc: 0.2153 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3858 - acc: 0.2027 - precision_m: 0.0000e+00 - val_loss: 1.4008 - val_acc: 0.1806 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3849 - acc: 0.2268 - precision_m: 0.0047 - val_loss: 1.3968 - val_acc: 0.2083 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3776 - acc: 0.2780 - precision_m: 7.8286e-04 - val_loss: 1.3948 - val_acc: 0.2153 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3835 - acc: 0.2734 - precision_m: 0.0010 - val_loss: 1.3996 - val_acc: 0.2361 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3794 - acc: 0.3411 - precision_m: 0.0000e+00 - val_loss: 1.4005 - val_acc: 0.2222 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3826 - acc: 0.3149 - precision_m: 0.0035 - val_loss: 1.3986 - val_acc: 0.2014 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3679 - acc: 0.2796 - precision_m: 0.0217 - val_loss: 1.4073 - val_acc: 0.2361 - val_precision_m: 0.0000e+00\n","Epoch 52/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3761 - acc: 0.2798 - precision_m: 0.0091 - val_loss: 1.4037 - val_acc: 0.2153 - val_precision_m: 0.0000e+00\n","Epoch 53/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3895 - acc: 0.2207 - precision_m: 0.0090 - val_loss: 1.4041 - val_acc: 0.2431 - val_precision_m: 0.0000e+00\n","Epoch 54/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3788 - acc: 0.2437 - precision_m: 0.0165 - val_loss: 1.4039 - val_acc: 0.2222 - val_precision_m: 0.0000e+00\n","Epoch 55/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3823 - acc: 0.2876 - precision_m: 0.0017 - val_loss: 1.4028 - val_acc: 0.2083 - val_precision_m: 0.0000e+00\n","Epoch 56/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3885 - acc: 0.2480 - precision_m: 0.0075 - val_loss: 1.4028 - val_acc: 0.2083 - val_precision_m: 0.0000e+00\n","Epoch 57/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3741 - acc: 0.3181 - precision_m: 0.0116 - val_loss: 1.4022 - val_acc: 0.2083 - val_precision_m: 0.0000e+00\n","Epoch 58/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3859 - acc: 0.2621 - precision_m: 0.0099 - val_loss: 1.4083 - val_acc: 0.2361 - val_precision_m: 0.0000e+00\n","Epoch 59/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3739 - acc: 0.2611 - precision_m: 0.0370 - val_loss: 1.4034 - val_acc: 0.2222 - val_precision_m: 0.0000e+00\n","Epoch 60/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3852 - acc: 0.2635 - precision_m: 3.2304e-04 - val_loss: 1.4010 - val_acc: 0.2153 - val_precision_m: 0.0000e+00\n","Epoch 61/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3802 - acc: 0.2978 - precision_m: 0.0048 - val_loss: 1.4005 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 62/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3762 - acc: 0.2676 - precision_m: 0.0098 - val_loss: 1.3999 - val_acc: 0.2083 - val_precision_m: 0.0000e+00\n","Epoch 63/500\n","168/168 [==============================] - 0s 2ms/step - loss: 1.3791 - acc: 0.2838 - precision_m: 0.0032 - val_loss: 1.4019 - val_acc: 0.2014 - val_precision_m: 0.0000e+00\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 15.7s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","148/148 [==============================] - 1s 3ms/step - loss: 1.6580 - acc: 0.2462 - precision_m: 0.1317 - val_loss: 1.3834 - val_acc: 0.2441 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.4156 - acc: 0.2107 - precision_m: 0.0000e+00 - val_loss: 1.3814 - val_acc: 0.2756 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3952 - acc: 0.2802 - precision_m: 0.0000e+00 - val_loss: 1.3786 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3887 - acc: 0.2893 - precision_m: 0.0000e+00 - val_loss: 1.3815 - val_acc: 0.2441 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3922 - acc: 0.2246 - precision_m: 0.0000e+00 - val_loss: 1.3809 - val_acc: 0.3071 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3854 - acc: 0.2824 - precision_m: 0.0000e+00 - val_loss: 1.3802 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3789 - acc: 0.2391 - precision_m: 0.0000e+00 - val_loss: 1.3800 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3894 - acc: 0.2243 - precision_m: 0.0000e+00 - val_loss: 1.3817 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3898 - acc: 0.2613 - precision_m: 0.0000e+00 - val_loss: 1.3811 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3826 - acc: 0.2609 - precision_m: 0.0000e+00 - val_loss: 1.3806 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3776 - acc: 0.2499 - precision_m: 0.0000e+00 - val_loss: 1.3803 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3832 - acc: 0.2898 - precision_m: 0.0000e+00 - val_loss: 1.3796 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3752 - acc: 0.3158 - precision_m: 0.0000e+00 - val_loss: 1.3796 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","148/148 [==============================] - 0s 3ms/step - loss: 1.3816 - acc: 0.3051 - precision_m: 0.0000e+00 - val_loss: 1.3797 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3729 - acc: 0.2728 - precision_m: 0.0000e+00 - val_loss: 1.3796 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3820 - acc: 0.2440 - precision_m: 0.0000e+00 - val_loss: 1.3796 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3834 - acc: 0.2646 - precision_m: 0.0000e+00 - val_loss: 1.3791 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3928 - acc: 0.2462 - precision_m: 0.0000e+00 - val_loss: 1.3791 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3751 - acc: 0.3007 - precision_m: 0.0000e+00 - val_loss: 1.3792 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3817 - acc: 0.2927 - precision_m: 0.0000e+00 - val_loss: 1.3792 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3830 - acc: 0.2232 - precision_m: 0.0000e+00 - val_loss: 1.3792 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3879 - acc: 0.2587 - precision_m: 0.0000e+00 - val_loss: 1.3792 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3677 - acc: 0.3214 - precision_m: 0.0000e+00 - val_loss: 1.3795 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3794 - acc: 0.3059 - precision_m: 0.0000e+00 - val_loss: 1.3791 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3850 - acc: 0.2768 - precision_m: 0.0000e+00 - val_loss: 1.3792 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3838 - acc: 0.2750 - precision_m: 0.0000e+00 - val_loss: 1.3797 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3805 - acc: 0.2912 - precision_m: 0.0000e+00 - val_loss: 1.3796 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3893 - acc: 0.2536 - precision_m: 0.0000e+00 - val_loss: 1.3794 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3791 - acc: 0.2845 - precision_m: 0.0000e+00 - val_loss: 1.3794 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3808 - acc: 0.2948 - precision_m: 0.0000e+00 - val_loss: 1.3793 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3832 - acc: 0.2750 - precision_m: 0.0000e+00 - val_loss: 1.3793 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3772 - acc: 0.2990 - precision_m: 0.0000e+00 - val_loss: 1.3794 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3852 - acc: 0.2640 - precision_m: 0.0000e+00 - val_loss: 1.3794 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3828 - acc: 0.2908 - precision_m: 0.0000e+00 - val_loss: 1.3796 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3808 - acc: 0.2770 - precision_m: 0.0000e+00 - val_loss: 1.3791 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3744 - acc: 0.2895 - precision_m: 0.0000e+00 - val_loss: 1.3790 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3785 - acc: 0.2571 - precision_m: 0.0000e+00 - val_loss: 1.3793 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3806 - acc: 0.2802 - precision_m: 0.0000e+00 - val_loss: 1.3792 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3783 - acc: 0.2880 - precision_m: 0.0000e+00 - val_loss: 1.3794 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3797 - acc: 0.2800 - precision_m: 0.0000e+00 - val_loss: 1.3794 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3858 - acc: 0.2724 - precision_m: 0.0000e+00 - val_loss: 1.3792 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3847 - acc: 0.2702 - precision_m: 0.0000e+00 - val_loss: 1.3793 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3833 - acc: 0.3202 - precision_m: 0.0000e+00 - val_loss: 1.3791 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3824 - acc: 0.2741 - precision_m: 0.0000e+00 - val_loss: 1.3791 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3813 - acc: 0.2698 - precision_m: 0.0000e+00 - val_loss: 1.3793 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3892 - acc: 0.2573 - precision_m: 0.0000e+00 - val_loss: 1.3791 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3799 - acc: 0.2890 - precision_m: 0.0000e+00 - val_loss: 1.3790 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3807 - acc: 0.2647 - precision_m: 0.0000e+00 - val_loss: 1.3793 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3839 - acc: 0.2689 - precision_m: 0.0000e+00 - val_loss: 1.3792 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3870 - acc: 0.2686 - precision_m: 0.0000e+00 - val_loss: 1.3793 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3885 - acc: 0.2531 - precision_m: 0.0000e+00 - val_loss: 1.3793 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 52/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3856 - acc: 0.2512 - precision_m: 0.0000e+00 - val_loss: 1.3794 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Epoch 53/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3777 - acc: 0.2749 - precision_m: 0.0000e+00 - val_loss: 1.3789 - val_acc: 0.2913 - val_precision_m: 0.0000e+00\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 17.5s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","157/157 [==============================] - 1s 3ms/step - loss: 1.4107 - acc: 0.2253 - precision_m: 0.0597 - val_loss: 1.3760 - val_acc: 0.3111 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.4046 - acc: 0.1817 - precision_m: 0.0000e+00 - val_loss: 1.3802 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3970 - acc: 0.2150 - precision_m: 0.0000e+00 - val_loss: 1.3806 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3893 - acc: 0.2545 - precision_m: 0.0000e+00 - val_loss: 1.3810 - val_acc: 0.2889 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3890 - acc: 0.2785 - precision_m: 0.0000e+00 - val_loss: 1.3799 - val_acc: 0.3111 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3844 - acc: 0.2835 - precision_m: 0.0000e+00 - val_loss: 1.3794 - val_acc: 0.3111 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3811 - acc: 0.2235 - precision_m: 0.0000e+00 - val_loss: 1.3801 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3843 - acc: 0.2800 - precision_m: 0.0000e+00 - val_loss: 1.3774 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3837 - acc: 0.2837 - precision_m: 0.0000e+00 - val_loss: 1.3802 - val_acc: 0.2889 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3840 - acc: 0.2750 - precision_m: 0.0000e+00 - val_loss: 1.3782 - val_acc: 0.3185 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3804 - acc: 0.3278 - precision_m: 0.0000e+00 - val_loss: 1.3779 - val_acc: 0.2963 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3790 - acc: 0.2849 - precision_m: 0.0000e+00 - val_loss: 1.3720 - val_acc: 0.2963 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3828 - acc: 0.2788 - precision_m: 0.0000e+00 - val_loss: 1.3799 - val_acc: 0.2889 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3763 - acc: 0.3320 - precision_m: 0.0000e+00 - val_loss: 1.3693 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3767 - acc: 0.3366 - precision_m: 0.0136 - val_loss: 1.3767 - val_acc: 0.2963 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3879 - acc: 0.2603 - precision_m: 0.0000e+00 - val_loss: 1.3683 - val_acc: 0.2963 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3695 - acc: 0.3167 - precision_m: 0.0046 - val_loss: 1.3611 - val_acc: 0.3259 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3721 - acc: 0.3053 - precision_m: 0.0000e+00 - val_loss: 1.3630 - val_acc: 0.3185 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3778 - acc: 0.2927 - precision_m: 0.0000e+00 - val_loss: 1.3699 - val_acc: 0.2889 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3741 - acc: 0.2907 - precision_m: 0.0072 - val_loss: 1.3563 - val_acc: 0.3333 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3631 - acc: 0.3364 - precision_m: 0.0148 - val_loss: 1.3575 - val_acc: 0.3111 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3661 - acc: 0.3660 - precision_m: 0.0118 - val_loss: 1.3572 - val_acc: 0.3037 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3664 - acc: 0.3017 - precision_m: 0.0142 - val_loss: 1.3560 - val_acc: 0.2667 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3558 - acc: 0.3080 - precision_m: 0.0000e+00 - val_loss: 1.3557 - val_acc: 0.3259 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3592 - acc: 0.2951 - precision_m: 0.0044 - val_loss: 1.3558 - val_acc: 0.3111 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3675 - acc: 0.2995 - precision_m: 0.0046 - val_loss: 1.3474 - val_acc: 0.3333 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3568 - acc: 0.3194 - precision_m: 0.0000e+00 - val_loss: 1.3465 - val_acc: 0.3185 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3396 - acc: 0.3639 - precision_m: 0.0124 - val_loss: 1.3546 - val_acc: 0.2889 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3477 - acc: 0.3086 - precision_m: 0.0082 - val_loss: 1.3417 - val_acc: 0.3407 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3475 - acc: 0.3216 - precision_m: 0.0049 - val_loss: 1.3329 - val_acc: 0.2963 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3284 - acc: 0.3884 - precision_m: 0.0185 - val_loss: 1.3427 - val_acc: 0.3259 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3477 - acc: 0.3530 - precision_m: 0.0137 - val_loss: 1.3380 - val_acc: 0.3259 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3488 - acc: 0.3444 - precision_m: 0.0013 - val_loss: 1.3417 - val_acc: 0.2889 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3370 - acc: 0.3344 - precision_m: 0.0414 - val_loss: 1.3414 - val_acc: 0.3259 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3403 - acc: 0.3305 - precision_m: 0.0000e+00 - val_loss: 1.3292 - val_acc: 0.3333 - val_precision_m: 0.0294\n","Epoch 36/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2999 - acc: 0.3604 - precision_m: 0.0496 - val_loss: 1.3341 - val_acc: 0.3407 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3688 - acc: 0.2846 - precision_m: 0.0052 - val_loss: 1.3217 - val_acc: 0.3556 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3217 - acc: 0.3025 - precision_m: 0.0149 - val_loss: 1.3275 - val_acc: 0.2667 - val_precision_m: 0.0294\n","Epoch 39/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3382 - acc: 0.3240 - precision_m: 0.0451 - val_loss: 1.3300 - val_acc: 0.3259 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3419 - acc: 0.3117 - precision_m: 0.0084 - val_loss: 1.3270 - val_acc: 0.3259 - val_precision_m: 0.0147\n","Epoch 41/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3127 - acc: 0.3825 - precision_m: 0.0416 - val_loss: 1.3089 - val_acc: 0.3778 - val_precision_m: 0.0294\n","Epoch 42/500\n","157/157 [==============================] - 0s 3ms/step - loss: 1.3429 - acc: 0.3097 - precision_m: 0.0551 - val_loss: 1.3133 - val_acc: 0.3630 - val_precision_m: 0.0147\n","Epoch 43/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3178 - acc: 0.3463 - precision_m: 0.0112 - val_loss: 1.3246 - val_acc: 0.3333 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3222 - acc: 0.3503 - precision_m: 0.0115 - val_loss: 1.3151 - val_acc: 0.3407 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3635 - acc: 0.2725 - precision_m: 0.0144 - val_loss: 1.3314 - val_acc: 0.2889 - val_precision_m: 0.0147\n","Epoch 46/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3382 - acc: 0.2892 - precision_m: 0.0284 - val_loss: 1.3234 - val_acc: 0.3333 - val_precision_m: 0.0147\n","Epoch 47/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3348 - acc: 0.3227 - precision_m: 0.0324 - val_loss: 1.3175 - val_acc: 0.3778 - val_precision_m: 0.0147\n","Epoch 48/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3071 - acc: 0.3433 - precision_m: 0.0465 - val_loss: 1.3070 - val_acc: 0.3481 - val_precision_m: 0.0147\n","Epoch 49/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3312 - acc: 0.3077 - precision_m: 2.0313e-04 - val_loss: 1.3018 - val_acc: 0.3630 - val_precision_m: 0.0147\n","Epoch 50/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3439 - acc: 0.3042 - precision_m: 0.0097 - val_loss: 1.3113 - val_acc: 0.3630 - val_precision_m: 0.0147\n","Epoch 51/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3431 - acc: 0.3215 - precision_m: 0.0188 - val_loss: 1.3233 - val_acc: 0.3037 - val_precision_m: 0.0294\n","Epoch 52/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3257 - acc: 0.3487 - precision_m: 0.0200 - val_loss: 1.2952 - val_acc: 0.3630 - val_precision_m: 0.0294\n","Epoch 53/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3035 - acc: 0.3410 - precision_m: 0.0367 - val_loss: 1.2944 - val_acc: 0.3704 - val_precision_m: 0.0147\n","Epoch 54/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3432 - acc: 0.3003 - precision_m: 0.0216 - val_loss: 1.2969 - val_acc: 0.3704 - val_precision_m: 0.0147\n","Epoch 55/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3254 - acc: 0.3058 - precision_m: 0.0259 - val_loss: 1.3036 - val_acc: 0.3630 - val_precision_m: 0.0147\n","Epoch 56/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3041 - acc: 0.3929 - precision_m: 0.0474 - val_loss: 1.2804 - val_acc: 0.3481 - val_precision_m: 0.0294\n","Epoch 57/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3033 - acc: 0.3806 - precision_m: 0.0276 - val_loss: 1.2763 - val_acc: 0.3111 - val_precision_m: 0.0294\n","Epoch 58/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2986 - acc: 0.3150 - precision_m: 0.0767 - val_loss: 1.2809 - val_acc: 0.3259 - val_precision_m: 0.0441\n","Epoch 59/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3289 - acc: 0.3589 - precision_m: 0.0485 - val_loss: 1.3154 - val_acc: 0.3037 - val_precision_m: 0.0441\n","Epoch 60/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3014 - acc: 0.3246 - precision_m: 0.0886 - val_loss: 1.2886 - val_acc: 0.3556 - val_precision_m: 0.0294\n","Epoch 61/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3075 - acc: 0.3235 - precision_m: 0.0055 - val_loss: 1.2796 - val_acc: 0.3556 - val_precision_m: 0.0294\n","Epoch 62/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3054 - acc: 0.3175 - precision_m: 0.0311 - val_loss: 1.2846 - val_acc: 0.3259 - val_precision_m: 0.0588\n","Epoch 63/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2706 - acc: 0.3309 - precision_m: 0.0792 - val_loss: 1.2745 - val_acc: 0.3630 - val_precision_m: 0.0294\n","Epoch 64/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3097 - acc: 0.3408 - precision_m: 0.0853 - val_loss: 1.2792 - val_acc: 0.3704 - val_precision_m: 0.0147\n","Epoch 65/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2693 - acc: 0.3258 - precision_m: 0.0968 - val_loss: 1.2638 - val_acc: 0.3481 - val_precision_m: 0.0294\n","Epoch 66/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2338 - acc: 0.3270 - precision_m: 0.1088 - val_loss: 1.2651 - val_acc: 0.3778 - val_precision_m: 0.0294\n","Epoch 67/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3181 - acc: 0.2944 - precision_m: 0.0708 - val_loss: 1.2745 - val_acc: 0.3926 - val_precision_m: 0.0294\n","Epoch 68/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2878 - acc: 0.3617 - precision_m: 0.0669 - val_loss: 1.2997 - val_acc: 0.3481 - val_precision_m: 0.0147\n","Epoch 69/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2765 - acc: 0.3591 - precision_m: 0.0230 - val_loss: 1.2760 - val_acc: 0.3630 - val_precision_m: 0.0294\n","Epoch 70/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3250 - acc: 0.2874 - precision_m: 0.0336 - val_loss: 1.2764 - val_acc: 0.3778 - val_precision_m: 0.0147\n","Epoch 71/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2721 - acc: 0.3524 - precision_m: 0.0854 - val_loss: 1.2851 - val_acc: 0.3852 - val_precision_m: 0.0147\n","Epoch 72/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2664 - acc: 0.3886 - precision_m: 0.0635 - val_loss: 1.2591 - val_acc: 0.3852 - val_precision_m: 0.0294\n","Epoch 73/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2820 - acc: 0.2853 - precision_m: 0.0783 - val_loss: 1.2557 - val_acc: 0.3556 - val_precision_m: 0.0294\n","Epoch 74/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2734 - acc: 0.3462 - precision_m: 0.0756 - val_loss: 1.2696 - val_acc: 0.3926 - val_precision_m: 0.0294\n","Epoch 75/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3004 - acc: 0.3003 - precision_m: 0.0592 - val_loss: 1.2679 - val_acc: 0.3926 - val_precision_m: 0.0294\n","Epoch 76/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3286 - acc: 0.3095 - precision_m: 0.0449 - val_loss: 1.2560 - val_acc: 0.3333 - val_precision_m: 0.0588\n","Epoch 77/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2366 - acc: 0.3963 - precision_m: 0.1933 - val_loss: 1.2702 - val_acc: 0.3852 - val_precision_m: 0.0294\n","Epoch 78/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2757 - acc: 0.3554 - precision_m: 0.0678 - val_loss: 1.2577 - val_acc: 0.3852 - val_precision_m: 0.0294\n","Epoch 79/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3040 - acc: 0.3506 - precision_m: 0.0560 - val_loss: 1.2528 - val_acc: 0.3704 - val_precision_m: 0.0441\n","Epoch 80/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2913 - acc: 0.3150 - precision_m: 0.0620 - val_loss: 1.2614 - val_acc: 0.3852 - val_precision_m: 0.0588\n","Epoch 81/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3015 - acc: 0.3534 - precision_m: 0.0499 - val_loss: 1.2675 - val_acc: 0.3778 - val_precision_m: 0.0294\n","Epoch 82/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2700 - acc: 0.3604 - precision_m: 0.0465 - val_loss: 1.2520 - val_acc: 0.3852 - val_precision_m: 0.0294\n","Epoch 83/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3158 - acc: 0.2876 - precision_m: 0.0430 - val_loss: 1.2397 - val_acc: 0.4074 - val_precision_m: 0.0294\n","Epoch 84/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2829 - acc: 0.3441 - precision_m: 0.0805 - val_loss: 1.2676 - val_acc: 0.3852 - val_precision_m: 0.0294\n","Epoch 85/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2746 - acc: 0.3215 - precision_m: 0.0641 - val_loss: 1.2519 - val_acc: 0.4148 - val_precision_m: 0.0294\n","Epoch 86/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2728 - acc: 0.3231 - precision_m: 0.0710 - val_loss: 1.3297 - val_acc: 0.3111 - val_precision_m: 0.0882\n","Epoch 87/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3094 - acc: 0.3324 - precision_m: 0.0709 - val_loss: 1.3298 - val_acc: 0.3333 - val_precision_m: 0.0882\n","Epoch 88/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3289 - acc: 0.3705 - precision_m: 0.0659 - val_loss: 1.2416 - val_acc: 0.3407 - val_precision_m: 0.0588\n","Epoch 89/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2576 - acc: 0.3746 - precision_m: 0.0228 - val_loss: 1.3804 - val_acc: 0.2667 - val_precision_m: 0.0735\n","Epoch 90/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2849 - acc: 0.3468 - precision_m: 0.0849 - val_loss: 1.2623 - val_acc: 0.3407 - val_precision_m: 0.0588\n","Epoch 91/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2836 - acc: 0.3109 - precision_m: 0.1013 - val_loss: 1.2383 - val_acc: 0.3852 - val_precision_m: 0.0147\n","Epoch 92/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2625 - acc: 0.3824 - precision_m: 0.0880 - val_loss: 1.2288 - val_acc: 0.4148 - val_precision_m: 0.0147\n","Epoch 93/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3044 - acc: 0.2915 - precision_m: 0.0721 - val_loss: 1.2398 - val_acc: 0.4000 - val_precision_m: 0.0147\n","Epoch 94/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2798 - acc: 0.3506 - precision_m: 0.0523 - val_loss: 1.2488 - val_acc: 0.3926 - val_precision_m: 0.0294\n","Epoch 95/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2514 - acc: 0.3139 - precision_m: 0.0619 - val_loss: 1.2451 - val_acc: 0.4074 - val_precision_m: 0.0147\n","Epoch 96/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2916 - acc: 0.3020 - precision_m: 0.0523 - val_loss: 1.2167 - val_acc: 0.4222 - val_precision_m: 0.0294\n","Epoch 97/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2184 - acc: 0.3312 - precision_m: 0.1101 - val_loss: 1.2164 - val_acc: 0.3704 - val_precision_m: 0.0735\n","Epoch 98/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2090 - acc: 0.3658 - precision_m: 0.1310 - val_loss: 1.2415 - val_acc: 0.3556 - val_precision_m: 0.0147\n","Epoch 99/500\n","157/157 [==============================] - 0s 3ms/step - loss: 1.2627 - acc: 0.3942 - precision_m: 0.1294 - val_loss: 1.2337 - val_acc: 0.4074 - val_precision_m: 0.0294\n","Epoch 100/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2495 - acc: 0.3764 - precision_m: 0.0925 - val_loss: 1.2353 - val_acc: 0.3926 - val_precision_m: 0.0294\n","Epoch 101/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3114 - acc: 0.3576 - precision_m: 0.0604 - val_loss: 1.2532 - val_acc: 0.3778 - val_precision_m: 0.0147\n","Epoch 102/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2364 - acc: 0.4425 - precision_m: 0.0835 - val_loss: 1.2833 - val_acc: 0.3185 - val_precision_m: 0.0882\n","Epoch 103/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2983 - acc: 0.2958 - precision_m: 0.1223 - val_loss: 1.2159 - val_acc: 0.3481 - val_precision_m: 0.0735\n","Epoch 104/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2601 - acc: 0.3773 - precision_m: 0.1008 - val_loss: 1.2216 - val_acc: 0.3926 - val_precision_m: 0.0441\n","Epoch 105/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2577 - acc: 0.3705 - precision_m: 0.0819 - val_loss: 1.2263 - val_acc: 0.4000 - val_precision_m: 0.0294\n","Epoch 106/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2048 - acc: 0.3724 - precision_m: 0.1790 - val_loss: 1.2498 - val_acc: 0.3852 - val_precision_m: 0.0294\n","Epoch 107/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2626 - acc: 0.3681 - precision_m: 0.0860 - val_loss: 1.2282 - val_acc: 0.3704 - val_precision_m: 0.0588\n","Epoch 108/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2984 - acc: 0.3679 - precision_m: 0.0509 - val_loss: 1.2099 - val_acc: 0.3704 - val_precision_m: 0.0882\n","Epoch 109/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2348 - acc: 0.3788 - precision_m: 0.1346 - val_loss: 1.2406 - val_acc: 0.3852 - val_precision_m: 0.0294\n","Epoch 110/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2617 - acc: 0.3742 - precision_m: 0.0834 - val_loss: 1.2369 - val_acc: 0.3630 - val_precision_m: 0.1029\n","Epoch 111/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2943 - acc: 0.2981 - precision_m: 0.0402 - val_loss: 1.2161 - val_acc: 0.3778 - val_precision_m: 0.0882\n","Epoch 112/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2308 - acc: 0.3899 - precision_m: 0.1239 - val_loss: 1.2596 - val_acc: 0.3778 - val_precision_m: 0.0294\n","Epoch 113/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2751 - acc: 0.3541 - precision_m: 0.0493 - val_loss: 1.2090 - val_acc: 0.3556 - val_precision_m: 0.0882\n","Epoch 114/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2486 - acc: 0.3502 - precision_m: 0.1178 - val_loss: 1.2131 - val_acc: 0.3704 - val_precision_m: 0.1029\n","Epoch 115/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2471 - acc: 0.3645 - precision_m: 0.0927 - val_loss: 1.2287 - val_acc: 0.3778 - val_precision_m: 0.0441\n","Epoch 116/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2554 - acc: 0.3733 - precision_m: 0.1163 - val_loss: 1.2348 - val_acc: 0.3704 - val_precision_m: 0.0735\n","Epoch 117/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2568 - acc: 0.3120 - precision_m: 0.1244 - val_loss: 1.2221 - val_acc: 0.3185 - val_precision_m: 0.1618\n","Epoch 118/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2405 - acc: 0.3725 - precision_m: 0.1042 - val_loss: 1.2465 - val_acc: 0.3926 - val_precision_m: 0.0294\n","Epoch 119/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2650 - acc: 0.3523 - precision_m: 0.0672 - val_loss: 1.2171 - val_acc: 0.3481 - val_precision_m: 0.0882\n","Epoch 120/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2944 - acc: 0.3664 - precision_m: 0.0756 - val_loss: 1.2642 - val_acc: 0.3852 - val_precision_m: 0.0294\n","Epoch 121/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2356 - acc: 0.3583 - precision_m: 0.0653 - val_loss: 1.2390 - val_acc: 0.3481 - val_precision_m: 0.0588\n","Epoch 122/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2626 - acc: 0.3788 - precision_m: 0.1139 - val_loss: 1.2142 - val_acc: 0.3704 - val_precision_m: 0.1176\n","Epoch 123/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2928 - acc: 0.3338 - precision_m: 0.1056 - val_loss: 1.1993 - val_acc: 0.3556 - val_precision_m: 0.1029\n","Epoch 124/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2545 - acc: 0.3877 - precision_m: 0.0854 - val_loss: 1.2372 - val_acc: 0.3407 - val_precision_m: 0.0588\n","Epoch 125/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2337 - acc: 0.3225 - precision_m: 0.0997 - val_loss: 1.2332 - val_acc: 0.3556 - val_precision_m: 0.1029\n","Epoch 126/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2624 - acc: 0.3605 - precision_m: 0.0963 - val_loss: 1.2275 - val_acc: 0.3778 - val_precision_m: 0.0588\n","Epoch 127/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2198 - acc: 0.3732 - precision_m: 0.1518 - val_loss: 1.2003 - val_acc: 0.4000 - val_precision_m: 0.0882\n","Epoch 128/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2452 - acc: 0.3610 - precision_m: 0.0845 - val_loss: 1.2090 - val_acc: 0.3407 - val_precision_m: 0.0882\n","Epoch 129/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2937 - acc: 0.3659 - precision_m: 0.1342 - val_loss: 1.2249 - val_acc: 0.3926 - val_precision_m: 0.0441\n","Epoch 130/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2610 - acc: 0.3222 - precision_m: 0.0767 - val_loss: 1.2070 - val_acc: 0.4148 - val_precision_m: 0.0441\n","Epoch 131/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2468 - acc: 0.3945 - precision_m: 0.1183 - val_loss: 1.2267 - val_acc: 0.3852 - val_precision_m: 0.0441\n","Epoch 132/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2607 - acc: 0.3164 - precision_m: 0.0854 - val_loss: 1.2063 - val_acc: 0.3704 - val_precision_m: 0.1471\n","Epoch 133/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2605 - acc: 0.3777 - precision_m: 0.1775 - val_loss: 1.2090 - val_acc: 0.3926 - val_precision_m: 0.0441\n","Epoch 134/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3200 - acc: 0.3275 - precision_m: 0.0222 - val_loss: 1.2023 - val_acc: 0.3926 - val_precision_m: 0.1176\n","Epoch 135/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3236 - acc: 0.3765 - precision_m: 0.1500 - val_loss: 1.2194 - val_acc: 0.3852 - val_precision_m: 0.0441\n","Epoch 136/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2620 - acc: 0.3833 - precision_m: 0.1016 - val_loss: 1.2136 - val_acc: 0.3556 - val_precision_m: 0.0735\n","Epoch 137/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2648 - acc: 0.3398 - precision_m: 0.1221 - val_loss: 1.1967 - val_acc: 0.3630 - val_precision_m: 0.0735\n","Epoch 138/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2349 - acc: 0.3218 - precision_m: 0.1213 - val_loss: 1.2714 - val_acc: 0.3704 - val_precision_m: 0.1397\n","Epoch 139/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2609 - acc: 0.3390 - precision_m: 0.1427 - val_loss: 1.2206 - val_acc: 0.4148 - val_precision_m: 0.0735\n","Epoch 140/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2487 - acc: 0.3745 - precision_m: 0.1237 - val_loss: 1.2225 - val_acc: 0.3778 - val_precision_m: 0.0441\n","Epoch 141/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2563 - acc: 0.2833 - precision_m: 0.1184 - val_loss: 1.2153 - val_acc: 0.4000 - val_precision_m: 0.0735\n","Epoch 142/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.1837 - acc: 0.3697 - precision_m: 0.0876 - val_loss: 1.3307 - val_acc: 0.3407 - val_precision_m: 0.1324\n","Epoch 143/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2123 - acc: 0.3821 - precision_m: 0.1712 - val_loss: 1.2136 - val_acc: 0.3481 - val_precision_m: 0.0882\n","Epoch 144/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2109 - acc: 0.3561 - precision_m: 0.1795 - val_loss: 1.2172 - val_acc: 0.3926 - val_precision_m: 0.0735\n","Epoch 145/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2289 - acc: 0.3743 - precision_m: 0.1526 - val_loss: 1.3063 - val_acc: 0.3407 - val_precision_m: 0.1250\n","Epoch 146/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2942 - acc: 0.3407 - precision_m: 0.1112 - val_loss: 1.2216 - val_acc: 0.3556 - val_precision_m: 0.0882\n","Epoch 147/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2630 - acc: 0.2902 - precision_m: 0.1075 - val_loss: 1.2463 - val_acc: 0.3630 - val_precision_m: 0.0882\n","Epoch 148/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2660 - acc: 0.3111 - precision_m: 0.0871 - val_loss: 1.2101 - val_acc: 0.3481 - val_precision_m: 0.0882\n","Epoch 149/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2140 - acc: 0.4179 - precision_m: 0.1818 - val_loss: 1.2320 - val_acc: 0.3481 - val_precision_m: 0.0882\n","Epoch 150/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2623 - acc: 0.3446 - precision_m: 0.1302 - val_loss: 1.2031 - val_acc: 0.3630 - val_precision_m: 0.0735\n","Epoch 151/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2332 - acc: 0.3219 - precision_m: 0.1169 - val_loss: 1.2234 - val_acc: 0.3407 - val_precision_m: 0.0882\n","Epoch 152/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2291 - acc: 0.3474 - precision_m: 0.1552 - val_loss: 1.2033 - val_acc: 0.4000 - val_precision_m: 0.0735\n","Epoch 153/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2137 - acc: 0.3867 - precision_m: 0.1935 - val_loss: 1.3641 - val_acc: 0.2963 - val_precision_m: 0.1176\n","Epoch 154/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3414 - acc: 0.3629 - precision_m: 0.0868 - val_loss: 1.1919 - val_acc: 0.3630 - val_precision_m: 0.0882\n","Epoch 155/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2410 - acc: 0.3446 - precision_m: 0.1151 - val_loss: 1.2178 - val_acc: 0.3926 - val_precision_m: 0.0588\n","Epoch 156/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2432 - acc: 0.3585 - precision_m: 0.1516 - val_loss: 1.2726 - val_acc: 0.3630 - val_precision_m: 0.0294\n","Epoch 157/500\n","157/157 [==============================] - 0s 3ms/step - loss: 1.2466 - acc: 0.3179 - precision_m: 0.0816 - val_loss: 1.2658 - val_acc: 0.3481 - val_precision_m: 0.1029\n","Epoch 158/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2968 - acc: 0.3956 - precision_m: 0.1162 - val_loss: 1.1987 - val_acc: 0.3704 - val_precision_m: 0.0735\n","Epoch 159/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2476 - acc: 0.3801 - precision_m: 0.1379 - val_loss: 1.1818 - val_acc: 0.4222 - val_precision_m: 0.1029\n","Epoch 160/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2558 - acc: 0.4340 - precision_m: 0.1495 - val_loss: 1.2045 - val_acc: 0.3630 - val_precision_m: 0.0588\n","Epoch 161/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2576 - acc: 0.3972 - precision_m: 0.1473 - val_loss: 1.1963 - val_acc: 0.3556 - val_precision_m: 0.0882\n","Epoch 162/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2651 - acc: 0.3622 - precision_m: 0.1072 - val_loss: 1.1838 - val_acc: 0.3333 - val_precision_m: 0.1176\n","Epoch 163/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.1965 - acc: 0.3871 - precision_m: 0.2035 - val_loss: 1.4052 - val_acc: 0.3259 - val_precision_m: 0.1471\n","Epoch 164/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2759 - acc: 0.3767 - precision_m: 0.1320 - val_loss: 1.2222 - val_acc: 0.3556 - val_precision_m: 0.0882\n","Epoch 165/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.1912 - acc: 0.3869 - precision_m: 0.1836 - val_loss: 1.1941 - val_acc: 0.3333 - val_precision_m: 0.1471\n","Epoch 166/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2628 - acc: 0.3681 - precision_m: 0.2707 - val_loss: 1.2011 - val_acc: 0.3778 - val_precision_m: 0.0441\n","Epoch 167/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2064 - acc: 0.3353 - precision_m: 0.1656 - val_loss: 1.2126 - val_acc: 0.3852 - val_precision_m: 0.0294\n","Epoch 168/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2338 - acc: 0.3717 - precision_m: 0.0947 - val_loss: 1.2282 - val_acc: 0.4074 - val_precision_m: 0.0441\n","Epoch 169/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.1900 - acc: 0.3636 - precision_m: 0.1877 - val_loss: 1.1871 - val_acc: 0.3556 - val_precision_m: 0.1176\n","Epoch 170/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2268 - acc: 0.3793 - precision_m: 0.1526 - val_loss: 1.2687 - val_acc: 0.3630 - val_precision_m: 0.0441\n","Epoch 171/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2447 - acc: 0.2973 - precision_m: 0.1157 - val_loss: 1.2045 - val_acc: 0.3852 - val_precision_m: 0.1029\n","Epoch 172/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2689 - acc: 0.3652 - precision_m: 0.1193 - val_loss: 1.2059 - val_acc: 0.3852 - val_precision_m: 0.0147\n","Epoch 173/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2680 - acc: 0.3183 - precision_m: 0.0760 - val_loss: 1.1983 - val_acc: 0.3926 - val_precision_m: 0.0441\n","Epoch 174/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2396 - acc: 0.3539 - precision_m: 0.1164 - val_loss: 1.3418 - val_acc: 0.3704 - val_precision_m: 0.0735\n","Epoch 175/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3196 - acc: 0.3844 - precision_m: 0.0874 - val_loss: 1.2040 - val_acc: 0.3852 - val_precision_m: 0.0882\n","Epoch 176/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2236 - acc: 0.3759 - precision_m: 0.1153 - val_loss: 1.2281 - val_acc: 0.3778 - val_precision_m: 0.0588\n","Epoch 177/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2424 - acc: 0.3179 - precision_m: 0.1134 - val_loss: 1.2062 - val_acc: 0.3852 - val_precision_m: 0.1029\n","Epoch 178/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.1936 - acc: 0.4192 - precision_m: 0.2389 - val_loss: 1.1817 - val_acc: 0.3704 - val_precision_m: 0.1176\n","Epoch 179/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2377 - acc: 0.3201 - precision_m: 0.1818 - val_loss: 1.1894 - val_acc: 0.3481 - val_precision_m: 0.1176\n","Epoch 180/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2202 - acc: 0.3184 - precision_m: 0.1729 - val_loss: 1.2140 - val_acc: 0.3926 - val_precision_m: 0.0588\n","Epoch 181/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.1920 - acc: 0.3690 - precision_m: 0.1038 - val_loss: 1.2251 - val_acc: 0.3852 - val_precision_m: 0.1029\n","Epoch 182/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2525 - acc: 0.3372 - precision_m: 0.1202 - val_loss: 1.2213 - val_acc: 0.3704 - val_precision_m: 0.0882\n","Epoch 183/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.1876 - acc: 0.3638 - precision_m: 0.1862 - val_loss: 1.1968 - val_acc: 0.3630 - val_precision_m: 0.1176\n","Epoch 184/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2222 - acc: 0.3968 - precision_m: 0.1513 - val_loss: 1.2258 - val_acc: 0.3556 - val_precision_m: 0.0882\n","Epoch 185/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2489 - acc: 0.3685 - precision_m: 0.0873 - val_loss: 1.2829 - val_acc: 0.3556 - val_precision_m: 0.1471\n","Epoch 186/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2424 - acc: 0.3860 - precision_m: 0.1414 - val_loss: 1.2091 - val_acc: 0.3926 - val_precision_m: 0.0882\n","Epoch 187/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2415 - acc: 0.3272 - precision_m: 0.1386 - val_loss: 1.1986 - val_acc: 0.3704 - val_precision_m: 0.1029\n","Epoch 188/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2299 - acc: 0.3541 - precision_m: 0.1678 - val_loss: 1.2195 - val_acc: 0.3333 - val_precision_m: 0.1176\n","Epoch 189/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2427 - acc: 0.3714 - precision_m: 0.1655 - val_loss: 1.1734 - val_acc: 0.3926 - val_precision_m: 0.1029\n","Epoch 190/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2291 - acc: 0.3623 - precision_m: 0.1430 - val_loss: 1.2190 - val_acc: 0.3556 - val_precision_m: 0.1618\n","Epoch 191/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2708 - acc: 0.3653 - precision_m: 0.0940 - val_loss: 1.2120 - val_acc: 0.3926 - val_precision_m: 0.0588\n","Epoch 192/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2408 - acc: 0.3837 - precision_m: 0.1049 - val_loss: 1.1844 - val_acc: 0.4222 - val_precision_m: 0.1029\n","Epoch 193/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2480 - acc: 0.3169 - precision_m: 0.1091 - val_loss: 1.1734 - val_acc: 0.3556 - val_precision_m: 0.1176\n","Epoch 194/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2369 - acc: 0.3211 - precision_m: 0.1475 - val_loss: 1.2017 - val_acc: 0.4074 - val_precision_m: 0.0882\n","Epoch 195/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2275 - acc: 0.4160 - precision_m: 0.1513 - val_loss: 1.1814 - val_acc: 0.3259 - val_precision_m: 0.1324\n","Epoch 196/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2272 - acc: 0.3546 - precision_m: 0.0992 - val_loss: 1.2032 - val_acc: 0.3407 - val_precision_m: 0.0882\n","Epoch 197/500\n","157/157 [==============================] - 0s 3ms/step - loss: 1.2772 - acc: 0.2946 - precision_m: 0.0918 - val_loss: 1.2129 - val_acc: 0.3556 - val_precision_m: 0.1765\n","Epoch 198/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2474 - acc: 0.3459 - precision_m: 0.1541 - val_loss: 1.2127 - val_acc: 0.3556 - val_precision_m: 0.1176\n","Epoch 199/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.1921 - acc: 0.3978 - precision_m: 0.2082 - val_loss: 1.2042 - val_acc: 0.3481 - val_precision_m: 0.1618\n","Epoch 200/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2145 - acc: 0.3378 - precision_m: 0.2639 - val_loss: 1.1958 - val_acc: 0.3778 - val_precision_m: 0.1176\n","Epoch 201/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2081 - acc: 0.4200 - precision_m: 0.1846 - val_loss: 1.2402 - val_acc: 0.3778 - val_precision_m: 0.0735\n","Epoch 202/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.1952 - acc: 0.3909 - precision_m: 0.1573 - val_loss: 1.1956 - val_acc: 0.4074 - val_precision_m: 0.1029\n","Epoch 203/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2053 - acc: 0.3392 - precision_m: 0.1575 - val_loss: 1.1977 - val_acc: 0.3926 - val_precision_m: 0.1176\n","Epoch 204/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2017 - acc: 0.3774 - precision_m: 0.1723 - val_loss: 1.2157 - val_acc: 0.3926 - val_precision_m: 0.0735\n","Epoch 205/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.1488 - acc: 0.4174 - precision_m: 0.1876 - val_loss: 1.2196 - val_acc: 0.3926 - val_precision_m: 0.0441\n","Epoch 206/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.1949 - acc: 0.3812 - precision_m: 0.1477 - val_loss: 1.1924 - val_acc: 0.3704 - val_precision_m: 0.1029\n","Epoch 207/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2711 - acc: 0.3469 - precision_m: 0.1267 - val_loss: 1.1992 - val_acc: 0.3926 - val_precision_m: 0.1029\n","Epoch 208/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.1890 - acc: 0.3740 - precision_m: 0.1862 - val_loss: 1.1865 - val_acc: 0.3481 - val_precision_m: 0.1324\n","Epoch 209/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.1976 - acc: 0.4090 - precision_m: 0.1658 - val_loss: 1.1909 - val_acc: 0.3481 - val_precision_m: 0.1324\n","Epoch 210/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.1824 - acc: 0.3808 - precision_m: 0.2211 - val_loss: 1.1844 - val_acc: 0.3556 - val_precision_m: 0.1324\n","Epoch 211/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2506 - acc: 0.3540 - precision_m: 0.1042 - val_loss: 1.2139 - val_acc: 0.3481 - val_precision_m: 0.1691\n","Epoch 212/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.1795 - acc: 0.3684 - precision_m: 0.2308 - val_loss: 1.2009 - val_acc: 0.3926 - val_precision_m: 0.1029\n","Epoch 213/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2332 - acc: 0.3963 - precision_m: 0.1265 - val_loss: 1.1938 - val_acc: 0.3852 - val_precision_m: 0.1176\n","Epoch 214/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2686 - acc: 0.4041 - precision_m: 0.1776 - val_loss: 1.2856 - val_acc: 0.3037 - val_precision_m: 0.0294\n","Epoch 215/500\n","157/157 [==============================] - 0s 3ms/step - loss: 1.2803 - acc: 0.3589 - precision_m: 0.1109 - val_loss: 1.1883 - val_acc: 0.3926 - val_precision_m: 0.1029\n","Epoch 216/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2024 - acc: 0.3388 - precision_m: 0.2071 - val_loss: 1.1876 - val_acc: 0.3778 - val_precision_m: 0.0882\n","Epoch 217/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2138 - acc: 0.4266 - precision_m: 0.1879 - val_loss: 1.2425 - val_acc: 0.3926 - val_precision_m: 0.0735\n","Epoch 218/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2191 - acc: 0.3953 - precision_m: 0.1643 - val_loss: 1.2160 - val_acc: 0.3926 - val_precision_m: 0.0588\n","Epoch 219/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2305 - acc: 0.3957 - precision_m: 0.1240 - val_loss: 1.2056 - val_acc: 0.3259 - val_precision_m: 0.1176\n","Epoch 220/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2157 - acc: 0.3648 - precision_m: 0.2094 - val_loss: 1.2121 - val_acc: 0.3407 - val_precision_m: 0.1029\n","Epoch 221/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2459 - acc: 0.3696 - precision_m: 0.1964 - val_loss: 1.1808 - val_acc: 0.3333 - val_precision_m: 0.1029\n","Epoch 222/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2221 - acc: 0.3830 - precision_m: 0.1711 - val_loss: 1.2157 - val_acc: 0.3333 - val_precision_m: 0.0882\n","Epoch 223/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.1755 - acc: 0.3765 - precision_m: 0.1718 - val_loss: 1.1791 - val_acc: 0.4148 - val_precision_m: 0.0882\n","Epoch 224/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2346 - acc: 0.3527 - precision_m: 0.2107 - val_loss: 1.2156 - val_acc: 0.3630 - val_precision_m: 0.0882\n","Epoch 225/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2458 - acc: 0.3237 - precision_m: 0.1463 - val_loss: 1.2300 - val_acc: 0.3630 - val_precision_m: 0.0588\n","Epoch 226/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2033 - acc: 0.3877 - precision_m: 0.1818 - val_loss: 1.1989 - val_acc: 0.3704 - val_precision_m: 0.1176\n","Epoch 227/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2112 - acc: 0.3822 - precision_m: 0.1756 - val_loss: 1.2110 - val_acc: 0.3778 - val_precision_m: 0.1176\n","Epoch 228/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2092 - acc: 0.2864 - precision_m: 0.1514 - val_loss: 1.2107 - val_acc: 0.3556 - val_precision_m: 0.1324\n","Epoch 229/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2349 - acc: 0.3662 - precision_m: 0.1613 - val_loss: 1.1874 - val_acc: 0.3481 - val_precision_m: 0.1324\n","Epoch 230/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2882 - acc: 0.3488 - precision_m: 0.2072 - val_loss: 1.2067 - val_acc: 0.3926 - val_precision_m: 0.1765\n","Epoch 231/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2225 - acc: 0.3782 - precision_m: 0.2043 - val_loss: 1.1962 - val_acc: 0.3778 - val_precision_m: 0.1471\n","Epoch 232/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3384 - acc: 0.3451 - precision_m: 0.1362 - val_loss: 1.2028 - val_acc: 0.3481 - val_precision_m: 0.1691\n","Epoch 233/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.1723 - acc: 0.4265 - precision_m: 0.1808 - val_loss: 1.2091 - val_acc: 0.3407 - val_precision_m: 0.1176\n","Epoch 234/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2244 - acc: 0.3732 - precision_m: 0.1452 - val_loss: 1.1721 - val_acc: 0.3407 - val_precision_m: 0.1324\n","Epoch 235/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2697 - acc: 0.3646 - precision_m: 0.2358 - val_loss: 1.1666 - val_acc: 0.3778 - val_precision_m: 0.1176\n","Epoch 236/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2701 - acc: 0.3984 - precision_m: 0.1341 - val_loss: 1.1798 - val_acc: 0.3926 - val_precision_m: 0.1029\n","Epoch 237/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2512 - acc: 0.3216 - precision_m: 0.1338 - val_loss: 1.2067 - val_acc: 0.3852 - val_precision_m: 0.0735\n","Epoch 238/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.1674 - acc: 0.4011 - precision_m: 0.1789 - val_loss: 1.2054 - val_acc: 0.3407 - val_precision_m: 0.1618\n","Epoch 239/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2378 - acc: 0.3411 - precision_m: 0.1844 - val_loss: 1.1639 - val_acc: 0.3556 - val_precision_m: 0.1029\n","Epoch 240/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.1940 - acc: 0.3838 - precision_m: 0.1460 - val_loss: 1.2228 - val_acc: 0.3333 - val_precision_m: 0.1544\n","Epoch 241/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.1829 - acc: 0.4038 - precision_m: 0.2319 - val_loss: 1.2000 - val_acc: 0.3926 - val_precision_m: 0.1029\n","Epoch 242/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.1887 - acc: 0.3853 - precision_m: 0.1956 - val_loss: 1.2218 - val_acc: 0.3333 - val_precision_m: 0.1397\n","Epoch 243/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2297 - acc: 0.4387 - precision_m: 0.1773 - val_loss: 1.2118 - val_acc: 0.3481 - val_precision_m: 0.0882\n","Epoch 244/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.1993 - acc: 0.3662 - precision_m: 0.1288 - val_loss: 1.1689 - val_acc: 0.3630 - val_precision_m: 0.1324\n","Epoch 245/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.1886 - acc: 0.4146 - precision_m: 0.2461 - val_loss: 1.1955 - val_acc: 0.3481 - val_precision_m: 0.0882\n","Epoch 246/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2764 - acc: 0.3340 - precision_m: 0.1333 - val_loss: 1.2039 - val_acc: 0.3407 - val_precision_m: 0.1029\n","Epoch 247/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2191 - acc: 0.3945 - precision_m: 0.1815 - val_loss: 1.2218 - val_acc: 0.3704 - val_precision_m: 0.0882\n","Epoch 248/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2112 - acc: 0.3810 - precision_m: 0.1399 - val_loss: 1.1858 - val_acc: 0.3778 - val_precision_m: 0.1176\n","Epoch 249/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.1658 - acc: 0.3604 - precision_m: 0.1929 - val_loss: 1.2084 - val_acc: 0.3704 - val_precision_m: 0.1029\n","Epoch 250/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2592 - acc: 0.3238 - precision_m: 0.1173 - val_loss: 1.2054 - val_acc: 0.3926 - val_precision_m: 0.1176\n","Epoch 251/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2276 - acc: 0.3781 - precision_m: 0.1210 - val_loss: 1.2578 - val_acc: 0.3407 - val_precision_m: 0.1544\n","Epoch 252/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.1883 - acc: 0.4421 - precision_m: 0.2383 - val_loss: 1.1939 - val_acc: 0.3481 - val_precision_m: 0.1176\n","Epoch 253/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.1572 - acc: 0.4267 - precision_m: 0.2137 - val_loss: 1.2448 - val_acc: 0.3259 - val_precision_m: 0.1544\n","Epoch 254/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.1720 - acc: 0.4236 - precision_m: 0.2104 - val_loss: 1.1901 - val_acc: 0.3407 - val_precision_m: 0.1176\n","Epoch 255/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2194 - acc: 0.3129 - precision_m: 0.1387 - val_loss: 1.2144 - val_acc: 0.3259 - val_precision_m: 0.1324\n","Epoch 256/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2126 - acc: 0.3404 - precision_m: 0.1967 - val_loss: 1.1703 - val_acc: 0.3704 - val_precision_m: 0.1471\n","Epoch 257/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.1300 - acc: 0.4209 - precision_m: 0.2713 - val_loss: 1.1847 - val_acc: 0.3556 - val_precision_m: 0.1471\n","Epoch 258/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2357 - acc: 0.3957 - precision_m: 0.1793 - val_loss: 1.1823 - val_acc: 0.3259 - val_precision_m: 0.1471\n","Epoch 259/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.1593 - acc: 0.3901 - precision_m: 0.1372 - val_loss: 1.2400 - val_acc: 0.3481 - val_precision_m: 0.0735\n","Epoch 260/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2233 - acc: 0.3155 - precision_m: 0.1574 - val_loss: 1.2030 - val_acc: 0.3407 - val_precision_m: 0.1324\n","Epoch 261/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3338 - acc: 0.3088 - precision_m: 0.1185 - val_loss: 1.3030 - val_acc: 0.3556 - val_precision_m: 0.1618\n","Epoch 262/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2589 - acc: 0.3821 - precision_m: 0.2290 - val_loss: 1.1721 - val_acc: 0.3481 - val_precision_m: 0.1618\n","Epoch 263/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.1923 - acc: 0.4334 - precision_m: 0.1870 - val_loss: 1.1714 - val_acc: 0.3926 - val_precision_m: 0.1176\n","Epoch 264/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.1934 - acc: 0.3602 - precision_m: 0.2093 - val_loss: 1.2223 - val_acc: 0.3926 - val_precision_m: 0.0588\n","Epoch 265/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2022 - acc: 0.3490 - precision_m: 0.1469 - val_loss: 1.2156 - val_acc: 0.3704 - val_precision_m: 0.1618\n","Epoch 266/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2295 - acc: 0.3140 - precision_m: 0.1261 - val_loss: 1.1712 - val_acc: 0.3630 - val_precision_m: 0.1618\n","Epoch 267/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2278 - acc: 0.4130 - precision_m: 0.2671 - val_loss: 1.1840 - val_acc: 0.3630 - val_precision_m: 0.1397\n","Epoch 268/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.1940 - acc: 0.3564 - precision_m: 0.1682 - val_loss: 1.2022 - val_acc: 0.3926 - val_precision_m: 0.2279\n","Epoch 269/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.1530 - acc: 0.3794 - precision_m: 0.2018 - val_loss: 1.1933 - val_acc: 0.3704 - val_precision_m: 0.1985\n","Epoch 270/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.1895 - acc: 0.3937 - precision_m: 0.2206 - val_loss: 1.1973 - val_acc: 0.3926 - val_precision_m: 0.1103\n","Epoch 271/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2008 - acc: 0.3709 - precision_m: 0.1462 - val_loss: 1.1938 - val_acc: 0.3778 - val_precision_m: 0.1838\n","Epoch 272/500\n","157/157 [==============================] - 0s 3ms/step - loss: 1.2207 - acc: 0.3952 - precision_m: 0.1914 - val_loss: 1.2788 - val_acc: 0.3185 - val_precision_m: 0.1618\n","Epoch 273/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2086 - acc: 0.3969 - precision_m: 0.1641 - val_loss: 1.1760 - val_acc: 0.3704 - val_precision_m: 0.1176\n","Epoch 274/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2027 - acc: 0.3959 - precision_m: 0.2258 - val_loss: 1.2295 - val_acc: 0.3556 - val_precision_m: 0.1176\n","Epoch 275/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2253 - acc: 0.3653 - precision_m: 0.1611 - val_loss: 1.1818 - val_acc: 0.3630 - val_precision_m: 0.1029\n","Epoch 276/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2777 - acc: 0.3381 - precision_m: 0.1232 - val_loss: 1.2006 - val_acc: 0.3630 - val_precision_m: 0.0882\n","Epoch 277/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2360 - acc: 0.3656 - precision_m: 0.1285 - val_loss: 1.2967 - val_acc: 0.3259 - val_precision_m: 0.1618\n","Epoch 278/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2325 - acc: 0.3754 - precision_m: 0.1867 - val_loss: 1.1994 - val_acc: 0.3704 - val_precision_m: 0.1691\n","Epoch 279/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.1698 - acc: 0.4143 - precision_m: 0.1827 - val_loss: 1.2127 - val_acc: 0.3481 - val_precision_m: 0.1691\n","Epoch 280/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.1950 - acc: 0.4168 - precision_m: 0.2151 - val_loss: 1.2191 - val_acc: 0.3259 - val_precision_m: 0.1397\n","Epoch 281/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2060 - acc: 0.4103 - precision_m: 0.2101 - val_loss: 1.1916 - val_acc: 0.3704 - val_precision_m: 0.1176\n","Epoch 282/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.1733 - acc: 0.4164 - precision_m: 0.2181 - val_loss: 1.2106 - val_acc: 0.3704 - val_precision_m: 0.1176\n","Epoch 283/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.1894 - acc: 0.4273 - precision_m: 0.1659 - val_loss: 1.1729 - val_acc: 0.4074 - val_precision_m: 0.1324\n","Epoch 284/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2237 - acc: 0.3738 - precision_m: 0.2305 - val_loss: 1.2251 - val_acc: 0.3556 - val_precision_m: 0.1397\n","Epoch 285/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2125 - acc: 0.3876 - precision_m: 0.1550 - val_loss: 1.1954 - val_acc: 0.3556 - val_precision_m: 0.1838\n","Epoch 286/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.1710 - acc: 0.4155 - precision_m: 0.2663 - val_loss: 1.2022 - val_acc: 0.3852 - val_precision_m: 0.1029\n","Epoch 287/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.1910 - acc: 0.3693 - precision_m: 0.1554 - val_loss: 1.2006 - val_acc: 0.3630 - val_precision_m: 0.1176\n","Epoch 288/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2036 - acc: 0.4056 - precision_m: 0.1987 - val_loss: 1.2254 - val_acc: 0.3852 - val_precision_m: 0.1838\n","Epoch 289/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2562 - acc: 0.3283 - precision_m: 0.1305 - val_loss: 1.1944 - val_acc: 0.3630 - val_precision_m: 0.1544\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 16.3s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","144/144 [==============================] - 1s 3ms/step - loss: 1.4730 - acc: 0.2843 - precision_m: 0.0204 - val_loss: 1.3835 - val_acc: 0.2419 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.4010 - acc: 0.2449 - precision_m: 0.0000e+00 - val_loss: 1.3796 - val_acc: 0.2177 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3958 - acc: 0.2023 - precision_m: 0.0000e+00 - val_loss: 1.3832 - val_acc: 0.3065 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3804 - acc: 0.2438 - precision_m: 0.0000e+00 - val_loss: 1.3826 - val_acc: 0.2742 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3922 - acc: 0.2060 - precision_m: 0.0000e+00 - val_loss: 1.3828 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3865 - acc: 0.2671 - precision_m: 0.0000e+00 - val_loss: 1.3826 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3849 - acc: 0.2603 - precision_m: 0.0000e+00 - val_loss: 1.3816 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3848 - acc: 0.2884 - precision_m: 0.0000e+00 - val_loss: 1.3815 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3851 - acc: 0.2855 - precision_m: 0.0000e+00 - val_loss: 1.3810 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3806 - acc: 0.2936 - precision_m: 0.0000e+00 - val_loss: 1.3808 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3842 - acc: 0.2865 - precision_m: 0.0000e+00 - val_loss: 1.3806 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3805 - acc: 0.2886 - precision_m: 0.0000e+00 - val_loss: 1.3802 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3867 - acc: 0.2500 - precision_m: 0.0000e+00 - val_loss: 1.3780 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3857 - acc: 0.2816 - precision_m: 0.0000e+00 - val_loss: 1.3801 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3863 - acc: 0.2496 - precision_m: 0.0000e+00 - val_loss: 1.3802 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3821 - acc: 0.2451 - precision_m: 0.0000e+00 - val_loss: 1.3782 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3808 - acc: 0.3249 - precision_m: 0.0000e+00 - val_loss: 1.3802 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3862 - acc: 0.2454 - precision_m: 0.0000e+00 - val_loss: 1.3802 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3838 - acc: 0.3086 - precision_m: 0.0000e+00 - val_loss: 1.3804 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3854 - acc: 0.2791 - precision_m: 0.0000e+00 - val_loss: 1.3802 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3856 - acc: 0.2491 - precision_m: 0.0000e+00 - val_loss: 1.3797 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3824 - acc: 0.2827 - precision_m: 0.0000e+00 - val_loss: 1.3797 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3808 - acc: 0.3140 - precision_m: 0.0000e+00 - val_loss: 1.3797 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3864 - acc: 0.2585 - precision_m: 0.0000e+00 - val_loss: 1.3798 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","144/144 [==============================] - 0s 3ms/step - loss: 1.3855 - acc: 0.2568 - precision_m: 0.0000e+00 - val_loss: 1.3797 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3794 - acc: 0.3191 - precision_m: 0.0000e+00 - val_loss: 1.3786 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3818 - acc: 0.2867 - precision_m: 0.0000e+00 - val_loss: 1.3796 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3785 - acc: 0.2969 - precision_m: 0.0000e+00 - val_loss: 1.3796 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3888 - acc: 0.2686 - precision_m: 0.0000e+00 - val_loss: 1.3797 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3822 - acc: 0.2988 - precision_m: 0.0000e+00 - val_loss: 1.3799 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3936 - acc: 0.2584 - precision_m: 0.0000e+00 - val_loss: 1.3794 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3877 - acc: 0.2251 - precision_m: 0.0000e+00 - val_loss: 1.3794 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3824 - acc: 0.2632 - precision_m: 0.0000e+00 - val_loss: 1.3789 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3863 - acc: 0.2565 - precision_m: 0.0000e+00 - val_loss: 1.3750 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3899 - acc: 0.2671 - precision_m: 0.0000e+00 - val_loss: 1.3790 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3808 - acc: 0.2882 - precision_m: 0.0000e+00 - val_loss: 1.3778 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3843 - acc: 0.2394 - precision_m: 0.0000e+00 - val_loss: 1.3756 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3790 - acc: 0.2601 - precision_m: 0.0000e+00 - val_loss: 1.3790 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3862 - acc: 0.3131 - precision_m: 0.0000e+00 - val_loss: 1.3784 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3778 - acc: 0.2859 - precision_m: 0.0000e+00 - val_loss: 1.3755 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3848 - acc: 0.2497 - precision_m: 0.0000e+00 - val_loss: 1.3783 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3805 - acc: 0.2548 - precision_m: 0.0000e+00 - val_loss: 1.3747 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3928 - acc: 0.2379 - precision_m: 0.0000e+00 - val_loss: 1.3755 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3814 - acc: 0.2569 - precision_m: 0.0000e+00 - val_loss: 1.3789 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3798 - acc: 0.3258 - precision_m: 0.0000e+00 - val_loss: 1.3794 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3836 - acc: 0.2734 - precision_m: 0.0000e+00 - val_loss: 1.3794 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3893 - acc: 0.2663 - precision_m: 0.0000e+00 - val_loss: 1.3794 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3814 - acc: 0.2434 - precision_m: 0.0000e+00 - val_loss: 1.3795 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3842 - acc: 0.2624 - precision_m: 0.0000e+00 - val_loss: 1.3794 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3891 - acc: 0.2563 - precision_m: 0.0000e+00 - val_loss: 1.3731 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3809 - acc: 0.2618 - precision_m: 0.0000e+00 - val_loss: 1.3780 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 52/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3821 - acc: 0.2795 - precision_m: 0.0000e+00 - val_loss: 1.3763 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 53/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3813 - acc: 0.2940 - precision_m: 0.0000e+00 - val_loss: 1.3754 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 54/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3833 - acc: 0.2533 - precision_m: 0.0000e+00 - val_loss: 1.3753 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 55/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3742 - acc: 0.3648 - precision_m: 0.0000e+00 - val_loss: 1.3796 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 56/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3799 - acc: 0.3245 - precision_m: 0.0000e+00 - val_loss: 1.3730 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 57/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3806 - acc: 0.2927 - precision_m: 0.0000e+00 - val_loss: 1.3783 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 58/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3832 - acc: 0.2881 - precision_m: 0.0000e+00 - val_loss: 1.3717 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 59/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3882 - acc: 0.2632 - precision_m: 0.0000e+00 - val_loss: 1.3715 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 60/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3752 - acc: 0.3195 - precision_m: 0.0000e+00 - val_loss: 1.3723 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 61/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3917 - acc: 0.2160 - precision_m: 0.0000e+00 - val_loss: 1.3781 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 62/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3772 - acc: 0.2886 - precision_m: 0.0000e+00 - val_loss: 1.3785 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 63/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3778 - acc: 0.2976 - precision_m: 0.0000e+00 - val_loss: 1.3727 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 64/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3860 - acc: 0.2685 - precision_m: 0.0000e+00 - val_loss: 1.3799 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 65/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3848 - acc: 0.2837 - precision_m: 0.0000e+00 - val_loss: 1.3743 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 66/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3823 - acc: 0.2484 - precision_m: 0.0000e+00 - val_loss: 1.3793 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 67/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3778 - acc: 0.2869 - precision_m: 0.0000e+00 - val_loss: 1.3696 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 68/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3878 - acc: 0.2449 - precision_m: 0.0000e+00 - val_loss: 1.3759 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 69/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3867 - acc: 0.2708 - precision_m: 0.0000e+00 - val_loss: 1.3724 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 70/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3806 - acc: 0.2495 - precision_m: 0.0000e+00 - val_loss: 1.3769 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 71/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3854 - acc: 0.2531 - precision_m: 0.0000e+00 - val_loss: 1.3707 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 72/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3910 - acc: 0.2819 - precision_m: 0.0000e+00 - val_loss: 1.3729 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 73/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3821 - acc: 0.2720 - precision_m: 0.0000e+00 - val_loss: 1.3747 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 74/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3883 - acc: 0.2249 - precision_m: 0.0000e+00 - val_loss: 1.3780 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 75/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3780 - acc: 0.2593 - precision_m: 0.0000e+00 - val_loss: 1.3724 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 76/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3892 - acc: 0.2649 - precision_m: 0.0000e+00 - val_loss: 1.3757 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 77/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3794 - acc: 0.2749 - precision_m: 0.0000e+00 - val_loss: 1.3752 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 78/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3766 - acc: 0.2449 - precision_m: 0.0000e+00 - val_loss: 1.3769 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 79/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3763 - acc: 0.2856 - precision_m: 0.0000e+00 - val_loss: 1.3710 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 80/500\n","144/144 [==============================] - 0s 3ms/step - loss: 1.3656 - acc: 0.3180 - precision_m: 0.0000e+00 - val_loss: 1.3695 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 81/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3787 - acc: 0.2770 - precision_m: 0.0000e+00 - val_loss: 1.3730 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 82/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3748 - acc: 0.2766 - precision_m: 0.0000e+00 - val_loss: 1.3772 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 83/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3826 - acc: 0.2750 - precision_m: 0.0000e+00 - val_loss: 1.3699 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 84/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3672 - acc: 0.2663 - precision_m: 0.0000e+00 - val_loss: 1.3798 - val_acc: 0.2823 - val_precision_m: 0.0000e+00\n","Epoch 85/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3849 - acc: 0.2247 - precision_m: 0.0000e+00 - val_loss: 1.3719 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 86/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3765 - acc: 0.2296 - precision_m: 0.0000e+00 - val_loss: 1.3705 - val_acc: 0.2823 - val_precision_m: 0.0000e+00\n","Epoch 87/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3746 - acc: 0.2574 - precision_m: 0.0000e+00 - val_loss: 1.3691 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 88/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3837 - acc: 0.2419 - precision_m: 0.0000e+00 - val_loss: 1.3700 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 89/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3796 - acc: 0.2729 - precision_m: 0.0000e+00 - val_loss: 1.3690 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 90/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3788 - acc: 0.3102 - precision_m: 0.0000e+00 - val_loss: 1.3791 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 91/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3902 - acc: 0.2157 - precision_m: 0.0115 - val_loss: 1.3744 - val_acc: 0.3790 - val_precision_m: 0.0000e+00\n","Epoch 92/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3752 - acc: 0.2617 - precision_m: 0.0000e+00 - val_loss: 1.3760 - val_acc: 0.3065 - val_precision_m: 0.0000e+00\n","Epoch 93/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3823 - acc: 0.3016 - precision_m: 0.0000e+00 - val_loss: 1.3751 - val_acc: 0.3548 - val_precision_m: 0.0000e+00\n","Epoch 94/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3591 - acc: 0.3328 - precision_m: 0.0000e+00 - val_loss: 1.3713 - val_acc: 0.3871 - val_precision_m: 0.0000e+00\n","Epoch 95/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3642 - acc: 0.3184 - precision_m: 0.0061 - val_loss: 1.3672 - val_acc: 0.2984 - val_precision_m: 0.0000e+00\n","Epoch 96/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3786 - acc: 0.2946 - precision_m: 0.0000e+00 - val_loss: 1.3671 - val_acc: 0.2984 - val_precision_m: 0.0000e+00\n","Epoch 97/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3756 - acc: 0.2310 - precision_m: 0.0000e+00 - val_loss: 1.3753 - val_acc: 0.3065 - val_precision_m: 0.0000e+00\n","Epoch 98/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3703 - acc: 0.3225 - precision_m: 0.0000e+00 - val_loss: 1.3679 - val_acc: 0.2984 - val_precision_m: 0.0000e+00\n","Epoch 99/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3775 - acc: 0.2795 - precision_m: 0.0000e+00 - val_loss: 1.3798 - val_acc: 0.2419 - val_precision_m: 0.0000e+00\n","Epoch 100/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3863 - acc: 0.3031 - precision_m: 0.0000e+00 - val_loss: 1.3800 - val_acc: 0.2581 - val_precision_m: 0.0000e+00\n","Epoch 101/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3768 - acc: 0.2851 - precision_m: 0.0000e+00 - val_loss: 1.3751 - val_acc: 0.3306 - val_precision_m: 0.0000e+00\n","Epoch 102/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3792 - acc: 0.3092 - precision_m: 0.0030 - val_loss: 1.3682 - val_acc: 0.3145 - val_precision_m: 0.0000e+00\n","Epoch 103/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3611 - acc: 0.2817 - precision_m: 8.5720e-04 - val_loss: 1.3678 - val_acc: 0.3226 - val_precision_m: 0.0000e+00\n","Epoch 104/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3796 - acc: 0.2436 - precision_m: 0.0028 - val_loss: 1.3809 - val_acc: 0.2258 - val_precision_m: 0.0000e+00\n","Epoch 105/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3838 - acc: 0.2754 - precision_m: 1.9258e-04 - val_loss: 1.3690 - val_acc: 0.3306 - val_precision_m: 0.0000e+00\n","Epoch 106/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3870 - acc: 0.3277 - precision_m: 0.0052 - val_loss: 1.3763 - val_acc: 0.3145 - val_precision_m: 0.0000e+00\n","Epoch 107/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3665 - acc: 0.3318 - precision_m: 0.0012 - val_loss: 1.3729 - val_acc: 0.3790 - val_precision_m: 0.0000e+00\n","Epoch 108/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3773 - acc: 0.2791 - precision_m: 0.0034 - val_loss: 1.3716 - val_acc: 0.2984 - val_precision_m: 0.0000e+00\n","Epoch 109/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3623 - acc: 0.3340 - precision_m: 0.0154 - val_loss: 1.3678 - val_acc: 0.3226 - val_precision_m: 0.0000e+00\n","Epoch 110/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3586 - acc: 0.3210 - precision_m: 0.0066 - val_loss: 1.3686 - val_acc: 0.2823 - val_precision_m: 0.0000e+00\n","Epoch 111/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3653 - acc: 0.3026 - precision_m: 0.0000e+00 - val_loss: 1.3811 - val_acc: 0.2419 - val_precision_m: 0.0000e+00\n","Epoch 112/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3613 - acc: 0.3157 - precision_m: 0.0000e+00 - val_loss: 1.3713 - val_acc: 0.3548 - val_precision_m: 0.0000e+00\n","Epoch 113/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3751 - acc: 0.2751 - precision_m: 0.0045 - val_loss: 1.3762 - val_acc: 0.3145 - val_precision_m: 0.0000e+00\n","Epoch 114/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3624 - acc: 0.3042 - precision_m: 0.0000e+00 - val_loss: 1.3697 - val_acc: 0.3710 - val_precision_m: 0.0000e+00\n","Epoch 115/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3579 - acc: 0.2781 - precision_m: 0.0257 - val_loss: 1.3710 - val_acc: 0.3952 - val_precision_m: 0.0000e+00\n","Epoch 116/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3714 - acc: 0.2968 - precision_m: 0.0012 - val_loss: 1.3688 - val_acc: 0.3710 - val_precision_m: 0.0000e+00\n","Epoch 117/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3651 - acc: 0.3000 - precision_m: 0.0056 - val_loss: 1.3763 - val_acc: 0.3065 - val_precision_m: 0.0000e+00\n","Epoch 118/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3663 - acc: 0.2582 - precision_m: 0.0000e+00 - val_loss: 1.3738 - val_acc: 0.3790 - val_precision_m: 0.0000e+00\n","Epoch 119/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3626 - acc: 0.2999 - precision_m: 0.0000e+00 - val_loss: 1.3854 - val_acc: 0.2984 - val_precision_m: 0.0161\n","Epoch 120/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3883 - acc: 0.2277 - precision_m: 0.0000e+00 - val_loss: 1.3768 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 121/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3754 - acc: 0.2841 - precision_m: 0.0033 - val_loss: 1.3763 - val_acc: 0.3145 - val_precision_m: 0.0000e+00\n","Epoch 122/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3636 - acc: 0.2967 - precision_m: 0.0069 - val_loss: 1.3799 - val_acc: 0.2177 - val_precision_m: 0.0000e+00\n","Epoch 123/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3719 - acc: 0.3570 - precision_m: 2.4149e-04 - val_loss: 1.3695 - val_acc: 0.3629 - val_precision_m: 0.0000e+00\n","Epoch 124/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3693 - acc: 0.3229 - precision_m: 0.0059 - val_loss: 1.3831 - val_acc: 0.2258 - val_precision_m: 0.0000e+00\n","Epoch 125/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3637 - acc: 0.3170 - precision_m: 0.0054 - val_loss: 1.3721 - val_acc: 0.3468 - val_precision_m: 0.0000e+00\n","Epoch 126/500\n","144/144 [==============================] - 0s 3ms/step - loss: 1.3648 - acc: 0.3128 - precision_m: 0.0050 - val_loss: 1.3748 - val_acc: 0.3306 - val_precision_m: 0.0000e+00\n","Epoch 127/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3647 - acc: 0.2842 - precision_m: 0.0260 - val_loss: 1.3811 - val_acc: 0.2177 - val_precision_m: 0.0000e+00\n","Epoch 128/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3725 - acc: 0.3055 - precision_m: 0.0104 - val_loss: 1.3736 - val_acc: 0.3226 - val_precision_m: 0.0000e+00\n","Epoch 129/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3646 - acc: 0.3071 - precision_m: 0.0033 - val_loss: 1.3701 - val_acc: 0.3710 - val_precision_m: 0.0000e+00\n","Epoch 130/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3735 - acc: 0.2493 - precision_m: 0.0024 - val_loss: 1.3678 - val_acc: 0.3629 - val_precision_m: 0.0000e+00\n","Epoch 131/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3617 - acc: 0.2834 - precision_m: 0.0000e+00 - val_loss: 1.3820 - val_acc: 0.2258 - val_precision_m: 0.0000e+00\n","Epoch 132/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3410 - acc: 0.3374 - precision_m: 0.0430 - val_loss: 1.3769 - val_acc: 0.3065 - val_precision_m: 0.0000e+00\n","Epoch 133/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3474 - acc: 0.3642 - precision_m: 0.0125 - val_loss: 1.3747 - val_acc: 0.2984 - val_precision_m: 0.0000e+00\n","Epoch 134/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3588 - acc: 0.3133 - precision_m: 0.0015 - val_loss: 1.3822 - val_acc: 0.2339 - val_precision_m: 0.0000e+00\n","Epoch 135/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3454 - acc: 0.3267 - precision_m: 0.0043 - val_loss: 1.3731 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 136/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3745 - acc: 0.2936 - precision_m: 0.0411 - val_loss: 1.3821 - val_acc: 0.2339 - val_precision_m: 0.0000e+00\n","Epoch 137/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3614 - acc: 0.3033 - precision_m: 0.0041 - val_loss: 1.3680 - val_acc: 0.3710 - val_precision_m: 0.0000e+00\n","Epoch 138/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3582 - acc: 0.2714 - precision_m: 0.0060 - val_loss: 1.3730 - val_acc: 0.3710 - val_precision_m: 0.0000e+00\n","Epoch 139/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3430 - acc: 0.3703 - precision_m: 0.0146 - val_loss: 1.3852 - val_acc: 0.2339 - val_precision_m: 0.0000e+00\n","Epoch 140/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3559 - acc: 0.3330 - precision_m: 0.0381 - val_loss: 1.3789 - val_acc: 0.2984 - val_precision_m: 0.0000e+00\n","Epoch 141/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3445 - acc: 0.3118 - precision_m: 0.1000 - val_loss: 1.3869 - val_acc: 0.2339 - val_precision_m: 0.0000e+00\n","Epoch 142/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3669 - acc: 0.2693 - precision_m: 0.0098 - val_loss: 1.3858 - val_acc: 0.2339 - val_precision_m: 0.0000e+00\n","Epoch 143/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3709 - acc: 0.2990 - precision_m: 0.0196 - val_loss: 1.3826 - val_acc: 0.2500 - val_precision_m: 0.0000e+00\n","Epoch 144/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3768 - acc: 0.2747 - precision_m: 0.0105 - val_loss: 1.3818 - val_acc: 0.2661 - val_precision_m: 0.0000e+00\n","Epoch 145/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3719 - acc: 0.2947 - precision_m: 0.0083 - val_loss: 1.3811 - val_acc: 0.2742 - val_precision_m: 0.0000e+00\n","Epoch 146/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3660 - acc: 0.3180 - precision_m: 0.0035 - val_loss: 1.3744 - val_acc: 0.3387 - val_precision_m: 0.0000e+00\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 15.6s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","138/138 [==============================] - 1s 3ms/step - loss: 1.4262 - acc: 0.2417 - precision_m: 0.0063 - val_loss: 1.3770 - val_acc: 0.3193 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3722 - acc: 0.3208 - precision_m: 0.0000e+00 - val_loss: 1.3881 - val_acc: 0.2689 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.4061 - acc: 0.2093 - precision_m: 0.0000e+00 - val_loss: 1.3792 - val_acc: 0.3109 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3823 - acc: 0.2574 - precision_m: 0.0000e+00 - val_loss: 1.3810 - val_acc: 0.3277 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3881 - acc: 0.2109 - precision_m: 0.0000e+00 - val_loss: 1.3769 - val_acc: 0.3193 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3947 - acc: 0.2754 - precision_m: 0.0000e+00 - val_loss: 1.3836 - val_acc: 0.2941 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3946 - acc: 0.1854 - precision_m: 0.0000e+00 - val_loss: 1.3833 - val_acc: 0.3025 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3837 - acc: 0.2907 - precision_m: 0.0000e+00 - val_loss: 1.3829 - val_acc: 0.3277 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3903 - acc: 0.2777 - precision_m: 0.0000e+00 - val_loss: 1.3829 - val_acc: 0.2689 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3866 - acc: 0.2370 - precision_m: 0.0000e+00 - val_loss: 1.3825 - val_acc: 0.2605 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3834 - acc: 0.3132 - precision_m: 0.0000e+00 - val_loss: 1.3825 - val_acc: 0.2269 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3848 - acc: 0.2575 - precision_m: 0.0000e+00 - val_loss: 1.3817 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3877 - acc: 0.2344 - precision_m: 0.0000e+00 - val_loss: 1.3804 - val_acc: 0.2353 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3940 - acc: 0.3043 - precision_m: 0.0000e+00 - val_loss: 1.3843 - val_acc: 0.3025 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3913 - acc: 0.2458 - precision_m: 0.0000e+00 - val_loss: 1.3838 - val_acc: 0.2269 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3911 - acc: 0.2665 - precision_m: 0.0000e+00 - val_loss: 1.3811 - val_acc: 0.2689 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3899 - acc: 0.2477 - precision_m: 0.0000e+00 - val_loss: 1.3827 - val_acc: 0.3025 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3857 - acc: 0.2235 - precision_m: 0.0000e+00 - val_loss: 1.3864 - val_acc: 0.3109 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3931 - acc: 0.2315 - precision_m: 0.0000e+00 - val_loss: 1.3817 - val_acc: 0.2353 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3848 - acc: 0.2525 - precision_m: 0.0000e+00 - val_loss: 1.3824 - val_acc: 0.2269 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3861 - acc: 0.2706 - precision_m: 0.0000e+00 - val_loss: 1.3829 - val_acc: 0.2353 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3868 - acc: 0.2766 - precision_m: 0.0000e+00 - val_loss: 1.3857 - val_acc: 0.2521 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3800 - acc: 0.2523 - precision_m: 0.0000e+00 - val_loss: 1.3821 - val_acc: 0.3445 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3822 - acc: 0.2788 - precision_m: 0.0000e+00 - val_loss: 1.3793 - val_acc: 0.2941 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3865 - acc: 0.2672 - precision_m: 0.0000e+00 - val_loss: 1.3818 - val_acc: 0.2185 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3893 - acc: 0.2639 - precision_m: 0.0000e+00 - val_loss: 1.3824 - val_acc: 0.2101 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3915 - acc: 0.2637 - precision_m: 0.0000e+00 - val_loss: 1.3845 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3960 - acc: 0.2351 - precision_m: 0.0000e+00 - val_loss: 1.3816 - val_acc: 0.3025 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3847 - acc: 0.3096 - precision_m: 0.0000e+00 - val_loss: 1.3820 - val_acc: 0.2941 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3834 - acc: 0.2663 - precision_m: 0.0000e+00 - val_loss: 1.3818 - val_acc: 0.3109 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3799 - acc: 0.3006 - precision_m: 0.0000e+00 - val_loss: 1.3814 - val_acc: 0.2605 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3805 - acc: 0.2662 - precision_m: 0.0000e+00 - val_loss: 1.3814 - val_acc: 0.3109 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3802 - acc: 0.2723 - precision_m: 0.0000e+00 - val_loss: 1.3816 - val_acc: 0.2941 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3823 - acc: 0.3093 - precision_m: 0.0000e+00 - val_loss: 1.3818 - val_acc: 0.3109 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3893 - acc: 0.2371 - precision_m: 0.0000e+00 - val_loss: 1.3826 - val_acc: 0.3193 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3921 - acc: 0.2464 - precision_m: 0.0000e+00 - val_loss: 1.3809 - val_acc: 0.2941 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3831 - acc: 0.3040 - precision_m: 0.0000e+00 - val_loss: 1.3819 - val_acc: 0.3025 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3881 - acc: 0.2962 - precision_m: 0.0000e+00 - val_loss: 1.3812 - val_acc: 0.2941 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3873 - acc: 0.3350 - precision_m: 0.0000e+00 - val_loss: 1.3812 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3885 - acc: 0.2710 - precision_m: 0.0000e+00 - val_loss: 1.3809 - val_acc: 0.2269 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3788 - acc: 0.2981 - precision_m: 0.0000e+00 - val_loss: 1.3823 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3775 - acc: 0.2551 - precision_m: 0.0000e+00 - val_loss: 1.3826 - val_acc: 0.2773 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","138/138 [==============================] - 0s 3ms/step - loss: 1.3792 - acc: 0.3222 - precision_m: 0.0000e+00 - val_loss: 1.3821 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3844 - acc: 0.2684 - precision_m: 0.0000e+00 - val_loss: 1.3827 - val_acc: 0.2773 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3799 - acc: 0.3056 - precision_m: 0.0000e+00 - val_loss: 1.3822 - val_acc: 0.3025 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3748 - acc: 0.2754 - precision_m: 0.0000e+00 - val_loss: 1.3829 - val_acc: 0.2689 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3854 - acc: 0.2468 - precision_m: 0.0000e+00 - val_loss: 1.3821 - val_acc: 0.3109 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3828 - acc: 0.2344 - precision_m: 0.0000e+00 - val_loss: 1.3810 - val_acc: 0.2521 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3809 - acc: 0.2267 - precision_m: 0.0000e+00 - val_loss: 1.3826 - val_acc: 0.2689 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3712 - acc: 0.3249 - precision_m: 0.0000e+00 - val_loss: 1.3805 - val_acc: 0.2353 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3703 - acc: 0.2721 - precision_m: 0.0000e+00 - val_loss: 1.3839 - val_acc: 0.2689 - val_precision_m: 0.0000e+00\n","Epoch 52/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3782 - acc: 0.2663 - precision_m: 0.0000e+00 - val_loss: 1.3867 - val_acc: 0.2689 - val_precision_m: 0.0000e+00\n","Epoch 53/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3855 - acc: 0.2710 - precision_m: 0.0000e+00 - val_loss: 1.3831 - val_acc: 0.3193 - val_precision_m: 0.0000e+00\n","Epoch 54/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3818 - acc: 0.2499 - precision_m: 0.0000e+00 - val_loss: 1.3802 - val_acc: 0.2689 - val_precision_m: 0.0000e+00\n","Epoch 55/500\n","138/138 [==============================] - 0s 2ms/step - loss: 1.3627 - acc: 0.3281 - precision_m: 0.0037 - val_loss: 1.3813 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 17.0s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","149/149 [==============================] - 1s 3ms/step - loss: 1.4549 - acc: 0.2242 - precision_m: 0.0166 - val_loss: 1.3851 - val_acc: 0.3101 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3750 - acc: 0.3447 - precision_m: 0.0000e+00 - val_loss: 1.3832 - val_acc: 0.3256 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3903 - acc: 0.2444 - precision_m: 0.0000e+00 - val_loss: 1.3805 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3848 - acc: 0.2536 - precision_m: 0.0000e+00 - val_loss: 1.3865 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3791 - acc: 0.3377 - precision_m: 0.0000e+00 - val_loss: 1.3855 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3791 - acc: 0.2977 - precision_m: 0.0000e+00 - val_loss: 1.3847 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3785 - acc: 0.3037 - precision_m: 0.0000e+00 - val_loss: 1.3847 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3752 - acc: 0.3252 - precision_m: 0.0000e+00 - val_loss: 1.3845 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3857 - acc: 0.2914 - precision_m: 0.0000e+00 - val_loss: 1.3828 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3766 - acc: 0.2976 - precision_m: 0.0000e+00 - val_loss: 1.3822 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3854 - acc: 0.2649 - precision_m: 0.0000e+00 - val_loss: 1.3826 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3793 - acc: 0.3063 - precision_m: 0.0000e+00 - val_loss: 1.3818 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3716 - acc: 0.3223 - precision_m: 0.0000e+00 - val_loss: 1.3814 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3706 - acc: 0.3094 - precision_m: 0.0000e+00 - val_loss: 1.3812 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3758 - acc: 0.3078 - precision_m: 0.0000e+00 - val_loss: 1.3821 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3877 - acc: 0.2705 - precision_m: 0.0000e+00 - val_loss: 1.3814 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3788 - acc: 0.3058 - precision_m: 0.0000e+00 - val_loss: 1.3823 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3787 - acc: 0.3147 - precision_m: 0.0000e+00 - val_loss: 1.3816 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3809 - acc: 0.2936 - precision_m: 0.0000e+00 - val_loss: 1.3813 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3792 - acc: 0.3188 - precision_m: 0.0000e+00 - val_loss: 1.3813 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3768 - acc: 0.2904 - precision_m: 0.0000e+00 - val_loss: 1.3814 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3762 - acc: 0.3029 - precision_m: 0.0000e+00 - val_loss: 1.3815 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3694 - acc: 0.3228 - precision_m: 0.0000e+00 - val_loss: 1.3811 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3882 - acc: 0.2616 - precision_m: 0.0000e+00 - val_loss: 1.3815 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3967 - acc: 0.2398 - precision_m: 0.0000e+00 - val_loss: 1.3816 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3840 - acc: 0.2664 - precision_m: 0.0000e+00 - val_loss: 1.3810 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3672 - acc: 0.3343 - precision_m: 0.0000e+00 - val_loss: 1.3806 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","149/149 [==============================] - 0s 3ms/step - loss: 1.3686 - acc: 0.3417 - precision_m: 0.0000e+00 - val_loss: 1.3808 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3704 - acc: 0.3242 - precision_m: 0.0000e+00 - val_loss: 1.3804 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3842 - acc: 0.2878 - precision_m: 0.0000e+00 - val_loss: 1.3803 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3778 - acc: 0.3119 - precision_m: 0.0000e+00 - val_loss: 1.3813 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3767 - acc: 0.2832 - precision_m: 0.0000e+00 - val_loss: 1.3815 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3798 - acc: 0.2579 - precision_m: 0.0000e+00 - val_loss: 1.3817 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3722 - acc: 0.3371 - precision_m: 0.0000e+00 - val_loss: 1.3817 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3935 - acc: 0.2802 - precision_m: 0.0000e+00 - val_loss: 1.3814 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3793 - acc: 0.2931 - precision_m: 0.0000e+00 - val_loss: 1.3815 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3819 - acc: 0.2872 - precision_m: 0.0000e+00 - val_loss: 1.3820 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3736 - acc: 0.3096 - precision_m: 0.0000e+00 - val_loss: 1.3812 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3759 - acc: 0.3269 - precision_m: 0.0000e+00 - val_loss: 1.3813 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3833 - acc: 0.2771 - precision_m: 0.0000e+00 - val_loss: 1.3818 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3900 - acc: 0.2918 - precision_m: 0.0000e+00 - val_loss: 1.3819 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3815 - acc: 0.3229 - precision_m: 0.0000e+00 - val_loss: 1.3813 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3739 - acc: 0.3199 - precision_m: 0.0000e+00 - val_loss: 1.3822 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3782 - acc: 0.3084 - precision_m: 0.0000e+00 - val_loss: 1.3820 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3727 - acc: 0.3382 - precision_m: 0.0000e+00 - val_loss: 1.3817 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3728 - acc: 0.3351 - precision_m: 0.0000e+00 - val_loss: 1.3820 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3744 - acc: 0.3242 - precision_m: 0.0000e+00 - val_loss: 1.3816 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3806 - acc: 0.2782 - precision_m: 0.0000e+00 - val_loss: 1.3825 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3659 - acc: 0.3533 - precision_m: 0.0000e+00 - val_loss: 1.3816 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3788 - acc: 0.2892 - precision_m: 0.0000e+00 - val_loss: 1.3820 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3796 - acc: 0.2812 - precision_m: 0.0000e+00 - val_loss: 1.3814 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 52/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3805 - acc: 0.3035 - precision_m: 0.0000e+00 - val_loss: 1.3823 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 53/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3648 - acc: 0.3517 - precision_m: 0.0000e+00 - val_loss: 1.3825 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 54/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3832 - acc: 0.3009 - precision_m: 0.0000e+00 - val_loss: 1.3824 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 55/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3779 - acc: 0.3036 - precision_m: 0.0000e+00 - val_loss: 1.3820 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 56/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3853 - acc: 0.2766 - precision_m: 0.0000e+00 - val_loss: 1.3819 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 57/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3623 - acc: 0.3434 - precision_m: 0.0000e+00 - val_loss: 1.3820 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 58/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3965 - acc: 0.2246 - precision_m: 0.0000e+00 - val_loss: 1.3818 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 59/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3596 - acc: 0.3755 - precision_m: 0.0000e+00 - val_loss: 1.3825 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 60/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3694 - acc: 0.3144 - precision_m: 0.0000e+00 - val_loss: 1.3820 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 61/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3771 - acc: 0.2874 - precision_m: 0.0000e+00 - val_loss: 1.3822 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 62/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3648 - acc: 0.3349 - precision_m: 0.0000e+00 - val_loss: 1.3822 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 63/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3880 - acc: 0.2721 - precision_m: 0.0000e+00 - val_loss: 1.3824 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 64/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3759 - acc: 0.3054 - precision_m: 0.0000e+00 - val_loss: 1.3822 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 65/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3914 - acc: 0.2634 - precision_m: 0.0000e+00 - val_loss: 1.3820 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 66/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3813 - acc: 0.3098 - precision_m: 0.0000e+00 - val_loss: 1.3818 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 67/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3798 - acc: 0.2736 - precision_m: 0.0000e+00 - val_loss: 1.3825 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 68/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3817 - acc: 0.2871 - precision_m: 0.0000e+00 - val_loss: 1.3819 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 69/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3778 - acc: 0.3097 - precision_m: 0.0000e+00 - val_loss: 1.3825 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 70/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3853 - acc: 0.2776 - precision_m: 0.0000e+00 - val_loss: 1.3818 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 71/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3667 - acc: 0.3462 - precision_m: 0.0000e+00 - val_loss: 1.3819 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 72/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3865 - acc: 0.2832 - precision_m: 0.0000e+00 - val_loss: 1.3820 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 73/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3758 - acc: 0.3072 - precision_m: 0.0000e+00 - val_loss: 1.3818 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 74/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3686 - acc: 0.3201 - precision_m: 0.0000e+00 - val_loss: 1.3823 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 75/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3831 - acc: 0.2695 - precision_m: 0.0000e+00 - val_loss: 1.3818 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 76/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3790 - acc: 0.2984 - precision_m: 0.0000e+00 - val_loss: 1.3818 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 77/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3819 - acc: 0.2614 - precision_m: 0.0000e+00 - val_loss: 1.3824 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 78/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3816 - acc: 0.3030 - precision_m: 0.0000e+00 - val_loss: 1.3821 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 79/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3720 - acc: 0.3286 - precision_m: 0.0000e+00 - val_loss: 1.3824 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 80/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3757 - acc: 0.3253 - precision_m: 0.0000e+00 - val_loss: 1.3823 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 17.5s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","156/156 [==============================] - 1s 3ms/step - loss: 1.4467 - acc: 0.2908 - precision_m: 0.0270 - val_loss: 1.3855 - val_acc: 0.2889 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3973 - acc: 0.2417 - precision_m: 0.0000e+00 - val_loss: 1.3863 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3739 - acc: 0.3210 - precision_m: 0.0000e+00 - val_loss: 1.3848 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3924 - acc: 0.2077 - precision_m: 0.0000e+00 - val_loss: 1.3884 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3868 - acc: 0.3068 - precision_m: 0.0000e+00 - val_loss: 1.3842 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3858 - acc: 0.2683 - precision_m: 0.0000e+00 - val_loss: 1.3848 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3788 - acc: 0.3314 - precision_m: 0.0000e+00 - val_loss: 1.3843 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3796 - acc: 0.3082 - precision_m: 0.0000e+00 - val_loss: 1.3849 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3831 - acc: 0.2884 - precision_m: 0.0000e+00 - val_loss: 1.3858 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3780 - acc: 0.3378 - precision_m: 0.0000e+00 - val_loss: 1.3863 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3816 - acc: 0.2922 - precision_m: 0.0000e+00 - val_loss: 1.3865 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3821 - acc: 0.2888 - precision_m: 0.0000e+00 - val_loss: 1.3866 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3886 - acc: 0.2782 - precision_m: 0.0000e+00 - val_loss: 1.3870 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3759 - acc: 0.3063 - precision_m: 0.0000e+00 - val_loss: 1.3875 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3865 - acc: 0.2460 - precision_m: 0.0000e+00 - val_loss: 1.3877 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3828 - acc: 0.2672 - precision_m: 0.0000e+00 - val_loss: 1.3877 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3812 - acc: 0.2813 - precision_m: 0.0000e+00 - val_loss: 1.3879 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3832 - acc: 0.3021 - precision_m: 0.0000e+00 - val_loss: 1.3877 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3864 - acc: 0.2873 - precision_m: 0.0000e+00 - val_loss: 1.3884 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3828 - acc: 0.2829 - precision_m: 0.0000e+00 - val_loss: 1.3879 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3811 - acc: 0.3215 - precision_m: 0.0000e+00 - val_loss: 1.3884 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3802 - acc: 0.2715 - precision_m: 0.0000e+00 - val_loss: 1.3886 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3842 - acc: 0.2813 - precision_m: 0.0000e+00 - val_loss: 1.3882 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3786 - acc: 0.3023 - precision_m: 0.0000e+00 - val_loss: 1.3886 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3788 - acc: 0.3044 - precision_m: 0.0000e+00 - val_loss: 1.3888 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3813 - acc: 0.2916 - precision_m: 0.0000e+00 - val_loss: 1.3883 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3752 - acc: 0.3122 - precision_m: 0.0000e+00 - val_loss: 1.3884 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3708 - acc: 0.3177 - precision_m: 0.0000e+00 - val_loss: 1.3888 