INFO7390_ADSProject.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Advance in Data Science INFO7390 - Project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Application of Deep Neural Networks in Diabetes Biomarkers prediction Development"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Abstract : \n",
    "\n",
    "Biomarkers are basically the indicators which helps in diagnosis of a health condition or disease. This plays an important role in diagnostic department of healthcare sector. Through this project, we are trying to implement deep learning technique in healthcare. \n",
    "\n",
    "This project aims using different machine learning and deep learning techniques to identify different diabetes biomarkers present in the blood sample of various patients. Developing a neural network model to predict whether person will have diabetes. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Purpose :\n",
    "\n",
    "Purpose of the project is to use different machine learning techniques and also deep learning techniques to predict the output and compare the performance of deep neural networks over traditional machine learning models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Techniques Used: \n",
    "\n",
    "We will be using different machine learning as well as deep learning techniques to understand and predict the given outcome.\n",
    "\n",
    "### Machine Learning techniques Used:\n",
    "Decision Tree, Random Forests, KNN etc. \n",
    "\n",
    "### Deep Learning Techniques Used: \n",
    "\n",
    "Artificial Neural Network with different number of layers, neurons and activation functions. \n",
    "\n",
    "### Evaluation Metrics Used:\n",
    "\n",
    "We will be employing Accuracy, AUC-ROC curve, binary cross entropy. We will be plotting the graphs to check training and validation accuracy as well as training and validation loss."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## About Dataset\n",
    "\n",
    "Dataset has been taken from Kaggle. \n",
    "This dataset comprises of blood sample of different patients used to depict whether person will have diabetes or not.\n",
    "\n",
    "Sample Size : 768\n",
    "\n",
    "No of features: 9\n",
    "\n",
    "Type of problem: Binary classification. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploring the dataset \n",
    "\n",
    "We will be reading the dataset and importing into a dataframe and understand different statistical aspects of the dataset. We will check if null values are present in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing sklearn and traditional Python machine learning libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import confusion_matrix, precision_recall_curve, roc_auc_score, roc_curve, accuracy_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# importing keras, tensorflow and related modules\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers\n",
    "import keras\n",
    "from keras.models import Sequential # intitialize the ANN\n",
    "from keras.layers import Dense, Dropout      # create layers\n",
    "from keras.optimizers import Adam, SGD, RMSprop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing the dataset and initializing a dataframe.\n",
    "diabetes_df=pd.read_csv(\"diabetes.csv\", decimal = ',')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploratory Data Analysis and Data Cleaning\n",
    "\n",
    "Here, we will be exploring the data and check for null values, improper formats and other data related issues. We will resolve these issues and clean the data which can be furthur used for building the model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "  DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                    0.627   50        1  \n",
       "1                    0.351   31        0  \n",
       "2                    0.672   32        1  \n",
       "3                    0.167   21        0  \n",
       "4                    2.288   33        1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 9)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of      Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0              6      148             72             35        0  33.6   \n",
       "1              1       85             66             29        0  26.6   \n",
       "2              8      183             64              0        0  23.3   \n",
       "3              1       89             66             23       94  28.1   \n",
       "4              0      137             40             35      168  43.1   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "763           10      101             76             48      180  32.9   \n",
       "764            2      122             70             27        0  36.8   \n",
       "765            5      121             72             23      112  26.2   \n",
       "766            1      126             60              0        0  30.1   \n",
       "767            1       93             70             31        0  30.4   \n",
       "\n",
       "    DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                      0.627   50        1  \n",
       "1                      0.351   31        0  \n",
       "2                      0.672   32        1  \n",
       "3                      0.167   21        0  \n",
       "4                      2.288   33        1  \n",
       "..                       ...  ...      ...  \n",
       "763                    0.171   63        0  \n",
       "764                     0.34   27        0  \n",
       "765                    0.245   30        0  \n",
       "766                    0.349   47        1  \n",
       "767                    0.315   23        0  \n",
       "\n",
       "[768 rows x 9 columns]>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes_df.info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Pre-processing and Exploratory Data Analysis\n",
    "\n",
    "Before any model building, it is essential to understand the data properly, understand the importance of various features of the dataset. It is essential to clean the dataset and prepare it for model building so that predicting outcome is easier and more efficient. \n",
    "\n",
    "One part of data pre-processing is checking for the missing values in the dataset. Below line of code executed shows number of missing values in the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking whether there are any missing values in the dataset.\n",
    "diabetes_df.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 518,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pregnancies</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.129459</td>\n",
       "      <td>0.141282</td>\n",
       "      <td>-0.081672</td>\n",
       "      <td>-0.073535</td>\n",
       "      <td>0.017683</td>\n",
       "      <td>-0.033523</td>\n",
       "      <td>0.544341</td>\n",
       "      <td>0.221898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glucose</th>\n",
       "      <td>0.129459</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.152590</td>\n",
       "      <td>0.057328</td>\n",
       "      <td>0.331357</td>\n",
       "      <td>0.221071</td>\n",
       "      <td>0.137337</td>\n",
       "      <td>0.263514</td>\n",
       "      <td>0.466581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BloodPressure</th>\n",
       "      <td>0.141282</td>\n",
       "      <td>0.152590</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.207371</td>\n",
       "      <td>0.088933</td>\n",
       "      <td>0.281805</td>\n",
       "      <td>0.041265</td>\n",
       "      <td>0.239528</td>\n",
       "      <td>0.065068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SkinThickness</th>\n",
       "      <td>-0.081672</td>\n",
       "      <td>0.057328</td>\n",
       "      <td>0.207371</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.436783</td>\n",
       "      <td>0.392573</td>\n",
       "      <td>0.183928</td>\n",
       "      <td>-0.113970</td>\n",
       "      <td>0.074752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Insulin</th>\n",
       "      <td>-0.073535</td>\n",
       "      <td>0.331357</td>\n",
       "      <td>0.088933</td>\n",
       "      <td>0.436783</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.197859</td>\n",
       "      <td>0.185071</td>\n",
       "      <td>-0.042163</td>\n",
       "      <td>0.130548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMI</th>\n",
       "      <td>0.017683</td>\n",
       "      <td>0.221071</td>\n",
       "      <td>0.281805</td>\n",
       "      <td>0.392573</td>\n",
       "      <td>0.197859</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.140647</td>\n",
       "      <td>0.036242</td>\n",
       "      <td>0.292695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <td>-0.033523</td>\n",
       "      <td>0.137337</td>\n",
       "      <td>0.041265</td>\n",
       "      <td>0.183928</td>\n",
       "      <td>0.185071</td>\n",
       "      <td>0.140647</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.033561</td>\n",
       "      <td>0.173844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.544341</td>\n",
       "      <td>0.263514</td>\n",
       "      <td>0.239528</td>\n",
       "      <td>-0.113970</td>\n",
       "      <td>-0.042163</td>\n",
       "      <td>0.036242</td>\n",
       "      <td>0.033561</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.238356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outcome</th>\n",
       "      <td>0.221898</td>\n",
       "      <td>0.466581</td>\n",
       "      <td>0.065068</td>\n",
       "      <td>0.074752</td>\n",
       "      <td>0.130548</td>\n",
       "      <td>0.292695</td>\n",
       "      <td>0.173844</td>\n",
       "      <td>0.238356</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Pregnancies   Glucose  BloodPressure  SkinThickness  \\\n",
       "Pregnancies                  1.000000  0.129459       0.141282      -0.081672   \n",
       "Glucose                      0.129459  1.000000       0.152590       0.057328   \n",
       "BloodPressure                0.141282  0.152590       1.000000       0.207371   \n",
       "SkinThickness               -0.081672  0.057328       0.207371       1.000000   \n",
       "Insulin                     -0.073535  0.331357       0.088933       0.436783   \n",
       "BMI                          0.017683  0.221071       0.281805       0.392573   \n",
       "DiabetesPedigreeFunction    -0.033523  0.137337       0.041265       0.183928   \n",
       "Age                          0.544341  0.263514       0.239528      -0.113970   \n",
       "Outcome                      0.221898  0.466581       0.065068       0.074752   \n",
       "\n",
       "                           Insulin       BMI  DiabetesPedigreeFunction  \\\n",
       "Pregnancies              -0.073535  0.017683                 -0.033523   \n",
       "Glucose                   0.331357  0.221071                  0.137337   \n",
       "BloodPressure             0.088933  0.281805                  0.041265   \n",
       "SkinThickness             0.436783  0.392573                  0.183928   \n",
       "Insulin                   1.000000  0.197859                  0.185071   \n",
       "BMI                       0.197859  1.000000                  0.140647   \n",
       "DiabetesPedigreeFunction  0.185071  0.140647                  1.000000   \n",
       "Age                      -0.042163  0.036242                  0.033561   \n",
       "Outcome                   0.130548  0.292695                  0.173844   \n",
       "\n",
       "                               Age   Outcome  \n",
       "Pregnancies               0.544341  0.221898  \n",
       "Glucose                   0.263514  0.466581  \n",
       "BloodPressure             0.239528  0.065068  \n",
       "SkinThickness            -0.113970  0.074752  \n",
       "Insulin                  -0.042163  0.130548  \n",
       "BMI                       0.036242  0.292695  \n",
       "DiabetesPedigreeFunction  0.033561  0.173844  \n",
       "Age                       1.000000  0.238356  \n",
       "Outcome                   0.238356  1.000000  "
      ]
     },
     "execution_count": 518,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes_df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 520,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,12))\n",
    "sns.heatmap(data=diabetes_df.iloc[:,2:].corr(),annot=True,fmt='.2f',cmap='coolwarm')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature Scaling and why is it important?\n",
    "\n",
    "It is a part of data pre-processing which is applied to independent variables or features of the data. \n",
    "\n",
    "It is important to normalize the data within a particular range and sometimes it helps in speeding up the calculations in an algorithm. \n",
    "\n",
    "Below line of code shows scaling of features in the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scale all the columns except the 'outcome'\n",
    "    scaler = StandardScaler()\n",
    "    diabetes_df[['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age']] = \\\n",
    "    scaler.fit_transform(diabetes_df[['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# defining variables X and Y. Y variable has the target value and X variable has other features in the dataset. \n",
    "X = diabetes_df.iloc[:, :-1].values\n",
    "y = diabetes_df[\"Outcome\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(537, 8) (537,) (231, 8) (231,)\n"
     ]
    }
   ],
   "source": [
    "# splitting the data into train and test data in the ratio 70% and 30%\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n",
    "print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)\n",
    "#return X_train, X_test, y_train, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# defining all functions used in the project\n",
    "\n",
    "#this function is used to plot auc-roc curve\n",
    "def plot_roc(y_test, y_pred, model_name):\n",
    "    fpr, tpr, thr = roc_curve(y_test, y_pred)\n",
    "    fig, ax = plt.subplots(figsize=(8, 8))\n",
    "    ax.plot(fpr, tpr, 'k-')\n",
    "    ax.plot([0, 1], [0, 1], 'k--', linewidth=.5)  # roc curve for random model\n",
    "    ax.grid(True)\n",
    "    ax.set(title='ROC Curve for {} on PIMA diabetes dataset'.format(model_name),\n",
    "           xlim=[-0.01, 1.01], ylim=[-0.01, 1.01])\n",
    "\n",
    "#function used to fit the training data into the model  \n",
    "def train_clf(clf, X_train, y_train):\n",
    "    \n",
    "    return clf.fit(X_train, y_train)\n",
    "    \n",
    "#function used to predict the outcome and return f1 score   \n",
    "def pred_clf(clf, features, target):\n",
    "    \n",
    "   y_pred = clf.predict(features)\n",
    "   return f1_score(target.values, y_pred, pos_label = 1)\n",
    "\n",
    "\n",
    "def train_predict(clf, X_train, y_train, X_test, y_test):\n",
    "    \n",
    "    train_clf(clf, X_train, y_train)\n",
    "    \n",
    "    print(\"F1 score for training set is: {:.4f}\".format(pred_clf(clf, X_train, y_train)))\n",
    "    print(\"F1 score for testing set is: {:.4f}\\n\".format(pred_clf(clf, X_test, y_test)))\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Applying Machine Learning Techniques\n",
    "\n",
    "We will use Random Forests, K-Nearest Neighbors, Decision Trees to predict the outcome. We will also check for accuracy, AUC-ROC score values. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest Classifier\n",
    "\n",
    "Random Forests are also known as random decision forests is a popular ensemble method useful for predicting the outcome. \n",
    "\n",
    "Random forest classifier creates a set of decision trees from randomly selected subset of training set. It then aggregates the votes from different decision trees to decide the final class of the test object.\n",
    "\n",
    "Hyperparameters for Random Forests Classifier: Total Number of trees to be generated and decision tree related parameters like minimum split, split criteria etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=300,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Train the RF Model\n",
    "rf_model = RandomForestClassifier(n_estimators=300)\n",
    "rf_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is 0.723\n",
      "ROC-AUC is 0.812\n"
     ]
    }
   ],
   "source": [
    "# Make predictions on the test set - both \"hard\" predictions, and the scores (percent of trees voting yes)\n",
    "y_pred_class_rf = rf_model.predict(X_test)\n",
    "y_pred_prob_rf = rf_model.predict_proba(X_test)\n",
    "\n",
    "print('Accuracy is {:.3f}'.format(accuracy_score(y_test,y_pred_class_rf)))\n",
    "print('ROC-AUC is {:.3f}'.format(roc_auc_score(y_test,y_pred_prob_rf[:,1])))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plot_roc(y_test, y_pred_prob_rf[:, 1], 'Random Forests')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accuracy obtained is 72.3 % and AUC-ROC value is 0.81 by employing Random Forest Model. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using K-Nearest neighbors method\n",
    "\n",
    "K-Nearest neighbor classifier is a popular algorithm useful for classification problems.\n",
    "\n",
    "K-nearest neighbor algorithm predicts the class of the data point as per the majority of the votes obtained from the neighboring points and calculates distance such as Euclidean distance, hamming distance, cosine distance etc. Based on the votes, label is assigned to the new data point which needs to be predicted.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_cols = ['Glucose', 'BloodPressure', 'SkinThickness', 'BMI', 'Insulin']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(537, 8) (537,) (231, 8) (231,)\n"
     ]
    }
   ],
   "source": [
    "X = diabetes_df.iloc[:, 0:8]\n",
    "y = diabetes_df.iloc[:, 8]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=11111, test_size=0.3)\n",
    "print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc_X = StandardScaler()\n",
    "X_train = sc_X.fit_transform(X_train)\n",
    "X_test = sc_X.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the model: Init K-NN\n",
    "classifier = KNeighborsClassifier(n_neighbors=7, p=2, metric='euclidean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='euclidean',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=7, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[127  23]\n",
      " [ 34  47]]\n",
      "0.6225165562913907\n"
     ]
    }
   ],
   "source": [
    "# Evaluate Model\n",
    "from sklearn.metrics import f1_score\n",
    "evaluate_cm = confusion_matrix(y_test, y_pred)\n",
    "print (evaluate_cm)\n",
    "print(f1_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[127  23]\n",
      " [ 34  47]]\n",
      "F1 score is  0.6225165562913907\n",
      "Accuracy is 0.7532467532467533\n"
     ]
    }
   ],
   "source": [
    "# creating the confusion matrix\n",
    "evaluate_cm = confusion_matrix(y_test, y_pred)\n",
    "print (evaluate_cm)\n",
    "print('F1 score is ' ,f1_score(y_test, y_pred))\n",
    "print('Accuracy is' , accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc(y_test, y_pred, 'KNN')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accuracy score obtained from KNN method = 75.3%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Gradient Boosting Classifier\n",
    "\n",
    "Gradient boosting is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees. \n",
    "\n",
    "It builds the model in a stage-wise fashion like other boosting methods do, and it generalizes them by allowing optimization of an arbitrary differentiable loss function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is 0.7835497835497836\n",
      "F1 score for training set is: 1.0000\n",
      "F1 score for testing set is: 0.6753\n",
      "\n"
     ]
    }
   ],
   "source": [
    "params = {'max_depth':9, 'subsample':0.5, 'learning_rate':0.01, 'min_samples_leaf':1, 'random_state':42}\n",
    "gbc = GradientBoostingClassifier(n_estimators=290, **params)\n",
    "clf_ = gbc.fit(X_train, y_train)\n",
    "y_pred = clf_.predict(X_test)\n",
    "print('Accuracy is {}'.format(accuracy_score(y_test,y_pred )))\n",
    "train_predict(gbc, X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc(y_test, y_pred, 'GBC')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accuracy obtained by applying Gradient Boosting Classifier is 78.3%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using all 3 techniques , we see that best accuracy obtained from Gradient Boosting Classifier which is 78.3%\n",
    "\n",
    "Now, we need to develop a neural network model to see how well it performs and if they can achieve a better accuracy from these models. Wide and deep neural networks usually performs better but we need to experiment and see which model performs better. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Developing Deep Learning Neural Network Model \n",
    "\n",
    "Artificial Neural Network is a computational model based on the biological neural network which consists of various neurons and attempts to simulate the network of neurons so that it can learn the pattern programmatically and make better decisions.\n",
    "\n",
    "ANN comprises of input layer , output layer and hidden layers. Weights are assigned to them and using the backward propagation mechanism, weights are updated accordingly. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Important Terminologies and Concepts to understand Neural Network Model \n",
    "\n",
    "#### Activation Functions:\n",
    "Activation functions play an important role in neural network model because they introduce non-linearity. The main purpose is to convert a input signal of a node in a ANN to an output signal. This output signal now used as an input in the next layer in the stack. \n",
    "\n",
    "In ANN, we compute the sum of products of inputs and their corresponding weights and then apply activation function f(x) to it to get the output layer and feed it as an input to the next layer. There are various kinds of activation function. Only discussing the most popular activation functions used in the project. \n",
    "\n",
    "#### Sigmoid activation function: \n",
    "It is an activation function of form f(x)=1/1+exp(-x) . Range is between 0 and 1. Useful for binary classification problems. \n",
    "\n",
    "#### Tanh activation function:\n",
    "Its mathematical formula is f(x)=1-exp(-2x)/1+exp(-2x). Its output is zero centered because its range in between -1 and 1. Optimization is easier in this case and generally preferred over sigmoid function. \n",
    "\n",
    "#### RELU activation function:\n",
    "It is also popularly called as linear rectified unit. It returns the value provided as input directly, or the value 0.0 if the input is less than zero. \n",
    "\n",
    "Function can be described as g(z) = max{0,z}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train_normalized is [[-0.21654016 -0.04466859  0.07176895 ...  1.43258932 -0.09568657\n",
      "  -0.25299319]\n",
      " [-1.0996951  -0.61882624 -0.23329642 ...  1.13123171  0.03565317\n",
      "  -1.0298141 ]\n",
      " [ 0.07784482  0.84846554 -0.43667334 ... -0.13971125 -0.99715482\n",
      "  -0.33930663]\n",
      " ...\n",
      " [ 0.66661477 -3.87238629 -0.02991951 ...  0.92159163  0.72518683\n",
      "   0.69645458]\n",
      " [-0.51092514  0.62518201 -0.5383618  ... -0.86034901  0.64160699\n",
      "  -0.7708738 ]\n",
      " [-0.80531012  0.21051259  0.68189969 ... -0.58519641 -1.10162962\n",
      "  -0.94350067]]\n",
      "X test normalized is  [[-0.51092514  0.05102436 -1.04680408 ...  1.32776928  0.10729303\n",
      "  -0.59824693]\n",
      " [ 0.66661477  0.40189848  0.58021123 ...  1.86497198 -0.73447533\n",
      "   1.12802175]\n",
      " [ 0.07784482 -0.20415682  0.17345741 ... -0.40176135 -0.32254613\n",
      "   1.12802175]\n",
      " ...\n",
      " [ 0.07784482 -1.22488154  0.88527661 ... -0.34935133 -0.49866079\n",
      "   0.09226054]\n",
      " [-0.80531012  0.72087495  0.68189969 ...  1.22294924  0.36698753\n",
      "  -0.42562006]\n",
      " [-0.21654016 -1.38436978  0.07176895 ...  0.06992882 -0.63895552\n",
      "   0.52382771]]\n"
     ]
    }
   ],
   "source": [
    "#normalizing values\n",
    "normalizer = StandardScaler()\n",
    "X_train_norm = normalizer.fit_transform(X_train)\n",
    "X_test_norm = normalizer.transform(X_test)\n",
    "print('X_train_normalized is' , X_train_norm)\n",
    "print('X test normalized is ',X_test_norm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Developing a neural network model with 1 hidden layer. \n",
    "\n",
    "Here, we have defined a model having 3 dense layers. \n",
    "First is the input layer wherein we have defined an input shape which is 8 and also defined activation function. I have used RELU activation function and as 12 neurons. \n",
    "\n",
    "Second layer consists of a hidden layer having 8 neurons and RELU activation function. \n",
    "\n",
    "Third layer is the output layer having just 1 neuron and used Sigmoid activation function as the output ranges between 0 and 1. Sigmoid functions are useful for binary classification problems. \n",
    "\n",
    "We have used Sequential class from Keras library wherein you can build a model, define the layers, input and output. \n",
    "\n",
    "There are 4 major steps involved in model building:\n",
    "1. Define a model\n",
    "2. Compile the model\n",
    "3. Fit the data into the model\n",
    "4. Predict the outcome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {},
   "outputs": [],
   "source": [
    "# definining a model with input, 1 hidden layer and output layer.\n",
    "def nn_model_1(model):\n",
    "    model.add(Dense(12, input_shape=(8,), activation=\"relu\"))\n",
    "    model.add(Dense(8, activation=\"relu\"))\n",
    "    model.add(Dense(1, activation=\"sigmoid\"))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calling the above function and initializing the model.\n",
    "model = Sequential()\n",
    "model = nn_model_1(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_85 (Dense)             (None, 12)                108       \n",
      "_________________________________________________________________\n",
      "dense_86 (Dense)             (None, 8)                 104       \n",
      "_________________________________________________________________\n",
      "dense_87 (Dense)             (None, 1)                 9         \n",
      "=================================================================\n",
      "Total params: 221\n",
      "Trainable params: 221\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#prints the model summary\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Understanding the model compile method:\n",
    "\n",
    "#### Optimizers used:\n",
    "\n",
    "There are different options of optimizers which can be used in the model such as Adam , SGD and RMSProp. Most popular is SGD which is nothing but Stochastic Gradient Descent.This is useful to minimize the loss function and computes the gradientat each iteration and then helps in updating the weights in the model using backward propagation algorithm. We have used SGD as an optimizer in the model in the whole project. There are others which can be used in the model as well such as Adam or RMSProp.\n",
    "\n",
    "#### Binary Cross Entropy Losses :\n",
    "It is also called as Sigmoid Cross entropy loss. It is basically a sigmoid function and cross-entropy loss. It is independent for each vector component class which is the loss computed for every output vector component not affected by other component values. Since this is a binary classification problem, we have used binary cross entropy loss. \n",
    "\n",
    "#### Learning Rates:\n",
    "Learning Rate is a hyperparameter that controls how much we are adjusting the weights of our network with respect the loss gradient. Lower the learning rate, slower we travel along the downward slope. Equation of new weight calculation is given by:\n",
    "\n",
    "new_weight = existing weight - learning rate * gradient\n",
    "\n",
    "During the model building, we need to test and check the performance of the model. If the learning rate is too low then gradient will be very slow and if the learning rate is too high then the gradient descent can overshoot the minimum and they might fail to converge. \n",
    "\n",
    "Optimal value of the learning rate can be found only when the user tests and check or can have intuition based on the past experiences. In this project, I have tried testing with different learning rates to see the model performance. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {},
   "outputs": [],
   "source": [
    "# through this code, we are compiling the above generated model. \n",
    "sgd = SGD(lr=0.05)\n",
    "model.compile(loss=\"binary_crossentropy\",optimizer=sgd, metrics=[\"accuracy\"],sample_weight_mode=None)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model fit method\n",
    "\n",
    "In below line of code, we have tried fitting the data into the model and the output of the method is stored in a variable called run_hist_1. \n",
    "\n",
    "Verbose =1 signifies that it will show the animated training progress in the output when you run the code. \n",
    "\n",
    "Epochs : It is the hyperparameter set before training the model. One epoch is when an entire dataset is passed both backward and forward through the neural network. It defines the number of times the learning algorithm will work through the entire dataset. We have used different epochs in different models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 537 samples, validate on 231 samples\n",
      "Epoch 1/200\n",
      "537/537 [==============================] - 2s 3ms/step - loss: 0.6674 - acc: 0.6480 - val_loss: 0.6699 - val_acc: 0.6494\n",
      "Epoch 2/200\n",
      "537/537 [==============================] - 0s 85us/step - loss: 0.6254 - acc: 0.6480 - val_loss: 0.6343 - val_acc: 0.6494\n",
      "Epoch 3/200\n",
      "537/537 [==============================] - 0s 184us/step - loss: 0.5958 - acc: 0.6611 - val_loss: 0.6058 - val_acc: 0.6797\n",
      "Epoch 4/200\n",
      "537/537 [==============================] - 0s 80us/step - loss: 0.5709 - acc: 0.6853 - val_loss: 0.5829 - val_acc: 0.6840\n",
      "Epoch 5/200\n",
      "537/537 [==============================] - 0s 217us/step - loss: 0.5500 - acc: 0.7132 - val_loss: 0.5646 - val_acc: 0.7056\n",
      "Epoch 6/200\n",
      "537/537 [==============================] - 0s 91us/step - loss: 0.5330 - acc: 0.7318 - val_loss: 0.5495 - val_acc: 0.7229\n",
      "Epoch 7/200\n",
      "537/537 [==============================] - 0s 106us/step - loss: 0.5191 - acc: 0.7486 - val_loss: 0.5367 - val_acc: 0.7489\n",
      "Epoch 8/200\n",
      "537/537 [==============================] - 0s 171us/step - loss: 0.5081 - acc: 0.7430 - val_loss: 0.5268 - val_acc: 0.7706\n",
      "Epoch 9/200\n",
      "537/537 [==============================] - 0s 72us/step - loss: 0.4991 - acc: 0.7467 - val_loss: 0.5206 - val_acc: 0.7749\n",
      "Epoch 10/200\n",
      "537/537 [==============================] - 0s 72us/step - loss: 0.4922 - acc: 0.7505 - val_loss: 0.5160 - val_acc: 0.7749\n",
      "Epoch 11/200\n",
      "537/537 [==============================] - 0s 223us/step - loss: 0.4868 - acc: 0.7505 - val_loss: 0.5121 - val_acc: 0.7879\n",
      "Epoch 12/200\n",
      "537/537 [==============================] - 0s 93us/step - loss: 0.4820 - acc: 0.7486 - val_loss: 0.5100 - val_acc: 0.7835\n",
      "Epoch 13/200\n",
      "537/537 [==============================] - 0s 72us/step - loss: 0.4790 - acc: 0.7523 - val_loss: 0.5080 - val_acc: 0.7879\n",
      "Epoch 14/200\n",
      "537/537 [==============================] - 0s 75us/step - loss: 0.4753 - acc: 0.7561 - val_loss: 0.5066 - val_acc: 0.7835\n",
      "Epoch 15/200\n",
      "537/537 [==============================] - 0s 152us/step - loss: 0.4727 - acc: 0.7598 - val_loss: 0.5053 - val_acc: 0.7835\n",
      "Epoch 16/200\n",
      "537/537 [==============================] - 0s 322us/step - loss: 0.4707 - acc: 0.7654 - val_loss: 0.5049 - val_acc: 0.7835\n",
      "Epoch 17/200\n",
      "537/537 [==============================] - 0s 262us/step - loss: 0.4687 - acc: 0.7654 - val_loss: 0.5043 - val_acc: 0.7792\n",
      "Epoch 18/200\n",
      "537/537 [==============================] - 0s 199us/step - loss: 0.4657 - acc: 0.7654 - val_loss: 0.5033 - val_acc: 0.7792\n",
      "Epoch 19/200\n",
      "537/537 [==============================] - 0s 93us/step - loss: 0.4630 - acc: 0.7691 - val_loss: 0.5028 - val_acc: 0.7835\n",
      "Epoch 20/200\n",
      "537/537 [==============================] - 0s 208us/step - loss: 0.4620 - acc: 0.7654 - val_loss: 0.5025 - val_acc: 0.7835\n",
      "Epoch 21/200\n",
      "537/537 [==============================] - 0s 96us/step - loss: 0.4596 - acc: 0.7691 - val_loss: 0.5035 - val_acc: 0.7749\n",
      "Epoch 22/200\n",
      "537/537 [==============================] - 0s 236us/step - loss: 0.4580 - acc: 0.7691 - val_loss: 0.5020 - val_acc: 0.7835\n",
      "Epoch 23/200\n",
      "537/537 [==============================] - 0s 89us/step - loss: 0.4575 - acc: 0.7672 - val_loss: 0.5018 - val_acc: 0.7835\n",
      "Epoch 24/200\n",
      "537/537 [==============================] - 0s 137us/step - loss: 0.4556 - acc: 0.7672 - val_loss: 0.5025 - val_acc: 0.7749\n",
      "Epoch 25/200\n",
      "537/537 [==============================] - 0s 238us/step - loss: 0.4545 - acc: 0.7747 - val_loss: 0.5014 - val_acc: 0.7749\n",
      "Epoch 26/200\n",
      "537/537 [==============================] - 0s 132us/step - loss: 0.4527 - acc: 0.7691 - val_loss: 0.5013 - val_acc: 0.7749\n",
      "Epoch 27/200\n",
      "537/537 [==============================] - 0s 155us/step - loss: 0.4511 - acc: 0.7728 - val_loss: 0.5013 - val_acc: 0.7749\n",
      "Epoch 28/200\n",
      "537/537 [==============================] - 0s 197us/step - loss: 0.4503 - acc: 0.7709 - val_loss: 0.5016 - val_acc: 0.7706\n",
      "Epoch 29/200\n",
      "537/537 [==============================] - 0s 78us/step - loss: 0.4491 - acc: 0.7728 - val_loss: 0.5022 - val_acc: 0.7792\n",
      "Epoch 30/200\n",
      "537/537 [==============================] - 0s 215us/step - loss: 0.4486 - acc: 0.7691 - val_loss: 0.5016 - val_acc: 0.7749\n",
      "Epoch 31/200\n",
      "537/537 [==============================] - 0s 128us/step - loss: 0.4465 - acc: 0.7765 - val_loss: 0.5022 - val_acc: 0.7835\n",
      "Epoch 32/200\n",
      "537/537 [==============================] - 0s 171us/step - loss: 0.4454 - acc: 0.7728 - val_loss: 0.5004 - val_acc: 0.7835\n",
      "Epoch 33/200\n",
      "537/537 [==============================] - 0s 190us/step - loss: 0.4447 - acc: 0.7747 - val_loss: 0.5018 - val_acc: 0.7749\n",
      "Epoch 34/200\n",
      "537/537 [==============================] - 0s 198us/step - loss: 0.4438 - acc: 0.7784 - val_loss: 0.5021 - val_acc: 0.7749\n",
      "Epoch 35/200\n",
      "537/537 [==============================] - 0s 191us/step - loss: 0.4431 - acc: 0.7747 - val_loss: 0.5021 - val_acc: 0.7749\n",
      "Epoch 36/200\n",
      "537/537 [==============================] - 0s 238us/step - loss: 0.4419 - acc: 0.7784 - val_loss: 0.5017 - val_acc: 0.7792\n",
      "Epoch 37/200\n",
      "537/537 [==============================] - 0s 100us/step - loss: 0.4422 - acc: 0.7858 - val_loss: 0.5026 - val_acc: 0.7749\n",
      "Epoch 38/200\n",
      "537/537 [==============================] - 0s 164us/step - loss: 0.4407 - acc: 0.7803 - val_loss: 0.5029 - val_acc: 0.7792\n",
      "Epoch 39/200\n",
      "537/537 [==============================] - 0s 104us/step - loss: 0.4398 - acc: 0.7803 - val_loss: 0.5026 - val_acc: 0.7792\n",
      "Epoch 40/200\n",
      "537/537 [==============================] - 0s 286us/step - loss: 0.4387 - acc: 0.7821 - val_loss: 0.5020 - val_acc: 0.7792\n",
      "Epoch 41/200\n",
      "537/537 [==============================] - 0s 199us/step - loss: 0.4384 - acc: 0.7877 - val_loss: 0.5014 - val_acc: 0.7792\n",
      "Epoch 42/200\n",
      "537/537 [==============================] - 0s 113us/step - loss: 0.4374 - acc: 0.7877 - val_loss: 0.5005 - val_acc: 0.7749\n",
      "Epoch 43/200\n",
      "537/537 [==============================] - 0s 203us/step - loss: 0.4378 - acc: 0.7877 - val_loss: 0.5012 - val_acc: 0.7749\n",
      "Epoch 44/200\n",
      "537/537 [==============================] - 0s 351us/step - loss: 0.4369 - acc: 0.7877 - val_loss: 0.5005 - val_acc: 0.7792\n",
      "Epoch 45/200\n",
      "537/537 [==============================] - 0s 134us/step - loss: 0.4355 - acc: 0.7784 - val_loss: 0.5013 - val_acc: 0.7792\n",
      "Epoch 46/200\n",
      "537/537 [==============================] - 0s 700us/step - loss: 0.4358 - acc: 0.7914 - val_loss: 0.5009 - val_acc: 0.7792\n",
      "Epoch 47/200\n",
      "537/537 [==============================] - 0s 228us/step - loss: 0.4345 - acc: 0.7896 - val_loss: 0.5009 - val_acc: 0.7749\n",
      "Epoch 48/200\n",
      "537/537 [==============================] - 0s 739us/step - loss: 0.4351 - acc: 0.7896 - val_loss: 0.5016 - val_acc: 0.7792\n",
      "Epoch 49/200\n",
      "537/537 [==============================] - 0s 351us/step - loss: 0.4332 - acc: 0.7877 - val_loss: 0.5027 - val_acc: 0.7792\n",
      "Epoch 50/200\n",
      "537/537 [==============================] - 0s 366us/step - loss: 0.4321 - acc: 0.7896 - val_loss: 0.5021 - val_acc: 0.7749\n",
      "Epoch 51/200\n",
      "537/537 [==============================] - 0s 436us/step - loss: 0.4316 - acc: 0.7896 - val_loss: 0.5025 - val_acc: 0.7749\n",
      "Epoch 52/200\n",
      "537/537 [==============================] - 0s 624us/step - loss: 0.4319 - acc: 0.7877 - val_loss: 0.5025 - val_acc: 0.7706\n",
      "Epoch 53/200\n",
      "537/537 [==============================] - 0s 444us/step - loss: 0.4315 - acc: 0.7914 - val_loss: 0.5034 - val_acc: 0.7662\n",
      "Epoch 54/200\n",
      "537/537 [==============================] - 0s 234us/step - loss: 0.4304 - acc: 0.7877 - val_loss: 0.5034 - val_acc: 0.7662\n",
      "Epoch 55/200\n",
      "537/537 [==============================] - 0s 195us/step - loss: 0.4297 - acc: 0.7933 - val_loss: 0.5033 - val_acc: 0.7662\n",
      "Epoch 56/200\n",
      "537/537 [==============================] - 0s 158us/step - loss: 0.4299 - acc: 0.7914 - val_loss: 0.5046 - val_acc: 0.7662\n",
      "Epoch 57/200\n",
      "537/537 [==============================] - 0s 273us/step - loss: 0.4297 - acc: 0.7933 - val_loss: 0.5035 - val_acc: 0.7749\n",
      "Epoch 58/200\n",
      "537/537 [==============================] - 0s 215us/step - loss: 0.4292 - acc: 0.7914 - val_loss: 0.5037 - val_acc: 0.7662\n",
      "Epoch 59/200\n",
      "537/537 [==============================] - 0s 167us/step - loss: 0.4284 - acc: 0.7914 - val_loss: 0.5044 - val_acc: 0.7619\n",
      "Epoch 60/200\n",
      "537/537 [==============================] - 0s 110us/step - loss: 0.4279 - acc: 0.7914 - val_loss: 0.5048 - val_acc: 0.7619\n",
      "Epoch 61/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "537/537 [==============================] - 0s 115us/step - loss: 0.4270 - acc: 0.7914 - val_loss: 0.5042 - val_acc: 0.7749\n",
      "Epoch 62/200\n",
      "537/537 [==============================] - 0s 85us/step - loss: 0.4272 - acc: 0.7970 - val_loss: 0.5041 - val_acc: 0.7749\n",
      "Epoch 63/200\n",
      "537/537 [==============================] - 0s 149us/step - loss: 0.4263 - acc: 0.7896 - val_loss: 0.5050 - val_acc: 0.7662\n",
      "Epoch 64/200\n",
      "537/537 [==============================] - 0s 169us/step - loss: 0.4255 - acc: 0.7970 - val_loss: 0.5064 - val_acc: 0.7619\n",
      "Epoch 65/200\n",
      "537/537 [==============================] - 0s 704us/step - loss: 0.4245 - acc: 0.7989 - val_loss: 0.5065 - val_acc: 0.7662\n",
      "Epoch 66/200\n",
      "537/537 [==============================] - 0s 327us/step - loss: 0.4254 - acc: 0.7933 - val_loss: 0.5053 - val_acc: 0.7662\n",
      "Epoch 67/200\n",
      "537/537 [==============================] - 0s 150us/step - loss: 0.4241 - acc: 0.7914 - val_loss: 0.5050 - val_acc: 0.7749\n",
      "Epoch 68/200\n",
      "537/537 [==============================] - 0s 199us/step - loss: 0.4241 - acc: 0.7989 - val_loss: 0.5064 - val_acc: 0.7619\n",
      "Epoch 69/200\n",
      "537/537 [==============================] - 0s 151us/step - loss: 0.4228 - acc: 0.7989 - val_loss: 0.5089 - val_acc: 0.7662\n",
      "Epoch 70/200\n",
      "537/537 [==============================] - 1s 1ms/step - loss: 0.4231 - acc: 0.7989 - val_loss: 0.5069 - val_acc: 0.7706\n",
      "Epoch 71/200\n",
      "537/537 [==============================] - 0s 856us/step - loss: 0.4219 - acc: 0.7970 - val_loss: 0.5071 - val_acc: 0.7706\n",
      "Epoch 72/200\n",
      "537/537 [==============================] - 0s 106us/step - loss: 0.4214 - acc: 0.8045 - val_loss: 0.5082 - val_acc: 0.7662\n",
      "Epoch 73/200\n",
      "537/537 [==============================] - 0s 225us/step - loss: 0.4211 - acc: 0.7989 - val_loss: 0.5069 - val_acc: 0.7706\n",
      "Epoch 74/200\n",
      "537/537 [==============================] - 0s 115us/step - loss: 0.4214 - acc: 0.8007 - val_loss: 0.5092 - val_acc: 0.7706\n",
      "Epoch 75/200\n",
      "537/537 [==============================] - 0s 128us/step - loss: 0.4202 - acc: 0.7989 - val_loss: 0.5094 - val_acc: 0.7749\n",
      "Epoch 76/200\n",
      "537/537 [==============================] - 0s 204us/step - loss: 0.4192 - acc: 0.8007 - val_loss: 0.5085 - val_acc: 0.7706\n",
      "Epoch 77/200\n",
      "537/537 [==============================] - 0s 147us/step - loss: 0.4194 - acc: 0.8045 - val_loss: 0.5084 - val_acc: 0.7662\n",
      "Epoch 78/200\n",
      "537/537 [==============================] - 0s 137us/step - loss: 0.4190 - acc: 0.8063 - val_loss: 0.5097 - val_acc: 0.7706\n",
      "Epoch 79/200\n",
      "537/537 [==============================] - 0s 156us/step - loss: 0.4198 - acc: 0.8026 - val_loss: 0.5103 - val_acc: 0.7706\n",
      "Epoch 80/200\n",
      "537/537 [==============================] - 0s 182us/step - loss: 0.4181 - acc: 0.8026 - val_loss: 0.5108 - val_acc: 0.7749\n",
      "Epoch 81/200\n",
      "537/537 [==============================] - 0s 135us/step - loss: 0.4182 - acc: 0.8045 - val_loss: 0.5093 - val_acc: 0.7706\n",
      "Epoch 82/200\n",
      "537/537 [==============================] - 0s 251us/step - loss: 0.4176 - acc: 0.8101 - val_loss: 0.5111 - val_acc: 0.7706\n",
      "Epoch 83/200\n",
      "537/537 [==============================] - 0s 197us/step - loss: 0.4171 - acc: 0.8045 - val_loss: 0.5114 - val_acc: 0.7706\n",
      "Epoch 84/200\n",
      "537/537 [==============================] - 0s 167us/step - loss: 0.4170 - acc: 0.8045 - val_loss: 0.5104 - val_acc: 0.7706\n",
      "Epoch 85/200\n",
      "537/537 [==============================] - 0s 180us/step - loss: 0.4159 - acc: 0.8082 - val_loss: 0.5131 - val_acc: 0.7749\n",
      "Epoch 86/200\n",
      "537/537 [==============================] - 0s 145us/step - loss: 0.4170 - acc: 0.8026 - val_loss: 0.5138 - val_acc: 0.7749\n",
      "Epoch 87/200\n",
      "537/537 [==============================] - 0s 176us/step - loss: 0.4164 - acc: 0.8082 - val_loss: 0.5122 - val_acc: 0.7706\n",
      "Epoch 88/200\n",
      "537/537 [==============================] - 0s 123us/step - loss: 0.4154 - acc: 0.8082 - val_loss: 0.5135 - val_acc: 0.7749\n",
      "Epoch 89/200\n",
      "537/537 [==============================] - 0s 145us/step - loss: 0.4151 - acc: 0.8045 - val_loss: 0.5119 - val_acc: 0.7706\n",
      "Epoch 90/200\n",
      "537/537 [==============================] - 0s 130us/step - loss: 0.4145 - acc: 0.8082 - val_loss: 0.5111 - val_acc: 0.7706\n",
      "Epoch 91/200\n",
      "537/537 [==============================] - ETA: 0s - loss: 0.4248 - acc: 0.800 - 0s 170us/step - loss: 0.4148 - acc: 0.8101 - val_loss: 0.5150 - val_acc: 0.7792\n",
      "Epoch 92/200\n",
      "537/537 [==============================] - 0s 265us/step - loss: 0.4141 - acc: 0.8082 - val_loss: 0.5138 - val_acc: 0.7662\n",
      "Epoch 93/200\n",
      "537/537 [==============================] - 0s 139us/step - loss: 0.4136 - acc: 0.8101 - val_loss: 0.5131 - val_acc: 0.7706\n",
      "Epoch 94/200\n",
      "537/537 [==============================] - 0s 91us/step - loss: 0.4138 - acc: 0.8063 - val_loss: 0.5132 - val_acc: 0.7706\n",
      "Epoch 95/200\n",
      "537/537 [==============================] - 0s 89us/step - loss: 0.4123 - acc: 0.8119 - val_loss: 0.5166 - val_acc: 0.7706\n",
      "Epoch 96/200\n",
      "537/537 [==============================] - 0s 95us/step - loss: 0.4132 - acc: 0.8119 - val_loss: 0.5151 - val_acc: 0.7662\n",
      "Epoch 97/200\n",
      "537/537 [==============================] - 0s 98us/step - loss: 0.4116 - acc: 0.8175 - val_loss: 0.5195 - val_acc: 0.7792\n",
      "Epoch 98/200\n",
      "537/537 [==============================] - 0s 87us/step - loss: 0.4120 - acc: 0.8138 - val_loss: 0.5191 - val_acc: 0.7706\n",
      "Epoch 99/200\n",
      "537/537 [==============================] - 0s 93us/step - loss: 0.4120 - acc: 0.8175 - val_loss: 0.5166 - val_acc: 0.7749\n",
      "Epoch 100/200\n",
      "537/537 [==============================] - 0s 392us/step - loss: 0.4110 - acc: 0.8194 - val_loss: 0.5161 - val_acc: 0.7749\n",
      "Epoch 101/200\n",
      "537/537 [==============================] - 0s 91us/step - loss: 0.4111 - acc: 0.8156 - val_loss: 0.5196 - val_acc: 0.7879\n",
      "Epoch 102/200\n",
      "537/537 [==============================] - 0s 227us/step - loss: 0.4101 - acc: 0.8194 - val_loss: 0.5183 - val_acc: 0.7749\n",
      "Epoch 103/200\n",
      "537/537 [==============================] - 0s 424us/step - loss: 0.4097 - acc: 0.8156 - val_loss: 0.5169 - val_acc: 0.7792\n",
      "Epoch 104/200\n",
      "537/537 [==============================] - 0s 234us/step - loss: 0.4094 - acc: 0.8156 - val_loss: 0.5185 - val_acc: 0.7792\n",
      "Epoch 105/200\n",
      "537/537 [==============================] - 0s 111us/step - loss: 0.4096 - acc: 0.8175 - val_loss: 0.5173 - val_acc: 0.7749\n",
      "Epoch 106/200\n",
      "537/537 [==============================] - 0s 113us/step - loss: 0.4091 - acc: 0.8250 - val_loss: 0.5195 - val_acc: 0.7706\n",
      "Epoch 107/200\n",
      "537/537 [==============================] - 0s 165us/step - loss: 0.4094 - acc: 0.8212 - val_loss: 0.5191 - val_acc: 0.7706\n",
      "Epoch 108/200\n",
      "537/537 [==============================] - 0s 160us/step - loss: 0.4080 - acc: 0.8175 - val_loss: 0.5181 - val_acc: 0.7749\n",
      "Epoch 109/200\n",
      "537/537 [==============================] - 0s 206us/step - loss: 0.4076 - acc: 0.8231 - val_loss: 0.5188 - val_acc: 0.7749\n",
      "Epoch 110/200\n",
      "537/537 [==============================] - 0s 263us/step - loss: 0.4072 - acc: 0.8212 - val_loss: 0.5194 - val_acc: 0.7749\n",
      "Epoch 111/200\n",
      "537/537 [==============================] - 0s 104us/step - loss: 0.4067 - acc: 0.8212 - val_loss: 0.5209 - val_acc: 0.7749\n",
      "Epoch 112/200\n",
      "537/537 [==============================] - 0s 121us/step - loss: 0.4066 - acc: 0.8250 - val_loss: 0.5224 - val_acc: 0.7749\n",
