demo_sentiment_classifier_trainer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2020-04-10T20:09:33.300Z"
    }
   },
   "outputs": [],
   "source": [
    "import pickle as pkl\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from zipfile import ZipFile\n",
    "from sklearn.metrics import accuracy_score, matthews_corrcoef, confusion_matrix\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras.backend import clear_session\n",
    "\n",
    "from twitter_nlp_toolkit.tweet_sentiment_classifier import tweet_sentiment_classifier\n",
    "from twitter_nlp_toolkit.file_fetcher import file_fetcher\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: en_core_web_sm==2.2.5 from https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.2.5/en_core_web_sm-2.2.5.tar.gz#egg=en_core_web_sm==2.2.5 in c:\\users\\erics\\anaconda3\\envs\\tf_gpu\\lib\\site-packages (2.2.5)\n",
      "Requirement already satisfied: spacy>=2.2.2 in c:\\users\\erics\\anaconda3\\envs\\tf_gpu\\lib\\site-packages (from en_core_web_sm==2.2.5) (2.2.4)\n",
      "Requirement already satisfied: setuptools in c:\\users\\erics\\anaconda3\\envs\\tf_gpu\\lib\\site-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (46.1.3.post20200330)\n",
      "Requirement already satisfied: srsly<1.1.0,>=1.0.2 in c:\\users\\erics\\anaconda3\\envs\\tf_gpu\\lib\\site-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.0.2)\n",
      "Requirement already satisfied: plac<1.2.0,>=0.9.6 in c:\\users\\erics\\anaconda3\\envs\\tf_gpu\\lib\\site-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.1.3)\n",
      "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in c:\\users\\erics\\anaconda3\\envs\\tf_gpu\\lib\\site-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (3.0.2)\n",
      "Requirement already satisfied: blis<0.5.0,>=0.4.0 in c:\\users\\erics\\anaconda3\\envs\\tf_gpu\\lib\\site-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (0.4.1)\n",
      "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in c:\\users\\erics\\anaconda3\\envs\\tf_gpu\\lib\\site-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (2.0.3)\n",
      "Requirement already satisfied: thinc==7.4.0 in c:\\users\\erics\\anaconda3\\envs\\tf_gpu\\lib\\site-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (7.4.0)\n",
      "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in c:\\users\\erics\\anaconda3\\envs\\tf_gpu\\lib\\site-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (4.45.0)\n",
      "Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in c:\\users\\erics\\anaconda3\\envs\\tf_gpu\\lib\\site-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (0.6.0)\n",
      "Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in c:\\users\\erics\\anaconda3\\envs\\tf_gpu\\lib\\site-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.0.0)\n",
      "Requirement already satisfied: requests<3.0.0,>=2.13.0 in c:\\users\\erics\\anaconda3\\envs\\tf_gpu\\lib\\site-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (2.23.0)\n",
      "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in c:\\users\\erics\\anaconda3\\envs\\tf_gpu\\lib\\site-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.0.2)\n",
      "Requirement already satisfied: numpy>=1.15.0 in c:\\users\\erics\\anaconda3\\envs\\tf_gpu\\lib\\site-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.18.1)\n",
      "Requirement already satisfied: importlib-metadata>=0.20; python_version < \"3.8\" in c:\\users\\erics\\anaconda3\\envs\\tf_gpu\\lib\\site-packages (from catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_sm==2.2.5) (1.6.0)\n",
      "Requirement already satisfied: idna<3,>=2.5 in c:\\users\\erics\\anaconda3\\envs\\tf_gpu\\lib\\site-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (2.9)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\erics\\anaconda3\\envs\\tf_gpu\\lib\\site-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (2020.4.5.1)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in c:\\users\\erics\\anaconda3\\envs\\tf_gpu\\lib\\site-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (1.25.8)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in c:\\users\\erics\\anaconda3\\envs\\tf_gpu\\lib\\site-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (3.0.4)\n",
      "Requirement already satisfied: zipp>=0.5 in c:\\users\\erics\\anaconda3\\envs\\tf_gpu\\lib\\site-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_sm==2.2.5) (3.1.0)\n",
      "[+] Download and installation successful\n",
      "You can now load the model via spacy.load('en_core_web_sm')\n"
     ]
    }
   ],
   "source": [
    "# Install Spacy library\n",
    "!python -m spacy download en_core_web_sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "clear_session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-04-10T20:09:34.083Z",
     "iopub.status.busy": "2020-04-10T20:09:34.067Z",
     "iopub.status.idle": "2020-04-10T20:09:34.099Z",
     "shell.execute_reply": "2020-04-10T20:09:34.107Z"
    }
   },
   "outputs": [],
   "source": [
    "chunk = 1 # Fraction of data to train on - you can reduce for debugging for speed\n",
    "model_path = '.models'\n",
    "redownload=True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we download pre-trained models and a validation dataset. The models have been pre-trained on the Sentiment140 dataset, taken form here: https://www.kaggle.com/kazanova/sentiment140\n",
    "\n",
    "The validation data is hand-labeled airline customer feedback taken from https://www.figure-eight.com/data-for-everyone/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-04-10T20:09:38.496Z",
     "iopub.status.busy": "2020-04-10T20:09:38.486Z",
     "iopub.status.idle": "2020-04-10T20:12:21.744Z",
     "shell.execute_reply": "2020-04-10T20:12:21.768Z"
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "tweets_airline.zip: 100%|#########################################################| 1.07M/1.07M [00:00<00:00, 3.78MB/s]\n"
     ]
    }
   ],
   "source": [
    "if redownload: \n",
    "    \n",
    "    # Validation data\n",
    "    file_fetcher.download_file('https://www.dropbox.com/s/muqov03s9vtbe92/tweets_airline.zip?dl=1',\"tweets_airline.zip\")\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the validation data\n",
    "\n",
    "test_data = pd.read_csv('tweets_airline.zip', header=0,\n",
    "                        names=['Index', 'Sentiment', 'Sentiment_confidence',\n",
    "                                'Negative_reason', 'Negative_reason_confidence',\n",
    "                                 'Airline', 'Airline_sentiment_gold', 'Handle',\n",
    "                                 'Negative_reason_gold', 'Retweet_count', 'Text',\n",
    "                                'Tweet_coord', 'Time', 'Location', 'Timezone'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Index</th>\n",
       "      <th>Sentiment</th>\n",
       "      <th>Sentiment_confidence</th>\n",
       "      <th>Negative_reason</th>\n",
       "      <th>Negative_reason_confidence</th>\n",
       "      <th>Airline</th>\n",
       "      <th>Airline_sentiment_gold</th>\n",
       "      <th>Handle</th>\n",
       "      <th>Negative_reason_gold</th>\n",
       "      <th>Retweet_count</th>\n",
       "      <th>Text</th>\n",
       "      <th>Tweet_coord</th>\n",
       "      <th>Time</th>\n",
       "      <th>Location</th>\n",
       "      <th>Timezone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>570306133677760513</td>\n",
       "      <td>neutral</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cairdin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica What @dhepburn said.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:35:52 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Eastern Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>570301130888122368</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.3486</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica plus you've added commercials t...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:15:59 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>570301083672813571</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.6837</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yvonnalynn</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica I didn't today... Must mean I n...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:15:48 -0800</td>\n",
       "      <td>Lets Play</td>\n",
       "      <td>Central Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>570301031407624196</td>\n",
       "      <td>negative</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Bad Flight</td>\n",
       "      <td>0.7033</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica it's really aggressive to blast...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:15:36 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>570300817074462722</td>\n",
       "      <td>negative</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Can't Tell</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica and it's a really big bad thing...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:14:45 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Index Sentiment  Sentiment_confidence Negative_reason  \\\n",
       "0  570306133677760513   neutral                1.0000             NaN   \n",
       "1  570301130888122368  positive                0.3486             NaN   \n",
       "2  570301083672813571   neutral                0.6837             NaN   \n",
       "3  570301031407624196  negative                1.0000      Bad Flight   \n",
       "4  570300817074462722  negative                1.0000      Can't Tell   \n",
       "\n",
       "   Negative_reason_confidence         Airline Airline_sentiment_gold  \\\n",
       "0                         NaN  Virgin America                    NaN   \n",
       "1                      0.0000  Virgin America                    NaN   \n",
       "2                         NaN  Virgin America                    NaN   \n",
       "3                      0.7033  Virgin America                    NaN   \n",
       "4                      1.0000  Virgin America                    NaN   \n",
       "\n",
       "       Handle Negative_reason_gold  Retweet_count  \\\n",
       "0     cairdin                  NaN              0   \n",
       "1    jnardino                  NaN              0   \n",
       "2  yvonnalynn                  NaN              0   \n",
       "3    jnardino                  NaN              0   \n",
       "4    jnardino                  NaN              0   \n",
       "\n",
       "                                                Text Tweet_coord  \\\n",
       "0                @VirginAmerica What @dhepburn said.         NaN   \n",
       "1  @VirginAmerica plus you've added commercials t...         NaN   \n",
       "2  @VirginAmerica I didn't today... Must mean I n...         NaN   \n",
       "3  @VirginAmerica it's really aggressive to blast...         NaN   \n",
       "4  @VirginAmerica and it's a really big bad thing...         NaN   \n",
       "\n",
       "                        Time   Location                    Timezone  \n",
       "0  2015-02-24 11:35:52 -0800        NaN  Eastern Time (US & Canada)  \n",
       "1  2015-02-24 11:15:59 -0800        NaN  Pacific Time (US & Canada)  \n",
       "2  2015-02-24 11:15:48 -0800  Lets Play  Central Time (US & Canada)  \n",
       "3  2015-02-24 11:15:36 -0800        NaN  Pacific Time (US & Canada)  \n",
       "4  2015-02-24 11:14:45 -0800        NaN  Pacific Time (US & Canada)  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We drop the tweets with \"neural\" sentiment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove the unlabeled test data\n",
    "\n",
    "test_data['Labels'] = (test_data['Sentiment'] == 'positive') * 2\n",
    "test_data['Labels'] = test_data['Labels'] + (test_data['Sentiment'] == 'neutral') * 1\n",
    "test_data['Labels'] = test_data['Labels'] / 2\n",
    "\n",
    "test_data.set_index('Labels')\n",
    "test_data = test_data[test_data.Labels != 0.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bow_1Mtweets\n",
      "Default parameters file not found or not working...\n",
      "Trying .models/small_ensemble/bow_1Mtweets\n",
      "Loading BoW model bow_1Mtweets from legacy parameter file\n",
      "BoW model bow_1Mtweets loaded successfully\n",
      "glove_100D_10N_340000V_719685T_Twitter_0\n",
      "Default parameters file not found or not working...\n",
      "Trying .models/small_ensemble/glove_100D_10N_340000V_719685T_Twitter_0\n",
      "Loading GloVE model glove_100D_10N_340000V_719685T_Twitter_0 from legacy parameter file\n",
      "Pre-trained embedding model glove_100D_10N_340000V_719685T_Twitter_0 loaded successfully\n",
      "glove_200D_10N_20000V_343332T_Twitter_7\n",
      "Default parameters file not found or not working...\n",
      "Trying .models/small_ensemble/glove_200D_10N_20000V_343332T_Twitter_7\n",
      "Loading GloVE model glove_200D_10N_20000V_343332T_Twitter_7 from legacy parameter file\n",
