04_q__FER_cnn-resnet50-0-trainable.ipynb

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    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "#for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    " #   for filename in filenames:\n",
    "  #      print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
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     "text": [
      "Collecting kaleido\r\n",
      "  Downloading kaleido-0.2.1-py2.py3-none-manylinux1_x86_64.whl (79.9 MB)\r\n",
      "     |████████████████████████████████| 79.9 MB 180 kB/s             \r\n",
      "\u001b[?25hInstalling collected packages: kaleido\r\n",
      "Successfully installed kaleido-0.2.1\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\r\n"
     ]
    }
   ],
   "source": [
    "!pip install kaleido"
   ]
  },
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import tensorflow.keras.backend as K\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras import datasets, layers, models\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from sklearn.model_selection import KFold, StratifiedKFold\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "import plotly.graph_objects as go\n",
    "import glob\n",
    "import os"
   ]
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   "source": [
    "def create_dir (direct):\n",
    "  if not os.path.exists(direct):\n",
    "    os.mkdir(direct)"
   ]
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   "source": [
    "ROOT_DIR = \"/kaggle/input/ferset\" #MT Directory\n",
    "DATASET_02 = ROOT_DIR + \"/02_FER\"\n",
    "DATASET_02_TRAIN = DATASET_02 + \"/train\"\n",
    "DATASET_02_TEST = DATASET_02+ \"/test\""
   ]
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     "text": [
      "L\n",
      "48 48\n"
     ]
    }
   ],
   "source": [
    "import PIL\n",
    "\n",
    "image = PIL.Image.open(DATASET_02_TRAIN + \"/surprise/Training_99984132.jpg\")\n",
    "#image to open\n",
    "\n",
    "width, height = image.size\n",
    "\n",
    "print(image.mode)\n",
    "print(width, height)"
   ]
  },
  {
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    {
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     "text": [
      "Found 7178 images belonging to 7 classes.\n",
      "Found 28709 images belonging to 7 classes.\n"
     ]
    }
   ],
   "source": [
    "datagen = ImageDataGenerator()\n",
    "image_size = (48, 48)\n",
    "colormode = 'rgb'\n",
    "test_set = datagen.flow_from_directory(DATASET_02_TEST, target_size=image_size, color_mode =colormode, batch_size=32, shuffle=False)\n",
    "train_set = datagen.flow_from_directory(DATASET_02_TRAIN, target_size=image_size, color_mode =colormode, batch_size=32)"
   ]
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 46, 46, 64)        1792      \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 23, 23, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 21, 21, 128)       73856     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 10, 10, 128)       0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 12800)             0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 256)               3277056   \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 7)                 1799      \n",
      "=================================================================\n",
      "Total params: 3,354,503\n",
      "Trainable params: 3,354,503\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-04-10 00:47:54.416438: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-10 00:47:54.503276: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-10 00:47:54.504170: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-10 00:47:54.505404: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2022-04-10 00:47:54.506720: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-10 00:47:54.507425: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-10 00:47:54.508059: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-10 00:47:56.613099: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-10 00:47:56.614048: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-10 00:47:56.614750: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-10 00:47:56.615382: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 15403 MB memory:  -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0\n"
     ]
    }
   ],
   "source": [
    "def create_model_V1():\n",
    "    model = models.Sequential()\n",
    "    model.add(layers.Conv2D(64, (3, 3), activation='relu', input_shape=(48, 48, 3)))\n",
    "    model.add(layers.MaxPooling2D((2, 2)))\n",
    "    #model.add(layers.Dropout(0.4))\n",
    "    #model.add(layers.Conv2D(32, (3, 3), activation='relu'))\n",
    "    #model.add(layers.MaxPooling2D((2, 2)))\n",
    "    #model.add(layers.Dropout(0.4))\n",
    "    #model.add(layers.Conv2D(16, (3, 3), activation='relu'))\n",
    "    #model.add(layers.MaxPooling2D((2, 2)))\n",
    "    model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "    model.add(layers.MaxPooling2D((2, 2)))\n",
    "    model.add(layers.Flatten())\n",
    "    model.add(layers.Dense(256, activation='relu'))\n",
    "    model.add(layers.Dense(7, activation='softmax'))\n",
    "    \n",
    "    return model\n",
    "\n",
    "\n",
    "model_1 = create_model_V1()\n",
    "model_1.summary()"
   ]
  },
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   "source": [
    "def get_data_df(dir):\n",
    "  # modified code from: https://www.kaggle.com/namgalielei/simple-load-images-and-count-number-of-each-class\n",
    "\n",
    "  train_df = pd.DataFrame()\n",
    "\n",
    "  trainset = glob.glob(dir)\n",
    "  #print(trainset)\n",
    "  train_df['filename'] = [img.split(\"/\")[-2]+\"/\"+img.split(\"/\")[-1] for img in trainset]\n",
    "  train_df['label'] = [img.split(\"/\")[-2] for img in trainset]\n",
    "\n",
    "  return train_df"
   ]
  },
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   ],
   "source": [
    "test_train = get_data_df(DATASET_02_TRAIN+\"/*/*.jpg\")\n",
    "test_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3fcf9fca",
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     "status": "completed"
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    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "surprise\n",
      "fear\n",
      "angry\n",
      "neutral\n",
      "sad\n",
      "disgust\n",
      "happy\n",
      "35887\n",
      "[4002, 5121, 4953, 6198, 6077, 547, 8989]\n",
      "{0: 1.2810380524023703, 1: 1.001115853488437, 2: 1.035072539009547, 3: 0.8271562255105334, 4: 0.8436258492207152, 5: 9.372420997649517, 6: 0.5703319930708962}\n"
     ]
    }
   ],
   "source": [
    "total = 0\n",
    "value_list = []\n",
    "for item in os.listdir(DATASET_02_TRAIN):\n",
    "    y = len(os.listdir(DATASET_02_TRAIN+'/'+item))+len(os.listdir(DATASET_02_TEST+'/'+item))\n",
    "    value_list.append(y)\n",
    "    total += y\n",
    "    print(item)\n",
    "print(total)\n",
    "print(value_list)\n",
    "weights = {}\n",
    "for n in range(0,len(value_list)):\n",
    "    w = total/(len(value_list)*value_list[n])\n",
    "    weights[n] = w\n",
    "    \n",
    "print(weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "582da1e8",
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    "tags": []
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   "outputs": [],
   "source": [
    "create_dir('/kaggle/working/Saved_CNN_Models')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fe850c34",
   "metadata": {
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   "source": [
    "from sklearn.model_selection import KFold, StratifiedKFold\n",
    "\n",
    "train_data = get_data_df(DATASET_02_TRAIN+\"/*/*.jpg\")\n",
    "Y = train_data[['label']]\n",
    "\n",
    "#Y = final_train[['label']]\n",
    "#n = len(final_train)   \n",
    "\n",
    "skf = StratifiedKFold(n_splits = 5, random_state = 7, shuffle = True) \n",
    "\n",
    "#Un needed preprocessing aside from rescale\n",
    "#idg = ImageDataGenerator(rescale=1./255)\n",
    "create_dir('/kaggle/working/Saved_CNN_Models')\n",
    "#model name\n",
    "def get_model_name(k):\n",
    "    return 'model_'+str(k)+'.h5'\n",
    "def model_test(fold_count, epoch, model_method, image_size, mode):\n",
    "    skf = StratifiedKFold(n_splits = fold_count, random_state = 7, shuffle = True)\n",
    "    train_data = get_data_df(DATASET_02_TRAIN+\"/*/*.jpg\")\n",
    "      #Incorperate preprocessing in the method \n",
    "      #####################################################################\n",
    "      #aug_train = get_data_df(DATASET_01_ROOT+\"/Aug_train/*/*.jpg\")\n",
    "      #aug_train['filename'] = aug_train['filename'].str[7:]\n",
    "      #final_train = test_train.append(aug_train, sort=False, ignore_index=True)\n",
    "\n",
    "\n",
    "      #######################################################################\n",
    "      #idg = ImageDataGenerator(rescale=1./255)\n",
    "    idg = ImageDataGenerator()\n",
    "    Y = train_data[['label']]\n",
    "    n = len(train_data)    \n",
    "    VALIDATION_ACCURACY = []\n",
    "    VALIDATION_LOSS = []\n",
    "\n",
    "      #create a directory\n",
    "    save_dir = '/kaggle/working/Saved_CNN_Models/'+model_method.__name__+'_'+str(epoch)+'epochs/'\n",
    "    create_dir(save_dir)\n",
    "    \n",
    "    img_dir = DATASET_02_TRAIN\n",
    "\n",
    "    fold_var = 1\n",
    "\n",