      "Epoch 113/200\n",
      "537/537 [==============================] - 0s 227us/step - loss: 0.4068 - acc: 0.8194 - val_loss: 0.5216 - val_acc: 0.7749\n",
      "Epoch 114/200\n",
      "537/537 [==============================] - 0s 180us/step - loss: 0.4071 - acc: 0.8250 - val_loss: 0.5215 - val_acc: 0.7706\n",
      "Epoch 115/200\n",
      "537/537 [==============================] - 0s 120us/step - loss: 0.4057 - acc: 0.8212 - val_loss: 0.5215 - val_acc: 0.7706\n",
      "Epoch 116/200\n",
      "537/537 [==============================] - 0s 160us/step - loss: 0.4055 - acc: 0.8268 - val_loss: 0.5226 - val_acc: 0.7749\n",
      "Epoch 117/200\n",
      "537/537 [==============================] - 0s 154us/step - loss: 0.4062 - acc: 0.8268 - val_loss: 0.5229 - val_acc: 0.7749\n",
      "Epoch 118/200\n",
      "537/537 [==============================] - 0s 221us/step - loss: 0.4046 - acc: 0.8231 - val_loss: 0.5213 - val_acc: 0.7706\n",
      "Epoch 119/200\n",
      "537/537 [==============================] - 0s 204us/step - loss: 0.4039 - acc: 0.8268 - val_loss: 0.5243 - val_acc: 0.7749\n",
      "Epoch 120/200\n",
      "537/537 [==============================] - 0s 154us/step - loss: 0.4044 - acc: 0.8194 - val_loss: 0.5250 - val_acc: 0.7749\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 121/200\n",
      "537/537 [==============================] - 0s 247us/step - loss: 0.4037 - acc: 0.8231 - val_loss: 0.5243 - val_acc: 0.7749\n",
      "Epoch 122/200\n",
      "537/537 [==============================] - 0s 135us/step - loss: 0.4042 - acc: 0.8250 - val_loss: 0.5253 - val_acc: 0.7792\n",
      "Epoch 123/200\n",
      "537/537 [==============================] - 0s 150us/step - loss: 0.4029 - acc: 0.8231 - val_loss: 0.5228 - val_acc: 0.7749\n",
      "Epoch 124/200\n",
      "537/537 [==============================] - 0s 147us/step - loss: 0.4030 - acc: 0.8212 - val_loss: 0.5251 - val_acc: 0.7706\n",
      "Epoch 125/200\n",
      "537/537 [==============================] - 0s 184us/step - loss: 0.4028 - acc: 0.8250 - val_loss: 0.5277 - val_acc: 0.7749\n",
      "Epoch 126/200\n",
      "537/537 [==============================] - 0s 109us/step - loss: 0.4021 - acc: 0.8231 - val_loss: 0.5298 - val_acc: 0.7792\n",
      "Epoch 127/200\n",
      "537/537 [==============================] - 0s 184us/step - loss: 0.4007 - acc: 0.8212 - val_loss: 0.5221 - val_acc: 0.7749\n",
      "Epoch 128/200\n",
      "537/537 [==============================] - 0s 134us/step - loss: 0.4018 - acc: 0.8231 - val_loss: 0.5249 - val_acc: 0.7706\n",
      "Epoch 129/200\n",
      "537/537 [==============================] - 0s 171us/step - loss: 0.4009 - acc: 0.8324 - val_loss: 0.5317 - val_acc: 0.7706\n",
      "Epoch 130/200\n",
      "537/537 [==============================] - 0s 98us/step - loss: 0.4000 - acc: 0.8305 - val_loss: 0.5306 - val_acc: 0.7749\n",
      "Epoch 131/200\n",
      "537/537 [==============================] - 0s 150us/step - loss: 0.4001 - acc: 0.8212 - val_loss: 0.5262 - val_acc: 0.7706\n",
      "Epoch 132/200\n",
      "537/537 [==============================] - 0s 217us/step - loss: 0.3993 - acc: 0.8268 - val_loss: 0.5266 - val_acc: 0.7792\n",
      "Epoch 133/200\n",
      "537/537 [==============================] - 0s 101us/step - loss: 0.3990 - acc: 0.8380 - val_loss: 0.5291 - val_acc: 0.7706\n",
      "Epoch 134/200\n",
      "537/537 [==============================] - 0s 137us/step - loss: 0.3985 - acc: 0.8380 - val_loss: 0.5307 - val_acc: 0.7706\n",
      "Epoch 135/200\n",
      "537/537 [==============================] - 0s 134us/step - loss: 0.3997 - acc: 0.8343 - val_loss: 0.5300 - val_acc: 0.7706\n",
      "Epoch 136/200\n",
      "537/537 [==============================] - 0s 106us/step - loss: 0.3982 - acc: 0.8380 - val_loss: 0.5275 - val_acc: 0.7749\n",
      "Epoch 137/200\n",
      "537/537 [==============================] - 0s 196us/step - loss: 0.3977 - acc: 0.8361 - val_loss: 0.5273 - val_acc: 0.7706\n",
      "Epoch 138/200\n",
      "537/537 [==============================] - 0s 123us/step - loss: 0.3970 - acc: 0.8324 - val_loss: 0.5270 - val_acc: 0.7706\n",
      "Epoch 139/200\n",
      "537/537 [==============================] - 0s 134us/step - loss: 0.3980 - acc: 0.8305 - val_loss: 0.5302 - val_acc: 0.7749\n",
      "Epoch 140/200\n",
      "537/537 [==============================] - 0s 126us/step - loss: 0.3976 - acc: 0.8380 - val_loss: 0.5315 - val_acc: 0.7619\n",
      "Epoch 141/200\n",
      "537/537 [==============================] - 0s 104us/step - loss: 0.3967 - acc: 0.8268 - val_loss: 0.5280 - val_acc: 0.7749\n",
      "Epoch 142/200\n",
      "537/537 [==============================] - 0s 163us/step - loss: 0.3974 - acc: 0.8343 - val_loss: 0.5312 - val_acc: 0.7662\n",
      "Epoch 143/200\n",
      "537/537 [==============================] - 0s 270us/step - loss: 0.3972 - acc: 0.8343 - val_loss: 0.5299 - val_acc: 0.7662\n",
      "Epoch 144/200\n",
      "537/537 [==============================] - ETA: 0s - loss: 0.4041 - acc: 0.830 - 0s 251us/step - loss: 0.3947 - acc: 0.8305 - val_loss: 0.5317 - val_acc: 0.7662\n",
      "Epoch 145/200\n",
      "537/537 [==============================] - 0s 119us/step - loss: 0.3953 - acc: 0.8343 - val_loss: 0.5293 - val_acc: 0.7662\n",
      "Epoch 146/200\n",
      "537/537 [==============================] - 0s 113us/step - loss: 0.3945 - acc: 0.8287 - val_loss: 0.5284 - val_acc: 0.7749\n",
      "Epoch 147/200\n",
      "537/537 [==============================] - 0s 134us/step - loss: 0.3946 - acc: 0.8305 - val_loss: 0.5338 - val_acc: 0.7662\n",
      "Epoch 148/200\n",
      "537/537 [==============================] - 0s 145us/step - loss: 0.3935 - acc: 0.8399 - val_loss: 0.5299 - val_acc: 0.7662\n",
      "Epoch 149/200\n",
      "537/537 [==============================] - 0s 156us/step - loss: 0.3936 - acc: 0.8361 - val_loss: 0.5359 - val_acc: 0.7662\n",
      "Epoch 150/200\n",
      "537/537 [==============================] - 0s 145us/step - loss: 0.3941 - acc: 0.8250 - val_loss: 0.5323 - val_acc: 0.7662\n",
      "Epoch 151/200\n",
      "537/537 [==============================] - 0s 197us/step - loss: 0.3932 - acc: 0.8324 - val_loss: 0.5321 - val_acc: 0.7662\n",
      "Epoch 152/200\n",
      "537/537 [==============================] - 0s 113us/step - loss: 0.3931 - acc: 0.8380 - val_loss: 0.5311 - val_acc: 0.7662\n",
      "Epoch 153/200\n",
      "537/537 [==============================] - 0s 124us/step - loss: 0.3922 - acc: 0.8287 - val_loss: 0.5353 - val_acc: 0.7662\n",
      "Epoch 154/200\n",
      "537/537 [==============================] - 0s 159us/step - loss: 0.3924 - acc: 0.8324 - val_loss: 0.5358 - val_acc: 0.7662\n",
      "Epoch 155/200\n",
      "537/537 [==============================] - 0s 258us/step - loss: 0.3914 - acc: 0.8305 - val_loss: 0.5318 - val_acc: 0.7706\n",
      "Epoch 156/200\n",
      "537/537 [==============================] - 0s 102us/step - loss: 0.3927 - acc: 0.8305 - val_loss: 0.5347 - val_acc: 0.7706\n",
      "Epoch 157/200\n",
      "537/537 [==============================] - 0s 303us/step - loss: 0.3906 - acc: 0.8436 - val_loss: 0.5315 - val_acc: 0.7749\n",
      "Epoch 158/200\n",
      "537/537 [==============================] - 0s 189us/step - loss: 0.3915 - acc: 0.8287 - val_loss: 0.5339 - val_acc: 0.7749\n",
      "Epoch 159/200\n",
      "537/537 [==============================] - 0s 108us/step - loss: 0.3897 - acc: 0.8305 - val_loss: 0.5350 - val_acc: 0.7706\n",
      "Epoch 160/200\n",
      "537/537 [==============================] - 0s 117us/step - loss: 0.3895 - acc: 0.8417 - val_loss: 0.5372 - val_acc: 0.7706\n",
      "Epoch 161/200\n",
      "537/537 [==============================] - 0s 198us/step - loss: 0.3894 - acc: 0.8361 - val_loss: 0.5332 - val_acc: 0.7706\n",
      "Epoch 162/200\n",
      "537/537 [==============================] - 0s 123us/step - loss: 0.3889 - acc: 0.8399 - val_loss: 0.5344 - val_acc: 0.7706\n",
      "Epoch 163/200\n",
      "537/537 [==============================] - 0s 109us/step - loss: 0.3893 - acc: 0.8343 - val_loss: 0.5352 - val_acc: 0.7662\n",
      "Epoch 164/200\n",
      "537/537 [==============================] - 0s 105us/step - loss: 0.3879 - acc: 0.8380 - val_loss: 0.5387 - val_acc: 0.7706\n",
      "Epoch 165/200\n",
      "537/537 [==============================] - 0s 116us/step - loss: 0.3879 - acc: 0.8399 - val_loss: 0.5398 - val_acc: 0.7706\n",
      "Epoch 166/200\n",
      "537/537 [==============================] - 0s 182us/step - loss: 0.3871 - acc: 0.8305 - val_loss: 0.5456 - val_acc: 0.7706\n",
      "Epoch 167/200\n",
      "537/537 [==============================] - 0s 188us/step - loss: 0.3886 - acc: 0.8361 - val_loss: 0.5391 - val_acc: 0.7706\n",
      "Epoch 168/200\n",
      "537/537 [==============================] - 0s 216us/step - loss: 0.3872 - acc: 0.8436 - val_loss: 0.5411 - val_acc: 0.7706\n",
      "Epoch 169/200\n",
      "537/537 [==============================] - 0s 197us/step - loss: 0.3859 - acc: 0.8380 - val_loss: 0.5418 - val_acc: 0.7706\n",
      "Epoch 170/200\n",
      "537/537 [==============================] - 0s 193us/step - loss: 0.3856 - acc: 0.8417 - val_loss: 0.5389 - val_acc: 0.7662\n",
      "Epoch 171/200\n",
      "537/537 [==============================] - 0s 164us/step - loss: 0.3851 - acc: 0.8343 - val_loss: 0.5419 - val_acc: 0.7662\n",
      "Epoch 172/200\n",
      "537/537 [==============================] - 0s 201us/step - loss: 0.3857 - acc: 0.8287 - val_loss: 0.5451 - val_acc: 0.7706\n",
      "Epoch 173/200\n",
      "537/537 [==============================] - 0s 234us/step - loss: 0.3854 - acc: 0.8343 - val_loss: 0.5414 - val_acc: 0.7662\n",
      "Epoch 174/200\n",
      "537/537 [==============================] - 0s 98us/step - loss: 0.3856 - acc: 0.8287 - val_loss: 0.5466 - val_acc: 0.7706\n",
      "Epoch 175/200\n",
      "537/537 [==============================] - 0s 119us/step - loss: 0.3847 - acc: 0.8343 - val_loss: 0.5457 - val_acc: 0.7749\n",
      "Epoch 176/200\n",
      "537/537 [==============================] - 0s 111us/step - loss: 0.3831 - acc: 0.8194 - val_loss: 0.5452 - val_acc: 0.7749\n",
      "Epoch 177/200\n",
      "537/537 [==============================] - 0s 241us/step - loss: 0.3833 - acc: 0.8417 - val_loss: 0.5448 - val_acc: 0.7706\n",
      "Epoch 178/200\n",
      "537/537 [==============================] - 0s 127us/step - loss: 0.3816 - acc: 0.8231 - val_loss: 0.5473 - val_acc: 0.7662\n",
      "Epoch 179/200\n",
      "537/537 [==============================] - 0s 179us/step - loss: 0.3822 - acc: 0.8324 - val_loss: 0.5491 - val_acc: 0.7662\n",
      "Epoch 180/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "537/537 [==============================] - 0s 171us/step - loss: 0.3820 - acc: 0.8268 - val_loss: 0.5481 - val_acc: 0.7749\n",
      "Epoch 181/200\n",
      "537/537 [==============================] - 0s 267us/step - loss: 0.3815 - acc: 0.8305 - val_loss: 0.5538 - val_acc: 0.7706\n",
      "Epoch 182/200\n",
      "537/537 [==============================] - 0s 227us/step - loss: 0.3805 - acc: 0.8361 - val_loss: 0.5507 - val_acc: 0.7706\n",
      "Epoch 183/200\n",
      "537/537 [==============================] - 0s 160us/step - loss: 0.3814 - acc: 0.8287 - val_loss: 0.5613 - val_acc: 0.7792\n",
      "Epoch 184/200\n",
      "537/537 [==============================] - 0s 188us/step - loss: 0.3821 - acc: 0.8287 - val_loss: 0.5515 - val_acc: 0.7662\n",
      "Epoch 185/200\n",
      "537/537 [==============================] - 0s 264us/step - loss: 0.3797 - acc: 0.8343 - val_loss: 0.5523 - val_acc: 0.7749\n",
      "Epoch 186/200\n",
      "537/537 [==============================] - 0s 199us/step - loss: 0.3791 - acc: 0.8343 - val_loss: 0.5520 - val_acc: 0.7749\n",
      "Epoch 187/200\n",
      "537/537 [==============================] - 0s 123us/step - loss: 0.3801 - acc: 0.8380 - val_loss: 0.5505 - val_acc: 0.7706\n",
      "Epoch 188/200\n",
      "537/537 [==============================] - 0s 137us/step - loss: 0.3787 - acc: 0.8268 - val_loss: 0.5486 - val_acc: 0.7749\n",
      "Epoch 189/200\n",
      "537/537 [==============================] - 0s 121us/step - loss: 0.3805 - acc: 0.8380 - val_loss: 0.5502 - val_acc: 0.7835\n",
      "Epoch 190/200\n",
      "537/537 [==============================] - 0s 166us/step - loss: 0.3773 - acc: 0.8324 - val_loss: 0.5530 - val_acc: 0.7706\n",
      "Epoch 191/200\n",
      "537/537 [==============================] - 0s 156us/step - loss: 0.3773 - acc: 0.8305 - val_loss: 0.5552 - val_acc: 0.7749\n",
      "Epoch 192/200\n",
      "537/537 [==============================] - 0s 137us/step - loss: 0.3772 - acc: 0.8361 - val_loss: 0.5528 - val_acc: 0.7749\n",
      "Epoch 193/200\n",
      "537/537 [==============================] - 0s 134us/step - loss: 0.3764 - acc: 0.8287 - val_loss: 0.5514 - val_acc: 0.7835\n",
      "Epoch 194/200\n",
      "537/537 [==============================] - 0s 186us/step - loss: 0.3759 - acc: 0.8194 - val_loss: 0.5522 - val_acc: 0.7792\n",
      "Epoch 195/200\n",
      "537/537 [==============================] - 0s 112us/step - loss: 0.3766 - acc: 0.8361 - val_loss: 0.5558 - val_acc: 0.7835\n",
      "Epoch 196/200\n",
      "537/537 [==============================] - 0s 134us/step - loss: 0.3749 - acc: 0.8380 - val_loss: 0.5559 - val_acc: 0.7792\n",
      "Epoch 197/200\n",
      "537/537 [==============================] - 0s 115us/step - loss: 0.3756 - acc: 0.8343 - val_loss: 0.5500 - val_acc: 0.7706\n",
      "Epoch 198/200\n",
      "537/537 [==============================] - 0s 141us/step - loss: 0.3757 - acc: 0.8305 - val_loss: 0.5539 - val_acc: 0.7706\n",
      "Epoch 199/200\n",
      "537/537 [==============================] - 0s 318us/step - loss: 0.3748 - acc: 0.8324 - val_loss: 0.5527 - val_acc: 0.7706\n",
      "Epoch 200/200\n",
      "537/537 [==============================] - 0s 104us/step - loss: 0.3737 - acc: 0.8324 - val_loss: 0.5566 - val_acc: 0.7749\n"
     ]
    }
   ],
   "source": [
    "run_hist_1 = model.fit(X_train_norm, y_train, validation_data=(X_test_norm, y_test), verbose=1, epochs=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_class_nn_1 = model.predict_classes(X_test_norm)\n",
    "y_pred_prob_nn_1 = model.predict(X_test_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1],\n",
       "       [1],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0]])"
      ]
     },
     "execution_count": 395,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's check out the outputs to get a feel for how keras apis work.\n",
    "y_pred_class_nn_1[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.70708334],\n",
       "       [0.89493465],\n",
       "       [0.518112  ],\n",
       "       [0.0581139 ],\n",
       "       [0.23702025],\n",
       "       [0.57479405],\n",
       "       [0.00375727],\n",
       "       [0.48315445],\n",
       "       [0.9495256 ],\n",
       "       [0.17577732]], dtype=float32)"
      ]
     },
     "execution_count": 396,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_prob_nn_1[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is 0.775\n",
      "ROC-AUC is 0.814\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Print model performance and plot the roc curve\n",
    "print('Accuracy is {:.3f}'.format(accuracy_score(y_test,y_pred_class_nn_1)))\n",
    "print('ROC-AUC is {:.3f}'.format(roc_auc_score(y_test,y_pred_prob_nn_1)))\n",
    "\n",
    "plot_roc(y_test, y_pred_prob_nn_1, 'NN')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accuracy obtained from this model is slightly better 77.5% and ROC-AUC value is 0.814 \n",
    "\n",
    "If we tune the hyperpaprameters and add more layers, epochs, learing rate then model might improve. \n",
    "\n",
    "Lets try running for more number of epoch iterations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])"
      ]
     },
     "execution_count": 398,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run_hist_1.history.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c78e000898>"
      ]
     },
     "execution_count": 399,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the curve to check training and validation accuracy\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(run_hist_1.history[\"acc\"],'r', marker='.', label=\"Training Accuracy\")\n",
    "ax.plot(run_hist_1.history[\"val_acc\"],'b', marker='.', label=\"Validation Accuracy\")\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c78deecd30>"
      ]
     },
     "execution_count": 400,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(run_hist_1.history[\"loss\"],'r', marker='.', label=\"Training Loss\")\n",
    "ax.plot(run_hist_1.history[\"val_loss\"],'b', marker='.', label=\"Validation Loss\")\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training loss= [0.667396244057064, 0.6254130719538286, 0.5957936708931595, 0.5708816987176181, 0.5500221115916801, 0.532988995147151, 0.519072246063133, 0.5081218436665597, 0.49905164942173097, 0.4921787594283759, 0.4867541404853764, 0.4819810397687127, 0.47898368706694305, 0.47529946941903184, 0.4727154449599637, 0.47074349699073664, 0.4687215073148632, 0.4656917346256405, 0.46298287584128994, 0.46203451571739806, 0.45959297161084534, 0.457968141002584, 0.4575342527092946, 0.45555174606028437, 0.45450002093776865, 0.452689578524293, 0.451127015456585, 0.45030251239709124, 0.4490982667027905, 0.4485761690295164, 0.44645637062896787, 0.4454214628404301, 0.44473576806555026, 0.4437639905864744, 0.4431102724825648, 0.44185843159144594, 0.4422457546717184, 0.4406720336589067, 0.4398247683958412, 0.43871851995226613, 0.43841959189214297, 0.43744796692104093, 0.43780545049539493, 0.4369372257640242, 0.4355056181522277, 0.4357536056632214, 0.4345020569680567, 0.4350529530638867, 0.4331617617851307, 0.432133271652005, 0.43158440250258207, 0.43189942997705116, 0.4314682817769894, 0.4303564514749765, 0.429693719195254, 0.4298834378936881, 0.4296845543983928, 0.42915283434883844, 0.4284237122069524, 0.42791817785419345, 0.42696694943713964, 0.4271650262163116, 0.42633085094350676, 0.4255309968465311, 0.42454539297679283, 0.42536504558344795, 0.42405910309910555, 0.4240663309115049, 0.4227860406790366, 0.4231412890792115, 0.42194880253775824, 0.42143411099133765, 0.4210778570263967, 0.42142736312397366, 0.4201891349014623, 0.4192464091369338, 0.41936516684082853, 0.4190186040139509, 0.4197788271824075, 0.41805020357643424, 0.41823853532917227, 0.4175751323029538, 0.417096659568879, 0.4170109051343893, 0.41589990752147343, 0.4169707011910124, 0.41637692837741785, 0.41540343823601633, 0.4150848082878958, 0.4145101779444036, 0.4147849514164738, 0.41412778817519574, 0.4136193359697331, 0.41380426798230885, 0.41232409335159503, 0.41324630351927916, 0.41161665403643133, 0.4120195305436239, 0.41198469196173954, 0.4110357094299416, 0.41108297247238446, 0.4101171496305164, 0.40968774589302376, 0.40938716172284034, 0.4096316666354458, 0.40908048738980424, 0.4093925188151596, 0.4080240990640509, 0.40760612798580687, 0.40718461747942025, 0.4066929433154882, 0.406595640286831, 0.40683032691589727, 0.4071378428731773, 0.40567235845649263, 0.4054687394443171, 0.4062036918528253, 0.40455556193321557, 0.4039191256244311, 0.4043679490990701, 0.40366021509055094, 0.4042406794745163, 0.4028895203738683, 0.40297320979045537, 0.4027561691331242, 0.4021252095144332, 0.4007061921906427, 0.40180258683208203, 0.40092844936434785, 0.3999877626447482, 0.4000628667725753, 0.39931690640178696, 0.3990216719149655, 0.3985371877694263, 0.39965549649695, 0.39817034548887326, 0.3976800459501242, 0.3969716795440937, 0.3980458304979726, 0.3975572488938408, 0.3967241408993634, 0.39744913150700334, 0.397216823624943, 0.39468477719100936, 0.39531784950021925, 0.39448496161004465, 0.39458603982152884, 0.393540044958587, 0.3936333936019983, 0.3941480611456173, 0.3932473359272245, 0.3930971532560594, 0.3921851085106976, 0.392398262601103, 0.3914043973833045, 0.3927328089318018, 0.39055652913656536, 0.39152160425647897, 0.38965277099076595, 0.38947945755509245, 0.38943040265495327, 0.3889029899566977, 0.38932618670845387, 0.38789453691832404, 0.3878842682145827, 0.3871269405887114, 0.3885851086739943, 0.38720797277029667, 0.38593801600751043, 0.38556918019023023, 0.3851499925445578, 0.3856664821644291, 0.3854495147087055, 0.385564899422381, 0.38467321499099943, 0.3831071172124625, 0.38330679083003677, 0.3816033221045908, 0.382162096114132, 0.38204733647225736, 0.38152805292628555, 0.38052168516251406, 0.38137546908700043, 0.3820535410715881, 0.3797414761237814, 0.3790951451109774, 0.3801035222283511, 0.3786665983151235, 0.38052061988210767, 0.37726651390171584, 0.3772677226439535, 0.37715933997759826, 0.3764175211029124, 0.37588180232536417, 0.37662200865576834, 0.3749183075610042, 0.375631938988492, 0.3756859405191695, 0.3747929741994407, 0.3737019415896254]\n",
      "Validation Loss= [0.6698603490730385, 0.6342750875464765, 0.6057606612965142, 0.5829151861079327, 0.5646215994636734, 0.549472561130276, 0.5367049285859773, 0.5267829211243303, 0.52061574361025, 0.5159600039065142, 0.5121473192652582, 0.509969318841959, 0.508042680494713, 0.5065985862568859, 0.505342790058681, 0.5048699649897489, 0.5042848568974119, 0.5032821549223615, 0.5028455152914122, 0.5024950275947522, 0.5035221687643043, 0.5019836692841022, 0.5017768998682757, 0.5025063177723905, 0.5014369633548703, 0.5012633600534299, 0.5013476866922337, 0.5016322937104609, 0.5021983310257717, 0.5015643171933822, 0.5022209165416238, 0.500427877619153, 0.5018319630777681, 0.5021491151351434, 0.5020849112566416, 0.5016897961948857, 0.5026137230200168, 0.5029459145420041, 0.5025894773470891, 0.5019527046711414, 0.5014293414431733, 0.5005451488546478, 0.50120235934402, 0.5005114059169571, 0.5013274262735854, 0.5009292856955425, 0.5008809264604147, 0.501573846066669, 0.502684288855755, 0.5021124135106156, 0.5024605932689848, 0.5024718829305657, 0.5034051231749646, 0.5034445088147085, 0.5033172686378677, 0.5046105264843285, 0.5034524322330177, 0.5036819857178312, 0.5043839790346303, 0.5047914802512049, 0.5042032670407068, 0.504078020910164, 0.5050058182957885, 0.5063518401864288, 0.506537116838224, 0.5053078474162461, 0.5049616967960869, 0.5063504162546876, 0.5089318402659841, 0.5069352153575781, 0.5070553798696179, 0.5082407933034938, 0.5069029693737691, 0.5091848828833857, 0.5093587111859095, 0.5085387664697903, 0.508403373487068, 0.5097339789330702, 0.5103109191248427, 0.510793366602489, 0.5092939441596275, 0.5110812846458319, 0.5114308025413777, 0.5104223214960718, 0.513104428589602, 0.5138413855781803, 0.5122122596868705, 0.5134893649326259, 0.5119321622373738, 0.5111018518606821, 0.515046343787924, 0.513803282083371, 0.513098917740248, 0.5131865916572091, 0.5166411212770454, 0.5151295658055838, 0.5194607793768763, 0.519136706729988, 0.5166263785455134, 0.5161046125156022, 0.5195892206776194, 0.5183053165029138, 0.5169073166527274, 0.5184539710030411, 0.5172735234617671, 0.5194967136238561, 0.5191437413682153, 0.518075775403481, 0.5188029494894532, 0.5193617103935836, 0.5208504016781266, 0.5224145418637759, 0.5216335855521165, 0.5214623021873045, 0.5214550937667037, 0.5225705202523764, 0.5229028603989324, 0.5213343342403313, 0.5242951314944726, 0.5249688015097663, 0.5243114691018026, 0.52531543915922, 0.5227803824009833, 0.5250658854777679, 0.527673048116428, 0.5297531728104595, 0.522097899413212, 0.52491277469185, 0.5317373234472234, 0.5305745302340685, 0.5261855680189091, 0.5265755334715823, 0.52913875974618, 0.5306826930541497, 0.5300154777574333, 0.5274761330771756, 0.5272739190301854, 0.5270168884492024, 0.5301817825862339, 0.5315260452367526, 0.5279750221477443, 0.5311743982168503, 0.5298597424319296, 0.5317201241528317, 0.5293403294953433, 0.5283509108411285, 0.5338387713803874, 0.529932321666123, 0.5359020885967073, 0.5323403802785006, 0.5320805877576142, 0.5311252307066153, 0.5352712286499156, 0.5357983965120274, 0.5317825235071636, 0.5346968328797972, 0.5315115953678693, 0.5339090128739675, 0.5350402410412247, 0.5371700206618288, 0.5331796647666337, 0.5343679043895755, 0.5352272023628284, 0.5386571409382346, 0.5397935095545533, 0.5456265100152977, 0.5390720022963239, 0.5411014122106296, 0.5418478929119193, 0.5389360878374669, 0.5419250693156089, 0.5450650994137768, 0.5413918622902462, 0.5465715012767098, 0.5456990350634505, 0.5452256165283583, 0.5448285303848647, 0.547322505738312, 0.5490909012365135, 0.5480531922408513, 0.5537525131588891, 0.5506757567455243, 0.561316541134021, 0.5514600205989111, 0.5522653171510408, 0.5519830670965699, 0.5504778037855635, 0.5486381268088436, 0.5502031624833227, 0.5530257212135182, 0.5552094887087355, 0.5528409015306662, 0.551361456577912, 0.5522348288591806, 0.5558308005590975, 0.5558882831753075, 0.5499547819296519, 0.5538555240476286, 0.5527371284249541, 0.5566337084099328]\n"
     ]
    }
   ],
   "source": [
    "print('Training loss=',run_hist_1.history[\"loss\"])\n",
    "\n",
    "print('Validation Loss=',run_hist_1.history[\"val_loss\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Increasing the epochs to 400 and checking the performance of the model. \n",
    "\n",
    "Lets increase the number of epochs to 400 and check the performance of the model. Rest all the parameter values will remain unchanged. Here we are trying to check if the number of epochs makes a difference in performance to the model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 537 samples, validate on 231 samples\n",
      "Epoch 1/400\n",
      "537/537 [==============================] - 0s 139us/step - loss: 0.3426 - acc: 0.8436 - val_loss: 0.6438 - val_acc: 0.7186\n",
      "Epoch 2/400\n",
      "537/537 [==============================] - 0s 54us/step - loss: 0.3408 - acc: 0.8510 - val_loss: 0.6470 - val_acc: 0.7359\n",
      "Epoch 3/400\n",
      "537/537 [==============================] - 0s 59us/step - loss: 0.3426 - acc: 0.8547 - val_loss: 0.6484 - val_acc: 0.7186\n",
      "Epoch 4/400\n",
      "537/537 [==============================] - 0s 61us/step - loss: 0.3430 - acc: 0.8454 - val_loss: 0.6456 - val_acc: 0.7273\n",
      "Epoch 5/400\n",
      "537/537 [==============================] - 0s 65us/step - loss: 0.3392 - acc: 0.8417 - val_loss: 0.6505 - val_acc: 0.7316\n",
      "Epoch 6/400\n",
      "537/537 [==============================] - 0s 65us/step - loss: 0.3422 - acc: 0.8492 - val_loss: 0.6466 - val_acc: 0.7186\n",
      "Epoch 7/400\n",
      "537/537 [==============================] - 0s 58us/step - loss: 0.3398 - acc: 0.8492 - val_loss: 0.6460 - val_acc: 0.7446\n",
      "Epoch 8/400\n",
      "537/537 [==============================] - 0s 56us/step - loss: 0.3412 - acc: 0.8566 - val_loss: 0.6533 - val_acc: 0.7229\n",
      "Epoch 9/400\n",
      "537/537 [==============================] - 0s 52us/step - loss: 0.3421 - acc: 0.8510 - val_loss: 0.6457 - val_acc: 0.7316\n",
      "Epoch 10/400\n",
      "537/537 [==============================] - 0s 56us/step - loss: 0.3390 - acc: 0.8473 - val_loss: 0.6524 - val_acc: 0.7229\n",
      "Epoch 11/400\n",
      "537/537 [==============================] - 0s 130us/step - loss: 0.3380 - acc: 0.8492 - val_loss: 0.6604 - val_acc: 0.7359\n",
      "Epoch 12/400\n",
      "537/537 [==============================] - 0s 271us/step - loss: 0.3405 - acc: 0.8454 - val_loss: 0.6517 - val_acc: 0.7186\n",
      "Epoch 13/400\n",
      "537/537 [==============================] - 0s 193us/step - loss: 0.3373 - acc: 0.8566 - val_loss: 0.6524 - val_acc: 0.7186\n",
      "Epoch 14/400\n",
      "537/537 [==============================] - 0s 104us/step - loss: 0.3380 - acc: 0.8492 - val_loss: 0.6485 - val_acc: 0.7186\n",
      "Epoch 15/400\n",
      "537/537 [==============================] - 0s 156us/step - loss: 0.3360 - acc: 0.8529 - val_loss: 0.6567 - val_acc: 0.7186\n",
      "Epoch 16/400\n",
      "537/537 [==============================] - 0s 162us/step - loss: 0.3359 - acc: 0.8510 - val_loss: 0.6566 - val_acc: 0.7359\n",
      "Epoch 17/400\n",
      "537/537 [==============================] - 0s 123us/step - loss: 0.3375 - acc: 0.8529 - val_loss: 0.6588 - val_acc: 0.7186\n",
      "Epoch 18/400\n",
      "537/537 [==============================] - 0s 80us/step - loss: 0.3368 - acc: 0.8547 - val_loss: 0.6548 - val_acc: 0.7316\n",
      "Epoch 19/400\n",
      "537/537 [==============================] - 0s 141us/step - loss: 0.3367 - acc: 0.8510 - val_loss: 0.6543 - val_acc: 0.7316\n",
      "Epoch 20/400\n",
      "537/537 [==============================] - 0s 124us/step - loss: 0.3324 - acc: 0.8566 - val_loss: 0.6564 - val_acc: 0.7359\n",
      "Epoch 21/400\n",
      "537/537 [==============================] - 0s 102us/step - loss: 0.3364 - acc: 0.8529 - val_loss: 0.6598 - val_acc: 0.7229\n",
      "Epoch 22/400\n",
      "537/537 [==============================] - 0s 174us/step - loss: 0.3355 - acc: 0.8547 - val_loss: 0.6583 - val_acc: 0.7316\n",
      "Epoch 23/400\n",
      "537/537 [==============================] - 0s 178us/step - loss: 0.3341 - acc: 0.8566 - val_loss: 0.6614 - val_acc: 0.7186\n",
      "Epoch 24/400\n",
      "537/537 [==============================] - 0s 78us/step - loss: 0.3353 - acc: 0.8566 - val_loss: 0.6602 - val_acc: 0.7403\n",
      "Epoch 25/400\n",
      "537/537 [==============================] - 0s 89us/step - loss: 0.3366 - acc: 0.8585 - val_loss: 0.6648 - val_acc: 0.7273\n",
      "Epoch 26/400\n",
      "537/537 [==============================] - 0s 169us/step - loss: 0.3345 - acc: 0.8585 - val_loss: 0.6607 - val_acc: 0.7316\n",
      "Epoch 27/400\n",
      "537/537 [==============================] - 0s 102us/step - loss: 0.3324 - acc: 0.8603 - val_loss: 0.6703 - val_acc: 0.7143\n",
      "Epoch 28/400\n",
      "537/537 [==============================] - 0s 110us/step - loss: 0.3302 - acc: 0.8547 - val_loss: 0.6679 - val_acc: 0.7403\n",
      "Epoch 29/400\n",
      "537/537 [==============================] - 0s 91us/step - loss: 0.3337 - acc: 0.8659 - val_loss: 0.6646 - val_acc: 0.7359\n",
      "Epoch 30/400\n",
      "537/537 [==============================] - 0s 171us/step - loss: 0.3288 - acc: 0.8529 - val_loss: 0.6750 - val_acc: 0.7143\n",
      "Epoch 31/400\n",
      "537/537 [==============================] - 0s 95us/step - loss: 0.3338 - acc: 0.8473 - val_loss: 0.6656 - val_acc: 0.7273\n",
      "Epoch 32/400\n",
      "537/537 [==============================] - 0s 121us/step - loss: 0.3299 - acc: 0.8547 - val_loss: 0.6700 - val_acc: 0.7273\n",
      "Epoch 33/400\n",
      "537/537 [==============================] - 0s 98us/step - loss: 0.3304 - acc: 0.8510 - val_loss: 0.6713 - val_acc: 0.7229\n",
      "Epoch 34/400\n",
      "537/537 [==============================] - 0s 102us/step - loss: 0.3296 - acc: 0.8641 - val_loss: 0.6714 - val_acc: 0.7273\n",
      "Epoch 35/400\n",
      "537/537 [==============================] - 0s 89us/step - loss: 0.3306 - acc: 0.8566 - val_loss: 0.6733 - val_acc: 0.7056\n",
      "Epoch 36/400\n",
      "537/537 [==============================] - 0s 262us/step - loss: 0.3348 - acc: 0.8641 - val_loss: 0.6796 - val_acc: 0.7056\n",
      "Epoch 37/400\n",
      "537/537 [==============================] - 0s 94us/step - loss: 0.3292 - acc: 0.8547 - val_loss: 0.6812 - val_acc: 0.7143\n",
      "Epoch 38/400\n",
      "537/537 [==============================] - 0s 82us/step - loss: 0.3293 - acc: 0.8622 - val_loss: 0.6720 - val_acc: 0.7186\n",
      "Epoch 39/400\n",
      "537/537 [==============================] - 0s 210us/step - loss: 0.3331 - acc: 0.8566 - val_loss: 0.6713 - val_acc: 0.7316\n",
      "Epoch 40/400\n",
      "537/537 [==============================] - 0s 139us/step - loss: 0.3310 - acc: 0.8547 - val_loss: 0.6755 - val_acc: 0.7273\n",
      "Epoch 41/400\n",
      "537/537 [==============================] - 0s 243us/step - loss: 0.3314 - acc: 0.8566 - val_loss: 0.6744 - val_acc: 0.7316\n",
      "Epoch 42/400\n",
      "537/537 [==============================] - 0s 205us/step - loss: 0.3296 - acc: 0.8547 - val_loss: 0.6804 - val_acc: 0.7186\n",
      "Epoch 43/400\n",
      "537/537 [==============================] - 0s 221us/step - loss: 0.3273 - acc: 0.8659 - val_loss: 0.6839 - val_acc: 0.7186\n",
      "Epoch 44/400\n",
      "537/537 [==============================] - 0s 659us/step - loss: 0.3317 - acc: 0.8678 - val_loss: 0.6808 - val_acc: 0.7143\n",
      "Epoch 45/400\n",
      "537/537 [==============================] - 0s 162us/step - loss: 0.3275 - acc: 0.8585 - val_loss: 0.6876 - val_acc: 0.7229\n",
      "Epoch 46/400\n",
      "537/537 [==============================] - 0s 124us/step - loss: 0.3267 - acc: 0.8659 - val_loss: 0.6833 - val_acc: 0.7186\n",
      "Epoch 47/400\n",
      "537/537 [==============================] - 0s 423us/step - loss: 0.3250 - acc: 0.8622 - val_loss: 0.6843 - val_acc: 0.7100\n",
      "Epoch 48/400\n",
      "537/537 [==============================] - 0s 871us/step - loss: 0.3260 - acc: 0.8603 - val_loss: 0.6775 - val_acc: 0.7359\n",
      "Epoch 49/400\n",
      "537/537 [==============================] - 0s 282us/step - loss: 0.3264 - acc: 0.8566 - val_loss: 0.6858 - val_acc: 0.7229\n",
      "Epoch 50/400\n",
      "537/537 [==============================] - 0s 260us/step - loss: 0.3256 - acc: 0.8622 - val_loss: 0.6893 - val_acc: 0.7056\n",
      "Epoch 51/400\n",
      "537/537 [==============================] - 0s 321us/step - loss: 0.3226 - acc: 0.8659 - val_loss: 0.6854 - val_acc: 0.7273\n",
      "Epoch 52/400\n",
      "537/537 [==============================] - 0s 529us/step - loss: 0.3241 - acc: 0.8715 - val_loss: 0.6895 - val_acc: 0.7229\n",
      "Epoch 53/400\n",
      "537/537 [==============================] - 0s 322us/step - loss: 0.3240 - acc: 0.8566 - val_loss: 0.6822 - val_acc: 0.7273\n",
      "Epoch 54/400\n",
      "537/537 [==============================] - 0s 478us/step - loss: 0.3226 - acc: 0.8622 - val_loss: 0.6824 - val_acc: 0.7316\n",
      "Epoch 55/400\n",
      "537/537 [==============================] - 0s 132us/step - loss: 0.3250 - acc: 0.8547 - val_loss: 0.6870 - val_acc: 0.7186\n",
      "Epoch 56/400\n",
      "537/537 [==============================] - 0s 418us/step - loss: 0.3234 - acc: 0.8603 - val_loss: 0.6899 - val_acc: 0.7403\n",
      "Epoch 57/400\n",
      "537/537 [==============================] - 0s 251us/step - loss: 0.3259 - acc: 0.8659 - val_loss: 0.6894 - val_acc: 0.7143\n",
      "Epoch 58/400\n",
      "537/537 [==============================] - 0s 176us/step - loss: 0.3211 - acc: 0.8641 - val_loss: 0.6881 - val_acc: 0.7229\n",
      "Epoch 59/400\n",
      "537/537 [==============================] - 0s 113us/step - loss: 0.3208 - acc: 0.8641 - val_loss: 0.6924 - val_acc: 0.7316\n",
      "Epoch 60/400\n",
      "537/537 [==============================] - 0s 182us/step - loss: 0.3234 - acc: 0.8641 - val_loss: 0.6872 - val_acc: 0.7229\n",
      "Epoch 61/400\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "537/537 [==============================] - 0s 156us/step - loss: 0.3178 - acc: 0.8696 - val_loss: 0.6903 - val_acc: 0.7273\n",
      "Epoch 62/400\n",
      "537/537 [==============================] - 0s 156us/step - loss: 0.3195 - acc: 0.8622 - val_loss: 0.6855 - val_acc: 0.7273\n",
      "Epoch 63/400\n",
      "537/537 [==============================] - 0s 630us/step - loss: 0.3196 - acc: 0.8566 - val_loss: 0.6910 - val_acc: 0.7316\n",
      "Epoch 64/400\n",
      "537/537 [==============================] - 0s 203us/step - loss: 0.3177 - acc: 0.8715 - val_loss: 0.6879 - val_acc: 0.7359\n",
      "Epoch 65/400\n",
      "537/537 [==============================] - 0s 84us/step - loss: 0.3198 - acc: 0.8603 - val_loss: 0.6901 - val_acc: 0.7229\n",
      "Epoch 66/400\n",
      "537/537 [==============================] - 0s 130us/step - loss: 0.3176 - acc: 0.8659 - val_loss: 0.6977 - val_acc: 0.7186\n",
      "Epoch 67/400\n",
      "537/537 [==============================] - 0s 85us/step - loss: 0.3174 - acc: 0.8734 - val_loss: 0.6982 - val_acc: 0.7143\n",
      "Epoch 68/400\n",
      "537/537 [==============================] - 0s 546us/step - loss: 0.3169 - acc: 0.8622 - val_loss: 0.6930 - val_acc: 0.7316\n",
      "Epoch 69/400\n",
      "537/537 [==============================] - 0s 336us/step - loss: 0.3211 - acc: 0.8603 - val_loss: 0.6965 - val_acc: 0.7229\n",
      "Epoch 70/400\n",
      "537/537 [==============================] - 0s 209us/step - loss: 0.3182 - acc: 0.8622 - val_loss: 0.6961 - val_acc: 0.7229\n",
      "Epoch 71/400\n",
      "537/537 [==============================] - 0s 645us/step - loss: 0.3199 - acc: 0.8641 - val_loss: 0.6930 - val_acc: 0.7186\n",
      "Epoch 72/400\n",
      "537/537 [==============================] - 0s 256us/step - loss: 0.3158 - acc: 0.8678 - val_loss: 0.6975 - val_acc: 0.7186\n",
      "Epoch 73/400\n",
      "537/537 [==============================] - 0s 117us/step - loss: 0.3175 - acc: 0.8566 - val_loss: 0.6950 - val_acc: 0.7229\n",
      "Epoch 74/400\n",
      "537/537 [==============================] - 0s 113us/step - loss: 0.3138 - acc: 0.8715 - val_loss: 0.6945 - val_acc: 0.7273\n",
      "Epoch 75/400\n",
      "537/537 [==============================] - 0s 626us/step - loss: 0.3144 - acc: 0.8603 - val_loss: 0.6931 - val_acc: 0.7186\n",
      "Epoch 76/400\n",
      "537/537 [==============================] - 0s 406us/step - loss: 0.3143 - acc: 0.8715 - val_loss: 0.6960 - val_acc: 0.7143\n",
      "Epoch 77/400\n",
      "537/537 [==============================] - 0s 276us/step - loss: 0.3157 - acc: 0.8659 - val_loss: 0.6995 - val_acc: 0.7186\n",
      "Epoch 78/400\n",
      "537/537 [==============================] - 0s 115us/step - loss: 0.3120 - acc: 0.8641 - val_loss: 0.6966 - val_acc: 0.7316\n",
      "Epoch 79/400\n",
      "537/537 [==============================] - 0s 190us/step - loss: 0.3153 - acc: 0.8678 - val_loss: 0.7061 - val_acc: 0.7359\n",
      "Epoch 80/400\n",
      "537/537 [==============================] - 0s 268us/step - loss: 0.3188 - acc: 0.8659 - val_loss: 0.6980 - val_acc: 0.7359\n",
      "Epoch 81/400\n",
      "537/537 [==============================] - 0s 195us/step - loss: 0.3118 - acc: 0.8696 - val_loss: 0.6978 - val_acc: 0.7316\n",
      "Epoch 82/400\n",
      "537/537 [==============================] - 0s 102us/step - loss: 0.3107 - acc: 0.8641 - val_loss: 0.6994 - val_acc: 0.7229\n",
      "Epoch 83/400\n",
      "537/537 [==============================] - 0s 121us/step - loss: 0.3119 - acc: 0.8696 - val_loss: 0.7019 - val_acc: 0.7359\n",
      "Epoch 84/400\n",
      "537/537 [==============================] - 0s 271us/step - loss: 0.3080 - acc: 0.8659 - val_loss: 0.7203 - val_acc: 0.7143\n",
      "Epoch 85/400\n",
      "537/537 [==============================] - 0s 141us/step - loss: 0.3156 - acc: 0.8734 - val_loss: 0.7004 - val_acc: 0.7316\n",
      "Epoch 86/400\n",
      "537/537 [==============================] - 0s 154us/step - loss: 0.3131 - acc: 0.8659 - val_loss: 0.7066 - val_acc: 0.7143\n",
      "Epoch 87/400\n",
      "537/537 [==============================] - 0s 232us/step - loss: 0.3137 - acc: 0.8678 - val_loss: 0.7031 - val_acc: 0.7316\n",
      "Epoch 88/400\n",
      "537/537 [==============================] - 0s 93us/step - loss: 0.3083 - acc: 0.8678 - val_loss: 0.7060 - val_acc: 0.7273\n",
      "Epoch 89/400\n",
      "537/537 [==============================] - 0s 150us/step - loss: 0.3091 - acc: 0.8696 - val_loss: 0.7039 - val_acc: 0.7143\n",
      "Epoch 90/400\n",
      "537/537 [==============================] - 0s 108us/step - loss: 0.3107 - acc: 0.8659 - val_loss: 0.6982 - val_acc: 0.7316\n",
      "Epoch 91/400\n",
      "537/537 [==============================] - 0s 136us/step - loss: 0.3116 - acc: 0.8696 - val_loss: 0.7023 - val_acc: 0.7229\n",
      "Epoch 92/400\n",
      "537/537 [==============================] - 0s 130us/step - loss: 0.3079 - acc: 0.8678 - val_loss: 0.7011 - val_acc: 0.7229\n",
      "Epoch 93/400\n",
      "537/537 [==============================] - 0s 150us/step - loss: 0.3103 - acc: 0.8622 - val_loss: 0.7044 - val_acc: 0.7359\n",
      "Epoch 94/400\n",
      "537/537 [==============================] - 0s 87us/step - loss: 0.3072 - acc: 0.8752 - val_loss: 0.7072 - val_acc: 0.7186\n",
      "Epoch 95/400\n",
      "537/537 [==============================] - 0s 197us/step - loss: 0.3071 - acc: 0.8678 - val_loss: 0.7040 - val_acc: 0.7403\n",
      "Epoch 96/400\n",
      "537/537 [==============================] - 0s 312us/step - loss: 0.3084 - acc: 0.8585 - val_loss: 0.7035 - val_acc: 0.7229\n",
      "Epoch 97/400\n",
      "537/537 [==============================] - 0s 213us/step - loss: 0.3099 - acc: 0.8641 - val_loss: 0.7057 - val_acc: 0.7229\n",
      "Epoch 98/400\n",
      "537/537 [==============================] - 0s 206us/step - loss: 0.3070 - acc: 0.8696 - val_loss: 0.7013 - val_acc: 0.7359\n",
      "Epoch 99/400\n",
      "537/537 [==============================] - 0s 130us/step - loss: 0.3068 - acc: 0.8771 - val_loss: 0.7020 - val_acc: 0.7359\n",
      "Epoch 100/400\n",
      "537/537 [==============================] - 0s 110us/step - loss: 0.3089 - acc: 0.8678 - val_loss: 0.7097 - val_acc: 0.7186\n",
      "Epoch 101/400\n",
      "537/537 [==============================] - 0s 80us/step - loss: 0.3106 - acc: 0.8585 - val_loss: 0.7185 - val_acc: 0.7359\n",
      "Epoch 102/400\n",
      "537/537 [==============================] - 0s 202us/step - loss: 0.3061 - acc: 0.8734 - val_loss: 0.7057 - val_acc: 0.7316\n",
      "Epoch 103/400\n",
      "537/537 [==============================] - 0s 150us/step - loss: 0.3078 - acc: 0.8696 - val_loss: 0.7106 - val_acc: 0.7143\n",
      "Epoch 104/400\n",
      "537/537 [==============================] - 0s 193us/step - loss: 0.3091 - acc: 0.8622 - val_loss: 0.7157 - val_acc: 0.7229\n",
      "Epoch 105/400\n",
      "537/537 [==============================] - 0s 86us/step - loss: 0.3060 - acc: 0.8696 - val_loss: 0.7107 - val_acc: 0.7316\n",
      "Epoch 106/400\n",
      "537/537 [==============================] - 0s 186us/step - loss: 0.3059 - acc: 0.8659 - val_loss: 0.7103 - val_acc: 0.7403\n",
      "Epoch 107/400\n",
      "537/537 [==============================] - 0s 95us/step - loss: 0.3075 - acc: 0.8752 - val_loss: 0.7075 - val_acc: 0.7273\n",
      "Epoch 108/400\n",
      "537/537 [==============================] - 0s 85us/step - loss: 0.3014 - acc: 0.8752 - val_loss: 0.7188 - val_acc: 0.7229\n",
      "Epoch 109/400\n",
      "537/537 [==============================] - 0s 139us/step - loss: 0.3083 - acc: 0.8715 - val_loss: 0.7145 - val_acc: 0.7316\n",
      "Epoch 110/400\n",
      "537/537 [==============================] - 0s 199us/step - loss: 0.3044 - acc: 0.8734 - val_loss: 0.7076 - val_acc: 0.7446\n",
      "Epoch 111/400\n",
      "537/537 [==============================] - 0s 226us/step - loss: 0.3062 - acc: 0.8641 - val_loss: 0.7098 - val_acc: 0.7359\n",
      "Epoch 112/400\n",
      "537/537 [==============================] - 0s 83us/step - loss: 0.3040 - acc: 0.8659 - val_loss: 0.7109 - val_acc: 0.7143\n",
      "Epoch 113/400\n",
      "537/537 [==============================] - 0s 254us/step - loss: 0.3033 - acc: 0.8715 - val_loss: 0.7137 - val_acc: 0.7273\n",
      "Epoch 114/400\n",
      "537/537 [==============================] - 0s 93us/step - loss: 0.3057 - acc: 0.8678 - val_loss: 0.7110 - val_acc: 0.7316\n",
      "Epoch 115/400\n",
      "537/537 [==============================] - 0s 134us/step - loss: 0.3026 - acc: 0.8641 - val_loss: 0.7259 - val_acc: 0.7186\n",
      "Epoch 116/400\n",
      "537/537 [==============================] - 0s 199us/step - loss: 0.3076 - acc: 0.8678 - val_loss: 0.7113 - val_acc: 0.7316\n",
      "Epoch 117/400\n",
      "537/537 [==============================] - 0s 124us/step - loss: 0.3011 - acc: 0.8752 - val_loss: 0.7170 - val_acc: 0.7489\n",
      "Epoch 118/400\n",
      "537/537 [==============================] - 0s 184us/step - loss: 0.3014 - acc: 0.8696 - val_loss: 0.7309 - val_acc: 0.7489\n",
      "Epoch 119/400\n",
      "537/537 [==============================] - 0s 141us/step - loss: 0.3011 - acc: 0.8752 - val_loss: 0.7194 - val_acc: 0.7229\n",
      "Epoch 120/400\n",
      "537/537 [==============================] - 0s 108us/step - loss: 0.3038 - acc: 0.8641 - val_loss: 0.7149 - val_acc: 0.7403\n",
      "Epoch 121/400\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "537/537 [==============================] - 0s 210us/step - loss: 0.2985 - acc: 0.8734 - val_loss: 0.7205 - val_acc: 0.7316\n",
      "Epoch 122/400\n",
      "537/537 [==============================] - 0s 132us/step - loss: 0.3053 - acc: 0.8752 - val_loss: 0.7259 - val_acc: 0.7143\n",
      "Epoch 123/400\n",
      "537/537 [==============================] - 0s 199us/step - loss: 0.3031 - acc: 0.8678 - val_loss: 0.7297 - val_acc: 0.7446\n",
      "Epoch 124/400\n",
      "537/537 [==============================] - 0s 253us/step - loss: 0.3012 - acc: 0.8696 - val_loss: 0.7155 - val_acc: 0.7316\n",
      "Epoch 125/400\n",
      "537/537 [==============================] - 0s 100us/step - loss: 0.3004 - acc: 0.8696 - val_loss: 0.7200 - val_acc: 0.7359\n",
      "Epoch 126/400\n",
      "537/537 [==============================] - 0s 178us/step - loss: 0.3037 - acc: 0.8622 - val_loss: 0.7185 - val_acc: 0.7359\n",
      "Epoch 127/400\n",
      "537/537 [==============================] - 0s 117us/step - loss: 0.3003 - acc: 0.8752 - val_loss: 0.7321 - val_acc: 0.7489\n",
      "Epoch 128/400\n",
      "537/537 [==============================] - 0s 126us/step - loss: 0.2999 - acc: 0.8715 - val_loss: 0.7169 - val_acc: 0.7446\n",
      "Epoch 129/400\n",
      "537/537 [==============================] - 0s 110us/step - loss: 0.2993 - acc: 0.8678 - val_loss: 0.7378 - val_acc: 0.7446\n",
      "Epoch 130/400\n",
      "537/537 [==============================] - 0s 108us/step - loss: 0.3040 - acc: 0.8641 - val_loss: 0.7199 - val_acc: 0.7316\n",
      "Epoch 131/400\n",
      "537/537 [==============================] - 0s 191us/step - loss: 0.3013 - acc: 0.8659 - val_loss: 0.7164 - val_acc: 0.7316\n",
      "Epoch 132/400\n",
      "537/537 [==============================] - 0s 186us/step - loss: 0.2980 - acc: 0.8641 - val_loss: 0.7424 - val_acc: 0.7143\n",
      "Epoch 133/400\n",
      "537/537 [==============================] - 0s 310us/step - loss: 0.3002 - acc: 0.8585 - val_loss: 0.7262 - val_acc: 0.7403\n",
      "Epoch 134/400\n",
      "537/537 [==============================] - 0s 231us/step - loss: 0.2997 - acc: 0.8603 - val_loss: 0.7250 - val_acc: 0.7359\n",
      "Epoch 135/400\n",
      "537/537 [==============================] - 0s 160us/step - loss: 0.2988 - acc: 0.8696 - val_loss: 0.7266 - val_acc: 0.7359\n",
      "Epoch 136/400\n",
      "537/537 [==============================] - 0s 110us/step - loss: 0.2973 - acc: 0.8603 - val_loss: 0.7431 - val_acc: 0.7446\n",
      "Epoch 137/400\n",
      "537/537 [==============================] - 0s 314us/step - loss: 0.3004 - acc: 0.8585 - val_loss: 0.7312 - val_acc: 0.7446\n",
      "Epoch 138/400\n",
      "537/537 [==============================] - 0s 119us/step - loss: 0.2973 - acc: 0.8696 - val_loss: 0.7249 - val_acc: 0.7359\n",
      "Epoch 139/400\n",
      "537/537 [==============================] - 0s 171us/step - loss: 0.3005 - acc: 0.8547 - val_loss: 0.7272 - val_acc: 0.7316\n",
      "Epoch 140/400\n",
      "537/537 [==============================] - 0s 188us/step - loss: 0.3012 - acc: 0.8696 - val_loss: 0.7251 - val_acc: 0.7403\n",