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3983 - acc: 0.2220 - precision_m: 0.0000e+00 - val_loss: 1.3891 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3860 - acc: 0.2759 - precision_m: 0.0000e+00 - val_loss: 1.3887 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3747 - acc: 0.3312 - precision_m: 0.0000e+00 - val_loss: 1.3888 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","156/156 [==============================] - 0s 3ms/step - loss: 1.3817 - acc: 0.2688 - precision_m: 0.0000e+00 - val_loss: 1.3886 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3871 - acc: 0.2636 - precision_m: 0.0000e+00 - val_loss: 1.3888 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3857 - acc: 0.2493 - precision_m: 0.0000e+00 - val_loss: 1.3888 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3816 - acc: 0.2681 - precision_m: 0.0000e+00 - val_loss: 1.3903 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3742 - acc: 0.3137 - precision_m: 0.0000e+00 - val_loss: 1.3892 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3809 - acc: 0.3100 - precision_m: 0.0000e+00 - val_loss: 1.3916 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3790 - acc: 0.2635 - precision_m: 0.0133 - val_loss: 1.3912 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3848 - acc: 0.2634 - precision_m: 0.0000e+00 - val_loss: 1.3907 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3813 - acc: 0.2730 - precision_m: 0.0000e+00 - val_loss: 1.3896 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3826 - acc: 0.2971 - precision_m: 0.0000e+00 - val_loss: 1.3896 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3816 - acc: 0.2457 - precision_m: 0.0000e+00 - val_loss: 1.3905 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3790 - acc: 0.3168 - precision_m: 0.0000e+00 - val_loss: 1.3909 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3820 - acc: 0.2911 - precision_m: 0.0000e+00 - val_loss: 1.3878 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3786 - acc: 0.2768 - precision_m: 0.0000e+00 - val_loss: 1.3890 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3699 - acc: 0.3263 - precision_m: 0.0148 - val_loss: 1.3907 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3817 - acc: 0.2928 - precision_m: 0.0000e+00 - val_loss: 1.3902 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3806 - acc: 0.2812 - precision_m: 0.0000e+00 - val_loss: 1.3905 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3765 - acc: 0.3229 - precision_m: 0.0000e+00 - val_loss: 1.3924 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3839 - acc: 0.2522 - precision_m: 0.0082 - val_loss: 1.3901 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3785 - acc: 0.3114 - precision_m: 0.0000e+00 - val_loss: 1.3894 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 52/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3804 - acc: 0.2918 - precision_m: 0.0000e+00 - val_loss: 1.3891 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 53/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3833 - acc: 0.2898 - precision_m: 0.0000e+00 - val_loss: 1.3890 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 54/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3730 - acc: 0.3083 - precision_m: 0.0127 - val_loss: 1.3884 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 55/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3825 - acc: 0.2666 - precision_m: 0.0064 - val_loss: 1.3879 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 17.5s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","160/160 [==============================] - 1s 3ms/step - loss: 1.4662 - acc: 0.2704 - precision_m: 0.0206 - val_loss: 1.3952 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.4063 - acc: 0.2334 - precision_m: 0.0000e+00 - val_loss: 1.3883 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","160/160 [==============================] - 0s 3ms/step - loss: 1.3872 - acc: 0.3010 - precision_m: 0.0000e+00 - val_loss: 1.3875 - val_acc: 0.2246 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3895 - acc: 0.2890 - precision_m: 0.0000e+00 - val_loss: 1.3901 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3813 - acc: 0.3195 - precision_m: 0.0000e+00 - val_loss: 1.3909 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3835 - acc: 0.3021 - precision_m: 0.0000e+00 - val_loss: 1.3910 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3861 - acc: 0.2711 - precision_m: 0.0000e+00 - val_loss: 1.3913 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3803 - acc: 0.3049 - precision_m: 0.0000e+00 - val_loss: 1.3916 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3839 - acc: 0.2841 - precision_m: 0.0000e+00 - val_loss: 1.3916 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3845 - acc: 0.2835 - precision_m: 0.0000e+00 - val_loss: 1.3923 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3849 - acc: 0.2919 - precision_m: 0.0000e+00 - val_loss: 1.3924 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3861 - acc: 0.2773 - precision_m: 0.0000e+00 - val_loss: 1.3927 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3861 - acc: 0.2724 - precision_m: 0.0000e+00 - val_loss: 1.3923 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3816 - acc: 0.3014 - precision_m: 0.0000e+00 - val_loss: 1.3925 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3840 - acc: 0.2834 - precision_m: 0.0000e+00 - val_loss: 1.3926 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3802 - acc: 0.2998 - precision_m: 0.0000e+00 - val_loss: 1.3924 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3822 - acc: 0.2816 - precision_m: 0.0000e+00 - val_loss: 1.3927 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3804 - acc: 0.2914 - precision_m: 0.0000e+00 - val_loss: 1.3938 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3825 - acc: 0.2884 - precision_m: 0.0000e+00 - val_loss: 1.3930 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3887 - acc: 0.2604 - precision_m: 0.0000e+00 - val_loss: 1.3933 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3837 - acc: 0.2729 - precision_m: 0.0000e+00 - val_loss: 1.3943 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3946 - acc: 0.2166 - precision_m: 0.0000e+00 - val_loss: 1.3945 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3799 - acc: 0.2991 - precision_m: 0.0000e+00 - val_loss: 1.3952 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3849 - acc: 0.2668 - precision_m: 0.0000e+00 - val_loss: 1.3947 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3842 - acc: 0.2961 - precision_m: 0.0000e+00 - val_loss: 1.3953 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3842 - acc: 0.2793 - precision_m: 0.0000e+00 - val_loss: 1.3931 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3882 - acc: 0.2579 - precision_m: 0.0000e+00 - val_loss: 1.3922 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3823 - acc: 0.2924 - precision_m: 0.0000e+00 - val_loss: 1.3931 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3831 - acc: 0.2639 - precision_m: 0.0000e+00 - val_loss: 1.3928 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3808 - acc: 0.3091 - precision_m: 0.0000e+00 - val_loss: 1.3935 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3949 - acc: 0.2863 - precision_m: 0.0000e+00 - val_loss: 1.3928 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3790 - acc: 0.3170 - precision_m: 0.0000e+00 - val_loss: 1.3933 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3857 - acc: 0.2841 - precision_m: 0.0000e+00 - val_loss: 1.3937 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3761 - acc: 0.3139 - precision_m: 0.0000e+00 - val_loss: 1.3936 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3895 - acc: 0.2450 - precision_m: 0.0000e+00 - val_loss: 1.3943 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3835 - acc: 0.2692 - precision_m: 0.0000e+00 - val_loss: 1.3945 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3842 - acc: 0.2705 - precision_m: 0.0000e+00 - val_loss: 1.3943 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3838 - acc: 0.2793 - precision_m: 0.0000e+00 - val_loss: 1.3948 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3867 - acc: 0.2641 - precision_m: 0.0000e+00 - val_loss: 1.3943 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3821 - acc: 0.2857 - precision_m: 0.0000e+00 - val_loss: 1.3951 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3833 - acc: 0.2873 - precision_m: 0.0000e+00 - val_loss: 1.3949 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3860 - acc: 0.2653 - precision_m: 0.0000e+00 - val_loss: 1.3945 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3808 - acc: 0.2680 - precision_m: 0.0089 - val_loss: 1.3949 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3652 - acc: 0.3628 - precision_m: 0.0063 - val_loss: 1.3950 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3901 - acc: 0.2477 - precision_m: 0.0000e+00 - val_loss: 1.3950 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3819 - acc: 0.2919 - precision_m: 0.0026 - val_loss: 1.3939 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3865 - acc: 0.2822 - precision_m: 0.0000e+00 - val_loss: 1.3939 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3820 - acc: 0.2843 - precision_m: 0.0024 - val_loss: 1.3941 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3871 - acc: 0.2541 - precision_m: 0.0029 - val_loss: 1.3937 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3761 - acc: 0.3187 - precision_m: 0.0000e+00 - val_loss: 1.3937 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3838 - acc: 0.2870 - precision_m: 0.0000e+00 - val_loss: 1.3937 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 52/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3813 - acc: 0.2956 - precision_m: 4.7980e-04 - val_loss: 1.3937 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Epoch 53/500\n","160/160 [==============================] - 0s 2ms/step - loss: 1.3823 - acc: 0.2829 - precision_m: 0.0000e+00 - val_loss: 1.3945 - val_acc: 0.2319 - val_precision_m: 0.0000e+00\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 17.3s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","157/157 [==============================] - 1s 3ms/step - loss: 1.4608 - acc: 0.2358 - precision_m: 0.0046 - val_loss: 1.3904 - val_acc: 0.2444 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3865 - acc: 0.2782 - precision_m: 0.0000e+00 - val_loss: 1.3890 - val_acc: 0.2148 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3952 - acc: 0.2371 - precision_m: 0.0000e+00 - val_loss: 1.3969 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3918 - acc: 0.2568 - precision_m: 0.0000e+00 - val_loss: 1.3951 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3828 - acc: 0.2723 - precision_m: 0.0000e+00 - val_loss: 1.3914 - val_acc: 0.2444 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3833 - acc: 0.2793 - precision_m: 0.0000e+00 - val_loss: 1.3963 - val_acc: 0.2148 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3878 - acc: 0.2461 - precision_m: 0.0000e+00 - val_loss: 1.3907 - val_acc: 0.2444 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3853 - acc: 0.2641 - precision_m: 0.0000e+00 - val_loss: 1.3910 - val_acc: 0.2444 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3840 - acc: 0.2448 - precision_m: 0.0000e+00 - val_loss: 1.3916 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3878 - acc: 0.2142 - precision_m: 0.0000e+00 - val_loss: 1.3931 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3823 - acc: 0.2792 - precision_m: 0.0000e+00 - val_loss: 1.3936 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3843 - acc: 0.3095 - precision_m: 0.0000e+00 - val_loss: 1.3930 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3862 - acc: 0.2596 - precision_m: 0.0000e+00 - val_loss: 1.3933 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3847 - acc: 0.2516 - precision_m: 0.0000e+00 - val_loss: 1.3937 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3814 - acc: 0.2770 - precision_m: 0.0000e+00 - val_loss: 1.3945 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3840 - acc: 0.2818 - precision_m: 0.0000e+00 - val_loss: 1.3953 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3742 - acc: 0.3046 - precision_m: 0.0000e+00 - val_loss: 1.3941 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3879 - acc: 0.2552 - precision_m: 0.0000e+00 - val_loss: 1.3937 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3859 - acc: 0.2501 - precision_m: 0.0000e+00 - val_loss: 1.3939 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3818 - acc: 0.3037 - precision_m: 0.0000e+00 - val_loss: 1.3945 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3819 - acc: 0.2854 - precision_m: 0.0000e+00 - val_loss: 1.3957 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3786 - acc: 0.2876 - precision_m: 0.0000e+00 - val_loss: 1.3944 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3848 - acc: 0.2708 - precision_m: 0.0000e+00 - val_loss: 1.3942 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3880 - acc: 0.2234 - precision_m: 0.0000e+00 - val_loss: 1.3949 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3805 - acc: 0.2855 - precision_m: 0.0000e+00 - val_loss: 1.3949 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3816 - acc: 0.2870 - precision_m: 0.0000e+00 - val_loss: 1.3960 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3767 - acc: 0.3126 - precision_m: 0.0000e+00 - val_loss: 1.3957 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3870 - acc: 0.2697 - precision_m: 0.0000e+00 - val_loss: 1.3963 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3850 - acc: 0.2713 - precision_m: 0.0000e+00 - val_loss: 1.3951 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3849 - acc: 0.2805 - precision_m: 0.0000e+00 - val_loss: 1.3956 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3864 - acc: 0.2648 - precision_m: 0.0000e+00 - val_loss: 1.3953 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3797 - acc: 0.2759 - precision_m: 0.0000e+00 - val_loss: 1.3955 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3832 - acc: 0.2810 - precision_m: 0.0000e+00 - val_loss: 1.3958 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3867 - acc: 0.2676 - precision_m: 0.0000e+00 - val_loss: 1.3957 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3826 - acc: 0.2837 - precision_m: 0.0000e+00 - val_loss: 1.3957 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3847 - acc: 0.2676 - precision_m: 0.0000e+00 - val_loss: 1.3960 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3912 - acc: 0.2237 - precision_m: 0.0000e+00 - val_loss: 1.3960 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3903 - acc: 0.2282 - precision_m: 0.0000e+00 - val_loss: 1.3969 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3853 - acc: 0.2768 - precision_m: 0.0000e+00 - val_loss: 1.3972 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3795 - acc: 0.2901 - precision_m: 0.0000e+00 - val_loss: 1.3972 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3838 - acc: 0.2709 - precision_m: 0.0000e+00 - val_loss: 1.3967 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3839 - acc: 0.3044 - precision_m: 0.0000e+00 - val_loss: 1.3968 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3843 - acc: 0.2874 - precision_m: 0.0000e+00 - val_loss: 1.3961 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3880 - acc: 0.2555 - precision_m: 0.0000e+00 - val_loss: 1.3965 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3835 - acc: 0.2936 - precision_m: 0.0000e+00 - val_loss: 1.3970 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3782 - acc: 0.3063 - precision_m: 0.0000e+00 - val_loss: 1.3968 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3845 - acc: 0.2769 - precision_m: 0.0000e+00 - val_loss: 1.3973 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3783 - acc: 0.2905 - precision_m: 0.0000e+00 - val_loss: 1.3970 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3823 - acc: 0.2702 - precision_m: 0.0000e+00 - val_loss: 1.3969 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3815 - acc: 0.2701 - precision_m: 0.0000e+00 - val_loss: 1.3958 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3854 - acc: 0.2540 - precision_m: 0.0000e+00 - val_loss: 1.3964 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 52/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3837 - acc: 0.2753 - precision_m: 0.0000e+00 - val_loss: 1.3964 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 15.8s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","152/152 [==============================] - 1s 3ms/step - loss: 1.5899 - acc: 0.2407 - precision_m: 0.0227 - val_loss: 1.3947 - val_acc: 0.1450 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3908 - acc: 0.2508 - precision_m: 0.0000e+00 - val_loss: 1.3915 - val_acc: 0.2366 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3891 - acc: 0.1970 - precision_m: 0.0000e+00 - val_loss: 1.3927 - val_acc: 0.2977 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3812 - acc: 0.2828 - precision_m: 0.0000e+00 - val_loss: 1.3950 - val_acc: 0.2901 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3962 - acc: 0.2335 - precision_m: 0.0000e+00 - val_loss: 1.4010 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3887 - acc: 0.2709 - precision_m: 0.0000e+00 - val_loss: 1.3984 - val_acc: 0.2672 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3794 - acc: 0.2728 - precision_m: 0.0000e+00 - val_loss: 1.3984 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3790 - acc: 0.2095 - precision_m: 0.0000e+00 - val_loss: 1.4077 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3662 - acc: 0.2907 - precision_m: 0.0000e+00 - val_loss: 1.4063 - val_acc: 0.2214 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3821 - acc: 0.3085 - precision_m: 0.0000e+00 - val_loss: 1.4059 - val_acc: 0.2061 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3691 - acc: 0.2695 - precision_m: 0.0000e+00 - val_loss: 1.4088 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3655 - acc: 0.2700 - precision_m: 0.0000e+00 - val_loss: 1.4092 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3740 - acc: 0.2066 - precision_m: 0.0000e+00 - val_loss: 1.4109 - val_acc: 0.2672 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","152/152 [==============================] - 0s 3ms/step - loss: 1.3732 - acc: 0.2939 - precision_m: 0.0000e+00 - val_loss: 1.4030 - val_acc: 0.2672 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3899 - acc: 0.1837 - precision_m: 0.0000e+00 - val_loss: 1.4034 - val_acc: 0.2672 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3826 - acc: 0.2771 - precision_m: 0.0000e+00 - val_loss: 1.4060 - val_acc: 0.2595 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3738 - acc: 0.2379 - precision_m: 0.0000e+00 - val_loss: 1.4025 - val_acc: 0.2748 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3782 - acc: 0.2750 - precision_m: 0.0000e+00 - val_loss: 1.4059 - val_acc: 0.2824 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3794 - acc: 0.2522 - precision_m: 0.0000e+00 - val_loss: 1.4075 - val_acc: 0.2366 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3825 - acc: 0.2342 - precision_m: 0.0000e+00 - val_loss: 1.4059 - val_acc: 0.2977 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3718 - acc: 0.2065 - precision_m: 0.0000e+00 - val_loss: 1.4024 - val_acc: 0.2977 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3795 - acc: 0.2714 - precision_m: 0.0000e+00 - val_loss: 1.4070 - val_acc: 0.3053 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3813 - acc: 0.2485 - precision_m: 0.0000e+00 - val_loss: 1.4052 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3914 - acc: 0.2707 - precision_m: 0.0000e+00 - val_loss: 1.4080 - val_acc: 0.2672 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3793 - acc: 0.3041 - precision_m: 0.0000e+00 - val_loss: 1.4069 - val_acc: 0.2366 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3758 - acc: 0.3135 - precision_m: 0.0000e+00 - val_loss: 1.4118 - val_acc: 0.2595 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3776 - acc: 0.3026 - precision_m: 0.0000e+00 - val_loss: 1.4029 - val_acc: 0.3206 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3630 - acc: 0.3103 - precision_m: 0.0000e+00 - val_loss: 1.4021 - val_acc: 0.1985 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3711 - acc: 0.2525 - precision_m: 0.0000e+00 - val_loss: 1.4039 - val_acc: 0.1985 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3771 - acc: 0.3168 - precision_m: 0.0000e+00 - val_loss: 1.4090 - val_acc: 0.2748 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3759 - acc: 0.2455 - precision_m: 0.0000e+00 - val_loss: 1.4127 - val_acc: 0.2595 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3715 - acc: 0.2985 - precision_m: 0.0000e+00 - val_loss: 1.4102 - val_acc: 0.3130 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3928 - acc: 0.2643 - precision_m: 0.0000e+00 - val_loss: 1.4128 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3600 - acc: 0.2904 - precision_m: 0.0025 - val_loss: 1.4125 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3862 - acc: 0.2637 - precision_m: 0.0000e+00 - val_loss: 1.4113 - val_acc: 0.2290 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3840 - acc: 0.2543 - precision_m: 0.0000e+00 - val_loss: 1.4107 - val_acc: 0.2977 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3717 - acc: 0.2570 - precision_m: 0.0028 - val_loss: 1.4136 - val_acc: 0.2824 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3701 - acc: 0.2425 - precision_m: 0.0032 - val_loss: 1.4139 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3793 - acc: 0.2568 - precision_m: 0.0000e+00 - val_loss: 1.4125 - val_acc: 0.2137 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3679 - acc: 0.2525 - precision_m: 0.0000e+00 - val_loss: 1.4155 - val_acc: 0.2748 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3814 - acc: 0.2646 - precision_m: 0.0085 - val_loss: 1.4138 - val_acc: 0.2061 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3830 - acc: 0.2629 - precision_m: 0.0000e+00 - val_loss: 1.4125 - val_acc: 0.1908 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3738 - acc: 0.2175 - precision_m: 0.0000e+00 - val_loss: 1.4158 - val_acc: 0.2137 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3504 - acc: 0.3124 - precision_m: 0.0217 - val_loss: 1.4138 - val_acc: 0.2290 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3561 - acc: 0.3584 - precision_m: 0.0071 - val_loss: 1.4100 - val_acc: 0.2061 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3471 - acc: 0.3757 - precision_m: 0.0023 - val_loss: 1.4172 - val_acc: 0.2214 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3788 - acc: 0.2860 - precision_m: 0.0000e+00 - val_loss: 1.4195 - val_acc: 0.1985 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3659 - acc: 0.2938 - precision_m: 0.0013 - val_loss: 1.4088 - val_acc: 0.1832 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3805 - acc: 0.2192 - precision_m: 0.0000e+00 - val_loss: 1.4241 - val_acc: 0.2824 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3665 - acc: 0.2774 - precision_m: 0.0000e+00 - val_loss: 1.4242 - val_acc: 0.2748 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3691 - acc: 0.2828 - precision_m: 0.0000e+00 - val_loss: 1.4196 - val_acc: 0.1756 - val_precision_m: 0.0000e+00\n","Epoch 52/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3507 - acc: 0.2990 - precision_m: 0.0015 - val_loss: 1.4132 - val_acc: 0.1985 - val_precision_m: 0.0000e+00\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 18.3s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","146/146 [==============================] - 1s 3ms/step - loss: 1.4269 - acc: 0.2465 - precision_m: 0.0000e+00 - val_loss: 1.3766 - val_acc: 0.2619 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3664 - acc: 0.3132 - precision_m: 0.0252 - val_loss: 1.3843 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3903 - acc: 0.1793 - precision_m: 0.0000e+00 - val_loss: 1.3840 - val_acc: 0.2937 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3866 - acc: 0.2777 - precision_m: 0.0000e+00 - val_loss: 1.3844 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3905 - acc: 0.2214 - precision_m: 0.0000e+00 - val_loss: 1.3833 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3820 - acc: 0.2848 - precision_m: 0.0000e+00 - val_loss: 1.3823 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3845 - acc: 0.2962 - precision_m: 0.0000e+00 - val_loss: 1.3814 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3855 - acc: 0.2695 - precision_m: 0.0000e+00 - val_loss: 1.3804 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3856 - acc: 0.2779 - precision_m: 0.0000e+00 - val_loss: 1.3800 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3816 - acc: 0.2998 - precision_m: 0.0000e+00 - val_loss: 1.3805 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3755 - acc: 0.3087 - precision_m: 0.0000e+00 - val_loss: 1.3788 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3828 - acc: 0.2979 - precision_m: 0.0000e+00 - val_loss: 1.3799 - val_acc: 0.2619 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3858 - acc: 0.2868 - precision_m: 0.0000e+00 - val_loss: 1.3787 - val_acc: 0.2381 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3891 - acc: 0.2678 - precision_m: 0.0000e+00 - val_loss: 1.3803 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3884 - acc: 0.2639 - precision_m: 0.0000e+00 - val_loss: 1.3773 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3809 - acc: 0.2852 - precision_m: 0.0000e+00 - val_loss: 1.3795 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3808 - acc: 0.2866 - precision_m: 0.0000e+00 - val_loss: 1.3800 - val_acc: 0.2619 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3832 - acc: 0.2933 - precision_m: 0.0000e+00 - val_loss: 1.3784 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3775 - acc: 0.2921 - precision_m: 0.0000e+00 - val_loss: 1.3792 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3833 - acc: 0.3015 - precision_m: 0.0000e+00 - val_loss: 1.3792 - val_acc: 0.2619 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3812 - acc: 0.3175 - precision_m: 0.0000e+00 - val_loss: 1.3782 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3840 - acc: 0.2805 - precision_m: 0.0000e+00 - val_loss: 1.3793 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3793 - acc: 0.3112 - precision_m: 0.0000e+00 - val_loss: 1.3806 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3791 - acc: 0.2804 - precision_m: 0.0072 - val_loss: 1.3743 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3835 - acc: 0.2511 - precision_m: 0.0000e+00 - val_loss: 1.3747 - val_acc: 0.2302 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3766 - acc: 0.3027 - precision_m: 0.0000e+00 - val_loss: 1.3756 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3802 - acc: 0.2829 - precision_m: 0.0000e+00 - val_loss: 1.3785 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3814 - acc: 0.2657 - precision_m: 0.0000e+00 - val_loss: 1.3822 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3774 - acc: 0.2909 - precision_m: 0.0000e+00 - val_loss: 1.3774 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3740 - acc: 0.2782 - precision_m: 0.0000e+00 - val_loss: 1.3737 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3574 - acc: 0.3114 - precision_m: 0.0193 - val_loss: 1.3790 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3831 - acc: 0.2575 - precision_m: 0.0000e+00 - val_loss: 1.3760 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3689 - acc: 0.2849 - precision_m: 0.0330 - val_loss: 1.3773 - val_acc: 0.2619 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3789 - acc: 0.2774 - precision_m: 0.0000e+00 - val_loss: 1.3828 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3817 - acc: 0.2515 - precision_m: 0.0000e+00 - val_loss: 1.3808 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3865 - acc: 0.2854 - precision_m: 0.0000e+00 - val_loss: 1.3793 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3747 - acc: 0.2754 - precision_m: 0.0017 - val_loss: 1.3711 - val_acc: 0.2381 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3690 - acc: 0.2751 - precision_m: 0.0150 - val_loss: 1.3824 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","146/146 [==============================] - 0s 3ms/step - loss: 1.3722 - acc: 0.2998 - precision_m: 0.0000e+00 - val_loss: 1.3801 - val_acc: 0.2619 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3877 - acc: 0.2363 - precision_m: 0.0000e+00 - val_loss: 1.3685 - val_acc: 0.2302 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3717 - acc: 0.2571 - precision_m: 0.0219 - val_loss: 1.3798 - val_acc: 0.2619 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3752 - acc: 0.2659 - precision_m: 0.0078 - val_loss: 1.3774 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3852 - acc: 0.2619 - precision_m: 0.0014 - val_loss: 1.3704 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3541 - acc: 0.2740 - precision_m: 0.0335 - val_loss: 1.3698 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3596 - acc: 0.2991 - precision_m: 0.0267 - val_loss: 1.3812 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3614 - acc: 0.3064 - precision_m: 0.0329 - val_loss: 1.3827 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3729 - acc: 0.2816 - precision_m: 0.0080 - val_loss: 1.3688 - val_acc: 0.2381 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3705 - acc: 0.2813 - precision_m: 0.0415 - val_loss: 1.3823 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3582 - acc: 0.2633 - precision_m: 0.0140 - val_loss: 1.3838 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3508 - acc: 0.2734 - precision_m: 0.0266 - val_loss: 1.3842 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3591 - acc: 0.3171 - precision_m: 0.0157 - val_loss: 1.3836 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 52/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3819 - acc: 0.2464 - precision_m: 0.0159 - val_loss: 1.3832 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 53/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3528 - acc: 0.2702 - precision_m: 0.0158 - val_loss: 1.3737 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 54/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3576 - acc: 0.3247 - precision_m: 0.0183 - val_loss: 1.3827 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 55/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3598 - acc: 0.3102 - precision_m: 0.0320 - val_loss: 1.3847 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 56/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3712 - acc: 0.2801 - precision_m: 0.0063 - val_loss: 1.3768 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 57/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3423 - acc: 0.3023 - precision_m: 0.0406 - val_loss: 1.3879 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 58/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3765 - acc: 0.2751 - precision_m: 0.0104 - val_loss: 1.3810 - val_acc: 0.2619 - val_precision_m: 0.0000e+00\n","Epoch 59/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3515 - acc: 0.3326 - precision_m: 0.0022 - val_loss: 1.3883 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 60/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3639 - acc: 0.3223 - precision_m: 0.0080 - val_loss: 1.3844 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 61/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3552 - acc: 0.3197 - precision_m: 0.0319 - val_loss: 1.3869 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 62/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3583 - acc: 0.3145 - precision_m: 0.0091 - val_loss: 1.3757 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 63/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3705 - acc: 0.2675 - precision_m: 0.0499 - val_loss: 1.3829 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 64/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3982 - acc: 0.3159 - precision_m: 0.0475 - val_loss: 1.3818 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 65/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3587 - acc: 0.2885 - precision_m: 0.0394 - val_loss: 1.3882 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 66/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3635 - acc: 0.2840 - precision_m: 0.0097 - val_loss: 1.3767 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 67/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3688 - acc: 0.3080 - precision_m: 0.0331 - val_loss: 1.3887 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 68/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3707 - acc: 0.2579 - precision_m: 0.0116 - val_loss: 1.3827 - val_acc: 0.2619 - val_precision_m: 0.0000e+00\n","Epoch 69/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3252 - acc: 0.3102 - precision_m: 0.0766 - val_loss: 1.3821 - val_acc: 0.2619 - val_precision_m: 0.0000e+00\n","Epoch 70/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3546 - acc: 0.2529 - precision_m: 0.0254 - val_loss: 1.3852 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 71/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3450 - acc: 0.3051 - precision_m: 0.0506 - val_loss: 1.3887 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 72/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3610 - acc: 0.2583 - precision_m: 0.0319 - val_loss: 1.3867 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 73/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3528 - acc: 0.3012 - precision_m: 0.0455 - val_loss: 1.3784 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 74/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3393 - acc: 0.2799 - precision_m: 0.0524 - val_loss: 1.3885 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 75/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3624 - acc: 0.2736 - precision_m: 0.0108 - val_loss: 1.3791 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 76/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3472 - acc: 0.2646 - precision_m: 0.0671 - val_loss: 1.3899 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 77/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3419 - acc: 0.3453 - precision_m: 0.0379 - val_loss: 1.3926 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 78/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3293 - acc: 0.3436 - precision_m: 0.0396 - val_loss: 1.3852 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 79/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3579 - acc: 0.2893 - precision_m: 0.0596 - val_loss: 1.3944 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 80/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3675 - acc: 0.2818 - precision_m: 0.0093 - val_loss: 1.3837 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 81/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3614 - acc: 0.2826 - precision_m: 0.0636 - val_loss: 1.3821 - val_acc: 0.2460 - val_precision_m: 0.0317\n","Epoch 82/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3606 - acc: 0.2579 - precision_m: 0.0219 - val_loss: 1.3888 - val_acc: 0.2619 - val_precision_m: 0.0000e+00\n","Epoch 83/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3333 - acc: 0.3300 - precision_m: 0.0162 - val_loss: 1.3829 - val_acc: 0.2460 - val_precision_m: 0.0159\n","Epoch 84/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3694 - acc: 0.2823 - precision_m: 0.0043 - val_loss: 1.3831 - val_acc: 0.2698 - val_precision_m: 0.0159\n","Epoch 85/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3878 - acc: 0.3008 - precision_m: 0.0236 - val_loss: 1.3934 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 86/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3553 - acc: 0.2899 - precision_m: 0.0057 - val_loss: 1.3853 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 87/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3511 - acc: 0.3232 - precision_m: 0.0189 - val_loss: 1.3926 - val_acc: 0.2619 - val_precision_m: 0.0000e+00\n","Epoch 88/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3605 - acc: 0.2840 - precision_m: 0.0022 - val_loss: 1.3908 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 89/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3801 - acc: 0.3005 - precision_m: 0.0247 - val_loss: 1.3899 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 90/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3567 - acc: 0.2640 - precision_m: 0.0538 - val_loss: 1.3820 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_split.py:667: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=5.\n"," % (min_groups, self.n_splits)), UserWarning)\n"],"name":"stderr"},{"output_type":"stream","text":["Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_split.py:667: UserWarning: The least populated class in y has only 3 members, which is less than n_splits=5.\n"," % (min_groups, self.n_splits)), UserWarning)\n","[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 8.5s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","85/85 [==============================] - 1s 4ms/step - loss: 1.8124 - acc: 0.3556 - precision_m: 0.3269 - val_loss: 1.0608 - val_acc: 0.4795 - val_precision_m: 0.0270\n","Epoch 2/500\n","85/85 [==============================] - 0s 2ms/step - loss: 1.0369 - acc: 0.5496 - precision_m: 0.4104 - val_loss: 0.9747 - val_acc: 0.5342 - val_precision_m: 0.3649\n","Epoch 3/500\n","85/85 [==============================] - 0s 2ms/step - loss: 1.0336 - acc: 0.5692 - precision_m: 0.3673 - val_loss: 0.9560 - val_acc: 0.5205 - val_precision_m: 0.4324\n","Epoch 4/500\n","85/85 [==============================] - 0s 2ms/step - loss: 1.1694 - acc: 0.4583 - precision_m: 0.3364 - val_loss: 0.9502 - val_acc: 0.5342 - val_precision_m: 0.3378\n","Epoch 5/500\n","85/85 [==============================] - 0s 2ms/step - loss: 1.0004 - acc: 0.5227 - precision_m: 0.3963 - val_loss: 0.9466 - val_acc: 0.5068 - val_precision_m: 0.0541\n","Epoch 6/500\n","85/85 [==============================] - 0s 2ms/step - loss: 1.0438 - acc: 0.3928 - precision_m: 0.3509 - val_loss: 0.9407 - val_acc: 0.4932 - val_precision_m: 0.1081\n","Epoch 7/500\n","85/85 [==============================] - 0s 2ms/step - loss: 1.0477 - acc: 0.5759 - precision_m: 0.4438 - val_loss: 0.9403 - val_acc: 0.5616 - val_precision_m: 0.2838\n","Epoch 8/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.9957 - acc: 0.5449 - precision_m: 0.4191 - val_loss: 0.9170 - val_acc: 0.5342 - val_precision_m: 0.5270\n","Epoch 9/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.9371 - acc: 0.4448 - precision_m: 0.3600 - val_loss: 0.9207 - val_acc: 0.5205 - val_precision_m: 0.3243\n","Epoch 10/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.9839 - acc: 0.4788 - precision_m: 0.3200 - val_loss: 0.9241 - val_acc: 0.5205 - val_precision_m: 0.1892\n","Epoch 11/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.9654 - acc: 0.5009 - precision_m: 0.2876 - val_loss: 0.9171 - val_acc: 0.5205 - val_precision_m: 0.3784\n","Epoch 12/500\n","85/85 [==============================] - 0s 2ms/step - loss: 1.1000 - acc: 0.4361 - precision_m: 0.3439 - val_loss: 0.9296 - val_acc: 0.5068 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8901 - acc: 0.5359 - precision_m: 0.3781 - val_loss: 0.9203 - val_acc: 0.5068 - val_precision_m: 0.3378\n","Epoch 14/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.9045 - acc: 0.5086 - precision_m: 0.3459 - val_loss: 0.9632 - val_acc: 0.4384 - val_precision_m: 0.3649\n","Epoch 15/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8777 - acc: 0.4978 - precision_m: 0.4245 - val_loss: 0.9179 - val_acc: 0.5890 - val_precision_m: 0.0270\n","Epoch 16/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8770 - acc: 0.5565 - precision_m: 0.5053 - val_loss: 0.9099 - val_acc: 0.5890 - val_precision_m: 0.2162\n","Epoch 17/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.9497 - acc: 0.5586 - precision_m: 0.4615 - val_loss: 0.9082 - val_acc: 0.5890 - val_precision_m: 0.2973\n","Epoch 18/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.9138 - acc: 0.4938 - precision_m: 0.4410 - val_loss: 0.9177 - val_acc: 0.4247 - val_precision_m: 0.2838\n","Epoch 19/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.9639 - acc: 0.4977 - precision_m: 0.4274 - val_loss: 0.9128 - val_acc: 0.5479 - val_precision_m: 0.1892\n","Epoch 20/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8623 - acc: 0.5582 - precision_m: 0.4477 - val_loss: 0.9516 - val_acc: 0.3973 - val_precision_m: 0.3649\n","Epoch 21/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8824 - acc: 0.5282 - precision_m: 0.5187 - val_loss: 0.9073 - val_acc: 0.5068 - val_precision_m: 0.3243\n","Epoch 22/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8795 - acc: 0.4892 - precision_m: 0.3564 - val_loss: 0.9159 - val_acc: 0.4247 - val_precision_m: 0.3649\n","Epoch 23/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8630 - acc: 0.4761 - precision_m: 0.3780 - val_loss: 0.9039 - val_acc: 0.5205 - val_precision_m: 0.5270\n","Epoch 24/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8826 - acc: 0.5338 - precision_m: 0.4789 - val_loss: 0.9058 - val_acc: 0.5205 - val_precision_m: 0.2568\n","Epoch 25/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8784 - acc: 0.5833 - precision_m: 0.4406 - val_loss: 0.9038 - val_acc: 0.5753 - val_precision_m: 0.0541\n","Epoch 26/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8376 - acc: 0.5756 - precision_m: 0.4728 - val_loss: 0.9093 - val_acc: 0.4247 - val_precision_m: 0.1892\n","Epoch 27/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8844 - acc: 0.6085 - precision_m: 0.5388 - val_loss: 0.9009 - val_acc: 0.5753 - val_precision_m: 0.1351\n","Epoch 28/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8323 - acc: 0.5441 - precision_m: 0.4728 - val_loss: 0.8981 - val_acc: 0.5342 - val_precision_m: 0.4865\n","Epoch 29/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.9397 - acc: 0.3868 - precision_m: 0.3769 - val_loss: 0.9064 - val_acc: 0.4932 - val_precision_m: 0.3378\n","Epoch 30/500\n","85/85 [==============================] - 0s 2ms/step - loss: 1.0339 - acc: 0.5235 - precision_m: 0.4443 - val_loss: 0.9003 - val_acc: 0.6027 - val_precision_m: 0.4189\n","Epoch 31/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.9833 - acc: 0.5277 - precision_m: 0.3687 - val_loss: 0.9105 - val_acc: 0.4247 - val_precision_m: 0.3649\n","Epoch 32/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8734 - acc: 0.4363 - precision_m: 0.4079 - val_loss: 0.9064 - val_acc: 0.4247 - val_precision_m: 0.4054\n","Epoch 33/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.9230 - acc: 0.5204 - precision_m: 0.4349 - val_loss: 0.8932 - val_acc: 0.5890 - val_precision_m: 0.4459\n","Epoch 34/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8591 - acc: 0.5162 - precision_m: 0.3871 - val_loss: 0.8956 - val_acc: 0.5616 - val_precision_m: 0.5541\n","Epoch 35/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8280 - acc: 0.4769 - precision_m: 0.4344 - val_loss: 0.8917 - val_acc: 0.5342 - val_precision_m: 0.4595\n","Epoch 36/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8435 - acc: 0.5729 - precision_m: 0.5845 - val_loss: 0.8969 - val_acc: 0.5342 - val_precision_m: 0.3919\n","Epoch 37/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8737 - acc: 0.4893 - precision_m: 0.3362 - val_loss: 0.8921 - val_acc: 0.5616 - val_precision_m: 0.3514\n","Epoch 38/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.9911 - acc: 0.4151 - precision_m: 0.3647 - val_loss: 0.9016 - val_acc: 0.4521 - val_precision_m: 0.3649\n","Epoch 39/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8806 - acc: 0.4882 - precision_m: 0.5431 - val_loss: 0.8960 - val_acc: 0.5753 - val_precision_m: 0.1351\n","Epoch 40/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8274 - acc: 0.4642 - precision_m: 0.4209 - val_loss: 0.8958 - val_acc: 0.5205 - val_precision_m: 0.3108\n","Epoch 41/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8082 - acc: 0.5807 - precision_m: 0.5186 - val_loss: 0.9078 - val_acc: 0.5205 - val_precision_m: 0.3919\n","Epoch 42/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8335 - acc: 0.5458 - precision_m: 0.4777 - val_loss: 0.9033 - val_acc: 0.4521 - val_precision_m: 0.3919\n","Epoch 43/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.7975 - acc: 0.5346 - precision_m: 0.4704 - val_loss: 0.8938 - val_acc: 0.6164 - val_precision_m: 0.4189\n","Epoch 44/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8118 - acc: 0.5986 - precision_m: 0.5390 - val_loss: 0.8890 - val_acc: 0.5342 - val_precision_m: 0.3649\n","Epoch 45/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8834 - acc: 0.4593 - precision_m: 0.5182 - val_loss: 0.8846 - val_acc: 0.5890 - val_precision_m: 0.3649\n","Epoch 46/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.7998 - acc: 0.5434 - precision_m: 0.5102 - val_loss: 0.9033 - val_acc: 0.4521 - val_precision_m: 0.4324\n","Epoch 47/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.7896 - acc: 0.5784 - precision_m: 0.5949 - val_loss: 0.9128 - val_acc: 0.4384 - val_precision_m: 0.4459\n","Epoch 48/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8600 - acc: 0.5024 - precision_m: 0.5281 - val_loss: 0.8950 - val_acc: 0.5205 - val_precision_m: 0.5405\n","Epoch 49/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.7820 - acc: 0.6821 - precision_m: 0.6906 - val_loss: 0.8857 - val_acc: 0.6027 - val_precision_m: 0.5541\n","Epoch 50/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8024 - acc: 0.6466 - precision_m: 0.6497 - val_loss: 0.8858 - val_acc: 0.5205 - val_precision_m: 0.3784\n","Epoch 51/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8848 - acc: 0.5383 - precision_m: 0.5216 - val_loss: 0.8816 - val_acc: 0.5753 - val_precision_m: 0.5541\n","Epoch 52/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8921 - acc: 0.5204 - precision_m: 0.4818 - val_loss: 0.8886 - val_acc: 0.5342 - val_precision_m: 0.4459\n","Epoch 53/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8137 - acc: 0.5163 - precision_m: 0.5134 - val_loss: 0.8859 - val_acc: 0.4932 - val_precision_m: 0.4324\n","Epoch 54/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8549 - acc: 0.5786 - precision_m: 0.5608 - val_loss: 0.8802 - val_acc: 0.6027 - val_precision_m: 0.5000\n","Epoch 55/500\n","85/85 [==============================] - 0s 3ms/step - loss: 0.8266 - acc: 0.5328 - precision_m: 0.4836 - val_loss: 0.8906 - val_acc: 0.5068 - val_precision_m: 0.4459\n","Epoch 56/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8163 - acc: 0.5843 - precision_m: 0.5213 - val_loss: 0.8994 - val_acc: 0.5342 - val_precision_m: 0.4865\n","Epoch 57/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.7660 - acc: 0.6084 - precision_m: 0.6511 - val_loss: 0.8809 - val_acc: 0.5616 - val_precision_m: 0.5676\n","Epoch 58/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.7948 - acc: 0.6587 - precision_m: 0.6193 - val_loss: 0.8745 - val_acc: 0.5479 - val_precision_m: 0.5405\n","Epoch 59/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8269 - acc: 0.5250 - precision_m: 0.4490 - val_loss: 0.8963 - val_acc: 0.5479 - val_precision_m: 0.5270\n","Epoch 60/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8175 - acc: 0.4722 - precision_m: 0.4427 - val_loss: 0.8881 - val_acc: 0.5205 - val_precision_m: 0.4189\n","Epoch 61/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8373 - acc: 0.6201 - precision_m: 0.6316 - val_loss: 0.8956 - val_acc: 0.5068 - val_precision_m: 0.4865\n","Epoch 62/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.7685 - acc: 0.5527 - precision_m: 0.5589 - val_loss: 0.8789 - val_acc: 0.5205 - val_precision_m: 0.5270\n","Epoch 63/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8106 - acc: 0.6162 - precision_m: 0.5316 - val_loss: 0.8815 - val_acc: 0.5616 - val_precision_m: 0.5676\n","Epoch 64/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.9513 - acc: 0.5584 - precision_m: 0.5434 - val_loss: 0.8968 - val_acc: 0.5479 - val_precision_m: 0.4730\n","Epoch 65/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8207 - acc: 0.5965 - precision_m: 0.5443 - val_loss: 0.8774 - val_acc: 0.5616 - val_precision_m: 0.5541\n","Epoch 66/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.7625 - acc: 0.6035 - precision_m: 0.6094 - val_loss: 0.8842 - val_acc: 0.5068 - val_precision_m: 0.4865\n","Epoch 67/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8491 - acc: 0.5796 - precision_m: 0.5377 - val_loss: 0.8767 - val_acc: 0.5890 - val_precision_m: 0.5676\n","Epoch 68/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.9164 - acc: 0.6264 - precision_m: 0.5817 - val_loss: 0.8802 - val_acc: 0.5205 - val_precision_m: 0.5541\n","Epoch 69/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.7837 - acc: 0.5910 - precision_m: 0.6045 - val_loss: 0.8965 - val_acc: 0.5342 - val_precision_m: 0.3649\n","Epoch 70/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.7774 - acc: 0.5865 - precision_m: 0.5622 - val_loss: 0.8933 - val_acc: 0.5616 - val_precision_m: 0.4595\n","Epoch 71/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8005 - acc: 0.5908 - precision_m: 0.5565 - val_loss: 0.9045 - val_acc: 0.5068 - val_precision_m: 0.5000\n","Epoch 72/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.7784 - acc: 0.5657 - precision_m: 0.5977 - val_loss: 0.8781 - val_acc: 0.5342 - val_precision_m: 0.5541\n","Epoch 73/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.7542 - acc: 0.7092 - precision_m: 0.7135 - val_loss: 0.8886 - val_acc: 0.5068 - val_precision_m: 0.5135\n","Epoch 74/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.7895 - acc: 0.5515 - precision_m: 0.5646 - val_loss: 0.8775 - val_acc: 0.5479 - val_precision_m: 0.5405\n","Epoch 75/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.7880 - acc: 0.6828 - precision_m: 0.6899 - val_loss: 0.8891 - val_acc: 0.5479 - val_precision_m: 0.5541\n","Epoch 76/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8605 - acc: 0.5667 - precision_m: 0.6080 - val_loss: 0.8898 - val_acc: 0.4932 - val_precision_m: 0.5405\n","Epoch 77/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.7635 - acc: 0.6085 - precision_m: 0.6405 - val_loss: 0.9145 - val_acc: 0.5068 - val_precision_m: 0.4189\n","Epoch 78/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.7812 - acc: 0.5907 - precision_m: 0.6035 - val_loss: 0.8900 - val_acc: 0.5479 - val_precision_m: 0.5405\n","Epoch 79/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.7650 - acc: 0.6245 - precision_m: 0.6315 - val_loss: 0.8976 - val_acc: 0.4932 - val_precision_m: 0.5135\n","Epoch 80/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.6675 - acc: 0.7019 - precision_m: 0.7392 - val_loss: 0.8910 - val_acc: 0.5342 - val_precision_m: 0.5270\n","Epoch 81/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.7792 - acc: 0.5974 - precision_m: 0.5988 - val_loss: 0.9228 - val_acc: 0.4658 - val_precision_m: 0.4459\n","Epoch 82/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.7990 - acc: 0.6530 - precision_m: 0.6140 - val_loss: 0.9001 - val_acc: 0.5205 - val_precision_m: 0.5270\n","Epoch 83/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8863 - acc: 0.5595 - precision_m: 0.6118 - val_loss: 0.8844 - val_acc: 0.5616 - val_precision_m: 0.5541\n","Epoch 84/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8358 - acc: 0.5145 - precision_m: 0.5160 - val_loss: 0.9087 - val_acc: 0.5342 - val_precision_m: 0.4865\n","Epoch 85/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.7215 - acc: 0.6347 - precision_m: 0.6557 - val_loss: 0.9557 - val_acc: 0.5342 - val_precision_m: 0.4730\n","Epoch 86/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8788 - acc: 0.5139 - precision_m: 0.4984 - val_loss: 0.9043 - val_acc: 0.5205 - val_precision_m: 0.5000\n","Epoch 87/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8564 - acc: 0.5921 - precision_m: 0.5885 - val_loss: 0.8974 - val_acc: 0.5068 - val_precision_m: 0.5135\n","Epoch 88/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.7277 - acc: 0.6159 - precision_m: 0.6551 - val_loss: 0.9135 - val_acc: 0.5068 - val_precision_m: 0.5000\n","Epoch 89/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8000 - acc: 0.6568 - precision_m: 0.6596 - val_loss: 0.8731 - val_acc: 0.6164 - val_precision_m: 0.5946\n","Epoch 90/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8647 - acc: 0.6457 - precision_m: 0.6268 - val_loss: 0.9157 - val_acc: 0.5342 - val_precision_m: 0.5135\n","Epoch 91/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.7884 - acc: 0.6581 - precision_m: 0.6367 - val_loss: 0.8705 - val_acc: 0.5890 - val_precision_m: 0.5946\n","Epoch 92/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.7419 - acc: 0.6533 - precision_m: 0.6334 - val_loss: 0.8698 - val_acc: 0.6027 - val_precision_m: 0.5811\n","Epoch 93/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.6870 - acc: 0.7155 - precision_m: 0.6889 - val_loss: 0.8879 - val_acc: 0.5753 - val_precision_m: 0.5541\n","Epoch 94/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.7065 - acc: 0.6479 - precision_m: 0.6563 - val_loss: 0.9092 - val_acc: 0.5068 - val_precision_m: 0.5000\n","Epoch 95/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.7980 - acc: 0.6226 - precision_m: 0.6709 - val_loss: 0.8699 - val_acc: 0.5890 - val_precision_m: 0.5676\n","Epoch 96/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.7289 - acc: 0.6304 - precision_m: 0.6349 - val_loss: 0.8832 - val_acc: 0.5342 - val_precision_m: 0.5405\n","Epoch 97/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8549 - acc: 0.6268 - precision_m: 0.5962 - val_loss: 0.9152 - val_acc: 0.5342 - val_precision_m: 0.5270\n","Epoch 98/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8038 - acc: 0.6269 - precision_m: 0.6455 - val_loss: 0.9983 - val_acc: 0.5068 - val_precision_m: 0.4730\n","Epoch 99/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.7302 - acc: 0.6662 - precision_m: 0.6859 - val_loss: 0.9483 - val_acc: 0.4795 - val_precision_m: 0.4865\n","Epoch 100/500\n","85/85 [==============================] - 0s 4ms/step - loss: 0.7504 - acc: 0.6999 - precision_m: 0.7181 - val_loss: 0.9558 - val_acc: 0.5205 - val_precision_m: 0.4730\n","Epoch 101/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.7691 - acc: 0.6725 - precision_m: 0.6421 - val_loss: 0.9300 - val_acc: 0.5205 - val_precision_m: 0.5135\n","Epoch 102/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.7174 - acc: 0.6633 - precision_m: 0.6751 - val_loss: 0.9066 - val_acc: 0.5205 - val_precision_m: 0.5270\n","Epoch 103/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.7978 - acc: 0.6451 - precision_m: 0.6877 - val_loss: 0.9271 - val_acc: 0.5479 - val_precision_m: 0.5000\n","Epoch 104/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.6006 - acc: 0.7283 - precision_m: 0.7697 - val_loss: 0.8978 - val_acc: 0.5616 - val_precision_m: 0.5811\n","Epoch 105/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.7044 - acc: 0.7302 - precision_m: 0.7344 - val_loss: 0.8955 - val_acc: 0.5616 - val_precision_m: 0.5270\n","Epoch 106/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8687 - acc: 0.5855 - precision_m: 0.6072 - val_loss: 0.8851 - val_acc: 0.6027 - val_precision_m: 0.5811\n","Epoch 107/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.6995 - acc: 0.6930 - precision_m: 0.7017 - val_loss: 0.9234 - val_acc: 0.5205 - val_precision_m: 0.5135\n","Epoch 108/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.6531 - acc: 0.7181 - precision_m: 0.7186 - val_loss: 0.9292 - val_acc: 0.5479 - val_precision_m: 0.5135\n","Epoch 109/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.7464 - acc: 0.6481 - precision_m: 0.6497 - val_loss: 0.8848 - val_acc: 0.6027 - val_precision_m: 0.5811\n","Epoch 110/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.6821 - acc: 0.7619 - precision_m: 0.7749 - val_loss: 0.9991 - val_acc: 0.4932 - val_precision_m: 0.5000\n","Epoch 111/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.6770 - acc: 0.7252 - precision_m: 0.7099 - val_loss: 0.9797 - val_acc: 0.5205 - val_precision_m: 0.5135\n","Epoch 112/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8169 - acc: 0.6233 - precision_m: 0.6024 - val_loss: 0.9543 - val_acc: 0.5616 - val_precision_m: 0.5676\n","Epoch 113/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.6888 - acc: 0.6597 - precision_m: 0.7050 - val_loss: 0.8841 - val_acc: 0.5753 - val_precision_m: 0.5811\n","Epoch 114/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.7534 - acc: 0.6353 - precision_m: 0.6417 - val_loss: 0.8947 - val_acc: 0.5616 - val_precision_m: 0.5541\n","Epoch 115/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.8170 - acc: 0.6617 - precision_m: 0.6444 - val_loss: 0.9068 - val_acc: 0.5205 - val_precision_m: 0.5270\n","Epoch 116/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.7528 - acc: 0.6871 - precision_m: 0.6687 - val_loss: 1.1017 - val_acc: 0.5205 - val_precision_m: 0.5405\n","Epoch 117/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.9364 - acc: 0.6520 - precision_m: 0.7088 - val_loss: 0.9001 - val_acc: 0.5753 - val_precision_m: 0.5676\n","Epoch 118/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.6913 - acc: 0.6743 - precision_m: 0.6825 - val_loss: 0.9574 - val_acc: 0.5616 - val_precision_m: 0.5541\n","Epoch 119/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.9322 - acc: 0.5964 - precision_m: 0.5979 - val_loss: 0.9591 - val_acc: 0.5205 - val_precision_m: 0.5135\n","Epoch 120/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.7094 - acc: 0.7036 - precision_m: 0.7209 - val_loss: 0.9809 - val_acc: 0.5342 - val_precision_m: 0.5135\n","Epoch 121/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.7350 - acc: 0.7020 - precision_m: 0.7101 - val_loss: 0.9480 - val_acc: 0.5342 - val_precision_m: 0.5270\n","Epoch 122/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.7647 - acc: 0.6812 - precision_m: 0.6796 - val_loss: 0.9376 - val_acc: 0.5479 - val_precision_m: 0.5405\n","Epoch 123/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.6311 - acc: 0.7262 - precision_m: 0.7341 - val_loss: 0.9216 - val_acc: 0.5342 - val_precision_m: 0.5270\n","Epoch 124/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.7228 - acc: 0.6504 - precision_m: 0.6476 - val_loss: 0.9734 - val_acc: 0.5068 - val_precision_m: 0.5000\n","Epoch 125/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.6552 - acc: 0.7064 - precision_m: 0.6910 - val_loss: 1.0296 - val_acc: 0.5479 - val_precision_m: 0.5135\n","Epoch 126/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.6951 - acc: 0.6985 - precision_m: 0.6948 - val_loss: 0.9020 - val_acc: 0.6164 - val_precision_m: 0.5676\n","Epoch 127/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.7092 - acc: 0.6467 - precision_m: 0.6565 - val_loss: 0.9895 - val_acc: 0.5205 - val_precision_m: 0.5000\n","Epoch 128/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.7840 - acc: 0.6672 - precision_m: 0.6715 - val_loss: 0.9642 - val_acc: 0.5616 - val_precision_m: 0.5811\n","Epoch 129/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.7063 - acc: 0.7036 - precision_m: 0.7120 - val_loss: 0.8851 - val_acc: 0.5890 - val_precision_m: 0.5811\n","Epoch 130/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.6648 - acc: 0.7141 - precision_m: 0.7429 - val_loss: 0.9616 - val_acc: 0.5342 - val_precision_m: 0.5270\n","Epoch 131/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.6305 - acc: 0.7453 - precision_m: 0.7702 - val_loss: 0.9179 - val_acc: 0.5342 - val_precision_m: 0.5541\n","Epoch 132/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.6980 - acc: 0.6836 - precision_m: 0.7071 - val_loss: 0.9635 - val_acc: 0.5616 - val_precision_m: 0.5270\n","Epoch 133/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.6361 - acc: 0.7417 - precision_m: 0.7533 - val_loss: 1.0180 - val_acc: 0.5890 - val_precision_m: 0.5811\n","Epoch 134/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.6789 - acc: 0.6840 - precision_m: 0.7007 - val_loss: 0.9584 - val_acc: 0.5616 - val_precision_m: 0.5541\n","Epoch 135/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.5509 - acc: 0.7499 - precision_m: 0.7105 - val_loss: 1.0137 - val_acc: 0.5479 - val_precision_m: 0.5135\n","Epoch 136/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.6539 - acc: 0.7239 - precision_m: 0.7369 - val_loss: 1.0501 - val_acc: 0.5342 - val_precision_m: 0.5541\n","Epoch 137/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.6649 - acc: 0.6824 - precision_m: 0.7022 - val_loss: 0.8993 - val_acc: 0.6027 - val_precision_m: 0.5946\n","Epoch 138/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.7080 - acc: 0.6403 - precision_m: 0.6198 - val_loss: 0.9412 - val_acc: 0.5479 - val_precision_m: 0.5270\n","Epoch 139/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.5931 - acc: 0.8001 - precision_m: 0.8006 - val_loss: 1.0471 - val_acc: 0.5342 - val_precision_m: 0.5405\n","Epoch 140/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.5949 - acc: 0.6976 - precision_m: 0.7377 - val_loss: 0.9609 - val_acc: 0.5479 - val_precision_m: 0.5270\n","Epoch 141/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.7018 - acc: 0.7017 - precision_m: 0.7263 - val_loss: 0.9764 - val_acc: 0.5342 - val_precision_m: 0.5405\n","Epoch 142/500\n","85/85 [==============================] - 0s 2ms/step - loss: 0.6653 - acc: 0.7129 - precision_m: 0.7194 - val_loss: 0.9479 - val_acc: 0.6027 - val_precision_m: 0.5676\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 20.4s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","148/148 [==============================] - 1s 4ms/step - loss: 1.4192 - acc: 0.2960 - precision_m: 0.0346 - val_loss: 1.3872 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.4030 - acc: 0.2522 - precision_m: 0.0000e+00 - val_loss: 1.3869 - val_acc: 0.2344 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3842 - acc: 0.2503 - precision_m: 0.0000e+00 - val_loss: 1.3863 - val_acc: 0.2578 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3948 - acc: 0.2351 - precision_m: 0.0000e+00 - val_loss: 1.3864 - val_acc: 0.2734 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3799 - acc: 0.2870 - precision_m: 0.0000e+00 - val_loss: 1.3859 - val_acc: 0.2812 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3749 - acc: 0.3132 - precision_m: 0.0000e+00 - val_loss: 1.3856 - val_acc: 0.2812 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3896 - acc: 0.2063 - precision_m: 0.0000e+00 - val_loss: 1.3860 - val_acc: 0.2578 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3857 - acc: 0.2728 - precision_m: 0.0000e+00 - val_loss: 1.3858 - val_acc: 0.2266 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3892 - acc: 0.2365 - precision_m: 0.0000e+00 - val_loss: 1.3861 - val_acc: 0.2578 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3811 - acc: 0.2672 - precision_m: 0.0053 - val_loss: 1.3886 - val_acc: 0.2578 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3806 - acc: 0.2661 - precision_m: 0.0000e+00 - val_loss: 1.3877 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3872 - acc: 0.2206 - precision_m: 0.0000e+00 - val_loss: 1.3853 - val_acc: 0.2109 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3931 - acc: 0.2454 - precision_m: 0.0000e+00 - val_loss: 1.3862 - val_acc: 0.2188 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3808 - acc: 0.2554 - precision_m: 0.0000e+00 - val_loss: 1.3861 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3820 - acc: 0.3001 - precision_m: 0.0000e+00 - val_loss: 1.3862 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3761 - acc: 0.2810 - precision_m: 0.0000e+00 - val_loss: 1.3861 - val_acc: 0.2266 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3740 - acc: 0.3138 - precision_m: 0.0044 - val_loss: 1.3860 - val_acc: 0.2734 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3804 - acc: 0.2521 - precision_m: 2.7521e-04 - val_loss: 1.3860 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3814 - acc: 0.2435 - precision_m: 0.0086 - val_loss: 1.3869 - val_acc: 0.2734 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3825 - acc: 0.2244 - precision_m: 0.0000e+00 - val_loss: 1.3857 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3778 - acc: 0.3199 - precision_m: 0.0111 - val_loss: 1.3855 - val_acc: 0.2578 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3855 - acc: 0.2315 - precision_m: 0.0000e+00 - val_loss: 1.3872 - val_acc: 0.2578 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3837 - acc: 0.2883 - precision_m: 0.0012 - val_loss: 1.3892 - val_acc: 0.2578 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3784 - acc: 0.3155 - precision_m: 0.0013 - val_loss: 1.3819 - val_acc: 0.2734 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3780 - acc: 0.2582 - precision_m: 0.0192 - val_loss: 1.3868 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3787 - acc: 0.2180 - precision_m: 0.0229 - val_loss: 1.3865 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3859 - acc: 0.2440 - precision_m: 0.0032 - val_loss: 1.3863 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3789 - acc: 0.3008 - precision_m: 0.0129 - val_loss: 1.3867 - val_acc: 0.2734 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3726 - acc: 0.2702 - precision_m: 0.0152 - val_loss: 1.3866 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3760 - acc: 0.3198 - precision_m: 9.6416e-04 - val_loss: 1.3862 - val_acc: 0.2734 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3544 - acc: 0.3348 - precision_m: 0.0537 - val_loss: 1.3853 - val_acc: 0.2500 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3840 - acc: 0.2749 - precision_m: 0.0000e+00 - val_loss: 1.3871 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3869 - acc: 0.2485 - precision_m: 0.0000e+00 - val_loss: 1.3864 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3618 - acc: 0.3112 - precision_m: 0.0362 - val_loss: 1.3869 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3655 - acc: 0.3104 - precision_m: 0.0320 - val_loss: 1.3934 - val_acc: 0.2578 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3609 - acc: 0.2914 - precision_m: 0.0368 - val_loss: 1.3923 - val_acc: 0.2422 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3511 - acc: 0.3146 - precision_m: 0.0525 - val_loss: 1.3882 - val_acc: 0.2734 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3821 - acc: 0.2720 - precision_m: 0.0124 - val_loss: 1.3879 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3398 - acc: 0.3219 - precision_m: 0.0849 - val_loss: 1.3879 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3871 - acc: 0.2901 - precision_m: 0.0119 - val_loss: 1.3868 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3817 - acc: 0.2622 - precision_m: 0.0081 - val_loss: 1.3870 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3814 - acc: 0.2538 - precision_m: 0.0107 - val_loss: 1.3867 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3832 - acc: 0.2579 - precision_m: 0.0086 - val_loss: 1.3872 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3598 - acc: 0.3039 - precision_m: 0.0241 - val_loss: 1.3872 - val_acc: 0.2734 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3818 - acc: 0.2351 - precision_m: 0.0065 - val_loss: 1.3875 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3611 - acc: 0.2928 - precision_m: 0.0356 - val_loss: 1.3862 - val_acc: 0.2734 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3859 - acc: 0.2567 - precision_m: 0.0049 - val_loss: 1.3864 - val_acc: 0.2734 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3716 - acc: 0.2978 - precision_m: 0.0120 - val_loss: 1.3875 - val_acc: 0.2578 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3691 - acc: 0.3132 - precision_m: 0.0160 - val_loss: 1.3871 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3789 - acc: 0.2900 - precision_m: 0.0094 - val_loss: 1.3869 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3718 - acc: 0.3059 - precision_m: 0.0220 - val_loss: 1.3869 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 52/500\n","148/148 [==============================] - 0s 3ms/step - loss: 1.3834 - acc: 0.2852 - precision_m: 0.0062 - val_loss: 1.3872 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 53/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3701 - acc: 0.3216 - precision_m: 0.0131 - val_loss: 1.3872 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 54/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3557 - acc: 0.2755 - precision_m: 0.0437 - val_loss: 1.3864 - val_acc: 0.2734 - val_precision_m: 0.0000e+00\n","Epoch 55/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3872 - acc: 0.2582 - precision_m: 0.0043 - val_loss: 1.3877 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 56/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3764 - acc: 0.3052 - precision_m: 0.0033 - val_loss: 1.3865 - val_acc: 0.2734 - val_precision_m: 0.0000e+00\n","Epoch 57/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3733 - acc: 0.2744 - precision_m: 0.0146 - val_loss: 1.3878 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 58/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3647 - acc: 0.3398 - precision_m: 0.0254 - val_loss: 1.3866 - val_acc: 0.2734 - val_precision_m: 0.0000e+00\n","Epoch 59/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3558 - acc: 0.2747 - precision_m: 0.0396 - val_loss: 1.3884 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 60/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3767 - acc: 0.2981 - precision_m: 0.0174 - val_loss: 1.3888 - val_acc: 0.2578 - val_precision_m: 0.0000e+00\n","Epoch 61/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3684 - acc: 0.2789 - precision_m: 0.0314 - val_loss: 1.3869 - val_acc: 0.2812 - val_precision_m: 0.0000e+00\n","Epoch 62/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3725 - acc: 0.2764 - precision_m: 0.0213 - val_loss: 1.3877 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 63/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3744 - acc: 0.2826 - precision_m: 0.0185 - val_loss: 1.3880 - val_acc: 0.2734 - val_precision_m: 0.0000e+00\n","Epoch 64/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3667 - acc: 0.2433 - precision_m: 0.0451 - val_loss: 1.3856 - val_acc: 0.2812 - val_precision_m: 0.0000e+00\n","Epoch 65/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3711 - acc: 0.2755 - precision_m: 0.0282 - val_loss: 1.3877 - val_acc: 0.2734 - val_precision_m: 0.0000e+00\n","Epoch 66/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3771 - acc: 0.2684 - precision_m: 0.0305 - val_loss: 1.3866 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 67/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3695 - acc: 0.2944 - precision_m: 0.0318 - val_loss: 1.3881 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 68/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3672 - acc: 0.2966 - precision_m: 0.0322 - val_loss: 1.3874 - val_acc: 0.2734 - val_precision_m: 0.0000e+00\n","Epoch 69/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3588 - acc: 0.2615 - precision_m: 0.0482 - val_loss: 1.3880 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 70/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3605 - acc: 0.3140 - precision_m: 0.0459 - val_loss: 1.3883 - val_acc: 0.2578 - val_precision_m: 0.0000e+00\n","Epoch 71/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3765 - acc: 0.2697 - precision_m: 0.0108 - val_loss: 1.3883 - val_acc: 0.2578 - val_precision_m: 0.0000e+00\n","Epoch 72/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3715 - acc: 0.3007 - precision_m: 0.0096 - val_loss: 1.3887 - val_acc: 0.2578 - val_precision_m: 0.0000e+00\n","Epoch 73/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3815 - acc: 0.2606 - precision_m: 0.0072 - val_loss: 1.3884 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 74/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3573 - acc: 0.2528 - precision_m: 0.0398 - val_loss: 1.3878 - val_acc: 0.2734 - val_precision_m: 0.0000e+00\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 15.6s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","139/139 [==============================] - 1s 3ms/step - loss: 1.3806 - acc: 0.2727 - precision_m: 0.0000e+00 - val_loss: 1.3829 - val_acc: 0.3250 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.4019 - acc: 0.2153 - precision_m: 0.0000e+00 - val_loss: 1.3867 - val_acc: 0.2167 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3945 - acc: 0.2215 - precision_m: 0.0000e+00 - val_loss: 1.3852 - val_acc: 0.2417 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3842 - acc: 0.2819 - precision_m: 0.0000e+00 - val_loss: 1.3872 - val_acc: 0.2167 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3869 - acc: 0.2259 - precision_m: 0.0000e+00 - val_loss: 1.3861 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3754 - acc: 0.3108 - precision_m: 0.0000e+00 - val_loss: 1.3913 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3866 - acc: 0.1927 - precision_m: 0.0000e+00 - val_loss: 1.3906 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3917 - acc: 0.2201 - precision_m: 0.0000e+00 - val_loss: 1.3863 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3868 - acc: 0.2361 - precision_m: 0.0000e+00 - val_loss: 1.3887 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3866 - acc: 0.2425 - precision_m: 0.0000e+00 - val_loss: 1.3886 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3865 - acc: 0.2368 - precision_m: 0.0000e+00 - val_loss: 1.3874 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3846 - acc: 0.2633 - precision_m: 0.0000e+00 - val_loss: 1.3866 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3828 - acc: 0.2709 - precision_m: 0.0000e+00 - val_loss: 1.3869 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3818 - acc: 0.2925 - precision_m: 0.0000e+00 - val_loss: 1.3864 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3844 - acc: 0.2569 - precision_m: 0.0000e+00 - val_loss: 1.3863 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3861 - acc: 0.2119 - precision_m: 0.0000e+00 - val_loss: 1.3865 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3843 - acc: 0.2844 - precision_m: 0.0000e+00 - val_loss: 1.3867 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3840 - acc: 0.2950 - precision_m: 0.0000e+00 - val_loss: 1.3866 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3871 - acc: 0.2708 - precision_m: 0.0000e+00 - val_loss: 1.3871 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3823 - acc: 0.3090 - precision_m: 0.0000e+00 - val_loss: 1.3867 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3782 - acc: 0.3146 - precision_m: 0.0000e+00 - val_loss: 1.3866 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3844 - acc: 0.2441 - precision_m: 0.0000e+00 - val_loss: 1.3867 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3869 - acc: 0.2282 - precision_m: 0.0000e+00 - val_loss: 1.3865 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3849 - acc: 0.2661 - precision_m: 0.0000e+00 - val_loss: 1.3866 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3887 - acc: 0.2694 - precision_m: 0.0000e+00 - val_loss: 1.3869 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","139/139 [==============================] - 0s 3ms/step - loss: 1.3819 - acc: 0.2435 - precision_m: 0.0000e+00 - val_loss: 1.3869 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3798 - acc: 0.3186 - precision_m: 0.0000e+00 - val_loss: 1.3869 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3764 - acc: 0.3050 - precision_m: 0.0000e+00 - val_loss: 1.3867 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3812 - acc: 0.2511 - precision_m: 0.0000e+00 - val_loss: 1.3873 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3915 - acc: 0.2519 - precision_m: 0.0000e+00 - val_loss: 1.3868 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3836 - acc: 0.2931 - precision_m: 0.0000e+00 - val_loss: 1.3868 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3845 - acc: 0.2892 - precision_m: 0.0000e+00 - val_loss: 1.3869 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3856 - acc: 0.2826 - precision_m: 0.0000e+00 - val_loss: 1.3872 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3884 - acc: 0.2696 - precision_m: 0.0000e+00 - val_loss: 1.3869 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3913 - acc: 0.2238 - precision_m: 0.0000e+00 - val_loss: 1.3868 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3824 - acc: 0.2799 - precision_m: 0.0000e+00 - val_loss: 1.3869 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3873 - acc: 0.2576 - precision_m: 0.0000e+00 - val_loss: 1.3868 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3807 - acc: 0.3010 - precision_m: 0.0000e+00 - val_loss: 1.3872 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3789 - acc: 0.2490 - precision_m: 0.0000e+00 - val_loss: 1.3871 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3814 - acc: 0.2832 - precision_m: 0.0000e+00 - val_loss: 1.3872 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3925 - acc: 0.2513 - precision_m: 0.0000e+00 - val_loss: 1.3876 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3863 - acc: 0.2839 - precision_m: 0.0000e+00 - val_loss: 1.3873 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3858 - acc: 0.2637 - precision_m: 0.0000e+00 - val_loss: 1.3869 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3829 - acc: 0.2422 - precision_m: 0.0000e+00 - val_loss: 1.3870 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3934 - acc: 0.2583 - precision_m: 0.0000e+00 - val_loss: 1.3869 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3822 - acc: 0.2873 - precision_m: 0.0000e+00 - val_loss: 1.3876 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3794 - acc: 0.2846 - precision_m: 0.0000e+00 - val_loss: 1.3870 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3859 - acc: 0.2793 - precision_m: 0.0000e+00 - val_loss: 1.3871 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3850 - acc: 0.2614 - precision_m: 0.0000e+00 - val_loss: 1.3872 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3852 - acc: 0.2196 - precision_m: 0.0000e+00 - val_loss: 1.3874 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3870 - acc: 0.2484 - precision_m: 0.0000e+00 - val_loss: 1.3872 - val_acc: 0.2333 - val_precision_m: 0.0000e+00\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 18.1s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","157/157 [==============================] - 1s 3ms/step - loss: 1.6317 - acc: 0.2420 - precision_m: 0.0594 - val_loss: 1.4030 - val_acc: 0.2222 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.4035 - acc: 0.2158 - precision_m: 0.0000e+00 - val_loss: 1.3915 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3931 - acc: 0.2495 - precision_m: 0.0000e+00 - val_loss: 1.3891 - val_acc: 0.2370 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3895 - acc: 0.2309 - precision_m: 0.0000e+00 - val_loss: 1.3873 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3797 - acc: 0.2872 - precision_m: 0.0000e+00 - val_loss: 1.3853 - val_acc: 0.2444 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3858 - acc: 0.2502 - precision_m: 0.0000e+00 - val_loss: 1.3869 - val_acc: 0.2444 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3855 - acc: 0.2325 - precision_m: 0.0000e+00 - val_loss: 1.3848 - val_acc: 0.2444 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3835 - acc: 0.2671 - precision_m: 0.0000e+00 - val_loss: 1.3848 - val_acc: 0.2444 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3951 - acc: 0.2433 - precision_m: 0.0000e+00 - val_loss: 1.3852 - val_acc: 0.2444 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3840 - acc: 0.2834 - precision_m: 0.0000e+00 - val_loss: 1.3854 - val_acc: 0.2444 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3830 - acc: 0.2837 - precision_m: 0.0000e+00 - val_loss: 1.3846 - val_acc: 0.2444 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3831 - acc: 0.2878 - precision_m: 0.0000e+00 - val_loss: 1.3888 - val_acc: 0.2148 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3879 - acc: 0.2633 - precision_m: 0.0000e+00 - val_loss: 1.3846 - val_acc: 0.2444 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3847 - acc: 0.2858 - precision_m: 0.0000e+00 - val_loss: 1.3843 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3940 - acc: 0.2286 - precision_m: 0.0000e+00 - val_loss: 1.3845 - val_acc: 0.2444 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3912 - acc: 0.2540 - precision_m: 0.0000e+00 - val_loss: 1.3850 - val_acc: 0.2444 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3855 - acc: 0.2444 - precision_m: 0.0000e+00 - val_loss: 1.3855 - val_acc: 0.2444 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3852 - acc: 0.2630 - precision_m: 0.0000e+00 - val_loss: 1.3851 - val_acc: 0.2444 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3828 - acc: 0.2839 - precision_m: 0.0000e+00 - val_loss: 1.3870 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3905 - acc: 0.2728 - precision_m: 0.0000e+00 - val_loss: 1.3857 - val_acc: 0.2444 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3865 - acc: 0.2574 - precision_m: 0.0000e+00 - val_loss: 1.3855 - val_acc: 0.2444 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3855 - acc: 0.2620 - precision_m: 0.0000e+00 - val_loss: 1.3851 - val_acc: 0.2444 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3906 - acc: 0.2283 - precision_m: 0.0000e+00 - val_loss: 1.3854 - val_acc: 0.2444 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3853 - acc: 0.2763 - precision_m: 0.0000e+00 - val_loss: 1.3851 - val_acc: 0.2444 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3883 - acc: 0.2357 - precision_m: 0.0000e+00 - val_loss: 1.3850 - val_acc: 0.2444 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3891 - acc: 0.2137 - precision_m: 0.0000e+00 - val_loss: 1.3924 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3907 - acc: 0.2378 - precision_m: 0.0000e+00 - val_loss: 1.3862 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3851 - acc: 0.2981 - precision_m: 0.0000e+00 - val_loss: 1.3851 - val_acc: 0.2444 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3856 - acc: 0.2385 - precision_m: 0.0000e+00 - val_loss: 1.3845 - val_acc: 0.2444 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3851 - acc: 0.2676 - precision_m: 0.0000e+00 - val_loss: 1.3853 - val_acc: 0.2444 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3840 - acc: 0.2841 - precision_m: 0.0000e+00 - val_loss: 1.3848 - val_acc: 0.2444 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3836 - acc: 0.2878 - precision_m: 0.0000e+00 - val_loss: 1.3849 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3933 - acc: 0.2875 - precision_m: 0.0000e+00 - val_loss: 1.3867 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3873 - acc: 0.2476 - precision_m: 0.0000e+00 - val_loss: 1.3848 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3839 - acc: 0.2620 - precision_m: 0.0000e+00 - val_loss: 1.3837 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3852 - acc: 0.2137 - precision_m: 0.0000e+00 - val_loss: 1.3844 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3849 - acc: 0.2228 - precision_m: 0.0000e+00 - val_loss: 1.3870 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3880 - acc: 0.2411 - precision_m: 0.0000e+00 - val_loss: 1.3847 - val_acc: 0.2444 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3873 - acc: 0.2716 - precision_m: 0.0000e+00 - val_loss: 1.3854 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3817 - acc: 0.2834 - precision_m: 0.0000e+00 - val_loss: 1.3846 - val_acc: 0.2444 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3824 - acc: 0.2814 - precision_m: 0.0000e+00 - val_loss: 1.3850 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3837 - acc: 0.2356 - precision_m: 0.0000e+00 - val_loss: 1.3842 - val_acc: 0.2222 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3830 - acc: 0.2782 - precision_m: 0.0000e+00 - val_loss: 1.3855 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3872 - acc: 0.2286 - precision_m: 0.0000e+00 - val_loss: 1.3834 - val_acc: 0.3037 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3871 - acc: 0.2148 - precision_m: 0.0000e+00 - val_loss: 1.3842 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3858 - acc: 0.2913 - precision_m: 0.0000e+00 - val_loss: 1.3842 - val_acc: 0.2370 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3843 - acc: 0.2805 - precision_m: 0.0000e+00 - val_loss: 1.3842 - val_acc: 0.2444 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3853 - acc: 0.2652 - precision_m: 0.0000e+00 - val_loss: 1.3831 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3892 - acc: 0.2490 - precision_m: 0.0000e+00 - val_loss: 1.3834 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3849 - acc: 0.2584 - precision_m: 0.0000e+00 - val_loss: 1.3827 - val_acc: 0.2667 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3842 - acc: 0.2041 - precision_m: 0.0000e+00 - val_loss: 1.3829 - val_acc: 0.2370 - val_precision_m: 0.0000e+00\n","Epoch 52/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3813 - acc: 0.2729 - precision_m: 0.0000e+00 - val_loss: 1.3821 - val_acc: 0.2741 - val_precision_m: 0.0000e+00\n","Epoch 53/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3807 - acc: 0.2689 - precision_m: 0.0000e+00 - val_loss: 1.3813 - val_acc: 0.2741 - val_precision_m: 0.0000e+00\n","Epoch 54/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3864 - acc: 0.2727 - precision_m: 0.0000e+00 - val_loss: 1.3816 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 55/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3834 - acc: 0.2359 - precision_m: 0.0000e+00 - val_loss: 1.3834 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 56/500\n","157/157 [==============================] - 0s 3ms/step - loss: 1.3785 - acc: 0.2453 - precision_m: 0.0000e+00 - val_loss: 1.3838 - val_acc: 0.1926 - val_precision_m: 0.0000e+00\n","Epoch 57/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3780 - acc: 0.2475 - precision_m: 0.0118 - val_loss: 1.3815 - val_acc: 0.2667 - val_precision_m: 0.0000e+00\n","Epoch 58/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3819 - acc: 0.2599 - precision_m: 0.0041 - val_loss: 1.3847 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 59/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3812 - acc: 0.2946 - precision_m: 0.0000e+00 - val_loss: 1.3832 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 60/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3878 - acc: 0.2872 - precision_m: 0.0000e+00 - val_loss: 1.3831 - val_acc: 0.2444 - val_precision_m: 0.0000e+00\n","Epoch 61/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3744 - acc: 0.2723 - precision_m: 0.0095 - val_loss: 1.3840 - val_acc: 0.2741 - val_precision_m: 0.0000e+00\n","Epoch 62/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3842 - acc: 0.2330 - precision_m: 1.6203e-04 - val_loss: 1.3819 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 63/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3785 - acc: 0.2664 - precision_m: 0.0000e+00 - val_loss: 1.3822 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 64/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3835 - acc: 0.2952 - precision_m: 0.0000e+00 - val_loss: 1.3821 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 65/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3681 - acc: 0.2840 - precision_m: 0.0118 - val_loss: 1.3825 - val_acc: 0.2222 - val_precision_m: 0.0000e+00\n","Epoch 66/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3781 - acc: 0.2499 - precision_m: 0.0050 - val_loss: 1.3815 - val_acc: 0.2667 - val_precision_m: 0.0000e+00\n","Epoch 67/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3817 - acc: 0.2270 - precision_m: 0.0000e+00 - val_loss: 1.3816 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 68/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3750 - acc: 0.2920 - precision_m: 0.0025 - val_loss: 1.3817 - val_acc: 0.2667 - val_precision_m: 0.0000e+00\n","Epoch 69/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3751 - acc: 0.2882 - precision_m: 0.0102 - val_loss: 1.3815 - val_acc: 0.2667 - val_precision_m: 0.0000e+00\n","Epoch 70/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3797 - acc: 0.2594 - precision_m: 0.0000e+00 - val_loss: 1.3812 - val_acc: 0.2667 - val_precision_m: 0.0000e+00\n","Epoch 71/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3813 - acc: 0.2475 - precision_m: 0.0000e+00 - val_loss: 1.3821 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 72/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3823 - acc: 0.2227 - precision_m: 0.0104 - val_loss: 1.3833 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 73/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3803 - acc: 0.2871 - precision_m: 0.0000e+00 - val_loss: 1.3817 - val_acc: 0.2370 - val_precision_m: 0.0000e+00\n","Epoch 74/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3859 - acc: 0.2827 - precision_m: 0.0064 - val_loss: 1.3856 - val_acc: 0.1852 - val_precision_m: 0.0000e+00\n","Epoch 75/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3700 - acc: 0.2719 - precision_m: 0.0000e+00 - val_loss: 1.3815 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 76/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3841 - acc: 0.2585 - precision_m: 0.0000e+00 - val_loss: 1.3830 - val_acc: 0.2667 - val_precision_m: 0.0000e+00\n","Epoch 77/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3739 - acc: 0.2989 - precision_m: 0.0040 - val_loss: 1.3819 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 78/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3741 - acc: 0.2097 - precision_m: 0.0143 - val_loss: 1.3811 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 79/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3702 - acc: 0.3212 - precision_m: 0.0104 - val_loss: 1.3829 - val_acc: 0.2741 - val_precision_m: 0.0000e+00\n","Epoch 80/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3737 - acc: 0.2735 - precision_m: 0.0109 - val_loss: 1.3835 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 81/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3818 - acc: 0.2587 - precision_m: 0.0148 - val_loss: 1.3848 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 82/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3755 - acc: 0.2297 - precision_m: 0.0000e+00 - val_loss: 1.3838 - val_acc: 0.2148 - val_precision_m: 0.0000e+00\n","Epoch 83/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3737 - acc: 0.3021 - precision_m: 0.0000e+00 - val_loss: 1.3872 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 84/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3850 - acc: 0.2711 - precision_m: 0.0113 - val_loss: 1.3856 - val_acc: 0.2667 - val_precision_m: 0.0000e+00\n","Epoch 85/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3585 - acc: 0.2266 - precision_m: 0.0537 - val_loss: 1.3817 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 86/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3723 - acc: 0.3070 - precision_m: 0.0141 - val_loss: 1.3806 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 87/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3768 - acc: 0.2258 - precision_m: 0.0052 - val_loss: 1.3818 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 88/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3740 - acc: 0.2771 - precision_m: 0.0112 - val_loss: 1.4052 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 89/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.4144 - acc: 0.2461 - precision_m: 0.0123 - val_loss: 1.3862 - val_acc: 0.2444 - val_precision_m: 0.0000e+00\n","Epoch 90/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3699 - acc: 0.2852 - precision_m: 0.0172 - val_loss: 1.3839 - val_acc: 0.2667 - val_precision_m: 0.0000e+00\n","Epoch 91/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3546 - acc: 0.3314 - precision_m: 0.0080 - val_loss: 1.3829 - val_acc: 0.2222 - val_precision_m: 0.0000e+00\n","Epoch 92/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3801 - acc: 0.2444 - precision_m: 0.0045 - val_loss: 1.3847 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 93/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3758 - acc: 0.2750 - precision_m: 0.0325 - val_loss: 1.3838 - val_acc: 0.2444 - val_precision_m: 0.0000e+00\n","Epoch 94/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3634 - acc: 0.3375 - precision_m: 0.0145 - val_loss: 1.3825 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 95/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3604 - acc: 0.2858 - precision_m: 0.0099 - val_loss: 1.3814 - val_acc: 0.2667 - val_precision_m: 0.0000e+00\n","Epoch 96/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3512 - acc: 0.3510 - precision_m: 0.0129 - val_loss: 1.3848 - val_acc: 0.2741 - val_precision_m: 0.0000e+00\n","Epoch 97/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3797 - acc: 0.2872 - precision_m: 0.0261 - val_loss: 1.3830 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 98/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3707 - acc: 0.3094 - precision_m: 0.0046 - val_loss: 1.3843 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 99/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3707 - acc: 0.2369 - precision_m: 0.0348 - val_loss: 1.3853 - val_acc: 0.2222 - val_precision_m: 0.0000e+00\n","Epoch 100/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3593 - acc: 0.2557 - precision_m: 0.0198 - val_loss: 1.3879 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 101/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3683 - acc: 0.2475 - precision_m: 0.0148 - val_loss: 1.3806 - val_acc: 0.2222 - val_precision_m: 0.0000e+00\n","Epoch 102/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3726 - acc: 0.2559 - precision_m: 0.0087 - val_loss: 1.3854 - val_acc: 0.2444 - val_precision_m: 0.0000e+00\n","Epoch 103/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3785 - acc: 0.3370 - precision_m: 0.0024 - val_loss: 1.3828 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 104/500\n","157/157 [==============================] - 0s 3ms/step - loss: 1.3737 - acc: 0.3194 - precision_m: 0.0350 - val_loss: 1.3852 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 105/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3782 - acc: 0.2857 - precision_m: 0.0000e+00 - val_loss: 1.3871 - val_acc: 0.2444 - val_precision_m: 0.0000e+00\n","Epoch 106/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3769 - acc: 0.2873 - precision_m: 0.0088 - val_loss: 1.3908 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 107/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3349 - acc: 0.3488 - precision_m: 0.0791 - val_loss: 1.3867 - val_acc: 0.2741 - val_precision_m: 0.0000e+00\n","Epoch 108/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3583 - acc: 0.2804 - precision_m: 0.0341 - val_loss: 1.3831 - val_acc: 0.2444 - val_precision_m: 0.0000e+00\n","Epoch 109/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3692 - acc: 0.2688 - precision_m: 0.0297 - val_loss: 1.3837 - val_acc: 0.3111 - val_precision_m: 0.0000e+00\n","Epoch 110/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3911 - acc: 0.2754 - precision_m: 0.0199 - val_loss: 1.3906 - val_acc: 0.2519 - val_precision_m: 0.0147\n","Epoch 111/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3454 - acc: 0.3515 - precision_m: 0.0640 - val_loss: 1.3794 - val_acc: 0.3037 - val_precision_m: 0.0000e+00\n","Epoch 112/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3996 - acc: 0.2196 - precision_m: 0.0035 - val_loss: 1.3810 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 113/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3550 - acc: 0.2678 - precision_m: 0.0094 - val_loss: 1.3862 - val_acc: 0.2000 - val_precision_m: 0.0000e+00\n","Epoch 114/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3649 - acc: 0.3008 - precision_m: 0.0106 - val_loss: 1.3835 - val_acc: 0.2444 - val_precision_m: 0.0000e+00\n","Epoch 115/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3689 - acc: 0.2259 - precision_m: 0.0166 - val_loss: 1.3842 - val_acc: 0.2148 - val_precision_m: 0.0000e+00\n","Epoch 116/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3637 - acc: 0.3174 - precision_m: 0.0222 - val_loss: 1.3822 - val_acc: 0.2667 - val_precision_m: 0.0147\n","Epoch 117/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3578 - acc: 0.3101 - precision_m: 0.0193 - val_loss: 1.3814 - val_acc: 0.2667 - val_precision_m: 0.0147\n","Epoch 118/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3730 - acc: 0.3416 - precision_m: 0.0207 - val_loss: 1.3804 - val_acc: 0.2667 - val_precision_m: 0.0147\n","Epoch 119/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3612 - acc: 0.3176 - precision_m: 0.0266 - val_loss: 1.3802 - val_acc: 0.2667 - val_precision_m: 0.0147\n","Epoch 120/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3665 - acc: 0.2772 - precision_m: 0.0091 - val_loss: 1.3845 - val_acc: 0.2370 - val_precision_m: 0.0147\n","Epoch 121/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3699 - acc: 0.1922 - precision_m: 0.0283 - val_loss: 1.3848 - val_acc: 0.2444 - val_precision_m: 0.0147\n","Epoch 122/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3880 - acc: 0.2445 - precision_m: 0.0027 - val_loss: 1.3873 - val_acc: 0.2370 - val_precision_m: 0.0147\n","Epoch 123/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3909 - acc: 0.2601 - precision_m: 0.0251 - val_loss: 1.3894 - val_acc: 0.2741 - val_precision_m: 0.0147\n","Epoch 124/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3638 - acc: 0.3384 - precision_m: 0.0208 - val_loss: 1.3894 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 125/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3643 - acc: 0.2857 - precision_m: 0.0154 - val_loss: 1.3855 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 126/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3701 - acc: 0.3353 - precision_m: 0.0166 - val_loss: 1.3845 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 127/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3793 - acc: 0.2720 - precision_m: 0.0063 - val_loss: 1.3863 - val_acc: 0.2741 - val_precision_m: 0.0000e+00\n","Epoch 128/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3614 - acc: 0.2553 - precision_m: 0.0148 - val_loss: 1.3889 - val_acc: 0.2667 - val_precision_m: 0.0000e+00\n","Epoch 129/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3690 - acc: 0.2437 - precision_m: 0.0186 - val_loss: 1.3880 - val_acc: 0.2222 - val_precision_m: 0.0000e+00\n","Epoch 130/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3717 - acc: 0.2332 - precision_m: 0.0260 - val_loss: 1.3859 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 131/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3485 - acc: 0.2919 - precision_m: 0.0358 - val_loss: 1.3985 - val_acc: 0.2148 - val_precision_m: 0.0147\n","Epoch 132/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3607 - acc: 0.2996 - precision_m: 0.0533 - val_loss: 1.3905 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 133/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3513 - acc: 0.3351 - precision_m: 0.0271 - val_loss: 1.3801 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 134/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3791 - acc: 0.2597 - precision_m: 0.0117 - val_loss: 1.3805 - val_acc: 0.2370 - val_precision_m: 0.0000e+00\n","Epoch 135/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3584 - acc: 0.2694 - precision_m: 0.0178 - val_loss: 1.3824 - val_acc: 0.2889 - val_precision_m: 0.0147\n","Epoch 136/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3551 - acc: 0.2585 - precision_m: 0.0477 - val_loss: 1.4109 - val_acc: 0.2519 - val_precision_m: 0.0441\n","Epoch 137/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3467 - acc: 0.3240 - precision_m: 0.0646 - val_loss: 1.3892 - val_acc: 0.2296 - val_precision_m: 0.0147\n","Epoch 138/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3486 - acc: 0.2865 - precision_m: 0.0967 - val_loss: 1.3934 - val_acc: 0.2815 - val_precision_m: 0.0147\n","Epoch 139/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3563 - acc: 0.2664 - precision_m: 0.0662 - val_loss: 1.3844 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 140/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3789 - acc: 0.2687 - precision_m: 0.0090 - val_loss: 1.3863 - val_acc: 0.2741 - val_precision_m: 0.0147\n","Epoch 141/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3546 - acc: 0.2839 - precision_m: 0.0379 - val_loss: 1.3904 - val_acc: 0.2444 - val_precision_m: 0.0147\n","Epoch 142/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3716 - acc: 0.2698 - precision_m: 0.0318 - val_loss: 1.3903 - val_acc: 0.2370 - val_precision_m: 0.0147\n","Epoch 143/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3686 - acc: 0.3031 - precision_m: 0.0112 - val_loss: 1.3870 - val_acc: 0.2667 - val_precision_m: 0.0147\n","Epoch 144/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3496 - acc: 0.3173 - precision_m: 0.0369 - val_loss: 1.3934 - val_acc: 0.2741 - val_precision_m: 0.0147\n","Epoch 145/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3698 - acc: 0.2782 - precision_m: 0.0297 - val_loss: 1.3862 - val_acc: 0.2296 - val_precision_m: 0.0147\n","Epoch 146/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3609 - acc: 0.2586 - precision_m: 0.0447 - val_loss: 1.3979 - val_acc: 0.2370 - val_precision_m: 0.0147\n","Epoch 147/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3611 - acc: 0.2590 - precision_m: 0.0245 - val_loss: 1.3847 - val_acc: 0.2593 - val_precision_m: 0.0147\n","Epoch 148/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3711 - acc: 0.2453 - precision_m: 0.0063 - val_loss: 1.3845 - val_acc: 0.2519 - val_precision_m: 0.0147\n","Epoch 149/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3788 - acc: 0.2210 - precision_m: 0.0172 - val_loss: 1.3810 - val_acc: 0.2222 - val_precision_m: 0.0147\n","Epoch 150/500\n","157/157 [==============================] - 0s 3ms/step - loss: 1.3571 - acc: 0.2779 - precision_m: 0.0500 - val_loss: 1.3871 - val_acc: 0.2593 - val_precision_m: 0.0147\n","Epoch 151/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3595 - acc: 0.2505 - precision_m: 0.0332 - val_loss: 1.3966 - val_acc: 0.2519 - val_precision_m: 0.0147\n","Epoch 152/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3490 - acc: 0.3383 - precision_m: 0.0364 - val_loss: 1.3929 - val_acc: 0.2444 - val_precision_m: 0.0147\n","Epoch 153/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3564 - acc: 0.2567 - precision_m: 0.0484 - val_loss: 1.3812 - val_acc: 0.2593 - val_precision_m: 0.0147\n","Epoch 154/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3567 - acc: 0.3117 - precision_m: 0.0396 - val_loss: 1.3795 - val_acc: 0.2370 - val_precision_m: 0.0147\n","Epoch 155/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3567 - acc: 0.3223 - precision_m: 0.0271 - val_loss: 1.3819 - val_acc: 0.2889 - val_precision_m: 0.0147\n","Epoch 156/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3479 - acc: 0.3770 - precision_m: 0.0638 - val_loss: 1.3796 - val_acc: 0.2519 - val_precision_m: 