    "      #n is number of samples, doesn't care about x values for split, just y\n",
    "    for train_index, val_index in skf.split(np.zeros(n),Y):\n",
    "\n",
    "        #Index value for fold\n",
    "        training_data = train_data.iloc[train_index]\n",
    "        validation_data = train_data.iloc[val_index]\n",
    "        #generator for train\n",
    "        #print(\"hi\")\n",
    "        #print(validation_data.head())\n",
    "        #Modify the training size, have an input adjustability \n",
    "        train_data_generator = idg.flow_from_dataframe(training_data, directory = img_dir,\n",
    "                                                       target_size=image_size, batch_size=32, color_mode =mode,\n",
    "                                                       x_col = \"filename\", y_col = \"label\",shuffle = True)\n",
    "        #print(\"hi\")\n",
    "        #generator for validation\n",
    "        valid_data_generator  = idg.flow_from_dataframe(validation_data, directory = img_dir,\n",
    "                                                        target_size=image_size, color_mode =mode,batch_size=32,\n",
    "                                                        x_col = \"filename\", y_col = \"label\",shuffle = True)\n",
    "        #print(\"hi\")\n",
    "        #create a method for this\n",
    "        # CREATE NEW MODEL\n",
    "        model = model_method()\n",
    "        # COMPILE NEW MODEL\n",
    "        model.compile(loss=tf.keras.losses.CategoricalCrossentropy(),\n",
    "                optimizer= tf.keras.optimizers.Adam(learning_rate=0.0001),\n",
    "                metrics=['accuracy'])\n",
    "\n",
    "        # CREATE CALLBACKS\n",
    "        #Unsure about checpoint\n",
    "        checkpoint = tf.keras.callbacks.ModelCheckpoint(save_dir+model_method.__name__+\"_\"+get_model_name(fold_var), \n",
    "                    monitor='val_accuracy', verbose=1, \n",
    "                    save_best_only=True, mode='max')\n",
    "\n",
    "        callbacks_list = [checkpoint]\n",
    "        # There can be other callbacks, but just showing one because it involves the model name\n",
    "        # This saves the best model\n",
    "        # FIT THE MODEL\n",
    "        #Include the weights for classes\n",
    "        history = model.fit(train_data_generator,\n",
    "                epochs=epoch,\n",
    "                callbacks=callbacks_list,\n",
    "                validation_data=valid_data_generator\n",
    "                ,verbose = 2)\n",
    "\n",
    "        #PLOT HISTORY\n",
    "        #Plots for whichever\n",
    "        #\t\t:\n",
    "        #plt.plot(history.history['binary_accuracy'], label='accuracy')\n",
    "        #plt.plot(history.history['val_binary_accuracy'], label = 'val_accuracy')\n",
    "        #plt.xlabel('Epoch')\n",
    "        #plt.ylabel('Accuracy')\n",
    "        #plt.ylim([0.3, 1])\n",
    "        #plt.legend(loc='lower right')\n",
    "        #plt.show()\n",
    "        #frame = {'Accuracy': history.history['binary_accuracy'], 'Val_Accuracy': history.history['val_binary_accuracy']}\n",
    "        #fig = px.line(frame)\n",
    "        #fig.show()\n",
    "        fig = go.Figure()\n",
    "        fig.add_trace(go.Scatter(y= history.history['accuracy'],\n",
    "                          mode='lines+markers',\n",
    "                          name='Training Accuracy'))\n",
    "        fig.add_trace(go.Scatter(y= history.history['val_accuracy'],\n",
    "                          mode='lines+markers',\n",
    "                          name='Validation Accuracy'))\n",
    "        fig.update_yaxes(range=[0, 1])\n",
    "        fig.update_layout(\n",
    "            title=\"Fold \"+ str(fold_var)+ \"'s performance\",\n",
    "            title_x=0.5,\n",
    "            xaxis_title=\"Epochs\",\n",
    "            yaxis_title=\"Accuracy\",\n",
    "            autosize=False,\n",
    "            width=800,\n",
    "            height=400,\n",
    "            margin=dict(l=20, r=20, t=40, b=20),)\n",
    "        fig.show()\n",
    "        graph_dir = '/kaggle/working/img'\n",
    "        create_dir(graph_dir)\n",
    "        fig.write_image(graph_dir+'/fold'+str(fold_var)+'.png') \n",
    "\n",
    "        #\t\t:\n",
    "        # LOAD BEST MODEL to evaluate the performance of the model\n",
    "        model.load_weights(save_dir+model_method.__name__+\"_\" + \"model_\"+str(fold_var)+\".h5\")\n",
    "\n",
    "        results = model.evaluate(test_set)\n",
    "        results = dict(zip(model.metrics_names,results))\n",
    "\n",
    "        VALIDATION_ACCURACY.append(results['accuracy'])\n",
    "        VALIDATION_LOSS.append(results['loss'])\n",
    "\n",
    "        tf.keras.backend.clear_session()\n",
    "\n",
    "        fold_var += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "20972ab7",
   "metadata": {
    "execution": {
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     "status": "completed"
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    "tags": []
   },
   "outputs": [],
   "source": [
    "#model_test(5, 1, create_model_V1, (48,48), 'rgb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "27dfcc02",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "94773248/94765736 [==============================] - 1s 0us/step\n",
      "94781440/94765736 [==============================] - 1s 0us/step\n"
     ]
    }
   ],
   "source": [
    "def transfer_learning_model_resnet_50():\n",
    "\n",
    "  IMG_SHAPE = (48,48,3,)\n",
    " \n",
    "  inputs = tf.keras.Input(shape=IMG_SHAPE)\n",
    "  #inputs = tf.image.grayscale_to_rgb(y)\n",
    " \n",
    "  x = tf.keras.applications.resnet50.preprocess_input(inputs)\n",
    "  base_model = tf.keras.applications.resnet50.ResNet50(input_shape=(48,48,3,),\n",
    "                                               include_top=False,\n",
    "                                               weights='imagenet')\n",
    "  #print(len(base_model.layers))\n",
    "  base_model.trainable = True\n",
    "\n",
    "  fine_tune_at = 400\n",
    "  for layer in base_model.layers[:fine_tune_at]:\n",
    "    layer.trainable = False\n",
    "\n",
    "  x = base_model(x)\n",
    "  global_average_layer = tf.keras.layers.GlobalAveragePooling2D()\n",
    "  x = global_average_layer(x)\n",
    "  x = tf.keras.layers.Dropout(0.2)(x)\n",
    "  prediction_layer = tf.keras.layers.Dense(7, activation = \"softmax\")\n",
    "  outputs = prediction_layer(x)\n",
    "  model = tf.keras.Model(inputs, outputs)\n",
    "  #print(len(model.trainable_variables))\n",
    "  return model\n",
    "\n",
    "transfer_model_resnet_50 = transfer_learning_model_resnet_50()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "08a70d4f",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 48, 48, 3)]       0         \n",
      "_________________________________________________________________\n",
      "tf.__operators__.getitem (Sl (None, 48, 48, 3)         0         \n",
      "_________________________________________________________________\n",
      "tf.nn.bias_add (TFOpLambda)  (None, 48, 48, 3)         0         \n",
      "_________________________________________________________________\n",
      "resnet50 (Functional)        (None, 2, 2, 2048)        23587712  \n",
      "_________________________________________________________________\n",
      "global_average_pooling2d (Gl (None, 2048)              0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 2048)              0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 7)                 14343     \n",
      "=================================================================\n",
      "Total params: 23,602,055\n",
      "Trainable params: 14,343\n",
      "Non-trainable params: 23,587,712\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "transfer_model_resnet_50.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "37457b57",
   "metadata": {
    "execution": {
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    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 22967 validated image filenames belonging to 7 classes.\n",
      "Found 5742 validated image filenames belonging to 7 classes.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-04-10 00:48:14.535467: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-04-10 00:48:18.118611: I tensorflow/stream_executor/cuda/cuda_dnn.cc:369] Loaded cuDNN version 8005\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "718/718 - 117s - loss: 2.7402 - accuracy: 0.2551 - val_loss: 1.8697 - val_accuracy: 0.3431\n",
      "\n",
      "Epoch 00001: val_accuracy improved from -inf to 0.34309, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_1.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/keras/utils/generic_utils.py:497: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
      "  category=CustomMaskWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/50\n",
      "718/718 - 24s - loss: 2.1340 - accuracy: 0.3127 - val_loss: 1.7247 - val_accuracy: 0.3649\n",
      "\n",
      "Epoch 00002: val_accuracy improved from 0.34309 to 0.36486, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_1.h5\n",
      "Epoch 3/50\n",
      "718/718 - 24s - loss: 1.9295 - accuracy: 0.3378 - val_loss: 1.6483 - val_accuracy: 0.3925\n",
      "\n",
      "Epoch 00003: val_accuracy improved from 0.36486 to 0.39255, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_1.h5\n",