      "Pre-trained embedding model glove_200D_10N_20000V_343332T_Twitter_7 loaded successfully\n"
     ]
    }
   ],
   "source": [
    "# Download the small ensemble\n",
    "# Executing this cell starts a 500MB download\n",
    "\n",
    "Classifier = tweet_sentiment_classifier.SentimentAnalyzer()\n",
    "Classifier.load_small_ensemble()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We sanity check the models: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 0., 1., 0.])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Classifier.predict(['I am happy', 'I am sad', 'I am cheerful', 'I am mad'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We test the model on an airline customer feedback dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Executing this cell takes several minuites on a laptop\n",
    "\n",
    "predictions = Classifier.predict(test_data['Text'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy:  0.812\n",
      "Test MCC:  0.570\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[7305, 1873],\n",
       "       [ 297, 2066]], dtype=int64)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Test Accuracy:  {:.3f}'.format(accuracy_score(test_data['Labels'], predictions)))\n",
    "print('Test MCC:  {:.3f}'.format(matthews_corrcoef(test_data['Labels'], predictions)))\n",
    "confusion_matrix(test_data['Labels'], predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have accuracy of just over 80%.\n",
    "\n",
    "We split our evaluation dataset into validation and testing and check for poor-performing models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "valX, testX, valY, testY = train_test_split(test_data['Text'], test_data['Labels'], test_size=0.5, stratify=test_data['Labels'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model bow_1Mtweets score: 0.795\n",
      "Model glove_100D_10N_340000V_719685T_Twitter_0 score: 0.788\n",
      "Model glove_200D_10N_20000V_343332T_Twitter_7 score: 0.786\n"
     ]
    }
   ],
   "source": [
    "# Executing this cell takes several minuites on a laptop\n",
    "\n",
    "Classifier.trim_models(valX, valY, threshold=0.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All three models perform well, so none have been pruned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy:  0.818\n",
      "Test MCC:  0.584\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[3670,  919],\n",
       "       [ 134, 1048]], dtype=int64)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = Classifier.predict(testX)\n",
    "\n",
    "print('Test Accuracy:  {:.3f}'.format(accuracy_score(testY, predictions)))\n",
    "print('Test MCC:  {:.3f}'.format(matthews_corrcoef(testY, predictions)))\n",
    "confusion_matrix(testY, predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To improve our accuracy, we can refine the models on our airline data. The early stopping procedure (enabled by default to use 20% of the training data for validation) should minimize overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Preprocessed 5770 tweets \n",
      "\n",
      " Filtered data\n",
      " Preprocessed 5770 tweets \n",
      "Filtered data\n",
      "WARNING:tensorflow:sample_weight modes were coerced from\n",
      "  ...\n",
      "    to  \n",
      "  ['...']\n",
      "WARNING:tensorflow:sample_weight modes were coerced from\n",
      "  ...\n",
      "    to  \n",
      "  ['...']\n",
      "Train on 4616 samples, validate on 1154 samples\n",
      "Epoch 1/500\n",
      "4616/4616 [==============================] - 4s 817us/sample - loss: 0.5021 - acc: 0.7695 - val_loss: 0.4576 - val_acc: 0.7998\n",
      "Epoch 2/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.4872 - acc: 0.7727 - val_loss: 0.4444 - val_acc: 0.8102\n",
      "Epoch 3/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.4747 - acc: 0.7938 - val_loss: 0.4319 - val_acc: 0.8206\n",
      "Epoch 4/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.4640 - acc: 0.7979 - val_loss: 0.4200 - val_acc: 0.8319\n",
      "Epoch 5/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.4554 - acc: 0.8061 - val_loss: 0.4089 - val_acc: 0.8380\n",
      "Epoch 6/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.4475 - acc: 0.8200 - val_loss: 0.3985 - val_acc: 0.8458\n",
      "Epoch 7/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.4348 - acc: 0.8211 - val_loss: 0.3888 - val_acc: 0.8492\n",
      "Epoch 8/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.4299 - acc: 0.8315 - val_loss: 0.3797 - val_acc: 0.8553\n",
      "Epoch 9/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.4159 - acc: 0.8384 - val_loss: 0.3712 - val_acc: 0.8640\n",
      "Epoch 10/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.4127 - acc: 0.8414 - val_loss: 0.3634 - val_acc: 0.8666\n",
      "Epoch 11/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.3999 - acc: 0.8531 - val_loss: 0.3561 - val_acc: 0.8709\n",
      "Epoch 12/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.3974 - acc: 0.8501 - val_loss: 0.3494 - val_acc: 0.8726\n",
      "Epoch 13/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.3854 - acc: 0.8596 - val_loss: 0.3432 - val_acc: 0.8761\n",
      "Epoch 14/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.3814 - acc: 0.8605 - val_loss: 0.3375 - val_acc: 0.8795\n",
      "Epoch 15/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.3725 - acc: 0.8728 - val_loss: 0.3323 - val_acc: 0.8839\n",
      "Epoch 16/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.3649 - acc: 0.8709 - val_loss: 0.3274 - val_acc: 0.8882\n",
      "Epoch 17/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.3645 - acc: 0.8687 - val_loss: 0.3232 - val_acc: 0.8873\n",
      "Epoch 18/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.3593 - acc: 0.8715 - val_loss: 0.3195 - val_acc: 0.8899\n",
      "Epoch 19/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.3563 - acc: 0.8767 - val_loss: 0.3162 - val_acc: 0.8917\n",
      "Epoch 20/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.3518 - acc: 0.8772 - val_loss: 0.3131 - val_acc: 0.8925\n",
      "Epoch 21/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.3546 - acc: 0.8800 - val_loss: 0.3103 - val_acc: 0.8925\n",
      "Epoch 22/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.3490 - acc: 0.8789 - val_loss: 0.3077 - val_acc: 0.8943\n",
      "Epoch 23/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.3419 - acc: 0.8733 - val_loss: 0.3053 - val_acc: 0.8934\n",
      "Epoch 24/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.3516 - acc: 0.8785 - val_loss: 0.3032 - val_acc: 0.8951\n",
      "Epoch 25/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.3506 - acc: 0.8761 - val_loss: 0.3012 - val_acc: 0.8951\n",
      "Epoch 26/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.3438 - acc: 0.8761 - val_loss: 0.2994 - val_acc: 0.8977\n",
      "Epoch 27/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.3524 - acc: 0.8776 - val_loss: 0.2977 - val_acc: 0.8960\n",
      "Epoch 28/500\n",
      "4616/4616 [==============================] - 0s 27us/sample - loss: 0.3340 - acc: 0.8815 - val_loss: 0.2961 - val_acc: 0.8951\n",
      "Epoch 29/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.3293 - acc: 0.8821 - val_loss: 0.2946 - val_acc: 0.8934\n",
      "Epoch 30/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.3528 - acc: 0.8769 - val_loss: 0.2932 - val_acc: 0.8943\n",
      "Epoch 31/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.3381 - acc: 0.8800 - val_loss: 0.2919 - val_acc: 0.8925\n",
      "Epoch 32/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.3294 - acc: 0.8791 - val_loss: 0.2908 - val_acc: 0.8934\n",
      "Epoch 33/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.3290 - acc: 0.8826 - val_loss: 0.2898 - val_acc: 0.8934\n",
      "Epoch 34/500\n",
      "4616/4616 [==============================] - 0s 27us/sample - loss: 0.3386 - acc: 0.8800 - val_loss: 0.2890 - val_acc: 0.8925\n",
      "Epoch 35/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.3331 - acc: 0.8808 - val_loss: 0.2887 - val_acc: 0.8917\n",
      "Epoch 36/500\n",
      "4616/4616 [==============================] - 0s 28us/sample - loss: 0.3385 - acc: 0.8828 - val_loss: 0.2946 - val_acc: 0.8917\n",
      "Epoch 37/500\n",
      "4616/4616 [==============================] - 0s 30us/sample - loss: 0.3306 - acc: 0.8841 - val_loss: 0.2936 - val_acc: 0.8917\n",
      "Epoch 38/500\n",
      "4616/4616 [==============================] - 0s 29us/sample - loss: 0.3232 - acc: 0.8834 - val_loss: 0.2926 - val_acc: 0.8925\n",
      "Epoch 39/500\n",
      "4616/4616 [==============================] - 0s 27us/sample - loss: 0.3414 - acc: 0.8778 - val_loss: 0.2916 - val_acc: 0.8925\n",
      "Epoch 40/500\n",
      "4616/4616 [==============================] - 0s 27us/sample - loss: 0.3314 - acc: 0.8830 - val_loss: 0.2907 - val_acc: 0.8925\n",
      "Epoch 41/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.3256 - acc: 0.8791 - val_loss: 0.2899 - val_acc: 0.8934\n",
      "Epoch 42/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.3345 - acc: 0.8841 - val_loss: 0.2892 - val_acc: 0.8934\n",
      "Epoch 43/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.3275 - acc: 0.8837 - val_loss: 0.2890 - val_acc: 0.8951\n",
      "Epoch 44/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.3258 - acc: 0.8834 - val_loss: 0.2944 - val_acc: 0.8951\n",
      "Epoch 45/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.3147 - acc: 0.8882 - val_loss: 0.2933 - val_acc: 0.8951\n",
      "Epoch 46/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.3283 - acc: 0.8884 - val_loss: 0.2921 - val_acc: 0.8960\n",
      "Epoch 47/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.3225 - acc: 0.8832 - val_loss: 0.2911 - val_acc: 0.8969\n",
      "Epoch 48/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.3134 - acc: 0.8845 - val_loss: 0.2901 - val_acc: 0.8969\n",
      "Epoch 49/500\n",
      "4616/4616 [==============================] - 0s 27us/sample - loss: 0.3140 - acc: 0.8876 - val_loss: 0.2891 - val_acc: 0.8969\n",
      "Epoch 50/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.3147 - acc: 0.8878 - val_loss: 0.2882 - val_acc: 0.8969\n",
      "Epoch 51/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.3308 - acc: 0.8891 - val_loss: 0.2874 - val_acc: 0.8969\n",
      "Epoch 52/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.3148 - acc: 0.8893 - val_loss: 0.2866 - val_acc: 0.8969\n",
      "Epoch 53/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.3166 - acc: 0.8860 - val_loss: 0.2858 - val_acc: 0.8960\n",
      "Epoch 54/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.3213 - acc: 0.8876 - val_loss: 0.2851 - val_acc: 0.8951\n",
      "Epoch 55/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.3112 - acc: 0.8808 - val_loss: 0.2843 - val_acc: 0.8960\n",
      "Epoch 56/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.3162 - acc: 0.8860 - val_loss: 0.2836 - val_acc: 0.8943\n",
      "Epoch 57/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.3111 - acc: 0.8891 - val_loss: 0.2831 - val_acc: 0.8951\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 58/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.3215 - acc: 0.8871 - val_loss: 0.2826 - val_acc: 0.8951\n",
      "Epoch 59/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.3073 - acc: 0.8889 - val_loss: 0.2821 - val_acc: 0.8943\n",
      "Epoch 60/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.3177 - acc: 0.8915 - val_loss: 0.2816 - val_acc: 0.8943\n",
      "Epoch 61/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.3077 - acc: 0.8923 - val_loss: 0.2811 - val_acc: 0.8943\n",
      "Epoch 62/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.3199 - acc: 0.8858 - val_loss: 0.2807 - val_acc: 0.8943\n",
      "Epoch 63/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.3072 - acc: 0.8915 - val_loss: 0.2802 - val_acc: 0.8943\n",
      "Epoch 64/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.3063 - acc: 0.8906 - val_loss: 0.2796 - val_acc: 0.8934\n",
      "Epoch 65/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.3147 - acc: 0.8876 - val_loss: 0.2791 - val_acc: 0.8934\n",
      "Epoch 66/500\n",
      "4616/4616 [==============================] - 0s 27us/sample - loss: 0.3061 - acc: 0.8923 - val_loss: 0.2786 - val_acc: 0.8951\n",
      "Epoch 67/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.3025 - acc: 0.8930 - val_loss: 0.2780 - val_acc: 0.8951\n",
      "Epoch 68/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.3112 - acc: 0.8889 - val_loss: 0.2774 - val_acc: 0.8960\n",
      "Epoch 69/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.3126 - acc: 0.8919 - val_loss: 0.2769 - val_acc: 0.8969\n",
      "Epoch 70/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.2995 - acc: 0.8923 - val_loss: 0.2763 - val_acc: 0.8960\n",
      "Epoch 71/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.3001 - acc: 0.8919 - val_loss: 0.2757 - val_acc: 0.8969\n",