      "Epoch 141/400\n",
      "537/537 [==============================] - 0s 189us/step - loss: 0.3013 - acc: 0.8696 - val_loss: 0.7336 - val_acc: 0.7446\n",
      "Epoch 142/400\n",
      "537/537 [==============================] - 0s 145us/step - loss: 0.3010 - acc: 0.8715 - val_loss: 0.7408 - val_acc: 0.7359\n",
      "Epoch 143/400\n",
      "537/537 [==============================] - 0s 147us/step - loss: 0.3034 - acc: 0.8659 - val_loss: 0.7284 - val_acc: 0.7229\n",
      "Epoch 144/400\n",
      "537/537 [==============================] - 0s 189us/step - loss: 0.2941 - acc: 0.8622 - val_loss: 0.7310 - val_acc: 0.7186\n",
      "Epoch 145/400\n",
      "537/537 [==============================] - 0s 124us/step - loss: 0.3019 - acc: 0.8678 - val_loss: 0.7246 - val_acc: 0.7316\n",
      "Epoch 146/400\n",
      "537/537 [==============================] - 0s 165us/step - loss: 0.2947 - acc: 0.8678 - val_loss: 0.7208 - val_acc: 0.7446\n",
      "Epoch 147/400\n",
      "537/537 [==============================] - 0s 163us/step - loss: 0.2967 - acc: 0.8734 - val_loss: 0.7358 - val_acc: 0.7403\n",
      "Epoch 148/400\n",
      "537/537 [==============================] - 0s 215us/step - loss: 0.2999 - acc: 0.8641 - val_loss: 0.7271 - val_acc: 0.7359\n",
      "Epoch 149/400\n",
      "537/537 [==============================] - ETA: 0s - loss: 0.3222 - acc: 0.812 - 0s 106us/step - loss: 0.3002 - acc: 0.8696 - val_loss: 0.7287 - val_acc: 0.7186\n",
      "Epoch 150/400\n",
      "537/537 [==============================] - 0s 100us/step - loss: 0.2962 - acc: 0.8696 - val_loss: 0.7299 - val_acc: 0.7229\n",
      "Epoch 151/400\n",
      "537/537 [==============================] - 0s 100us/step - loss: 0.2936 - acc: 0.8715 - val_loss: 0.7324 - val_acc: 0.7186\n",
      "Epoch 152/400\n",
      "537/537 [==============================] - 0s 132us/step - loss: 0.2990 - acc: 0.8641 - val_loss: 0.7514 - val_acc: 0.7403\n",
      "Epoch 153/400\n",
      "537/537 [==============================] - 0s 78us/step - loss: 0.2989 - acc: 0.8585 - val_loss: 0.7302 - val_acc: 0.7273\n",
      "Epoch 154/400\n",
      "537/537 [==============================] - 0s 100us/step - loss: 0.2945 - acc: 0.8734 - val_loss: 0.7385 - val_acc: 0.7403\n",
      "Epoch 155/400\n",
      "537/537 [==============================] - 0s 128us/step - loss: 0.2992 - acc: 0.8659 - val_loss: 0.7357 - val_acc: 0.7143\n",
      "Epoch 156/400\n",
      "537/537 [==============================] - 0s 136us/step - loss: 0.3001 - acc: 0.8696 - val_loss: 0.7303 - val_acc: 0.7186\n",
      "Epoch 157/400\n",
      "537/537 [==============================] - 0s 124us/step - loss: 0.2941 - acc: 0.8641 - val_loss: 0.7383 - val_acc: 0.7143\n",
      "Epoch 158/400\n",
      "537/537 [==============================] - 0s 176us/step - loss: 0.2975 - acc: 0.8715 - val_loss: 0.7490 - val_acc: 0.7403\n",
      "Epoch 159/400\n",
      "537/537 [==============================] - 0s 178us/step - loss: 0.2992 - acc: 0.8715 - val_loss: 0.7300 - val_acc: 0.7229\n",
      "Epoch 160/400\n",
      "537/537 [==============================] - 0s 137us/step - loss: 0.3045 - acc: 0.8678 - val_loss: 0.7368 - val_acc: 0.7229\n",
      "Epoch 161/400\n",
      "537/537 [==============================] - 0s 117us/step - loss: 0.3001 - acc: 0.8641 - val_loss: 0.7403 - val_acc: 0.7446\n",
      "Epoch 162/400\n",
      "537/537 [==============================] - 0s 175us/step - loss: 0.2892 - acc: 0.8734 - val_loss: 0.7336 - val_acc: 0.7273\n",
      "Epoch 163/400\n",
      "537/537 [==============================] - 0s 249us/step - loss: 0.2963 - acc: 0.8715 - val_loss: 0.7316 - val_acc: 0.7316\n",
      "Epoch 164/400\n",
      "537/537 [==============================] - 0s 156us/step - loss: 0.2916 - acc: 0.8752 - val_loss: 0.7415 - val_acc: 0.7143\n",
      "Epoch 165/400\n",
      "537/537 [==============================] - 0s 93us/step - loss: 0.2952 - acc: 0.8659 - val_loss: 0.7468 - val_acc: 0.7273\n",
      "Epoch 166/400\n",
      "537/537 [==============================] - 0s 123us/step - loss: 0.2948 - acc: 0.8678 - val_loss: 0.7362 - val_acc: 0.7359\n",
      "Epoch 167/400\n",
      "537/537 [==============================] - 0s 297us/step - loss: 0.2947 - acc: 0.8678 - val_loss: 0.7375 - val_acc: 0.7359\n",
      "Epoch 168/400\n",
      "537/537 [==============================] - 0s 130us/step - loss: 0.2937 - acc: 0.8715 - val_loss: 0.7370 - val_acc: 0.7359\n",
      "Epoch 169/400\n",
      "537/537 [==============================] - 0s 173us/step - loss: 0.2932 - acc: 0.8734 - val_loss: 0.7410 - val_acc: 0.7056\n",
      "Epoch 170/400\n",
      "537/537 [==============================] - 0s 134us/step - loss: 0.2911 - acc: 0.8790 - val_loss: 0.7393 - val_acc: 0.7229\n",
      "Epoch 171/400\n",
      "537/537 [==============================] - 0s 206us/step - loss: 0.2941 - acc: 0.8678 - val_loss: 0.7383 - val_acc: 0.7100\n",
      "Epoch 172/400\n",
      "537/537 [==============================] - 0s 132us/step - loss: 0.2944 - acc: 0.8659 - val_loss: 0.7340 - val_acc: 0.7273\n",
      "Epoch 173/400\n",
      "537/537 [==============================] - 0s 104us/step - loss: 0.2939 - acc: 0.8790 - val_loss: 0.7447 - val_acc: 0.7316\n",
      "Epoch 174/400\n",
      "537/537 [==============================] - 0s 67us/step - loss: 0.2954 - acc: 0.8678 - val_loss: 0.7383 - val_acc: 0.7100\n",
      "Epoch 175/400\n",
      "537/537 [==============================] - 0s 58us/step - loss: 0.2922 - acc: 0.8808 - val_loss: 0.7376 - val_acc: 0.7403\n",
      "Epoch 176/400\n",
      "537/537 [==============================] - 0s 171us/step - loss: 0.2918 - acc: 0.8715 - val_loss: 0.7568 - val_acc: 0.7186\n",
      "Epoch 177/400\n",
      "537/537 [==============================] - 0s 65us/step - loss: 0.2928 - acc: 0.8696 - val_loss: 0.7372 - val_acc: 0.7359\n",
      "Epoch 178/400\n",
      "537/537 [==============================] - 0s 67us/step - loss: 0.2973 - acc: 0.8585 - val_loss: 0.7411 - val_acc: 0.7273\n",
      "Epoch 179/400\n",
      "537/537 [==============================] - 0s 87us/step - loss: 0.2943 - acc: 0.8715 - val_loss: 0.7424 - val_acc: 0.7100\n",
      "Epoch 180/400\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "537/537 [==============================] - 0s 193us/step - loss: 0.2934 - acc: 0.8696 - val_loss: 0.7407 - val_acc: 0.7143\n",
      "Epoch 181/400\n",
      "537/537 [==============================] - 0s 78us/step - loss: 0.2904 - acc: 0.8659 - val_loss: 0.7473 - val_acc: 0.7359\n",
      "Epoch 182/400\n",
      "537/537 [==============================] - 0s 85us/step - loss: 0.2917 - acc: 0.8752 - val_loss: 0.7465 - val_acc: 0.7186\n",
      "Epoch 183/400\n",
      "537/537 [==============================] - 0s 109us/step - loss: 0.2981 - acc: 0.8659 - val_loss: 0.7425 - val_acc: 0.7186\n",
      "Epoch 184/400\n",
      "537/537 [==============================] - 0s 132us/step - loss: 0.2929 - acc: 0.8734 - val_loss: 0.7496 - val_acc: 0.7229\n",
      "Epoch 185/400\n",
      "537/537 [==============================] - 0s 238us/step - loss: 0.2936 - acc: 0.8659 - val_loss: 0.7501 - val_acc: 0.7273\n",
      "Epoch 186/400\n",
      "537/537 [==============================] - 0s 251us/step - loss: 0.2915 - acc: 0.8752 - val_loss: 0.7422 - val_acc: 0.7186\n",
      "Epoch 187/400\n",
      "537/537 [==============================] - 0s 162us/step - loss: 0.2946 - acc: 0.8771 - val_loss: 0.7752 - val_acc: 0.7359\n",
      "Epoch 188/400\n",
      "537/537 [==============================] - 0s 176us/step - loss: 0.3033 - acc: 0.8678 - val_loss: 0.7478 - val_acc: 0.7143\n",
      "Epoch 189/400\n",
      "537/537 [==============================] - 0s 104us/step - loss: 0.2917 - acc: 0.8808 - val_loss: 0.7485 - val_acc: 0.7229\n",
      "Epoch 190/400\n",
      "537/537 [==============================] - 0s 95us/step - loss: 0.2955 - acc: 0.8696 - val_loss: 0.7655 - val_acc: 0.7229\n",
      "Epoch 191/400\n",
      "537/537 [==============================] - 0s 191us/step - loss: 0.2938 - acc: 0.8734 - val_loss: 0.7441 - val_acc: 0.7186\n",
      "Epoch 192/400\n",
      "537/537 [==============================] - 0s 221us/step - loss: 0.2939 - acc: 0.8641 - val_loss: 0.7384 - val_acc: 0.7403\n",
      "Epoch 193/400\n",
      "537/537 [==============================] - 0s 183us/step - loss: 0.2881 - acc: 0.8715 - val_loss: 0.7568 - val_acc: 0.7359\n",
      "Epoch 194/400\n",
      "537/537 [==============================] - 0s 173us/step - loss: 0.2909 - acc: 0.8622 - val_loss: 0.7477 - val_acc: 0.7403\n",
      "Epoch 195/400\n",
      "537/537 [==============================] - 0s 141us/step - loss: 0.2907 - acc: 0.8734 - val_loss: 0.7455 - val_acc: 0.7273\n",
      "Epoch 196/400\n",
      "537/537 [==============================] - 0s 201us/step - loss: 0.2924 - acc: 0.8585 - val_loss: 0.7528 - val_acc: 0.7273\n",
      "Epoch 197/400\n",
      "537/537 [==============================] - 0s 232us/step - loss: 0.2861 - acc: 0.8734 - val_loss: 0.7441 - val_acc: 0.7186\n",
      "Epoch 198/400\n",
      "537/537 [==============================] - 0s 104us/step - loss: 0.2941 - acc: 0.8715 - val_loss: 0.7564 - val_acc: 0.7273\n",
      "Epoch 199/400\n",
      "537/537 [==============================] - 0s 179us/step - loss: 0.2896 - acc: 0.8734 - val_loss: 0.7508 - val_acc: 0.7186\n",
      "Epoch 200/400\n",
      "537/537 [==============================] - 0s 95us/step - loss: 0.2969 - acc: 0.8603 - val_loss: 0.7477 - val_acc: 0.7229\n",
      "Epoch 201/400\n",
      "537/537 [==============================] - 0s 180us/step - loss: 0.2930 - acc: 0.8696 - val_loss: 0.7525 - val_acc: 0.7316\n",
      "Epoch 202/400\n",
      "537/537 [==============================] - 0s 119us/step - loss: 0.2903 - acc: 0.8641 - val_loss: 0.7558 - val_acc: 0.7229\n",
      "Epoch 203/400\n",
      "537/537 [==============================] - 0s 119us/step - loss: 0.2925 - acc: 0.8622 - val_loss: 0.7718 - val_acc: 0.7446\n",
      "Epoch 204/400\n",
      "537/537 [==============================] - 0s 104us/step - loss: 0.2908 - acc: 0.8696 - val_loss: 0.7541 - val_acc: 0.7359\n",
      "Epoch 205/400\n",
      "537/537 [==============================] - 0s 115us/step - loss: 0.2947 - acc: 0.8659 - val_loss: 0.7702 - val_acc: 0.7316\n",
      "Epoch 206/400\n",
      "537/537 [==============================] - 0s 212us/step - loss: 0.2941 - acc: 0.8641 - val_loss: 0.7495 - val_acc: 0.7316\n",
      "Epoch 207/400\n",
      "537/537 [==============================] - 0s 113us/step - loss: 0.2886 - acc: 0.8734 - val_loss: 0.7492 - val_acc: 0.7273\n",
      "Epoch 208/400\n",
      "537/537 [==============================] - 0s 150us/step - loss: 0.2902 - acc: 0.8734 - val_loss: 0.7546 - val_acc: 0.7273\n",
      "Epoch 209/400\n",
      "537/537 [==============================] - 0s 191us/step - loss: 0.2909 - acc: 0.8659 - val_loss: 0.7543 - val_acc: 0.7056\n",
      "Epoch 210/400\n",
      "537/537 [==============================] - 0s 111us/step - loss: 0.2882 - acc: 0.8734 - val_loss: 0.7872 - val_acc: 0.7359\n",
      "Epoch 211/400\n",
      "537/537 [==============================] - 0s 104us/step - loss: 0.2897 - acc: 0.8715 - val_loss: 0.7501 - val_acc: 0.7100\n",
      "Epoch 212/400\n",
      "537/537 [==============================] - 0s 171us/step - loss: 0.2912 - acc: 0.8659 - val_loss: 0.7675 - val_acc: 0.7446\n",
      "Epoch 213/400\n",
      "537/537 [==============================] - 0s 165us/step - loss: 0.3003 - acc: 0.8696 - val_loss: 0.7530 - val_acc: 0.7229\n",
      "Epoch 214/400\n",
      "537/537 [==============================] - 0s 297us/step - loss: 0.2891 - acc: 0.8678 - val_loss: 0.7687 - val_acc: 0.7316\n",
      "Epoch 215/400\n",
      "537/537 [==============================] - 0s 78us/step - loss: 0.2904 - acc: 0.8641 - val_loss: 0.7564 - val_acc: 0.7403\n",
      "Epoch 216/400\n",
      "537/537 [==============================] - 0s 113us/step - loss: 0.2902 - acc: 0.8715 - val_loss: 0.7505 - val_acc: 0.7359\n",
      "Epoch 217/400\n",
      "537/537 [==============================] - ETA: 0s - loss: 0.2986 - acc: 0.863 - 0s 154us/step - loss: 0.2863 - acc: 0.8752 - val_loss: 0.7628 - val_acc: 0.7359\n",
      "Epoch 218/400\n",
      "537/537 [==============================] - 0s 102us/step - loss: 0.2873 - acc: 0.8771 - val_loss: 0.7644 - val_acc: 0.7100\n",
      "Epoch 219/400\n",
      "537/537 [==============================] - 0s 193us/step - loss: 0.2915 - acc: 0.8715 - val_loss: 0.7541 - val_acc: 0.7446\n",
      "Epoch 220/400\n",
      "537/537 [==============================] - 0s 124us/step - loss: 0.2912 - acc: 0.8771 - val_loss: 0.7549 - val_acc: 0.7359\n",
      "Epoch 221/400\n",
      "537/537 [==============================] - 0s 212us/step - loss: 0.2921 - acc: 0.8715 - val_loss: 0.7697 - val_acc: 0.7446\n",
      "Epoch 222/400\n",
      "537/537 [==============================] - 0s 160us/step - loss: 0.2825 - acc: 0.8808 - val_loss: 0.7965 - val_acc: 0.7446\n",
      "Epoch 223/400\n",
      "537/537 [==============================] - 0s 204us/step - loss: 0.2926 - acc: 0.8659 - val_loss: 0.7714 - val_acc: 0.7143\n",
      "Epoch 224/400\n",
      "537/537 [==============================] - 0s 249us/step - loss: 0.2917 - acc: 0.8678 - val_loss: 0.7629 - val_acc: 0.7143\n",
      "Epoch 225/400\n",
      "537/537 [==============================] - 0s 152us/step - loss: 0.2884 - acc: 0.8827 - val_loss: 0.7549 - val_acc: 0.7229\n",
      "Epoch 226/400\n",
      "537/537 [==============================] - 0s 108us/step - loss: 0.2896 - acc: 0.8696 - val_loss: 0.7614 - val_acc: 0.7229\n",
      "Epoch 227/400\n",
      "537/537 [==============================] - 0s 85us/step - loss: 0.2919 - acc: 0.8715 - val_loss: 0.7724 - val_acc: 0.7403\n",
      "Epoch 228/400\n",
      "537/537 [==============================] - 0s 154us/step - loss: 0.2885 - acc: 0.8771 - val_loss: 0.7521 - val_acc: 0.7446\n",
      "Epoch 229/400\n",
      "537/537 [==============================] - 0s 156us/step - loss: 0.2877 - acc: 0.8808 - val_loss: 0.7606 - val_acc: 0.7359\n",
      "Epoch 230/400\n",
      "537/537 [==============================] - 0s 143us/step - loss: 0.2876 - acc: 0.8845 - val_loss: 0.7539 - val_acc: 0.7273\n",
      "Epoch 231/400\n",
      "537/537 [==============================] - 0s 154us/step - loss: 0.2908 - acc: 0.8678 - val_loss: 0.7734 - val_acc: 0.7532\n",
      "Epoch 232/400\n",
      "537/537 [==============================] - 0s 80us/step - loss: 0.2918 - acc: 0.8808 - val_loss: 0.7806 - val_acc: 0.7316\n",
      "Epoch 233/400\n",
      "537/537 [==============================] - 0s 71us/step - loss: 0.2962 - acc: 0.8696 - val_loss: 0.7668 - val_acc: 0.7186\n",
      "Epoch 234/400\n",
      "537/537 [==============================] - 0s 74us/step - loss: 0.2924 - acc: 0.8734 - val_loss: 0.7601 - val_acc: 0.7186\n",
      "Epoch 235/400\n",
      "537/537 [==============================] - 0s 93us/step - loss: 0.2882 - acc: 0.8715 - val_loss: 0.7573 - val_acc: 0.7316\n",
      "Epoch 236/400\n",
      "537/537 [==============================] - 0s 102us/step - loss: 0.2857 - acc: 0.8790 - val_loss: 0.7587 - val_acc: 0.7186\n",
      "Epoch 237/400\n",
      "537/537 [==============================] - 0s 124us/step - loss: 0.3052 - acc: 0.8622 - val_loss: 0.8334 - val_acc: 0.7359\n",
      "Epoch 238/400\n",
      "537/537 [==============================] - 0s 123us/step - loss: 0.2993 - acc: 0.8678 - val_loss: 0.7576 - val_acc: 0.7316\n",
      "Epoch 239/400\n",
      "537/537 [==============================] - 0s 91us/step - loss: 0.2850 - acc: 0.8752 - val_loss: 0.7681 - val_acc: 0.7359\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 240/400\n",
      "537/537 [==============================] - 0s 173us/step - loss: 0.2831 - acc: 0.8808 - val_loss: 0.7710 - val_acc: 0.7143\n",
      "Epoch 241/400\n",
      "537/537 [==============================] - 0s 100us/step - loss: 0.2974 - acc: 0.8678 - val_loss: 0.7637 - val_acc: 0.7359\n",
      "Epoch 242/400\n",
      "537/537 [==============================] - 0s 247us/step - loss: 0.2881 - acc: 0.8715 - val_loss: 0.7808 - val_acc: 0.7186\n",
      "Epoch 243/400\n",
      "537/537 [==============================] - 0s 139us/step - loss: 0.2907 - acc: 0.8715 - val_loss: 0.7614 - val_acc: 0.7403\n",
      "Epoch 244/400\n",
      "537/537 [==============================] - 0s 104us/step - loss: 0.2880 - acc: 0.8696 - val_loss: 0.7503 - val_acc: 0.7403\n",
      "Epoch 245/400\n",
      "537/537 [==============================] - 0s 113us/step - loss: 0.2918 - acc: 0.8696 - val_loss: 0.7640 - val_acc: 0.7403\n",
      "Epoch 246/400\n",
      "537/537 [==============================] - 0s 130us/step - loss: 0.2876 - acc: 0.8771 - val_loss: 0.7619 - val_acc: 0.7273\n",
      "Epoch 247/400\n",
      "537/537 [==============================] - 0s 299us/step - loss: 0.2851 - acc: 0.8752 - val_loss: 0.7729 - val_acc: 0.7273\n",
      "Epoch 248/400\n",
      "537/537 [==============================] - 0s 104us/step - loss: 0.2911 - acc: 0.8715 - val_loss: 0.7582 - val_acc: 0.7100\n",
      "Epoch 249/400\n",
      "537/537 [==============================] - 0s 89us/step - loss: 0.2832 - acc: 0.8659 - val_loss: 0.7672 - val_acc: 0.7446\n",
      "Epoch 250/400\n",
      "537/537 [==============================] - 0s 106us/step - loss: 0.2858 - acc: 0.8827 - val_loss: 0.7574 - val_acc: 0.7273\n",
      "Epoch 251/400\n",
      "537/537 [==============================] - 0s 247us/step - loss: 0.2886 - acc: 0.8808 - val_loss: 0.7584 - val_acc: 0.7273\n",
      "Epoch 252/400\n",
      "537/537 [==============================] - 0s 243us/step - loss: 0.2938 - acc: 0.8790 - val_loss: 0.7679 - val_acc: 0.7273\n",
      "Epoch 253/400\n",
      "537/537 [==============================] - 0s 111us/step - loss: 0.2902 - acc: 0.8678 - val_loss: 0.7630 - val_acc: 0.7273\n",
      "Epoch 254/400\n",
      "537/537 [==============================] - 0s 286us/step - loss: 0.2894 - acc: 0.8771 - val_loss: 0.7613 - val_acc: 0.7316\n",
      "Epoch 255/400\n",
      "537/537 [==============================] - 0s 136us/step - loss: 0.2889 - acc: 0.8827 - val_loss: 0.7621 - val_acc: 0.7143\n",
      "Epoch 256/400\n",
      "537/537 [==============================] - 0s 227us/step - loss: 0.2892 - acc: 0.8678 - val_loss: 0.7565 - val_acc: 0.7273\n",
      "Epoch 257/400\n",
      "537/537 [==============================] - 0s 147us/step - loss: 0.2847 - acc: 0.8696 - val_loss: 0.7624 - val_acc: 0.7403\n",
      "Epoch 258/400\n",
      "537/537 [==============================] - 0s 139us/step - loss: 0.2826 - acc: 0.8790 - val_loss: 0.7824 - val_acc: 0.7446\n",
      "Epoch 259/400\n",
      "537/537 [==============================] - 0s 186us/step - loss: 0.2925 - acc: 0.8715 - val_loss: 0.7626 - val_acc: 0.7489\n",
      "Epoch 260/400\n",
      "537/537 [==============================] - 0s 253us/step - loss: 0.2875 - acc: 0.8845 - val_loss: 0.7853 - val_acc: 0.7446\n",
      "Epoch 261/400\n",
      "537/537 [==============================] - 0s 121us/step - loss: 0.2888 - acc: 0.8827 - val_loss: 0.7696 - val_acc: 0.7273\n",
      "Epoch 262/400\n",
      "537/537 [==============================] - 0s 229us/step - loss: 0.2817 - acc: 0.8734 - val_loss: 0.7597 - val_acc: 0.7403\n",
      "Epoch 263/400\n",
      "537/537 [==============================] - 0s 154us/step - loss: 0.2880 - acc: 0.8752 - val_loss: 0.7665 - val_acc: 0.7316\n",
      "Epoch 264/400\n",
      "537/537 [==============================] - 0s 176us/step - loss: 0.2864 - acc: 0.8734 - val_loss: 0.7685 - val_acc: 0.7316\n",
      "Epoch 265/400\n",
      "537/537 [==============================] - 0s 188us/step - loss: 0.2798 - acc: 0.8808 - val_loss: 0.7760 - val_acc: 0.7359\n",
      "Epoch 266/400\n",
      "537/537 [==============================] - 0s 297us/step - loss: 0.2865 - acc: 0.8734 - val_loss: 0.7720 - val_acc: 0.7359\n",
      "Epoch 267/400\n",
      "537/537 [==============================] - 0s 126us/step - loss: 0.2876 - acc: 0.8771 - val_loss: 0.7713 - val_acc: 0.7359\n",
      "Epoch 268/400\n",
      "537/537 [==============================] - 0s 225us/step - loss: 0.2807 - acc: 0.8678 - val_loss: 0.7686 - val_acc: 0.7143\n",
      "Epoch 269/400\n",
      "537/537 [==============================] - 0s 119us/step - loss: 0.2794 - acc: 0.8864 - val_loss: 0.7885 - val_acc: 0.7273\n",
      "Epoch 270/400\n",
      "537/537 [==============================] - 0s 98us/step - loss: 0.2846 - acc: 0.8790 - val_loss: 0.7874 - val_acc: 0.7316\n",
      "Epoch 271/400\n",
      "537/537 [==============================] - 0s 100us/step - loss: 0.2894 - acc: 0.8790 - val_loss: 0.7723 - val_acc: 0.7273\n",
      "Epoch 272/400\n",
      "537/537 [==============================] - 0s 280us/step - loss: 0.2800 - acc: 0.8920 - val_loss: 0.7789 - val_acc: 0.7316\n",
      "Epoch 273/400\n",
      "537/537 [==============================] - 0s 125us/step - loss: 0.2848 - acc: 0.8715 - val_loss: 0.7739 - val_acc: 0.7403\n",
      "Epoch 274/400\n",
      "537/537 [==============================] - 0s 60us/step - loss: 0.2840 - acc: 0.8734 - val_loss: 0.7724 - val_acc: 0.7143\n",
      "Epoch 275/400\n",
      "537/537 [==============================] - 0s 63us/step - loss: 0.2861 - acc: 0.8715 - val_loss: 0.7789 - val_acc: 0.7316\n",
      "Epoch 276/400\n",
      "537/537 [==============================] - 0s 74us/step - loss: 0.2769 - acc: 0.8752 - val_loss: 0.7999 - val_acc: 0.7446\n",
      "Epoch 277/400\n",
      "537/537 [==============================] - 0s 74us/step - loss: 0.2838 - acc: 0.8771 - val_loss: 0.7729 - val_acc: 0.7316\n",
      "Epoch 278/400\n",
      "537/537 [==============================] - 0s 126us/step - loss: 0.2882 - acc: 0.8790 - val_loss: 0.7657 - val_acc: 0.7446\n",
      "Epoch 279/400\n",
      "537/537 [==============================] - 0s 106us/step - loss: 0.2757 - acc: 0.8845 - val_loss: 0.7629 - val_acc: 0.7489\n",
      "Epoch 280/400\n",
      "537/537 [==============================] - 0s 72us/step - loss: 0.2825 - acc: 0.8827 - val_loss: 0.7706 - val_acc: 0.7229\n",
      "Epoch 281/400\n",
      "537/537 [==============================] - 0s 76us/step - loss: 0.2816 - acc: 0.8827 - val_loss: 0.7682 - val_acc: 0.7403\n",
      "Epoch 282/400\n",
      "537/537 [==============================] - 0s 166us/step - loss: 0.2823 - acc: 0.8827 - val_loss: 0.7776 - val_acc: 0.7359\n",
      "Epoch 283/400\n",
      "537/537 [==============================] - 0s 59us/step - loss: 0.2908 - acc: 0.8641 - val_loss: 0.7758 - val_acc: 0.7359\n",
      "Epoch 284/400\n",
      "537/537 [==============================] - 0s 141us/step - loss: 0.2796 - acc: 0.8827 - val_loss: 0.8081 - val_acc: 0.7489\n",
      "Epoch 285/400\n",
      "537/537 [==============================] - 0s 100us/step - loss: 0.2964 - acc: 0.8659 - val_loss: 0.7844 - val_acc: 0.7446\n",
      "Epoch 286/400\n",
      "537/537 [==============================] - 0s 65us/step - loss: 0.2783 - acc: 0.8790 - val_loss: 0.7663 - val_acc: 0.7489\n",
      "Epoch 287/400\n",
      "537/537 [==============================] - 0s 71us/step - loss: 0.2845 - acc: 0.8734 - val_loss: 0.7734 - val_acc: 0.7316\n",
      "Epoch 288/400\n",
      "537/537 [==============================] - 0s 141us/step - loss: 0.2786 - acc: 0.8845 - val_loss: 0.8057 - val_acc: 0.7446\n",
      "Epoch 289/400\n",
      "537/537 [==============================] - 0s 323us/step - loss: 0.2826 - acc: 0.8752 - val_loss: 0.7694 - val_acc: 0.7403\n",
      "Epoch 290/400\n",
      "537/537 [==============================] - 0s 76us/step - loss: 0.2882 - acc: 0.8678 - val_loss: 0.7787 - val_acc: 0.7403\n",
      "Epoch 291/400\n",
      "537/537 [==============================] - 0s 72us/step - loss: 0.2836 - acc: 0.8715 - val_loss: 0.8178 - val_acc: 0.7403\n",
      "Epoch 292/400\n",
      "537/537 [==============================] - 0s 65us/step - loss: 0.2802 - acc: 0.8771 - val_loss: 0.7809 - val_acc: 0.7186\n",
      "Epoch 293/400\n",
      "537/537 [==============================] - 0s 100us/step - loss: 0.2829 - acc: 0.8771 - val_loss: 0.7828 - val_acc: 0.7532\n",
      "Epoch 294/400\n",
      "537/537 [==============================] - 0s 65us/step - loss: 0.2828 - acc: 0.8845 - val_loss: 0.7743 - val_acc: 0.7403\n",
      "Epoch 295/400\n",
      "537/537 [==============================] - 0s 71us/step - loss: 0.2800 - acc: 0.8808 - val_loss: 0.7779 - val_acc: 0.7359\n",
      "Epoch 296/400\n",
      "537/537 [==============================] - 0s 158us/step - loss: 0.2793 - acc: 0.8808 - val_loss: 0.7826 - val_acc: 0.7446\n",
      "Epoch 297/400\n",
      "537/537 [==============================] - 0s 74us/step - loss: 0.2746 - acc: 0.8845 - val_loss: 0.7737 - val_acc: 0.7403\n",
      "Epoch 298/400\n",
      "537/537 [==============================] - 0s 89us/step - loss: 0.2772 - acc: 0.8883 - val_loss: 0.7804 - val_acc: 0.7359\n",
      "Epoch 299/400\n",
      "537/537 [==============================] - 0s 165us/step - loss: 0.2832 - acc: 0.8752 - val_loss: 0.7834 - val_acc: 0.7229\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 300/400\n",
      "537/537 [==============================] - 0s 85us/step - loss: 0.2826 - acc: 0.8845 - val_loss: 0.7863 - val_acc: 0.7186\n",
      "Epoch 301/400\n",
      "537/537 [==============================] - 0s 65us/step - loss: 0.2779 - acc: 0.8864 - val_loss: 0.7880 - val_acc: 0.7229\n",
      "Epoch 302/400\n",
      "537/537 [==============================] - 0s 63us/step - loss: 0.2819 - acc: 0.8790 - val_loss: 0.7980 - val_acc: 0.7446\n",
      "Epoch 303/400\n",
      "537/537 [==============================] - 0s 69us/step - loss: 0.2795 - acc: 0.8790 - val_loss: 0.7978 - val_acc: 0.7359\n",
      "Epoch 304/400\n",
      "537/537 [==============================] - 0s 65us/step - loss: 0.2757 - acc: 0.8752 - val_loss: 0.7864 - val_acc: 0.7100\n",
      "Epoch 305/400\n",
      "537/537 [==============================] - 0s 63us/step - loss: 0.2755 - acc: 0.8864 - val_loss: 0.7922 - val_acc: 0.7403\n",
      "Epoch 306/400\n",
      "537/537 [==============================] - 0s 63us/step - loss: 0.2904 - acc: 0.8771 - val_loss: 0.7807 - val_acc: 0.7359\n",
      "Epoch 307/400\n",
      "537/537 [==============================] - 0s 61us/step - loss: 0.2785 - acc: 0.8883 - val_loss: 0.7807 - val_acc: 0.7489\n",
      "Epoch 308/400\n",
      "537/537 [==============================] - 0s 73us/step - loss: 0.2785 - acc: 0.8808 - val_loss: 0.7851 - val_acc: 0.7489\n",
      "Epoch 309/400\n",
      "537/537 [==============================] - 0s 67us/step - loss: 0.2772 - acc: 0.8845 - val_loss: 0.7830 - val_acc: 0.7489\n",
      "Epoch 310/400\n",
      "537/537 [==============================] - 0s 76us/step - loss: 0.2868 - acc: 0.8752 - val_loss: 0.7867 - val_acc: 0.7446\n",
      "Epoch 311/400\n",
      "537/537 [==============================] - 0s 59us/step - loss: 0.2736 - acc: 0.8827 - val_loss: 0.7890 - val_acc: 0.7273\n",
      "Epoch 312/400\n",
      "537/537 [==============================] - 0s 69us/step - loss: 0.2827 - acc: 0.8790 - val_loss: 0.7863 - val_acc: 0.7316\n",
      "Epoch 313/400\n",
      "537/537 [==============================] - 0s 58us/step - loss: 0.2795 - acc: 0.8790 - val_loss: 0.7982 - val_acc: 0.7316\n",
      "Epoch 314/400\n",
      "537/537 [==============================] - 0s 193us/step - loss: 0.2820 - acc: 0.8790 - val_loss: 0.7947 - val_acc: 0.7403\n",
      "Epoch 315/400\n",
      "537/537 [==============================] - 0s 150us/step - loss: 0.2788 - acc: 0.8864 - val_loss: 0.7982 - val_acc: 0.7316\n",
      "Epoch 316/400\n",
      "537/537 [==============================] - 0s 182us/step - loss: 0.2771 - acc: 0.8827 - val_loss: 0.8087 - val_acc: 0.7489\n",
      "Epoch 317/400\n",
      "537/537 [==============================] - 0s 208us/step - loss: 0.2751 - acc: 0.8715 - val_loss: 0.7864 - val_acc: 0.7316\n",
      "Epoch 318/400\n",
      "537/537 [==============================] - 0s 130us/step - loss: 0.2761 - acc: 0.8845 - val_loss: 0.7799 - val_acc: 0.7489\n",
      "Epoch 319/400\n",
      "537/537 [==============================] - 0s 115us/step - loss: 0.2739 - acc: 0.8957 - val_loss: 0.7893 - val_acc: 0.7359\n",
      "Epoch 320/400\n",
      "537/537 [==============================] - 0s 242us/step - loss: 0.2721 - acc: 0.8901 - val_loss: 0.7866 - val_acc: 0.7489\n",
      "Epoch 321/400\n",
      "537/537 [==============================] - 0s 195us/step - loss: 0.2750 - acc: 0.8845 - val_loss: 0.8104 - val_acc: 0.7403\n",
      "Epoch 322/400\n",
      "537/537 [==============================] - 0s 147us/step - loss: 0.2811 - acc: 0.8864 - val_loss: 0.8056 - val_acc: 0.7446\n",
      "Epoch 323/400\n",
      "537/537 [==============================] - 0s 191us/step - loss: 0.2722 - acc: 0.8827 - val_loss: 0.8013 - val_acc: 0.7489\n",
      "Epoch 324/400\n",
      "537/537 [==============================] - 0s 184us/step - loss: 0.2772 - acc: 0.8864 - val_loss: 0.7919 - val_acc: 0.7143\n",
      "Epoch 325/400\n",
      "537/537 [==============================] - 0s 176us/step - loss: 0.2749 - acc: 0.8827 - val_loss: 0.7901 - val_acc: 0.7229\n",
      "Epoch 326/400\n",
      "537/537 [==============================] - 0s 202us/step - loss: 0.2793 - acc: 0.8827 - val_loss: 0.7890 - val_acc: 0.7316\n",
      "Epoch 327/400\n",
      "537/537 [==============================] - 0s 96us/step - loss: 0.2733 - acc: 0.8901 - val_loss: 0.7894 - val_acc: 0.7359\n",
      "Epoch 328/400\n",
      "537/537 [==============================] - 0s 134us/step - loss: 0.2728 - acc: 0.8957 - val_loss: 0.8000 - val_acc: 0.7056\n",
      "Epoch 329/400\n",
      "537/537 [==============================] - 0s 197us/step - loss: 0.2725 - acc: 0.8808 - val_loss: 0.7866 - val_acc: 0.7359\n",
      "Epoch 330/400\n",
      "537/537 [==============================] - 0s 106us/step - loss: 0.2777 - acc: 0.8845 - val_loss: 0.7933 - val_acc: 0.7316\n",
      "Epoch 331/400\n",
      "537/537 [==============================] - 0s 162us/step - loss: 0.2783 - acc: 0.8883 - val_loss: 0.7989 - val_acc: 0.7619\n",
      "Epoch 332/400\n",
      "537/537 [==============================] - 0s 139us/step - loss: 0.2788 - acc: 0.8808 - val_loss: 0.7862 - val_acc: 0.7316\n",
      "Epoch 333/400\n",
      "537/537 [==============================] - 0s 95us/step - loss: 0.2775 - acc: 0.8715 - val_loss: 0.7852 - val_acc: 0.7576\n",
      "Epoch 334/400\n",
      "537/537 [==============================] - 0s 147us/step - loss: 0.2690 - acc: 0.8939 - val_loss: 0.7840 - val_acc: 0.7619\n",
      "Epoch 335/400\n",
      "537/537 [==============================] - 0s 219us/step - loss: 0.2667 - acc: 0.8939 - val_loss: 0.8066 - val_acc: 0.7316\n",
      "Epoch 336/400\n",
      "537/537 [==============================] - 0s 189us/step - loss: 0.2663 - acc: 0.8845 - val_loss: 0.8014 - val_acc: 0.7489\n",
      "Epoch 337/400\n",
      "537/537 [==============================] - 0s 104us/step - loss: 0.2798 - acc: 0.8864 - val_loss: 0.7943 - val_acc: 0.7273\n",
      "Epoch 338/400\n",
      "537/537 [==============================] - 0s 193us/step - loss: 0.2663 - acc: 0.8864 - val_loss: 0.7929 - val_acc: 0.7619\n",
      "Epoch 339/400\n",
      "537/537 [==============================] - 0s 104us/step - loss: 0.2672 - acc: 0.8920 - val_loss: 0.7901 - val_acc: 0.7576\n",
      "Epoch 340/400\n",
      "537/537 [==============================] - 0s 82us/step - loss: 0.2674 - acc: 0.8864 - val_loss: 0.7974 - val_acc: 0.7532\n",
      "Epoch 341/400\n",
      "537/537 [==============================] - 0s 139us/step - loss: 0.2740 - acc: 0.8808 - val_loss: 0.8102 - val_acc: 0.7359\n",
      "Epoch 342/400\n",
      "537/537 [==============================] - 0s 63us/step - loss: 0.2671 - acc: 0.8939 - val_loss: 0.7976 - val_acc: 0.7359\n",
      "Epoch 343/400\n",
      "537/537 [==============================] - 0s 78us/step - loss: 0.2710 - acc: 0.8864 - val_loss: 0.8294 - val_acc: 0.7359\n",
      "Epoch 344/400\n",
      "537/537 [==============================] - 0s 108us/step - loss: 0.2756 - acc: 0.8920 - val_loss: 0.8021 - val_acc: 0.7446\n",
      "Epoch 345/400\n",
      "537/537 [==============================] - 0s 67us/step - loss: 0.2700 - acc: 0.8901 - val_loss: 0.8002 - val_acc: 0.7532\n",
      "Epoch 346/400\n",
      "537/537 [==============================] - 0s 72us/step - loss: 0.2729 - acc: 0.8808 - val_loss: 0.8129 - val_acc: 0.7532\n",
      "Epoch 347/400\n",
      "537/537 [==============================] - 0s 200us/step - loss: 0.2654 - acc: 0.8864 - val_loss: 0.8197 - val_acc: 0.7403\n",
      "Epoch 348/400\n",
      "537/537 [==============================] - 0s 197us/step - loss: 0.2708 - acc: 0.8901 - val_loss: 0.8017 - val_acc: 0.7489\n",
      "Epoch 349/400\n",
      "537/537 [==============================] - 0s 87us/step - loss: 0.2672 - acc: 0.8790 - val_loss: 0.7967 - val_acc: 0.7446\n",
      "Epoch 350/400\n",
      "537/537 [==============================] - 0s 117us/step - loss: 0.2660 - acc: 0.8957 - val_loss: 0.8105 - val_acc: 0.7446\n",
      "Epoch 351/400\n",
      "537/537 [==============================] - 0s 87us/step - loss: 0.2678 - acc: 0.8976 - val_loss: 0.8090 - val_acc: 0.7316\n",
      "Epoch 352/400\n",
      "537/537 [==============================] - 0s 122us/step - loss: 0.2674 - acc: 0.8883 - val_loss: 0.8055 - val_acc: 0.7273\n",
      "Epoch 353/400\n",
      "537/537 [==============================] - 0s 206us/step - loss: 0.2673 - acc: 0.8920 - val_loss: 0.8025 - val_acc: 0.7532\n",
      "Epoch 354/400\n",
      "537/537 [==============================] - 0s 80us/step - loss: 0.2637 - acc: 0.8939 - val_loss: 0.8025 - val_acc: 0.7446\n",
      "Epoch 355/400\n",
      "537/537 [==============================] - 0s 188us/step - loss: 0.2626 - acc: 0.8976 - val_loss: 0.8057 - val_acc: 0.7316\n",
      "Epoch 356/400\n",
      "537/537 [==============================] - 0s 193us/step - loss: 0.2628 - acc: 0.8901 - val_loss: 0.8165 - val_acc: 0.7316\n",
      "Epoch 357/400\n",
      "537/537 [==============================] - 0s 175us/step - loss: 0.2680 - acc: 0.8883 - val_loss: 0.8124 - val_acc: 0.7403\n",
      "Epoch 358/400\n",
      "537/537 [==============================] - 0s 182us/step - loss: 0.2634 - acc: 0.8901 - val_loss: 0.8100 - val_acc: 0.7359\n",
      "Epoch 359/400\n",
      "537/537 [==============================] - 0s 167us/step - loss: 0.2617 - acc: 0.9013 - val_loss: 0.8270 - val_acc: 0.7273\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 360/400\n",
      "537/537 [==============================] - 0s 124us/step - loss: 0.2696 - acc: 0.8957 - val_loss: 0.8276 - val_acc: 0.7359\n",
      "Epoch 361/400\n",
      "537/537 [==============================] - 0s 182us/step - loss: 0.2611 - acc: 0.8790 - val_loss: 0.8235 - val_acc: 0.7403\n",
      "Epoch 362/400\n",
      "537/537 [==============================] - 0s 199us/step - loss: 0.2660 - acc: 0.8901 - val_loss: 0.8155 - val_acc: 0.7403\n",
      "Epoch 363/400\n",
      "537/537 [==============================] - 0s 195us/step - loss: 0.2677 - acc: 0.8994 - val_loss: 0.8252 - val_acc: 0.7403\n",
      "Epoch 364/400\n",
      "537/537 [==============================] - 0s 128us/step - loss: 0.2694 - acc: 0.8845 - val_loss: 0.8162 - val_acc: 0.7576\n",
      "Epoch 365/400\n",
      "537/537 [==============================] - 0s 163us/step - loss: 0.2586 - acc: 0.9032 - val_loss: 0.8155 - val_acc: 0.7446\n",
      "Epoch 366/400\n",
      "537/537 [==============================] - 0s 162us/step - loss: 0.2638 - acc: 0.8939 - val_loss: 0.8904 - val_acc: 0.7143\n",
      "Epoch 367/400\n",
      "537/537 [==============================] - 0s 121us/step - loss: 0.2755 - acc: 0.8883 - val_loss: 0.8634 - val_acc: 0.7273\n",
      "Epoch 368/400\n",
      "537/537 [==============================] - 0s 126us/step - loss: 0.2608 - acc: 0.8827 - val_loss: 0.8912 - val_acc: 0.7273\n",
      "Epoch 369/400\n",
      "537/537 [==============================] - 0s 119us/step - loss: 0.2668 - acc: 0.8845 - val_loss: 0.8242 - val_acc: 0.7273\n",
      "Epoch 370/400\n",
      "537/537 [==============================] - 0s 217us/step - loss: 0.2578 - acc: 0.9032 - val_loss: 0.8399 - val_acc: 0.7229\n",
      "Epoch 371/400\n",
      "537/537 [==============================] - 0s 124us/step - loss: 0.2707 - acc: 0.8939 - val_loss: 0.8398 - val_acc: 0.7489\n",
      "Epoch 372/400\n",
      "537/537 [==============================] - 0s 217us/step - loss: 0.2690 - acc: 0.8920 - val_loss: 0.8163 - val_acc: 0.7359\n",
      "Epoch 373/400\n",
      "537/537 [==============================] - 0s 108us/step - loss: 0.2638 - acc: 0.9088 - val_loss: 0.8887 - val_acc: 0.7056\n",
      "Epoch 374/400\n",
      "537/537 [==============================] - 0s 102us/step - loss: 0.2660 - acc: 0.8864 - val_loss: 0.8221 - val_acc: 0.7489\n",
      "Epoch 375/400\n",
      "537/537 [==============================] - 0s 89us/step - loss: 0.2626 - acc: 0.8976 - val_loss: 0.8318 - val_acc: 0.7273\n",
      "Epoch 376/400\n",
      "537/537 [==============================] - 0s 152us/step - loss: 0.2576 - acc: 0.9032 - val_loss: 0.8426 - val_acc: 0.7229\n",
      "Epoch 377/400\n",
      "537/537 [==============================] - 0s 132us/step - loss: 0.2598 - acc: 0.9050 - val_loss: 0.8380 - val_acc: 0.7143\n",
      "Epoch 378/400\n",
      "537/537 [==============================] - 0s 217us/step - loss: 0.2669 - acc: 0.8920 - val_loss: 0.8506 - val_acc: 0.7532\n",
      "Epoch 379/400\n",
      "537/537 [==============================] - 0s 132us/step - loss: 0.2575 - acc: 0.8994 - val_loss: 0.8260 - val_acc: 0.7359\n",
      "Epoch 380/400\n",
      "537/537 [==============================] - 0s 176us/step - loss: 0.2613 - acc: 0.9050 - val_loss: 0.8203 - val_acc: 0.7273\n",
      "Epoch 381/400\n",
      "537/537 [==============================] - 0s 124us/step - loss: 0.2578 - acc: 0.9013 - val_loss: 0.8399 - val_acc: 0.7489\n",
      "Epoch 382/400\n",
      "537/537 [==============================] - 0s 102us/step - loss: 0.2517 - acc: 0.9050 - val_loss: 0.8319 - val_acc: 0.7446\n",
      "Epoch 383/400\n",
      "537/537 [==============================] - 0s 184us/step - loss: 0.2543 - acc: 0.8994 - val_loss: 0.8199 - val_acc: 0.7576\n",
      "Epoch 384/400\n",
      "537/537 [==============================] - 0s 136us/step - loss: 0.2692 - acc: 0.8920 - val_loss: 0.8234 - val_acc: 0.7532\n",
      "Epoch 385/400\n",
      "537/537 [==============================] - 0s 139us/step - loss: 0.2643 - acc: 0.8864 - val_loss: 0.8236 - val_acc: 0.7489\n",
      "Epoch 386/400\n",
      "537/537 [==============================] - 0s 240us/step - loss: 0.2581 - acc: 0.8939 - val_loss: 0.8442 - val_acc: 0.7489\n",
      "Epoch 387/400\n",
      "537/537 [==============================] - 0s 97us/step - loss: 0.2709 - acc: 0.8994 - val_loss: 0.8328 - val_acc: 0.7403\n",
      "Epoch 388/400\n",
      "537/537 [==============================] - 0s 176us/step - loss: 0.2540 - acc: 0.8976 - val_loss: 0.8325 - val_acc: 0.7489\n",
      "Epoch 389/400\n",
      "537/537 [==============================] - 0s 212us/step - loss: 0.2579 - acc: 0.8994 - val_loss: 0.8384 - val_acc: 0.7316\n",
      "Epoch 390/400\n",
      "537/537 [==============================] - 0s 124us/step - loss: 0.2654 - acc: 0.9013 - val_loss: 0.8258 - val_acc: 0.7576\n",
      "Epoch 391/400\n",
      "537/537 [==============================] - 0s 199us/step - loss: 0.2627 - acc: 0.8976 - val_loss: 0.8383 - val_acc: 0.7316\n",
      "Epoch 392/400\n",
      "537/537 [==============================] - 0s 145us/step - loss: 0.2574 - acc: 0.8994 - val_loss: 0.8517 - val_acc: 0.7359\n",
      "Epoch 393/400\n",
      "537/537 [==============================] - 0s 121us/step - loss: 0.2652 - acc: 0.9032 - val_loss: 0.8355 - val_acc: 0.7316\n",
      "Epoch 394/400\n",
      "537/537 [==============================] - 0s 245us/step - loss: 0.2578 - acc: 0.8920 - val_loss: 0.8299 - val_acc: 0.7532\n",
      "Epoch 395/400\n",
      "537/537 [==============================] - 0s 95us/step - loss: 0.2570 - acc: 0.9032 - val_loss: 0.8297 - val_acc: 0.7446\n",
      "Epoch 396/400\n",
      "537/537 [==============================] - 0s 212us/step - loss: 0.2521 - acc: 0.9013 - val_loss: 0.8325 - val_acc: 0.7532\n",
      "Epoch 397/400\n",
      "537/537 [==============================] - 0s 113us/step - loss: 0.2493 - acc: 0.9069 - val_loss: 0.8492 - val_acc: 0.7273\n",
      "Epoch 398/400\n",
      "537/537 [==============================] - 0s 113us/step - loss: 0.2538 - acc: 0.9032 - val_loss: 0.8357 - val_acc: 0.7532\n",
      "Epoch 399/400\n",
      "537/537 [==============================] - 0s 319us/step - loss: 0.2569 - acc: 0.9013 - val_loss: 0.8454 - val_acc: 0.7359\n",
      "Epoch 400/400\n",
      "537/537 [==============================] - 0s 173us/step - loss: 0.2572 - acc: 0.9069 - val_loss: 0.8632 - val_acc: 0.7273\n"
     ]
    }
   ],
   "source": [
    "run_hist_400 = model.fit(X_train_norm, y_train, validation_data=(X_test_norm, y_test), epochs=400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_class_nn_2 = model.predict_classes(X_test_norm)\n",
    "y_pred_prob_nn_2 = model.predict(X_test_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is 0.727\n",
      "ROC-AUC is 0.765\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Print model performance and plot the roc curve\n",
    "print('Accuracy is {:.3f}'.format(accuracy_score(y_test,y_pred_class_nn_2)))\n",
    "print('ROC-AUC is {:.3f}'.format(roc_auc_score(y_test,y_pred_prob_nn_2)))\n",
    "\n",
    "plot_roc(y_test, y_pred_prob_nn_2, 'NN')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accuracy obtained is 72.7% and AUC-ROC curve is 0.765\n",
    "\n",
    "We see that there is a slight drop in accuracy and AUC-ROC curve. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run_hist_400.history.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c6dbf62080>"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the curve to check training and validation accuracy\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(run_hist_400.history[\"acc\"],'r', marker='.', label=\"Training Accuracy\")\n",
    "ax.plot(run_hist_400.history[\"val_acc\"],'b', marker='.', label=\"Validation Accuracy\")\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c6dbfdc3c8>"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the curve to see the training and validation loss of the model.\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(run_hist_400.history[\"loss\"],'r', marker='.', label=\"Training Loss\")\n",
    "ax.plot(run_hist_400.history[\"val_loss\"],'b', marker='.', label=\"Validation Loss\")\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Increasing the number of iterations to 600 and check the performance of the model. \n",
    "\n",
    "Lets now increase number of epochs to 600 and check the performance of the model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 537 samples, validate on 231 samples\n",
      "Epoch 1/600\n",
      "537/537 [==============================] - 0s 377us/step - loss: 0.0887 - acc: 0.9683 - val_loss: 1.9734 - val_acc: 0.7403\n",
      "Epoch 2/600\n",
      "537/537 [==============================] - 0s 65us/step - loss: 0.1212 - acc: 0.9553 - val_loss: 1.9484 - val_acc: 0.7143\n",
      "Epoch 3/600\n",
      "537/537 [==============================] - 0s 63us/step - loss: 0.0970 - acc: 0.9646 - val_loss: 1.9048 - val_acc: 0.7359\n",
      "Epoch 4/600\n",
      "537/537 [==============================] - 0s 69us/step - loss: 0.0909 - acc: 0.9758 - val_loss: 1.9194 - val_acc: 0.7229\n",
      "Epoch 5/600\n",
      "537/537 [==============================] - 0s 74us/step - loss: 0.0920 - acc: 0.9646 - val_loss: 1.9392 - val_acc: 0.7316\n",
      "Epoch 6/600\n",
      "537/537 [==============================] - 0s 63us/step - loss: 0.0859 - acc: 0.9702 - val_loss: 1.9666 - val_acc: 0.7359\n",
      "Epoch 7/600\n",
      "537/537 [==============================] - 0s 158us/step - loss: 0.0862 - acc: 0.9683 - val_loss: 2.0328 - val_acc: 0.7186\n",
      "Epoch 8/600\n",
      "537/537 [==============================] - 0s 215us/step - loss: 0.1324 - acc: 0.9423 - val_loss: 1.9576 - val_acc: 0.7273\n",
      "Epoch 9/600\n",
      "537/537 [==============================] - 0s 141us/step - loss: 0.0919 - acc: 0.9702 - val_loss: 2.0809 - val_acc: 0.7186\n",
      "Epoch 10/600\n",
      "537/537 [==============================] - 0s 202us/step - loss: 0.0981 - acc: 0.9721 - val_loss: 1.9867 - val_acc: 0.7100\n",
      "Epoch 11/600\n",
      "537/537 [==============================] - 0s 97us/step - loss: 0.1279 - acc: 0.9646 - val_loss: 2.0089 - val_acc: 0.7186\n",
      "Epoch 12/600\n",
      "537/537 [==============================] - 0s 191us/step - loss: 0.0913 - acc: 0.9646 - val_loss: 1.9371 - val_acc: 0.7186\n",
      "Epoch 13/600\n",
      "537/537 [==============================] - 0s 89us/step - loss: 0.0828 - acc: 0.9721 - val_loss: 1.9871 - val_acc: 0.7316\n",
      "Epoch 14/600\n",
      "537/537 [==============================] - 0s 318us/step - loss: 0.1345 - acc: 0.9385 - val_loss: 1.9299 - val_acc: 0.7273\n",
      "Epoch 15/600\n",
      "537/537 [==============================] - 0s 204us/step - loss: 0.0943 - acc: 0.9702 - val_loss: 1.9278 - val_acc: 0.7273\n",
      "Epoch 16/600\n",
      "537/537 [==============================] - 0s 93us/step - loss: 0.0903 - acc: 0.9702 - val_loss: 1.9496 - val_acc: 0.7100\n",
      "Epoch 17/600\n",
      "537/537 [==============================] - 0s 98us/step - loss: 0.0810 - acc: 0.9721 - val_loss: 1.9693 - val_acc: 0.7143\n",
      "Epoch 18/600\n",
      "537/537 [==============================] - 0s 150us/step - loss: 0.0849 - acc: 0.9702 - val_loss: 1.9177 - val_acc: 0.7186\n",
      "Epoch 19/600\n",
      "537/537 [==============================] - 0s 208us/step - loss: 0.1202 - acc: 0.9572 - val_loss: 1.9504 - val_acc: 0.7229\n",
      "Epoch 20/600\n",
      "537/537 [==============================] - 0s 169us/step - loss: 0.1045 - acc: 0.9572 - val_loss: 1.9507 - val_acc: 0.7186\n",
      "Epoch 21/600\n",
      "537/537 [==============================] - 0s 183us/step - loss: 0.0974 - acc: 0.9628 - val_loss: 1.9529 - val_acc: 0.7186\n",
      "Epoch 22/600\n",
      "537/537 [==============================] - 0s 161us/step - loss: 0.1208 - acc: 0.9628 - val_loss: 2.0331 - val_acc: 0.7273\n",
      "Epoch 23/600\n",
      "537/537 [==============================] - 0s 201us/step - loss: 0.1067 - acc: 0.9628 - val_loss: 1.9500 - val_acc: 0.7229\n",
      "Epoch 24/600\n",
      "537/537 [==============================] - 0s 162us/step - loss: 0.0996 - acc: 0.9590 - val_loss: 1.9958 - val_acc: 0.7186\n",
      "Epoch 25/600\n",
      "537/537 [==============================] - 0s 74us/step - loss: 0.1141 - acc: 0.9665 - val_loss: 2.0365 - val_acc: 0.7013\n",
      "Epoch 26/600\n",
      "537/537 [==============================] - 0s 54us/step - loss: 0.0977 - acc: 0.9609 - val_loss: 1.9121 - val_acc: 0.7186\n",
      "Epoch 27/600\n",
      "537/537 [==============================] - 0s 52us/step - loss: 0.1483 - acc: 0.9385 - val_loss: 1.9149 - val_acc: 0.7273\n",
      "Epoch 28/600\n",