      "Epoch 4/50\n",
      "718/718 - 25s - loss: 1.8049 - accuracy: 0.3642 - val_loss: 1.6061 - val_accuracy: 0.3967\n",
      "\n",
      "Epoch 00004: val_accuracy improved from 0.39255 to 0.39673, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_1.h5\n",
      "Epoch 5/50\n",
      "718/718 - 25s - loss: 1.7102 - accuracy: 0.3825 - val_loss: 1.5758 - val_accuracy: 0.4101\n",
      "\n",
      "Epoch 00005: val_accuracy improved from 0.39673 to 0.41014, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_1.h5\n",
      "Epoch 6/50\n",
      "718/718 - 24s - loss: 1.6512 - accuracy: 0.3900 - val_loss: 1.5482 - val_accuracy: 0.4246\n",
      "\n",
      "Epoch 00006: val_accuracy improved from 0.41014 to 0.42459, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_1.h5\n",
      "Epoch 7/50\n",
      "718/718 - 24s - loss: 1.6027 - accuracy: 0.4037 - val_loss: 1.5463 - val_accuracy: 0.4225\n",
      "\n",
      "Epoch 00007: val_accuracy did not improve from 0.42459\n",
      "Epoch 8/50\n",
      "718/718 - 25s - loss: 1.5602 - accuracy: 0.4169 - val_loss: 1.5146 - val_accuracy: 0.4383\n",
      "\n",
      "Epoch 00008: val_accuracy improved from 0.42459 to 0.43835, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_1.h5\n",
      "Epoch 9/50\n",
      "718/718 - 25s - loss: 1.5310 - accuracy: 0.4220 - val_loss: 1.5124 - val_accuracy: 0.4333\n",
      "\n",
      "Epoch 00009: val_accuracy did not improve from 0.43835\n",
      "Epoch 10/50\n",
      "718/718 - 24s - loss: 1.5001 - accuracy: 0.4325 - val_loss: 1.4968 - val_accuracy: 0.4448\n",
      "\n",
      "Epoch 00010: val_accuracy improved from 0.43835 to 0.44479, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_1.h5\n",
      "Epoch 11/50\n",
      "718/718 - 25s - loss: 1.4851 - accuracy: 0.4342 - val_loss: 1.4956 - val_accuracy: 0.4448\n",
      "\n",
      "Epoch 00011: val_accuracy did not improve from 0.44479\n",
      "Epoch 12/50\n",
      "718/718 - 26s - loss: 1.4656 - accuracy: 0.4420 - val_loss: 1.4879 - val_accuracy: 0.4469\n",
      "\n",
      "Epoch 00012: val_accuracy improved from 0.44479 to 0.44688, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_1.h5\n",
      "Epoch 13/50\n",
      "718/718 - 26s - loss: 1.4516 - accuracy: 0.4515 - val_loss: 1.4920 - val_accuracy: 0.4469\n",
      "\n",
      "Epoch 00013: val_accuracy did not improve from 0.44688\n",
      "Epoch 14/50\n",
      "718/718 - 25s - loss: 1.4360 - accuracy: 0.4572 - val_loss: 1.4856 - val_accuracy: 0.4432\n",
      "\n",
      "Epoch 00014: val_accuracy did not improve from 0.44688\n",
      "Epoch 15/50\n",
      "718/718 - 24s - loss: 1.4282 - accuracy: 0.4567 - val_loss: 1.4891 - val_accuracy: 0.4497\n",
      "\n",
      "Epoch 00015: val_accuracy improved from 0.44688 to 0.44967, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_1.h5\n",
      "Epoch 16/50\n",
      "718/718 - 25s - loss: 1.4231 - accuracy: 0.4621 - val_loss: 1.4757 - val_accuracy: 0.4472\n",
      "\n",
      "Epoch 00016: val_accuracy did not improve from 0.44967\n",
      "Epoch 17/50\n",
      "718/718 - 26s - loss: 1.4096 - accuracy: 0.4641 - val_loss: 1.4761 - val_accuracy: 0.4479\n",
      "\n",
      "Epoch 00017: val_accuracy did not improve from 0.44967\n",
      "Epoch 18/50\n",
      "718/718 - 24s - loss: 1.4046 - accuracy: 0.4666 - val_loss: 1.4783 - val_accuracy: 0.4521\n",
      "\n",
      "Epoch 00018: val_accuracy improved from 0.44967 to 0.45211, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_1.h5\n",
      "Epoch 19/50\n",
      "718/718 - 24s - loss: 1.3977 - accuracy: 0.4685 - val_loss: 1.4789 - val_accuracy: 0.4498\n",
      "\n",
      "Epoch 00019: val_accuracy did not improve from 0.45211\n",
      "Epoch 20/50\n",
      "718/718 - 24s - loss: 1.3943 - accuracy: 0.4720 - val_loss: 1.4727 - val_accuracy: 0.4485\n",
      "\n",
      "Epoch 00020: val_accuracy did not improve from 0.45211\n",
      "Epoch 21/50\n",
      "718/718 - 25s - loss: 1.3875 - accuracy: 0.4731 - val_loss: 1.4636 - val_accuracy: 0.4509\n",
      "\n",
      "Epoch 00021: val_accuracy did not improve from 0.45211\n",
      "Epoch 22/50\n",
      "718/718 - 24s - loss: 1.3852 - accuracy: 0.4744 - val_loss: 1.4673 - val_accuracy: 0.4532\n",
      "\n",
      "Epoch 00022: val_accuracy improved from 0.45211 to 0.45315, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_1.h5\n",
      "Epoch 23/50\n",
      "718/718 - 24s - loss: 1.3805 - accuracy: 0.4770 - val_loss: 1.4675 - val_accuracy: 0.4471\n",
      "\n",
      "Epoch 00023: val_accuracy did not improve from 0.45315\n",
      "Epoch 24/50\n",
      "718/718 - 25s - loss: 1.3715 - accuracy: 0.4790 - val_loss: 1.4654 - val_accuracy: 0.4523\n",
      "\n",
      "Epoch 00024: val_accuracy did not improve from 0.45315\n",
      "Epoch 25/50\n",
      "718/718 - 26s - loss: 1.3751 - accuracy: 0.4781 - val_loss: 1.4791 - val_accuracy: 0.4453\n",
      "\n",
      "Epoch 00025: val_accuracy did not improve from 0.45315\n",
      "Epoch 26/50\n",
      "718/718 - 26s - loss: 1.3696 - accuracy: 0.4799 - val_loss: 1.4605 - val_accuracy: 0.4568\n",
      "\n",
      "Epoch 00026: val_accuracy improved from 0.45315 to 0.45681, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_1.h5\n",
      "Epoch 27/50\n",
      "718/718 - 26s - loss: 1.3679 - accuracy: 0.4796 - val_loss: 1.4639 - val_accuracy: 0.4521\n",
      "\n",
      "Epoch 00027: val_accuracy did not improve from 0.45681\n",
      "Epoch 28/50\n",
      "718/718 - 25s - loss: 1.3639 - accuracy: 0.4801 - val_loss: 1.4769 - val_accuracy: 0.4498\n",
      "\n",
      "Epoch 00028: val_accuracy did not improve from 0.45681\n",
      "Epoch 29/50\n",
      "718/718 - 26s - loss: 1.3655 - accuracy: 0.4809 - val_loss: 1.4593 - val_accuracy: 0.4572\n",
      "\n",
      "Epoch 00029: val_accuracy improved from 0.45681 to 0.45716, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_1.h5\n",
      "Epoch 30/50\n",
      "718/718 - 27s - loss: 1.3630 - accuracy: 0.4846 - val_loss: 1.4671 - val_accuracy: 0.4516\n",
      "\n",
      "Epoch 00030: val_accuracy did not improve from 0.45716\n",
      "Epoch 31/50\n",
      "718/718 - 25s - loss: 1.3591 - accuracy: 0.4849 - val_loss: 1.4558 - val_accuracy: 0.4589\n",
      "\n",
      "Epoch 00031: val_accuracy improved from 0.45716 to 0.45890, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_1.h5\n",
      "Epoch 32/50\n",
      "718/718 - 27s - loss: 1.3614 - accuracy: 0.4843 - val_loss: 1.4657 - val_accuracy: 0.4551\n",
      "\n",
      "Epoch 00032: val_accuracy did not improve from 0.45890\n",
      "Epoch 33/50\n",
      "718/718 - 25s - loss: 1.3535 - accuracy: 0.4835 - val_loss: 1.4765 - val_accuracy: 0.4547\n",
      "\n",
      "Epoch 00033: val_accuracy did not improve from 0.45890\n",
      "Epoch 34/50\n",
      "718/718 - 25s - loss: 1.3530 - accuracy: 0.4890 - val_loss: 1.4638 - val_accuracy: 0.4544\n",
      "\n",
      "Epoch 00034: val_accuracy did not improve from 0.45890\n",
      "Epoch 35/50\n",
      "718/718 - 25s - loss: 1.3504 - accuracy: 0.4889 - val_loss: 1.4640 - val_accuracy: 0.4580\n",
      "\n",
      "Epoch 00035: val_accuracy did not improve from 0.45890\n",
      "Epoch 36/50\n",
      "718/718 - 25s - loss: 1.3512 - accuracy: 0.4845 - val_loss: 1.4685 - val_accuracy: 0.4498\n",
      "\n",
      "Epoch 00036: val_accuracy did not improve from 0.45890\n",
      "Epoch 37/50\n",
      "718/718 - 24s - loss: 1.3523 - accuracy: 0.4844 - val_loss: 1.4622 - val_accuracy: 0.4512\n",
      "\n",
      "Epoch 00037: val_accuracy did not improve from 0.45890\n",
      "Epoch 38/50\n",
      "718/718 - 25s - loss: 1.3489 - accuracy: 0.4893 - val_loss: 1.4685 - val_accuracy: 0.4512\n",
      "\n",
      "Epoch 00038: val_accuracy did not improve from 0.45890\n",
      "Epoch 39/50\n",
      "718/718 - 25s - loss: 1.3518 - accuracy: 0.4846 - val_loss: 1.4685 - val_accuracy: 0.4540\n",
      "\n",
      "Epoch 00039: val_accuracy did not improve from 0.45890\n",
      "Epoch 40/50\n",
      "718/718 - 24s - loss: 1.3465 - accuracy: 0.4903 - val_loss: 1.4822 - val_accuracy: 0.4420\n",
      "\n",
      "Epoch 00040: val_accuracy did not improve from 0.45890\n",
      "Epoch 41/50\n",
      "718/718 - 24s - loss: 1.3439 - accuracy: 0.4898 - val_loss: 1.4706 - val_accuracy: 0.4568\n",
      "\n",
      "Epoch 00041: val_accuracy did not improve from 0.45890\n",
      "Epoch 42/50\n",
      "718/718 - 26s - loss: 1.3469 - accuracy: 0.4869 - val_loss: 1.4792 - val_accuracy: 0.4558\n",
      "\n",
      "Epoch 00042: val_accuracy did not improve from 0.45890\n",
      "Epoch 43/50\n",
      "718/718 - 24s - loss: 1.3451 - accuracy: 0.4877 - val_loss: 1.4672 - val_accuracy: 0.4521\n",
      "\n",
      "Epoch 00043: val_accuracy did not improve from 0.45890\n",
      "Epoch 44/50\n",
      "718/718 - 24s - loss: 1.3440 - accuracy: 0.4917 - val_loss: 1.4668 - val_accuracy: 0.4502\n",
      "\n",
      "Epoch 00044: val_accuracy did not improve from 0.45890\n",
      "Epoch 45/50\n",
      "718/718 - 25s - loss: 1.3392 - accuracy: 0.4929 - val_loss: 1.4695 - val_accuracy: 0.4504\n",
      "\n",
      "Epoch 00045: val_accuracy did not improve from 0.45890\n",
      "Epoch 46/50\n",
      "718/718 - 25s - loss: 1.3433 - accuracy: 0.4899 - val_loss: 1.4642 - val_accuracy: 0.4572\n",
      "\n",
      "Epoch 00046: val_accuracy did not improve from 0.45890\n",
      "Epoch 47/50\n",