      "Epoch 72/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.3050 - acc: 0.8936 - val_loss: 0.2751 - val_acc: 0.8960\n",
      "Epoch 73/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.2992 - acc: 0.8941 - val_loss: 0.2745 - val_acc: 0.8960\n",
      "Epoch 74/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.3048 - acc: 0.8960 - val_loss: 0.2739 - val_acc: 0.8960\n",
      "Epoch 75/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.3005 - acc: 0.8923 - val_loss: 0.2733 - val_acc: 0.8951\n",
      "Epoch 76/500\n",
      "4616/4616 [==============================] - 0s 28us/sample - loss: 0.3037 - acc: 0.8930 - val_loss: 0.2727 - val_acc: 0.8951\n",
      "Epoch 77/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.3069 - acc: 0.8915 - val_loss: 0.2724 - val_acc: 0.8951\n",
      "Epoch 78/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2974 - acc: 0.8906 - val_loss: 0.2720 - val_acc: 0.8951\n",
      "Epoch 79/500\n",
      "4616/4616 [==============================] - 0s 27us/sample - loss: 0.3003 - acc: 0.8884 - val_loss: 0.2716 - val_acc: 0.8951\n",
      "Epoch 80/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2982 - acc: 0.8919 - val_loss: 0.2713 - val_acc: 0.8951\n",
      "Epoch 81/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2959 - acc: 0.8910 - val_loss: 0.2711 - val_acc: 0.8951\n",
      "Epoch 82/500\n",
      "4616/4616 [==============================] - 0s 27us/sample - loss: 0.2834 - acc: 0.8973 - val_loss: 0.2709 - val_acc: 0.8960\n",
      "Epoch 83/500\n",
      "4616/4616 [==============================] - 0s 28us/sample - loss: 0.2982 - acc: 0.8960 - val_loss: 0.2708 - val_acc: 0.8960\n",
      "Epoch 84/500\n",
      "4616/4616 [==============================] - 0s 27us/sample - loss: 0.3005 - acc: 0.8919 - val_loss: 0.2634 - val_acc: 0.8960\n",
      "Epoch 85/500\n",
      "4616/4616 [==============================] - 0s 27us/sample - loss: 0.2911 - acc: 0.8964 - val_loss: 0.2628 - val_acc: 0.8960\n",
      "Epoch 86/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.3069 - acc: 0.8969 - val_loss: 0.2624 - val_acc: 0.8960\n",
      "Epoch 87/500\n",
      "4616/4616 [==============================] - 0s 28us/sample - loss: 0.2895 - acc: 0.8973 - val_loss: 0.2621 - val_acc: 0.8960\n",
      "Epoch 88/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2958 - acc: 0.8856 - val_loss: 0.2618 - val_acc: 0.8969\n",
      "Epoch 89/500\n",
      "4616/4616 [==============================] - 0s 27us/sample - loss: 0.2897 - acc: 0.8938 - val_loss: 0.2614 - val_acc: 0.8969\n",
      "Epoch 90/500\n",
      "4616/4616 [==============================] - 0s 29us/sample - loss: 0.2836 - acc: 0.8915 - val_loss: 0.2610 - val_acc: 0.8969\n",
      "Epoch 91/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2932 - acc: 0.8941 - val_loss: 0.2607 - val_acc: 0.8969\n",
      "Epoch 92/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2845 - acc: 0.8949 - val_loss: 0.2539 - val_acc: 0.8969\n",
      "Epoch 93/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2871 - acc: 0.8962 - val_loss: 0.2532 - val_acc: 0.8969\n",
      "Epoch 94/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2956 - acc: 0.8951 - val_loss: 0.2528 - val_acc: 0.8969\n",
      "Epoch 95/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2834 - acc: 0.9008 - val_loss: 0.2523 - val_acc: 0.8977\n",
      "Epoch 96/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2846 - acc: 0.8977 - val_loss: 0.2521 - val_acc: 0.8977\n",
      "Epoch 97/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.2850 - acc: 0.8977 - val_loss: 0.2516 - val_acc: 0.8977\n",
      "Epoch 98/500\n",
      "4616/4616 [==============================] - 0s 28us/sample - loss: 0.2898 - acc: 0.8917 - val_loss: 0.2512 - val_acc: 0.8977\n",
      "Epoch 99/500\n",
      "4616/4616 [==============================] - 0s 29us/sample - loss: 0.2877 - acc: 0.8956 - val_loss: 0.2511 - val_acc: 0.8977\n",
      "Epoch 100/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2918 - acc: 0.8964 - val_loss: 0.2526 - val_acc: 0.8977\n",
      "Epoch 101/500\n",
      "4616/4616 [==============================] - 0s 27us/sample - loss: 0.3007 - acc: 0.8947 - val_loss: 0.2570 - val_acc: 0.8977\n",
      "Epoch 102/500\n",
      "4616/4616 [==============================] - 0s 28us/sample - loss: 0.2848 - acc: 0.9012 - val_loss: 0.2567 - val_acc: 0.8977\n",
      "Epoch 103/500\n",
      "4616/4616 [==============================] - 0s 27us/sample - loss: 0.2868 - acc: 0.8964 - val_loss: 0.2564 - val_acc: 0.8977\n",
      "Epoch 104/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2887 - acc: 0.8960 - val_loss: 0.2562 - val_acc: 0.8986\n",
      "Epoch 105/500\n",
      "4616/4616 [==============================] - 0s 28us/sample - loss: 0.2972 - acc: 0.8999 - val_loss: 0.2559 - val_acc: 0.8986\n",
      "Epoch 106/500\n",
      "4616/4616 [==============================] - 0s 29us/sample - loss: 0.2790 - acc: 0.8949 - val_loss: 0.2556 - val_acc: 0.8986\n",
      "Epoch 107/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2849 - acc: 0.8967 - val_loss: 0.2554 - val_acc: 0.8986\n",
      "Epoch 108/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2787 - acc: 0.9023 - val_loss: 0.2553 - val_acc: 0.8977\n",
      "Epoch 109/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2813 - acc: 0.8954 - val_loss: 0.2554 - val_acc: 0.8977\n",
      "Epoch 110/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2864 - acc: 0.8951 - val_loss: 0.2466 - val_acc: 0.8977\n",
      "Epoch 111/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2860 - acc: 0.8995 - val_loss: 0.2458 - val_acc: 0.8977\n",
      "Epoch 112/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.2789 - acc: 0.8988 - val_loss: 0.2456 - val_acc: 0.8986\n",
      "Epoch 113/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.2819 - acc: 0.8988 - val_loss: 0.2455 - val_acc: 0.8995\n",
      "Epoch 114/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2797 - acc: 0.8988 - val_loss: 0.2456 - val_acc: 0.8995\n",
      "Epoch 115/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2741 - acc: 0.9029 - val_loss: 0.2456 - val_acc: 0.9003\n",
      "Epoch 116/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.2756 - acc: 0.9003 - val_loss: 0.2456 - val_acc: 0.9003\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 117/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2887 - acc: 0.8988 - val_loss: 0.2457 - val_acc: 0.9012\n",
      "Epoch 118/500\n",
      "4616/4616 [==============================] - 0s 27us/sample - loss: 0.2786 - acc: 0.9032 - val_loss: 0.2457 - val_acc: 0.9012\n",
      "Epoch 119/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2782 - acc: 0.9003 - val_loss: 0.2456 - val_acc: 0.9012\n",
      "Epoch 120/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2688 - acc: 0.9014 - val_loss: 0.2456 - val_acc: 0.9021\n",
      "Epoch 121/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2687 - acc: 0.9040 - val_loss: 0.2455 - val_acc: 0.9029\n",
      "Epoch 122/500\n",
      "4616/4616 [==============================] - 0s 31us/sample - loss: 0.2775 - acc: 0.8980 - val_loss: 0.2454 - val_acc: 0.9029\n",
      "Epoch 123/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2809 - acc: 0.8958 - val_loss: 0.2452 - val_acc: 0.9029\n",
      "Epoch 124/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2711 - acc: 0.9034 - val_loss: 0.2450 - val_acc: 0.9038\n",
      "Epoch 125/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2825 - acc: 0.8984 - val_loss: 0.2449 - val_acc: 0.9038\n",
      "Epoch 126/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2768 - acc: 0.8982 - val_loss: 0.2446 - val_acc: 0.9038\n",
      "Epoch 127/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2746 - acc: 0.9010 - val_loss: 0.2444 - val_acc: 0.9038\n",
      "Epoch 128/500\n",
      "4616/4616 [==============================] - 0s 28us/sample - loss: 0.2714 - acc: 0.8988 - val_loss: 0.2442 - val_acc: 0.9038\n",
      "Epoch 129/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2771 - acc: 0.9016 - val_loss: 0.2440 - val_acc: 0.9038\n",
      "Epoch 130/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2769 - acc: 0.9019 - val_loss: 0.2437 - val_acc: 0.9038\n",
      "Epoch 131/500\n",
      "4616/4616 [==============================] - 0s 27us/sample - loss: 0.2883 - acc: 0.8960 - val_loss: 0.2435 - val_acc: 0.9038\n",
      "Epoch 132/500\n",
      "4616/4616 [==============================] - 0s 28us/sample - loss: 0.2720 - acc: 0.8999 - val_loss: 0.2432 - val_acc: 0.9038\n",
      "Epoch 133/500\n",
      "4616/4616 [==============================] - 0s 27us/sample - loss: 0.2701 - acc: 0.9025 - val_loss: 0.2429 - val_acc: 0.9038\n",
      "Epoch 134/500\n",
      "4616/4616 [==============================] - 0s 27us/sample - loss: 0.2751 - acc: 0.9016 - val_loss: 0.2427 - val_acc: 0.9038\n",
      "Epoch 135/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2768 - acc: 0.8990 - val_loss: 0.2424 - val_acc: 0.9038\n",
      "Epoch 136/500\n",
      "4616/4616 [==============================] - 0s 28us/sample - loss: 0.2686 - acc: 0.9019 - val_loss: 0.2421 - val_acc: 0.9038\n",
      "Epoch 137/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2689 - acc: 0.9019 - val_loss: 0.2418 - val_acc: 0.9038\n",
      "Epoch 138/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2729 - acc: 0.9010 - val_loss: 0.2416 - val_acc: 0.9055\n",
      "Epoch 139/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2765 - acc: 0.9001 - val_loss: 0.2415 - val_acc: 0.9055\n",
      "Epoch 140/500\n",
      "4616/4616 [==============================] - 0s 29us/sample - loss: 0.2783 - acc: 0.8971 - val_loss: 0.2413 - val_acc: 0.9055\n",
      "Epoch 141/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2685 - acc: 0.9040 - val_loss: 0.2411 - val_acc: 0.9055\n",
      "Epoch 142/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2700 - acc: 0.9047 - val_loss: 0.2408 - val_acc: 0.9064\n",
      "Epoch 143/500\n",
      "4616/4616 [==============================] - 0s 30us/sample - loss: 0.2777 - acc: 0.9021 - val_loss: 0.2406 - val_acc: 0.9064\n",
      "Epoch 144/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2694 - acc: 0.9010 - val_loss: 0.2404 - val_acc: 0.9064\n",
      "Epoch 145/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2649 - acc: 0.9014 - val_loss: 0.2402 - val_acc: 0.9064\n",
      "Epoch 146/500\n",
      "4616/4616 [==============================] - 0s 28us/sample - loss: 0.2792 - acc: 0.9025 - val_loss: 0.2400 - val_acc: 0.9073\n",
      "Epoch 147/500\n",
      "4616/4616 [==============================] - 0s 27us/sample - loss: 0.2630 - acc: 0.9040 - val_loss: 0.2398 - val_acc: 0.9073\n",
      "Epoch 148/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2726 - acc: 0.8995 - val_loss: 0.2396 - val_acc: 0.9073\n",
      "Epoch 149/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.2805 - acc: 0.9023 - val_loss: 0.2394 - val_acc: 0.9073\n",
      "Epoch 150/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2736 - acc: 0.8993 - val_loss: 0.2392 - val_acc: 0.9073\n",
      "Epoch 151/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2631 - acc: 0.9066 - val_loss: 0.2390 - val_acc: 0.9073\n",
      "Epoch 152/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2643 - acc: 0.9021 - val_loss: 0.2388 - val_acc: 0.9073\n",
      "Epoch 153/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.2706 - acc: 0.9001 - val_loss: 0.2386 - val_acc: 0.9073\n",
      "Epoch 154/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.2737 - acc: 0.8993 - val_loss: 0.2384 - val_acc: 0.9081\n",
      "Epoch 155/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2784 - acc: 0.9010 - val_loss: 0.2382 - val_acc: 0.9081\n",
      "Epoch 156/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.2672 - acc: 0.9077 - val_loss: 0.2380 - val_acc: 0.9090\n",
      "Epoch 157/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.2642 - acc: 0.9029 - val_loss: 0.2378 - val_acc: 0.9090\n",
      "Epoch 158/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.2669 - acc: 0.9003 - val_loss: 0.2376 - val_acc: 0.9090\n",
      "Epoch 159/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2718 - acc: 0.9045 - val_loss: 0.2373 - val_acc: 0.9081\n",
      "Epoch 160/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.2634 - acc: 0.9032 - val_loss: 0.2371 - val_acc: 0.9081\n",
      "Epoch 161/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.2617 - acc: 0.9055 - val_loss: 0.2369 - val_acc: 0.9090\n",
      "Epoch 162/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.2690 - acc: 0.9036 - val_loss: 0.2367 - val_acc: 0.9090\n",
      "Epoch 163/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.2674 - acc: 0.9029 - val_loss: 0.2366 - val_acc: 0.9090\n",
      "Epoch 164/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2591 - acc: 0.9051 - val_loss: 0.2365 - val_acc: 0.9090\n",
      "Epoch 165/500\n",
      "4616/4616 [==============================] - 0s 27us/sample - loss: 0.2756 - acc: 0.9040 - val_loss: 0.2364 - val_acc: 0.9090\n",