      "537/537 [==============================] - 0s 58us/step - loss: 0.1151 - acc: 0.9553 - val_loss: 1.9765 - val_acc: 0.7143\n",
      "Epoch 29/600\n",
      "537/537 [==============================] - 0s 84us/step - loss: 0.1017 - acc: 0.9628 - val_loss: 1.9449 - val_acc: 0.7100\n",
      "Epoch 30/600\n",
      "537/537 [==============================] - 0s 65us/step - loss: 0.1474 - acc: 0.9385 - val_loss: 2.0212 - val_acc: 0.6926\n",
      "Epoch 31/600\n",
      "537/537 [==============================] - 0s 48us/step - loss: 0.0838 - acc: 0.9683 - val_loss: 2.0017 - val_acc: 0.7143\n",
      "Epoch 32/600\n",
      "537/537 [==============================] - 0s 130us/step - loss: 0.0800 - acc: 0.9758 - val_loss: 1.9642 - val_acc: 0.7186\n",
      "Epoch 33/600\n",
      "537/537 [==============================] - 0s 113us/step - loss: 0.0884 - acc: 0.9721 - val_loss: 1.9438 - val_acc: 0.7186\n",
      "Epoch 34/600\n",
      "537/537 [==============================] - 0s 227us/step - loss: 0.1458 - acc: 0.9441 - val_loss: 2.1342 - val_acc: 0.6926\n",
      "Epoch 35/600\n",
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      "537/537 [==============================] - 0s 191us/step - loss: 0.0959 - acc: 0.9628 - val_loss: 2.0328 - val_acc: 0.7186\n",
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      "537/537 [==============================] - 0s 188us/step - loss: 0.1039 - acc: 0.9646 - val_loss: 1.9536 - val_acc: 0.7273\n",
      "Epoch 38/600\n",
      "537/537 [==============================] - 0s 182us/step - loss: 0.1008 - acc: 0.9590 - val_loss: 1.9484 - val_acc: 0.7143\n",
      "Epoch 39/600\n",
      "537/537 [==============================] - 0s 175us/step - loss: 0.1181 - acc: 0.9534 - val_loss: 1.9631 - val_acc: 0.7143\n",
      "Epoch 40/600\n",
      "537/537 [==============================] - 0s 149us/step - loss: 0.0885 - acc: 0.9665 - val_loss: 1.9384 - val_acc: 0.7186\n",
      "Epoch 41/600\n",
      "537/537 [==============================] - 0s 277us/step - loss: 0.0926 - acc: 0.9683 - val_loss: 1.9861 - val_acc: 0.7143\n",
      "Epoch 42/600\n",
      "537/537 [==============================] - 0s 110us/step - loss: 0.0932 - acc: 0.9721 - val_loss: 1.9966 - val_acc: 0.7056\n",
      "Epoch 43/600\n",
      "537/537 [==============================] - 0s 748us/step - loss: 0.1281 - acc: 0.9590 - val_loss: 1.9804 - val_acc: 0.7229\n",
      "Epoch 44/600\n",
      "537/537 [==============================] - 0s 258us/step - loss: 0.1021 - acc: 0.9590 - val_loss: 1.9683 - val_acc: 0.7186\n",
      "Epoch 45/600\n",
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      "Epoch 46/600\n",
      "537/537 [==============================] - 0s 175us/step - loss: 0.1263 - acc: 0.9497 - val_loss: 2.0083 - val_acc: 0.7056\n",
      "Epoch 47/600\n",
      "537/537 [==============================] - 0s 208us/step - loss: 0.0888 - acc: 0.9739 - val_loss: 1.9670 - val_acc: 0.7273\n",
      "Epoch 48/600\n",
      "537/537 [==============================] - 0s 212us/step - loss: 0.0902 - acc: 0.9758 - val_loss: 1.9311 - val_acc: 0.7143\n",
      "Epoch 49/600\n",
      "537/537 [==============================] - 0s 125us/step - loss: 0.0969 - acc: 0.9665 - val_loss: 2.0229 - val_acc: 0.7273\n",
      "Epoch 50/600\n",
      "537/537 [==============================] - 0s 128us/step - loss: 0.0994 - acc: 0.9665 - val_loss: 1.9519 - val_acc: 0.7229\n",
      "Epoch 51/600\n",
      "537/537 [==============================] - 0s 145us/step - loss: 0.1381 - acc: 0.9404 - val_loss: 1.9378 - val_acc: 0.7229\n",
      "Epoch 52/600\n",
      "537/537 [==============================] - 0s 210us/step - loss: 0.0970 - acc: 0.9628 - val_loss: 2.0035 - val_acc: 0.7143\n",
      "Epoch 53/600\n",
      "537/537 [==============================] - 0s 130us/step - loss: 0.0926 - acc: 0.9665 - val_loss: 1.9527 - val_acc: 0.7229\n",
      "Epoch 54/600\n",
      "537/537 [==============================] - 0s 61us/step - loss: 0.0970 - acc: 0.9646 - val_loss: 1.9333 - val_acc: 0.7143\n",
      "Epoch 55/600\n",
      "537/537 [==============================] - 0s 106us/step - loss: 0.0813 - acc: 0.9814 - val_loss: 1.9543 - val_acc: 0.7186\n",
      "Epoch 56/600\n",
      "537/537 [==============================] - 0s 137us/step - loss: 0.1229 - acc: 0.9628 - val_loss: 1.9528 - val_acc: 0.7359\n",
      "Epoch 57/600\n",
      "537/537 [==============================] - 0s 87us/step - loss: 0.1002 - acc: 0.9628 - val_loss: 1.9865 - val_acc: 0.7100\n",
      "Epoch 58/600\n",
      "537/537 [==============================] - 0s 225us/step - loss: 0.0840 - acc: 0.9758 - val_loss: 1.9389 - val_acc: 0.7229\n",
      "Epoch 59/600\n",
      "537/537 [==============================] - 0s 297us/step - loss: 0.0878 - acc: 0.9646 - val_loss: 1.9373 - val_acc: 0.7273\n",
      "Epoch 60/600\n",
      "537/537 [==============================] - 0s 74us/step - loss: 0.1221 - acc: 0.9553 - val_loss: 1.9374 - val_acc: 0.7143\n",
      "Epoch 61/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "537/537 [==============================] - 0s 71us/step - loss: 0.1213 - acc: 0.9590 - val_loss: 2.2151 - val_acc: 0.6926\n",
      "Epoch 62/600\n",
      "537/537 [==============================] - 0s 59us/step - loss: 0.1452 - acc: 0.9460 - val_loss: 1.9782 - val_acc: 0.7316\n",
      "Epoch 63/600\n",
      "537/537 [==============================] - 0s 65us/step - loss: 0.1623 - acc: 0.9367 - val_loss: 2.1092 - val_acc: 0.7186\n",
      "Epoch 64/600\n",
      "537/537 [==============================] - 0s 61us/step - loss: 0.1235 - acc: 0.9628 - val_loss: 1.9735 - val_acc: 0.7143\n",
      "Epoch 65/600\n",
      "537/537 [==============================] - 0s 123us/step - loss: 0.0961 - acc: 0.9609 - val_loss: 2.0310 - val_acc: 0.7056\n",
      "Epoch 66/600\n",
      "537/537 [==============================] - 0s 67us/step - loss: 0.1208 - acc: 0.9609 - val_loss: 1.9706 - val_acc: 0.7143\n",
      "Epoch 67/600\n",
      "537/537 [==============================] - 0s 70us/step - loss: 0.1109 - acc: 0.9609 - val_loss: 2.0209 - val_acc: 0.6970\n",
      "Epoch 68/600\n",
      "537/537 [==============================] - 0s 89us/step - loss: 0.0980 - acc: 0.9721 - val_loss: 1.9929 - val_acc: 0.7100\n",
      "Epoch 69/600\n",
      "537/537 [==============================] - 0s 156us/step - loss: 0.1080 - acc: 0.9572 - val_loss: 1.9603 - val_acc: 0.7273\n",
      "Epoch 70/600\n",
      "537/537 [==============================] - 0s 205us/step - loss: 0.0988 - acc: 0.9646 - val_loss: 2.0314 - val_acc: 0.7100\n",
      "Epoch 71/600\n",
      "537/537 [==============================] - 0s 103us/step - loss: 0.0976 - acc: 0.9646 - val_loss: 1.9645 - val_acc: 0.7143\n",
      "Epoch 72/600\n",
      "537/537 [==============================] - 0s 91us/step - loss: 0.1140 - acc: 0.9572 - val_loss: 2.0104 - val_acc: 0.7143\n",
      "Epoch 73/600\n",
      "537/537 [==============================] - 0s 154us/step - loss: 0.1156 - acc: 0.9609 - val_loss: 2.0461 - val_acc: 0.7013\n",
      "Epoch 74/600\n",
      "537/537 [==============================] - 0s 93us/step - loss: 0.0947 - acc: 0.9739 - val_loss: 1.9790 - val_acc: 0.7100\n",
      "Epoch 75/600\n",
      "537/537 [==============================] - 0s 94us/step - loss: 0.0803 - acc: 0.9739 - val_loss: 1.9560 - val_acc: 0.7316\n",
      "Epoch 76/600\n",
      "537/537 [==============================] - 0s 141us/step - loss: 0.0943 - acc: 0.9646 - val_loss: 2.0418 - val_acc: 0.7056\n",
      "Epoch 77/600\n",
      "537/537 [==============================] - 0s 100us/step - loss: 0.0916 - acc: 0.9628 - val_loss: 1.9895 - val_acc: 0.7100\n",
      "Epoch 78/600\n",
      "537/537 [==============================] - 0s 86us/step - loss: 0.0863 - acc: 0.9665 - val_loss: 2.0069 - val_acc: 0.7186\n",
      "Epoch 79/600\n",
      "537/537 [==============================] - 0s 145us/step - loss: 0.0850 - acc: 0.9721 - val_loss: 2.1322 - val_acc: 0.7013\n",
      "Epoch 80/600\n",
      "537/537 [==============================] - 0s 162us/step - loss: 0.1349 - acc: 0.9609 - val_loss: 2.0965 - val_acc: 0.7143\n",
      "Epoch 81/600\n",
      "537/537 [==============================] - 0s 197us/step - loss: 0.1190 - acc: 0.9553 - val_loss: 2.0128 - val_acc: 0.6926\n",
      "Epoch 82/600\n",
      "537/537 [==============================] - 1s 1ms/step - loss: 0.1025 - acc: 0.9665 - val_loss: 2.0249 - val_acc: 0.6970\n",
      "Epoch 83/600\n",
      "537/537 [==============================] - 0s 206us/step - loss: 0.0791 - acc: 0.9777 - val_loss: 2.0285 - val_acc: 0.7056\n",
      "Epoch 84/600\n",
      "537/537 [==============================] - 0s 132us/step - loss: 0.0818 - acc: 0.9777 - val_loss: 2.0196 - val_acc: 0.7013\n",
      "Epoch 85/600\n",
      "537/537 [==============================] - 0s 113us/step - loss: 0.0848 - acc: 0.9702 - val_loss: 2.1130 - val_acc: 0.6970\n",
      "Epoch 86/600\n",
      "537/537 [==============================] - 0s 223us/step - loss: 0.0916 - acc: 0.9665 - val_loss: 2.0403 - val_acc: 0.7056\n",
      "Epoch 87/600\n",
      "537/537 [==============================] - 0s 878us/step - loss: 0.0870 - acc: 0.9739 - val_loss: 2.0037 - val_acc: 0.7143\n",
      "Epoch 88/600\n",
      "537/537 [==============================] - 0s 219us/step - loss: 0.0936 - acc: 0.9665 - val_loss: 2.0058 - val_acc: 0.6970\n",
      "Epoch 89/600\n",
      "537/537 [==============================] - 0s 104us/step - loss: 0.0919 - acc: 0.9590 - val_loss: 2.0258 - val_acc: 0.7186\n",
      "Epoch 90/600\n",
      "537/537 [==============================] - 0s 108us/step - loss: 0.0821 - acc: 0.9721 - val_loss: 2.0335 - val_acc: 0.7056\n",
      "Epoch 91/600\n",
      "537/537 [==============================] - 0s 258us/step - loss: 0.1015 - acc: 0.9609 - val_loss: 1.9930 - val_acc: 0.7100\n",
      "Epoch 92/600\n",
      "537/537 [==============================] - 0s 91us/step - loss: 0.0999 - acc: 0.9609 - val_loss: 2.0903 - val_acc: 0.7056\n",
      "Epoch 93/600\n",
      "537/537 [==============================] - 0s 202us/step - loss: 0.1086 - acc: 0.9534 - val_loss: 2.0159 - val_acc: 0.6926\n",
      "Epoch 94/600\n",
      "537/537 [==============================] - 0s 147us/step - loss: 0.0874 - acc: 0.9739 - val_loss: 2.0039 - val_acc: 0.7186\n",
      "Epoch 95/600\n",
      "537/537 [==============================] - 0s 221us/step - loss: 0.0733 - acc: 0.9777 - val_loss: 2.0601 - val_acc: 0.7100\n",
      "Epoch 96/600\n",
      "537/537 [==============================] - 0s 111us/step - loss: 0.1765 - acc: 0.9330 - val_loss: 2.0401 - val_acc: 0.7229\n",
      "Epoch 97/600\n",
      "537/537 [==============================] - 0s 162us/step - loss: 0.1165 - acc: 0.9534 - val_loss: 2.0684 - val_acc: 0.7143\n",
      "Epoch 98/600\n",
      "537/537 [==============================] - 0s 124us/step - loss: 0.0923 - acc: 0.9665 - val_loss: 2.0828 - val_acc: 0.7229\n",
      "Epoch 99/600\n",
      "537/537 [==============================] - 0s 154us/step - loss: 0.1003 - acc: 0.9702 - val_loss: 2.2133 - val_acc: 0.7013\n",
      "Epoch 100/600\n",
      "537/537 [==============================] - 0s 158us/step - loss: 0.1051 - acc: 0.9497 - val_loss: 1.9688 - val_acc: 0.7143\n",
      "Epoch 101/600\n",
      "537/537 [==============================] - 0s 186us/step - loss: 0.1494 - acc: 0.9497 - val_loss: 2.0135 - val_acc: 0.7229\n",
      "Epoch 102/600\n",
      "537/537 [==============================] - 0s 219us/step - loss: 0.0837 - acc: 0.9683 - val_loss: 2.1541 - val_acc: 0.7316\n",
      "Epoch 103/600\n",
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      "Epoch 104/600\n",
      "537/537 [==============================] - 0s 121us/step - loss: 0.1032 - acc: 0.9609 - val_loss: 2.0782 - val_acc: 0.7143\n",
      "Epoch 105/600\n",
      "537/537 [==============================] - 0s 160us/step - loss: 0.1048 - acc: 0.9646 - val_loss: 2.0142 - val_acc: 0.7229\n",
      "Epoch 106/600\n",
      "537/537 [==============================] - 0s 195us/step - loss: 0.1042 - acc: 0.9628 - val_loss: 2.0566 - val_acc: 0.7100\n",
      "Epoch 107/600\n",
      "537/537 [==============================] - 0s 186us/step - loss: 0.0953 - acc: 0.9683 - val_loss: 1.9985 - val_acc: 0.7100\n",
      "Epoch 108/600\n",
      "537/537 [==============================] - 0s 132us/step - loss: 0.0795 - acc: 0.9721 - val_loss: 2.0111 - val_acc: 0.7100\n",
      "Epoch 109/600\n",
      "537/537 [==============================] - 0s 258us/step - loss: 0.0806 - acc: 0.9739 - val_loss: 2.0121 - val_acc: 0.7316\n",
      "Epoch 110/600\n",
      "537/537 [==============================] - 0s 164us/step - loss: 0.0912 - acc: 0.9646 - val_loss: 2.0379 - val_acc: 0.7056\n",
      "Epoch 111/600\n",
      "537/537 [==============================] - 0s 158us/step - loss: 0.0901 - acc: 0.9702 - val_loss: 2.0031 - val_acc: 0.7143\n",
      "Epoch 112/600\n",
      "537/537 [==============================] - 0s 219us/step - loss: 0.0930 - acc: 0.9646 - val_loss: 2.1475 - val_acc: 0.6970\n",
      "Epoch 113/600\n",
      "537/537 [==============================] - 0s 243us/step - loss: 0.1683 - acc: 0.9460 - val_loss: 2.0576 - val_acc: 0.7316\n",
      "Epoch 114/600\n",
      "537/537 [==============================] - 0s 182us/step - loss: 0.1537 - acc: 0.9497 - val_loss: 2.1539 - val_acc: 0.7186\n",
      "Epoch 115/600\n",
      "537/537 [==============================] - 0s 418us/step - loss: 0.0949 - acc: 0.9628 - val_loss: 2.0345 - val_acc: 0.7056\n",
      "Epoch 116/600\n",
      "537/537 [==============================] - 0s 254us/step - loss: 0.0846 - acc: 0.9702 - val_loss: 1.9749 - val_acc: 0.7186\n",
      "Epoch 117/600\n",
      "537/537 [==============================] - 0s 115us/step - loss: 0.0882 - acc: 0.9646 - val_loss: 2.0003 - val_acc: 0.7143\n",
      "Epoch 118/600\n",
      "537/537 [==============================] - 0s 71us/step - loss: 0.1015 - acc: 0.9609 - val_loss: 2.2052 - val_acc: 0.7143\n",
      "Epoch 119/600\n",
      "537/537 [==============================] - 0s 63us/step - loss: 0.1310 - acc: 0.9516 - val_loss: 2.2479 - val_acc: 0.7100\n",
      "Epoch 120/600\n",
      "537/537 [==============================] - 0s 206us/step - loss: 0.1156 - acc: 0.9553 - val_loss: 2.0177 - val_acc: 0.7100\n",
      "Epoch 121/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "537/537 [==============================] - 0s 219us/step - loss: 0.0969 - acc: 0.9628 - val_loss: 2.0612 - val_acc: 0.7143\n",
      "Epoch 122/600\n",
      "537/537 [==============================] - 0s 68us/step - loss: 0.0860 - acc: 0.9739 - val_loss: 1.9917 - val_acc: 0.7229\n",
      "Epoch 123/600\n",
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      "Epoch 124/600\n",
      "537/537 [==============================] - 0s 182us/step - loss: 0.1069 - acc: 0.9628 - val_loss: 1.9741 - val_acc: 0.7273\n",
      "Epoch 125/600\n",
      "537/537 [==============================] - 0s 141us/step - loss: 0.1043 - acc: 0.9590 - val_loss: 1.9577 - val_acc: 0.7229\n",
      "Epoch 126/600\n",
      "537/537 [==============================] - 0s 332us/step - loss: 0.1021 - acc: 0.9590 - val_loss: 2.0125 - val_acc: 0.7100\n",
      "Epoch 127/600\n",
      "537/537 [==============================] - 0s 109us/step - loss: 0.1327 - acc: 0.9479 - val_loss: 1.9727 - val_acc: 0.7229\n",
      "Epoch 128/600\n",
      "537/537 [==============================] - 0s 149us/step - loss: 0.0948 - acc: 0.9628 - val_loss: 2.0125 - val_acc: 0.7273\n",
      "Epoch 129/600\n",
      "537/537 [==============================] - 0s 269us/step - loss: 0.0807 - acc: 0.9665 - val_loss: 2.0286 - val_acc: 0.7143\n",
      "Epoch 130/600\n",
      "537/537 [==============================] - 0s 206us/step - loss: 0.1038 - acc: 0.9683 - val_loss: 1.9833 - val_acc: 0.7100\n",
      "Epoch 131/600\n",
      "537/537 [==============================] - 0s 102us/step - loss: 0.0860 - acc: 0.9758 - val_loss: 2.0263 - val_acc: 0.7143\n",
      "Epoch 132/600\n",
      "537/537 [==============================] - 0s 197us/step - loss: 0.0761 - acc: 0.9721 - val_loss: 2.0020 - val_acc: 0.7100\n",
      "Epoch 133/600\n",
      "537/537 [==============================] - 0s 98us/step - loss: 0.0871 - acc: 0.9646 - val_loss: 1.9945 - val_acc: 0.7186\n",
      "Epoch 134/600\n",
      "537/537 [==============================] - 0s 139us/step - loss: 0.1039 - acc: 0.9572 - val_loss: 2.0141 - val_acc: 0.7100\n",
      "Epoch 135/600\n",
      "537/537 [==============================] - 0s 295us/step - loss: 0.0974 - acc: 0.9683 - val_loss: 1.9817 - val_acc: 0.7143\n",
      "Epoch 136/600\n",
      "537/537 [==============================] - 0s 217us/step - loss: 0.0913 - acc: 0.9721 - val_loss: 2.0105 - val_acc: 0.7100\n",
      "Epoch 137/600\n",
      "537/537 [==============================] - 0s 95us/step - loss: 0.0940 - acc: 0.9665 - val_loss: 2.0653 - val_acc: 0.6970\n",
      "Epoch 138/600\n",
      "537/537 [==============================] - 0s 184us/step - loss: 0.1031 - acc: 0.9609 - val_loss: 1.9798 - val_acc: 0.7186\n",
      "Epoch 139/600\n",
      "537/537 [==============================] - 0s 323us/step - loss: 0.1035 - acc: 0.9609 - val_loss: 2.0163 - val_acc: 0.7186\n",
      "Epoch 140/600\n",
      "537/537 [==============================] - 0s 232us/step - loss: 0.1075 - acc: 0.9572 - val_loss: 2.0386 - val_acc: 0.7056\n",
      "Epoch 141/600\n",
      "537/537 [==============================] - 0s 232us/step - loss: 0.0933 - acc: 0.9683 - val_loss: 1.9944 - val_acc: 0.7229\n",
      "Epoch 142/600\n",
      "537/537 [==============================] - 0s 163us/step - loss: 0.1401 - acc: 0.9590 - val_loss: 1.9750 - val_acc: 0.7229\n",
      "Epoch 143/600\n",
      "537/537 [==============================] - 0s 132us/step - loss: 0.1086 - acc: 0.9590 - val_loss: 2.0861 - val_acc: 0.7186\n",
      "Epoch 144/600\n",
      "537/537 [==============================] - 0s 228us/step - loss: 0.1084 - acc: 0.9590 - val_loss: 2.0267 - val_acc: 0.7229\n",
      "Epoch 145/600\n",
      "537/537 [==============================] - 0s 176us/step - loss: 0.0799 - acc: 0.9683 - val_loss: 2.0248 - val_acc: 0.6970\n",
      "Epoch 146/600\n",
      "537/537 [==============================] - 0s 202us/step - loss: 0.0981 - acc: 0.9683 - val_loss: 1.9737 - val_acc: 0.7316\n",
      "Epoch 147/600\n",
      "537/537 [==============================] - 0s 130us/step - loss: 0.0793 - acc: 0.9739 - val_loss: 2.0990 - val_acc: 0.7273\n",
      "Epoch 148/600\n",
      "537/537 [==============================] - 0s 158us/step - loss: 0.0862 - acc: 0.9721 - val_loss: 2.0257 - val_acc: 0.7143\n",
      "Epoch 149/600\n",
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      "Epoch 150/600\n",
      "537/537 [==============================] - 0s 126us/step - loss: 0.1115 - acc: 0.9534 - val_loss: 1.9705 - val_acc: 0.7143\n",
      "Epoch 151/600\n",
      "537/537 [==============================] - 0s 217us/step - loss: 0.1418 - acc: 0.9534 - val_loss: 2.0258 - val_acc: 0.7056\n",
      "Epoch 152/600\n",
      "537/537 [==============================] - 0s 162us/step - loss: 0.0915 - acc: 0.9702 - val_loss: 1.9830 - val_acc: 0.7143\n",
      "Epoch 153/600\n",
      "537/537 [==============================] - 0s 104us/step - loss: 0.0937 - acc: 0.9758 - val_loss: 2.0083 - val_acc: 0.7229\n",
      "Epoch 154/600\n",
      "537/537 [==============================] - 0s 82us/step - loss: 0.0994 - acc: 0.9665 - val_loss: 2.0422 - val_acc: 0.7056\n",
      "Epoch 155/600\n",
      "537/537 [==============================] - 1s 1ms/step - loss: 0.0939 - acc: 0.9665 - val_loss: 2.1115 - val_acc: 0.7273\n",
      "Epoch 156/600\n",
      "537/537 [==============================] - 0s 175us/step - loss: 0.0844 - acc: 0.9683 - val_loss: 1.9704 - val_acc: 0.7143\n",
      "Epoch 157/600\n",
      "537/537 [==============================] - 0s 171us/step - loss: 0.0799 - acc: 0.9758 - val_loss: 2.1924 - val_acc: 0.7013\n",
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      "Epoch 159/600\n",
      "537/537 [==============================] - 0s 282us/step - loss: 0.1153 - acc: 0.9479 - val_loss: 2.0196 - val_acc: 0.7186\n",
      "Epoch 160/600\n",
      "537/537 [==============================] - 0s 349us/step - loss: 0.1100 - acc: 0.9628 - val_loss: 1.9905 - val_acc: 0.7186\n",
      "Epoch 161/600\n",
      "537/537 [==============================] - 0s 119us/step - loss: 0.0925 - acc: 0.9683 - val_loss: 2.0185 - val_acc: 0.7186\n",
      "Epoch 162/600\n",
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      "Epoch 163/600\n",
      "537/537 [==============================] - 0s 104us/step - loss: 0.1365 - acc: 0.9534 - val_loss: 2.1039 - val_acc: 0.7186\n",
      "Epoch 164/600\n",
      "537/537 [==============================] - 0s 124us/step - loss: 0.1147 - acc: 0.9609 - val_loss: 2.1159 - val_acc: 0.7056\n",
      "Epoch 165/600\n",
      "537/537 [==============================] - 0s 169us/step - loss: 0.0957 - acc: 0.9683 - val_loss: 2.1075 - val_acc: 0.7273\n",
      "Epoch 166/600\n",
      "537/537 [==============================] - ETA: 0s - loss: 0.1018 - acc: 0.971 - 0s 275us/step - loss: 0.1022 - acc: 0.9628 - val_loss: 2.0004 - val_acc: 0.7143\n",
      "Epoch 167/600\n",
      "537/537 [==============================] - 0s 145us/step - loss: 0.0933 - acc: 0.9702 - val_loss: 2.0009 - val_acc: 0.7273\n",
      "Epoch 168/600\n",
      "537/537 [==============================] - 0s 225us/step - loss: 0.0957 - acc: 0.9609 - val_loss: 2.0486 - val_acc: 0.7186\n",
      "Epoch 169/600\n",
      "537/537 [==============================] - ETA: 0s - loss: 0.2056 - acc: 0.937 - 0s 100us/step - loss: 0.1157 - acc: 0.9590 - val_loss: 2.0076 - val_acc: 0.7143\n",
      "Epoch 170/600\n",
      "537/537 [==============================] - 0s 214us/step - loss: 0.1284 - acc: 0.9534 - val_loss: 2.1158 - val_acc: 0.7229\n",
      "Epoch 171/600\n",
      "537/537 [==============================] - 0s 148us/step - loss: 0.2682 - acc: 0.9143 - val_loss: 2.1834 - val_acc: 0.7229\n",
      "Epoch 172/600\n",
      "537/537 [==============================] - 0s 182us/step - loss: 0.2266 - acc: 0.9404 - val_loss: 1.9512 - val_acc: 0.7229\n",
      "Epoch 173/600\n",
      "537/537 [==============================] - 0s 124us/step - loss: 0.1235 - acc: 0.9479 - val_loss: 2.0233 - val_acc: 0.7186\n",
      "Epoch 174/600\n",
      "537/537 [==============================] - 0s 126us/step - loss: 0.0986 - acc: 0.9628 - val_loss: 1.9788 - val_acc: 0.7273\n",
      "Epoch 175/600\n",
      "537/537 [==============================] - 0s 217us/step - loss: 0.0936 - acc: 0.9683 - val_loss: 1.9813 - val_acc: 0.7186\n",
      "Epoch 176/600\n",
      "537/537 [==============================] - 0s 238us/step - loss: 0.0799 - acc: 0.9702 - val_loss: 2.0276 - val_acc: 0.7056\n",
      "Epoch 177/600\n",
      "537/537 [==============================] - 0s 191us/step - loss: 0.0993 - acc: 0.9646 - val_loss: 2.0107 - val_acc: 0.7186\n",
      "Epoch 178/600\n",
      "537/537 [==============================] - 0s 244us/step - loss: 0.1391 - acc: 0.9534 - val_loss: 2.0495 - val_acc: 0.7186\n",
      "Epoch 179/600\n",
      "537/537 [==============================] - 0s 308us/step - loss: 0.1275 - acc: 0.9572 - val_loss: 2.0481 - val_acc: 0.7143\n",
      "Epoch 180/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "537/537 [==============================] - 0s 113us/step - loss: 0.0900 - acc: 0.9683 - val_loss: 2.0383 - val_acc: 0.7229\n",
      "Epoch 181/600\n",
      "537/537 [==============================] - 0s 152us/step - loss: 0.0973 - acc: 0.9646 - val_loss: 1.9938 - val_acc: 0.7229\n",
      "Epoch 182/600\n",
      "537/537 [==============================] - 0s 119us/step - loss: 0.0820 - acc: 0.9665 - val_loss: 2.0032 - val_acc: 0.7143\n",
      "Epoch 183/600\n",
      "537/537 [==============================] - 0s 117us/step - loss: 0.0793 - acc: 0.9758 - val_loss: 2.0208 - val_acc: 0.7229\n",
      "Epoch 184/600\n",
      "537/537 [==============================] - 0s 195us/step - loss: 0.0800 - acc: 0.9777 - val_loss: 1.9640 - val_acc: 0.7229\n",
      "Epoch 185/600\n",
      "537/537 [==============================] - 0s 321us/step - loss: 0.0962 - acc: 0.9609 - val_loss: 1.9881 - val_acc: 0.7186\n",
      "Epoch 186/600\n",
      "537/537 [==============================] - 0s 85us/step - loss: 0.0783 - acc: 0.9721 - val_loss: 1.9991 - val_acc: 0.7229\n",
      "Epoch 187/600\n",
      "537/537 [==============================] - 0s 221us/step - loss: 0.0841 - acc: 0.9739 - val_loss: 2.0108 - val_acc: 0.7186\n",
      "Epoch 188/600\n",
      "537/537 [==============================] - 0s 262us/step - loss: 0.0908 - acc: 0.9665 - val_loss: 2.0446 - val_acc: 0.7316\n",
      "Epoch 189/600\n",
      "537/537 [==============================] - 0s 195us/step - loss: 0.1659 - acc: 0.9385 - val_loss: 2.0793 - val_acc: 0.7056\n",
      "Epoch 190/600\n",
      "537/537 [==============================] - 0s 169us/step - loss: 0.1045 - acc: 0.9572 - val_loss: 2.0509 - val_acc: 0.7013\n",
      "Epoch 191/600\n",
      "537/537 [==============================] - 0s 197us/step - loss: 0.0930 - acc: 0.9646 - val_loss: 2.0584 - val_acc: 0.7143\n",
      "Epoch 192/600\n",
      "537/537 [==============================] - 0s 221us/step - loss: 0.0886 - acc: 0.9739 - val_loss: 2.0414 - val_acc: 0.7186\n",
      "Epoch 193/600\n",
      "537/537 [==============================] - 0s 154us/step - loss: 0.0911 - acc: 0.9665 - val_loss: 2.0388 - val_acc: 0.7273\n",
      "Epoch 194/600\n",
      "537/537 [==============================] - 0s 119us/step - loss: 0.1391 - acc: 0.9590 - val_loss: 2.3041 - val_acc: 0.7056\n",
      "Epoch 195/600\n",
      "537/537 [==============================] - 0s 106us/step - loss: 0.1151 - acc: 0.9497 - val_loss: 2.0776 - val_acc: 0.7056\n",
      "Epoch 196/600\n",
      "537/537 [==============================] - 0s 104us/step - loss: 0.1402 - acc: 0.9534 - val_loss: 2.0965 - val_acc: 0.7143\n",
      "Epoch 197/600\n",
      "537/537 [==============================] - 0s 344us/step - loss: 0.0861 - acc: 0.9721 - val_loss: 2.3876 - val_acc: 0.7013\n",
      "Epoch 198/600\n",
      "537/537 [==============================] - 0s 256us/step - loss: 0.1266 - acc: 0.9534 - val_loss: 2.0265 - val_acc: 0.7273\n",
      "Epoch 199/600\n",
      "537/537 [==============================] - 0s 125us/step - loss: 0.1040 - acc: 0.9646 - val_loss: 2.0395 - val_acc: 0.7186\n",
      "Epoch 200/600\n",
      "537/537 [==============================] - 0s 150us/step - loss: 0.0812 - acc: 0.9665 - val_loss: 2.0956 - val_acc: 0.7143\n",
      "Epoch 201/600\n",
      "537/537 [==============================] - 0s 206us/step - loss: 0.1244 - acc: 0.9479 - val_loss: 2.0860 - val_acc: 0.7316\n",
      "Epoch 202/600\n",
      "537/537 [==============================] - 0s 167us/step - loss: 0.1391 - acc: 0.9497 - val_loss: 2.0405 - val_acc: 0.7229\n",
      "Epoch 203/600\n",
      "537/537 [==============================] - 0s 126us/step - loss: 0.0926 - acc: 0.9665 - val_loss: 2.0428 - val_acc: 0.7100\n",
      "Epoch 204/600\n",
      "537/537 [==============================] - 0s 241us/step - loss: 0.1147 - acc: 0.9590 - val_loss: 2.0291 - val_acc: 0.7229\n",
      "Epoch 205/600\n",
      "537/537 [==============================] - 0s 247us/step - loss: 0.0717 - acc: 0.9739 - val_loss: 2.0425 - val_acc: 0.7229\n",
      "Epoch 206/600\n",
      "537/537 [==============================] - 0s 106us/step - loss: 0.1629 - acc: 0.9385 - val_loss: 2.0362 - val_acc: 0.7186\n",
      "Epoch 207/600\n",
      "537/537 [==============================] - 0s 195us/step - loss: 0.0946 - acc: 0.9646 - val_loss: 2.0185 - val_acc: 0.7186\n",
      "Epoch 208/600\n",
      "537/537 [==============================] - 0s 152us/step - loss: 0.0873 - acc: 0.9628 - val_loss: 2.0960 - val_acc: 0.7186\n",
      "Epoch 209/600\n",
      "537/537 [==============================] - 0s 137us/step - loss: 0.0962 - acc: 0.9665 - val_loss: 2.1346 - val_acc: 0.7056\n",
      "Epoch 210/600\n",
      "537/537 [==============================] - 0s 75us/step - loss: 0.1060 - acc: 0.9665 - val_loss: 2.0188 - val_acc: 0.7273\n",
      "Epoch 211/600\n",
      "537/537 [==============================] - 0s 301us/step - loss: 0.1074 - acc: 0.9497 - val_loss: 2.0085 - val_acc: 0.7273\n",
      "Epoch 212/600\n",
      "537/537 [==============================] - 0s 76us/step - loss: 0.1027 - acc: 0.9665 - val_loss: 2.0147 - val_acc: 0.7143\n",
      "Epoch 213/600\n",
      "537/537 [==============================] - 0s 98us/step - loss: 0.0915 - acc: 0.9628 - val_loss: 2.1826 - val_acc: 0.7273\n",
      "Epoch 214/600\n",
      "537/537 [==============================] - 0s 104us/step - loss: 0.0966 - acc: 0.9572 - val_loss: 2.0650 - val_acc: 0.7273\n",
      "Epoch 215/600\n",
      "537/537 [==============================] - 0s 271us/step - loss: 0.1528 - acc: 0.9497 - val_loss: 2.0622 - val_acc: 0.7186\n",
      "Epoch 216/600\n",
      "537/537 [==============================] - 0s 269us/step - loss: 0.0891 - acc: 0.9665 - val_loss: 2.0596 - val_acc: 0.7100\n",
      "Epoch 217/600\n",
      "537/537 [==============================] - 0s 210us/step - loss: 0.0940 - acc: 0.9702 - val_loss: 2.0753 - val_acc: 0.7229\n",
      "Epoch 218/600\n",
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      "Epoch 219/600\n",
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      "Epoch 220/600\n",
      "537/537 [==============================] - 0s 340us/step - loss: 0.1017 - acc: 0.9646 - val_loss: 2.1907 - val_acc: 0.7056\n",
      "Epoch 221/600\n",
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      "Epoch 222/600\n",
      "537/537 [==============================] - 0s 160us/step - loss: 0.0863 - acc: 0.9683 - val_loss: 2.0201 - val_acc: 0.7186\n",
      "Epoch 223/600\n",
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      "Epoch 224/600\n",
      "537/537 [==============================] - 0s 158us/step - loss: 0.0880 - acc: 0.9721 - val_loss: 2.0237 - val_acc: 0.7273\n",
      "Epoch 225/600\n",
      "537/537 [==============================] - 0s 87us/step - loss: 0.1534 - acc: 0.9460 - val_loss: 2.0864 - val_acc: 0.7013\n",
      "Epoch 226/600\n",
      "537/537 [==============================] - 0s 62us/step - loss: 0.0933 - acc: 0.9702 - val_loss: 2.0201 - val_acc: 0.7229\n",
      "Epoch 227/600\n",
      "537/537 [==============================] - 0s 72us/step - loss: 0.1056 - acc: 0.9572 - val_loss: 2.0954 - val_acc: 0.7143\n",
      "Epoch 228/600\n",
      "537/537 [==============================] - 0s 106us/step - loss: 0.1041 - acc: 0.9646 - val_loss: 2.0971 - val_acc: 0.7186\n",
      "Epoch 229/600\n",
      "537/537 [==============================] - 0s 80us/step - loss: 0.1155 - acc: 0.9553 - val_loss: 2.0203 - val_acc: 0.7186\n",
      "Epoch 230/600\n",
      "537/537 [==============================] - 0s 97us/step - loss: 0.0803 - acc: 0.9777 - val_loss: 2.0718 - val_acc: 0.7186\n",
      "Epoch 231/600\n",
      "537/537 [==============================] - 0s 273us/step - loss: 0.0849 - acc: 0.9721 - val_loss: 2.0341 - val_acc: 0.7186\n",
      "Epoch 232/600\n",
      "537/537 [==============================] - 0s 86us/step - loss: 0.0850 - acc: 0.9721 - val_loss: 2.1123 - val_acc: 0.7316\n",
      "Epoch 233/600\n",
      "537/537 [==============================] - 0s 84us/step - loss: 0.0724 - acc: 0.9758 - val_loss: 2.0252 - val_acc: 0.7056\n",
      "Epoch 234/600\n",
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      "Epoch 235/600\n",
      "537/537 [==============================] - 0s 100us/step - loss: 0.0964 - acc: 0.9646 - val_loss: 2.0439 - val_acc: 0.7056\n",
      "Epoch 236/600\n",
      "537/537 [==============================] - 0s 178us/step - loss: 0.0946 - acc: 0.9628 - val_loss: 1.9904 - val_acc: 0.7143\n",
      "Epoch 237/600\n",
      "537/537 [==============================] - 0s 61us/step - loss: 0.0712 - acc: 0.9758 - val_loss: 1.9990 - val_acc: 0.7100\n",
      "Epoch 238/600\n",
      "537/537 [==============================] - 0s 77us/step - loss: 0.0884 - acc: 0.9702 - val_loss: 2.0437 - val_acc: 0.7056\n",
      "Epoch 239/600\n",
      "537/537 [==============================] - 0s 165us/step - loss: 0.0923 - acc: 0.9628 - val_loss: 2.0650 - val_acc: 0.7143\n",
      "Epoch 240/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "537/537 [==============================] - 0s 178us/step - loss: 0.1044 - acc: 0.9646 - val_loss: 2.0791 - val_acc: 0.7316\n",
      "Epoch 241/600\n",
      "537/537 [==============================] - 0s 171us/step - loss: 0.0856 - acc: 0.9683 - val_loss: 2.1194 - val_acc: 0.7229\n",
      "Epoch 242/600\n",
      "537/537 [==============================] - 0s 65us/step - loss: 0.1693 - acc: 0.9330 - val_loss: 2.0249 - val_acc: 0.7316\n",
      "Epoch 243/600\n",
      "537/537 [==============================] - 0s 72us/step - loss: 0.1515 - acc: 0.9330 - val_loss: 2.1296 - val_acc: 0.7359\n",
      "Epoch 244/600\n",
      "537/537 [==============================] - 0s 69us/step - loss: 0.1752 - acc: 0.9367 - val_loss: 2.1580 - val_acc: 0.7186\n",
      "Epoch 245/600\n",
      "537/537 [==============================] - 0s 180us/step - loss: 0.1125 - acc: 0.9646 - val_loss: 1.9832 - val_acc: 0.7316\n",
      "Epoch 246/600\n",
      "537/537 [==============================] - 0s 98us/step - loss: 0.0972 - acc: 0.9609 - val_loss: 2.0070 - val_acc: 0.7186\n",
      "Epoch 247/600\n",
      "537/537 [==============================] - 0s 115us/step - loss: 0.0845 - acc: 0.9628 - val_loss: 2.0889 - val_acc: 0.7273\n",
      "Epoch 248/600\n",
      "537/537 [==============================] - 0s 125us/step - loss: 0.1133 - acc: 0.9553 - val_loss: 2.0268 - val_acc: 0.7143\n",
      "Epoch 249/600\n",
      "537/537 [==============================] - 0s 221us/step - loss: 0.0891 - acc: 0.9721 - val_loss: 2.1119 - val_acc: 0.7229\n",
      "Epoch 250/600\n",
      "537/537 [==============================] - 0s 201us/step - loss: 0.1069 - acc: 0.9646 - val_loss: 2.0713 - val_acc: 0.7229\n",
      "Epoch 251/600\n",
      "537/537 [==============================] - 0s 215us/step - loss: 0.0862 - acc: 0.9777 - val_loss: 2.0282 - val_acc: 0.7229\n",
      "Epoch 252/600\n",
      "537/537 [==============================] - 0s 128us/step - loss: 0.1041 - acc: 0.9646 - val_loss: 2.0581 - val_acc: 0.7143\n",
      "Epoch 253/600\n",
      "537/537 [==============================] - 0s 108us/step - loss: 0.0899 - acc: 0.9702 - val_loss: 2.0935 - val_acc: 0.7273\n",
      "Epoch 254/600\n",
      "537/537 [==============================] - 0s 173us/step - loss: 0.1195 - acc: 0.9628 - val_loss: 2.0145 - val_acc: 0.7316\n",
      "Epoch 255/600\n",
      "537/537 [==============================] - 0s 214us/step - loss: 0.1120 - acc: 0.9665 - val_loss: 2.0649 - val_acc: 0.7186\n",
      "Epoch 256/600\n",
      "537/537 [==============================] - 0s 258us/step - loss: 0.1016 - acc: 0.9628 - val_loss: 2.0868 - val_acc: 0.7143\n",
      "Epoch 257/600\n",
      "537/537 [==============================] - 0s 191us/step - loss: 0.0791 - acc: 0.9721 - val_loss: 2.0982 - val_acc: 0.7229\n",
      "Epoch 258/600\n",
      "537/537 [==============================] - 0s 182us/step - loss: 0.0996 - acc: 0.9646 - val_loss: 2.0504 - val_acc: 0.7229\n",
      "Epoch 259/600\n",
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      "Epoch 260/600\n",
      "537/537 [==============================] - 0s 204us/step - loss: 0.1740 - acc: 0.9441 - val_loss: 2.0567 - val_acc: 0.7229\n",
      "Epoch 261/600\n",
      "537/537 [==============================] - 0s 96us/step - loss: 0.0958 - acc: 0.9683 - val_loss: 2.0496 - val_acc: 0.7186\n",
      "Epoch 262/600\n",
      "537/537 [==============================] - 0s 197us/step - loss: 0.1444 - acc: 0.9497 - val_loss: 2.0404 - val_acc: 0.7316\n",
      "Epoch 263/600\n",
      "537/537 [==============================] - 0s 294us/step - loss: 0.1091 - acc: 0.9590 - val_loss: 2.0856 - val_acc: 0.7143\n",
      "Epoch 264/600\n",
      "537/537 [==============================] - 0s 82us/step - loss: 0.0857 - acc: 0.9758 - val_loss: 2.0571 - val_acc: 0.7273\n",
      "Epoch 265/600\n",
      "537/537 [==============================] - 0s 189us/step - loss: 0.0881 - acc: 0.9702 - val_loss: 2.1617 - val_acc: 0.7013\n",
      "Epoch 266/600\n",
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      "Epoch 267/600\n",
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      "Epoch 268/600\n",
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      "Epoch 269/600\n",
      "537/537 [==============================] - 0s 178us/step - loss: 0.1203 - acc: 0.9534 - val_loss: 2.1173 - val_acc: 0.7056\n",
      "Epoch 270/600\n",
      "537/537 [==============================] - 0s 74us/step - loss: 0.0887 - acc: 0.9702 - val_loss: 2.0284 - val_acc: 0.7229\n",
      "Epoch 271/600\n",
      "537/537 [==============================] - 0s 59us/step - loss: 0.0786 - acc: 0.9721 - val_loss: 2.0679 - val_acc: 0.7186\n",
      "Epoch 272/600\n",
      "537/537 [==============================] - 0s 78us/step - loss: 0.1595 - acc: 0.9460 - val_loss: 2.0396 - val_acc: 0.7100\n",
      "Epoch 273/600\n",
      "537/537 [==============================] - 0s 100us/step - loss: 0.0933 - acc: 0.9628 - val_loss: 2.3386 - val_acc: 0.6970\n",
      "Epoch 274/600\n",
      "537/537 [==============================] - 0s 74us/step - loss: 0.1022 - acc: 0.9646 - val_loss: 2.0489 - val_acc: 0.7229\n",
      "Epoch 275/600\n",
      "537/537 [==============================] - 0s 63us/step - loss: 0.0879 - acc: 0.9665 - val_loss: 2.0444 - val_acc: 0.7229\n",
      "Epoch 276/600\n",
      "537/537 [==============================] - 0s 67us/step - loss: 0.1052 - acc: 0.9590 - val_loss: 2.0237 - val_acc: 0.7100\n",
      "Epoch 277/600\n",
      "537/537 [==============================] - 0s 72us/step - loss: 0.1556 - acc: 0.9516 - val_loss: 2.1077 - val_acc: 0.7186\n",
      "Epoch 278/600\n",
      "537/537 [==============================] - 0s 76us/step - loss: 0.1499 - acc: 0.9479 - val_loss: 2.0766 - val_acc: 0.7143\n",
      "Epoch 279/600\n",
      "537/537 [==============================] - 0s 98us/step - loss: 0.0933 - acc: 0.9683 - val_loss: 2.0194 - val_acc: 0.7229\n",
      "Epoch 280/600\n",
      "537/537 [==============================] - 0s 82us/step - loss: 0.0918 - acc: 0.9665 - val_loss: 2.0812 - val_acc: 0.7056\n",
      "Epoch 281/600\n",
      "537/537 [==============================] - 0s 63us/step - loss: 0.0777 - acc: 0.9702 - val_loss: 2.0637 - val_acc: 0.7056\n",
      "Epoch 282/600\n",
      "537/537 [==============================] - 0s 67us/step - loss: 0.1053 - acc: 0.9665 - val_loss: 2.0464 - val_acc: 0.7056\n",
      "Epoch 283/600\n",
      "537/537 [==============================] - 0s 61us/step - loss: 0.0891 - acc: 0.9683 - val_loss: 2.0168 - val_acc: 0.7316\n",
      "Epoch 284/600\n",
      "537/537 [==============================] - 0s 72us/step - loss: 0.0835 - acc: 0.9702 - val_loss: 2.0115 - val_acc: 0.7359\n",
      "Epoch 285/600\n",
      "537/537 [==============================] - 0s 176us/step - loss: 0.0880 - acc: 0.9646 - val_loss: 2.0179 - val_acc: 0.7100\n",
      "Epoch 286/600\n",
      "537/537 [==============================] - 0s 61us/step - loss: 0.1072 - acc: 0.9460 - val_loss: 1.9784 - val_acc: 0.7359\n",
      "Epoch 287/600\n",
      "537/537 [==============================] - 0s 74us/step - loss: 0.1544 - acc: 0.9516 - val_loss: 2.0611 - val_acc: 0.7273\n",
      "Epoch 288/600\n",
      "537/537 [==============================] - 0s 66us/step - loss: 0.1233 - acc: 0.9646 - val_loss: 2.0963 - val_acc: 0.7229\n",
      "Epoch 289/600\n",
      "537/537 [==============================] - 0s 71us/step - loss: 0.1405 - acc: 0.9553 - val_loss: 2.1331 - val_acc: 0.7229\n",
      "Epoch 290/600\n",
      "537/537 [==============================] - 0s 63us/step - loss: 0.1049 - acc: 0.9628 - val_loss: 2.0589 - val_acc: 0.7186\n",
      "Epoch 291/600\n",
      "537/537 [==============================] - 0s 63us/step - loss: 0.1058 - acc: 0.9609 - val_loss: 2.0447 - val_acc: 0.7273\n",
      "Epoch 292/600\n",
      "537/537 [==============================] - 0s 65us/step - loss: 0.0876 - acc: 0.9702 - val_loss: 2.0463 - val_acc: 0.7186\n",
      "Epoch 293/600\n",
      "537/537 [==============================] - 0s 58us/step - loss: 0.1051 - acc: 0.9534 - val_loss: 2.0810 - val_acc: 0.7056\n",
      "Epoch 294/600\n",
      "537/537 [==============================] - 0s 174us/step - loss: 0.1405 - acc: 0.9497 - val_loss: 2.1405 - val_acc: 0.7056\n",
      "Epoch 295/600\n",
      "537/537 [==============================] - 0s 80us/step - loss: 0.0968 - acc: 0.9665 - val_loss: 2.0973 - val_acc: 0.7013\n",
      "Epoch 296/600\n",
      "537/537 [==============================] - 0s 137us/step - loss: 0.0922 - acc: 0.9609 - val_loss: 2.0337 - val_acc: 0.7186\n",
      "Epoch 297/600\n",
      "537/537 [==============================] - 0s 143us/step - loss: 0.0841 - acc: 0.9739 - val_loss: 2.0197 - val_acc: 0.7316\n",
      "Epoch 298/600\n",
      "537/537 [==============================] - 0s 141us/step - loss: 0.0998 - acc: 0.9572 - val_loss: 2.0343 - val_acc: 0.7143\n",
      "Epoch 299/600\n",
      "537/537 [==============================] - 0s 228us/step - loss: 0.0996 - acc: 0.9628 - val_loss: 2.0288 - val_acc: 0.7273\n",
      "Epoch 300/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "537/537 [==============================] - ETA: 0s - loss: 0.0714 - acc: 0.972 - 0s 318us/step - loss: 0.0699 - acc: 0.9739 - val_loss: 2.0532 - val_acc: 0.7143\n",
      "Epoch 301/600\n",
      "537/537 [==============================] - 0s 137us/step - loss: 0.0746 - acc: 0.9702 - val_loss: 2.0788 - val_acc: 0.7186\n",
      "Epoch 302/600\n",
      "537/537 [==============================] - 0s 212us/step - loss: 0.0921 - acc: 0.9609 - val_loss: 2.0604 - val_acc: 0.7186\n",
      "Epoch 303/600\n",
      "537/537 [==============================] - 0s 173us/step - loss: 0.0831 - acc: 0.9665 - val_loss: 2.0594 - val_acc: 0.7186\n",
      "Epoch 304/600\n",
      "537/537 [==============================] - 0s 215us/step - loss: 0.1184 - acc: 0.9516 - val_loss: 2.1053 - val_acc: 0.7100\n",
      "Epoch 305/600\n",
      "537/537 [==============================] - 0s 174us/step - loss: 0.0858 - acc: 0.9739 - val_loss: 2.0296 - val_acc: 0.7143\n",
      "Epoch 306/600\n",
      "537/537 [==============================] - 0s 296us/step - loss: 0.1565 - acc: 0.9497 - val_loss: 2.0371 - val_acc: 0.7186\n",
      "Epoch 307/600\n",
      "537/537 [==============================] - 0s 115us/step - loss: 0.0968 - acc: 0.9628 - val_loss: 2.0707 - val_acc: 0.7100\n",
      "Epoch 308/600\n",
      "537/537 [==============================] - 0s 306us/step - loss: 0.0780 - acc: 0.9702 - val_loss: 2.0729 - val_acc: 0.7013\n",
      "Epoch 309/600\n",
      "537/537 [==============================] - 0s 93us/step - loss: 0.1436 - acc: 0.9553 - val_loss: 2.1060 - val_acc: 0.7143\n",
      "Epoch 310/600\n",
      "537/537 [==============================] - 0s 123us/step - loss: 0.1040 - acc: 0.9609 - val_loss: 2.0686 - val_acc: 0.7100\n",
      "Epoch 311/600\n",
      "537/537 [==============================] - 0s 191us/step - loss: 0.0843 - acc: 0.9683 - val_loss: 2.0578 - val_acc: 0.7186\n",
      "Epoch 312/600\n",
      "537/537 [==============================] - 0s 212us/step - loss: 0.0975 - acc: 0.9646 - val_loss: 2.0231 - val_acc: 0.7229\n",
      "Epoch 313/600\n",
      "537/537 [==============================] - 0s 154us/step - loss: 0.1116 - acc: 0.9665 - val_loss: 2.0752 - val_acc: 0.7273\n",
      "Epoch 314/600\n",
      "537/537 [==============================] - 0s 143us/step - loss: 0.1009 - acc: 0.9665 - val_loss: 2.1434 - val_acc: 0.7229\n",
      "Epoch 315/600\n",
      "537/537 [==============================] - 0s 295us/step - loss: 0.0970 - acc: 0.9683 - val_loss: 2.0365 - val_acc: 0.7186\n",
      "Epoch 316/600\n",
      "537/537 [==============================] - 0s 126us/step - loss: 0.0918 - acc: 0.9665 - val_loss: 2.0332 - val_acc: 0.7143\n",
      "Epoch 317/600\n",
      "537/537 [==============================] - 0s 173us/step - loss: 0.1750 - acc: 0.9534 - val_loss: 2.0592 - val_acc: 0.7273\n",
      "Epoch 318/600\n",
      "537/537 [==============================] - 0s 201us/step - loss: 0.1429 - acc: 0.9460 - val_loss: 2.0563 - val_acc: 0.7229\n",
      "Epoch 319/600\n",
      "537/537 [==============================] - 0s 292us/step - loss: 0.1322 - acc: 0.9553 - val_loss: 2.1513 - val_acc: 0.7013\n",
      "Epoch 320/600\n",
      "537/537 [==============================] - 0s 186us/step - loss: 0.0911 - acc: 0.9683 - val_loss: 2.0730 - val_acc: 0.7186\n",
      "Epoch 321/600\n",
      "537/537 [==============================] - 0s 145us/step - loss: 0.1274 - acc: 0.9646 - val_loss: 2.0685 - val_acc: 0.7186\n",
      "Epoch 322/600\n",
      "537/537 [==============================] - 0s 95us/step - loss: 0.1260 - acc: 0.9572 - val_loss: 2.0902 - val_acc: 0.7186\n",
      "Epoch 323/600\n",
      "537/537 [==============================] - 0s 258us/step - loss: 0.0822 - acc: 0.9665 - val_loss: 2.0420 - val_acc: 0.7186\n",
      "Epoch 324/600\n",
      "537/537 [==============================] - 0s 111us/step - loss: 0.1184 - acc: 0.9665 - val_loss: 2.0767 - val_acc: 0.7273\n",
      "Epoch 325/600\n",
      "537/537 [==============================] - 0s 141us/step - loss: 0.0929 - acc: 0.9683 - val_loss: 2.0839 - val_acc: 0.7229\n",
      "Epoch 326/600\n",
      "537/537 [==============================] - 0s 106us/step - loss: 0.0838 - acc: 0.9739 - val_loss: 2.1070 - val_acc: 0.7143\n",
      "Epoch 327/600\n",
      "537/537 [==============================] - 0s 193us/step - loss: 0.0811 - acc: 0.9646 - val_loss: 2.0865 - val_acc: 0.7056\n",
      "Epoch 328/600\n",
      "537/537 [==============================] - 0s 342us/step - loss: 0.0791 - acc: 0.9758 - val_loss: 2.0588 - val_acc: 0.7143\n",
      "Epoch 329/600\n",
      "537/537 [==============================] - 0s 225us/step - loss: 0.0736 - acc: 0.9721 - val_loss: 2.1234 - val_acc: 0.7013\n",
      "Epoch 330/600\n",
      "537/537 [==============================] - 0s 63us/step - loss: 0.1114 - acc: 0.9590 - val_loss: 2.0765 - val_acc: 0.7143\n",
      "Epoch 331/600\n",
      "537/537 [==============================] - 0s 236us/step - loss: 0.1073 - acc: 0.9665 - val_loss: 2.1147 - val_acc: 0.7229\n",
      "Epoch 332/600\n",
      "537/537 [==============================] - 0s 67us/step - loss: 0.1122 - acc: 0.9590 - val_loss: 2.0480 - val_acc: 0.7229\n",
      "Epoch 333/600\n",
      "537/537 [==============================] - 0s 78us/step - loss: 0.0804 - acc: 0.9665 - val_loss: 2.1780 - val_acc: 0.7186\n",
      "Epoch 334/600\n",
      "537/537 [==============================] - 0s 271us/step - loss: 0.1410 - acc: 0.9516 - val_loss: 2.1092 - val_acc: 0.7186\n",
      "Epoch 335/600\n",
      "537/537 [==============================] - 0s 193us/step - loss: 0.1079 - acc: 0.9628 - val_loss: 2.0636 - val_acc: 0.7100\n",
      "Epoch 336/600\n",