      "718/718 - 24s - loss: 1.3407 - accuracy: 0.4909 - val_loss: 1.4716 - val_accuracy: 0.4552\n",
      "\n",
      "Epoch 00047: val_accuracy did not improve from 0.45890\n",
      "Epoch 48/50\n",
      "718/718 - 24s - loss: 1.3418 - accuracy: 0.4904 - val_loss: 1.4689 - val_accuracy: 0.4544\n",
      "\n",
      "Epoch 00048: val_accuracy did not improve from 0.45890\n",
      "Epoch 49/50\n",
      "718/718 - 25s - loss: 1.3418 - accuracy: 0.4886 - val_loss: 1.4657 - val_accuracy: 0.4556\n",
      "\n",
      "Epoch 00049: val_accuracy did not improve from 0.45890\n",
      "Epoch 50/50\n",
      "718/718 - 24s - loss: 1.3404 - accuracy: 0.4909 - val_loss: 1.4650 - val_accuracy: 0.4572\n",
      "\n",
      "Epoch 00050: val_accuracy did not improve from 0.45890\n"
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     "text": [
      "225/225 [==============================] - 30s 133ms/step - loss: 1.4507 - accuracy: 0.4571\n",
      "Found 22967 validated image filenames belonging to 7 classes.\n",
      "Found 5742 validated image filenames belonging to 7 classes.\n",
      "Epoch 1/50\n",
      "718/718 - 27s - loss: 2.7262 - accuracy: 0.2489 - val_loss: 1.8399 - val_accuracy: 0.3509\n",
      "\n",
      "Epoch 00001: val_accuracy improved from -inf to 0.35092, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_2.h5\n"
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      "/opt/conda/lib/python3.7/site-packages/keras/utils/generic_utils.py:497: CustomMaskWarning:\n",
      "\n",
      "Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
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     "text": [
      "Epoch 2/50\n",
      "718/718 - 25s - loss: 2.1572 - accuracy: 0.3089 - val_loss: 1.7109 - val_accuracy: 0.3797\n",
      "\n",
      "Epoch 00002: val_accuracy improved from 0.35092 to 0.37966, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_2.h5\n",
      "Epoch 3/50\n",
      "718/718 - 24s - loss: 1.9369 - accuracy: 0.3427 - val_loss: 1.6450 - val_accuracy: 0.3988\n",
      "\n",
      "Epoch 00003: val_accuracy improved from 0.37966 to 0.39882, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_2.h5\n",
      "Epoch 4/50\n",
      "718/718 - 24s - loss: 1.8029 - accuracy: 0.3619 - val_loss: 1.6111 - val_accuracy: 0.4028\n",
      "\n",
      "Epoch 00004: val_accuracy improved from 0.39882 to 0.40282, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_2.h5\n",
      "Epoch 5/50\n",
      "718/718 - 24s - loss: 1.7205 - accuracy: 0.3810 - val_loss: 1.5855 - val_accuracy: 0.4110\n",
      "\n",
      "Epoch 00005: val_accuracy improved from 0.40282 to 0.41101, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_2.h5\n",
      "Epoch 6/50\n",
      "718/718 - 24s - loss: 1.6411 - accuracy: 0.3985 - val_loss: 1.5556 - val_accuracy: 0.4222\n",
      "\n",
      "Epoch 00006: val_accuracy improved from 0.41101 to 0.42215, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_2.h5\n",
      "Epoch 7/50\n",
      "718/718 - 25s - loss: 1.5948 - accuracy: 0.4060 - val_loss: 1.5436 - val_accuracy: 0.4206\n",
      "\n",
      "Epoch 00007: val_accuracy did not improve from 0.42215\n",
      "Epoch 8/50\n",
      "718/718 - 26s - loss: 1.5547 - accuracy: 0.4156 - val_loss: 1.5224 - val_accuracy: 0.4336\n",
      "\n",
      "Epoch 00008: val_accuracy improved from 0.42215 to 0.43365, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_2.h5\n",
      "Epoch 9/50\n",
      "718/718 - 27s - loss: 1.5221 - accuracy: 0.4271 - val_loss: 1.5160 - val_accuracy: 0.4296\n",
      "\n",
      "Epoch 00009: val_accuracy did not improve from 0.43365\n",
      "Epoch 10/50\n",
      "718/718 - 26s - loss: 1.5043 - accuracy: 0.4333 - val_loss: 1.5110 - val_accuracy: 0.4368\n",
      "\n",
      "Epoch 00010: val_accuracy improved from 0.43365 to 0.43678, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_2.h5\n",
      "Epoch 11/50\n",
      "718/718 - 26s - loss: 1.4780 - accuracy: 0.4428 - val_loss: 1.5196 - val_accuracy: 0.4291\n",
      "\n",
      "Epoch 00011: val_accuracy did not improve from 0.43678\n",
      "Epoch 12/50\n",
      "718/718 - 25s - loss: 1.4684 - accuracy: 0.4463 - val_loss: 1.5038 - val_accuracy: 0.4432\n",
      "\n",
      "Epoch 00012: val_accuracy improved from 0.43678 to 0.44323, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_2.h5\n",
      "Epoch 13/50\n",
      "718/718 - 26s - loss: 1.4428 - accuracy: 0.4529 - val_loss: 1.4957 - val_accuracy: 0.4408\n",
      "\n",
      "Epoch 00013: val_accuracy did not improve from 0.44323\n",
      "Epoch 14/50\n",
      "718/718 - 24s - loss: 1.4379 - accuracy: 0.4546 - val_loss: 1.4943 - val_accuracy: 0.4427\n",
      "\n",
      "Epoch 00014: val_accuracy did not improve from 0.44323\n",
      "Epoch 15/50\n",
      "718/718 - 24s - loss: 1.4264 - accuracy: 0.4594 - val_loss: 1.5025 - val_accuracy: 0.4309\n",
      "\n",
      "Epoch 00015: val_accuracy did not improve from 0.44323\n",
      "Epoch 16/50\n",
      "718/718 - 26s - loss: 1.4112 - accuracy: 0.4648 - val_loss: 1.4920 - val_accuracy: 0.4397\n",
      "\n",
      "Epoch 00016: val_accuracy did not improve from 0.44323\n",
      "Epoch 17/50\n",
      "718/718 - 25s - loss: 1.4088 - accuracy: 0.4668 - val_loss: 1.4795 - val_accuracy: 0.4450\n",
      "\n",
      "Epoch 00017: val_accuracy improved from 0.44323 to 0.44497, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_2.h5\n",
      "Epoch 18/50\n",
      "718/718 - 24s - loss: 1.4013 - accuracy: 0.4677 - val_loss: 1.4856 - val_accuracy: 0.4455\n",
      "\n",
      "Epoch 00018: val_accuracy improved from 0.44497 to 0.44549, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_2.h5\n",
      "Epoch 19/50\n",
      "718/718 - 25s - loss: 1.3899 - accuracy: 0.4710 - val_loss: 1.4793 - val_accuracy: 0.4455\n",
      "\n",
      "Epoch 00019: val_accuracy did not improve from 0.44549\n",
      "Epoch 20/50\n",
      "718/718 - 24s - loss: 1.3866 - accuracy: 0.4739 - val_loss: 1.4871 - val_accuracy: 0.4498\n",
      "\n",
      "Epoch 00020: val_accuracy improved from 0.44549 to 0.44984, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_2.h5\n",
      "Epoch 21/50\n",
      "718/718 - 25s - loss: 1.3793 - accuracy: 0.4773 - val_loss: 1.4845 - val_accuracy: 0.4479\n",
      "\n",
      "Epoch 00021: val_accuracy did not improve from 0.44984\n",
      "Epoch 22/50\n",
      "718/718 - 25s - loss: 1.3852 - accuracy: 0.4740 - val_loss: 1.4803 - val_accuracy: 0.4443\n",
      "\n",
      "Epoch 00022: val_accuracy did not improve from 0.44984\n",
      "Epoch 23/50\n",
      "718/718 - 25s - loss: 1.3778 - accuracy: 0.4732 - val_loss: 1.4862 - val_accuracy: 0.4424\n",
      "\n",
      "Epoch 00023: val_accuracy did not improve from 0.44984\n",
      "Epoch 24/50\n",
      "718/718 - 25s - loss: 1.3711 - accuracy: 0.4795 - val_loss: 1.5011 - val_accuracy: 0.4401\n",
      "\n",
      "Epoch 00024: val_accuracy did not improve from 0.44984\n",
      "Epoch 25/50\n",
      "718/718 - 26s - loss: 1.3667 - accuracy: 0.4819 - val_loss: 1.4744 - val_accuracy: 0.4455\n",
      "\n",
      "Epoch 00025: val_accuracy did not improve from 0.44984\n",
      "Epoch 26/50\n",
      "718/718 - 26s - loss: 1.3656 - accuracy: 0.4835 - val_loss: 1.4745 - val_accuracy: 0.4558\n",
      "\n",
      "Epoch 00026: val_accuracy improved from 0.44984 to 0.45576, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_2.h5\n",
      "Epoch 27/50\n",
      "718/718 - 26s - loss: 1.3616 - accuracy: 0.4861 - val_loss: 1.4704 - val_accuracy: 0.4563\n",
      "\n",
      "Epoch 00027: val_accuracy improved from 0.45576 to 0.45629, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_2.h5\n",
      "Epoch 28/50\n",
      "718/718 - 24s - loss: 1.3645 - accuracy: 0.4852 - val_loss: 1.4841 - val_accuracy: 0.4422\n",
      "\n",
      "Epoch 00028: val_accuracy did not improve from 0.45629\n",
      "Epoch 29/50\n",
      "718/718 - 26s - loss: 1.3547 - accuracy: 0.4901 - val_loss: 1.4720 - val_accuracy: 0.4521\n",
      "\n",
      "Epoch 00029: val_accuracy did not improve from 0.45629\n",
      "Epoch 30/50\n",
      "718/718 - 25s - loss: 1.3557 - accuracy: 0.4883 - val_loss: 1.4784 - val_accuracy: 0.4495\n",
      "\n",
      "Epoch 00030: val_accuracy did not improve from 0.45629\n",
      "Epoch 31/50\n",
      "718/718 - 25s - loss: 1.3534 - accuracy: 0.4841 - val_loss: 1.4830 - val_accuracy: 0.4429\n",
      "\n",
      "Epoch 00031: val_accuracy did not improve from 0.45629\n",
      "Epoch 32/50\n",
      "718/718 - 24s - loss: 1.3540 - accuracy: 0.4859 - val_loss: 1.4821 - val_accuracy: 0.4491\n",
      "\n",
      "Epoch 00032: val_accuracy did not improve from 0.45629\n",
      "Epoch 33/50\n",
      "718/718 - 25s - loss: 1.3540 - accuracy: 0.4880 - val_loss: 1.4824 - val_accuracy: 0.4460\n",
      "\n",
      "Epoch 00033: val_accuracy did not improve from 0.45629\n",
      "Epoch 34/50\n",
      "718/718 - 24s - loss: 1.3464 - accuracy: 0.4894 - val_loss: 1.4824 - val_accuracy: 0.4511\n",
      "\n",
      "Epoch 00034: val_accuracy did not improve from 0.45629\n",
      "Epoch 35/50\n",
      "718/718 - 24s - loss: 1.3436 - accuracy: 0.4914 - val_loss: 1.4788 - val_accuracy: 0.4521\n",
      "\n",
      "Epoch 00035: val_accuracy did not improve from 0.45629\n",
      "Epoch 36/50\n",
      "718/718 - 26s - loss: 1.3446 - accuracy: 0.4904 - val_loss: 1.4849 - val_accuracy: 0.4383\n",
      "\n",
      "Epoch 00036: val_accuracy did not improve from 0.45629\n",
      "Epoch 37/50\n",