      "Epoch 166/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2665 - acc: 0.9040 - val_loss: 0.2363 - val_acc: 0.9090\n",
      "Epoch 167/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.2697 - acc: 0.9012 - val_loss: 0.2362 - val_acc: 0.9090\n",
      "Epoch 168/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2646 - acc: 0.9016 - val_loss: 0.2360 - val_acc: 0.9090\n",
      "Epoch 169/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2633 - acc: 0.9025 - val_loss: 0.2359 - val_acc: 0.9099\n",
      "Epoch 170/500\n",
      "4616/4616 [==============================] - 0s 27us/sample - loss: 0.2553 - acc: 0.9058 - val_loss: 0.2357 - val_acc: 0.9099\n",
      "Epoch 171/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2555 - acc: 0.9097 - val_loss: 0.2356 - val_acc: 0.9107\n",
      "Epoch 172/500\n",
      "4616/4616 [==============================] - 0s 27us/sample - loss: 0.2620 - acc: 0.9047 - val_loss: 0.2355 - val_acc: 0.9107\n",
      "Epoch 173/500\n",
      "4616/4616 [==============================] - 0s 28us/sample - loss: 0.2695 - acc: 0.9034 - val_loss: 0.2359 - val_acc: 0.9125\n",
      "Epoch 174/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2706 - acc: 0.8995 - val_loss: 0.2367 - val_acc: 0.9116\n",
      "Epoch 175/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2612 - acc: 0.9014 - val_loss: 0.2375 - val_acc: 0.9107\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 176/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2650 - acc: 0.9027 - val_loss: 0.2384 - val_acc: 0.9099\n",
      "Epoch 177/500\n",
      "4616/4616 [==============================] - 0s 29us/sample - loss: 0.2646 - acc: 0.9055 - val_loss: 0.2394 - val_acc: 0.9099\n",
      "Epoch 178/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2598 - acc: 0.9086 - val_loss: 0.2403 - val_acc: 0.9099\n",
      "Epoch 179/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2594 - acc: 0.9064 - val_loss: 0.2411 - val_acc: 0.9081\n",
      "Epoch 180/500\n",
      "4616/4616 [==============================] - 0s 28us/sample - loss: 0.2675 - acc: 0.9001 - val_loss: 0.2418 - val_acc: 0.9064\n",
      "Epoch 181/500\n",
      "4616/4616 [==============================] - 0s 27us/sample - loss: 0.2635 - acc: 0.9068 - val_loss: 0.2425 - val_acc: 0.9055\n",
      "Epoch 182/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2713 - acc: 0.9029 - val_loss: 0.2430 - val_acc: 0.9064\n",
      "Epoch 183/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2649 - acc: 0.9047 - val_loss: 0.2435 - val_acc: 0.9064\n",
      "Epoch 184/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2686 - acc: 0.9001 - val_loss: 0.2438 - val_acc: 0.9064\n",
      "Epoch 185/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2701 - acc: 0.9012 - val_loss: 0.2440 - val_acc: 0.9073\n",
      "Epoch 186/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2699 - acc: 0.9021 - val_loss: 0.2441 - val_acc: 0.9073\n",
      "Epoch 187/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2684 - acc: 0.9073 - val_loss: 0.2442 - val_acc: 0.9081\n",
      "Epoch 188/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2636 - acc: 0.9088 - val_loss: 0.2442 - val_acc: 0.9073\n",
      "Epoch 189/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.2682 - acc: 0.9084 - val_loss: 0.2441 - val_acc: 0.9064\n",
      "Epoch 190/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2684 - acc: 0.9025 - val_loss: 0.2440 - val_acc: 0.9064\n",
      "Epoch 191/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.2703 - acc: 0.9038 - val_loss: 0.2438 - val_acc: 0.9055\n",
      "Epoch 192/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2679 - acc: 0.9016 - val_loss: 0.2436 - val_acc: 0.9081\n",
      "Epoch 193/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2696 - acc: 0.9055 - val_loss: 0.2433 - val_acc: 0.9081\n",
      "Epoch 194/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.2630 - acc: 0.9060 - val_loss: 0.2431 - val_acc: 0.9090\n",
      "Epoch 195/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.2754 - acc: 0.8986 - val_loss: 0.2428 - val_acc: 0.9090\n",
      "Epoch 196/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2683 - acc: 0.9029 - val_loss: 0.2425 - val_acc: 0.9099\n",
      "Epoch 197/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2638 - acc: 0.9034 - val_loss: 0.2422 - val_acc: 0.9099\n",
      "Epoch 198/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2742 - acc: 0.9071 - val_loss: 0.2419 - val_acc: 0.9099\n",
      "Epoch 199/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.2649 - acc: 0.9084 - val_loss: 0.2416 - val_acc: 0.9099\n",
      "Epoch 200/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2713 - acc: 0.9025 - val_loss: 0.2413 - val_acc: 0.9099\n",
      "Epoch 201/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.2719 - acc: 0.9055 - val_loss: 0.2410 - val_acc: 0.9099\n",
      "Epoch 202/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2638 - acc: 0.9040 - val_loss: 0.2407 - val_acc: 0.9099\n",
      "Epoch 203/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.2719 - acc: 0.9042 - val_loss: 0.2404 - val_acc: 0.9090\n",
      "Epoch 204/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2716 - acc: 0.9012 - val_loss: 0.2402 - val_acc: 0.9099\n",
      "Epoch 205/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.2653 - acc: 0.9086 - val_loss: 0.2400 - val_acc: 0.9107\n",
      "Epoch 206/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.2641 - acc: 0.9032 - val_loss: 0.2398 - val_acc: 0.9116\n",
      "Epoch 207/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.2699 - acc: 0.9064 - val_loss: 0.2396 - val_acc: 0.9116\n",
      "Epoch 208/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.2616 - acc: 0.9068 - val_loss: 0.2394 - val_acc: 0.9116\n",
      "Epoch 209/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2697 - acc: 0.9062 - val_loss: 0.2392 - val_acc: 0.9116\n",
      "Epoch 210/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.2629 - acc: 0.9040 - val_loss: 0.2390 - val_acc: 0.9116\n",
      "Epoch 211/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.2621 - acc: 0.9088 - val_loss: 0.2388 - val_acc: 0.9107\n",
      "Epoch 212/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.2625 - acc: 0.9075 - val_loss: 0.2387 - val_acc: 0.9107\n",
      "Epoch 213/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.2608 - acc: 0.9053 - val_loss: 0.2385 - val_acc: 0.9107\n",
      "Epoch 214/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2637 - acc: 0.9042 - val_loss: 0.2383 - val_acc: 0.9107\n",
      "Epoch 215/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.2646 - acc: 0.9008 - val_loss: 0.2382 - val_acc: 0.9107\n",
      "Epoch 216/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2581 - acc: 0.9051 - val_loss: 0.2380 - val_acc: 0.9099\n",
      "Epoch 217/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2635 - acc: 0.9032 - val_loss: 0.2379 - val_acc: 0.9107\n",
      "Epoch 218/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.2664 - acc: 0.9060 - val_loss: 0.2377 - val_acc: 0.9107\n",
      "Epoch 219/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2696 - acc: 0.9068 - val_loss: 0.2376 - val_acc: 0.9107\n",
      "Epoch 220/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2676 - acc: 0.9029 - val_loss: 0.2375 - val_acc: 0.9107\n",
      "Epoch 221/500\n",
      "4616/4616 [==============================] - 0s 26us/sample - loss: 0.2640 - acc: 0.9064 - val_loss: 0.2374 - val_acc: 0.9107\n",
      "Epoch 222/500\n",
      "4616/4616 [==============================] - 0s 25us/sample - loss: 0.2572 - acc: 0.9090 - val_loss: 0.2372 - val_acc: 0.9107\n",
      "Epoch 00222: early stopping\n",
      " Preprocessed 5770 tweets \n",
      "Filtered data\n",
      "WARNING:tensorflow:sample_weight modes were coerced from\n",
      "  ...\n",
      "    to  \n",
      "  ['...']\n",
      "WARNING:tensorflow:sample_weight modes were coerced from\n",
      "  ...\n",
      "    to  \n",
      "  ['...']\n",
      "Train on 4616 samples, validate on 1154 samples\n",
      "Epoch 1/500\n",
      "4616/4616 [==============================] - 4s 770us/sample - loss: 0.5032 - acc: 0.7712 - val_loss: 0.4620 - val_acc: 0.7825\n",
      "Epoch 2/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.4755 - acc: 0.7823 - val_loss: 0.4385 - val_acc: 0.7938\n",
      "Epoch 3/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.4565 - acc: 0.7903 - val_loss: 0.4227 - val_acc: 0.8085\n",
      "Epoch 4/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.4452 - acc: 0.8050 - val_loss: 0.4082 - val_acc: 0.8137\n",
      "Epoch 5/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.4284 - acc: 0.8146 - val_loss: 0.3948 - val_acc: 0.8267\n",
      "Epoch 6/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.4199 - acc: 0.8232 - val_loss: 0.3823 - val_acc: 0.8397\n",
      "Epoch 7/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.4096 - acc: 0.8302 - val_loss: 0.3707 - val_acc: 0.8536\n",
      "Epoch 8/500\n",
      "4616/4616 [==============================] - 0s 36us/sample - loss: 0.3999 - acc: 0.8401 - val_loss: 0.3598 - val_acc: 0.8588\n",
      "Epoch 9/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.3896 - acc: 0.8490 - val_loss: 0.3500 - val_acc: 0.8666\n",
      "Epoch 10/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.3762 - acc: 0.8510 - val_loss: 0.3409 - val_acc: 0.8674\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 11/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.3693 - acc: 0.8588 - val_loss: 0.3326 - val_acc: 0.8718\n",
      "Epoch 12/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.3637 - acc: 0.8689 - val_loss: 0.3250 - val_acc: 0.8709\n",
      "Epoch 13/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.3645 - acc: 0.8657 - val_loss: 0.3181 - val_acc: 0.8726\n",
      "Epoch 14/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.3424 - acc: 0.8815 - val_loss: 0.3120 - val_acc: 0.8804\n",
      "Epoch 15/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.3384 - acc: 0.8744 - val_loss: 0.3063 - val_acc: 0.8821\n",
      "Epoch 16/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.3364 - acc: 0.8761 - val_loss: 0.3013 - val_acc: 0.8873\n",
      "Epoch 17/500\n",
      "4616/4616 [==============================] - 0s 35us/sample - loss: 0.3468 - acc: 0.8748 - val_loss: 0.2975 - val_acc: 0.8891\n",
      "Epoch 18/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.3218 - acc: 0.8798 - val_loss: 0.2940 - val_acc: 0.8891\n",
      "Epoch 19/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.3296 - acc: 0.8826 - val_loss: 0.2909 - val_acc: 0.8882\n",
      "Epoch 20/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.3158 - acc: 0.8886 - val_loss: 0.2881 - val_acc: 0.8882\n",
      "Epoch 21/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.3294 - acc: 0.8858 - val_loss: 0.2857 - val_acc: 0.8856\n",
      "Epoch 22/500\n",
      "4616/4616 [==============================] - 0s 35us/sample - loss: 0.3357 - acc: 0.8889 - val_loss: 0.2837 - val_acc: 0.8856\n",
      "Epoch 23/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.3153 - acc: 0.8873 - val_loss: 0.2819 - val_acc: 0.8873\n",
      "Epoch 24/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.3263 - acc: 0.8902 - val_loss: 0.2805 - val_acc: 0.8865\n",
      "Epoch 25/500\n",
      "4616/4616 [==============================] - 0s 35us/sample - loss: 0.3185 - acc: 0.8856 - val_loss: 0.2794 - val_acc: 0.8873\n",
      "Epoch 26/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.3265 - acc: 0.8865 - val_loss: 0.2786 - val_acc: 0.8865\n",
      "Epoch 27/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.3174 - acc: 0.8873 - val_loss: 0.2778 - val_acc: 0.8856\n",
      "Epoch 28/500\n",
      "4616/4616 [==============================] - 0s 35us/sample - loss: 0.3196 - acc: 0.8880 - val_loss: 0.2771 - val_acc: 0.8839\n",
      "Epoch 29/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.3031 - acc: 0.8886 - val_loss: 0.2765 - val_acc: 0.8856\n",
      "Epoch 30/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.3146 - acc: 0.8882 - val_loss: 0.2758 - val_acc: 0.8865\n",
      "Epoch 31/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.3154 - acc: 0.8908 - val_loss: 0.2752 - val_acc: 0.8873\n",
      "Epoch 32/500\n",
      "4616/4616 [==============================] - 0s 35us/sample - loss: 0.3112 - acc: 0.8951 - val_loss: 0.2745 - val_acc: 0.8882\n",
      "Epoch 33/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.3149 - acc: 0.8925 - val_loss: 0.2737 - val_acc: 0.8899\n",
      "Epoch 34/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2998 - acc: 0.8902 - val_loss: 0.2730 - val_acc: 0.8908\n",
      "Epoch 35/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.3253 - acc: 0.8876 - val_loss: 0.2723 - val_acc: 0.8899\n",
      "Epoch 36/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.3159 - acc: 0.8906 - val_loss: 0.2717 - val_acc: 0.8899\n",
      "Epoch 37/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.3026 - acc: 0.8921 - val_loss: 0.2710 - val_acc: 0.8899\n",