      "537/537 [==============================] - 0s 267us/step - loss: 0.2256 - acc: 0.9181 - val_loss: 2.1029 - val_acc: 0.7229\n",
      "Epoch 337/600\n",
      "537/537 [==============================] - 0s 208us/step - loss: 0.0903 - acc: 0.9702 - val_loss: 2.1095 - val_acc: 0.7143\n",
      "Epoch 338/600\n",
      "537/537 [==============================] - 0s 143us/step - loss: 0.0836 - acc: 0.9758 - val_loss: 2.0735 - val_acc: 0.7100\n",
      "Epoch 339/600\n",
      "537/537 [==============================] - 0s 69us/step - loss: 0.1034 - acc: 0.9721 - val_loss: 2.0833 - val_acc: 0.7186\n",
      "Epoch 340/600\n",
      "537/537 [==============================] - 0s 59us/step - loss: 0.0964 - acc: 0.9609 - val_loss: 2.1405 - val_acc: 0.7056\n",
      "Epoch 341/600\n",
      "537/537 [==============================] - 0s 160us/step - loss: 0.0919 - acc: 0.9572 - val_loss: 2.0823 - val_acc: 0.7143\n",
      "Epoch 342/600\n",
      "537/537 [==============================] - 0s 61us/step - loss: 0.0774 - acc: 0.9739 - val_loss: 2.1107 - val_acc: 0.7013\n",
      "Epoch 343/600\n",
      "537/537 [==============================] - 0s 67us/step - loss: 0.0975 - acc: 0.9628 - val_loss: 2.0737 - val_acc: 0.7143\n",
      "Epoch 344/600\n",
      "537/537 [==============================] - 0s 67us/step - loss: 0.0840 - acc: 0.9609 - val_loss: 2.1368 - val_acc: 0.7013\n",
      "Epoch 345/600\n",
      "537/537 [==============================] - 0s 150us/step - loss: 0.0902 - acc: 0.9590 - val_loss: 2.1116 - val_acc: 0.7056\n",
      "Epoch 346/600\n",
      "537/537 [==============================] - 0s 69us/step - loss: 0.0721 - acc: 0.9758 - val_loss: 2.1074 - val_acc: 0.7143\n",
      "Epoch 347/600\n",
      "537/537 [==============================] - 0s 148us/step - loss: 0.0793 - acc: 0.9758 - val_loss: 2.0880 - val_acc: 0.7100\n",
      "Epoch 348/600\n",
      "537/537 [==============================] - 0s 83us/step - loss: 0.1027 - acc: 0.9590 - val_loss: 2.0681 - val_acc: 0.7186\n",
      "Epoch 349/600\n",
      "537/537 [==============================] - 0s 85us/step - loss: 0.0901 - acc: 0.9665 - val_loss: 2.1016 - val_acc: 0.7229\n",
      "Epoch 350/600\n",
      "537/537 [==============================] - 0s 87us/step - loss: 0.0816 - acc: 0.9739 - val_loss: 2.0973 - val_acc: 0.7100\n",
      "Epoch 351/600\n",
      "537/537 [==============================] - 0s 85us/step - loss: 0.0817 - acc: 0.9683 - val_loss: 2.0785 - val_acc: 0.7143\n",
      "Epoch 352/600\n",
      "537/537 [==============================] - 0s 78us/step - loss: 0.1138 - acc: 0.9609 - val_loss: 2.1075 - val_acc: 0.7273\n",
      "Epoch 353/600\n",
      "537/537 [==============================] - 0s 163us/step - loss: 0.0993 - acc: 0.9683 - val_loss: 2.0634 - val_acc: 0.7186\n",
      "Epoch 354/600\n",
      "537/537 [==============================] - 0s 89us/step - loss: 0.0868 - acc: 0.9683 - val_loss: 2.0687 - val_acc: 0.7143\n",
      "Epoch 355/600\n",
      "537/537 [==============================] - 0s 175us/step - loss: 0.1236 - acc: 0.9553 - val_loss: 2.0361 - val_acc: 0.7316\n",
      "Epoch 356/600\n",
      "537/537 [==============================] - 0s 74us/step - loss: 0.0868 - acc: 0.9665 - val_loss: 2.0851 - val_acc: 0.7143\n",
      "Epoch 357/600\n",
      "537/537 [==============================] - 0s 219us/step - loss: 0.1018 - acc: 0.9702 - val_loss: 2.0724 - val_acc: 0.7273\n",
      "Epoch 358/600\n",
      "537/537 [==============================] - 0s 108us/step - loss: 0.0756 - acc: 0.9758 - val_loss: 2.1219 - val_acc: 0.7143\n",
      "Epoch 359/600\n",
      "537/537 [==============================] - 0s 58us/step - loss: 0.1234 - acc: 0.9553 - val_loss: 2.1169 - val_acc: 0.7229\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 360/600\n",
      "537/537 [==============================] - 0s 58us/step - loss: 0.1123 - acc: 0.9590 - val_loss: 2.0403 - val_acc: 0.7186\n",
      "Epoch 361/600\n",
      "537/537 [==============================] - 0s 74us/step - loss: 0.0833 - acc: 0.9665 - val_loss: 2.1100 - val_acc: 0.7316\n",
      "Epoch 362/600\n",
      "537/537 [==============================] - 0s 137us/step - loss: 0.0849 - acc: 0.9721 - val_loss: 2.1330 - val_acc: 0.7143\n",
      "Epoch 363/600\n",
      "537/537 [==============================] - 0s 106us/step - loss: 0.0788 - acc: 0.9702 - val_loss: 2.0569 - val_acc: 0.7273\n",
      "Epoch 364/600\n",
      "537/537 [==============================] - 0s 327us/step - loss: 0.0687 - acc: 0.9739 - val_loss: 2.0578 - val_acc: 0.7316\n",
      "Epoch 365/600\n",
      "537/537 [==============================] - 0s 117us/step - loss: 0.0776 - acc: 0.9777 - val_loss: 2.0895 - val_acc: 0.7186\n",
      "Epoch 366/600\n",
      "537/537 [==============================] - 0s 171us/step - loss: 0.0981 - acc: 0.9665 - val_loss: 2.1161 - val_acc: 0.7100\n",
      "Epoch 367/600\n",
      "537/537 [==============================] - 0s 381us/step - loss: 0.1052 - acc: 0.9646 - val_loss: 2.1013 - val_acc: 0.7186\n",
      "Epoch 368/600\n",
      "537/537 [==============================] - 0s 240us/step - loss: 0.0949 - acc: 0.9665 - val_loss: 2.0868 - val_acc: 0.7186\n",
      "Epoch 369/600\n",
      "537/537 [==============================] - 0s 110us/step - loss: 0.0984 - acc: 0.9665 - val_loss: 2.1468 - val_acc: 0.7100\n",
      "Epoch 370/600\n",
      "537/537 [==============================] - 0s 221us/step - loss: 0.0881 - acc: 0.9702 - val_loss: 2.1318 - val_acc: 0.7186\n",
      "Epoch 371/600\n",
      "537/537 [==============================] - 0s 130us/step - loss: 0.1527 - acc: 0.9497 - val_loss: 2.1423 - val_acc: 0.7229\n",
      "Epoch 372/600\n",
      "537/537 [==============================] - 0s 217us/step - loss: 0.1005 - acc: 0.9628 - val_loss: 2.1090 - val_acc: 0.7056\n",
      "Epoch 373/600\n",
      "537/537 [==============================] - 0s 124us/step - loss: 0.1469 - acc: 0.9423 - val_loss: 2.1614 - val_acc: 0.7186\n",
      "Epoch 374/600\n",
      "537/537 [==============================] - 0s 124us/step - loss: 0.1223 - acc: 0.9553 - val_loss: 2.1017 - val_acc: 0.7100\n",
      "Epoch 375/600\n",
      "537/537 [==============================] - 0s 134us/step - loss: 0.0895 - acc: 0.9721 - val_loss: 2.0539 - val_acc: 0.7229\n",
      "Epoch 376/600\n",
      "537/537 [==============================] - 0s 256us/step - loss: 0.0721 - acc: 0.9777 - val_loss: 2.0348 - val_acc: 0.7186\n",
      "Epoch 377/600\n",
      "537/537 [==============================] - 0s 226us/step - loss: 0.1040 - acc: 0.9572 - val_loss: 2.3012 - val_acc: 0.7143\n",
      "Epoch 378/600\n",
      "537/537 [==============================] - 0s 102us/step - loss: 0.1778 - acc: 0.9479 - val_loss: 2.1116 - val_acc: 0.7013\n",
      "Epoch 379/600\n",
      "537/537 [==============================] - 0s 69us/step - loss: 0.1035 - acc: 0.9646 - val_loss: 2.1010 - val_acc: 0.7013\n",
      "Epoch 380/600\n",
      "537/537 [==============================] - 0s 137us/step - loss: 0.1073 - acc: 0.9516 - val_loss: 2.7058 - val_acc: 0.6840\n",
      "Epoch 381/600\n",
      "537/537 [==============================] - 0s 83us/step - loss: 0.1757 - acc: 0.9367 - val_loss: 2.1728 - val_acc: 0.7100\n",
      "Epoch 382/600\n",
      "537/537 [==============================] - 0s 178us/step - loss: 0.1001 - acc: 0.9646 - val_loss: 2.1293 - val_acc: 0.7056\n",
      "Epoch 383/600\n",
      "537/537 [==============================] - 0s 63us/step - loss: 0.0866 - acc: 0.9721 - val_loss: 2.1177 - val_acc: 0.7100\n",
      "Epoch 384/600\n",
      "537/537 [==============================] - 0s 135us/step - loss: 0.0801 - acc: 0.9721 - val_loss: 2.1324 - val_acc: 0.7186\n",
      "Epoch 385/600\n",
      "537/537 [==============================] - 0s 85us/step - loss: 0.1134 - acc: 0.9534 - val_loss: 2.1393 - val_acc: 0.7446\n",
      "Epoch 386/600\n",
      "537/537 [==============================] - 0s 173us/step - loss: 0.0923 - acc: 0.9628 - val_loss: 2.1539 - val_acc: 0.7100\n",
      "Epoch 387/600\n",
      "537/537 [==============================] - 0s 185us/step - loss: 0.0756 - acc: 0.9683 - val_loss: 2.1501 - val_acc: 0.6926\n",
      "Epoch 388/600\n",
      "537/537 [==============================] - 0s 89us/step - loss: 0.1447 - acc: 0.9553 - val_loss: 2.1014 - val_acc: 0.7403\n",
      "Epoch 389/600\n",
      "537/537 [==============================] - 0s 67us/step - loss: 0.2640 - acc: 0.9404 - val_loss: 2.1864 - val_acc: 0.7186\n",
      "Epoch 390/600\n",
      "537/537 [==============================] - 0s 72us/step - loss: 0.1382 - acc: 0.9534 - val_loss: 2.1680 - val_acc: 0.7186\n",
      "Epoch 391/600\n",
      "537/537 [==============================] - 0s 69us/step - loss: 0.0795 - acc: 0.9683 - val_loss: 2.1110 - val_acc: 0.7273\n",
      "Epoch 392/600\n",
      "537/537 [==============================] - 0s 63us/step - loss: 0.1883 - acc: 0.9460 - val_loss: 2.4954 - val_acc: 0.7056\n",
      "Epoch 393/600\n",
      "537/537 [==============================] - 0s 97us/step - loss: 0.1229 - acc: 0.9534 - val_loss: 2.1488 - val_acc: 0.7186\n",
      "Epoch 394/600\n",
      "537/537 [==============================] - 0s 186us/step - loss: 0.1404 - acc: 0.9553 - val_loss: 2.1730 - val_acc: 0.7229\n",
      "Epoch 395/600\n",
      "537/537 [==============================] - 0s 72us/step - loss: 0.0941 - acc: 0.9609 - val_loss: 2.1467 - val_acc: 0.7143\n",
      "Epoch 396/600\n",
      "537/537 [==============================] - 0s 67us/step - loss: 0.1128 - acc: 0.9665 - val_loss: 2.1081 - val_acc: 0.7186\n",
      "Epoch 397/600\n",
      "537/537 [==============================] - 0s 87us/step - loss: 0.0913 - acc: 0.9665 - val_loss: 2.0978 - val_acc: 0.7100\n",
      "Epoch 398/600\n",
      "537/537 [==============================] - 0s 229us/step - loss: 0.0865 - acc: 0.9702 - val_loss: 2.1443 - val_acc: 0.7056\n",
      "Epoch 399/600\n",
      "537/537 [==============================] - 0s 72us/step - loss: 0.0821 - acc: 0.9628 - val_loss: 2.1843 - val_acc: 0.7013\n",
      "Epoch 400/600\n",
      "537/537 [==============================] - 0s 74us/step - loss: 0.1436 - acc: 0.9479 - val_loss: 2.0912 - val_acc: 0.7143\n",
      "Epoch 401/600\n",
      "537/537 [==============================] - 0s 186us/step - loss: 0.1207 - acc: 0.9497 - val_loss: 2.1422 - val_acc: 0.7100\n",
      "Epoch 402/600\n",
      "537/537 [==============================] - 0s 225us/step - loss: 0.0941 - acc: 0.9665 - val_loss: 2.1256 - val_acc: 0.7143\n",
      "Epoch 403/600\n",
      "537/537 [==============================] - 0s 213us/step - loss: 0.0995 - acc: 0.9665 - val_loss: 2.0983 - val_acc: 0.7100\n",
      "Epoch 404/600\n",
      "537/537 [==============================] - 0s 308us/step - loss: 0.0840 - acc: 0.9721 - val_loss: 2.1520 - val_acc: 0.7056\n",
      "Epoch 405/600\n",
      "537/537 [==============================] - 0s 126us/step - loss: 0.0748 - acc: 0.9758 - val_loss: 2.1695 - val_acc: 0.7056\n",
      "Epoch 406/600\n",
      "537/537 [==============================] - 0s 295us/step - loss: 0.0839 - acc: 0.9739 - val_loss: 2.0808 - val_acc: 0.7143\n",
      "Epoch 407/600\n",
      "537/537 [==============================] - 0s 134us/step - loss: 0.1330 - acc: 0.9628 - val_loss: 2.1057 - val_acc: 0.7056\n",
      "Epoch 408/600\n",
      "537/537 [==============================] - 0s 63us/step - loss: 0.0950 - acc: 0.9609 - val_loss: 2.2762 - val_acc: 0.7013\n",
      "Epoch 409/600\n",
      "537/537 [==============================] - 0s 63us/step - loss: 0.0961 - acc: 0.9646 - val_loss: 2.1038 - val_acc: 0.7186\n",
      "Epoch 410/600\n",
      "537/537 [==============================] - 0s 84us/step - loss: 0.1421 - acc: 0.9479 - val_loss: 2.1414 - val_acc: 0.7186\n",
      "Epoch 411/600\n",
      "537/537 [==============================] - 0s 84us/step - loss: 0.0879 - acc: 0.9646 - val_loss: 2.1321 - val_acc: 0.7056\n",
      "Epoch 412/600\n",
      "537/537 [==============================] - ETA: 0s - loss: 0.0816 - acc: 0.976 - 0s 169us/step - loss: 0.1122 - acc: 0.9683 - val_loss: 2.1097 - val_acc: 0.7229\n",
      "Epoch 413/600\n",
      "537/537 [==============================] - 0s 104us/step - loss: 0.1184 - acc: 0.9572 - val_loss: 2.1501 - val_acc: 0.7186\n",
      "Epoch 414/600\n",
      "537/537 [==============================] - 0s 76us/step - loss: 0.0834 - acc: 0.9702 - val_loss: 2.1383 - val_acc: 0.7143\n",
      "Epoch 415/600\n",
      "537/537 [==============================] - 0s 150us/step - loss: 0.0861 - acc: 0.9665 - val_loss: 2.1072 - val_acc: 0.7229\n",
      "Epoch 416/600\n",
      "537/537 [==============================] - 0s 147us/step - loss: 0.0721 - acc: 0.9739 - val_loss: 2.1135 - val_acc: 0.7186\n",
      "Epoch 417/600\n",
      "537/537 [==============================] - 0s 73us/step - loss: 0.0856 - acc: 0.9646 - val_loss: 2.1226 - val_acc: 0.7186\n",
      "Epoch 418/600\n",
      "537/537 [==============================] - 0s 113us/step - loss: 0.0846 - acc: 0.9646 - val_loss: 2.1142 - val_acc: 0.7056\n",
      "Epoch 419/600\n",
      "537/537 [==============================] - 0s 176us/step - loss: 0.0961 - acc: 0.9739 - val_loss: 2.2356 - val_acc: 0.7229\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 420/600\n",
      "537/537 [==============================] - 0s 147us/step - loss: 0.1290 - acc: 0.9572 - val_loss: 2.1111 - val_acc: 0.7056\n",
      "Epoch 421/600\n",
      "537/537 [==============================] - 0s 74us/step - loss: 0.0939 - acc: 0.9646 - val_loss: 2.1557 - val_acc: 0.6970\n",
      "Epoch 422/600\n",
      "537/537 [==============================] - 0s 283us/step - loss: 0.1348 - acc: 0.9423 - val_loss: 2.1944 - val_acc: 0.6970\n",
      "Epoch 423/600\n",
      "537/537 [==============================] - 0s 204us/step - loss: 0.1158 - acc: 0.9665 - val_loss: 2.1260 - val_acc: 0.7100\n",
      "Epoch 424/600\n",
      "537/537 [==============================] - 0s 149us/step - loss: 0.1128 - acc: 0.9646 - val_loss: 2.1121 - val_acc: 0.7229\n",
      "Epoch 425/600\n",
      "537/537 [==============================] - 0s 95us/step - loss: 0.1055 - acc: 0.9628 - val_loss: 2.1253 - val_acc: 0.7229\n",
      "Epoch 426/600\n",
      "537/537 [==============================] - 0s 176us/step - loss: 0.0853 - acc: 0.9646 - val_loss: 2.1332 - val_acc: 0.7100\n",
      "Epoch 427/600\n",
      "537/537 [==============================] - 0s 147us/step - loss: 0.0813 - acc: 0.9777 - val_loss: 2.1197 - val_acc: 0.7143\n",
      "Epoch 428/600\n",
      "537/537 [==============================] - 0s 260us/step - loss: 0.0944 - acc: 0.9683 - val_loss: 2.1149 - val_acc: 0.7186\n",
      "Epoch 429/600\n",
      "537/537 [==============================] - 0s 206us/step - loss: 0.0879 - acc: 0.9665 - val_loss: 2.0507 - val_acc: 0.7186\n",
      "Epoch 430/600\n",
      "537/537 [==============================] - 0s 210us/step - loss: 0.0809 - acc: 0.9739 - val_loss: 2.1157 - val_acc: 0.7100\n",
      "Epoch 431/600\n",
      "537/537 [==============================] - 0s 323us/step - loss: 0.0784 - acc: 0.9758 - val_loss: 2.1179 - val_acc: 0.7143\n",
      "Epoch 432/600\n",
      "537/537 [==============================] - 0s 91us/step - loss: 0.0964 - acc: 0.9683 - val_loss: 2.1565 - val_acc: 0.7229\n",
      "Epoch 433/600\n",
      "537/537 [==============================] - 0s 61us/step - loss: 0.0831 - acc: 0.9702 - val_loss: 2.0788 - val_acc: 0.7056\n",
      "Epoch 434/600\n",
      "537/537 [==============================] - 0s 63us/step - loss: 0.1010 - acc: 0.9590 - val_loss: 2.1349 - val_acc: 0.7186\n",
      "Epoch 435/600\n",
      "537/537 [==============================] - 0s 59us/step - loss: 0.0945 - acc: 0.9683 - val_loss: 2.1148 - val_acc: 0.7273\n",
      "Epoch 436/600\n",
      "537/537 [==============================] - 0s 67us/step - loss: 0.0819 - acc: 0.9665 - val_loss: 2.0752 - val_acc: 0.7186\n",
      "Epoch 437/600\n",
      "537/537 [==============================] - 0s 69us/step - loss: 0.0810 - acc: 0.9702 - val_loss: 2.0954 - val_acc: 0.7056\n",
      "Epoch 438/600\n",
      "537/537 [==============================] - 0s 59us/step - loss: 0.0738 - acc: 0.9777 - val_loss: 2.1224 - val_acc: 0.7056\n",
      "Epoch 439/600\n",
      "537/537 [==============================] - 0s 106us/step - loss: 0.0919 - acc: 0.9702 - val_loss: 2.0886 - val_acc: 0.7100\n",
      "Epoch 440/600\n",
      "537/537 [==============================] - 0s 193us/step - loss: 0.0823 - acc: 0.9702 - val_loss: 2.1055 - val_acc: 0.7013\n",
      "Epoch 441/600\n",
      "537/537 [==============================] - 0s 201us/step - loss: 0.0770 - acc: 0.9683 - val_loss: 2.1179 - val_acc: 0.7056\n",
      "Epoch 442/600\n",
      "537/537 [==============================] - 0s 119us/step - loss: 0.0798 - acc: 0.9665 - val_loss: 2.1084 - val_acc: 0.7013\n",
      "Epoch 443/600\n",
      "537/537 [==============================] - 0s 188us/step - loss: 0.0877 - acc: 0.9739 - val_loss: 2.1037 - val_acc: 0.7143\n",
      "Epoch 444/600\n",
      "537/537 [==============================] - 0s 145us/step - loss: 0.0987 - acc: 0.9665 - val_loss: 2.1870 - val_acc: 0.7229\n",
      "Epoch 445/600\n",
      "537/537 [==============================] - 0s 219us/step - loss: 0.1697 - acc: 0.9441 - val_loss: 2.1452 - val_acc: 0.7143\n",
      "Epoch 446/600\n",
      "537/537 [==============================] - 0s 191us/step - loss: 0.0917 - acc: 0.9646 - val_loss: 2.1151 - val_acc: 0.6970\n",
      "Epoch 447/600\n",
      "537/537 [==============================] - 0s 217us/step - loss: 0.0854 - acc: 0.9665 - val_loss: 2.1167 - val_acc: 0.7056\n",
      "Epoch 448/600\n",
      "537/537 [==============================] - 0s 98us/step - loss: 0.1135 - acc: 0.9702 - val_loss: 2.2602 - val_acc: 0.7013\n",
      "Epoch 449/600\n",
      "537/537 [==============================] - 0s 87us/step - loss: 0.0878 - acc: 0.9609 - val_loss: 2.1324 - val_acc: 0.7229\n",
      "Epoch 450/600\n",
      "537/537 [==============================] - 0s 240us/step - loss: 0.0914 - acc: 0.9721 - val_loss: 2.0765 - val_acc: 0.7273\n",
      "Epoch 451/600\n",
      "537/537 [==============================] - 0s 293us/step - loss: 0.0888 - acc: 0.9646 - val_loss: 2.1713 - val_acc: 0.7186\n",
      "Epoch 452/600\n",
      "537/537 [==============================] - 0s 97us/step - loss: 0.0929 - acc: 0.9665 - val_loss: 2.1458 - val_acc: 0.7186\n",
      "Epoch 453/600\n",
      "537/537 [==============================] - 0s 128us/step - loss: 0.0821 - acc: 0.9702 - val_loss: 2.1638 - val_acc: 0.7100\n",
      "Epoch 454/600\n",
      "537/537 [==============================] - 0s 186us/step - loss: 0.0850 - acc: 0.9683 - val_loss: 2.0969 - val_acc: 0.7229\n",
      "Epoch 455/600\n",
      "537/537 [==============================] - 0s 180us/step - loss: 0.0947 - acc: 0.9628 - val_loss: 2.1062 - val_acc: 0.7056\n",
      "Epoch 456/600\n",
      "537/537 [==============================] - 0s 218us/step - loss: 0.0937 - acc: 0.9665 - val_loss: 2.1558 - val_acc: 0.7186\n",
      "Epoch 457/600\n",
      "537/537 [==============================] - 0s 225us/step - loss: 0.1021 - acc: 0.9609 - val_loss: 2.1134 - val_acc: 0.7186\n",
      "Epoch 458/600\n",
      "537/537 [==============================] - 0s 178us/step - loss: 0.0995 - acc: 0.9572 - val_loss: 2.2094 - val_acc: 0.7143\n",
      "Epoch 459/600\n",
      "537/537 [==============================] - 0s 278us/step - loss: 0.1539 - acc: 0.9423 - val_loss: 2.1071 - val_acc: 0.7273\n",
      "Epoch 460/600\n",
      "537/537 [==============================] - 0s 125us/step - loss: 0.1391 - acc: 0.9553 - val_loss: 2.0946 - val_acc: 0.7316\n",
      "Epoch 461/600\n",
      "537/537 [==============================] - 0s 143us/step - loss: 0.0880 - acc: 0.9702 - val_loss: 2.1755 - val_acc: 0.7229\n",
      "Epoch 462/600\n",
      "537/537 [==============================] - 0s 182us/step - loss: 0.0947 - acc: 0.9628 - val_loss: 2.2036 - val_acc: 0.7013\n",
      "Epoch 463/600\n",
      "537/537 [==============================] - 0s 111us/step - loss: 0.0803 - acc: 0.9683 - val_loss: 2.1054 - val_acc: 0.7186\n",
      "Epoch 464/600\n",
      "537/537 [==============================] - 0s 175us/step - loss: 0.0921 - acc: 0.9739 - val_loss: 2.1177 - val_acc: 0.7273\n",
      "Epoch 465/600\n",
      "537/537 [==============================] - 0s 331us/step - loss: 0.1125 - acc: 0.9628 - val_loss: 2.1063 - val_acc: 0.7229\n",
      "Epoch 466/600\n",
      "537/537 [==============================] - 0s 115us/step - loss: 0.1072 - acc: 0.9572 - val_loss: 2.0853 - val_acc: 0.7229\n",
      "Epoch 467/600\n",
      "537/537 [==============================] - 0s 208us/step - loss: 0.0726 - acc: 0.9758 - val_loss: 2.0843 - val_acc: 0.7143\n",
      "Epoch 468/600\n",
      "537/537 [==============================] - 0s 122us/step - loss: 0.0788 - acc: 0.9721 - val_loss: 2.1322 - val_acc: 0.7056\n",
      "Epoch 469/600\n",
      "537/537 [==============================] - 0s 99us/step - loss: 0.1637 - acc: 0.9441 - val_loss: 2.0967 - val_acc: 0.7273\n",
      "Epoch 470/600\n",
      "537/537 [==============================] - 0s 248us/step - loss: 0.1340 - acc: 0.9553 - val_loss: 2.1374 - val_acc: 0.7143\n",
      "Epoch 471/600\n",
      "537/537 [==============================] - 0s 149us/step - loss: 0.1090 - acc: 0.9646 - val_loss: 2.0835 - val_acc: 0.7229\n",
      "Epoch 472/600\n",
      "537/537 [==============================] - 0s 145us/step - loss: 0.0844 - acc: 0.9702 - val_loss: 2.1111 - val_acc: 0.7100\n",
      "Epoch 473/600\n",
      "537/537 [==============================] - 0s 102us/step - loss: 0.0977 - acc: 0.9646 - val_loss: 2.2790 - val_acc: 0.7316\n",
      "Epoch 474/600\n",
      "537/537 [==============================] - 0s 243us/step - loss: 0.1131 - acc: 0.9572 - val_loss: 2.1566 - val_acc: 0.7229\n",
      "Epoch 475/600\n",
      "537/537 [==============================] - 0s 173us/step - loss: 0.0829 - acc: 0.9777 - val_loss: 2.1382 - val_acc: 0.7100\n",
      "Epoch 476/600\n",
      "537/537 [==============================] - 0s 199us/step - loss: 0.0823 - acc: 0.9721 - val_loss: 2.0797 - val_acc: 0.7316\n",
      "Epoch 477/600\n",
      "537/537 [==============================] - 0s 159us/step - loss: 0.1337 - acc: 0.9516 - val_loss: 2.0908 - val_acc: 0.7229\n",
      "Epoch 478/600\n",
      "537/537 [==============================] - 0s 173us/step - loss: 0.1063 - acc: 0.9609 - val_loss: 2.2249 - val_acc: 0.7273\n",
      "Epoch 479/600\n",
      "537/537 [==============================] - 0s 186us/step - loss: 0.2270 - acc: 0.9236 - val_loss: 2.0842 - val_acc: 0.7273\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 480/600\n",
      "537/537 [==============================] - 0s 95us/step - loss: 0.1851 - acc: 0.9423 - val_loss: 2.2024 - val_acc: 0.7359\n",
      "Epoch 481/600\n",
      "537/537 [==============================] - 0s 284us/step - loss: 0.1213 - acc: 0.9572 - val_loss: 2.2285 - val_acc: 0.7143\n",
      "Epoch 482/600\n",
      "537/537 [==============================] - 0s 169us/step - loss: 0.1201 - acc: 0.9665 - val_loss: 2.0968 - val_acc: 0.7186\n",
      "Epoch 483/600\n",
      "537/537 [==============================] - 0s 167us/step - loss: 0.0983 - acc: 0.9628 - val_loss: 2.1128 - val_acc: 0.7273\n",
      "Epoch 484/600\n",
      "537/537 [==============================] - 0s 108us/step - loss: 0.0865 - acc: 0.9665 - val_loss: 2.1604 - val_acc: 0.7186\n",
      "Epoch 485/600\n",
      "537/537 [==============================] - 0s 145us/step - loss: 0.0768 - acc: 0.9721 - val_loss: 2.0983 - val_acc: 0.7143\n",
      "Epoch 486/600\n",
      "537/537 [==============================] - 0s 195us/step - loss: 0.1837 - acc: 0.9423 - val_loss: 2.0630 - val_acc: 0.7359\n",
      "Epoch 487/600\n",
      "537/537 [==============================] - 0s 299us/step - loss: 0.0958 - acc: 0.9665 - val_loss: 2.1047 - val_acc: 0.7229\n",
      "Epoch 488/600\n",
      "537/537 [==============================] - 0s 84us/step - loss: 0.1365 - acc: 0.9609 - val_loss: 2.0893 - val_acc: 0.7316\n",
      "Epoch 489/600\n",
      "537/537 [==============================] - 0s 110us/step - loss: 0.1095 - acc: 0.9609 - val_loss: 2.1459 - val_acc: 0.7186\n",
      "Epoch 490/600\n",
      "537/537 [==============================] - 0s 162us/step - loss: 0.0814 - acc: 0.9721 - val_loss: 2.1133 - val_acc: 0.7359\n",
      "Epoch 491/600\n",
      "537/537 [==============================] - 0s 254us/step - loss: 0.1165 - acc: 0.9590 - val_loss: 2.0940 - val_acc: 0.7316\n",
      "Epoch 492/600\n",
      "537/537 [==============================] - 0s 280us/step - loss: 0.0901 - acc: 0.9665 - val_loss: 2.1150 - val_acc: 0.7229\n",
      "Epoch 493/600\n",
      "537/537 [==============================] - 0s 123us/step - loss: 0.0893 - acc: 0.9665 - val_loss: 2.0926 - val_acc: 0.7273\n",
      "Epoch 494/600\n",
      "537/537 [==============================] - 0s 200us/step - loss: 0.0895 - acc: 0.9646 - val_loss: 2.0671 - val_acc: 0.7273\n",
      "Epoch 495/600\n",
      "537/537 [==============================] - 0s 158us/step - loss: 0.1036 - acc: 0.9646 - val_loss: 2.1068 - val_acc: 0.7186\n",
      "Epoch 496/600\n",
      "537/537 [==============================] - 0s 264us/step - loss: 0.1042 - acc: 0.9665 - val_loss: 2.0747 - val_acc: 0.7056\n",
      "Epoch 497/600\n",
      "537/537 [==============================] - 0s 199us/step - loss: 0.0971 - acc: 0.9646 - val_loss: 2.1398 - val_acc: 0.7100\n",
      "Epoch 498/600\n",
      "537/537 [==============================] - 0s 325us/step - loss: 0.0882 - acc: 0.9553 - val_loss: 2.1279 - val_acc: 0.7143\n",
      "Epoch 499/600\n",
      "537/537 [==============================] - 0s 206us/step - loss: 0.0706 - acc: 0.9777 - val_loss: 2.1689 - val_acc: 0.7143\n",
      "Epoch 500/600\n",
      "537/537 [==============================] - 0s 316us/step - loss: 0.1373 - acc: 0.9534 - val_loss: 2.5441 - val_acc: 0.6753\n",
      "Epoch 501/600\n",
      "537/537 [==============================] - 0s 414us/step - loss: 0.1671 - acc: 0.9348 - val_loss: 2.1274 - val_acc: 0.7229\n",
      "Epoch 502/600\n",
      "537/537 [==============================] - 0s 414us/step - loss: 0.0834 - acc: 0.9758 - val_loss: 2.1423 - val_acc: 0.7186\n",
      "Epoch 503/600\n",
      "537/537 [==============================] - 0s 282us/step - loss: 0.0913 - acc: 0.9646 - val_loss: 2.1418 - val_acc: 0.7186\n",
      "Epoch 504/600\n",
      "537/537 [==============================] - 0s 234us/step - loss: 0.0735 - acc: 0.9758 - val_loss: 2.1481 - val_acc: 0.7013\n",
      "Epoch 505/600\n",
      "537/537 [==============================] - 0s 440us/step - loss: 0.1139 - acc: 0.9572 - val_loss: 2.1338 - val_acc: 0.7056\n",
      "Epoch 506/600\n",
      "537/537 [==============================] - 0s 435us/step - loss: 0.0863 - acc: 0.9702 - val_loss: 2.1538 - val_acc: 0.7186\n",
      "Epoch 507/600\n",
      "537/537 [==============================] - 0s 199us/step - loss: 0.0897 - acc: 0.9609 - val_loss: 2.2474 - val_acc: 0.7100\n",
      "Epoch 508/600\n",
      "537/537 [==============================] - 0s 331us/step - loss: 0.0932 - acc: 0.9665 - val_loss: 2.1469 - val_acc: 0.6970\n",
      "Epoch 509/600\n",
      "537/537 [==============================] - 0s 295us/step - loss: 0.0783 - acc: 0.9721 - val_loss: 2.1657 - val_acc: 0.7100\n",
      "Epoch 510/600\n",
      "537/537 [==============================] - 0s 275us/step - loss: 0.1366 - acc: 0.9572 - val_loss: 2.1095 - val_acc: 0.7100\n",
      "Epoch 511/600\n",
      "537/537 [==============================] - 0s 399us/step - loss: 0.1169 - acc: 0.9628 - val_loss: 2.1099 - val_acc: 0.7316\n",
      "Epoch 512/600\n",
      "537/537 [==============================] - 0s 448us/step - loss: 0.1095 - acc: 0.9572 - val_loss: 2.0734 - val_acc: 0.7403\n",
      "Epoch 513/600\n",
      "537/537 [==============================] - 0s 295us/step - loss: 0.1672 - acc: 0.9479 - val_loss: 2.0832 - val_acc: 0.7229\n",
      "Epoch 514/600\n",
      "537/537 [==============================] - 0s 266us/step - loss: 0.0992 - acc: 0.9609 - val_loss: 2.1494 - val_acc: 0.7229\n",
      "Epoch 515/600\n",
      "537/537 [==============================] - 0s 345us/step - loss: 0.0965 - acc: 0.9665 - val_loss: 2.1396 - val_acc: 0.7056\n",
      "Epoch 516/600\n",
      "537/537 [==============================] - 0s 299us/step - loss: 0.2660 - acc: 0.9330 - val_loss: 2.2960 - val_acc: 0.7186\n",
      "Epoch 517/600\n",
      "537/537 [==============================] - 0s 189us/step - loss: 0.1898 - acc: 0.9367 - val_loss: 2.1531 - val_acc: 0.7229\n",
      "Epoch 518/600\n",
      "537/537 [==============================] - ETA: 0s - loss: 0.1327 - acc: 0.943 - 0s 340us/step - loss: 0.1437 - acc: 0.9423 - val_loss: 2.1954 - val_acc: 0.7316\n",
      "Epoch 519/600\n",
      "537/537 [==============================] - 0s 379us/step - loss: 0.0956 - acc: 0.9628 - val_loss: 2.2910 - val_acc: 0.6970\n",
      "Epoch 520/600\n",
      "537/537 [==============================] - 0s 286us/step - loss: 0.1066 - acc: 0.9646 - val_loss: 2.2090 - val_acc: 0.7056\n",
      "Epoch 521/600\n",
      "537/537 [==============================] - 0s 397us/step - loss: 0.1083 - acc: 0.9628 - val_loss: 2.1479 - val_acc: 0.7143\n",
      "Epoch 522/600\n",
      "537/537 [==============================] - 0s 241us/step - loss: 0.0818 - acc: 0.9739 - val_loss: 2.1040 - val_acc: 0.7143\n",
      "Epoch 523/600\n",
      "537/537 [==============================] - 0s 349us/step - loss: 0.0951 - acc: 0.9646 - val_loss: 2.0958 - val_acc: 0.7229\n",
      "Epoch 524/600\n",
      "537/537 [==============================] - 0s 357us/step - loss: 0.0872 - acc: 0.9683 - val_loss: 2.1647 - val_acc: 0.7186\n",
      "Epoch 525/600\n",
      "537/537 [==============================] - 0s 245us/step - loss: 0.0974 - acc: 0.9702 - val_loss: 2.2513 - val_acc: 0.7013\n",
      "Epoch 526/600\n",
      "537/537 [==============================] - 0s 489us/step - loss: 0.0849 - acc: 0.9758 - val_loss: 2.1369 - val_acc: 0.7186\n",
      "Epoch 527/600\n",
      "537/537 [==============================] - 0s 412us/step - loss: 0.0940 - acc: 0.9665 - val_loss: 2.1430 - val_acc: 0.7186\n",
      "Epoch 528/600\n",
      "537/537 [==============================] - ETA: 0s - loss: 0.0500 - acc: 0.982 - 0s 321us/step - loss: 0.0943 - acc: 0.9758 - val_loss: 2.1894 - val_acc: 0.7186\n",
      "Epoch 529/600\n",
      "537/537 [==============================] - 0s 405us/step - loss: 0.1130 - acc: 0.9572 - val_loss: 2.1173 - val_acc: 0.7273\n",
      "Epoch 530/600\n",
      "537/537 [==============================] - 0s 249us/step - loss: 0.0752 - acc: 0.9758 - val_loss: 2.1845 - val_acc: 0.7143\n",
      "Epoch 531/600\n",
      "537/537 [==============================] - 0s 392us/step - loss: 0.1008 - acc: 0.9739 - val_loss: 2.1153 - val_acc: 0.7186\n",
      "Epoch 532/600\n",
      "537/537 [==============================] - 0s 338us/step - loss: 0.0795 - acc: 0.9702 - val_loss: 2.1223 - val_acc: 0.7100\n",
      "Epoch 533/600\n",
      "537/537 [==============================] - 0s 520us/step - loss: 0.0817 - acc: 0.9739 - val_loss: 2.1087 - val_acc: 0.7316\n",
      "Epoch 534/600\n",
      "537/537 [==============================] - 0s 272us/step - loss: 0.1226 - acc: 0.9534 - val_loss: 2.1645 - val_acc: 0.7100\n",
      "Epoch 535/600\n",
      "537/537 [==============================] - 0s 316us/step - loss: 0.0997 - acc: 0.9646 - val_loss: 2.1209 - val_acc: 0.7186\n",
      "Epoch 536/600\n",
      "537/537 [==============================] - 0s 186us/step - loss: 0.0794 - acc: 0.9683 - val_loss: 2.1653 - val_acc: 0.7056\n",
      "Epoch 537/600\n",
      "537/537 [==============================] - 0s 87us/step - loss: 0.1265 - acc: 0.9590 - val_loss: 2.2465 - val_acc: 0.7143\n",
      "Epoch 538/600\n",
      "537/537 [==============================] - 0s 147us/step - loss: 0.1240 - acc: 0.9665 - val_loss: 2.1204 - val_acc: 0.7056\n",
      "Epoch 539/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "537/537 [==============================] - 0s 188us/step - loss: 0.0842 - acc: 0.9628 - val_loss: 2.1927 - val_acc: 0.7100\n",
      "Epoch 540/600\n",
      "537/537 [==============================] - 0s 191us/step - loss: 0.0962 - acc: 0.9665 - val_loss: 2.1382 - val_acc: 0.7143\n",
      "Epoch 541/600\n",
      "537/537 [==============================] - 0s 128us/step - loss: 0.1989 - acc: 0.9367 - val_loss: 2.2932 - val_acc: 0.7143\n",
      "Epoch 542/600\n",
      "537/537 [==============================] - 0s 108us/step - loss: 0.0843 - acc: 0.9702 - val_loss: 2.1504 - val_acc: 0.7229\n",
      "Epoch 543/600\n",
      "537/537 [==============================] - 0s 117us/step - loss: 0.1179 - acc: 0.9553 - val_loss: 2.1303 - val_acc: 0.7229\n",
      "Epoch 544/600\n",
      "537/537 [==============================] - 0s 141us/step - loss: 0.0874 - acc: 0.9646 - val_loss: 2.1346 - val_acc: 0.7229\n",
      "Epoch 545/600\n",
      "537/537 [==============================] - 0s 373us/step - loss: 0.0981 - acc: 0.9590 - val_loss: 2.1783 - val_acc: 0.7273\n",
      "Epoch 546/600\n",
      "537/537 [==============================] - 0s 214us/step - loss: 0.1399 - acc: 0.9553 - val_loss: 2.1807 - val_acc: 0.7273\n",
      "Epoch 547/600\n",
      "537/537 [==============================] - 0s 83us/step - loss: 0.0988 - acc: 0.9628 - val_loss: 2.1091 - val_acc: 0.7186\n",
      "Epoch 548/600\n",
      "537/537 [==============================] - 0s 145us/step - loss: 0.0823 - acc: 0.9758 - val_loss: 2.1503 - val_acc: 0.7100\n",
      "Epoch 549/600\n",
      "537/537 [==============================] - 0s 145us/step - loss: 0.0785 - acc: 0.9795 - val_loss: 2.1272 - val_acc: 0.7229\n",
      "Epoch 550/600\n",
      "537/537 [==============================] - 0s 241us/step - loss: 0.0866 - acc: 0.9646 - val_loss: 2.1847 - val_acc: 0.7100\n",
      "Epoch 551/600\n",
      "537/537 [==============================] - 0s 652us/step - loss: 0.0975 - acc: 0.9683 - val_loss: 2.1645 - val_acc: 0.7100\n",
      "Epoch 552/600\n",
      "537/537 [==============================] - 0s 332us/step - loss: 0.0723 - acc: 0.9721 - val_loss: 2.1289 - val_acc: 0.7273\n",
      "Epoch 553/600\n",
      "537/537 [==============================] - 0s 318us/step - loss: 0.0784 - acc: 0.9721 - val_loss: 2.1597 - val_acc: 0.7056\n",
      "Epoch 554/600\n",
      "537/537 [==============================] - 0s 132us/step - loss: 0.0904 - acc: 0.9665 - val_loss: 2.1466 - val_acc: 0.7186\n",
      "Epoch 555/600\n",
      "537/537 [==============================] - 0s 228us/step - loss: 0.0851 - acc: 0.9628 - val_loss: 2.1485 - val_acc: 0.7143\n",
      "Epoch 556/600\n",
      "537/537 [==============================] - 0s 187us/step - loss: 0.0762 - acc: 0.9683 - val_loss: 2.1835 - val_acc: 0.7013\n",
      "Epoch 557/600\n",
      "537/537 [==============================] - 0s 189us/step - loss: 0.0905 - acc: 0.9702 - val_loss: 2.1267 - val_acc: 0.7186\n",
      "Epoch 558/600\n",
      "537/537 [==============================] - 0s 232us/step - loss: 0.0863 - acc: 0.9758 - val_loss: 2.1204 - val_acc: 0.7186\n",
      "Epoch 559/600\n",
      "537/537 [==============================] - 0s 232us/step - loss: 0.0889 - acc: 0.9683 - val_loss: 2.1318 - val_acc: 0.7143\n",
      "Epoch 560/600\n",
      "537/537 [==============================] - 0s 176us/step - loss: 0.0867 - acc: 0.9683 - val_loss: 2.2071 - val_acc: 0.7186\n",
      "Epoch 561/600\n",
      "537/537 [==============================] - 0s 171us/step - loss: 0.1002 - acc: 0.9646 - val_loss: 2.1646 - val_acc: 0.7013\n",
      "Epoch 562/600\n",
      "537/537 [==============================] - 0s 212us/step - loss: 0.0835 - acc: 0.9665 - val_loss: 2.1933 - val_acc: 0.7186\n",
      "Epoch 563/600\n",
      "537/537 [==============================] - 0s 327us/step - loss: 0.1032 - acc: 0.9572 - val_loss: 2.1792 - val_acc: 0.7186\n",
      "Epoch 564/600\n",
      "537/537 [==============================] - 0s 234us/step - loss: 0.1072 - acc: 0.9497 - val_loss: 2.1431 - val_acc: 0.7273\n",
      "Epoch 565/600\n",
      "537/537 [==============================] - 0s 97us/step - loss: 0.0911 - acc: 0.9646 - val_loss: 2.1421 - val_acc: 0.7056\n",
      "Epoch 566/600\n",
      "537/537 [==============================] - 0s 126us/step - loss: 0.0842 - acc: 0.9665 - val_loss: 2.2611 - val_acc: 0.7100\n",
      "Epoch 567/600\n",
      "537/537 [==============================] - 0s 95us/step - loss: 0.1814 - acc: 0.9367 - val_loss: 2.1952 - val_acc: 0.7186\n",
      "Epoch 568/600\n",
      "537/537 [==============================] - 0s 169us/step - loss: 0.1010 - acc: 0.9628 - val_loss: 2.1224 - val_acc: 0.7186\n",
      "Epoch 569/600\n",
      "537/537 [==============================] - 0s 197us/step - loss: 0.0864 - acc: 0.9665 - val_loss: 2.3662 - val_acc: 0.6926\n",
      "Epoch 570/600\n",
      "537/537 [==============================] - 0s 232us/step - loss: 0.0886 - acc: 0.9721 - val_loss: 2.1584 - val_acc: 0.7143\n",
      "Epoch 571/600\n",
      "537/537 [==============================] - 0s 212us/step - loss: 0.0975 - acc: 0.9758 - val_loss: 2.1135 - val_acc: 0.7143\n",
      "Epoch 572/600\n",
      "537/537 [==============================] - 0s 175us/step - loss: 0.0931 - acc: 0.9646 - val_loss: 2.1076 - val_acc: 0.7186\n",
      "Epoch 573/600\n",
      "537/537 [==============================] - 0s 139us/step - loss: 0.1420 - acc: 0.9460 - val_loss: 2.2127 - val_acc: 0.7100\n",
      "Epoch 574/600\n",
      "537/537 [==============================] - 0s 321us/step - loss: 0.0797 - acc: 0.9739 - val_loss: 2.1991 - val_acc: 0.7056\n",
      "Epoch 575/600\n",
      "537/537 [==============================] - 0s 80us/step - loss: 0.0877 - acc: 0.9739 - val_loss: 2.1529 - val_acc: 0.7229\n",
      "Epoch 576/600\n",
      "537/537 [==============================] - 0s 128us/step - loss: 0.0712 - acc: 0.9739 - val_loss: 2.1605 - val_acc: 0.7056\n",
      "Epoch 577/600\n",
      "537/537 [==============================] - 0s 317us/step - loss: 0.0706 - acc: 0.9739 - val_loss: 2.1426 - val_acc: 0.7186\n",
      "Epoch 578/600\n",
      "537/537 [==============================] - 0s 147us/step - loss: 0.0666 - acc: 0.9739 - val_loss: 2.1472 - val_acc: 0.7186\n",
      "Epoch 579/600\n",
      "537/537 [==============================] - 0s 84us/step - loss: 0.1948 - acc: 0.9385 - val_loss: 2.0925 - val_acc: 0.7229\n",
      "Epoch 580/600\n",
      "537/537 [==============================] - 0s 191us/step - loss: 0.1083 - acc: 0.9665 - val_loss: 2.1042 - val_acc: 0.7273\n",
      "Epoch 581/600\n",
      "537/537 [==============================] - 0s 72us/step - loss: 0.0894 - acc: 0.9646 - val_loss: 2.2415 - val_acc: 0.7186\n",
      "Epoch 582/600\n",
      "537/537 [==============================] - 0s 82us/step - loss: 0.0770 - acc: 0.9683 - val_loss: 2.2279 - val_acc: 0.7013\n",
      "Epoch 583/600\n",
      "537/537 [==============================] - 0s 175us/step - loss: 0.1680 - acc: 0.9404 - val_loss: 2.2676 - val_acc: 0.7186\n",
      "Epoch 584/600\n",
      "537/537 [==============================] - 0s 126us/step - loss: 0.1838 - acc: 0.9479 - val_loss: 2.3221 - val_acc: 0.7013\n",
      "Epoch 585/600\n",
      "537/537 [==============================] - 0s 215us/step - loss: 0.1750 - acc: 0.9423 - val_loss: 2.1370 - val_acc: 0.7229\n",
      "Epoch 586/600\n",
      "537/537 [==============================] - 0s 89us/step - loss: 0.0766 - acc: 0.9702 - val_loss: 2.1033 - val_acc: 0.7186\n",
      "Epoch 587/600\n",
      "537/537 [==============================] - 0s 247us/step - loss: 0.1077 - acc: 0.9646 - val_loss: 2.1451 - val_acc: 0.7100\n",
      "Epoch 588/600\n",
      "537/537 [==============================] - 0s 225us/step - loss: 0.0895 - acc: 0.9646 - val_loss: 2.1858 - val_acc: 0.7229\n",
      "Epoch 589/600\n",
      "537/537 [==============================] - 0s 305us/step - loss: 0.0877 - acc: 0.9702 - val_loss: 2.1577 - val_acc: 0.7056\n",
      "Epoch 590/600\n",
      "537/537 [==============================] - 0s 303us/step - loss: 0.1556 - acc: 0.9441 - val_loss: 2.1345 - val_acc: 0.7273\n",
      "Epoch 591/600\n",
      "537/537 [==============================] - 0s 295us/step - loss: 0.0838 - acc: 0.9702 - val_loss: 2.1403 - val_acc: 0.7143\n",
      "Epoch 592/600\n",
      "537/537 [==============================] - 0s 188us/step - loss: 0.0910 - acc: 0.9702 - val_loss: 2.1120 - val_acc: 0.7273\n",
      "Epoch 593/600\n",
      "537/537 [==============================] - ETA: 0s - loss: 0.0986 - acc: 0.966 - 0s 229us/step - loss: 0.1019 - acc: 0.9646 - val_loss: 2.1459 - val_acc: 0.7143\n",
      "Epoch 594/600\n",
      "537/537 [==============================] - 0s 241us/step - loss: 0.0831 - acc: 0.9721 - val_loss: 2.1490 - val_acc: 0.7186\n",
      "Epoch 595/600\n",
      "537/537 [==============================] - 0s 126us/step - loss: 0.0906 - acc: 0.9665 - val_loss: 2.1185 - val_acc: 0.7316\n",
      "Epoch 596/600\n",
      "537/537 [==============================] - 0s 123us/step - loss: 0.0741 - acc: 0.9739 - val_loss: 2.1765 - val_acc: 0.7056\n",
      "Epoch 597/600\n",
      "537/537 [==============================] - 0s 228us/step - loss: 0.0745 - acc: 0.9739 - val_loss: 2.1360 - val_acc: 0.7100\n",
      "Epoch 598/600\n",
      "537/537 [==============================] - 0s 100us/step - loss: 0.0730 - acc: 0.9758 - val_loss: 2.1301 - val_acc: 0.7273\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 599/600\n",
      "537/537 [==============================] - 0s 150us/step - loss: 0.0876 - acc: 0.9683 - val_loss: 2.1920 - val_acc: 0.7186\n",
      "Epoch 600/600\n",
      "537/537 [==============================] - 0s 160us/step - loss: 0.0943 - acc: 0.9665 - val_loss: 2.1339 - val_acc: 0.7186\n"
     ]
    }
   ],
   "source": [
    "run_hist_600 = model.fit(X_train_norm, y_train, validation_data=(X_test_norm, y_test), epochs=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predicting the outcome after the data is fit into the model.\n",
    "y_pred_class_nn_600 = model.predict_classes(X_test_norm)\n",
    "y_pred_prob_nn_600 = model.predict(X_test_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is 0.719\n",
      "ROC-AUC is 0.750\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Print model performance and plot the roc curve\n",
    "print('Accuracy is {:.3f}'.format(accuracy_score(y_test,y_pred_class_nn_600)))\n",
    "print('ROC-AUC is {:.3f}'.format(roc_auc_score(y_test,y_pred_prob_nn_600)))\n",
    "\n",
    "plot_roc(y_test, y_pred_prob_nn_600, 'NN')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accuracy obtained is 71.9% and AUC-ROC curve is 0.750\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run_hist_600.history.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c6ebf5c6d8>"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the curve to check training and validation accuracy\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(run_hist_600.history[\"acc\"],'r', marker='.', label=\"Training Accuracy\")\n",
    "ax.plot(run_hist_600.history[\"val_acc\"],'b', marker='.', label=\"Validation Accuracy\")\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c6ebfd2208>"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(run_hist_600.history[\"loss\"],'r', marker='.', label=\"Training Loss\")\n",
    "ax.plot(run_hist_600.history[\"val_loss\"],'b', marker='.', label=\"Validation Loss\")\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that 200 epochs in the above model gave the best accuracy, when we started increasing the number of epochs to 400 and 600 then accuracy was either constant or slightly dropped which means that model is not learning any furthur with increase in number of epochs. \n",
    "\n",
    "Inorder to improve the model, we need to modify the learning rate to see if there is any improvement. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "metadata": {},
   "outputs": [],
   "source": [
    "sgd = SGD(lr=1e-3)\n",
    "model.compile(loss=\"binary_crossentropy\",optimizer=adam, metrics=[\"accuracy\"],sample_weight_mode=None)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 537 samples, validate on 231 samples\n",
      "Epoch 1/200\n",
      "537/537 [==============================] - 2s 4ms/step - loss: 0.1128 - acc: 0.9683 - val_loss: 1.4446 - val_acc: 0.7273\n",
      "Epoch 2/200\n",
      "537/537 [==============================] - 0s 126us/step - loss: 0.1121 - acc: 0.9683 - val_loss: 1.4423 - val_acc: 0.7229\n",
      "Epoch 3/200\n",
      "537/537 [==============================] - 0s 357us/step - loss: 0.1120 - acc: 0.9702 - val_loss: 1.4509 - val_acc: 0.7229\n",
      "Epoch 4/200\n",
      "537/537 [==============================] - 0s 152us/step - loss: 0.1118 - acc: 0.9683 - val_loss: 1.4422 - val_acc: 0.7229\n",
      "Epoch 5/200\n",
      " 32/537 [>.............................] - ETA: 0s - loss: 0.1069 - acc: 0.9375"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ashmita\\Anaconda3\\Anaconda_new\\envs\\tensorflow_cpu\\lib\\site-packages\\keras\\callbacks.py:122: UserWarning: Method on_batch_end() is slow compared to the batch update (0.113869). Check your callbacks.\n",
      "  % delta_t_median)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "537/537 [==============================] - 0s 397us/step - loss: 0.1116 - acc: 0.9683 - val_loss: 1.4482 - val_acc: 0.7229\n",
      "Epoch 6/200\n",
      "537/537 [==============================] - 0s 407us/step - loss: 0.1117 - acc: 0.9665 - val_loss: 1.4442 - val_acc: 0.7229\n",
      "Epoch 7/200\n",
      "537/537 [==============================] - 0s 212us/step - loss: 0.1107 - acc: 0.9665 - val_loss: 1.4463 - val_acc: 0.7229\n",
      "Epoch 8/200\n",
      "537/537 [==============================] - 0s 186us/step - loss: 0.1109 - acc: 0.9665 - val_loss: 1.4504 - val_acc: 0.7229\n",
      "Epoch 9/200\n",
      "537/537 [==============================] - 0s 93us/step - loss: 0.1108 - acc: 0.9665 - val_loss: 1.4499 - val_acc: 0.7229\n",
      "Epoch 10/200\n",
      "537/537 [==============================] - 0s 221us/step - loss: 0.1108 - acc: 0.9665 - val_loss: 1.4486 - val_acc: 0.7229\n",
      "Epoch 11/200\n",
      "537/537 [==============================] - 0s 110us/step - loss: 0.1108 - acc: 0.9665 - val_loss: 1.4478 - val_acc: 0.7229\n",
      "Epoch 12/200\n",
      "537/537 [==============================] - 0s 310us/step - loss: 0.1106 - acc: 0.9665 - val_loss: 1.4487 - val_acc: 0.7229\n",
      "Epoch 13/200\n",
      "537/537 [==============================] - 0s 160us/step - loss: 0.1109 - acc: 0.9665 - val_loss: 1.4478 - val_acc: 0.7229\n",
      "Epoch 14/200\n",
      "537/537 [==============================] - 0s 255us/step - loss: 0.1109 - acc: 0.9665 - val_loss: 1.4510 - val_acc: 0.7229\n",
      "Epoch 15/200\n",
      "537/537 [==============================] - 0s 318us/step - loss: 0.1106 - acc: 0.9665 - val_loss: 1.4491 - val_acc: 0.7229\n",
      "Epoch 16/200\n",
      "537/537 [==============================] - 0s 147us/step - loss: 0.1105 - acc: 0.9665 - val_loss: 1.4482 - val_acc: 0.7229\n",
      "Epoch 17/200\n",
      "537/537 [==============================] - 0s 318us/step - loss: 0.1106 - acc: 0.9665 - val_loss: 1.4495 - val_acc: 0.7229\n",
      "Epoch 18/200\n",