      "718/718 - 24s - loss: 1.3449 - accuracy: 0.4901 - val_loss: 1.4977 - val_accuracy: 0.4387\n",
      "\n",
      "Epoch 00037: val_accuracy did not improve from 0.45629\n",
      "Epoch 38/50\n",
      "718/718 - 24s - loss: 1.3470 - accuracy: 0.4933 - val_loss: 1.4782 - val_accuracy: 0.4457\n",
      "\n",
      "Epoch 00038: val_accuracy did not improve from 0.45629\n",
      "Epoch 39/50\n",
      "718/718 - 27s - loss: 1.3423 - accuracy: 0.4927 - val_loss: 1.4808 - val_accuracy: 0.4516\n",
      "\n",
      "Epoch 00039: val_accuracy did not improve from 0.45629\n",
      "Epoch 40/50\n",
      "718/718 - 28s - loss: 1.3411 - accuracy: 0.4911 - val_loss: 1.4819 - val_accuracy: 0.4490\n",
      "\n",
      "Epoch 00040: val_accuracy did not improve from 0.45629\n",
      "Epoch 41/50\n",
      "718/718 - 25s - loss: 1.3417 - accuracy: 0.4937 - val_loss: 1.4771 - val_accuracy: 0.4537\n",
      "\n",
      "Epoch 00041: val_accuracy did not improve from 0.45629\n",
      "Epoch 42/50\n",
      "718/718 - 26s - loss: 1.3360 - accuracy: 0.4939 - val_loss: 1.4823 - val_accuracy: 0.4509\n",
      "\n",
      "Epoch 00042: val_accuracy did not improve from 0.45629\n",
      "Epoch 43/50\n",
      "718/718 - 27s - loss: 1.3397 - accuracy: 0.4920 - val_loss: 1.4853 - val_accuracy: 0.4471\n",
      "\n",
      "Epoch 00043: val_accuracy did not improve from 0.45629\n",
      "Epoch 44/50\n",
      "718/718 - 26s - loss: 1.3399 - accuracy: 0.4926 - val_loss: 1.4790 - val_accuracy: 0.4533\n",
      "\n",
      "Epoch 00044: val_accuracy did not improve from 0.45629\n",
      "Epoch 45/50\n",
      "718/718 - 25s - loss: 1.3372 - accuracy: 0.4963 - val_loss: 1.4782 - val_accuracy: 0.4526\n",
      "\n",
      "Epoch 00045: val_accuracy did not improve from 0.45629\n",
      "Epoch 46/50\n",
      "718/718 - 25s - loss: 1.3360 - accuracy: 0.4941 - val_loss: 1.4975 - val_accuracy: 0.4437\n",
      "\n",
      "Epoch 00046: val_accuracy did not improve from 0.45629\n",
      "Epoch 47/50\n",
      "718/718 - 27s - loss: 1.3365 - accuracy: 0.4914 - val_loss: 1.4851 - val_accuracy: 0.4481\n",
      "\n",
      "Epoch 00047: val_accuracy did not improve from 0.45629\n",
      "Epoch 48/50\n",
      "718/718 - 26s - loss: 1.3400 - accuracy: 0.4932 - val_loss: 1.4889 - val_accuracy: 0.4486\n",
      "\n",
      "Epoch 00048: val_accuracy did not improve from 0.45629\n",
      "Epoch 49/50\n",
      "718/718 - 24s - loss: 1.3338 - accuracy: 0.4942 - val_loss: 1.4830 - val_accuracy: 0.4505\n",
      "\n",
      "Epoch 00049: val_accuracy did not improve from 0.45629\n",
      "Epoch 50/50\n",
      "718/718 - 23s - loss: 1.3372 - accuracy: 0.4938 - val_loss: 1.4912 - val_accuracy: 0.4472\n",
      "\n",
      "Epoch 00050: val_accuracy did not improve from 0.45629\n"
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     "text": [
      "225/225 [==============================] - 7s 29ms/step - loss: 1.4571 - accuracy: 0.4455\n",
      "Found 22967 validated image filenames belonging to 7 classes.\n",
      "Found 5742 validated image filenames belonging to 7 classes.\n",
      "Epoch 1/50\n",
      "718/718 - 31s - loss: 2.7590 - accuracy: 0.2441 - val_loss: 1.8731 - val_accuracy: 0.3351\n",
      "\n",
      "Epoch 00001: val_accuracy improved from -inf to 0.33507, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_3.h5\n",
      "Epoch 2/50\n",
      "718/718 - 24s - loss: 2.1657 - accuracy: 0.3136 - val_loss: 1.7231 - val_accuracy: 0.3664\n",
      "\n",
      "Epoch 00002: val_accuracy improved from 0.33507 to 0.36642, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_3.h5\n",
      "Epoch 3/50\n",
      "718/718 - 24s - loss: 1.9507 - accuracy: 0.3391 - val_loss: 1.6533 - val_accuracy: 0.3929\n",
      "\n",
      "Epoch 00003: val_accuracy improved from 0.36642 to 0.39289, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_3.h5\n",
      "Epoch 4/50\n",
      "718/718 - 25s - loss: 1.8114 - accuracy: 0.3607 - val_loss: 1.6049 - val_accuracy: 0.3941\n",
      "\n",
      "Epoch 00004: val_accuracy improved from 0.39289 to 0.39411, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_3.h5\n",
      "Epoch 5/50\n",
      "718/718 - 24s - loss: 1.7245 - accuracy: 0.3781 - val_loss: 1.5778 - val_accuracy: 0.4075\n",
      "\n",
      "Epoch 00005: val_accuracy improved from 0.39411 to 0.40752, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_3.h5\n",
      "Epoch 6/50\n",
      "718/718 - 24s - loss: 1.6632 - accuracy: 0.3898 - val_loss: 1.5503 - val_accuracy: 0.4141\n",
      "\n",
      "Epoch 00006: val_accuracy improved from 0.40752 to 0.41414, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_3.h5\n",
      "Epoch 7/50\n",
      "718/718 - 24s - loss: 1.5954 - accuracy: 0.4080 - val_loss: 1.5336 - val_accuracy: 0.4235\n",
      "\n",
      "Epoch 00007: val_accuracy improved from 0.41414 to 0.42355, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_3.h5\n",
      "Epoch 8/50\n",
      "718/718 - 25s - loss: 1.5634 - accuracy: 0.4165 - val_loss: 1.5318 - val_accuracy: 0.4242\n",
      "\n",
      "Epoch 00008: val_accuracy improved from 0.42355 to 0.42424, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_3.h5\n",
      "Epoch 9/50\n",
      "718/718 - 24s - loss: 1.5272 - accuracy: 0.4254 - val_loss: 1.5107 - val_accuracy: 0.4279\n",
      "\n",
      "Epoch 00009: val_accuracy improved from 0.42424 to 0.42790, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_3.h5\n",
      "Epoch 10/50\n",
      "718/718 - 25s - loss: 1.5070 - accuracy: 0.4337 - val_loss: 1.5051 - val_accuracy: 0.4335\n",
      "\n",
      "Epoch 00010: val_accuracy improved from 0.42790 to 0.43347, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_3.h5\n",
      "Epoch 11/50\n",
      "718/718 - 25s - loss: 1.4774 - accuracy: 0.4436 - val_loss: 1.5046 - val_accuracy: 0.4342\n",
      "\n",
      "Epoch 00011: val_accuracy improved from 0.43347 to 0.43417, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_3.h5\n",
      "Epoch 12/50\n",
      "718/718 - 24s - loss: 1.4644 - accuracy: 0.4487 - val_loss: 1.4904 - val_accuracy: 0.4387\n",
      "\n",
      "Epoch 00012: val_accuracy improved from 0.43417 to 0.43870, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_3.h5\n",
      "Epoch 13/50\n",
      "718/718 - 24s - loss: 1.4506 - accuracy: 0.4523 - val_loss: 1.4951 - val_accuracy: 0.4396\n",
      "\n",
      "Epoch 00013: val_accuracy improved from 0.43870 to 0.43957, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_3.h5\n",
      "Epoch 14/50\n",
      "718/718 - 24s - loss: 1.4356 - accuracy: 0.4543 - val_loss: 1.4838 - val_accuracy: 0.4382\n",
      "\n",
      "Epoch 00014: val_accuracy did not improve from 0.43957\n",
      "Epoch 15/50\n",
      "718/718 - 24s - loss: 1.4277 - accuracy: 0.4608 - val_loss: 1.4785 - val_accuracy: 0.4418\n",
      "\n",
      "Epoch 00015: val_accuracy improved from 0.43957 to 0.44183, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_3.h5\n",
      "Epoch 16/50\n",
      "718/718 - 26s - loss: 1.4177 - accuracy: 0.4619 - val_loss: 1.4795 - val_accuracy: 0.4413\n",
      "\n",
      "Epoch 00016: val_accuracy did not improve from 0.44183\n",
      "Epoch 17/50\n",
      "718/718 - 25s - loss: 1.4037 - accuracy: 0.4637 - val_loss: 1.4820 - val_accuracy: 0.4415\n",
      "\n",
      "Epoch 00017: val_accuracy did not improve from 0.44183\n",
      "Epoch 18/50\n",
      "718/718 - 25s - loss: 1.4022 - accuracy: 0.4715 - val_loss: 1.4783 - val_accuracy: 0.4446\n",
      "\n",
      "Epoch 00018: val_accuracy improved from 0.44183 to 0.44462, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_3.h5\n",
      "Epoch 19/50\n",
      "718/718 - 25s - loss: 1.3937 - accuracy: 0.4687 - val_loss: 1.4825 - val_accuracy: 0.4380\n",
      "\n",
      "Epoch 00019: val_accuracy did not improve from 0.44462\n",
      "Epoch 20/50\n",
      "718/718 - 25s - loss: 1.3878 - accuracy: 0.4751 - val_loss: 1.4788 - val_accuracy: 0.4429\n",
      "\n",
      "Epoch 00020: val_accuracy did not improve from 0.44462\n",
      "Epoch 21/50\n",
      "718/718 - 25s - loss: 1.3849 - accuracy: 0.4749 - val_loss: 1.4808 - val_accuracy: 0.4467\n",
      "\n",
      "Epoch 00021: val_accuracy improved from 0.44462 to 0.44671, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_3.h5\n",
      "Epoch 22/50\n",
      "718/718 - 24s - loss: 1.3854 - accuracy: 0.4751 - val_loss: 1.4779 - val_accuracy: 0.4465\n",
      "\n",
      "Epoch 00022: val_accuracy did not improve from 0.44671\n",
      "Epoch 23/50\n",
      "718/718 - 25s - loss: 1.3754 - accuracy: 0.4772 - val_loss: 1.4729 - val_accuracy: 0.4471\n",
      "\n",
      "Epoch 00023: val_accuracy improved from 0.44671 to 0.44706, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_3.h5\n",
      "Epoch 24/50\n",
      "718/718 - 24s - loss: 1.3692 - accuracy: 0.4810 - val_loss: 1.4850 - val_accuracy: 0.4429\n",
      "\n",
      "Epoch 00024: val_accuracy did not improve from 0.44706\n",
      "Epoch 25/50\n",
      "718/718 - 25s - loss: 1.3696 - accuracy: 0.4773 - val_loss: 1.4827 - val_accuracy: 0.4411\n",
      "\n",
      "Epoch 00025: val_accuracy did not improve from 0.44706\n",
      "Epoch 26/50\n",
      "718/718 - 24s - loss: 1.3701 - accuracy: 0.4801 - val_loss: 1.4985 - val_accuracy: 0.4359\n",
      "\n",
      "Epoch 00026: val_accuracy did not improve from 0.44706\n",
      "Epoch 27/50\n",
      "718/718 - 25s - loss: 1.3607 - accuracy: 0.4887 - val_loss: 1.4805 - val_accuracy: 0.4443\n",