      "Epoch 38/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.3078 - acc: 0.8921 - val_loss: 0.2704 - val_acc: 0.8917\n",
      "Epoch 39/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.3129 - acc: 0.8921 - val_loss: 0.2698 - val_acc: 0.8934\n",
      "Epoch 40/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.3064 - acc: 0.8941 - val_loss: 0.2692 - val_acc: 0.8917\n",
      "Epoch 41/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.3137 - acc: 0.8923 - val_loss: 0.2686 - val_acc: 0.8934\n",
      "Epoch 42/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.3019 - acc: 0.8947 - val_loss: 0.2680 - val_acc: 0.8925\n",
      "Epoch 43/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.3004 - acc: 0.8867 - val_loss: 0.2674 - val_acc: 0.8934\n",
      "Epoch 44/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.3043 - acc: 0.8958 - val_loss: 0.2668 - val_acc: 0.8934\n",
      "Epoch 45/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2958 - acc: 0.8923 - val_loss: 0.2662 - val_acc: 0.8943\n",
      "Epoch 46/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.3101 - acc: 0.8891 - val_loss: 0.2657 - val_acc: 0.8943\n",
      "Epoch 47/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.3013 - acc: 0.8973 - val_loss: 0.2652 - val_acc: 0.8943\n",
      "Epoch 48/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2940 - acc: 0.8928 - val_loss: 0.2647 - val_acc: 0.8960\n",
      "Epoch 49/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2857 - acc: 0.8969 - val_loss: 0.2642 - val_acc: 0.8951\n",
      "Epoch 50/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2879 - acc: 0.8936 - val_loss: 0.2637 - val_acc: 0.8951\n",
      "Epoch 51/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2942 - acc: 0.8941 - val_loss: 0.2632 - val_acc: 0.8951\n",
      "Epoch 52/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2864 - acc: 0.8975 - val_loss: 0.2628 - val_acc: 0.8960\n",
      "Epoch 53/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2951 - acc: 0.8928 - val_loss: 0.2624 - val_acc: 0.8951\n",
      "Epoch 54/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2829 - acc: 0.8960 - val_loss: 0.2620 - val_acc: 0.8960\n",
      "Epoch 55/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2798 - acc: 0.8949 - val_loss: 0.2615 - val_acc: 0.8960\n",
      "Epoch 56/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2931 - acc: 0.8949 - val_loss: 0.2611 - val_acc: 0.8960\n",
      "Epoch 57/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2878 - acc: 0.8954 - val_loss: 0.2607 - val_acc: 0.8969\n",
      "Epoch 58/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2798 - acc: 0.8962 - val_loss: 0.2603 - val_acc: 0.8969\n",
      "Epoch 59/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2913 - acc: 0.9019 - val_loss: 0.2598 - val_acc: 0.8977\n",
      "Epoch 60/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2792 - acc: 0.8932 - val_loss: 0.2594 - val_acc: 0.8977\n",
      "Epoch 61/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2855 - acc: 0.9008 - val_loss: 0.2592 - val_acc: 0.8986\n",
      "Epoch 62/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2820 - acc: 0.8954 - val_loss: 0.2590 - val_acc: 0.8986\n",
      "Epoch 63/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2770 - acc: 0.8999 - val_loss: 0.2588 - val_acc: 0.8995\n",
      "Epoch 64/500\n",
      "4616/4616 [==============================] - 0s 35us/sample - loss: 0.2808 - acc: 0.9023 - val_loss: 0.2586 - val_acc: 0.9003\n",
      "Epoch 65/500\n",
      "4616/4616 [==============================] - 0s 35us/sample - loss: 0.2765 - acc: 0.8999 - val_loss: 0.2584 - val_acc: 0.9003\n",
      "Epoch 66/500\n",
      "4616/4616 [==============================] - 0s 36us/sample - loss: 0.2786 - acc: 0.8980 - val_loss: 0.2582 - val_acc: 0.9003\n",
      "Epoch 67/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.2807 - acc: 0.9001 - val_loss: 0.2580 - val_acc: 0.9012\n",
      "Epoch 68/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.2730 - acc: 0.8984 - val_loss: 0.2578 - val_acc: 0.9012\n",
      "Epoch 69/500\n",
      "4616/4616 [==============================] - 0s 38us/sample - loss: 0.2726 - acc: 0.8973 - val_loss: 0.2576 - val_acc: 0.9021\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 70/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.2765 - acc: 0.9045 - val_loss: 0.2574 - val_acc: 0.9029\n",
      "Epoch 71/500\n",
      "4616/4616 [==============================] - 0s 35us/sample - loss: 0.2817 - acc: 0.9001 - val_loss: 0.2572 - val_acc: 0.9038\n",
      "Epoch 72/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2761 - acc: 0.8999 - val_loss: 0.2571 - val_acc: 0.9038\n",
      "Epoch 73/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2787 - acc: 0.8990 - val_loss: 0.2571 - val_acc: 0.9038\n",
      "Epoch 74/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2730 - acc: 0.9019 - val_loss: 0.2574 - val_acc: 0.9047\n",
      "Epoch 75/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2749 - acc: 0.8986 - val_loss: 0.2635 - val_acc: 0.9055\n",
      "Epoch 76/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2689 - acc: 0.9021 - val_loss: 0.2630 - val_acc: 0.9055\n",
      "Epoch 77/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.2764 - acc: 0.8986 - val_loss: 0.2625 - val_acc: 0.9055\n",
      "Epoch 78/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2725 - acc: 0.8986 - val_loss: 0.2621 - val_acc: 0.9055\n",
      "Epoch 79/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2750 - acc: 0.8984 - val_loss: 0.2616 - val_acc: 0.9055\n",
      "Epoch 80/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2761 - acc: 0.8975 - val_loss: 0.2612 - val_acc: 0.9047\n",
      "Epoch 81/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2732 - acc: 0.8975 - val_loss: 0.2607 - val_acc: 0.9038\n",
      "Epoch 82/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2702 - acc: 0.9032 - val_loss: 0.2603 - val_acc: 0.9038\n",
      "Epoch 83/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2732 - acc: 0.9036 - val_loss: 0.2599 - val_acc: 0.9047\n",
      "Epoch 84/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2696 - acc: 0.9034 - val_loss: 0.2595 - val_acc: 0.9047\n",
      "Epoch 85/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2714 - acc: 0.9049 - val_loss: 0.2591 - val_acc: 0.9047\n",
      "Epoch 86/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2693 - acc: 0.9023 - val_loss: 0.2587 - val_acc: 0.9047\n",
      "Epoch 87/500\n",
      "4616/4616 [==============================] - 0s 35us/sample - loss: 0.2686 - acc: 0.9036 - val_loss: 0.2583 - val_acc: 0.9047\n",
      "Epoch 88/500\n",
      "4616/4616 [==============================] - 0s 35us/sample - loss: 0.2636 - acc: 0.9040 - val_loss: 0.2579 - val_acc: 0.9047\n",
      "Epoch 89/500\n",
      "4616/4616 [==============================] - 0s 35us/sample - loss: 0.2774 - acc: 0.9014 - val_loss: 0.2576 - val_acc: 0.9038\n",
      "Epoch 90/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2705 - acc: 0.9001 - val_loss: 0.2572 - val_acc: 0.9047\n",
      "Epoch 91/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.2680 - acc: 0.9032 - val_loss: 0.2569 - val_acc: 0.9047\n",
      "Epoch 92/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2639 - acc: 0.9055 - val_loss: 0.2566 - val_acc: 0.9047\n",
      "Epoch 93/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2710 - acc: 0.9025 - val_loss: 0.2562 - val_acc: 0.9055\n",
      "Epoch 94/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2649 - acc: 0.9025 - val_loss: 0.2559 - val_acc: 0.9055\n",
      "Epoch 95/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2700 - acc: 0.9071 - val_loss: 0.2556 - val_acc: 0.9055\n",
      "Epoch 96/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2658 - acc: 0.9062 - val_loss: 0.2552 - val_acc: 0.9064\n",
      "Epoch 97/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2678 - acc: 0.9047 - val_loss: 0.2549 - val_acc: 0.9064\n",
      "Epoch 98/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2622 - acc: 0.8993 - val_loss: 0.2546 - val_acc: 0.9064\n",
      "Epoch 99/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.2608 - acc: 0.9032 - val_loss: 0.2543 - val_acc: 0.9064\n",
      "Epoch 100/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2588 - acc: 0.9038 - val_loss: 0.2540 - val_acc: 0.9064\n",
      "Epoch 101/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2632 - acc: 0.9034 - val_loss: 0.2537 - val_acc: 0.9064\n",
      "Epoch 102/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2652 - acc: 0.8995 - val_loss: 0.2535 - val_acc: 0.9064\n",
      "Epoch 103/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.2579 - acc: 0.9058 - val_loss: 0.2532 - val_acc: 0.9064\n",
      "Epoch 104/500\n",
      "4616/4616 [==============================] - 0s 35us/sample - loss: 0.2670 - acc: 0.9068 - val_loss: 0.2530 - val_acc: 0.9064\n",
      "Epoch 105/500\n",
      "4616/4616 [==============================] - 0s 35us/sample - loss: 0.2617 - acc: 0.9016 - val_loss: 0.2529 - val_acc: 0.9081\n",
      "Epoch 106/500\n",
      "4616/4616 [==============================] - 0s 36us/sample - loss: 0.2570 - acc: 0.9051 - val_loss: 0.2531 - val_acc: 0.9081\n",
      "Epoch 107/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.2536 - acc: 0.9068 - val_loss: 0.2532 - val_acc: 0.9081\n",
      "Epoch 108/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2645 - acc: 0.8997 - val_loss: 0.2534 - val_acc: 0.9081\n",
      "Epoch 109/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2567 - acc: 0.9049 - val_loss: 0.2536 - val_acc: 0.9073\n",
      "Epoch 110/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2665 - acc: 0.9055 - val_loss: 0.2538 - val_acc: 0.9073\n",
      "Epoch 111/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2681 - acc: 0.9016 - val_loss: 0.2540 - val_acc: 0.9081\n",
      "Epoch 112/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2577 - acc: 0.9073 - val_loss: 0.2541 - val_acc: 0.9081\n",
      "Epoch 113/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2609 - acc: 0.9081 - val_loss: 0.2542 - val_acc: 0.9081\n",
      "Epoch 114/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2604 - acc: 0.9019 - val_loss: 0.2542 - val_acc: 0.9081\n",
      "Epoch 115/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2554 - acc: 0.9086 - val_loss: 0.2541 - val_acc: 0.9090\n",
      "Epoch 116/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2595 - acc: 0.9092 - val_loss: 0.2541 - val_acc: 0.9090\n",
      "Epoch 117/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.2564 - acc: 0.9025 - val_loss: 0.2539 - val_acc: 0.9090\n",
      "Epoch 118/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2610 - acc: 0.9049 - val_loss: 0.2537 - val_acc: 0.9081\n",
      "Epoch 119/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2586 - acc: 0.9081 - val_loss: 0.2535 - val_acc: 0.9081\n",
      "Epoch 120/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2595 - acc: 0.9062 - val_loss: 0.2533 - val_acc: 0.9081\n",
      "Epoch 121/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2546 - acc: 0.9066 - val_loss: 0.2530 - val_acc: 0.9081\n",
      "Epoch 122/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2675 - acc: 0.9036 - val_loss: 0.2527 - val_acc: 0.9073\n",
      "Epoch 123/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2588 - acc: 0.9016 - val_loss: 0.2524 - val_acc: 0.9073\n",
      "Epoch 124/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2546 - acc: 0.9058 - val_loss: 0.2521 - val_acc: 0.9073\n",
      "Epoch 125/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2561 - acc: 0.9071 - val_loss: 0.2518 - val_acc: 0.9081\n",
      "Epoch 126/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2572 - acc: 0.9025 - val_loss: 0.2515 - val_acc: 0.9081\n",
      "Epoch 127/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2540 - acc: 0.9090 - val_loss: 0.2513 - val_acc: 0.9073\n",
      "Epoch 128/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2624 - acc: 0.9053 - val_loss: 0.2510 - val_acc: 0.9073\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 129/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2686 - acc: 0.9071 - val_loss: 0.2507 - val_acc: 0.9064\n",
      "Epoch 130/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.2553 - acc: 0.9066 - val_loss: 0.2505 - val_acc: 0.9064\n",
      "Epoch 131/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2479 - acc: 0.9053 - val_loss: 0.2502 - val_acc: 0.9073\n",
      "Epoch 132/500\n",
      "4616/4616 [==============================] - 0s 36us/sample - loss: 0.2556 - acc: 0.9066 - val_loss: 0.2500 - val_acc: 0.9081\n",
      "Epoch 133/500\n",
      "4616/4616 [==============================] - 0s 35us/sample - loss: 0.2478 - acc: 0.9073 - val_loss: 0.2498 - val_acc: 0.9081\n",
      "Epoch 134/500\n",
      "4616/4616 [==============================] - 0s 35us/sample - loss: 0.2507 - acc: 0.9038 - val_loss: 0.2496 - val_acc: 0.9073\n",