      "537/537 [==============================] - 0s 115us/step - loss: 0.1105 - acc: 0.9665 - val_loss: 1.4484 - val_acc: 0.7229\n",
      "Epoch 19/200\n",
      "537/537 [==============================] - 0s 93us/step - loss: 0.1106 - acc: 0.9665 - val_loss: 1.4484 - val_acc: 0.7229\n",
      "Epoch 20/200\n",
      "537/537 [==============================] - 0s 277us/step - loss: 0.1105 - acc: 0.9665 - val_loss: 1.4482 - val_acc: 0.7229\n",
      "Epoch 21/200\n",
      "537/537 [==============================] - 0s 145us/step - loss: 0.1105 - acc: 0.9665 - val_loss: 1.4496 - val_acc: 0.7229\n",
      "Epoch 22/200\n",
      "537/537 [==============================] - 0s 241us/step - loss: 0.1104 - acc: 0.9665 - val_loss: 1.4479 - val_acc: 0.7229\n",
      "Epoch 23/200\n",
      "537/537 [==============================] - 0s 163us/step - loss: 0.1104 - acc: 0.9665 - val_loss: 1.4494 - val_acc: 0.7229\n",
      "Epoch 24/200\n",
      "537/537 [==============================] - 0s 121us/step - loss: 0.1104 - acc: 0.9665 - val_loss: 1.4481 - val_acc: 0.7229\n",
      "Epoch 25/200\n",
      "537/537 [==============================] - 0s 186us/step - loss: 0.1104 - acc: 0.9665 - val_loss: 1.4483 - val_acc: 0.7229\n",
      "Epoch 26/200\n",
      "537/537 [==============================] - 0s 325us/step - loss: 0.1103 - acc: 0.9665 - val_loss: 1.4502 - val_acc: 0.7229\n",
      "Epoch 27/200\n",
      "537/537 [==============================] - 0s 199us/step - loss: 0.1104 - acc: 0.9665 - val_loss: 1.4507 - val_acc: 0.7229\n",
      "Epoch 28/200\n",
      "537/537 [==============================] - ETA: 0s - loss: 0.0727 - acc: 0.968 - 0s 143us/step - loss: 0.1105 - acc: 0.9665 - val_loss: 1.4503 - val_acc: 0.7229\n",
      "Epoch 29/200\n",
      "537/537 [==============================] - 0s 223us/step - loss: 0.1104 - acc: 0.9665 - val_loss: 1.4491 - val_acc: 0.7229\n",
      "Epoch 30/200\n",
      "537/537 [==============================] - 0s 86us/step - loss: 0.1103 - acc: 0.9665 - val_loss: 1.4484 - val_acc: 0.7229\n",
      "Epoch 31/200\n",
      "537/537 [==============================] - 0s 116us/step - loss: 0.1103 - acc: 0.9665 - val_loss: 1.4508 - val_acc: 0.7229\n",
      "Epoch 32/200\n",
      "537/537 [==============================] - 0s 147us/step - loss: 0.1103 - acc: 0.9665 - val_loss: 1.4518 - val_acc: 0.7229\n",
      "Epoch 33/200\n",
      "537/537 [==============================] - 0s 301us/step - loss: 0.1104 - acc: 0.9665 - val_loss: 1.4482 - val_acc: 0.7229\n",
      "Epoch 34/200\n",
      "537/537 [==============================] - 0s 160us/step - loss: 0.1101 - acc: 0.9665 - val_loss: 1.4490 - val_acc: 0.7229\n",
      "Epoch 35/200\n",
      "537/537 [==============================] - 0s 122us/step - loss: 0.1104 - acc: 0.9665 - val_loss: 1.4512 - val_acc: 0.7229\n",
      "Epoch 36/200\n",
      "537/537 [==============================] - 0s 100us/step - loss: 0.1102 - acc: 0.9665 - val_loss: 1.4501 - val_acc: 0.7229\n",
      "Epoch 37/200\n",
      "537/537 [==============================] - 0s 136us/step - loss: 0.1102 - acc: 0.9665 - val_loss: 1.4482 - val_acc: 0.7229\n",
      "Epoch 38/200\n",
      "537/537 [==============================] - 0s 206us/step - loss: 0.1102 - acc: 0.9665 - val_loss: 1.4500 - val_acc: 0.7229\n",
      "Epoch 39/200\n",
      "537/537 [==============================] - 0s 87us/step - loss: 0.1101 - acc: 0.9665 - val_loss: 1.4483 - val_acc: 0.7229\n",
      "Epoch 40/200\n",
      "537/537 [==============================] - 0s 295us/step - loss: 0.1103 - acc: 0.9665 - val_loss: 1.4495 - val_acc: 0.7229\n",
      "Epoch 41/200\n",
      "537/537 [==============================] - 0s 147us/step - loss: 0.1101 - acc: 0.9665 - val_loss: 1.4500 - val_acc: 0.7229\n",
      "Epoch 42/200\n",
      "537/537 [==============================] - 0s 128us/step - loss: 0.1101 - acc: 0.9665 - val_loss: 1.4507 - val_acc: 0.7229\n",
      "Epoch 43/200\n",
      "537/537 [==============================] - 0s 574us/step - loss: 0.1103 - acc: 0.9665 - val_loss: 1.4504 - val_acc: 0.7229\n",
      "Epoch 44/200\n",
      "537/537 [==============================] - 0s 390us/step - loss: 0.1102 - acc: 0.9665 - val_loss: 1.4505 - val_acc: 0.7229\n",
      "Epoch 45/200\n",
      "537/537 [==============================] - 0s 282us/step - loss: 0.1101 - acc: 0.9665 - val_loss: 1.4499 - val_acc: 0.7229\n",
      "Epoch 46/200\n",
      "537/537 [==============================] - 0s 204us/step - loss: 0.1103 - acc: 0.9665 - val_loss: 1.4512 - val_acc: 0.7229\n",
      "Epoch 47/200\n",
      "537/537 [==============================] - 0s 555us/step - loss: 0.1101 - acc: 0.9665 - val_loss: 1.4504 - val_acc: 0.7229\n",
      "Epoch 48/200\n",
      "537/537 [==============================] - 0s 349us/step - loss: 0.1101 - acc: 0.9665 - val_loss: 1.4510 - val_acc: 0.7229\n",
      "Epoch 49/200\n",
      "537/537 [==============================] - 0s 165us/step - loss: 0.1101 - acc: 0.9665 - val_loss: 1.4507 - val_acc: 0.7229\n",
      "Epoch 50/200\n",
      "537/537 [==============================] - 0s 557us/step - loss: 0.1104 - acc: 0.9665 - val_loss: 1.4516 - val_acc: 0.7229\n",
      "Epoch 51/200\n",
      "537/537 [==============================] - 0s 425us/step - loss: 0.1100 - acc: 0.9665 - val_loss: 1.4503 - val_acc: 0.7229\n",
      "Epoch 52/200\n",
      "537/537 [==============================] - 0s 594us/step - loss: 0.1101 - acc: 0.9665 - val_loss: 1.4500 - val_acc: 0.7229\n",
      "Epoch 53/200\n",
      "537/537 [==============================] - 0s 275us/step - loss: 0.1100 - acc: 0.9665 - val_loss: 1.4516 - val_acc: 0.7229\n",
      "Epoch 54/200\n",
      "537/537 [==============================] - 0s 178us/step - loss: 0.1101 - acc: 0.9665 - val_loss: 1.4494 - val_acc: 0.7229\n",
      "Epoch 55/200\n",
      "537/537 [==============================] - 0s 344us/step - loss: 0.1100 - acc: 0.9665 - val_loss: 1.4505 - val_acc: 0.7229\n",
      "Epoch 56/200\n",
      "537/537 [==============================] - 0s 141us/step - loss: 0.1102 - acc: 0.9665 - val_loss: 1.4521 - val_acc: 0.7229\n",
      "Epoch 57/200\n",
      "537/537 [==============================] - 0s 145us/step - loss: 0.1101 - acc: 0.9665 - val_loss: 1.4510 - val_acc: 0.7229\n",
      "Epoch 58/200\n",
      "537/537 [==============================] - 0s 308us/step - loss: 0.1100 - acc: 0.9665 - val_loss: 1.4509 - val_acc: 0.7229\n",
      "Epoch 59/200\n",
      "537/537 [==============================] - 0s 672us/step - loss: 0.1101 - acc: 0.9665 - val_loss: 1.4503 - val_acc: 0.7229\n",
      "Epoch 60/200\n",
      "537/537 [==============================] - 0s 217us/step - loss: 0.1100 - acc: 0.9665 - val_loss: 1.4512 - val_acc: 0.7229\n",
      "Epoch 61/200\n",
      "537/537 [==============================] - 0s 188us/step - loss: 0.1100 - acc: 0.9665 - val_loss: 1.4529 - val_acc: 0.7229\n",
      "Epoch 62/200\n",
      "537/537 [==============================] - 0s 648us/step - loss: 0.1099 - acc: 0.9665 - val_loss: 1.4518 - val_acc: 0.7229\n",
      "Epoch 63/200\n",
      "537/537 [==============================] - 0s 236us/step - loss: 0.1099 - acc: 0.9665 - val_loss: 1.4514 - val_acc: 0.7229\n",
      "Epoch 64/200\n",
      "537/537 [==============================] - 0s 271us/step - loss: 0.1100 - acc: 0.9665 - val_loss: 1.4512 - val_acc: 0.7229\n",
      "Epoch 65/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "537/537 [==============================] - 0s 273us/step - loss: 0.1100 - acc: 0.9665 - val_loss: 1.4502 - val_acc: 0.7229\n",
      "Epoch 66/200\n",
      "537/537 [==============================] - 0s 144us/step - loss: 0.1099 - acc: 0.9665 - val_loss: 1.4506 - val_acc: 0.7229\n",
      "Epoch 67/200\n",
      "537/537 [==============================] - 0s 223us/step - loss: 0.1099 - acc: 0.9665 - val_loss: 1.4525 - val_acc: 0.7229\n",
      "Epoch 68/200\n",
      "537/537 [==============================] - 0s 133us/step - loss: 0.1099 - acc: 0.9665 - val_loss: 1.4512 - val_acc: 0.7229\n",
      "Epoch 69/200\n",
      "537/537 [==============================] - 0s 130us/step - loss: 0.1099 - acc: 0.9665 - val_loss: 1.4510 - val_acc: 0.7229\n",
      "Epoch 70/200\n",
      "537/537 [==============================] - 0s 108us/step - loss: 0.1099 - acc: 0.9665 - val_loss: 1.4522 - val_acc: 0.7229\n",
      "Epoch 71/200\n",
      "537/537 [==============================] - 0s 191us/step - loss: 0.1100 - acc: 0.9665 - val_loss: 1.4520 - val_acc: 0.7229\n",
      "Epoch 72/200\n",
      "537/537 [==============================] - 0s 240us/step - loss: 0.1099 - acc: 0.9665 - val_loss: 1.4529 - val_acc: 0.7229\n",
      "Epoch 73/200\n",
      "537/537 [==============================] - 0s 135us/step - loss: 0.1099 - acc: 0.9665 - val_loss: 1.4529 - val_acc: 0.7229\n",
      "Epoch 74/200\n",
      "537/537 [==============================] - 0s 165us/step - loss: 0.1098 - acc: 0.9665 - val_loss: 1.4520 - val_acc: 0.7229\n",
      "Epoch 75/200\n",
      "537/537 [==============================] - 0s 213us/step - loss: 0.1099 - acc: 0.9665 - val_loss: 1.4527 - val_acc: 0.7229\n",
      "Epoch 76/200\n",
      "537/537 [==============================] - 0s 160us/step - loss: 0.1100 - acc: 0.9665 - val_loss: 1.4517 - val_acc: 0.7229\n",
      "Epoch 77/200\n",
      "537/537 [==============================] - 0s 659us/step - loss: 0.1099 - acc: 0.9665 - val_loss: 1.4526 - val_acc: 0.7229\n",
      "Epoch 78/200\n",
      "537/537 [==============================] - 0s 274us/step - loss: 0.1099 - acc: 0.9665 - val_loss: 1.4520 - val_acc: 0.7229\n",
      "Epoch 79/200\n",
      "537/537 [==============================] - 0s 160us/step - loss: 0.1098 - acc: 0.9665 - val_loss: 1.4522 - val_acc: 0.7229\n",
      "Epoch 80/200\n",
      "537/537 [==============================] - 0s 154us/step - loss: 0.1099 - acc: 0.9665 - val_loss: 1.4520 - val_acc: 0.7229\n",
      "Epoch 81/200\n",
      "537/537 [==============================] - 0s 87us/step - loss: 0.1098 - acc: 0.9665 - val_loss: 1.4516 - val_acc: 0.7229\n",
      "Epoch 82/200\n",
      "537/537 [==============================] - 0s 93us/step - loss: 0.1098 - acc: 0.9665 - val_loss: 1.4523 - val_acc: 0.7229\n",
      "Epoch 83/200\n",
      "537/537 [==============================] - 0s 95us/step - loss: 0.1097 - acc: 0.9665 - val_loss: 1.4535 - val_acc: 0.7229\n",
      "Epoch 84/200\n",
      "537/537 [==============================] - 0s 85us/step - loss: 0.1098 - acc: 0.9665 - val_loss: 1.4528 - val_acc: 0.7229\n",
      "Epoch 85/200\n",
      "537/537 [==============================] - 0s 241us/step - loss: 0.1098 - acc: 0.9665 - val_loss: 1.4521 - val_acc: 0.7229\n",
      "Epoch 86/200\n",
      "537/537 [==============================] - 0s 225us/step - loss: 0.1097 - acc: 0.9665 - val_loss: 1.4526 - val_acc: 0.7229\n",
      "Epoch 87/200\n",
      "537/537 [==============================] - 0s 152us/step - loss: 0.1099 - acc: 0.9665 - val_loss: 1.4519 - val_acc: 0.7229\n",
      "Epoch 88/200\n",
      "537/537 [==============================] - 0s 253us/step - loss: 0.1098 - acc: 0.9665 - val_loss: 1.4530 - val_acc: 0.7229\n",
      "Epoch 89/200\n",
      "537/537 [==============================] - 0s 260us/step - loss: 0.1098 - acc: 0.9665 - val_loss: 1.4535 - val_acc: 0.7229\n",
      "Epoch 90/200\n",
      "537/537 [==============================] - 0s 184us/step - loss: 0.1097 - acc: 0.9665 - val_loss: 1.4530 - val_acc: 0.7229\n",
      "Epoch 91/200\n",
      "537/537 [==============================] - 0s 206us/step - loss: 0.1098 - acc: 0.9665 - val_loss: 1.4534 - val_acc: 0.7229\n",
      "Epoch 92/200\n",
      "537/537 [==============================] - 0s 154us/step - loss: 0.1098 - acc: 0.9665 - val_loss: 1.4522 - val_acc: 0.7229\n",
      "Epoch 93/200\n",
      "537/537 [==============================] - 0s 210us/step - loss: 0.1098 - acc: 0.9665 - val_loss: 1.4534 - val_acc: 0.7229\n",
      "Epoch 94/200\n",
      "537/537 [==============================] - 0s 176us/step - loss: 0.1098 - acc: 0.9665 - val_loss: 1.4539 - val_acc: 0.7229\n",
      "Epoch 95/200\n",
      "537/537 [==============================] - 0s 217us/step - loss: 0.1097 - acc: 0.9665 - val_loss: 1.4527 - val_acc: 0.7229\n",
      "Epoch 96/200\n",
      "537/537 [==============================] - 0s 374us/step - loss: 0.1097 - acc: 0.9665 - val_loss: 1.4530 - val_acc: 0.7229\n",
      "Epoch 97/200\n",
      "537/537 [==============================] - 0s 368us/step - loss: 0.1097 - acc: 0.9665 - val_loss: 1.4524 - val_acc: 0.7229\n",
      "Epoch 98/200\n",
      "537/537 [==============================] - 0s 189us/step - loss: 0.1097 - acc: 0.9665 - val_loss: 1.4559 - val_acc: 0.7229\n",
      "Epoch 99/200\n",
      "537/537 [==============================] - 0s 136us/step - loss: 0.1097 - acc: 0.9665 - val_loss: 1.4542 - val_acc: 0.7229\n",
      "Epoch 100/200\n",
      "537/537 [==============================] - 0s 199us/step - loss: 0.1097 - acc: 0.9665 - val_loss: 1.4534 - val_acc: 0.7229\n",
      "Epoch 101/200\n",
      "537/537 [==============================] - 0s 411us/step - loss: 0.1097 - acc: 0.9665 - val_loss: 1.4540 - val_acc: 0.7229\n",
      "Epoch 102/200\n",
      "537/537 [==============================] - 0s 286us/step - loss: 0.1096 - acc: 0.9665 - val_loss: 1.4535 - val_acc: 0.7229\n",
      "Epoch 103/200\n",
      "537/537 [==============================] - 0s 232us/step - loss: 0.1099 - acc: 0.9665 - val_loss: 1.4549 - val_acc: 0.7229\n",
      "Epoch 104/200\n",
      "537/537 [==============================] - 0s 212us/step - loss: 0.1096 - acc: 0.9665 - val_loss: 1.4538 - val_acc: 0.7229\n",
      "Epoch 105/200\n",
      "537/537 [==============================] - 0s 124us/step - loss: 0.1096 - acc: 0.9665 - val_loss: 1.4536 - val_acc: 0.7229\n",
      "Epoch 106/200\n",
      "537/537 [==============================] - 0s 260us/step - loss: 0.1097 - acc: 0.9665 - val_loss: 1.4565 - val_acc: 0.7229\n",
      "Epoch 107/200\n",
      "537/537 [==============================] - 0s 210us/step - loss: 0.1097 - acc: 0.9665 - val_loss: 1.4525 - val_acc: 0.7229\n",
      "Epoch 108/200\n",
      "537/537 [==============================] - ETA: 0s - loss: 0.1852 - acc: 0.906 - 0s 119us/step - loss: 0.1096 - acc: 0.9665 - val_loss: 1.4528 - val_acc: 0.7229\n",
      "Epoch 109/200\n",
      "537/537 [==============================] - 0s 117us/step - loss: 0.1096 - acc: 0.9665 - val_loss: 1.4541 - val_acc: 0.7229\n",
      "Epoch 110/200\n",
      "537/537 [==============================] - 0s 128us/step - loss: 0.1097 - acc: 0.9665 - val_loss: 1.4534 - val_acc: 0.7229\n",
      "Epoch 111/200\n",
      "537/537 [==============================] - 0s 288us/step - loss: 0.1098 - acc: 0.9665 - val_loss: 1.4544 - val_acc: 0.7229\n",
      "Epoch 112/200\n",
      "537/537 [==============================] - 0s 106us/step - loss: 0.1096 - acc: 0.9665 - val_loss: 1.4533 - val_acc: 0.7229\n",
      "Epoch 113/200\n",
      "537/537 [==============================] - 0s 243us/step - loss: 0.1096 - acc: 0.9665 - val_loss: 1.4537 - val_acc: 0.7229\n",
      "Epoch 114/200\n",
      "537/537 [==============================] - 0s 128us/step - loss: 0.1096 - acc: 0.9665 - val_loss: 1.4537 - val_acc: 0.7229\n",
      "Epoch 115/200\n",
      "537/537 [==============================] - 0s 152us/step - loss: 0.1096 - acc: 0.9665 - val_loss: 1.4536 - val_acc: 0.7229\n",
      "Epoch 116/200\n",
      "537/537 [==============================] - 0s 154us/step - loss: 0.1096 - acc: 0.9665 - val_loss: 1.4552 - val_acc: 0.7229\n",
      "Epoch 117/200\n",
      "537/537 [==============================] - 0s 167us/step - loss: 0.1096 - acc: 0.9665 - val_loss: 1.4545 - val_acc: 0.7229\n",
      "Epoch 118/200\n",
      "537/537 [==============================] - 0s 154us/step - loss: 0.1096 - acc: 0.9665 - val_loss: 1.4548 - val_acc: 0.7229\n",
      "Epoch 119/200\n",
      "537/537 [==============================] - 0s 152us/step - loss: 0.1096 - acc: 0.9665 - val_loss: 1.4572 - val_acc: 0.7229\n",
      "Epoch 120/200\n",
      "537/537 [==============================] - 0s 126us/step - loss: 0.1096 - acc: 0.9665 - val_loss: 1.4541 - val_acc: 0.7229\n",
      "Epoch 121/200\n",
      "537/537 [==============================] - 0s 143us/step - loss: 0.1096 - acc: 0.9683 - val_loss: 1.4564 - val_acc: 0.7229\n",
      "Epoch 122/200\n",
      "537/537 [==============================] - 0s 117us/step - loss: 0.1095 - acc: 0.9683 - val_loss: 1.4551 - val_acc: 0.7229\n",
      "Epoch 123/200\n",
      "537/537 [==============================] - 0s 308us/step - loss: 0.1095 - acc: 0.9665 - val_loss: 1.4548 - val_acc: 0.7229\n",
      "Epoch 124/200\n",
      "537/537 [==============================] - 0s 217us/step - loss: 0.1095 - acc: 0.9665 - val_loss: 1.4554 - val_acc: 0.7229\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 125/200\n",
      "537/537 [==============================] - 0s 230us/step - loss: 0.1096 - acc: 0.9665 - val_loss: 1.4544 - val_acc: 0.7229\n",
      "Epoch 126/200\n",
      "537/537 [==============================] - 0s 188us/step - loss: 0.1096 - acc: 0.9665 - val_loss: 1.4559 - val_acc: 0.7229\n",
      "Epoch 127/200\n",
      "537/537 [==============================] - 0s 145us/step - loss: 0.1095 - acc: 0.9665 - val_loss: 1.4553 - val_acc: 0.7229\n",
      "Epoch 128/200\n",
      "537/537 [==============================] - 0s 204us/step - loss: 0.1095 - acc: 0.9665 - val_loss: 1.4551 - val_acc: 0.7229\n",
      "Epoch 129/200\n",
      "537/537 [==============================] - 0s 171us/step - loss: 0.1096 - acc: 0.9665 - val_loss: 1.4549 - val_acc: 0.7229\n",
      "Epoch 130/200\n",
      "537/537 [==============================] - 0s 203us/step - loss: 0.1094 - acc: 0.9665 - val_loss: 1.4550 - val_acc: 0.7229\n",
      "Epoch 131/200\n",
      "537/537 [==============================] - 0s 204us/step - loss: 0.1095 - acc: 0.9665 - val_loss: 1.4557 - val_acc: 0.7229\n",
      "Epoch 132/200\n",
      "537/537 [==============================] - 0s 173us/step - loss: 0.1095 - acc: 0.9665 - val_loss: 1.4553 - val_acc: 0.7229\n",
      "Epoch 133/200\n",
      "537/537 [==============================] - 0s 212us/step - loss: 0.1095 - acc: 0.9665 - val_loss: 1.4549 - val_acc: 0.7229\n",
      "Epoch 134/200\n",
      "537/537 [==============================] - 0s 202us/step - loss: 0.1094 - acc: 0.9665 - val_loss: 1.4555 - val_acc: 0.7229\n",
      "Epoch 135/200\n",
      "537/537 [==============================] - 0s 150us/step - loss: 0.1095 - acc: 0.9665 - val_loss: 1.4553 - val_acc: 0.7229\n",
      "Epoch 136/200\n",
      "537/537 [==============================] - 0s 271us/step - loss: 0.1095 - acc: 0.9665 - val_loss: 1.4551 - val_acc: 0.7229\n",
      "Epoch 137/200\n",
      "537/537 [==============================] - 0s 644us/step - loss: 0.1095 - acc: 0.9665 - val_loss: 1.4560 - val_acc: 0.7229\n",
      "Epoch 138/200\n",
      "537/537 [==============================] - 0s 230us/step - loss: 0.1095 - acc: 0.9665 - val_loss: 1.4547 - val_acc: 0.7229\n",
      "Epoch 139/200\n",
      "537/537 [==============================] - 0s 371us/step - loss: 0.1095 - acc: 0.9665 - val_loss: 1.4589 - val_acc: 0.7229\n",
      "Epoch 140/200\n",
      "537/537 [==============================] - 0s 678us/step - loss: 0.1096 - acc: 0.9665 - val_loss: 1.4543 - val_acc: 0.7229\n",
      "Epoch 141/200\n",
      "537/537 [==============================] - 0s 299us/step - loss: 0.1095 - acc: 0.9665 - val_loss: 1.4572 - val_acc: 0.7229\n",
      "Epoch 142/200\n",
      "537/537 [==============================] - 0s 164us/step - loss: 0.1095 - acc: 0.9683 - val_loss: 1.4568 - val_acc: 0.7229\n",
      "Epoch 143/200\n",
      "537/537 [==============================] - 0s 217us/step - loss: 0.1094 - acc: 0.9665 - val_loss: 1.4550 - val_acc: 0.7229\n",
      "Epoch 144/200\n",
      "537/537 [==============================] - 0s 657us/step - loss: 0.1094 - acc: 0.9665 - val_loss: 1.4557 - val_acc: 0.7229\n",
      "Epoch 145/200\n",
      "537/537 [==============================] - 0s 196us/step - loss: 0.1094 - acc: 0.9665 - val_loss: 1.4561 - val_acc: 0.7229\n",
      "Epoch 146/200\n",
      "537/537 [==============================] - 0s 132us/step - loss: 0.1094 - acc: 0.9665 - val_loss: 1.4554 - val_acc: 0.7229\n",
      "Epoch 147/200\n",
      "537/537 [==============================] - 0s 234us/step - loss: 0.1094 - acc: 0.9665 - val_loss: 1.4542 - val_acc: 0.7229\n",
      "Epoch 148/200\n",
      "537/537 [==============================] - 0s 119us/step - loss: 0.1093 - acc: 0.9665 - val_loss: 1.4558 - val_acc: 0.7229\n",
      "Epoch 149/200\n",
      "537/537 [==============================] - 0s 151us/step - loss: 0.1094 - acc: 0.9665 - val_loss: 1.4556 - val_acc: 0.7229\n",
      "Epoch 150/200\n",
      "537/537 [==============================] - 0s 249us/step - loss: 0.1094 - acc: 0.9665 - val_loss: 1.4544 - val_acc: 0.7229\n",
      "Epoch 151/200\n",
      "537/537 [==============================] - 0s 247us/step - loss: 0.1094 - acc: 0.9665 - val_loss: 1.4548 - val_acc: 0.7229\n",
      "Epoch 152/200\n",
      "537/537 [==============================] - 0s 206us/step - loss: 0.1094 - acc: 0.9665 - val_loss: 1.4554 - val_acc: 0.7229\n",
      "Epoch 153/200\n",
      "537/537 [==============================] - 0s 115us/step - loss: 0.1094 - acc: 0.9665 - val_loss: 1.4562 - val_acc: 0.7229\n",
      "Epoch 154/200\n",
      "537/537 [==============================] - 0s 262us/step - loss: 0.1093 - acc: 0.9665 - val_loss: 1.4561 - val_acc: 0.7229\n",
      "Epoch 155/200\n",
      "537/537 [==============================] - 0s 184us/step - loss: 0.1094 - acc: 0.9665 - val_loss: 1.4580 - val_acc: 0.7229\n",
      "Epoch 156/200\n",
      "537/537 [==============================] - 0s 150us/step - loss: 0.1094 - acc: 0.9665 - val_loss: 1.4567 - val_acc: 0.7229\n",
      "Epoch 157/200\n",
      "537/537 [==============================] - 0s 199us/step - loss: 0.1094 - acc: 0.9665 - val_loss: 1.4564 - val_acc: 0.7229\n",
      "Epoch 158/200\n",
      "537/537 [==============================] - 0s 206us/step - loss: 0.1093 - acc: 0.9665 - val_loss: 1.4548 - val_acc: 0.7229\n",
      "Epoch 159/200\n",
      "537/537 [==============================] - 0s 121us/step - loss: 0.1093 - acc: 0.9665 - val_loss: 1.4540 - val_acc: 0.7229\n",
      "Epoch 160/200\n",
      "537/537 [==============================] - 0s 132us/step - loss: 0.1093 - acc: 0.9665 - val_loss: 1.4551 - val_acc: 0.7229\n",
      "Epoch 161/200\n",
      "537/537 [==============================] - ETA: 0s - loss: 0.1248 - acc: 0.957 - 0s 204us/step - loss: 0.1093 - acc: 0.9665 - val_loss: 1.4565 - val_acc: 0.7229\n",
      "Epoch 162/200\n",
      "537/537 [==============================] - 0s 171us/step - loss: 0.1093 - acc: 0.9665 - val_loss: 1.4565 - val_acc: 0.7229\n",
      "Epoch 163/200\n",
      "537/537 [==============================] - 0s 152us/step - loss: 0.1092 - acc: 0.9665 - val_loss: 1.4561 - val_acc: 0.7229\n",
      "Epoch 164/200\n",
      "537/537 [==============================] - 0s 316us/step - loss: 0.1094 - acc: 0.9665 - val_loss: 1.4560 - val_acc: 0.7229\n",
      "Epoch 165/200\n",
      "537/537 [==============================] - 0s 150us/step - loss: 0.1092 - acc: 0.9665 - val_loss: 1.4567 - val_acc: 0.7229\n",
      "Epoch 166/200\n",
      "537/537 [==============================] - 0s 156us/step - loss: 0.1093 - acc: 0.9665 - val_loss: 1.4566 - val_acc: 0.7229\n",
      "Epoch 167/200\n",
      "537/537 [==============================] - 0s 189us/step - loss: 0.1093 - acc: 0.9665 - val_loss: 1.4565 - val_acc: 0.7229\n",
      "Epoch 168/200\n",
      "537/537 [==============================] - 0s 149us/step - loss: 0.1093 - acc: 0.9665 - val_loss: 1.4565 - val_acc: 0.7229\n",
      "Epoch 169/200\n",
      "537/537 [==============================] - 0s 130us/step - loss: 0.1094 - acc: 0.9665 - val_loss: 1.4570 - val_acc: 0.7229\n",
      "Epoch 170/200\n",
      "537/537 [==============================] - 0s 115us/step - loss: 0.1092 - acc: 0.9665 - val_loss: 1.4564 - val_acc: 0.7229\n",
      "Epoch 171/200\n",
      "537/537 [==============================] - 0s 117us/step - loss: 0.1093 - acc: 0.9665 - val_loss: 1.4563 - val_acc: 0.7229\n",
      "Epoch 172/200\n",
      "537/537 [==============================] - 0s 113us/step - loss: 0.1092 - acc: 0.9665 - val_loss: 1.4561 - val_acc: 0.7229\n",
      "Epoch 173/200\n",
      "537/537 [==============================] - 0s 102us/step - loss: 0.1092 - acc: 0.9665 - val_loss: 1.4558 - val_acc: 0.7229\n",
      "Epoch 174/200\n",
      "537/537 [==============================] - 0s 126us/step - loss: 0.1092 - acc: 0.9665 - val_loss: 1.4595 - val_acc: 0.7229\n",
      "Epoch 175/200\n",
      "537/537 [==============================] - 0s 173us/step - loss: 0.1092 - acc: 0.9665 - val_loss: 1.4571 - val_acc: 0.7229\n",
      "Epoch 176/200\n",
      "537/537 [==============================] - 0s 158us/step - loss: 0.1093 - acc: 0.9665 - val_loss: 1.4572 - val_acc: 0.7229\n",
      "Epoch 177/200\n",
      "537/537 [==============================] - 0s 199us/step - loss: 0.1092 - acc: 0.9665 - val_loss: 1.4572 - val_acc: 0.7229\n",
      "Epoch 178/200\n",
      "537/537 [==============================] - 0s 133us/step - loss: 0.1092 - acc: 0.9665 - val_loss: 1.4579 - val_acc: 0.7229\n",
      "Epoch 179/200\n",
      "537/537 [==============================] - 0s 176us/step - loss: 0.1092 - acc: 0.9665 - val_loss: 1.4564 - val_acc: 0.7229\n",
      "Epoch 180/200\n",
      "537/537 [==============================] - 0s 253us/step - loss: 0.1092 - acc: 0.9665 - val_loss: 1.4564 - val_acc: 0.7229\n",
      "Epoch 181/200\n",
      "537/537 [==============================] - 0s 264us/step - loss: 0.1093 - acc: 0.9665 - val_loss: 1.4550 - val_acc: 0.7229\n",
      "Epoch 182/200\n",
      "537/537 [==============================] - 0s 110us/step - loss: 0.1092 - acc: 0.9665 - val_loss: 1.4562 - val_acc: 0.7229\n",
      "Epoch 183/200\n",
      "537/537 [==============================] - 0s 189us/step - loss: 0.1092 - acc: 0.9665 - val_loss: 1.4555 - val_acc: 0.7229\n",
      "Epoch 184/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "537/537 [==============================] - 0s 165us/step - loss: 0.1093 - acc: 0.9665 - val_loss: 1.4573 - val_acc: 0.7229\n",
      "Epoch 185/200\n",
      "537/537 [==============================] - 0s 176us/step - loss: 0.1092 - acc: 0.9665 - val_loss: 1.4574 - val_acc: 0.7229\n",
      "Epoch 186/200\n",
      "537/537 [==============================] - 0s 169us/step - loss: 0.1091 - acc: 0.9665 - val_loss: 1.4573 - val_acc: 0.7229\n",
      "Epoch 187/200\n",
      "537/537 [==============================] - 0s 111us/step - loss: 0.1091 - acc: 0.9665 - val_loss: 1.4573 - val_acc: 0.7229\n",
      "Epoch 188/200\n",
      "537/537 [==============================] - 0s 121us/step - loss: 0.1091 - acc: 0.9665 - val_loss: 1.4576 - val_acc: 0.7229\n",
      "Epoch 189/200\n",
      "537/537 [==============================] - 0s 117us/step - loss: 0.1091 - acc: 0.9665 - val_loss: 1.4568 - val_acc: 0.7229\n",
      "Epoch 190/200\n",
      "537/537 [==============================] - 0s 184us/step - loss: 0.1091 - acc: 0.9665 - val_loss: 1.4573 - val_acc: 0.7229\n",
      "Epoch 191/200\n",
      "537/537 [==============================] - 0s 202us/step - loss: 0.1091 - acc: 0.9665 - val_loss: 1.4576 - val_acc: 0.7229\n",
      "Epoch 192/200\n",
      "537/537 [==============================] - 0s 115us/step - loss: 0.1092 - acc: 0.9683 - val_loss: 1.4594 - val_acc: 0.7229\n",
      "Epoch 193/200\n",
      "537/537 [==============================] - 0s 184us/step - loss: 0.1091 - acc: 0.9665 - val_loss: 1.4570 - val_acc: 0.7229\n",
      "Epoch 194/200\n",
      "537/537 [==============================] - 0s 115us/step - loss: 0.1091 - acc: 0.9665 - val_loss: 1.4575 - val_acc: 0.7229\n",
      "Epoch 195/200\n",
      "537/537 [==============================] - 0s 134us/step - loss: 0.1091 - acc: 0.9665 - val_loss: 1.4568 - val_acc: 0.7229\n",
      "Epoch 196/200\n",
      "537/537 [==============================] - 0s 204us/step - loss: 0.1092 - acc: 0.9665 - val_loss: 1.4575 - val_acc: 0.7229\n",
      "Epoch 197/200\n",
      "537/537 [==============================] - 0s 136us/step - loss: 0.1091 - acc: 0.9665 - val_loss: 1.4597 - val_acc: 0.7229\n",
      "Epoch 198/200\n",
      "537/537 [==============================] - 0s 149us/step - loss: 0.1091 - acc: 0.9665 - val_loss: 1.4579 - val_acc: 0.7229\n",
      "Epoch 199/200\n",
      "537/537 [==============================] - 0s 180us/step - loss: 0.1090 - acc: 0.9665 - val_loss: 1.4570 - val_acc: 0.7229\n",
      "Epoch 200/200\n",
      "537/537 [==============================] - 0s 195us/step - loss: 0.1090 - acc: 0.9665 - val_loss: 1.4594 - val_acc: 0.7229\n"
     ]
    }
   ],
   "source": [
    "run_hist_200 = model.fit(X_train_norm, y_train, validation_data=(X_test_norm, y_test), verbose=1, epochs=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_class_nn_200 = model.predict_classes(X_test_norm)\n",
    "y_pred_prob_nn_200 = model.predict(X_test_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is 0.723\n",
      "ROC-AUC is 0.781\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Print model performance and plot the roc curve\n",
    "print('Accuracy is {:.3f}'.format(accuracy_score(y_test,y_pred_class_nn_200)))\n",
    "print('ROC-AUC is {:.3f}'.format(roc_auc_score(y_test,y_pred_prob_nn_200)))\n",
    "\n",
    "plot_roc(y_test, y_pred_prob_nn_2, 'NN')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accuracy obtained is 72.3% and ROC-AUC is 0.781\n",
    "\n",
    "Plotting the curve for training and validation accuracy. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c777118898>"
      ]
     },
     "execution_count": 437,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the curve to check training and validation accuracy\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(run_hist_600.history[\"acc\"],'r', marker='.', label=\"Training Accuracy\")\n",
    "ax.plot(run_hist_600.history[\"val_acc\"],'b', marker='.', label=\"Validation Accuracy\")\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the curve for training and validation loss of the model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c77d669f28>"
      ]
     },
     "execution_count": 438,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5uRzjAk9j8Otf/5orrriCnJwccnNzGTx4MCCrDw0aNIh+/fpFlN4dP348I0eO5JBDDmGhi4NCyvVefvnl+/u4+uqrGTRoUMLuFYC77757/8QnQElJSdQ+58+fz+9+9zvS0tLIyMjgkUce4fvvv2f06NHs27cPay0PPvhgwvdVWibOp9wUgh4eW37ppTKJ+eab8MYb0sa5akEE3drQVYqqqsQ1E/RsYq232EVFhfjTu3UTa/2TT6BXL2l/yCFev23aSH/l5Q0bex5CrDKM7gcR6M+BI4BMYCXQN6zNdODXwf2+wKaa+tXyuamH/v4UR+fOUjL2xhsb976LFlmbmWltWpq1bdrI8bXXWpuV5V03RsYG1h55pGy/+cbaV1/1zoO1/fp5/blzxx8v20DA2uOOs/aMM6zNy5P32amT3KN1a2lz6aUyDtf+v/+7ft4jccrnJuJyGQxssNZutNaWA3OA0eHPBSAYeUpHYEtdHjKKoiQ3jeVyCQ9bXrAgND68qEgyNV2IYkEB+NNKRoyQ7datkUEV2dnSfupU79zKlbKtqhL3SseOsHo1rF/vxd677YgR4uZpqAnQaCQi6IcCX/mOS4Ln/EwCxhpjSoB5wI3ROjLGjDfGLDXGLN2RaAkzRVGSivJycUtAqKAvWlS/FRhdUIR/8eWf/MS7np4uIrptW+j5yy7z9k8+WbZbt8Izz4ibJBg0hsvp+/ZbmVAFEXIX5JWRAS+/LO91/nwv8iW4eBiHHtr4K2QmIujRAq3DQyTGAE9aa7OBUcD/GWMi+rbWTrfW5ltr87t16xb1ZrYOiyMrTYf+3hSHs87BE/TiYjjlFLj9dknOmT697vcpKpJoEr81/s03cs0Yr77K11+HCvrJJ4v4pqd7Qv3uu/D66zLZ+fHHcs7ltBQWhoYaumiXPXs8X7y1cu/p02FL0D9x1lmybagJ0GgkIuglgG/ulmwiXSpXAc8BWGuLgdZAV2pJ69atKS0tVXFIMqy1lJaW0rp166YeitIMiCboCxZ44ldZCddfX3dLvbDQW/fTWePvvCOCa4y4R4YNE4H1C/rKlTKWykq46io59+qrXnanG+cnn8gYw6PpevSQ69nZIvDuobBrl7yvxqjZEotEolyWAH2MMb2BzcDFwCVhbb4EhgFPGmOORQS91j6V7OxsSkpKUHdM8tG6dWuyXRCu0qJxES7gCbrLuHRUV9c9S7KgQKzwNWvghhtEoBcs8JKBQKoeVld7PnSQ+zoRLi+Xh8Hq1XIcCMhPeblEuPijUwoKROBd3ZZZs+Avf5GSuP/8pwh5dbX0Z23j+c391Cjo1tpKY8wNwHwk4mWGtXaNMWYyMts6F/gt8Jgx5jeIO+ZyewBmdkZGBr17967tyxRFaUY4C71LF0/Q3Yo9aWme6NWH2LkY8n//G04/XYT4s8+k//Jy735+C72wUMTWJSR27ixWfH6+VIn88ktxnURLzy8qCv2mUVoqcevvvef1N2WKnG+IxKGaSCgO3UpM+bywc3f69tcCJ4W/TlGUlocT9EMO8QTXJej87W9wySVw4411F7udOz2f+UcfeRmh1sIVV0js+VFHwbp1oYLuXCguVv1XvxJBHz5c/N3FxfDUU55A+x88hYUSueK/Ft5fU5YLbrap/4qiJCd+QV+/XvY//VTS33/xCxg71osUqQtuObcePbxIFOfquOwyEXm3BIFf0CHUheJKKU2dKrVW4gl0rGsNkeB4IKigK0oSE2vFnabECXr37mLJlpeLoB99tPinDz5YIk/CSeS9+Ns4q3/0aHjkEdkvLIT//m95fU4OLF7sjSUaRUWRk5hOnGONobmIdzRU0BUlSXGr5lRUNK/FmP0WOogf/bPPYORIOe7ePVLQ58/3wvwyM6O/F1c50fnGhw+XmO+zzvIE3Yk5iKA7YkRJR3WhJDMq6IqSpPgrCTZEbe0DxUW5OEHfvFkE/Kc/lWMn6MXFUl9lxAhZqs7VGC8vlwiScGv9H/+QuHOQicn586FnTxg4UM61bev1ATBggGyzsrxkn3Cak/+7PlBBV5QkxcVhV1fX3bqsT9dNuIUeXPRqv2uje3dYvlwqG5aVSfbooEHe6wMBeOIJ7305a91VTXRYCz/+KAtMgOyPGOG1d4IeCHjx5NFozi6U2pLU5XMVpSVTUOAlucyff+CiFC2Fvi7s2SNujM6d5fi++2T7X//lLf22c2fot4t16zw/d1pa9LU+P/8c2rf3UuxB+rn+ei/ByN/+s89konT79oZZ7q05ooKuKEmMc0HUUM05LkVFXgJOfWQ37tkjceft28uxK0lbUSF9d+8u93LJPdZKYaxf/lLO+dd7cd88qqrkoXXeeZKmP2KEiLhL5nEJQf5vKuEJRI2dtdkUqMtFUZIUayVcDySRJdbEX034XTf1kfCzZw+0ayc/fpzYfvmlHLdq5VnhIL52F3rotscfL9eefFLe45FHyjeRRJJ5Um3CMxFU0BUlSdmzx5sEdEudHQgFBbIa/cqV4nYBmDxZMi+juXFq8rc7QXcWOoSGEzoB37sX/uM/4Pnn5fill2TysrJSrO2KCrHGnYUOcO+9Yp0nMpmZahOeiaCCrihJirPO4cAE3QnzkCHin3Z9+icr3347VAiLi6V9ZaWUmo0WXvjDD5GC/rvfee38MeHWet8OqqrgmmskcuXLL+HRR6VNRYU3oercNjXFijtSacIzEVTQFSVJ8Qt6aWntXusmQsvKxB3h/NYrVni1zKOFQvqXafNf91vt0Sz0E0/09v2CftZZsl6nc4uMG+f1N3OmjC8QkHsa03JcJweKCrqiJCl1sdBdLXFrRTQdnTuLgLqJxnDxzM/39jMy5LpLcKqqEsHNzoZjj/VW6+nSRbI63YOhQwdpV1Eha3HGSqWfP1+Sh376U6moeMklEtHSkizu2qJRLoqSpOza5e3v3Bm5HFs8CgtDo0zS0iQEMhCQ1epB6qHEE8+775br//iHWNhugrO0VKJciovlwbBzZ2jY4Icfem6UUaPkXLRFIE49FX72MxFzgAcfVDGvCRV0RUlSnIWeliYFpk49VVYESiTm+oQTRHRdkazqasne/PxzT+j92ZXvvy+Tmi5JCLx4cH90jbVi+W/YINmeLlrFHzboDx+sKZzQLRHXrZv0qcRHXS6KkqQ4QT/8cNi40fN9l5XVXAZg7VqpsXL++RJdAiLqJSVe7fJt22Trd6mkpcERR4gV7qoduvsaI338+CMsWSJRMy5qxe/7dku6JRJOmJUlW2flN5d6Nc0VFXRFSVJcydjevT3xBTlX08The+/JdswYWUvThRJWVno1zF0BrXnzvLBBl3zUo4cn6CtXil/8hx9Ca6n4o1bC/eOJhhN+951s/Va+CnpsVNAVpRlRm5oq334rK9N36ya1UUAs34oKiRyB6BULi4pktfr27UWYnbiuWwezZ3uTpO4h4cTesXmzWOruG8KKFVI3ZflyL+M0LS00aiWcRMMJzzhDSge0pOSgumCaakHm/Px8u9RVn1cUhUWL4JRTxBpNpBzuL38pE4wjR8K0aXLu5ptloQZjIvtw5Wf9qfX+WPIHHoDf/lbOp6XJtT17IDcXvvhC6pkvXeql7btM1a5dYeJECUEsKhI3SX0uwdYca743JcaYZdba/GjX1EJXlGbCSy9561Um4l749lsJM+za1TvnJjmdi2LWLPlx+EMUw+/jn9w85hjxs8+eLS4VY8QS92dylpfD+PHiWhk4sOGSeFpaclBdUEFXlCbGWaDbt3vnEnEvOEF3E4edO8PZZ8Of/+zFkT/6qJdlmZHhWdbguUXcffwPhtxcEfQnnpBja0N94t9/LyGSLm3frR6kNC0atqgoTUhxsYQb/uEP8Mwzcu6gg+K7W1y8+ZYtoRZ6v34St33VVXJ82mmeeINY1h07ivX96KMSR+6/j99Cd/XJN2+WratkOG6cxIy3ahU6psmTW0Z52uaOWuiK0oTMmeOF/Tl++EEs5Gj4l2GrrhbhdYJeXi7X/+M/4LHHvCgURyAgPvGrrhJXSTjhFjrAJ5/IhOeYMaE+7BEj5IHgygBUVWkESnMgIQvdGHOmMeZTY8wGY8zEKNcfNMasCP58ZozZFa0fRVFCcRawo3dv2ZaURG/vUvadr/3bb70+liyRWG13beNGmWS9+movlLGiQtLyo+Es9PR06NvXOz98eGQmZ0GBTMRmZIjrplUrjUBpDtQo6MaYADANGAn0BcYYY/r621hrf2OtzbXW5gIPAS81xGAVJZV4+21Jmz/2WC870wm5qxkezpAhocd9+8pr/BmZy5fDYYfJ9cJCsdb79ZMoGoi9GEbbtuJWads2NGPU1SQPZ/x4eOedSNeN0nQk4nIZDGyw1m4EMMbMAUYDa2O0HwP8sX6GpyjJhz/MDqKH3BUXS7hhebm3VJpbfQdiC7q/UiFI8s+gQaGZl1lZ4l8HuP9+uU9uLnz8sZyLJeiuxkp5ucR/t2snk5/pcVRCI1CaF4kI+qHAV77jEuCEaA2NMYcDvYG36z40RUkuiovh4YfhuedEnF3ZVxCXhN+KLSryfOeurSsPu3dvpKC/9RZ88IE3GTlgAKxeDXPnSlVC/4o9RUXeZKgLS8zNhb/9DQ4+WKofRsP/urIyGQdIka7sbBXuZCARQTdRzsXKRroYeMFaWxXtojFmPDAeoGfPngkNUFGaG9ESXfwLPziqq0OFddYs73Wu2qG1ItJ+Qb7gAhF0d5/OneG666Sts5YLC0XQXSp+aan4uR3hS6/9+KOcb9tW+o0mzoWFkkxUXu7VZXFj1wnP5CARQS8BDvMdZwNbYrS9GLg+VkfW2unAdJBM0QTHqChNjhPXrCypyV1dHSrEX34ZKuYQGvMdCMBf/+pNIC5YIH1lZ8vkol8sDz8cVq2SlYNcn66fykqJRhkzBh5/PHpKfLRaKa+/Lte++CJ2kSv/67KyYMIETblPNhIR9CVAH2NMb2AzItqXhDcyxvwU6AxoNKqSUvhT8v1ulLIyuPZa2XelZo3xllRzKwF17AiDB8M//ynn9+2ThJ0dO+CmmyKFtWdPqcUSntXpGDCg5gJX4b7tFSsiS9nWVGNlwABNuU82ahR0a22lMeYGYD4QAGZYa9cYYyYDS621c4NNxwBzbFMVh1GUBuLllz33g9uCCLcTd7e9/HKp4X3VVSLc+flS/8S5PEBE9f/+T/aPPjryfunpEovuJxAQd8n338vK91C7CcnalKx16IRn8qHFuRTFRzT/+O23S2YmiCXuJjNHj4ZXXpH9QECSa7Zvh/XrRdSdf9xZ2r16waZNsu+s5eXLvaxMd/9TT5V7hK8oBJ7lfyA+bS1ylRpocS5FqYHiYvFlz54tx/6oFH+ST48eItp794amugcCElLYrZv4tp1g+/3qP/uZhBn6qx0edVToOIqKvG8Bxnjre/onKQ80K1Mt7tRHa7koLR6XTv/00yKaLnLEhfH5Y8q/+EIs6OxsEfbjjxchLy/30uULC70Fkl04Ikg1xalTZTEIa+GQQ6QmuZ/CQrHAAwHp4+GH4a675GHj+tRJSiUWaqErLR6XTu/HJeiMHQtffQW/+IVkT371lQhxWZlY7n36iCh//bVXC8U/Yfnll1IIC8SNUloqtVaeeEIEOjyEMN5kp05SKjWhPnQlqTlQv3B4NudJJ3lRLFVVssL8Lbd44YLp6Z717mLBKytD96P5touLJUzQTUYuWCA/d9wRfREKRakJ9aErKYmbQKyqiszEjNXexVjfdJMnwi+9JMJ9xhmSJj9hgkS2hJeedYSvm+nfDxf0aBb3vHlyTdfJVOobFXQlaVmwwIs4qUkYFy6Ukq9OpJ0Ql5dLCVuQZdQGDYLf/EbS7P24qoJutR5jIvdj+bbDJyNHjZIaKxUV6g9X6hcVdKVZkEhBq3D69Ak9zsqK7YJ54onITE4QMd65U8Q6P18KUvXqBf/+t0yUujjxceNCxxVrPxFLu6BAKi2qP1ypb1TQlSbHv2iD3yddkxulTRtvv6oKbrxRBLqiIrI+ir9iYFqaWNxlZXKfefOkXsrq1XLdFcb64AMpDRvuQqlpPxE0hFBpCFTQlSbHH2VSURFZKTCW8Ll1LF3Mt/+1+/bBDTd4/vWcHDmfmSnHRx8tSSBAcCMAAB4DSURBVD3Wys8338jk5WWXhdZNUf+2kkyooCtNjr/yoMvEdPvOpRHNJbNokVjW+/ZJoo8/sxJEkK0VS3z1avjJT2DbNnlQjBwpiyC7ErEg5yGyUqGiJAuaWKQ0OG5R41iLCOfkeCI8ebK3f9ddYh0vWuQtpFxYKPt33CGuksMPF7fMz37mlavt0CG00iFILZWbbvJEPzdXXnfttaEJO+PGyfm77tJwQiX5UAtdaTCKi+Gpp2DGjPihhf7FjP2i7wpUTZvmRbP43SogVnxBgbhX3BJre/eGps+7/YMP9oT+0ktlLI88IiIePkGpQq4kIyroSoPgEmr8Lo19+2SRB/+iEP6Ve9LSZI1NEPfI8uWyZuWrr3p9pKd77QFOPFG2mzZFr5/izqWlwb/+5Z33++d1glJJFVTQlQYhWjq9tWKtuxBAtwp9IOAdv/22FMAaOlSWVnv9dU/AAwGxrGfM8Pp0C1/5y8OGx4mXl4uVPmuWWPTxYsYVJZlRQVcaBP9Ep0unBy9yxGVJunOdOokf/O23xQfepYvEhzvS0qSPN98Uv/lXX4lI33GHpO2HZ2SCV0tl+nRpW1UF11wjDwGN/1ZSERV0pV5xbpSCAnGP5OdLKOCECV4kSmEhfPJJ6OuOPtqLK//0UymE5Sc9XR4AJSVw3HEi6CDWeyzXSUGB58d3USvjxqmQK6mLCrpSbzi/eVmZuDbKy2VxiLPOkkqBV18Nu3aJoL71lrymTRsR+kDAiyuP5ge/4gp4/nmJF+/bV0IOEwktrGmpNkVJJVTQlTrjrPIvvxS/uYv9dtUEQYT0qqvgt7+VSc7HH4djj4X+/UWoP/wQli2TSJhoNVLy8uCxx6Sv55+XuuIuC7QmkdZJT6WloIKu1Am/VZ4e9tdkLZxzjheqeNJJcv6887wytP36eW3DfdzgWdZFRV6/rq74bbc17HtTlGRDBV05IKJZ5VVVMnnZvTts3izt/OGBbm1N/4LLXbuK2yWWj9u/rxmcihIfFXSl1vgXMs7MDE30qaqSJJ/JkyPFN7wkbSAgAh4tsScc9YUrSs2ooCsxiVWK1p8M5I8Rr6oSv/f48SL44a8tLBRrvKxMLPmHH65dZqb6whUlPgkJujHmTGAqEAAet9beG6XNL4BJgAVWWmsvqcdxKnUgXJhrWratuFhKz77wggi0WzrNte3f32vr4syvvlrWzuzeXaJVoomvWtmK0rDUKOjGmAAwDTgdKAGWGGPmWmvX+tr0AW4DTrLWfmuMObihBqzEJ1ys33vPc3m4GuETJoiV7K+t4l+e7cYbvaQfiCxjW1oq2wEDpIphVRXMnCnnvv5aJkljFbZSK1tRGo5ELPTBwAZr7UYAY8wcYDSw1tfmGmCatfZbAGvt9voeqJKYZT10qAiwW3z4ySe9ScjycnjxRW8Sc98+mDQJLrhA/N4VFZFVCkEs9KwsqUxordRYaddO6qi4RSH8r9V1MhWlaUhE0A8FvvIdlwAnhLU5GsAY8wHilplkrX2jXkaoAJ5Yu9V4olnARUVeJIkTVWdNg4QJXnCBvLaqSsT3n/+UdHuXyOPEPC1NXCkVFbIk23XXhS6IDJKBGStuXKNQFKXxSUTQTZRzYTYc6UAfoBDIBt4zxvS31u4K6ciY8cB4gJ6uqpKSEG++GSnW4YJ+6qnefiAg9VHeekt83p98Ar17i5ukSxcR52++8cIN/RgDw4eL+F97LaxbF31M8eLG1TpXlMYnEUEvAQ7zHWcDW6K0+dBaWwH82xjzKSLwS/yNrLXTgekA+fn54Q+FlKUmV0kir3/zTe/Yv5KPn+xsb//442X1+rIyEfPqatmedpq4Wn7zG4kycfXFDzpIHhSubvmkSTLmaC4YEAu+prhxRVEal0QEfQnQxxjTG9gMXAyER7D8HRgDPGmM6Yq4YDbW50CTlVdfhdGjRRhrWvQ4GtHqil9/ffQ+PvxQtr17w5o1nkXvt8BdSduxY6FPH3GlgLSdNi0ynd4l8wQCMGqURLEMGpR42r2iKI1HjYJura00xtwAzEf84zOstWuMMZOBpdbaucFrI4wxa4Eq4HfW2tLYvaYufmvcWgnncwsRH8hkod8vnpYGHTuGrvDj58MPZTL05pslkgXkQZKRIVvXT3q6rAa0a5f06ZZuC0+n1zBDRUkuEopDt9bOA+aFnbvTt2+BW4I/jUZdXRn1jX/iMj3dW4ne4aJF7rkn/pj978vVFQexlocOlbU077pL/Nxuzc0FC+CVV2Sln++/9/oKBOChh8R3ftddsmBEZSWccYaEMNaUTq9hhoqSPCRtpmhxsQhQeblkH9bHgr51fUBMmeJZwf61L50f+uijxcVhrRcT/tFH0sb5ol1avfNlv/WW1Dvp2lWqDb7/Prz0kvi477lHKhhOmxbq5/6v//Lu6SzvggI4+WQRdJDPrbRULXBFSSWSVtD96edlZaGujAMR5uJimTAsL4/t647X78KF8Nxz3rFbYce5PMrLYeVK73pZGfz6116M+MyZ0sdTT3nva98+KRO7bZvURnEuEJDX7d0rE5vhuEqG1dWhlvfQoaGFsNz7UCFXlNQgaQU9fIkzJ1puEnHfPi+5Jp5ghVcNhOi+7nDLecoUb2IQZK1LP5mZIrgu6/LRR0OtaGNCJyvLy0XUX3jBO2et1P4GsdBB3ttdd4VmcvpJS4scn79eilrkipK6JK2gFxRAt25ivZ55pidObnFit8iCE+Zo1vX06eICcRato7paBL642Gv73HOh3wiuv96zwN1rQAQVRMw7d4aLLpI2bhk0t2hxeOy3tbLog1uh3vXnHgJjx3oPpyuvjHxApKfDLbdI7HlNVQtVyBUlNUlaQf/+exFzgO3bvVjtQw7x3B3Wirg5f3tFhbg/rrwScnM9MQe5lpYGRxwBGzaI2D/1lOfiePVV797+JdL8opqWJhOV3bvLCvO7dsnxggWeZZyV5bla0tPh7LOl/smHH3p9WeutTu/O+b81jBsX+oC48kpdK1NRlCQWdJe92LOn+KbdBGnr1mK5b98uovnMM1JvxLkonPvDbwU7qqth0yZvf98+uO8+mUh0JV9diF84Ls580iRv4tEfqnjbbSK499zjvcZaGDxYti6GHEToH35YJkxnzoxMp1fXiaIo0UhaQV8bLA120UXwP//jnS8rE4t3zBiYPTv6pKE/3d25SFxESHV1qHDPnRvp/nC416anR1rJ998fPRywsDB6qGB4nfDx4+V8rMUf1HWiKEo4SSvoa9aIMI4eHSroTnQPOih62rr/XCAgtUgGDZJEHCeyZ5wBf/+7tPFb8enpIrjOYo428QjxLehY1+K1V+FWFCURkk7Q3eTm66/LpOOyZd41Z1kbAzk54n4pK/Os7latQsUaxGUzfrwk3jhBBZg/35tcBenzqqsSWy4N4gtxrMUfVLgVRakLxkZzCDcC+fn5dunSpbV6jYsVd+GFIFazm6D0+8XbtPEs6Kys0BDDYcM8azxWWGNxsUxs+n3Y9ZG8pCiKUheMMcustfnRriWVhe6va+Jw0SLWhmZHukxIf20SRyITis5iTtQiVxRFaWqSStALC6O7UfyWuN8XHmuRhdq4N9QVoihKspBUgu6fUPS7UfyC6/eFqxAritKSSCofuqIoSksnng89rbEHoyiKojQMKuiKoigpggq6oihKiqCCriiKkiKooCuKoqQIKuiKoigpggq6oihKiqCCriiKkiKooCuKoqQICQm6MeZMY8ynxpgNxpiJUa5fbozZYYxZEfy5uv6HqiiKosSjxlouxpgAMA04HSgBlhhj5lpr14Y1fdZae0MDjFFRFEVJgEQs9MHABmvtRmttOTAHGN2ww1IURVFqSyKCfijwle+4JHgunAuMMauMMS8YYw6L1pExZrwxZqkxZumOHTsOYLiKoihKLBIRdBPlXHiJxleBXtbaHOAt4KloHVlrp1tr8621+d26davdSBVFUZS4JCLoJYDf4s4GtvgbWGtLrbVuLaHHgOPqZ3iKoihKoiQi6EuAPsaY3saYTOBiYK6/gTHmEN/hucC6+huioiiKkgg1RrlYayuNMTcA84EAMMNau8YYMxlYaq2dC9xkjDkXqAS+AS5vwDEriqIoUdAVixRFUZIIXbFIURSlBaCCriiKkiKooCuKoqQIKuiKoigpggq6oihKiqCCriiKkiKooCuKoqQIKuiKoigpggq6oihKiqCCriiKkiKooCuKoqQIKuiKoigpggq6oihKiqCCriiKkiKooCuKoqQIKuiKoigpggq6oihKiqCCriiKkiKooCuKoqQIKuiKoigpggq6oihKiqCCriiKkiIkJOjGmDONMZ8aYzYYYybGaXehMcYaY/Lrb4iKoihKItQo6MaYADANGAn0BcYYY/pGadceuAn4V30PUlEURamZRCz0wcAGa+1Ga205MAcYHaXdXcB9wL56HJ+iKIqSIIkI+qHAV77jkuC5/RhjBgGHWWv/UY9jUxRFUWpBIoJuopyz+y8akwY8CPy2xo6MGW+MWWqMWbpjx47ER6koiqLUSCKCXgIc5jvOBrb4jtsD/YEiY8wm4ERgbrSJUWvtdGttvrU2v1u3bgc+akVRFCWCRAR9CdDHGNPbGJMJXAzMdRettbuttV2ttb2stb2AD4FzrbVLG2TEiqIoSlRqFHRrbSVwAzAfWAc8Z61dY4yZbIw5t6EHqCiKoiRGeiKNrLXzgHlh5+6M0baw7sOKQ3ExFBVBVhaUlkJhIRQUeOfdsaIoSgsjIUFvNhQXi2CXl8uxMRAIwIUXwvPPQ3W1HJ92GmRnw9VXe+Ie60GgKIqSIiSXoBcVQWWld2ytHM+Z452rrIQ335T9J5+EMWOgfXuYPl0EH7wHwbRpMH58Y41eURSlQUkuQS8shFatoKzME+d4VFfD009HnncPguuug3nzoFs3OP54+OgjuT5okGfFg7pyFEVJCoy1tuZWDUB+fr5duvQAAmGc62TXLnjwQRFma8XqTgvO8VZV1c8g09Lkx1rIzIQFCyJdOLGEXn36iqI0AMaYZdbaqPWykstCBxFHJ5DnnRfpFweYNQu+/hpefx0qKsRSd26Wiy6C557zHgTxqK72vgns2wd//jN07w5btsAbb8i1zEw5X1oKZ5whbZ96CmbM8K77HwSKoigNRPJZ6LUhXkTMrFnwxBMi+PVFICAPCb87yBj41a/gkUfq7z6KorRY4lnoqS3oNeGEHcRv7nzoHTqIO6eqSgS5ri6c9HQ4+2yx7seNS9xtoyiKEoYK+oHgt+4nTIiciHW+9VifnzHRrzlxt1YmZKuqpK9bboHvvpM248bJVsVeUZQwVNDril/c/ZEwEyZITHwgAKNGeT57J9APPSS+99p+xmlp8kCorobWrSN98OGWvVr6itJiSK1J0abAPxHrZ8CA+MJ63nkH5qv3fxPYuxduvhmOOw6OOUZi61es8CZ5b7kFpkyR/jMy4MorPbfOokXwz3/CiBEq9IrSAlALvTFwvvqvv4bXXvPEPRCQbXV17a34eKSni9A/8IBE87RuDW+/raKuKCmAulyaE/6JWL+v3Llz/OGWkFgCVSIcd5wkT/knf9VXryhJhwp6suH32d94o1e7BkKTncIt+1gTsbHwf0No3VpcNzt2SOZsaanc/8MP5fqvfiVtayP+6ttXlHpHBT2ZCQ+tDE+gmjlT3CqZmSL+/uzZ2hLvgeBi7K31xH/nThg6NHam7NChEh3Upo0mVylKPaGToslMrAlZd23cuOgTsU7onRAnMikb7yHgj8Xfu9ez2DMz4cwzoUsXub9z54CIOUikT1FR5PtQC15R6hW10FMVv1hCZAJVTb762rpvwklLC+3znHPg0EO9bxldusD118uDok0bsfidm8fv448WrqklkJUWjLpclOhES55yMfSdOoVO1M6b55Uurs1EbbiwO8IfGNEeIIGAPAi6d5fs3f/9X++bgjHRY/QVJcVRQVdqJtHqkX7xN0auxSuNUFdLPx6BAFxzDfTsGflNJNy6BxH/xYvVsleSGhV0pX5J1J1Tkw/fGOjdGzZuPPCxGCM/6elyP/dtICMDzjpLrPtBg2RO4cMP5RtDq1bi4onm2tEsXKWZo4KuND6xRN9f+MwJa7i757vvIpOwILQE8jPP1J/l78S/utqrr+OycP/yFxmbuneUZoIKutK8SNQKjhWyWVQEd9xRfwuZxMLv/09Lk+UKDz0Uhg3TiplKk6GCrqQWxcUiqi7hygl7Whrk50OPHpHWvSt4VpeHgJsPaN0apk6FDz6A2bMjFzJ56y1YskSXMFQahDoLujHmTGAqEAAet9beG3b9WuB6oArYA4y31q6N16cKulInwidpy8tDRTWade9v6ypkQvT6On7hjzaxGy1KZ/RoybT94AN5gKSnS7/GeO6lxYthzx7Iy5N7RPtWEv7tZcGC0G8FSoumToJujAkAnwGnAyXAEmCMX7CNMR2std8F988FrrPWnhmvXxV0pd6ojdsjWtto9XUmTRJL2y1f2FDfZNPTYdo0qdzpli50k8kXXeR9A2jVChYurHtylrqIkp66CnoBMMlae0bw+DYAa+09MdqPAcZZa0fG61cFXWnW+N06gYCIerQkrPoQe7e4eU3x/bm50L8/XHediPH06bJfXR1ZOjnWeyoslPekFTiTlrqm/h8KfOU7LgFOiHKT64FbgEzgtAMYp6I0HwoKxNXhj9SJlYT10EPeilbhvvpEBD/RRK0VK+Rn9mxZ9WruXK/v8nL461/h8ce9xLBwK7yoyJt3KCuLXo5BSWoSEXQT5VzEX6i1dhowzRhzCfAH4LKIjowZD4wH6NmzZ+1GqiiNTXgdHbcfvrDJeeeFliQI99Ub47lRRo3yMl+jFVLzr1ZlrRdn7xf9qip45ZXoY66shPvuk32/O6eoSKx4h7WwapVY7RpvnzI0hMslDfjWWtsxXr/qclFSmvA4/HhhmTNneksX+gXY/4AIL6PsxyVvxSqx4L4lpKXJw+Ckk2TiFqSOzjXXyH1jLXmY6PvUB0GjUFcfejoyKToM2IxMil5irV3ja9PHWrs+uH8O8MdYN3SooCtKkERE0b/qlT8L1/nNV6+GG25IrHSyy6qF6LV2Bg+GnBzpO9ok7MyZ8mA44QR50FRUxH4QqODXO/URtjgKmIKELc6w1v7JGDMZWGqtnWuMmQoMByqAb4Eb/IIfDRV0RTlA4iViFRXBrl3x6+K7RVKcqMciIwPeeUf2Z82SEg0u8gdC5wfS0uDuu+G227yxzJwpUTvV1V7mrd+3r2UWDghNLFKUlka4O8cYzxp3i6E4XztE99UD9OkjQp5IQtbo0XDIIRL3P2GC1M0Px19L5+abpVZ+RgYMHy4LmruJ5WnTJDNXiUAFXVFaKrF8+UVF8Pvfe7714cPhggvi++oTpabIHmPgmGNg3brYbdy3g0RdOC3IutcVixSlpRIrUgfE7+0ybCdNkmsDBohlv3w5LF0aabG7Qmbg+fJdFI/DL+b+dWvdeWvjizlIf5MmeeMqLoYnnxQXjiue5qz44mI47TT5JuLPFo5FIhPWSYpa6IrSUoln1YYnVrlwy2ilhmNF4RgjSxW6ZRIXL4a//z1yHE70nWXvd++kp0vG7LPPRvr8MzLg4YfhkUckPt/1MXq0TOxGe19vvikx/M6vD3K/zExv1axmbv2ry0VRlNpT25IK4VE44daye0j4k7CcP92/+PnNN0txs0SI595xi5ODvI927aRK5+7dkW1dtq4xkTWBhg4NfT+uvwMV+EWLxJ10gK9Xl4uiKLUn3gLl8drGehD4s2/jrQs7dSoMGRI9Cic8EzeeQbpvH0ycKAJaU0SP37VUXu5l0c6d6y12Xl4uD60nn5T9Vq1q594pKJByC8OHewXb6rnGvgq6oij1S7wHQSIPiYIC8Y/74+rd4iYu8eqPf5SoGEcg4FW3dOJsLbz7bmT/fqs+ViXNXbvgnntg/XrvfEaGRPzs2yfHZWUi8K++KmvfghRQ69pVHla7dsk6uP6EralT5X7Whj446gl1uSiK0jzx++jDrXm/+yY8w3bxYimNECsGPz3dm8h11r57aISXRAbo2FHE+cgj4fPPQ/ty8fw1FVhLS5OM3DlzZEWutLTEJnCjoD50RVFSj3ghjH5fvbPu/YlNEFlPPzxaxzFgAHz8ceQDwp9xWxNpaTKGigp53dVXx6+MGQf1oSuKknrEct8k6qsPL7bmr6Tpt7TXhq3VY4y4X+LF6ztr320POwy++EKuWQs9ezZIxIwKuqIoqceBTuhGc9tYK1a1C3W88kro2xduukleEwh4hc9cSeVOnbyHyYIFXnSMi6Jx3xLqGRV0RVEUhxP34mKYP99LvAqPUb/nntDCZlddJVZ3tG8C1dWeoAcC0lcDxbOroCuKooQTvsBJuAAXFkrYoRP8eP5w/zKG1sqDoYFQQVcURYlGTeGX8QTfz/DhYtE78W8gdwuooCuKohwYifrpayP+dUQFXVEUpaGpzSRtHUhr8DsoiqIojYIKuqIoSoqggq4oipIiqKAriqKkCCroiqIoKYIKuqIoSorQZNUWjTE7gC8O8OVdgZ31OJz6pLmOTcdVO3Rctae5ji3VxnW4tbZbtAtNJuh1wRizNFb5yKamuY5Nx1U7dFy1p7mOrSWNS10uiqIoKYIKuqIoSoqQrII+vakHEIfmOjYdV+3QcdWe5jq2FjOupPShK4qiKJEkq4WuKIqihJF0gm6MOdMY86kxZoMxZmITjuMwY8xCY8w6Y8waY8zNwfOTjDGbjTErgj+jmmBsm4wxq4P3Xxo818UY809jzPrgtnMjj+mnvs9khTHmO2PMhKb6vIwxM4wx240xH/vORf2MjPCX4N/cKmNMXiOP63+MMZ8E7/2yMaZT8HwvY8xe32f310YeV8zfnTHmtuDn9akx5oyGGlecsT3rG9cmY8yK4PlG+czi6EPD/o1Za5PmBwgAnwNHAJnASqBvE43lECAvuN8e+AzoC0wCbm3iz2kT0DXs3H3AxOD+RODPTfx7/Bo4vKk+L2AIkAd8XNNnBIwCXgcMcCLwr0Ye1wggPbj/Z9+4evnbNcHnFfV3F/w/WAm0AnoH/2cDjTm2sOv/C9zZmJ9ZHH1o0L+xZLPQBwMbrLUbrbXlwBxgdFMMxFq71Vq7PLj/PbAOOLQpxpIgo4GngvtPAec14ViGAZ9baw80sazOWGvfBb4JOx3rMxoNzLLCh0AnY8whjTUua+2b1trK4OGHQHZD3Lu244rDaGCOtbbMWvtvYAPyv9voYzPGGOAXwOyGun+MMcXShwb9G0s2QT8U+Mp3XEIzEFFjTC9gEPCv4Kkbgl+bZjS2ayOIBd40xiwzxowPnvuJtXYryB8bcHATjMtxMaH/YE39eTlifUbN6e/uSsSSc/Q2xnxkjHnHGHNKE4wn2u+uOX1epwDbrLXrfeca9TML04cG/RtLNkE3Uc41aZiOMaYd8CIwwVr7HfAIcCSQC2xFvu41NidZa/OAkcD1xpghTTCGqBhjMoFzgeeDp5rD51UTzeLvzhjze6ASeDp4aivQ01o7CLgFeMYY06ERhxTrd9csPq8gYwg1Hhr1M4uiDzGbRjlX688s2QS9BDjMd5wNbGmisWCMyUB+WU9ba18CsNZus9ZWWWurgcdowK+asbDWbglutwMvB8ewzX2FC263N/a4gowElltrtwXH2OSfl49Yn1GT/90ZYy4DzgZ+aYNO16BLozS4vwzxVR/dWGOK87tr8s8LwBiTDpwPPOvONeZnFk0faOC/sWQT9CVAH2NM76CldzEwtykGEvTNPQGss9Y+4Dvv93v9HPg4/LUNPK6DjDHt3T4yofYx8jldFmx2GfBKY47LR4jF1NSfVxixPqO5wLhgJMKJwG73tbkxMMacCfwncK619kff+W7GmEBw/wigD7CxEccV63c3F7jYGNPKGNM7OK7FjTUuH8OBT6y1Je5EY31msfSBhv4ba+jZ3gaYPR6FzBh/Dvy+CcdxMvKVaBWwIvgzCvg/YHXw/FzgkEYe1xFIhMFKYI37jIAsYAGwPrjt0gSfWVugFOjoO9cknxfyUNkKVCDW0VWxPiPk6/C04N/caiC/kce1AfGvur+zvwbbXhD8Ha8ElgPnNPK4Yv7ugN8HP69PgZGN/bsMnn8SuDasbaN8ZnH0oUH/xjRTVFEUJUVINpeLoiiKEgMVdEVRlBRBBV1RFCVFUEFXFEVJEVTQFUVRUgQVdEVRlBRBBV1RFCVFUEFXFEVJEf4/EJu2+20Iw7wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(run_hist_600.history[\"loss\"],'r', marker='.', label=\"Training Loss\")\n",
    "ax.plot(run_hist_600.history[\"val_loss\"],'b', marker='.', label=\"Validation Loss\")\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that there is not much improvement in Accuracy and ROC-AUC curve values. Hence we need to fine tune the model to improve the performance. We need to check for different learning rates, optimizers such as adam or RMSprop or sgd if the values improve. \n",
    "\n",