0.0147\n","Epoch 157/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3404 - acc: 0.2984 - precision_m: 0.0581 - val_loss: 1.3801 - val_acc: 0.2519 - val_precision_m: 0.0147\n","Epoch 158/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3654 - acc: 0.3181 - precision_m: 0.0263 - val_loss: 1.3877 - val_acc: 0.2593 - val_precision_m: 0.0147\n","Epoch 159/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3794 - acc: 0.2720 - precision_m: 0.0186 - val_loss: 1.3965 - val_acc: 0.2296 - val_precision_m: 0.0147\n","Epoch 160/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3708 - acc: 0.2801 - precision_m: 0.0207 - val_loss: 1.4014 - val_acc: 0.2593 - val_precision_m: 0.0294\n","Epoch 161/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3664 - acc: 0.2957 - precision_m: 0.0669 - val_loss: 1.3985 - val_acc: 0.2444 - val_precision_m: 0.0147\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 14.6s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","122/122 [==============================] - 1s 4ms/step - loss: 1.4087 - acc: 0.3056 - precision_m: 0.0193 - val_loss: 1.3994 - val_acc: 0.2667 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.4291 - acc: 0.2670 - precision_m: 0.0000e+00 - val_loss: 1.3980 - val_acc: 0.2476 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.4218 - acc: 0.1617 - precision_m: 0.0000e+00 - val_loss: 1.3901 - val_acc: 0.2571 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3988 - acc: 0.2156 - precision_m: 0.0000e+00 - val_loss: 1.3860 - val_acc: 0.2381 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3876 - acc: 0.2482 - precision_m: 0.0000e+00 - val_loss: 1.3859 - val_acc: 0.2381 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3836 - acc: 0.2942 - precision_m: 0.0000e+00 - val_loss: 1.3827 - val_acc: 0.2476 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3807 - acc: 0.2841 - precision_m: 0.0000e+00 - val_loss: 1.3849 - val_acc: 0.2476 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3769 - acc: 0.3374 - precision_m: 0.0000e+00 - val_loss: 1.3836 - val_acc: 0.2571 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3887 - acc: 0.2705 - precision_m: 0.0000e+00 - val_loss: 1.3824 - val_acc: 0.2571 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3840 - acc: 0.2912 - precision_m: 0.0000e+00 - val_loss: 1.3845 - val_acc: 0.2476 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3725 - acc: 0.3064 - precision_m: 0.0000e+00 - val_loss: 1.3887 - val_acc: 0.2476 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3810 - acc: 0.3201 - precision_m: 0.0000e+00 - val_loss: 1.3814 - val_acc: 0.2476 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3865 - acc: 0.2621 - precision_m: 0.0000e+00 - val_loss: 1.3818 - val_acc: 0.2476 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3859 - acc: 0.2488 - precision_m: 0.0000e+00 - val_loss: 1.3819 - val_acc: 0.2476 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3873 - acc: 0.2723 - precision_m: 0.0000e+00 - val_loss: 1.3822 - val_acc: 0.2476 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3801 - acc: 0.2489 - precision_m: 0.0000e+00 - val_loss: 1.3821 - val_acc: 0.2476 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3779 - acc: 0.2988 - precision_m: 0.0000e+00 - val_loss: 1.3821 - val_acc: 0.2476 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3794 - acc: 0.2866 - precision_m: 0.0000e+00 - val_loss: 1.3821 - val_acc: 0.2476 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3812 - acc: 0.3169 - precision_m: 0.0000e+00 - val_loss: 1.3823 - val_acc: 0.2476 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3824 - acc: 0.2747 - precision_m: 0.0000e+00 - val_loss: 1.3822 - val_acc: 0.2476 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3770 - acc: 0.2763 - precision_m: 0.0000e+00 - val_loss: 1.3823 - val_acc: 0.2476 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3803 - acc: 0.2880 - precision_m: 0.0000e+00 - val_loss: 1.3841 - val_acc: 0.2476 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3887 - acc: 0.2832 - precision_m: 0.0000e+00 - val_loss: 1.3831 - val_acc: 0.2476 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3727 - acc: 0.3099 - precision_m: 0.0032 - val_loss: 1.3829 - val_acc: 0.2476 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3882 - acc: 0.3242 - precision_m: 0.0000e+00 - val_loss: 1.3827 - val_acc: 0.3238 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3800 - acc: 0.2863 - precision_m: 0.0000e+00 - val_loss: 1.3819 - val_acc: 0.2952 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3811 - acc: 0.3347 - precision_m: 0.0000e+00 - val_loss: 1.3831 - val_acc: 0.2476 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3824 - acc: 0.2655 - precision_m: 0.0000e+00 - val_loss: 1.3831 - val_acc: 0.2476 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3798 - acc: 0.3098 - precision_m: 0.0000e+00 - val_loss: 1.3831 - val_acc: 0.2476 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3843 - acc: 0.2806 - precision_m: 0.0000e+00 - val_loss: 1.3827 - val_acc: 0.2476 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3857 - acc: 0.2464 - precision_m: 0.0000e+00 - val_loss: 1.3833 - val_acc: 0.2476 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3747 - acc: 0.2915 - precision_m: 0.0044 - val_loss: 1.3833 - val_acc: 0.2381 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3783 - acc: 0.2921 - precision_m: 0.0000e+00 - val_loss: 1.3839 - val_acc: 0.2476 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3768 - acc: 0.3078 - precision_m: 0.0018 - val_loss: 1.3838 - val_acc: 0.2476 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3827 - acc: 0.2828 - precision_m: 0.0042 - val_loss: 1.3836 - val_acc: 0.2381 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3659 - acc: 0.3219 - precision_m: 0.0000e+00 - val_loss: 1.3849 - val_acc: 0.2381 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3753 - acc: 0.3172 - precision_m: 0.0000e+00 - val_loss: 1.3839 - val_acc: 0.2381 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3621 - acc: 0.3416 - precision_m: 0.0038 - val_loss: 1.3837 - val_acc: 0.2476 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3709 - acc: 0.3073 - precision_m: 0.0070 - val_loss: 1.3842 - val_acc: 0.2476 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3806 - acc: 0.3007 - precision_m: 0.0048 - val_loss: 1.3852 - val_acc: 0.2476 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3797 - acc: 0.2899 - precision_m: 0.0022 - val_loss: 1.3854 - val_acc: 0.2381 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3804 - acc: 0.2535 - precision_m: 0.0029 - val_loss: 1.3865 - val_acc: 0.2381 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3846 - acc: 0.2697 - precision_m: 0.0000e+00 - val_loss: 1.3861 - val_acc: 0.2381 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3788 - acc: 0.2818 - precision_m: 0.0000e+00 - val_loss: 1.3847 - val_acc: 0.2381 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3931 - acc: 0.2448 - precision_m: 0.0033 - val_loss: 1.3867 - val_acc: 0.2381 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3853 - acc: 0.2616 - precision_m: 0.0022 - val_loss: 1.3856 - val_acc: 0.2381 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3753 - acc: 0.2682 - precision_m: 0.0037 - val_loss: 1.3874 - val_acc: 0.2381 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3774 - acc: 0.2669 - precision_m: 0.0033 - val_loss: 1.3852 - val_acc: 0.2381 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3793 - acc: 0.2784 - precision_m: 0.0000e+00 - val_loss: 1.3848 - val_acc: 0.2476 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3725 - acc: 0.3083 - precision_m: 0.0081 - val_loss: 1.3884 - val_acc: 0.2381 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3675 - acc: 0.3162 - precision_m: 0.0000e+00 - val_loss: 1.3860 - val_acc: 0.2381 - val_precision_m: 0.0000e+00\n","Epoch 52/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3772 - acc: 0.2855 - precision_m: 0.0025 - val_loss: 1.3850 - val_acc: 0.2381 - val_precision_m: 0.0000e+00\n","Epoch 53/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3823 - acc: 0.2628 - precision_m: 0.0065 - val_loss: 1.3891 - val_acc: 0.2476 - val_precision_m: 0.0000e+00\n","Epoch 54/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3754 - acc: 0.3132 - precision_m: 0.0014 - val_loss: 1.3892 - val_acc: 0.2476 - val_precision_m: 0.0000e+00\n","Epoch 55/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3793 - acc: 0.2785 - precision_m: 0.0000e+00 - val_loss: 1.3888 - val_acc: 0.2476 - val_precision_m: 0.0000e+00\n","Epoch 56/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3662 - acc: 0.2516 - precision_m: 0.0038 - val_loss: 1.3928 - val_acc: 0.2381 - val_precision_m: 0.0000e+00\n","Epoch 57/500\n","122/122 [==============================] - 0s 3ms/step - loss: 1.3836 - acc: 0.2773 - precision_m: 0.0000e+00 - val_loss: 1.3880 - val_acc: 0.2381 - val_precision_m: 0.0000e+00\n","Epoch 58/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3750 - acc: 0.2994 - precision_m: 1.3328e-04 - val_loss: 1.3889 - val_acc: 0.2381 - val_precision_m: 0.0000e+00\n","Epoch 59/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3681 - acc: 0.2990 - precision_m: 0.0154 - val_loss: 1.3902 - val_acc: 0.2381 - val_precision_m: 0.0000e+00\n","Epoch 60/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3822 - acc: 0.2670 - precision_m: 0.0000e+00 - val_loss: 1.3867 - val_acc: 0.2381 - val_precision_m: 0.0000e+00\n","Epoch 61/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3750 - acc: 0.2935 - precision_m: 0.0000e+00 - val_loss: 1.3885 - val_acc: 0.2381 - val_precision_m: 0.0000e+00\n","Epoch 62/500\n","122/122 [==============================] - 0s 2ms/step - loss: 1.3605 - acc: 0.3097 - precision_m: 0.0026 - val_loss: 1.3918 - val_acc: 0.2381 - val_precision_m: 0.0000e+00\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 19.7s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","165/165 [==============================] - 1s 3ms/step - loss: 1.4020 - acc: 0.2593 - precision_m: 0.0115 - val_loss: 1.3912 - val_acc: 0.2746 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3861 - acc: 0.2566 - precision_m: 0.0000e+00 - val_loss: 1.4005 - val_acc: 0.2465 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3889 - acc: 0.2481 - precision_m: 0.0000e+00 - val_loss: 1.3862 - val_acc: 0.3239 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3932 - acc: 0.2165 - precision_m: 0.0000e+00 - val_loss: 1.3888 - val_acc: 0.2465 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3856 - acc: 0.2720 - precision_m: 0.0000e+00 - val_loss: 1.3856 - val_acc: 0.3169 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3893 - acc: 0.2271 - precision_m: 0.0000e+00 - val_loss: 1.3869 - val_acc: 0.2606 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3813 - acc: 0.2742 - precision_m: 0.0000e+00 - val_loss: 1.3876 - val_acc: 0.2254 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3859 - acc: 0.2238 - precision_m: 0.0000e+00 - val_loss: 1.3855 - val_acc: 0.3380 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3887 - acc: 0.2320 - precision_m: 0.0000e+00 - val_loss: 1.3857 - val_acc: 0.2887 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3819 - acc: 0.3135 - precision_m: 0.0000e+00 - val_loss: 1.3891 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3866 - acc: 0.2735 - precision_m: 0.0000e+00 - val_loss: 1.3872 - val_acc: 0.2394 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3843 - acc: 0.2722 - precision_m: 0.0000e+00 - val_loss: 1.3879 - val_acc: 0.2394 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3881 - acc: 0.2927 - precision_m: 0.0000e+00 - val_loss: 1.3881 - val_acc: 0.2394 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3820 - acc: 0.2897 - precision_m: 0.0000e+00 - val_loss: 1.3911 - val_acc: 0.2676 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3861 - acc: 0.2409 - precision_m: 0.0000e+00 - val_loss: 1.3885 - val_acc: 0.2254 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3891 - acc: 0.2575 - precision_m: 0.0000e+00 - val_loss: 1.3869 - val_acc: 0.2394 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3816 - acc: 0.2779 - precision_m: 0.0000e+00 - val_loss: 1.3883 - val_acc: 0.2535 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3915 - acc: 0.1927 - precision_m: 0.0000e+00 - val_loss: 1.4022 - val_acc: 0.2042 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3992 - acc: 0.2184 - precision_m: 0.0000e+00 - val_loss: 1.3881 - val_acc: 0.2394 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3834 - acc: 0.3047 - precision_m: 0.0000e+00 - val_loss: 1.3920 - val_acc: 0.2676 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3844 - acc: 0.2798 - precision_m: 0.0000e+00 - val_loss: 1.3884 - val_acc: 0.2254 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3889 - acc: 0.2205 - precision_m: 0.0000e+00 - val_loss: 1.3876 - val_acc: 0.2394 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3807 - acc: 0.2845 - precision_m: 0.0000e+00 - val_loss: 1.3893 - val_acc: 0.2394 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3795 - acc: 0.3164 - precision_m: 0.0000e+00 - val_loss: 1.3882 - val_acc: 0.2394 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3861 - acc: 0.2575 - precision_m: 0.0000e+00 - val_loss: 1.3870 - val_acc: 0.2394 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.4023 - acc: 0.3054 - precision_m: 0.0000e+00 - val_loss: 1.3892 - val_acc: 0.2394 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3848 - acc: 0.3014 - precision_m: 0.0000e+00 - val_loss: 1.3884 - val_acc: 0.2394 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3854 - acc: 0.2900 - precision_m: 0.0000e+00 - val_loss: 1.3883 - val_acc: 0.2394 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3922 - acc: 0.2382 - precision_m: 0.0000e+00 - val_loss: 1.3888 - val_acc: 0.2394 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3807 - acc: 0.2918 - precision_m: 0.0000e+00 - val_loss: 1.3881 - val_acc: 0.2394 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3804 - acc: 0.2786 - precision_m: 0.0000e+00 - val_loss: 1.3906 - val_acc: 0.2254 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3854 - acc: 0.2561 - precision_m: 0.0105 - val_loss: 1.3907 - val_acc: 0.2113 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3870 - acc: 0.2912 - precision_m: 0.0000e+00 - val_loss: 1.3895 - val_acc: 0.2465 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3818 - acc: 0.2488 - precision_m: 0.0000e+00 - val_loss: 1.3885 - val_acc: 0.2394 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3791 - acc: 0.2849 - precision_m: 3.7335e-04 - val_loss: 1.3905 - val_acc: 0.2254 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3815 - acc: 0.2886 - precision_m: 0.0000e+00 - val_loss: 1.3890 - val_acc: 0.2394 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3815 - acc: 0.2768 - precision_m: 0.0000e+00 - val_loss: 1.3920 - val_acc: 0.2183 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3856 - acc: 0.3020 - precision_m: 1.4671e-04 - val_loss: 1.3923 - val_acc: 0.2254 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3849 - acc: 0.2696 - precision_m: 0.0123 - val_loss: 1.3892 - val_acc: 0.2606 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3789 - acc: 0.2537 - precision_m: 0.0000e+00 - val_loss: 1.3887 - val_acc: 0.2465 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3771 - acc: 0.2846 - precision_m: 0.0151 - val_loss: 1.3883 - val_acc: 0.2394 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3789 - acc: 0.3124 - precision_m: 0.0000e+00 - val_loss: 1.3882 - val_acc: 0.2465 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3814 - acc: 0.2426 - precision_m: 0.0062 - val_loss: 1.3880 - val_acc: 0.2535 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3784 - acc: 0.3104 - precision_m: 0.0024 - val_loss: 1.3876 - val_acc: 0.2465 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3769 - acc: 0.2389 - precision_m: 0.0000e+00 - val_loss: 1.3874 - val_acc: 0.2394 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3813 - acc: 0.2675 - precision_m: 0.0041 - val_loss: 1.3874 - val_acc: 0.2394 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3791 - acc: 0.2363 - precision_m: 0.0144 - val_loss: 1.3876 - val_acc: 0.2465 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3756 - acc: 0.3124 - precision_m: 0.0171 - val_loss: 1.3874 - val_acc: 0.2535 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3742 - acc: 0.2718 - precision_m: 0.0121 - val_loss: 1.3914 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3896 - acc: 0.2601 - precision_m: 0.0016 - val_loss: 1.3881 - val_acc: 0.2394 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3834 - acc: 0.2654 - precision_m: 0.0124 - val_loss: 1.3876 - val_acc: 0.2394 - val_precision_m: 0.0000e+00\n","Epoch 52/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3756 - acc: 0.2387 - precision_m: 0.0156 - val_loss: 1.3884 - val_acc: 0.2465 - val_precision_m: 0.0000e+00\n","Epoch 53/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3728 - acc: 0.2942 - precision_m: 0.0022 - val_loss: 1.3882 - val_acc: 0.2465 - val_precision_m: 0.0000e+00\n","Epoch 54/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3854 - acc: 0.2660 - precision_m: 7.3014e-04 - val_loss: 1.3881 - val_acc: 0.2183 - val_precision_m: 0.0000e+00\n","Epoch 55/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3741 - acc: 0.2715 - precision_m: 0.0187 - val_loss: 1.3916 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 56/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3665 - acc: 0.2397 - precision_m: 0.0401 - val_loss: 1.3875 - val_acc: 0.2394 - val_precision_m: 0.0000e+00\n","Epoch 57/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3685 - acc: 0.2696 - precision_m: 0.0396 - val_loss: 1.3869 - val_acc: 0.2465 - val_precision_m: 0.0000e+00\n","Epoch 58/500\n","165/165 [==============================] - 0s 2ms/step - loss: 1.3782 - acc: 0.3248 - precision_m: 0.0027 - val_loss: 1.3881 - val_acc: 0.2465 - val_precision_m: 0.0000e+00\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 18.3s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","145/145 [==============================] - 1s 3ms/step - loss: 1.4575 - acc: 0.3281 - precision_m: 0.0484 - val_loss: 1.3858 - val_acc: 0.2581 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.4110 - acc: 0.2280 - precision_m: 0.0000e+00 - val_loss: 1.3798 - val_acc: 0.2984 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3813 - acc: 0.2853 - precision_m: 0.0000e+00 - val_loss: 1.3872 - val_acc: 0.2419 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3786 - acc: 0.2581 - precision_m: 0.0000e+00 - val_loss: 1.3850 - val_acc: 0.2581 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3692 - acc: 0.3539 - precision_m: 0.0000e+00 - val_loss: 1.3833 - val_acc: 0.2500 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3763 - acc: 0.3538 - precision_m: 0.0000e+00 - val_loss: 1.3821 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3880 - acc: 0.3151 - precision_m: 0.0000e+00 - val_loss: 1.3827 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3754 - acc: 0.3158 - precision_m: 0.0000e+00 - val_loss: 1.3832 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3720 - acc: 0.3382 - precision_m: 0.0000e+00 - val_loss: 1.3825 - val_acc: 0.2823 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3703 - acc: 0.3011 - precision_m: 0.0000e+00 - val_loss: 1.3841 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3735 - acc: 0.2466 - precision_m: 0.0000e+00 - val_loss: 1.3828 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3970 - acc: 0.2865 - precision_m: 0.0000e+00 - val_loss: 1.3806 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3879 - acc: 0.2685 - precision_m: 0.0000e+00 - val_loss: 1.3827 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3842 - acc: 0.2982 - precision_m: 0.0000e+00 - val_loss: 1.3795 - val_acc: 0.3065 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3841 - acc: 0.2467 - precision_m: 0.0000e+00 - val_loss: 1.3938 - val_acc: 0.2500 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3925 - acc: 0.2549 - precision_m: 0.0000e+00 - val_loss: 1.3818 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3846 - acc: 0.2902 - precision_m: 0.0000e+00 - val_loss: 1.3823 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3746 - acc: 0.3070 - precision_m: 0.0000e+00 - val_loss: 1.3892 - val_acc: 0.2661 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3764 - acc: 0.2702 - precision_m: 0.0000e+00 - val_loss: 1.3804 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3718 - acc: 0.2807 - precision_m: 0.0000e+00 - val_loss: 1.3806 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3790 - acc: 0.2680 - precision_m: 0.0000e+00 - val_loss: 1.3828 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3696 - acc: 0.3248 - precision_m: 0.0000e+00 - val_loss: 1.3824 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3731 - acc: 0.3165 - precision_m: 0.0000e+00 - val_loss: 1.3813 - val_acc: 0.2984 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3577 - acc: 0.3523 - precision_m: 0.0058 - val_loss: 1.3831 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3689 - acc: 0.3162 - precision_m: 0.0000e+00 - val_loss: 1.3834 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3705 - acc: 0.3052 - precision_m: 0.0000e+00 - val_loss: 1.3848 - val_acc: 0.3145 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3752 - acc: 0.2579 - precision_m: 0.0000e+00 - val_loss: 1.3835 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3780 - acc: 0.2641 - precision_m: 0.0000e+00 - val_loss: 1.3834 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3735 - acc: 0.2915 - precision_m: 0.0000e+00 - val_loss: 1.3836 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3809 - acc: 0.2695 - precision_m: 0.0000e+00 - val_loss: 1.3847 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3737 - acc: 0.2400 - precision_m: 0.0000e+00 - val_loss: 1.3842 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3618 - acc: 0.2716 - precision_m: 0.0000e+00 - val_loss: 1.3829 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3651 - acc: 0.2974 - precision_m: 0.0000e+00 - val_loss: 1.3832 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3807 - acc: 0.2655 - precision_m: 0.0000e+00 - val_loss: 1.3853 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3789 - acc: 0.2798 - precision_m: 0.0000e+00 - val_loss: 1.3867 - val_acc: 0.2984 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3822 - acc: 0.2529 - precision_m: 0.0000e+00 - val_loss: 1.3877 - val_acc: 0.2984 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3904 - acc: 0.2766 - precision_m: 0.0029 - val_loss: 1.3860 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3791 - acc: 0.2775 - precision_m: 7.4017e-04 - val_loss: 1.3879 - val_acc: 0.2984 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","145/145 [==============================] - 0s 3ms/step - loss: 1.3649 - acc: 0.2730 - precision_m: 0.0131 - val_loss: 1.3848 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3667 - acc: 0.2805 - precision_m: 0.0000e+00 - val_loss: 1.3872 - val_acc: 0.2984 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3793 - acc: 0.2746 - precision_m: 0.0146 - val_loss: 1.3904 - val_acc: 0.2984 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3618 - acc: 0.3003 - precision_m: 0.0142 - val_loss: 1.3873 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3613 - acc: 0.3148 - precision_m: 0.0125 - val_loss: 1.3885 - val_acc: 0.2984 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3638 - acc: 0.3265 - precision_m: 0.0053 - val_loss: 1.3869 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3679 - acc: 0.2951 - precision_m: 0.0000e+00 - val_loss: 1.3935 - val_acc: 0.3065 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3886 - acc: 0.2364 - precision_m: 0.0038 - val_loss: 1.3913 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3712 - acc: 0.3415 - precision_m: 0.0131 - val_loss: 1.3946 - val_acc: 0.2581 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3636 - acc: 0.2946 - precision_m: 0.0262 - val_loss: 1.3950 - val_acc: 0.2581 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3662 - acc: 0.2504 - precision_m: 0.0090 - val_loss: 1.3911 - val_acc: 0.3065 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3907 - acc: 0.2391 - precision_m: 3.3567e-04 - val_loss: 1.4004 - val_acc: 0.2419 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3768 - acc: 0.2674 - precision_m: 0.0072 - val_loss: 1.3949 - val_acc: 0.2984 - val_precision_m: 0.0000e+00\n","Epoch 52/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3675 - acc: 0.2504 - precision_m: 0.0173 - val_loss: 1.3891 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 53/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3780 - acc: 0.3085 - precision_m: 0.0000e+00 - val_loss: 1.3965 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 54/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3788 - acc: 0.2826 - precision_m: 0.0000e+00 - val_loss: 1.3908 - val_acc: 0.2984 - val_precision_m: 0.0000e+00\n","Epoch 55/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3748 - acc: 0.2693 - precision_m: 0.0187 - val_loss: 1.3902 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 56/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3921 - acc: 0.2564 - precision_m: 0.0053 - val_loss: 1.3913 - val_acc: 0.2984 - val_precision_m: 0.0000e+00\n","Epoch 57/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3699 - acc: 0.3080 - precision_m: 0.0131 - val_loss: 1.3897 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 58/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3565 - acc: 0.3020 - precision_m: 0.0262 - val_loss: 1.3910 - val_acc: 0.2984 - val_precision_m: 0.0000e+00\n","Epoch 59/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3572 - acc: 0.2730 - precision_m: 0.0224 - val_loss: 1.3918 - val_acc: 0.2984 - val_precision_m: 0.0000e+00\n","Epoch 60/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3761 - acc: 0.2757 - precision_m: 0.0195 - val_loss: 1.3933 - val_acc: 0.2984 - val_precision_m: 0.0000e+00\n","Epoch 61/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3662 - acc: 0.3099 - precision_m: 0.0213 - val_loss: 1.3954 - val_acc: 0.2984 - val_precision_m: 0.0000e+00\n","Epoch 62/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3705 - acc: 0.3246 - precision_m: 0.0161 - val_loss: 1.3954 - val_acc: 0.2984 - val_precision_m: 0.0000e+00\n","Epoch 63/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3646 - acc: 0.2835 - precision_m: 0.0000e+00 - val_loss: 1.3880 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 64/500\n","145/145 [==============================] - 0s 2ms/step - loss: 1.3726 - acc: 0.2970 - precision_m: 0.0000e+00 - val_loss: 1.3900 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 15.0s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","135/135 [==============================] - 1s 3ms/step - loss: 1.4816 - acc: 0.3121 - precision_m: 0.1076 - val_loss: 1.3851 - val_acc: 0.3162 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3913 - acc: 0.2588 - precision_m: 0.0024 - val_loss: 1.3712 - val_acc: 0.3162 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.4064 - acc: 0.2515 - precision_m: 0.0000e+00 - val_loss: 1.3804 - val_acc: 0.2393 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3765 - acc: 0.2710 - precision_m: 0.0000e+00 - val_loss: 1.3826 - val_acc: 0.2222 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3887 - acc: 0.2436 - precision_m: 0.0000e+00 - val_loss: 1.3882 - val_acc: 0.2393 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3854 - acc: 0.2143 - precision_m: 0.0000e+00 - val_loss: 1.3858 - val_acc: 0.2308 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3869 - acc: 0.2640 - precision_m: 0.0000e+00 - val_loss: 1.3864 - val_acc: 0.2308 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3783 - acc: 0.2859 - precision_m: 0.0000e+00 - val_loss: 1.3809 - val_acc: 0.2906 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3943 - acc: 0.2288 - precision_m: 0.0000e+00 - val_loss: 1.3868 - val_acc: 0.2308 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3846 - acc: 0.2521 - precision_m: 0.0000e+00 - val_loss: 1.3855 - val_acc: 0.2308 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3790 - acc: 0.3114 - precision_m: 0.0000e+00 - val_loss: 1.3853 - val_acc: 0.2308 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3820 - acc: 0.3137 - precision_m: 0.0000e+00 - val_loss: 1.3843 - val_acc: 0.2308 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3861 - acc: 0.2706 - precision_m: 0.0000e+00 - val_loss: 1.3797 - val_acc: 0.2308 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3854 - acc: 0.2776 - precision_m: 0.0000e+00 - val_loss: 1.3806 - val_acc: 0.2735 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3720 - acc: 0.3106 - precision_m: 0.0000e+00 - val_loss: 1.3837 - val_acc: 0.2308 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3801 - acc: 0.2684 - precision_m: 0.0000e+00 - val_loss: 1.3841 - val_acc: 0.2308 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3868 - acc: 0.2492 - precision_m: 0.0000e+00 - val_loss: 1.3840 - val_acc: 0.2308 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3788 - acc: 0.3187 - precision_m: 0.0000e+00 - val_loss: 1.3741 - val_acc: 0.2564 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3851 - acc: 0.2940 - precision_m: 0.0000e+00 - val_loss: 1.3841 - val_acc: 0.2308 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3773 - acc: 0.3048 - precision_m: 0.0000e+00 - val_loss: 1.3840 - val_acc: 0.2308 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3755 - acc: 0.3024 - precision_m: 0.0000e+00 - val_loss: 1.3834 - val_acc: 0.2308 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3824 - acc: 0.2638 - precision_m: 0.0000e+00 - val_loss: 1.3833 - val_acc: 0.2308 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3747 - acc: 0.3159 - precision_m: 0.0000e+00 - val_loss: 1.3836 - val_acc: 0.2308 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3892 - acc: 0.2757 - precision_m: 0.0000e+00 - val_loss: 1.3833 - val_acc: 0.2308 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3767 - acc: 0.3181 - precision_m: 0.0000e+00 - val_loss: 1.3827 - val_acc: 0.2308 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3830 - acc: 0.2973 - precision_m: 0.0000e+00 - val_loss: 1.3825 - val_acc: 0.2308 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3696 - acc: 0.2970 - precision_m: 0.0000e+00 - val_loss: 1.3805 - val_acc: 0.2308 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3755 - acc: 0.2968 - precision_m: 0.0000e+00 - val_loss: 1.3793 - val_acc: 0.2308 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3699 - acc: 0.3494 - precision_m: 0.0000e+00 - val_loss: 1.3797 - val_acc: 0.2308 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3819 - acc: 0.2677 - precision_m: 0.0000e+00 - val_loss: 1.3789 - val_acc: 0.2308 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3871 - acc: 0.2737 - precision_m: 0.0000e+00 - val_loss: 1.3800 - val_acc: 0.2308 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3702 - acc: 0.3357 - precision_m: 0.0000e+00 - val_loss: 1.3804 - val_acc: 0.2308 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3821 - acc: 0.2955 - precision_m: 0.0000e+00 - val_loss: 1.3813 - val_acc: 0.2308 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3827 - acc: 0.2887 - precision_m: 5.0193e-04 - val_loss: 1.3783 - val_acc: 0.2650 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3801 - acc: 0.3209 - precision_m: 0.0029 - val_loss: 1.3768 - val_acc: 0.2821 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3727 - acc: 0.2822 - precision_m: 0.0000e+00 - val_loss: 1.3824 - val_acc: 0.2308 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3657 - acc: 0.3005 - precision_m: 0.0048 - val_loss: 1.3776 - val_acc: 0.2906 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3887 - acc: 0.2425 - precision_m: 0.0000e+00 - val_loss: 1.3786 - val_acc: 0.2821 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3831 - acc: 0.3098 - precision_m: 0.0000e+00 - val_loss: 1.3823 - val_acc: 0.2308 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3846 - acc: 0.3064 - precision_m: 7.3630e-04 - val_loss: 1.3823 - val_acc: 0.2308 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3752 - acc: 0.2775 - precision_m: 0.0000e+00 - val_loss: 1.3825 - val_acc: 0.2308 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","135/135 [==============================] - 0s 3ms/step - loss: 1.3748 - acc: 0.3463 - precision_m: 0.0000e+00 - val_loss: 1.3837 - val_acc: 0.2308 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3798 - acc: 0.2908 - precision_m: 0.0000e+00 - val_loss: 1.3789 - val_acc: 0.2735 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3608 - acc: 0.3493 - precision_m: 0.0000e+00 - val_loss: 1.3834 - val_acc: 0.2222 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3786 - acc: 0.2814 - precision_m: 0.0000e+00 - val_loss: 1.3839 - val_acc: 0.2308 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3826 - acc: 0.2626 - precision_m: 0.0000e+00 - val_loss: 1.3842 - val_acc: 0.2222 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3792 - acc: 0.2934 - precision_m: 0.0035 - val_loss: 1.3822 - val_acc: 0.2308 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3835 - acc: 0.3597 - precision_m: 0.0000e+00 - val_loss: 1.3849 - val_acc: 0.2308 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3914 - acc: 0.2366 - precision_m: 0.0041 - val_loss: 1.3837 - val_acc: 0.2308 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3738 - acc: 0.2483 - precision_m: 0.0126 - val_loss: 1.3824 - val_acc: 0.2308 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3687 - acc: 0.3037 - precision_m: 0.0032 - val_loss: 1.3818 - val_acc: 0.2821 - val_precision_m: 0.0000e+00\n","Epoch 52/500\n","135/135 [==============================] - 0s 2ms/step - loss: 1.3642 - acc: 0.3354 - precision_m: 0.0000e+00 - val_loss: 1.3822 - val_acc: 0.2564 - val_precision_m: 0.0000e+00\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 15.3s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","139/139 [==============================] - 1s 3ms/step - loss: 1.4764 - acc: 0.2911 - precision_m: 0.0246 - val_loss: 1.3836 - val_acc: 0.2773 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3743 - acc: 0.2737 - precision_m: 0.0000e+00 - val_loss: 1.3871 - val_acc: 0.2185 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3841 - acc: 0.3165 - precision_m: 0.0038 - val_loss: 1.3876 - val_acc: 0.2185 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3847 - acc: 0.2878 - precision_m: 0.0000e+00 - val_loss: 1.3864 - val_acc: 0.2269 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3861 - acc: 0.2026 - precision_m: 0.0000e+00 - val_loss: 1.3871 - val_acc: 0.2185 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3867 - acc: 0.2296 - precision_m: 0.0000e+00 - val_loss: 1.3869 - val_acc: 0.2185 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","139/139 [==============================] - 0s 3ms/step - loss: 1.3862 - acc: 0.2885 - precision_m: 0.0000e+00 - val_loss: 1.3862 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3868 - acc: 0.2767 - precision_m: 0.0000e+00 - val_loss: 1.3860 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3846 - acc: 0.2667 - precision_m: 0.0000e+00 - val_loss: 1.3861 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3848 - acc: 0.2707 - precision_m: 0.0000e+00 - val_loss: 1.3861 - val_acc: 0.2773 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3853 - acc: 0.2197 - precision_m: 0.0000e+00 - val_loss: 1.3855 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3875 - acc: 0.2268 - precision_m: 0.0000e+00 - val_loss: 1.3854 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3859 - acc: 0.1997 - precision_m: 0.0000e+00 - val_loss: 1.3856 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3842 - acc: 0.2825 - precision_m: 0.0000e+00 - val_loss: 1.3847 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3911 - acc: 0.2517 - precision_m: 0.0000e+00 - val_loss: 1.3851 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3874 - acc: 0.2494 - precision_m: 0.0000e+00 - val_loss: 1.3853 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3883 - acc: 0.2356 - precision_m: 0.0000e+00 - val_loss: 1.3853 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3843 - acc: 0.2628 - precision_m: 0.0000e+00 - val_loss: 1.3854 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3914 - acc: 0.2337 - precision_m: 0.0000e+00 - val_loss: 1.3854 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3893 - acc: 0.2654 - precision_m: 0.0000e+00 - val_loss: 1.3854 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3837 - acc: 0.2881 - precision_m: 0.0000e+00 - val_loss: 1.3851 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3874 - acc: 0.2270 - precision_m: 0.0000e+00 - val_loss: 1.3853 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3867 - acc: 0.2430 - precision_m: 0.0000e+00 - val_loss: 1.3857 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3864 - acc: 0.2723 - precision_m: 0.0000e+00 - val_loss: 1.3859 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3816 - acc: 0.3191 - precision_m: 0.0000e+00 - val_loss: 1.3855 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3857 - acc: 0.2733 - precision_m: 0.0000e+00 - val_loss: 1.3857 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3878 - acc: 0.2515 - precision_m: 0.0000e+00 - val_loss: 1.3859 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3871 - acc: 0.2671 - precision_m: 0.0000e+00 - val_loss: 1.3856 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3876 - acc: 0.2347 - precision_m: 0.0000e+00 - val_loss: 1.3849 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3873 - acc: 0.2447 - precision_m: 0.0000e+00 - val_loss: 1.3852 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3855 - acc: 0.2559 - precision_m: 0.0000e+00 - val_loss: 1.3851 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3838 - acc: 0.2550 - precision_m: 0.0000e+00 - val_loss: 1.3852 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3879 - acc: 0.2456 - precision_m: 0.0000e+00 - val_loss: 1.3848 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3817 - acc: 0.3224 - precision_m: 0.0000e+00 - val_loss: 1.3848 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3839 - acc: 0.2890 - precision_m: 0.0000e+00 - val_loss: 1.3854 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3842 - acc: 0.2435 - precision_m: 0.0000e+00 - val_loss: 1.3852 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3814 - acc: 0.3062 - precision_m: 0.0000e+00 - val_loss: 1.3854 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3767 - acc: 0.3503 - precision_m: 0.0000e+00 - val_loss: 1.3854 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3857 - acc: 0.2600 - precision_m: 0.0000e+00 - val_loss: 1.3856 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3874 - acc: 0.2392 - precision_m: 0.0000e+00 - val_loss: 1.3855 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3876 - acc: 0.2530 - precision_m: 0.0000e+00 - val_loss: 1.3855 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3829 - acc: 0.2844 - precision_m: 0.0000e+00 - val_loss: 1.3855 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3893 - acc: 0.2081 - precision_m: 0.0000e+00 - val_loss: 1.3856 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3802 - acc: 0.3060 - precision_m: 0.0068 - val_loss: 1.3862 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3817 - acc: 0.2921 - precision_m: 0.0059 - val_loss: 1.3866 - val_acc: 0.2773 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3870 - acc: 0.2561 - precision_m: 0.0035 - val_loss: 1.3855 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3841 - acc: 0.2428 - precision_m: 0.0000e+00 - val_loss: 1.3854 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3873 - acc: 0.2681 - precision_m: 0.0000e+00 - val_loss: 1.3855 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3817 - acc: 0.2927 - precision_m: 0.0000e+00 - val_loss: 1.3857 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3851 - acc: 0.2508 - precision_m: 0.0000e+00 - val_loss: 1.3857 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","139/139 [==============================] - 0s 2ms/step - loss: 1.3839 - acc: 0.2872 - precision_m: 0.0000e+00 - val_loss: 1.3855 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 17.2s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","147/147 [==============================] - 1s 3ms/step - loss: 1.3991 - acc: 0.2279 - precision_m: 0.0000e+00 - val_loss: 1.3900 - val_acc: 0.2441 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.4100 - acc: 0.2239 - precision_m: 0.0015 - val_loss: 1.3859 - val_acc: 0.2598 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3963 - acc: 0.1491 - precision_m: 0.0000e+00 - val_loss: 1.3857 - val_acc: 0.2047 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3897 - acc: 0.2743 - precision_m: 0.0000e+00 - val_loss: 1.3857 - val_acc: 0.2756 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3867 - acc: 0.2270 - precision_m: 0.0000e+00 - val_loss: 1.3856 - val_acc: 0.2835 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3852 - acc: 0.2531 - precision_m: 0.0000e+00 - val_loss: 1.3859 - val_acc: 0.2835 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3839 - acc: 0.2319 - precision_m: 0.0000e+00 - val_loss: 1.3863 - val_acc: 0.2835 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3846 - acc: 0.2311 - precision_m: 0.0000e+00 - val_loss: 1.3865 - val_acc: 0.2126 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3828 - acc: 0.2745 - precision_m: 0.0000e+00 - val_loss: 1.3868 - val_acc: 0.2126 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3805 - acc: 0.2768 - precision_m: 0.0000e+00 - val_loss: 1.3877 - val_acc: 0.2126 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3814 - acc: 0.2522 - precision_m: 0.0000e+00 - val_loss: 1.3900 - val_acc: 0.2126 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","147/147 [==============================] - 0s 3ms/step - loss: 1.3878 - acc: 0.2265 - precision_m: 0.0000e+00 - val_loss: 1.3874 - val_acc: 0.2126 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3831 - acc: 0.3002 - precision_m: 0.0000e+00 - val_loss: 1.3872 - val_acc: 0.2126 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3878 - acc: 0.1900 - precision_m: 0.0000e+00 - val_loss: 1.3878 - val_acc: 0.2126 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3795 - acc: 0.2712 - precision_m: 0.0000e+00 - val_loss: 1.3880 - val_acc: 0.2126 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3824 - acc: 0.2652 - precision_m: 0.0000e+00 - val_loss: 1.3866 - val_acc: 0.2598 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3840 - acc: 0.2798 - precision_m: 0.0000e+00 - val_loss: 1.3883 - val_acc: 0.2126 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3839 - acc: 0.2695 - precision_m: 0.0000e+00 - val_loss: 1.3875 - val_acc: 0.2205 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3786 - acc: 0.2491 - precision_m: 0.0000e+00 - val_loss: 1.3879 - val_acc: 0.2520 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3870 - acc: 0.2280 - precision_m: 0.0000e+00 - val_loss: 1.3884 - val_acc: 0.2677 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3818 - acc: 0.3059 - precision_m: 0.0000e+00 - val_loss: 1.3873 - val_acc: 0.2362 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3859 - acc: 0.2048 - precision_m: 0.0000e+00 - val_loss: 1.3863 - val_acc: 0.2598 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3821 - acc: 0.2582 - precision_m: 0.0000e+00 - val_loss: 1.3871 - val_acc: 0.2205 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3837 - acc: 0.2247 - precision_m: 0.0000e+00 - val_loss: 1.3871 - val_acc: 0.2520 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3852 - acc: 0.2455 - precision_m: 0.0000e+00 - val_loss: 1.3946 - val_acc: 0.2441 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3879 - acc: 0.2173 - precision_m: 0.0000e+00 - val_loss: 1.3876 - val_acc: 0.2520 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3783 - acc: 0.3010 - precision_m: 0.0000e+00 - val_loss: 1.3870 - val_acc: 0.2520 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3748 - acc: 0.2809 - precision_m: 0.0000e+00 - val_loss: 1.3900 - val_acc: 0.2205 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3883 - acc: 0.2304 - precision_m: 0.0000e+00 - val_loss: 1.3885 - val_acc: 0.2362 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3804 - acc: 0.2993 - precision_m: 0.0174 - val_loss: 1.3885 - val_acc: 0.2441 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3806 - acc: 0.2914 - precision_m: 0.0000e+00 - val_loss: 1.3876 - val_acc: 0.2441 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3878 - acc: 0.3134 - precision_m: 0.0000e+00 - val_loss: 1.3915 - val_acc: 0.2441 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3834 - acc: 0.2675 - precision_m: 0.0000e+00 - val_loss: 1.3863 - val_acc: 0.2520 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3862 - acc: 0.2384 - precision_m: 0.0000e+00 - val_loss: 1.3888 - val_acc: 0.2283 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3852 - acc: 0.2793 - precision_m: 0.0000e+00 - val_loss: 1.3876 - val_acc: 0.2441 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3805 - acc: 0.2916 - precision_m: 0.0034 - val_loss: 1.3867 - val_acc: 0.2520 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3807 - acc: 0.2480 - precision_m: 0.0000e+00 - val_loss: 1.3870 - val_acc: 0.2520 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3790 - acc: 0.2386 - precision_m: 0.0040 - val_loss: 1.3902 - val_acc: 0.2598 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3858 - acc: 0.2667 - precision_m: 0.0072 - val_loss: 1.3943 - val_acc: 0.2126 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3777 - acc: 0.2667 - precision_m: 0.0114 - val_loss: 1.3906 - val_acc: 0.2283 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3757 - acc: 0.2542 - precision_m: 0.0017 - val_loss: 1.3903 - val_acc: 0.2362 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3898 - acc: 0.2305 - precision_m: 0.0000e+00 - val_loss: 1.4034 - val_acc: 0.2205 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3899 - acc: 0.2991 - precision_m: 0.0065 - val_loss: 1.3912 - val_acc: 0.2283 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3816 - acc: 0.2742 - precision_m: 0.0058 - val_loss: 1.3883 - val_acc: 0.2520 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3775 - acc: 0.3136 - precision_m: 0.0000e+00 - val_loss: 1.3905 - val_acc: 0.2441 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3826 - acc: 0.2802 - precision_m: 0.0045 - val_loss: 1.3902 - val_acc: 0.2441 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3645 - acc: 0.2225 - precision_m: 0.0130 - val_loss: 1.3985 - val_acc: 0.2205 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3589 - acc: 0.2834 - precision_m: 0.0443 - val_loss: 1.4032 - val_acc: 0.2520 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3856 - acc: 0.2762 - precision_m: 0.0323 - val_loss: 1.3911 - val_acc: 0.2362 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3705 - acc: 0.3026 - precision_m: 0.0424 - val_loss: 1.3910 - val_acc: 0.2441 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3870 - acc: 0.3088 - precision_m: 0.0406 - val_loss: 1.3931 - val_acc: 0.2441 - val_precision_m: 0.0000e+00\n","Epoch 52/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3805 - acc: 0.2826 - precision_m: 0.0143 - val_loss: 1.3893 - val_acc: 0.2520 - val_precision_m: 0.0000e+00\n","Epoch 53/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3747 - acc: 0.2749 - precision_m: 0.0023 - val_loss: 1.3944 - val_acc: 0.2441 - val_precision_m: 0.0000e+00\n","Epoch 54/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3590 - acc: 0.2553 - precision_m: 0.0294 - val_loss: 1.3906 - val_acc: 0.2362 - val_precision_m: 0.0000e+00\n","Epoch 55/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3812 - acc: 0.2509 - precision_m: 0.0153 - val_loss: 1.3932 - val_acc: 0.2283 - val_precision_m: 0.0000e+00\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 18.2s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","150/150 [==============================] - 1s 3ms/step - loss: 1.5066 - acc: 0.2450 - precision_m: 0.1213 - val_loss: 1.3863 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3866 - acc: 0.2419 - precision_m: 0.0000e+00 - val_loss: 1.3894 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3853 - acc: 0.2875 - precision_m: 0.0000e+00 - val_loss: 1.3905 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3849 - acc: 0.2607 - precision_m: 0.0000e+00 - val_loss: 1.3910 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3880 - acc: 0.2056 - precision_m: 0.0000e+00 - val_loss: 1.3912 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3862 - acc: 0.2525 - precision_m: 0.0000e+00 - val_loss: 1.3918 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3856 - acc: 0.2750 - precision_m: 0.0000e+00 - val_loss: 1.3917 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3848 - acc: 0.2575 - precision_m: 0.0000e+00 - val_loss: 1.3927 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3922 - acc: 0.2406 - precision_m: 0.0000e+00 - val_loss: 1.3926 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3854 - acc: 0.2585 - precision_m: 0.0000e+00 - val_loss: 1.3934 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3864 - acc: 0.2735 - precision_m: 0.0000e+00 - val_loss: 1.3933 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3866 - acc: 0.2789 - precision_m: 0.0000e+00 - val_loss: 1.3940 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3855 - acc: 0.2914 - precision_m: 0.0000e+00 - val_loss: 1.3936 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3830 - acc: 0.3088 - precision_m: 0.0000e+00 - val_loss: 1.3945 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3859 - acc: 0.2616 - precision_m: 0.0000e+00 - val_loss: 1.4006 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3936 - acc: 0.2555 - precision_m: 0.0000e+00 - val_loss: 1.3936 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3877 - acc: 0.2258 - precision_m: 0.0000e+00 - val_loss: 1.3944 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3885 - acc: 0.2701 - precision_m: 0.0000e+00 - val_loss: 1.3947 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3806 - acc: 0.2805 - precision_m: 0.0000e+00 - val_loss: 1.3960 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3878 - acc: 0.2648 - precision_m: 0.0000e+00 - val_loss: 1.3952 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3844 - acc: 0.2907 - precision_m: 0.0000e+00 - val_loss: 1.3951 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3826 - acc: 0.2763 - precision_m: 0.0000e+00 - val_loss: 1.3950 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3847 - acc: 0.2976 - precision_m: 0.0000e+00 - val_loss: 1.3950 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3811 - acc: 0.2782 - precision_m: 0.0000e+00 - val_loss: 1.3946 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3864 - acc: 0.2461 - precision_m: 0.0000e+00 - val_loss: 1.3948 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3820 - acc: 0.2914 - precision_m: 0.0000e+00 - val_loss: 1.3941 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3864 - acc: 0.2739 - precision_m: 0.0000e+00 - val_loss: 1.3934 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3843 - acc: 0.2928 - precision_m: 0.0000e+00 - val_loss: 1.3934 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3841 - acc: 0.2916 - precision_m: 0.0000e+00 - val_loss: 1.3935 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3859 - acc: 0.2549 - precision_m: 0.0000e+00 - val_loss: 1.3947 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","150/150 [==============================] - 0s 3ms/step - loss: 1.3869 - acc: 0.2603 - precision_m: 0.0000e+00 - val_loss: 1.3944 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3842 - acc: 0.3030 - precision_m: 0.0000e+00 - val_loss: 1.3941 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3855 - acc: 0.2717 - precision_m: 0.0000e+00 - val_loss: 1.3938 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3870 - acc: 0.2628 - precision_m: 0.0000e+00 - val_loss: 1.3927 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3843 - acc: 0.2688 - precision_m: 0.0000e+00 - val_loss: 1.3933 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3872 - acc: 0.2276 - precision_m: 0.0000e+00 - val_loss: 1.3931 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3853 - acc: 0.2384 - precision_m: 0.0000e+00 - val_loss: 1.3926 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3844 - acc: 0.3002 - precision_m: 0.0000e+00 - val_loss: 1.3952 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3809 - acc: 0.2703 - precision_m: 0.0000e+00 - val_loss: 1.3953 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3785 - acc: 0.3083 - precision_m: 0.0000e+00 - val_loss: 1.3963 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3868 - acc: 0.2426 - precision_m: 0.0000e+00 - val_loss: 1.3959 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3861 - acc: 0.2418 - precision_m: 0.0000e+00 - val_loss: 1.3958 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3844 - acc: 0.2749 - precision_m: 0.0000e+00 - val_loss: 1.3951 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3747 - acc: 0.2836 - precision_m: 0.0271 - val_loss: 1.3950 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3878 - acc: 0.2440 - precision_m: 0.0000e+00 - val_loss: 1.3949 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3850 - acc: 0.2638 - precision_m: 0.0000e+00 - val_loss: 1.3948 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3843 - acc: 0.2695 - precision_m: 0.0000e+00 - val_loss: 1.3890 - val_acc: 0.2636 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3867 - acc: 0.2561 - precision_m: 0.0065 - val_loss: 1.3959 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3880 - acc: 0.2542 - precision_m: 0.0000e+00 - val_loss: 1.3966 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3902 - acc: 0.2477 - precision_m: 0.0000e+00 - val_loss: 1.3958 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3824 - acc: 0.2608 - precision_m: 0.0106 - val_loss: 1.3964 - val_acc: 0.1860 - val_precision_m: 0.0000e+00\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 17.1s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","147/147 [==============================] - 1s 3ms/step - loss: 1.4693 - acc: 0.2379 - precision_m: 0.0296 - val_loss: 1.3852 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3970 - acc: 0.2329 - precision_m: 0.0000e+00 - val_loss: 1.3888 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3889 - acc: 0.2603 - precision_m: 0.0000e+00 - val_loss: 1.3861 - val_acc: 0.2302 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3854 - acc: 0.2974 - precision_m: 0.0000e+00 - val_loss: 1.3866 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","147/147 [==============================] - 0s 3ms/step - loss: 1.3909 - acc: 0.2327 - precision_m: 0.0000e+00 - val_loss: 1.3886 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3897 - acc: 0.2587 - precision_m: 0.0000e+00 - val_loss: 1.3865 - val_acc: 0.2381 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3858 - acc: 0.2830 - precision_m: 0.0000e+00 - val_loss: 1.3866 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3885 - acc: 0.2635 - precision_m: 0.0000e+00 - val_loss: 1.3881 - val_acc: 0.2381 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3896 - acc: 0.1973 - precision_m: 0.0000e+00 - val_loss: 1.3854 - val_acc: 0.2381 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3871 - acc: 0.2959 - precision_m: 0.0000e+00 - val_loss: 1.3847 - val_acc: 0.2619 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3863 - acc: 0.2633 - precision_m: 0.0000e+00 - val_loss: 1.3851 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3858 - acc: 0.3006 - precision_m: 0.0000e+00 - val_loss: 1.3847 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3852 - acc: 0.2833 - precision_m: 0.0000e+00 - val_loss: 1.3851 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3856 - acc: 0.2833 - precision_m: 0.0000e+00 - val_loss: 1.3850 - val_acc: 0.2302 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3875 - acc: 0.2314 - precision_m: 0.0000e+00 - val_loss: 1.3854 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3844 - acc: 0.2979 - precision_m: 0.0000e+00 - val_loss: 1.3850 - val_acc: 0.2302 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3853 - acc: 0.2275 - precision_m: 0.0000e+00 - val_loss: 1.3848 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3849 - acc: 0.2340 - precision_m: 0.0000e+00 - val_loss: 1.3847 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3855 - acc: 0.3268 - precision_m: 0.0000e+00 - val_loss: 1.3846 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3857 - acc: 0.2023 - precision_m: 0.0000e+00 - val_loss: 1.3849 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3853 - acc: 0.2486 - precision_m: 0.0000e+00 - val_loss: 1.3850 - val_acc: 0.2143 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3863 - acc: 0.2551 - precision_m: 0.0000e+00 - val_loss: 1.3850 - val_acc: 0.2222 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3842 - acc: 0.3342 - precision_m: 0.0000e+00 - val_loss: 1.3883 - val_acc: 0.2619 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3844 - acc: 0.2526 - precision_m: 0.0000e+00 - val_loss: 1.3839 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3858 - acc: 0.2664 - precision_m: 0.0000e+00 - val_loss: 1.3839 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3849 - acc: 0.2439 - precision_m: 0.0000e+00 - val_loss: 1.3839 - val_acc: 0.2619 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3862 - acc: 0.2627 - precision_m: 0.0000e+00 - val_loss: 1.3839 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3850 - acc: 0.2763 - precision_m: 0.0000e+00 - val_loss: 1.3833 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3840 - acc: 0.2462 - precision_m: 0.0000e+00 - val_loss: 1.3839 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3826 - acc: 0.2654 - precision_m: 0.0000e+00 - val_loss: 1.3831 - val_acc: 0.2619 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3882 - acc: 0.2779 - precision_m: 0.0000e+00 - val_loss: 1.3835 - val_acc: 0.3016 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3827 - acc: 0.2886 - precision_m: 0.0000e+00 - val_loss: 1.3837 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3853 - acc: 0.1957 - precision_m: 0.0000e+00 - val_loss: 1.3832 - val_acc: 0.2619 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3866 - acc: 0.2500 - precision_m: 0.0000e+00 - val_loss: 1.3835 - val_acc: 0.2937 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3789 - acc: 0.2167 - precision_m: 0.0000e+00 - val_loss: 1.3838 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3810 - acc: 0.2834 - precision_m: 0.0000e+00 - val_loss: 1.3823 - val_acc: 0.3095 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3806 - acc: 0.3005 - precision_m: 0.0000e+00 - val_loss: 1.3821 - val_acc: 0.2937 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3869 - acc: 0.2692 - precision_m: 0.0000e+00 - val_loss: 1.3822 - val_acc: 0.3413 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3827 - acc: 0.2034 - precision_m: 0.0000e+00 - val_loss: 1.3822 - val_acc: 0.3016 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3850 - acc: 0.2715 - precision_m: 0.0000e+00 - val_loss: 1.3828 - val_acc: 0.2619 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3814 - acc: 0.2977 - precision_m: 0.0000e+00 - val_loss: 1.3826 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3892 - acc: 0.2571 - precision_m: 0.0000e+00 - val_loss: 1.3827 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3853 - acc: 0.2894 - precision_m: 0.0000e+00 - val_loss: 1.3827 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3883 - acc: 0.2331 - precision_m: 0.0000e+00 - val_loss: 1.3830 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3823 - acc: 0.2934 - precision_m: 0.0000e+00 - val_loss: 1.3827 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3811 - acc: 0.3308 - precision_m: 0.0000e+00 - val_loss: 1.3830 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3844 - acc: 0.2397 - precision_m: 0.0000e+00 - val_loss: 1.3845 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3972 - acc: 0.1889 - precision_m: 0.0000e+00 - val_loss: 1.3835 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3786 - acc: 0.2542 - precision_m: 0.0000e+00 - val_loss: 1.3837 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3823 - acc: 0.2809 - precision_m: 0.0000e+00 - val_loss: 1.3827 - val_acc: 0.3095 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3879 - acc: 0.2395 - precision_m: 0.0000e+00 - val_loss: 1.3832 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 52/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3891 - acc: 0.2819 - precision_m: 0.0000e+00 - val_loss: 1.3827 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 53/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3816 - acc: 0.2607 - precision_m: 0.0000e+00 - val_loss: 1.3827 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 54/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3821 - acc: 0.2546 - precision_m: 0.0000e+00 - val_loss: 1.3870 - val_acc: 0.2619 - val_precision_m: 0.0000e+00\n","Epoch 55/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3985 - acc: 0.2759 - precision_m: 0.0000e+00 - val_loss: 1.3834 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 56/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3806 - acc: 0.2322 - precision_m: 0.0000e+00 - val_loss: 1.3835 - val_acc: 0.2143 - val_precision_m: 0.0000e+00\n","Epoch 57/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3852 - acc: 0.2378 - precision_m: 0.0000e+00 - val_loss: 1.3827 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 58/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3800 - acc: 0.2721 - precision_m: 0.0000e+00 - val_loss: 1.3833 - val_acc: 0.2619 - val_precision_m: 0.0000e+00\n","Epoch 59/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3793 - acc: 0.3506 - precision_m: 0.0000e+00 - val_loss: 1.3869 - val_acc: 0.2619 - val_precision_m: 0.0000e+00\n","Epoch 60/500\n","147/147 [==============================] - 0s 3ms/step - loss: 1.3872 - acc: 0.2405 - precision_m: 0.0000e+00 - val_loss: 1.3830 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 61/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3792 - acc: 0.2896 - precision_m: 0.0000e+00 - val_loss: 1.3849 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 62/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3956 - acc: 0.2112 - precision_m: 0.0000e+00 - val_loss: 1.3827 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 63/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3845 - acc: 0.2881 - precision_m: 0.0000e+00 - val_loss: 1.3843 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 64/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3965 - acc: 0.2309 - precision_m: 0.0000e+00 - val_loss: 1.3931 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 65/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.4120 - acc: 0.2864 - precision_m: 0.0000e+00 - val_loss: 1.3867 - val_acc: 0.2619 - val_precision_m: 0.0000e+00\n","Epoch 66/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3933 - acc: 0.2625 - precision_m: 0.0000e+00 - val_loss: 1.3857 - val_acc: 0.2937 - val_precision_m: 0.0000e+00\n","Epoch 67/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3929 - acc: 0.2104 - precision_m: 0.0000e+00 - val_loss: 1.3826 - val_acc: 0.2619 - val_precision_m: 0.0000e+00\n","Epoch 68/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3811 - acc: 0.2745 - precision_m: 0.0000e+00 - val_loss: 1.3824 - val_acc: 0.2619 - val_precision_m: 0.0000e+00\n","Epoch 69/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3809 - acc: 0.2421 - precision_m: 0.0000e+00 - val_loss: 1.3836 - val_acc: 0.2302 - val_precision_m: 0.0000e+00\n","Epoch 70/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3781 - acc: 0.3183 - precision_m: 0.0000e+00 - val_loss: 1.3833 - val_acc: 0.2619 - val_precision_m: 0.0000e+00\n","Epoch 71/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3798 - acc: 0.3133 - precision_m: 0.0000e+00 - val_loss: 1.3916 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 72/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3903 - acc: 0.2653 - precision_m: 0.0000e+00 - val_loss: 1.3852 - val_acc: 0.2222 - val_precision_m: 0.0000e+00\n","Epoch 73/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3733 - acc: 0.3031 - precision_m: 0.0000e+00 - val_loss: 1.3840 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 74/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3803 - acc: 0.2779 - precision_m: 3.2656e-04 - val_loss: 1.3841 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 75/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3840 - acc: 0.2092 - precision_m: 0.0000e+00 - val_loss: 1.3840 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 76/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3806 - acc: 0.2882 - precision_m: 0.0012 - val_loss: 1.3842 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 77/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3748 - acc: 0.2531 - precision_m: 0.0000e+00 - val_loss: 1.3832 - val_acc: 0.2619 - val_precision_m: 0.0000e+00\n","Epoch 78/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3626 - acc: 0.2666 - precision_m: 0.0499 - val_loss: 1.3838 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 79/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3869 - acc: 0.2469 - precision_m: 5.6963e-04 - val_loss: 1.3850 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 80/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3786 - acc: 0.2667 - precision_m: 0.0134 - val_loss: 1.3854 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 81/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3970 - acc: 0.2435 - precision_m: 0.0027 - val_loss: 1.3851 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 82/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3750 - acc: 0.2317 - precision_m: 0.0169 - val_loss: 1.3847 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 83/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3767 - acc: 0.2570 - precision_m: 0.0116 - val_loss: 1.3827 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 84/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3826 - acc: 0.2448 - precision_m: 0.0000e+00 - val_loss: 1.3824 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 85/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3766 - acc: 0.2477 - precision_m: 0.0012 - val_loss: 1.3860 - val_acc: 0.2937 - val_precision_m: 0.0000e+00\n","Epoch 86/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3866 - acc: 0.2323 - precision_m: 0.0150 - val_loss: 1.3824 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 87/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3652 - acc: 0.2922 - precision_m: 0.0402 - val_loss: 1.3827 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 17.1s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","143/143 [==============================] - 1s 3ms/step - loss: 1.4927 - acc: 0.1899 - precision_m: 0.0477 - val_loss: 1.3887 - val_acc: 0.2439 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3909 - acc: 0.2749 - precision_m: 0.0065 - val_loss: 1.3935 - val_acc: 0.2114 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3814 - acc: 0.3106 - precision_m: 0.0110 - val_loss: 1.3850 - val_acc: 0.2520 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3739 - acc: 0.2376 - precision_m: 0.0000e+00 - val_loss: 1.3858 - val_acc: 0.2439 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3838 - acc: 0.2634 - precision_m: 0.0000e+00 - val_loss: 1.3792 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3750 - acc: 0.3438 - precision_m: 0.0000e+00 - val_loss: 1.3801 - val_acc: 0.2358 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3785 - acc: 0.2562 - precision_m: 0.0000e+00 - val_loss: 1.3849 - val_acc: 0.2439 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3832 - acc: 0.2265 - precision_m: 0.0000e+00 - val_loss: 1.3850 - val_acc: 0.2602 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3807 - acc: 0.2433 - precision_m: 0.0000e+00 - val_loss: 1.3795 - val_acc: 0.2439 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3687 - acc: 0.2716 - precision_m: 0.0000e+00 - val_loss: 1.3854 - val_acc: 0.2602 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3720 - acc: 0.2833 - precision_m: 0.0155 - val_loss: 1.3764 - val_acc: 0.2276 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3538 - acc: 0.2630 - precision_m: 0.0000e+00 - val_loss: 1.3798 - val_acc: 0.2602 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3683 - acc: 0.2674 - precision_m: 0.0102 - val_loss: 1.3737 - val_acc: 0.2683 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3679 - acc: 0.2847 - precision_m: 0.0000e+00 - val_loss: 1.3627 - val_acc: 0.2358 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3536 - acc: 0.2891 - precision_m: 0.0036 - val_loss: 1.3681 - val_acc: 0.2439 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3695 - acc: 0.3033 - precision_m: 0.0040 - val_loss: 1.3533 - val_acc: 0.3008 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3373 - acc: 0.3508 - precision_m: 0.0085 - val_loss: 1.3903 - val_acc: 0.2683 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3783 - acc: 0.2689 - precision_m: 0.0239 - val_loss: 1.3621 - val_acc: 0.2276 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3478 - acc: 0.3777 - precision_m: 0.0131 - val_loss: 1.3485 - val_acc: 0.3171 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3689 - acc: 0.2957 - precision_m: 0.0034 - val_loss: 1.3501 - val_acc: 0.2439 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3292 - acc: 0.3029 - precision_m: 0.0259 - val_loss: 1.3469 - val_acc: 0.3577 - val_precision_m: 0.0161\n","Epoch 22/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3370 - acc: 0.3394 - precision_m: 0.1212 - val_loss: 1.3437 - val_acc: 0.3008 - val_precision_m: 0.0161\n","Epoch 23/500\n","143/143 [==============================] - 0s 3ms/step - loss: 1.3088 - acc: 0.3704 - precision_m: 0.0444 - val_loss: 1.3431 - val_acc: 0.3171 - val_precision_m: 0.0161\n","Epoch 24/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3521 - acc: 0.3266 - precision_m: 0.0068 - val_loss: 1.3636 - val_acc: 0.2276 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3158 - acc: 0.3429 - precision_m: 0.0310 - val_loss: 1.3503 - val_acc: 0.2520 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3004 - acc: 0.3475 - precision_m: 0.0499 - val_loss: 1.3425 - val_acc: 0.2195 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3612 - acc: 0.2992 - precision_m: 0.0110 - val_loss: 1.3365 - val_acc: 0.2602 - val_precision_m: 0.0161\n","Epoch 28/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3398 - acc: 0.3583 - precision_m: 0.0281 - val_loss: 1.3429 - val_acc: 0.2276 - val_precision_m: 0.0161\n","Epoch 29/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3116 - acc: 0.3295 - precision_m: 0.1101 - val_loss: 1.3301 - val_acc: 0.3496 - val_precision_m: 0.0161\n","Epoch 30/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2755 - acc: 0.3493 - precision_m: 0.0652 - val_loss: 1.3322 - val_acc: 0.2520 - val_precision_m: 0.0161\n","Epoch 31/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3309 - acc: 0.3898 - precision_m: 0.0585 - val_loss: 1.3240 - val_acc: 0.2602 - val_precision_m: 0.0161\n","Epoch 32/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2721 - acc: 0.4584 - precision_m: 0.0856 - val_loss: 1.3345 - val_acc: 0.2520 - val_precision_m: 0.0161\n","Epoch 33/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3120 - acc: 0.3507 - precision_m: 0.0846 - val_loss: 1.3247 - val_acc: 0.3415 - val_precision_m: 0.0484\n","Epoch 34/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2886 - acc: 0.3860 - precision_m: 0.0747 - val_loss: 1.3222 - val_acc: 0.3333 - val_precision_m: 0.0484\n","Epoch 35/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3197 - acc: 0.4041 - precision_m: 0.0618 - val_loss: 1.3194 - val_acc: 0.2683 - val_precision_m: 0.0484\n","Epoch 36/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3301 - acc: 0.3209 - precision_m: 0.0830 - val_loss: 1.3309 - val_acc: 0.2195 - val_precision_m: 0.0161\n","Epoch 37/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3329 - acc: 0.3189 - precision_m: 0.0763 - val_loss: 1.3365 - val_acc: 0.2439 - val_precision_m: 0.0161\n","Epoch 38/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2782 - acc: 0.3412 - precision_m: 0.1113 - val_loss: 1.3399 - val_acc: 0.2846 - val_precision_m: 0.0484\n","Epoch 39/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3112 - acc: 0.3699 - precision_m: 0.0410 - val_loss: 1.3336 - val_acc: 0.2683 - val_precision_m: 0.0323\n","Epoch 40/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3119 - acc: 0.3092 - precision_m: 0.0678 - val_loss: 1.3376 - val_acc: 0.2520 - val_precision_m: 0.0161\n","Epoch 41/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3075 - acc: 0.3809 - precision_m: 0.0310 - val_loss: 1.3267 - val_acc: 0.2439 - val_precision_m: 0.0323\n","Epoch 42/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2558 - acc: 0.3919 - precision_m: 0.1035 - val_loss: 1.3266 - val_acc: 0.2846 - val_precision_m: 0.0484\n","Epoch 43/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2689 - acc: 0.3775 - precision_m: 0.0889 - val_loss: 1.3386 - val_acc: 0.2276 - val_precision_m: 0.0323\n","Epoch 44/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3019 - acc: 0.3385 - precision_m: 0.0392 - val_loss: 1.3236 - val_acc: 0.2520 - val_precision_m: 0.0484\n","Epoch 45/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3422 - acc: 0.3989 - precision_m: 0.0684 - val_loss: 1.3328 - val_acc: 0.2358 - val_precision_m: 0.0323\n","Epoch 46/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2909 - acc: 0.3578 - precision_m: 0.0507 - val_loss: 1.3232 - val_acc: 0.2846 - val_precision_m: 0.0484\n","Epoch 47/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2711 - acc: 0.3424 - precision_m: 0.0800 - val_loss: 1.3262 - val_acc: 0.2683 - val_precision_m: 0.0484\n","Epoch 48/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3056 - acc: 0.3410 - precision_m: 0.0857 - val_loss: 1.3238 - val_acc: 0.2683 - val_precision_m: 0.0484\n","Epoch 49/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2422 - acc: 0.3610 - precision_m: 0.1354 - val_loss: 1.3224 - val_acc: 0.2764 - val_precision_m: 0.0484\n","Epoch 50/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2616 - acc: 0.3684 - precision_m: 0.0907 - val_loss: 1.3151 - val_acc: 0.2846 - val_precision_m: 0.0484\n","Epoch 51/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2583 - acc: 0.3940 - precision_m: 0.0712 - val_loss: 1.3149 - val_acc: 0.2439 - val_precision_m: 0.0484\n","Epoch 52/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2519 - acc: 0.3733 - precision_m: 0.1063 - val_loss: 1.3343 - val_acc: 0.2439 - val_precision_m: 0.0323\n","Epoch 53/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2649 - acc: 0.3638 - precision_m: 0.1226 - val_loss: 1.3177 - val_acc: 0.2764 - val_precision_m: 0.0484\n","Epoch 54/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2556 - acc: 0.3711 - precision_m: 0.0973 - val_loss: 1.3126 - val_acc: 0.2602 - val_precision_m: 0.0484\n","Epoch 55/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2537 - acc: 0.3853 - precision_m: 0.1240 - val_loss: 1.3382 - val_acc: 0.3577 - val_precision_m: 0.1129\n","Epoch 56/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2465 - acc: 0.4081 - precision_m: 0.1354 - val_loss: 1.3157 - val_acc: 0.2520 - val_precision_m: 0.0484\n","Epoch 57/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2618 - acc: 0.3830 - precision_m: 0.1136 - val_loss: 1.3055 - val_acc: 0.3089 - val_precision_m: 0.0484\n","Epoch 58/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2794 - acc: 0.3831 - precision_m: 0.0925 - val_loss: 1.3328 - val_acc: 0.2927 - val_precision_m: 0.0323\n","Epoch 59/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2215 - acc: 0.3933 - precision_m: 0.0832 - val_loss: 1.3104 - val_acc: 0.2439 - val_precision_m: 0.0484\n","Epoch 60/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2243 - acc: 0.3797 - precision_m: 0.0950 - val_loss: 1.3397 - val_acc: 0.2846 - val_precision_m: 0.0645\n","Epoch 61/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2999 - acc: 0.3232 - precision_m: 0.0810 - val_loss: 1.3038 - val_acc: 0.3252 - val_precision_m: 0.0484\n","Epoch 62/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2597 - acc: 0.3802 - precision_m: 0.1279 - val_loss: 1.3085 - val_acc: 0.2520 - val_precision_m: 0.0161\n","Epoch 63/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2799 - acc: 0.3115 - precision_m: 0.0620 - val_loss: 1.3884 - val_acc: 0.3659 - val_precision_m: 0.0806\n","Epoch 64/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2505 - acc: 0.3684 - precision_m: 0.1155 - val_loss: 1.3152 - val_acc: 0.2114 - val_precision_m: 0.0161\n","Epoch 65/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.1866 - acc: 0.4145 - precision_m: 0.1403 - val_loss: 1.3028 - val_acc: 0.3008 - val_precision_m: 0.0484\n","Epoch 66/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2553 - acc: 0.3632 - precision_m: 0.1147 - val_loss: 1.3121 - val_acc: 0.2520 - val_precision_m: 0.0484\n","Epoch 67/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2578 - acc: 0.3756 - precision_m: 0.1069 - val_loss: 1.3116 - val_acc: 0.2683 - val_precision_m: 0.0645\n","Epoch 68/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2248 - acc: 0.4133 - precision_m: 0.1548 - val_loss: 1.3221 - val_acc: 0.2602 - val_precision_m: 0.0323\n","Epoch 69/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2344 - acc: 0.3820 - precision_m: 0.1058 - val_loss: 1.3147 - val_acc: 0.3252 - val_precision_m: 0.0806\n","Epoch 70/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3040 - acc: 0.3497 - precision_m: 0.0831 - val_loss: 1.3035 - val_acc: 0.2276 - val_precision_m: 0.0484\n","Epoch 71/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2012 - acc: 0.4452 - precision_m: 0.1412 - val_loss: 1.3267 - val_acc: 0.2520 - val_precision_m: 0.0484\n","Epoch 72/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2012 - acc: 0.3918 - precision_m: 0.2302 - val_loss: 1.3100 - val_acc: 0.2764 - val_precision_m: 0.0484\n","Epoch 73/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.1897 - acc: 0.4124 - precision_m: 0.1941 - val_loss: 1.3107 - val_acc: 0.2602 - val_precision_m: 0.0484\n","Epoch 74/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2237 - acc: 0.4052 - precision_m: 0.1681 - val_loss: 1.3085 - val_acc: 0.2927 - val_precision_m: 0.0484\n","Epoch 75/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2320 - acc: 0.3513 - precision_m: 0.0859 - val_loss: 1.3208 - val_acc: 0.2439 - val_precision_m: 0.0806\n","Epoch 76/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2564 - acc: 0.4081 - precision_m: 0.1236 - val_loss: 1.3159 - val_acc: 0.3089 - val_precision_m: 0.0806\n","Epoch 77/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2502 - acc: 0.3714 - precision_m: 0.1643 - val_loss: 1.2931 - val_acc: 0.2846 - val_precision_m: 0.0484\n","Epoch 78/500\n","143/143 [==============================] - 0s 3ms/step - loss: 1.2939 - acc: 0.3669 - precision_m: 0.0993 - val_loss: 1.3046 - val_acc: 0.2683 - val_precision_m: 0.0806\n","Epoch 79/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2490 - acc: 0.3791 - precision_m: 0.1472 - val_loss: 1.3221 - val_acc: 0.3089 - val_precision_m: 0.0806\n","Epoch 80/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2473 - acc: 0.3707 - precision_m: 0.0746 - val_loss: 1.2948 - val_acc: 0.2602 - val_precision_m: 0.0484\n","Epoch 81/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2268 - acc: 0.4059 - precision_m: 0.0897 - val_loss: 1.3015 - val_acc: 0.2520 - val_precision_m: 0.0645\n","Epoch 82/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2123 - acc: 0.3678 - precision_m: 0.0979 - val_loss: 1.3096 - val_acc: 0.2683 - val_precision_m: 0.0484\n","Epoch 83/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2550 - acc: 0.3525 - precision_m: 0.0298 - val_loss: 1.3065 - val_acc: 0.2114 - val_precision_m: 0.0484\n","Epoch 84/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2528 - acc: 0.3826 - precision_m: 0.0936 - val_loss: 1.3168 - val_acc: 0.2846 - val_precision_m: 0.0968\n","Epoch 85/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2025 - acc: 0.3891 - precision_m: 0.1408 - val_loss: 1.3176 - val_acc: 0.2927 - val_precision_m: 0.0645\n","Epoch 86/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2796 - acc: 0.2757 - precision_m: 0.1001 - val_loss: 1.3256 - val_acc: 0.2927 - val_precision_m: 0.0806\n","Epoch 87/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.1807 - acc: 0.4581 - precision_m: 0.1594 - val_loss: 1.3082 - val_acc: 0.3171 - val_precision_m: 0.0806\n","Epoch 88/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2084 - acc: 0.4188 - precision_m: 0.1073 - val_loss: 1.2976 - val_acc: 0.2764 - val_precision_m: 0.0645\n","Epoch 89/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2554 - acc: 0.3478 - precision_m: 0.0615 - val_loss: 1.3084 - val_acc: 0.2927 - val_precision_m: 0.0806\n","Epoch 90/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2043 - acc: 0.4721 - precision_m: 0.0942 - val_loss: 1.3141 - val_acc: 0.2846 - val_precision_m: 0.0645\n","Epoch 91/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.1866 - acc: 0.3409 - precision_m: 0.1048 - val_loss: 1.3218 - val_acc: 0.2683 - val_precision_m: 0.0806\n","Epoch 92/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2312 - acc: 0.3431 - precision_m: 0.1453 - val_loss: 1.3051 - val_acc: 0.2846 - val_precision_m: 0.0645\n","Epoch 93/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.1495 - acc: 0.4556 - precision_m: 0.1589 - val_loss: 1.2940 - val_acc: 0.2764 - val_precision_m: 0.0484\n","Epoch 94/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2185 - acc: 0.3933 - precision_m: 0.0662 - val_loss: 1.3499 - val_acc: 0.3496 - val_precision_m: 0.1048\n","Epoch 95/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2172 - acc: 0.3834 - precision_m: 0.1296 - val_loss: 1.3002 - val_acc: 0.2439 - val_precision_m: 0.0645\n","Epoch 96/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.1942 - acc: 0.4198 - precision_m: 0.1172 - val_loss: 1.2918 - val_acc: 0.2927 - val_precision_m: 0.0484\n","Epoch 97/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.1701 - acc: 0.4110 - precision_m: 0.0699 - val_loss: 1.2915 - val_acc: 0.2439 - val_precision_m: 0.0645\n","Epoch 98/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2415 - acc: 0.3889 - precision_m: 0.1058 - val_loss: 1.3200 - val_acc: 0.2520 - val_precision_m: 0.0484\n","Epoch 99/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2231 - acc: 0.4065 - precision_m: 0.1030 - val_loss: 1.3166 - val_acc: 0.2520 - val_precision_m: 0.0806\n","Epoch 100/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.1445 - acc: 0.4125 - precision_m: 0.1727 - val_loss: 1.3058 - val_acc: 0.2683 - val_precision_m: 0.0484\n","Epoch 101/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.1691 - acc: 0.4246 - precision_m: 0.1613 - val_loss: 1.2944 - val_acc: 0.2358 - val_precision_m: 0.0645\n","Epoch 102/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.1391 - acc: 0.3882 - precision_m: 0.2142 - val_loss: 1.3016 - val_acc: 0.2520 - val_precision_m: 0.0645\n","Epoch 103/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.1347 - acc: 0.4082 - precision_m: 0.1684 - val_loss: 1.3076 - val_acc: 0.2927 - val_precision_m: 0.0806\n","Epoch 104/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2088 - acc: 0.3693 - precision_m: 0.0938 - val_loss: 1.3203 - val_acc: 0.2602 - val_precision_m: 0.0806\n","Epoch 105/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2044 - acc: 0.4201 - precision_m: 0.1849 - val_loss: 1.3199 - val_acc: 0.3089 - val_precision_m: 0.0806\n","Epoch 106/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2257 - acc: 0.3755 - precision_m: 0.1668 - val_loss: 1.3231 - val_acc: 0.2764 - val_precision_m: 0.0968\n","Epoch 107/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.1626 - acc: 0.4751 - precision_m: 0.1284 - val_loss: 1.3122 - val_acc: 0.2602 - val_precision_m: 0.0806\n","Epoch 108/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2035 - acc: 0.3795 - precision_m: 0.1402 - val_loss: 1.3441 - val_acc: 0.3089 - val_precision_m: 0.1129\n","Epoch 109/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.1935 - acc: 0.3904 - precision_m: 0.1490 - val_loss: 1.3028 - val_acc: 0.2764 - val_precision_m: 0.0806\n","Epoch 110/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.1939 - acc: 0.4264 - precision_m: 0.1651 - val_loss: 1.3201 - val_acc: 0.2683 - val_precision_m: 0.0968\n","Epoch 111/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.1536 - acc: 0.4463 - precision_m: 0.1457 - val_loss: 1.3145 - val_acc: 0.2276 - val_precision_m: 0.0645\n","Epoch 112/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2551 - acc: 0.3954 - precision_m: 0.0847 - val_loss: 1.3052 - val_acc: 0.2764 - val_precision_m: 0.0968\n","Epoch 113/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2693 - acc: 0.4692 - precision_m: 0.1099 - val_loss: 1.3392 - val_acc: 0.3333 - val_precision_m: 0.0806\n","Epoch 114/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2081 - acc: 0.3593 - precision_m: 0.2110 - val_loss: 1.3125 - val_acc: 0.2764 - val_precision_m: 0.0645\n","Epoch 115/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.1483 - acc: 0.4978 - precision_m: 0.2157 - val_loss: 1.3022 - val_acc: 0.3008 - val_precision_m: 0.0806\n","Epoch 116/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.1806 - acc: 0.4584 - precision_m: 0.2269 - val_loss: 1.3128 - val_acc: 0.2764 - val_precision_m: 0.0968\n","Epoch 117/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2129 - acc: 0.3915 - precision_m: 0.0666 - val_loss: 1.3457 - val_acc: 0.2602 - val_precision_m: 0.0806\n","Epoch 118/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.1838 - acc: 0.4110 - precision_m: 0.1229 - val_loss: 1.3138 - val_acc: 0.2764 - val_precision_m: 0.0968\n","Epoch 119/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.1411 - acc: 0.3869 - precision_m: 0.1490 - val_loss: 1.3101 - val_acc: 0.2602 - val_precision_m: 0.0645\n","Epoch 120/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.1189 - acc: 0.4306 - precision_m: 0.1268 - val_loss: 1.4419 - val_acc: 0.3415 - val_precision_m: 0.1774\n","Epoch 121/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.1763 - acc: 0.3926 - precision_m: 0.1889 - val_loss: 1.3155 - val_acc: 0.3089 - val_precision_m: 0.1129\n","Epoch 122/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.1973 - acc: 0.4198 - precision_m: 0.1737 - val_loss: 1.3098 - val_acc: 0.2439 - val_precision_m: 0.0645\n","Epoch 123/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.1688 - acc: 0.4453 - precision_m: 0.1859 - val_loss: 1.3190 - val_acc: 0.2602 - val_precision_m: 0.0323\n","Epoch 124/500\n","143/143 [==============================] - 0s 3ms/step - loss: 1.2011 - acc: 0.4346 - precision_m: 0.1783 - val_loss: 1.3451 - val_acc: 0.2846 - val_precision_m: 0.0968\n","Epoch 125/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.1627 - acc: 0.4297 - precision_m: 0.1311 - val_loss: 1.3211 - val_acc: 0.3171 - val_precision_m: 0.0968\n","Epoch 126/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.1806 - acc: 0.4406 - precision_m: 0.1499 - val_loss: 1.3614 - val_acc: 0.2520 - val_precision_m: 0.0806\n","Epoch 127/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.1132 - acc: 0.4311 - precision_m: 0.1463 - val_loss: 1.3106 - val_acc: 0.2520 - val_precision_m: 0.0806\n","Epoch 128/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.1594 - acc: 0.4258 - precision_m: 0.1452 - val_loss: 1.3887 - val_acc: 0.3496 - val_precision_m: 0.1210\n","Epoch 129/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.1212 - acc: 0.4523 - precision_m: 0.1606 - val_loss: 1.3313 - val_acc: 0.2846 - val_precision_m: 0.1129\n","Epoch 130/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.1555 - acc: 0.4136 - precision_m: 0.2098 - val_loss: 1.3640 - val_acc: 0.3821 - val_precision_m: 0.1129\n","Epoch 131/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.1355 - acc: 0.4309 - precision_m: 0.1639 - val_loss: 1.3448 - val_acc: 0.3659 - val_precision_m: 0.1129\n","Epoch 132/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.1897 - acc: 0.4134 - precision_m: 0.2045 - val_loss: 1.3299 - val_acc: 0.2358 - val_precision_m: 0.0645\n","Epoch 133/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2192 - acc: 0.3814 - precision_m: 0.1223 - val_loss: 1.3405 - val_acc: 0.2764 - val_precision_m: 0.0968\n","Epoch 134/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.1340 - acc: 0.4702 - precision_m: 0.1591 - val_loss: 1.3037 - val_acc: 0.2276 - val_precision_m: 0.0645\n","Epoch 135/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.1766 - acc: 0.4167 - precision_m: 0.1183 - val_loss: 1.3077 - val_acc: 0.3008 - val_precision_m: 0.0968\n","Epoch 136/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.1527 - acc: 0.3865 - precision_m: 0.1294 - val_loss: 1.2957 - val_acc: 0.2683 - val_precision_m: 0.0806\n","Epoch 137/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2350 - acc: 0.3880 - precision_m: 0.0985 - val_loss: 1.4606 - val_acc: 0.3577 - val_precision_m: 0.1452\n","Epoch 138/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2360 - acc: 0.4164 - precision_m: 0.1520 - val_loss: 1.3079 - val_acc: 0.2764 - val_precision_m: 0.0968\n","Epoch 139/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.1654 - acc: 0.4448 - precision_m: 0.1656 - val_loss: 1.3089 - val_acc: 0.2764 - val_precision_m: 0.0806\n","Epoch 140/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.1883 - acc: 0.4330 - precision_m: 0.1270 - val_loss: 1.3047 - val_acc: 0.3089 - val_precision_m: 0.0968\n","Epoch 141/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.1378 - acc: 0.4117 - precision_m: 0.2124 - val_loss: 1.3460 - val_acc: 0.3333 - val_precision_m: 0.1129\n","Epoch 142/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.1200 - acc: 0.4695 - precision_m: 0.2292 - val_loss: 1.3417 - val_acc: 0.2683 - val_precision_m: 0.0968\n","Epoch 143/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.1199 - acc: 0.4728 - precision_m: 0.2482 - val_loss: 1.3156 - val_acc: 0.2276 - val_precision_m: 0.0645\n","Epoch 144/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.0965 - acc: 0.4358 - precision_m: 0.1713 - val_loss: 1.3266 - val_acc: 0.2846 - val_precision_m: 0.0645\n","Epoch 145/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.1532 - acc: 0.4125 - precision_m: 0.1067 - val_loss: 1.4121 - val_acc: 0.3415 - val_precision_m: 0.1129\n","Epoch 146/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.1651 - acc: 0.3871 - precision_m: 0.1232 - val_loss: 1.3128 - val_acc: 0.2764 - val_precision_m: 0.0645\n","Epoch 147/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.1418 - acc: 0.4256 - precision_m: 0.2147 - val_loss: 1.3090 - val_acc: 0.2358 - val_precision_m: 0.0645\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 18.4s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","173/173 [==============================] - 1s 3ms/step - loss: 1.4961 - acc: 0.2780 - precision_m: 0.1006 - val_loss: 1.3941 - val_acc: 0.2416 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3942 - acc: 0.2575 - precision_m: 0.0000e+00 - val_loss: 1.3857 - val_acc: 0.2752 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3840 - acc: 0.2985 - precision_m: 0.0000e+00 - val_loss: 1.3863 - val_acc: 0.2752 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3848 - acc: 0.2443 - precision_m: 0.0000e+00 - val_loss: 1.3868 - val_acc: 0.2752 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3845 - acc: 0.2437 - precision_m: 0.0000e+00 - val_loss: 1.3870 - val_acc: 0.2685 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3849 - acc: 0.2476 - precision_m: 0.0000e+00 - val_loss: 1.3875 - val_acc: 0.2416 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3842 - acc: 0.2204 - precision_m: 0.0000e+00 - val_loss: 1.3878 - val_acc: 0.2416 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3851 - acc: 0.2459 - precision_m: 0.0000e+00 - val_loss: 1.3881 - val_acc: 0.2416 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3838 - acc: 0.2120 - precision_m: 0.0000e+00 - val_loss: 1.3882 - val_acc: 0.2416 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3845 - acc: 0.2403 - precision_m: 0.0000e+00 - val_loss: 1.3887 - val_acc: 0.2215 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3861 - acc: 0.2389 - precision_m: 0.0000e+00 - val_loss: 1.3889 - val_acc: 0.2215 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3865 - acc: 0.2599 - precision_m: 0.0000e+00 - val_loss: 1.3898 - val_acc: 0.2215 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3853 - acc: 0.2812 - precision_m: 0.0000e+00 - val_loss: 1.3890 - val_acc: 0.2215 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3886 - acc: 0.2187 - precision_m: 0.0000e+00 - val_loss: 1.3891 - val_acc: 0.2215 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3825 - acc: 0.2654 - precision_m: 0.0000e+00 - val_loss: 1.3894 - val_acc: 0.2215 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3853 - acc: 0.2739 - precision_m: 0.0000e+00 - val_loss: 1.3895 - val_acc: 0.2215 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3814 - acc: 0.2874 - precision_m: 0.0000e+00 - val_loss: 1.3897 - val_acc: 0.2215 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3886 - acc: 0.2596 - precision_m: 0.0000e+00 - val_loss: 1.3897 - val_acc: 0.2215 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3890 - acc: 0.2387 - precision_m: 0.0000e+00 - val_loss: 1.3896 - val_acc: 0.2215 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3876 - acc: 0.2465 - precision_m: 0.0000e+00 - val_loss: 1.3899 - val_acc: 0.2215 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3876 - acc: 0.2210 - precision_m: 0.0000e+00 - val_loss: 1.3899 - val_acc: 0.2215 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3898 - acc: 0.2386 - precision_m: 0.0000e+00 - val_loss: 1.3898 - val_acc: 0.2215 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3867 - acc: 0.2278 - precision_m: 0.0000e+00 - val_loss: 1.3900 - val_acc: 0.2215 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3856 - acc: 0.2521 - precision_m: 0.0000e+00 - val_loss: 1.3901 - val_acc: 0.2215 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3842 - acc: 0.2517 - precision_m: 0.0000e+00 - val_loss: 1.3900 - val_acc: 0.2215 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3859 - acc: 0.2289 - precision_m: 0.0000e+00 - val_loss: 1.3901 - val_acc: 0.2215 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3867 - acc: 0.2499 - precision_m: 0.0000e+00 - val_loss: 1.3900 - val_acc: 0.2215 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3885 - acc: 0.2664 - precision_m: 0.0000e+00 - val_loss: 1.3900 - val_acc: 0.2215 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3819 - acc: 0.3003 - precision_m: 0.0000e+00 - val_loss: 1.3904 - val_acc: 0.2215 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3835 - acc: 0.2991 - precision_m: 0.0000e+00 - val_loss: 1.3903 - val_acc: 0.2215 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3873 - acc: 0.2295 - precision_m: 0.0000e+00 - val_loss: 1.3902 - val_acc: 0.2215 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3859 - acc: 0.2318 - precision_m: 0.0000e+00 - val_loss: 1.3902 - val_acc: 0.2215 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3852 - acc: 0.2445 - precision_m: 0.0000e+00 - val_loss: 1.3902 - val_acc: 0.2215 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3854 - acc: 0.2680 - precision_m: 0.0000e+00 - val_loss: 1.3901 - val_acc: 0.2215 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3900 - acc: 0.2541 - precision_m: 0.0000e+00 - val_loss: 1.3902 - val_acc: 0.2215 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3885 - acc: 0.2428 - precision_m: 0.0000e+00 - val_loss: 1.3903 - val_acc: 0.2215 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3803 - acc: 0.2936 - precision_m: 0.0000e+00 - val_loss: 1.3902 - val_acc: 0.2215 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3856 - acc: 0.2526 - precision_m: 0.0000e+00 - val_loss: 1.3901 - val_acc: 0.2215 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3883 - acc: 0.2597 - precision_m: 0.0000e+00 - val_loss: 1.3902 - val_acc: 0.2215 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3880 - acc: 0.2174 - precision_m: 0.0000e+00 - val_loss: 1.3904 - val_acc: 0.2215 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3832 - acc: 0.2638 - precision_m: 0.0000e+00 - val_loss: 1.3903 - val_acc: 0.2215 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3834 - acc: 0.2784 - precision_m: 0.0000e+00 - val_loss: 1.3904 - val_acc: 0.2215 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3869 - acc: 0.2522 - precision_m: 0.0000e+00 - val_loss: 1.3899 - val_acc: 0.2215 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3841 - acc: 0.2651 - precision_m: 0.0000e+00 - val_loss: 1.3902 - val_acc: 0.2215 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3876 - acc: 0.2108 - precision_m: 0.0000e+00 - val_loss: 1.3901 - val_acc: 0.2215 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3903 - acc: 0.2325 - precision_m: 0.0000e+00 - val_loss: 1.3903 - val_acc: 0.2215 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3799 - acc: 0.2891 - precision_m: 0.0000e+00 - val_loss: 1.3903 - val_acc: 0.2215 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3897 - acc: 0.2219 - precision_m: 0.0000e+00 - val_loss: 1.3902 - val_acc: 0.2215 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3836 - acc: 0.3019 - precision_m: 0.0000e+00 - val_loss: 1.3905 - val_acc: 0.2215 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3855 - acc: 0.2784 - precision_m: 0.0000e+00 - val_loss: 1.3903 - val_acc: 0.2215 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3907 - acc: 0.2505 - precision_m: 0.0000e+00 - val_loss: 1.3902 - val_acc: 0.2215 - val_precision_m: 0.0000e+00\n","Epoch 52/500\n","173/173 [==============================] - 0s 2ms/step - loss: 1.3851 - acc: 0.2725 - precision_m: 0.0000e+00 - val_loss: 1.3902 - val_acc: 0.2215 - val_precision_m: 0.0000e+00\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 19.8s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","166/166 [==============================] - 1s 3ms/step - loss: 1.4793 - acc: 0.2406 - precision_m: 0.0000e+00 - val_loss: 1.3911 - val_acc: 0.2254 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.4217 - acc: 0.2779 - precision_m: 0.0000e+00 - val_loss: 1.3868 - val_acc: 0.2254 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3924 - acc: 0.2347 - precision_m: 0.0000e+00 - val_loss: 1.3851 - val_acc: 0.2958 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.4010 - acc: 0.2230 - precision_m: 0.0000e+00 - val_loss: 1.3854 - val_acc: 0.2887 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3911 - acc: 0.2506 - precision_m: 0.0000e+00 - val_loss: 1.3874 - val_acc: 0.2113 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3913 - acc: 0.2112 - precision_m: 0.0000e+00 - val_loss: 1.3870 - val_acc: 0.2183 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3854 - acc: 0.2667 - precision_m: 0.0000e+00 - val_loss: 1.3878 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3853 - acc: 0.2684 - precision_m: 0.0000e+00 - val_loss: 1.3882 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3868 - acc: 0.2542 - precision_m: 0.0000e+00 - val_loss: 1.3881 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3858 - acc: 0.2544 - precision_m: 0.0000e+00 - val_loss: 1.3886 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3831 - acc: 0.2902 - precision_m: 0.0000e+00 - val_loss: 1.3878 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3836 - acc: 0.2916 - precision_m: 0.0000e+00 - val_loss: 1.3944 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3864 - acc: 0.2654 - precision_m: 0.0000e+00 - val_loss: 1.3905 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3874 - acc: 0.2455 - precision_m: 0.0000e+00 - val_loss: 1.3896 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3857 - acc: 0.2669 - precision_m: 0.0000e+00 - val_loss: 1.3881 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3900 - acc: 0.2413 - precision_m: 0.0000e+00 - val_loss: 1.3880 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3836 - acc: 0.2845 - precision_m: 0.0000e+00 - val_loss: 1.3887 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3833 - acc: 0.2786 - precision_m: 0.0000e+00 - val_loss: 1.3887 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3852 - acc: 0.2490 - precision_m: 0.0000e+00 - val_loss: 1.3890 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3868 - acc: 0.2744 - precision_m: 0.0000e+00 - val_loss: 1.3886 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3885 - acc: 0.2586 - precision_m: 0.0000e+00 - val_loss: 1.3884 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3814 - acc: 0.2943 - precision_m: 0.0000e+00 - val_loss: 1.3886 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3863 - acc: 0.2454 - precision_m: 0.0000e+00 - val_loss: 1.3882 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3865 - acc: 0.2786 - precision_m: 0.0000e+00 - val_loss: 1.3884 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3852 - acc: 0.2619 - precision_m: 0.0019 - val_loss: 1.3886 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3854 - acc: 0.2873 - precision_m: 0.0000e+00 - val_loss: 1.3882 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3878 - acc: 0.2450 - precision_m: 0.0000e+00 - val_loss: 1.3884 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3857 - acc: 0.2779 - precision_m: 0.0000e+00 - val_loss: 1.3882 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3906 - acc: 0.2261 - precision_m: 0.0000e+00 - val_loss: 1.3877 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3798 - acc: 0.3120 - precision_m: 0.0000e+00 - val_loss: 1.3880 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3844 - acc: 0.2745 - precision_m: 0.0000e+00 - val_loss: 1.3885 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3851 - acc: 0.2801 - precision_m: 0.0000e+00 - val_loss: 1.3885 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3832 - acc: 0.2875 - precision_m: 0.0000e+00 - val_loss: 1.3889 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3743 - acc: 0.3368 - precision_m: 0.0000e+00 - val_loss: 1.3888 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3837 - acc: 0.2914 - precision_m: 0.0000e+00 - val_loss: 1.3890 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3802 - acc: 0.3057 - precision_m: 0.0000e+00 - val_loss: 1.3901 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3879 - acc: 0.2466 - precision_m: 0.0000e+00 - val_loss: 1.3885 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3849 - acc: 0.2559 - precision_m: 0.0000e+00 - val_loss: 1.3896 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3858 - acc: 0.2725 - precision_m: 0.0000e+00 - val_loss: 1.3901 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","166/166 [==============================] - 0s 3ms/step - loss: 1.3847 - acc: 0.2994 - precision_m: 0.0000e+00 - val_loss: 1.3889 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3805 - acc: 0.3299 - precision_m: 0.0000e+00 - val_loss: 1.3889 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3803 - acc: 0.3001 - precision_m: 0.0000e+00 - val_loss: 1.3897 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3870 - acc: 0.2625 - precision_m: 0.0000e+00 - val_loss: 1.3896 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3788 - acc: 0.3118 - precision_m: 0.0000e+00 - val_loss: 1.3891 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3859 - acc: 0.2580 - precision_m: 0.0000e+00 - val_loss: 1.3887 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3869 - acc: 0.2456 - precision_m: 0.0000e+00 - val_loss: 1.3898 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3814 - acc: 0.2935 - precision_m: 0.0000e+00 - val_loss: 1.3900 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3780 - acc: 0.3302 - precision_m: 0.0150 - val_loss: 1.3914 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3799 - acc: 0.2855 - precision_m: 0.0000e+00 - val_loss: 1.3907 - val_acc: 0.2324 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3930 - acc: 0.2113 - precision_m: 0.0000e+00 - val_loss: 1.3906 - val_acc: 0.2254 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3825 - acc: 0.2855 - precision_m: 0.0000e+00 - val_loss: 1.3842 - val_acc: 0.2887 - val_precision_m: 0.0000e+00\n","Epoch 52/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3907 - acc: 0.2420 - precision_m: 0.0000e+00 - val_loss: 1.3922 - val_acc: 0.2254 - val_precision_m: 0.0000e+00\n","Epoch 53/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3817 - acc: 0.2894 - precision_m: 0.0015 - val_loss: 1.3909 - val_acc: 0.2254 - val_precision_m: 0.0000e+00\n","Epoch 54/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3813 - acc: 0.3070 - precision_m: 0.0000e+00 - val_loss: 1.3909 - val_acc: 0.2254 - val_precision_m: 0.0000e+00\n","Epoch 55/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3855 - acc: 0.2567 - precision_m: 0.0025 - val_loss: 1.3908 - val_acc: 0.2254 - val_precision_m: 0.0000e+00\n","Epoch 56/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3848 - acc: 0.2548 - precision_m: 0.0000e+00 - val_loss: 1.3909 - val_acc: 0.2254 - val_precision_m: 0.0000e+00\n","Epoch 57/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3857 - acc: 0.2884 - precision_m: 0.0000e+00 - val_loss: 1.3908 - val_acc: 0.2254 - val_precision_m: 0.0000e+00\n","Epoch 58/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3730 - acc: 0.3367 - precision_m: 0.0032 - val_loss: 1.3903 - val_acc: 0.2254 - val_precision_m: 0.0000e+00\n","Epoch 59/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3800 - acc: 0.3179 - precision_m: 0.0000e+00 - val_loss: 1.3851 - val_acc: 0.2887 - val_precision_m: 0.0000e+00\n","Epoch 60/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3782 - acc: 0.3246 - precision_m: 0.0000e+00 - val_loss: 1.3906 - val_acc: 0.2254 - val_precision_m: 0.0000e+00\n","Epoch 61/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3882 - acc: 0.2272 - precision_m: 0.0000e+00 - val_loss: 1.3901 - val_acc: 0.2254 - val_precision_m: 0.0000e+00\n","Epoch 62/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3874 - acc: 0.2363 - precision_m: 0.0000e+00 - val_loss: 1.3912 - val_acc: 0.2183 - val_precision_m: 0.0000e+00\n","Epoch 63/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3786 - acc: 0.2595 - precision_m: 0.0178 - val_loss: 1.3913 - val_acc: 0.2113 - val_precision_m: 0.0000e+00\n","Epoch 64/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3851 - acc: 0.2893 - precision_m: 0.0011 - val_loss: 1.3906 - val_acc: 0.2254 - val_precision_m: 0.0000e+00\n","Epoch 65/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3867 - acc: 0.2460 - precision_m: 0.0000e+00 - val_loss: 1.3912 - val_acc: 0.2254 - val_precision_m: 0.0000e+00\n","Epoch 66/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3827 - acc: 0.3056 - precision_m: 0.0000e+00 - val_loss: 1.3916 - val_acc: 0.2254 - val_precision_m: 0.0000e+00\n","Epoch 67/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3840 - acc: 0.2961 - precision_m: 0.0000e+00 - val_loss: 1.3906 - val_acc: 0.2254 - val_precision_m: 0.0000e+00\n","Epoch 68/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3729 - acc: 0.3187 - precision_m: 0.0000e+00 - val_loss: 1.3916 - val_acc: 0.2183 - val_precision_m: 0.0000e+00\n","Epoch 69/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3780 - acc: 0.2712 - precision_m: 0.0000e+00 - val_loss: 1.3922 - val_acc: 0.2113 - val_precision_m: 0.0000e+00\n","Epoch 70/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3839 - acc: 0.2840 - precision_m: 0.0000e+00 - val_loss: 1.3926 - val_acc: 0.2183 - val_precision_m: 0.0000e+00\n","Epoch 71/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3813 - acc: 0.3054 - precision_m: 0.0000e+00 - val_loss: 1.3897 - val_acc: 0.2183 - val_precision_m: 0.0000e+00\n","Epoch 72/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3847 - acc: 0.2259 - precision_m: 0.0000e+00 - val_loss: 1.3893 - val_acc: 0.2254 - val_precision_m: 0.0000e+00\n","Epoch 73/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3848 - acc: 0.2587 - precision_m: 0.0000e+00 - val_loss: 1.3896 - val_acc: 0.2254 - val_precision_m: 0.0000e+00\n","Epoch 74/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3901 - acc: 0.2413 - precision_m: 6.4105e-04 - val_loss: 1.3898 - val_acc: 0.2113 - val_precision_m: 0.0000e+00\n","Epoch 75/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3849 - acc: 0.2603 - precision_m: 0.0000e+00 - val_loss: 1.3894 - val_acc: 0.2254 - val_precision_m: 0.0000e+00\n","Epoch 76/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3796 - acc: 0.2789 - precision_m: 0.0000e+00 - val_loss: 1.3906 - val_acc: 0.2183 - val_precision_m: 0.0000e+00\n","Epoch 77/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3744 - acc: 0.2868 - precision_m: 0.0129 - val_loss: 1.3917 - val_acc: 0.2183 - val_precision_m: 0.0000e+00\n","Epoch 78/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3852 - acc: 0.2762 - precision_m: 0.0000e+00 - val_loss: 1.3916 - val_acc: 0.2183 - val_precision_m: 0.0000e+00\n","Epoch 79/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3797 - acc: 0.3320 - precision_m: 0.0079 - val_loss: 1.3903 - val_acc: 0.2183 - val_precision_m: 0.0000e+00\n","Epoch 80/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3832 - acc: 0.2904 - precision_m: 0.0037 - val_loss: 1.3894 - val_acc: 0.2254 - val_precision_m: 0.0000e+00\n","Epoch 81/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3846 - acc: 0.2111 - precision_m: 0.0000e+00 - val_loss: 1.3897 - val_acc: 0.2113 - val_precision_m: 0.0000e+00\n","Epoch 82/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3763 - acc: 0.3043 - precision_m: 0.0186 - val_loss: 1.3900 - val_acc: 0.2254 - val_precision_m: 0.0000e+00\n","Epoch 83/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3795 - acc: 0.2889 - precision_m: 0.0000e+00 - val_loss: 1.3910 - val_acc: 0.2113 - val_precision_m: 0.0000e+00\n","Epoch 84/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3726 - acc: 0.2807 - precision_m: 0.0182 - val_loss: 1.3915 - val_acc: 0.2113 - val_precision_m: 0.0000e+00\n","Epoch 85/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3777 - acc: 0.3078 - precision_m: 0.0014 - val_loss: 1.3920 - val_acc: 0.2113 - val_precision_m: 0.0000e+00\n","Epoch 86/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3751 - acc: 0.2958 - precision_m: 0.0082 - val_loss: 1.3915 - val_acc: 0.2254 - val_precision_m: 0.0000e+00\n","Epoch 87/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3771 - acc: 0.2796 - precision_m: 0.0034 - val_loss: 1.3914 - val_acc: 0.2183 - val_precision_m: 0.0000e+00\n","Epoch 88/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3777 - acc: 0.2956 - precision_m: 0.0041 - val_loss: 1.3927 - val_acc: 0.2113 - val_precision_m: 0.0000e+00\n","Epoch 89/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3780 - acc: 0.2875 - precision_m: 0.0078 - val_loss: 1.3912 - val_acc: 0.2254 - val_precision_m: 0.0000e+00\n","Epoch 90/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3824 - acc: 0.2890 - precision_m: 0.0030 - val_loss: 1.3920 - val_acc: 0.2183 - val_precision_m: 0.0000e+00\n","Epoch 91/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3739 - acc: 0.2836 - precision_m: 0.0145 - val_loss: 1.3923 - val_acc: 0.2183 - val_precision_m: 0.0000e+00\n","Epoch 92/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3781 - acc: 0.3350 - precision_m: 0.0027 - val_loss: 1.3908 - val_acc: 0.2183 - val_precision_m: 0.0000e+00\n","Epoch 93/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3795 - acc: 0.2858 - precision_m: 0.0036 - val_loss: 1.3918 - val_acc: 0.2183 - val_precision_m: 0.0000e+00\n","Epoch 94/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3793 - acc: 0.2652 - precision_m: 0.0112 - val_loss: 1.3919 - val_acc: 0.2254 - val_precision_m: 0.0000e+00\n","Epoch 95/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3706 - acc: 0.2582 - precision_m: 0.0374 - val_loss: 1.3926 - val_acc: 0.2113 - val_precision_m: 0.0000e+00\n","Epoch 96/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3799 - acc: 0.2996 - precision_m: 2.9327e-04 - val_loss: 1.3931 - val_acc: 0.2183 - val_precision_m: 0.0000e+00\n","Epoch 97/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3807 - acc: 0.3140 - precision_m: 0.0108 - val_loss: 1.3933 - val_acc: 0.2183 - val_precision_m: 0.0000e+00\n","Epoch 98/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3795 - acc: 0.2518 - precision_m: 0.0052 - val_loss: 1.3927 - val_acc: 0.2183 - val_precision_m: 0.0000e+00\n","Epoch 99/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3746 - acc: 0.2606 - precision_m: 0.0183 - val_loss: 1.3936 - val_acc: 0.2183 - val_precision_m: 0.0000e+00\n","Epoch 100/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3782 - acc: 0.2982 - precision_m: 0.0000e+00 - val_loss: 1.3949 - val_acc: 0.2183 - val_precision_m: 0.0000e+00\n","Epoch 101/500\n","166/166 [==============================] - 0s 2ms/step - loss: 1.3798 - acc: 0.2808 - precision_m: 0.0050 - val_loss: 1.3976 - val_acc: 0.2113 - val_precision_m: 0.0000e+00\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 19.5s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","157/157 [==============================] - 1s 3ms/step - loss: 1.4394 - acc: 0.2427 - precision_m: 0.0172 - val_loss: 1.3980 - val_acc: 0.2222 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3976 - acc: 0.2314 - precision_m: 0.0000e+00 - val_loss: 1.3878 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3859 - acc: 0.2872 - precision_m: 0.0000e+00 - val_loss: 1.3833 - val_acc: 0.2667 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3855 - acc: 0.2546 - precision_m: 0.0000e+00 - val_loss: 1.3818 - val_acc: 0.2667 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3901 - acc: 0.3067 - precision_m: 0.0000e+00 - val_loss: 1.3853 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.4008 - acc: 0.2427 - precision_m: 0.0000e+00 - val_loss: 1.3823 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3802 - acc: 0.2907 - precision_m: 0.0000e+00 - val_loss: 1.3802 - val_acc: 0.2741 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3756 - acc: 0.2859 - precision_m: 0.0000e+00 - val_loss: 1.3806 - val_acc: 0.2741 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3845 - acc: 0.2613 - precision_m: 0.0000e+00 - val_loss: 1.3814 - val_acc: 0.2741 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3815 - acc: 0.2805 - precision_m: 0.0000e+00 - val_loss: 1.3812 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3858 - acc: 0.2434 - precision_m: 0.0000e+00 - val_loss: 1.3839 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3853 - acc: 0.2550 - precision_m: 0.0000e+00 - val_loss: 1.3837 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3820 - acc: 0.2998 - precision_m: 0.0000e+00 - val_loss: 1.3839 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3839 - acc: 0.2903 - precision_m: 0.0000e+00 - val_loss: 1.3830 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3864 - acc: 0.2655 - precision_m: 0.0000e+00 - val_loss: 1.3832 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3813 - acc: 0.2900 - precision_m: 0.0000e+00 - val_loss: 1.3834 - val_acc: 0.2741 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3898 - acc: 0.2203 - precision_m: 0.0000e+00 - val_loss: 1.3813 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3842 - acc: 0.2844 - precision_m: 0.0000e+00 - val_loss: 1.3807 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3790 - acc: 0.3216 - precision_m: 0.0000e+00 - val_loss: 1.3808 - val_acc: 0.3111 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3884 - acc: 0.2717 - precision_m: 0.0000e+00 - val_loss: 1.3832 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3861 - acc: 0.2526 - precision_m: 0.0000e+00 - val_loss: 1.3820 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3880 - acc: 0.2452 - precision_m: 0.0000e+00 - val_loss: 1.3826 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3856 - acc: 0.2500 - precision_m: 0.0000e+00 - val_loss: 1.3826 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3887 - acc: 0.2300 - precision_m: 0.0000e+00 - val_loss: 1.3825 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3851 - acc: 0.2827 - precision_m: 0.0000e+00 - val_loss: 1.3829 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3876 - acc: 0.2256 - precision_m: 0.0000e+00 - val_loss: 1.3826 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3776 - acc: 0.3122 - precision_m: 0.0000e+00 - val_loss: 1.3822 - val_acc: 0.2889 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3776 - acc: 0.2977 - precision_m: 0.0000e+00 - val_loss: 1.3826 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3934 - acc: 0.2320 - precision_m: 0.0000e+00 - val_loss: 1.3826 - val_acc: 0.2741 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3827 - acc: 0.2769 - precision_m: 0.0000e+00 - val_loss: 1.3818 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3744 - acc: 0.2812 - precision_m: 0.0029 - val_loss: 1.3829 - val_acc: 0.2963 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","157/157 [==============================] - 0s 3ms/step - loss: 1.3815 - acc: 0.2673 - precision_m: 0.0000e+00 - val_loss: 1.3835 - val_acc: 0.2889 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3786 - acc: 0.3334 - precision_m: 0.0000e+00 - val_loss: 1.3836 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3863 - acc: 0.2829 - precision_m: 0.0000e+00 - val_loss: 1.3827 - val_acc: 0.2889 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3862 - acc: 0.2731 - precision_m: 0.0000e+00 - val_loss: 1.3831 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3863 - acc: 0.2682 - precision_m: 0.0000e+00 - val_loss: 1.3832 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3864 - acc: 0.2566 - precision_m: 0.0000e+00 - val_loss: 1.3827 - val_acc: 0.2741 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3778 - acc: 0.2850 - precision_m: 0.0000e+00 - val_loss: 1.3838 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3838 - acc: 0.2900 - precision_m: 0.0000e+00 - val_loss: 1.3834 - val_acc: 0.2741 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3886 - acc: 0.2455 - precision_m: 0.0000e+00 - val_loss: 1.3835 - val_acc: 0.2741 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3800 - acc: 0.3311 - precision_m: 0.0000e+00 - val_loss: 1.3832 - val_acc: 0.2741 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3826 - acc: 0.3152 - precision_m: 0.0000e+00 - val_loss: 1.3836 - val_acc: 0.2889 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3814 - acc: 0.2655 - precision_m: 0.0000e+00 - val_loss: 1.3829 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3852 - acc: 0.3095 - precision_m: 0.0000e+00 - val_loss: 1.3854 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3944 - acc: 0.2268 - precision_m: 0.0000e+00 - val_loss: 1.3829 - val_acc: 0.2741 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3748 - acc: 0.3407 - precision_m: 0.0000e+00 - val_loss: 1.3821 - val_acc: 0.2889 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3768 - acc: 0.2741 - precision_m: 0.0000e+00 - val_loss: 1.3821 - val_acc: 0.3037 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3820 - acc: 0.2757 - precision_m: 0.0000e+00 - val_loss: 1.3818 - val_acc: 0.2963 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3828 - acc: 0.2966 - precision_m: 0.0000e+00 - val_loss: 1.3822 - val_acc: 0.2889 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3820 - acc: 0.2970 - precision_m: 0.0000e+00 - val_loss: 1.3810 - val_acc: 0.2889 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3790 - acc: 0.2681 - precision_m: 0.0000e+00 - val_loss: 1.3810 - val_acc: 0.2963 - val_precision_m: 0.0000e+00\n","Epoch 52/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3889 - acc: 0.2685 - precision_m: 0.0000e+00 - val_loss: 1.3823 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 53/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3839 - acc: 0.3021 - precision_m: 0.0000e+00 - val_loss: 1.3824 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 54/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3795 - acc: 0.2987 - precision_m: 0.0000e+00 - val_loss: 1.3812 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 55/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3815 - acc: 0.2676 - precision_m: 0.0000e+00 - val_loss: 1.3826 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 56/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3856 - acc: 0.2686 - precision_m: 0.0000e+00 - val_loss: 1.3821 - val_acc: 0.2889 - val_precision_m: 0.0000e+00\n","Epoch 57/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3798 - acc: 0.2889 - precision_m: 0.0000e+00 - val_loss: 1.3812 - val_acc: 0.2889 - val_precision_m: 0.0000e+00\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 15.9s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","142/142 [==============================] - 1s 3ms/step - loss: 1.3931 - acc: 0.2788 - precision_m: 0.0163 - val_loss: 1.3731 - val_acc: 0.3115 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.4177 - acc: 0.2555 - precision_m: 0.0000e+00 - val_loss: 1.3824 - val_acc: 0.2869 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3888 - acc: 0.2303 - precision_m: 0.0000e+00 - val_loss: 1.3913 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3843 - acc: 0.3302 - precision_m: 0.0000e+00 - val_loss: 1.3783 - val_acc: 0.2951 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3818 - acc: 0.2210 - precision_m: 0.0000e+00 - val_loss: 1.3862 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3764 - acc: 0.3081 - precision_m: 0.0000e+00 - val_loss: 1.3869 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3766 - acc: 0.3072 - precision_m: 0.0000e+00 - val_loss: 1.3841 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3668 - acc: 0.3161 - precision_m: 0.0000e+00 - val_loss: 1.3818 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3707 - acc: 0.2942 - precision_m: 0.0000e+00 - val_loss: 1.3872 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3664 - acc: 0.3306 - precision_m: 0.0000e+00 - val_loss: 1.3892 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3771 - acc: 0.2903 - precision_m: 0.0000e+00 - val_loss: 1.3868 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3773 - acc: 0.3050 - precision_m: 0.0000e+00 - val_loss: 1.3923 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3875 - acc: 0.2762 - precision_m: 0.0000e+00 - val_loss: 1.3909 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3836 - acc: 0.2813 - precision_m: 0.0000e+00 - val_loss: 1.3897 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3727 - acc: 0.3238 - precision_m: 0.0000e+00 - val_loss: 1.3890 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3678 - acc: 0.3390 - precision_m: 0.0000e+00 - val_loss: 1.3890 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3799 - acc: 0.3093 - precision_m: 0.0000e+00 - val_loss: 1.3882 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3763 - acc: 0.2884 - precision_m: 0.0000e+00 - val_loss: 1.3935 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3789 - acc: 0.2971 - precision_m: 0.0000e+00 - val_loss: 1.3884 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3834 - acc: 0.2808 - precision_m: 0.0000e+00 - val_loss: 1.3886 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","142/142 [==============================] - 0s 3ms/step - loss: 1.3649 - acc: 0.3360 - precision_m: 0.0000e+00 - val_loss: 1.3884 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3574 - acc: 0.3530 - precision_m: 0.0000e+00 - val_loss: 1.3819 - val_acc: 0.2541 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3762 - acc: 0.2887 - precision_m: 0.0000e+00 - val_loss: 1.3855 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3664 - acc: 0.2943 - precision_m: 0.0000e+00 - val_loss: 1.3891 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3661 - acc: 0.3253 - precision_m: 0.0000e+00 - val_loss: 1.3882 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3834 - acc: 0.2622 - precision_m: 0.0000e+00 - val_loss: 1.3876 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3789 - acc: 0.3135 - precision_m: 0.0000e+00 - val_loss: 1.3891 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3702 - acc: 0.3425 - precision_m: 0.0000e+00 - val_loss: 1.3871 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3577 - acc: 0.3394 - precision_m: 0.0000e+00 - val_loss: 1.3869 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3572 - acc: 0.3667 - precision_m: 0.0000e+00 - val_loss: 1.3934 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3934 - acc: 0.2541 - precision_m: 0.0000e+00 - val_loss: 1.3857 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3671 - acc: 0.3241 - precision_m: 0.0000e+00 - val_loss: 1.3867 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3631 - acc: 0.3198 - precision_m: 0.0283 - val_loss: 1.3861 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3852 - acc: 0.2777 - precision_m: 0.0000e+00 - val_loss: 1.3860 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3577 - acc: 0.3197 - precision_m: 0.0000e+00 - val_loss: 1.3892 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3655 - acc: 0.3624 - precision_m: 0.0000e+00 - val_loss: 1.3864 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3709 - acc: 0.3138 - precision_m: 0.0013 - val_loss: 1.4023 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3669 - acc: 0.3672 - precision_m: 0.0079 - val_loss: 1.3889 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3780 - acc: 0.2922 - precision_m: 0.0000e+00 - val_loss: 1.3867 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3688 - acc: 0.3206 - precision_m: 0.0000e+00 - val_loss: 1.3869 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3781 - acc: 0.2774 - precision_m: 0.0058 - val_loss: 1.3868 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3720 - acc: 0.2969 - precision_m: 0.0000e+00 - val_loss: 1.3903 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3875 - acc: 0.2605 - precision_m: 8.2704e-04 - val_loss: 1.3865 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3589 - acc: 0.3265 - precision_m: 0.0000e+00 - val_loss: 1.3897 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3609 - acc: 0.3381 - precision_m: 0.0186 - val_loss: 1.3898 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3773 - acc: 0.2855 - precision_m: 0.0100 - val_loss: 1.3911 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3589 - acc: 0.3118 - precision_m: 0.0250 - val_loss: 1.3884 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3530 - acc: 0.3415 - precision_m: 0.0664 - val_loss: 1.3860 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3760 - acc: 0.2778 - precision_m: 0.0049 - val_loss: 1.3976 - val_acc: 0.2295 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3642 - acc: 0.2922 - precision_m: 0.0216 - val_loss: 1.3885 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3587 - acc: 0.3036 - precision_m: 0.0164 - val_loss: 1.3899 - val_acc: 0.2377 - val_precision_m: 0.0000e+00\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 21.2s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","157/157 [==============================] - 1s 3ms/step - loss: 1.6741 - acc: 0.2547 - precision_m: 0.0879 - val_loss: 1.3907 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3905 - acc: 0.2550 - precision_m: 0.0000e+00 - val_loss: 1.3970 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3822 - acc: 0.2715 - precision_m: 0.0000e+00 - val_loss: 1.3922 - val_acc: 0.2148 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3933 - acc: 0.2116 - precision_m: 0.0000e+00 - val_loss: 1.3874 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3872 - acc: 0.2562 - precision_m: 0.0000e+00 - val_loss: 1.3873 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3863 - acc: 0.2361 - precision_m: 0.0000e+00 - val_loss: 1.3886 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3846 - acc: 0.3133 - precision_m: 0.0000e+00 - val_loss: 1.3896 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3846 - acc: 0.2740 - precision_m: 0.0000e+00 - val_loss: 1.3901 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3877 - acc: 0.2498 - precision_m: 0.0000e+00 - val_loss: 1.3910 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3835 - acc: 0.2843 - precision_m: 0.0000e+00 - val_loss: 1.3921 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3851 - acc: 0.2827 - precision_m: 0.0000e+00 - val_loss: 1.3916 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3872 - acc: 0.2648 - precision_m: 0.0000e+00 - val_loss: 1.3925 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3848 - acc: 0.2631 - precision_m: 0.0000e+00 - val_loss: 1.3933 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3803 - acc: 0.2978 - precision_m: 0.0000e+00 - val_loss: 1.3936 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3805 - acc: 0.3109 - precision_m: 0.0000e+00 - val_loss: 1.3936 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3844 - acc: 0.2900 - precision_m: 0.0000e+00 - val_loss: 1.3943 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3855 - acc: 0.2635 - precision_m: 0.0000e+00 - val_loss: 1.3945 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3829 - acc: 0.2868 - precision_m: 0.0000e+00 - val_loss: 1.3951 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3833 - acc: 0.2973 - precision_m: 0.0000e+00 - val_loss: 1.3949 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3848 - acc: 0.2709 - precision_m: 0.0000e+00 - val_loss: 1.3949 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3862 - acc: 0.2690 - precision_m: 0.0000e+00 - val_loss: 1.3953 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3888 - acc: 0.2320 - precision_m: 0.0000e+00 - val_loss: 1.3953 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3884 - acc: 0.2332 - precision_m: 0.0000e+00 - val_loss: 1.3956 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3842 - acc: 0.2609 - precision_m: 0.0000e+00 - val_loss: 1.3962 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3835 - acc: 0.2861 - precision_m: 0.0000e+00 - val_loss: 1.3959 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3909 - acc: 0.2168 - precision_m: 0.0000e+00 - val_loss: 1.3955 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3868 - acc: 0.2416 - precision_m: 0.0000e+00 - val_loss: 1.3962 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3814 - acc: 0.2732 - precision_m: 0.0000e+00 - val_loss: 1.3964 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3824 - acc: 0.2897 - precision_m: 0.0000e+00 - val_loss: 1.3963 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3834 - acc: 0.2795 - precision_m: 0.0000e+00 - val_loss: 1.3960 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3859 - acc: 0.2451 - precision_m: 0.0000e+00 - val_loss: 1.3966 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3812 - acc: 0.2890 - precision_m: 0.0000e+00 - val_loss: 1.3961 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3824 - acc: 0.2830 - precision_m: 0.0000e+00 - val_loss: 1.3961 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3811 - acc: 0.3164 - precision_m: 0.0000e+00 - val_loss: 1.3961 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3831 - acc: 0.2734 - precision_m: 0.0000e+00 - val_loss: 1.3964 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3822 - acc: 0.2896 - precision_m: 0.0000e+00 - val_loss: 1.3962 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3875 - acc: 0.2654 - precision_m: 0.0000e+00 - val_loss: 1.3966 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3861 - acc: 0.2524 - precision_m: 0.0000e+00 - val_loss: 1.3963 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3904 - acc: 0.2372 - precision_m: 0.0000e+00 - val_loss: 1.3965 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3856 - acc: 0.2727 - precision_m: 0.0000e+00 - val_loss: 1.3962 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3883 - acc: 0.2303 - precision_m: 0.0000e+00 - val_loss: 1.3964 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3864 - acc: 0.2940 - precision_m: 0.0000e+00 - val_loss: 1.3968 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3812 - acc: 0.2990 - precision_m: 0.0000e+00 - val_loss: 1.3968 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3814 - acc: 0.2969 - precision_m: 0.0000e+00 - val_loss: 1.3968 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3859 - acc: 0.2913 - precision_m: 0.0000e+00 - val_loss: 1.3964 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3831 - acc: 0.2992 - precision_m: 0.0000e+00 - val_loss: 1.3970 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3855 - acc: 0.2673 - precision_m: 0.0000e+00 - val_loss: 1.3966 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3781 - acc: 0.3319 - precision_m: 0.0000e+00 - val_loss: 1.3967 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3812 - acc: 0.2908 - precision_m: 0.0000e+00 - val_loss: 1.3976 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","157/157 [==============================] - 0s 3ms/step - loss: 1.3875 - acc: 0.2597 - precision_m: 0.0000e+00 - val_loss: 1.3961 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3822 - acc: 0.3049 - precision_m: 0.0000e+00 - val_loss: 1.3970 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 52/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3861 - acc: 0.2546 - precision_m: 0.0000e+00 - val_loss: 1.3968 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 53/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3862 - acc: 0.2818 - precision_m: 0.0000e+00 - val_loss: 1.3968 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 54/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3851 - acc: 0.2645 - precision_m: 0.0000e+00 - val_loss: 1.3967 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 55/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3850 - acc: 0.2713 - precision_m: 0.0000e+00 - val_loss: 1.3970 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 16.5s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","147/147 [==============================] - 1s 3ms/step - loss: 1.4196 - acc: 0.1928 - precision_m: 0.0081 - val_loss: 1.3957 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3906 - acc: 0.2484 - precision_m: 0.0000e+00 - val_loss: 1.3895 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3949 - acc: 0.2471 - precision_m: 0.0000e+00 - val_loss: 1.3806 - val_acc: 0.3016 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3738 - acc: 0.2853 - precision_m: 0.0000e+00 - val_loss: 1.3804 - val_acc: 0.3333 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3762 - acc: 0.2989 - precision_m: 0.0000e+00 - val_loss: 1.3806 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3800 - acc: 0.2999 - precision_m: 0.0000e+00 - val_loss: 1.3804 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3718 - acc: 0.2851 - precision_m: 0.0000e+00 - val_loss: 1.3813 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3847 - acc: 0.2609 - precision_m: 0.0000e+00 - val_loss: 1.3800 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3614 - acc: 0.3220 - precision_m: 0.0000e+00 - val_loss: 1.3806 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3616 - acc: 0.3575 - precision_m: 0.0000e+00 - val_loss: 1.3802 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3912 - acc: 0.2731 - precision_m: 0.0000e+00 - val_loss: 1.3811 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3782 - acc: 0.3168 - precision_m: 0.0000e+00 - val_loss: 1.3804 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3697 - acc: 0.3025 - precision_m: 0.0000e+00 - val_loss: 1.3799 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3761 - acc: 0.2703 - precision_m: 0.0000e+00 - val_loss: 1.3807 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3514 - acc: 0.3440 - precision_m: 0.0000e+00 - val_loss: 1.3811 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3838 - acc: 0.2817 - precision_m: 0.0000e+00 - val_loss: 1.3814 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3659 - acc: 0.3219 - precision_m: 0.0000e+00 - val_loss: 1.3813 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3581 - acc: 0.3409 - precision_m: 0.0000e+00 - val_loss: 1.3815 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3710 - acc: 0.3171 - precision_m: 0.0000e+00 - val_loss: 1.3818 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3742 - acc: 0.3058 - precision_m: 0.0000e+00 - val_loss: 1.3813 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3561 - acc: 0.3228 - precision_m: 0.0000e+00 - val_loss: 1.3812 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3919 - acc: 0.2614 - precision_m: 0.0000e+00 - val_loss: 1.3815 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3784 - acc: 0.2697 - precision_m: 0.0000e+00 - val_loss: 1.3821 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3666 - acc: 0.3369 - precision_m: 0.0000e+00 - val_loss: 1.3818 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3766 - acc: 0.2724 - precision_m: 0.0000e+00 - val_loss: 1.3820 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3757 - acc: 0.3077 - precision_m: 0.0000e+00 - val_loss: 1.3817 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3815 - acc: 0.2924 - precision_m: 0.0000e+00 - val_loss: 1.3816 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3757 - acc: 0.3081 - precision_m: 0.0000e+00 - val_loss: 1.3821 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3773 - acc: 0.2718 - precision_m: 0.0000e+00 - val_loss: 1.3819 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3664 - acc: 0.3096 - precision_m: 0.0000e+00 - val_loss: 1.3816 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3697 - acc: 0.3070 - precision_m: 0.0000e+00 - val_loss: 1.3816 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3814 - acc: 0.2796 - precision_m: 0.0000e+00 - val_loss: 1.3815 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3804 - acc: 0.2834 - precision_m: 0.0000e+00 - val_loss: 1.3811 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3684 - acc: 0.3373 - precision_m: 0.0000e+00 - val_loss: 1.3812 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3820 - acc: 0.2867 - precision_m: 0.0000e+00 - val_loss: 1.3811 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3945 - acc: 0.2795 - precision_m: 0.0000e+00 - val_loss: 1.3807 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3658 - acc: 0.3212 - precision_m: 0.0000e+00 - val_loss: 1.3810 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3607 - acc: 0.3500 - precision_m: 0.0000e+00 - val_loss: 1.3809 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3923 - acc: 0.2750 - precision_m: 0.0000e+00 - val_loss: 1.3821 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3767 - acc: 0.3163 - precision_m: 0.0000e+00 - val_loss: 1.3823 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3493 - acc: 0.3203 - precision_m: 0.0000e+00 - val_loss: 1.3825 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3824 - acc: 0.2929 - precision_m: 0.0000e+00 - val_loss: 1.3826 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3655 - acc: 0.3248 - precision_m: 0.0000e+00 - val_loss: 1.3822 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3576 - acc: 0.3067 - precision_m: 0.0000e+00 - val_loss: 1.3827 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3999 - acc: 0.2743 - precision_m: 0.0000e+00 - val_loss: 1.3808 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3752 - acc: 0.2943 - precision_m: 0.0000e+00 - val_loss: 1.3812 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3743 - acc: 0.3277 - precision_m: 0.0000e+00 - val_loss: 1.3816 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","147/147 [==============================] - 0s 3ms/step - loss: 1.3732 - acc: 0.2792 - precision_m: 0.0000e+00 - val_loss: 1.3810 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3639 - acc: 0.3534 - precision_m: 0.0000e+00 - val_loss: 1.3813 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3856 - acc: 0.2513 - precision_m: 0.0000e+00 - val_loss: 1.3812 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3654 - acc: 0.3018 - precision_m: 0.0000e+00 - val_loss: 1.3810 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 52/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3719 - acc: 0.2372 - precision_m: 0.0000e+00 - val_loss: 1.3813 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 53/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3788 - acc: 0.2594 - precision_m: 0.0000e+00 - val_loss: 1.3821 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 54/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3784 - acc: 0.2895 - precision_m: 0.0000e+00 - val_loss: 1.3822 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 55/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3844 - acc: 0.2651 - precision_m: 0.0000e+00 - val_loss: 1.3820 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 56/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3767 - acc: 0.2757 - precision_m: 0.0000e+00 - val_loss: 1.3821 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 57/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3601 - acc: 0.3418 - precision_m: 0.0000e+00 - val_loss: 1.3821 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 58/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3766 - acc: 0.2970 - precision_m: 0.0000e+00 - val_loss: 1.3816 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 59/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3672 - acc: 0.3091 - precision_m: 0.0000e+00 - val_loss: 1.3818 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 60/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3799 - acc: 0.2981 - precision_m: 0.0000e+00 - val_loss: 1.3822 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 61/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3724 - acc: 0.3083 - precision_m: 0.0000e+00 - val_loss: 1.3819 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 62/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3761 - acc: 0.2729 - precision_m: 0.0000e+00 - val_loss: 1.3830 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 63/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3643 - acc: 0.3088 - precision_m: 0.0000e+00 - val_loss: 1.3829 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 19.3s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","157/157 [==============================] - 1s 4ms/step - loss: 1.4708 - acc: 0.2251 - precision_m: 0.0151 - val_loss: 1.3878 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3904 - acc: 0.2839 - precision_m: 0.0000e+00 - val_loss: 1.3810 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3861 - acc: 0.2591 - precision_m: 0.0000e+00 - val_loss: 1.3840 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3841 - acc: 0.2576 - precision_m: 0.0000e+00 - val_loss: 1.3843 - val_acc: 0.2667 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3850 - acc: 0.3130 - precision_m: 0.0000e+00 - val_loss: 1.3899 - val_acc: 0.2444 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3916 - acc: 0.2565 - precision_m: 0.0000e+00 - val_loss: 1.3917 - val_acc: 0.2444 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3934 - acc: 0.2168 - precision_m: 0.0000e+00 - val_loss: 1.3828 - val_acc: 0.2444 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3830 - acc: 0.2890 - precision_m: 0.0000e+00 - val_loss: 1.3822 - val_acc: 0.2889 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3847 - acc: 0.2707 - precision_m: 0.0000e+00 - val_loss: 1.3774 - val_acc: 0.2963 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3966 - acc: 0.2503 - precision_m: 0.0000e+00 - val_loss: 1.3864 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3907 - acc: 0.2581 - precision_m: 0.0000e+00 - val_loss: 1.3855 - val_acc: 0.2370 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3825 - acc: 0.2877 - precision_m: 0.0000e+00 - val_loss: 1.3851 - val_acc: 0.2444 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3897 - acc: 0.2753 - precision_m: 0.0000e+00 - val_loss: 1.3830 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3801 - acc: 0.3112 - precision_m: 0.0000e+00 - val_loss: 1.3823 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3830 - acc: 0.3041 - precision_m: 0.0000e+00 - val_loss: 1.3737 - val_acc: 0.3185 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.4001 - acc: 0.2429 - precision_m: 0.0000e+00 - val_loss: 1.3781 - val_acc: 0.3185 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3833 - acc: 0.2910 - precision_m: 0.0000e+00 - val_loss: 1.3835 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3798 - acc: 0.2983 - precision_m: 0.0000e+00 - val_loss: 1.3851 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3828 - acc: 0.2712 - precision_m: 0.0000e+00 - val_loss: 1.3797 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3773 - acc: 0.2910 - precision_m: 0.0000e+00 - val_loss: 1.3797 - val_acc: 0.2889 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3860 - acc: 0.2860 - precision_m: 0.0000e+00 - val_loss: 1.3808 - val_acc: 0.2741 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3881 - acc: 0.2495 - precision_m: 0.0000e+00 - val_loss: 1.3820 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3826 - acc: 0.2524 - precision_m: 0.0000e+00 - val_loss: 1.3758 - val_acc: 0.3111 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3835 - acc: 0.2454 - precision_m: 0.0000e+00 - val_loss: 1.3799 - val_acc: 0.2741 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3821 - acc: 0.2484 - precision_m: 0.0000e+00 - val_loss: 1.3832 - val_acc: 0.2667 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3823 - acc: 0.2745 - precision_m: 0.0000e+00 - val_loss: 1.3835 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3846 - acc: 0.2761 - precision_m: 0.0095 - val_loss: 1.3805 - val_acc: 0.2889 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3814 - acc: 0.2613 - precision_m: 0.0030 - val_loss: 1.3754 - val_acc: 0.3259 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3899 - acc: 0.2757 - precision_m: 0.0000e+00 - val_loss: 1.3774 - val_acc: 0.3111 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3854 - acc: 0.2815 - precision_m: 0.0000e+00 - val_loss: 1.3786 - val_acc: 0.3111 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3869 - acc: 0.2780 - precision_m: 0.0000e+00 - val_loss: 1.3943 - val_acc: 0.2667 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3906 - acc: 0.2702 - precision_m: 0.0000e+00 - val_loss: 1.3787 - val_acc: 0.3037 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3675 - acc: 0.3059 - precision_m: 0.0029 - val_loss: 1.3795 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3799 - acc: 0.3036 - precision_m: 0.0000e+00 - val_loss: 1.3880 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3826 - acc: 0.2847 - precision_m: 0.0000e+00 - val_loss: 1.3814 - val_acc: 0.2667 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3819 - acc: 0.2632 - precision_m: 0.0051 - val_loss: 1.3788 - val_acc: 0.2667 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3861 - acc: 0.2100 - precision_m: 0.0085 - val_loss: 1.3858 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3853 - acc: 0.2868 - precision_m: 0.0000e+00 - val_loss: 1.3862 - val_acc: 0.2444 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3766 - acc: 0.3104 - precision_m: 0.0000e+00 - val_loss: 1.3818 - val_acc: 0.2741 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3902 - acc: 0.2474 - precision_m: 0.0000e+00 - val_loss: 1.3791 - val_acc: 0.2963 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3686 - acc: 0.3343 - precision_m: 2.4449e-04 - val_loss: 1.3730 - val_acc: 0.3481 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3864 - acc: 0.2425 - precision_m: 0.0242 - val_loss: 1.3777 - val_acc: 0.3259 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3838 - acc: 0.3039 - precision_m: 0.0000e+00 - val_loss: 1.3834 - val_acc: 0.2667 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3775 - acc: 0.3158 - precision_m: 6.2949e-04 - val_loss: 1.3730 - val_acc: 0.3259 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3873 - acc: 0.2630 - precision_m: 0.0000e+00 - val_loss: 1.3763 - val_acc: 0.3111 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3571 - acc: 0.3395 - precision_m: 0.0509 - val_loss: 1.3843 - val_acc: 0.3037 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3769 - acc: 0.2736 - precision_m: 0.0055 - val_loss: 1.3753 - val_acc: 0.2889 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3612 - acc: 0.3060 - precision_m: 0.0156 - val_loss: 1.3794 - val_acc: 0.2667 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3694 - acc: 0.2816 - precision_m: 0.0000e+00 - val_loss: 1.3898 - val_acc: 0.2074 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3758 - acc: 0.2819 - precision_m: 0.0012 - val_loss: 1.3717 - val_acc: 0.2963 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3783 - acc: 0.3019 - precision_m: 0.0031 - val_loss: 1.3830 - val_acc: 0.2370 - val_precision_m: 0.0000e+00\n","Epoch 52/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3750 - acc: 0.3179 - precision_m: 0.0040 - val_loss: 1.3824 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 53/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3756 - acc: 0.3053 - precision_m: 0.0083 - val_loss: 1.3825 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 54/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3747 - acc: 0.2736 - precision_m: 0.0000e+00 - val_loss: 1.3831 - val_acc: 0.2444 - val_precision_m: 0.0000e+00\n","Epoch 55/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3649 - acc: 0.3460 - precision_m: 0.0146 - val_loss: 1.3722 - val_acc: 0.3111 - val_precision_m: 0.0000e+00\n","Epoch 56/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3684 - acc: 0.3263 - precision_m: 0.0000e+00 - val_loss: 1.3721 - val_acc: 0.2741 - val_precision_m: 0.0000e+00\n","Epoch 57/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3835 - acc: 0.2695 - precision_m: 0.0090 - val_loss: 1.3788 - val_acc: 0.2667 - val_precision_m: 0.0000e+00\n","Epoch 58/500\n","157/157 [==============================] - 0s 3ms/step - loss: 1.3760 - acc: 0.2968 - precision_m: 0.0059 - val_loss: 1.3753 - val_acc: 0.2889 - val_precision_m: 0.0000e+00\n","Epoch 59/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3844 - acc: 0.2895 - precision_m: 0.0126 - val_loss: 1.3811 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 60/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3708 - acc: 0.3144 - precision_m: 0.0000e+00 - val_loss: 1.3898 - val_acc: 0.3037 - val_precision_m: 0.0000e+00\n","Epoch 61/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3709 - acc: 0.3214 - precision_m: 0.0017 - val_loss: 1.3754 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 62/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3703 - acc: 0.3214 - precision_m: 0.0030 - val_loss: 1.3670 - val_acc: 0.3185 - val_precision_m: 0.0000e+00\n","Epoch 63/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3379 - acc: 0.3930 - precision_m: 0.0457 - val_loss: 1.3792 - val_acc: 0.3111 - val_precision_m: 0.0000e+00\n","Epoch 64/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3326 - acc: 0.3562 - precision_m: 0.0166 - val_loss: 1.3781 - val_acc: 0.2889 - val_precision_m: 0.0000e+00\n","Epoch 65/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3677 - acc: 0.2637 - precision_m: 0.0262 - val_loss: 1.3804 - val_acc: 0.2889 - val_precision_m: 0.0000e+00\n","Epoch 66/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3434 - acc: 0.3473 - precision_m: 0.0297 - val_loss: 1.3863 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 67/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3596 - acc: 0.2893 - precision_m: 0.0032 - val_loss: 1.3711 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 68/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3896 - acc: 0.2711 - precision_m: 0.0223 - val_loss: 1.3901 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 69/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3718 - acc: 0.2669 - precision_m: 0.0000e+00 - val_loss: 1.3797 - val_acc: 0.2741 - val_precision_m: 0.0000e+00\n","Epoch 70/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3700 - acc: 0.3088 - precision_m: 0.0058 - val_loss: 1.3861 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 71/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3812 - acc: 0.2813 - precision_m: 0.0000e+00 - val_loss: 1.3839 - val_acc: 0.2889 - val_precision_m: 0.0000e+00\n","Epoch 72/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3438 - acc: 0.3814 - precision_m: 0.0459 - val_loss: 1.3916 - val_acc: 0.2741 - val_precision_m: 0.0000e+00\n","Epoch 73/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3657 - acc: 0.2999 - precision_m: 0.0084 - val_loss: 1.3914 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 74/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3519 - acc: 0.3659 - precision_m: 6.2949e-04 - val_loss: 1.3755 - val_acc: 0.2741 - val_precision_m: 0.0000e+00\n","Epoch 75/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3483 - acc: 0.3220 - precision_m: 0.0268 - val_loss: 1.3852 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 76/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3836 - acc: 0.2913 - precision_m: 0.0017 - val_loss: 1.3792 - val_acc: 0.3111 - val_precision_m: 0.0000e+00\n","Epoch 77/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3423 - acc: 0.3634 - precision_m: 0.0190 - val_loss: 1.3790 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 78/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3595 - acc: 0.2831 - precision_m: 0.0581 - val_loss: 1.3818 - val_acc: 0.2741 - val_precision_m: 0.0000e+00\n","Epoch 79/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3316 - acc: 0.3758 - precision_m: 0.0246 - val_loss: 1.3841 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 80/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3490 - acc: 0.3597 - precision_m: 0.0198 - val_loss: 1.3767 - val_acc: 0.2889 - val_precision_m: 0.0000e+00\n","Epoch 81/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3346 - acc: 0.3215 - precision_m: 0.0524 - val_loss: 1.4033 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 82/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3495 - acc: 0.3180 - precision_m: 0.0271 - val_loss: 1.4019 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 83/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3377 - acc: 0.3214 - precision_m: 0.0195 - val_loss: 1.3850 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 84/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3644 - acc: 0.3072 - precision_m: 0.0056 - val_loss: 1.3769 - val_acc: 0.3185 - val_precision_m: 0.0000e+00\n","Epoch 85/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3592 - acc: 0.3034 - precision_m: 0.0155 - val_loss: 1.3851 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 86/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3411 - acc: 0.3247 - precision_m: 0.0137 - val_loss: 1.3999 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 87/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3653 - acc: 0.3171 - precision_m: 0.0018 - val_loss: 1.3886 - val_acc: 0.2667 - val_precision_m: 0.0000e+00\n","Epoch 88/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3500 - acc: 0.3173 - precision_m: 0.0585 - val_loss: 1.3864 - val_acc: 0.2741 - val_precision_m: 0.0000e+00\n","Epoch 89/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3513 - acc: 0.3275 - precision_m: 0.0164 - val_loss: 1.3885 - val_acc: 0.2889 - val_precision_m: 0.0147\n","Epoch 90/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3248 - acc: 0.3560 - precision_m: 0.0560 - val_loss: 1.3797 - val_acc: 0.2963 - val_precision_m: 0.0000e+00\n","Epoch 91/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3644 - acc: 0.2821 - precision_m: 0.0048 - val_loss: 1.3953 - val_acc: 0.2741 - val_precision_m: 0.0000e+00\n","Epoch 92/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3531 - acc: 0.3737 - precision_m: 0.0345 - val_loss: 1.3895 - val_acc: 0.3111 - val_precision_m: 0.0000e+00\n","Epoch 93/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3359 - acc: 0.3362 - precision_m: 0.0450 - val_loss: 1.4027 - val_acc: 0.2741 - val_precision_m: 0.0147\n","Epoch 94/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3601 - acc: 0.3301 - precision_m: 0.0264 - val_loss: 1.3897 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 95/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3418 - acc: 0.3094 - precision_m: 0.0435 - val_loss: 1.3797 - val_acc: 0.3111 - val_precision_m: 0.0147\n","Epoch 96/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3214 - acc: 0.3470 - precision_m: 0.0912 - val_loss: 1.3895 - val_acc: 0.2889 - val_precision_m: 0.0000e+00\n","Epoch 97/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3542 - acc: 0.3045 - precision_m: 0.0041 - val_loss: 1.3838 - val_acc: 0.2889 - val_precision_m: 0.0000e+00\n","Epoch 98/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3370 - acc: 0.3293 - precision_m: 0.0506 - val_loss: 1.3966 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 99/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3514 - acc: 0.3719 - precision_m: 0.0104 - val_loss: 1.3895 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 100/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3346 - acc: 0.3703 - precision_m: 0.0491 - val_loss: 1.3883 - val_acc: 0.2889 - val_precision_m: 0.0000e+00\n","Epoch 101/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3067 - acc: 0.3707 - precision_m: 0.0985 - val_loss: 1.3921 - val_acc: 0.2815 - val_precision_m: 0.0000e+00\n","Epoch 102/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3355 - acc: 0.3317 - precision_m: 0.0287 - val_loss: 1.4072 - val_acc: 0.2889 - val_precision_m: 0.0147\n","Epoch 103/500\n","157/157 [==============================] - 0s 3ms/step - loss: 1.3397 - acc: 0.3776 - precision_m: 0.0114 - val_loss: 1.3934 - val_acc: 0.2889 - val_precision_m: 0.0000e+00\n","Epoch 104/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3186 - acc: 0.4079 - precision_m: 0.0477 - val_loss: 1.3996 - val_acc: 0.2741 - val_precision_m: 0.0000e+00\n","Epoch 105/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3281 - acc: 0.3243 - precision_m: 0.0456 - val_loss: 1.4104 - val_acc: 0.2667 - val_precision_m: 0.0147\n","Epoch 106/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3289 - acc: 0.3953 - precision_m: 0.0473 - val_loss: 1.4126 - val_acc: 0.3037 - val_precision_m: 0.0147\n","Epoch 107/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3403 - acc: 0.3041 - precision_m: 0.0365 - val_loss: 1.3939 - val_acc: 0.2815 - val_precision_m: 0.0147\n","Epoch 108/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.2896 - acc: 0.3674 - precision_m: 0.0999 - val_loss: 1.4055 - val_acc: 0.2889 - val_precision_m: 0.0294\n","Epoch 109/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3268 - acc: 0.3093 - precision_m: 0.0276 - val_loss: 1.3861 - val_acc: 0.2889 - val_precision_m: 0.0147\n","Epoch 110/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3398 - acc: 0.3846 - precision_m: 0.0574 - val_loss: 1.4010 - val_acc: 0.2963 - val_precision_m: 0.0147\n","Epoch 111/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3351 - acc: 0.3082 - precision_m: 0.0573 - val_loss: 1.4119 - val_acc: 0.2889 - val_precision_m: 0.0147\n","Epoch 112/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3374 - acc: 0.3649 - precision_m: 0.0495 - val_loss: 1.4171 - val_acc: 0.2741 - val_precision_m: 0.0147\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 18.0s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","156/156 [==============================] - 1s 4ms/step - loss: 1.4463 - acc: 0.2349 - precision_m: 0.0000e+00 - val_loss: 1.3759 - val_acc: 0.2910 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3815 - acc: 0.2252 - precision_m: 0.0000e+00 - val_loss: 1.3843 - val_acc: 0.1940 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3764 - acc: 0.2472 - precision_m: 0.0000e+00 - val_loss: 1.3840 - val_acc: 0.2090 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3873 - acc: 0.2736 - precision_m: 0.0000e+00 - val_loss: 1.3857 - val_acc: 0.2463 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3792 - acc: 0.3058 - precision_m: 0.0000e+00 - val_loss: 1.3800 - val_acc: 0.2090 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3787 - acc: 0.2545 - precision_m: 0.0000e+00 - val_loss: 1.3806 - val_acc: 0.2612 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3753 - acc: 0.3307 - precision_m: 0.0000e+00 - val_loss: 1.3798 - val_acc: 0.2836 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3751 - acc: 0.2700 - precision_m: 0.0000e+00 - val_loss: 1.3821 - val_acc: 0.2164 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3822 - acc: 0.2764 - precision_m: 0.0000e+00 - val_loss: 1.3783 - val_acc: 0.2836 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3827 - acc: 0.2403 - precision_m: 0.0000e+00 - val_loss: 1.3736 - val_acc: 0.2836 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3762 - acc: 0.3118 - precision_m: 0.0000e+00 - val_loss: 1.3712 - val_acc: 0.2537 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3851 - acc: 0.2405 - precision_m: 0.0000e+00 - val_loss: 1.3717 - val_acc: 0.2910 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3706 - acc: 0.2627 - precision_m: 0.0000e+00 - val_loss: 1.3740 - val_acc: 0.2836 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3811 - acc: 0.2791 - precision_m: 0.0000e+00 - val_loss: 1.3805 - val_acc: 0.2910 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3686 - acc: 0.3336 - precision_m: 0.0000e+00 - val_loss: 1.3840 - val_acc: 0.2015 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3878 - acc: 0.2843 - precision_m: 0.0000e+00 - val_loss: 1.3764 - val_acc: 0.2537 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3646 - acc: 0.3051 - precision_m: 0.0000e+00 - val_loss: 1.3742 - val_acc: 0.2537 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3817 - acc: 0.2777 - precision_m: 0.0000e+00 - val_loss: 1.3751 - val_acc: 0.2836 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3650 - acc: 0.3408 - precision_m: 0.0000e+00 - val_loss: 1.3703 - val_acc: 0.2836 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3758 - acc: 0.2846 - precision_m: 0.0000e+00 - val_loss: 1.3630 - val_acc: 0.2985 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3865 - acc: 0.2382 - precision_m: 0.0000e+00 - val_loss: 1.3657 - val_acc: 0.2910 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3698 - acc: 0.2765 - precision_m: 0.0000e+00 - val_loss: 1.3703 - val_acc: 0.2537 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3752 - acc: 0.2923 - precision_m: 0.0000e+00 - val_loss: 1.3647 - val_acc: 0.2463 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3631 - acc: 0.2741 - precision_m: 0.0000e+00 - val_loss: 1.3781 - val_acc: 0.2612 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3817 - acc: 0.2990 - precision_m: 0.0000e+00 - val_loss: 1.3612 - val_acc: 0.2910 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3407 - acc: 0.3169 - precision_m: 0.0000e+00 - val_loss: 1.3520 - val_acc: 0.2985 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3506 - acc: 0.3382 - precision_m: 0.0000e+00 - val_loss: 1.3541 - val_acc: 0.2836 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3639 - acc: 0.2703 - precision_m: 0.0021 - val_loss: 1.3441 - val_acc: 0.2985 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3492 - acc: 0.3076 - precision_m: 0.0011 - val_loss: 1.3637 - val_acc: 0.2239 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3803 - acc: 0.3417 - precision_m: 0.0036 - val_loss: 1.3508 - val_acc: 0.2761 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3347 - acc: 0.3368 - precision_m: 0.0509 - val_loss: 1.3465 - val_acc: 0.3060 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3721 - acc: 0.2858 - precision_m: 0.0261 - val_loss: 1.3445 - val_acc: 0.3134 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3334 - acc: 0.3995 - precision_m: 0.0093 - val_loss: 1.3387 - val_acc: 0.3060 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3180 - acc: 0.3465 - precision_m: 0.0238 - val_loss: 1.3198 - val_acc: 0.3358 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3451 - acc: 0.3582 - precision_m: 0.0357 - val_loss: 1.3164 - val_acc: 0.3507 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.2740 - acc: 0.3997 - precision_m: 0.0620 - val_loss: 1.3205 - val_acc: 0.3284 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.2879 - acc: 0.3297 - precision_m: 0.0267 - val_loss: 1.3217 - val_acc: 0.3209 - val_precision_m: 0.0149\n","Epoch 38/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3004 - acc: 0.3778 - precision_m: 0.0782 - val_loss: 1.3214 - val_acc: 0.2836 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3162 - acc: 0.3711 - precision_m: 0.0247 - val_loss: 1.3168 - val_acc: 0.3358 - val_precision_m: 0.0149\n","Epoch 40/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3109 - acc: 0.3505 - precision_m: 0.0308 - val_loss: 1.3221 - val_acc: 0.3060 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.2911 - acc: 0.3354 - precision_m: 0.0439 - val_loss: 1.3048 - val_acc: 0.3060 - val_precision_m: 0.0299\n","Epoch 42/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.2758 - acc: 0.4157 - precision_m: 0.0443 - val_loss: 1.3010 - val_acc: 0.3507 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.2627 - acc: 0.4054 - precision_m: 0.0897 - val_loss: 1.2995 - val_acc: 0.3358 - val_precision_m: 0.0149\n","Epoch 44/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.2539 - acc: 0.3895 - precision_m: 0.0478 - val_loss: 1.3076 - val_acc: 0.3358 - val_precision_m: 0.0448\n","Epoch 45/500\n","156/156 [==============================] - 0s 3ms/step - loss: 1.2789 - acc: 0.3857 - precision_m: 0.1038 - val_loss: 1.2883 - val_acc: 0.3433 - val_precision_m: 0.0149\n","Epoch 46/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.2642 - acc: 0.3704 - precision_m: 0.0764 - val_loss: 1.2831 - val_acc: 0.3433 - val_precision_m: 0.0149\n","Epoch 47/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.2546 - acc: 0.3812 - precision_m: 0.0270 - val_loss: 1.3091 - val_acc: 0.3060 - val_precision_m: 0.0149\n","Epoch 48/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.2802 - acc: 0.3654 - precision_m: 0.0447 - val_loss: 1.2863 - val_acc: 0.3433 - val_precision_m: 0.0149\n","Epoch 49/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.2789 - acc: 0.3661 - precision_m: 0.0502 - val_loss: 1.2896 - val_acc: 0.2761 - val_precision_m: 0.0448\n","Epoch 50/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.2029 - acc: 0.4043 - precision_m: 0.1708 - val_loss: 1.2900 - val_acc: 0.3209 - val_precision_m: 0.0149\n","Epoch 51/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.2730 - acc: 0.3600 - precision_m: 0.1243 - val_loss: 1.2604 - val_acc: 0.3657 - val_precision_m: 0.0149\n","Epoch 52/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1951 - acc: 0.4347 - precision_m: 0.1193 - val_loss: 1.2565 - val_acc: 0.3433 - val_precision_m: 0.0149\n","Epoch 53/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.2850 - acc: 0.3159 - precision_m: 0.0611 - val_loss: 1.2578 - val_acc: 0.3582 - val_precision_m: 0.0299\n","Epoch 54/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.2663 - acc: 0.3586 - precision_m: 0.0587 - val_loss: 1.2595 - val_acc: 0.3284 - val_precision_m: 0.0448\n","Epoch 55/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.2391 - acc: 0.3958 - precision_m: 0.0973 - val_loss: 1.2619 - val_acc: 0.3433 - val_precision_m: 0.0299\n","Epoch 56/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.2061 - acc: 0.4174 - precision_m: 0.0612 - val_loss: 1.2597 - val_acc: 0.3284 - val_precision_m: 0.0299\n","Epoch 57/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.2137 - acc: 0.3830 - precision_m: 0.0975 - val_loss: 1.2525 - val_acc: 0.3433 - val_precision_m: 0.0746\n","Epoch 58/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.2723 - acc: 0.3211 - precision_m: 0.0633 - val_loss: 1.2699 - val_acc: 0.3358 - val_precision_m: 0.0448\n","Epoch 59/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.2125 - acc: 0.3860 - precision_m: 0.1390 - val_loss: 1.2700 - val_acc: 0.3209 - val_precision_m: 0.0149\n","Epoch 60/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.2418 - acc: 0.3543 - precision_m: 0.1093 - val_loss: 1.2705 - val_acc: 0.3209 - val_precision_m: 0.0597\n","Epoch 61/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.2117 - acc: 0.4467 - precision_m: 0.1057 - val_loss: 1.2474 - val_acc: 0.3582 - val_precision_m: 0.0299\n","Epoch 62/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.2016 - acc: 0.4122 - precision_m: 0.0791 - val_loss: 1.2356 - val_acc: 0.3284 - val_precision_m: 0.0896\n","Epoch 63/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.2304 - acc: 0.3196 - precision_m: 0.0811 - val_loss: 1.2369 - val_acc: 0.3358 - val_precision_m: 0.0746\n","Epoch 64/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1764 - acc: 0.4292 - precision_m: 0.1562 - val_loss: 1.2523 - val_acc: 0.3433 - val_precision_m: 0.0299\n","Epoch 65/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1617 - acc: 0.4220 - precision_m: 0.1264 - val_loss: 1.2432 - val_acc: 0.3284 - val_precision_m: 0.0149\n","Epoch 66/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.2052 - acc: 0.3329 - precision_m: 0.0907 - val_loss: 1.2423 - val_acc: 0.3507 - val_precision_m: 0.0597\n","Epoch 67/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.2120 - acc: 0.3825 - precision_m: 0.1166 - val_loss: 1.2473 - val_acc: 0.3507 - val_precision_m: 0.0746\n","Epoch 68/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.2421 - acc: 0.3384 - precision_m: 0.0924 - val_loss: 1.2262 - val_acc: 0.3582 - val_precision_m: 0.0597\n","Epoch 69/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.2187 - acc: 0.4157 - precision_m: 0.1306 - val_loss: 1.2316 - val_acc: 0.3657 - val_precision_m: 0.0597\n","Epoch 70/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1846 - acc: 0.4262 - precision_m: 0.1386 - val_loss: 1.2318 - val_acc: 0.3582 - val_precision_m: 0.0896\n","Epoch 71/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1898 - acc: 0.3972 - precision_m: 0.0844 - val_loss: 1.2354 - val_acc: 0.3433 - val_precision_m: 0.0149\n","Epoch 72/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.2306 - acc: 0.3806 - precision_m: 0.0581 - val_loss: 1.2208 - val_acc: 0.3582 - val_precision_m: 0.0746\n","Epoch 73/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1730 - acc: 0.4045 - precision_m: 0.1767 - val_loss: 1.2127 - val_acc: 0.3657 - val_precision_m: 0.0746\n","Epoch 74/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1755 - acc: 0.3608 - precision_m: 0.1376 - val_loss: 1.2275 - val_acc: 0.3582 - val_precision_m: 0.0896\n","Epoch 75/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1727 - acc: 0.3515 - precision_m: 0.1341 - val_loss: 1.2071 - val_acc: 0.3657 - val_precision_m: 0.0746\n","Epoch 76/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1918 - acc: 0.3831 - precision_m: 0.0919 - val_loss: 1.2407 - val_acc: 0.3731 - val_precision_m: 0.1045\n","Epoch 77/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1828 - acc: 0.4140 - precision_m: 0.1103 - val_loss: 1.2150 - val_acc: 0.3731 - val_precision_m: 0.0597\n","Epoch 78/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1774 - acc: 0.3838 - precision_m: 0.1645 - val_loss: 1.2196 - val_acc: 0.3507 - val_precision_m: 0.0746\n","Epoch 79/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1957 - acc: 0.4314 - precision_m: 0.1605 - val_loss: 1.2769 - val_acc: 0.3582 - val_precision_m: 0.1269\n","Epoch 80/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1712 - acc: 0.4053 - precision_m: 0.1168 - val_loss: 1.2174 - val_acc: 0.3284 - val_precision_m: 0.0746\n","Epoch 81/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1784 - acc: 0.3643 - precision_m: 0.1219 - val_loss: 1.2261 - val_acc: 0.3806 - val_precision_m: 0.1269\n","Epoch 82/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.2087 - acc: 0.3681 - precision_m: 0.1106 - val_loss: 1.1989 - val_acc: 0.3358 - val_precision_m: 0.0522\n","Epoch 83/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1715 - acc: 0.4336 - precision_m: 0.1441 - val_loss: 1.2059 - val_acc: 0.3731 - val_precision_m: 0.0821\n","Epoch 84/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.2199 - acc: 0.3957 - precision_m: 0.1278 - val_loss: 1.1906 - val_acc: 0.3582 - val_precision_m: 0.0896\n","Epoch 85/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1695 - acc: 0.4092 - precision_m: 0.1539 - val_loss: 1.1925 - val_acc: 0.3284 - val_precision_m: 0.0672\n","Epoch 86/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1571 - acc: 0.3641 - precision_m: 0.1218 - val_loss: 1.2543 - val_acc: 0.3433 - val_precision_m: 0.1045\n","Epoch 87/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.2261 - acc: 0.3460 - precision_m: 0.1428 - val_loss: 1.1842 - val_acc: 0.3433 - val_precision_m: 0.0448\n","Epoch 88/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1731 - acc: 0.3895 - precision_m: 0.1500 - val_loss: 1.1928 - val_acc: 0.3507 - val_precision_m: 0.0672\n","Epoch 89/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1389 - acc: 0.4256 - precision_m: 0.1756 - val_loss: 1.2185 - val_acc: 0.3731 - val_precision_m: 0.0896\n","Epoch 90/500\n","156/156 [==============================] - 0s 3ms/step - loss: 1.1755 - acc: 0.4434 - precision_m: 0.1313 - val_loss: 1.2135 - val_acc: 0.3358 - val_precision_m: 0.0149\n","Epoch 91/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.2134 - acc: 0.3952 - precision_m: 0.1055 - val_loss: 1.2030 - val_acc: 0.3507 - val_precision_m: 0.0597\n","Epoch 92/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1367 - acc: 0.3900 - precision_m: 0.1486 - val_loss: 1.2668 - val_acc: 0.3284 - val_precision_m: 0.1045\n","Epoch 93/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.2633 - acc: 0.3637 - precision_m: 0.1047 - val_loss: 1.1946 - val_acc: 0.3657 - val_precision_m: 0.0896\n","Epoch 94/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1636 - acc: 0.4769 - precision_m: 0.1195 - val_loss: 1.2224 - val_acc: 0.3433 - val_precision_m: 0.1119\n","Epoch 95/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1501 - acc: 0.3967 - precision_m: 0.1928 - val_loss: 1.2035 - val_acc: 0.3507 - val_precision_m: 0.0597\n","Epoch 96/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1996 - acc: 0.3699 - precision_m: 0.0926 - val_loss: 1.2204 - val_acc: 0.3657 - val_precision_m: 0.0448\n","Epoch 97/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1404 - acc: 0.4727 - precision_m: 0.1210 - val_loss: 1.1815 - val_acc: 0.3507 - val_precision_m: 0.0896\n","Epoch 98/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1366 - acc: 0.4570 - precision_m: 0.1662 - val_loss: 1.2095 - val_acc: 0.3433 - val_precision_m: 0.0597\n","Epoch 99/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1509 - acc: 0.3907 - precision_m: 0.1375 - val_loss: 1.1791 - val_acc: 0.3433 - val_precision_m: 0.1119\n","Epoch 100/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1964 - acc: 0.3775 - precision_m: 0.1461 - val_loss: 1.2061 - val_acc: 0.3731 - val_precision_m: 0.0970\n","Epoch 101/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1675 - acc: 0.3882 - precision_m: 0.1545 - val_loss: 1.1928 - val_acc: 0.3433 - val_precision_m: 0.0821\n","Epoch 102/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1639 - acc: 0.4416 - precision_m: 0.1113 - val_loss: 1.2184 - val_acc: 0.3284 - val_precision_m: 0.0448\n","Epoch 103/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1640 - acc: 0.3836 - precision_m: 0.1436 - val_loss: 1.2065 - val_acc: 0.3881 - val_precision_m: 0.1269\n","Epoch 104/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1884 - acc: 0.4473 - precision_m: 0.1620 - val_loss: 1.1859 - val_acc: 0.3507 - val_precision_m: 0.1194\n","Epoch 105/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1027 - acc: 0.4258 - precision_m: 0.1707 - val_loss: 1.1850 - val_acc: 0.3582 - val_precision_m: 0.1045\n","Epoch 106/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0990 - acc: 0.4108 - precision_m: 0.2480 - val_loss: 1.2287 - val_acc: 0.3134 - val_precision_m: 0.0896\n","Epoch 107/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0995 - acc: 0.4222 - precision_m: 0.1604 - val_loss: 1.3266 - val_acc: 0.3507 - val_precision_m: 0.1567\n","Epoch 108/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1542 - acc: 0.4576 - precision_m: 0.2750 - val_loss: 1.1679 - val_acc: 0.3507 - val_precision_m: 0.0672\n","Epoch 109/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1006 - acc: 0.4713 - precision_m: 0.2046 - val_loss: 1.1825 - val_acc: 0.3731 - val_precision_m: 0.1119\n","Epoch 110/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0671 - acc: 0.5128 - precision_m: 0.2128 - val_loss: 1.1790 - val_acc: 0.3433 - val_precision_m: 0.1269\n","Epoch 111/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0642 - acc: 0.4630 - precision_m: 0.1812 - val_loss: 1.1967 - val_acc: 0.3358 - val_precision_m: 0.1119\n","Epoch 112/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1236 - acc: 0.4723 - precision_m: 0.2095 - val_loss: 1.1789 - val_acc: 0.3582 - val_precision_m: 0.0896\n","Epoch 113/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1152 - acc: 0.4427 - precision_m: 0.1521 - val_loss: 1.1895 - val_acc: 0.3657 - val_precision_m: 0.1418\n","Epoch 114/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.2000 - acc: 0.3733 - precision_m: 0.1336 - val_loss: 1.1897 - val_acc: 0.3657 - val_precision_m: 0.1045\n","Epoch 115/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1912 - acc: 0.3887 - precision_m: 0.1232 - val_loss: 1.1565 - val_acc: 0.3731 - val_precision_m: 0.1045\n","Epoch 116/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1070 - acc: 0.4508 - precision_m: 0.2378 - val_loss: 1.2320 - val_acc: 0.3582 - val_precision_m: 0.1119\n","Epoch 117/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1257 - acc: 0.4214 - precision_m: 0.1311 - val_loss: 1.1515 - val_acc: 0.3433 - val_precision_m: 0.1119\n","Epoch 118/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1139 - acc: 0.4511 - precision_m: 0.2355 - val_loss: 1.1739 - val_acc: 0.4254 - val_precision_m: 0.1194\n","Epoch 119/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1092 - acc: 0.4158 - precision_m: 0.2443 - val_loss: 1.1850 - val_acc: 0.3433 - val_precision_m: 0.0746\n","Epoch 120/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1943 - acc: 0.4097 - precision_m: 0.1640 - val_loss: 1.1789 - val_acc: 0.3955 - val_precision_m: 0.1194\n","Epoch 121/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.2035 - acc: 0.4194 - precision_m: 0.1344 - val_loss: 1.2911 - val_acc: 0.3433 - val_precision_m: 0.0896\n","Epoch 122/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1888 - acc: 0.3783 - precision_m: 0.1406 - val_loss: 1.2476 - val_acc: 0.3731 - val_precision_m: 0.1866\n","Epoch 123/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1301 - acc: 0.3917 - precision_m: 0.1303 - val_loss: 1.1765 - val_acc: 0.3433 - val_precision_m: 0.1194\n","Epoch 124/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1083 - acc: 0.4079 - precision_m: 0.2437 - val_loss: 1.1950 - val_acc: 0.3881 - val_precision_m: 0.1418\n","Epoch 125/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1506 - acc: 0.4235 - precision_m: 0.2007 - val_loss: 1.2195 - val_acc: 0.3582 - val_precision_m: 0.1045\n","Epoch 126/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1060 - acc: 0.4546 - precision_m: 0.2397 - val_loss: 1.2323 - val_acc: 0.3582 - val_precision_m: 0.1343\n","Epoch 127/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1612 - acc: 0.3649 - precision_m: 0.1884 - val_loss: 1.3052 - val_acc: 0.3806 - val_precision_m: 0.1866\n","Epoch 128/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.2018 - acc: 0.4036 - precision_m: 0.2093 - val_loss: 1.1797 - val_acc: 0.3657 - val_precision_m: 0.1343\n","Epoch 129/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1539 - acc: 0.4582 - precision_m: 0.2182 - val_loss: 1.1630 - val_acc: 0.4030 - val_precision_m: 0.1343\n","Epoch 130/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1176 - acc: 0.4513 - precision_m: 0.2663 - val_loss: 1.1633 - val_acc: 0.4104 - val_precision_m: 0.1194\n","Epoch 131/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1537 - acc: 0.4173 - precision_m: 0.1719 - val_loss: 1.2340 - val_acc: 0.3582 - val_precision_m: 0.1567\n","Epoch 132/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0995 - acc: 0.4344 - precision_m: 0.1516 - val_loss: 1.1698 - val_acc: 0.3731 - val_precision_m: 0.1194\n","Epoch 133/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1126 - acc: 0.4604 - precision_m: 0.1981 - val_loss: 1.1646 - val_acc: 0.3433 - val_precision_m: 0.0970\n","Epoch 134/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0944 - acc: 0.3823 - precision_m: 0.1458 - val_loss: 1.1978 - val_acc: 0.3731 - val_precision_m: 0.2015\n","Epoch 135/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0778 - acc: 0.4517 - precision_m: 0.3039 - val_loss: 1.1934 - val_acc: 0.4328 - val_precision_m: 0.1642\n","Epoch 136/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1776 - acc: 0.3780 - precision_m: 0.1453 - val_loss: 1.2542 - val_acc: 0.3582 - val_precision_m: 0.1567\n","Epoch 137/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1368 - acc: 0.4737 - precision_m: 0.1808 - val_loss: 1.1879 - val_acc: 0.3657 - val_precision_m: 0.1045\n","Epoch 138/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1027 - acc: 0.4224 - precision_m: 0.1723 - val_loss: 1.1532 - val_acc: 0.3881 - val_precision_m: 0.1269\n","Epoch 139/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1830 - acc: 0.4259 - precision_m: 0.2207 - val_loss: 1.1685 - val_acc: 0.3806 - val_precision_m: 0.0896\n","Epoch 140/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1580 - acc: 0.4169 - precision_m: 0.1648 - val_loss: 1.1758 - val_acc: 0.3955 - val_precision_m: 0.1045\n","Epoch 141/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1143 - acc: 0.4961 - precision_m: 0.2492 - val_loss: 1.1703 - val_acc: 0.3731 - val_precision_m: 0.1045\n","Epoch 142/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1346 - acc: 0.4699 - precision_m: 0.2173 - val_loss: 1.1687 - val_acc: 0.3806 - val_precision_m: 0.1119\n","Epoch 143/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0664 - acc: 0.4568 - precision_m: 0.2395 - val_loss: 1.1674 - val_acc: 0.3806 - val_precision_m: 0.1194\n","Epoch 144/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1320 - acc: 0.4547 - precision_m: 0.2322 - val_loss: 1.1320 - val_acc: 0.3806 - val_precision_m: 0.0746\n","Epoch 145/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1409 - acc: 0.4266 - precision_m: 0.2235 - val_loss: 1.1250 - val_acc: 0.4104 - val_precision_m: 0.1269\n","Epoch 146/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0983 - acc: 0.4254 - precision_m: 0.2540 - val_loss: 1.1703 - val_acc: 0.3582 - val_precision_m: 0.0821\n","Epoch 147/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0962 - acc: 0.5084 - precision_m: 0.2791 - val_loss: 1.1948 - val_acc: 0.3881 - val_precision_m: 0.0896\n","Epoch 148/500\n","156/156 [==============================] - 0s 3ms/step - loss: 1.0574 - acc: 0.4611 - precision_m: 0.1228 - val_loss: 1.1399 - val_acc: 0.3433 - val_precision_m: 0.1119\n","Epoch 149/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1104 - acc: 0.4056 - precision_m: 0.1493 - val_loss: 1.1410 - val_acc: 0.4104 - val_precision_m: 0.1791\n","Epoch 150/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0342 - acc: 0.4279 - precision_m: 0.1541 - val_loss: 1.1436 - val_acc: 0.3657 - val_precision_m: 0.1119\n","Epoch 151/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0733 - acc: 0.4690 - precision_m: 0.2332 - val_loss: 1.1609 - val_acc: 0.3955 - val_precision_m: 0.1119\n","Epoch 152/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1206 - acc: 0.4674 - precision_m: 0.2372 - val_loss: 1.1798 - val_acc: 0.3881 - val_precision_m: 0.1045\n","Epoch 153/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1324 - acc: 0.4038 - precision_m: 0.0857 - val_loss: 1.1735 - val_acc: 0.4030 - val_precision_m: 0.1194\n","Epoch 154/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1289 - acc: 0.3854 - precision_m: 0.2062 - val_loss: 1.2112 - val_acc: 0.3881 - val_precision_m: 0.0970\n","Epoch 155/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0903 - acc: 0.4396 - precision_m: 0.1808 - val_loss: 1.1593 - val_acc: 0.3806 - val_precision_m: 0.1269\n","Epoch 156/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1287 - acc: 0.4252 - precision_m: 0.1513 - val_loss: 1.1941 - val_acc: 0.3806 - val_precision_m: 0.1194\n","Epoch 157/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0889 - acc: 0.4442 - precision_m: 0.2806 - val_loss: 1.1914 - val_acc: 0.3657 - val_precision_m: 0.1493\n","Epoch 158/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1005 - acc: 0.4283 - precision_m: 0.2331 - val_loss: 1.1972 - val_acc: 0.4030 - val_precision_m: 0.1418\n","Epoch 159/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1778 - acc: 0.4477 - precision_m: 0.2256 - val_loss: 1.1500 - val_acc: 0.3657 - val_precision_m: 0.1343\n","Epoch 160/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1418 - acc: 0.4038 - precision_m: 0.1530 - val_loss: 1.2512 - val_acc: 0.3881 - val_precision_m: 0.1642\n","Epoch 161/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1089 - acc: 0.4859 - precision_m: 0.1827 - val_loss: 1.1517 - val_acc: 0.4403 - val_precision_m: 0.1493\n","Epoch 162/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0604 - acc: 0.4048 - precision_m: 0.1313 - val_loss: 1.1559 - val_acc: 0.3955 - val_precision_m: 0.1045\n","Epoch 163/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1238 - acc: 0.3941 - precision_m: 0.2011 - val_loss: 1.1737 - val_acc: 0.3881 - val_precision_m: 0.1791\n","Epoch 164/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0551 - acc: 0.4412 - precision_m: 0.1788 - val_loss: 1.1417 - val_acc: 0.4030 - val_precision_m: 0.1716\n","Epoch 165/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1342 - acc: 0.4701 - precision_m: 0.1832 - val_loss: 1.1681 - val_acc: 0.4030 - val_precision_m: 0.1716\n","Epoch 166/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1838 - acc: 0.4633 - precision_m: 0.1500 - val_loss: 1.1573 - val_acc: 0.4030 - val_precision_m: 0.1716\n","Epoch 167/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0882 - acc: 0.4296 - precision_m: 0.2157 - val_loss: 1.1663 - val_acc: 0.3955 - val_precision_m: 0.1343\n","Epoch 168/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0906 - acc: 0.4444 - precision_m: 0.2139 - val_loss: 1.1415 - val_acc: 0.4179 - val_precision_m: 0.1493\n","Epoch 169/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1960 - acc: 0.3985 - precision_m: 0.2055 - val_loss: 1.2320 - val_acc: 0.3358 - val_precision_m: 0.1493\n","Epoch 170/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1085 - acc: 0.4382 - precision_m: 0.2306 - val_loss: 1.1108 - val_acc: 0.4179 - val_precision_m: 0.1567\n","Epoch 171/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1952 - acc: 0.3474 - precision_m: 0.1775 - val_loss: 1.1853 - val_acc: 0.3507 - val_precision_m: 0.1119\n","Epoch 172/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0877 - acc: 0.4557 - precision_m: 0.2406 - val_loss: 1.1769 - val_acc: 0.3358 - val_precision_m: 0.0896\n","Epoch 173/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0608 - acc: 0.4966 - precision_m: 0.2257 - val_loss: 1.1870 - val_acc: 0.4030 - val_precision_m: 0.1493\n","Epoch 174/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1447 - acc: 0.4957 - precision_m: 0.2058 - val_loss: 1.1672 - val_acc: 0.3955 - val_precision_m: 0.1418\n","Epoch 175/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0809 - acc: 0.4595 - precision_m: 0.2010 - val_loss: 1.1904 - val_acc: 0.4478 - val_precision_m: 0.1716\n","Epoch 176/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0748 - acc: 0.4457 - precision_m: 0.2036 - val_loss: 1.1588 - val_acc: 0.4104 - val_precision_m: 0.1493\n","Epoch 177/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1567 - acc: 0.4295 - precision_m: 0.1993 - val_loss: 1.1237 - val_acc: 0.3881 - val_precision_m: 0.1119\n","Epoch 178/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1086 - acc: 0.4855 - precision_m: 0.2018 - val_loss: 1.1134 - val_acc: 0.4104 - val_precision_m: 0.1567\n","Epoch 179/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0722 - acc: 0.5243 - precision_m: 0.2009 - val_loss: 1.1015 - val_acc: 0.3955 - val_precision_m: 0.1343\n","Epoch 180/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1397 - acc: 0.4313 - precision_m: 0.1525 - val_loss: 1.1595 - val_acc: 0.3582 - val_precision_m: 0.1269\n","Epoch 181/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1247 - acc: 0.4435 - precision_m: 0.2215 - val_loss: 1.2496 - val_acc: 0.3955 - val_precision_m: 0.1418\n","Epoch 182/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1084 - acc: 0.4533 - precision_m: 0.1926 - val_loss: 1.1190 - val_acc: 0.4179 - val_precision_m: 0.1866\n","Epoch 183/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0297 - acc: 0.5065 - precision_m: 0.2939 - val_loss: 1.1103 - val_acc: 0.4030 - val_precision_m: 0.1418\n","Epoch 184/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0297 - acc: 0.4640 - precision_m: 0.2792 - val_loss: 1.1462 - val_acc: 0.3881 - val_precision_m: 0.1269\n","Epoch 185/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0172 - acc: 0.4460 - precision_m: 0.2051 - val_loss: 1.1092 - val_acc: 0.4254 - val_precision_m: 0.1493\n","Epoch 186/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0599 - acc: 0.4573 - precision_m: 0.2755 - val_loss: 1.1776 - val_acc: 0.4403 - val_precision_m: 0.1866\n","Epoch 187/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0712 - acc: 0.4202 - precision_m: 0.2385 - val_loss: 1.1259 - val_acc: 0.4328 - val_precision_m: 0.2090\n","Epoch 188/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0547 - acc: 0.4770 - precision_m: 0.2727 - val_loss: 1.1435 - val_acc: 0.4254 - val_precision_m: 0.1194\n","Epoch 189/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0768 - acc: 0.5095 - precision_m: 0.2807 - val_loss: 1.1445 - val_acc: 0.4403 - val_precision_m: 0.1791\n","Epoch 190/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0711 - acc: 0.4447 - precision_m: 0.2821 - val_loss: 1.1549 - val_acc: 0.4104 - val_precision_m: 0.1716\n","Epoch 191/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0757 - acc: 0.4329 - precision_m: 0.2289 - val_loss: 1.1777 - val_acc: 0.3657 - val_precision_m: 0.1194\n","Epoch 192/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1330 - acc: 0.4476 - precision_m: 0.2092 - val_loss: 1.1167 - val_acc: 0.4328 - val_precision_m: 0.1716\n","Epoch 193/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1338 - acc: 0.3644 - precision_m: 0.1465 - val_loss: 1.4315 - val_acc: 0.3657 - val_precision_m: 0.1866\n","Epoch 194/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.2522 - acc: 0.3583 - precision_m: 0.1579 - val_loss: 1.1330 - val_acc: 0.3806 - val_precision_m: 0.1716\n","Epoch 195/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0953 - acc: 0.4720 - precision_m: 0.2245 - val_loss: 1.1904 - val_acc: 0.3731 - val_precision_m: 0.1791\n","Epoch 196/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1110 - acc: 0.4322 - precision_m: 0.2007 - val_loss: 1.1081 - val_acc: 0.4179 - val_precision_m: 0.1866\n","Epoch 197/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0532 - acc: 0.4530 - precision_m: 0.2303 - val_loss: 1.1570 - val_acc: 0.4179 - val_precision_m: 0.1791\n","Epoch 198/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0242 - acc: 0.4574 - precision_m: 0.2910 - val_loss: 1.1327 - val_acc: 0.4179 - val_precision_m: 0.1269\n","Epoch 199/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0420 - acc: 0.4905 - precision_m: 0.2014 - val_loss: 1.0919 - val_acc: 0.3881 - val_precision_m: 0.1418\n","Epoch 200/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0697 - acc: 0.4493 - precision_m: 0.2059 - val_loss: 1.1085 - val_acc: 0.4179 - val_precision_m: 0.1716\n","Epoch 201/500\n","156/156 [==============================] - 0s 2ms/step - loss: 0.9914 - acc: 0.4469 - precision_m: 0.2760 - val_loss: 1.1161 - val_acc: 0.4179 - val_precision_m: 0.1343\n","Epoch 202/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0357 - acc: 0.4847 - precision_m: 0.2683 - val_loss: 1.1073 - val_acc: 0.4254 - val_precision_m: 0.1716\n","Epoch 203/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1386 - acc: 0.4253 - precision_m: 0.2203 - val_loss: 1.0789 - val_acc: 0.4179 - val_precision_m: 0.1567\n","Epoch 204/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0781 - acc: 0.4291 - precision_m: 0.2235 - val_loss: 1.1195 - val_acc: 0.4328 - val_precision_m: 0.1716\n","Epoch 205/500\n","156/156 [==============================] - 0s 3ms/step - loss: 1.0314 - acc: 0.4602 - precision_m: 0.2522 - val_loss: 1.1016 - val_acc: 0.4254 - val_precision_m: 0.2015\n","Epoch 206/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1509 - acc: 0.4718 - precision_m: 0.1968 - val_loss: 1.1408 - val_acc: 0.3507 - val_precision_m: 0.1269\n","Epoch 207/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0678 - acc: 0.4590 - precision_m: 0.2827 - val_loss: 1.1465 - val_acc: 0.4254 - val_precision_m: 0.1866\n","Epoch 208/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0591 - acc: 0.4397 - precision_m: 0.2280 - val_loss: 1.1390 - val_acc: 0.3955 - val_precision_m: 0.2239\n","Epoch 209/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0621 - acc: 0.4832 - precision_m: 0.2957 - val_loss: 1.1042 - val_acc: 0.3806 - val_precision_m: 0.1866\n","Epoch 210/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1249 - acc: 0.4720 - precision_m: 0.1726 - val_loss: 1.1183 - val_acc: 0.4179 - val_precision_m: 0.1493\n","Epoch 211/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0710 - acc: 0.4629 - precision_m: 0.2086 - val_loss: 1.1348 - val_acc: 0.3731 - val_precision_m: 0.1343\n","Epoch 212/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1353 - acc: 0.4516 - precision_m: 0.2469 - val_loss: 1.1215 - val_acc: 0.3955 - val_precision_m: 0.1567\n","Epoch 213/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.2096 - acc: 0.3591 - precision_m: 0.1694 - val_loss: 1.1145 - val_acc: 0.4030 - val_precision_m: 0.1567\n","Epoch 214/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1268 - acc: 0.3584 - precision_m: 0.1396 - val_loss: 1.1745 - val_acc: 0.3731 - val_precision_m: 0.1194\n","Epoch 215/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1319 - acc: 0.4163 - precision_m: 0.2100 - val_loss: 1.1405 - val_acc: 0.4179 - val_precision_m: 0.2090\n","Epoch 216/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0661 - acc: 0.4639 - precision_m: 0.2481 - val_loss: 1.1026 - val_acc: 0.4328 - val_precision_m: 0.1791\n","Epoch 217/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0075 - acc: 0.4623 - precision_m: 0.3008 - val_loss: 1.1136 - val_acc: 0.4552 - val_precision_m: 0.2090\n","Epoch 218/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0674 - acc: 0.5382 - precision_m: 0.2194 - val_loss: 1.1157 - val_acc: 0.3881 - val_precision_m: 0.1269\n","Epoch 219/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1149 - acc: 0.4726 - precision_m: 0.2627 - val_loss: 1.1479 - val_acc: 0.4179 - val_precision_m: 0.2090\n","Epoch 220/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0812 - acc: 0.4313 - precision_m: 0.1945 - val_loss: 1.0955 - val_acc: 0.4403 - val_precision_m: 0.2164\n","Epoch 221/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0719 - acc: 0.4696 - precision_m: 0.2582 - val_loss: 1.1162 - val_acc: 0.4030 - val_precision_m: 0.1791\n","Epoch 222/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1581 - acc: 0.4269 - precision_m: 0.2248 - val_loss: 1.1040 - val_acc: 0.4254 - val_precision_m: 0.1940\n","Epoch 223/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0336 - acc: 0.5152 - precision_m: 0.2933 - val_loss: 1.0662 - val_acc: 0.4403 - val_precision_m: 0.1418\n","Epoch 224/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0103 - acc: 0.5258 - precision_m: 0.2309 - val_loss: 1.2839 - val_acc: 0.4030 - val_precision_m: 0.1866\n","Epoch 225/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.2052 - acc: 0.3771 - precision_m: 0.2370 - val_loss: 1.1551 - val_acc: 0.3955 - val_precision_m: 0.1269\n","Epoch 226/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0227 - acc: 0.4848 - precision_m: 0.3087 - val_loss: 1.1020 - val_acc: 0.4254 - val_precision_m: 0.1493\n","Epoch 227/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0774 - acc: 0.4395 - precision_m: 0.2418 - val_loss: 1.0960 - val_acc: 0.4328 - val_precision_m: 0.1418\n","Epoch 228/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0899 - acc: 0.4293 - precision_m: 0.2088 - val_loss: 1.1992 - val_acc: 0.3358 - val_precision_m: 0.1567\n","Epoch 229/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1166 - acc: 0.4411 - precision_m: 0.2510 - val_loss: 1.1404 - val_acc: 0.4478 - val_precision_m: 0.2313\n","Epoch 230/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0288 - acc: 0.4557 - precision_m: 0.2131 - val_loss: 1.1853 - val_acc: 0.4328 - val_precision_m: 0.2687\n","Epoch 231/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0823 - acc: 0.4299 - precision_m: 0.1781 - val_loss: 1.1075 - val_acc: 0.4030 - val_precision_m: 0.1866\n","Epoch 232/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0128 - acc: 0.4951 - precision_m: 0.2970 - val_loss: 1.2142 - val_acc: 0.3731 - val_precision_m: 0.2313\n","Epoch 233/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0822 - acc: 0.4179 - precision_m: 0.2231 - val_loss: 1.1933 - val_acc: 0.4254 - val_precision_m: 0.1716\n","Epoch 234/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.2044 - acc: 0.3675 - precision_m: 0.1387 - val_loss: 1.0751 - val_acc: 0.4403 - val_precision_m: 0.2015\n","Epoch 235/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0978 - acc: 0.4536 - precision_m: 0.3216 - val_loss: 1.1141 - val_acc: 0.4104 - val_precision_m: 0.1791\n","Epoch 236/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1025 - acc: 0.5075 - precision_m: 0.3089 - val_loss: 1.1623 - val_acc: 0.4104 - val_precision_m: 0.2164\n","Epoch 237/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0667 - acc: 0.4833 - precision_m: 0.2857 - val_loss: 1.1209 - val_acc: 0.3881 - val_precision_m: 0.1791\n","Epoch 238/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0174 - acc: 0.4385 - precision_m: 0.2339 - val_loss: 1.2133 - val_acc: 0.4254 - val_precision_m: 0.2388\n","Epoch 239/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0364 - acc: 0.5272 - precision_m: 0.2839 - val_loss: 1.0654 - val_acc: 0.4104 - val_precision_m: 0.2015\n","Epoch 240/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0696 - acc: 0.4336 - precision_m: 0.2763 - val_loss: 1.0948 - val_acc: 0.4179 - val_precision_m: 0.1791\n","Epoch 241/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0309 - acc: 0.4883 - precision_m: 0.2099 - val_loss: 1.1250 - val_acc: 0.4403 - val_precision_m: 0.2313\n","Epoch 242/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0430 - acc: 0.4963 - precision_m: 0.4245 - val_loss: 1.1400 - val_acc: 0.4104 - val_precision_m: 0.2090\n","Epoch 243/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0572 - acc: 0.4819 - precision_m: 0.2676 - val_loss: 1.1957 - val_acc: 0.4030 - val_precision_m: 0.1866\n","Epoch 244/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1116 - acc: 0.4266 - precision_m: 0.2662 - val_loss: 1.1120 - val_acc: 0.4030 - val_precision_m: 0.1940\n","Epoch 245/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0853 - acc: 0.4669 - precision_m: 0.2847 - val_loss: 1.1410 - val_acc: 0.4179 - val_precision_m: 0.2090\n","Epoch 246/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0266 - acc: 0.5242 - precision_m: 0.2827 - val_loss: 1.1241 - val_acc: 0.4179 - val_precision_m: 0.1866\n","Epoch 247/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1370 - acc: 0.4153 - precision_m: 0.2913 - val_loss: 1.1605 - val_acc: 0.4627 - val_precision_m: 0.2090\n","Epoch 248/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0851 - acc: 0.4991 - precision_m: 0.3213 - val_loss: 1.0807 - val_acc: 0.4254 - val_precision_m: 0.2090\n","Epoch 249/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0942 - acc: 0.4630 - precision_m: 0.2366 - val_loss: 1.1578 - val_acc: 0.4104 - val_precision_m: 0.2164\n","Epoch 250/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0415 - acc: 0.4821 - precision_m: 0.2685 - val_loss: 1.1124 - val_acc: 0.3731 - val_precision_m: 0.2239\n","Epoch 251/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0391 - acc: 0.4779 - precision_m: 0.3121 - val_loss: 1.1554 - val_acc: 0.4403 - val_precision_m: 0.2388\n","Epoch 252/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0601 - acc: 0.4905 - precision_m: 0.2224 - val_loss: 1.1165 - val_acc: 0.4328 - val_precision_m: 0.1866\n","Epoch 253/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0420 - acc: 0.4561 - precision_m: 0.2833 - val_loss: 1.1144 - val_acc: 0.4403 - val_precision_m: 0.2463\n","Epoch 254/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0811 - acc: 0.4249 - precision_m: 0.3053 - val_loss: 1.1127 - val_acc: 0.3881 - val_precision_m: 0.2090\n","Epoch 255/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0593 - acc: 0.4873 - precision_m: 0.2467 - val_loss: 1.0603 - val_acc: 0.4030 - val_precision_m: 0.2090\n","Epoch 256/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0789 - acc: 0.4557 - precision_m: 0.2362 - val_loss: 1.1019 - val_acc: 0.4403 - val_precision_m: 0.1791\n","Epoch 257/500\n","156/156 [==============================] - 0s 2ms/step - loss: 0.9871 - acc: 0.5211 - precision_m: 0.2425 - val_loss: 1.1483 - val_acc: 0.4254 - val_precision_m: 0.1567\n","Epoch 258/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1049 - acc: 0.4843 - precision_m: 0.2203 - val_loss: 1.1033 - val_acc: 0.4627 - val_precision_m: 0.2463\n","Epoch 259/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1092 - acc: 0.4659 - precision_m: 0.2663 - val_loss: 1.1143 - val_acc: 0.4627 - val_precision_m: 0.2015\n","Epoch 260/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0724 - acc: 0.4542 - precision_m: 0.3125 - val_loss: 1.2126 - val_acc: 0.4030 - val_precision_m: 0.2537\n","Epoch 261/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0546 - acc: 0.4909 - precision_m: 0.3689 - val_loss: 1.0746 - val_acc: 0.4104 - val_precision_m: 0.2015\n","Epoch 262/500\n","156/156 [==============================] - 0s 2ms/step - loss: 0.9797 - acc: 0.4801 - precision_m: 0.3031 - val_loss: 1.1057 - val_acc: 0.4104 - val_precision_m: 0.2090\n","Epoch 263/500\n","156/156 [==============================] - 0s 3ms/step - loss: 1.0493 - acc: 0.4993 - precision_m: 0.2812 - val_loss: 1.2465 - val_acc: 0.3657 - val_precision_m: 0.2537\n","Epoch 264/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1109 - acc: 0.4397 - precision_m: 0.2780 - val_loss: 1.1646 - val_acc: 0.4254 - val_precision_m: 0.2313\n","Epoch 265/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0428 - acc: 0.4644 - precision_m: 0.2726 - val_loss: 1.1151 - val_acc: 0.4030 - val_precision_m: 0.1567\n","Epoch 266/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1091 - acc: 0.4188 - precision_m: 0.2433 - val_loss: 1.0828 - val_acc: 0.4403 - val_precision_m: 0.2761\n","Epoch 267/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0475 - acc: 0.4637 - precision_m: 0.2678 - val_loss: 1.1712 - val_acc: 0.4254 - val_precision_m: 0.2388\n","Epoch 268/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0616 - acc: 0.4464 - precision_m: 0.2285 - val_loss: 1.1338 - val_acc: 0.4478 - val_precision_m: 0.2687\n","Epoch 269/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0463 - acc: 0.4764 - precision_m: 0.3411 - val_loss: 1.0831 - val_acc: 0.4254 - val_precision_m: 0.2836\n","Epoch 270/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0835 - acc: 0.4511 - precision_m: 0.2139 - val_loss: 1.1220 - val_acc: 0.3881 - val_precision_m: 0.1418\n","Epoch 271/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1693 - acc: 0.4154 - precision_m: 0.2267 - val_loss: 1.1308 - val_acc: 0.4104 - val_precision_m: 0.2239\n","Epoch 272/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0717 - acc: 0.4661 - precision_m: 0.2706 - val_loss: 1.1275 - val_acc: 0.3806 - val_precision_m: 0.1119\n","Epoch 273/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1591 - acc: 0.4923 - precision_m: 0.2967 - val_loss: 1.0887 - val_acc: 0.4179 - val_precision_m: 0.2239\n","Epoch 274/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0798 - acc: 0.4745 - precision_m: 0.3172 - val_loss: 1.1598 - val_acc: 0.3433 - val_precision_m: 0.2313\n","Epoch 275/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0500 - acc: 0.4672 - precision_m: 0.2191 - val_loss: 1.2869 - val_acc: 0.4403 - val_precision_m: 0.3060\n","Epoch 276/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0345 - acc: 0.4505 - precision_m: 0.2184 - val_loss: 1.1072 - val_acc: 0.3955 - val_precision_m: 0.2463\n","Epoch 277/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0505 - acc: 0.4340 - precision_m: 0.2184 - val_loss: 1.1828 - val_acc: 0.4627 - val_precision_m: 0.2910\n","Epoch 278/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1241 - acc: 0.4096 - precision_m: 0.2824 - val_loss: 1.1922 - val_acc: 0.3507 - val_precision_m: 0.2164\n","Epoch 279/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0316 - acc: 0.5047 - precision_m: 0.3082 - val_loss: 1.0855 - val_acc: 0.4552 - val_precision_m: 0.2910\n","Epoch 280/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0115 - acc: 0.4805 - precision_m: 0.3703 - val_loss: 1.1921 - val_acc: 0.4552 - val_precision_m: 0.2761\n","Epoch 281/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0415 - acc: 0.5036 - precision_m: 0.2643 - val_loss: 1.0778 - val_acc: 0.4328 - val_precision_m: 0.2388\n","Epoch 282/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0219 - acc: 0.4575 - precision_m: 0.3317 - val_loss: 1.1259 - val_acc: 0.3657 - val_precision_m: 0.2463\n","Epoch 283/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0497 - acc: 0.4829 - precision_m: 0.2693 - val_loss: 1.0850 - val_acc: 0.4403 - val_precision_m: 0.2537\n","Epoch 284/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0391 - acc: 0.4575 - precision_m: 0.3249 - val_loss: 1.0487 - val_acc: 0.4328 - val_precision_m: 0.2537\n","Epoch 285/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0364 - acc: 0.4953 - precision_m: 0.2914 - val_loss: 1.6099 - val_acc: 0.3955 - val_precision_m: 0.3060\n","Epoch 286/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0991 - acc: 0.4579 - precision_m: 0.3015 - val_loss: 1.0519 - val_acc: 0.4030 - val_precision_m: 0.2910\n","Epoch 287/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0164 - acc: 0.4954 - precision_m: 0.3534 - val_loss: 1.0412 - val_acc: 0.4478 - val_precision_m: 0.2836\n","Epoch 288/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1239 - acc: 0.4450 - precision_m: 0.2772 - val_loss: 1.1156 - val_acc: 0.4030 - val_precision_m: 0.2388\n","Epoch 289/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0255 - acc: 0.4547 - precision_m: 0.3616 - val_loss: 1.0747 - val_acc: 0.4179 - val_precision_m: 0.2463\n","Epoch 290/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0390 - acc: 0.4614 - precision_m: 0.2487 - val_loss: 1.0795 - val_acc: 0.4030 - val_precision_m: 0.2388\n","Epoch 291/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1285 - acc: 0.4636 - precision_m: 0.2417 - val_loss: 1.1221 - val_acc: 0.4403 - val_precision_m: 0.2388\n","Epoch 292/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0195 - acc: 0.4934 - precision_m: 0.2738 - val_loss: 1.0776 - val_acc: 0.4254 - val_precision_m: 0.2687\n","Epoch 293/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0786 - acc: 0.4179 - precision_m: 0.2971 - val_loss: 1.1131 - val_acc: 0.4328 - val_precision_m: 0.2463\n","Epoch 294/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0706 - acc: 0.4824 - precision_m: 0.2792 - val_loss: 1.1375 - val_acc: 0.4552 - val_precision_m: 0.2687\n","Epoch 295/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0942 - acc: 0.4324 - precision_m: 0.2345 - val_loss: 1.1578 - val_acc: 0.4627 - val_precision_m: 0.2836\n","Epoch 296/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0705 - acc: 0.4517 - precision_m: 0.3159 - val_loss: 1.1071 - val_acc: 0.4328 - val_precision_m: 0.2687\n","Epoch 297/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1352 - acc: 0.4683 - precision_m: 0.2491 - val_loss: 1.1651 - val_acc: 0.4328 - val_precision_m: 0.2164\n","Epoch 298/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0435 - acc: 0.4930 - precision_m: 0.3622 - val_loss: 1.2122 - val_acc: 0.4701 - val_precision_m: 0.3209\n","Epoch 299/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0840 - acc: 0.4333 - precision_m: 0.2918 - val_loss: 1.0630 - val_acc: 0.4478 - val_precision_m: 0.2537\n","Epoch 300/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0294 - acc: 0.5135 - precision_m: 0.3308 - val_loss: 1.0920 - val_acc: 0.4254 - val_precision_m: 0.2239\n","Epoch 301/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0612 - acc: 0.4763 - precision_m: 0.3490 - val_loss: 1.1167 - val_acc: 0.4328 - val_precision_m: 0.2761\n","Epoch 302/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0318 - acc: 0.4975 - precision_m: 0.3315 - val_loss: 1.0989 - val_acc: 0.3881 - val_precision_m: 0.2612\n","Epoch 303/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0108 - acc: 0.4905 - precision_m: 0.3919 - val_loss: 1.0767 - val_acc: 0.4179 - val_precision_m: 0.2761\n","Epoch 304/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0162 - acc: 0.4602 - precision_m: 0.2802 - val_loss: 1.4163 - val_acc: 0.4104 - val_precision_m: 0.3358\n","Epoch 305/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1240 - acc: 0.4311 - precision_m: 0.1992 - val_loss: 1.1042 - val_acc: 0.4478 - val_precision_m: 0.2463\n","Epoch 306/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0256 - acc: 0.5176 - precision_m: 0.3488 - val_loss: 1.1401 - val_acc: 0.4328 - val_precision_m: 0.2836\n","Epoch 307/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0346 - acc: 0.4207 - precision_m: 0.2691 - val_loss: 1.1233 - val_acc: 0.3806 - val_precision_m: 0.1940\n","Epoch 308/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0181 - acc: 0.4903 - precision_m: 0.3664 - val_loss: 1.0989 - val_acc: 0.4030 - val_precision_m: 0.2239\n","Epoch 309/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0447 - acc: 0.4272 - precision_m: 0.2965 - val_loss: 1.1716 - val_acc: 0.4478 - val_precision_m: 0.2836\n","Epoch 310/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0442 - acc: 0.5158 - precision_m: 0.3685 - val_loss: 1.0952 - val_acc: 0.3806 - val_precision_m: 0.2164\n","Epoch 311/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0452 - acc: 0.4831 - precision_m: 0.2543 - val_loss: 1.0712 - val_acc: 0.4478 - val_precision_m: 0.2836\n","Epoch 312/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0282 - acc: 0.5329 - precision_m: 0.3355 - val_loss: 1.0883 - val_acc: 0.4851 - val_precision_m: 0.2761\n","Epoch 313/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0510 - acc: 0.4953 - precision_m: 0.3031 - val_loss: 1.1932 - val_acc: 0.4701 - val_precision_m: 0.2985\n","Epoch 314/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0769 - acc: 0.4892 - precision_m: 0.3327 - val_loss: 1.1243 - val_acc: 0.3433 - val_precision_m: 0.2015\n","Epoch 315/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1161 - acc: 0.4416 - precision_m: 0.2837 - val_loss: 1.1079 - val_acc: 0.3955 - val_precision_m: 0.2015\n","Epoch 316/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1077 - acc: 0.4545 - precision_m: 0.2730 - val_loss: 1.0878 - val_acc: 0.4701 - val_precision_m: 0.2463\n","Epoch 317/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0554 - acc: 0.4732 - precision_m: 0.2934 - val_loss: 1.1204 - val_acc: 0.4776 - val_precision_m: 0.2612\n","Epoch 318/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0472 - acc: 0.4791 - precision_m: 0.3092 - val_loss: 1.1031 - val_acc: 0.4552 - val_precision_m: 0.2537\n","Epoch 319/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0656 - acc: 0.4126 - precision_m: 0.2810 - val_loss: 1.1143 - val_acc: 0.3881 - val_precision_m: 0.2015\n","Epoch 320/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1347 - acc: 0.4244 - precision_m: 0.2795 - val_loss: 1.2924 - val_acc: 0.4254 - val_precision_m: 0.2687\n","Epoch 321/500\n","156/156 [==============================] - 0s 3ms/step - loss: 1.2056 - acc: 0.4347 - precision_m: 0.2087 - val_loss: 1.1648 - val_acc: 0.4104 - val_precision_m: 0.2612\n","Epoch 322/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0369 - acc: 0.5286 - precision_m: 0.2967 - val_loss: 1.1207 - val_acc: 0.4403 - val_precision_m: 0.2537\n","Epoch 323/500\n","156/156 [==============================] - 0s 2ms/step - loss: 0.9812 - acc: 0.5122 - precision_m: 0.3215 - val_loss: 1.1046 - val_acc: 0.4403 - val_precision_m: 0.2537\n","Epoch 324/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0569 - acc: 0.4173 - precision_m: 0.3075 - val_loss: 1.1055 - val_acc: 0.4403 - val_precision_m: 0.2836\n","Epoch 325/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0573 - acc: 0.4588 - precision_m: 0.2915 - val_loss: 1.0859 - val_acc: 0.4627 - val_precision_m: 0.2761\n","Epoch 326/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0005 - acc: 0.4700 - precision_m: 0.2974 - val_loss: 1.2050 - val_acc: 0.4179 - val_precision_m: 0.3284\n","Epoch 327/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0392 - acc: 0.4467 - precision_m: 0.3050 - val_loss: 1.0537 - val_acc: 0.4328 - val_precision_m: 0.2537\n","Epoch 328/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1357 - acc: 0.4324 - precision_m: 0.2357 - val_loss: 1.0831 - val_acc: 0.4403 - val_precision_m: 0.2687\n","Epoch 329/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.1116 - acc: 0.4267 - precision_m: 0.2516 - val_loss: 1.1451 - val_acc: 0.4627 - val_precision_m: 0.2537\n","Epoch 330/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0015 - acc: 0.5113 - precision_m: 0.4362 - val_loss: 1.0635 - val_acc: 0.4552 - val_precision_m: 0.2836\n","Epoch 331/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0434 - acc: 0.5754 - precision_m: 0.3357 - val_loss: 1.1140 - val_acc: 0.4627 - val_precision_m: 0.3134\n","Epoch 332/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0122 - acc: 0.4792 - precision_m: 0.3154 - val_loss: 1.1166 - val_acc: 0.4403 - val_precision_m: 0.2313\n","Epoch 333/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0484 - acc: 0.5281 - precision_m: 0.3409 - val_loss: 1.1832 - val_acc: 0.4328 - val_precision_m: 0.2463\n","Epoch 334/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0757 - acc: 0.4758 - precision_m: 0.2439 - val_loss: 1.1321 - val_acc: 0.4104 - val_precision_m: 0.2313\n","Epoch 335/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0676 - acc: 0.4864 - precision_m: 0.2620 - val_loss: 1.1136 - val_acc: 0.4030 - val_precision_m: 0.1866\n","Epoch 336/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0823 - acc: 0.4368 - precision_m: 0.2433 - val_loss: 1.1385 - val_acc: 0.4328 - val_precision_m: 0.2537\n","Epoch 337/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.0572 - acc: 0.4864 - precision_m: 0.3575 - val_loss: 1.1911 - val_acc: 0.4627 - val_precision_m: 0.2985\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 17.8s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","149/149 [==============================] - 1s 3ms/step - loss: 1.4876 - acc: 0.2468 - precision_m: 0.0574 - val_loss: 1.3814 - val_acc: 0.2734 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3785 - acc: 0.3147 - precision_m: 0.0000e+00 - val_loss: 1.3950 - val_acc: 0.1797 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3981 - acc: 0.2346 - precision_m: 0.0000e+00 - val_loss: 1.3853 - val_acc: 0.2031 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3876 - acc: 0.2542 - precision_m: 0.0000e+00 - val_loss: 1.3813 - val_acc: 0.2500 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3772 - acc: 0.2587 - precision_m: 0.0000e+00 - val_loss: 1.3828 - val_acc: 0.2734 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3891 - acc: 0.3068 - precision_m: 0.0000e+00 - val_loss: 1.3748 - val_acc: 0.3203 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3649 - acc: 0.3734 - precision_m: 0.0000e+00 - val_loss: 1.3701 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3718 - acc: 0.2876 - precision_m: 0.0000e+00 - val_loss: 1.3732 - val_acc: 0.2734 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3865 - acc: 0.2980 - precision_m: 0.0000e+00 - val_loss: 1.3855 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3810 - acc: 0.2721 - precision_m: 0.0000e+00 - val_loss: 1.3741 - val_acc: 0.2734 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3827 - acc: 0.2801 - precision_m: 0.0000e+00 - val_loss: 1.3723 - val_acc: 0.2812 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3628 - acc: 0.3360 - precision_m: 0.0000e+00 - val_loss: 1.3750 - val_acc: 0.2812 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3629 - acc: 0.3319 - precision_m: 0.0000e+00 - val_loss: 1.3796 - val_acc: 0.2891 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3713 - acc: 0.2464 - precision_m: 0.0000e+00 - val_loss: 1.3848 - val_acc: 0.2891 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.4017 - acc: 0.2908 - precision_m: 0.0000e+00 - val_loss: 1.3743 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3531 - acc: 0.3118 - precision_m: 0.0000e+00 - val_loss: 1.3751 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3648 - acc: 0.3129 - precision_m: 0.0000e+00 - val_loss: 1.3722 - val_acc: 0.3125 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3695 - acc: 0.2807 - precision_m: 0.0000e+00 - val_loss: 1.3768 - val_acc: 0.2734 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3543 - acc: 0.3210 - precision_m: 0.0000e+00 - val_loss: 1.3864 - val_acc: 0.2734 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3732 - acc: 0.2885 - precision_m: 0.0000e+00 - val_loss: 1.3739 - val_acc: 0.3047 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3881 - acc: 0.2687 - precision_m: 0.0000e+00 - val_loss: 1.3739 - val_acc: 0.2578 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3470 - acc: 0.3264 - precision_m: 0.0259 - val_loss: 1.3728 - val_acc: 0.3125 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3826 - acc: 0.2869 - precision_m: 0.0000e+00 - val_loss: 1.3717 - val_acc: 0.2422 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3570 - acc: 0.3108 - precision_m: 0.0000e+00 - val_loss: 1.3705 - val_acc: 0.2891 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3693 - acc: 0.3223 - precision_m: 0.0000e+00 - val_loss: 1.3681 - val_acc: 0.3047 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3733 - acc: 0.3087 - precision_m: 0.0166 - val_loss: 1.3714 - val_acc: 0.2969 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3608 - acc: 0.3392 - precision_m: 0.0000e+00 - val_loss: 1.3689 - val_acc: 0.3281 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3857 - acc: 0.2922 - precision_m: 0.0000e+00 - val_loss: 1.3720 - val_acc: 0.2969 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3780 - acc: 0.2819 - precision_m: 0.0053 - val_loss: 1.3746 - val_acc: 0.2812 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3794 - acc: 0.2973 - precision_m: 3.6444e-04 - val_loss: 1.3763 - val_acc: 0.2891 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3769 - acc: 0.2643 - precision_m: 0.0036 - val_loss: 1.3746 - val_acc: 0.3047 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3651 - acc: 0.3064 - precision_m: 0.0000e+00 - val_loss: 1.3701 - val_acc: 0.3203 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3416 - acc: 0.3408 - precision_m: 0.0097 - val_loss: 1.3784 - val_acc: 0.2891 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3755 - acc: 0.2711 - precision_m: 0.0059 - val_loss: 1.3831 - val_acc: 0.2734 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","149/149 [==============================] - 0s 3ms/step - loss: 1.3627 - acc: 0.3062 - precision_m: 0.0121 - val_loss: 1.3684 - val_acc: 0.2734 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3599 - acc: 0.3197 - precision_m: 1.7988e-04 - val_loss: 1.3714 - val_acc: 0.2891 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3486 - acc: 0.3013 - precision_m: 0.0084 - val_loss: 1.3691 - val_acc: 0.2969 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3680 - acc: 0.3113 - precision_m: 4.5867e-04 - val_loss: 1.3691 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3865 - acc: 0.2224 - precision_m: 0.0019 - val_loss: 1.3728 - val_acc: 0.2891 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3482 - acc: 0.3086 - precision_m: 0.0901 - val_loss: 1.3714 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3643 - acc: 0.3294 - precision_m: 0.0182 - val_loss: 1.3703 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3425 - acc: 0.3551 - precision_m: 0.0225 - val_loss: 1.3675 - val_acc: 0.3203 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3627 - acc: 0.3333 - precision_m: 0.0000e+00 - val_loss: 1.3762 - val_acc: 0.2891 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3552 - acc: 0.3391 - precision_m: 0.0100 - val_loss: 1.3693 - val_acc: 0.3125 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3748 - acc: 0.3043 - precision_m: 0.0173 - val_loss: 1.3730 - val_acc: 0.2734 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3501 - acc: 0.3224 - precision_m: 0.0169 - val_loss: 1.3749 - val_acc: 0.2734 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3209 - acc: 0.3673 - precision_m: 0.0588 - val_loss: 1.3747 - val_acc: 0.3047 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3696 - acc: 0.2970 - precision_m: 8.8886e-04 - val_loss: 1.3714 - val_acc: 0.3125 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3375 - acc: 0.3232 - precision_m: 0.1087 - val_loss: 1.3718 - val_acc: 0.2969 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3105 - acc: 0.3735 - precision_m: 0.0289 - val_loss: 1.3727 - val_acc: 0.3125 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3730 - acc: 0.2948 - precision_m: 0.0013 - val_loss: 1.3743 - val_acc: 0.3125 - val_precision_m: 0.0000e+00\n","Epoch 52/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3463 - acc: 0.2761 - precision_m: 0.0480 - val_loss: 1.3749 - val_acc: 0.2891 - val_precision_m: 0.0000e+00\n","Epoch 53/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3395 - acc: 0.3087 - precision_m: 0.0084 - val_loss: 1.3718 - val_acc: 0.2891 - val_precision_m: 0.0000e+00\n","Epoch 54/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3355 - acc: 0.3328 - precision_m: 0.0349 - val_loss: 1.3736 - val_acc: 0.3047 - val_precision_m: 0.0000e+00\n","Epoch 55/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3247 - acc: 0.3088 - precision_m: 0.0595 - val_loss: 1.3767 - val_acc: 0.3125 - val_precision_m: 0.0000e+00\n","Epoch 56/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3528 - acc: 0.3451 - precision_m: 0.0698 - val_loss: 1.3781 - val_acc: 0.2891 - val_precision_m: 0.0000e+00\n","Epoch 57/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.2871 - acc: 0.4220 - precision_m: 0.1753 - val_loss: 1.3776 - val_acc: 0.2734 - val_precision_m: 0.0000e+00\n","Epoch 58/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3190 - acc: 0.3719 - precision_m: 0.0821 - val_loss: 1.4020 - val_acc: 0.2812 - val_precision_m: 0.0000e+00\n","Epoch 59/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3622 - acc: 0.3100 - precision_m: 0.0251 - val_loss: 1.4034 - val_acc: 0.2578 - val_precision_m: 0.0000e+00\n","Epoch 60/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3484 - acc: 0.3350 - precision_m: 0.0392 - val_loss: 1.3756 - val_acc: 0.3359 - val_precision_m: 0.0000e+00\n","Epoch 61/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3341 - acc: 0.3247 - precision_m: 0.0826 - val_loss: 1.3899 - val_acc: 0.2891 - val_precision_m: 0.0000e+00\n","Epoch 62/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3235 - acc: 0.3100 - precision_m: 0.0319 - val_loss: 1.3705 - val_acc: 0.3047 - val_precision_m: 0.0000e+00\n","Epoch 63/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3250 - acc: 0.3371 - precision_m: 0.0683 - val_loss: 1.4044 - val_acc: 0.2734 - val_precision_m: 0.0000e+00\n","Epoch 64/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3364 - acc: 0.3537 - precision_m: 0.1367 - val_loss: 1.3727 - val_acc: 0.3125 - val_precision_m: 0.0000e+00\n","Epoch 65/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3629 - acc: 0.3069 - precision_m: 0.0319 - val_loss: 1.3817 - val_acc: 0.3438 - val_precision_m: 0.0000e+00\n","Epoch 66/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3427 - acc: 0.3381 - precision_m: 0.0886 - val_loss: 1.3868 - val_acc: 0.2891 - val_precision_m: 0.0469\n","Epoch 67/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3252 - acc: 0.2910 - precision_m: 0.1329 - val_loss: 1.3753 - val_acc: 0.2578 - val_precision_m: 0.0000e+00\n","Epoch 68/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3240 - acc: 0.4000 - precision_m: 0.1293 - val_loss: 1.3821 - val_acc: 0.2812 - val_precision_m: 0.0156\n","Epoch 69/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3195 - acc: 0.3609 - precision_m: 0.0588 - val_loss: 1.3940 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 70/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3387 - acc: 0.3320 - precision_m: 0.0806 - val_loss: 1.3813 - val_acc: 0.3125 - val_precision_m: 0.0000e+00\n","Epoch 71/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3346 - acc: 0.3281 - precision_m: 0.0694 - val_loss: 1.3760 - val_acc: 0.3516 - val_precision_m: 0.0000e+00\n","Epoch 72/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3177 - acc: 0.3593 - precision_m: 0.0708 - val_loss: 1.3855 - val_acc: 0.2969 - val_precision_m: 0.0000e+00\n","Epoch 73/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3284 - acc: 0.3823 - precision_m: 0.0585 - val_loss: 1.3976 - val_acc: 0.2422 - val_precision_m: 0.0000e+00\n","Epoch 74/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3330 - acc: 0.3624 - precision_m: 0.1152 - val_loss: 1.3798 - val_acc: 0.2969 - val_precision_m: 0.0000e+00\n","Epoch 75/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.2707 - acc: 0.3398 - precision_m: 0.1752 - val_loss: 1.3793 - val_acc: 0.3203 - val_precision_m: 0.0000e+00\n","Epoch 76/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3107 - acc: 0.3358 - precision_m: 0.0942 - val_loss: 1.3800 - val_acc: 0.3359 - val_precision_m: 0.0000e+00\n","Epoch 77/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3183 - acc: 0.3557 - precision_m: 0.1232 - val_loss: 1.3779 - val_acc: 0.3125 - val_precision_m: 0.0000e+00\n","Epoch 78/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3346 - acc: 0.3817 - precision_m: 0.0561 - val_loss: 1.3773 - val_acc: 0.3125 - val_precision_m: 0.0000e+00\n","Epoch 79/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.2934 - acc: 0.3589 - precision_m: 0.0852 - val_loss: 1.4134 - val_acc: 0.2500 - val_precision_m: 0.0000e+00\n","Epoch 80/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3523 - acc: 0.3102 - precision_m: 0.0693 - val_loss: 1.3715 - val_acc: 0.2812 - val_precision_m: 0.0000e+00\n","Epoch 81/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3031 - acc: 0.3530 - precision_m: 0.1281 - val_loss: 1.3831 - val_acc: 0.3125 - val_precision_m: 0.0000e+00\n","Epoch 82/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3115 - acc: 0.3660 - precision_m: 0.1195 - val_loss: 1.3821 - val_acc: 0.2812 - val_precision_m: 0.0000e+00\n","Epoch 83/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.2925 - acc: 0.3847 - precision_m: 0.1123 - val_loss: 1.4112 - val_acc: 0.2656 - val_precision_m: 0.0625\n","Epoch 84/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.2610 - acc: 0.4670 - precision_m: 0.1925 - val_loss: 1.3769 - val_acc: 0.3125 - val_precision_m: 0.0000e+00\n","Epoch 85/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3151 - acc: 0.3538 - precision_m: 0.1357 - val_loss: 1.3907 - val_acc: 0.2812 - val_precision_m: 0.0000e+00\n","Epoch 86/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.2894 - acc: 0.3757 - precision_m: 0.1128 - val_loss: 1.3827 - val_acc: 0.2812 - val_precision_m: 0.0000e+00\n","Epoch 87/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.2653 - acc: 0.3956 - precision_m: 0.1328 - val_loss: 1.3948 - val_acc: 0.2891 - val_precision_m: 0.0000e+00\n","Epoch 88/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3300 - acc: 0.3979 - precision_m: 0.1309 - val_loss: 1.4333 - val_acc: 0.2734 - val_precision_m: 0.0156\n","Epoch 89/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.2695 - acc: 0.3823 - precision_m: 0.1522 - val_loss: 1.3797 - val_acc: 0.3203 - val_precision_m: 0.0000e+00\n","Epoch 90/500\n","149/149 [==============================] - 0s 3ms/step - loss: 1.3165 - acc: 0.3942 - precision_m: 0.0734 - val_loss: 1.3884 - val_acc: 0.3047 - val_precision_m: 0.0000e+00\n","Epoch 91/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.2928 - acc: 0.3453 - precision_m: 0.0786 - val_loss: 1.4019 - val_acc: 0.2734 - val_precision_m: 0.0781\n","Epoch 92/500\n","149/149 [==============================] - 0s 2ms/step - loss: 1.3201 - acc: 0.3865 - precision_m: 0.1365 - val_loss: 1.4231 - val_acc: 0.2422 - val_precision_m: 0.0156\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 17.3s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","154/154 [==============================] - 1s 3ms/step - loss: 1.4065 - acc: 0.2948 - precision_m: 0.0000e+00 - val_loss: 1.3925 - val_acc: 0.2576 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3884 - acc: 0.2767 - precision_m: 0.0000e+00 - val_loss: 1.3878 - val_acc: 0.2652 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3944 - acc: 0.2684 - precision_m: 0.0000e+00 - val_loss: 1.3867 - val_acc: 0.2348 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3852 - acc: 0.2626 - precision_m: 0.0000e+00 - val_loss: 1.3838 - val_acc: 0.2879 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3879 - acc: 0.2953 - precision_m: 0.0000e+00 - val_loss: 1.3864 - val_acc: 0.2576 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3891 - acc: 0.2302 - precision_m: 0.0000e+00 - val_loss: 1.3879 - val_acc: 0.1894 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3898 - acc: 0.1881 - precision_m: 0.0000e+00 - val_loss: 1.3877 - val_acc: 0.2197 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3858 - acc: 0.2964 - precision_m: 0.0000e+00 - val_loss: 1.3884 - val_acc: 0.2121 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3845 - acc: 0.2605 - precision_m: 0.0000e+00 - val_loss: 1.3891 - val_acc: 0.2045 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3854 - acc: 0.2541 - precision_m: 0.0000e+00 - val_loss: 1.3895 - val_acc: 0.2045 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3763 - acc: 0.2936 - precision_m: 0.0000e+00 - val_loss: 1.3880 - val_acc: 0.2045 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3850 - acc: 0.2670 - precision_m: 0.0000e+00 - val_loss: 1.3876 - val_acc: 0.2121 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3857 - acc: 0.2597 - precision_m: 0.0000e+00 - val_loss: 1.3890 - val_acc: 0.2045 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3842 - acc: 0.2649 - precision_m: 0.0153 - val_loss: 1.3889 - val_acc: 0.2045 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3807 - acc: 0.3235 - precision_m: 0.0000e+00 - val_loss: 1.3894 - val_acc: 0.2045 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3816 - acc: 0.2991 - precision_m: 7.0079e-04 - val_loss: 1.4096 - val_acc: 0.2500 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3563 - acc: 0.3001 - precision_m: 0.0000e+00 - val_loss: 1.3876 - val_acc: 0.2045 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3806 - acc: 0.3036 - precision_m: 0.0050 - val_loss: 1.3878 - val_acc: 0.2045 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3805 - acc: 0.3507 - precision_m: 7.0079e-04 - val_loss: 1.3883 - val_acc: 0.2045 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3835 - acc: 0.2614 - precision_m: 0.0000e+00 - val_loss: 1.3885 - val_acc: 0.2045 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3822 - acc: 0.2919 - precision_m: 0.0000e+00 - val_loss: 1.3888 - val_acc: 0.2045 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3831 - acc: 0.2674 - precision_m: 0.0000e+00 - val_loss: 1.3891 - val_acc: 0.2045 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3787 - acc: 0.3287 - precision_m: 0.0000e+00 - val_loss: 1.3880 - val_acc: 0.2045 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3838 - acc: 0.2650 - precision_m: 0.0000e+00 - val_loss: 1.3879 - val_acc: 0.2273 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3788 - acc: 0.2591 - precision_m: 0.0000e+00 - val_loss: 1.3895 - val_acc: 0.2045 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3828 - acc: 0.2356 - precision_m: 0.0000e+00 - val_loss: 1.3898 - val_acc: 0.2045 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3832 - acc: 0.2638 - precision_m: 0.0000e+00 - val_loss: 1.3894 - val_acc: 0.2045 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3867 - acc: 0.2831 - precision_m: 0.0000e+00 - val_loss: 1.3903 - val_acc: 0.2045 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3728 - acc: 0.3280 - precision_m: 0.0000e+00 - val_loss: 1.3901 - val_acc: 0.2197 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3809 - acc: 0.3205 - precision_m: 0.0000e+00 - val_loss: 1.3897 - val_acc: 0.2045 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3823 - acc: 0.2900 - precision_m: 0.0000e+00 - val_loss: 1.3895 - val_acc: 0.2045 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3829 - acc: 0.2424 - precision_m: 0.0000e+00 - val_loss: 1.3865 - val_acc: 0.2879 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3862 - acc: 0.3198 - precision_m: 0.0000e+00 - val_loss: 1.3892 - val_acc: 0.2045 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3890 - acc: 0.2489 - precision_m: 0.0000e+00 - val_loss: 1.3891 - val_acc: 0.2197 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3816 - acc: 0.3012 - precision_m: 0.0000e+00 - val_loss: 1.3889 - val_acc: 0.2273 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3621 - acc: 0.3063 - precision_m: 0.0363 - val_loss: 1.3876 - val_acc: 0.2273 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3895 - acc: 0.2163 - precision_m: 0.0000e+00 - val_loss: 1.3891 - val_acc: 0.2197 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3814 - acc: 0.2769 - precision_m: 0.0000e+00 - val_loss: 1.3884 - val_acc: 0.2348 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","154/154 [==============================] - 0s 3ms/step - loss: 1.3817 - acc: 0.2742 - precision_m: 0.0022 - val_loss: 1.3904 - val_acc: 0.2045 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3815 - acc: 0.3097 - precision_m: 4.2911e-04 - val_loss: 1.3906 - val_acc: 0.2121 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3862 - acc: 0.2584 - precision_m: 0.0026 - val_loss: 1.3892 - val_acc: 0.2348 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3780 - acc: 0.2848 - precision_m: 0.0038 - val_loss: 1.3880 - val_acc: 0.2424 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3822 - acc: 0.2691 - precision_m: 1.6840e-04 - val_loss: 1.3891 - val_acc: 0.2273 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3821 - acc: 0.2748 - precision_m: 0.0000e+00 - val_loss: 1.3900 - val_acc: 0.2273 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3804 - acc: 0.2675 - precision_m: 5.1840e-04 - val_loss: 1.3898 - val_acc: 0.2273 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3884 - acc: 0.2673 - precision_m: 0.0000e+00 - val_loss: 1.3900 - val_acc: 0.2273 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3829 - acc: 0.2779 - precision_m: 0.0000e+00 - val_loss: 1.3898 - val_acc: 0.2121 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3925 - acc: 0.2353 - precision_m: 0.0013 - val_loss: 1.3898 - val_acc: 0.2273 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3760 - acc: 0.3009 - precision_m: 0.0062 - val_loss: 1.3831 - val_acc: 0.2955 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3826 - acc: 0.2382 - precision_m: 0.0026 - val_loss: 1.3899 - val_acc: 0.2197 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3781 - acc: 0.3134 - precision_m: 0.0018 - val_loss: 1.3912 - val_acc: 0.2121 - val_precision_m: 0.0000e+00\n","Epoch 52/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3757 - acc: 0.2853 - precision_m: 0.0068 - val_loss: 1.3896 - val_acc: 0.2197 - val_precision_m: 0.0000e+00\n","Epoch 53/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3791 - acc: 0.2866 - precision_m: 0.0000e+00 - val_loss: 1.3882 - val_acc: 0.2197 - val_precision_m: 0.0000e+00\n","Epoch 54/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3810 - acc: 0.2054 - precision_m: 0.0105 - val_loss: 1.3895 - val_acc: 0.2273 - val_precision_m: 0.0000e+00\n","Epoch 55/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3825 - acc: 0.2783 - precision_m: 0.0000e+00 - val_loss: 1.3890 - val_acc: 0.2197 - val_precision_m: 0.0000e+00\n","Epoch 56/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3740 - acc: 0.3029 - precision_m: 0.0051 - val_loss: 1.3850 - val_acc: 0.2273 - val_precision_m: 0.0000e+00\n","Epoch 57/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3850 - acc: 0.3158 - precision_m: 0.0000e+00 - val_loss: 1.3892 - val_acc: 0.2273 - val_precision_m: 0.0000e+00\n","Epoch 58/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3784 - acc: 0.2905 - precision_m: 0.0000e+00 - val_loss: 1.3890 - val_acc: 0.2273 - val_precision_m: 0.0000e+00\n","Epoch 59/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3877 - acc: 0.2590 - precision_m: 0.0000e+00 - val_loss: 1.3861 - val_acc: 0.2348 - val_precision_m: 0.0000e+00\n","Epoch 60/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3841 - acc: 0.2121 - precision_m: 0.0000e+00 - val_loss: 1.3891 - val_acc: 0.2273 - val_precision_m: 0.0000e+00\n","Epoch 61/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3815 - acc: 0.3063 - precision_m: 0.0000e+00 - val_loss: 1.3885 - val_acc: 0.2424 - val_precision_m: 0.0000e+00\n","Epoch 62/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3760 - acc: 0.2550 - precision_m: 0.0069 - val_loss: 1.3867 - val_acc: 0.2273 - val_precision_m: 0.0000e+00\n","Epoch 63/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3828 - acc: 0.2122 - precision_m: 0.0000e+00 - val_loss: 1.3902 - val_acc: 0.2197 - val_precision_m: 0.0000e+00\n","Epoch 64/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3833 - acc: 0.2971 - precision_m: 0.0000e+00 - val_loss: 1.3910 - val_acc: 0.2197 - val_precision_m: 0.0000e+00\n","Epoch 65/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3687 - acc: 0.3203 - precision_m: 0.0107 - val_loss: 1.3884 - val_acc: 0.2273 - val_precision_m: 0.0000e+00\n","Epoch 66/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3680 - acc: 0.3052 - precision_m: 0.0119 - val_loss: 1.3863 - val_acc: 0.2348 - val_precision_m: 0.0000e+00\n","Epoch 67/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3790 - acc: 0.2968 - precision_m: 0.0000e+00 - val_loss: 1.3884 - val_acc: 0.2348 - val_precision_m: 0.0000e+00\n","Epoch 68/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3829 - acc: 0.2773 - precision_m: 0.0000e+00 - val_loss: 1.3882 - val_acc: 0.2348 - val_precision_m: 0.0000e+00\n","Epoch 69/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3741 - acc: 0.3312 - precision_m: 0.0000e+00 - val_loss: 1.3791 - val_acc: 0.2879 - val_precision_m: 0.0000e+00\n","Epoch 70/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3833 - acc: 0.2871 - precision_m: 0.0103 - val_loss: 1.3873 - val_acc: 0.2424 - val_precision_m: 0.0000e+00\n","Epoch 71/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3707 - acc: 0.3190 - precision_m: 4.2911e-04 - val_loss: 1.3895 - val_acc: 0.1970 - val_precision_m: 0.0000e+00\n","Epoch 72/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3725 - acc: 0.2980 - precision_m: 0.0075 - val_loss: 1.3908 - val_acc: 0.1970 - val_precision_m: 0.0000e+00\n","Epoch 73/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3776 - acc: 0.2534 - precision_m: 0.0026 - val_loss: 1.3866 - val_acc: 0.2197 - val_precision_m: 0.0000e+00\n","Epoch 74/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3629 - acc: 0.2254 - precision_m: 0.0117 - val_loss: 1.3885 - val_acc: 0.2197 - val_precision_m: 0.0000e+00\n","Epoch 75/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3804 - acc: 0.2637 - precision_m: 0.0000e+00 - val_loss: 1.3882 - val_acc: 0.2273 - val_precision_m: 0.0000e+00\n","Epoch 76/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3828 - acc: 0.2525 - precision_m: 0.0029 - val_loss: 1.3864 - val_acc: 0.2424 - val_precision_m: 0.0000e+00\n","Epoch 77/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3871 - acc: 0.2580 - precision_m: 0.0000e+00 - val_loss: 1.3873 - val_acc: 0.2500 - val_precision_m: 0.0000e+00\n","Epoch 78/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3777 - acc: 0.3220 - precision_m: 0.0000e+00 - val_loss: 1.3890 - val_acc: 0.2121 - val_precision_m: 0.0000e+00\n","Epoch 79/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3805 - acc: 0.2633 - precision_m: 0.0012 - val_loss: 1.3902 - val_acc: 0.2197 - val_precision_m: 0.0000e+00\n","Epoch 80/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3723 - acc: 0.3078 - precision_m: 0.0041 - val_loss: 1.3878 - val_acc: 0.2121 - val_precision_m: 0.0000e+00\n","Epoch 81/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3719 - acc: 0.2598 - precision_m: 0.0097 - val_loss: 1.3844 - val_acc: 0.1970 - val_precision_m: 0.0000e+00\n","Epoch 82/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3811 - acc: 0.2800 - precision_m: 0.0057 - val_loss: 1.3854 - val_acc: 0.2273 - val_precision_m: 0.0000e+00\n","Epoch 83/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3784 - acc: 0.3035 - precision_m: 0.0020 - val_loss: 1.3855 - val_acc: 0.2197 - val_precision_m: 0.0000e+00\n","Epoch 84/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3792 - acc: 0.2923 - precision_m: 0.0011 - val_loss: 1.3881 - val_acc: 0.2121 - val_precision_m: 0.0000e+00\n","Epoch 85/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3841 - acc: 0.2316 - precision_m: 7.9396e-04 - val_loss: 1.3853 - val_acc: 0.2348 - val_precision_m: 0.0000e+00\n","Epoch 86/500\n","154/154 [==============================] - 0s 3ms/step - loss: 1.3685 - acc: 0.3178 - precision_m: 6.5471e-04 - val_loss: 1.3855 - val_acc: 0.2424 - val_precision_m: 0.0000e+00\n","Epoch 87/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3705 - acc: 0.2948 - precision_m: 0.0187 - val_loss: 1.3877 - val_acc: 0.2273 - val_precision_m: 0.0000e+00\n","Epoch 88/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3530 - acc: 0.3516 - precision_m: 0.0205 - val_loss: 1.3840 - val_acc: 0.2197 - val_precision_m: 0.0000e+00\n","Epoch 89/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3839 - acc: 0.2728 - precision_m: 0.0014 - val_loss: 1.3887 - val_acc: 0.2197 - val_precision_m: 0.0000e+00\n","Epoch 90/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3701 - acc: 0.2890 - precision_m: 0.0047 - val_loss: 1.3853 - val_acc: 0.2273 - val_precision_m: 0.0000e+00\n","Epoch 91/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3718 - acc: 0.2871 - precision_m: 0.0040 - val_loss: 1.3820 - val_acc: 0.2121 - val_precision_m: 0.0000e+00\n","Epoch 92/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3717 - acc: 0.3008 - precision_m: 0.0068 - val_loss: 1.3875 - val_acc: 0.2348 - val_precision_m: 0.0000e+00\n","Epoch 93/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3617 - acc: 0.3283 - precision_m: 0.0132 - val_loss: 1.3856 - val_acc: 0.2424 - val_precision_m: 0.0000e+00\n","Epoch 94/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3687 - acc: 0.3158 - precision_m: 0.0078 - val_loss: 1.3818 - val_acc: 0.2197 - val_precision_m: 0.0000e+00\n","Epoch 95/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3748 - acc: 0.2437 - precision_m: 2.5414e-04 - val_loss: 1.3912 - val_acc: 0.2197 - val_precision_m: 0.0000e+00\n","Epoch 96/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3603 - acc: 0.3601 - precision_m: 0.0061 - val_loss: 1.3830 - val_acc: 0.2424 - val_precision_m: 0.0000e+00\n","Epoch 97/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3675 - acc: 0.3058 - precision_m: 0.0123 - val_loss: 1.3842 - val_acc: 0.2348 - val_precision_m: 0.0000e+00\n","Epoch 98/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3690 - acc: 0.2799 - precision_m: 0.0011 - val_loss: 1.3849 - val_acc: 0.2273 - val_precision_m: 0.0000e+00\n","Epoch 99/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3745 - acc: 0.2889 - precision_m: 0.0095 - val_loss: 1.3854 - val_acc: 0.2348 - val_precision_m: 0.0000e+00\n","Epoch 100/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3537 - acc: 0.2972 - precision_m: 0.0405 - val_loss: 1.3822 - val_acc: 0.2197 - val_precision_m: 0.0000e+00\n","Epoch 101/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3680 - acc: 0.2756 - precision_m: 0.0000e+00 - val_loss: 1.3928 - val_acc: 0.2197 - val_precision_m: 0.0000e+00\n","Epoch 102/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3700 - acc: 0.3350 - precision_m: 0.0031 - val_loss: 1.3893 - val_acc: 0.2348 - val_precision_m: 0.0000e+00\n","Epoch 103/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3656 - acc: 0.3185 - precision_m: 0.0245 - val_loss: 1.3837 - val_acc: 0.2348 - val_precision_m: 0.0000e+00\n","Epoch 104/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3620 - acc: 0.3241 - precision_m: 0.0134 - val_loss: 1.3866 - val_acc: 0.2500 - val_precision_m: 0.0000e+00\n","Epoch 105/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3644 - acc: 0.2886 - precision_m: 0.0187 - val_loss: 1.3880 - val_acc: 0.2273 - val_precision_m: 0.0000e+00\n","Epoch 106/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3636 - acc: 0.2581 - precision_m: 0.0093 - val_loss: 1.3864 - val_acc: 0.2197 - val_precision_m: 0.0000e+00\n","Epoch 107/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3570 - acc: 0.2892 - precision_m: 0.0271 - val_loss: 1.3936 - val_acc: 0.2121 - val_precision_m: 0.0000e+00\n","Epoch 108/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3669 - acc: 0.2986 - precision_m: 0.0000e+00 - val_loss: 1.3904 - val_acc: 0.2273 - val_precision_m: 0.0000e+00\n","Epoch 109/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3718 - acc: 0.2792 - precision_m: 0.0076 - val_loss: 1.3920 - val_acc: 0.2045 - val_precision_m: 0.0000e+00\n","Epoch 110/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3623 - acc: 0.2761 - precision_m: 0.0033 - val_loss: 1.3891 - val_acc: 0.2348 - val_precision_m: 0.0000e+00\n","Epoch 111/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3647 - acc: 0.3099 - precision_m: 0.0185 - val_loss: 1.3849 - val_acc: 0.2348 - val_precision_m: 0.0000e+00\n","Epoch 112/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3563 - acc: 0.2953 - precision_m: 0.0150 - val_loss: 1.3897 - val_acc: 0.2197 - val_precision_m: 0.0000e+00\n","Epoch 113/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3591 - acc: 0.2943 - precision_m: 0.0310 - val_loss: 1.3867 - val_acc: 0.2424 - val_precision_m: 0.0000e+00\n","Epoch 114/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3720 - acc: 0.3035 - precision_m: 5.6352e-04 - val_loss: 1.3843 - val_acc: 0.2121 - val_precision_m: 0.0152\n","Epoch 115/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3729 - acc: 0.2701 - precision_m: 0.0000e+00 - val_loss: 1.3796 - val_acc: 0.2424 - val_precision_m: 0.0000e+00\n","Epoch 116/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3494 - acc: 0.3108 - precision_m: 0.0182 - val_loss: 1.3869 - val_acc: 0.2121 - val_precision_m: 0.0000e+00\n","Epoch 117/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3719 - acc: 0.2610 - precision_m: 0.0098 - val_loss: 1.3829 - val_acc: 0.2424 - val_precision_m: 0.0152\n","Epoch 118/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3578 - acc: 0.3247 - precision_m: 0.0163 - val_loss: 1.3829 - val_acc: 0.2348 - val_precision_m: 0.0000e+00\n","Epoch 119/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3687 - acc: 0.3230 - precision_m: 0.0053 - val_loss: 1.3791 - val_acc: 0.2348 - val_precision_m: 0.0152\n","Epoch 120/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3682 - acc: 0.2640 - precision_m: 0.0208 - val_loss: 1.3859 - val_acc: 0.2348 - val_precision_m: 0.0000e+00\n","Epoch 121/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3611 - acc: 0.3214 - precision_m: 0.0029 - val_loss: 1.3869 - val_acc: 0.2348 - val_precision_m: 0.0000e+00\n","Epoch 122/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3567 - acc: 0.3426 - precision_m: 0.0094 - val_loss: 1.3858 - val_acc: 0.2348 - val_precision_m: 0.0000e+00\n","Epoch 123/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3658 - acc: 0.3021 - precision_m: 0.0234 - val_loss: 1.3826 - val_acc: 0.2273 - val_precision_m: 0.0152\n","Epoch 124/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3634 - acc: 0.2903 - precision_m: 0.0195 - val_loss: 1.3857 - val_acc: 0.2500 - val_precision_m: 0.0000e+00\n","Epoch 125/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3523 - acc: 0.3068 - precision_m: 0.0071 - val_loss: 1.3875 - val_acc: 0.2273 - val_precision_m: 0.0000e+00\n","Epoch 126/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3631 - acc: 0.2991 - precision_m: 0.0312 - val_loss: 1.3782 - val_acc: 0.2348 - val_precision_m: 0.0152\n","Epoch 127/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3653 - acc: 0.2802 - precision_m: 0.0151 - val_loss: 1.3825 - val_acc: 0.2424 - val_precision_m: 0.0000e+00\n","Epoch 128/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3619 - acc: 0.2942 - precision_m: 0.0000e+00 - val_loss: 1.3851 - val_acc: 0.2273 - val_precision_m: 0.0152\n","Epoch 129/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3514 - acc: 0.3246 - precision_m: 0.0136 - val_loss: 1.3842 - val_acc: 0.2348 - val_precision_m: 0.0000e+00\n","Epoch 130/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3637 - acc: 0.3244 - precision_m: 0.0028 - val_loss: 1.3849 - val_acc: 0.2652 - val_precision_m: 0.0000e+00\n","Epoch 131/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3566 - acc: 0.2957 - precision_m: 4.2911e-04 - val_loss: 1.3885 - val_acc: 0.2273 - val_precision_m: 0.0000e+00\n","Epoch 132/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3809 - acc: 0.2491 - precision_m: 0.0000e+00 - val_loss: 1.3875 - val_acc: 0.2273 - val_precision_m: 0.0000e+00\n","Epoch 133/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3558 - acc: 0.3240 - precision_m: 0.0034 - val_loss: 1.3812 - val_acc: 0.2121 - val_precision_m: 0.0152\n","Epoch 134/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3849 - acc: 0.2572 - precision_m: 0.0130 - val_loss: 1.3874 - val_acc: 0.2273 - val_precision_m: 0.0000e+00\n","Epoch 135/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3493 - acc: 0.2821 - precision_m: 0.0231 - val_loss: 1.3811 - val_acc: 0.2273 - val_precision_m: 0.0000e+00\n","Epoch 136/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3630 - acc: 0.3307 - precision_m: 0.0243 - val_loss: 1.3798 - val_acc: 0.2424 - val_precision_m: 0.0152\n","Epoch 137/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3893 - acc: 0.3030 - precision_m: 0.0513 - val_loss: 1.3831 - val_acc: 0.2424 - val_precision_m: 0.0152\n","Epoch 138/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3518 - acc: 0.3379 - precision_m: 0.0062 - val_loss: 1.3912 - val_acc: 0.2197 - val_precision_m: 0.0000e+00\n","Epoch 139/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3573 - acc: 0.3046 - precision_m: 0.0063 - val_loss: 1.3887 - val_acc: 0.2424 - val_precision_m: 0.0000e+00\n","Epoch 140/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3440 - acc: 0.3226 - precision_m: 0.0217 - val_loss: 1.3899 - val_acc: 0.2348 - val_precision_m: 0.0000e+00\n","Epoch 141/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3593 - acc: 0.2997 - precision_m: 0.0223 - val_loss: 1.3908 - val_acc: 0.2424 - val_precision_m: 0.0000e+00\n","Epoch 142/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3794 - acc: 0.2511 - precision_m: 0.0127 - val_loss: 1.3829 - val_acc: 0.2424 - val_precision_m: 0.0000e+00\n","Epoch 143/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3746 - acc: 0.2253 - precision_m: 0.0094 - val_loss: 1.3820 - val_acc: 0.2424 - val_precision_m: 0.0152\n","Epoch 144/500\n","154/154 [==============================] - 0s 3ms/step - loss: 1.3547 - acc: 0.3266 - precision_m: 0.0296 - val_loss: 1.3910 - val_acc: 0.2197 - val_precision_m: 0.0000e+00\n","Epoch 145/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3762 - acc: 0.2692 - precision_m: 0.0039 - val_loss: 1.3774 - val_acc: 0.2348 - val_precision_m: 0.0152\n","Epoch 146/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3386 - acc: 0.3146 - precision_m: 0.0191 - val_loss: 1.3837 - val_acc: 0.2500 - val_precision_m: 0.0000e+00\n","Epoch 147/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3732 - acc: 0.2579 - precision_m: 0.0000e+00 - val_loss: 1.3874 - val_acc: 0.2273 - val_precision_m: 0.0000e+00\n","Epoch 148/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3616 - acc: 0.3232 - precision_m: 0.0120 - val_loss: 1.3867 - val_acc: 0.2424 - val_precision_m: 0.0000e+00\n","Epoch 149/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3347 - acc: 0.2939 - precision_m: 0.0282 - val_loss: 1.3765 - val_acc: 0.2424 - val_precision_m: 0.0152\n","Epoch 150/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3554 - acc: 0.3250 - precision_m: 0.0359 - val_loss: 1.3814 - val_acc: 0.2500 - val_precision_m: 0.0000e+00\n","Epoch 151/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3643 - acc: 0.2368 - precision_m: 0.0260 - val_loss: 1.3797 - val_acc: 0.2424 - val_precision_m: 0.0000e+00\n","Epoch 152/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3574 - acc: 0.3220 - precision_m: 0.0136 - val_loss: 1.3969 - val_acc: 0.1970 - val_precision_m: 0.0000e+00\n","Epoch 153/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3544 - acc: 0.2975 - precision_m: 0.0091 - val_loss: 1.3898 - val_acc: 0.2121 - val_precision_m: 0.0000e+00\n","Epoch 154/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3687 - acc: 0.2674 - precision_m: 0.0138 - val_loss: 1.3801 - val_acc: 0.2500 - val_precision_m: 0.0152\n","Epoch 155/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3521 - acc: 0.3258 - precision_m: 0.0251 - val_loss: 1.3806 - val_acc: 0.2348 - val_precision_m: 0.0000e+00\n","Epoch 156/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3551 - acc: 0.3063 - precision_m: 0.0112 - val_loss: 1.3821 - val_acc: 0.2348 - val_precision_m: 0.0000e+00\n","Epoch 157/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3629 - acc: 0.3403 - precision_m: 0.0317 - val_loss: 1.3818 - val_acc: 0.2652 - val_precision_m: 0.0152\n","Epoch 158/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3654 - acc: 0.2631 - precision_m: 0.0047 - val_loss: 1.3845 - val_acc: 0.2576 - val_precision_m: 0.0000e+00\n","Epoch 159/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3660 - acc: 0.3094 - precision_m: 0.0047 - val_loss: 1.3821 - val_acc: 0.2197 - val_precision_m: 0.0000e+00\n","Epoch 160/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3336 - acc: 0.3369 - precision_m: 0.0194 - val_loss: 1.3873 - val_acc: 0.2652 - val_precision_m: 0.0000e+00\n","Epoch 161/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3523 - acc: 0.3325 - precision_m: 0.0118 - val_loss: 1.3911 - val_acc: 0.2045 - val_precision_m: 0.0000e+00\n","Epoch 162/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3370 - acc: 0.2861 - precision_m: 0.0266 - val_loss: 1.3816 - val_acc: 0.2273 - val_precision_m: 0.0152\n","Epoch 163/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3612 - acc: 0.3051 - precision_m: 0.0017 - val_loss: 1.3835 - val_acc: 0.2197 - val_precision_m: 0.0152\n","Epoch 164/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3419 - acc: 0.3927 - precision_m: 0.0071 - val_loss: 1.3813 - val_acc: 0.2652 - val_precision_m: 0.0152\n","Epoch 165/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3591 - acc: 0.2853 - precision_m: 0.0153 - val_loss: 1.3781 - val_acc: 0.2652 - val_precision_m: 0.0152\n","Epoch 166/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3654 - acc: 0.2652 - precision_m: 0.0169 - val_loss: 1.3834 - val_acc: 0.2348 - val_precision_m: 0.0000e+00\n","Epoch 167/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3258 - acc: 0.3442 - precision_m: 0.0201 - val_loss: 1.3895 - val_acc: 0.2348 - val_precision_m: 0.0000e+00\n","Epoch 168/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3482 - acc: 0.2798 - precision_m: 0.0442 - val_loss: 1.3762 - val_acc: 0.2348 - val_precision_m: 0.0152\n","Epoch 169/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3368 - acc: 0.3005 - precision_m: 0.0449 - val_loss: 1.3870 - val_acc: 0.2273 - val_precision_m: 0.0152\n","Epoch 170/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3468 - acc: 0.3488 - precision_m: 0.0222 - val_loss: 1.3826 - val_acc: 0.2348 - val_precision_m: 0.0152\n","Epoch 171/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3736 - acc: 0.2965 - precision_m: 0.0043 - val_loss: 1.3933 - val_acc: 0.2121 - val_precision_m: 0.0000e+00\n","Epoch 172/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3658 - acc: 0.2622 - precision_m: 0.0094 - val_loss: 1.3791 - val_acc: 0.2803 - val_precision_m: 0.0000e+00\n","Epoch 173/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3581 - acc: 0.3481 - precision_m: 0.0082 - val_loss: 1.3850 - val_acc: 0.2348 - val_precision_m: 0.0000e+00\n","Epoch 174/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3670 - acc: 0.2706 - precision_m: 0.0054 - val_loss: 1.3789 - val_acc: 0.2803 - val_precision_m: 0.0152\n","Epoch 175/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3277 - acc: 0.3339 - precision_m: 0.0346 - val_loss: 1.3790 - val_acc: 0.2576 - val_precision_m: 0.0152\n","Epoch 176/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3499 - acc: 0.3519 - precision_m: 0.0028 - val_loss: 1.3811 - val_acc: 0.2273 - val_precision_m: 0.0152\n","Epoch 177/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3542 - acc: 0.2815 - precision_m: 0.0049 - val_loss: 1.3858 - val_acc: 0.2197 - val_precision_m: 0.0000e+00\n","Epoch 178/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3512 - acc: 0.3379 - precision_m: 0.0178 - val_loss: 1.3898 - val_acc: 0.2197 - val_precision_m: 0.0000e+00\n","Epoch 179/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3457 - acc: 0.2584 - precision_m: 0.0324 - val_loss: 1.3785 - val_acc: 0.2652 - val_precision_m: 0.0152\n","Epoch 180/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3623 - acc: 0.2855 - precision_m: 0.0190 - val_loss: 1.3813 - val_acc: 0.2348 - val_precision_m: 0.0152\n","Epoch 181/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3458 - acc: 0.3262 - precision_m: 0.0494 - val_loss: 1.3891 - val_acc: 0.2045 - val_precision_m: 0.0000e+00\n","Epoch 182/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3378 - acc: 0.3366 - precision_m: 0.0085 - val_loss: 1.3840 - val_acc: 0.2348 - val_precision_m: 0.0152\n","Epoch 183/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3343 - acc: 0.3360 - precision_m: 0.0313 - val_loss: 1.3794 - val_acc: 0.2273 - val_precision_m: 0.0152\n","Epoch 184/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3564 - acc: 0.3378 - precision_m: 0.0038 - val_loss: 1.3785 - val_acc: 0.2727 - val_precision_m: 0.0152\n","Epoch 185/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3513 - acc: 0.2756 - precision_m: 0.0181 - val_loss: 1.3895 - val_acc: 0.2197 - val_precision_m: 0.0000e+00\n","Epoch 186/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3590 - acc: 0.2946 - precision_m: 0.0194 - val_loss: 1.3941 - val_acc: 0.2045 - val_precision_m: 0.0000e+00\n","Epoch 187/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3483 - acc: 0.3441 - precision_m: 0.0137 - val_loss: 1.3821 - val_acc: 0.2652 - val_precision_m: 0.0152\n","Epoch 188/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3349 - acc: 0.2982 - precision_m: 0.0432 - val_loss: 1.3801 - val_acc: 0.2424 - val_precision_m: 0.0152\n","Epoch 189/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3537 - acc: 0.3376 - precision_m: 0.0208 - val_loss: 1.3774 - val_acc: 0.2500 - val_precision_m: 0.0152\n","Epoch 190/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3696 - acc: 0.2974 - precision_m: 0.0040 - val_loss: 1.3891 - val_acc: 0.2348 - val_precision_m: 0.0000e+00\n","Epoch 191/500\n","154/154 [==============================] - 0s 3ms/step - loss: 1.3516 - acc: 0.3226 - precision_m: 0.0186 - val_loss: 1.3821 - val_acc: 0.2197 - val_precision_m: 0.0000e+00\n","Epoch 192/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3216 - acc: 0.3685 - precision_m: 0.0354 - val_loss: 1.3923 - val_acc: 0.1970 - val_precision_m: 0.0000e+00\n","Epoch 193/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3721 - acc: 0.2620 - precision_m: 0.0084 - val_loss: 1.3834 - val_acc: 0.2500 - val_precision_m: 0.0000e+00\n","Epoch 194/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3285 - acc: 0.3596 - precision_m: 0.0341 - val_loss: 1.3773 - val_acc: 0.2576 - val_precision_m: 0.0152\n","Epoch 195/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3555 - acc: 0.3394 - precision_m: 0.0243 - val_loss: 1.3828 - val_acc: 0.2652 - val_precision_m: 0.0152\n","Epoch 196/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3511 - acc: 0.2874 - precision_m: 0.0032 - val_loss: 1.3857 - val_acc: 0.2348 - val_precision_m: 0.0000e+00\n","Epoch 197/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3286 - acc: 0.3413 - precision_m: 0.0173 - val_loss: 1.3916 - val_acc: 0.2197 - val_precision_m: 0.0000e+00\n","Epoch 198/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3273 - acc: 0.3860 - precision_m: 0.0151 - val_loss: 1.3922 - val_acc: 0.2045 - val_precision_m: 0.0000e+00\n","Epoch 199/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3296 - acc: 0.3491 - precision_m: 0.0450 - val_loss: 1.3927 - val_acc: 0.2045 - val_precision_m: 0.0000e+00\n","Epoch 200/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3529 - acc: 0.2994 - precision_m: 0.0053 - val_loss: 1.3880 - val_acc: 0.2273 - val_precision_m: 0.0000e+00\n","Epoch 201/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3642 - acc: 0.2897 - precision_m: 0.0141 - val_loss: 1.3858 - val_acc: 0.2424 - val_precision_m: 0.0000e+00\n","Epoch 202/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3401 - acc: 0.3544 - precision_m: 0.0444 - val_loss: 1.3909 - val_acc: 0.2045 - val_precision_m: 0.0000e+00\n","Epoch 203/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3360 - acc: 0.3468 - precision_m: 0.0276 - val_loss: 1.3814 - val_acc: 0.2500 - val_precision_m: 0.0152\n","Epoch 204/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3729 - acc: 0.2917 - precision_m: 0.0182 - val_loss: 1.3833 - val_acc: 0.2197 - val_precision_m: 0.0000e+00\n","Epoch 205/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3545 - acc: 0.3290 - precision_m: 0.0229 - val_loss: 1.3849 - val_acc: 0.2500 - val_precision_m: 0.0000e+00\n","Epoch 206/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3612 - acc: 0.3235 - precision_m: 0.0037 - val_loss: 1.3935 - val_acc: 0.2273 - val_precision_m: 0.0000e+00\n","Epoch 207/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3365 - acc: 0.3404 - precision_m: 0.0171 - val_loss: 1.3872 - val_acc: 0.2273 - val_precision_m: 0.0000e+00\n","Epoch 208/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3255 - acc: 0.2951 - precision_m: 0.0126 - val_loss: 1.3855 - val_acc: 0.2424 - val_precision_m: 0.0000e+00\n","Epoch 209/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3379 - acc: 0.2877 - precision_m: 0.0564 - val_loss: 1.3828 - val_acc: 0.2197 - val_precision_m: 0.0000e+00\n","Epoch 210/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3539 - acc: 0.3238 - precision_m: 0.0182 - val_loss: 1.3878 - val_acc: 0.2121 - val_precision_m: 0.0000e+00\n","Epoch 211/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3498 - acc: 0.3081 - precision_m: 0.0293 - val_loss: 1.3885 - val_acc: 0.2273 - val_precision_m: 0.0000e+00\n","Epoch 212/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3556 - acc: 0.2963 - precision_m: 0.0081 - val_loss: 1.3767 - val_acc: 0.2273 - val_precision_m: 0.0152\n","Epoch 213/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3408 - acc: 0.3069 - precision_m: 0.0141 - val_loss: 1.3969 - val_acc: 0.2045 - val_precision_m: 0.0000e+00\n","Epoch 214/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3446 - acc: 0.3351 - precision_m: 0.0114 - val_loss: 1.3908 - val_acc: 0.2045 - val_precision_m: 0.0000e+00\n","Epoch 215/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3408 - acc: 0.2992 - precision_m: 0.0145 - val_loss: 1.3862 - val_acc: 0.2348 - val_precision_m: 0.0000e+00\n","Epoch 216/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3500 - acc: 0.3042 - precision_m: 0.0088 - val_loss: 1.3916 - val_acc: 0.2121 - val_precision_m: 0.0000e+00\n","Epoch 217/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3486 - acc: 0.2861 - precision_m: 0.0191 - val_loss: 1.3860 - val_acc: 0.3030 - val_precision_m: 0.0152\n","Epoch 218/500\n","154/154 [==============================] - 0s 2ms/step - loss: 1.3390 - acc: 0.3340 - precision_m: 0.0304 - val_loss: 1.3900 - val_acc: 0.2273 - val_precision_m: 0.0000e+00\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 15.9s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","143/143 [==============================] - 1s 3ms/step - loss: 1.4778 - acc: 0.1947 - precision_m: 0.0098 - val_loss: 1.3806 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3899 - acc: 0.2431 - precision_m: 0.0000e+00 - val_loss: 1.3819 - val_acc: 0.2114 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3897 - acc: 0.2278 - precision_m: 0.0000e+00 - val_loss: 1.3790 - val_acc: 0.3333 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3840 - acc: 0.3068 - precision_m: 0.0000e+00 - val_loss: 1.3770 - val_acc: 0.3089 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3829 - acc: 0.2701 - precision_m: 0.0000e+00 - val_loss: 1.3753 - val_acc: 0.3008 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3740 - acc: 0.3071 - precision_m: 0.0000e+00 - val_loss: 1.3659 - val_acc: 0.3089 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3800 - acc: 0.2736 - precision_m: 0.0000e+00 - val_loss: 1.3769 - val_acc: 0.3008 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3779 - acc: 0.3087 - precision_m: 0.0000e+00 - val_loss: 1.3741 - val_acc: 0.3008 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3814 - acc: 0.3337 - precision_m: 0.0000e+00 - val_loss: 1.3736 - val_acc: 0.3008 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3803 - acc: 0.3066 - precision_m: 0.0000e+00 - val_loss: 1.3732 - val_acc: 0.3008 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3896 - acc: 0.2834 - precision_m: 0.0000e+00 - val_loss: 1.3742 - val_acc: 0.3008 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3796 - acc: 0.3370 - precision_m: 0.0000e+00 - val_loss: 1.3744 - val_acc: 0.3008 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3909 - acc: 0.2043 - precision_m: 0.0000e+00 - val_loss: 1.3695 - val_acc: 0.3089 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3866 - acc: 0.2362 - precision_m: 0.0000e+00 - val_loss: 1.3722 - val_acc: 0.3008 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3823 - acc: 0.2972 - precision_m: 0.0000e+00 - val_loss: 1.3754 - val_acc: 0.2927 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3799 - acc: 0.3188 - precision_m: 0.0000e+00 - val_loss: 1.3705 - val_acc: 0.3008 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3824 - acc: 0.3152 - precision_m: 0.0000e+00 - val_loss: 1.3718 - val_acc: 0.3008 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3927 - acc: 0.2445 - precision_m: 0.0000e+00 - val_loss: 1.3727 - val_acc: 0.3008 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3837 - acc: 0.2642 - precision_m: 0.0000e+00 - val_loss: 1.3713 - val_acc: 0.3008 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3792 - acc: 0.2886 - precision_m: 0.0000e+00 - val_loss: 1.3706 - val_acc: 0.3089 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3726 - acc: 0.3657 - precision_m: 0.0000e+00 - val_loss: 1.3699 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3857 - acc: 0.2852 - precision_m: 0.0000e+00 - val_loss: 1.3712 - val_acc: 0.2927 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3858 - acc: 0.2697 - precision_m: 0.0000e+00 - val_loss: 1.3707 - val_acc: 0.2927 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3790 - acc: 0.2665 - precision_m: 0.0000e+00 - val_loss: 1.3719 - val_acc: 0.2927 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3733 - acc: 0.3441 - precision_m: 0.0000e+00 - val_loss: 1.3727 - val_acc: 0.3333 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3784 - acc: 0.2614 - precision_m: 0.0022 - val_loss: 1.3755 - val_acc: 0.3089 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3789 - acc: 0.2495 - precision_m: 0.0143 - val_loss: 1.3694 - val_acc: 0.3008 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3775 - acc: 0.2638 - precision_m: 0.0000e+00 - val_loss: 1.3704 - val_acc: 0.3089 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3855 - acc: 0.3220 - precision_m: 1.9528e-04 - val_loss: 1.3708 - val_acc: 0.3171 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3737 - acc: 0.3172 - precision_m: 0.0032 - val_loss: 1.3717 - val_acc: 0.3171 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3761 - acc: 0.2969 - precision_m: 0.0016 - val_loss: 1.3686 - val_acc: 0.3333 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3563 - acc: 0.2626 - precision_m: 0.0126 - val_loss: 1.3720 - val_acc: 0.3089 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3783 - acc: 0.2918 - precision_m: 0.0000e+00 - val_loss: 1.3720 - val_acc: 0.3252 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3700 - acc: 0.3112 - precision_m: 0.0000e+00 - val_loss: 1.3792 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3876 - acc: 0.2760 - precision_m: 0.0000e+00 - val_loss: 1.3704 - val_acc: 0.3089 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3729 - acc: 0.3169 - precision_m: 5.5018e-04 - val_loss: 1.3734 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3853 - acc: 0.2709 - precision_m: 0.0000e+00 - val_loss: 1.3739 - val_acc: 0.3008 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3750 - acc: 0.3013 - precision_m: 0.0063 - val_loss: 1.3679 - val_acc: 0.2927 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3798 - acc: 0.2746 - precision_m: 0.0104 - val_loss: 1.3697 - val_acc: 0.3171 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3830 - acc: 0.2848 - precision_m: 0.0000e+00 - val_loss: 1.3693 - val_acc: 0.3089 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3729 - acc: 0.2981 - precision_m: 0.0027 - val_loss: 1.3693 - val_acc: 0.3089 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3742 - acc: 0.2618 - precision_m: 0.0154 - val_loss: 1.3720 - val_acc: 0.3008 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3637 - acc: 0.2956 - precision_m: 0.0048 - val_loss: 1.3694 - val_acc: 0.2927 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3684 - acc: 0.2753 - precision_m: 0.0154 - val_loss: 1.3728 - val_acc: 0.2927 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3709 - acc: 0.3010 - precision_m: 0.0104 - val_loss: 1.3704 - val_acc: 0.3171 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3577 - acc: 0.2986 - precision_m: 0.0123 - val_loss: 1.3731 - val_acc: 0.3171 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3567 - acc: 0.3747 - precision_m: 0.0158 - val_loss: 1.3691 - val_acc: 0.3171 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3817 - acc: 0.2874 - precision_m: 0.0095 - val_loss: 1.3686 - val_acc: 0.3252 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3640 - acc: 0.3140 - precision_m: 0.0033 - val_loss: 1.3644 - val_acc: 0.3089 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3532 - acc: 0.3459 - precision_m: 0.0211 - val_loss: 1.3725 - val_acc: 0.2927 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3723 - acc: 0.2646 - precision_m: 0.0154 - val_loss: 1.3699 - val_acc: 0.3008 - val_precision_m: 0.0000e+00\n","Epoch 52/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3764 - acc: 0.2853 - precision_m: 0.0145 - val_loss: 1.3711 - val_acc: 0.3089 - val_precision_m: 0.0000e+00\n","Epoch 53/500\n","143/143 [==============================] - 0s 3ms/step - loss: 1.3701 - acc: 0.3370 - precision_m: 0.0000e+00 - val_loss: 1.3762 - val_acc: 0.3171 - val_precision_m: 0.0000e+00\n","Epoch 54/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3560 - acc: 0.2903 - precision_m: 0.0346 - val_loss: 1.3689 - val_acc: 0.3171 - val_precision_m: 0.0000e+00\n","Epoch 55/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3489 - acc: 0.3491 - precision_m: 0.0425 - val_loss: 1.3728 - val_acc: 0.3008 - val_precision_m: 0.0000e+00\n","Epoch 56/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3544 - acc: 0.3742 - precision_m: 0.0455 - val_loss: 1.3705 - val_acc: 0.3171 - val_precision_m: 0.0000e+00\n","Epoch 57/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3602 - acc: 0.3248 - precision_m: 0.0189 - val_loss: 1.3705 - val_acc: 0.3089 - val_precision_m: 0.0000e+00\n","Epoch 58/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3634 - acc: 0.2873 - precision_m: 0.0298 - val_loss: 1.3877 - val_acc: 0.2439 - val_precision_m: 0.0000e+00\n","Epoch 59/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3569 - acc: 0.3081 - precision_m: 0.0238 - val_loss: 1.3798 - val_acc: 0.3171 - val_precision_m: 0.0000e+00\n","Epoch 60/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3744 - acc: 0.2651 - precision_m: 0.0128 - val_loss: 1.3730 - val_acc: 0.2927 - val_precision_m: 0.0000e+00\n","Epoch 61/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3369 - acc: 0.3452 - precision_m: 0.0385 - val_loss: 1.3696 - val_acc: 0.3089 - val_precision_m: 0.0000e+00\n","Epoch 62/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3556 - acc: 0.3279 - precision_m: 0.0146 - val_loss: 1.3645 - val_acc: 0.3089 - val_precision_m: 0.0000e+00\n","Epoch 63/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3403 - acc: 0.3045 - precision_m: 0.0390 - val_loss: 1.3748 - val_acc: 0.2846 - val_precision_m: 0.0161\n","Epoch 64/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3754 - acc: 0.3194 - precision_m: 0.0179 - val_loss: 1.3684 - val_acc: 0.3252 - val_precision_m: 0.0000e+00\n","Epoch 65/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3623 - acc: 0.2851 - precision_m: 0.0299 - val_loss: 1.3732 - val_acc: 0.3171 - val_precision_m: 0.0000e+00\n","Epoch 66/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3539 - acc: 0.2857 - precision_m: 0.0279 - val_loss: 1.3764 - val_acc: 0.3008 - val_precision_m: 0.0161\n","Epoch 67/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3533 - acc: 0.2960 - precision_m: 0.0355 - val_loss: 1.3808 - val_acc: 0.2846 - val_precision_m: 0.0161\n","Epoch 68/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3645 - acc: 0.3258 - precision_m: 0.0303 - val_loss: 1.3734 - val_acc: 0.3008 - val_precision_m: 0.0161\n","Epoch 69/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3597 - acc: 0.3044 - precision_m: 0.0166 - val_loss: 1.3786 - val_acc: 0.3008 - val_precision_m: 0.0161\n","Epoch 70/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3627 - acc: 0.2935 - precision_m: 0.0250 - val_loss: 1.3778 - val_acc: 0.3008 - val_precision_m: 0.0161\n","Epoch 71/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3545 - acc: 0.3325 - precision_m: 0.0264 - val_loss: 1.3711 - val_acc: 0.3008 - val_precision_m: 0.0161\n","Epoch 72/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3480 - acc: 0.3087 - precision_m: 0.0474 - val_loss: 1.3769 - val_acc: 0.2846 - val_precision_m: 0.0161\n","Epoch 73/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3488 - acc: 0.3288 - precision_m: 0.0422 - val_loss: 1.3887 - val_acc: 0.3089 - val_precision_m: 0.0161\n","Epoch 74/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3572 - acc: 0.3129 - precision_m: 0.0493 - val_loss: 1.3770 - val_acc: 0.3089 - val_precision_m: 0.0000e+00\n","Epoch 75/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3282 - acc: 0.3583 - precision_m: 0.0606 - val_loss: 1.3859 - val_acc: 0.3415 - val_precision_m: 0.0323\n","Epoch 76/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3637 - acc: 0.2992 - precision_m: 0.0432 - val_loss: 1.3914 - val_acc: 0.2846 - val_precision_m: 0.0161\n","Epoch 77/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3793 - acc: 0.3061 - precision_m: 0.0598 - val_loss: 1.3996 - val_acc: 0.3008 - val_precision_m: 0.0161\n","Epoch 78/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3079 - acc: 0.3685 - precision_m: 0.1088 - val_loss: 1.3803 - val_acc: 0.3008 - val_precision_m: 0.0161\n","Epoch 79/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3181 - acc: 0.3762 - precision_m: 0.0558 - val_loss: 1.3828 - val_acc: 0.2927 - val_precision_m: 0.0161\n","Epoch 80/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3274 - acc: 0.3365 - precision_m: 0.0659 - val_loss: 1.3780 - val_acc: 0.2927 - val_precision_m: 0.0161\n","Epoch 81/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3390 - acc: 0.3520 - precision_m: 0.0277 - val_loss: 1.3904 - val_acc: 0.2846 - val_precision_m: 0.0161\n","Epoch 82/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3140 - acc: 0.3404 - precision_m: 0.0848 - val_loss: 1.3743 - val_acc: 0.3171 - val_precision_m: 0.0000e+00\n","Epoch 83/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3399 - acc: 0.3332 - precision_m: 0.0456 - val_loss: 1.3842 - val_acc: 0.2846 - val_precision_m: 0.0161\n","Epoch 84/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3270 - acc: 0.2902 - precision_m: 0.0473 - val_loss: 1.3783 - val_acc: 0.3089 - val_precision_m: 0.0161\n","Epoch 85/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3372 - acc: 0.3023 - precision_m: 0.0368 - val_loss: 1.3762 - val_acc: 0.3089 - val_precision_m: 0.0161\n","Epoch 86/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3318 - acc: 0.3468 - precision_m: 0.0549 - val_loss: 1.3838 - val_acc: 0.3089 - val_precision_m: 0.0161\n","Epoch 87/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3163 - acc: 0.4289 - precision_m: 0.0472 - val_loss: 1.3708 - val_acc: 0.3171 - val_precision_m: 0.0161\n","Epoch 88/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3501 - acc: 0.3016 - precision_m: 0.0376 - val_loss: 1.3859 - val_acc: 0.2927 - val_precision_m: 0.0161\n","Epoch 89/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3145 - acc: 0.3619 - precision_m: 0.0892 - val_loss: 1.3836 - val_acc: 0.3008 - val_precision_m: 0.0161\n","Epoch 90/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3412 - acc: 0.3097 - precision_m: 0.0464 - val_loss: 1.4303 - val_acc: 0.2520 - val_precision_m: 0.0161\n","Epoch 91/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3382 - acc: 0.3367 - precision_m: 0.0538 - val_loss: 1.3970 - val_acc: 0.2927 - val_precision_m: 0.0323\n","Epoch 92/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3341 - acc: 0.3332 - precision_m: 0.0597 - val_loss: 1.4238 - val_acc: 0.2846 - val_precision_m: 0.0484\n","Epoch 93/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3357 - acc: 0.3162 - precision_m: 0.0773 - val_loss: 1.3985 - val_acc: 0.2927 - val_precision_m: 0.0323\n","Epoch 94/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3551 - acc: 0.3438 - precision_m: 0.0708 - val_loss: 1.3884 - val_acc: 0.3089 - val_precision_m: 0.0161\n","Epoch 95/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3156 - acc: 0.4030 - precision_m: 0.0812 - val_loss: 1.4007 - val_acc: 0.3008 - val_precision_m: 0.0161\n","Epoch 96/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3643 - acc: 0.3472 - precision_m: 0.0200 - val_loss: 1.3784 - val_acc: 0.2764 - val_precision_m: 0.0161\n","Epoch 97/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3127 - acc: 0.3345 - precision_m: 0.0688 - val_loss: 1.4000 - val_acc: 0.2846 - val_precision_m: 0.0161\n","Epoch 98/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3285 - acc: 0.3537 - precision_m: 0.1026 - val_loss: 1.3998 - val_acc: 0.2927 - val_precision_m: 0.0323\n","Epoch 99/500\n","143/143 [==============================] - 0s 3ms/step - loss: 1.3315 - acc: 0.3155 - precision_m: 0.0736 - val_loss: 1.4156 - val_acc: 0.2846 - val_precision_m: 0.0323\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 19.0s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","162/162 [==============================] - 1s 3ms/step - loss: 1.6887 - acc: 0.2753 - precision_m: 0.1011 - val_loss: 1.3958 - val_acc: 0.1871 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3990 - acc: 0.2450 - precision_m: 0.0000e+00 - val_loss: 1.3922 - val_acc: 0.2086 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3894 - acc: 0.2552 - precision_m: 0.0000e+00 - val_loss: 1.3936 - val_acc: 0.1942 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3875 - acc: 0.2867 - precision_m: 0.0000e+00 - val_loss: 1.3894 - val_acc: 0.2518 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3800 - acc: 0.3559 - precision_m: 0.0000e+00 - val_loss: 1.3926 - val_acc: 0.2446 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3862 - acc: 0.2674 - precision_m: 0.0000e+00 - val_loss: 1.3946 - val_acc: 0.2446 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3833 - acc: 0.2671 - precision_m: 0.0000e+00 - val_loss: 1.4015 - val_acc: 0.1727 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3864 - acc: 0.2958 - precision_m: 0.0000e+00 - val_loss: 1.4406 - val_acc: 0.1727 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3878 - acc: 0.3121 - precision_m: 0.0000e+00 - val_loss: 1.4028 - val_acc: 0.2086 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3821 - acc: 0.2662 - precision_m: 0.0000e+00 - val_loss: 1.4074 - val_acc: 0.1942 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3997 - acc: 0.2271 - precision_m: 0.0000e+00 - val_loss: 1.3995 - val_acc: 0.2446 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3835 - acc: 0.2776 - precision_m: 0.0000e+00 - val_loss: 1.4139 - val_acc: 0.1799 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3948 - acc: 0.2989 - precision_m: 0.0000e+00 - val_loss: 1.4017 - val_acc: 0.2446 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3778 - acc: 0.2839 - precision_m: 0.0000e+00 - val_loss: 1.4054 - val_acc: 0.1871 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3788 - acc: 0.3102 - precision_m: 0.0000e+00 - val_loss: 1.4037 - val_acc: 0.2014 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3937 - acc: 0.2625 - precision_m: 0.0000e+00 - val_loss: 1.4027 - val_acc: 0.2302 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3964 - acc: 0.2159 - precision_m: 0.0000e+00 - val_loss: 1.4057 - val_acc: 0.1727 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3867 - acc: 0.2534 - precision_m: 0.0000e+00 - val_loss: 1.4067 - val_acc: 0.1727 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3863 - acc: 0.2632 - precision_m: 0.0000e+00 - val_loss: 1.4079 - val_acc: 0.1727 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3750 - acc: 0.3139 - precision_m: 0.0000e+00 - val_loss: 1.4078 - val_acc: 0.1727 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","162/162 [==============================] - 0s 3ms/step - loss: 1.3817 - acc: 0.3080 - precision_m: 0.0000e+00 - val_loss: 1.4084 - val_acc: 0.1727 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","162/162 [==============================] - 0s 3ms/step - loss: 1.3740 - acc: 0.3059 - precision_m: 0.0000e+00 - val_loss: 1.4095 - val_acc: 0.1727 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","162/162 [==============================] - 0s 3ms/step - loss: 1.3861 - acc: 0.2693 - precision_m: 0.0000e+00 - val_loss: 1.4090 - val_acc: 0.1727 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","162/162 [==============================] - 1s 3ms/step - loss: 1.3787 - acc: 0.2969 - precision_m: 0.0000e+00 - val_loss: 1.4153 - val_acc: 0.1727 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","162/162 [==============================] - 0s 3ms/step - loss: 1.3736 - acc: 0.2991 - precision_m: 0.0000e+00 - val_loss: 1.4059 - val_acc: 0.1942 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","162/162 [==============================] - 1s 3ms/step - loss: 1.3886 - acc: 0.2858 - precision_m: 0.0000e+00 - val_loss: 1.4084 - val_acc: 0.1727 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","162/162 [==============================] - 0s 3ms/step - loss: 1.3750 - acc: 0.2822 - precision_m: 0.0000e+00 - val_loss: 1.4057 - val_acc: 0.1871 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","162/162 [==============================] - 1s 3ms/step - loss: 1.3759 - acc: 0.3095 - precision_m: 0.0000e+00 - val_loss: 1.4077 - val_acc: 0.1871 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","162/162 [==============================] - 0s 3ms/step - loss: 1.3807 - acc: 0.2733 - precision_m: 0.0000e+00 - val_loss: 1.4131 - val_acc: 0.1727 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3790 - acc: 0.2676 - precision_m: 0.0000e+00 - val_loss: 1.4121 - val_acc: 0.1727 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3809 - acc: 0.2965 - precision_m: 0.0000e+00 - val_loss: 1.4124 - val_acc: 0.1727 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3744 - acc: 0.3013 - precision_m: 0.0000e+00 - val_loss: 1.4096 - val_acc: 0.1727 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3872 - acc: 0.2676 - precision_m: 0.0000e+00 - val_loss: 1.4117 - val_acc: 0.1727 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3765 - acc: 0.2821 - precision_m: 0.0000e+00 - val_loss: 1.4023 - val_acc: 0.2302 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3859 - acc: 0.2519 - precision_m: 0.0000e+00 - val_loss: 1.4135 - val_acc: 0.1727 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3743 - acc: 0.3000 - precision_m: 0.0000e+00 - val_loss: 1.4131 - val_acc: 0.1727 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3859 - acc: 0.2577 - precision_m: 0.0000e+00 - val_loss: 1.4137 - val_acc: 0.1727 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3819 - acc: 0.2818 - precision_m: 0.0000e+00 - val_loss: 1.4141 - val_acc: 0.1727 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3831 - acc: 0.2984 - precision_m: 0.0000e+00 - val_loss: 1.4133 - val_acc: 0.1727 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3802 - acc: 0.3336 - precision_m: 0.0000e+00 - val_loss: 1.4122 - val_acc: 0.1727 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3792 - acc: 0.3021 - precision_m: 0.0000e+00 - val_loss: 1.4122 - val_acc: 0.1727 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3776 - acc: 0.2915 - precision_m: 0.0000e+00 - val_loss: 1.4129 - val_acc: 0.1727 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3745 - acc: 0.3147 - precision_m: 0.0000e+00 - val_loss: 1.4135 - val_acc: 0.1727 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3762 - acc: 0.2639 - precision_m: 0.0000e+00 - val_loss: 1.4132 - val_acc: 0.1727 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3817 - acc: 0.2565 - precision_m: 0.0000e+00 - val_loss: 1.4121 - val_acc: 0.1727 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3753 - acc: 0.3172 - precision_m: 0.0000e+00 - val_loss: 1.4122 - val_acc: 0.1727 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3756 - acc: 0.2923 - precision_m: 0.0000e+00 - val_loss: 1.4127 - val_acc: 0.1727 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3776 - acc: 0.2875 - precision_m: 0.0000e+00 - val_loss: 1.4122 - val_acc: 0.1727 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3699 - acc: 0.3238 - precision_m: 0.0000e+00 - val_loss: 1.4124 - val_acc: 0.1727 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3931 - acc: 0.2556 - precision_m: 0.0000e+00 - val_loss: 1.4122 - val_acc: 0.1727 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3763 - acc: 0.3388 - precision_m: 0.0000e+00 - val_loss: 1.4129 - val_acc: 0.1727 - val_precision_m: 0.0000e+00\n","Epoch 52/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3752 - acc: 0.3250 - precision_m: 0.0000e+00 - val_loss: 1.4126 - val_acc: 0.1727 - val_precision_m: 0.0000e+00\n","Epoch 53/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3831 - acc: 0.2798 - precision_m: 0.0000e+00 - val_loss: 1.4123 - val_acc: 0.1727 - val_precision_m: 0.0000e+00\n","Epoch 54/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3825 - acc: 0.2996 - precision_m: 0.0000e+00 - val_loss: 1.4128 - val_acc: 0.1727 - val_precision_m: 0.0000e+00\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 16.2s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","142/142 [==============================] - 1s 3ms/step - loss: 1.5673 - acc: 0.2770 - precision_m: 0.1021 - val_loss: 1.3848 - val_acc: 0.3008 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3906 - acc: 0.2291 - precision_m: 0.0000e+00 - val_loss: 1.3864 - val_acc: 0.2439 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3853 - acc: 0.2379 - precision_m: 0.0000e+00 - val_loss: 1.3855 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3780 - acc: 0.2711 - precision_m: 0.0000e+00 - val_loss: 1.3858 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3832 - acc: 0.2488 - precision_m: 0.0000e+00 - val_loss: 1.3859 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3841 - acc: 0.3029 - precision_m: 0.0000e+00 - val_loss: 1.3860 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3772 - acc: 0.3096 - precision_m: 0.0000e+00 - val_loss: 1.3862 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3795 - acc: 0.2761 - precision_m: 0.0000e+00 - val_loss: 1.3875 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3713 - acc: 0.3115 - precision_m: 0.0000e+00 - val_loss: 1.3873 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3828 - acc: 0.2858 - precision_m: 0.0000e+00 - val_loss: 1.3853 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3660 - acc: 0.3413 - precision_m: 0.0000e+00 - val_loss: 1.3855 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3762 - acc: 0.2931 - precision_m: 0.0000e+00 - val_loss: 1.3864 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3793 - acc: 0.2854 - precision_m: 0.0000e+00 - val_loss: 1.3866 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3754 - acc: 0.2574 - precision_m: 0.0000e+00 - val_loss: 1.3870 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3713 - acc: 0.3072 - precision_m: 0.0000e+00 - val_loss: 1.3877 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3698 - acc: 0.2655 - precision_m: 0.0000e+00 - val_loss: 1.3862 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3653 - acc: 0.3089 - precision_m: 0.0000e+00 - val_loss: 1.3876 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3548 - acc: 0.3213 - precision_m: 0.0000e+00 - val_loss: 1.3876 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3905 - acc: 0.2800 - precision_m: 0.0000e+00 - val_loss: 1.3876 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3729 - acc: 0.3024 - precision_m: 0.0000e+00 - val_loss: 1.3872 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3756 - acc: 0.2945 - precision_m: 0.0000e+00 - val_loss: 1.3866 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3836 - acc: 0.2796 - precision_m: 0.0000e+00 - val_loss: 1.3875 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","142/142 [==============================] - 0s 3ms/step - loss: 1.3705 - acc: 0.3154 - precision_m: 0.0000e+00 - val_loss: 1.3884 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3811 - acc: 0.2791 - precision_m: 0.0000e+00 - val_loss: 1.3880 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3829 - acc: 0.2879 - precision_m: 0.0000e+00 - val_loss: 1.3880 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3662 - acc: 0.2977 - precision_m: 0.0000e+00 - val_loss: 1.3857 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3826 - acc: 0.2624 - precision_m: 0.0000e+00 - val_loss: 1.3872 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3607 - acc: 0.2924 - precision_m: 0.0000e+00 - val_loss: 1.3879 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3686 - acc: 0.2938 - precision_m: 0.0000e+00 - val_loss: 1.3869 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3843 - acc: 0.2466 - precision_m: 0.0000e+00 - val_loss: 1.3876 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3805 - acc: 0.2520 - precision_m: 0.0000e+00 - val_loss: 1.3882 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3745 - acc: 0.2763 - precision_m: 0.0000e+00 - val_loss: 1.3923 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3800 - acc: 0.2726 - precision_m: 0.0000e+00 - val_loss: 1.3907 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3709 - acc: 0.3046 - precision_m: 0.0000e+00 - val_loss: 1.3897 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3812 - acc: 0.2511 - precision_m: 0.0000e+00 - val_loss: 1.3883 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3852 - acc: 0.3010 - precision_m: 0.0000e+00 - val_loss: 1.3872 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3559 - acc: 0.3126 - precision_m: 0.0000e+00 - val_loss: 1.3879 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3745 - acc: 0.2757 - precision_m: 0.0000e+00 - val_loss: 1.3886 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3657 - acc: 0.3021 - precision_m: 0.0000e+00 - val_loss: 1.3892 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3923 - acc: 0.2806 - precision_m: 0.0000e+00 - val_loss: 1.3873 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3885 - acc: 0.2588 - precision_m: 0.0000e+00 - val_loss: 1.3871 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3616 - acc: 0.3463 - precision_m: 0.0000e+00 - val_loss: 1.3868 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3612 - acc: 0.3709 - precision_m: 0.0000e+00 - val_loss: 1.3873 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3865 - acc: 0.2772 - precision_m: 0.0000e+00 - val_loss: 1.3877 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3877 - acc: 0.2712 - precision_m: 0.0000e+00 - val_loss: 1.3864 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3719 - acc: 0.2709 - precision_m: 0.0000e+00 - val_loss: 1.3876 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3600 - acc: 0.3400 - precision_m: 0.0000e+00 - val_loss: 1.3867 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3726 - acc: 0.3222 - precision_m: 0.0000e+00 - val_loss: 1.3869 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3786 - acc: 0.3066 - precision_m: 0.0000e+00 - val_loss: 1.3859 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3830 - acc: 0.2770 - precision_m: 0.0000e+00 - val_loss: 1.3863 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","142/142 [==============================] - 0s 2ms/step - loss: 1.3837 - acc: 0.2970 - precision_m: 0.0000e+00 - val_loss: 1.3863 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 19.0s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","150/150 [==============================] - 1s 3ms/step - loss: 1.5348 - acc: 0.2406 - precision_m: 0.0390 - val_loss: 1.4177 - val_acc: 0.2093 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3946 - acc: 0.2703 - precision_m: 0.0000e+00 - val_loss: 1.3905 - val_acc: 0.2326 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3861 - acc: 0.2279 - precision_m: 0.0000e+00 - val_loss: 1.3844 - val_acc: 0.3256 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3885 - acc: 0.2701 - precision_m: 0.0000e+00 - val_loss: 1.3846 - val_acc: 0.2481 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3877 - acc: 0.2200 - precision_m: 0.0000e+00 - val_loss: 1.3864 - val_acc: 0.2481 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3876 - acc: 0.2192 - precision_m: 0.0000e+00 - val_loss: 1.3868 - val_acc: 0.2481 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3860 - acc: 0.2600 - precision_m: 0.0000e+00 - val_loss: 1.3873 - val_acc: 0.2481 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3854 - acc: 0.2382 - precision_m: 0.0000e+00 - val_loss: 1.3879 - val_acc: 0.2481 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3877 - acc: 0.2460 - precision_m: 0.0000e+00 - val_loss: 1.3882 - val_acc: 0.2481 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3843 - acc: 0.2590 - precision_m: 0.0000e+00 - val_loss: 1.3922 - val_acc: 0.2481 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3797 - acc: 0.2876 - precision_m: 0.0000e+00 - val_loss: 1.3908 - val_acc: 0.2481 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3897 - acc: 0.2496 - precision_m: 0.0000e+00 - val_loss: 1.3897 - val_acc: 0.2481 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3824 - acc: 0.2850 - precision_m: 0.0000e+00 - val_loss: 1.3898 - val_acc: 0.2481 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3854 - acc: 0.2539 - precision_m: 0.0000e+00 - val_loss: 1.3900 - val_acc: 0.2481 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3892 - acc: 0.2356 - precision_m: 0.0000e+00 - val_loss: 1.3910 - val_acc: 0.2481 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3822 - acc: 0.3003 - precision_m: 0.0000e+00 - val_loss: 1.3918 - val_acc: 0.2481 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3854 - acc: 0.2503 - precision_m: 0.0000e+00 - val_loss: 1.3898 - val_acc: 0.2481 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3880 - acc: 0.2603 - precision_m: 0.0000e+00 - val_loss: 1.3909 - val_acc: 0.2481 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3842 - acc: 0.2739 - precision_m: 0.0000e+00 - val_loss: 1.3915 - val_acc: 0.2481 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3859 - acc: 0.2628 - precision_m: 0.0000e+00 - val_loss: 1.3927 - val_acc: 0.2481 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3854 - acc: 0.2793 - precision_m: 0.0000e+00 - val_loss: 1.3915 - val_acc: 0.2481 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3802 - acc: 0.2618 - precision_m: 0.0000e+00 - val_loss: 1.3927 - val_acc: 0.2481 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3838 - acc: 0.2728 - precision_m: 0.0000e+00 - val_loss: 1.3923 - val_acc: 0.2481 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3903 - acc: 0.2397 - precision_m: 0.0000e+00 - val_loss: 1.3935 - val_acc: 0.2481 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3858 - acc: 0.2568 - precision_m: 0.0000e+00 - val_loss: 1.3952 - val_acc: 0.2481 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3883 - acc: 0.2859 - precision_m: 0.0000e+00 - val_loss: 1.3925 - val_acc: 0.2481 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3882 - acc: 0.2452 - precision_m: 0.0000e+00 - val_loss: 1.3945 - val_acc: 0.2481 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3803 - acc: 0.2884 - precision_m: 0.0000e+00 - val_loss: 1.3929 - val_acc: 0.2481 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3835 - acc: 0.2569 - precision_m: 0.0000e+00 - val_loss: 1.3941 - val_acc: 0.2481 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3868 - acc: 0.2415 - precision_m: 0.0000e+00 - val_loss: 1.3918 - val_acc: 0.2481 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3903 - acc: 0.2477 - precision_m: 0.0000e+00 - val_loss: 1.3952 - val_acc: 0.2481 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3813 - acc: 0.2941 - precision_m: 0.0000e+00 - val_loss: 1.3955 - val_acc: 0.2481 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3835 - acc: 0.2929 - precision_m: 0.0000e+00 - val_loss: 1.3942 - val_acc: 0.2481 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3884 - acc: 0.2510 - precision_m: 0.0000e+00 - val_loss: 1.3961 - val_acc: 0.2481 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3799 - acc: 0.3139 - precision_m: 0.0000e+00 - val_loss: 1.3996 - val_acc: 0.2481 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3840 - acc: 0.2853 - precision_m: 0.0000e+00 - val_loss: 1.3926 - val_acc: 0.2481 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3856 - acc: 0.2802 - precision_m: 0.0000e+00 - val_loss: 1.3942 - val_acc: 0.2481 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3801 - acc: 0.3138 - precision_m: 0.0000e+00 - val_loss: 1.3929 - val_acc: 0.2481 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3832 - acc: 0.2541 - precision_m: 0.0000e+00 - val_loss: 1.3919 - val_acc: 0.2481 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3869 - acc: 0.2847 - precision_m: 0.0000e+00 - val_loss: 1.3900 - val_acc: 0.2481 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3822 - acc: 0.3338 - precision_m: 0.0000e+00 - val_loss: 1.3923 - val_acc: 0.2481 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3826 - acc: 0.2749 - precision_m: 0.0000e+00 - val_loss: 1.3909 - val_acc: 0.2481 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3873 - acc: 0.2417 - precision_m: 0.0014 - val_loss: 1.3907 - val_acc: 0.2481 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3827 - acc: 0.2542 - precision_m: 0.0048 - val_loss: 1.3913 - val_acc: 0.2558 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3856 - acc: 0.2758 - precision_m: 0.0014 - val_loss: 1.3931 - val_acc: 0.2481 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3816 - acc: 0.2583 - precision_m: 2.2254e-04 - val_loss: 1.3922 - val_acc: 0.2558 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3749 - acc: 0.3425 - precision_m: 0.0123 - val_loss: 1.3910 - val_acc: 0.2558 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3803 - acc: 0.2850 - precision_m: 0.0139 - val_loss: 1.3894 - val_acc: 0.2558 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3812 - acc: 0.2758 - precision_m: 9.3790e-04 - val_loss: 1.4071 - val_acc: 0.2481 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3960 - acc: 0.2975 - precision_m: 6.3230e-04 - val_loss: 1.4016 - val_acc: 0.2093 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3822 - acc: 0.2072 - precision_m: 0.0036 - val_loss: 1.3891 - val_acc: 0.2558 - val_precision_m: 0.0000e+00\n","Epoch 52/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3822 - acc: 0.2622 - precision_m: 0.0119 - val_loss: 1.3906 - val_acc: 0.2481 - val_precision_m: 0.0000e+00\n","Epoch 53/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3831 - acc: 0.2376 - precision_m: 0.0048 - val_loss: 1.3920 - val_acc: 0.2481 - val_precision_m: 0.0000e+00\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 16.2s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","144/144 [==============================] - 1s 3ms/step - loss: 1.4721 - acc: 0.2544 - precision_m: 0.0176 - val_loss: 1.3861 - val_acc: 0.2602 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.4030 - acc: 0.2238 - precision_m: 0.0000e+00 - val_loss: 1.3843 - val_acc: 0.2520 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.4029 - acc: 0.2150 - precision_m: 0.0000e+00 - val_loss: 1.3855 - val_acc: 0.2276 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3962 - acc: 0.2518 - precision_m: 0.0000e+00 - val_loss: 1.3829 - val_acc: 0.2358 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3824 - acc: 0.2634 - precision_m: 0.0000e+00 - val_loss: 1.3834 - val_acc: 0.2683 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3758 - acc: 0.2962 - precision_m: 0.0000e+00 - val_loss: 1.3814 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3953 - acc: 0.2378 - precision_m: 0.0000e+00 - val_loss: 1.3818 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3804 - acc: 0.2444 - precision_m: 0.0000e+00 - val_loss: 1.3808 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3809 - acc: 0.2398 - precision_m: 0.0000e+00 - val_loss: 1.3842 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3867 - acc: 0.2714 - precision_m: 0.0000e+00 - val_loss: 1.3809 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3873 - acc: 0.2727 - precision_m: 0.0000e+00 - val_loss: 1.3818 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3744 - acc: 0.2771 - precision_m: 0.0000e+00 - val_loss: 1.3807 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","144/144 [==============================] - 0s 3ms/step - loss: 1.3898 - acc: 0.2795 - precision_m: 0.0000e+00 - val_loss: 1.3817 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3763 - acc: 0.3065 - precision_m: 0.0000e+00 - val_loss: 1.3816 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3814 - acc: 0.2873 - precision_m: 0.0000e+00 - val_loss: 1.3819 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3884 - acc: 0.2139 - precision_m: 0.0000e+00 - val_loss: 1.3840 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3893 - acc: 0.2317 - precision_m: 0.0000e+00 - val_loss: 1.3825 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3801 - acc: 0.3159 - precision_m: 0.0000e+00 - val_loss: 1.3826 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3896 - acc: 0.2460 - precision_m: 0.0000e+00 - val_loss: 1.3832 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3791 - acc: 0.3166 - precision_m: 0.0000e+00 - val_loss: 1.3835 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3820 - acc: 0.2440 - precision_m: 0.0000e+00 - val_loss: 1.3842 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3852 - acc: 0.2963 - precision_m: 0.0000e+00 - val_loss: 1.3837 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3654 - acc: 0.3113 - precision_m: 0.0000e+00 - val_loss: 1.3839 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3811 - acc: 0.2818 - precision_m: 0.0000e+00 - val_loss: 1.3835 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3784 - acc: 0.3021 - precision_m: 0.0000e+00 - val_loss: 1.3841 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3799 - acc: 0.2960 - precision_m: 0.0000e+00 - val_loss: 1.3837 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3831 - acc: 0.2923 - precision_m: 0.0000e+00 - val_loss: 1.3837 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3885 - acc: 0.2751 - precision_m: 0.0000e+00 - val_loss: 1.3827 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3834 - acc: 0.2392 - precision_m: 0.0000e+00 - val_loss: 1.3825 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3904 - acc: 0.2287 - precision_m: 0.0000e+00 - val_loss: 1.3838 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3744 - acc: 0.2928 - precision_m: 0.0000e+00 - val_loss: 1.3835 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3812 - acc: 0.2748 - precision_m: 0.0000e+00 - val_loss: 1.3836 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3798 - acc: 0.2753 - precision_m: 0.0000e+00 - val_loss: 1.3827 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3823 - acc: 0.2442 - precision_m: 0.0000e+00 - val_loss: 1.3827 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3747 - acc: 0.3415 - precision_m: 0.0000e+00 - val_loss: 1.3831 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3756 - acc: 0.3284 - precision_m: 0.0000e+00 - val_loss: 1.3825 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3898 - acc: 0.2775 - precision_m: 0.0000e+00 - val_loss: 1.3832 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3770 - acc: 0.3027 - precision_m: 0.0000e+00 - val_loss: 1.3833 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3795 - acc: 0.3034 - precision_m: 0.0000e+00 - val_loss: 1.3827 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3731 - acc: 0.2876 - precision_m: 0.0000e+00 - val_loss: 1.3827 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3764 - acc: 0.3068 - precision_m: 0.0000e+00 - val_loss: 1.3890 