      "Epoch 135/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2540 - acc: 0.9094 - val_loss: 0.2494 - val_acc: 0.9073\n",
      "Epoch 136/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.2518 - acc: 0.9073 - val_loss: 0.2492 - val_acc: 0.9073\n",
      "Epoch 137/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2554 - acc: 0.9042 - val_loss: 0.2490 - val_acc: 0.9081\n",
      "Epoch 138/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2584 - acc: 0.9051 - val_loss: 0.2489 - val_acc: 0.9081\n",
      "Epoch 139/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2493 - acc: 0.9053 - val_loss: 0.2487 - val_acc: 0.9081\n",
      "Epoch 140/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2543 - acc: 0.9077 - val_loss: 0.2485 - val_acc: 0.9081\n",
      "Epoch 141/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2556 - acc: 0.9064 - val_loss: 0.2484 - val_acc: 0.9081\n",
      "Epoch 142/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.2565 - acc: 0.9055 - val_loss: 0.2482 - val_acc: 0.9090\n",
      "Epoch 143/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2492 - acc: 0.9073 - val_loss: 0.2481 - val_acc: 0.9090\n",
      "Epoch 144/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2426 - acc: 0.9112 - val_loss: 0.2479 - val_acc: 0.9090\n",
      "Epoch 145/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2580 - acc: 0.9075 - val_loss: 0.2478 - val_acc: 0.9099\n",
      "Epoch 146/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.2555 - acc: 0.9068 - val_loss: 0.2476 - val_acc: 0.9099\n",
      "Epoch 147/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.2505 - acc: 0.9066 - val_loss: 0.2475 - val_acc: 0.9107\n",
      "Epoch 148/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2519 - acc: 0.9036 - val_loss: 0.2473 - val_acc: 0.9107\n",
      "Epoch 149/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2614 - acc: 0.9097 - val_loss: 0.2472 - val_acc: 0.9107\n",
      "Epoch 150/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2484 - acc: 0.9118 - val_loss: 0.2470 - val_acc: 0.9107\n",
      "Epoch 151/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2477 - acc: 0.9064 - val_loss: 0.2469 - val_acc: 0.9107\n",
      "Epoch 152/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2519 - acc: 0.9073 - val_loss: 0.2467 - val_acc: 0.9107\n",
      "Epoch 153/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2459 - acc: 0.9092 - val_loss: 0.2466 - val_acc: 0.9107\n",
      "Epoch 154/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2455 - acc: 0.9075 - val_loss: 0.2464 - val_acc: 0.9107\n",
      "Epoch 155/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.2492 - acc: 0.9088 - val_loss: 0.2466 - val_acc: 0.9107\n",
      "Epoch 156/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2553 - acc: 0.9086 - val_loss: 0.2468 - val_acc: 0.9090\n",
      "Epoch 157/500\n",
      "4616/4616 [==============================] - 0s 36us/sample - loss: 0.2498 - acc: 0.9034 - val_loss: 0.2469 - val_acc: 0.9099\n",
      "Epoch 158/500\n",
      "4616/4616 [==============================] - 0s 37us/sample - loss: 0.2494 - acc: 0.9103 - val_loss: 0.2470 - val_acc: 0.9099\n",
      "Epoch 159/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2536 - acc: 0.9068 - val_loss: 0.2471 - val_acc: 0.9099\n",
      "Epoch 160/500\n",
      "4616/4616 [==============================] - 0s 35us/sample - loss: 0.2491 - acc: 0.9103 - val_loss: 0.2472 - val_acc: 0.9090\n",
      "Epoch 161/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.2536 - acc: 0.9081 - val_loss: 0.2472 - val_acc: 0.9090\n",
      "Epoch 162/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2483 - acc: 0.9120 - val_loss: 0.2473 - val_acc: 0.9090\n",
      "Epoch 163/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.2501 - acc: 0.9066 - val_loss: 0.2473 - val_acc: 0.9090\n",
      "Epoch 164/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2427 - acc: 0.9092 - val_loss: 0.2472 - val_acc: 0.9090\n",
      "Epoch 165/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2476 - acc: 0.9081 - val_loss: 0.2472 - val_acc: 0.9090\n",
      "Epoch 166/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2536 - acc: 0.9081 - val_loss: 0.2471 - val_acc: 0.9090\n",
      "Epoch 167/500\n",
      "4616/4616 [==============================] - 0s 31us/sample - loss: 0.2528 - acc: 0.9086 - val_loss: 0.2470 - val_acc: 0.9090\n",
      "Epoch 168/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2497 - acc: 0.9092 - val_loss: 0.2469 - val_acc: 0.9090\n",
      "Epoch 169/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2414 - acc: 0.9136 - val_loss: 0.2467 - val_acc: 0.9099\n",
      "Epoch 170/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2497 - acc: 0.9112 - val_loss: 0.2466 - val_acc: 0.9099\n",
      "Epoch 171/500\n",
      "4616/4616 [==============================] - 0s 31us/sample - loss: 0.2581 - acc: 0.9042 - val_loss: 0.2465 - val_acc: 0.9099\n",
      "Epoch 172/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2431 - acc: 0.9064 - val_loss: 0.2463 - val_acc: 0.9099\n",
      "Epoch 173/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2586 - acc: 0.9071 - val_loss: 0.2462 - val_acc: 0.9099\n",
      "Epoch 174/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2544 - acc: 0.9077 - val_loss: 0.2460 - val_acc: 0.9099\n",
      "Epoch 175/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2524 - acc: 0.9090 - val_loss: 0.2459 - val_acc: 0.9090\n",
      "Epoch 176/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2465 - acc: 0.9058 - val_loss: 0.2457 - val_acc: 0.9090\n",
      "Epoch 177/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2539 - acc: 0.9071 - val_loss: 0.2456 - val_acc: 0.9090\n",
      "Epoch 178/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2383 - acc: 0.9127 - val_loss: 0.2454 - val_acc: 0.9090\n",
      "Epoch 179/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2372 - acc: 0.9099 - val_loss: 0.2452 - val_acc: 0.9107\n",
      "Epoch 180/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2475 - acc: 0.9099 - val_loss: 0.2451 - val_acc: 0.9107\n",
      "Epoch 181/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2447 - acc: 0.9084 - val_loss: 0.2450 - val_acc: 0.9107\n",
      "Epoch 182/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2430 - acc: 0.9092 - val_loss: 0.2448 - val_acc: 0.9107\n",
      "Epoch 183/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2409 - acc: 0.9125 - val_loss: 0.2446 - val_acc: 0.9099\n",
      "Epoch 184/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2470 - acc: 0.9116 - val_loss: 0.2445 - val_acc: 0.9090\n",
      "Epoch 185/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2501 - acc: 0.9097 - val_loss: 0.2444 - val_acc: 0.9090\n",
      "Epoch 186/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2541 - acc: 0.9040 - val_loss: 0.2443 - val_acc: 0.9090\n",
      "Epoch 187/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2476 - acc: 0.9084 - val_loss: 0.2442 - val_acc: 0.9099\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 188/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2411 - acc: 0.9127 - val_loss: 0.2441 - val_acc: 0.9107\n",
      "Epoch 189/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2368 - acc: 0.9114 - val_loss: 0.2440 - val_acc: 0.9116\n",
      "Epoch 190/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2468 - acc: 0.9114 - val_loss: 0.2440 - val_acc: 0.9116\n",
      "Epoch 191/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2452 - acc: 0.9092 - val_loss: 0.2439 - val_acc: 0.9107\n",
      "Epoch 192/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2413 - acc: 0.9120 - val_loss: 0.2438 - val_acc: 0.9107\n",
      "Epoch 193/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2434 - acc: 0.9129 - val_loss: 0.2438 - val_acc: 0.9107\n",
      "Epoch 194/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2399 - acc: 0.9081 - val_loss: 0.2437 - val_acc: 0.9107\n",
      "Epoch 195/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2389 - acc: 0.9120 - val_loss: 0.2436 - val_acc: 0.9116\n",
      "Epoch 196/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2412 - acc: 0.9127 - val_loss: 0.2435 - val_acc: 0.9116\n",
      "Epoch 197/500\n",
      "4616/4616 [==============================] - 0s 35us/sample - loss: 0.2450 - acc: 0.9073 - val_loss: 0.2434 - val_acc: 0.9116\n",
      "Epoch 198/500\n",
      "4616/4616 [==============================] - 0s 38us/sample - loss: 0.2447 - acc: 0.9105 - val_loss: 0.2434 - val_acc: 0.9116\n",
      "Epoch 199/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2402 - acc: 0.9114 - val_loss: 0.2435 - val_acc: 0.9116\n",
      "Epoch 200/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2374 - acc: 0.9131 - val_loss: 0.2434 - val_acc: 0.9116\n",
      "Epoch 201/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2378 - acc: 0.9149 - val_loss: 0.2434 - val_acc: 0.9107\n",
      "Epoch 202/500\n",
      "4616/4616 [==============================] - 0s 35us/sample - loss: 0.2473 - acc: 0.9112 - val_loss: 0.2434 - val_acc: 0.9107\n",
      "Epoch 203/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2398 - acc: 0.9129 - val_loss: 0.2433 - val_acc: 0.9107\n",
      "Epoch 204/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2402 - acc: 0.9101 - val_loss: 0.2433 - val_acc: 0.9107\n",
      "Epoch 205/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2394 - acc: 0.9138 - val_loss: 0.2432 - val_acc: 0.9107\n",
      "Epoch 206/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2385 - acc: 0.9118 - val_loss: 0.2431 - val_acc: 0.9107\n",
      "Epoch 207/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2407 - acc: 0.9103 - val_loss: 0.2430 - val_acc: 0.9116\n",
      "Epoch 208/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2356 - acc: 0.9123 - val_loss: 0.2429 - val_acc: 0.9116\n",
      "Epoch 209/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.2421 - acc: 0.9101 - val_loss: 0.2428 - val_acc: 0.9116\n",
      "Epoch 210/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.2431 - acc: 0.9103 - val_loss: 0.2427 - val_acc: 0.9125\n",
      "Epoch 211/500\n",
      "4616/4616 [==============================] - 0s 35us/sample - loss: 0.2448 - acc: 0.9151 - val_loss: 0.2426 - val_acc: 0.9125\n",
      "Epoch 212/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.2392 - acc: 0.9123 - val_loss: 0.2425 - val_acc: 0.9125\n",
      "Epoch 213/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2370 - acc: 0.9120 - val_loss: 0.2423 - val_acc: 0.9125\n",
      "Epoch 214/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2404 - acc: 0.9162 - val_loss: 0.2422 - val_acc: 0.9116\n",
      "Epoch 215/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.2385 - acc: 0.9120 - val_loss: 0.2421 - val_acc: 0.9116\n",
      "Epoch 216/500\n",
      "4616/4616 [==============================] - 0s 35us/sample - loss: 0.2490 - acc: 0.9079 - val_loss: 0.2420 - val_acc: 0.9116\n",
      "Epoch 217/500\n",
      "4616/4616 [==============================] - 0s 38us/sample - loss: 0.2373 - acc: 0.9101 - val_loss: 0.2419 - val_acc: 0.9125\n",
      "Epoch 218/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.2370 - acc: 0.9107 - val_loss: 0.2418 - val_acc: 0.9116\n",
      "Epoch 219/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2395 - acc: 0.9084 - val_loss: 0.2418 - val_acc: 0.9125\n",
      "Epoch 220/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2409 - acc: 0.9079 - val_loss: 0.2417 - val_acc: 0.9125\n",
      "Epoch 221/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2430 - acc: 0.9112 - val_loss: 0.2416 - val_acc: 0.9133\n",
      "Epoch 222/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2375 - acc: 0.9123 - val_loss: 0.2416 - val_acc: 0.9133\n",
      "Epoch 223/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2400 - acc: 0.9114 - val_loss: 0.2415 - val_acc: 0.9133\n",
      "Epoch 224/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2527 - acc: 0.9153 - val_loss: 0.2415 - val_acc: 0.9133\n",
      "Epoch 225/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2340 - acc: 0.9116 - val_loss: 0.2415 - val_acc: 0.9133\n",
      "Epoch 226/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2359 - acc: 0.9136 - val_loss: 0.2414 - val_acc: 0.9133\n",
      "Epoch 227/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2376 - acc: 0.9105 - val_loss: 0.2414 - val_acc: 0.9133\n",
      "Epoch 228/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2380 - acc: 0.9101 - val_loss: 0.2414 - val_acc: 0.9133\n",
      "Epoch 229/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2299 - acc: 0.9153 - val_loss: 0.2414 - val_acc: 0.9133\n",
      "Epoch 230/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2378 - acc: 0.9112 - val_loss: 0.2414 - val_acc: 0.9133\n",
      "Epoch 231/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2378 - acc: 0.9133 - val_loss: 0.2414 - val_acc: 0.9133\n",
      "Epoch 232/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2369 - acc: 0.9131 - val_loss: 0.2414 - val_acc: 0.9133\n",