    "Now let's try developing a more complex model with more number of layers and check the performance of the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trying to build another model with more number of hidden layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "metadata": {},
   "outputs": [],
   "source": [
    "nn_model_2 = Sequential()\n",
    "nn_model_2.add(Dense(64, input_shape=(8,), activation=\"relu\"))\n",
    "nn_model_2.add(Dense(32, activation=\"relu\"))\n",
    "nn_model_2.add(Dense(16, activation=\"relu\"))\n",
    "nn_model_2.add(Dense(8,  activation=\"relu\"))\n",
    "nn_model_2.add(Dense(1, activation =\"sigmoid\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_98 (Dense)             (None, 64)                576       \n",
      "_________________________________________________________________\n",
      "dense_99 (Dense)             (None, 32)                2080      \n",
      "_________________________________________________________________\n",
      "dense_100 (Dense)            (None, 16)                528       \n",
      "_________________________________________________________________\n",
      "dense_101 (Dense)            (None, 8)                 136       \n",
      "_________________________________________________________________\n",
      "dense_102 (Dense)            (None, 1)                 9         \n",
      "=================================================================\n",
      "Total params: 3,329\n",
      "Trainable params: 3,329\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "nn_model_2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 537 samples, validate on 231 samples\n",
      "Epoch 1/200\n",
      "537/537 [==============================] - 3s 5ms/step - loss: 0.5365 - acc: 0.7076 - val_loss: 0.5408 - val_acc: 0.7316\n",
      "Epoch 2/200\n",
      "537/537 [==============================] - 0s 143us/step - loss: 0.5328 - acc: 0.7244 - val_loss: 0.5362 - val_acc: 0.7316\n",
      "Epoch 3/200\n",
      "537/537 [==============================] - 0s 208us/step - loss: 0.5281 - acc: 0.7263 - val_loss: 0.5317 - val_acc: 0.7489\n",
      "Epoch 4/200\n",
      "537/537 [==============================] - 0s 134us/step - loss: 0.5233 - acc: 0.7337 - val_loss: 0.5279 - val_acc: 0.7662\n",
      "Epoch 5/200\n",
      "537/537 [==============================] - 0s 240us/step - loss: 0.5193 - acc: 0.7374 - val_loss: 0.5235 - val_acc: 0.7749\n",
      "Epoch 6/200\n",
      "537/537 [==============================] - 0s 236us/step - loss: 0.5151 - acc: 0.7393 - val_loss: 0.5197 - val_acc: 0.7835\n",
      "Epoch 7/200\n",
      "537/537 [==============================] - 0s 100us/step - loss: 0.5109 - acc: 0.7486 - val_loss: 0.5161 - val_acc: 0.7879\n",
      "Epoch 8/200\n",
      "537/537 [==============================] - 0s 102us/step - loss: 0.5070 - acc: 0.7523 - val_loss: 0.5125 - val_acc: 0.7922\n",
      "Epoch 9/200\n",
      "537/537 [==============================] - 0s 117us/step - loss: 0.5037 - acc: 0.7505 - val_loss: 0.5087 - val_acc: 0.7792\n",
      "Epoch 10/200\n",
      "537/537 [==============================] - 0s 93us/step - loss: 0.5002 - acc: 0.7579 - val_loss: 0.5056 - val_acc: 0.7706\n",
      "Epoch 11/200\n",
      "537/537 [==============================] - 0s 310us/step - loss: 0.4966 - acc: 0.7579 - val_loss: 0.5026 - val_acc: 0.7619\n",
      "Epoch 12/200\n",
      "537/537 [==============================] - 0s 238us/step - loss: 0.4936 - acc: 0.7672 - val_loss: 0.5000 - val_acc: 0.7532\n",
      "Epoch 13/200\n",
      "537/537 [==============================] - 0s 351us/step - loss: 0.4906 - acc: 0.7672 - val_loss: 0.4972 - val_acc: 0.7532\n",
      "Epoch 14/200\n",
      "537/537 [==============================] - 0s 273us/step - loss: 0.4877 - acc: 0.7654 - val_loss: 0.4946 - val_acc: 0.7532\n",
      "Epoch 15/200\n",
      "537/537 [==============================] - 0s 178us/step - loss: 0.4853 - acc: 0.7691 - val_loss: 0.4927 - val_acc: 0.7532\n",
      "Epoch 16/200\n",
      "537/537 [==============================] - 0s 247us/step - loss: 0.4824 - acc: 0.7709 - val_loss: 0.4905 - val_acc: 0.7576\n",
      "Epoch 17/200\n",
      "537/537 [==============================] - 0s 173us/step - loss: 0.4802 - acc: 0.7709 - val_loss: 0.4882 - val_acc: 0.7532\n",
      "Epoch 18/200\n",
      "537/537 [==============================] - 0s 514us/step - loss: 0.4776 - acc: 0.7747 - val_loss: 0.4865 - val_acc: 0.7532\n",
      "Epoch 19/200\n",
      "537/537 [==============================] - 0s 102us/step - loss: 0.4753 - acc: 0.7672 - val_loss: 0.4844 - val_acc: 0.7576\n",
      "Epoch 20/200\n",
      "537/537 [==============================] - 0s 184us/step - loss: 0.4730 - acc: 0.7654 - val_loss: 0.4830 - val_acc: 0.7576\n",
      "Epoch 21/200\n",
      "537/537 [==============================] - 0s 236us/step - loss: 0.4710 - acc: 0.7691 - val_loss: 0.4814 - val_acc: 0.7576\n",
      "Epoch 22/200\n",
      "537/537 [==============================] - 0s 234us/step - loss: 0.4692 - acc: 0.7672 - val_loss: 0.4798 - val_acc: 0.7619\n",
      "Epoch 23/200\n",
      "537/537 [==============================] - 0s 336us/step - loss: 0.4673 - acc: 0.7691 - val_loss: 0.4790 - val_acc: 0.7619\n",
      "Epoch 24/200\n",
      "537/537 [==============================] - 0s 102us/step - loss: 0.4655 - acc: 0.7672 - val_loss: 0.4777 - val_acc: 0.7619\n",
      "Epoch 25/200\n",
      "537/537 [==============================] - 0s 314us/step - loss: 0.4639 - acc: 0.7616 - val_loss: 0.4769 - val_acc: 0.7576\n",
      "Epoch 26/200\n",
      "537/537 [==============================] - 0s 293us/step - loss: 0.4619 - acc: 0.7635 - val_loss: 0.4762 - val_acc: 0.7576\n",
      "Epoch 27/200\n",
      "537/537 [==============================] - 0s 269us/step - loss: 0.4604 - acc: 0.7672 - val_loss: 0.4751 - val_acc: 0.7576\n",
      "Epoch 28/200\n",
      "537/537 [==============================] - 0s 134us/step - loss: 0.4591 - acc: 0.7709 - val_loss: 0.4743 - val_acc: 0.7532\n",
      "Epoch 29/200\n",
      "537/537 [==============================] - 0s 171us/step - loss: 0.4572 - acc: 0.7672 - val_loss: 0.4739 - val_acc: 0.7576\n",
      "Epoch 30/200\n",
      "537/537 [==============================] - ETA: 0s - loss: 0.4527 - acc: 0.778 - 0s 299us/step - loss: 0.4565 - acc: 0.7728 - val_loss: 0.4736 - val_acc: 0.7576\n",
      "Epoch 31/200\n",
      "537/537 [==============================] - 0s 273us/step - loss: 0.4546 - acc: 0.7728 - val_loss: 0.4725 - val_acc: 0.7576\n",
      "Epoch 32/200\n",
      "537/537 [==============================] - 0s 225us/step - loss: 0.4533 - acc: 0.7728 - val_loss: 0.4719 - val_acc: 0.7489\n",
      "Epoch 33/200\n",
      "537/537 [==============================] - 0s 204us/step - loss: 0.4521 - acc: 0.7709 - val_loss: 0.4713 - val_acc: 0.7576\n",
      "Epoch 34/200\n",
      "537/537 [==============================] - 0s 334us/step - loss: 0.4505 - acc: 0.7709 - val_loss: 0.4709 - val_acc: 0.7532\n",
      "Epoch 35/200\n",
      "537/537 [==============================] - 0s 279us/step - loss: 0.4493 - acc: 0.7728 - val_loss: 0.4706 - val_acc: 0.7532\n",
      "Epoch 36/200\n",
      "537/537 [==============================] - 0s 193us/step - loss: 0.4485 - acc: 0.7728 - val_loss: 0.4700 - val_acc: 0.7532\n",
      "Epoch 37/200\n",
      "537/537 [==============================] - 0s 234us/step - loss: 0.4470 - acc: 0.7709 - val_loss: 0.4699 - val_acc: 0.7532\n",
      "Epoch 38/200\n",
      "537/537 [==============================] - 0s 215us/step - loss: 0.4460 - acc: 0.7709 - val_loss: 0.4696 - val_acc: 0.7532\n",
      "Epoch 39/200\n",
      "537/537 [==============================] - 0s 293us/step - loss: 0.4448 - acc: 0.7728 - val_loss: 0.4693 - val_acc: 0.7532\n",
      "Epoch 40/200\n",
      "537/537 [==============================] - 0s 258us/step - loss: 0.4441 - acc: 0.7728 - val_loss: 0.4692 - val_acc: 0.7532\n",
      "Epoch 41/200\n",
      "537/537 [==============================] - 0s 319us/step - loss: 0.4430 - acc: 0.7765 - val_loss: 0.4693 - val_acc: 0.7532\n",
      "Epoch 42/200\n",
      "537/537 [==============================] - 0s 373us/step - loss: 0.4418 - acc: 0.7765 - val_loss: 0.4688 - val_acc: 0.7532\n",
      "Epoch 43/200\n",
      "537/537 [==============================] - 0s 266us/step - loss: 0.4408 - acc: 0.7821 - val_loss: 0.4691 - val_acc: 0.7532\n",
      "Epoch 44/200\n",
      "537/537 [==============================] - 0s 169us/step - loss: 0.4398 - acc: 0.7840 - val_loss: 0.4692 - val_acc: 0.7576\n",
      "Epoch 45/200\n",
      "537/537 [==============================] - 0s 318us/step - loss: 0.4387 - acc: 0.7784 - val_loss: 0.4692 - val_acc: 0.7619\n",
      "Epoch 46/200\n",
      "537/537 [==============================] - 0s 145us/step - loss: 0.4387 - acc: 0.7803 - val_loss: 0.4692 - val_acc: 0.7619\n",
      "Epoch 47/200\n",
      "537/537 [==============================] - 0s 188us/step - loss: 0.4370 - acc: 0.7840 - val_loss: 0.4697 - val_acc: 0.7662\n",
      "Epoch 48/200\n",
      "537/537 [==============================] - 0s 236us/step - loss: 0.4367 - acc: 0.7821 - val_loss: 0.4694 - val_acc: 0.7706\n",
      "Epoch 49/200\n",
      "537/537 [==============================] - 0s 147us/step - loss: 0.4352 - acc: 0.7896 - val_loss: 0.4696 - val_acc: 0.7706\n",
      "Epoch 50/200\n",
      "537/537 [==============================] - 0s 331us/step - loss: 0.4343 - acc: 0.7896 - val_loss: 0.4694 - val_acc: 0.7662\n",
      "Epoch 51/200\n",
      "537/537 [==============================] - 0s 126us/step - loss: 0.4333 - acc: 0.7858 - val_loss: 0.4699 - val_acc: 0.7706\n",
      "Epoch 52/200\n",
      "537/537 [==============================] - 0s 279us/step - loss: 0.4325 - acc: 0.7877 - val_loss: 0.4699 - val_acc: 0.7662\n",
      "Epoch 53/200\n",
      "537/537 [==============================] - 0s 223us/step - loss: 0.4319 - acc: 0.7877 - val_loss: 0.4699 - val_acc: 0.7792\n",
      "Epoch 54/200\n",
      "537/537 [==============================] - 0s 223us/step - loss: 0.4306 - acc: 0.7896 - val_loss: 0.4706 - val_acc: 0.7749\n",
      "Epoch 55/200\n",
      "537/537 [==============================] - 0s 277us/step - loss: 0.4297 - acc: 0.7896 - val_loss: 0.4706 - val_acc: 0.7749\n",
      "Epoch 56/200\n",
      "537/537 [==============================] - 0s 234us/step - loss: 0.4291 - acc: 0.7877 - val_loss: 0.4707 - val_acc: 0.7749\n",
      "Epoch 57/200\n",
      "537/537 [==============================] - 0s 189us/step - loss: 0.4280 - acc: 0.7914 - val_loss: 0.4709 - val_acc: 0.7792\n",
      "Epoch 58/200\n",
      "537/537 [==============================] - 0s 263us/step - loss: 0.4273 - acc: 0.7914 - val_loss: 0.4710 - val_acc: 0.7749\n",
      "Epoch 59/200\n",
      "537/537 [==============================] - 0s 206us/step - loss: 0.4265 - acc: 0.7914 - val_loss: 0.4718 - val_acc: 0.7749\n",
      "Epoch 60/200\n",
      "537/537 [==============================] - 0s 183us/step - loss: 0.4255 - acc: 0.7914 - val_loss: 0.4718 - val_acc: 0.7792\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 61/200\n",
      "537/537 [==============================] - 0s 199us/step - loss: 0.4253 - acc: 0.7970 - val_loss: 0.4724 - val_acc: 0.7792\n",
      "Epoch 62/200\n",
      "537/537 [==============================] - 0s 243us/step - loss: 0.4239 - acc: 0.7914 - val_loss: 0.4729 - val_acc: 0.7792\n",
      "Epoch 63/200\n",
      "537/537 [==============================] - 0s 188us/step - loss: 0.4229 - acc: 0.7896 - val_loss: 0.4729 - val_acc: 0.7792\n",
      "Epoch 64/200\n",
      "537/537 [==============================] - 0s 147us/step - loss: 0.4236 - acc: 0.7933 - val_loss: 0.4739 - val_acc: 0.7835\n",
      "Epoch 65/200\n",
      "537/537 [==============================] - 0s 249us/step - loss: 0.4230 - acc: 0.7970 - val_loss: 0.4730 - val_acc: 0.7792\n",
      "Epoch 66/200\n",
      "537/537 [==============================] - 0s 150us/step - loss: 0.4206 - acc: 0.7952 - val_loss: 0.4735 - val_acc: 0.7792\n",
      "Epoch 67/200\n",
      "537/537 [==============================] - 0s 279us/step - loss: 0.4199 - acc: 0.7952 - val_loss: 0.4740 - val_acc: 0.7835\n",
      "Epoch 68/200\n",
      "537/537 [==============================] - 0s 124us/step - loss: 0.4189 - acc: 0.7970 - val_loss: 0.4746 - val_acc: 0.7879\n",
      "Epoch 69/200\n",
      "537/537 [==============================] - 0s 128us/step - loss: 0.4185 - acc: 0.7989 - val_loss: 0.4747 - val_acc: 0.7835\n",
      "Epoch 70/200\n",
      "537/537 [==============================] - 0s 186us/step - loss: 0.4172 - acc: 0.7970 - val_loss: 0.4756 - val_acc: 0.7879\n",
      "Epoch 71/200\n",
      "537/537 [==============================] - 0s 214us/step - loss: 0.4168 - acc: 0.7970 - val_loss: 0.4756 - val_acc: 0.7879\n",
      "Epoch 72/200\n",
      "537/537 [==============================] - 0s 133us/step - loss: 0.4158 - acc: 0.7989 - val_loss: 0.4764 - val_acc: 0.7879\n",
      "Epoch 73/200\n",
      "537/537 [==============================] - 0s 333us/step - loss: 0.4150 - acc: 0.7989 - val_loss: 0.4769 - val_acc: 0.7792\n",
      "Epoch 74/200\n",
      "537/537 [==============================] - 0s 431us/step - loss: 0.4143 - acc: 0.8026 - val_loss: 0.4770 - val_acc: 0.7835\n",
      "Epoch 75/200\n",
      "537/537 [==============================] - 0s 225us/step - loss: 0.4143 - acc: 0.8007 - val_loss: 0.4773 - val_acc: 0.7879\n",
      "Epoch 76/200\n",
      "537/537 [==============================] - 0s 637us/step - loss: 0.4128 - acc: 0.8026 - val_loss: 0.4781 - val_acc: 0.7879\n",
      "Epoch 77/200\n",
      "537/537 [==============================] - 0s 199us/step - loss: 0.4121 - acc: 0.8026 - val_loss: 0.4786 - val_acc: 0.7879\n",
      "Epoch 78/200\n",
      "537/537 [==============================] - 0s 251us/step - loss: 0.4112 - acc: 0.8026 - val_loss: 0.4786 - val_acc: 0.7835\n",
      "Epoch 79/200\n",
      "537/537 [==============================] - 0s 798us/step - loss: 0.4105 - acc: 0.8026 - val_loss: 0.4788 - val_acc: 0.7879\n",
      "Epoch 80/200\n",
      "537/537 [==============================] - 0s 165us/step - loss: 0.4098 - acc: 0.8045 - val_loss: 0.4798 - val_acc: 0.7792\n",
      "Epoch 81/200\n",
      "537/537 [==============================] - 0s 260us/step - loss: 0.4095 - acc: 0.8007 - val_loss: 0.4805 - val_acc: 0.7792\n",
      "Epoch 82/200\n",
      "537/537 [==============================] - 0s 290us/step - loss: 0.4084 - acc: 0.8063 - val_loss: 0.4806 - val_acc: 0.7792\n",
      "Epoch 83/200\n",
      "537/537 [==============================] - 0s 306us/step - loss: 0.4079 - acc: 0.8045 - val_loss: 0.4819 - val_acc: 0.7749\n",
      "Epoch 84/200\n",
      "537/537 [==============================] - 0s 219us/step - loss: 0.4078 - acc: 0.8007 - val_loss: 0.4818 - val_acc: 0.7879\n",
      "Epoch 85/200\n",
      "537/537 [==============================] - 0s 108us/step - loss: 0.4060 - acc: 0.8045 - val_loss: 0.4825 - val_acc: 0.7792\n",
      "Epoch 86/200\n",
      "537/537 [==============================] - 0s 236us/step - loss: 0.4054 - acc: 0.8045 - val_loss: 0.4827 - val_acc: 0.7792\n",
      "Epoch 87/200\n",
      "537/537 [==============================] - 0s 187us/step - loss: 0.4051 - acc: 0.8101 - val_loss: 0.4834 - val_acc: 0.7749\n",
      "Epoch 88/200\n",
      "537/537 [==============================] - 0s 193us/step - loss: 0.4039 - acc: 0.8063 - val_loss: 0.4835 - val_acc: 0.7835\n",
      "Epoch 89/200\n",
      "537/537 [==============================] - 0s 203us/step - loss: 0.4033 - acc: 0.8026 - val_loss: 0.4844 - val_acc: 0.7879\n",
      "Epoch 90/200\n",
      "537/537 [==============================] - 0s 275us/step - loss: 0.4026 - acc: 0.8007 - val_loss: 0.4845 - val_acc: 0.7879\n",
      "Epoch 91/200\n",
      "537/537 [==============================] - 0s 137us/step - loss: 0.4016 - acc: 0.8063 - val_loss: 0.4856 - val_acc: 0.7835\n",
      "Epoch 92/200\n",
      "537/537 [==============================] - 0s 264us/step - loss: 0.4014 - acc: 0.8063 - val_loss: 0.4866 - val_acc: 0.7835\n",
      "Epoch 93/200\n",
      "537/537 [==============================] - 0s 141us/step - loss: 0.4019 - acc: 0.8007 - val_loss: 0.4865 - val_acc: 0.7879\n",
      "Epoch 94/200\n",
      "537/537 [==============================] - 0s 225us/step - loss: 0.3995 - acc: 0.8045 - val_loss: 0.4873 - val_acc: 0.7835\n",
      "Epoch 95/200\n",
      "537/537 [==============================] - 0s 221us/step - loss: 0.4003 - acc: 0.8026 - val_loss: 0.4876 - val_acc: 0.7835\n",
      "Epoch 96/200\n",
      "537/537 [==============================] - 0s 260us/step - loss: 0.3981 - acc: 0.8063 - val_loss: 0.4876 - val_acc: 0.7835\n",
      "Epoch 97/200\n",
      "537/537 [==============================] - 0s 286us/step - loss: 0.3989 - acc: 0.8045 - val_loss: 0.4882 - val_acc: 0.7835\n",
      "Epoch 98/200\n",
      "537/537 [==============================] - 0s 247us/step - loss: 0.3967 - acc: 0.8082 - val_loss: 0.4886 - val_acc: 0.7835\n",
      "Epoch 99/200\n",
      "537/537 [==============================] - 0s 175us/step - loss: 0.3961 - acc: 0.8119 - val_loss: 0.4899 - val_acc: 0.7835\n",
      "Epoch 100/200\n",
      "537/537 [==============================] - 0s 192us/step - loss: 0.3956 - acc: 0.8119 - val_loss: 0.4911 - val_acc: 0.7792\n",
      "Epoch 101/200\n",
      "537/537 [==============================] - 0s 243us/step - loss: 0.3944 - acc: 0.8138 - val_loss: 0.4916 - val_acc: 0.7792\n",
      "Epoch 102/200\n",
      "537/537 [==============================] - 0s 258us/step - loss: 0.3938 - acc: 0.8138 - val_loss: 0.4918 - val_acc: 0.7879\n",
      "Epoch 103/200\n",
      "537/537 [==============================] - 0s 356us/step - loss: 0.3932 - acc: 0.8119 - val_loss: 0.4932 - val_acc: 0.7879\n",
      "Epoch 104/200\n",
      "537/537 [==============================] - 0s 136us/step - loss: 0.3920 - acc: 0.8156 - val_loss: 0.4936 - val_acc: 0.7792\n",
      "Epoch 105/200\n",
      "537/537 [==============================] - 0s 175us/step - loss: 0.3917 - acc: 0.8156 - val_loss: 0.4944 - val_acc: 0.7835\n",
      "Epoch 106/200\n",
      "537/537 [==============================] - 0s 271us/step - loss: 0.3908 - acc: 0.8156 - val_loss: 0.4947 - val_acc: 0.7792\n",
      "Epoch 107/200\n",
      "537/537 [==============================] - 0s 147us/step - loss: 0.3901 - acc: 0.8156 - val_loss: 0.4960 - val_acc: 0.7792\n",
      "Epoch 108/200\n",
      "537/537 [==============================] - 0s 295us/step - loss: 0.3893 - acc: 0.8138 - val_loss: 0.4964 - val_acc: 0.7835\n",
      "Epoch 109/200\n",
      "537/537 [==============================] - 0s 162us/step - loss: 0.3887 - acc: 0.8156 - val_loss: 0.4974 - val_acc: 0.7835\n",
      "Epoch 110/200\n",
      "537/537 [==============================] - 0s 172us/step - loss: 0.3882 - acc: 0.8119 - val_loss: 0.4985 - val_acc: 0.7835\n",
      "Epoch 111/200\n",
      "537/537 [==============================] - 0s 242us/step - loss: 0.3876 - acc: 0.8175 - val_loss: 0.4988 - val_acc: 0.7835\n",
      "Epoch 112/200\n",
      "537/537 [==============================] - 0s 295us/step - loss: 0.3863 - acc: 0.8194 - val_loss: 0.4996 - val_acc: 0.7792\n",
      "Epoch 113/200\n",
      "537/537 [==============================] - 0s 225us/step - loss: 0.3859 - acc: 0.8194 - val_loss: 0.5006 - val_acc: 0.7792\n",
      "Epoch 114/200\n",
      "537/537 [==============================] - 0s 258us/step - loss: 0.3849 - acc: 0.8212 - val_loss: 0.5023 - val_acc: 0.7792\n",
      "Epoch 115/200\n",
      "537/537 [==============================] - 0s 285us/step - loss: 0.3844 - acc: 0.8194 - val_loss: 0.5026 - val_acc: 0.7792\n",
      "Epoch 116/200\n",
      "537/537 [==============================] - 0s 241us/step - loss: 0.3840 - acc: 0.8212 - val_loss: 0.5036 - val_acc: 0.7792\n",
      "Epoch 117/200\n",
      "537/537 [==============================] - 0s 295us/step - loss: 0.3835 - acc: 0.8231 - val_loss: 0.5033 - val_acc: 0.7792\n",
      "Epoch 118/200\n",
      "537/537 [==============================] - 0s 214us/step - loss: 0.3827 - acc: 0.8194 - val_loss: 0.5044 - val_acc: 0.7792\n",
      "Epoch 119/200\n",
      "537/537 [==============================] - 0s 229us/step - loss: 0.3819 - acc: 0.8194 - val_loss: 0.5054 - val_acc: 0.7792\n",
      "Epoch 120/200\n",
      "537/537 [==============================] - 0s 175us/step - loss: 0.3810 - acc: 0.8212 - val_loss: 0.5059 - val_acc: 0.7749\n",
      "Epoch 121/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "537/537 [==============================] - 0s 195us/step - loss: 0.3807 - acc: 0.8212 - val_loss: 0.5067 - val_acc: 0.7749\n",
      "Epoch 122/200\n",
      "537/537 [==============================] - 0s 199us/step - loss: 0.3800 - acc: 0.8212 - val_loss: 0.5073 - val_acc: 0.7792\n",
      "Epoch 123/200\n",
      "537/537 [==============================] - 0s 191us/step - loss: 0.3799 - acc: 0.8212 - val_loss: 0.5076 - val_acc: 0.7792\n",
      "Epoch 124/200\n",
      "537/537 [==============================] - 0s 176us/step - loss: 0.3782 - acc: 0.8250 - val_loss: 0.5087 - val_acc: 0.7792\n",
      "Epoch 125/200\n",
      "537/537 [==============================] - 0s 234us/step - loss: 0.3780 - acc: 0.8212 - val_loss: 0.5094 - val_acc: 0.7792\n",
      "Epoch 126/200\n",
      "537/537 [==============================] - 0s 160us/step - loss: 0.3774 - acc: 0.8231 - val_loss: 0.5100 - val_acc: 0.7792\n",
      "Epoch 127/200\n",
      "537/537 [==============================] - 0s 225us/step - loss: 0.3767 - acc: 0.8268 - val_loss: 0.5109 - val_acc: 0.7835\n",
      "Epoch 128/200\n",
      "537/537 [==============================] - 0s 223us/step - loss: 0.3763 - acc: 0.8287 - val_loss: 0.5113 - val_acc: 0.7835\n",
      "Epoch 129/200\n",
      "537/537 [==============================] - 0s 154us/step - loss: 0.3755 - acc: 0.8231 - val_loss: 0.5126 - val_acc: 0.7792\n",
      "Epoch 130/200\n",
      "537/537 [==============================] - 0s 206us/step - loss: 0.3754 - acc: 0.8305 - val_loss: 0.5125 - val_acc: 0.7835\n",
      "Epoch 131/200\n",
      "537/537 [==============================] - 0s 132us/step - loss: 0.3775 - acc: 0.8194 - val_loss: 0.5146 - val_acc: 0.7835\n",
      "Epoch 132/200\n",
      "537/537 [==============================] - 0s 117us/step - loss: 0.3751 - acc: 0.8287 - val_loss: 0.5132 - val_acc: 0.7879\n",
      "Epoch 133/200\n",
      "537/537 [==============================] - 0s 284us/step - loss: 0.3731 - acc: 0.8287 - val_loss: 0.5151 - val_acc: 0.7835\n",
      "Epoch 134/200\n",
      "537/537 [==============================] - 0s 311us/step - loss: 0.3724 - acc: 0.8287 - val_loss: 0.5165 - val_acc: 0.7792\n",
      "Epoch 135/200\n",
      "537/537 [==============================] - 0s 249us/step - loss: 0.3732 - acc: 0.8324 - val_loss: 0.5172 - val_acc: 0.7792\n",
      "Epoch 136/200\n",
      "537/537 [==============================] - 0s 193us/step - loss: 0.3711 - acc: 0.8343 - val_loss: 0.5178 - val_acc: 0.7835\n",
      "Epoch 137/200\n",
      "537/537 [==============================] - 0s 271us/step - loss: 0.3704 - acc: 0.8343 - val_loss: 0.5180 - val_acc: 0.7879\n",
      "Epoch 138/200\n",
      "537/537 [==============================] - 0s 298us/step - loss: 0.3715 - acc: 0.8361 - val_loss: 0.5182 - val_acc: 0.7835\n",
      "Epoch 139/200\n",
      "537/537 [==============================] - 0s 410us/step - loss: 0.3687 - acc: 0.8380 - val_loss: 0.5207 - val_acc: 0.7879\n",
      "Epoch 140/200\n",
      "537/537 [==============================] - 0s 171us/step - loss: 0.3686 - acc: 0.8361 - val_loss: 0.5211 - val_acc: 0.7879\n",
      "Epoch 141/200\n",
      "537/537 [==============================] - 0s 247us/step - loss: 0.3680 - acc: 0.8436 - val_loss: 0.5212 - val_acc: 0.7792\n",
      "Epoch 142/200\n",
      "537/537 [==============================] - 0s 126us/step - loss: 0.3681 - acc: 0.8324 - val_loss: 0.5221 - val_acc: 0.7835\n",
      "Epoch 143/200\n",
      "537/537 [==============================] - 0s 226us/step - loss: 0.3670 - acc: 0.8417 - val_loss: 0.5223 - val_acc: 0.7792\n",
      "Epoch 144/200\n",
      "537/537 [==============================] - 0s 150us/step - loss: 0.3664 - acc: 0.8399 - val_loss: 0.5233 - val_acc: 0.7879\n",
      "Epoch 145/200\n",
      "537/537 [==============================] - 0s 189us/step - loss: 0.3661 - acc: 0.8305 - val_loss: 0.5240 - val_acc: 0.7879\n",
      "Epoch 146/200\n",
      "537/537 [==============================] - 0s 197us/step - loss: 0.3639 - acc: 0.8454 - val_loss: 0.5238 - val_acc: 0.7749\n",
      "Epoch 147/200\n",
      "537/537 [==============================] - 0s 165us/step - loss: 0.3646 - acc: 0.8454 - val_loss: 0.5247 - val_acc: 0.7835\n",
      "Epoch 148/200\n",
      "537/537 [==============================] - 0s 163us/step - loss: 0.3632 - acc: 0.8454 - val_loss: 0.5268 - val_acc: 0.7835\n",
      "Epoch 149/200\n",
      "537/537 [==============================] - 0s 256us/step - loss: 0.3640 - acc: 0.8399 - val_loss: 0.5279 - val_acc: 0.7792\n",
      "Epoch 150/200\n",
      "537/537 [==============================] - 0s 132us/step - loss: 0.3623 - acc: 0.8436 - val_loss: 0.5273 - val_acc: 0.7835\n",
      "Epoch 151/200\n",
      "537/537 [==============================] - 0s 204us/step - loss: 0.3623 - acc: 0.8361 - val_loss: 0.5276 - val_acc: 0.7835\n",
      "Epoch 152/200\n",
      "537/537 [==============================] - 0s 148us/step - loss: 0.3612 - acc: 0.8454 - val_loss: 0.5280 - val_acc: 0.7835\n",
      "Epoch 153/200\n",
      "537/537 [==============================] - ETA: 0s - loss: 0.3566 - acc: 0.828 - 0s 186us/step - loss: 0.3613 - acc: 0.8343 - val_loss: 0.5311 - val_acc: 0.7879\n",
      "Epoch 154/200\n",
      "537/537 [==============================] - 0s 213us/step - loss: 0.3594 - acc: 0.8454 - val_loss: 0.5301 - val_acc: 0.7749\n",
      "Epoch 155/200\n",
      "537/537 [==============================] - 0s 214us/step - loss: 0.3588 - acc: 0.8492 - val_loss: 0.5307 - val_acc: 0.7792\n",
      "Epoch 156/200\n",
      "537/537 [==============================] - 0s 208us/step - loss: 0.3578 - acc: 0.8436 - val_loss: 0.5339 - val_acc: 0.7879\n",
      "Epoch 157/200\n",
      "537/537 [==============================] - 0s 297us/step - loss: 0.3579 - acc: 0.8454 - val_loss: 0.5327 - val_acc: 0.7835\n",
      "Epoch 158/200\n",
      "537/537 [==============================] - 0s 128us/step - loss: 0.3563 - acc: 0.8454 - val_loss: 0.5339 - val_acc: 0.7835\n",
      "Epoch 159/200\n",
      "537/537 [==============================] - 0s 841us/step - loss: 0.3557 - acc: 0.8454 - val_loss: 0.5354 - val_acc: 0.7879\n",
      "Epoch 160/200\n",
      "537/537 [==============================] - 0s 349us/step - loss: 0.3553 - acc: 0.8454 - val_loss: 0.5353 - val_acc: 0.7749\n",
      "Epoch 161/200\n",
      "537/537 [==============================] - 0s 312us/step - loss: 0.3553 - acc: 0.8473 - val_loss: 0.5363 - val_acc: 0.7749\n",
      "Epoch 162/200\n",
      "537/537 [==============================] - 0s 593us/step - loss: 0.3541 - acc: 0.8473 - val_loss: 0.5372 - val_acc: 0.7835\n",
      "Epoch 163/200\n",
      "537/537 [==============================] - 0s 191us/step - loss: 0.3531 - acc: 0.8454 - val_loss: 0.5378 - val_acc: 0.7835\n",
      "Epoch 164/200\n",
      "537/537 [==============================] - 0s 271us/step - loss: 0.3525 - acc: 0.8510 - val_loss: 0.5384 - val_acc: 0.7835\n",
      "Epoch 165/200\n",
      "537/537 [==============================] - 0s 325us/step - loss: 0.3518 - acc: 0.8473 - val_loss: 0.5398 - val_acc: 0.7879\n",
      "Epoch 166/200\n",
      "537/537 [==============================] - 0s 229us/step - loss: 0.3517 - acc: 0.8510 - val_loss: 0.5396 - val_acc: 0.7835\n",
      "Epoch 167/200\n",
      "537/537 [==============================] - 0s 165us/step - loss: 0.3515 - acc: 0.8454 - val_loss: 0.5412 - val_acc: 0.7835\n",
      "Epoch 168/200\n",
      "537/537 [==============================] - 0s 212us/step - loss: 0.3495 - acc: 0.8529 - val_loss: 0.5405 - val_acc: 0.7835\n",
      "Epoch 169/200\n",
      "537/537 [==============================] - 0s 232us/step - loss: 0.3488 - acc: 0.8547 - val_loss: 0.5426 - val_acc: 0.7835\n",
      "Epoch 170/200\n",
      "537/537 [==============================] - 0s 173us/step - loss: 0.3481 - acc: 0.8547 - val_loss: 0.5434 - val_acc: 0.7792\n",
      "Epoch 171/200\n",
      "537/537 [==============================] - 0s 347us/step - loss: 0.3478 - acc: 0.8529 - val_loss: 0.5430 - val_acc: 0.7835\n",
      "Epoch 172/200\n",
      "537/537 [==============================] - 0s 221us/step - loss: 0.3472 - acc: 0.8529 - val_loss: 0.5439 - val_acc: 0.7706\n",
      "Epoch 173/200\n",
      "537/537 [==============================] - 0s 132us/step - loss: 0.3455 - acc: 0.8566 - val_loss: 0.5459 - val_acc: 0.7835\n",
      "Epoch 174/200\n",
      "537/537 [==============================] - 0s 274us/step - loss: 0.3455 - acc: 0.8547 - val_loss: 0.5461 - val_acc: 0.7879\n",
      "Epoch 175/200\n",
      "537/537 [==============================] - 0s 150us/step - loss: 0.3446 - acc: 0.8529 - val_loss: 0.5463 - val_acc: 0.7792\n",
      "Epoch 176/200\n",
      "537/537 [==============================] - 0s 225us/step - loss: 0.3444 - acc: 0.8529 - val_loss: 0.5472 - val_acc: 0.7835\n",
      "Epoch 177/200\n",
      "537/537 [==============================] - 0s 134us/step - loss: 0.3433 - acc: 0.8566 - val_loss: 0.5474 - val_acc: 0.7879\n",
      "Epoch 178/200\n",
      "537/537 [==============================] - 0s 284us/step - loss: 0.3421 - acc: 0.8585 - val_loss: 0.5487 - val_acc: 0.7879\n",
      "Epoch 179/200\n",
      "537/537 [==============================] - 0s 571us/step - loss: 0.3437 - acc: 0.8529 - val_loss: 0.5497 - val_acc: 0.7835\n",
      "Epoch 180/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "537/537 [==============================] - 0s 306us/step - loss: 0.3406 - acc: 0.8566 - val_loss: 0.5493 - val_acc: 0.7792\n",
      "Epoch 181/200\n",
      "537/537 [==============================] - 0s 583us/step - loss: 0.3413 - acc: 0.8566 - val_loss: 0.5505 - val_acc: 0.7835\n",
      "Epoch 182/200\n",
      "537/537 [==============================] - 0s 556us/step - loss: 0.3401 - acc: 0.8585 - val_loss: 0.5509 - val_acc: 0.7792\n",
      "Epoch 183/200\n",
      "537/537 [==============================] - 0s 272us/step - loss: 0.3394 - acc: 0.8566 - val_loss: 0.5516 - val_acc: 0.7835\n",
      "Epoch 184/200\n",
      "537/537 [==============================] - 0s 243us/step - loss: 0.3387 - acc: 0.8622 - val_loss: 0.5528 - val_acc: 0.7749\n",
      "Epoch 185/200\n",
      "537/537 [==============================] - 0s 280us/step - loss: 0.3381 - acc: 0.8566 - val_loss: 0.5557 - val_acc: 0.7749\n",
      "Epoch 186/200\n",
      "537/537 [==============================] - 0s 686us/step - loss: 0.3361 - acc: 0.8622 - val_loss: 0.5539 - val_acc: 0.7792\n",
      "Epoch 187/200\n",
      "537/537 [==============================] - 0s 332us/step - loss: 0.3368 - acc: 0.8585 - val_loss: 0.5551 - val_acc: 0.7835\n",
      "Epoch 188/200\n",
      "537/537 [==============================] - 0s 248us/step - loss: 0.3350 - acc: 0.8622 - val_loss: 0.5557 - val_acc: 0.7749\n",
      "Epoch 189/200\n",
      "537/537 [==============================] - 0s 156us/step - loss: 0.3343 - acc: 0.8603 - val_loss: 0.5563 - val_acc: 0.7792\n",
      "Epoch 190/200\n",
      "537/537 [==============================] - 0s 257us/step - loss: 0.3337 - acc: 0.8622 - val_loss: 0.5582 - val_acc: 0.7749\n",
      "Epoch 191/200\n",
      "537/537 [==============================] - 0s 231us/step - loss: 0.3337 - acc: 0.8622 - val_loss: 0.5578 - val_acc: 0.7749\n",
      "Epoch 192/200\n",
      "537/537 [==============================] - 0s 164us/step - loss: 0.3327 - acc: 0.8603 - val_loss: 0.5579 - val_acc: 0.7706\n",
      "Epoch 193/200\n",
      "537/537 [==============================] - 0s 243us/step - loss: 0.3315 - acc: 0.8603 - val_loss: 0.5597 - val_acc: 0.7749\n",
      "Epoch 194/200\n",
      "537/537 [==============================] - 0s 246us/step - loss: 0.3309 - acc: 0.8678 - val_loss: 0.5602 - val_acc: 0.7706\n",
      "Epoch 195/200\n",
      "537/537 [==============================] - 0s 238us/step - loss: 0.3296 - acc: 0.8622 - val_loss: 0.5607 - val_acc: 0.7662\n",
      "Epoch 196/200\n",
      "537/537 [==============================] - 0s 204us/step - loss: 0.3295 - acc: 0.8659 - val_loss: 0.5599 - val_acc: 0.7619\n",
      "Epoch 197/200\n",
      "537/537 [==============================] - 0s 130us/step - loss: 0.3283 - acc: 0.8659 - val_loss: 0.5631 - val_acc: 0.7619\n",
      "Epoch 198/200\n",
      "537/537 [==============================] - 0s 278us/step - loss: 0.3276 - acc: 0.8566 - val_loss: 0.5616 - val_acc: 0.7706\n",
      "Epoch 199/200\n",
      "537/537 [==============================] - 0s 223us/step - loss: 0.3262 - acc: 0.8678 - val_loss: 0.5634 - val_acc: 0.7662\n",
      "Epoch 200/200\n",
      "537/537 [==============================] - 0s 160us/step - loss: 0.3269 - acc: 0.8641 - val_loss: 0.5665 - val_acc: 0.7706\n"
     ]
    }
   ],
   "source": [
    "sgd = SGD(lr=1e-3, momentum=0.9)\n",
    "nn_model_2.compile(loss=\"binary_crossentropy\", optimizer=sgd,metrics=[\"accuracy\"])\n",
    "run_hist_2 = nn_model_2.fit(X_train_norm, y_train, validation_data=(X_test_norm, y_test), epochs=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 461,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_class_nn_mod2 = model.predict_classes(X_test_norm)\n",
    "y_pred_prob_nn_mod2 = model.predict(X_test_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 462,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c7ad26eb00>"
      ]
     },
     "execution_count": 462,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the curve to check training and validation accuracy\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(run_hist_2.history[\"acc\"],'r', marker='.', label=\"Training Accuracy\")\n",
    "ax.plot(run_hist_2.history[\"val_acc\"],'b', marker='.', label=\"Validation Accuracy\")\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c7af267978>"
      ]
     },
     "execution_count": 463,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting curves for Training Loss and Validation loss\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(run_hist_2.history[\"loss\"],'r', marker='.', label=\"Training Loss\")\n",
    "ax.plot(run_hist_2.history[\"val_loss\"],'b', marker='.', label=\"Validation Loss\")\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy is 0.723\n",
      "roc-auc is 0.781\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Print model performance and plot the roc curve\n",
    "print('accuracy is {:.3f}'.format(accuracy_score(y_test,y_pred_class_nn_mod2)))\n",
    "print('roc-auc is {:.3f}'.format(roc_auc_score(y_test,y_pred_prob_nn_mod2)))\n",
    "\n",
    "plot_roc(y_test, y_pred_prob_nn_mod2, 'NN')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accuracy obtained from this model is 72.3% for epochs=200 , learning rate=1e-3. \n",
    "\n",
    "Now let's try for 400 epochs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 537 samples, validate on 231 samples\n",
      "Epoch 1/400\n",
      "537/537 [==============================] - 0s 158us/step - loss: 0.3285 - acc: 0.8547 - val_loss: 0.5635 - val_acc: 0.7662\n",
      "Epoch 2/400\n",
      "537/537 [==============================] - 0s 86us/step - loss: 0.3258 - acc: 0.8622 - val_loss: 0.5679 - val_acc: 0.7706\n",
      "Epoch 3/400\n",
      "537/537 [==============================] - 0s 80us/step - loss: 0.3259 - acc: 0.8585 - val_loss: 0.5669 - val_acc: 0.7662\n",
      "Epoch 4/400\n",
      "537/537 [==============================] - 0s 87us/step - loss: 0.3230 - acc: 0.8678 - val_loss: 0.5677 - val_acc: 0.7749\n",
      "Epoch 5/400\n",
      "537/537 [==============================] - 0s 87us/step - loss: 0.3233 - acc: 0.8603 - val_loss: 0.5673 - val_acc: 0.7662\n",
      "Epoch 6/400\n",
      "537/537 [==============================] - 0s 87us/step - loss: 0.3222 - acc: 0.8641 - val_loss: 0.5702 - val_acc: 0.7706\n",
      "Epoch 7/400\n",
      "537/537 [==============================] - 0s 100us/step - loss: 0.3206 - acc: 0.8678 - val_loss: 0.5694 - val_acc: 0.7749\n",
      "Epoch 8/400\n",
      "537/537 [==============================] - 0s 87us/step - loss: 0.3200 - acc: 0.8659 - val_loss: 0.5695 - val_acc: 0.7792\n",
      "Epoch 9/400\n",
      "537/537 [==============================] - 0s 150us/step - loss: 0.3202 - acc: 0.8641 - val_loss: 0.5701 - val_acc: 0.7706\n",
      "Epoch 10/400\n",
      "537/537 [==============================] - 0s 247us/step - loss: 0.3188 - acc: 0.8734 - val_loss: 0.5729 - val_acc: 0.7706\n",
      "Epoch 11/400\n",
      "537/537 [==============================] - 0s 338us/step - loss: 0.3170 - acc: 0.8696 - val_loss: 0.5725 - val_acc: 0.7749\n",
      "Epoch 12/400\n",
      "537/537 [==============================] - 0s 299us/step - loss: 0.3162 - acc: 0.8696 - val_loss: 0.5725 - val_acc: 0.7792\n",
      "Epoch 13/400\n",
      "537/537 [==============================] - 0s 269us/step - loss: 0.3160 - acc: 0.8678 - val_loss: 0.5728 - val_acc: 0.7792\n",
      "Epoch 14/400\n",
      "537/537 [==============================] - 0s 204us/step - loss: 0.3167 - acc: 0.8696 - val_loss: 0.5745 - val_acc: 0.7706\n",
      "Epoch 15/400\n",
      "537/537 [==============================] - 0s 340us/step - loss: 0.3144 - acc: 0.8659 - val_loss: 0.5745 - val_acc: 0.7792\n",
      "Epoch 16/400\n",
      "537/537 [==============================] - 0s 490us/step - loss: 0.3124 - acc: 0.8734 - val_loss: 0.5749 - val_acc: 0.7749\n",
      "Epoch 17/400\n",
      "537/537 [==============================] - 0s 318us/step - loss: 0.3113 - acc: 0.8771 - val_loss: 0.5758 - val_acc: 0.7792\n",
      "Epoch 18/400\n",
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      "Epoch 20/400\n",
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      "Epoch 21/400\n",
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      "Epoch 22/400\n",
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      "Epoch 23/400\n",
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      "Epoch 24/400\n",
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      "Epoch 26/400\n",
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      "Epoch 27/400\n",
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      "Epoch 28/400\n",
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      "Epoch 29/400\n",
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      "Epoch 30/400\n",
      "537/537 [==============================] - 0s 230us/step - loss: 0.3008 - acc: 0.8790 - val_loss: 0.5864 - val_acc: 0.7835\n",
      "Epoch 31/400\n",
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      "Epoch 32/400\n",
      "537/537 [==============================] - 0s 145us/step - loss: 0.2993 - acc: 0.8808 - val_loss: 0.5905 - val_acc: 0.7792\n",
      "Epoch 33/400\n",
      "537/537 [==============================] - 0s 236us/step - loss: 0.2971 - acc: 0.8771 - val_loss: 0.5886 - val_acc: 0.7792\n",
      "Epoch 34/400\n",
      "537/537 [==============================] - 0s 241us/step - loss: 0.2968 - acc: 0.8771 - val_loss: 0.5888 - val_acc: 0.7835\n",
      "Epoch 35/400\n",
      "537/537 [==============================] - 0s 271us/step - loss: 0.2957 - acc: 0.8808 - val_loss: 0.5902 - val_acc: 0.7792\n",
      "Epoch 36/400\n",
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      "Epoch 37/400\n",
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      "Epoch 38/400\n",
      "537/537 [==============================] - 0s 257us/step - loss: 0.2920 - acc: 0.8864 - val_loss: 0.5931 - val_acc: 0.7792\n",
      "Epoch 39/400\n",
      "537/537 [==============================] - 0s 147us/step - loss: 0.2920 - acc: 0.8864 - val_loss: 0.5943 - val_acc: 0.7749\n",
      "Epoch 40/400\n",
      "537/537 [==============================] - 0s 158us/step - loss: 0.2911 - acc: 0.8790 - val_loss: 0.5957 - val_acc: 0.7792\n",
      "Epoch 41/400\n",
      "537/537 [==============================] - 0s 188us/step - loss: 0.2899 - acc: 0.8864 - val_loss: 0.5944 - val_acc: 0.7749\n",
      "Epoch 42/400\n",
      "537/537 [==============================] - 0s 262us/step - loss: 0.2895 - acc: 0.8827 - val_loss: 0.5950 - val_acc: 0.7792\n",
      "Epoch 43/400\n",
      "537/537 [==============================] - 0s 323us/step - loss: 0.2884 - acc: 0.8845 - val_loss: 0.5986 - val_acc: 0.7792\n",
      "Epoch 44/400\n",
      "537/537 [==============================] - 0s 683us/step - loss: 0.2864 - acc: 0.8901 - val_loss: 0.5960 - val_acc: 0.7706\n",
      "Epoch 45/400\n",
      "537/537 [==============================] - 0s 438us/step - loss: 0.2865 - acc: 0.8864 - val_loss: 0.5983 - val_acc: 0.7749\n",
      "Epoch 46/400\n",
      "537/537 [==============================] - 0s 228us/step - loss: 0.2860 - acc: 0.8883 - val_loss: 0.5983 - val_acc: 0.7706\n",
      "Epoch 47/400\n",
      "537/537 [==============================] - 0s 880us/step - loss: 0.2846 - acc: 0.8901 - val_loss: 0.6016 - val_acc: 0.7749\n",
      "Epoch 48/400\n",
      "537/537 [==============================] - 0s 931us/step - loss: 0.2832 - acc: 0.8939 - val_loss: 0.6010 - val_acc: 0.7706\n",
      "Epoch 49/400\n",
      "537/537 [==============================] - 0s 381us/step - loss: 0.2826 - acc: 0.8939 - val_loss: 0.6040 - val_acc: 0.7706\n",
      "Epoch 50/400\n",
      "537/537 [==============================] - 0s 331us/step - loss: 0.2814 - acc: 0.8939 - val_loss: 0.5999 - val_acc: 0.7749\n",
      "Epoch 51/400\n",
      "537/537 [==============================] - 0s 369us/step - loss: 0.2844 - acc: 0.8864 - val_loss: 0.6063 - val_acc: 0.7749\n",
      "Epoch 52/400\n",
      "537/537 [==============================] - 0s 390us/step - loss: 0.2829 - acc: 0.8901 - val_loss: 0.6046 - val_acc: 0.7749\n",
      "Epoch 53/400\n",
      "537/537 [==============================] - 0s 214us/step - loss: 0.2794 - acc: 0.8957 - val_loss: 0.6034 - val_acc: 0.7835\n",
      "Epoch 54/400\n",
      "537/537 [==============================] - 0s 314us/step - loss: 0.2776 - acc: 0.9013 - val_loss: 0.6054 - val_acc: 0.7706\n",
      "Epoch 55/400\n",
      "537/537 [==============================] - 0s 154us/step - loss: 0.2792 - acc: 0.8957 - val_loss: 0.6068 - val_acc: 0.7706\n",
      "Epoch 56/400\n",
      "537/537 [==============================] - 0s 191us/step - loss: 0.2768 - acc: 0.9013 - val_loss: 0.6092 - val_acc: 0.7706\n",
      "Epoch 57/400\n",
      "537/537 [==============================] - 0s 122us/step - loss: 0.2754 - acc: 0.8994 - val_loss: 0.6119 - val_acc: 0.7749\n",
      "Epoch 58/400\n",
      "537/537 [==============================] - 0s 587us/step - loss: 0.2744 - acc: 0.8957 - val_loss: 0.6063 - val_acc: 0.7749\n",
      "Epoch 59/400\n",
      "537/537 [==============================] - 0s 162us/step - loss: 0.2736 - acc: 0.9013 - val_loss: 0.6115 - val_acc: 0.7706\n",
      "Epoch 60/400\n",
      "537/537 [==============================] - 0s 277us/step - loss: 0.2720 - acc: 0.8976 - val_loss: 0.6103 - val_acc: 0.7749\n",
      "Epoch 61/400\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "537/537 [==============================] - 0s 113us/step - loss: 0.2712 - acc: 0.8994 - val_loss: 0.6138 - val_acc: 0.7749\n",
      "Epoch 62/400\n",
      "537/537 [==============================] - 0s 100us/step - loss: 0.2705 - acc: 0.9013 - val_loss: 0.6156 - val_acc: 0.7749\n",
      "Epoch 63/400\n",
      "537/537 [==============================] - 0s 108us/step - loss: 0.2700 - acc: 0.9032 - val_loss: 0.6094 - val_acc: 0.7749\n",
      "Epoch 64/400\n",
      "537/537 [==============================] - 0s 154us/step - loss: 0.2710 - acc: 0.9013 - val_loss: 0.6184 - val_acc: 0.7792\n",
      "Epoch 65/400\n",
      "537/537 [==============================] - 0s 184us/step - loss: 0.2680 - acc: 0.9032 - val_loss: 0.6148 - val_acc: 0.7706\n",
      "Epoch 66/400\n",
      "537/537 [==============================] - 0s 191us/step - loss: 0.2662 - acc: 0.9032 - val_loss: 0.6168 - val_acc: 0.7749\n",
      "Epoch 67/400\n",
      "537/537 [==============================] - 0s 141us/step - loss: 0.2679 - acc: 0.9069 - val_loss: 0.6204 - val_acc: 0.7706\n",
      "Epoch 68/400\n",
      "537/537 [==============================] - 0s 106us/step - loss: 0.2660 - acc: 0.9125 - val_loss: 0.6201 - val_acc: 0.7706\n",
      "Epoch 69/400\n",
      "537/537 [==============================] - 0s 115us/step - loss: 0.2636 - acc: 0.9032 - val_loss: 0.6236 - val_acc: 0.7749\n",
      "Epoch 70/400\n",
      "537/537 [==============================] - 0s 162us/step - loss: 0.2648 - acc: 0.9106 - val_loss: 0.6187 - val_acc: 0.7749\n",
      "Epoch 71/400\n",
      "537/537 [==============================] - 0s 135us/step - loss: 0.2625 - acc: 0.9069 - val_loss: 0.6201 - val_acc: 0.7706\n",
      "Epoch 72/400\n",
      "537/537 [==============================] - 0s 125us/step - loss: 0.2613 - acc: 0.9162 - val_loss: 0.6234 - val_acc: 0.7749\n",
      "Epoch 73/400\n",
      "537/537 [==============================] - 0s 167us/step - loss: 0.2594 - acc: 0.9125 - val_loss: 0.6222 - val_acc: 0.7749\n",
      "Epoch 74/400\n",
      "537/537 [==============================] - 0s 132us/step - loss: 0.2598 - acc: 0.9106 - val_loss: 0.6243 - val_acc: 0.7749\n",
      "Epoch 75/400\n",
      "537/537 [==============================] - 0s 110us/step - loss: 0.2584 - acc: 0.9125 - val_loss: 0.6280 - val_acc: 0.7706\n",
      "Epoch 76/400\n",
      "537/537 [==============================] - 0s 100us/step - loss: 0.2577 - acc: 0.9088 - val_loss: 0.6265 - val_acc: 0.7706\n",
      "Epoch 77/400\n",
      "537/537 [==============================] - 0s 180us/step - loss: 0.2561 - acc: 0.9088 - val_loss: 0.6256 - val_acc: 0.7749\n",
      "Epoch 78/400\n",
      "537/537 [==============================] - 0s 143us/step - loss: 0.2560 - acc: 0.9125 - val_loss: 0.6272 - val_acc: 0.7706\n",
      "Epoch 79/400\n",
      "537/537 [==============================] - 0s 111us/step - loss: 0.2547 - acc: 0.9125 - val_loss: 0.6287 - val_acc: 0.7749\n",
      "Epoch 80/400\n",
      "537/537 [==============================] - 0s 117us/step - loss: 0.2544 - acc: 0.9162 - val_loss: 0.6327 - val_acc: 0.7706\n",
      "Epoch 81/400\n",
      "537/537 [==============================] - 0s 420us/step - loss: 0.2527 - acc: 0.9069 - val_loss: 0.6273 - val_acc: 0.7706\n",
      "Epoch 82/400\n",
      "537/537 [==============================] - 0s 522us/step - loss: 0.2521 - acc: 0.9162 - val_loss: 0.6284 - val_acc: 0.7706\n",
      "Epoch 83/400\n",
      "537/537 [==============================] - 0s 788us/step - loss: 0.2510 - acc: 0.9236 - val_loss: 0.6352 - val_acc: 0.7662\n",
      "Epoch 84/400\n",
      "537/537 [==============================] - 0s 206us/step - loss: 0.2494 - acc: 0.9218 - val_loss: 0.6369 - val_acc: 0.7706\n",
      "Epoch 85/400\n",
      "537/537 [==============================] - 0s 254us/step - loss: 0.2489 - acc: 0.9125 - val_loss: 0.6300 - val_acc: 0.7706\n",
      "Epoch 86/400\n",
      "537/537 [==============================] - 0s 241us/step - loss: 0.2466 - acc: 0.9218 - val_loss: 0.6362 - val_acc: 0.7706\n",
      "Epoch 87/400\n",