      "\n",
      "Epoch 00027: val_accuracy did not improve from 0.44706\n",
      "Epoch 28/50\n",
      "718/718 - 24s - loss: 1.3639 - accuracy: 0.4837 - val_loss: 1.4747 - val_accuracy: 0.4497\n",
      "\n",
      "Epoch 00028: val_accuracy improved from 0.44706 to 0.44967, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_3.h5\n",
      "Epoch 29/50\n",
      "718/718 - 25s - loss: 1.3631 - accuracy: 0.4853 - val_loss: 1.4709 - val_accuracy: 0.4497\n",
      "\n",
      "Epoch 00029: val_accuracy did not improve from 0.44967\n",
      "Epoch 30/50\n",
      "718/718 - 25s - loss: 1.3601 - accuracy: 0.4802 - val_loss: 1.4696 - val_accuracy: 0.4446\n",
      "\n",
      "Epoch 00030: val_accuracy did not improve from 0.44967\n",
      "Epoch 31/50\n",
      "718/718 - 25s - loss: 1.3569 - accuracy: 0.4862 - val_loss: 1.4647 - val_accuracy: 0.4483\n",
      "\n",
      "Epoch 00031: val_accuracy did not improve from 0.44967\n",
      "Epoch 32/50\n",
      "718/718 - 24s - loss: 1.3510 - accuracy: 0.4918 - val_loss: 1.4840 - val_accuracy: 0.4446\n",
      "\n",
      "Epoch 00032: val_accuracy did not improve from 0.44967\n",
      "Epoch 33/50\n",
      "718/718 - 25s - loss: 1.3566 - accuracy: 0.4858 - val_loss: 1.4858 - val_accuracy: 0.4443\n",
      "\n",
      "Epoch 00033: val_accuracy did not improve from 0.44967\n",
      "Epoch 34/50\n",
      "718/718 - 25s - loss: 1.3592 - accuracy: 0.4852 - val_loss: 1.4674 - val_accuracy: 0.4511\n",
      "\n",
      "Epoch 00034: val_accuracy improved from 0.44967 to 0.45106, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_3.h5\n",
      "Epoch 35/50\n",
      "718/718 - 25s - loss: 1.3495 - accuracy: 0.4913 - val_loss: 1.4744 - val_accuracy: 0.4453\n",
      "\n",
      "Epoch 00035: val_accuracy did not improve from 0.45106\n",
      "Epoch 36/50\n",
      "718/718 - 26s - loss: 1.3503 - accuracy: 0.4899 - val_loss: 1.4753 - val_accuracy: 0.4486\n",
      "\n",
      "Epoch 00036: val_accuracy did not improve from 0.45106\n",
      "Epoch 37/50\n",
      "718/718 - 25s - loss: 1.3492 - accuracy: 0.4908 - val_loss: 1.4770 - val_accuracy: 0.4450\n",
      "\n",
      "Epoch 00037: val_accuracy did not improve from 0.45106\n",
      "Epoch 38/50\n",
      "718/718 - 24s - loss: 1.3428 - accuracy: 0.4907 - val_loss: 1.4754 - val_accuracy: 0.4431\n",
      "\n",
      "Epoch 00038: val_accuracy did not improve from 0.45106\n",
      "Epoch 39/50\n",
      "718/718 - 25s - loss: 1.3429 - accuracy: 0.4931 - val_loss: 1.4768 - val_accuracy: 0.4504\n",
      "\n",
      "Epoch 00039: val_accuracy did not improve from 0.45106\n",
      "Epoch 40/50\n",
      "718/718 - 25s - loss: 1.3441 - accuracy: 0.4938 - val_loss: 1.4806 - val_accuracy: 0.4512\n",
      "\n",
      "Epoch 00040: val_accuracy improved from 0.45106 to 0.45124, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_3.h5\n",
      "Epoch 41/50\n",
      "718/718 - 25s - loss: 1.3453 - accuracy: 0.4903 - val_loss: 1.4774 - val_accuracy: 0.4514\n",
      "\n",
      "Epoch 00041: val_accuracy improved from 0.45124 to 0.45141, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_3.h5\n",
      "Epoch 42/50\n",
      "718/718 - 27s - loss: 1.3437 - accuracy: 0.4931 - val_loss: 1.4760 - val_accuracy: 0.4497\n",
      "\n",
      "Epoch 00042: val_accuracy did not improve from 0.45141\n",
      "Epoch 43/50\n",
      "718/718 - 26s - loss: 1.3409 - accuracy: 0.4912 - val_loss: 1.4802 - val_accuracy: 0.4444\n",
      "\n",
      "Epoch 00043: val_accuracy did not improve from 0.45141\n",
      "Epoch 44/50\n",
      "718/718 - 24s - loss: 1.3418 - accuracy: 0.4959 - val_loss: 1.4791 - val_accuracy: 0.4498\n",
      "\n",
      "Epoch 00044: val_accuracy did not improve from 0.45141\n",
      "Epoch 45/50\n",
      "718/718 - 24s - loss: 1.3401 - accuracy: 0.4930 - val_loss: 1.4831 - val_accuracy: 0.4460\n",
      "\n",
      "Epoch 00045: val_accuracy did not improve from 0.45141\n",
      "Epoch 46/50\n",
      "718/718 - 25s - loss: 1.3418 - accuracy: 0.4907 - val_loss: 1.4792 - val_accuracy: 0.4443\n",
      "\n",
      "Epoch 00046: val_accuracy did not improve from 0.45141\n",
      "Epoch 47/50\n",
      "718/718 - 25s - loss: 1.3354 - accuracy: 0.4966 - val_loss: 1.4803 - val_accuracy: 0.4472\n",
      "\n",
      "Epoch 00047: val_accuracy did not improve from 0.45141\n",
      "Epoch 48/50\n",
      "718/718 - 24s - loss: 1.3363 - accuracy: 0.4931 - val_loss: 1.4732 - val_accuracy: 0.4544\n",
      "\n",
      "Epoch 00048: val_accuracy improved from 0.45141 to 0.45437, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_3.h5\n",
      "Epoch 49/50\n",
      "718/718 - 24s - loss: 1.3348 - accuracy: 0.4931 - val_loss: 1.4930 - val_accuracy: 0.4455\n",
      "\n",
      "Epoch 00049: val_accuracy did not improve from 0.45437\n",
      "Epoch 50/50\n",
      "718/718 - 25s - loss: 1.3349 - accuracy: 0.4950 - val_loss: 1.4833 - val_accuracy: 0.4490\n",
      "\n",
      "Epoch 00050: val_accuracy did not improve from 0.45437\n"
     ]
    },
    {
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     "text": [
      "225/225 [==============================] - 6s 28ms/step - loss: 1.4631 - accuracy: 0.4561\n",
      "Found 22967 validated image filenames belonging to 7 classes.\n",
      "Found 5742 validated image filenames belonging to 7 classes.\n",
      "Epoch 1/50\n",
      "718/718 - 27s - loss: 2.8005 - accuracy: 0.2407 - val_loss: 1.8692 - val_accuracy: 0.3438\n",
      "\n",
      "Epoch 00001: val_accuracy improved from -inf to 0.34378, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_4.h5\n",
      "Epoch 2/50\n",
      "718/718 - 24s - loss: 2.1807 - accuracy: 0.3102 - val_loss: 1.7248 - val_accuracy: 0.3770\n",
      "\n",
      "Epoch 00002: val_accuracy improved from 0.34378 to 0.37705, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_4.h5\n",
      "Epoch 3/50\n",
      "718/718 - 24s - loss: 1.9470 - accuracy: 0.3462 - val_loss: 1.6557 - val_accuracy: 0.3901\n",
      "\n",
      "Epoch 00003: val_accuracy improved from 0.37705 to 0.39011, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_4.h5\n",
      "Epoch 4/50\n",
      "718/718 - 24s - loss: 1.8152 - accuracy: 0.3648 - val_loss: 1.6152 - val_accuracy: 0.3953\n",
      "\n",
      "Epoch 00004: val_accuracy improved from 0.39011 to 0.39533, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_4.h5\n",
      "Epoch 5/50\n",
      "718/718 - 23s - loss: 1.7181 - accuracy: 0.3798 - val_loss: 1.5869 - val_accuracy: 0.4070\n",
      "\n",
      "Epoch 00005: val_accuracy improved from 0.39533 to 0.40700, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_4.h5\n",
      "Epoch 6/50\n",
      "718/718 - 24s - loss: 1.6527 - accuracy: 0.3905 - val_loss: 1.5679 - val_accuracy: 0.4197\n",
      "\n",
      "Epoch 00006: val_accuracy improved from 0.40700 to 0.41971, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_4.h5\n",
      "Epoch 7/50\n",
      "718/718 - 24s - loss: 1.6010 - accuracy: 0.4092 - val_loss: 1.5477 - val_accuracy: 0.4223\n",
      "\n",
      "Epoch 00007: val_accuracy improved from 0.41971 to 0.42233, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_4.h5\n",
      "Epoch 8/50\n",
      "718/718 - 23s - loss: 1.5583 - accuracy: 0.4181 - val_loss: 1.5294 - val_accuracy: 0.4265\n",
      "\n",
      "Epoch 00008: val_accuracy improved from 0.42233 to 0.42651, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_4.h5\n",
      "Epoch 9/50\n",
      "718/718 - 24s - loss: 1.5224 - accuracy: 0.4273 - val_loss: 1.5271 - val_accuracy: 0.4258\n",
      "\n",
      "Epoch 00009: val_accuracy did not improve from 0.42651\n",
      "Epoch 10/50\n",
      "718/718 - 24s - loss: 1.4907 - accuracy: 0.4382 - val_loss: 1.5175 - val_accuracy: 0.4289\n",
      "\n",
      "Epoch 00010: val_accuracy improved from 0.42651 to 0.42894, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_4.h5\n",
      "Epoch 11/50\n",
      "718/718 - 24s - loss: 1.4746 - accuracy: 0.4424 - val_loss: 1.5041 - val_accuracy: 0.4333\n",
      "\n",
      "Epoch 00011: val_accuracy improved from 0.42894 to 0.43330, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_4.h5\n",
      "Epoch 12/50\n",
      "718/718 - 24s - loss: 1.4611 - accuracy: 0.4468 - val_loss: 1.5009 - val_accuracy: 0.4399\n",
      "\n",
      "Epoch 00012: val_accuracy improved from 0.43330 to 0.43992, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_4.h5\n",
      "Epoch 13/50\n",
      "718/718 - 24s - loss: 1.4482 - accuracy: 0.4516 - val_loss: 1.4939 - val_accuracy: 0.4418\n",
      "\n",
      "Epoch 00013: val_accuracy improved from 0.43992 to 0.44183, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_4.h5\n",
      "Epoch 14/50\n",
      "718/718 - 24s - loss: 1.4302 - accuracy: 0.4586 - val_loss: 1.4986 - val_accuracy: 0.4396\n",
      "\n",
      "Epoch 00014: val_accuracy did not improve from 0.44183\n",
      "Epoch 15/50\n",
      "718/718 - 24s - loss: 1.4227 - accuracy: 0.4619 - val_loss: 1.4865 - val_accuracy: 0.4450\n",
      "\n",
      "Epoch 00015: val_accuracy improved from 0.44183 to 0.44497, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_4.h5\n",
      "Epoch 16/50\n",
      "718/718 - 24s - loss: 1.4082 - accuracy: 0.4694 - val_loss: 1.4886 - val_accuracy: 0.4399\n",
      "\n",
      "Epoch 00016: val_accuracy did not improve from 0.44497\n",
      "Epoch 17/50\n",
      "718/718 - 24s - loss: 1.4059 - accuracy: 0.4681 - val_loss: 1.4893 - val_accuracy: 0.4377\n",