      "Epoch 233/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2347 - acc: 0.9120 - val_loss: 0.2414 - val_acc: 0.9133\n",
      "Epoch 234/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2300 - acc: 0.9155 - val_loss: 0.2414 - val_acc: 0.9133\n",
      "Epoch 235/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2445 - acc: 0.9142 - val_loss: 0.2414 - val_acc: 0.9133\n",
      "Epoch 236/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2352 - acc: 0.9103 - val_loss: 0.2413 - val_acc: 0.9133\n",
      "Epoch 237/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2490 - acc: 0.9090 - val_loss: 0.2413 - val_acc: 0.9133\n",
      "Epoch 238/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2452 - acc: 0.9097 - val_loss: 0.2412 - val_acc: 0.9133\n",
      "Epoch 239/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2409 - acc: 0.9105 - val_loss: 0.2412 - val_acc: 0.9133\n",
      "Epoch 240/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2370 - acc: 0.9105 - val_loss: 0.2411 - val_acc: 0.9125\n",
      "Epoch 241/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2293 - acc: 0.9164 - val_loss: 0.2410 - val_acc: 0.9125\n",
      "Epoch 242/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.2328 - acc: 0.9144 - val_loss: 0.2409 - val_acc: 0.9125\n",
      "Epoch 243/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2359 - acc: 0.9136 - val_loss: 0.2409 - val_acc: 0.9125\n",
      "Epoch 244/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2371 - acc: 0.9110 - val_loss: 0.2408 - val_acc: 0.9133\n",
      "Epoch 245/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2366 - acc: 0.9092 - val_loss: 0.2407 - val_acc: 0.9125\n",
      "Epoch 246/500\n",
      "4616/4616 [==============================] - 0s 31us/sample - loss: 0.2331 - acc: 0.9107 - val_loss: 0.2406 - val_acc: 0.9125\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 247/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2350 - acc: 0.9105 - val_loss: 0.2405 - val_acc: 0.9125\n",
      "Epoch 248/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2393 - acc: 0.9107 - val_loss: 0.2404 - val_acc: 0.9125\n",
      "Epoch 249/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.2353 - acc: 0.9123 - val_loss: 0.2403 - val_acc: 0.9133\n",
      "Epoch 250/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.2311 - acc: 0.9146 - val_loss: 0.2401 - val_acc: 0.9133\n",
      "Epoch 251/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2338 - acc: 0.9140 - val_loss: 0.2400 - val_acc: 0.9133\n",
      "Epoch 252/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2396 - acc: 0.9084 - val_loss: 0.2398 - val_acc: 0.9125\n",
      "Epoch 253/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2293 - acc: 0.9153 - val_loss: 0.2397 - val_acc: 0.9125\n",
      "Epoch 254/500\n",
      "4616/4616 [==============================] - 0s 38us/sample - loss: 0.2348 - acc: 0.9092 - val_loss: 0.2396 - val_acc: 0.9125\n",
      "Epoch 255/500\n",
      "4616/4616 [==============================] - 0s 35us/sample - loss: 0.2303 - acc: 0.9112 - val_loss: 0.2395 - val_acc: 0.9125\n",
      "Epoch 256/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.2292 - acc: 0.9092 - val_loss: 0.2394 - val_acc: 0.9125\n",
      "Epoch 257/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2260 - acc: 0.9131 - val_loss: 0.2394 - val_acc: 0.9125\n",
      "Epoch 258/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2309 - acc: 0.9170 - val_loss: 0.2393 - val_acc: 0.9125\n",
      "Epoch 259/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2325 - acc: 0.9194 - val_loss: 0.2392 - val_acc: 0.9125\n",
      "Epoch 260/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2413 - acc: 0.9107 - val_loss: 0.2392 - val_acc: 0.9125\n",
      "Epoch 261/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2285 - acc: 0.9164 - val_loss: 0.2392 - val_acc: 0.9125\n",
      "Epoch 262/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2263 - acc: 0.9168 - val_loss: 0.2391 - val_acc: 0.9125\n",
      "Epoch 263/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2358 - acc: 0.9120 - val_loss: 0.2391 - val_acc: 0.9125\n",
      "Epoch 264/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2272 - acc: 0.9162 - val_loss: 0.2391 - val_acc: 0.9125\n",
      "Epoch 265/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2353 - acc: 0.9123 - val_loss: 0.2390 - val_acc: 0.9125\n",
      "Epoch 266/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2337 - acc: 0.9133 - val_loss: 0.2390 - val_acc: 0.9125\n",
      "Epoch 267/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2333 - acc: 0.9136 - val_loss: 0.2389 - val_acc: 0.9125\n",
      "Epoch 268/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2291 - acc: 0.9114 - val_loss: 0.2389 - val_acc: 0.9125\n",
      "Epoch 269/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2353 - acc: 0.9157 - val_loss: 0.2389 - val_acc: 0.9125\n",
      "Epoch 270/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2381 - acc: 0.9142 - val_loss: 0.2388 - val_acc: 0.9125\n",
      "Epoch 271/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2368 - acc: 0.9129 - val_loss: 0.2388 - val_acc: 0.9125\n",
      "Epoch 272/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2366 - acc: 0.9155 - val_loss: 0.2388 - val_acc: 0.9125\n",
      "Epoch 273/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2362 - acc: 0.9118 - val_loss: 0.2388 - val_acc: 0.9116\n",
      "Epoch 274/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2238 - acc: 0.9125 - val_loss: 0.2388 - val_acc: 0.9116\n",
      "Epoch 275/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2340 - acc: 0.9170 - val_loss: 0.2389 - val_acc: 0.9116\n",
      "Epoch 276/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2294 - acc: 0.9151 - val_loss: 0.2389 - val_acc: 0.9116\n",
      "Epoch 277/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2351 - acc: 0.9129 - val_loss: 0.2390 - val_acc: 0.9116\n",
      "Epoch 278/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2319 - acc: 0.9123 - val_loss: 0.2391 - val_acc: 0.9116\n",
      "Epoch 279/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2252 - acc: 0.9123 - val_loss: 0.2392 - val_acc: 0.9116\n",
      "Epoch 280/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.2304 - acc: 0.9127 - val_loss: 0.2393 - val_acc: 0.9116\n",
      "Epoch 281/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.2311 - acc: 0.9157 - val_loss: 0.2393 - val_acc: 0.9116\n",
      "Epoch 282/500\n",
      "4616/4616 [==============================] - 0s 38us/sample - loss: 0.2305 - acc: 0.9099 - val_loss: 0.2395 - val_acc: 0.9116\n",
      "Epoch 283/500\n",
      "4616/4616 [==============================] - 0s 35us/sample - loss: 0.2255 - acc: 0.9155 - val_loss: 0.2398 - val_acc: 0.9116\n",
      "Epoch 284/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.2319 - acc: 0.9138 - val_loss: 0.2402 - val_acc: 0.9116\n",
      "Epoch 285/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2279 - acc: 0.9181 - val_loss: 0.2411 - val_acc: 0.9116\n",
      "Epoch 286/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2318 - acc: 0.9105 - val_loss: 0.2412 - val_acc: 0.9116\n",
      "Epoch 287/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2248 - acc: 0.9131 - val_loss: 0.2417 - val_acc: 0.9116\n",
      "Epoch 288/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2232 - acc: 0.9127 - val_loss: 0.2461 - val_acc: 0.9116\n",
      "Epoch 289/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2365 - acc: 0.9105 - val_loss: 0.2460 - val_acc: 0.9116\n",
      "Epoch 290/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2247 - acc: 0.9172 - val_loss: 0.2460 - val_acc: 0.9116\n",
      "Epoch 291/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2356 - acc: 0.9190 - val_loss: 0.2459 - val_acc: 0.9116\n",
      "Epoch 292/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2289 - acc: 0.9110 - val_loss: 0.2459 - val_acc: 0.9116\n",
      "Epoch 293/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2262 - acc: 0.9138 - val_loss: 0.2458 - val_acc: 0.9116\n",
      "Epoch 294/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2289 - acc: 0.9112 - val_loss: 0.2392 - val_acc: 0.9116\n",
      "Epoch 295/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2318 - acc: 0.9151 - val_loss: 0.2383 - val_acc: 0.9116\n",
      "Epoch 296/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2327 - acc: 0.9175 - val_loss: 0.2378 - val_acc: 0.9116\n",
      "Epoch 297/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2278 - acc: 0.9157 - val_loss: 0.2375 - val_acc: 0.9116\n",
      "Epoch 298/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2395 - acc: 0.9116 - val_loss: 0.2372 - val_acc: 0.9116\n",
      "Epoch 299/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2217 - acc: 0.9157 - val_loss: 0.2370 - val_acc: 0.9116\n",
      "Epoch 300/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2255 - acc: 0.9101 - val_loss: 0.2369 - val_acc: 0.9116\n",
      "Epoch 301/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2227 - acc: 0.9175 - val_loss: 0.2368 - val_acc: 0.9116\n",
      "Epoch 302/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.2254 - acc: 0.9149 - val_loss: 0.2367 - val_acc: 0.9116\n",
      "Epoch 303/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2341 - acc: 0.9153 - val_loss: 0.2366 - val_acc: 0.9116\n",
      "Epoch 304/500\n",
      "4616/4616 [==============================] - 0s 35us/sample - loss: 0.2253 - acc: 0.9138 - val_loss: 0.2366 - val_acc: 0.9116\n",
      "Epoch 305/500\n",
      "4616/4616 [==============================] - 0s 37us/sample - loss: 0.2176 - acc: 0.9136 - val_loss: 0.2366 - val_acc: 0.9116\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 306/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2408 - acc: 0.9175 - val_loss: 0.2366 - val_acc: 0.9116\n",
      "Epoch 307/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.2309 - acc: 0.9114 - val_loss: 0.2366 - val_acc: 0.9116\n",
      "Epoch 308/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2262 - acc: 0.9146 - val_loss: 0.2367 - val_acc: 0.9116\n",
      "Epoch 309/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2274 - acc: 0.9144 - val_loss: 0.2368 - val_acc: 0.9116\n",
      "Epoch 310/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2200 - acc: 0.9155 - val_loss: 0.2370 - val_acc: 0.9116\n",
      "Epoch 311/500\n",
      "4616/4616 [==============================] - 0s 35us/sample - loss: 0.2238 - acc: 0.9166 - val_loss: 0.2372 - val_acc: 0.9116\n",
      "Epoch 312/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2247 - acc: 0.9179 - val_loss: 0.2375 - val_acc: 0.9116\n",
      "Epoch 313/500\n",
      "4616/4616 [==============================] - 0s 35us/sample - loss: 0.2347 - acc: 0.9151 - val_loss: 0.2380 - val_acc: 0.9116\n",
      "Epoch 314/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.2232 - acc: 0.9172 - val_loss: 0.2406 - val_acc: 0.9125\n",
      "Epoch 315/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2293 - acc: 0.9157 - val_loss: 0.2441 - val_acc: 0.9125\n",
      "Epoch 316/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2241 - acc: 0.9151 - val_loss: 0.2440 - val_acc: 0.9125\n",
      "Epoch 317/500\n",
      "4616/4616 [==============================] - 0s 35us/sample - loss: 0.2240 - acc: 0.9142 - val_loss: 0.2440 - val_acc: 0.9125\n",
      "Epoch 318/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2362 - acc: 0.9151 - val_loss: 0.2440 - val_acc: 0.9125\n",
      "Epoch 319/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2244 - acc: 0.9120 - val_loss: 0.2438 - val_acc: 0.9125\n",
      "Epoch 320/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2179 - acc: 0.9196 - val_loss: 0.2437 - val_acc: 0.9125\n",
      "Epoch 321/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2289 - acc: 0.9172 - val_loss: 0.2436 - val_acc: 0.9125\n",
      "Epoch 322/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2231 - acc: 0.9155 - val_loss: 0.2435 - val_acc: 0.9125\n",
      "Epoch 323/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.2204 - acc: 0.9125 - val_loss: 0.2434 - val_acc: 0.9125\n",
      "Epoch 324/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.2229 - acc: 0.9146 - val_loss: 0.2434 - val_acc: 0.9125\n",
      "Epoch 325/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.2186 - acc: 0.9179 - val_loss: 0.2433 - val_acc: 0.9125\n",
      "Epoch 326/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.2227 - acc: 0.9175 - val_loss: 0.2432 - val_acc: 0.9125\n",
      "Epoch 327/500\n",
      "4616/4616 [==============================] - 0s 35us/sample - loss: 0.2219 - acc: 0.9177 - val_loss: 0.2431 - val_acc: 0.9125\n",
      "Epoch 328/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.2191 - acc: 0.9179 - val_loss: 0.2430 - val_acc: 0.9125\n",
      "Epoch 329/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.2257 - acc: 0.9142 - val_loss: 0.2429 - val_acc: 0.9125\n",
      "Epoch 330/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2340 - acc: 0.9105 - val_loss: 0.2428 - val_acc: 0.9125\n",