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3967 - acc: 0.2831 - precision_m: 0.0000e+00 - val_loss: 1.3817 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3745 - acc: 0.2919 - precision_m: 0.0000e+00 - val_loss: 1.3843 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3914 - acc: 0.2864 - precision_m: 0.0000e+00 - val_loss: 1.3812 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3923 - acc: 0.2116 - precision_m: 0.0000e+00 - val_loss: 1.3831 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3819 - acc: 0.2729 - precision_m: 0.0000e+00 - val_loss: 1.3843 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3741 - acc: 0.2975 - precision_m: 0.0000e+00 - val_loss: 1.3834 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3805 - acc: 0.2919 - precision_m: 0.0000e+00 - val_loss: 1.3828 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3753 - acc: 0.2865 - precision_m: 0.0000e+00 - val_loss: 1.3823 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3669 - acc: 0.3186 - precision_m: 0.0000e+00 - val_loss: 1.3828 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3808 - acc: 0.2567 - precision_m: 0.0000e+00 - val_loss: 1.3826 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 52/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3880 - acc: 0.2473 - precision_m: 0.0000e+00 - val_loss: 1.3830 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 53/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3697 - acc: 0.3250 - precision_m: 0.0000e+00 - val_loss: 1.3802 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 54/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3832 - acc: 0.2974 - precision_m: 0.0000e+00 - val_loss: 1.3806 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 55/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3824 - acc: 0.2171 - precision_m: 0.0000e+00 - val_loss: 1.3820 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 56/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3732 - acc: 0.2973 - precision_m: 0.0000e+00 - val_loss: 1.3838 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 57/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3714 - acc: 0.2923 - precision_m: 0.0000e+00 - val_loss: 1.3813 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 58/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3727 - acc: 0.2993 - precision_m: 0.0000e+00 - val_loss: 1.3827 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 59/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3824 - acc: 0.2692 - precision_m: 0.0000e+00 - val_loss: 1.3837 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 60/500\n","144/144 [==============================] - 0s 3ms/step - loss: 1.3701 - acc: 0.2858 - precision_m: 0.0000e+00 - val_loss: 1.3839 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 61/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3811 - acc: 0.2560 - precision_m: 0.0000e+00 - val_loss: 1.3908 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 62/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3911 - acc: 0.2803 - precision_m: 0.0000e+00 - val_loss: 1.3834 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 63/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3727 - acc: 0.2942 - precision_m: 0.0000e+00 - val_loss: 1.3865 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 64/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3754 - acc: 0.2809 - precision_m: 0.0000e+00 - val_loss: 1.3835 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 65/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3793 - acc: 0.2490 - precision_m: 0.0000e+00 - val_loss: 1.3817 - val_acc: 0.2683 - val_precision_m: 0.0000e+00\n","Epoch 66/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3751 - acc: 0.2811 - precision_m: 0.0000e+00 - val_loss: 1.3900 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 67/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3821 - acc: 0.2792 - precision_m: 0.0056 - val_loss: 1.3793 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 68/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3793 - acc: 0.2824 - precision_m: 0.0126 - val_loss: 1.3850 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 69/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3625 - acc: 0.3057 - precision_m: 0.0085 - val_loss: 1.3856 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 70/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3867 - acc: 0.2973 - precision_m: 8.0374e-04 - val_loss: 1.3901 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 71/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3605 - acc: 0.2954 - precision_m: 0.0000e+00 - val_loss: 1.3837 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 72/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3850 - acc: 0.2399 - precision_m: 0.0018 - val_loss: 1.3824 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 73/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3752 - acc: 0.2822 - precision_m: 0.0000e+00 - val_loss: 1.3834 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 74/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3706 - acc: 0.2945 - precision_m: 0.0150 - val_loss: 1.3826 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 75/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3832 - acc: 0.2489 - precision_m: 0.0000e+00 - val_loss: 1.3861 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 76/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3770 - acc: 0.2615 - precision_m: 0.0033 - val_loss: 1.3846 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 77/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3726 - acc: 0.2911 - precision_m: 5.4248e-04 - val_loss: 1.3860 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 78/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3655 - acc: 0.2925 - precision_m: 9.1108e-04 - val_loss: 1.3825 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 79/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3633 - acc: 0.3425 - precision_m: 0.0000e+00 - val_loss: 1.3800 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 80/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3457 - acc: 0.3370 - precision_m: 0.0127 - val_loss: 1.3766 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 81/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3733 - acc: 0.2386 - precision_m: 0.0000e+00 - val_loss: 1.3831 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 82/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3668 - acc: 0.2624 - precision_m: 0.0174 - val_loss: 1.3773 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 83/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3789 - acc: 0.2830 - precision_m: 6.4580e-04 - val_loss: 1.3836 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 84/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3767 - acc: 0.2712 - precision_m: 0.0000e+00 - val_loss: 1.3829 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 85/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3530 - acc: 0.2921 - precision_m: 0.0020 - val_loss: 1.3784 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 86/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3755 - acc: 0.2825 - precision_m: 0.0012 - val_loss: 1.3859 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 87/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3629 - acc: 0.2657 - precision_m: 0.0000e+00 - val_loss: 1.3857 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 88/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3612 - acc: 0.2704 - precision_m: 0.0000e+00 - val_loss: 1.3925 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 89/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3706 - acc: 0.2699 - precision_m: 0.0139 - val_loss: 1.3808 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 90/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3910 - acc: 0.2979 - precision_m: 0.0000e+00 - val_loss: 1.3824 - val_acc: 0.2683 - val_precision_m: 0.0000e+00\n","Epoch 91/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3717 - acc: 0.2807 - precision_m: 0.0000e+00 - val_loss: 1.3829 - val_acc: 0.2683 - val_precision_m: 0.0000e+00\n","Epoch 92/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3639 - acc: 0.3022 - precision_m: 0.0079 - val_loss: 1.3840 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 93/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3674 - acc: 0.2817 - precision_m: 0.0000e+00 - val_loss: 1.4064 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 94/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3686 - acc: 0.2920 - precision_m: 0.0037 - val_loss: 1.3838 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 95/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3773 - acc: 0.2731 - precision_m: 0.0000e+00 - val_loss: 1.3877 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 96/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3688 - acc: 0.3124 - precision_m: 0.0066 - val_loss: 1.3823 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 97/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3619 - acc: 0.2923 - precision_m: 0.0169 - val_loss: 1.3845 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 98/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3581 - acc: 0.2895 - precision_m: 2.4149e-04 - val_loss: 1.3817 - val_acc: 0.2602 - val_precision_m: 0.0000e+00\n","Epoch 99/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3731 - acc: 0.2831 - precision_m: 0.0059 - val_loss: 1.3861 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 100/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3656 - acc: 0.2847 - precision_m: 0.0000e+00 - val_loss: 1.3857 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 101/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3185 - acc: 0.3514 - precision_m: 0.0249 - val_loss: 1.3851 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 102/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3767 - acc: 0.2359 - precision_m: 0.0031 - val_loss: 1.3891 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 103/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3660 - acc: 0.2692 - precision_m: 0.0000e+00 - val_loss: 1.3875 - val_acc: 0.2439 - val_precision_m: 0.0000e+00\n","Epoch 104/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3449 - acc: 0.3006 - precision_m: 0.0224 - val_loss: 1.3887 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 105/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3466 - acc: 0.2840 - precision_m: 6.9805e-04 - val_loss: 1.3798 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 106/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3722 - acc: 0.2571 - precision_m: 0.0000e+00 - val_loss: 1.3805 - val_acc: 0.2683 - val_precision_m: 0.0000e+00\n","Epoch 107/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3719 - acc: 0.2802 - precision_m: 7.5069e-04 - val_loss: 1.3871 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 108/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3602 - acc: 0.2643 - precision_m: 0.0270 - val_loss: 1.3916 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 109/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3616 - acc: 0.3366 - precision_m: 0.0017 - val_loss: 1.3931 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 110/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3404 - acc: 0.3060 - precision_m: 0.0106 - val_loss: 1.3957 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 111/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3598 - acc: 0.2851 - precision_m: 0.0034 - val_loss: 1.3905 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 112/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3419 - acc: 0.3379 - precision_m: 0.0000e+00 - val_loss: 1.3951 - val_acc: 0.2927 - val_precision_m: 0.0000e+00\n","Epoch 113/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3196 - acc: 0.3071 - precision_m: 0.0178 - val_loss: 1.3876 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 114/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3552 - acc: 0.2521 - precision_m: 0.0000e+00 - val_loss: 1.3855 - val_acc: 0.2927 - val_precision_m: 0.0000e+00\n","Epoch 115/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3566 - acc: 0.2924 - precision_m: 0.0099 - val_loss: 1.3978 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 116/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3522 - acc: 0.2177 - precision_m: 0.0334 - val_loss: 1.3774 - val_acc: 0.2683 - val_precision_m: 0.0000e+00\n","Epoch 117/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3570 - acc: 0.2276 - precision_m: 0.0053 - val_loss: 1.3865 - val_acc: 0.2683 - val_precision_m: 0.0000e+00\n","Epoch 118/500\n","144/144 [==============================] - 0s 3ms/step - loss: 1.3668 - acc: 0.2863 - precision_m: 0.0057 - val_loss: 1.3994 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 119/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3372 - acc: 0.3087 - precision_m: 0.0236 - val_loss: 1.3891 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 120/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3500 - acc: 0.2698 - precision_m: 0.0048 - val_loss: 1.3895 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 121/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3552 - acc: 0.2691 - precision_m: 0.0184 - val_loss: 1.3906 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 122/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3305 - acc: 0.2713 - precision_m: 0.0013 - val_loss: 1.3937 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 123/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3549 - acc: 0.2714 - precision_m: 0.0262 - val_loss: 1.3862 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 124/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3652 - acc: 0.2551 - precision_m: 0.0023 - val_loss: 1.3835 - val_acc: 0.2683 - val_precision_m: 0.0000e+00\n","Epoch 125/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3502 - acc: 0.2561 - precision_m: 0.0030 - val_loss: 1.3876 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 126/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3423 - acc: 0.3141 - precision_m: 0.0120 - val_loss: 1.4067 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 127/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3239 - acc: 0.3238 - precision_m: 0.0038 - val_loss: 1.3932 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 128/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3342 - acc: 0.2891 - precision_m: 0.0000e+00 - val_loss: 1.3894 - val_acc: 0.2683 - val_precision_m: 0.0000e+00\n","Epoch 129/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3732 - acc: 0.1833 - precision_m: 0.0164 - val_loss: 1.4025 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 130/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3561 - acc: 0.2504 - precision_m: 0.0201 - val_loss: 1.3942 - val_acc: 0.2927 - val_precision_m: 0.0000e+00\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 19.8s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","150/150 [==============================] - 1s 3ms/step - loss: 1.5165 - acc: 0.1715 - precision_m: 0.0000e+00 - val_loss: 1.3820 - val_acc: 0.3023 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3848 - acc: 0.2924 - precision_m: 0.0000e+00 - val_loss: 1.3853 - val_acc: 0.2868 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3856 - acc: 0.2952 - precision_m: 0.0000e+00 - val_loss: 1.3855 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3863 - acc: 0.2552 - precision_m: 0.0000e+00 - val_loss: 1.3849 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3853 - acc: 0.2640 - precision_m: 0.0000e+00 - val_loss: 1.3842 - val_acc: 0.2791 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3819 - acc: 0.3166 - precision_m: 0.0000e+00 - val_loss: 1.3841 - val_acc: 0.2791 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3871 - acc: 0.2657 - precision_m: 0.0000e+00 - val_loss: 1.3835 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3823 - acc: 0.2757 - precision_m: 0.0000e+00 - val_loss: 1.3820 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3867 - acc: 0.2630 - precision_m: 0.0000e+00 - val_loss: 1.3822 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3830 - acc: 0.2895 - precision_m: 0.0000e+00 - val_loss: 1.3817 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3841 - acc: 0.2677 - precision_m: 0.0000e+00 - val_loss: 1.3807 - val_acc: 0.2868 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3852 - acc: 0.2860 - precision_m: 0.0000e+00 - val_loss: 1.3811 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3814 - acc: 0.3283 - precision_m: 0.0000e+00 - val_loss: 1.3803 - val_acc: 0.3178 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3967 - acc: 0.2176 - precision_m: 0.0000e+00 - val_loss: 1.3807 - val_acc: 0.2791 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3824 - acc: 0.2813 - precision_m: 0.0000e+00 - val_loss: 1.3810 - val_acc: 0.2791 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3835 - acc: 0.2655 - precision_m: 0.0000e+00 - val_loss: 1.3816 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3833 - acc: 0.2884 - precision_m: 0.0000e+00 - val_loss: 1.3828 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3840 - acc: 0.2882 - precision_m: 0.0000e+00 - val_loss: 1.3826 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3862 - acc: 0.2708 - precision_m: 0.0000e+00 - val_loss: 1.3830 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3860 - acc: 0.2984 - precision_m: 0.0000e+00 - val_loss: 1.3824 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3834 - acc: 0.2947 - precision_m: 0.0000e+00 - val_loss: 1.3812 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3833 - acc: 0.2139 - precision_m: 0.0000e+00 - val_loss: 1.3815 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3821 - acc: 0.2795 - precision_m: 0.0000e+00 - val_loss: 1.3811 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3859 - acc: 0.2905 - precision_m: 0.0000e+00 - val_loss: 1.3821 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3843 - acc: 0.2815 - precision_m: 0.0000e+00 - val_loss: 1.3821 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3876 - acc: 0.2825 - precision_m: 0.0000e+00 - val_loss: 1.3820 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3865 - acc: 0.2401 - precision_m: 0.0000e+00 - val_loss: 1.3814 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3845 - acc: 0.2605 - precision_m: 0.0000e+00 - val_loss: 1.3818 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3899 - acc: 0.2403 - precision_m: 0.0000e+00 - val_loss: 1.3825 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3920 - acc: 0.2673 - precision_m: 0.0000e+00 - val_loss: 1.3816 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3844 - acc: 0.2605 - precision_m: 0.0000e+00 - val_loss: 1.3815 - val_acc: 0.2791 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3829 - acc: 0.2476 - precision_m: 0.0000e+00 - val_loss: 1.3821 - val_acc: 0.2791 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3873 - acc: 0.2170 - precision_m: 0.0000e+00 - val_loss: 1.3807 - val_acc: 0.2868 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3803 - acc: 0.3151 - precision_m: 4.1897e-04 - val_loss: 1.3890 - val_acc: 0.2093 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3871 - acc: 0.2330 - precision_m: 0.0000e+00 - val_loss: 1.3836 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3825 - acc: 0.2783 - precision_m: 0.0000e+00 - val_loss: 1.3820 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3864 - acc: 0.2722 - precision_m: 0.0000e+00 - val_loss: 1.3826 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3853 - acc: 0.2655 - precision_m: 0.0000e+00 - val_loss: 1.3830 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3836 - acc: 0.3089 - precision_m: 0.0000e+00 - val_loss: 1.3837 - val_acc: 0.2791 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3847 - acc: 0.2530 - precision_m: 0.0000e+00 - val_loss: 1.3831 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3845 - acc: 0.2515 - precision_m: 0.0000e+00 - val_loss: 1.3831 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3819 - acc: 0.2520 - precision_m: 0.0000e+00 - val_loss: 1.3822 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3841 - acc: 0.2932 - precision_m: 0.0000e+00 - val_loss: 1.3816 - val_acc: 0.2791 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3795 - acc: 0.2891 - precision_m: 0.0000e+00 - val_loss: 1.3816 - val_acc: 0.2791 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3836 - acc: 0.2720 - precision_m: 0.0000e+00 - val_loss: 1.3828 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3856 - acc: 0.2493 - precision_m: 0.0000e+00 - val_loss: 1.3835 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3868 - acc: 0.2596 - precision_m: 0.0000e+00 - val_loss: 1.3844 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3839 - acc: 0.2744 - precision_m: 0.0000e+00 - val_loss: 1.3841 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3855 - acc: 0.2600 - precision_m: 0.0000e+00 - val_loss: 1.3842 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3903 - acc: 0.2478 - precision_m: 0.0000e+00 - val_loss: 1.3834 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3846 - acc: 0.2875 - precision_m: 0.0000e+00 - val_loss: 1.3829 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 52/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3858 - acc: 0.2426 - precision_m: 0.0000e+00 - val_loss: 1.3807 - val_acc: 0.2791 - val_precision_m: 0.0000e+00\n","Epoch 53/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3843 - acc: 0.2629 - precision_m: 0.0000e+00 - val_loss: 1.3828 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 54/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3846 - acc: 0.2705 - precision_m: 0.0000e+00 - val_loss: 1.3840 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 55/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3819 - acc: 0.3022 - precision_m: 0.0000e+00 - val_loss: 1.3831 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 56/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3807 - acc: 0.2460 - precision_m: 0.0000e+00 - val_loss: 1.3837 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 57/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3835 - acc: 0.2643 - precision_m: 0.0000e+00 - val_loss: 1.3821 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 58/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3820 - acc: 0.2301 - precision_m: 0.0000e+00 - val_loss: 1.3838 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 59/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3810 - acc: 0.2693 - precision_m: 0.0000e+00 - val_loss: 1.3822 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 60/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3858 - acc: 0.2444 - precision_m: 0.0000e+00 - val_loss: 1.3846 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 61/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3763 - acc: 0.2796 - precision_m: 0.0077 - val_loss: 1.3847 - val_acc: 0.2558 - val_precision_m: 0.0000e+00\n","Epoch 62/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3894 - acc: 0.2388 - precision_m: 0.0140 - val_loss: 1.3833 - val_acc: 0.2713 - val_precision_m: 0.0000e+00\n","Epoch 63/500\n","150/150 [==============================] - 0s 2ms/step - loss: 1.3746 - acc: 0.2748 - precision_m: 0.0077 - val_loss: 1.3828 - val_acc: 0.2791 - val_precision_m: 0.0000e+00\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 16.4s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","148/148 [==============================] - 1s 3ms/step - loss: 1.5137 - acc: 0.2323 - precision_m: 0.0578 - val_loss: 1.3877 - val_acc: 0.2344 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.4003 - acc: 0.2464 - precision_m: 0.0000e+00 - val_loss: 1.3875 - val_acc: 0.2734 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3870 - acc: 0.2353 - precision_m: 0.0000e+00 - val_loss: 1.3885 - val_acc: 0.2500 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3936 - acc: 0.2204 - precision_m: 0.0000e+00 - val_loss: 1.3927 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3795 - acc: 0.2453 - precision_m: 0.0000e+00 - val_loss: 1.3870 - val_acc: 0.2266 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3985 - acc: 0.2439 - precision_m: 0.0000e+00 - val_loss: 1.3856 - val_acc: 0.2578 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3874 - acc: 0.2007 - precision_m: 0.0000e+00 - val_loss: 1.3907 - val_acc: 0.2188 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3893 - acc: 0.2359 - precision_m: 0.0000e+00 - val_loss: 1.3889 - val_acc: 0.2266 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3836 - acc: 0.2767 - precision_m: 0.0000e+00 - val_loss: 1.3896 - val_acc: 0.2266 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3819 - acc: 0.2027 - precision_m: 0.0000e+00 - val_loss: 1.3901 - val_acc: 0.2578 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3843 - acc: 0.2900 - precision_m: 0.0000e+00 - val_loss: 1.3891 - val_acc: 0.2500 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3776 - acc: 0.3250 - precision_m: 0.0000e+00 - val_loss: 1.3872 - val_acc: 0.2266 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3811 - acc: 0.2708 - precision_m: 0.0000e+00 - val_loss: 1.3869 - val_acc: 0.2266 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3869 - acc: 0.2853 - precision_m: 0.0000e+00 - val_loss: 1.3877 - val_acc: 0.2344 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3938 - acc: 0.2378 - precision_m: 0.0000e+00 - val_loss: 1.3884 - val_acc: 0.2266 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3842 - acc: 0.2983 - precision_m: 0.0000e+00 - val_loss: 1.3874 - val_acc: 0.2500 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3832 - acc: 0.2692 - precision_m: 0.0000e+00 - val_loss: 1.3878 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3853 - acc: 0.2667 - precision_m: 0.0000e+00 - val_loss: 1.3887 - val_acc: 0.2422 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3842 - acc: 0.2859 - precision_m: 0.0000e+00 - val_loss: 1.3886 - val_acc: 0.2344 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3893 - acc: 0.2351 - precision_m: 0.0000e+00 - val_loss: 1.3887 - val_acc: 0.2422 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3783 - acc: 0.2670 - precision_m: 0.0000e+00 - val_loss: 1.3888 - val_acc: 0.2266 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3760 - acc: 0.3336 - precision_m: 0.0000e+00 - val_loss: 1.3886 - val_acc: 0.2266 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3822 - acc: 0.2567 - precision_m: 0.0000e+00 - val_loss: 1.3881 - val_acc: 0.2344 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3835 - acc: 0.3046 - precision_m: 0.0000e+00 - val_loss: 1.3883 - val_acc: 0.2344 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3817 - acc: 0.2933 - precision_m: 0.0000e+00 - val_loss: 1.3883 - val_acc: 0.2578 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3838 - acc: 0.2836 - precision_m: 0.0000e+00 - val_loss: 1.3881 - val_acc: 0.2422 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3836 - acc: 0.2485 - precision_m: 0.0000e+00 - val_loss: 1.3880 - val_acc: 0.2734 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3810 - acc: 0.2713 - precision_m: 0.0000e+00 - val_loss: 1.3879 - val_acc: 0.2734 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3725 - acc: 0.2969 - precision_m: 0.0000e+00 - val_loss: 1.3879 - val_acc: 0.2812 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3786 - acc: 0.2978 - precision_m: 0.0000e+00 - val_loss: 1.3884 - val_acc: 0.2812 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3781 - acc: 0.2813 - precision_m: 0.0000e+00 - val_loss: 1.3876 - val_acc: 0.2812 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3802 - acc: 0.3173 - precision_m: 0.0000e+00 - val_loss: 1.3881 - val_acc: 0.2812 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3831 - acc: 0.2800 - precision_m: 0.0000e+00 - val_loss: 1.3891 - val_acc: 0.2891 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3781 - acc: 0.3089 - precision_m: 0.0000e+00 - val_loss: 1.3901 - val_acc: 0.2812 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3806 - acc: 0.3204 - precision_m: 0.0000e+00 - val_loss: 1.3893 - val_acc: 0.2578 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3838 - acc: 0.2688 - precision_m: 0.0000e+00 - val_loss: 1.3885 - val_acc: 0.2812 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3767 - acc: 0.2486 - precision_m: 0.0000e+00 - val_loss: 1.3817 - val_acc: 0.2812 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3928 - acc: 0.2570 - precision_m: 0.0000e+00 - val_loss: 1.3878 - val_acc: 0.2578 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3842 - acc: 0.3118 - precision_m: 0.0000e+00 - val_loss: 1.3884 - val_acc: 0.2891 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3748 - acc: 0.3112 - precision_m: 0.0000e+00 - val_loss: 1.3878 - val_acc: 0.2266 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3905 - acc: 0.2112 - precision_m: 0.0000e+00 - val_loss: 1.3875 - val_acc: 0.2344 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3814 - acc: 0.2740 - precision_m: 0.0000e+00 - val_loss: 1.3882 - val_acc: 0.2422 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3825 - acc: 0.3021 - precision_m: 0.0000e+00 - val_loss: 1.3887 - val_acc: 0.2812 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3845 - acc: 0.2726 - precision_m: 0.0000e+00 - val_loss: 1.3883 - val_acc: 0.2812 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3832 - acc: 0.2431 - precision_m: 0.0000e+00 - val_loss: 1.3880 - val_acc: 0.2812 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3806 - acc: 0.2391 - precision_m: 0.0000e+00 - val_loss: 1.3854 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3880 - acc: 0.2540 - precision_m: 0.0130 - val_loss: 1.3877 - val_acc: 0.2422 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3821 - acc: 0.2661 - precision_m: 0.0000e+00 - val_loss: 1.3874 - val_acc: 0.2344 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3796 - acc: 0.3065 - precision_m: 0.0000e+00 - val_loss: 1.3874 - val_acc: 0.2578 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3751 - acc: 0.2431 - precision_m: 0.0000e+00 - val_loss: 1.3869 - val_acc: 0.2422 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3818 - acc: 0.2867 - precision_m: 0.0000e+00 - val_loss: 1.3846 - val_acc: 0.2812 - val_precision_m: 0.0000e+00\n","Epoch 52/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3709 - acc: 0.2944 - precision_m: 0.0000e+00 - val_loss: 1.3867 - val_acc: 0.2812 - val_precision_m: 0.0000e+00\n","Epoch 53/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3840 - acc: 0.2941 - precision_m: 0.0000e+00 - val_loss: 1.3858 - val_acc: 0.2812 - val_precision_m: 0.0000e+00\n","Epoch 54/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3812 - acc: 0.2684 - precision_m: 0.0000e+00 - val_loss: 1.3842 - val_acc: 0.2812 - val_precision_m: 0.0000e+00\n","Epoch 55/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3761 - acc: 0.2887 - precision_m: 0.0000e+00 - val_loss: 1.3858 - val_acc: 0.2891 - val_precision_m: 0.0000e+00\n","Epoch 56/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3754 - acc: 0.2805 - precision_m: 0.0000e+00 - val_loss: 1.3868 - val_acc: 0.2578 - val_precision_m: 0.0000e+00\n","Epoch 57/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3775 - acc: 0.2938 - precision_m: 0.0000e+00 - val_loss: 1.3869 - val_acc: 0.2422 - val_precision_m: 0.0000e+00\n","Epoch 58/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3806 - acc: 0.2642 - precision_m: 0.0028 - val_loss: 1.3868 - val_acc: 0.2734 - val_precision_m: 0.0000e+00\n","Epoch 59/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3752 - acc: 0.2879 - precision_m: 0.0116 - val_loss: 1.3858 - val_acc: 0.2578 - val_precision_m: 0.0000e+00\n","Epoch 60/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3731 - acc: 0.2778 - precision_m: 0.0077 - val_loss: 1.3859 - val_acc: 0.2422 - val_precision_m: 0.0000e+00\n","Epoch 61/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3839 - acc: 0.2341 - precision_m: 0.0051 - val_loss: 1.3846 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 62/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3766 - acc: 0.3082 - precision_m: 0.0000e+00 - val_loss: 1.3821 - val_acc: 0.2578 - val_precision_m: 0.0000e+00\n","Epoch 63/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3656 - acc: 0.3218 - precision_m: 0.0000e+00 - val_loss: 1.3862 - val_acc: 0.2578 - val_precision_m: 0.0000e+00\n","Epoch 64/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3752 - acc: 0.3133 - precision_m: 0.0000e+00 - val_loss: 1.3874 - val_acc: 0.2344 - val_precision_m: 0.0000e+00\n","Epoch 65/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3783 - acc: 0.2399 - precision_m: 0.0000e+00 - val_loss: 1.3837 - val_acc: 0.2500 - val_precision_m: 0.0000e+00\n","Epoch 66/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3756 - acc: 0.2662 - precision_m: 0.0000e+00 - val_loss: 1.3837 - val_acc: 0.2656 - val_precision_m: 0.0000e+00\n","Epoch 67/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3684 - acc: 0.2990 - precision_m: 0.0134 - val_loss: 1.3853 - val_acc: 0.2734 - val_precision_m: 0.0000e+00\n","Epoch 68/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3714 - acc: 0.3290 - precision_m: 0.0057 - val_loss: 1.3883 - val_acc: 0.2500 - val_precision_m: 0.0000e+00\n","Epoch 69/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3803 - acc: 0.2718 - precision_m: 0.0000e+00 - val_loss: 1.3842 - val_acc: 0.2734 - val_precision_m: 0.0000e+00\n","Epoch 70/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3640 - acc: 0.3028 - precision_m: 0.0102 - val_loss: 1.3867 - val_acc: 0.2422 - val_precision_m: 0.0000e+00\n","Epoch 71/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3771 - acc: 0.2541 - precision_m: 0.0000e+00 - val_loss: 1.3829 - val_acc: 0.2500 - val_precision_m: 0.0000e+00\n","Epoch 72/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3828 - acc: 0.2826 - precision_m: 0.0000e+00 - val_loss: 1.3870 - val_acc: 0.2266 - val_precision_m: 0.0000e+00\n","Epoch 73/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3868 - acc: 0.2295 - precision_m: 0.0000e+00 - val_loss: 1.3892 - val_acc: 0.2578 - val_precision_m: 0.0000e+00\n","Epoch 74/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3813 - acc: 0.2678 - precision_m: 0.0000e+00 - val_loss: 1.3866 - val_acc: 0.2344 - val_precision_m: 0.0000e+00\n","Epoch 75/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3566 - acc: 0.3301 - precision_m: 0.0192 - val_loss: 1.3868 - val_acc: 0.2891 - val_precision_m: 0.0000e+00\n","Epoch 76/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3939 - acc: 0.2676 - precision_m: 5.1323e-04 - val_loss: 1.3905 - val_acc: 0.2500 - val_precision_m: 0.0000e+00\n","Epoch 77/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3819 - acc: 0.2627 - precision_m: 0.0045 - val_loss: 1.3878 - val_acc: 0.2500 - val_precision_m: 0.0000e+00\n","Epoch 78/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3628 - acc: 0.3122 - precision_m: 0.0099 - val_loss: 1.3877 - val_acc: 0.2422 - val_precision_m: 0.0000e+00\n","Epoch 79/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3681 - acc: 0.2390 - precision_m: 0.0042 - val_loss: 1.3862 - val_acc: 0.2422 - val_precision_m: 0.0000e+00\n","Epoch 80/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3720 - acc: 0.2957 - precision_m: 0.0060 - val_loss: 1.3850 - val_acc: 0.2188 - val_precision_m: 0.0000e+00\n","Epoch 81/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3688 - acc: 0.3004 - precision_m: 0.0018 - val_loss: 1.3869 - val_acc: 0.2734 - val_precision_m: 0.0000e+00\n","Epoch 82/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3863 - acc: 0.2553 - precision_m: 0.0000e+00 - val_loss: 1.3822 - val_acc: 0.2344 - val_precision_m: 0.0000e+00\n","Epoch 83/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3594 - acc: 0.2958 - precision_m: 0.0000e+00 - val_loss: 1.3887 - val_acc: 0.2578 - val_precision_m: 0.0000e+00\n","Epoch 84/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3764 - acc: 0.2849 - precision_m: 0.0014 - val_loss: 1.3926 - val_acc: 0.2422 - val_precision_m: 0.0000e+00\n","Epoch 85/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3755 - acc: 0.2676 - precision_m: 0.0011 - val_loss: 1.3857 - val_acc: 0.2188 - val_precision_m: 0.0000e+00\n","Epoch 86/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3599 - acc: 0.3229 - precision_m: 0.0072 - val_loss: 1.3922 - val_acc: 0.2500 - val_precision_m: 0.0000e+00\n","Epoch 87/500\n","148/148 [==============================] - 0s 2ms/step - loss: 1.3653 - acc: 0.3145 - precision_m: 0.0011 - val_loss: 1.3825 - val_acc: 0.2734 - val_precision_m: 0.0000e+00\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 17.3s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","158/158 [==============================] - 1s 3ms/step - loss: 1.4134 - acc: 0.2272 - precision_m: 0.0000e+00 - val_loss: 1.3846 - val_acc: 0.2647 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3888 - acc: 0.2458 - precision_m: 0.0000e+00 - val_loss: 1.3884 - val_acc: 0.2279 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3852 - acc: 0.2789 - precision_m: 0.0000e+00 - val_loss: 1.3863 - val_acc: 0.2353 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3921 - acc: 0.2046 - precision_m: 0.0000e+00 - val_loss: 1.3896 - val_acc: 0.2426 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3849 - acc: 0.2877 - precision_m: 0.0000e+00 - val_loss: 1.3877 - val_acc: 0.2426 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3855 - acc: 0.2558 - precision_m: 0.0000e+00 - val_loss: 1.3875 - val_acc: 0.2426 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3876 - acc: 0.2612 - precision_m: 0.0000e+00 - val_loss: 1.3881 - val_acc: 0.2426 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3848 - acc: 0.2312 - precision_m: 0.0000e+00 - val_loss: 1.3864 - val_acc: 0.2647 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3891 - acc: 0.2566 - precision_m: 0.0000e+00 - val_loss: 1.3866 - val_acc: 0.2279 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3852 - acc: 0.2755 - precision_m: 0.0000e+00 - val_loss: 1.3861 - val_acc: 0.2279 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3856 - acc: 0.2666 - precision_m: 0.0000e+00 - val_loss: 1.3866 - val_acc: 0.2353 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3877 - acc: 0.2045 - precision_m: 0.0000e+00 - val_loss: 1.3865 - val_acc: 0.2353 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3843 - acc: 0.3198 - precision_m: 0.0000e+00 - val_loss: 1.3873 - val_acc: 0.2353 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3835 - acc: 0.2651 - precision_m: 0.0000e+00 - val_loss: 1.3859 - val_acc: 0.2353 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3835 - acc: 0.2855 - precision_m: 0.0000e+00 - val_loss: 1.3865 - val_acc: 0.2353 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3843 - acc: 0.2582 - precision_m: 0.0000e+00 - val_loss: 1.3867 - val_acc: 0.2353 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3883 - acc: 0.2065 - precision_m: 0.0000e+00 - val_loss: 1.3855 - val_acc: 0.2353 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3816 - acc: 0.2824 - precision_m: 0.0000e+00 - val_loss: 1.3874 - val_acc: 0.2279 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3853 - acc: 0.2117 - precision_m: 0.0000e+00 - val_loss: 1.3880 - val_acc: 0.2353 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3834 - acc: 0.2671 - precision_m: 0.0000e+00 - val_loss: 1.3865 - val_acc: 0.2279 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3820 - acc: 0.1989 - precision_m: 0.0000e+00 - val_loss: 1.3869 - val_acc: 0.2279 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3776 - acc: 0.2341 - precision_m: 0.0000e+00 - val_loss: 1.3878 - val_acc: 0.2279 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3869 - acc: 0.2170 - precision_m: 0.0000e+00 - val_loss: 1.3874 - val_acc: 0.2279 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3888 - acc: 0.2647 - precision_m: 0.0000e+00 - val_loss: 1.3883 - val_acc: 0.2353 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3882 - acc: 0.2962 - precision_m: 0.0000e+00 - val_loss: 1.3877 - val_acc: 0.2353 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3864 - acc: 0.2911 - precision_m: 0.0000e+00 - val_loss: 1.3924 - val_acc: 0.2353 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3853 - acc: 0.3041 - precision_m: 0.0000e+00 - val_loss: 1.3871 - val_acc: 0.2426 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3879 - acc: 0.2323 - precision_m: 0.0000e+00 - val_loss: 1.3886 - val_acc: 0.2353 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3884 - acc: 0.2333 - precision_m: 0.0000e+00 - val_loss: 1.3875 - val_acc: 0.2574 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3843 - acc: 0.2637 - precision_m: 0.0000e+00 - val_loss: 1.3859 - val_acc: 0.2426 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3872 - acc: 0.2771 - precision_m: 0.0000e+00 - val_loss: 1.3859 - val_acc: 0.2794 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3858 - acc: 0.2831 - precision_m: 0.0000e+00 - val_loss: 1.3872 - val_acc: 0.2279 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3884 - acc: 0.2326 - precision_m: 0.0000e+00 - val_loss: 1.3885 - val_acc: 0.2132 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3839 - acc: 0.2349 - precision_m: 0.0000e+00 - val_loss: 1.3886 - val_acc: 0.2426 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3839 - acc: 0.2719 - precision_m: 0.0000e+00 - val_loss: 1.3884 - val_acc: 0.2426 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3798 - acc: 0.2653 - precision_m: 0.0000e+00 - val_loss: 1.3885 - val_acc: 0.2279 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3795 - acc: 0.2800 - precision_m: 0.0000e+00 - val_loss: 1.3880 - val_acc: 0.2426 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3786 - acc: 0.2660 - precision_m: 0.0000e+00 - val_loss: 1.3924 - val_acc: 0.2353 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3809 - acc: 0.2366 - precision_m: 0.0000e+00 - val_loss: 1.3864 - val_acc: 0.2574 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3824 - acc: 0.2102 - precision_m: 0.0000e+00 - val_loss: 1.3871 - val_acc: 0.2353 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3789 - acc: 0.3087 - precision_m: 0.0000e+00 - val_loss: 1.3915 - val_acc: 0.2426 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3858 - acc: 0.2608 - precision_m: 0.0000e+00 - val_loss: 1.3865 - val_acc: 0.2574 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3813 - acc: 0.2621 - precision_m: 0.0000e+00 - val_loss: 1.3881 - val_acc: 0.2500 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3836 - acc: 0.2112 - precision_m: 0.0000e+00 - val_loss: 1.3908 - val_acc: 0.2279 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3848 - acc: 0.2416 - precision_m: 0.0000e+00 - val_loss: 1.3927 - val_acc: 0.2426 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3835 - acc: 0.2532 - precision_m: 0.0000e+00 - val_loss: 1.3978 - val_acc: 0.2574 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3719 - acc: 0.2683 - precision_m: 0.0000e+00 - val_loss: 1.3906 - val_acc: 0.2868 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3822 - acc: 0.2794 - precision_m: 0.0000e+00 - val_loss: 1.3905 - val_acc: 0.2426 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3776 - acc: 0.2770 - precision_m: 0.0000e+00 - val_loss: 1.3891 - val_acc: 0.2500 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3753 - acc: 0.2602 - precision_m: 0.0000e+00 - val_loss: 1.3902 - val_acc: 0.2353 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","158/158 [==============================] - 0s 2ms/step - loss: 1.3732 - acc: 0.3185 - precision_m: 0.0000e+00 - val_loss: 1.3910 - val_acc: 0.2426 - val_precision_m: 0.0000e+00\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 22.0s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","162/162 [==============================] - 1s 3ms/step - loss: 1.4338 - acc: 0.2497 - precision_m: 0.0168 - val_loss: 1.3960 - val_acc: 0.1799 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3794 - acc: 0.2554 - precision_m: 0.0000e+00 - val_loss: 1.3804 - val_acc: 0.2446 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3822 - acc: 0.3533 - precision_m: 0.0000e+00 - val_loss: 1.3929 - val_acc: 0.2662 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3876 - acc: 0.3007 - precision_m: 0.0000e+00 - val_loss: 1.3848 - val_acc: 0.2590 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3852 - acc: 0.2829 - precision_m: 0.0000e+00 - val_loss: 1.3895 - val_acc: 0.2518 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3758 - acc: 0.3128 - precision_m: 0.0000e+00 - val_loss: 1.3922 - val_acc: 0.2230 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3870 - acc: 0.2430 - precision_m: 0.0000e+00 - val_loss: 1.3798 - val_acc: 0.3022 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3865 - acc: 0.2757 - precision_m: 0.0000e+00 - val_loss: 1.4010 - val_acc: 0.2230 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3856 - acc: 0.3311 - precision_m: 0.0000e+00 - val_loss: 1.3822 - val_acc: 0.2518 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3803 - acc: 0.2718 - precision_m: 0.0000e+00 - val_loss: 1.3916 - val_acc: 0.2662 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3956 - acc: 0.2670 - precision_m: 0.0000e+00 - val_loss: 1.3832 - val_acc: 0.2806 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3750 - acc: 0.3182 - precision_m: 0.0000e+00 - val_loss: 1.3756 - val_acc: 0.3165 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3794 - acc: 0.2624 - precision_m: 0.0000e+00 - val_loss: 1.3888 - val_acc: 0.2302 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3715 - acc: 0.3041 - precision_m: 0.0000e+00 - val_loss: 1.3723 - val_acc: 0.2590 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3701 - acc: 0.2981 - precision_m: 0.0000e+00 - val_loss: 1.3731 - val_acc: 0.2806 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3546 - acc: 0.3352 - precision_m: 0.0000e+00 - val_loss: 1.3710 - val_acc: 0.2950 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3710 - acc: 0.2806 - precision_m: 0.0000e+00 - val_loss: 1.4184 - val_acc: 0.1655 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3810 - acc: 0.3533 - precision_m: 0.0000e+00 - val_loss: 1.3765 - val_acc: 0.2590 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3658 - acc: 0.3047 - precision_m: 0.0000e+00 - val_loss: 1.3579 - val_acc: 0.2734 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3476 - acc: 0.3209 - precision_m: 0.0000e+00 - val_loss: 1.3545 - val_acc: 0.3381 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3712 - acc: 0.2869 - precision_m: 0.0000e+00 - val_loss: 1.3579 - val_acc: 0.2806 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3646 - acc: 0.3077 - precision_m: 0.0000e+00 - val_loss: 1.3624 - val_acc: 0.2950 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3408 - acc: 0.3061 - precision_m: 0.0000e+00 - val_loss: 1.3480 - val_acc: 0.2734 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3478 - acc: 0.2842 - precision_m: 0.0000e+00 - val_loss: 1.3590 - val_acc: 0.3094 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3286 - acc: 0.3376 - precision_m: 0.0000e+00 - val_loss: 1.3495 - val_acc: 0.2878 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3373 - acc: 0.3098 - precision_m: 0.0000e+00 - val_loss: 1.3447 - val_acc: 0.2806 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3444 - acc: 0.3179 - precision_m: 0.0000e+00 - val_loss: 1.3415 - val_acc: 0.2950 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3500 - acc: 0.3175 - precision_m: 0.0040 - val_loss: 1.3487 - val_acc: 0.2950 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3671 - acc: 0.3019 - precision_m: 0.0000e+00 - val_loss: 1.3524 - val_acc: 0.2806 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3477 - acc: 0.3773 - precision_m: 0.0019 - val_loss: 1.3375 - val_acc: 0.2878 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3480 - acc: 0.2982 - precision_m: 0.0000e+00 - val_loss: 1.3419 - val_acc: 0.2806 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3493 - acc: 0.2760 - precision_m: 0.0000e+00 - val_loss: 1.3599 - val_acc: 0.2734 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3339 - acc: 0.3256 - precision_m: 0.0019 - val_loss: 1.3303 - val_acc: 0.2806 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3441 - acc: 0.3483 - precision_m: 0.0016 - val_loss: 1.3307 - val_acc: 0.2950 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3170 - acc: 0.3025 - precision_m: 0.0385 - val_loss: 1.3312 - val_acc: 0.2878 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3225 - acc: 0.2959 - precision_m: 0.0000e+00 - val_loss: 1.3162 - val_acc: 0.2950 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3359 - acc: 0.3355 - precision_m: 0.0086 - val_loss: 1.3114 - val_acc: 0.3094 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3237 - acc: 0.3452 - precision_m: 0.0020 - val_loss: 1.3087 - val_acc: 0.2950 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3068 - acc: 0.3795 - precision_m: 0.0158 - val_loss: 1.3226 - val_acc: 0.3309 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3422 - acc: 0.3242 - precision_m: 0.0020 - val_loss: 1.3040 - val_acc: 0.3094 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3522 - acc: 0.2900 - precision_m: 0.0000e+00 - val_loss: 1.3056 - val_acc: 0.3165 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3454 - acc: 0.3636 - precision_m: 0.0000e+00 - val_loss: 1.2992 - val_acc: 0.3165 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2723 - acc: 0.3796 - precision_m: 0.0175 - val_loss: 1.3163 - val_acc: 0.2806 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3163 - acc: 0.4247 - precision_m: 0.0321 - val_loss: 1.2921 - val_acc: 0.2950 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3004 - acc: 0.3668 - precision_m: 0.0468 - val_loss: 1.2862 - val_acc: 0.3094 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2837 - acc: 0.3793 - precision_m: 0.0130 - val_loss: 1.3064 - val_acc: 0.2950 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3207 - acc: 0.3350 - precision_m: 0.0127 - val_loss: 1.2850 - val_acc: 0.3381 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3408 - acc: 0.3625 - precision_m: 0.0243 - val_loss: 1.2858 - val_acc: 0.2950 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2825 - acc: 0.3547 - precision_m: 0.0306 - val_loss: 1.2816 - val_acc: 0.3165 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2874 - acc: 0.3351 - precision_m: 0.0267 - val_loss: 1.3057 - val_acc: 0.2806 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2952 - acc: 0.3862 - precision_m: 0.0100 - val_loss: 1.2799 - val_acc: 0.3669 - val_precision_m: 0.0000e+00\n","Epoch 52/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2699 - acc: 0.3651 - precision_m: 0.0772 - val_loss: 1.2760 - val_acc: 0.3741 - val_precision_m: 0.0000e+00\n","Epoch 53/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2875 - acc: 0.3170 - precision_m: 0.0249 - val_loss: 1.3012 - val_acc: 0.2806 - val_precision_m: 0.0000e+00\n","Epoch 54/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2714 - acc: 0.3480 - precision_m: 0.0589 - val_loss: 1.2918 - val_acc: 0.2950 - val_precision_m: 0.0000e+00\n","Epoch 55/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3070 - acc: 0.3740 - precision_m: 0.0056 - val_loss: 1.2652 - val_acc: 0.3525 - val_precision_m: 0.0000e+00\n","Epoch 56/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2070 - acc: 0.4026 - precision_m: 0.0203 - val_loss: 1.2574 - val_acc: 0.3309 - val_precision_m: 0.0000e+00\n","Epoch 57/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2961 - acc: 0.4138 - precision_m: 0.0157 - val_loss: 1.2802 - val_acc: 0.3165 - val_precision_m: 0.0000e+00\n","Epoch 58/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3016 - acc: 0.3450 - precision_m: 0.0325 - val_loss: 1.2554 - val_acc: 0.3813 - val_precision_m: 0.0000e+00\n","Epoch 59/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3017 - acc: 0.3244 - precision_m: 0.0148 - val_loss: 1.2632 - val_acc: 0.3525 - val_precision_m: 0.0000e+00\n","Epoch 60/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2702 - acc: 0.3612 - precision_m: 0.0376 - val_loss: 1.2530 - val_acc: 0.3813 - val_precision_m: 0.0143\n","Epoch 61/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2707 - acc: 0.4054 - precision_m: 0.0389 - val_loss: 1.2472 - val_acc: 0.3885 - val_precision_m: 0.0000e+00\n","Epoch 62/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2564 - acc: 0.3589 - precision_m: 0.0226 - val_loss: 1.2517 - val_acc: 0.3309 - val_precision_m: 0.0000e+00\n","Epoch 63/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2510 - acc: 0.4479 - precision_m: 0.0212 - val_loss: 1.2509 - val_acc: 0.3453 - val_precision_m: 0.0000e+00\n","Epoch 64/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2744 - acc: 0.3472 - precision_m: 0.0158 - val_loss: 1.2679 - val_acc: 0.3237 - val_precision_m: 0.0000e+00\n","Epoch 65/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2934 - acc: 0.3796 - precision_m: 0.0320 - val_loss: 1.2566 - val_acc: 0.3525 - val_precision_m: 0.0143\n","Epoch 66/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3192 - acc: 0.3163 - precision_m: 0.0388 - val_loss: 1.2645 - val_acc: 0.3165 - val_precision_m: 0.0000e+00\n","Epoch 67/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2704 - acc: 0.3649 - precision_m: 0.0540 - val_loss: 1.2645 - val_acc: 0.3165 - val_precision_m: 0.0000e+00\n","Epoch 68/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2437 - acc: 0.4109 - precision_m: 0.0468 - val_loss: 1.2636 - val_acc: 0.3094 - val_precision_m: 0.0000e+00\n","Epoch 69/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2650 - acc: 0.4066 - precision_m: 0.0436 - val_loss: 1.2691 - val_acc: 0.2950 - val_precision_m: 0.0000e+00\n","Epoch 70/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2511 - acc: 0.3821 - precision_m: 0.0129 - val_loss: 1.2659 - val_acc: 0.3022 - val_precision_m: 0.0000e+00\n","Epoch 71/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2388 - acc: 0.4099 - precision_m: 0.0460 - val_loss: 1.2653 - val_acc: 0.3022 - val_precision_m: 0.0000e+00\n","Epoch 72/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2552 - acc: 0.3645 - precision_m: 0.0627 - val_loss: 1.2368 - val_acc: 0.3309 - val_precision_m: 0.0000e+00\n","Epoch 73/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2903 - acc: 0.3285 - precision_m: 2.2793e-04 - val_loss: 1.2366 - val_acc: 0.3165 - val_precision_m: 0.0000e+00\n","Epoch 74/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2804 - acc: 0.3732 - precision_m: 0.0317 - val_loss: 1.2455 - val_acc: 0.3165 - val_precision_m: 0.0000e+00\n","Epoch 75/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2730 - acc: 0.3303 - precision_m: 0.0438 - val_loss: 1.2611 - val_acc: 0.3094 - val_precision_m: 0.0000e+00\n","Epoch 76/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2356 - acc: 0.4016 - precision_m: 0.0705 - val_loss: 1.2543 - val_acc: 0.3309 - val_precision_m: 0.0000e+00\n","Epoch 77/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3026 - acc: 0.3137 - precision_m: 0.0260 - val_loss: 1.2422 - val_acc: 0.3094 - val_precision_m: 0.0000e+00\n","Epoch 78/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2711 - acc: 0.4040 - precision_m: 0.0819 - val_loss: 1.2424 - val_acc: 0.3381 - val_precision_m: 0.0143\n","Epoch 79/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2621 - acc: 0.3724 - precision_m: 0.0873 - val_loss: 1.2708 - val_acc: 0.3022 - val_precision_m: 0.0000e+00\n","Epoch 80/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2477 - acc: 0.4031 - precision_m: 0.0336 - val_loss: 1.2864 - val_acc: 0.2950 - val_precision_m: 0.0143\n","Epoch 81/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3014 - acc: 0.3382 - precision_m: 0.0664 - val_loss: 1.2461 - val_acc: 0.3309 - val_precision_m: 0.0143\n","Epoch 82/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1764 - acc: 0.4264 - precision_m: 0.1911 - val_loss: 1.2380 - val_acc: 0.3165 - val_precision_m: 0.0143\n","Epoch 83/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2597 - acc: 0.3575 - precision_m: 0.0416 - val_loss: 1.2304 - val_acc: 0.3165 - val_precision_m: 0.0000e+00\n","Epoch 84/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2567 - acc: 0.3312 - precision_m: 0.0417 - val_loss: 1.2338 - val_acc: 0.3381 - val_precision_m: 0.0143\n","Epoch 85/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3061 - acc: 0.3274 - precision_m: 0.0680 - val_loss: 1.2518 - val_acc: 0.3094 - val_precision_m: 0.0000e+00\n","Epoch 86/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2166 - acc: 0.3618 - precision_m: 0.0677 - val_loss: 1.2294 - val_acc: 0.3237 - val_precision_m: 0.0143\n","Epoch 87/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2332 - acc: 0.4010 - precision_m: 0.0300 - val_loss: 1.2414 - val_acc: 0.3094 - val_precision_m: 0.0143\n","Epoch 88/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2555 - acc: 0.3701 - precision_m: 0.0631 - val_loss: 1.2777 - val_acc: 0.2878 - val_precision_m: 0.0143\n","Epoch 89/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3007 - acc: 0.3613 - precision_m: 0.1000 - val_loss: 1.2493 - val_acc: 0.3094 - val_precision_m: 0.0286\n","Epoch 90/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2276 - acc: 0.4165 - precision_m: 0.0932 - val_loss: 1.3129 - val_acc: 0.2878 - val_precision_m: 0.0429\n","Epoch 91/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2649 - acc: 0.3549 - precision_m: 0.0486 - val_loss: 1.2574 - val_acc: 0.3165 - val_precision_m: 0.0143\n","Epoch 92/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2679 - acc: 0.3292 - precision_m: 0.0483 - val_loss: 1.2204 - val_acc: 0.3453 - val_precision_m: 0.0000e+00\n","Epoch 93/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2189 - acc: 0.3868 - precision_m: 0.0213 - val_loss: 1.2293 - val_acc: 0.3597 - val_precision_m: 0.0143\n","Epoch 94/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2247 - acc: 0.4037 - precision_m: 0.1376 - val_loss: 1.2416 - val_acc: 0.3741 - val_precision_m: 0.0286\n","Epoch 95/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2500 - acc: 0.3279 - precision_m: 0.0342 - val_loss: 1.2462 - val_acc: 0.3453 - val_precision_m: 0.0429\n","Epoch 96/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2269 - acc: 0.3772 - precision_m: 0.0881 - val_loss: 1.2420 - val_acc: 0.3309 - val_precision_m: 0.0143\n","Epoch 97/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2503 - acc: 0.4353 - precision_m: 0.1063 - val_loss: 1.2636 - val_acc: 0.2878 - val_precision_m: 0.0143\n","Epoch 98/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2663 - acc: 0.4158 - precision_m: 0.1256 - val_loss: 1.2783 - val_acc: 0.2662 - val_precision_m: 0.0000e+00\n","Epoch 99/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2702 - acc: 0.3898 - precision_m: 0.1019 - val_loss: 1.2686 - val_acc: 0.2806 - val_precision_m: 0.0143\n","Epoch 100/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1921 - acc: 0.4327 - precision_m: 0.0655 - val_loss: 1.2212 - val_acc: 0.3094 - val_precision_m: 0.0000e+00\n","Epoch 101/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2529 - acc: 0.3838 - precision_m: 0.0700 - val_loss: 1.2883 - val_acc: 0.3094 - val_precision_m: 0.0143\n","Epoch 102/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2413 - acc: 0.3502 - precision_m: 0.1185 - val_loss: 1.2313 - val_acc: 0.3165 - val_precision_m: 0.0000e+00\n","Epoch 103/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3003 - acc: 0.3913 - precision_m: 0.1267 - val_loss: 1.2212 - val_acc: 0.3309 - val_precision_m: 0.0000e+00\n","Epoch 104/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2010 - acc: 0.3558 - precision_m: 0.0690 - val_loss: 1.2252 - val_acc: 0.3525 - val_precision_m: 0.0143\n","Epoch 105/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2779 - acc: 0.3951 - precision_m: 0.0561 - val_loss: 1.2391 - val_acc: 0.3165 - val_precision_m: 0.0000e+00\n","Epoch 106/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2560 - acc: 0.3939 - precision_m: 0.0974 - val_loss: 1.2469 - val_acc: 0.3309 - val_precision_m: 0.0143\n","Epoch 107/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2396 - acc: 0.4227 - precision_m: 0.0917 - val_loss: 1.2163 - val_acc: 0.3453 - val_precision_m: 0.0000e+00\n","Epoch 108/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1940 - acc: 0.3804 - precision_m: 0.0422 - val_loss: 1.2154 - val_acc: 0.3957 - val_precision_m: 0.0571\n","Epoch 109/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2457 - acc: 0.3734 - precision_m: 0.0862 - val_loss: 1.2247 - val_acc: 0.3813 - val_precision_m: 0.0571\n","Epoch 110/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2879 - acc: 0.3538 - precision_m: 0.0374 - val_loss: 1.2384 - val_acc: 0.3022 - val_precision_m: 0.0000e+00\n","Epoch 111/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2552 - acc: 0.3815 - precision_m: 0.0297 - val_loss: 1.2305 - val_acc: 0.3165 - val_precision_m: 0.0000e+00\n","Epoch 112/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2430 - acc: 0.3917 - precision_m: 0.1076 - val_loss: 1.2238 - val_acc: 0.3094 - val_precision_m: 0.0000e+00\n","Epoch 113/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2048 - acc: 0.3615 - precision_m: 0.1145 - val_loss: 1.2172 - val_acc: 0.2950 - val_precision_m: 0.0000e+00\n","Epoch 114/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2135 - acc: 0.4355 - precision_m: 0.1359 - val_loss: 1.2926 - val_acc: 0.2734 - val_precision_m: 0.0286\n","Epoch 115/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1780 - acc: 0.3932 - precision_m: 0.1226 - val_loss: 1.2216 - val_acc: 0.3094 - val_precision_m: 0.0143\n","Epoch 116/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2484 - acc: 0.3571 - precision_m: 0.1194 - val_loss: 1.2087 - val_acc: 0.3165 - val_precision_m: 0.0143\n","Epoch 117/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2264 - acc: 0.3736 - precision_m: 0.0454 - val_loss: 1.2175 - val_acc: 0.3094 - val_precision_m: 0.0000e+00\n","Epoch 118/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2259 - acc: 0.3994 - precision_m: 0.0760 - val_loss: 1.2264 - val_acc: 0.3094 - val_precision_m: 0.0286\n","Epoch 119/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2127 - acc: 0.3777 - precision_m: 0.0648 - val_loss: 1.2239 - val_acc: 0.3022 - val_precision_m: 0.0000e+00\n","Epoch 120/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2463 - acc: 0.3621 - precision_m: 0.1628 - val_loss: 1.2367 - val_acc: 0.3165 - val_precision_m: 0.0286\n","Epoch 121/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2545 - acc: 0.3665 - precision_m: 0.0758 - val_loss: 1.2990 - val_acc: 0.2662 - val_precision_m: 0.0286\n","Epoch 122/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1754 - acc: 0.4078 - precision_m: 0.0904 - val_loss: 1.2073 - val_acc: 0.3237 - val_precision_m: 0.0000e+00\n","Epoch 123/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2211 - acc: 0.3969 - precision_m: 0.0712 - val_loss: 1.2627 - val_acc: 0.2950 - val_precision_m: 0.0571\n","Epoch 124/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2029 - acc: 0.4652 - precision_m: 0.1255 - val_loss: 1.2519 - val_acc: 0.2950 - val_precision_m: 0.0143\n","Epoch 125/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2867 - acc: 0.3684 - precision_m: 0.1062 - val_loss: 1.2224 - val_acc: 0.3165 - val_precision_m: 0.0143\n","Epoch 126/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2619 - acc: 0.3640 - precision_m: 0.1164 - val_loss: 1.2070 - val_acc: 0.3453 - val_precision_m: 0.0000e+00\n","Epoch 127/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2634 - acc: 0.4209 - precision_m: 0.1006 - val_loss: 1.2148 - val_acc: 0.3237 - val_precision_m: 0.0000e+00\n","Epoch 128/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1942 - acc: 0.3796 - precision_m: 0.0478 - val_loss: 1.2791 - val_acc: 0.2734 - val_precision_m: 0.0000e+00\n","Epoch 129/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3193 - acc: 0.3357 - precision_m: 0.0556 - val_loss: 1.2370 - val_acc: 0.3237 - val_precision_m: 0.0143\n","Epoch 130/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2214 - acc: 0.3804 - precision_m: 0.0725 - val_loss: 1.2169 - val_acc: 0.3309 - val_precision_m: 0.0143\n","Epoch 131/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2506 - acc: 0.3813 - precision_m: 0.0945 - val_loss: 1.2055 - val_acc: 0.3094 - val_precision_m: 0.0000e+00\n","Epoch 132/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2578 - acc: 0.3996 - precision_m: 0.0658 - val_loss: 1.2220 - val_acc: 0.3453 - val_precision_m: 0.0143\n","Epoch 133/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2250 - acc: 0.3858 - precision_m: 0.0909 - val_loss: 1.2322 - val_acc: 0.3453 - val_precision_m: 0.0286\n","Epoch 134/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3081 - acc: 0.3355 - precision_m: 0.0878 - val_loss: 1.2181 - val_acc: 0.2950 - val_precision_m: 0.0000e+00\n","Epoch 135/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2662 - acc: 0.3547 - precision_m: 0.1147 - val_loss: 1.2142 - val_acc: 0.3669 - val_precision_m: 0.0429\n","Epoch 136/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2699 - acc: 0.3365 - precision_m: 0.0880 - val_loss: 1.2071 - val_acc: 0.3813 - val_precision_m: 0.0286\n","Epoch 137/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2411 - acc: 0.3621 - precision_m: 0.1173 - val_loss: 1.2139 - val_acc: 0.3309 - val_precision_m: 0.0000e+00\n","Epoch 138/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2332 - acc: 0.3857 - precision_m: 0.0851 - val_loss: 1.3784 - val_acc: 0.2662 - val_precision_m: 0.0286\n","Epoch 139/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2194 - acc: 0.3710 - precision_m: 0.0896 - val_loss: 1.2932 - val_acc: 0.2734 - val_precision_m: 0.0000e+00\n","Epoch 140/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2774 - acc: 0.3389 - precision_m: 0.0921 - val_loss: 1.2656 - val_acc: 0.2950 - val_precision_m: 0.0000e+00\n","Epoch 141/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2504 - acc: 0.3698 - precision_m: 0.0890 - val_loss: 1.2195 - val_acc: 0.3309 - val_precision_m: 0.0143\n","Epoch 142/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2639 - acc: 0.3376 - precision_m: 0.0507 - val_loss: 1.2577 - val_acc: 0.3309 - val_precision_m: 0.0000e+00\n","Epoch 143/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2492 - acc: 0.3726 - precision_m: 0.1579 - val_loss: 1.2194 - val_acc: 0.3165 - val_precision_m: 0.0286\n","Epoch 144/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2419 - acc: 0.3593 - precision_m: 0.0709 - val_loss: 1.2729 - val_acc: 0.2950 - val_precision_m: 0.0857\n","Epoch 145/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2355 - acc: 0.3703 - precision_m: 0.1275 - val_loss: 1.2077 - val_acc: 0.3237 - val_precision_m: 0.0143\n","Epoch 146/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1794 - acc: 0.4040 - precision_m: 0.1520 - val_loss: 1.2555 - val_acc: 0.2950 - val_precision_m: 0.0143\n","Epoch 147/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2323 - acc: 0.3784 - precision_m: 0.0895 - val_loss: 1.2062 - val_acc: 0.3237 - val_precision_m: 0.0143\n","Epoch 148/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2156 - acc: 0.3874 - precision_m: 0.1345 - val_loss: 1.2560 - val_acc: 0.2950 - val_precision_m: 0.1000\n","Epoch 149/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2619 - acc: 0.3989 - precision_m: 0.1770 - val_loss: 1.2197 - val_acc: 0.3381 - val_precision_m: 0.0429\n","Epoch 150/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3095 - acc: 0.3568 - precision_m: 0.1009 - val_loss: 1.2415 - val_acc: 0.2878 - val_precision_m: 0.0143\n","Epoch 151/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2002 - acc: 0.4256 - precision_m: 0.1728 - val_loss: 1.2401 - val_acc: 0.3309 - val_precision_m: 0.0286\n","Epoch 152/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2261 - acc: 0.3762 - precision_m: 0.1090 - val_loss: 1.2522 - val_acc: 0.3022 - val_precision_m: 0.0571\n","Epoch 153/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2262 - acc: 0.3793 - precision_m: 0.1122 - val_loss: 1.2517 - val_acc: 0.3165 - val_precision_m: 0.0571\n","Epoch 154/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1826 - acc: 0.4745 - precision_m: 0.1605 - val_loss: 1.2003 - val_acc: 0.3813 - val_precision_m: 0.0571\n","Epoch 155/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2671 - acc: 0.3181 - precision_m: 0.0585 - val_loss: 1.3462 - val_acc: 0.2806 - val_precision_m: 0.0286\n","Epoch 156/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2328 - acc: 0.3845 - precision_m: 0.1355 - val_loss: 1.1952 - val_acc: 0.3309 - val_precision_m: 0.0571\n","Epoch 157/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2077 - acc: 0.4391 - precision_m: 0.1727 - val_loss: 1.2081 - val_acc: 0.3165 - val_precision_m: 0.0143\n","Epoch 158/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2303 - acc: 0.3487 - precision_m: 0.0920 - val_loss: 1.2235 - val_acc: 0.3381 - val_precision_m: 0.0429\n","Epoch 159/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2272 - acc: 0.3435 - precision_m: 0.1854 - val_loss: 1.1986 - val_acc: 0.3597 - val_precision_m: 0.0000e+00\n","Epoch 160/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2139 - acc: 0.3773 - precision_m: 0.1014 - val_loss: 1.2103 - val_acc: 0.3669 - val_precision_m: 0.0286\n","Epoch 161/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1812 - acc: 0.3922 - precision_m: 0.1306 - val_loss: 1.2001 - val_acc: 0.3381 - val_precision_m: 0.0429\n","Epoch 162/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2203 - acc: 0.3922 - precision_m: 0.1119 - val_loss: 1.2213 - val_acc: 0.3237 - val_precision_m: 0.0429\n","Epoch 163/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1866 - acc: 0.4153 - precision_m: 0.1494 - val_loss: 1.2086 - val_acc: 0.3381 - val_precision_m: 0.0143\n","Epoch 164/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2083 - acc: 0.4287 - precision_m: 0.1174 - val_loss: 1.1900 - val_acc: 0.3165 - val_precision_m: 0.0571\n","Epoch 165/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1841 - acc: 0.4044 - precision_m: 0.1896 - val_loss: 1.2308 - val_acc: 0.3022 - val_precision_m: 0.0143\n","Epoch 166/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1481 - acc: 0.4419 - precision_m: 0.1894 - val_loss: 1.2610 - val_acc: 0.3237 - val_precision_m: 0.0286\n","Epoch 167/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2294 - acc: 0.3992 - precision_m: 0.1475 - val_loss: 1.2003 - val_acc: 0.3381 - val_precision_m: 0.0714\n","Epoch 168/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2301 - acc: 0.3783 - precision_m: 0.1453 - val_loss: 1.2135 - val_acc: 0.3237 - val_precision_m: 0.0143\n","Epoch 169/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2195 - acc: 0.3611 - precision_m: 0.0871 - val_loss: 1.2168 - val_acc: 0.3309 - val_precision_m: 0.0286\n","Epoch 170/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1880 - acc: 0.4149 - precision_m: 0.1345 - val_loss: 1.1892 - val_acc: 0.3381 - val_precision_m: 0.0286\n","Epoch 171/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1510 - acc: 0.3798 - precision_m: 0.0954 - val_loss: 1.1836 - val_acc: 0.3237 - val_precision_m: 0.0286\n","Epoch 172/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1657 - acc: 0.4117 - precision_m: 0.1030 - val_loss: 1.1922 - val_acc: 0.3309 - val_precision_m: 0.0143\n","Epoch 173/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2135 - acc: 0.4039 - precision_m: 0.1159 - val_loss: 1.1839 - val_acc: 0.3453 - val_precision_m: 0.0286\n","Epoch 174/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2363 - acc: 0.3628 - precision_m: 0.0841 - val_loss: 1.1871 - val_acc: 0.3309 - val_precision_m: 0.0714\n","Epoch 175/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2273 - acc: 0.3902 - precision_m: 0.0975 - val_loss: 1.1918 - val_acc: 0.3022 - val_precision_m: 0.0714\n","Epoch 176/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2294 - acc: 0.4160 - precision_m: 0.1387 - val_loss: 1.2225 - val_acc: 0.3094 - val_precision_m: 0.1000\n","Epoch 177/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1715 - acc: 0.4225 - precision_m: 0.2104 - val_loss: 1.2184 - val_acc: 0.3165 - val_precision_m: 0.0429\n","Epoch 178/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2929 - acc: 0.3417 - precision_m: 0.0663 - val_loss: 1.2786 - val_acc: 0.2806 - val_precision_m: 0.0000e+00\n","Epoch 179/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2129 - acc: 0.4038 - precision_m: 0.1246 - val_loss: 1.2322 - val_acc: 0.3094 - val_precision_m: 0.0857\n","Epoch 180/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1594 - acc: 0.4371 - precision_m: 0.1923 - val_loss: 1.1910 - val_acc: 0.3525 - val_precision_m: 0.0286\n","Epoch 181/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2137 - acc: 0.4058 - precision_m: 0.1401 - val_loss: 1.2192 - val_acc: 0.3453 - val_precision_m: 0.0571\n","Epoch 182/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2140 - acc: 0.4172 - precision_m: 0.2298 - val_loss: 1.2085 - val_acc: 0.3669 - val_precision_m: 0.0714\n","Epoch 183/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2431 - acc: 0.3515 - precision_m: 0.0621 - val_loss: 1.1923 - val_acc: 0.3525 - val_precision_m: 0.0143\n","Epoch 184/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1641 - acc: 0.4391 - precision_m: 0.2265 - val_loss: 1.1976 - val_acc: 0.3525 - val_precision_m: 0.0000e+00\n","Epoch 185/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1590 - acc: 0.3860 - precision_m: 0.0526 - val_loss: 1.1884 - val_acc: 0.4101 - val_precision_m: 0.1143\n","Epoch 186/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2510 - acc: 0.3947 - precision_m: 0.1037 - val_loss: 1.2052 - val_acc: 0.3741 - val_precision_m: 0.0429\n","Epoch 187/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1980 - acc: 0.3717 - precision_m: 0.0880 - val_loss: 1.1964 - val_acc: 0.3741 - val_precision_m: 0.0714\n","Epoch 188/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2211 - acc: 0.3505 - precision_m: 0.0572 - val_loss: 1.1881 - val_acc: 0.3669 - val_precision_m: 0.0000e+00\n","Epoch 189/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1916 - acc: 0.3880 - precision_m: 0.1335 - val_loss: 1.1880 - val_acc: 0.3669 - val_precision_m: 0.0286\n","Epoch 190/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2045 - acc: 0.4022 - precision_m: 0.1382 - val_loss: 1.1808 - val_acc: 0.3741 - val_precision_m: 0.0286\n","Epoch 191/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2318 - acc: 0.3977 - precision_m: 0.0871 - val_loss: 1.1969 - val_acc: 0.3381 - val_precision_m: 0.0857\n","Epoch 192/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1982 - acc: 0.4106 - precision_m: 0.2122 - val_loss: 1.3048 - val_acc: 0.2878 - val_precision_m: 0.1000\n","Epoch 193/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2250 - acc: 0.3554 - precision_m: 0.1429 - val_loss: 1.1960 - val_acc: 0.3525 - val_precision_m: 0.0857\n","Epoch 194/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1943 - acc: 0.3671 - precision_m: 0.1039 - val_loss: 1.1813 - val_acc: 0.3741 - val_precision_m: 0.0286\n","Epoch 195/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2106 - acc: 0.3819 - precision_m: 0.0741 - val_loss: 1.1976 - val_acc: 0.3813 - val_precision_m: 0.0714\n","Epoch 196/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2186 - acc: 0.3691 - precision_m: 0.1096 - val_loss: 1.1923 - val_acc: 0.4029 - val_precision_m: 0.1286\n","Epoch 197/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1336 - acc: 0.4906 - precision_m: 0.2019 - val_loss: 1.2039 - val_acc: 0.3597 - val_precision_m: 0.0286\n","Epoch 198/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1486 - acc: 0.4148 - precision_m: 0.1583 - val_loss: 1.1899 - val_acc: 0.3309 - val_precision_m: 0.0286\n","Epoch 199/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2015 - acc: 0.3863 - precision_m: 0.1229 - val_loss: 1.2089 - val_acc: 0.3165 - val_precision_m: 0.1000\n","Epoch 200/500\n","162/162 [==============================] - 0s 3ms/step - loss: 1.2001 - acc: 0.4546 - precision_m: 0.1862 - val_loss: 1.2516 - val_acc: 0.2878 - val_precision_m: 0.0286\n","Epoch 201/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2703 - acc: 0.3707 - precision_m: 0.1214 - val_loss: 1.2093 - val_acc: 0.3525 - val_precision_m: 0.0143\n","Epoch 202/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2173 - acc: 0.3793 - precision_m: 0.1130 - val_loss: 1.2071 - val_acc: 0.3741 - val_precision_m: 0.0143\n","Epoch 203/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1952 - acc: 0.3418 - precision_m: 0.1361 - val_loss: 1.2068 - val_acc: 0.3453 - val_precision_m: 0.0143\n","Epoch 204/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2337 - acc: 0.3789 - precision_m: 0.1608 - val_loss: 1.1910 - val_acc: 0.3669 - val_precision_m: 0.0286\n","Epoch 205/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2196 - acc: 0.3544 - precision_m: 0.1329 - val_loss: 1.2030 - val_acc: 0.3453 - val_precision_m: 0.0286\n","Epoch 206/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1418 - acc: 0.4432 - precision_m: 0.1458 - val_loss: 1.1839 - val_acc: 0.3165 - val_precision_m: 0.0857\n","Epoch 207/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1984 - acc: 0.3361 - precision_m: 0.1791 - val_loss: 1.1901 - val_acc: 0.3381 - val_precision_m: 0.0857\n","Epoch 208/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2286 - acc: 0.4098 - precision_m: 0.1386 - val_loss: 1.2322 - val_acc: 0.3094 - val_precision_m: 0.0714\n","Epoch 209/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2176 - acc: 0.4243 - precision_m: 0.1445 - val_loss: 1.1941 - val_acc: 0.3453 - val_precision_m: 0.0714\n","Epoch 210/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1847 - acc: 0.4283 - precision_m: 0.2079 - val_loss: 1.1831 - val_acc: 0.3597 - val_precision_m: 0.0714\n","Epoch 211/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1759 - acc: 0.3997 - precision_m: 0.1672 - val_loss: 1.1819 - val_acc: 0.3741 - val_precision_m: 0.0571\n","Epoch 212/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1975 - acc: 0.3732 - precision_m: 0.0754 - val_loss: 1.1943 - val_acc: 0.3597 - val_precision_m: 0.0857\n","Epoch 213/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2072 - acc: 0.3573 - precision_m: 0.1543 - val_loss: 1.1882 - val_acc: 0.3597 - val_precision_m: 0.0429\n","Epoch 214/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2135 - acc: 0.3997 - precision_m: 0.1441 - val_loss: 1.2053 - val_acc: 0.3669 - val_precision_m: 0.0429\n","Epoch 215/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1825 - acc: 0.3784 - precision_m: 0.1670 - val_loss: 1.2330 - val_acc: 0.3309 - val_precision_m: 0.0857\n","Epoch 216/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2021 - acc: 0.3966 - precision_m: 0.1404 - val_loss: 1.3699 - val_acc: 0.3165 - val_precision_m: 0.1143\n","Epoch 217/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1519 - acc: 0.4542 - precision_m: 0.2301 - val_loss: 1.2873 - val_acc: 0.3381 - val_precision_m: 0.1000\n","Epoch 218/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1921 - acc: 0.4292 - precision_m: 0.1285 - val_loss: 1.1939 - val_acc: 0.3741 - val_precision_m: 0.0429\n","Epoch 219/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1984 - acc: 0.3309 - precision_m: 0.0833 - val_loss: 1.1795 - val_acc: 0.3957 - val_precision_m: 0.1286\n","Epoch 220/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1634 - acc: 0.3944 - precision_m: 0.1222 - val_loss: 1.2368 - val_acc: 0.3741 - val_precision_m: 0.0857\n","Epoch 221/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1757 - acc: 0.4178 - precision_m: 0.1793 - val_loss: 1.1925 - val_acc: 0.3957 - val_precision_m: 0.1429\n","Epoch 222/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2372 - acc: 0.4348 - precision_m: 0.1490 - val_loss: 1.2236 - val_acc: 0.3597 - val_precision_m: 0.0857\n","Epoch 223/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2030 - acc: 0.3494 - precision_m: 0.1136 - val_loss: 1.1897 - val_acc: 0.3957 - val_precision_m: 0.1714\n","Epoch 224/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1790 - acc: 0.4176 - precision_m: 0.2399 - val_loss: 1.1894 - val_acc: 0.3957 - val_precision_m: 0.1429\n","Epoch 225/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2287 - acc: 0.3614 - precision_m: 0.1296 - val_loss: 1.2016 - val_acc: 0.3813 - val_precision_m: 0.1429\n","Epoch 226/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2205 - acc: 0.3871 - precision_m: 0.0944 - val_loss: 1.2487 - val_acc: 0.3597 - val_precision_m: 0.0571\n","Epoch 227/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1857 - acc: 0.3726 - precision_m: 0.1307 - val_loss: 1.1962 - val_acc: 0.4245 - val_precision_m: 0.1714\n","Epoch 228/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1849 - acc: 0.3935 - precision_m: 0.1115 - val_loss: 1.2018 - val_acc: 0.4029 - val_precision_m: 0.1714\n","Epoch 229/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1487 - acc: 0.3687 - precision_m: 0.1185 - val_loss: 1.2356 - val_acc: 0.3741 - val_precision_m: 0.1429\n","Epoch 230/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1603 - acc: 0.4096 - precision_m: 0.1648 - val_loss: 1.2016 - val_acc: 0.3237 - val_precision_m: 0.0857\n","Epoch 231/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2389 - acc: 0.3421 - precision_m: 0.1063 - val_loss: 1.1768 - val_acc: 0.3957 - val_precision_m: 0.1143\n","Epoch 232/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1588 - acc: 0.3423 - precision_m: 0.1201 - val_loss: 1.2348 - val_acc: 0.3165 - val_precision_m: 0.1000\n","Epoch 233/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.3304 - acc: 0.3148 - precision_m: 0.0826 - val_loss: 1.2512 - val_acc: 0.2950 - val_precision_m: 0.0286\n","Epoch 234/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1782 - acc: 0.3935 - precision_m: 0.1131 - val_loss: 1.2149 - val_acc: 0.3309 - val_precision_m: 0.0857\n","Epoch 235/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1390 - acc: 0.4418 - precision_m: 0.2343 - val_loss: 1.2461 - val_acc: 0.3022 - val_precision_m: 0.1143\n","Epoch 236/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2547 - acc: 0.3657 - precision_m: 0.1093 - val_loss: 1.2063 - val_acc: 0.3237 - val_precision_m: 0.0857\n","Epoch 237/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1908 - acc: 0.4155 - precision_m: 0.1385 - val_loss: 1.2157 - val_acc: 0.3453 - val_precision_m: 0.0857\n","Epoch 238/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1549 - acc: 0.3951 - precision_m: 0.1294 - val_loss: 1.1822 - val_acc: 0.3381 - val_precision_m: 0.0714\n","Epoch 239/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2146 - acc: 0.3743 - precision_m: 0.1372 - val_loss: 1.2170 - val_acc: 0.3309 - val_precision_m: 0.0857\n","Epoch 240/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1380 - acc: 0.4387 - precision_m: 0.1315 - val_loss: 1.1909 - val_acc: 0.3381 - val_precision_m: 0.0857\n","Epoch 241/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1595 - acc: 0.4142 - precision_m: 0.1252 - val_loss: 1.1975 - val_acc: 0.3669 - val_precision_m: 0.0571\n","Epoch 242/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2024 - acc: 0.4122 - precision_m: 0.1661 - val_loss: 1.2204 - val_acc: 0.3237 - val_precision_m: 0.1000\n","Epoch 243/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1985 - acc: 0.4220 - precision_m: 0.1295 - val_loss: 1.2115 - val_acc: 0.3381 - val_precision_m: 0.1000\n","Epoch 244/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2139 - acc: 0.4058 - precision_m: 0.1718 - val_loss: 1.2054 - val_acc: 0.3381 - val_precision_m: 0.0857\n","Epoch 245/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1271 - acc: 0.4248 - precision_m: 0.0991 - val_loss: 1.1974 - val_acc: 0.3741 - val_precision_m: 0.0571\n","Epoch 246/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1836 - acc: 0.4063 - precision_m: 0.1760 - val_loss: 1.1979 - val_acc: 0.3525 - val_precision_m: 0.0857\n","Epoch 247/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1912 - acc: 0.4139 - precision_m: 0.1502 - val_loss: 1.2078 - val_acc: 0.3381 - val_precision_m: 0.0857\n","Epoch 248/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2115 - acc: 0.3877 - precision_m: 0.1883 - val_loss: 1.1911 - val_acc: 0.3669 - val_precision_m: 0.0714\n","Epoch 249/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1730 - acc: 0.3814 - precision_m: 0.1787 - val_loss: 1.2110 - val_acc: 0.3525 - val_precision_m: 0.0857\n","Epoch 250/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1795 - acc: 0.4285 - precision_m: 0.1349 - val_loss: 1.2386 - val_acc: 0.3381 - val_precision_m: 0.0857\n","Epoch 251/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1790 - acc: 0.4417 - precision_m: 0.1729 - val_loss: 1.1844 - val_acc: 0.3525 - val_precision_m: 0.0286\n","Epoch 252/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2518 - acc: 0.3514 - precision_m: 0.0931 - val_loss: 1.2190 - val_acc: 0.3669 - val_precision_m: 0.1000\n","Epoch 253/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1691 - acc: 0.3885 - precision_m: 0.1759 - val_loss: 1.2114 - val_acc: 0.3741 - val_precision_m: 0.0571\n","Epoch 254/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.0999 - acc: 0.4237 - precision_m: 0.2344 - val_loss: 1.2316 - val_acc: 0.3381 - val_precision_m: 0.0714\n","Epoch 255/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1672 - acc: 0.3942 - precision_m: 0.1865 - val_loss: 1.1854 - val_acc: 0.3453 - val_precision_m: 0.0714\n","Epoch 256/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1887 - acc: 0.3929 - precision_m: 0.1425 - val_loss: 1.1882 - val_acc: 0.3309 - val_precision_m: 0.0714\n","Epoch 257/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1875 - acc: 0.4577 - precision_m: 0.1771 - val_loss: 1.2345 - val_acc: 0.3309 - val_precision_m: 0.1000\n","Epoch 258/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1770 - acc: 0.4276 - precision_m: 0.2212 - val_loss: 1.2005 - val_acc: 0.3525 - val_precision_m: 0.0429\n","Epoch 259/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1548 - acc: 0.3823 - precision_m: 0.1015 - val_loss: 1.1959 - val_acc: 0.3813 - val_precision_m: 0.1286\n","Epoch 260/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1448 - acc: 0.4493 - precision_m: 0.1192 - val_loss: 1.2573 - val_acc: 0.3525 - val_precision_m: 0.1286\n","Epoch 261/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2017 - acc: 0.3690 - precision_m: 0.1520 - val_loss: 1.2840 - val_acc: 0.3165 - val_precision_m: 0.0429\n","Epoch 262/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2333 - acc: 0.3384 - precision_m: 0.1017 - val_loss: 1.2290 - val_acc: 0.3309 - val_precision_m: 0.0857\n","Epoch 263/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1939 - acc: 0.3704 - precision_m: 0.1545 - val_loss: 1.2540 - val_acc: 0.3309 - val_precision_m: 0.1000\n","Epoch 264/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2300 - acc: 0.3534 - precision_m: 0.1705 - val_loss: 1.2188 - val_acc: 0.3381 - val_precision_m: 0.1000\n","Epoch 265/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2036 - acc: 0.4376 - precision_m: 0.1487 - val_loss: 1.1896 - val_acc: 0.3381 - val_precision_m: 0.0714\n","Epoch 266/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1382 - acc: 0.4583 - precision_m: 0.1399 - val_loss: 1.2019 - val_acc: 0.3525 - val_precision_m: 0.0714\n","Epoch 267/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1548 - acc: 0.4640 - precision_m: 0.1937 - val_loss: 1.2261 - val_acc: 0.3381 - val_precision_m: 0.0429\n","Epoch 268/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1487 - acc: 0.4066 - precision_m: 0.1599 - val_loss: 1.2184 - val_acc: 0.3381 - val_precision_m: 0.0714\n","Epoch 269/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1665 - acc: 0.4331 - precision_m: 0.1797 - val_loss: 1.2172 - val_acc: 0.3525 - val_precision_m: 0.0857\n","Epoch 270/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2207 - acc: 0.3753 - precision_m: 0.0799 - val_loss: 1.2024 - val_acc: 0.3741 - val_precision_m: 0.1571\n","Epoch 271/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2123 - acc: 0.3908 - precision_m: 0.1045 - val_loss: 1.2253 - val_acc: 0.3525 - val_precision_m: 0.0857\n","Epoch 272/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2274 - acc: 0.4144 - precision_m: 0.1614 - val_loss: 1.2395 - val_acc: 0.3309 - val_precision_m: 0.1000\n","Epoch 273/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1666 - acc: 0.4167 - precision_m: 0.1601 - val_loss: 1.2119 - val_acc: 0.3885 - val_precision_m: 0.1286\n","Epoch 274/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1776 - acc: 0.4316 - precision_m: 0.1979 - val_loss: 1.2682 - val_acc: 0.3381 - val_precision_m: 0.0857\n","Epoch 275/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2359 - acc: 0.3713 - precision_m: 0.1728 - val_loss: 1.2283 - val_acc: 0.3237 - val_precision_m: 0.0857\n","Epoch 276/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1744 - acc: 0.4412 - precision_m: 0.1927 - val_loss: 1.2075 - val_acc: 0.3453 - val_precision_m: 0.0857\n","Epoch 277/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1970 - acc: 0.3711 - precision_m: 0.1232 - val_loss: 1.2261 - val_acc: 0.3597 - val_precision_m: 0.0857\n","Epoch 278/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2148 - acc: 0.4186 - precision_m: 0.1750 - val_loss: 1.2053 - val_acc: 0.3309 - val_precision_m: 0.0857\n","Epoch 279/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1593 - acc: 0.3862 - precision_m: 0.1835 - val_loss: 1.2058 - val_acc: 0.3237 - val_precision_m: 0.0857\n","Epoch 280/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.2672 - acc: 0.3794 - precision_m: 0.1303 - val_loss: 1.2149 - val_acc: 0.3381 - val_precision_m: 0.0857\n","Epoch 281/500\n","162/162 [==============================] - 0s 2ms/step - loss: 1.1387 - acc: 0.4263 - precision_m: 0.1876 - val_loss: 1.2537 - val_acc: 0.3094 - val_precision_m: 0.1143\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 18.8s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","152/152 [==============================] - 1s 3ms/step - loss: 1.5198 - acc: 0.2131 - precision_m: 0.0182 - val_loss: 1.3873 - val_acc: 0.3077 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3947 - acc: 0.2663 - precision_m: 0.0000e+00 - val_loss: 1.3885 - val_acc: 0.2769 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3945 - acc: 0.2493 - precision_m: 0.0000e+00 - val_loss: 1.3852 - val_acc: 0.2385 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3862 - acc: 0.2493 - precision_m: 0.0000e+00 - val_loss: 1.3891 - val_acc: 0.1769 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3929 - acc: 0.2769 - precision_m: 0.0000e+00 - val_loss: 1.3955 - val_acc: 0.1769 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3997 - acc: 0.2533 - precision_m: 0.0000e+00 - val_loss: 1.3880 - val_acc: 0.2385 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3933 - acc: 0.2427 - precision_m: 0.0000e+00 - val_loss: 1.3855 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3859 - acc: 0.2836 - precision_m: 0.0000e+00 - val_loss: 1.3855 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3851 - acc: 0.2913 - precision_m: 0.0000e+00 - val_loss: 1.3855 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3866 - acc: 0.2337 - precision_m: 0.0000e+00 - val_loss: 1.3859 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3837 - acc: 0.3281 - precision_m: 0.0000e+00 - val_loss: 1.3856 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3885 - acc: 0.2049 - precision_m: 0.0000e+00 - val_loss: 1.3852 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3862 - acc: 0.2777 - precision_m: 0.0000e+00 - val_loss: 1.3849 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3855 - acc: 0.2808 - precision_m: 0.0000e+00 - val_loss: 1.3849 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3875 - acc: 0.2623 - precision_m: 0.0000e+00 - val_loss: 1.3856 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3863 - acc: 0.2564 - precision_m: 0.0000e+00 - val_loss: 1.3854 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3855 - acc: 0.2445 - precision_m: 0.0000e+00 - val_loss: 1.3851 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3842 - acc: 0.2879 - precision_m: 0.0000e+00 - val_loss: 1.3854 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3875 - acc: 0.2720 - precision_m: 0.0000e+00 - val_loss: 1.3855 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3855 - acc: 0.2752 - precision_m: 0.0000e+00 - val_loss: 1.3856 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3902 - acc: 0.1968 - precision_m: 0.0000e+00 - val_loss: 1.3848 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3849 - acc: 0.2901 - precision_m: 0.0000e+00 - val_loss: 1.3851 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3882 - acc: 0.2485 - precision_m: 0.0000e+00 - val_loss: 1.3849 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3887 - acc: 0.2264 - precision_m: 0.0000e+00 - val_loss: 1.3849 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3904 - acc: 0.2155 - precision_m: 0.0000e+00 - val_loss: 1.3846 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3851 - acc: 0.2675 - precision_m: 0.0000e+00 - val_loss: 1.3846 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3865 - acc: 0.2450 - precision_m: 0.0000e+00 - val_loss: 1.3841 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3841 - acc: 0.2315 - precision_m: 0.0000e+00 - val_loss: 1.3842 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3859 - acc: 0.2804 - precision_m: 0.0000e+00 - val_loss: 1.3841 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3894 - acc: 0.2259 - precision_m: 0.0000e+00 - val_loss: 1.3850 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3860 - acc: 0.2977 - precision_m: 0.0000e+00 - val_loss: 1.3842 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3847 - acc: 0.2699 - precision_m: 0.0000e+00 - val_loss: 1.3845 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3860 - acc: 0.2554 - precision_m: 0.0000e+00 - val_loss: 1.3850 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3859 - acc: 0.2740 - precision_m: 0.0000e+00 - val_loss: 1.3853 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3894 - acc: 0.2314 - precision_m: 0.0000e+00 - val_loss: 1.3858 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3866 - acc: 0.2579 - precision_m: 0.0000e+00 - val_loss: 1.3858 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3829 - acc: 0.2741 - precision_m: 0.0000e+00 - val_loss: 1.3847 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3809 - acc: 0.2710 - precision_m: 0.0000e+00 - val_loss: 1.3850 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3876 - acc: 0.2449 - precision_m: 0.0000e+00 - val_loss: 1.3855 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3843 - acc: 0.2753 - precision_m: 0.0000e+00 - val_loss: 1.3852 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3899 - acc: 0.2280 - precision_m: 0.0000e+00 - val_loss: 1.3854 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3854 - acc: 0.2779 - precision_m: 0.0000e+00 - val_loss: 1.3856 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3851 - acc: 0.2489 - precision_m: 0.0000e+00 - val_loss: 1.3844 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3856 - acc: 0.2051 - precision_m: 0.0000e+00 - val_loss: 1.3841 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3887 - acc: 0.2388 - precision_m: 0.0000e+00 - val_loss: 1.3850 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3879 - acc: 0.2410 - precision_m: 0.0000e+00 - val_loss: 1.3851 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3899 - acc: 0.2216 - precision_m: 0.0000e+00 - val_loss: 1.3851 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3836 - acc: 0.2735 - precision_m: 0.0000e+00 - val_loss: 1.3851 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3846 - acc: 0.2658 - precision_m: 0.0000e+00 - val_loss: 1.3844 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3864 - acc: 0.2444 - precision_m: 0.0000e+00 - val_loss: 1.3849 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3851 - acc: 0.2499 - precision_m: 0.0000e+00 - val_loss: 1.3847 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 52/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3879 - acc: 0.2318 - precision_m: 0.0000e+00 - val_loss: 1.3849 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 53/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3825 - acc: 0.2703 - precision_m: 0.0000e+00 - val_loss: 1.3849 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 54/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3849 - acc: 0.2815 - precision_m: 0.0000e+00 - val_loss: 1.3847 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 55/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3844 - acc: 0.2715 - precision_m: 0.0000e+00 - val_loss: 1.3853 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 56/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3898 - acc: 0.2159 - precision_m: 0.0000e+00 - val_loss: 1.3846 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 57/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3827 - acc: 0.2766 - precision_m: 0.0096 - val_loss: 1.3851 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 58/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3854 - acc: 0.2443 - precision_m: 0.0085 - val_loss: 1.3855 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 59/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3894 - acc: 0.2794 - precision_m: 0.0000e+00 - val_loss: 1.3856 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 60/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3889 - acc: 0.2489 - precision_m: 0.0000e+00 - val_loss: 1.3851 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 61/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3832 - acc: 0.3051 - precision_m: 0.0026 - val_loss: 1.3848 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 62/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3849 - acc: 0.2771 - precision_m: 0.0000e+00 - val_loss: 1.3849 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 63/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3864 - acc: 0.2721 - precision_m: 0.0000e+00 - val_loss: 1.3851 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 64/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3829 - acc: 0.2736 - precision_m: 0.0000e+00 - val_loss: 1.3840 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 65/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3885 - acc: 0.2230 - precision_m: 0.0000e+00 - val_loss: 1.3845 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 66/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3845 - acc: 0.2639 - precision_m: 0.0000e+00 - val_loss: 1.3852 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 67/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3824 - acc: 0.2709 - precision_m: 0.0000e+00 - val_loss: 1.3851 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 68/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3859 - acc: 0.2429 - precision_m: 0.0073 - val_loss: 1.3853 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 69/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3911 - acc: 0.1873 - precision_m: 0.0000e+00 - val_loss: 1.3849 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 70/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3914 - acc: 0.1992 - precision_m: 0.0000e+00 - val_loss: 1.3854 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 71/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3830 - acc: 0.2758 - precision_m: 0.0061 - val_loss: 1.3849 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 72/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3847 - acc: 0.2826 - precision_m: 0.0000e+00 - val_loss: 1.3850 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 73/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3838 - acc: 0.2890 - precision_m: 0.0000e+00 - val_loss: 1.3848 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 74/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3910 - acc: 0.2102 - precision_m: 0.0000e+00 - val_loss: 1.3851 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 75/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3812 - acc: 0.2485 - precision_m: 0.0000e+00 - val_loss: 1.3852 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 76/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3819 - acc: 0.3005 - precision_m: 0.0000e+00 - val_loss: 1.3852 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 77/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3856 - acc: 0.2497 - precision_m: 0.0000e+00 - val_loss: 1.3846 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 78/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3843 - acc: 0.2598 - precision_m: 0.0000e+00 - val_loss: 1.3853 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 79/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3874 - acc: 0.2334 - precision_m: 0.0000e+00 - val_loss: 1.3851 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 80/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3847 - acc: 0.2315 - precision_m: 0.0000e+00 - val_loss: 1.3847 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 81/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3845 - acc: 0.2681 - precision_m: 0.0000e+00 - val_loss: 1.3851 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 82/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3767 - acc: 0.2471 - precision_m: 0.0217 - val_loss: 1.3846 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 83/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3846 - acc: 0.2436 - precision_m: 0.0000e+00 - val_loss: 1.3852 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 84/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3877 - acc: 0.2471 - precision_m: 0.0000e+00 - val_loss: 1.3851 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 85/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3760 - acc: 0.2219 - precision_m: 0.0197 - val_loss: 1.3850 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 86/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3857 - acc: 0.2564 - precision_m: 0.0000e+00 - val_loss: 1.3853 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 87/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3868 - acc: 0.2572 - precision_m: 0.0000e+00 - val_loss: 1.3849 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 88/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3847 - acc: 0.2430 - precision_m: 0.0047 - val_loss: 1.3852 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 89/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3839 - acc: 0.2723 - precision_m: 0.0000e+00 - val_loss: 1.3850 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 90/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3838 - acc: 0.2895 - precision_m: 0.0031 - val_loss: 1.3845 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 91/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3868 - acc: 0.2617 - precision_m: 0.0000e+00 - val_loss: 1.3848 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 92/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3877 - acc: 0.2233 - precision_m: 0.0000e+00 - val_loss: 1.3849 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 93/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3827 - acc: 0.3196 - precision_m: 7.6744e-04 - val_loss: 1.3851 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 94/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3818 - acc: 0.2490 - precision_m: 0.0079 - val_loss: 1.3854 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 95/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3790 - acc: 0.3040 - precision_m: 0.0049 - val_loss: 1.3859 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 96/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3795 - acc: 0.2792 - precision_m: 0.0078 - val_loss: 1.3853 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 97/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3862 - acc: 0.2290 - precision_m: 0.0000e+00 - val_loss: 1.3859 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 98/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3784 - acc: 0.3325 - precision_m: 0.0026 - val_loss: 1.3857 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 99/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3875 - acc: 0.2564 - precision_m: 5.3231e-04 - val_loss: 1.3852 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 100/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3864 - acc: 0.2671 - precision_m: 0.0000e+00 - val_loss: 1.3851 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 101/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3833 - acc: 0.2400 - precision_m: 0.0101 - val_loss: 1.3856 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 102/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3832 - acc: 0.2819 - precision_m: 0.0000e+00 - val_loss: 1.3847 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 103/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3835 - acc: 0.2725 - precision_m: 0.0000e+00 - val_loss: 1.3853 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 104/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3746 - acc: 0.2836 - precision_m: 0.0132 - val_loss: 1.3852 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 105/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3813 - acc: 0.2500 - precision_m: 0.0087 - val_loss: 1.3845 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 106/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3847 - acc: 0.2377 - precision_m: 0.0085 - val_loss: 1.3846 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 107/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3864 - acc: 0.2456 - precision_m: 5.3231e-04 - val_loss: 1.3851 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 108/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3885 - acc: 0.2616 - precision_m: 0.0000e+00 - val_loss: 1.3844 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 109/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3842 - acc: 0.2973 - precision_m: 0.0000e+00 - val_loss: 1.3845 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 110/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3826 - acc: 0.2995 - precision_m: 0.0032 - val_loss: 1.3848 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 111/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3829 - acc: 0.2525 - precision_m: 0.0073 - val_loss: 1.3847 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 112/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3863 - acc: 0.2322 - precision_m: 0.0000e+00 - val_loss: 1.3851 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 113/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3814 - acc: 0.2663 - precision_m: 0.0058 - val_loss: 1.3848 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 114/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3849 - acc: 0.2837 - precision_m: 0.0019 - val_loss: 1.3849 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 16.6s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","144/144 [==============================] - 1s 3ms/step - loss: 1.4272 - acc: 0.2773 - precision_m: 0.0000e+00 - val_loss: 1.4039 - val_acc: 0.1694 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.4020 - acc: 0.1995 - precision_m: 0.0000e+00 - val_loss: 1.3844 - val_acc: 0.2823 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3955 - acc: 0.2698 - precision_m: 0.0000e+00 - val_loss: 1.3800 - val_acc: 0.2823 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3860 - acc: 0.2738 - precision_m: 0.0000e+00 - val_loss: 1.3743 - val_acc: 0.3065 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3866 - acc: 0.2478 - precision_m: 0.0000e+00 - val_loss: 1.3777 - val_acc: 0.3065 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3831 - acc: 0.2567 - precision_m: 0.0000e+00 - val_loss: 1.3825 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3894 - acc: 0.2747 - precision_m: 0.0000e+00 - val_loss: 1.3800 - val_acc: 0.2823 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3876 - acc: 0.2356 - precision_m: 0.0000e+00 - val_loss: 1.3806 - val_acc: 0.2581 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3862 - acc: 0.2912 - precision_m: 0.0000e+00 - val_loss: 1.3784 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3889 - acc: 0.2500 - precision_m: 0.0000e+00 - val_loss: 1.3778 - val_acc: 0.2823 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3883 - acc: 0.2810 - precision_m: 0.0000e+00 - val_loss: 1.3777 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3813 - acc: 0.2509 - precision_m: 0.0000e+00 - val_loss: 1.3777 - val_acc: 0.3065 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3835 - acc: 0.2816 - precision_m: 0.0000e+00 - val_loss: 1.3741 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3859 - acc: 0.2770 - precision_m: 0.0000e+00 - val_loss: 1.3768 - val_acc: 0.2984 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3711 - acc: 0.2990 - precision_m: 0.0000e+00 - val_loss: 1.3778 - val_acc: 0.3226 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3855 - acc: 0.2458 - precision_m: 0.0000e+00 - val_loss: 1.3764 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3810 - acc: 0.2646 - precision_m: 0.0000e+00 - val_loss: 1.3762 - val_acc: 0.3145 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3838 - acc: 0.2886 - precision_m: 0.0000e+00 - val_loss: 1.3784 - val_acc: 0.3065 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3857 - acc: 0.2868 - precision_m: 0.0000e+00 - val_loss: 1.3789 - val_acc: 0.2823 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3812 - acc: 0.3132 - precision_m: 0.0000e+00 - val_loss: 1.3776 - val_acc: 0.3145 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3806 - acc: 0.2545 - precision_m: 0.0000e+00 - val_loss: 1.3784 - val_acc: 0.2984 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3821 - acc: 0.2440 - precision_m: 0.0000e+00 - val_loss: 1.3743 - val_acc: 0.2984 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3858 - acc: 0.2527 - precision_m: 0.0000e+00 - val_loss: 1.3826 - val_acc: 0.2742 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3822 - acc: 0.2382 - precision_m: 0.0000e+00 - val_loss: 1.3722 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3705 - acc: 0.2506 - precision_m: 0.0000e+00 - val_loss: 1.3889 - val_acc: 0.2177 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.4015 - acc: 0.2388 - precision_m: 0.0000e+00 - val_loss: 1.3761 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3788 - acc: 0.2762 - precision_m: 0.0000e+00 - val_loss: 1.3718 - val_acc: 0.3065 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3780 - acc: 0.2893 - precision_m: 0.0000e+00 - val_loss: 1.3752 - val_acc: 0.2661 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3747 - acc: 0.2933 - precision_m: 0.0000e+00 - val_loss: 1.3772 - val_acc: 0.3226 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3787 - acc: 0.2703 - precision_m: 0.0000e+00 - val_loss: 1.3761 - val_acc: 0.2823 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3853 - acc: 0.2808 - precision_m: 0.0000e+00 - val_loss: 1.3671 - val_acc: 0.2742 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3860 - acc: 0.2936 - precision_m: 0.0000e+00 - val_loss: 1.3773 - val_acc: 0.3065 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3754 - acc: 0.2897 - precision_m: 0.0000e+00 - val_loss: 1.3667 - val_acc: 0.2823 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3785 - acc: 0.2637 - precision_m: 0.0000e+00 - val_loss: 1.3781 - val_acc: 0.3548 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3767 - acc: 0.2987 - precision_m: 0.0000e+00 - val_loss: 1.3702 - val_acc: 0.2984 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3822 - acc: 0.2293 - precision_m: 0.0000e+00 - val_loss: 1.3714 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3785 - acc: 0.2856 - precision_m: 0.0057 - val_loss: 1.3749 - val_acc: 0.3145 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3676 - acc: 0.2798 - precision_m: 0.0000e+00 - val_loss: 1.3625 - val_acc: 0.2823 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3886 - acc: 0.2673 - precision_m: 0.0015 - val_loss: 1.3765 - val_acc: 0.2581 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3864 - acc: 0.2390 - precision_m: 0.0000e+00 - val_loss: 1.3698 - val_acc: 0.2823 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3652 - acc: 0.3234 - precision_m: 0.0000e+00 - val_loss: 1.3783 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3739 - acc: 0.2842 - precision_m: 0.0026 - val_loss: 1.3720 - val_acc: 0.3145 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3773 - acc: 0.3131 - precision_m: 0.0000e+00 - val_loss: 1.3766 - val_acc: 0.2823 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3744 - acc: 0.2619 - precision_m: 0.0000e+00 - val_loss: 1.3815 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3954 - acc: 0.2506 - precision_m: 0.0193 - val_loss: 1.3727 - val_acc: 0.3145 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3655 - acc: 0.3334 - precision_m: 0.0000e+00 - val_loss: 1.3846 - val_acc: 0.2419 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3727 - acc: 0.2751 - precision_m: 0.0073 - val_loss: 1.3785 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3538 - acc: 0.3057 - precision_m: 0.0023 - val_loss: 1.3765 - val_acc: 0.2500 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3912 - acc: 0.2352 - precision_m: 0.0010 - val_loss: 1.3660 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3716 - acc: 0.2899 - precision_m: 0.0026 - val_loss: 1.3721 - val_acc: 0.3145 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3748 - acc: 0.2772 - precision_m: 0.0000e+00 - val_loss: 1.3769 - val_acc: 0.2742 - val_precision_m: 0.0000e+00\n","Epoch 52/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3709 - acc: 0.2908 - precision_m: 0.0000e+00 - val_loss: 1.3648 - val_acc: 0.3468 - val_precision_m: 0.0000e+00\n","Epoch 53/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3688 - acc: 0.2948 - precision_m: 0.0000e+00 - val_loss: 1.3603 - val_acc: 0.3145 - val_precision_m: 0.0000e+00\n","Epoch 54/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3725 - acc: 0.2843 - precision_m: 9.6539e-04 - val_loss: 1.3637 - val_acc: 0.2984 - val_precision_m: 0.0000e+00\n","Epoch 55/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3664 - acc: 0.2949 - precision_m: 0.0000e+00 - val_loss: 1.3819 - val_acc: 0.2339 - val_precision_m: 0.0000e+00\n","Epoch 56/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3710 - acc: 0.3085 - precision_m: 0.0000e+00 - val_loss: 1.3718 - val_acc: 0.3629 - val_precision_m: 0.0000e+00\n","Epoch 57/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3705 - acc: 0.3220 - precision_m: 0.0037 - val_loss: 1.3625 - val_acc: 0.3306 - val_precision_m: 0.0000e+00\n","Epoch 58/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3649 - acc: 0.2684 - precision_m: 0.0257 - val_loss: 1.3728 - val_acc: 0.3226 - val_precision_m: 0.0000e+00\n","Epoch 59/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3456 - acc: 0.3013 - precision_m: 0.0383 - val_loss: 1.3719 - val_acc: 0.2984 - val_precision_m: 0.0000e+00\n","Epoch 60/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3706 - acc: 0.2389 - precision_m: 0.0039 - val_loss: 1.3531 - val_acc: 0.3145 - val_precision_m: 0.0000e+00\n","Epoch 61/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3824 - acc: 0.3146 - precision_m: 0.0059 - val_loss: 1.3666 - val_acc: 0.3065 - val_precision_m: 0.0000e+00\n","Epoch 62/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3521 - acc: 0.3038 - precision_m: 0.0033 - val_loss: 1.3600 - val_acc: 0.3306 - val_precision_m: 0.0000e+00\n","Epoch 63/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3706 - acc: 0.2991 - precision_m: 0.0000e+00 - val_loss: 1.3687 - val_acc: 0.2823 - val_precision_m: 0.0000e+00\n","Epoch 64/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3734 - acc: 0.2485 - precision_m: 0.0000e+00 - val_loss: 1.3717 - val_acc: 0.3226 - val_precision_m: 0.0000e+00\n","Epoch 65/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3587 - acc: 0.3138 - precision_m: 0.0000e+00 - val_loss: 1.3701 - val_acc: 0.3145 - val_precision_m: 0.0000e+00\n","Epoch 66/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3705 - acc: 0.2642 - precision_m: 0.0000e+00 - val_loss: 1.3649 - val_acc: 0.3306 - val_precision_m: 0.0000e+00\n","Epoch 67/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3574 - acc: 0.3098 - precision_m: 0.0149 - val_loss: 1.3716 - val_acc: 0.3226 - val_precision_m: 0.0000e+00\n","Epoch 68/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3817 - acc: 0.2992 - precision_m: 0.0000e+00 - val_loss: 1.3791 - val_acc: 0.2419 - val_precision_m: 0.0000e+00\n","Epoch 69/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3667 - acc: 0.2884 - precision_m: 0.0018 - val_loss: 1.3775 - val_acc: 0.2258 - val_precision_m: 0.0000e+00\n","Epoch 70/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3821 - acc: 0.2252 - precision_m: 0.0120 - val_loss: 1.3652 - val_acc: 0.3306 - val_precision_m: 0.0000e+00\n","Epoch 71/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3722 - acc: 0.2624 - precision_m: 0.0051 - val_loss: 1.3716 - val_acc: 0.3065 - val_precision_m: 0.0000e+00\n","Epoch 72/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3722 - acc: 0.2538 - precision_m: 0.0060 - val_loss: 1.3724 - val_acc: 0.3145 - val_precision_m: 0.0000e+00\n","Epoch 73/500\n","144/144 [==============================] - 0s 3ms/step - loss: 1.3787 - acc: 0.2540 - precision_m: 3.4037e-04 - val_loss: 1.3679 - val_acc: 0.3468 - val_precision_m: 0.0000e+00\n","Epoch 74/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3446 - acc: 0.3888 - precision_m: 0.0154 - val_loss: 1.3697 - val_acc: 0.3065 - val_precision_m: 0.0000e+00\n","Epoch 75/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3641 - acc: 0.3275 - precision_m: 0.0774 - val_loss: 1.3702 - val_acc: 0.3065 - val_precision_m: 0.0000e+00\n","Epoch 76/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3624 - acc: 0.3319 - precision_m: 0.0079 - val_loss: 1.3712 - val_acc: 0.3145 - val_precision_m: 0.0000e+00\n","Epoch 77/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3669 - acc: 0.2793 - precision_m: 6.9805e-04 - val_loss: 1.3536 - val_acc: 0.3710 - val_precision_m: 0.0000e+00\n","Epoch 78/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.4032 - acc: 0.2765 - precision_m: 0.0018 - val_loss: 1.3604 - val_acc: 0.3387 - val_precision_m: 0.0000e+00\n","Epoch 79/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3448 - acc: 0.3881 - precision_m: 0.0625 - val_loss: 1.3676 - val_acc: 0.2984 - val_precision_m: 0.0000e+00\n","Epoch 80/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3345 - acc: 0.3664 - precision_m: 0.0441 - val_loss: 1.3735 - val_acc: 0.2984 - val_precision_m: 0.0000e+00\n","Epoch 81/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3373 - acc: 0.3100 - precision_m: 0.0608 - val_loss: 1.3637 - val_acc: 0.2984 - val_precision_m: 0.0000e+00\n","Epoch 82/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3729 - acc: 0.3037 - precision_m: 0.0401 - val_loss: 1.3738 - val_acc: 0.3065 - val_precision_m: 0.0000e+00\n","Epoch 83/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3771 - acc: 0.2503 - precision_m: 0.0138 - val_loss: 1.3709 - val_acc: 0.2984 - val_precision_m: 0.0000e+00\n","Epoch 84/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3584 - acc: 0.3317 - precision_m: 0.0218 - val_loss: 1.3525 - val_acc: 0.3306 - val_precision_m: 0.0000e+00\n","Epoch 85/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3765 - acc: 0.2929 - precision_m: 0.0619 - val_loss: 1.3705 - val_acc: 0.3065 - val_precision_m: 0.0000e+00\n","Epoch 86/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3609 - acc: 0.3073 - precision_m: 0.0395 - val_loss: 1.3610 - val_acc: 0.3145 - val_precision_m: 0.0000e+00\n","Epoch 87/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3519 - acc: 0.3196 - precision_m: 0.0238 - val_loss: 1.3544 - val_acc: 0.3468 - val_precision_m: 0.0000e+00\n","Epoch 88/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3663 - acc: 0.2802 - precision_m: 0.0074 - val_loss: 1.3645 - val_acc: 0.3065 - val_precision_m: 0.0000e+00\n","Epoch 89/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3412 - acc: 0.3373 - precision_m: 0.0466 - val_loss: 1.3581 - val_acc: 0.3387 - val_precision_m: 0.0000e+00\n","Epoch 90/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3700 - acc: 0.2929 - precision_m: 0.0167 - val_loss: 1.3642 - val_acc: 0.3306 - val_precision_m: 0.0000e+00\n","Epoch 91/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3686 - acc: 0.2608 - precision_m: 0.0159 - val_loss: 1.3603 - val_acc: 0.3306 - val_precision_m: 0.0000e+00\n","Epoch 92/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3554 - acc: 0.2831 - precision_m: 0.0205 - val_loss: 1.3665 - val_acc: 0.2984 - val_precision_m: 0.0000e+00\n","Epoch 93/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3451 - acc: 0.3047 - precision_m: 0.0642 - val_loss: 1.3686 - val_acc: 0.2661 - val_precision_m: 0.0000e+00\n","Epoch 94/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3688 - acc: 0.2756 - precision_m: 0.0166 - val_loss: 1.3638 - val_acc: 0.3145 - val_precision_m: 0.0000e+00\n","Epoch 95/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3315 - acc: 0.4028 - precision_m: 0.0650 - val_loss: 1.3653 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 96/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3520 - acc: 0.2932 - precision_m: 0.0344 - val_loss: 1.3635 - val_acc: 0.2984 - val_precision_m: 0.0000e+00\n","Epoch 97/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3435 - acc: 0.3147 - precision_m: 0.0844 - val_loss: 1.3597 - val_acc: 0.3387 - val_precision_m: 0.0000e+00\n","Epoch 98/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3423 - acc: 0.3341 - precision_m: 0.0753 - val_loss: 1.3729 - val_acc: 0.2742 - val_precision_m: 0.0000e+00\n","Epoch 99/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3576 - acc: 0.2776 - precision_m: 0.0387 - val_loss: 1.3660 - val_acc: 0.3226 - val_precision_m: 0.0000e+00\n","Epoch 100/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3635 - acc: 0.3171 - precision_m: 0.0026 - val_loss: 1.3713 - val_acc: 0.2661 - val_precision_m: 0.0000e+00\n","Epoch 101/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3464 - acc: 0.3396 - precision_m: 0.0284 - val_loss: 1.3433 - val_acc: 0.3468 - val_precision_m: 0.0000e+00\n","Epoch 102/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3591 - acc: 0.2787 - precision_m: 0.0292 - val_loss: 1.3622 - val_acc: 0.3145 - val_precision_m: 0.0000e+00\n","Epoch 103/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3499 - acc: 0.3203 - precision_m: 0.0425 - val_loss: 1.3560 - val_acc: 0.3226 - val_precision_m: 0.0000e+00\n","Epoch 104/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3609 - acc: 0.2770 - precision_m: 0.0327 - val_loss: 1.3838 - val_acc: 0.2823 - val_precision_m: 0.0000e+00\n","Epoch 105/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3231 - acc: 0.3642 - precision_m: 0.0612 - val_loss: 1.3681 - val_acc: 0.2661 - val_precision_m: 0.0000e+00\n","Epoch 106/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3349 - acc: 0.3472 - precision_m: 0.0598 - val_loss: 1.3753 - val_acc: 0.3226 - val_precision_m: 0.0000e+00\n","Epoch 107/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3681 - acc: 0.3057 - precision_m: 0.0179 - val_loss: 1.3614 - val_acc: 0.2823 - val_precision_m: 0.0000e+00\n","Epoch 108/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3532 - acc: 0.2612 - precision_m: 0.0338 - val_loss: 1.3720 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 109/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3212 - acc: 0.3462 - precision_m: 0.0772 - val_loss: 1.3577 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 110/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3573 - acc: 0.3062 - precision_m: 0.0343 - val_loss: 1.3714 - val_acc: 0.2339 - val_precision_m: 0.0000e+00\n","Epoch 111/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3423 - acc: 0.2706 - precision_m: 0.0289 - val_loss: 1.3679 - val_acc: 0.3145 - val_precision_m: 0.0000e+00\n","Epoch 112/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3300 - acc: 0.3536 - precision_m: 0.0485 - val_loss: 1.3668 - val_acc: 0.2742 - val_precision_m: 0.0000e+00\n","Epoch 113/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3450 - acc: 0.3064 - precision_m: 0.0520 - val_loss: 1.3808 - val_acc: 0.2339 - val_precision_m: 0.0000e+00\n","Epoch 114/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3673 - acc: 0.2852 - precision_m: 0.0482 - val_loss: 1.3366 - val_acc: 0.3468 - val_precision_m: 0.0161\n","Epoch 115/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3577 - acc: 0.3032 - precision_m: 0.0832 - val_loss: 1.3785 - val_acc: 0.2339 - val_precision_m: 0.0000e+00\n","Epoch 116/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3244 - acc: 0.2975 - precision_m: 0.0962 - val_loss: 1.3592 - val_acc: 0.3226 - val_precision_m: 0.0000e+00\n","Epoch 117/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3502 - acc: 0.3223 - precision_m: 0.0099 - val_loss: 1.3662 - val_acc: 0.2339 - val_precision_m: 0.0000e+00\n","Epoch 118/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3388 - acc: 0.3421 - precision_m: 0.0337 - val_loss: 1.3548 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 119/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3311 - acc: 0.3342 - precision_m: 0.0580 - val_loss: 1.3597 - val_acc: 0.2581 - val_precision_m: 0.0000e+00\n","Epoch 120/500\n","144/144 [==============================] - 0s 3ms/step - loss: 1.3264 - acc: 0.3142 - precision_m: 0.1208 - val_loss: 1.3503 - val_acc: 0.3468 - val_precision_m: 0.0000e+00\n","Epoch 121/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3538 - acc: 0.2990 - precision_m: 0.0335 - val_loss: 1.3578 - val_acc: 0.2742 - val_precision_m: 0.0000e+00\n","Epoch 122/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3408 - acc: 0.3655 - precision_m: 0.0571 - val_loss: 1.3537 - val_acc: 0.2742 - val_precision_m: 0.0161\n","Epoch 123/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3499 - acc: 0.2978 - precision_m: 0.0556 - val_loss: 1.3606 - val_acc: 0.2984 - val_precision_m: 0.0000e+00\n","Epoch 124/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3093 - acc: 0.3792 - precision_m: 0.1025 - val_loss: 1.3488 - val_acc: 0.3387 - val_precision_m: 0.0000e+00\n","Epoch 125/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3637 - acc: 0.3208 - precision_m: 0.0194 - val_loss: 1.3804 - val_acc: 0.2661 - val_precision_m: 0.0161\n","Epoch 126/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3794 - acc: 0.3348 - precision_m: 0.0447 - val_loss: 1.3530 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 127/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3616 - acc: 0.3014 - precision_m: 0.0297 - val_loss: 1.3647 - val_acc: 0.2500 - val_precision_m: 0.0000e+00\n","Epoch 128/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3527 - acc: 0.2815 - precision_m: 0.0333 - val_loss: 1.3587 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 129/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3464 - acc: 0.3283 - precision_m: 0.0374 - val_loss: 1.3504 - val_acc: 0.3065 - val_precision_m: 0.0161\n","Epoch 130/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3422 - acc: 0.3291 - precision_m: 0.0712 - val_loss: 1.3562 - val_acc: 0.3306 - val_precision_m: 0.0000e+00\n","Epoch 131/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3563 - acc: 0.3495 - precision_m: 0.0354 - val_loss: 1.3638 - val_acc: 0.2177 - val_precision_m: 0.0000e+00\n","Epoch 132/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3518 - acc: 0.2732 - precision_m: 0.0541 - val_loss: 1.3626 - val_acc: 0.2419 - val_precision_m: 0.0000e+00\n","Epoch 133/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3424 - acc: 0.3208 - precision_m: 0.0352 - val_loss: 1.3711 - val_acc: 0.2742 - val_precision_m: 0.0000e+00\n","Epoch 134/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3444 - acc: 0.3742 - precision_m: 0.0283 - val_loss: 1.3506 - val_acc: 0.3226 - val_precision_m: 0.0000e+00\n","Epoch 135/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3684 - acc: 0.2728 - precision_m: 0.0216 - val_loss: 1.3595 - val_acc: 0.2581 - val_precision_m: 0.0000e+00\n","Epoch 136/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3152 - acc: 0.3732 - precision_m: 0.1024 - val_loss: 1.3626 - val_acc: 0.3306 - val_precision_m: 0.0000e+00\n","Epoch 137/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3351 - acc: 0.3203 - precision_m: 0.0673 - val_loss: 1.3608 - val_acc: 0.3306 - val_precision_m: 0.0000e+00\n","Epoch 138/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3513 - acc: 0.2870 - precision_m: 0.0483 - val_loss: 1.3457 - val_acc: 0.3145 - val_precision_m: 0.0000e+00\n","Epoch 139/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3520 - acc: 0.2763 - precision_m: 0.0549 - val_loss: 1.3849 - val_acc: 0.2258 - val_precision_m: 0.0000e+00\n","Epoch 140/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3109 - acc: 0.3455 - precision_m: 0.0554 - val_loss: 1.3595 - val_acc: 0.2823 - val_precision_m: 0.0000e+00\n","Epoch 141/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3260 - acc: 0.3110 - precision_m: 0.0793 - val_loss: 1.3587 - val_acc: 0.2823 - val_precision_m: 0.0000e+00\n","Epoch 142/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3357 - acc: 0.3869 - precision_m: 0.1279 - val_loss: 1.3632 - val_acc: 0.3145 - val_precision_m: 0.0000e+00\n","Epoch 143/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3415 - acc: 0.3100 - precision_m: 0.0347 - val_loss: 1.4178 - val_acc: 0.2258 - val_precision_m: 0.0000e+00\n","Epoch 144/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3341 - acc: 0.3196 - precision_m: 0.1754 - val_loss: 1.3736 - val_acc: 0.2500 - val_precision_m: 0.0000e+00\n","Epoch 145/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3434 - acc: 0.3725 - precision_m: 0.0420 - val_loss: 1.3398 - val_acc: 0.3548 - val_precision_m: 0.0161\n","Epoch 146/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3429 - acc: 0.3164 - precision_m: 0.0556 - val_loss: 1.3892 - val_acc: 0.2177 - val_precision_m: 0.0000e+00\n","Epoch 147/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3283 - acc: 0.3542 - precision_m: 0.0907 - val_loss: 1.3658 - val_acc: 0.2581 - val_precision_m: 0.0000e+00\n","Epoch 148/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3115 - acc: 0.3554 - precision_m: 0.1193 - val_loss: 1.3678 - val_acc: 0.2823 - val_precision_m: 0.0000e+00\n","Epoch 149/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3459 - acc: 0.3010 - precision_m: 0.0536 - val_loss: 1.3719 - val_acc: 0.2742 - val_precision_m: 0.0000e+00\n","Epoch 150/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3442 - acc: 0.3064 - precision_m: 0.0200 - val_loss: 1.3504 - val_acc: 0.2742 - val_precision_m: 0.0000e+00\n","Epoch 151/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3163 - acc: 0.3208 - precision_m: 0.0535 - val_loss: 1.3458 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 152/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.2980 - acc: 0.3732 - precision_m: 0.0761 - val_loss: 1.3594 - val_acc: 0.2500 - val_precision_m: 0.0000e+00\n","Epoch 153/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3472 - acc: 0.3110 - precision_m: 0.0514 - val_loss: 1.3430 - val_acc: 0.2903 - val_precision_m: 0.0000e+00\n","Epoch 154/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3301 - acc: 0.3468 - precision_m: 0.1167 - val_loss: 1.3787 - val_acc: 0.2419 - val_precision_m: 0.0000e+00\n","Epoch 155/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.2921 - acc: 0.3246 - precision_m: 0.0395 - val_loss: 1.3915 - val_acc: 0.2742 - val_precision_m: 0.0000e+00\n","Epoch 156/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3767 - acc: 0.2381 - precision_m: 0.0246 - val_loss: 1.3670 - val_acc: 0.2339 - val_precision_m: 0.0000e+00\n","Epoch 157/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3272 - acc: 0.3609 - precision_m: 0.0576 - val_loss: 1.3534 - val_acc: 0.2661 - val_precision_m: 0.0323\n","Epoch 158/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3546 - acc: 0.3219 - precision_m: 0.0719 - val_loss: 1.3835 - val_acc: 0.2258 - val_precision_m: 0.0000e+00\n","Epoch 159/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3484 - acc: 0.3306 - precision_m: 0.0661 - val_loss: 1.3698 - val_acc: 0.2419 - val_precision_m: 0.0161\n","Epoch 160/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3542 - acc: 0.3219 - precision_m: 0.0257 - val_loss: 1.3662 - val_acc: 0.2742 - val_precision_m: 0.0000e+00\n","Epoch 161/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3326 - acc: 0.3714 - precision_m: 0.0620 - val_loss: 1.3626 - val_acc: 0.2661 - val_precision_m: 0.0000e+00\n","Epoch 162/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3277 - acc: 0.3499 - precision_m: 0.0749 - val_loss: 1.3870 - val_acc: 0.2016 - val_precision_m: 0.0000e+00\n","Epoch 163/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3618 - acc: 0.3004 - precision_m: 0.0466 - val_loss: 1.3728 - val_acc: 0.2661 - val_precision_m: 0.0323\n","Epoch 164/500\n","144/144 [==============================] - 0s 2ms/step - loss: 1.3359 - acc: 0.3017 - precision_m: 0.0611 - val_loss: 1.3581 - val_acc: 0.2581 - val_precision_m: 0.0000e+00\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 20.0s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","167/167 [==============================] - 1s 3ms/step - loss: 1.4628 - acc: 0.2830 - precision_m: 0.0150 - val_loss: 1.3915 - val_acc: 0.2014 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3862 - acc: 0.2845 - precision_m: 0.0000e+00 - val_loss: 1.3918 - val_acc: 0.2292 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3918 - acc: 0.2760 - precision_m: 0.0000e+00 - val_loss: 1.3909 - val_acc: 0.2292 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3927 - acc: 0.2456 - precision_m: 0.0000e+00 - val_loss: 1.3927 - val_acc: 0.2361 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3919 - acc: 0.2683 - precision_m: 0.0000e+00 - val_loss: 1.3920 - val_acc: 0.2431 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3952 - acc: 0.2733 - precision_m: 0.0000e+00 - val_loss: 1.3924 - val_acc: 0.2153 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3848 - acc: 0.2491 - precision_m: 0.0000e+00 - val_loss: 1.3939 - val_acc: 0.1806 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3799 - acc: 0.2539 - precision_m: 0.0000e+00 - val_loss: 1.3986 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3796 - acc: 0.2839 - precision_m: 0.0000e+00 - val_loss: 1.3938 - val_acc: 0.1944 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3860 - acc: 0.2733 - precision_m: 0.0000e+00 - val_loss: 1.3933 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3874 - acc: 0.2634 - precision_m: 0.0000e+00 - val_loss: 1.3912 - val_acc: 0.2708 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3816 - acc: 0.2820 - precision_m: 0.0000e+00 - val_loss: 1.3969 - val_acc: 0.2222 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3675 - acc: 0.3528 - precision_m: 0.0000e+00 - val_loss: 1.4003 - val_acc: 0.1944 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3875 - acc: 0.2870 - precision_m: 0.0000e+00 - val_loss: 1.3980 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3829 - acc: 0.2912 - precision_m: 0.0000e+00 - val_loss: 1.3951 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3809 - acc: 0.2869 - precision_m: 0.0000e+00 - val_loss: 1.3955 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3823 - acc: 0.2488 - precision_m: 0.0000e+00 - val_loss: 1.3967 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3842 - acc: 0.2587 - precision_m: 0.0000e+00 - val_loss: 1.3957 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3820 - acc: 0.2798 - precision_m: 0.0000e+00 - val_loss: 1.3971 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3790 - acc: 0.2606 - precision_m: 0.0000e+00 - val_loss: 1.3972 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3849 - acc: 0.2339 - precision_m: 0.0000e+00 - val_loss: 1.3961 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3826 - acc: 0.2751 - precision_m: 0.0000e+00 - val_loss: 1.3967 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3825 - acc: 0.2596 - precision_m: 0.0000e+00 - val_loss: 1.3978 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3826 - acc: 0.2767 - precision_m: 0.0000e+00 - val_loss: 1.3973 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3872 - acc: 0.2510 - precision_m: 0.0000e+00 - val_loss: 1.3977 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3860 - acc: 0.2713 - precision_m: 0.0000e+00 - val_loss: 1.3980 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3808 - acc: 0.3226 - precision_m: 0.0000e+00 - val_loss: 1.3988 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3856 - acc: 0.2746 - precision_m: 0.0000e+00 - val_loss: 1.4037 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3834 - acc: 0.2768 - precision_m: 0.0000e+00 - val_loss: 1.3985 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3742 - acc: 0.3006 - precision_m: 0.0000e+00 - val_loss: 1.4006 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3831 - acc: 0.2258 - precision_m: 0.0000e+00 - val_loss: 1.3985 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3833 - acc: 0.2466 - precision_m: 0.0000e+00 - val_loss: 1.3980 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3847 - acc: 0.2500 - precision_m: 0.0000e+00 - val_loss: 1.4005 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3757 - acc: 0.2866 - precision_m: 0.0000e+00 - val_loss: 1.3994 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3760 - acc: 0.2894 - precision_m: 0.0000e+00 - val_loss: 1.3990 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3949 - acc: 0.2356 - precision_m: 0.0000e+00 - val_loss: 1.3998 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3797 - acc: 0.2772 - precision_m: 0.0000e+00 - val_loss: 1.3989 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3877 - acc: 0.2395 - precision_m: 0.0000e+00 - val_loss: 1.3990 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3818 - acc: 0.2555 - precision_m: 0.0000e+00 - val_loss: 1.3987 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3803 - acc: 0.2880 - precision_m: 0.0000e+00 - val_loss: 1.4002 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3800 - acc: 0.2885 - precision_m: 0.0000e+00 - val_loss: 1.3993 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3832 - acc: 0.2736 - precision_m: 0.0000e+00 - val_loss: 1.4025 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3875 - acc: 0.2556 - precision_m: 0.0000e+00 - val_loss: 1.3990 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3858 - acc: 0.2121 - precision_m: 0.0000e+00 - val_loss: 1.4034 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3840 - acc: 0.2646 - precision_m: 0.0000e+00 - val_loss: 1.3988 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3805 - acc: 0.1989 - precision_m: 0.0000e+00 - val_loss: 1.3991 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3812 - acc: 0.2944 - precision_m: 0.0000e+00 - val_loss: 1.4002 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3826 - acc: 0.2505 - precision_m: 0.0000e+00 - val_loss: 1.4004 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3782 - acc: 0.2987 - precision_m: 0.0000e+00 - val_loss: 1.4001 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3852 - acc: 0.2109 - precision_m: 0.0000e+00 - val_loss: 1.4001 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3869 - acc: 0.2621 - precision_m: 0.0000e+00 - val_loss: 1.3998 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 52/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3828 - acc: 0.2559 - precision_m: 0.0000e+00 - val_loss: 1.4015 - val_acc: 0.1875 - val_precision_m: 0.0000e+00\n","Epoch 53/500\n","167/167 [==============================] - 0s 2ms/step - loss: 1.3770 - acc: 0.2885 - precision_m: 0.0000e+00 - val_loss: 1.4027 - val_acc: 0.1528 - val_precision_m: 0.0000e+00\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 17.9s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","143/143 [==============================] - 1s 3ms/step - loss: 1.3978 - acc: 0.3256 - precision_m: 0.0000e+00 - val_loss: 1.3837 - val_acc: 0.3008 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3937 - acc: 0.2657 - precision_m: 0.0000e+00 - val_loss: 1.3828 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3880 - acc: 0.2345 - precision_m: 0.0000e+00 - val_loss: 1.3823 - val_acc: 0.2602 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3847 - acc: 0.2723 - precision_m: 0.0000e+00 - val_loss: 1.3806 - val_acc: 0.2927 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3730 - acc: 0.2817 - precision_m: 0.0000e+00 - val_loss: 1.3822 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3795 - acc: 0.2979 - precision_m: 0.0000e+00 - val_loss: 1.3797 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3867 - acc: 0.3146 - precision_m: 0.0000e+00 - val_loss: 1.3789 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3879 - acc: 0.2421 - precision_m: 0.0000e+00 - val_loss: 1.3798 - val_acc: 0.2683 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3838 - acc: 0.2569 - precision_m: 0.0000e+00 - val_loss: 1.3796 - val_acc: 0.2520 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3815 - acc: 0.2649 - precision_m: 0.0000e+00 - val_loss: 1.3791 - val_acc: 0.2683 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3799 - acc: 0.3214 - precision_m: 0.0000e+00 - val_loss: 1.3776 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","143/143 [==============================] - 0s 3ms/step - loss: 1.3849 - acc: 0.2238 - precision_m: 0.0000e+00 - val_loss: 1.3789 - val_acc: 0.3171 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3937 - acc: 0.2952 - precision_m: 0.0085 - val_loss: 1.3780 - val_acc: 0.2683 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3858 - acc: 0.2460 - precision_m: 0.0000e+00 - val_loss: 1.3769 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3758 - acc: 0.2681 - precision_m: 0.0000e+00 - val_loss: 1.3776 - val_acc: 0.2683 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3815 - acc: 0.2547 - precision_m: 0.0000e+00 - val_loss: 1.3781 - val_acc: 0.2683 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3823 - acc: 0.2386 - precision_m: 0.0000e+00 - val_loss: 1.3778 - val_acc: 0.2683 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3832 - acc: 0.2380 - precision_m: 0.0000e+00 - val_loss: 1.3775 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3823 - acc: 0.2910 - precision_m: 0.0000e+00 - val_loss: 1.3777 - val_acc: 0.2683 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3833 - acc: 0.2689 - precision_m: 0.0000e+00 - val_loss: 1.3775 - val_acc: 0.2683 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3800 - acc: 0.2557 - precision_m: 0.0000e+00 - val_loss: 1.3774 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3892 - acc: 0.2243 - precision_m: 0.0000e+00 - val_loss: 1.3787 - val_acc: 0.2602 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3802 - acc: 0.2627 - precision_m: 0.0000e+00 - val_loss: 1.3787 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3890 - acc: 0.2776 - precision_m: 0.0000e+00 - val_loss: 1.3772 - val_acc: 0.2602 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3888 - acc: 0.2529 - precision_m: 0.0000e+00 - val_loss: 1.3770 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3822 - acc: 0.2575 - precision_m: 0.0017 - val_loss: 1.3777 - val_acc: 0.3008 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3819 - acc: 0.3078 - precision_m: 0.0000e+00 - val_loss: 1.3787 - val_acc: 0.2683 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3892 - acc: 0.2365 - precision_m: 3.4517e-04 - val_loss: 1.3781 - val_acc: 0.2683 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3799 - acc: 0.2799 - precision_m: 0.0000e+00 - val_loss: 1.3914 - val_acc: 0.2927 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3992 - acc: 0.2682 - precision_m: 0.0000e+00 - val_loss: 1.3825 - val_acc: 0.3171 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3792 - acc: 0.2615 - precision_m: 0.0027 - val_loss: 1.3768 - val_acc: 0.3252 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3896 - acc: 0.2307 - precision_m: 0.0052 - val_loss: 1.3759 - val_acc: 0.2683 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3903 - acc: 0.2430 - precision_m: 0.0000e+00 - val_loss: 1.3744 - val_acc: 0.2683 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3812 - acc: 0.2301 - precision_m: 2.9484e-04 - val_loss: 1.3736 - val_acc: 0.2683 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3754 - acc: 0.2973 - precision_m: 0.0017 - val_loss: 1.3732 - val_acc: 0.2683 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3843 - acc: 0.2007 - precision_m: 1.9528e-04 - val_loss: 1.3745 - val_acc: 0.2683 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3794 - acc: 0.2611 - precision_m: 0.0053 - val_loss: 1.3878 - val_acc: 0.2520 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3816 - acc: 0.3051 - precision_m: 0.0073 - val_loss: 1.3749 - val_acc: 0.2683 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3908 - acc: 0.2398 - precision_m: 0.0000e+00 - val_loss: 1.3752 - val_acc: 0.2683 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3687 - acc: 0.3252 - precision_m: 0.0077 - val_loss: 1.3842 - val_acc: 0.2195 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3850 - acc: 0.2437 - precision_m: 9.2420e-04 - val_loss: 1.3731 - val_acc: 0.3171 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3744 - acc: 0.2619 - precision_m: 0.0027 - val_loss: 1.3743 - val_acc: 0.2927 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3592 - acc: 0.3093 - precision_m: 0.0132 - val_loss: 1.3766 - val_acc: 0.3008 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3732 - acc: 0.2532 - precision_m: 0.0178 - val_loss: 1.3721 - val_acc: 0.2683 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3731 - acc: 0.2527 - precision_m: 0.0000e+00 - val_loss: 1.3777 - val_acc: 0.2927 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3620 - acc: 0.2916 - precision_m: 0.0374 - val_loss: 1.3736 - val_acc: 0.2683 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3677 - acc: 0.3047 - precision_m: 0.0058 - val_loss: 1.3752 - val_acc: 0.2927 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3708 - acc: 0.2739 - precision_m: 0.0129 - val_loss: 1.3761 - val_acc: 0.2683 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3801 - acc: 0.2561 - precision_m: 6.0240e-04 - val_loss: 1.3732 - val_acc: 0.2439 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3571 - acc: 0.3271 - precision_m: 0.0467 - val_loss: 1.3743 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3707 - acc: 0.2787 - precision_m: 0.0227 - val_loss: 1.3714 - val_acc: 0.2276 - val_precision_m: 0.0000e+00\n","Epoch 52/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3818 - acc: 0.2785 - precision_m: 0.0033 - val_loss: 1.3702 - val_acc: 0.2927 - val_precision_m: 0.0000e+00\n","Epoch 53/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3755 - acc: 0.2837 - precision_m: 0.0112 - val_loss: 1.3683 - val_acc: 0.2927 - val_precision_m: 0.0000e+00\n","Epoch 54/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3771 - acc: 0.2288 - precision_m: 0.0127 - val_loss: 1.3696 - val_acc: 0.2927 - val_precision_m: 0.0000e+00\n","Epoch 55/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3660 - acc: 0.3287 - precision_m: 0.0113 - val_loss: 1.3681 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 56/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3780 - acc: 0.2998 - precision_m: 0.0014 - val_loss: 1.3737 - val_acc: 0.3171 - val_precision_m: 0.0000e+00\n","Epoch 57/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3857 - acc: 0.2503 - precision_m: 0.0120 - val_loss: 1.3682 - val_acc: 0.3008 - val_precision_m: 0.0000e+00\n","Epoch 58/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3658 - acc: 0.2564 - precision_m: 3.9586e-04 - val_loss: 1.3699 - val_acc: 0.3089 - val_precision_m: 0.0000e+00\n","Epoch 59/500\n","143/143 [==============================] - 0s 3ms/step - loss: 1.3757 - acc: 0.2477 - precision_m: 0.0122 - val_loss: 1.3685 - val_acc: 0.2276 - val_precision_m: 0.0000e+00\n","Epoch 60/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3620 - acc: 0.2942 - precision_m: 0.0394 - val_loss: 1.3675 - val_acc: 0.2683 - val_precision_m: 0.0000e+00\n","Epoch 61/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3781 - acc: 0.2315 - precision_m: 0.0093 - val_loss: 1.3686 - val_acc: 0.3008 - val_precision_m: 0.0000e+00\n","Epoch 62/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3798 - acc: 0.2895 - precision_m: 0.0027 - val_loss: 1.3670 - val_acc: 0.3008 - val_precision_m: 0.0000e+00\n","Epoch 63/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3758 - acc: 0.2862 - precision_m: 0.0056 - val_loss: 1.3675 - val_acc: 0.2520 - val_precision_m: 0.0000e+00\n","Epoch 64/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3718 - acc: 0.2631 - precision_m: 0.0224 - val_loss: 1.3693 - val_acc: 0.2602 - val_precision_m: 0.0000e+00\n","Epoch 65/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3780 - acc: 0.2372 - precision_m: 0.0000e+00 - val_loss: 1.3684 - val_acc: 0.2927 - val_precision_m: 0.0000e+00\n","Epoch 66/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3761 - acc: 0.2226 - precision_m: 0.0118 - val_loss: 1.3673 - val_acc: 0.2358 - val_precision_m: 0.0000e+00\n","Epoch 67/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3906 - acc: 0.2911 - precision_m: 0.0022 - val_loss: 1.3684 - val_acc: 0.2683 - val_precision_m: 0.0000e+00\n","Epoch 68/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3881 - acc: 0.2507 - precision_m: 2.9484e-04 - val_loss: 1.3684 - val_acc: 0.2358 - val_precision_m: 0.0000e+00\n","Epoch 69/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3592 - acc: 0.3263 - precision_m: 0.0090 - val_loss: 1.3654 - val_acc: 0.3171 - val_precision_m: 0.0000e+00\n","Epoch 70/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3718 - acc: 0.3102 - precision_m: 0.0258 - val_loss: 1.3655 - val_acc: 0.3089 - val_precision_m: 0.0000e+00\n","Epoch 71/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3798 - acc: 0.2361 - precision_m: 0.0027 - val_loss: 1.3671 - val_acc: 0.3089 - val_precision_m: 0.0000e+00\n","Epoch 72/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3684 - acc: 0.2682 - precision_m: 0.0100 - val_loss: 1.3663 - val_acc: 0.2764 - val_precision_m: 0.0000e+00\n","Epoch 73/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3768 - acc: 0.2845 - precision_m: 0.0416 - val_loss: 1.3636 - val_acc: 0.3171 - val_precision_m: 0.0000e+00\n","Epoch 74/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3764 - acc: 0.2591 - precision_m: 0.0000e+00 - val_loss: 1.3664 - val_acc: 0.2927 - val_precision_m: 0.0000e+00\n","Epoch 75/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3686 - acc: 0.3223 - precision_m: 0.0134 - val_loss: 1.3654 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 76/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3795 - acc: 0.2694 - precision_m: 0.0015 - val_loss: 1.3644 - val_acc: 0.3171 - val_precision_m: 0.0161\n","Epoch 77/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3561 - acc: 0.3213 - precision_m: 0.0123 - val_loss: 1.3667 - val_acc: 0.3089 - val_precision_m: 0.0000e+00\n","Epoch 78/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3838 - acc: 0.2943 - precision_m: 0.0077 - val_loss: 1.3675 - val_acc: 0.3008 - val_precision_m: 0.0000e+00\n","Epoch 79/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3537 - acc: 0.3038 - precision_m: 0.0302 - val_loss: 1.3753 - val_acc: 0.2602 - val_precision_m: 0.0000e+00\n","Epoch 80/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3732 - acc: 0.2639 - precision_m: 0.0151 - val_loss: 1.3664 - val_acc: 0.2927 - val_precision_m: 0.0000e+00\n","Epoch 81/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3658 - acc: 0.2901 - precision_m: 0.0228 - val_loss: 1.3775 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 82/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3693 - acc: 0.2900 - precision_m: 0.0275 - val_loss: 1.3652 - val_acc: 0.3171 - val_precision_m: 0.0000e+00\n","Epoch 83/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3725 - acc: 0.2662 - precision_m: 0.0177 - val_loss: 1.3660 - val_acc: 0.2927 - val_precision_m: 0.0000e+00\n","Epoch 84/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3809 - acc: 0.2828 - precision_m: 0.0089 - val_loss: 1.3679 - val_acc: 0.3089 - val_precision_m: 0.0000e+00\n","Epoch 85/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3785 - acc: 0.2965 - precision_m: 0.0073 - val_loss: 1.3825 - val_acc: 0.2520 - val_precision_m: 0.0000e+00\n","Epoch 86/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3790 - acc: 0.2714 - precision_m: 0.0244 - val_loss: 1.3626 - val_acc: 0.3333 - val_precision_m: 0.0000e+00\n","Epoch 87/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3728 - acc: 0.3099 - precision_m: 0.0071 - val_loss: 1.3628 - val_acc: 0.3008 - val_precision_m: 0.0000e+00\n","Epoch 88/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3682 - acc: 0.2995 - precision_m: 0.0231 - val_loss: 1.3694 - val_acc: 0.3171 - val_precision_m: 0.0000e+00\n","Epoch 89/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3813 - acc: 0.2332 - precision_m: 0.0104 - val_loss: 1.3652 - val_acc: 0.3089 - val_precision_m: 0.0000e+00\n","Epoch 90/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3656 - acc: 0.3122 - precision_m: 0.0166 - val_loss: 1.3626 - val_acc: 0.2927 - val_precision_m: 0.0000e+00\n","Epoch 91/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3703 - acc: 0.2721 - precision_m: 0.0185 - val_loss: 1.3638 - val_acc: 0.3089 - val_precision_m: 0.0000e+00\n","Epoch 92/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3661 - acc: 0.2890 - precision_m: 0.0062 - val_loss: 1.3620 - val_acc: 0.3008 - val_precision_m: 0.0000e+00\n","Epoch 93/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3723 - acc: 0.2835 - precision_m: 0.0408 - val_loss: 1.3636 - val_acc: 0.3252 - val_precision_m: 0.0161\n","Epoch 94/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3628 - acc: 0.2810 - precision_m: 0.0361 - val_loss: 1.3591 - val_acc: 0.3740 - val_precision_m: 0.0000e+00\n","Epoch 95/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3600 - acc: 0.2348 - precision_m: 0.0221 - val_loss: 1.3690 - val_acc: 0.2358 - val_precision_m: 0.0161\n","Epoch 96/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3699 - acc: 0.2631 - precision_m: 0.0660 - val_loss: 1.3636 - val_acc: 0.2927 - val_precision_m: 0.0161\n","Epoch 97/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3708 - acc: 0.2626 - precision_m: 0.0534 - val_loss: 1.3639 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 98/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3673 - acc: 0.3433 - precision_m: 0.0255 - val_loss: 1.3635 - val_acc: 0.3089 - val_precision_m: 0.0161\n","Epoch 99/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3774 - acc: 0.2461 - precision_m: 0.0033 - val_loss: 1.3623 - val_acc: 0.2927 - val_precision_m: 0.0161\n","Epoch 100/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3567 - acc: 0.3113 - precision_m: 0.0608 - val_loss: 1.3628 - val_acc: 0.3089 - val_precision_m: 0.0161\n","Epoch 101/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3650 - acc: 0.3199 - precision_m: 0.0099 - val_loss: 1.3649 - val_acc: 0.3008 - val_precision_m: 0.0161\n","Epoch 102/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3748 - acc: 0.2642 - precision_m: 0.0311 - val_loss: 1.3642 - val_acc: 0.2520 - val_precision_m: 0.0161\n","Epoch 103/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3655 - acc: 0.2983 - precision_m: 0.0316 - val_loss: 1.3723 - val_acc: 0.3089 - val_precision_m: 0.0000e+00\n","Epoch 104/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3806 - acc: 0.2789 - precision_m: 0.0226 - val_loss: 1.3650 - val_acc: 0.2927 - val_precision_m: 0.0161\n","Epoch 105/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3773 - acc: 0.3123 - precision_m: 0.0045 - val_loss: 1.3641 - val_acc: 0.3171 - val_precision_m: 0.0161\n","Epoch 106/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3598 - acc: 0.3067 - precision_m: 0.0287 - val_loss: 1.3647 - val_acc: 0.3171 - val_precision_m: 0.0161\n","Epoch 107/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3596 - acc: 0.2815 - precision_m: 0.0722 - val_loss: 1.3646 - val_acc: 0.2683 - val_precision_m: 0.0161\n","Epoch 108/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3764 - acc: 0.2594 - precision_m: 0.0075 - val_loss: 1.3653 - val_acc: 0.3171 - val_precision_m: 0.0161\n","Epoch 109/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3858 - acc: 0.2422 - precision_m: 0.0076 - val_loss: 1.3639 - val_acc: 0.2276 - val_precision_m: 0.0161\n","Epoch 110/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3869 - acc: 0.2517 - precision_m: 0.0042 - val_loss: 1.3646 - val_acc: 0.2927 - val_precision_m: 0.0161\n","Epoch 111/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3809 - acc: 0.2917 - precision_m: 0.0092 - val_loss: 1.3643 - val_acc: 0.2846 - val_precision_m: 0.0161\n","Epoch 112/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3793 - acc: 0.2634 - precision_m: 0.0012 - val_loss: 1.3636 - val_acc: 0.2927 - val_precision_m: 0.0161\n","Epoch 113/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3527 - acc: 0.3394 - precision_m: 0.0601 - val_loss: 1.3625 - val_acc: 0.2602 - val_precision_m: 0.0161\n","Epoch 114/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3663 - acc: 0.2630 - precision_m: 0.0136 - val_loss: 1.3639 - val_acc: 0.2927 - val_precision_m: 0.0161\n","Epoch 115/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3785 - acc: 0.2272 - precision_m: 0.0150 - val_loss: 1.3635 - val_acc: 0.2602 - val_precision_m: 0.0161\n","Epoch 116/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3715 - acc: 0.2406 - precision_m: 0.0285 - val_loss: 1.3668 - val_acc: 0.3089 - val_precision_m: 0.0161\n","Epoch 117/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3697 - acc: 0.3364 - precision_m: 0.0042 - val_loss: 1.3614 - val_acc: 0.3171 - val_precision_m: 0.0161\n","Epoch 118/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3749 - acc: 0.3193 - precision_m: 0.0045 - val_loss: 1.3612 - val_acc: 0.3008 - val_precision_m: 0.0161\n","Epoch 119/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3595 - acc: 0.3433 - precision_m: 0.0550 - val_loss: 1.3611 - val_acc: 0.3089 - val_precision_m: 0.0161\n","Epoch 120/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3756 - acc: 0.2799 - precision_m: 0.0216 - val_loss: 1.3599 - val_acc: 0.2927 - val_precision_m: 0.0161\n","Epoch 121/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3638 - acc: 0.3235 - precision_m: 0.0323 - val_loss: 1.3623 - val_acc: 0.3171 - val_precision_m: 0.0161\n","Epoch 122/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3832 - acc: 0.1966 - precision_m: 0.0130 - val_loss: 1.3619 - val_acc: 0.3171 - val_precision_m: 0.0161\n","Epoch 123/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3585 - acc: 0.3057 - precision_m: 0.0617 - val_loss: 1.3625 - val_acc: 0.2927 - val_precision_m: 0.0161\n","Epoch 124/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3486 - acc: 0.2825 - precision_m: 0.0529 - val_loss: 1.3607 - val_acc: 0.3008 - val_precision_m: 0.0161\n","Epoch 125/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3669 - acc: 0.2933 - precision_m: 0.0287 - val_loss: 1.3623 - val_acc: 0.3171 - val_precision_m: 0.0161\n","Epoch 126/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3589 - acc: 0.3164 - precision_m: 0.0279 - val_loss: 1.3631 - val_acc: 0.3171 - val_precision_m: 0.0161\n","Epoch 127/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3894 - acc: 0.2749 - precision_m: 0.0068 - val_loss: 1.3613 - val_acc: 0.3171 - val_precision_m: 0.0161\n","Epoch 128/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3611 - acc: 0.3243 - precision_m: 0.0217 - val_loss: 1.3600 - val_acc: 0.2846 - val_precision_m: 0.0161\n","Epoch 129/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3626 - acc: 0.2865 - precision_m: 0.0406 - val_loss: 1.3590 - val_acc: 0.3333 - val_precision_m: 0.0161\n","Epoch 130/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3712 - acc: 0.2890 - precision_m: 0.0231 - val_loss: 1.3634 - val_acc: 0.3171 - val_precision_m: 0.0161\n","Epoch 131/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3847 - acc: 0.2521 - precision_m: 0.0126 - val_loss: 1.3631 - val_acc: 0.3171 - val_precision_m: 0.0161\n","Epoch 132/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3698 - acc: 0.3231 - precision_m: 0.0289 - val_loss: 1.3586 - val_acc: 0.3333 - val_precision_m: 0.0161\n","Epoch 133/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3740 - acc: 0.2699 - precision_m: 0.0442 - val_loss: 1.3624 - val_acc: 0.3089 - val_precision_m: 0.0161\n","Epoch 134/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3708 - acc: 0.2622 - precision_m: 0.0184 - val_loss: 1.3626 - val_acc: 0.3008 - val_precision_m: 0.0161\n","Epoch 135/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3782 - acc: 0.2741 - precision_m: 0.0076 - val_loss: 1.3609 - val_acc: 0.2846 - val_precision_m: 0.0161\n","Epoch 136/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3892 - acc: 0.2627 - precision_m: 0.0145 - val_loss: 1.3577 - val_acc: 0.3089 - val_precision_m: 0.0161\n","Epoch 137/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3811 - acc: 0.3134 - precision_m: 0.0251 - val_loss: 1.3625 - val_acc: 0.3171 - val_precision_m: 0.0161\n","Epoch 138/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3687 - acc: 0.2532 - precision_m: 0.0337 - val_loss: 1.3624 - val_acc: 0.3252 - val_precision_m: 0.0161\n","Epoch 139/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3751 - acc: 0.2674 - precision_m: 0.0139 - val_loss: 1.3638 - val_acc: 0.3008 - val_precision_m: 0.0161\n","Epoch 140/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3856 - acc: 0.2562 - precision_m: 0.0069 - val_loss: 1.3708 - val_acc: 0.3171 - val_precision_m: 0.0484\n","Epoch 141/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3162 - acc: 0.3711 - precision_m: 0.1414 - val_loss: 1.3653 - val_acc: 0.2683 - val_precision_m: 0.0161\n","Epoch 142/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3762 - acc: 0.2403 - precision_m: 0.0290 - val_loss: 1.3596 - val_acc: 0.3089 - val_precision_m: 0.0161\n","Epoch 143/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3692 - acc: 0.2983 - precision_m: 0.0401 - val_loss: 1.3617 - val_acc: 0.2927 - val_precision_m: 0.0161\n","Epoch 144/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3674 - acc: 0.2766 - precision_m: 0.0248 - val_loss: 1.3647 - val_acc: 0.2764 - val_precision_m: 0.0161\n","Epoch 145/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3681 - acc: 0.2249 - precision_m: 0.0338 - val_loss: 1.3626 - val_acc: 0.3089 - val_precision_m: 0.0161\n","Epoch 146/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3515 - acc: 0.3352 - precision_m: 0.0565 - val_loss: 1.3609 - val_acc: 0.2927 - val_precision_m: 0.0161\n","Epoch 147/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3703 - acc: 0.2740 - precision_m: 0.0266 - val_loss: 1.3572 - val_acc: 0.3171 - val_precision_m: 0.0161\n","Epoch 148/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3403 - acc: 0.3033 - precision_m: 0.0478 - val_loss: 1.3592 - val_acc: 0.2764 - val_precision_m: 0.0161\n","Epoch 149/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3699 - acc: 0.3020 - precision_m: 0.0319 - val_loss: 1.3667 - val_acc: 0.3171 - val_precision_m: 0.0161\n","Epoch 150/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3741 - acc: 0.3068 - precision_m: 0.0522 - val_loss: 1.3601 - val_acc: 0.3171 - val_precision_m: 0.0161\n","Epoch 151/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3466 - acc: 0.2864 - precision_m: 0.0653 - val_loss: 1.3586 - val_acc: 0.3008 - val_precision_m: 0.0161\n","Epoch 152/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3728 - acc: 0.2513 - precision_m: 0.0388 - val_loss: 1.3633 - val_acc: 0.2927 - val_precision_m: 0.0161\n","Epoch 153/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3588 - acc: 0.2703 - precision_m: 0.0509 - val_loss: 1.3598 - val_acc: 0.3171 - val_precision_m: 0.0161\n","Epoch 154/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3606 - acc: 0.3104 - precision_m: 0.0507 - val_loss: 1.3588 - val_acc: 0.2927 - val_precision_m: 0.0161\n","Epoch 155/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3759 - acc: 0.2787 - precision_m: 0.0248 - val_loss: 1.3601 - val_acc: 0.3089 - val_precision_m: 0.0161\n","Epoch 156/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3626 - acc: 0.2914 - precision_m: 0.0424 - val_loss: 1.3591 - val_acc: 0.2846 - val_precision_m: 0.0161\n","Epoch 157/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3549 - acc: 0.2167 - precision_m: 0.0505 - val_loss: 1.3602 - val_acc: 0.3008 - val_precision_m: 0.0161\n","Epoch 158/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3733 - acc: 0.3242 - precision_m: 0.0232 - val_loss: 1.3650 - val_acc: 0.3252 - val_precision_m: 0.0161\n","Epoch 159/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3655 - acc: 0.2865 - precision_m: 0.0546 - val_loss: 1.3609 - val_acc: 0.3089 - val_precision_m: 0.0161\n","Epoch 160/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3481 - acc: 0.3327 - precision_m: 0.0462 - val_loss: 1.3617 - val_acc: 0.3008 - val_precision_m: 0.0161\n","Epoch 161/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3538 - acc: 0.2827 - precision_m: 0.0342 - val_loss: 1.3623 - val_acc: 0.2846 - val_precision_m: 0.0161\n","Epoch 162/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3505 - acc: 0.3540 - precision_m: 0.0661 - val_loss: 1.3633 - val_acc: 0.3089 - val_precision_m: 0.0161\n","Epoch 163/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3715 - acc: 0.2340 - precision_m: 0.0149 - val_loss: 1.3626 - val_acc: 0.3008 - val_precision_m: 0.0161\n","Epoch 164/500\n","143/143 [==============================] - 0s 3ms/step - loss: 1.3537 - acc: 0.2969 - precision_m: 0.0594 - val_loss: 1.3631 - val_acc: 0.3008 - val_precision_m: 0.0161\n","Epoch 165/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3562 - acc: 0.2551 - precision_m: 0.0573 - val_loss: 1.3584 - val_acc: 0.2927 - val_precision_m: 0.0161\n","Epoch 166/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3331 - acc: 0.2977 - precision_m: 0.0920 - val_loss: 1.3627 - val_acc: 0.2927 - val_precision_m: 0.0161\n","Epoch 167/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3723 - acc: 0.2837 - precision_m: 0.0250 - val_loss: 1.3577 - val_acc: 0.2927 - val_precision_m: 0.0161\n","Epoch 168/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3650 - acc: 0.2864 - precision_m: 0.0218 - val_loss: 1.3621 - val_acc: 0.3008 - val_precision_m: 0.0161\n","Epoch 169/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3641 - acc: 0.3063 - precision_m: 0.0072 - val_loss: 1.3587 - val_acc: 0.3008 - val_precision_m: 0.0161\n","Epoch 170/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3635 - acc: 0.2819 - precision_m: 0.0339 - val_loss: 1.3565 - val_acc: 0.3171 - val_precision_m: 0.0161\n","Epoch 171/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3535 - acc: 0.2706 - precision_m: 0.0519 - val_loss: 1.3596 - val_acc: 0.3089 - val_precision_m: 0.0161\n","Epoch 172/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3631 - acc: 0.2493 - precision_m: 0.0413 - val_loss: 1.3605 - val_acc: 0.3008 - val_precision_m: 0.0161\n","Epoch 173/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3798 - acc: 0.2451 - precision_m: 0.0067 - val_loss: 1.3578 - val_acc: 0.3171 - val_precision_m: 0.0000e+00\n","Epoch 174/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3669 - acc: 0.2620 - precision_m: 0.0306 - val_loss: 1.3583 - val_acc: 0.3089 - val_precision_m: 0.0161\n","Epoch 175/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3920 - acc: 0.2649 - precision_m: 0.0086 - val_loss: 1.3667 - val_acc: 0.3089 - val_precision_m: 0.0161\n","Epoch 176/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3678 - acc: 0.2566 - precision_m: 0.0161 - val_loss: 1.3590 - val_acc: 0.3089 - val_precision_m: 0.0161\n","Epoch 177/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3353 - acc: 0.3731 - precision_m: 0.0612 - val_loss: 1.3619 - val_acc: 0.2927 - val_precision_m: 0.0161\n","Epoch 178/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3847 - acc: 0.2293 - precision_m: 0.0086 - val_loss: 1.3588 - val_acc: 0.2927 - val_precision_m: 0.0161\n","Epoch 179/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3514 - acc: 0.2816 - precision_m: 0.0559 - val_loss: 1.3655 - val_acc: 0.3089 - val_precision_m: 0.0161\n","Epoch 180/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3735 - acc: 0.2779 - precision_m: 0.0192 - val_loss: 1.3576 - val_acc: 0.3171 - val_precision_m: 0.0161\n","Epoch 181/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3637 - acc: 0.2703 - precision_m: 0.0325 - val_loss: 1.3554 - val_acc: 0.3089 - val_precision_m: 0.0161\n","Epoch 182/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3357 - acc: 0.3377 - precision_m: 0.0738 - val_loss: 1.3536 - val_acc: 0.3089 - val_precision_m: 0.0161\n","Epoch 183/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3858 - acc: 0.2145 - precision_m: 0.0310 - val_loss: 1.3526 - val_acc: 0.3171 - val_precision_m: 0.0161\n","Epoch 184/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3814 - acc: 0.2518 - precision_m: 0.0333 - val_loss: 1.3524 - val_acc: 0.3089 - val_precision_m: 0.0161\n","Epoch 185/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3745 - acc: 0.2698 - precision_m: 0.0252 - val_loss: 1.3512 - val_acc: 0.3171 - val_precision_m: 0.0161\n","Epoch 186/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3824 - acc: 0.2932 - precision_m: 0.0333 - val_loss: 1.3554 - val_acc: 0.3008 - val_precision_m: 0.0161\n","Epoch 187/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3487 - acc: 0.3173 - precision_m: 0.0466 - val_loss: 1.3555 - val_acc: 0.3008 - val_precision_m: 0.0161\n","Epoch 188/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3722 - acc: 0.2861 - precision_m: 0.0154 - val_loss: 1.3681 - val_acc: 0.3333 - val_precision_m: 0.0645\n","Epoch 189/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3662 - acc: 0.2789 - precision_m: 0.0451 - val_loss: 1.3516 - val_acc: 0.3171 - val_precision_m: 0.0161\n","Epoch 190/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3461 - acc: 0.2772 - precision_m: 0.0518 - val_loss: 1.3597 - val_acc: 0.3333 - val_precision_m: 0.0484\n","Epoch 191/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3758 - acc: 0.2992 - precision_m: 0.0644 - val_loss: 1.3534 - val_acc: 0.3008 - val_precision_m: 0.0645\n","Epoch 192/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3801 - acc: 0.3152 - precision_m: 0.0440 - val_loss: 1.3583 - val_acc: 0.3008 - val_precision_m: 0.0323\n","Epoch 193/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3540 - acc: 0.2576 - precision_m: 0.0937 - val_loss: 1.3647 - val_acc: 0.2683 - val_precision_m: 0.0484\n","Epoch 194/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3958 - acc: 0.2840 - precision_m: 0.0256 - val_loss: 1.3574 - val_acc: 0.3171 - val_precision_m: 0.0161\n","Epoch 195/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3693 - acc: 0.2314 - precision_m: 0.0279 - val_loss: 1.3525 - val_acc: 0.3496 - val_precision_m: 0.0161\n","Epoch 196/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3532 - acc: 0.3373 - precision_m: 0.0261 - val_loss: 1.3575 - val_acc: 0.3008 - val_precision_m: 0.0484\n","Epoch 197/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3650 - acc: 0.2415 - precision_m: 0.0493 - val_loss: 1.3517 - val_acc: 0.3496 - val_precision_m: 0.0161\n","Epoch 198/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3610 - acc: 0.2970 - precision_m: 0.0268 - val_loss: 1.3513 - val_acc: 0.3333 - val_precision_m: 0.0161\n","Epoch 199/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3469 - acc: 0.3405 - precision_m: 0.0807 - val_loss: 1.3566 - val_acc: 0.3089 - val_precision_m: 0.0161\n","Epoch 200/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3601 - acc: 0.2824 - precision_m: 0.0182 - val_loss: 1.3500 - val_acc: 0.3089 - val_precision_m: 0.0323\n","Epoch 201/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3261 - acc: 0.3438 - precision_m: 0.1166 - val_loss: 1.3480 - val_acc: 0.3496 - val_precision_m: 0.0161\n","Epoch 202/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3465 - acc: 0.2979 - precision_m: 0.0733 - val_loss: 1.3486 - val_acc: 0.2927 - val_precision_m: 0.0323\n","Epoch 203/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3448 - acc: 0.2548 - precision_m: 0.1119 - val_loss: 1.3487 - val_acc: 0.3008 - val_precision_m: 0.0484\n","Epoch 204/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3302 - acc: 0.3242 - precision_m: 0.1307 - val_loss: 1.3511 - val_acc: 0.3415 - val_precision_m: 0.0161\n","Epoch 205/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3550 - acc: 0.3270 - precision_m: 0.0210 - val_loss: 1.3572 - val_acc: 0.3171 - val_precision_m: 0.0161\n","Epoch 206/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3749 - acc: 0.2655 - precision_m: 0.0122 - val_loss: 1.3522 - val_acc: 0.3415 - val_precision_m: 0.0161\n","Epoch 207/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3691 - acc: 0.3133 - precision_m: 0.0236 - val_loss: 1.3525 - val_acc: 0.3171 - val_precision_m: 0.0161\n","Epoch 208/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3151 - acc: 0.3779 - precision_m: 0.0891 - val_loss: 1.3515 - val_acc: 0.3252 - val_precision_m: 0.0161\n","Epoch 209/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3566 - acc: 0.3244 - precision_m: 0.0335 - val_loss: 1.3499 - val_acc: 0.3089 - val_precision_m: 0.0323\n","Epoch 210/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3613 - acc: 0.3661 - precision_m: 0.0526 - val_loss: 1.3494 - val_acc: 0.3415 - val_precision_m: 0.0323\n","Epoch 211/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3391 - acc: 0.3016 - precision_m: 0.0963 - val_loss: 1.3491 - val_acc: 0.3496 - val_precision_m: 0.0161\n","Epoch 212/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3396 - acc: 0.3363 - precision_m: 0.0620 - val_loss: 1.3498 - val_acc: 0.3415 - val_precision_m: 0.0161\n","Epoch 213/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3705 - acc: 0.3072 - precision_m: 0.0534 - val_loss: 1.3511 - val_acc: 0.2927 - val_precision_m: 0.0323\n","Epoch 214/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3720 - acc: 0.2556 - precision_m: 0.0333 - val_loss: 1.3528 - val_acc: 0.3089 - val_precision_m: 0.0161\n","Epoch 215/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3403 - acc: 0.3267 - precision_m: 0.0531 - val_loss: 1.3506 - val_acc: 0.3171 - val_precision_m: 0.0161\n","Epoch 216/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3821 - acc: 0.2942 - precision_m: 0.0119 - val_loss: 1.3525 - val_acc: 0.3171 - val_precision_m: 0.0323\n","Epoch 217/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3784 - acc: 0.3051 - precision_m: 0.0097 - val_loss: 1.3498 - val_acc: 0.3171 - val_precision_m: 0.0161\n","Epoch 218/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3293 - acc: 0.3205 - precision_m: 0.0819 - val_loss: 1.3475 - val_acc: 0.3252 - val_precision_m: 0.0323\n","Epoch 219/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3455 - acc: 0.3257 - precision_m: 0.0665 - val_loss: 1.3498 - val_acc: 0.3089 - val_precision_m: 0.0323\n","Epoch 220/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3276 - acc: 0.3377 - precision_m: 0.0874 - val_loss: 1.3496 - val_acc: 0.3333 - val_precision_m: 0.0323\n","Epoch 221/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3486 - acc: 0.3005 - precision_m: 0.0617 - val_loss: 1.3526 - val_acc: 0.3089 - val_precision_m: 0.0323\n","Epoch 222/500\n","143/143 [==============================] - 0s 3ms/step - loss: 1.3485 - acc: 0.3099 - precision_m: 0.0511 - val_loss: 1.3604 - val_acc: 0.3333 - val_precision_m: 0.0645\n","Epoch 223/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3661 - acc: 0.3232 - precision_m: 0.0662 - val_loss: 1.3514 - val_acc: 0.3171 - val_precision_m: 0.0323\n","Epoch 224/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3685 - acc: 0.2837 - precision_m: 0.0266 - val_loss: 1.3538 - val_acc: 0.3171 - val_precision_m: 0.0323\n","Epoch 225/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3610 - acc: 0.2680 - precision_m: 0.0365 - val_loss: 1.3471 - val_acc: 0.3252 - val_precision_m: 0.0323\n","Epoch 226/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3343 - acc: 0.2970 - precision_m: 0.0857 - val_loss: 1.3496 - val_acc: 0.3171 - val_precision_m: 0.0323\n","Epoch 227/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3458 - acc: 0.2925 - precision_m: 0.0663 - val_loss: 1.3535 - val_acc: 0.3252 - val_precision_m: 0.0323\n","Epoch 228/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3640 - acc: 0.2791 - precision_m: 0.0623 - val_loss: 1.3462 - val_acc: 0.3171 - val_precision_m: 0.0645\n","Epoch 229/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3568 - acc: 0.3071 - precision_m: 0.0442 - val_loss: 1.3459 - val_acc: 0.3415 - val_precision_m: 0.0484\n","Epoch 230/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3269 - acc: 0.3269 - precision_m: 0.0818 - val_loss: 1.3496 - val_acc: 0.3252 - val_precision_m: 0.0645\n","Epoch 231/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3677 - acc: 0.2824 - precision_m: 0.0450 - val_loss: 1.3558 - val_acc: 0.2927 - val_precision_m: 0.0323\n","Epoch 232/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3610 - acc: 0.2449 - precision_m: 0.0744 - val_loss: 1.3484 - val_acc: 0.3415 - val_precision_m: 0.0323\n","Epoch 233/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3591 - acc: 0.2517 - precision_m: 0.0483 - val_loss: 1.3482 - val_acc: 0.3415 - val_precision_m: 0.0484\n","Epoch 234/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3562 - acc: 0.2850 - precision_m: 0.0656 - val_loss: 1.3551 - val_acc: 0.3089 - val_precision_m: 0.0645\n","Epoch 235/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3543 - acc: 0.3026 - precision_m: 0.0636 - val_loss: 1.3497 - val_acc: 0.3333 - val_precision_m: 0.0161\n","Epoch 236/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3668 - acc: 0.2633 - precision_m: 0.0430 - val_loss: 1.3540 - val_acc: 0.3171 - val_precision_m: 0.0161\n","Epoch 237/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3250 - acc: 0.3330 - precision_m: 0.1051 - val_loss: 1.3540 - val_acc: 0.3008 - val_precision_m: 0.0323\n","Epoch 238/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3541 - acc: 0.2741 - precision_m: 0.0419 - val_loss: 1.3527 - val_acc: 0.3171 - val_precision_m: 0.0323\n","Epoch 239/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3486 - acc: 0.2696 - precision_m: 0.0444 - val_loss: 1.3509 - val_acc: 0.3252 - val_precision_m: 0.0323\n","Epoch 240/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3547 - acc: 0.3336 - precision_m: 0.0419 - val_loss: 1.3502 - val_acc: 0.3171 - val_precision_m: 0.0645\n","Epoch 241/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3339 - acc: 0.3181 - precision_m: 0.0690 - val_loss: 1.3547 - val_acc: 0.2927 - val_precision_m: 0.0645\n","Epoch 242/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3880 - acc: 0.3004 - precision_m: 0.0150 - val_loss: 1.3520 - val_acc: 0.3008 - val_precision_m: 0.0645\n","Epoch 243/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3871 - acc: 0.2643 - precision_m: 0.0192 - val_loss: 1.3484 - val_acc: 0.3171 - val_precision_m: 0.0323\n","Epoch 244/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3328 - acc: 0.2994 - precision_m: 0.1056 - val_loss: 1.3491 - val_acc: 0.3089 - val_precision_m: 0.0323\n","Epoch 245/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3654 - acc: 0.2976 - precision_m: 0.0400 - val_loss: 1.3530 - val_acc: 0.3252 - val_precision_m: 0.0484\n","Epoch 246/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3657 - acc: 0.3698 - precision_m: 0.0690 - val_loss: 1.3437 - val_acc: 0.3577 - val_precision_m: 0.0484\n","Epoch 247/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3565 - acc: 0.2938 - precision_m: 0.0362 - val_loss: 1.3470 - val_acc: 0.3496 - val_precision_m: 0.0323\n","Epoch 248/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3448 - acc: 0.2662 - precision_m: 0.0921 - val_loss: 1.3468 - val_acc: 0.3496 - val_precision_m: 0.0323\n","Epoch 249/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3452 - acc: 0.3329 - precision_m: 0.0806 - val_loss: 1.3445 - val_acc: 0.3496 - val_precision_m: 0.0323\n","Epoch 250/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3599 - acc: 0.2606 - precision_m: 0.0495 - val_loss: 1.3509 - val_acc: 0.3008 - val_precision_m: 0.0323\n","Epoch 251/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3408 - acc: 0.3429 - precision_m: 0.0656 - val_loss: 1.3517 - val_acc: 0.3171 - val_precision_m: 0.0323\n","Epoch 252/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3417 - acc: 0.2860 - precision_m: 0.0609 - val_loss: 1.3484 - val_acc: 0.3415 - val_precision_m: 0.0323\n","Epoch 253/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3632 - acc: 0.2520 - precision_m: 0.0437 - val_loss: 1.3443 - val_acc: 0.3415 - val_precision_m: 0.0323\n","Epoch 254/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3423 - acc: 0.2651 - precision_m: 0.0711 - val_loss: 1.3478 - val_acc: 0.3171 - val_precision_m: 0.0323\n","Epoch 255/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3464 - acc: 0.3220 - precision_m: 0.0573 - val_loss: 1.3464 - val_acc: 0.3089 - val_precision_m: 0.0323\n","Epoch 256/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3359 - acc: 0.2809 - precision_m: 0.0762 - val_loss: 1.3476 - val_acc: 0.3171 - val_precision_m: 0.0323\n","Epoch 257/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3450 - acc: 0.3026 - precision_m: 0.0558 - val_loss: 1.3470 - val_acc: 0.3252 - val_precision_m: 0.0323\n","Epoch 258/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3728 - acc: 0.2924 - precision_m: 0.0134 - val_loss: 1.3500 - val_acc: 0.3171 - val_precision_m: 0.0323\n","Epoch 259/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3503 - acc: 0.3027 - precision_m: 0.0358 - val_loss: 1.3528 - val_acc: 0.3089 - val_precision_m: 0.0161\n","Epoch 260/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3364 - acc: 0.3056 - precision_m: 0.0827 - val_loss: 1.3435 - val_acc: 0.3171 - val_precision_m: 0.0323\n","Epoch 261/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3427 - acc: 0.3178 - precision_m: 0.0808 - val_loss: 1.3613 - val_acc: 0.2846 - val_precision_m: 0.0323\n","Epoch 262/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3548 - acc: 0.3297 - precision_m: 0.0418 - val_loss: 1.3442 - val_acc: 0.3415 - val_precision_m: 0.0323\n","Epoch 263/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3479 - acc: 0.3097 - precision_m: 0.0373 - val_loss: 1.3434 - val_acc: 0.3252 - val_precision_m: 0.0645\n","Epoch 264/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3339 - acc: 0.3371 - precision_m: 0.0935 - val_loss: 1.3486 - val_acc: 0.3171 - val_precision_m: 0.0323\n","Epoch 265/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3755 - acc: 0.2908 - precision_m: 0.0130 - val_loss: 1.3511 - val_acc: 0.3252 - val_precision_m: 0.0323\n","Epoch 266/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3445 - acc: 0.3489 - precision_m: 0.0316 - val_loss: 1.3435 - val_acc: 0.3496 - val_precision_m: 0.0323\n","Epoch 267/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3532 - acc: 0.2611 - precision_m: 0.0628 - val_loss: 1.3454 - val_acc: 0.3333 - val_precision_m: 0.0323\n","Epoch 268/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3685 - acc: 0.2295 - precision_m: 0.0409 - val_loss: 1.3512 - val_acc: 0.3171 - val_precision_m: 0.0323\n","Epoch 269/500\n","143/143 [==============================] - 0s 3ms/step - loss: 1.3441 - acc: 0.2729 - precision_m: 0.0501 - val_loss: 1.3533 - val_acc: 0.3171 - val_precision_m: 0.0323\n","Epoch 270/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3504 - acc: 0.2990 - precision_m: 0.0411 - val_loss: 1.3518 - val_acc: 0.3252 - val_precision_m: 0.0323\n","Epoch 271/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3585 - acc: 0.2780 - precision_m: 0.0346 - val_loss: 1.3546 - val_acc: 0.3008 - val_precision_m: 0.0323\n","Epoch 272/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3580 - acc: 0.2442 - precision_m: 0.0645 - val_loss: 1.3625 - val_acc: 0.3008 - val_precision_m: 0.0645\n","Epoch 273/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3351 - acc: 0.2875 - precision_m: 0.1340 - val_loss: 1.3514 - val_acc: 0.3171 - val_precision_m: 0.0323\n","Epoch 274/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3520 - acc: 0.3006 - precision_m: 0.0585 - val_loss: 1.3474 - val_acc: 0.3496 - val_precision_m: 0.0323\n","Epoch 275/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3427 - acc: 0.3761 - precision_m: 0.0778 - val_loss: 1.3468 - val_acc: 0.3171 - val_precision_m: 0.0323\n","Epoch 276/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3337 - acc: 0.2757 - precision_m: 0.0736 - val_loss: 1.3490 - val_acc: 0.3089 - val_precision_m: 0.0323\n","Epoch 277/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3518 - acc: 0.2777 - precision_m: 0.0446 - val_loss: 1.3669 - val_acc: 0.2927 - val_precision_m: 0.0323\n","Epoch 278/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3448 - acc: 0.3244 - precision_m: 0.0842 - val_loss: 1.3425 - val_acc: 0.3171 - val_precision_m: 0.0323\n","Epoch 279/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3640 - acc: 0.2836 - precision_m: 0.0401 - val_loss: 1.3621 - val_acc: 0.3008 - val_precision_m: 0.0323\n","Epoch 280/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3201 - acc: 0.3502 - precision_m: 0.0857 - val_loss: 1.3418 - val_acc: 0.3659 - val_precision_m: 0.0645\n","Epoch 281/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3745 - acc: 0.2613 - precision_m: 0.0422 - val_loss: 1.3397 - val_acc: 0.3415 - val_precision_m: 0.0484\n","Epoch 282/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3354 - acc: 0.2734 - precision_m: 0.0761 - val_loss: 1.3525 - val_acc: 0.3415 - val_precision_m: 0.0323\n","Epoch 283/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3587 - acc: 0.2704 - precision_m: 0.0390 - val_loss: 1.3563 - val_acc: 0.2927 - val_precision_m: 0.0323\n","Epoch 284/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3711 - acc: 0.3290 - precision_m: 0.0286 - val_loss: 1.3440 - val_acc: 0.3333 - val_precision_m: 0.0323\n","Epoch 285/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3686 - acc: 0.2894 - precision_m: 0.0307 - val_loss: 1.3491 - val_acc: 0.3171 - val_precision_m: 0.0323\n","Epoch 286/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3461 - acc: 0.2954 - precision_m: 0.0843 - val_loss: 1.3403 - val_acc: 0.3577 - val_precision_m: 0.0806\n","Epoch 287/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3832 - acc: 0.2642 - precision_m: 0.0154 - val_loss: 1.3487 - val_acc: 0.3008 - val_precision_m: 0.0323\n","Epoch 288/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3401 - acc: 0.3069 - precision_m: 0.0497 - val_loss: 1.3816 - val_acc: 0.3171 - val_precision_m: 0.1048\n","Epoch 289/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3270 - acc: 0.3280 - precision_m: 0.0935 - val_loss: 1.3430 - val_acc: 0.3252 - val_precision_m: 0.0323\n","Epoch 290/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3370 - acc: 0.2999 - precision_m: 0.0760 - val_loss: 1.3584 - val_acc: 0.2846 - val_precision_m: 0.0323\n","Epoch 291/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3585 - acc: 0.3159 - precision_m: 0.0102 - val_loss: 1.3488 - val_acc: 0.3333 - val_precision_m: 0.0484\n","Epoch 292/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3457 - acc: 0.2591 - precision_m: 0.0722 - val_loss: 1.3659 - val_acc: 0.3171 - val_precision_m: 0.0323\n","Epoch 293/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3338 - acc: 0.3132 - precision_m: 0.0524 - val_loss: 1.3533 - val_acc: 0.3333 - val_precision_m: 0.0161\n","Epoch 294/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3425 - acc: 0.2930 - precision_m: 0.0553 - val_loss: 1.3765 - val_acc: 0.3008 - val_precision_m: 0.0323\n","Epoch 295/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3761 - acc: 0.2893 - precision_m: 0.0264 - val_loss: 1.3472 - val_acc: 0.3496 - val_precision_m: 0.0484\n","Epoch 296/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3600 - acc: 0.2884 - precision_m: 0.0403 - val_loss: 1.3500 - val_acc: 0.3252 - val_precision_m: 0.0161\n","Epoch 297/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3428 - acc: 0.3127 - precision_m: 0.0661 - val_loss: 1.3507 - val_acc: 0.3496 - val_precision_m: 0.0484\n","Epoch 298/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3208 - acc: 0.3780 - precision_m: 0.0844 - val_loss: 1.3459 - val_acc: 0.3415 - val_precision_m: 0.0161\n","Epoch 299/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3525 - acc: 0.3011 - precision_m: 0.0619 - val_loss: 1.3512 - val_acc: 0.3252 - val_precision_m: 0.0161\n","Epoch 300/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3364 - acc: 0.2678 - precision_m: 0.0495 - val_loss: 1.3509 - val_acc: 0.3252 - val_precision_m: 0.0161\n","Epoch 301/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3556 - acc: 0.2837 - precision_m: 0.0289 - val_loss: 1.3446 - val_acc: 0.3333 - val_precision_m: 0.0161\n","Epoch 302/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3604 - acc: 0.2909 - precision_m: 0.0438 - val_loss: 1.3506 - val_acc: 0.3252 - val_precision_m: 0.0323\n","Epoch 303/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3501 - acc: 0.2805 - precision_m: 0.0573 - val_loss: 1.3457 - val_acc: 0.3333 - val_precision_m: 0.0323\n","Epoch 304/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3570 - acc: 0.2632 - precision_m: 0.0474 - val_loss: 1.3455 - val_acc: 0.3333 - val_precision_m: 0.0323\n","Epoch 305/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3493 - acc: 0.3289 - precision_m: 0.0400 - val_loss: 1.3518 - val_acc: 0.3333 - val_precision_m: 0.0323\n","Epoch 306/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3174 - acc: 0.3370 - precision_m: 0.0887 - val_loss: 1.3508 - val_acc: 0.3333 - val_precision_m: 0.0323\n","Epoch 307/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3547 - acc: 0.3258 - precision_m: 0.0352 - val_loss: 1.3431 - val_acc: 0.3577 - val_precision_m: 0.0645\n","Epoch 308/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3396 - acc: 0.3048 - precision_m: 0.0675 - val_loss: 1.3476 - val_acc: 0.3008 - val_precision_m: 0.0323\n","Epoch 309/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.2941 - acc: 0.3344 - precision_m: 0.1368 - val_loss: 1.3802 - val_acc: 0.2927 - val_precision_m: 0.0323\n","Epoch 310/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3316 - acc: 0.3294 - precision_m: 0.0851 - val_loss: 1.3485 - val_acc: 0.3333 - val_precision_m: 0.0323\n","Epoch 311/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3452 - acc: 0.3153 - precision_m: 0.0639 - val_loss: 1.3525 - val_acc: 0.2927 - val_precision_m: 0.0323\n","Epoch 312/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3206 - acc: 0.3381 - precision_m: 0.0814 - val_loss: 1.3616 - val_acc: 0.3008 - val_precision_m: 0.0323\n","Epoch 313/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3508 - acc: 0.2956 - precision_m: 0.0569 - val_loss: 1.3486 - val_acc: 0.3659 - val_precision_m: 0.0484\n","Epoch 314/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3471 - acc: 0.3204 - precision_m: 0.0576 - val_loss: 1.3432 - val_acc: 0.3415 - val_precision_m: 0.0323\n","Epoch 315/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3219 - acc: 0.3387 - precision_m: 0.1374 - val_loss: 1.3470 - val_acc: 0.3577 - val_precision_m: 0.0645\n","Epoch 316/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3110 - acc: 0.3861 - precision_m: 0.0917 - val_loss: 1.3431 - val_acc: 0.3577 - val_precision_m: 0.0323\n","Epoch 317/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3394 - acc: 0.3196 - precision_m: 0.0526 - val_loss: 1.3429 - val_acc: 0.3496 - val_precision_m: 0.0484\n","Epoch 318/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3170 - acc: 0.3498 - precision_m: 0.0889 - val_loss: 1.3473 - val_acc: 0.3415 - val_precision_m: 0.0323\n","Epoch 319/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3442 - acc: 0.2738 - precision_m: 0.0658 - val_loss: 1.3508 - val_acc: 0.3333 - val_precision_m: 0.0323\n","Epoch 320/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3576 - acc: 0.2759 - precision_m: 0.0324 - val_loss: 1.3508 - val_acc: 0.3333 - val_precision_m: 0.0323\n","Epoch 321/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3180 - acc: 0.3957 - precision_m: 0.0620 - val_loss: 1.3481 - val_acc: 0.3333 - val_precision_m: 0.0323\n","Epoch 322/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3380 - acc: 0.3515 - precision_m: 0.0483 - val_loss: 1.3442 - val_acc: 0.3415 - val_precision_m: 0.0323\n","Epoch 323/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3651 - acc: 0.3032 - precision_m: 0.0305 - val_loss: 1.3466 - val_acc: 0.3659 - val_precision_m: 0.0323\n","Epoch 324/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3471 - acc: 0.3224 - precision_m: 0.0658 - val_loss: 1.3658 - val_acc: 0.3333 - val_precision_m: 0.0484\n","Epoch 325/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3641 - acc: 0.2824 - precision_m: 0.0518 - val_loss: 1.3462 - val_acc: 0.3252 - val_precision_m: 0.0323\n","Epoch 326/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3574 - acc: 0.2712 - precision_m: 0.0094 - val_loss: 1.3885 - val_acc: 0.3171 - val_precision_m: 0.0323\n","Epoch 327/500\n","143/143 [==============================] - 0s 3ms/step - loss: 1.3089 - acc: 0.3544 - precision_m: 0.0811 - val_loss: 1.3544 - val_acc: 0.3496 - val_precision_m: 0.0323\n","Epoch 328/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3308 - acc: 0.3347 - precision_m: 0.0856 - val_loss: 1.3500 - val_acc: 0.3333 - val_precision_m: 0.0161\n","Epoch 329/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3357 - acc: 0.3178 - precision_m: 0.0641 - val_loss: 1.3498 - val_acc: 0.3252 - val_precision_m: 0.0161\n","Epoch 330/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3454 - acc: 0.3144 - precision_m: 0.0514 - val_loss: 1.3535 - val_acc: 0.3496 - val_precision_m: 0.0484\n","Epoch 331/500\n","143/143 [==============================] - 0s 2ms/step - loss: 1.3229 - acc: 0.3138 - precision_m: 0.0728 - val_loss: 1.3678 - val_acc: 0.3415 - val_precision_m: 0.0645\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 17.2s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","153/153 [==============================] - 1s 3ms/step - loss: 1.4636 - acc: 0.2028 - precision_m: 0.0423 - val_loss: 1.3891 - val_acc: 0.2595 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3886 - acc: 0.2843 - precision_m: 0.0000e+00 - val_loss: 1.3863 - val_acc: 0.2595 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3895 - acc: 0.2227 - precision_m: 0.0000e+00 - val_loss: 1.3838 - val_acc: 0.3053 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3778 - acc: 0.2525 - precision_m: 0.0000e+00 - val_loss: 1.3889 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3868 - acc: 0.2489 - precision_m: 0.0000e+00 - val_loss: 1.3864 - val_acc: 0.2901 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3864 - acc: 0.2731 - precision_m: 0.0000e+00 - val_loss: 1.3907 - val_acc: 0.2290 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3832 - acc: 0.2455 - precision_m: 0.0000e+00 - val_loss: 1.3893 - val_acc: 0.2595 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3917 - acc: 0.2420 - precision_m: 0.0000e+00 - val_loss: 1.3888 - val_acc: 0.2366 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3876 - acc: 0.2430 - precision_m: 0.0000e+00 - val_loss: 1.3839 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3880 - acc: 0.2299 - precision_m: 0.0000e+00 - val_loss: 1.3835 - val_acc: 0.3053 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3795 - acc: 0.3222 - precision_m: 0.0000e+00 - val_loss: 1.3858 - val_acc: 0.2061 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.4116 - acc: 0.2773 - precision_m: 0.0000e+00 - val_loss: 1.3886 - val_acc: 0.2290 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3959 - acc: 0.2086 - precision_m: 0.0000e+00 - val_loss: 1.3920 - val_acc: 0.2061 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3950 - acc: 0.2296 - precision_m: 0.0000e+00 - val_loss: 1.3833 - val_acc: 0.2977 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3792 - acc: 0.2525 - precision_m: 0.0000e+00 - val_loss: 1.3815 - val_acc: 0.2748 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3859 - acc: 0.2272 - precision_m: 0.0000e+00 - val_loss: 1.3820 - val_acc: 0.2748 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3889 - acc: 0.2522 - precision_m: 0.0000e+00 - val_loss: 1.3835 - val_acc: 0.3206 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3804 - acc: 0.2890 - precision_m: 0.0000e+00 - val_loss: 1.3827 - val_acc: 0.2977 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3839 - acc: 0.3076 - precision_m: 0.0000e+00 - val_loss: 1.3827 - val_acc: 0.2748 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3780 - acc: 0.2668 - precision_m: 0.0000e+00 - val_loss: 1.3824 - val_acc: 0.2824 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3793 - acc: 0.2202 - precision_m: 0.0000e+00 - val_loss: 1.3867 - val_acc: 0.2214 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3862 - acc: 0.2777 - precision_m: 0.0000e+00 - val_loss: 1.3824 - val_acc: 0.2901 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3865 - acc: 0.2565 - precision_m: 0.0000e+00 - val_loss: 1.3837 - val_acc: 0.3130 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3767 - acc: 0.2888 - precision_m: 0.0000e+00 - val_loss: 1.3877 - val_acc: 0.2672 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3840 - acc: 0.2580 - precision_m: 0.0000e+00 - val_loss: 1.3812 - val_acc: 0.2595 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3844 - acc: 0.2476 - precision_m: 0.0000e+00 - val_loss: 1.3810 - val_acc: 0.2672 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3861 - acc: 0.2334 - precision_m: 0.0000e+00 - val_loss: 1.3836 - val_acc: 0.2977 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3806 - acc: 0.2744 - precision_m: 0.0000e+00 - val_loss: 1.3811 - val_acc: 0.2977 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3823 - acc: 0.2650 - precision_m: 0.0000e+00 - val_loss: 1.3830 - val_acc: 0.3053 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3800 - acc: 0.2839 - precision_m: 0.0000e+00 - val_loss: 1.3915 - val_acc: 0.1679 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3910 - acc: 0.2194 - precision_m: 0.0000e+00 - val_loss: 1.3855 - val_acc: 0.2748 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3834 - acc: 0.2657 - precision_m: 0.0000e+00 - val_loss: 1.3888 - val_acc: 0.2290 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3863 - acc: 0.2729 - precision_m: 0.0000e+00 - val_loss: 1.3832 - val_acc: 0.3053 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3810 - acc: 0.2361 - precision_m: 0.0000e+00 - val_loss: 1.3836 - val_acc: 0.3053 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3805 - acc: 0.2368 - precision_m: 0.0000e+00 - val_loss: 1.3846 - val_acc: 0.2061 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3811 - acc: 0.2931 - precision_m: 0.0000e+00 - val_loss: 1.3878 - val_acc: 0.2061 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3715 - acc: 0.3256 - precision_m: 0.0000e+00 - val_loss: 1.3944 - val_acc: 0.2061 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3829 - acc: 0.2633 - precision_m: 0.0000e+00 - val_loss: 1.3855 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3828 - acc: 0.2451 - precision_m: 0.0000e+00 - val_loss: 1.3830 - val_acc: 0.3206 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3782 - acc: 0.2863 - precision_m: 0.0000e+00 - val_loss: 1.3826 - val_acc: 0.2672 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3767 - acc: 0.2754 - precision_m: 0.0000e+00 - val_loss: 1.3855 - val_acc: 0.3206 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3831 - acc: 0.2681 - precision_m: 0.0000e+00 - val_loss: 1.3854 - val_acc: 0.3130 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3649 - acc: 0.2940 - precision_m: 0.0000e+00 - val_loss: 1.3885 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3785 - acc: 0.2779 - precision_m: 0.0000e+00 - val_loss: 1.3869 - val_acc: 0.2977 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3761 - acc: 0.3698 - precision_m: 0.0000e+00 - val_loss: 1.3838 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3868 - acc: 0.2409 - precision_m: 0.0000e+00 - val_loss: 1.3852 - val_acc: 0.2366 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3686 - acc: 0.3320 - precision_m: 0.0000e+00 - val_loss: 1.3853 - val_acc: 0.3053 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3742 - acc: 0.2704 - precision_m: 0.0000e+00 - val_loss: 1.3870 - val_acc: 0.2595 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3739 - acc: 0.3067 - precision_m: 0.0000e+00 - val_loss: 1.3886 - val_acc: 0.2137 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3884 - acc: 0.2981 - precision_m: 0.0000e+00 - val_loss: 1.3888 - val_acc: 0.2901 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3723 - acc: 0.3209 - precision_m: 0.0000e+00 - val_loss: 1.3852 - val_acc: 0.2901 - val_precision_m: 0.0000e+00\n","Epoch 52/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3643 - acc: 0.3730 - precision_m: 0.0000e+00 - val_loss: 1.3915 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 53/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3747 - acc: 0.2635 - precision_m: 0.0000e+00 - val_loss: 1.3874 - val_acc: 0.2672 - val_precision_m: 0.0000e+00\n","Epoch 54/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3842 - acc: 0.2361 - precision_m: 0.0000e+00 - val_loss: 1.3810 - val_acc: 0.2977 - val_precision_m: 0.0000e+00\n","Epoch 55/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3783 - acc: 0.2845 - precision_m: 0.0000e+00 - val_loss: 1.3831 - val_acc: 0.2595 - val_precision_m: 0.0000e+00\n","Epoch 56/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3808 - acc: 0.2559 - precision_m: 0.0011 - val_loss: 1.3835 - val_acc: 0.2901 - val_precision_m: 0.0000e+00\n","Epoch 57/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3755 - acc: 0.2561 - precision_m: 0.0024 - val_loss: 1.3852 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 58/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3732 - acc: 0.2393 - precision_m: 0.0000e+00 - val_loss: 1.3799 - val_acc: 0.2977 - val_precision_m: 0.0000e+00\n","Epoch 59/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3741 - acc: 0.2710 - precision_m: 0.0000e+00 - val_loss: 1.3841 - val_acc: 0.2748 - val_precision_m: 0.0000e+00\n","Epoch 60/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3709 - acc: 0.3097 - precision_m: 0.0000e+00 - val_loss: 1.3869 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 61/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3620 - acc: 0.3245 - precision_m: 0.0000e+00 - val_loss: 1.3928 - val_acc: 0.2137 - val_precision_m: 0.0000e+00\n","Epoch 62/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3669 - acc: 0.2720 - precision_m: 0.0000e+00 - val_loss: 1.3860 - val_acc: 0.2214 - val_precision_m: 0.0000e+00\n","Epoch 63/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3721 - acc: 0.2976 - precision_m: 0.0000e+00 - val_loss: 1.3862 - val_acc: 0.2595 - val_precision_m: 0.0000e+00\n","Epoch 64/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3850 - acc: 0.2266 - precision_m: 0.0000e+00 - val_loss: 1.3841 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 65/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3625 - acc: 0.3290 - precision_m: 6.6345e-04 - val_loss: 1.3903 - val_acc: 0.1908 - val_precision_m: 0.0000e+00\n","Epoch 66/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3784 - acc: 0.2577 - precision_m: 0.0000e+00 - val_loss: 1.3857 - val_acc: 0.2595 - val_precision_m: 0.0000e+00\n","Epoch 67/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3689 - acc: 0.2821 - precision_m: 0.0012 - val_loss: 1.3866 - val_acc: 0.1985 - val_precision_m: 0.0000e+00\n","Epoch 68/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3717 - acc: 0.3032 - precision_m: 0.0000e+00 - val_loss: 1.3835 - val_acc: 0.2366 - val_precision_m: 0.0000e+00\n","Epoch 69/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3347 - acc: 0.3307 - precision_m: 0.0181 - val_loss: 1.3902 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 70/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3605 - acc: 0.3058 - precision_m: 0.0045 - val_loss: 1.3863 - val_acc: 0.2214 - val_precision_m: 0.0000e+00\n","Epoch 71/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3836 - acc: 0.2480 - precision_m: 0.0000e+00 - val_loss: 1.3796 - val_acc: 0.3206 - val_precision_m: 