      "\n",
      "Epoch 00017: val_accuracy did not improve from 0.44497\n",
      "Epoch 18/50\n",
      "718/718 - 24s - loss: 1.3968 - accuracy: 0.4719 - val_loss: 1.4867 - val_accuracy: 0.4462\n",
      "\n",
      "Epoch 00018: val_accuracy improved from 0.44497 to 0.44619, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_4.h5\n",
      "Epoch 19/50\n",
      "718/718 - 24s - loss: 1.3872 - accuracy: 0.4729 - val_loss: 1.4823 - val_accuracy: 0.4443\n",
      "\n",
      "Epoch 00019: val_accuracy did not improve from 0.44619\n",
      "Epoch 20/50\n",
      "718/718 - 24s - loss: 1.3838 - accuracy: 0.4762 - val_loss: 1.4808 - val_accuracy: 0.4451\n",
      "\n",
      "Epoch 00020: val_accuracy did not improve from 0.44619\n",
      "Epoch 21/50\n",
      "718/718 - 25s - loss: 1.3800 - accuracy: 0.4782 - val_loss: 1.4808 - val_accuracy: 0.4424\n",
      "\n",
      "Epoch 00021: val_accuracy did not improve from 0.44619\n",
      "Epoch 22/50\n",
      "718/718 - 26s - loss: 1.3798 - accuracy: 0.4783 - val_loss: 1.4912 - val_accuracy: 0.4497\n",
      "\n",
      "Epoch 00022: val_accuracy improved from 0.44619 to 0.44967, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_4.h5\n",
      "Epoch 23/50\n",
      "718/718 - 26s - loss: 1.3697 - accuracy: 0.4798 - val_loss: 1.4866 - val_accuracy: 0.4457\n",
      "\n",
      "Epoch 00023: val_accuracy did not improve from 0.44967\n",
      "Epoch 24/50\n",
      "718/718 - 26s - loss: 1.3665 - accuracy: 0.4802 - val_loss: 1.4922 - val_accuracy: 0.4361\n",
      "\n",
      "Epoch 00024: val_accuracy did not improve from 0.44967\n",
      "Epoch 25/50\n",
      "718/718 - 26s - loss: 1.3663 - accuracy: 0.4834 - val_loss: 1.4868 - val_accuracy: 0.4432\n",
      "\n",
      "Epoch 00025: val_accuracy did not improve from 0.44967\n",
      "Epoch 26/50\n",
      "718/718 - 25s - loss: 1.3646 - accuracy: 0.4841 - val_loss: 1.4821 - val_accuracy: 0.4471\n",
      "\n",
      "Epoch 00026: val_accuracy did not improve from 0.44967\n",
      "Epoch 27/50\n",
      "718/718 - 25s - loss: 1.3599 - accuracy: 0.4848 - val_loss: 1.4798 - val_accuracy: 0.4498\n",
      "\n",
      "Epoch 00027: val_accuracy improved from 0.44967 to 0.44984, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_4.h5\n",
      "Epoch 28/50\n",
      "718/718 - 24s - loss: 1.3560 - accuracy: 0.4869 - val_loss: 1.4846 - val_accuracy: 0.4495\n",
      "\n",
      "Epoch 00028: val_accuracy did not improve from 0.44984\n",
      "Epoch 29/50\n",
      "718/718 - 23s - loss: 1.3559 - accuracy: 0.4838 - val_loss: 1.4925 - val_accuracy: 0.4406\n",
      "\n",
      "Epoch 00029: val_accuracy did not improve from 0.44984\n",
      "Epoch 30/50\n",
      "718/718 - 23s - loss: 1.3511 - accuracy: 0.4902 - val_loss: 1.5041 - val_accuracy: 0.4418\n",
      "\n",
      "Epoch 00030: val_accuracy did not improve from 0.44984\n",
      "Epoch 31/50\n",
      "718/718 - 23s - loss: 1.3511 - accuracy: 0.4877 - val_loss: 1.4884 - val_accuracy: 0.4453\n",
      "\n",
      "Epoch 00031: val_accuracy did not improve from 0.44984\n",
      "Epoch 32/50\n",
      "718/718 - 24s - loss: 1.3528 - accuracy: 0.4869 - val_loss: 1.4937 - val_accuracy: 0.4481\n",
      "\n",
      "Epoch 00032: val_accuracy did not improve from 0.44984\n",
      "Epoch 33/50\n",
      "718/718 - 25s - loss: 1.3466 - accuracy: 0.4895 - val_loss: 1.4907 - val_accuracy: 0.4481\n",
      "\n",
      "Epoch 00033: val_accuracy did not improve from 0.44984\n",
      "Epoch 34/50\n",
      "718/718 - 24s - loss: 1.3455 - accuracy: 0.4902 - val_loss: 1.4783 - val_accuracy: 0.4540\n",
      "\n",
      "Epoch 00034: val_accuracy improved from 0.44984 to 0.45402, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_4.h5\n",
      "Epoch 35/50\n",
      "718/718 - 24s - loss: 1.3506 - accuracy: 0.4872 - val_loss: 1.4871 - val_accuracy: 0.4551\n",
      "\n",
      "Epoch 00035: val_accuracy improved from 0.45402 to 0.45507, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_4.h5\n",
      "Epoch 36/50\n",
      "718/718 - 24s - loss: 1.3411 - accuracy: 0.4924 - val_loss: 1.4817 - val_accuracy: 0.4502\n",
      "\n",
      "Epoch 00036: val_accuracy did not improve from 0.45507\n",
      "Epoch 37/50\n",
      "718/718 - 24s - loss: 1.3462 - accuracy: 0.4886 - val_loss: 1.4838 - val_accuracy: 0.4471\n",
      "\n",
      "Epoch 00037: val_accuracy did not improve from 0.45507\n",
      "Epoch 38/50\n",
      "718/718 - 23s - loss: 1.3404 - accuracy: 0.4935 - val_loss: 1.4915 - val_accuracy: 0.4497\n",
      "\n",
      "Epoch 00038: val_accuracy did not improve from 0.45507\n",
      "Epoch 39/50\n",
      "718/718 - 23s - loss: 1.3412 - accuracy: 0.4903 - val_loss: 1.4891 - val_accuracy: 0.4511\n",
      "\n",
      "Epoch 00039: val_accuracy did not improve from 0.45507\n",
      "Epoch 40/50\n",
      "718/718 - 24s - loss: 1.3411 - accuracy: 0.4898 - val_loss: 1.4947 - val_accuracy: 0.4511\n",
      "\n",
      "Epoch 00040: val_accuracy did not improve from 0.45507\n",
      "Epoch 41/50\n",
      "718/718 - 23s - loss: 1.3397 - accuracy: 0.4932 - val_loss: 1.4884 - val_accuracy: 0.4493\n",
      "\n",
      "Epoch 00041: val_accuracy did not improve from 0.45507\n",
      "Epoch 42/50\n",
      "718/718 - 24s - loss: 1.3365 - accuracy: 0.4904 - val_loss: 1.4890 - val_accuracy: 0.4485\n",
      "\n",
      "Epoch 00042: val_accuracy did not improve from 0.45507\n",
      "Epoch 43/50\n",
      "718/718 - 24s - loss: 1.3340 - accuracy: 0.4942 - val_loss: 1.4974 - val_accuracy: 0.4434\n",
      "\n",
      "Epoch 00043: val_accuracy did not improve from 0.45507\n",
      "Epoch 44/50\n",
      "718/718 - 24s - loss: 1.3389 - accuracy: 0.4921 - val_loss: 1.4844 - val_accuracy: 0.4558\n",
      "\n",
      "Epoch 00044: val_accuracy improved from 0.45507 to 0.45576, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_4.h5\n",
      "Epoch 45/50\n",
      "718/718 - 23s - loss: 1.3364 - accuracy: 0.4897 - val_loss: 1.4828 - val_accuracy: 0.4502\n",
      "\n",
      "Epoch 00045: val_accuracy did not improve from 0.45576\n",
      "Epoch 46/50\n",
      "718/718 - 24s - loss: 1.3349 - accuracy: 0.4918 - val_loss: 1.4923 - val_accuracy: 0.4411\n",
      "\n",
      "Epoch 00046: val_accuracy did not improve from 0.45576\n",
      "Epoch 47/50\n",
      "718/718 - 24s - loss: 1.3345 - accuracy: 0.4942 - val_loss: 1.4986 - val_accuracy: 0.4460\n",
      "\n",
      "Epoch 00047: val_accuracy did not improve from 0.45576\n",
      "Epoch 48/50\n",
      "718/718 - 26s - loss: 1.3355 - accuracy: 0.4921 - val_loss: 1.4948 - val_accuracy: 0.4424\n",
      "\n",
      "Epoch 00048: val_accuracy did not improve from 0.45576\n",
      "Epoch 49/50\n",
      "718/718 - 24s - loss: 1.3360 - accuracy: 0.4928 - val_loss: 1.5079 - val_accuracy: 0.4408\n",
      "\n",
      "Epoch 00049: val_accuracy did not improve from 0.45576\n",
      "Epoch 50/50\n",
      "718/718 - 24s - loss: 1.3349 - accuracy: 0.4954 - val_loss: 1.5018 - val_accuracy: 0.4392\n",
      "\n",
      "Epoch 00050: val_accuracy did not improve from 0.45576\n"
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      "225/225 [==============================] - 7s 31ms/step - loss: 1.4581 - accuracy: 0.4564\n",
      "Found 22968 validated image filenames belonging to 7 classes.\n",
      "Found 5741 validated image filenames belonging to 7 classes.\n",
      "Epoch 1/50\n",
      "718/718 - 29s - loss: 2.7952 - accuracy: 0.2426 - val_loss: 1.8641 - val_accuracy: 0.3374\n",
      "\n",
      "Epoch 00001: val_accuracy improved from -inf to 0.33740, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_5.h5\n",
      "Epoch 2/50\n",
      "718/718 - 25s - loss: 2.1902 - accuracy: 0.3066 - val_loss: 1.7171 - val_accuracy: 0.3729\n",
      "\n",
      "Epoch 00002: val_accuracy improved from 0.33740 to 0.37293, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_5.h5\n",
      "Epoch 3/50\n",
      "718/718 - 25s - loss: 1.9773 - accuracy: 0.3388 - val_loss: 1.6354 - val_accuracy: 0.3858\n",
      "\n",
      "Epoch 00003: val_accuracy improved from 0.37293 to 0.38582, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_5.h5\n",
      "Epoch 4/50\n",
      "718/718 - 24s - loss: 1.8269 - accuracy: 0.3603 - val_loss: 1.6054 - val_accuracy: 0.3984\n",
      "\n",
      "Epoch 00004: val_accuracy improved from 0.38582 to 0.39836, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_5.h5\n",
      "Epoch 5/50\n",
      "718/718 - 24s - loss: 1.7339 - accuracy: 0.3763 - val_loss: 1.5573 - val_accuracy: 0.4113\n",
      "\n",
      "Epoch 00005: val_accuracy improved from 0.39836 to 0.41125, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_5.h5\n",
      "Epoch 6/50\n",
      "718/718 - 25s - loss: 1.6534 - accuracy: 0.3950 - val_loss: 1.5447 - val_accuracy: 0.4114\n",
      "\n",
      "Epoch 00006: val_accuracy improved from 0.41125 to 0.41143, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_5.h5\n",
      "Epoch 7/50\n",
      "718/718 - 25s - loss: 1.6168 - accuracy: 0.4008 - val_loss: 1.5345 - val_accuracy: 0.4147\n",
      "\n",
      "Epoch 00007: val_accuracy improved from 0.41143 to 0.41474, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_5.h5\n",
      "Epoch 8/50\n",
      "718/718 - 25s - loss: 1.5684 - accuracy: 0.4198 - val_loss: 1.5074 - val_accuracy: 0.4302\n",
      "\n",
      "Epoch 00008: val_accuracy improved from 0.41474 to 0.43024, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_5.h5\n",