      "Epoch 331/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2273 - acc: 0.9166 - val_loss: 0.2428 - val_acc: 0.9125\n",
      "Epoch 332/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2222 - acc: 0.9194 - val_loss: 0.2427 - val_acc: 0.9125\n",
      "Epoch 333/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2250 - acc: 0.9146 - val_loss: 0.2426 - val_acc: 0.9125\n",
      "Epoch 334/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2189 - acc: 0.9164 - val_loss: 0.2426 - val_acc: 0.9125\n",
      "Epoch 335/500\n",
      "4616/4616 [==============================] - 0s 35us/sample - loss: 0.2204 - acc: 0.9188 - val_loss: 0.2364 - val_acc: 0.9125\n",
      "Epoch 336/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.2220 - acc: 0.9192 - val_loss: 0.2355 - val_acc: 0.9125\n",
      "Epoch 337/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2216 - acc: 0.9149 - val_loss: 0.2351 - val_acc: 0.9125\n",
      "Epoch 338/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2244 - acc: 0.9168 - val_loss: 0.2348 - val_acc: 0.9125\n",
      "Epoch 339/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2276 - acc: 0.9144 - val_loss: 0.2346 - val_acc: 0.9125\n",
      "Epoch 340/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2258 - acc: 0.9123 - val_loss: 0.2345 - val_acc: 0.9125\n",
      "Epoch 341/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2194 - acc: 0.9146 - val_loss: 0.2344 - val_acc: 0.9125\n",
      "Epoch 342/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2227 - acc: 0.9151 - val_loss: 0.2343 - val_acc: 0.9125\n",
      "Epoch 343/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2223 - acc: 0.9123 - val_loss: 0.2342 - val_acc: 0.9125\n",
      "Epoch 344/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2199 - acc: 0.9177 - val_loss: 0.2341 - val_acc: 0.9125\n",
      "Epoch 345/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2225 - acc: 0.9155 - val_loss: 0.2340 - val_acc: 0.9125\n",
      "Epoch 346/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2262 - acc: 0.9166 - val_loss: 0.2339 - val_acc: 0.9125\n",
      "Epoch 347/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2311 - acc: 0.9142 - val_loss: 0.2339 - val_acc: 0.9125\n",
      "Epoch 348/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2244 - acc: 0.9138 - val_loss: 0.2338 - val_acc: 0.9125\n",
      "Epoch 349/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2164 - acc: 0.9168 - val_loss: 0.2338 - val_acc: 0.9125\n",
      "Epoch 350/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2361 - acc: 0.9138 - val_loss: 0.2338 - val_acc: 0.9125\n",
      "Epoch 351/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2173 - acc: 0.9188 - val_loss: 0.2338 - val_acc: 0.9125\n",
      "Epoch 352/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2270 - acc: 0.9168 - val_loss: 0.2336 - val_acc: 0.9125\n",
      "Epoch 353/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2217 - acc: 0.9162 - val_loss: 0.2334 - val_acc: 0.9125\n",
      "Epoch 354/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2206 - acc: 0.9157 - val_loss: 0.2333 - val_acc: 0.9125\n",
      "Epoch 355/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2126 - acc: 0.9194 - val_loss: 0.2332 - val_acc: 0.9125\n",
      "Epoch 356/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2245 - acc: 0.9133 - val_loss: 0.2331 - val_acc: 0.9125\n",
      "Epoch 357/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2211 - acc: 0.9144 - val_loss: 0.2330 - val_acc: 0.9125\n",
      "Epoch 358/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2231 - acc: 0.9131 - val_loss: 0.2330 - val_acc: 0.9116\n",
      "Epoch 359/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2224 - acc: 0.9146 - val_loss: 0.2330 - val_acc: 0.9116\n",
      "Epoch 360/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2236 - acc: 0.9177 - val_loss: 0.2329 - val_acc: 0.9116\n",
      "Epoch 361/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2201 - acc: 0.9175 - val_loss: 0.2330 - val_acc: 0.9116\n",
      "Epoch 362/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2208 - acc: 0.9166 - val_loss: 0.2329 - val_acc: 0.9125\n",
      "Epoch 363/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2237 - acc: 0.9168 - val_loss: 0.2328 - val_acc: 0.9125\n",
      "Epoch 364/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2255 - acc: 0.9166 - val_loss: 0.2328 - val_acc: 0.9116\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 365/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2276 - acc: 0.9116 - val_loss: 0.2327 - val_acc: 0.9116\n",
      "Epoch 366/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.2281 - acc: 0.9168 - val_loss: 0.2327 - val_acc: 0.9116\n",
      "Epoch 367/500\n",
      "4616/4616 [==============================] - 0s 36us/sample - loss: 0.2231 - acc: 0.9168 - val_loss: 0.2327 - val_acc: 0.9116\n",
      "Epoch 368/500\n",
      "4616/4616 [==============================] - 0s 35us/sample - loss: 0.2229 - acc: 0.9142 - val_loss: 0.2327 - val_acc: 0.9116\n",
      "Epoch 369/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.2247 - acc: 0.9177 - val_loss: 0.2327 - val_acc: 0.9116\n",
      "Epoch 370/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.2180 - acc: 0.9183 - val_loss: 0.2328 - val_acc: 0.9116\n",
      "Epoch 371/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2205 - acc: 0.9164 - val_loss: 0.2329 - val_acc: 0.9116\n",
      "Epoch 372/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2253 - acc: 0.9172 - val_loss: 0.2329 - val_acc: 0.9125\n",
      "Epoch 373/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2226 - acc: 0.9164 - val_loss: 0.2330 - val_acc: 0.9125\n",
      "Epoch 374/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2174 - acc: 0.9138 - val_loss: 0.2332 - val_acc: 0.9125\n",
      "Epoch 375/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2184 - acc: 0.9162 - val_loss: 0.2334 - val_acc: 0.9125\n",
      "Epoch 376/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2190 - acc: 0.9198 - val_loss: 0.2338 - val_acc: 0.9125\n",
      "Epoch 377/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2203 - acc: 0.9166 - val_loss: 0.2347 - val_acc: 0.9125\n",
      "Epoch 378/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2193 - acc: 0.9170 - val_loss: 0.2401 - val_acc: 0.9133\n",
      "Epoch 379/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2277 - acc: 0.9175 - val_loss: 0.2400 - val_acc: 0.9133\n",
      "Epoch 380/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2209 - acc: 0.9188 - val_loss: 0.2400 - val_acc: 0.9133\n",
      "Epoch 381/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2135 - acc: 0.9159 - val_loss: 0.2399 - val_acc: 0.9125\n",
      "Epoch 382/500\n",
      "4616/4616 [==============================] - 0s 35us/sample - loss: 0.2337 - acc: 0.9164 - val_loss: 0.2399 - val_acc: 0.9125\n",
      "Epoch 383/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2164 - acc: 0.9172 - val_loss: 0.2399 - val_acc: 0.9125\n",
      "Epoch 384/500\n",
      "4616/4616 [==============================] - 0s 36us/sample - loss: 0.2177 - acc: 0.9110 - val_loss: 0.2398 - val_acc: 0.9125\n",
      "Epoch 385/500\n",
      "4616/4616 [==============================] - 0s 35us/sample - loss: 0.2249 - acc: 0.9196 - val_loss: 0.2398 - val_acc: 0.9125\n",
      "Epoch 386/500\n",
      "4616/4616 [==============================] - 0s 34us/sample - loss: 0.2226 - acc: 0.9168 - val_loss: 0.2397 - val_acc: 0.9125\n",
      "Epoch 387/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2129 - acc: 0.9175 - val_loss: 0.2397 - val_acc: 0.9125\n",
      "Epoch 388/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2185 - acc: 0.9179 - val_loss: 0.2397 - val_acc: 0.9125\n",
      "Epoch 389/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2203 - acc: 0.9129 - val_loss: 0.2396 - val_acc: 0.9116\n",
      "Epoch 390/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2177 - acc: 0.9203 - val_loss: 0.2395 - val_acc: 0.9116\n",
      "Epoch 391/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2193 - acc: 0.9142 - val_loss: 0.2394 - val_acc: 0.9116\n",
      "Epoch 392/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2202 - acc: 0.9170 - val_loss: 0.2393 - val_acc: 0.9116\n",
      "Epoch 393/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2209 - acc: 0.9142 - val_loss: 0.2392 - val_acc: 0.9116\n",
      "Epoch 394/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2173 - acc: 0.9166 - val_loss: 0.2392 - val_acc: 0.9116\n",
      "Epoch 395/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2205 - acc: 0.9112 - val_loss: 0.2391 - val_acc: 0.9116\n",
      "Epoch 396/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2155 - acc: 0.9198 - val_loss: 0.2390 - val_acc: 0.9116\n",
      "Epoch 397/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2179 - acc: 0.9194 - val_loss: 0.2390 - val_acc: 0.9116\n",
      "Epoch 398/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2161 - acc: 0.9151 - val_loss: 0.2389 - val_acc: 0.9116\n",
      "Epoch 399/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2120 - acc: 0.9233 - val_loss: 0.2389 - val_acc: 0.9116\n",
      "Epoch 400/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2076 - acc: 0.9209 - val_loss: 0.2389 - val_acc: 0.9116\n",
      "Epoch 401/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2157 - acc: 0.9211 - val_loss: 0.2389 - val_acc: 0.9116\n",
      "Epoch 402/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2152 - acc: 0.9153 - val_loss: 0.2389 - val_acc: 0.9116\n",
      "Epoch 403/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2113 - acc: 0.9194 - val_loss: 0.2390 - val_acc: 0.9116\n",
      "Epoch 404/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2164 - acc: 0.9211 - val_loss: 0.2390 - val_acc: 0.9116\n",
      "Epoch 405/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2151 - acc: 0.9177 - val_loss: 0.2390 - val_acc: 0.9116\n",
      "Epoch 406/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2112 - acc: 0.9172 - val_loss: 0.2391 - val_acc: 0.9116\n",
      "Epoch 407/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2099 - acc: 0.9216 - val_loss: 0.2391 - val_acc: 0.9116\n",
      "Epoch 408/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2189 - acc: 0.9159 - val_loss: 0.2392 - val_acc: 0.9116\n",
      "Epoch 409/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2144 - acc: 0.9179 - val_loss: 0.2393 - val_acc: 0.9116\n",
      "Epoch 410/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2268 - acc: 0.9194 - val_loss: 0.2393 - val_acc: 0.9116\n",
      "Epoch 411/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2122 - acc: 0.9201 - val_loss: 0.2394 - val_acc: 0.9116\n",
      "Epoch 412/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2194 - acc: 0.9177 - val_loss: 0.2394 - val_acc: 0.9116\n",
      "Epoch 413/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2167 - acc: 0.9170 - val_loss: 0.2394 - val_acc: 0.9116\n",
      "Epoch 414/500\n",
      "4616/4616 [==============================] - 0s 33us/sample - loss: 0.2120 - acc: 0.9159 - val_loss: 0.2395 - val_acc: 0.9116\n",
      "Epoch 415/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2240 - acc: 0.9164 - val_loss: 0.2394 - val_acc: 0.9116\n",
      "Epoch 416/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2259 - acc: 0.9153 - val_loss: 0.2393 - val_acc: 0.9116\n",
      "Epoch 417/500\n",
      "4616/4616 [==============================] - 0s 32us/sample - loss: 0.2214 - acc: 0.9110 - val_loss: 0.2392 - val_acc: 0.9116\n",
      "Epoch 00417: early stopping\n"
     ]
    }
   ],
   "source": [
    "Classifier.refine(valX, valY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy:  0.915\n",
      "Test MCC:  0.723\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[3670,  919],\n",
       "       [ 134, 1048]], dtype=int64)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_predictions = Classifier.predict(testX)\n",
    "\n",
    "print('Test Accuracy:  {:.3f}'.format(accuracy_score(testY, test_predictions)))\n",
    "print('Test MCC:  {:.3f}'.format(matthews_corrcoef(testY, test_predictions)))\n",
    "confusion_matrix(testY, predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have accuracies of over 90%! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model bow_1Mtweets accuracy_score: 0.887\n",
      "Model glove_100D_10N_340000V_719685T_Twitter_0 accuracy_score: 0.912\n",
      "Model glove_200D_10N_20000V_343332T_Twitter_7 accuracy_score: 0.919\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'bow_1Mtweets': 0.8866747530757234,\n",
       " 'glove_100D_10N_340000V_719685T_Twitter_0': 0.9116271010223531,\n",
       " 'glove_200D_10N_20000V_343332T_Twitter_7': 0.9189048691734535,\n",
       " 'ensembled': 0.9149194247097556}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Classifier.evaluate(testX, testY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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