0.0000e+00\n","Epoch 72/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3606 - acc: 0.3161 - precision_m: 0.0204 - val_loss: 1.3850 - val_acc: 0.2672 - val_precision_m: 0.0000e+00\n","Epoch 73/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3915 - acc: 0.2923 - precision_m: 5.7102e-04 - val_loss: 1.3973 - val_acc: 0.2290 - val_precision_m: 0.0000e+00\n","Epoch 74/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3857 - acc: 0.2387 - precision_m: 0.0000e+00 - val_loss: 1.3865 - val_acc: 0.2290 - val_precision_m: 0.0000e+00\n","Epoch 75/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3649 - acc: 0.3019 - precision_m: 0.0023 - val_loss: 1.3852 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 76/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3615 - acc: 0.3302 - precision_m: 0.0147 - val_loss: 1.3878 - val_acc: 0.2366 - val_precision_m: 0.0000e+00\n","Epoch 77/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3654 - acc: 0.3190 - precision_m: 0.0028 - val_loss: 1.3851 - val_acc: 0.1908 - val_precision_m: 0.0000e+00\n","Epoch 78/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3655 - acc: 0.2908 - precision_m: 0.0127 - val_loss: 1.3925 - val_acc: 0.2061 - val_precision_m: 0.0000e+00\n","Epoch 79/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3684 - acc: 0.3149 - precision_m: 0.0128 - val_loss: 1.3833 - val_acc: 0.2137 - val_precision_m: 0.0000e+00\n","Epoch 80/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3624 - acc: 0.2758 - precision_m: 0.0087 - val_loss: 1.3905 - val_acc: 0.2061 - val_precision_m: 0.0000e+00\n","Epoch 81/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3386 - acc: 0.3651 - precision_m: 0.0378 - val_loss: 1.4012 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 82/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3592 - acc: 0.3096 - precision_m: 0.0000e+00 - val_loss: 1.3909 - val_acc: 0.2595 - val_precision_m: 0.0000e+00\n","Epoch 83/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3523 - acc: 0.2986 - precision_m: 0.0385 - val_loss: 1.3866 - val_acc: 0.2290 - val_precision_m: 0.0000e+00\n","Epoch 84/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3494 - acc: 0.3284 - precision_m: 0.0206 - val_loss: 1.3833 - val_acc: 0.2366 - val_precision_m: 0.0000e+00\n","Epoch 85/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3666 - acc: 0.2795 - precision_m: 0.0000e+00 - val_loss: 1.3802 - val_acc: 0.2901 - val_precision_m: 0.0000e+00\n","Epoch 86/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3612 - acc: 0.3031 - precision_m: 0.0035 - val_loss: 1.3885 - val_acc: 0.1985 - val_precision_m: 0.0000e+00\n","Epoch 87/500\n","153/153 [==============================] - 0s 3ms/step - loss: 1.3545 - acc: 0.3591 - precision_m: 0.0411 - val_loss: 1.3960 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 88/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3661 - acc: 0.3397 - precision_m: 0.0148 - val_loss: 1.3878 - val_acc: 0.2290 - val_precision_m: 0.0000e+00\n","Epoch 89/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3607 - acc: 0.3004 - precision_m: 0.0058 - val_loss: 1.3820 - val_acc: 0.2595 - val_precision_m: 0.0000e+00\n","Epoch 90/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3562 - acc: 0.3585 - precision_m: 0.0103 - val_loss: 1.3996 - val_acc: 0.1832 - val_precision_m: 0.0152\n","Epoch 91/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3755 - acc: 0.3113 - precision_m: 0.0087 - val_loss: 1.3959 - val_acc: 0.1985 - val_precision_m: 0.0152\n","Epoch 92/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3651 - acc: 0.3368 - precision_m: 0.0365 - val_loss: 1.3918 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 93/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3495 - acc: 0.3225 - precision_m: 0.0261 - val_loss: 1.3826 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 94/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3631 - acc: 0.2879 - precision_m: 0.0000e+00 - val_loss: 1.4000 - val_acc: 0.2290 - val_precision_m: 0.0152\n","Epoch 95/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3613 - acc: 0.3317 - precision_m: 0.0303 - val_loss: 1.3785 - val_acc: 0.2824 - val_precision_m: 0.0000e+00\n","Epoch 96/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3634 - acc: 0.3310 - precision_m: 0.0118 - val_loss: 1.3916 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 97/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3578 - acc: 0.2770 - precision_m: 0.0223 - val_loss: 1.3785 - val_acc: 0.2748 - val_precision_m: 0.0000e+00\n","Epoch 98/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3620 - acc: 0.2952 - precision_m: 0.0112 - val_loss: 1.3891 - val_acc: 0.2672 - val_precision_m: 0.0152\n","Epoch 99/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3580 - acc: 0.2461 - precision_m: 0.0266 - val_loss: 1.3850 - val_acc: 0.2824 - val_precision_m: 0.0000e+00\n","Epoch 100/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3703 - acc: 0.2897 - precision_m: 0.0021 - val_loss: 1.3828 - val_acc: 0.2137 - val_precision_m: 0.0152\n","Epoch 101/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3555 - acc: 0.3435 - precision_m: 0.0000e+00 - val_loss: 1.3831 - val_acc: 0.2443 - val_precision_m: 0.0152\n","Epoch 102/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3471 - acc: 0.3319 - precision_m: 0.0081 - val_loss: 1.4083 - val_acc: 0.1832 - val_precision_m: 0.0152\n","Epoch 103/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3597 - acc: 0.2819 - precision_m: 0.0395 - val_loss: 1.3881 - val_acc: 0.2290 - val_precision_m: 0.0152\n","Epoch 104/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3598 - acc: 0.3300 - precision_m: 9.6007e-04 - val_loss: 1.3890 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 105/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3541 - acc: 0.2815 - precision_m: 0.0057 - val_loss: 1.3987 - val_acc: 0.1679 - val_precision_m: 0.0152\n","Epoch 106/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3812 - acc: 0.3303 - precision_m: 0.0118 - val_loss: 1.3774 - val_acc: 0.3053 - val_precision_m: 0.0000e+00\n","Epoch 107/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3724 - acc: 0.2919 - precision_m: 0.0062 - val_loss: 1.3922 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 108/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3909 - acc: 0.3127 - precision_m: 0.0090 - val_loss: 1.3807 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 109/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3740 - acc: 0.3013 - precision_m: 0.0022 - val_loss: 1.3909 - val_acc: 0.1756 - val_precision_m: 0.0152\n","Epoch 110/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3412 - acc: 0.3469 - precision_m: 0.0276 - val_loss: 1.3862 - val_acc: 0.2366 - val_precision_m: 0.0000e+00\n","Epoch 111/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3513 - acc: 0.3218 - precision_m: 0.0261 - val_loss: 1.3975 - val_acc: 0.2290 - val_precision_m: 0.0000e+00\n","Epoch 112/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3713 - acc: 0.2943 - precision_m: 0.0169 - val_loss: 1.3981 - val_acc: 0.1985 - val_precision_m: 0.0152\n","Epoch 113/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3475 - acc: 0.3556 - precision_m: 0.0272 - val_loss: 1.4019 - val_acc: 0.2443 - val_precision_m: 0.0152\n","Epoch 114/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3715 - acc: 0.2881 - precision_m: 0.0307 - val_loss: 1.3854 - val_acc: 0.2748 - val_precision_m: 0.0152\n","Epoch 115/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3508 - acc: 0.3128 - precision_m: 0.0352 - val_loss: 1.3964 - val_acc: 0.2366 - val_precision_m: 0.0152\n","Epoch 116/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3742 - acc: 0.2862 - precision_m: 0.0032 - val_loss: 1.3962 - val_acc: 0.2137 - val_precision_m: 0.0152\n","Epoch 117/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3632 - acc: 0.2876 - precision_m: 0.0513 - val_loss: 1.3801 - val_acc: 0.2901 - val_precision_m: 0.0152\n","Epoch 118/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3479 - acc: 0.2891 - precision_m: 0.0358 - val_loss: 1.3939 - val_acc: 0.1908 - val_precision_m: 0.0152\n","Epoch 119/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3618 - acc: 0.3155 - precision_m: 0.0170 - val_loss: 1.3917 - val_acc: 0.2443 - val_precision_m: 0.0152\n","Epoch 120/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3578 - acc: 0.3284 - precision_m: 0.0071 - val_loss: 1.3946 - val_acc: 0.2824 - val_precision_m: 0.0152\n","Epoch 121/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3351 - acc: 0.3809 - precision_m: 0.0355 - val_loss: 1.3893 - val_acc: 0.2290 - val_precision_m: 0.0152\n","Epoch 122/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3723 - acc: 0.2818 - precision_m: 0.0100 - val_loss: 1.3859 - val_acc: 0.3053 - val_precision_m: 0.0152\n","Epoch 123/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3557 - acc: 0.3227 - precision_m: 0.0317 - val_loss: 1.4033 - val_acc: 0.1985 - val_precision_m: 0.0152\n","Epoch 124/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3633 - acc: 0.3086 - precision_m: 0.0449 - val_loss: 1.3860 - val_acc: 0.2443 - val_precision_m: 0.0152\n","Epoch 125/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3552 - acc: 0.3324 - precision_m: 0.0103 - val_loss: 1.3903 - val_acc: 0.2519 - val_precision_m: 0.0152\n","Epoch 126/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3492 - acc: 0.3266 - precision_m: 0.0459 - val_loss: 1.4080 - val_acc: 0.1985 - val_precision_m: 0.0152\n","Epoch 127/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3678 - acc: 0.3065 - precision_m: 0.0305 - val_loss: 1.3860 - val_acc: 0.2595 - val_precision_m: 0.0152\n","Epoch 128/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3677 - acc: 0.3597 - precision_m: 0.0014 - val_loss: 1.3897 - val_acc: 0.2748 - val_precision_m: 0.0152\n","Epoch 129/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3539 - acc: 0.3206 - precision_m: 0.0161 - val_loss: 1.3919 - val_acc: 0.2214 - val_precision_m: 0.0152\n","Epoch 130/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3491 - acc: 0.3434 - precision_m: 0.0364 - val_loss: 1.3913 - val_acc: 0.2443 - val_precision_m: 0.0152\n","Epoch 131/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3419 - acc: 0.3116 - precision_m: 0.0602 - val_loss: 1.3898 - val_acc: 0.2748 - val_precision_m: 0.0152\n","Epoch 132/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3151 - acc: 0.4135 - precision_m: 0.0384 - val_loss: 1.3824 - val_acc: 0.2443 - val_precision_m: 0.0152\n","Epoch 133/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3359 - acc: 0.3475 - precision_m: 0.0417 - val_loss: 1.3877 - val_acc: 0.2748 - val_precision_m: 0.0152\n","Epoch 134/500\n","153/153 [==============================] - 0s 3ms/step - loss: 1.3342 - acc: 0.3591 - precision_m: 0.0566 - val_loss: 1.3956 - val_acc: 0.2519 - val_precision_m: 0.0152\n","Epoch 135/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3712 - acc: 0.2644 - precision_m: 0.0205 - val_loss: 1.3867 - val_acc: 0.2290 - val_precision_m: 0.0152\n","Epoch 136/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3336 - acc: 0.3690 - precision_m: 0.0468 - val_loss: 1.3918 - val_acc: 0.2443 - val_precision_m: 0.0152\n","Epoch 137/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3678 - acc: 0.2656 - precision_m: 0.0209 - val_loss: 1.3891 - val_acc: 0.2672 - val_precision_m: 0.0152\n","Epoch 138/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3296 - acc: 0.3664 - precision_m: 0.0300 - val_loss: 1.3908 - val_acc: 0.2443 - val_precision_m: 0.0152\n","Epoch 139/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3525 - acc: 0.3027 - precision_m: 0.0204 - val_loss: 1.3907 - val_acc: 0.2366 - val_precision_m: 0.0000e+00\n","Epoch 140/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3587 - acc: 0.2907 - precision_m: 0.0257 - val_loss: 1.3832 - val_acc: 0.2595 - val_precision_m: 0.0152\n","Epoch 141/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3497 - acc: 0.3794 - precision_m: 0.0333 - val_loss: 1.4249 - val_acc: 0.2214 - val_precision_m: 0.0152\n","Epoch 142/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3544 - acc: 0.3828 - precision_m: 0.0397 - val_loss: 1.4062 - val_acc: 0.2443 - val_precision_m: 0.0152\n","Epoch 143/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3372 - acc: 0.3216 - precision_m: 0.0451 - val_loss: 1.3820 - val_acc: 0.2366 - val_precision_m: 0.0152\n","Epoch 144/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3331 - acc: 0.3222 - precision_m: 0.0336 - val_loss: 1.3945 - val_acc: 0.2214 - val_precision_m: 0.0152\n","Epoch 145/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3169 - acc: 0.3491 - precision_m: 0.0516 - val_loss: 1.3911 - val_acc: 0.2366 - val_precision_m: 0.0152\n","Epoch 146/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3223 - acc: 0.3470 - precision_m: 0.0451 - val_loss: 1.3783 - val_acc: 0.2595 - val_precision_m: 0.0152\n","Epoch 147/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3483 - acc: 0.3337 - precision_m: 0.0252 - val_loss: 1.3856 - val_acc: 0.2366 - val_precision_m: 0.0152\n","Epoch 148/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3599 - acc: 0.3046 - precision_m: 0.0197 - val_loss: 1.3852 - val_acc: 0.2672 - val_precision_m: 0.0152\n","Epoch 149/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3450 - acc: 0.3118 - precision_m: 0.0292 - val_loss: 1.4090 - val_acc: 0.2443 - val_precision_m: 0.0152\n","Epoch 150/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3593 - acc: 0.2883 - precision_m: 0.0376 - val_loss: 1.4023 - val_acc: 0.2137 - val_precision_m: 0.0152\n","Epoch 151/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3317 - acc: 0.3250 - precision_m: 0.0457 - val_loss: 1.3955 - val_acc: 0.2595 - val_precision_m: 0.0152\n","Epoch 152/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3294 - acc: 0.3314 - precision_m: 0.0738 - val_loss: 1.4043 - val_acc: 0.2519 - val_precision_m: 0.0152\n","Epoch 153/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3493 - acc: 0.3474 - precision_m: 0.0710 - val_loss: 1.3839 - val_acc: 0.2672 - val_precision_m: 0.0152\n","Epoch 154/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3336 - acc: 0.3686 - precision_m: 0.0086 - val_loss: 1.3909 - val_acc: 0.2443 - val_precision_m: 0.0152\n","Epoch 155/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3536 - acc: 0.3431 - precision_m: 0.0302 - val_loss: 1.3996 - val_acc: 0.2061 - val_precision_m: 0.0303\n","Epoch 156/500\n","153/153 [==============================] - 0s 2ms/step - loss: 1.3415 - acc: 0.3823 - precision_m: 0.0180 - val_loss: 1.4172 - val_acc: 0.1756 - val_precision_m: 0.0303\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 15.9s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","146/146 [==============================] - 1s 3ms/step - loss: 1.4041 - acc: 0.1951 - precision_m: 0.0705 - val_loss: 1.3814 - val_acc: 0.3016 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3764 - acc: 0.2858 - precision_m: 0.0000e+00 - val_loss: 1.3843 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3981 - acc: 0.2148 - precision_m: 0.0000e+00 - val_loss: 1.3858 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.4060 - acc: 0.2589 - precision_m: 0.0000e+00 - val_loss: 1.3821 - val_acc: 0.3016 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3864 - acc: 0.2823 - precision_m: 0.0000e+00 - val_loss: 1.3832 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3981 - acc: 0.2424 - precision_m: 0.0000e+00 - val_loss: 1.3836 - val_acc: 0.3175 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3866 - acc: 0.2415 - precision_m: 0.0000e+00 - val_loss: 1.3838 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3891 - acc: 0.2239 - precision_m: 0.0000e+00 - val_loss: 1.3835 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3986 - acc: 0.2435 - precision_m: 0.0000e+00 - val_loss: 1.3815 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3807 - acc: 0.2916 - precision_m: 0.0000e+00 - val_loss: 1.3805 - val_acc: 0.3095 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3795 - acc: 0.2544 - precision_m: 0.0000e+00 - val_loss: 1.3810 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3746 - acc: 0.3078 - precision_m: 0.0000e+00 - val_loss: 1.3831 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3946 - acc: 0.1859 - precision_m: 0.0000e+00 - val_loss: 1.3837 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3711 - acc: 0.2772 - precision_m: 0.0000e+00 - val_loss: 1.3830 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3922 - acc: 0.2654 - precision_m: 0.0000e+00 - val_loss: 1.3829 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3612 - acc: 0.3336 - precision_m: 0.0000e+00 - val_loss: 1.3840 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3825 - acc: 0.2254 - precision_m: 0.0000e+00 - val_loss: 1.3845 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3647 - acc: 0.3104 - precision_m: 0.0000e+00 - val_loss: 1.3822 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3760 - acc: 0.3099 - precision_m: 0.0000e+00 - val_loss: 1.3819 - val_acc: 0.3333 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3817 - acc: 0.2728 - precision_m: 0.0000e+00 - val_loss: 1.3819 - val_acc: 0.3016 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3782 - acc: 0.3077 - precision_m: 0.0000e+00 - val_loss: 1.3827 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3714 - acc: 0.2924 - precision_m: 0.0000e+00 - val_loss: 1.3828 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3835 - acc: 0.2439 - precision_m: 0.0000e+00 - val_loss: 1.3822 - val_acc: 0.3016 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3690 - acc: 0.3226 - precision_m: 0.0000e+00 - val_loss: 1.3843 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3686 - acc: 0.3152 - precision_m: 0.0000e+00 - val_loss: 1.3859 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3725 - acc: 0.3359 - precision_m: 0.0000e+00 - val_loss: 1.3809 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3783 - acc: 0.2758 - precision_m: 0.0000e+00 - val_loss: 1.3836 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3856 - acc: 0.2184 - precision_m: 0.0000e+00 - val_loss: 1.3804 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3766 - acc: 0.3136 - precision_m: 0.0000e+00 - val_loss: 1.3855 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3802 - acc: 0.2696 - precision_m: 0.0000e+00 - val_loss: 1.3805 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3828 - acc: 0.2211 - precision_m: 0.0000e+00 - val_loss: 1.3822 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3575 - acc: 0.3468 - precision_m: 0.0000e+00 - val_loss: 1.3820 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3795 - acc: 0.2965 - precision_m: 0.0000e+00 - val_loss: 1.3787 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3709 - acc: 0.2925 - precision_m: 0.0000e+00 - val_loss: 1.3795 - val_acc: 0.3016 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3764 - acc: 0.3032 - precision_m: 0.0000e+00 - val_loss: 1.3870 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3772 - acc: 0.3029 - precision_m: 0.0000e+00 - val_loss: 1.3813 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3671 - acc: 0.3367 - precision_m: 0.0000e+00 - val_loss: 1.3800 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3749 - acc: 0.2974 - precision_m: 0.0000e+00 - val_loss: 1.3800 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3821 - acc: 0.2845 - precision_m: 0.0000e+00 - val_loss: 1.3807 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3728 - acc: 0.3126 - precision_m: 0.0000e+00 - val_loss: 1.3815 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3643 - acc: 0.2917 - precision_m: 0.0000e+00 - val_loss: 1.3815 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3736 - acc: 0.3018 - precision_m: 0.0000e+00 - val_loss: 1.3897 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3854 - acc: 0.2410 - precision_m: 0.0000e+00 - val_loss: 1.3811 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3813 - acc: 0.2899 - precision_m: 0.0000e+00 - val_loss: 1.3816 - val_acc: 0.3016 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3925 - acc: 0.2617 - precision_m: 0.0000e+00 - val_loss: 1.3892 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3683 - acc: 0.3487 - precision_m: 0.0000e+00 - val_loss: 1.3881 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3772 - acc: 0.2199 - precision_m: 0.0000e+00 - val_loss: 1.3845 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3842 - acc: 0.2567 - precision_m: 0.0000e+00 - val_loss: 1.3834 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3650 - acc: 0.2786 - precision_m: 0.0000e+00 - val_loss: 1.3858 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3660 - acc: 0.2615 - precision_m: 0.0000e+00 - val_loss: 1.3925 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3965 - acc: 0.3204 - precision_m: 0.0000e+00 - val_loss: 1.3833 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 52/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3723 - acc: 0.2568 - precision_m: 0.0000e+00 - val_loss: 1.3845 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 53/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3779 - acc: 0.2794 - precision_m: 0.0000e+00 - val_loss: 1.3894 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 54/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3819 - acc: 0.2588 - precision_m: 0.0033 - val_loss: 1.3868 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 55/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3698 - acc: 0.2828 - precision_m: 0.0069 - val_loss: 1.3948 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 56/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3564 - acc: 0.2897 - precision_m: 0.0105 - val_loss: 1.3846 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 57/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3672 - acc: 0.3028 - precision_m: 0.0000e+00 - val_loss: 1.3850 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 58/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3627 - acc: 0.3049 - precision_m: 9.3188e-05 - val_loss: 1.3963 - val_acc: 0.3333 - val_precision_m: 0.0000e+00\n","Epoch 59/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3803 - acc: 0.3013 - precision_m: 0.0010 - val_loss: 1.3899 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 60/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3852 - acc: 0.2730 - precision_m: 0.0000e+00 - val_loss: 1.3840 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 61/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3673 - acc: 0.2958 - precision_m: 0.0070 - val_loss: 1.3924 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 62/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3520 - acc: 0.3204 - precision_m: 0.0162 - val_loss: 1.3863 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 63/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3604 - acc: 0.3152 - precision_m: 0.0198 - val_loss: 1.3967 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 64/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3559 - acc: 0.2741 - precision_m: 0.0123 - val_loss: 1.3857 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 65/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3681 - acc: 0.2864 - precision_m: 0.0170 - val_loss: 1.3904 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 66/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3534 - acc: 0.3158 - precision_m: 0.0187 - val_loss: 1.3889 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 67/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3512 - acc: 0.3155 - precision_m: 0.0038 - val_loss: 1.3899 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 68/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3861 - acc: 0.2447 - precision_m: 0.0090 - val_loss: 1.3887 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 69/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3743 - acc: 0.3021 - precision_m: 6.2796e-04 - val_loss: 1.3853 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 70/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3363 - acc: 0.3474 - precision_m: 0.0371 - val_loss: 1.4006 - val_acc: 0.2619 - val_precision_m: 0.0000e+00\n","Epoch 71/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3429 - acc: 0.3198 - precision_m: 0.0144 - val_loss: 1.3947 - val_acc: 0.2619 - val_precision_m: 0.0000e+00\n","Epoch 72/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3515 - acc: 0.3211 - precision_m: 0.0243 - val_loss: 1.4077 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 73/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3937 - acc: 0.2796 - precision_m: 0.0203 - val_loss: 1.3979 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 74/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3410 - acc: 0.3176 - precision_m: 0.0212 - val_loss: 1.3880 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 75/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3385 - acc: 0.2464 - precision_m: 0.0421 - val_loss: 1.3894 - val_acc: 0.3095 - val_precision_m: 0.0000e+00\n","Epoch 76/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3480 - acc: 0.3257 - precision_m: 0.0130 - val_loss: 1.3926 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 77/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3639 - acc: 0.2952 - precision_m: 0.0174 - val_loss: 1.3875 - val_acc: 0.3016 - val_precision_m: 0.0000e+00\n","Epoch 78/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3335 - acc: 0.2915 - precision_m: 0.0506 - val_loss: 1.3907 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 79/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3588 - acc: 0.3191 - precision_m: 0.0119 - val_loss: 1.3897 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 80/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3492 - acc: 0.3344 - precision_m: 0.0062 - val_loss: 1.3889 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 81/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3465 - acc: 0.3385 - precision_m: 0.0203 - val_loss: 1.3903 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 82/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3336 - acc: 0.3480 - precision_m: 0.0273 - val_loss: 1.3963 - val_acc: 0.2937 - val_precision_m: 0.0000e+00\n","Epoch 83/500\n","146/146 [==============================] - 0s 2ms/step - loss: 1.3631 - acc: 0.2313 - precision_m: 0.0239 - val_loss: 1.4035 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 18.8s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","147/147 [==============================] - 1s 3ms/step - loss: 1.5931 - acc: 0.2669 - precision_m: 0.1467 - val_loss: 1.3986 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.4009 - acc: 0.2870 - precision_m: 0.0153 - val_loss: 1.3907 - val_acc: 0.2222 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3894 - acc: 0.2204 - precision_m: 0.0000e+00 - val_loss: 1.3934 - val_acc: 0.2302 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3822 - acc: 0.2505 - precision_m: 0.0000e+00 - val_loss: 1.3940 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3815 - acc: 0.2840 - precision_m: 0.0000e+00 - val_loss: 1.3941 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3859 - acc: 0.3000 - precision_m: 0.0000e+00 - val_loss: 1.3951 - val_acc: 0.2302 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3746 - acc: 0.2670 - precision_m: 0.0000e+00 - val_loss: 1.3952 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3955 - acc: 0.2480 - precision_m: 0.0000e+00 - val_loss: 1.3945 - val_acc: 0.2222 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3738 - acc: 0.2715 - precision_m: 0.0000e+00 - val_loss: 1.3921 - val_acc: 0.1984 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3809 - acc: 0.2761 - precision_m: 0.0000e+00 - val_loss: 1.3913 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3833 - acc: 0.2802 - precision_m: 0.0000e+00 - val_loss: 1.3905 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3879 - acc: 0.2811 - precision_m: 0.0000e+00 - val_loss: 1.3904 - val_acc: 0.2381 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3914 - acc: 0.2177 - precision_m: 0.0000e+00 - val_loss: 1.3923 - val_acc: 0.1667 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3783 - acc: 0.2776 - precision_m: 0.0000e+00 - val_loss: 1.3889 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3856 - acc: 0.2581 - precision_m: 0.0000e+00 - val_loss: 1.3891 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3838 - acc: 0.3073 - precision_m: 0.0000e+00 - val_loss: 1.3879 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3887 - acc: 0.2476 - precision_m: 0.0000e+00 - val_loss: 1.3893 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3900 - acc: 0.2445 - precision_m: 0.0000e+00 - val_loss: 1.3895 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3894 - acc: 0.2402 - precision_m: 0.0000e+00 - val_loss: 1.3895 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3831 - acc: 0.2945 - precision_m: 0.0000e+00 - val_loss: 1.3889 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3868 - acc: 0.2539 - precision_m: 0.0000e+00 - val_loss: 1.3890 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","147/147 [==============================] - 0s 3ms/step - loss: 1.3762 - acc: 0.2412 - precision_m: 0.0000e+00 - val_loss: 1.3892 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","147/147 [==============================] - 0s 3ms/step - loss: 1.3860 - acc: 0.2248 - precision_m: 0.0000e+00 - val_loss: 1.3881 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","147/147 [==============================] - 0s 3ms/step - loss: 1.3876 - acc: 0.2596 - precision_m: 0.0000e+00 - val_loss: 1.3899 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","147/147 [==============================] - 0s 3ms/step - loss: 1.3825 - acc: 0.2899 - precision_m: 0.0000e+00 - val_loss: 1.3893 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3859 - acc: 0.2851 - precision_m: 0.0000e+00 - val_loss: 1.3897 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3844 - acc: 0.2335 - precision_m: 0.0000e+00 - val_loss: 1.3892 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3761 - acc: 0.2602 - precision_m: 0.0073 - val_loss: 1.3883 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3763 - acc: 0.2778 - precision_m: 0.0000e+00 - val_loss: 1.3887 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3753 - acc: 0.2645 - precision_m: 0.0000e+00 - val_loss: 1.3891 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3867 - acc: 0.2985 - precision_m: 0.0000e+00 - val_loss: 1.3883 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3808 - acc: 0.3033 - precision_m: 0.0000e+00 - val_loss: 1.3872 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3856 - acc: 0.2392 - precision_m: 0.0000e+00 - val_loss: 1.3890 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3796 - acc: 0.2467 - precision_m: 0.0000e+00 - val_loss: 1.3885 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3782 - acc: 0.3004 - precision_m: 0.0000e+00 - val_loss: 1.3886 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3827 - acc: 0.2898 - precision_m: 0.0000e+00 - val_loss: 1.3884 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3786 - acc: 0.2896 - precision_m: 0.0000e+00 - val_loss: 1.3889 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3732 - acc: 0.3132 - precision_m: 0.0000e+00 - val_loss: 1.3878 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3785 - acc: 0.2743 - precision_m: 0.0000e+00 - val_loss: 1.3862 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3903 - acc: 0.2451 - precision_m: 0.0000e+00 - val_loss: 1.3884 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3727 - acc: 0.3088 - precision_m: 0.0069 - val_loss: 1.3894 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3788 - acc: 0.2125 - precision_m: 0.0000e+00 - val_loss: 1.3896 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3735 - acc: 0.2860 - precision_m: 0.0236 - val_loss: 1.3900 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3852 - acc: 0.2725 - precision_m: 0.0000e+00 - val_loss: 1.3893 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3692 - acc: 0.3127 - precision_m: 0.0000e+00 - val_loss: 1.3886 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3755 - acc: 0.2786 - precision_m: 0.0079 - val_loss: 1.3882 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3836 - acc: 0.3004 - precision_m: 0.0000e+00 - val_loss: 1.3873 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3852 - acc: 0.2259 - precision_m: 0.0063 - val_loss: 1.3893 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3741 - acc: 0.2575 - precision_m: 0.0200 - val_loss: 1.3877 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3749 - acc: 0.2365 - precision_m: 0.0073 - val_loss: 1.3879 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3692 - acc: 0.2988 - precision_m: 0.0087 - val_loss: 1.3914 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 52/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3780 - acc: 0.2680 - precision_m: 0.0072 - val_loss: 1.3916 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 53/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3829 - acc: 0.2146 - precision_m: 0.0026 - val_loss: 1.3889 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 54/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3869 - acc: 0.2774 - precision_m: 0.0032 - val_loss: 1.3924 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 55/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3659 - acc: 0.2655 - precision_m: 0.0024 - val_loss: 1.3900 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 56/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3792 - acc: 0.3140 - precision_m: 0.0101 - val_loss: 1.3905 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 57/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3691 - acc: 0.2703 - precision_m: 0.0115 - val_loss: 1.3898 - val_acc: 0.2937 - val_precision_m: 0.0000e+00\n","Epoch 58/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3745 - acc: 0.2647 - precision_m: 0.0000e+00 - val_loss: 1.3917 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 59/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3700 - acc: 0.3029 - precision_m: 0.0072 - val_loss: 1.3886 - val_acc: 0.2302 - val_precision_m: 0.0000e+00\n","Epoch 60/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3778 - acc: 0.2947 - precision_m: 0.0036 - val_loss: 1.3875 - val_acc: 0.2619 - val_precision_m: 0.0000e+00\n","Epoch 61/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3722 - acc: 0.2870 - precision_m: 0.0155 - val_loss: 1.3902 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 62/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3506 - acc: 0.3150 - precision_m: 0.0474 - val_loss: 1.3903 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 63/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3827 - acc: 0.2783 - precision_m: 0.0073 - val_loss: 1.3886 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 64/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3652 - acc: 0.2860 - precision_m: 0.0287 - val_loss: 1.3919 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 65/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3674 - acc: 0.3006 - precision_m: 0.0228 - val_loss: 1.3883 - val_acc: 0.3016 - val_precision_m: 0.0000e+00\n","Epoch 66/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3684 - acc: 0.3107 - precision_m: 0.0419 - val_loss: 1.3904 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 67/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3898 - acc: 0.2438 - precision_m: 0.0096 - val_loss: 1.3923 - val_acc: 0.2381 - val_precision_m: 0.0000e+00\n","Epoch 68/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3802 - acc: 0.2552 - precision_m: 0.0277 - val_loss: 1.3909 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 69/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3846 - acc: 0.2675 - precision_m: 0.0000e+00 - val_loss: 1.3910 - val_acc: 0.2619 - val_precision_m: 0.0000e+00\n","Epoch 70/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3646 - acc: 0.2674 - precision_m: 0.0148 - val_loss: 1.3913 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 71/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3657 - acc: 0.3383 - precision_m: 0.0017 - val_loss: 1.3915 - val_acc: 0.2619 - val_precision_m: 0.0000e+00\n","Epoch 72/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3729 - acc: 0.2915 - precision_m: 0.0173 - val_loss: 1.3945 - val_acc: 0.3016 - val_precision_m: 0.0000e+00\n","Epoch 73/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3867 - acc: 0.2749 - precision_m: 0.0249 - val_loss: 1.3938 - val_acc: 0.3016 - val_precision_m: 0.0000e+00\n","Epoch 74/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3480 - acc: 0.3225 - precision_m: 0.0572 - val_loss: 1.3894 - val_acc: 0.2778 - val_precision_m: 0.0000e+00\n","Epoch 75/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3709 - acc: 0.2733 - precision_m: 0.0193 - val_loss: 1.3911 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 76/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3651 - acc: 0.3135 - precision_m: 0.0195 - val_loss: 1.3976 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 77/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3891 - acc: 0.2757 - precision_m: 0.0222 - val_loss: 1.3938 - val_acc: 0.2619 - val_precision_m: 0.0000e+00\n","Epoch 78/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3551 - acc: 0.3260 - precision_m: 0.0600 - val_loss: 1.3913 - val_acc: 0.2381 - val_precision_m: 0.0000e+00\n","Epoch 79/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3823 - acc: 0.2456 - precision_m: 0.0168 - val_loss: 1.3939 - val_acc: 0.2619 - val_precision_m: 0.0000e+00\n","Epoch 80/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3640 - acc: 0.3166 - precision_m: 0.0088 - val_loss: 1.3918 - val_acc: 0.2063 - val_precision_m: 0.0000e+00\n","Epoch 81/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3593 - acc: 0.3167 - precision_m: 0.0179 - val_loss: 1.3931 - val_acc: 0.2381 - val_precision_m: 0.0000e+00\n","Epoch 82/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3611 - acc: 0.3062 - precision_m: 0.0109 - val_loss: 1.3952 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 83/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3668 - acc: 0.3258 - precision_m: 0.0170 - val_loss: 1.3939 - val_acc: 0.2540 - val_precision_m: 0.0000e+00\n","Epoch 84/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3571 - acc: 0.2719 - precision_m: 0.0568 - val_loss: 1.3959 - val_acc: 0.2460 - val_precision_m: 0.0000e+00\n","Epoch 85/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3502 - acc: 0.3024 - precision_m: 0.0674 - val_loss: 1.3956 - val_acc: 0.2937 - val_precision_m: 0.0000e+00\n","Epoch 86/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3455 - acc: 0.3042 - precision_m: 0.0581 - val_loss: 1.3938 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Epoch 87/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3615 - acc: 0.3210 - precision_m: 0.0281 - val_loss: 1.3932 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 88/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3708 - acc: 0.2603 - precision_m: 0.0015 - val_loss: 1.3976 - val_acc: 0.2381 - val_precision_m: 0.0000e+00\n","Epoch 89/500\n","147/147 [==============================] - 0s 2ms/step - loss: 1.3618 - acc: 0.2652 - precision_m: 0.0568 - val_loss: 1.3930 - val_acc: 0.2698 - val_precision_m: 0.0000e+00\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 18.7s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","156/156 [==============================] - 1s 3ms/step - loss: 1.5101 - acc: 0.2501 - precision_m: 0.0612 - val_loss: 1.3837 - val_acc: 0.2910 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3864 - acc: 0.2481 - precision_m: 0.0000e+00 - val_loss: 1.3897 - val_acc: 0.2313 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3925 - acc: 0.2292 - precision_m: 0.0000e+00 - val_loss: 1.3897 - val_acc: 0.2239 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3912 - acc: 0.1960 - precision_m: 0.0000e+00 - val_loss: 1.3878 - val_acc: 0.2388 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3892 - acc: 0.2222 - precision_m: 0.0000e+00 - val_loss: 1.3911 - val_acc: 0.2239 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3921 - acc: 0.2089 - precision_m: 0.0000e+00 - val_loss: 1.3900 - val_acc: 0.2388 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3811 - acc: 0.3078 - precision_m: 0.0000e+00 - val_loss: 1.3888 - val_acc: 0.2313 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3868 - acc: 0.2912 - precision_m: 0.0000e+00 - val_loss: 1.3893 - val_acc: 0.2463 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3857 - acc: 0.2261 - precision_m: 0.0000e+00 - val_loss: 1.3890 - val_acc: 0.2463 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3848 - acc: 0.2171 - precision_m: 0.0000e+00 - val_loss: 1.3882 - val_acc: 0.2463 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3836 - acc: 0.3082 - precision_m: 0.0000e+00 - val_loss: 1.3870 - val_acc: 0.2463 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3815 - acc: 0.2745 - precision_m: 0.0000e+00 - val_loss: 1.3880 - val_acc: 0.2388 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3877 - acc: 0.2245 - precision_m: 0.0000e+00 - val_loss: 1.3879 - val_acc: 0.2463 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3831 - acc: 0.2807 - precision_m: 0.0000e+00 - val_loss: 1.3876 - val_acc: 0.2463 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3867 - acc: 0.2603 - precision_m: 0.0000e+00 - val_loss: 1.3868 - val_acc: 0.2388 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3870 - acc: 0.1900 - precision_m: 0.0000e+00 - val_loss: 1.3882 - val_acc: 0.2463 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3871 - acc: 0.2707 - precision_m: 0.0000e+00 - val_loss: 1.3875 - val_acc: 0.2463 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3836 - acc: 0.2842 - precision_m: 0.0000e+00 - val_loss: 1.3879 - val_acc: 0.2463 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3886 - acc: 0.2556 - precision_m: 0.0000e+00 - val_loss: 1.3868 - val_acc: 0.2463 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3919 - acc: 0.2641 - precision_m: 0.0000e+00 - val_loss: 1.3870 - val_acc: 0.2463 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3787 - acc: 0.2741 - precision_m: 0.0000e+00 - val_loss: 1.3856 - val_acc: 0.2463 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3836 - acc: 0.2537 - precision_m: 0.0000e+00 - val_loss: 1.3870 - val_acc: 0.2463 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3826 - acc: 0.2730 - precision_m: 0.0000e+00 - val_loss: 1.3868 - val_acc: 0.2463 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3844 - acc: 0.2802 - precision_m: 0.0000e+00 - val_loss: 1.3871 - val_acc: 0.2463 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3763 - acc: 0.3014 - precision_m: 0.0000e+00 - val_loss: 1.3800 - val_acc: 0.2537 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3858 - acc: 0.2452 - precision_m: 0.0000e+00 - val_loss: 1.3838 - val_acc: 0.2537 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3738 - acc: 0.3238 - precision_m: 0.0000e+00 - val_loss: 1.3857 - val_acc: 0.2463 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3882 - acc: 0.2655 - precision_m: 0.0000e+00 - val_loss: 1.3853 - val_acc: 0.2313 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3781 - acc: 0.2498 - precision_m: 0.0044 - val_loss: 1.3860 - val_acc: 0.2313 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3848 - acc: 0.3255 - precision_m: 0.0000e+00 - val_loss: 1.3845 - val_acc: 0.2463 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3783 - acc: 0.3107 - precision_m: 0.0000e+00 - val_loss: 1.3854 - val_acc: 0.2313 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3795 - acc: 0.2446 - precision_m: 0.0000e+00 - val_loss: 1.3855 - val_acc: 0.2612 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3674 - acc: 0.2713 - precision_m: 0.0151 - val_loss: 1.3850 - val_acc: 0.2463 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3837 - acc: 0.2439 - precision_m: 0.0049 - val_loss: 1.3848 - val_acc: 0.2463 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3760 - acc: 0.2535 - precision_m: 0.0091 - val_loss: 1.3745 - val_acc: 0.2985 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3964 - acc: 0.2684 - precision_m: 0.0082 - val_loss: 1.3853 - val_acc: 0.2463 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3806 - acc: 0.2886 - precision_m: 0.0107 - val_loss: 1.3862 - val_acc: 0.2463 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3856 - acc: 0.2460 - precision_m: 0.0000e+00 - val_loss: 1.3837 - val_acc: 0.2388 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3787 - acc: 0.2847 - precision_m: 0.0000e+00 - val_loss: 1.3824 - val_acc: 0.2612 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3775 - acc: 0.2419 - precision_m: 0.0083 - val_loss: 1.3844 - val_acc: 0.2388 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3812 - acc: 0.2744 - precision_m: 0.0000e+00 - val_loss: 1.3832 - val_acc: 0.2463 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3651 - acc: 0.3138 - precision_m: 0.0261 - val_loss: 1.3867 - val_acc: 0.2313 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3760 - acc: 0.2524 - precision_m: 0.0000e+00 - val_loss: 1.3859 - val_acc: 0.2388 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3832 - acc: 0.2864 - precision_m: 0.0015 - val_loss: 1.3845 - val_acc: 0.2313 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3699 - acc: 0.3026 - precision_m: 0.0127 - val_loss: 1.3846 - val_acc: 0.2313 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3731 - acc: 0.2443 - precision_m: 0.0138 - val_loss: 1.3823 - val_acc: 0.2463 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3644 - acc: 0.3264 - precision_m: 0.0119 - val_loss: 1.3847 - val_acc: 0.2313 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3799 - acc: 0.3062 - precision_m: 0.0047 - val_loss: 1.3802 - val_acc: 0.2687 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3684 - acc: 0.2284 - precision_m: 0.0324 - val_loss: 1.3787 - val_acc: 0.2612 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3773 - acc: 0.2690 - precision_m: 0.0047 - val_loss: 1.3845 - val_acc: 0.2463 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3770 - acc: 0.2567 - precision_m: 0.0108 - val_loss: 1.3818 - val_acc: 0.2463 - val_precision_m: 0.0000e+00\n","Epoch 52/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3752 - acc: 0.2738 - precision_m: 0.0079 - val_loss: 1.3819 - val_acc: 0.2463 - val_precision_m: 0.0000e+00\n","Epoch 53/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3737 - acc: 0.2382 - precision_m: 0.0201 - val_loss: 1.3800 - val_acc: 0.2463 - val_precision_m: 0.0000e+00\n","Epoch 54/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3719 - acc: 0.3019 - precision_m: 0.0094 - val_loss: 1.3776 - val_acc: 0.2612 - val_precision_m: 0.0000e+00\n","Epoch 55/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3873 - acc: 0.2734 - precision_m: 0.0194 - val_loss: 1.3813 - val_acc: 0.2836 - val_precision_m: 0.0000e+00\n","Epoch 56/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3758 - acc: 0.2864 - precision_m: 0.0000e+00 - val_loss: 1.3792 - val_acc: 0.2687 - val_precision_m: 0.0000e+00\n","Epoch 57/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3743 - acc: 0.3134 - precision_m: 0.0022 - val_loss: 1.3840 - val_acc: 0.2612 - val_precision_m: 0.0000e+00\n","Epoch 58/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3771 - acc: 0.2477 - precision_m: 0.0080 - val_loss: 1.3837 - val_acc: 0.2612 - val_precision_m: 0.0000e+00\n","Epoch 59/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3762 - acc: 0.2839 - precision_m: 0.0126 - val_loss: 1.3800 - val_acc: 0.2761 - val_precision_m: 0.0000e+00\n","Epoch 60/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3720 - acc: 0.2900 - precision_m: 0.0316 - val_loss: 1.3848 - val_acc: 0.2313 - val_precision_m: 0.0000e+00\n","Epoch 61/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3728 - acc: 0.2600 - precision_m: 0.0090 - val_loss: 1.3836 - val_acc: 0.2537 - val_precision_m: 0.0000e+00\n","Epoch 62/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3766 - acc: 0.2769 - precision_m: 0.0107 - val_loss: 1.3803 - val_acc: 0.2388 - val_precision_m: 0.0000e+00\n","Epoch 63/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3911 - acc: 0.2144 - precision_m: 0.0020 - val_loss: 1.3786 - val_acc: 0.2612 - val_precision_m: 0.0000e+00\n","Epoch 64/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3686 - acc: 0.3196 - precision_m: 0.0192 - val_loss: 1.3853 - val_acc: 0.2313 - val_precision_m: 0.0000e+00\n","Epoch 65/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3534 - acc: 0.3243 - precision_m: 0.0344 - val_loss: 1.3809 - val_acc: 0.2761 - val_precision_m: 0.0000e+00\n","Epoch 66/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3674 - acc: 0.2989 - precision_m: 0.0392 - val_loss: 1.3781 - val_acc: 0.2612 - val_precision_m: 0.0000e+00\n","Epoch 67/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3839 - acc: 0.2436 - precision_m: 0.0190 - val_loss: 1.3874 - val_acc: 0.2313 - val_precision_m: 0.0000e+00\n","Epoch 68/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3608 - acc: 0.3017 - precision_m: 0.0186 - val_loss: 1.3854 - val_acc: 0.2313 - val_precision_m: 0.0000e+00\n","Epoch 69/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3670 - acc: 0.2801 - precision_m: 0.0265 - val_loss: 1.3828 - val_acc: 0.2463 - val_precision_m: 0.0000e+00\n","Epoch 70/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3742 - acc: 0.2589 - precision_m: 6.8259e-04 - val_loss: 1.3851 - val_acc: 0.2463 - val_precision_m: 0.0000e+00\n","Epoch 71/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3593 - acc: 0.2674 - precision_m: 0.0321 - val_loss: 1.3837 - val_acc: 0.2537 - val_precision_m: 0.0000e+00\n","Epoch 72/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3730 - acc: 0.2001 - precision_m: 0.0137 - val_loss: 1.3805 - val_acc: 0.2164 - val_precision_m: 0.0000e+00\n","Epoch 73/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3886 - acc: 0.2436 - precision_m: 0.0055 - val_loss: 1.3853 - val_acc: 0.2239 - val_precision_m: 0.0000e+00\n","Epoch 74/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3734 - acc: 0.2861 - precision_m: 0.0170 - val_loss: 1.3810 - val_acc: 0.2612 - val_precision_m: 0.0000e+00\n","Epoch 75/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3668 - acc: 0.2365 - precision_m: 0.0355 - val_loss: 1.3829 - val_acc: 0.2388 - val_precision_m: 0.0000e+00\n","Epoch 76/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3502 - acc: 0.3281 - precision_m: 0.0454 - val_loss: 1.3815 - val_acc: 0.2463 - val_precision_m: 0.0000e+00\n","Epoch 77/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3792 - acc: 0.2591 - precision_m: 0.0030 - val_loss: 1.3859 - val_acc: 0.2239 - val_precision_m: 0.0000e+00\n","Epoch 78/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3775 - acc: 0.2667 - precision_m: 0.0200 - val_loss: 1.3836 - val_acc: 0.2090 - val_precision_m: 0.0000e+00\n","Epoch 79/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3551 - acc: 0.3386 - precision_m: 0.0503 - val_loss: 1.3784 - val_acc: 0.2612 - val_precision_m: 0.0000e+00\n","Epoch 80/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3343 - acc: 0.3225 - precision_m: 0.0530 - val_loss: 1.3833 - val_acc: 0.2463 - val_precision_m: 0.0000e+00\n","Epoch 81/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3513 - acc: 0.3114 - precision_m: 0.0419 - val_loss: 1.3851 - val_acc: 0.2537 - val_precision_m: 0.0000e+00\n","Epoch 82/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3461 - acc: 0.2905 - precision_m: 0.0549 - val_loss: 1.3828 - val_acc: 0.2463 - val_precision_m: 0.0000e+00\n","Epoch 83/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3550 - acc: 0.3712 - precision_m: 0.0366 - val_loss: 1.3853 - val_acc: 0.2463 - val_precision_m: 0.0000e+00\n","Epoch 84/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3572 - acc: 0.2924 - precision_m: 0.0312 - val_loss: 1.3888 - val_acc: 0.2537 - val_precision_m: 0.0000e+00\n","Epoch 85/500\n","156/156 [==============================] - 0s 2ms/step - loss: 1.3679 - acc: 0.2864 - precision_m: 0.0092 - val_loss: 1.3821 - val_acc: 0.2761 - val_precision_m: 0.0000e+00\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 17.4s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","157/157 [==============================] - 1s 3ms/step - loss: 1.4484 - acc: 0.2437 - precision_m: 0.0420 - val_loss: 1.3897 - val_acc: 0.2222 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.4064 - acc: 0.2158 - precision_m: 0.0000e+00 - val_loss: 1.3912 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3983 - acc: 0.2087 - precision_m: 0.0000e+00 - val_loss: 1.3889 - val_acc: 0.2519 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3858 - acc: 0.2705 - precision_m: 0.0000e+00 - val_loss: 1.3857 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3909 - acc: 0.2571 - precision_m: 0.0000e+00 - val_loss: 1.3870 - val_acc: 0.2296 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3860 - acc: 0.2618 - precision_m: 0.0000e+00 - val_loss: 1.3882 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3867 - acc: 0.2874 - precision_m: 0.0000e+00 - val_loss: 1.3868 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3843 - acc: 0.2519 - precision_m: 0.0000e+00 - val_loss: 1.3873 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3881 - acc: 0.2724 - precision_m: 0.0000e+00 - val_loss: 1.3898 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3896 - acc: 0.2878 - precision_m: 0.0000e+00 - val_loss: 1.3881 - val_acc: 0.2667 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3835 - acc: 0.3097 - precision_m: 0.0000e+00 - val_loss: 1.3888 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3881 - acc: 0.2604 - precision_m: 0.0000e+00 - val_loss: 1.3876 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3837 - acc: 0.2483 - precision_m: 0.0000e+00 - val_loss: 1.3886 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3798 - acc: 0.3293 - precision_m: 0.0000e+00 - val_loss: 1.3890 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3838 - acc: 0.2683 - precision_m: 0.0000e+00 - val_loss: 1.3903 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3868 - acc: 0.2666 - precision_m: 0.0000e+00 - val_loss: 1.3898 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3847 - acc: 0.2813 - precision_m: 0.0000e+00 - val_loss: 1.3894 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3873 - acc: 0.2800 - precision_m: 0.0000e+00 - val_loss: 1.3883 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3854 - acc: 0.2680 - precision_m: 0.0000e+00 - val_loss: 1.3884 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3876 - acc: 0.2531 - precision_m: 0.0000e+00 - val_loss: 1.3884 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3858 - acc: 0.2558 - precision_m: 0.0000e+00 - val_loss: 1.3889 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3861 - acc: 0.2390 - precision_m: 0.0000e+00 - val_loss: 1.3891 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3880 - acc: 0.2527 - precision_m: 0.0000e+00 - val_loss: 1.3884 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3857 - acc: 0.2673 - precision_m: 0.0000e+00 - val_loss: 1.3886 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3839 - acc: 0.2942 - precision_m: 0.0000e+00 - val_loss: 1.3886 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3841 - acc: 0.2727 - precision_m: 0.0000e+00 - val_loss: 1.3893 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3833 - acc: 0.2679 - precision_m: 0.0000e+00 - val_loss: 1.3892 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3775 - acc: 0.3315 - precision_m: 0.0000e+00 - val_loss: 1.3892 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3853 - acc: 0.2715 - precision_m: 0.0000e+00 - val_loss: 1.3889 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3835 - acc: 0.2696 - precision_m: 0.0000e+00 - val_loss: 1.3889 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3822 - acc: 0.2976 - precision_m: 0.0000e+00 - val_loss: 1.3881 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3833 - acc: 0.2937 - precision_m: 0.0000e+00 - val_loss: 1.3881 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3839 - acc: 0.2799 - precision_m: 0.0000e+00 - val_loss: 1.3883 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3835 - acc: 0.2970 - precision_m: 0.0000e+00 - val_loss: 1.3885 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","157/157 [==============================] - 0s 3ms/step - loss: 1.3869 - acc: 0.2596 - precision_m: 0.0000e+00 - val_loss: 1.3889 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3841 - acc: 0.2923 - precision_m: 0.0000e+00 - val_loss: 1.3877 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3895 - acc: 0.2726 - precision_m: 0.0000e+00 - val_loss: 1.3883 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3812 - acc: 0.3053 - precision_m: 0.0000e+00 - val_loss: 1.3880 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3842 - acc: 0.2866 - precision_m: 0.0000e+00 - val_loss: 1.3883 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3829 - acc: 0.2845 - precision_m: 0.0000e+00 - val_loss: 1.3886 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3813 - acc: 0.2925 - precision_m: 0.0000e+00 - val_loss: 1.3882 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3803 - acc: 0.3180 - precision_m: 0.0000e+00 - val_loss: 1.3885 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3852 - acc: 0.2578 - precision_m: 0.0000e+00 - val_loss: 1.3885 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3848 - acc: 0.2661 - precision_m: 0.0000e+00 - val_loss: 1.3876 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3877 - acc: 0.2518 - precision_m: 0.0000e+00 - val_loss: 1.3887 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3808 - acc: 0.2749 - precision_m: 0.0000e+00 - val_loss: 1.3878 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3811 - acc: 0.2762 - precision_m: 0.0000e+00 - val_loss: 1.3887 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3832 - acc: 0.2812 - precision_m: 0.0000e+00 - val_loss: 1.3882 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3881 - acc: 0.2692 - precision_m: 0.0000e+00 - val_loss: 1.3879 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3829 - acc: 0.2974 - precision_m: 0.0000e+00 - val_loss: 1.3884 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3808 - acc: 0.2844 - precision_m: 0.0000e+00 - val_loss: 1.3887 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 52/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3809 - acc: 0.2862 - precision_m: 0.0000e+00 - val_loss: 1.3890 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 53/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3810 - acc: 0.2934 - precision_m: 0.0000e+00 - val_loss: 1.3896 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Epoch 54/500\n","157/157 [==============================] - 0s 2ms/step - loss: 1.3911 - acc: 0.2199 - precision_m: 0.0000e+00 - val_loss: 1.3901 - val_acc: 0.2593 - val_precision_m: 0.0000e+00\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 18.3s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","155/155 [==============================] - 1s 3ms/step - loss: 1.5377 - acc: 0.2416 - precision_m: 0.0279 - val_loss: 1.3881 - val_acc: 0.2331 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3879 - acc: 0.2297 - precision_m: 0.0000e+00 - val_loss: 1.3851 - val_acc: 0.2632 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3817 - acc: 0.2467 - precision_m: 0.0000e+00 - val_loss: 1.3796 - val_acc: 0.2782 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3844 - acc: 0.2760 - precision_m: 0.0114 - val_loss: 1.3830 - val_acc: 0.2932 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3925 - acc: 0.2782 - precision_m: 0.0000e+00 - val_loss: 1.3859 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3781 - acc: 0.2482 - precision_m: 0.0000e+00 - val_loss: 1.3871 - val_acc: 0.2707 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3823 - acc: 0.2667 - precision_m: 0.0000e+00 - val_loss: 1.3865 - val_acc: 0.2782 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3807 - acc: 0.2829 - precision_m: 0.0000e+00 - val_loss: 1.3858 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3807 - acc: 0.2939 - precision_m: 0.0000e+00 - val_loss: 1.3874 - val_acc: 0.2782 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3845 - acc: 0.2648 - precision_m: 0.0000e+00 - val_loss: 1.3858 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3809 - acc: 0.2591 - precision_m: 0.0000e+00 - val_loss: 1.3862 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3756 - acc: 0.2385 - precision_m: 0.0000e+00 - val_loss: 1.3866 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3765 - acc: 0.2997 - precision_m: 0.0000e+00 - val_loss: 1.3861 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3821 - acc: 0.2850 - precision_m: 0.0000e+00 - val_loss: 1.3861 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3700 - acc: 0.2814 - precision_m: 0.0000e+00 - val_loss: 1.3862 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3646 - acc: 0.2644 - precision_m: 0.0000e+00 - val_loss: 1.3868 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3878 - acc: 0.2457 - precision_m: 0.0000e+00 - val_loss: 1.3888 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3841 - acc: 0.2526 - precision_m: 0.0000e+00 - val_loss: 1.3868 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3799 - acc: 0.2997 - precision_m: 0.0000e+00 - val_loss: 1.3871 - val_acc: 0.2932 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3838 - acc: 0.2564 - precision_m: 0.0000e+00 - val_loss: 1.3868 - val_acc: 0.2932 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3709 - acc: 0.2989 - precision_m: 0.0000e+00 - val_loss: 1.3918 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3815 - acc: 0.3120 - precision_m: 0.0000e+00 - val_loss: 1.3875 - val_acc: 0.2782 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3782 - acc: 0.2201 - precision_m: 0.0000e+00 - val_loss: 1.3873 - val_acc: 0.2932 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3842 - acc: 0.2858 - precision_m: 0.0000e+00 - val_loss: 1.3868 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3779 - acc: 0.2679 - precision_m: 0.0000e+00 - val_loss: 1.3863 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3650 - acc: 0.3082 - precision_m: 0.0000e+00 - val_loss: 1.3873 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3898 - acc: 0.2845 - precision_m: 0.0000e+00 - val_loss: 1.3874 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3828 - acc: 0.2545 - precision_m: 0.0000e+00 - val_loss: 1.3896 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3929 - acc: 0.2102 - precision_m: 0.0000e+00 - val_loss: 1.3892 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3751 - acc: 0.2661 - precision_m: 0.0000e+00 - val_loss: 1.3844 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3781 - acc: 0.3219 - precision_m: 0.0000e+00 - val_loss: 1.3852 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3822 - acc: 0.2853 - precision_m: 0.0000e+00 - val_loss: 1.3887 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3790 - acc: 0.2317 - precision_m: 0.0000e+00 - val_loss: 1.3888 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3762 - acc: 0.2534 - precision_m: 0.0000e+00 - val_loss: 1.3897 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3773 - acc: 0.2761 - precision_m: 0.0000e+00 - val_loss: 1.3877 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3801 - acc: 0.3202 - precision_m: 0.0000e+00 - val_loss: 1.3875 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3765 - acc: 0.2646 - precision_m: 0.0000e+00 - val_loss: 1.3868 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3923 - acc: 0.2592 - precision_m: 0.0000e+00 - val_loss: 1.3901 - val_acc: 0.2782 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3733 - acc: 0.2576 - precision_m: 0.0000e+00 - val_loss: 1.3848 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3869 - acc: 0.2768 - precision_m: 0.0000e+00 - val_loss: 1.3880 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3729 - acc: 0.2405 - precision_m: 0.0000e+00 - val_loss: 1.3883 - val_acc: 0.2782 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3769 - acc: 0.2633 - precision_m: 0.0000e+00 - val_loss: 1.3867 - val_acc: 0.2932 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3773 - acc: 0.2629 - precision_m: 0.0000e+00 - val_loss: 1.3865 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3764 - acc: 0.2564 - precision_m: 0.0000e+00 - val_loss: 1.3865 - val_acc: 0.2932 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3941 - acc: 0.2435 - precision_m: 0.0000e+00 - val_loss: 1.3862 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3865 - acc: 0.2309 - precision_m: 0.0000e+00 - val_loss: 1.3867 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3865 - acc: 0.2538 - precision_m: 0.0000e+00 - val_loss: 1.3868 - val_acc: 0.2932 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3825 - acc: 0.2517 - precision_m: 0.0000e+00 - val_loss: 1.3875 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3753 - acc: 0.2627 - precision_m: 0.0000e+00 - val_loss: 1.3876 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3922 - acc: 0.2638 - precision_m: 0.0000e+00 - val_loss: 1.3880 - val_acc: 0.2707 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3713 - acc: 0.2642 - precision_m: 0.0000e+00 - val_loss: 1.3868 - val_acc: 0.3008 - val_precision_m: 0.0000e+00\n","Epoch 52/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3978 - acc: 0.2683 - precision_m: 0.0000e+00 - val_loss: 1.3875 - val_acc: 0.2857 - val_precision_m: 0.0000e+00\n","Epoch 53/500\n","155/155 [==============================] - 0s 2ms/step - loss: 1.3737 - acc: 0.2754 - precision_m: 0.0000e+00 - val_loss: 1.3885 - val_acc: 0.2932 - val_precision_m: 0.0000e+00\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 18.8s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","151/151 [==============================] - 1s 3ms/step - loss: 1.6422 - acc: 0.2305 - precision_m: 0.0907 - val_loss: 1.3729 - val_acc: 0.3462 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.4097 - acc: 0.2144 - precision_m: 0.0000e+00 - val_loss: 1.3846 - val_acc: 0.2385 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3923 - acc: 0.2291 - precision_m: 0.0000e+00 - val_loss: 1.3794 - val_acc: 0.3231 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3822 - acc: 0.2330 - precision_m: 0.0000e+00 - val_loss: 1.3781 - val_acc: 0.3154 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3816 - acc: 0.2187 - precision_m: 0.0000e+00 - val_loss: 1.3751 - val_acc: 0.2385 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3787 - acc: 0.2569 - precision_m: 0.0000e+00 - val_loss: 1.3760 - val_acc: 0.2385 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3868 - acc: 0.2550 - precision_m: 0.0000e+00 - val_loss: 1.3742 - val_acc: 0.2615 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3744 - acc: 0.3218 - precision_m: 0.0000e+00 - val_loss: 1.3735 - val_acc: 0.2385 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3775 - acc: 0.2180 - precision_m: 0.0000e+00 - val_loss: 1.3733 - val_acc: 0.2462 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3776 - acc: 0.2683 - precision_m: 0.0000e+00 - val_loss: 1.3727 - val_acc: 0.2385 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3740 - acc: 0.2544 - precision_m: 0.0000e+00 - val_loss: 1.3725 - val_acc: 0.2385 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3834 - acc: 0.2860 - precision_m: 0.0000e+00 - val_loss: 1.3718 - val_acc: 0.2385 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3871 - acc: 0.2653 - precision_m: 0.0000e+00 - val_loss: 1.3682 - val_acc: 0.3231 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3735 - acc: 0.3053 - precision_m: 0.0000e+00 - val_loss: 1.3723 - val_acc: 0.2462 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3808 - acc: 0.3040 - precision_m: 0.0000e+00 - val_loss: 1.3718 - val_acc: 0.2385 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3800 - acc: 0.3076 - precision_m: 0.0000e+00 - val_loss: 1.3715 - val_acc: 0.2385 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3895 - acc: 0.2280 - precision_m: 0.0000e+00 - val_loss: 1.3713 - val_acc: 0.2462 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3703 - acc: 0.2741 - precision_m: 0.0000e+00 - val_loss: 1.3664 - val_acc: 0.3231 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3946 - acc: 0.3234 - precision_m: 0.0000e+00 - val_loss: 1.3708 - val_acc: 0.2462 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3699 - acc: 0.3138 - precision_m: 0.0000e+00 - val_loss: 1.3704 - val_acc: 0.2462 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","151/151 [==============================] - 0s 3ms/step - loss: 1.3800 - acc: 0.2325 - precision_m: 0.0000e+00 - val_loss: 1.3704 - val_acc: 0.2462 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3765 - acc: 0.2701 - precision_m: 0.0000e+00 - val_loss: 1.3702 - val_acc: 0.2462 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3812 - acc: 0.1877 - precision_m: 0.0000e+00 - val_loss: 1.3703 - val_acc: 0.2462 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3751 - acc: 0.2888 - precision_m: 0.0000e+00 - val_loss: 1.3700 - val_acc: 0.2462 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3737 - acc: 0.2121 - precision_m: 0.0000e+00 - val_loss: 1.3700 - val_acc: 0.2462 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3783 - acc: 0.2283 - precision_m: 0.0000e+00 - val_loss: 1.3698 - val_acc: 0.2462 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3756 - acc: 0.2849 - precision_m: 0.0000e+00 - val_loss: 1.3699 - val_acc: 0.2231 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3641 - acc: 0.3345 - precision_m: 0.0000e+00 - val_loss: 1.3695 - val_acc: 0.2538 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3730 - acc: 0.2389 - precision_m: 0.0000e+00 - val_loss: 1.3698 - val_acc: 0.2385 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3812 - acc: 0.1988 - precision_m: 0.0000e+00 - val_loss: 1.3695 - val_acc: 0.2385 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3799 - acc: 0.2728 - precision_m: 0.0000e+00 - val_loss: 1.3695 - val_acc: 0.2385 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3720 - acc: 0.2866 - precision_m: 0.0000e+00 - val_loss: 1.3695 - val_acc: 0.2615 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3779 - acc: 0.2845 - precision_m: 0.0000e+00 - val_loss: 1.3689 - val_acc: 0.2538 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3949 - acc: 0.2553 - precision_m: 0.0000e+00 - val_loss: 1.3680 - val_acc: 0.3231 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3753 - acc: 0.2886 - precision_m: 0.0000e+00 - val_loss: 1.3680 - val_acc: 0.3154 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3720 - acc: 0.2748 - precision_m: 0.0000e+00 - val_loss: 1.3675 - val_acc: 0.3231 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3752 - acc: 0.2520 - precision_m: 0.0000e+00 - val_loss: 1.3685 - val_acc: 0.3231 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3765 - acc: 0.2510 - precision_m: 0.0000e+00 - val_loss: 1.3685 - val_acc: 0.3231 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3757 - acc: 0.2832 - precision_m: 0.0000e+00 - val_loss: 1.3692 - val_acc: 0.2385 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3811 - acc: 0.2560 - precision_m: 0.0000e+00 - val_loss: 1.3687 - val_acc: 0.3154 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3843 - acc: 0.2923 - precision_m: 0.0000e+00 - val_loss: 1.3692 - val_acc: 0.2615 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3788 - acc: 0.2416 - precision_m: 0.0000e+00 - val_loss: 1.3691 - val_acc: 0.2538 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3846 - acc: 0.2407 - precision_m: 0.0000e+00 - val_loss: 1.3687 - val_acc: 0.3154 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3963 - acc: 0.2077 - precision_m: 0.0000e+00 - val_loss: 1.3694 - val_acc: 0.3231 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3834 - acc: 0.2563 - precision_m: 0.0000e+00 - val_loss: 1.3686 - val_acc: 0.3231 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3714 - acc: 0.2562 - precision_m: 0.0000e+00 - val_loss: 1.3683 - val_acc: 0.3231 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3830 - acc: 0.2578 - precision_m: 0.0000e+00 - val_loss: 1.3687 - val_acc: 0.2462 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3779 - acc: 0.3724 - precision_m: 0.0000e+00 - val_loss: 1.3688 - val_acc: 0.3154 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3807 - acc: 0.2599 - precision_m: 0.0000e+00 - val_loss: 1.3693 - val_acc: 0.2615 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3755 - acc: 0.3146 - precision_m: 0.0000e+00 - val_loss: 1.3670 - val_acc: 0.3231 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3678 - acc: 0.2721 - precision_m: 0.0000e+00 - val_loss: 1.3678 - val_acc: 0.3154 - val_precision_m: 0.0000e+00\n","Epoch 52/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3866 - acc: 0.2677 - precision_m: 0.0000e+00 - val_loss: 1.3674 - val_acc: 0.3231 - val_precision_m: 0.0000e+00\n","Epoch 53/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3670 - acc: 0.2781 - precision_m: 0.0000e+00 - val_loss: 1.3685 - val_acc: 0.3231 - val_precision_m: 0.0000e+00\n","Epoch 54/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3817 - acc: 0.2276 - precision_m: 0.0000e+00 - val_loss: 1.3682 - val_acc: 0.2538 - val_precision_m: 0.0000e+00\n","Epoch 55/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3891 - acc: 0.2469 - precision_m: 0.0000e+00 - val_loss: 1.3691 - val_acc: 0.2385 - val_precision_m: 0.0000e+00\n","Epoch 56/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3855 - acc: 0.2644 - precision_m: 0.0000e+00 - val_loss: 1.3698 - val_acc: 0.2385 - val_precision_m: 0.0000e+00\n","Epoch 57/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3712 - acc: 0.2958 - precision_m: 0.0000e+00 - val_loss: 1.3702 - val_acc: 0.2385 - val_precision_m: 0.0000e+00\n","Epoch 58/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3780 - acc: 0.2582 - precision_m: 0.0000e+00 - val_loss: 1.3701 - val_acc: 0.2385 - val_precision_m: 0.0000e+00\n","Epoch 59/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3895 - acc: 0.3009 - precision_m: 0.0000e+00 - val_loss: 1.3709 - val_acc: 0.2462 - val_precision_m: 0.0000e+00\n","Epoch 60/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3931 - acc: 0.2272 - precision_m: 0.0000e+00 - val_loss: 1.3705 - val_acc: 0.2385 - val_precision_m: 0.0000e+00\n","Epoch 61/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3706 - acc: 0.2658 - precision_m: 0.0000e+00 - val_loss: 1.3706 - val_acc: 0.2615 - val_precision_m: 0.0000e+00\n","Epoch 62/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3725 - acc: 0.2506 - precision_m: 0.0000e+00 - val_loss: 1.3702 - val_acc: 0.2462 - val_precision_m: 0.0000e+00\n","Epoch 63/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3807 - acc: 0.3629 - precision_m: 0.0000e+00 - val_loss: 1.3703 - val_acc: 0.2385 - val_precision_m: 0.0000e+00\n","Epoch 64/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3689 - acc: 0.2865 - precision_m: 0.0000e+00 - val_loss: 1.3703 - val_acc: 0.2538 - val_precision_m: 0.0000e+00\n","Epoch 65/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3907 - acc: 0.2406 - precision_m: 0.0000e+00 - val_loss: 1.3701 - val_acc: 0.2462 - val_precision_m: 0.0000e+00\n","Epoch 66/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3752 - acc: 0.2913 - precision_m: 0.0000e+00 - val_loss: 1.3699 - val_acc: 0.2385 - val_precision_m: 0.0000e+00\n","Epoch 67/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3835 - acc: 0.2829 - precision_m: 0.0000e+00 - val_loss: 1.3698 - val_acc: 0.2385 - val_precision_m: 0.0000e+00\n","Epoch 68/500\n","151/151 [==============================] - 0s 2ms/step - loss: 1.3745 - acc: 0.2328 - precision_m: 0.0000e+00 - val_loss: 1.3699 - val_acc: 0.2462 - val_precision_m: 0.0000e+00\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 19.6s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","152/152 [==============================] - 1s 3ms/step - loss: 1.4664 - acc: 0.2848 - precision_m: 0.0475 - val_loss: 1.3805 - val_acc: 0.2748 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.4043 - acc: 0.2420 - precision_m: 0.0000e+00 - val_loss: 1.3846 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3890 - acc: 0.2748 - precision_m: 0.0000e+00 - val_loss: 1.3859 - val_acc: 0.2672 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3858 - acc: 0.2531 - precision_m: 0.0000e+00 - val_loss: 1.3856 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3868 - acc: 0.3032 - precision_m: 0.0000e+00 - val_loss: 1.3866 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3847 - acc: 0.3304 - precision_m: 0.0000e+00 - val_loss: 1.3881 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3855 - acc: 0.2538 - precision_m: 0.0000e+00 - val_loss: 1.3886 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3820 - acc: 0.3083 - precision_m: 0.0000e+00 - val_loss: 1.3883 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3839 - acc: 0.3029 - precision_m: 0.0000e+00 - val_loss: 1.3885 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3852 - acc: 0.2641 - precision_m: 0.0000e+00 - val_loss: 1.3881 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3794 - acc: 0.2659 - precision_m: 0.0000e+00 - val_loss: 1.3888 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3870 - acc: 0.2271 - precision_m: 0.0000e+00 - val_loss: 1.3888 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3850 - acc: 0.2561 - precision_m: 0.0000e+00 - val_loss: 1.3889 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3805 - acc: 0.3165 - precision_m: 0.0000e+00 - val_loss: 1.3892 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3834 - acc: 0.2970 - precision_m: 0.0000e+00 - val_loss: 1.3894 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3822 - acc: 0.2757 - precision_m: 0.0000e+00 - val_loss: 1.3896 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3809 - acc: 0.3013 - precision_m: 0.0000e+00 - val_loss: 1.3900 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3860 - acc: 0.2355 - precision_m: 0.0000e+00 - val_loss: 1.3896 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3833 - acc: 0.2641 - precision_m: 0.0000e+00 - val_loss: 1.3897 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3851 - acc: 0.2454 - precision_m: 0.0000e+00 - val_loss: 1.3897 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3840 - acc: 0.2706 - precision_m: 0.0000e+00 - val_loss: 1.3898 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3851 - acc: 0.2677 - precision_m: 0.0000e+00 - val_loss: 1.3898 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3863 - acc: 0.2399 - precision_m: 0.0000e+00 - val_loss: 1.3910 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3813 - acc: 0.2636 - precision_m: 0.0000e+00 - val_loss: 1.3900 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3859 - acc: 0.2692 - precision_m: 0.0000e+00 - val_loss: 1.3903 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3760 - acc: 0.2848 - precision_m: 0.0000e+00 - val_loss: 1.3900 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3865 - acc: 0.2258 - precision_m: 0.0000e+00 - val_loss: 1.3902 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3879 - acc: 0.2571 - precision_m: 0.0000e+00 - val_loss: 1.3901 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3805 - acc: 0.3154 - precision_m: 0.0000e+00 - val_loss: 1.3911 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3864 - acc: 0.2722 - precision_m: 0.0000e+00 - val_loss: 1.3903 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3820 - acc: 0.2961 - precision_m: 0.0000e+00 - val_loss: 1.3902 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3845 - acc: 0.2448 - precision_m: 0.0000e+00 - val_loss: 1.4029 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3892 - acc: 0.3164 - precision_m: 0.0000e+00 - val_loss: 1.3904 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3794 - acc: 0.2881 - precision_m: 0.0000e+00 - val_loss: 1.3910 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3935 - acc: 0.2217 - precision_m: 0.0000e+00 - val_loss: 1.3933 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3887 - acc: 0.2529 - precision_m: 0.0000e+00 - val_loss: 1.3915 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3842 - acc: 0.2654 - precision_m: 0.0000e+00 - val_loss: 1.3908 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3747 - acc: 0.3049 - precision_m: 0.0000e+00 - val_loss: 1.3911 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3890 - acc: 0.2247 - precision_m: 0.0000e+00 - val_loss: 1.3907 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3829 - acc: 0.2771 - precision_m: 0.0000e+00 - val_loss: 1.3906 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3877 - acc: 0.2425 - precision_m: 0.0000e+00 - val_loss: 1.3900 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3762 - acc: 0.2811 - precision_m: 0.0000e+00 - val_loss: 1.3900 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3771 - acc: 0.3154 - precision_m: 0.0000e+00 - val_loss: 1.3903 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3772 - acc: 0.3388 - precision_m: 0.0000e+00 - val_loss: 1.3903 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3797 - acc: 0.3046 - precision_m: 0.0000e+00 - val_loss: 1.3899 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3830 - acc: 0.2624 - precision_m: 0.0000e+00 - val_loss: 1.3901 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3838 - acc: 0.2779 - precision_m: 0.0000e+00 - val_loss: 1.3902 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3733 - acc: 0.3260 - precision_m: 0.0000e+00 - val_loss: 1.3898 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3860 - acc: 0.2521 - precision_m: 0.0000e+00 - val_loss: 1.3897 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3842 - acc: 0.2635 - precision_m: 0.0000e+00 - val_loss: 1.3895 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3856 - acc: 0.2394 - precision_m: 0.0000e+00 - val_loss: 1.3898 - val_acc: 0.2443 - val_precision_m: 0.0000e+00\n","Fitting 5 folds for each of 294 candidates, totalling 1470 fits\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[Parallel(n_jobs=1)]: Done 1470 out of 1470 | elapsed: 19.2s finished\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/500\n","152/152 [==============================] - 1s 4ms/step - loss: 1.4482 - acc: 0.2102 - precision_m: 0.0000e+00 - val_loss: 1.3825 - val_acc: 0.3231 - val_precision_m: 0.0000e+00\n","Epoch 2/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3999 - acc: 0.1873 - precision_m: 0.0000e+00 - val_loss: 1.3890 - val_acc: 0.2385 - val_precision_m: 0.0000e+00\n","Epoch 3/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3908 - acc: 0.2900 - precision_m: 0.0000e+00 - val_loss: 1.3894 - val_acc: 0.1923 - val_precision_m: 0.0000e+00\n","Epoch 4/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3870 - acc: 0.2559 - precision_m: 0.0000e+00 - val_loss: 1.3840 - val_acc: 0.2923 - val_precision_m: 0.0000e+00\n","Epoch 5/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3832 - acc: 0.2554 - precision_m: 0.0000e+00 - val_loss: 1.3897 - val_acc: 0.2231 - val_precision_m: 0.0000e+00\n","Epoch 6/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3944 - acc: 0.2127 - precision_m: 0.0000e+00 - val_loss: 1.3875 - val_acc: 0.2462 - val_precision_m: 0.0000e+00\n","Epoch 7/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3924 - acc: 0.2393 - precision_m: 0.0000e+00 - val_loss: 1.3903 - val_acc: 0.1923 - val_precision_m: 0.0000e+00\n","Epoch 8/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3866 - acc: 0.2792 - precision_m: 0.0000e+00 - val_loss: 1.3898 - val_acc: 0.1923 - val_precision_m: 0.0000e+00\n","Epoch 9/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3797 - acc: 0.2684 - precision_m: 0.0000e+00 - val_loss: 1.3894 - val_acc: 0.1923 - val_precision_m: 0.0000e+00\n","Epoch 10/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3847 - acc: 0.2455 - precision_m: 0.0000e+00 - val_loss: 1.3894 - val_acc: 0.1923 - val_precision_m: 0.0000e+00\n","Epoch 11/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3821 - acc: 0.2758 - precision_m: 0.0000e+00 - val_loss: 1.3890 - val_acc: 0.1923 - val_precision_m: 0.0000e+00\n","Epoch 12/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3835 - acc: 0.2992 - precision_m: 0.0000e+00 - val_loss: 1.3889 - val_acc: 0.1923 - val_precision_m: 0.0000e+00\n","Epoch 13/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3826 - acc: 0.2331 - precision_m: 0.0000e+00 - val_loss: 1.3886 - val_acc: 0.1923 - val_precision_m: 0.0000e+00\n","Epoch 14/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3887 - acc: 0.2309 - precision_m: 0.0000e+00 - val_loss: 1.3891 - val_acc: 0.1923 - val_precision_m: 0.0000e+00\n","Epoch 15/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3855 - acc: 0.2822 - precision_m: 0.0000e+00 - val_loss: 1.3887 - val_acc: 0.1923 - val_precision_m: 0.0000e+00\n","Epoch 16/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3849 - acc: 0.2937 - precision_m: 0.0000e+00 - val_loss: 1.3885 - val_acc: 0.1923 - val_precision_m: 0.0000e+00\n","Epoch 17/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3864 - acc: 0.2779 - precision_m: 0.0000e+00 - val_loss: 1.3885 - val_acc: 0.2769 - val_precision_m: 0.0000e+00\n","Epoch 18/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3797 - acc: 0.3240 - precision_m: 0.0000e+00 - val_loss: 1.3882 - val_acc: 0.2000 - val_precision_m: 0.0000e+00\n","Epoch 19/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3838 - acc: 0.2385 - precision_m: 0.0115 - val_loss: 1.3883 - val_acc: 0.2462 - val_precision_m: 0.0000e+00\n","Epoch 20/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3850 - acc: 0.2252 - precision_m: 0.0000e+00 - val_loss: 1.3884 - val_acc: 0.2462 - val_precision_m: 0.0000e+00\n","Epoch 21/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3860 - acc: 0.2620 - precision_m: 0.0000e+00 - val_loss: 1.3887 - val_acc: 0.2462 - val_precision_m: 0.0000e+00\n","Epoch 22/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3845 - acc: 0.2101 - precision_m: 0.0000e+00 - val_loss: 1.3881 - val_acc: 0.2615 - val_precision_m: 0.0000e+00\n","Epoch 23/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3836 - acc: 0.2834 - precision_m: 0.0000e+00 - val_loss: 1.3882 - val_acc: 0.2385 - val_precision_m: 0.0000e+00\n","Epoch 24/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3879 - acc: 0.2383 - precision_m: 0.0000e+00 - val_loss: 1.3896 - val_acc: 0.2615 - val_precision_m: 0.0000e+00\n","Epoch 25/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3849 - acc: 0.2263 - precision_m: 0.0000e+00 - val_loss: 1.3894 - val_acc: 0.2000 - val_precision_m: 0.0000e+00\n","Epoch 26/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3827 - acc: 0.3104 - precision_m: 0.0000e+00 - val_loss: 1.3899 - val_acc: 0.2538 - val_precision_m: 0.0000e+00\n","Epoch 27/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3851 - acc: 0.2302 - precision_m: 0.0000e+00 - val_loss: 1.3885 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 28/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3859 - acc: 0.2768 - precision_m: 0.0000e+00 - val_loss: 1.3937 - val_acc: 0.2077 - val_precision_m: 0.0000e+00\n","Epoch 29/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3796 - acc: 0.2873 - precision_m: 0.0000e+00 - val_loss: 1.3917 - val_acc: 0.2538 - val_precision_m: 0.0000e+00\n","Epoch 30/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3850 - acc: 0.2751 - precision_m: 0.0000e+00 - val_loss: 1.3906 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 31/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3796 - acc: 0.3114 - precision_m: 0.0000e+00 - val_loss: 1.3897 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 32/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3841 - acc: 0.2666 - precision_m: 0.0000e+00 - val_loss: 1.3892 - val_acc: 0.2615 - val_precision_m: 0.0000e+00\n","Epoch 33/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3849 - acc: 0.2834 - precision_m: 0.0000e+00 - val_loss: 1.3892 - val_acc: 0.2538 - val_precision_m: 0.0000e+00\n","Epoch 34/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3855 - acc: 0.2377 - precision_m: 0.0000e+00 - val_loss: 1.3889 - val_acc: 0.2769 - val_precision_m: 0.0000e+00\n","Epoch 35/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3831 - acc: 0.2544 - precision_m: 0.0000e+00 - val_loss: 1.3889 - val_acc: 0.2462 - val_precision_m: 0.0000e+00\n","Epoch 36/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3867 - acc: 0.2708 - precision_m: 0.0000e+00 - val_loss: 1.3885 - val_acc: 0.2538 - val_precision_m: 0.0000e+00\n","Epoch 37/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3808 - acc: 0.2646 - precision_m: 0.0000e+00 - val_loss: 1.3888 - val_acc: 0.2769 - val_precision_m: 0.0000e+00\n","Epoch 38/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3793 - acc: 0.3131 - precision_m: 0.0000e+00 - val_loss: 1.3889 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 39/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3856 - acc: 0.2287 - precision_m: 0.0000e+00 - val_loss: 1.3888 - val_acc: 0.2769 - val_precision_m: 0.0000e+00\n","Epoch 40/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3784 - acc: 0.2356 - precision_m: 0.0000e+00 - val_loss: 1.3879 - val_acc: 0.2615 - val_precision_m: 0.0000e+00\n","Epoch 41/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3851 - acc: 0.2567 - precision_m: 0.0000e+00 - val_loss: 1.3896 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 42/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3838 - acc: 0.2471 - precision_m: 0.0000e+00 - val_loss: 1.3886 - val_acc: 0.2846 - val_precision_m: 0.0000e+00\n","Epoch 43/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3830 - acc: 0.2762 - precision_m: 0.0027 - val_loss: 1.3914 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 44/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3790 - acc: 0.2812 - precision_m: 0.0016 - val_loss: 1.3896 - val_acc: 0.2538 - val_precision_m: 0.0000e+00\n","Epoch 45/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3760 - acc: 0.2874 - precision_m: 0.0017 - val_loss: 1.3901 - val_acc: 0.2615 - val_precision_m: 0.0000e+00\n","Epoch 46/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3871 - acc: 0.2421 - precision_m: 0.0000e+00 - val_loss: 1.3897 - val_acc: 0.2385 - val_precision_m: 0.0000e+00\n","Epoch 47/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3850 - acc: 0.2804 - precision_m: 0.0094 - val_loss: 1.3913 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 48/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3871 - acc: 0.2650 - precision_m: 0.0055 - val_loss: 1.3927 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 49/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3782 - acc: 0.2094 - precision_m: 0.0310 - val_loss: 1.3901 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n","Epoch 50/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3844 - acc: 0.2681 - precision_m: 0.0012 - val_loss: 1.3907 - val_acc: 0.2769 - val_precision_m: 0.0000e+00\n","Epoch 51/500\n","152/152 [==============================] - 0s 2ms/step - loss: 1.3666 - acc: 0.3077 - precision_m: 0.0190 - val_loss: 1.3916 - val_acc: 0.2692 - val_precision_m: 0.0000e+00\n"],"name":"stdout"},{"output_type":"error","ename":"NotADirectoryError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNotADirectoryError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;31m# iterate all the folders\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mfile\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparticipant\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;31m# iterate all files in every folder, find out the one end with 'Cong.csv' and 'Incong.csv' as input data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNotADirectoryError\u001b[0m: [Errno 20] Not a directory: 'output'"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":210},"id":"NG3Li6ZBvFsD","executionInfo":{"status":"ok","timestamp":1610658637818,"user_tz":300,"elapsed":568,"user":{"displayName":"Changhong Ma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg4a7esTogCkibNImqhE9gCGWTIpBdm_K1v1bWc=s64","userId":"04324339310204701212"}},"outputId":"fc96b990-5515-41b4-bfa8-9ad4478f45c9"},"source":["df"],"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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Participant 001Participant 007Participant 004Participant 003Participant 006Participant 010Participant 012Participant 011Participant 009Participant 016Participant 020Participant 023Participant 017Participant 013Participant 021Participant 019Participant 015Participant 014Participant 018Participant 033Participant 026Participant 027Participant 029Participant 031Participant 030Participant 025Participant 034Participant 024Participant 032Participant 044Participant 038Participant 040Participant 043Participant 041Participant 036Participant 039Participant 042Participant 037Participant 035Participant 048Participant 049Participant 055Participant 053Participant 054Participant 046Participant 047Participant 050Participant 051Participant 052Participant 059Participant 061Participant 063Participant 060Participant 058Participant 057Participant 056
SVC0.330.270.400.350.390.260.360.410.390.370.300.550.300.310.290.270.270.270.360.240.310.250.250.330.300.280.310.310.270.250.310.320.300.230.280.350.320.260.270.260.230.210.370.320.280.260.280.210.280.250.250.250.260.230.240.25
DTC0.250.240.330.270.240.280.340.410.190.250.250.520.240.230.300.240.250.270.210.240.220.280.250.410.230.240.200.250.230.210.350.360.250.280.250.150.220.230.230.360.280.310.340.320.310.190.280.210.260.240.260.290.260.220.230.22
NB0.230.240.310.240.270.250.300.290.230.210.280.560.200.230.240.260.230.290.210.230.260.300.210.280.240.180.260.260.260.220.270.370.270.250.230.210.260.220.240.230.240.260.520.240.230.290.330.210.190.210.280.290.230.200.220.19
NN0.200.290.360.340.290.260.260.230.230.200.250.600.270.230.240.240.250.290.260.290.230.190.280.240.220.210.290.240.210.250.270.460.240.230.280.170.280.250.290.280.270.240.310.270.260.150.340.180.280.270.280.260.290.250.240.27
\n","
"],"text/plain":[" Participant 001 Participant 007 ... Participant 057 Participant 056\n","SVC 0.33 0.27 ... 0.24 0.25\n","DTC 0.25 0.24 ... 0.23 0.22\n","NB 0.23 0.24 ... 0.22 0.19\n","NN 0.20 0.29 ... 0.24 0.27\n","\n","[4 rows x 56 columns]"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"code","metadata":{"id":"Hx54gxyR4cjc","executionInfo":{"status":"ok","timestamp":1610658734705,"user_tz":300,"elapsed":365,"user":{"displayName":"Changhong Ma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg4a7esTogCkibNImqhE9gCGWTIpBdm_K1v1bWc=s64","userId":"04324339310204701212"}}},"source":["df.to_csv('accuracy_multi_c4.csv') "],"execution_count":12,"outputs":[]}]} \ No newline at end of file diff --git a/multi_participants/case_4/multi_participants_case_4 (precision).ipynb b/multi_participants/case_4/multi_participants_case_4 (precision).ipynb deleted file mode 100644 index f90631a..0000000 --- a/multi_participants/case_4/multi_participants_case_4 (precision).ipynb +++ /dev/null @@ -1 +0,0 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"multi_participants_case_4 (precision).ipynb","provenance":[],"collapsed_sections":[],"authorship_tag":"ABX9TyPhoHBTM7jYkAIrULcJubv3"},"kernelspec":{"display_name":"Python 3","name":"python3"}},"cells":[{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sOg2lGMlThR2","executionInfo":{"elapsed":21298,"status":"ok","timestamp":1610636456526,"user":{"displayName":"Changhong Ma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg4a7esTogCkibNImqhE9gCGWTIpBdm_K1v1bWc=s64","userId":"04324339310204701212"},"user_tz":300},"outputId":"61ea6628-3b0e-4575-d8aa-de4d644892b7"},"source":["from google.colab import drive\n","drive.mount('/content/gdrive')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Mounted at /content/gdrive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hV5sT-YaYhdN","executionInfo":{"elapsed":3941,"status":"ok","timestamp":1610636456527,"user":{"displayName":"Changhong Ma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg4a7esTogCkibNImqhE9gCGWTIpBdm_K1v1bWc=s64","userId":"04324339310204701212"},"user_tz":300},"outputId":"a7be0149-c263-4ac7-e4c1-a021637f7ade"},"source":["import os\n","os.chdir(\"/content/gdrive/MyDrive\")\n","!ls"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" 2020fall-ml\n","'CBT test Diagram.drawio'\n","'CEN 5011 '\n","'Changhong Ma.pdf'\n","'Colab Notebooks'\n","'Copy of multi_participants_case_3 (acc).ipynb'\n","'CPT_Changhong Ma.pdf'\n"," CV.pdf\n"," Data_by_Participant\n","'Getting started.pdf'\n"," mind_reading.py\n"," mind_reading_v2.py\n","'New Panther Virtual Check In Module CERTIFICATE OF COMPLETION-Quiz Passed.pdf'\n"," oa\n"," __pycache__\n"," testing.ipynb\n","'UCD-User Account Home Page.drawio'\n","'Untitled Diagram.drawio'\n","'Untitled spreadsheet.gsheet'\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"KokWiR8kannV"},"source":["import mind_reading_v2 as mr\n","import pandas as pd\n","import re"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5Es9ZTM-TqT9","executionInfo":{"elapsed":493,"status":"ok","timestamp":1610636477121,"user":{"displayName":"Changhong Ma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg4a7esTogCkibNImqhE9gCGWTIpBdm_K1v1bWc=s64","userId":"04324339310204701212"},"user_tz":300},"outputId":"267dd1e9-8781-4b4f-ead1-c9ea0d5f81c5"},"source":["%cd \"/content/gdrive/MyDrive/Data_by_Participant\""],"execution_count":null,"outputs":[{"output_type":"stream","text":["/content/gdrive/MyDrive/Data_by_Participant\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PJtXxGsTV6Fm","executionInfo":{"elapsed":481,"status":"ok","timestamp":1610636479041,"user":{"displayName":"Changhong Ma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg4a7esTogCkibNImqhE9gCGWTIpBdm_K1v1bWc=s64","userId":"04324339310204701212"},"user_tz":300},"outputId":"e2d946d8-6a18-4989-d715-f64a32b8d287"},"source":["directory = os.fsencode(\"/content/gdrive/MyDrive/Data_by_Participant\")\n","os.listdir(directory)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[b'001',\n"," b'007',\n"," b'004',\n"," b'003',\n"," b'006',\n"," b'010',\n"," b'012',\n"," b'011',\n"," b'009',\n"," b'016',\n"," b'020',\n"," b'023',\n"," b'017',\n"," b'013',\n"," b'021',\n"," b'019',\n"," 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Ma","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg4a7esTogCkibNImqhE9gCGWTIpBdm_K1v1bWc=s64","userId":"04324339310204701212"},"user_tz":300},"outputId":"fc0d0e6c-2e3f-49d7-e146-d1452c6a52d2"},"source":["orig_participants = os.listdir(directory)\n","participants = []\n","\n","for participant in orig_participants:\n"," # decode byte, make sure use the string type\n"," participant = participant.decode('utf-8')\n"," participants.append(participant)\n","\n","# remove the 'cha' folder\n","participants.remove('cha')\n","participants"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['001',\n"," '007',\n"," '004',\n"," '003',\n"," '006',\n"," '010',\n"," '012',\n"," '011',\n"," '009',\n"," '016',\n"," '020',\n"," '023',\n"," '017',\n"," '013',\n"," '021',\n"," '019',\n"," '015',\n"," '014',\n"," '018',\n"," '033',\n"," '026',\n"," '027',\n"," '029',\n"," '031',\n"," '030',\n"," '025',\n"," '034',\n"," '024',\n"," '032',\n"," '044',\n"," '038',\n"," '040',\n"," '043',\n"," '041',\n"," '036',\n"," '039',\n"," '042',\n"," '037',\n"," '035',\n"," '048',\n"," '049',\n"," '055',\n"," '053',\n"," '054',\n"," '046',\n"," '047',\n"," '050',\n"," '051',\n"," '052',\n"," '059',\n"," '061',\n"," '063',\n"," '060',\n"," '058',\n"," '057',\n"," '056',\n"," 'output',\n"," 'accuracy.csv']"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"code","metadata":{"id":"Yi5ZlWGc71ts"},"source":["df = pd.DataFrame(index = ['SVC', 'DTC', 'NB', 'NN'])\n","\n","def precision_df(df, precision_column, participant):\n"," '''\n"," Add precision for every participant to the whole results\n"," Args: \n"," df: the dataframe of all results we have had\n"," precision_column: the dataframe of result we want to add \n"," participant: participant number\n"," returns: \n"," all precision results\n"," '''\n","\n"," data = pd.DataFrame({f\"Participant {participant}\": precision_column})\n"," df[f\"Participant {participant}\"] = data[f\"Participant {participant}\"].values\n"," return df"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"background_save":true},"id":"_sLrZfHhcDHi"},"source":["for participant in participants:\n"," # iterate all the folders\n","\n"," for file in os.listdir(participant):\n"," # iterate all files in every folder, find out the one end with 'Cong.csv' and 'Incong.csv' as input data\n","\n"," if file.endswith('Cong.csv'): file1 = f\"{participant}/{file}\" \n"," if file.endswith('Incong.csv'): file2 = f\"{participant}/{file}\"\n","\n"," # load in cong and incong data for them\n"," df1 = mr.load_data(file1)\n"," df2 = mr.load_data(file2)\n","\n"," # concatenate such data \n"," data = mr.concatenate_data(df1, df2)\n","\n"," # find trials to later separate\n"," trials_index = mr.find_trials(data)\n","\n"," # separate trials\n"," trials = mr.separate_trials(data, trials_index)\n","\n"," # create the label column \n"," labels = mr.create_multi_labels(data)\n","\n"," # Go through each trial, reset the columns, we split from 100-300ms ((308th sample to 513th sample))\n"," pro_trials = mr.process_trials(trials)\n","\n"," # Find the mean across channels\n"," avg_trials = mr.average_trials(pro_trials)\n","\n"," # concatenates the average trials dataframe with labels\n"," ml_df = mr.create_ml_df(avg_trials, labels)\n","\n"," # train models\n"," X_train, X_test, y_train, y_test = mr.prepare_ml_df(ml_df)\n","\n"," acc_svc, precision_svc = mr.train_svc_multi(X_train, X_test, y_train, y_test)\n","\n"," acc_dtc, precision_dtc = mr.train_dtc_multi(X_train, X_test, y_train, y_test)\n","\n"," acc_nb, precision_nb = mr.train_nb_multi(X_train, X_test, y_train, y_test)\n","\n"," acc_nn, precision_nn = mr.train_nn_multi(64, X_train, X_test, y_train, y_test)\n","\n"," # add every participant's precision together\n"," precision_list = [f\"{precision_svc:.2f}\", f\"{precision_dtc:.2f}\", f\"{precision_nb:.2f}\", f\"{precision_nn:.2f}\"]\n","\n"," df = precision_df(df, precision_list, participant) \n","\n"," \n","\n"," "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"NG3Li6ZBvFsD"},"source":["df"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Hx54gxyR4cjc"},"source":["df.to_csv('case_4_precision.csv') "],"execution_count":null,"outputs":[]}]} \ No newline at end of file