      "Epoch 9/50\n",
      "718/718 - 26s - loss: 1.5312 - accuracy: 0.4252 - val_loss: 1.5011 - val_accuracy: 0.4313\n",
      "\n",
      "Epoch 00009: val_accuracy improved from 0.43024 to 0.43128, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_5.h5\n",
      "Epoch 10/50\n",
      "718/718 - 25s - loss: 1.5075 - accuracy: 0.4348 - val_loss: 1.5087 - val_accuracy: 0.4264\n",
      "\n",
      "Epoch 00010: val_accuracy did not improve from 0.43128\n",
      "Epoch 11/50\n",
      "718/718 - 24s - loss: 1.4841 - accuracy: 0.4369 - val_loss: 1.4914 - val_accuracy: 0.4355\n",
      "\n",
      "Epoch 00011: val_accuracy improved from 0.43128 to 0.43546, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_5.h5\n",
      "Epoch 12/50\n",
      "718/718 - 24s - loss: 1.4678 - accuracy: 0.4403 - val_loss: 1.4739 - val_accuracy: 0.4391\n",
      "\n",
      "Epoch 00012: val_accuracy improved from 0.43546 to 0.43912, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_5.h5\n",
      "Epoch 13/50\n",
      "718/718 - 24s - loss: 1.4519 - accuracy: 0.4539 - val_loss: 1.4803 - val_accuracy: 0.4405\n",
      "\n",
      "Epoch 00013: val_accuracy improved from 0.43912 to 0.44052, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_5.h5\n",
      "Epoch 14/50\n",
      "718/718 - 23s - loss: 1.4404 - accuracy: 0.4542 - val_loss: 1.4895 - val_accuracy: 0.4363\n",
      "\n",
      "Epoch 00014: val_accuracy did not improve from 0.44052\n",
      "Epoch 15/50\n",
      "718/718 - 24s - loss: 1.4283 - accuracy: 0.4586 - val_loss: 1.4747 - val_accuracy: 0.4450\n",
      "\n",
      "Epoch 00015: val_accuracy improved from 0.44052 to 0.44504, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_5.h5\n",
      "Epoch 16/50\n",
      "718/718 - 23s - loss: 1.4182 - accuracy: 0.4596 - val_loss: 1.4883 - val_accuracy: 0.4362\n",
      "\n",
      "Epoch 00016: val_accuracy did not improve from 0.44504\n",
      "Epoch 17/50\n",
      "718/718 - 24s - loss: 1.4168 - accuracy: 0.4667 - val_loss: 1.4686 - val_accuracy: 0.4438\n",
      "\n",
      "Epoch 00017: val_accuracy did not improve from 0.44504\n",
      "Epoch 18/50\n",
      "718/718 - 25s - loss: 1.4016 - accuracy: 0.4723 - val_loss: 1.4623 - val_accuracy: 0.4438\n",
      "\n",
      "Epoch 00018: val_accuracy did not improve from 0.44504\n",
      "Epoch 19/50\n",
      "718/718 - 24s - loss: 1.4031 - accuracy: 0.4703 - val_loss: 1.4675 - val_accuracy: 0.4452\n",
      "\n",
      "Epoch 00019: val_accuracy improved from 0.44504 to 0.44522, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_5.h5\n",
      "Epoch 20/50\n",
      "718/718 - 24s - loss: 1.3920 - accuracy: 0.4734 - val_loss: 1.4537 - val_accuracy: 0.4491\n",
      "\n",
      "Epoch 00020: val_accuracy improved from 0.44522 to 0.44905, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_5.h5\n",
      "Epoch 21/50\n",
      "718/718 - 24s - loss: 1.3873 - accuracy: 0.4746 - val_loss: 1.4595 - val_accuracy: 0.4510\n",
      "\n",
      "Epoch 00021: val_accuracy improved from 0.44905 to 0.45097, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_5.h5\n",
      "Epoch 22/50\n",
      "718/718 - 24s - loss: 1.3835 - accuracy: 0.4736 - val_loss: 1.4599 - val_accuracy: 0.4464\n",
      "\n",
      "Epoch 00022: val_accuracy did not improve from 0.45097\n",
      "Epoch 23/50\n",
      "718/718 - 24s - loss: 1.3801 - accuracy: 0.4736 - val_loss: 1.4578 - val_accuracy: 0.4470\n",
      "\n",
      "Epoch 00023: val_accuracy did not improve from 0.45097\n",
      "Epoch 24/50\n",
      "718/718 - 24s - loss: 1.3782 - accuracy: 0.4781 - val_loss: 1.4618 - val_accuracy: 0.4449\n",
      "\n",
      "Epoch 00024: val_accuracy did not improve from 0.45097\n",
      "Epoch 25/50\n",
      "718/718 - 27s - loss: 1.3691 - accuracy: 0.4859 - val_loss: 1.4562 - val_accuracy: 0.4496\n",
      "\n",
      "Epoch 00025: val_accuracy did not improve from 0.45097\n",
      "Epoch 26/50\n",
      "718/718 - 24s - loss: 1.3728 - accuracy: 0.4772 - val_loss: 1.4611 - val_accuracy: 0.4513\n",
      "\n",
      "Epoch 00026: val_accuracy improved from 0.45097 to 0.45132, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_5.h5\n",
      "Epoch 27/50\n",
      "718/718 - 24s - loss: 1.3688 - accuracy: 0.4818 - val_loss: 1.4543 - val_accuracy: 0.4538\n",
      "\n",
      "Epoch 00027: val_accuracy improved from 0.45132 to 0.45375, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_5.h5\n",
      "Epoch 28/50\n",
      "718/718 - 27s - loss: 1.3722 - accuracy: 0.4816 - val_loss: 1.4613 - val_accuracy: 0.4492\n",
      "\n",
      "Epoch 00028: val_accuracy did not improve from 0.45375\n",
      "Epoch 29/50\n",
      "718/718 - 26s - loss: 1.3634 - accuracy: 0.4844 - val_loss: 1.4650 - val_accuracy: 0.4470\n",
      "\n",
      "Epoch 00029: val_accuracy did not improve from 0.45375\n",
      "Epoch 30/50\n",
      "718/718 - 24s - loss: 1.3599 - accuracy: 0.4858 - val_loss: 1.4548 - val_accuracy: 0.4541\n",
      "\n",
      "Epoch 00030: val_accuracy improved from 0.45375 to 0.45410, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_5.h5\n",
      "Epoch 31/50\n",
      "718/718 - 24s - loss: 1.3604 - accuracy: 0.4874 - val_loss: 1.4546 - val_accuracy: 0.4524\n",
      "\n",
      "Epoch 00031: val_accuracy did not improve from 0.45410\n",
      "Epoch 32/50\n",
      "718/718 - 24s - loss: 1.3589 - accuracy: 0.4844 - val_loss: 1.4656 - val_accuracy: 0.4529\n",
      "\n",
      "Epoch 00032: val_accuracy did not improve from 0.45410\n",
      "Epoch 33/50\n",
      "718/718 - 24s - loss: 1.3523 - accuracy: 0.4859 - val_loss: 1.4606 - val_accuracy: 0.4489\n",
      "\n",
      "Epoch 00033: val_accuracy did not improve from 0.45410\n",
      "Epoch 34/50\n",
      "718/718 - 24s - loss: 1.3536 - accuracy: 0.4903 - val_loss: 1.4595 - val_accuracy: 0.4564\n",
      "\n",
      "Epoch 00034: val_accuracy improved from 0.45410 to 0.45637, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_5.h5\n",
      "Epoch 35/50\n",
      "718/718 - 25s - loss: 1.3506 - accuracy: 0.4889 - val_loss: 1.4592 - val_accuracy: 0.4487\n",
      "\n",
      "Epoch 00035: val_accuracy did not improve from 0.45637\n",
      "Epoch 36/50\n",
      "718/718 - 24s - loss: 1.3506 - accuracy: 0.4855 - val_loss: 1.4624 - val_accuracy: 0.4496\n",
      "\n",
      "Epoch 00036: val_accuracy did not improve from 0.45637\n",
      "Epoch 37/50\n",
      "718/718 - 25s - loss: 1.3488 - accuracy: 0.4888 - val_loss: 1.4563 - val_accuracy: 0.4536\n",
      "\n",
      "Epoch 00037: val_accuracy did not improve from 0.45637\n",
      "Epoch 38/50\n",
      "718/718 - 25s - loss: 1.3548 - accuracy: 0.4865 - val_loss: 1.4577 - val_accuracy: 0.4524\n",
      "\n",
      "Epoch 00038: val_accuracy did not improve from 0.45637\n",
      "Epoch 39/50\n",
      "718/718 - 24s - loss: 1.3456 - accuracy: 0.4892 - val_loss: 1.4573 - val_accuracy: 0.4529\n",
      "\n",
      "Epoch 00039: val_accuracy did not improve from 0.45637\n",
      "Epoch 40/50\n",
      "718/718 - 24s - loss: 1.3489 - accuracy: 0.4915 - val_loss: 1.4635 - val_accuracy: 0.4548\n",
      "\n",
      "Epoch 00040: val_accuracy did not improve from 0.45637\n",
      "Epoch 41/50\n",
      "718/718 - 25s - loss: 1.3467 - accuracy: 0.4934 - val_loss: 1.4603 - val_accuracy: 0.4497\n",
      "\n",
      "Epoch 00041: val_accuracy did not improve from 0.45637\n",
      "Epoch 42/50\n",
      "718/718 - 24s - loss: 1.3506 - accuracy: 0.4911 - val_loss: 1.4634 - val_accuracy: 0.4551\n",
      "\n",
      "Epoch 00042: val_accuracy did not improve from 0.45637\n",
      "Epoch 43/50\n",
      "718/718 - 24s - loss: 1.3460 - accuracy: 0.4903 - val_loss: 1.4591 - val_accuracy: 0.4489\n",
      "\n",
      "Epoch 00043: val_accuracy did not improve from 0.45637\n",
      "Epoch 44/50\n",
      "718/718 - 24s - loss: 1.3449 - accuracy: 0.4907 - val_loss: 1.4623 - val_accuracy: 0.4501\n",
      "\n",
      "Epoch 00044: val_accuracy did not improve from 0.45637\n",
      "Epoch 45/50\n",
      "718/718 - 25s - loss: 1.3426 - accuracy: 0.4889 - val_loss: 1.4592 - val_accuracy: 0.4520\n",
      "\n",
      "Epoch 00045: val_accuracy did not improve from 0.45637\n",
      "Epoch 46/50\n",
      "718/718 - 25s - loss: 1.3500 - accuracy: 0.4894 - val_loss: 1.4615 - val_accuracy: 0.4511\n",
      "\n",
      "Epoch 00046: val_accuracy did not improve from 0.45637\n",
      "Epoch 47/50\n",
      "718/718 - 24s - loss: 1.3461 - accuracy: 0.4899 - val_loss: 1.4655 - val_accuracy: 0.4532\n",
      "\n",
      "Epoch 00047: val_accuracy did not improve from 0.45637\n",
      "Epoch 48/50\n",
      "718/718 - 26s - loss: 1.3415 - accuracy: 0.4931 - val_loss: 1.4760 - val_accuracy: 0.4452\n",
      "\n",
      "Epoch 00048: val_accuracy did not improve from 0.45637\n",
      "Epoch 49/50\n",
      "718/718 - 24s - loss: 1.3439 - accuracy: 0.4912 - val_loss: 1.4685 - val_accuracy: 0.4447\n",
      "\n",
      "Epoch 00049: val_accuracy did not improve from 0.45637\n",
      "Epoch 50/50\n",
      "718/718 - 26s - loss: 1.3407 - accuracy: 0.4954 - val_loss: 1.4613 - val_accuracy: 0.4581\n",
      "\n",
      "Epoch 00050: val_accuracy improved from 0.45637 to 0.45811, saving model to /kaggle/working/Saved_CNN_Models/transfer_learning_model_resnet_50_50epochs/transfer_learning_model_resnet_50_model_5.h5\n"
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    {
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