      "537/537 [==============================] - 0s 789us/step - loss: 0.2464 - acc: 0.9106 - val_loss: 0.6359 - val_acc: 0.7749\n",
      "Epoch 88/400\n",
      "537/537 [==============================] - 0s 230us/step - loss: 0.2450 - acc: 0.9274 - val_loss: 0.6377 - val_acc: 0.7706\n",
      "Epoch 89/400\n",
      "537/537 [==============================] - 0s 639us/step - loss: 0.2427 - acc: 0.9181 - val_loss: 0.6357 - val_acc: 0.7706\n",
      "Epoch 90/400\n",
      "537/537 [==============================] - 0s 399us/step - loss: 0.2430 - acc: 0.9255 - val_loss: 0.6417 - val_acc: 0.7662\n",
      "Epoch 91/400\n",
      "537/537 [==============================] - 0s 253us/step - loss: 0.2436 - acc: 0.9181 - val_loss: 0.6398 - val_acc: 0.7662\n",
      "Epoch 92/400\n",
      "537/537 [==============================] - 1s 982us/step - loss: 0.2418 - acc: 0.9218 - val_loss: 0.6447 - val_acc: 0.7792\n",
      "Epoch 93/400\n",
      "537/537 [==============================] - 0s 318us/step - loss: 0.2402 - acc: 0.9181 - val_loss: 0.6445 - val_acc: 0.7619\n",
      "Epoch 94/400\n",
      "537/537 [==============================] - 0s 488us/step - loss: 0.2404 - acc: 0.9218 - val_loss: 0.6459 - val_acc: 0.7619\n",
      "Epoch 95/400\n",
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      "Epoch 96/400\n",
      "537/537 [==============================] - 0s 314us/step - loss: 0.2409 - acc: 0.9125 - val_loss: 0.6395 - val_acc: 0.7749\n",
      "Epoch 97/400\n",
      "537/537 [==============================] - 0s 631us/step - loss: 0.2362 - acc: 0.9292 - val_loss: 0.6571 - val_acc: 0.7749\n",
      "Epoch 98/400\n",
      "537/537 [==============================] - 0s 284us/step - loss: 0.2396 - acc: 0.9143 - val_loss: 0.6409 - val_acc: 0.7662\n",
      "Epoch 99/400\n",
      "537/537 [==============================] - 0s 838us/step - loss: 0.2342 - acc: 0.9292 - val_loss: 0.6605 - val_acc: 0.7749\n",
      "Epoch 100/400\n",
      "537/537 [==============================] - 0s 150us/step - loss: 0.2340 - acc: 0.9236 - val_loss: 0.6459 - val_acc: 0.7706\n",
      "Epoch 101/400\n",
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      "Epoch 102/400\n",
      "537/537 [==============================] - 0s 210us/step - loss: 0.2309 - acc: 0.9330 - val_loss: 0.6511 - val_acc: 0.7706\n",
      "Epoch 103/400\n",
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      "Epoch 104/400\n",
      "537/537 [==============================] - 0s 292us/step - loss: 0.2304 - acc: 0.9218 - val_loss: 0.6530 - val_acc: 0.7662\n",
      "Epoch 105/400\n",
      "537/537 [==============================] - 0s 178us/step - loss: 0.2295 - acc: 0.9292 - val_loss: 0.6609 - val_acc: 0.7662\n",
      "Epoch 106/400\n",
      "537/537 [==============================] - 0s 152us/step - loss: 0.2269 - acc: 0.9348 - val_loss: 0.6564 - val_acc: 0.7662\n",
      "Epoch 107/400\n",
      "537/537 [==============================] - 0s 286us/step - loss: 0.2312 - acc: 0.9218 - val_loss: 0.6640 - val_acc: 0.7662\n",
      "Epoch 108/400\n",
      "537/537 [==============================] - 0s 267us/step - loss: 0.2277 - acc: 0.9181 - val_loss: 0.6653 - val_acc: 0.7706\n",
      "Epoch 109/400\n",
      "537/537 [==============================] - 0s 213us/step - loss: 0.2235 - acc: 0.9292 - val_loss: 0.6586 - val_acc: 0.7619\n",
      "Epoch 110/400\n",
      "537/537 [==============================] - 0s 154us/step - loss: 0.2222 - acc: 0.9330 - val_loss: 0.6665 - val_acc: 0.7619\n",
      "Epoch 111/400\n",
      "537/537 [==============================] - 0s 227us/step - loss: 0.2235 - acc: 0.9292 - val_loss: 0.6660 - val_acc: 0.7619\n",
      "Epoch 112/400\n",
      "537/537 [==============================] - 0s 286us/step - loss: 0.2204 - acc: 0.9274 - val_loss: 0.6645 - val_acc: 0.7619\n",
      "Epoch 113/400\n",
      "537/537 [==============================] - 0s 251us/step - loss: 0.2209 - acc: 0.9330 - val_loss: 0.6697 - val_acc: 0.7662\n",
      "Epoch 114/400\n",
      "537/537 [==============================] - 0s 240us/step - loss: 0.2209 - acc: 0.9236 - val_loss: 0.6621 - val_acc: 0.7576\n",
      "Epoch 115/400\n",
      "537/537 [==============================] - 0s 132us/step - loss: 0.2175 - acc: 0.9330 - val_loss: 0.6745 - val_acc: 0.7619\n",
      "Epoch 116/400\n",
      "537/537 [==============================] - 0s 507us/step - loss: 0.2207 - acc: 0.9292 - val_loss: 0.6650 - val_acc: 0.7576\n",
      "Epoch 117/400\n",
      "537/537 [==============================] - 0s 249us/step - loss: 0.2185 - acc: 0.9330 - val_loss: 0.6699 - val_acc: 0.7619\n",
      "Epoch 118/400\n",
      "537/537 [==============================] - 0s 273us/step - loss: 0.2153 - acc: 0.9348 - val_loss: 0.6660 - val_acc: 0.7662\n",
      "Epoch 119/400\n",
      "537/537 [==============================] - 0s 318us/step - loss: 0.2121 - acc: 0.9348 - val_loss: 0.6780 - val_acc: 0.7619\n",
      "Epoch 120/400\n",
      "537/537 [==============================] - 0s 186us/step - loss: 0.2127 - acc: 0.9404 - val_loss: 0.6868 - val_acc: 0.7619\n",
      "Epoch 121/400\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "537/537 [==============================] - 0s 247us/step - loss: 0.2111 - acc: 0.9385 - val_loss: 0.6813 - val_acc: 0.7619\n",
      "Epoch 122/400\n",
      "537/537 [==============================] - 0s 199us/step - loss: 0.2091 - acc: 0.9385 - val_loss: 0.6863 - val_acc: 0.7576\n",
      "Epoch 123/400\n",
      "537/537 [==============================] - 0s 143us/step - loss: 0.2080 - acc: 0.9292 - val_loss: 0.6766 - val_acc: 0.7619\n",
      "Epoch 124/400\n",
      "537/537 [==============================] - 0s 237us/step - loss: 0.2179 - acc: 0.9311 - val_loss: 0.7190 - val_acc: 0.7576\n",
      "Epoch 125/400\n",
      "537/537 [==============================] - 0s 297us/step - loss: 0.2120 - acc: 0.9311 - val_loss: 0.6869 - val_acc: 0.7662\n",
      "Epoch 126/400\n",
      "537/537 [==============================] - 0s 262us/step - loss: 0.2039 - acc: 0.9348 - val_loss: 0.6827 - val_acc: 0.7576\n",
      "Epoch 127/400\n",
      "537/537 [==============================] - 0s 165us/step - loss: 0.2056 - acc: 0.9441 - val_loss: 0.7006 - val_acc: 0.7662\n",
      "Epoch 128/400\n",
      "537/537 [==============================] - 0s 264us/step - loss: 0.2074 - acc: 0.9367 - val_loss: 0.6801 - val_acc: 0.7619\n",
      "Epoch 129/400\n",
      "537/537 [==============================] - 0s 126us/step - loss: 0.2048 - acc: 0.9348 - val_loss: 0.7019 - val_acc: 0.7619\n",
      "Epoch 130/400\n",
      "537/537 [==============================] - 0s 162us/step - loss: 0.2008 - acc: 0.9423 - val_loss: 0.6941 - val_acc: 0.7532\n",
      "Epoch 131/400\n",
      "537/537 [==============================] - 0s 232us/step - loss: 0.1998 - acc: 0.9441 - val_loss: 0.7136 - val_acc: 0.7619\n",
      "Epoch 132/400\n",
      "537/537 [==============================] - 0s 136us/step - loss: 0.2022 - acc: 0.9367 - val_loss: 0.6953 - val_acc: 0.7532\n",
      "Epoch 133/400\n",
      "537/537 [==============================] - 0s 227us/step - loss: 0.2018 - acc: 0.9292 - val_loss: 0.6891 - val_acc: 0.7273\n",
      "Epoch 134/400\n",
      "537/537 [==============================] - 0s 130us/step - loss: 0.2078 - acc: 0.9218 - val_loss: 0.7633 - val_acc: 0.7446\n",
      "Epoch 135/400\n",
      "537/537 [==============================] - 0s 173us/step - loss: 0.2063 - acc: 0.9218 - val_loss: 0.7107 - val_acc: 0.7619\n",
      "Epoch 136/400\n",
      "537/537 [==============================] - 0s 212us/step - loss: 0.1955 - acc: 0.9404 - val_loss: 0.7076 - val_acc: 0.7489\n",
      "Epoch 137/400\n",
      "537/537 [==============================] - 0s 373us/step - loss: 0.1951 - acc: 0.9385 - val_loss: 0.7050 - val_acc: 0.7489\n",
      "Epoch 138/400\n",
      "537/537 [==============================] - 0s 163us/step - loss: 0.1918 - acc: 0.9404 - val_loss: 0.7095 - val_acc: 0.7619\n",
      "Epoch 139/400\n",
      "537/537 [==============================] - 0s 249us/step - loss: 0.1937 - acc: 0.9441 - val_loss: 0.7022 - val_acc: 0.7576\n",
      "Epoch 140/400\n",
      "537/537 [==============================] - 0s 137us/step - loss: 0.1932 - acc: 0.9404 - val_loss: 0.7157 - val_acc: 0.7489\n",
      "Epoch 141/400\n",
      "537/537 [==============================] - 0s 256us/step - loss: 0.1918 - acc: 0.9441 - val_loss: 0.7092 - val_acc: 0.7532\n",
      "Epoch 142/400\n",
      "537/537 [==============================] - 0s 256us/step - loss: 0.1921 - acc: 0.9441 - val_loss: 0.7428 - val_acc: 0.7532\n",
      "Epoch 143/400\n",
      "537/537 [==============================] - 0s 212us/step - loss: 0.2005 - acc: 0.9348 - val_loss: 0.7492 - val_acc: 0.7403\n",
      "Epoch 144/400\n",
      "537/537 [==============================] - 0s 162us/step - loss: 0.2032 - acc: 0.9218 - val_loss: 0.7176 - val_acc: 0.7186\n",
      "Epoch 145/400\n",
      "537/537 [==============================] - 0s 279us/step - loss: 0.2055 - acc: 0.9311 - val_loss: 0.7415 - val_acc: 0.7489\n",
      "Epoch 146/400\n",
      "537/537 [==============================] - 0s 132us/step - loss: 0.1910 - acc: 0.9441 - val_loss: 0.7226 - val_acc: 0.7532\n",
      "Epoch 147/400\n",
      "537/537 [==============================] - 0s 357us/step - loss: 0.1838 - acc: 0.9479 - val_loss: 0.7134 - val_acc: 0.7576\n",
      "Epoch 148/400\n",
      "537/537 [==============================] - 0s 236us/step - loss: 0.1827 - acc: 0.9441 - val_loss: 0.7339 - val_acc: 0.7446\n",
      "Epoch 149/400\n",
      "537/537 [==============================] - 0s 286us/step - loss: 0.1875 - acc: 0.9441 - val_loss: 0.7338 - val_acc: 0.7403\n",
      "Epoch 150/400\n",
      "537/537 [==============================] - 0s 236us/step - loss: 0.1850 - acc: 0.9441 - val_loss: 0.7162 - val_acc: 0.7446\n",
      "Epoch 151/400\n",
      "537/537 [==============================] - 0s 219us/step - loss: 0.1818 - acc: 0.9441 - val_loss: 0.7261 - val_acc: 0.7446\n",
      "Epoch 152/400\n",
      "537/537 [==============================] - 0s 303us/step - loss: 0.1822 - acc: 0.9404 - val_loss: 0.7541 - val_acc: 0.7446\n",
      "Epoch 153/400\n",
      "537/537 [==============================] - 0s 405us/step - loss: 0.1788 - acc: 0.9460 - val_loss: 0.7411 - val_acc: 0.7403\n",
      "Epoch 154/400\n",
      "537/537 [==============================] - 0s 344us/step - loss: 0.1758 - acc: 0.9460 - val_loss: 0.7385 - val_acc: 0.7446\n",
      "Epoch 155/400\n",
      "537/537 [==============================] - 0s 119us/step - loss: 0.1763 - acc: 0.9460 - val_loss: 0.7384 - val_acc: 0.7489\n",
      "Epoch 156/400\n",
      "537/537 [==============================] - 0s 409us/step - loss: 0.1749 - acc: 0.9516 - val_loss: 0.7309 - val_acc: 0.7532\n",
      "Epoch 157/400\n",
      "537/537 [==============================] - 0s 318us/step - loss: 0.1824 - acc: 0.9441 - val_loss: 0.7331 - val_acc: 0.7316\n",
      "Epoch 158/400\n",
      "537/537 [==============================] - 0s 167us/step - loss: 0.1891 - acc: 0.9367 - val_loss: 0.7898 - val_acc: 0.7403\n",
      "Epoch 159/400\n",
      "537/537 [==============================] - 0s 226us/step - loss: 0.1761 - acc: 0.9479 - val_loss: 0.7640 - val_acc: 0.7489\n",
      "Epoch 160/400\n",
      "537/537 [==============================] - 0s 373us/step - loss: 0.1702 - acc: 0.9460 - val_loss: 0.7406 - val_acc: 0.7489\n",
      "Epoch 161/400\n",
      "537/537 [==============================] - 0s 182us/step - loss: 0.1740 - acc: 0.9479 - val_loss: 0.7438 - val_acc: 0.7489\n",
      "Epoch 162/400\n",
      "537/537 [==============================] - 1s 2ms/step - loss: 0.1759 - acc: 0.9423 - val_loss: 0.7630 - val_acc: 0.7446\n",
      "Epoch 163/400\n",
      "537/537 [==============================] - 0s 663us/step - loss: 0.1671 - acc: 0.9497 - val_loss: 0.7658 - val_acc: 0.7489\n",
      "Epoch 164/400\n",
      "537/537 [==============================] - 0s 217us/step - loss: 0.1703 - acc: 0.9460 - val_loss: 0.7701 - val_acc: 0.7446\n",
      "Epoch 165/400\n",
      "537/537 [==============================] - 0s 338us/step - loss: 0.1699 - acc: 0.9460 - val_loss: 0.7700 - val_acc: 0.7446\n",
      "Epoch 166/400\n",
      "537/537 [==============================] - 0s 232us/step - loss: 0.1638 - acc: 0.9516 - val_loss: 0.7558 - val_acc: 0.7359\n",
      "Epoch 167/400\n",
      "537/537 [==============================] - 0s 173us/step - loss: 0.1665 - acc: 0.9479 - val_loss: 0.7934 - val_acc: 0.7403\n",
      "Epoch 168/400\n",
      "537/537 [==============================] - 0s 175us/step - loss: 0.1640 - acc: 0.9534 - val_loss: 0.7764 - val_acc: 0.7403\n",
      "Epoch 169/400\n",
      "537/537 [==============================] - 0s 274us/step - loss: 0.1621 - acc: 0.9534 - val_loss: 0.7650 - val_acc: 0.7446\n",
      "Epoch 170/400\n",
      "537/537 [==============================] - 0s 130us/step - loss: 0.1603 - acc: 0.9460 - val_loss: 0.7748 - val_acc: 0.7446\n",
      "Epoch 171/400\n",
      "537/537 [==============================] - 0s 223us/step - loss: 0.1604 - acc: 0.9497 - val_loss: 0.7811 - val_acc: 0.7403\n",
      "Epoch 172/400\n",
      "537/537 [==============================] - 0s 186us/step - loss: 0.1582 - acc: 0.9497 - val_loss: 0.7778 - val_acc: 0.7403\n",
      "Epoch 173/400\n",
      "537/537 [==============================] - 0s 921us/step - loss: 0.1642 - acc: 0.9479 - val_loss: 0.7732 - val_acc: 0.7316\n",
      "Epoch 174/400\n",
      "537/537 [==============================] - 0s 579us/step - loss: 0.1587 - acc: 0.9590 - val_loss: 0.7792 - val_acc: 0.7489\n",
      "Epoch 175/400\n",
      "537/537 [==============================] - 0s 330us/step - loss: 0.1587 - acc: 0.9534 - val_loss: 0.7771 - val_acc: 0.7403\n",
      "Epoch 176/400\n",
      "537/537 [==============================] - 0s 318us/step - loss: 0.1575 - acc: 0.9460 - val_loss: 0.7811 - val_acc: 0.7489\n",
      "Epoch 177/400\n",
      "537/537 [==============================] - 0s 306us/step - loss: 0.1545 - acc: 0.9534 - val_loss: 0.7959 - val_acc: 0.7489\n",
      "Epoch 178/400\n",
      "537/537 [==============================] - 0s 299us/step - loss: 0.1563 - acc: 0.9534 - val_loss: 0.8041 - val_acc: 0.7359\n",
      "Epoch 179/400\n",
      "537/537 [==============================] - 0s 214us/step - loss: 0.1554 - acc: 0.9516 - val_loss: 0.8240 - val_acc: 0.7446\n",
      "Epoch 180/400\n",
      "537/537 [==============================] - 0s 182us/step - loss: 0.1531 - acc: 0.9516 - val_loss: 0.8001 - val_acc: 0.7446\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 181/400\n",
      "537/537 [==============================] - 0s 431us/step - loss: 0.1495 - acc: 0.9534 - val_loss: 0.8108 - val_acc: 0.7316\n",
      "Epoch 182/400\n",
      "537/537 [==============================] - 1s 1ms/step - loss: 0.1490 - acc: 0.9497 - val_loss: 0.8019 - val_acc: 0.7403\n",
      "Epoch 183/400\n",
      "537/537 [==============================] - 0s 241us/step - loss: 0.1516 - acc: 0.9516 - val_loss: 0.8055 - val_acc: 0.7359\n",
      "Epoch 184/400\n",
      "537/537 [==============================] - 0s 158us/step - loss: 0.1480 - acc: 0.9497 - val_loss: 0.8100 - val_acc: 0.7359\n",
      "Epoch 185/400\n",
      "537/537 [==============================] - 0s 226us/step - loss: 0.1450 - acc: 0.9534 - val_loss: 0.8077 - val_acc: 0.7316\n",
      "Epoch 186/400\n",
      "537/537 [==============================] - 0s 141us/step - loss: 0.1456 - acc: 0.9572 - val_loss: 0.8091 - val_acc: 0.7446\n",
      "Epoch 187/400\n",
      "537/537 [==============================] - 0s 292us/step - loss: 0.1466 - acc: 0.9516 - val_loss: 0.8142 - val_acc: 0.7359\n",
      "Epoch 188/400\n",
      "537/537 [==============================] - 0s 273us/step - loss: 0.1410 - acc: 0.9534 - val_loss: 0.8048 - val_acc: 0.7359\n",
      "Epoch 189/400\n",
      "537/537 [==============================] - 0s 195us/step - loss: 0.1449 - acc: 0.9609 - val_loss: 0.8195 - val_acc: 0.7316\n",
      "Epoch 190/400\n",
      "537/537 [==============================] - 0s 251us/step - loss: 0.1398 - acc: 0.9590 - val_loss: 0.8405 - val_acc: 0.7403\n",
      "Epoch 191/400\n",
      "537/537 [==============================] - 0s 247us/step - loss: 0.1382 - acc: 0.9609 - val_loss: 0.8129 - val_acc: 0.7273\n",
      "Epoch 192/400\n",
      "537/537 [==============================] - 0s 286us/step - loss: 0.1388 - acc: 0.9553 - val_loss: 0.8576 - val_acc: 0.7316\n",
      "Epoch 193/400\n",
      "537/537 [==============================] - 0s 197us/step - loss: 0.1457 - acc: 0.9516 - val_loss: 0.8518 - val_acc: 0.7316\n",
      "Epoch 194/400\n",
      "537/537 [==============================] - 0s 136us/step - loss: 0.1399 - acc: 0.9590 - val_loss: 0.8296 - val_acc: 0.7403\n",
      "Epoch 195/400\n",
      "537/537 [==============================] - 0s 310us/step - loss: 0.1408 - acc: 0.9572 - val_loss: 0.8624 - val_acc: 0.7359\n",
      "Epoch 196/400\n",
      "537/537 [==============================] - 0s 189us/step - loss: 0.1400 - acc: 0.9572 - val_loss: 0.8247 - val_acc: 0.7273\n",
      "Epoch 197/400\n",
      "537/537 [==============================] - 0s 306us/step - loss: 0.1358 - acc: 0.9609 - val_loss: 0.8241 - val_acc: 0.7359\n",
      "Epoch 198/400\n",
      "537/537 [==============================] - 0s 307us/step - loss: 0.1326 - acc: 0.9609 - val_loss: 0.8270 - val_acc: 0.7446\n",
      "Epoch 199/400\n",
      "537/537 [==============================] - 0s 163us/step - loss: 0.1329 - acc: 0.9683 - val_loss: 0.8502 - val_acc: 0.7359\n",
      "Epoch 200/400\n",
      "537/537 [==============================] - 0s 290us/step - loss: 0.1308 - acc: 0.9609 - val_loss: 0.8295 - val_acc: 0.7446\n",
      "Epoch 201/400\n",
      "537/537 [==============================] - 0s 384us/step - loss: 0.1288 - acc: 0.9590 - val_loss: 0.8510 - val_acc: 0.7316\n",
      "Epoch 202/400\n",
      "537/537 [==============================] - 0s 168us/step - loss: 0.1284 - acc: 0.9628 - val_loss: 0.8266 - val_acc: 0.7403\n",
      "Epoch 203/400\n",
      "537/537 [==============================] - 0s 263us/step - loss: 0.1294 - acc: 0.9628 - val_loss: 0.8227 - val_acc: 0.7316\n",
      "Epoch 204/400\n",
      "537/537 [==============================] - 0s 162us/step - loss: 0.1343 - acc: 0.9609 - val_loss: 0.8413 - val_acc: 0.7316\n",
      "Epoch 205/400\n",
      "537/537 [==============================] - 0s 275us/step - loss: 0.1343 - acc: 0.9534 - val_loss: 0.8388 - val_acc: 0.7273\n",
      "Epoch 206/400\n",
      "537/537 [==============================] - 0s 308us/step - loss: 0.1311 - acc: 0.9646 - val_loss: 0.9128 - val_acc: 0.7229\n",
      "Epoch 207/400\n",
      "537/537 [==============================] - 0s 299us/step - loss: 0.1309 - acc: 0.9609 - val_loss: 0.8608 - val_acc: 0.7316\n",
      "Epoch 208/400\n",
      "537/537 [==============================] - 0s 256us/step - loss: 0.1259 - acc: 0.9590 - val_loss: 0.8715 - val_acc: 0.7186\n",
      "Epoch 209/400\n",
      "537/537 [==============================] - 0s 356us/step - loss: 0.1236 - acc: 0.9665 - val_loss: 0.8391 - val_acc: 0.7446\n",
      "Epoch 210/400\n",
      "537/537 [==============================] - 0s 201us/step - loss: 0.1258 - acc: 0.9665 - val_loss: 0.8419 - val_acc: 0.7316\n",
      "Epoch 211/400\n",
      "537/537 [==============================] - 0s 303us/step - loss: 0.1300 - acc: 0.9609 - val_loss: 0.8733 - val_acc: 0.7273\n",
      "Epoch 212/400\n",
      "537/537 [==============================] - 0s 186us/step - loss: 0.1291 - acc: 0.9590 - val_loss: 0.8773 - val_acc: 0.7229\n",
      "Epoch 213/400\n",
      "537/537 [==============================] - 0s 258us/step - loss: 0.1225 - acc: 0.9665 - val_loss: 0.8836 - val_acc: 0.7316\n",
      "Epoch 214/400\n",
      "537/537 [==============================] - 0s 191us/step - loss: 0.1195 - acc: 0.9665 - val_loss: 0.8579 - val_acc: 0.7403\n",
      "Epoch 215/400\n",
      "537/537 [==============================] - 0s 299us/step - loss: 0.1176 - acc: 0.9739 - val_loss: 0.8811 - val_acc: 0.7316\n",
      "Epoch 216/400\n",
      "537/537 [==============================] - 0s 210us/step - loss: 0.1193 - acc: 0.9683 - val_loss: 0.8668 - val_acc: 0.7316\n",
      "Epoch 217/400\n",
      "537/537 [==============================] - 0s 230us/step - loss: 0.1192 - acc: 0.9628 - val_loss: 0.8744 - val_acc: 0.7316\n",
      "Epoch 218/400\n",
      "537/537 [==============================] - 0s 137us/step - loss: 0.1157 - acc: 0.9683 - val_loss: 0.8980 - val_acc: 0.7186\n",
      "Epoch 219/400\n",
      "537/537 [==============================] - 0s 214us/step - loss: 0.1188 - acc: 0.9646 - val_loss: 0.8636 - val_acc: 0.7359\n",
      "Epoch 220/400\n",
      "537/537 [==============================] - 0s 200us/step - loss: 0.1134 - acc: 0.9739 - val_loss: 0.8915 - val_acc: 0.7273\n",
      "Epoch 221/400\n",
      "537/537 [==============================] - 0s 266us/step - loss: 0.1137 - acc: 0.9683 - val_loss: 0.8607 - val_acc: 0.7446\n",
      "Epoch 222/400\n",
      "537/537 [==============================] - 0s 200us/step - loss: 0.1150 - acc: 0.9683 - val_loss: 0.8652 - val_acc: 0.7316\n",
      "Epoch 223/400\n",
      "537/537 [==============================] - 0s 171us/step - loss: 0.1163 - acc: 0.9702 - val_loss: 0.8615 - val_acc: 0.7359\n",
      "Epoch 224/400\n",
      "537/537 [==============================] - 0s 215us/step - loss: 0.1143 - acc: 0.9702 - val_loss: 0.8951 - val_acc: 0.7316\n",
      "Epoch 225/400\n",
      "537/537 [==============================] - 0s 221us/step - loss: 0.1237 - acc: 0.9572 - val_loss: 0.9155 - val_acc: 0.7273\n",
      "Epoch 226/400\n",
      "537/537 [==============================] - 0s 269us/step - loss: 0.1156 - acc: 0.9628 - val_loss: 0.8866 - val_acc: 0.7359\n",
      "Epoch 227/400\n",
      "537/537 [==============================] - 0s 264us/step - loss: 0.1115 - acc: 0.9646 - val_loss: 0.8718 - val_acc: 0.7316\n",
      "Epoch 228/400\n",
      "537/537 [==============================] - 0s 215us/step - loss: 0.1108 - acc: 0.9683 - val_loss: 0.8682 - val_acc: 0.7316\n",
      "Epoch 229/400\n",
      "537/537 [==============================] - 0s 408us/step - loss: 0.1106 - acc: 0.9721 - val_loss: 0.8773 - val_acc: 0.7359\n",
      "Epoch 230/400\n",
      "537/537 [==============================] - 0s 108us/step - loss: 0.1101 - acc: 0.9702 - val_loss: 0.8901 - val_acc: 0.7273\n",
      "Epoch 231/400\n",
      "537/537 [==============================] - 0s 254us/step - loss: 0.1083 - acc: 0.9702 - val_loss: 0.8644 - val_acc: 0.7359\n",
      "Epoch 232/400\n",
      "537/537 [==============================] - 0s 283us/step - loss: 0.1088 - acc: 0.9721 - val_loss: 0.8781 - val_acc: 0.7359\n",
      "Epoch 233/400\n",
      "537/537 [==============================] - 0s 329us/step - loss: 0.1059 - acc: 0.9721 - val_loss: 0.8893 - val_acc: 0.7359\n",
      "Epoch 234/400\n",
      "537/537 [==============================] - 0s 121us/step - loss: 0.1024 - acc: 0.9702 - val_loss: 0.9261 - val_acc: 0.7316\n",
      "Epoch 235/400\n",
      "537/537 [==============================] - 0s 224us/step - loss: 0.1030 - acc: 0.9758 - val_loss: 0.8934 - val_acc: 0.7359\n",
      "Epoch 236/400\n",
      "537/537 [==============================] - 0s 184us/step - loss: 0.1032 - acc: 0.9702 - val_loss: 0.9723 - val_acc: 0.7273\n",
      "Epoch 237/400\n",
      "537/537 [==============================] - 0s 249us/step - loss: 0.1052 - acc: 0.9739 - val_loss: 0.9487 - val_acc: 0.7186\n",
      "Epoch 238/400\n",
      "537/537 [==============================] - 0s 405us/step - loss: 0.1061 - acc: 0.9721 - val_loss: 0.9113 - val_acc: 0.7359\n",
      "Epoch 239/400\n",
      "537/537 [==============================] - 0s 223us/step - loss: 0.1002 - acc: 0.9795 - val_loss: 0.9070 - val_acc: 0.7403\n",
      "Epoch 240/400\n",
      "537/537 [==============================] - 0s 269us/step - loss: 0.1027 - acc: 0.9721 - val_loss: 0.9162 - val_acc: 0.7359\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 241/400\n",
      "537/537 [==============================] - 0s 111us/step - loss: 0.1001 - acc: 0.9795 - val_loss: 0.9264 - val_acc: 0.7403\n",
      "Epoch 242/400\n",
      "537/537 [==============================] - 0s 172us/step - loss: 0.1008 - acc: 0.9739 - val_loss: 0.9039 - val_acc: 0.7403\n",
      "Epoch 243/400\n",
      "537/537 [==============================] - 0s 245us/step - loss: 0.0950 - acc: 0.9777 - val_loss: 0.9116 - val_acc: 0.7446\n",
      "Epoch 244/400\n",
      "537/537 [==============================] - 0s 203us/step - loss: 0.1072 - acc: 0.9628 - val_loss: 0.9059 - val_acc: 0.7403\n",
      "Epoch 245/400\n",
      "537/537 [==============================] - 0s 260us/step - loss: 0.1062 - acc: 0.9702 - val_loss: 0.9040 - val_acc: 0.7446\n",
      "Epoch 246/400\n",
      "537/537 [==============================] - 0s 221us/step - loss: 0.0979 - acc: 0.9739 - val_loss: 0.9256 - val_acc: 0.7186\n",
      "Epoch 247/400\n",
      "537/537 [==============================] - 0s 150us/step - loss: 0.0960 - acc: 0.9758 - val_loss: 0.9130 - val_acc: 0.7446\n",
      "Epoch 248/400\n",
      "537/537 [==============================] - 0s 173us/step - loss: 0.1001 - acc: 0.9683 - val_loss: 0.9204 - val_acc: 0.7403\n",
      "Epoch 249/400\n",
      "537/537 [==============================] - 0s 241us/step - loss: 0.0930 - acc: 0.9777 - val_loss: 0.9475 - val_acc: 0.7316\n",
      "Epoch 250/400\n",
      "537/537 [==============================] - 0s 364us/step - loss: 0.0916 - acc: 0.9795 - val_loss: 0.9371 - val_acc: 0.7359\n",
      "Epoch 251/400\n",
      "537/537 [==============================] - 0s 247us/step - loss: 0.0922 - acc: 0.9758 - val_loss: 1.0020 - val_acc: 0.7273\n",
      "Epoch 252/400\n",
      "537/537 [==============================] - 0s 290us/step - loss: 0.0975 - acc: 0.9739 - val_loss: 0.9486 - val_acc: 0.7273\n",
      "Epoch 253/400\n",
      "537/537 [==============================] - 0s 137us/step - loss: 0.0977 - acc: 0.9739 - val_loss: 0.9457 - val_acc: 0.7359\n",
      "Epoch 254/400\n",
      "537/537 [==============================] - 0s 163us/step - loss: 0.0918 - acc: 0.9739 - val_loss: 0.9941 - val_acc: 0.7229\n",
      "Epoch 255/400\n",
      "537/537 [==============================] - 0s 208us/step - loss: 0.0888 - acc: 0.9777 - val_loss: 0.9326 - val_acc: 0.7316\n",
      "Epoch 256/400\n",
      "537/537 [==============================] - 0s 232us/step - loss: 0.0883 - acc: 0.9777 - val_loss: 0.9767 - val_acc: 0.7403\n",
      "Epoch 257/400\n",
      "537/537 [==============================] - 0s 260us/step - loss: 0.0909 - acc: 0.9777 - val_loss: 0.9534 - val_acc: 0.7273\n",
      "Epoch 258/400\n",
      "537/537 [==============================] - 0s 238us/step - loss: 0.0909 - acc: 0.9758 - val_loss: 0.9468 - val_acc: 0.7359\n",
      "Epoch 259/400\n",
      "537/537 [==============================] - 0s 163us/step - loss: 0.0955 - acc: 0.9721 - val_loss: 0.9403 - val_acc: 0.7446\n",
      "Epoch 260/400\n",
      "537/537 [==============================] - 0s 332us/step - loss: 0.0868 - acc: 0.9777 - val_loss: 0.9677 - val_acc: 0.7316\n",
      "Epoch 261/400\n",
      "537/537 [==============================] - 0s 217us/step - loss: 0.0880 - acc: 0.9777 - val_loss: 0.9812 - val_acc: 0.7359\n",
      "Epoch 262/400\n",
      "537/537 [==============================] - 0s 247us/step - loss: 0.0910 - acc: 0.9777 - val_loss: 1.0209 - val_acc: 0.7316\n",
      "Epoch 263/400\n",
      "537/537 [==============================] - 0s 225us/step - loss: 0.0979 - acc: 0.9721 - val_loss: 0.9738 - val_acc: 0.7186\n",
      "Epoch 264/400\n",
      "537/537 [==============================] - 0s 186us/step - loss: 0.0867 - acc: 0.9758 - val_loss: 1.0131 - val_acc: 0.7316\n",
      "Epoch 265/400\n",
      "537/537 [==============================] - 0s 175us/step - loss: 0.0899 - acc: 0.9758 - val_loss: 0.9729 - val_acc: 0.7359\n",
      "Epoch 266/400\n",
      "537/537 [==============================] - 0s 206us/step - loss: 0.0987 - acc: 0.9683 - val_loss: 1.0042 - val_acc: 0.7229\n",
      "Epoch 267/400\n",
      "537/537 [==============================] - 0s 171us/step - loss: 0.0913 - acc: 0.9795 - val_loss: 1.0482 - val_acc: 0.7273\n",
      "Epoch 268/400\n",
      "537/537 [==============================] - 0s 329us/step - loss: 0.0888 - acc: 0.9739 - val_loss: 1.0045 - val_acc: 0.7273\n",
      "Epoch 269/400\n",
      "537/537 [==============================] - 0s 132us/step - loss: 0.0843 - acc: 0.9814 - val_loss: 0.9468 - val_acc: 0.7316\n",
      "Epoch 270/400\n",
      "537/537 [==============================] - 0s 262us/step - loss: 0.0858 - acc: 0.9795 - val_loss: 0.9778 - val_acc: 0.7316\n",
      "Epoch 271/400\n",
      "537/537 [==============================] - 0s 253us/step - loss: 0.0800 - acc: 0.9795 - val_loss: 0.9500 - val_acc: 0.7316\n",
      "Epoch 272/400\n",
      "537/537 [==============================] - 0s 173us/step - loss: 0.0826 - acc: 0.9777 - val_loss: 0.9625 - val_acc: 0.7316\n",
      "Epoch 273/400\n",
      "537/537 [==============================] - 0s 342us/step - loss: 0.0865 - acc: 0.9777 - val_loss: 0.9643 - val_acc: 0.7359\n",
      "Epoch 274/400\n",
      "537/537 [==============================] - 0s 301us/step - loss: 0.0802 - acc: 0.9851 - val_loss: 1.0173 - val_acc: 0.7273\n",
      "Epoch 275/400\n",
      "537/537 [==============================] - 0s 152us/step - loss: 0.0878 - acc: 0.9758 - val_loss: 1.0073 - val_acc: 0.7273\n",
      "Epoch 276/400\n",
      "537/537 [==============================] - 0s 192us/step - loss: 0.0871 - acc: 0.9777 - val_loss: 1.0118 - val_acc: 0.7316\n",
      "Epoch 277/400\n",
      "537/537 [==============================] - 0s 243us/step - loss: 0.0801 - acc: 0.9814 - val_loss: 1.0019 - val_acc: 0.7446\n",
      "Epoch 278/400\n",
      "537/537 [==============================] - 0s 305us/step - loss: 0.0756 - acc: 0.9832 - val_loss: 1.0093 - val_acc: 0.7229\n",
      "Epoch 279/400\n",
      "537/537 [==============================] - 0s 269us/step - loss: 0.0755 - acc: 0.9832 - val_loss: 0.9931 - val_acc: 0.7359\n",
      "Epoch 280/400\n",
      "537/537 [==============================] - 0s 316us/step - loss: 0.0749 - acc: 0.9851 - val_loss: 1.0299 - val_acc: 0.7273\n",
      "Epoch 281/400\n",
      "537/537 [==============================] - 0s 323us/step - loss: 0.0799 - acc: 0.9739 - val_loss: 1.0252 - val_acc: 0.7229\n",
      "Epoch 282/400\n",
      "537/537 [==============================] - 0s 368us/step - loss: 0.0751 - acc: 0.9814 - val_loss: 1.0250 - val_acc: 0.7229\n",
      "Epoch 283/400\n",
      "537/537 [==============================] - 0s 228us/step - loss: 0.0759 - acc: 0.9851 - val_loss: 1.0381 - val_acc: 0.7316\n",
      "Epoch 284/400\n",
      "537/537 [==============================] - 0s 186us/step - loss: 0.0715 - acc: 0.9851 - val_loss: 1.0153 - val_acc: 0.7316\n",
      "Epoch 285/400\n",
      "537/537 [==============================] - 0s 240us/step - loss: 0.0719 - acc: 0.9870 - val_loss: 1.0623 - val_acc: 0.7229\n",
      "Epoch 286/400\n",
      "537/537 [==============================] - 0s 126us/step - loss: 0.0748 - acc: 0.9814 - val_loss: 1.0523 - val_acc: 0.7186\n",
      "Epoch 287/400\n",
      "537/537 [==============================] - 0s 238us/step - loss: 0.0719 - acc: 0.9832 - val_loss: 0.9979 - val_acc: 0.7359\n",
      "Epoch 288/400\n",
      "537/537 [==============================] - 0s 436us/step - loss: 0.0728 - acc: 0.9758 - val_loss: 1.0300 - val_acc: 0.7273\n",
      "Epoch 289/400\n",
      "537/537 [==============================] - 0s 119us/step - loss: 0.0707 - acc: 0.9851 - val_loss: 1.0218 - val_acc: 0.7359\n",
      "Epoch 290/400\n",
      "537/537 [==============================] - 0s 249us/step - loss: 0.0723 - acc: 0.9832 - val_loss: 1.0490 - val_acc: 0.7403\n",
      "Epoch 291/400\n",
      "537/537 [==============================] - 0s 190us/step - loss: 0.0694 - acc: 0.9851 - val_loss: 1.0403 - val_acc: 0.7229\n",
      "Epoch 292/400\n",
      "537/537 [==============================] - 0s 420us/step - loss: 0.0720 - acc: 0.9870 - val_loss: 1.1133 - val_acc: 0.7273\n",
      "Epoch 293/400\n",
      "537/537 [==============================] - 0s 353us/step - loss: 0.0738 - acc: 0.9832 - val_loss: 1.1054 - val_acc: 0.7273\n",
      "Epoch 294/400\n",
      "537/537 [==============================] - 0s 262us/step - loss: 0.0790 - acc: 0.9777 - val_loss: 1.0716 - val_acc: 0.7186\n",
      "Epoch 295/400\n",
      "537/537 [==============================] - 0s 150us/step - loss: 0.0726 - acc: 0.9814 - val_loss: 1.0493 - val_acc: 0.7359\n",
      "Epoch 296/400\n",
      "537/537 [==============================] - 0s 139us/step - loss: 0.0666 - acc: 0.9888 - val_loss: 1.0815 - val_acc: 0.7186\n",
      "Epoch 297/400\n",
      "537/537 [==============================] - 0s 150us/step - loss: 0.0678 - acc: 0.9870 - val_loss: 1.0673 - val_acc: 0.7229\n",
      "Epoch 298/400\n",
      "537/537 [==============================] - 0s 212us/step - loss: 0.0671 - acc: 0.9888 - val_loss: 1.0382 - val_acc: 0.7359\n",
      "Epoch 299/400\n",
      "537/537 [==============================] - 0s 204us/step - loss: 0.0646 - acc: 0.9870 - val_loss: 1.0574 - val_acc: 0.7316\n",
      "Epoch 300/400\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "537/537 [==============================] - 0s 297us/step - loss: 0.0634 - acc: 0.9907 - val_loss: 1.0640 - val_acc: 0.7359\n",
      "Epoch 301/400\n",
      "537/537 [==============================] - 0s 143us/step - loss: 0.0623 - acc: 0.9870 - val_loss: 1.0616 - val_acc: 0.7316\n",
      "Epoch 302/400\n",
      "537/537 [==============================] - 0s 162us/step - loss: 0.0620 - acc: 0.9870 - val_loss: 1.0948 - val_acc: 0.7273\n",
      "Epoch 303/400\n",
      "537/537 [==============================] - 0s 169us/step - loss: 0.0648 - acc: 0.9851 - val_loss: 1.0450 - val_acc: 0.7273\n",
      "Epoch 304/400\n",
      "537/537 [==============================] - 0s 303us/step - loss: 0.0632 - acc: 0.9888 - val_loss: 1.0597 - val_acc: 0.7316\n",
      "Epoch 305/400\n",
      "537/537 [==============================] - 0s 165us/step - loss: 0.0655 - acc: 0.9888 - val_loss: 1.0847 - val_acc: 0.7403\n",
      "Epoch 306/400\n",
      "537/537 [==============================] - 0s 264us/step - loss: 0.0647 - acc: 0.9870 - val_loss: 1.0686 - val_acc: 0.7273\n",
      "Epoch 307/400\n",
      "537/537 [==============================] - 0s 205us/step - loss: 0.0613 - acc: 0.9888 - val_loss: 1.0874 - val_acc: 0.7359\n",
      "Epoch 308/400\n",
      "537/537 [==============================] - 0s 143us/step - loss: 0.0604 - acc: 0.9888 - val_loss: 1.0813 - val_acc: 0.7316\n",
      "Epoch 309/400\n",
      "537/537 [==============================] - 0s 306us/step - loss: 0.0614 - acc: 0.9870 - val_loss: 1.0907 - val_acc: 0.7403\n",
      "Epoch 310/400\n",
      "537/537 [==============================] - 0s 268us/step - loss: 0.0624 - acc: 0.9888 - val_loss: 1.0935 - val_acc: 0.7403\n",
      "Epoch 311/400\n",
      "537/537 [==============================] - 0s 206us/step - loss: 0.0593 - acc: 0.9888 - val_loss: 1.0648 - val_acc: 0.7359\n",
      "Epoch 312/400\n",
      "537/537 [==============================] - 0s 334us/step - loss: 0.0594 - acc: 0.9870 - val_loss: 1.0616 - val_acc: 0.7316\n",
      "Epoch 313/400\n",
      "537/537 [==============================] - 0s 254us/step - loss: 0.0579 - acc: 0.9888 - val_loss: 1.0839 - val_acc: 0.7403\n",
      "Epoch 314/400\n",
      "537/537 [==============================] - 0s 179us/step - loss: 0.0570 - acc: 0.9907 - val_loss: 1.1042 - val_acc: 0.7316\n",
      "Epoch 315/400\n",
      "537/537 [==============================] - 0s 358us/step - loss: 0.0582 - acc: 0.9907 - val_loss: 1.0893 - val_acc: 0.7359\n",
      "Epoch 316/400\n",
      "537/537 [==============================] - 0s 165us/step - loss: 0.0582 - acc: 0.9888 - val_loss: 1.1352 - val_acc: 0.7273\n",
      "Epoch 317/400\n",
      "537/537 [==============================] - 0s 236us/step - loss: 0.0595 - acc: 0.9888 - val_loss: 1.1030 - val_acc: 0.7316\n",
      "Epoch 318/400\n",
      "537/537 [==============================] - 0s 223us/step - loss: 0.0569 - acc: 0.9907 - val_loss: 1.1427 - val_acc: 0.7229\n",
      "Epoch 319/400\n",
      "537/537 [==============================] - 0s 195us/step - loss: 0.0611 - acc: 0.9870 - val_loss: 1.1422 - val_acc: 0.7316\n",
      "Epoch 320/400\n",
      "537/537 [==============================] - 0s 214us/step - loss: 0.0597 - acc: 0.9907 - val_loss: 1.1348 - val_acc: 0.7273\n",
      "Epoch 321/400\n",
      "537/537 [==============================] - 0s 199us/step - loss: 0.0568 - acc: 0.9870 - val_loss: 1.1144 - val_acc: 0.7359\n",
      "Epoch 322/400\n",
      "537/537 [==============================] - 0s 171us/step - loss: 0.0578 - acc: 0.9888 - val_loss: 1.0890 - val_acc: 0.7359\n",
      "Epoch 323/400\n",
      "537/537 [==============================] - 0s 266us/step - loss: 0.0543 - acc: 0.9907 - val_loss: 1.1182 - val_acc: 0.7403\n",
      "Epoch 324/400\n",
      "537/537 [==============================] - 0s 132us/step - loss: 0.0528 - acc: 0.9907 - val_loss: 1.0985 - val_acc: 0.7273\n",
      "Epoch 325/400\n",
      "537/537 [==============================] - 0s 152us/step - loss: 0.0539 - acc: 0.9907 - val_loss: 1.1020 - val_acc: 0.7273\n",
      "Epoch 326/400\n",
      "537/537 [==============================] - 0s 154us/step - loss: 0.0553 - acc: 0.9907 - val_loss: 1.0957 - val_acc: 0.7316\n",
      "Epoch 327/400\n",
      "537/537 [==============================] - 0s 308us/step - loss: 0.0530 - acc: 0.9907 - val_loss: 1.0952 - val_acc: 0.7359\n",
      "Epoch 328/400\n",
      "537/537 [==============================] - 0s 187us/step - loss: 0.0532 - acc: 0.9907 - val_loss: 1.1452 - val_acc: 0.7316\n",
      "Epoch 329/400\n",
      "537/537 [==============================] - 0s 256us/step - loss: 0.0502 - acc: 0.9907 - val_loss: 1.1043 - val_acc: 0.7359\n",
      "Epoch 330/400\n",
      "537/537 [==============================] - 0s 163us/step - loss: 0.0506 - acc: 0.9907 - val_loss: 1.1654 - val_acc: 0.7359\n",
      "Epoch 331/400\n",
      "537/537 [==============================] - 0s 295us/step - loss: 0.0512 - acc: 0.9907 - val_loss: 1.1356 - val_acc: 0.7403\n",
      "Epoch 332/400\n",
      "537/537 [==============================] - 0s 438us/step - loss: 0.0542 - acc: 0.9907 - val_loss: 1.1478 - val_acc: 0.7359\n",
      "Epoch 333/400\n",
      "537/537 [==============================] - 0s 145us/step - loss: 0.0525 - acc: 0.9888 - val_loss: 1.1066 - val_acc: 0.7316\n",
      "Epoch 334/400\n",
      "537/537 [==============================] - 0s 193us/step - loss: 0.0576 - acc: 0.9888 - val_loss: 1.1077 - val_acc: 0.7273\n",
      "Epoch 335/400\n",
      "537/537 [==============================] - 0s 219us/step - loss: 0.0616 - acc: 0.9870 - val_loss: 1.0854 - val_acc: 0.7273\n",
      "Epoch 336/400\n",
      "537/537 [==============================] - 0s 270us/step - loss: 0.0609 - acc: 0.9870 - val_loss: 1.1570 - val_acc: 0.7229\n",
      "Epoch 337/400\n",
      "537/537 [==============================] - 0s 201us/step - loss: 0.0579 - acc: 0.9851 - val_loss: 1.1420 - val_acc: 0.7273\n",
      "Epoch 338/400\n",
      "537/537 [==============================] - 0s 171us/step - loss: 0.0519 - acc: 0.9888 - val_loss: 1.1520 - val_acc: 0.7229\n",
      "Epoch 339/400\n",
      "537/537 [==============================] - 0s 273us/step - loss: 0.0527 - acc: 0.9907 - val_loss: 1.2004 - val_acc: 0.7316\n",
      "Epoch 340/400\n",
      "537/537 [==============================] - 0s 195us/step - loss: 0.0508 - acc: 0.9907 - val_loss: 1.1586 - val_acc: 0.7359\n",
      "Epoch 341/400\n",
      "537/537 [==============================] - 0s 221us/step - loss: 0.0482 - acc: 0.9907 - val_loss: 1.1720 - val_acc: 0.7359\n",
      "Epoch 342/400\n",
      "537/537 [==============================] - 0s 203us/step - loss: 0.0487 - acc: 0.9907 - val_loss: 1.2028 - val_acc: 0.7316\n",
      "Epoch 343/400\n",
      "537/537 [==============================] - 0s 290us/step - loss: 0.0479 - acc: 0.9907 - val_loss: 1.1685 - val_acc: 0.7273\n",
      "Epoch 344/400\n",
      "537/537 [==============================] - 0s 182us/step - loss: 0.0497 - acc: 0.9870 - val_loss: 1.1470 - val_acc: 0.7359\n",
      "Epoch 345/400\n",
      "537/537 [==============================] - 0s 249us/step - loss: 0.0470 - acc: 0.9907 - val_loss: 1.1609 - val_acc: 0.7316\n",
      "Epoch 346/400\n",
      "537/537 [==============================] - 0s 186us/step - loss: 0.0460 - acc: 0.9907 - val_loss: 1.1793 - val_acc: 0.7359\n",
      "Epoch 347/400\n",
      "537/537 [==============================] - 0s 349us/step - loss: 0.0452 - acc: 0.9907 - val_loss: 1.1691 - val_acc: 0.7316\n",
      "Epoch 348/400\n",
      "537/537 [==============================] - 0s 124us/step - loss: 0.0452 - acc: 0.9888 - val_loss: 1.1534 - val_acc: 0.7316\n",
      "Epoch 349/400\n",
      "537/537 [==============================] - 0s 195us/step - loss: 0.0490 - acc: 0.9907 - val_loss: 1.1809 - val_acc: 0.7316\n",
      "Epoch 350/400\n",
      "537/537 [==============================] - 0s 224us/step - loss: 0.0470 - acc: 0.9907 - val_loss: 1.1752 - val_acc: 0.7359\n",
      "Epoch 351/400\n",
      "537/537 [==============================] - 0s 141us/step - loss: 0.0448 - acc: 0.9907 - val_loss: 1.1435 - val_acc: 0.7273\n",
      "Epoch 352/400\n",
      "537/537 [==============================] - 0s 137us/step - loss: 0.0512 - acc: 0.9907 - val_loss: 1.1548 - val_acc: 0.7359\n",
      "Epoch 353/400\n",
      "537/537 [==============================] - 0s 256us/step - loss: 0.0470 - acc: 0.9907 - val_loss: 1.2204 - val_acc: 0.7446\n",
      "Epoch 354/400\n",
      "537/537 [==============================] - 0s 228us/step - loss: 0.0456 - acc: 0.9888 - val_loss: 1.1961 - val_acc: 0.7359\n",
      "Epoch 355/400\n",
      "537/537 [==============================] - 0s 186us/step - loss: 0.0471 - acc: 0.9907 - val_loss: 1.2097 - val_acc: 0.7359\n",
      "Epoch 356/400\n",
      "537/537 [==============================] - 0s 231us/step - loss: 0.0433 - acc: 0.9907 - val_loss: 1.1830 - val_acc: 0.7273\n",
      "Epoch 357/400\n",
      "537/537 [==============================] - 0s 219us/step - loss: 0.0460 - acc: 0.9907 - val_loss: 1.2097 - val_acc: 0.7273\n",
      "Epoch 358/400\n",
      "537/537 [==============================] - 0s 156us/step - loss: 0.0457 - acc: 0.9907 - val_loss: 1.2106 - val_acc: 0.7403\n",
      "Epoch 359/400\n",
      "537/537 [==============================] - 0s 240us/step - loss: 0.0422 - acc: 0.9907 - val_loss: 1.2048 - val_acc: 0.7273\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 360/400\n",
      "537/537 [==============================] - 0s 217us/step - loss: 0.0421 - acc: 0.9907 - val_loss: 1.2041 - val_acc: 0.7359\n",
      "Epoch 361/400\n",
      "537/537 [==============================] - 0s 118us/step - loss: 0.0420 - acc: 0.9888 - val_loss: 1.2350 - val_acc: 0.7273\n",
      "Epoch 362/400\n",
      "537/537 [==============================] - 0s 286us/step - loss: 0.0431 - acc: 0.9907 - val_loss: 1.1923 - val_acc: 0.7273\n",
      "Epoch 363/400\n",
      "537/537 [==============================] - 0s 201us/step - loss: 0.0459 - acc: 0.9907 - val_loss: 1.2487 - val_acc: 0.7273\n",
      "Epoch 364/400\n",
      "537/537 [==============================] - 0s 178us/step - loss: 0.0500 - acc: 0.9832 - val_loss: 1.1796 - val_acc: 0.7229\n",
      "Epoch 365/400\n",
      "537/537 [==============================] - 0s 304us/step - loss: 0.0477 - acc: 0.9870 - val_loss: 1.2648 - val_acc: 0.7359\n",
      "Epoch 366/400\n",
      "537/537 [==============================] - 0s 212us/step - loss: 0.0419 - acc: 0.9870 - val_loss: 1.2157 - val_acc: 0.7316\n",
      "Epoch 367/400\n",
      "537/537 [==============================] - 0s 208us/step - loss: 0.0425 - acc: 0.9888 - val_loss: 1.2208 - val_acc: 0.7446\n",
      "Epoch 368/400\n",
      "537/537 [==============================] - 0s 117us/step - loss: 0.0392 - acc: 0.9907 - val_loss: 1.2102 - val_acc: 0.7359\n",
      "Epoch 369/400\n",
      "537/537 [==============================] - 0s 128us/step - loss: 0.0394 - acc: 0.9907 - val_loss: 1.2106 - val_acc: 0.7359\n",
      "Epoch 370/400\n",
      "537/537 [==============================] - 0s 191us/step - loss: 0.0390 - acc: 0.9907 - val_loss: 1.2027 - val_acc: 0.7403\n",
      "Epoch 371/400\n",
      "537/537 [==============================] - 0s 256us/step - loss: 0.0413 - acc: 0.9888 - val_loss: 1.1988 - val_acc: 0.7316\n",
      "Epoch 372/400\n",
      "537/537 [==============================] - 0s 182us/step - loss: 0.0612 - acc: 0.9795 - val_loss: 1.1610 - val_acc: 0.7143\n",
      "Epoch 373/400\n",
      "537/537 [==============================] - 0s 224us/step - loss: 0.0765 - acc: 0.9758 - val_loss: 1.2006 - val_acc: 0.7403\n",
      "Epoch 374/400\n",
      "537/537 [==============================] - 0s 136us/step - loss: 0.0554 - acc: 0.9870 - val_loss: 1.1875 - val_acc: 0.7186\n",
      "Epoch 375/400\n",
      "537/537 [==============================] - 0s 282us/step - loss: 0.0577 - acc: 0.9795 - val_loss: 1.2235 - val_acc: 0.7056\n",
      "Epoch 376/400\n",
      "537/537 [==============================] - 0s 215us/step - loss: 0.0784 - acc: 0.9758 - val_loss: 1.2308 - val_acc: 0.7316\n",
      "Epoch 377/400\n",
      "537/537 [==============================] - 0s 219us/step - loss: 0.0444 - acc: 0.9888 - val_loss: 1.2900 - val_acc: 0.7316\n",
      "Epoch 378/400\n",
      "537/537 [==============================] - 0s 139us/step - loss: 0.0396 - acc: 0.9888 - val_loss: 1.2029 - val_acc: 0.7316\n",
      "Epoch 379/400\n",
      "537/537 [==============================] - 0s 282us/step - loss: 0.0427 - acc: 0.9907 - val_loss: 1.1998 - val_acc: 0.7359\n",
      "Epoch 380/400\n",
      "537/537 [==============================] - 0s 123us/step - loss: 0.0420 - acc: 0.9907 - val_loss: 1.3122 - val_acc: 0.7229\n",
      "Epoch 381/400\n",
      "537/537 [==============================] - 0s 265us/step - loss: 0.0401 - acc: 0.9907 - val_loss: 1.2912 - val_acc: 0.7186\n",
      "Epoch 382/400\n",
      "537/537 [==============================] - 0s 154us/step - loss: 0.0385 - acc: 0.9907 - val_loss: 1.2494 - val_acc: 0.7316\n",
      "Epoch 383/400\n",
      "537/537 [==============================] - 0s 312us/step - loss: 0.0380 - acc: 0.9907 - val_loss: 1.2321 - val_acc: 0.7273\n",
      "Epoch 384/400\n",
      "537/537 [==============================] - 0s 189us/step - loss: 0.0365 - acc: 0.9907 - val_loss: 1.2433 - val_acc: 0.7359\n",
      "Epoch 385/400\n",
      "537/537 [==============================] - 0s 262us/step - loss: 0.0372 - acc: 0.9907 - val_loss: 1.2675 - val_acc: 0.7316\n",
      "Epoch 386/400\n",
      "537/537 [==============================] - 0s 262us/step - loss: 0.0359 - acc: 0.9907 - val_loss: 1.2436 - val_acc: 0.7403\n",
      "Epoch 387/400\n",
      "537/537 [==============================] - 0s 175us/step - loss: 0.0355 - acc: 0.9907 - val_loss: 1.2831 - val_acc: 0.7316\n",
      "Epoch 388/400\n",
      "537/537 [==============================] - 0s 188us/step - loss: 0.0356 - acc: 0.9907 - val_loss: 1.2567 - val_acc: 0.7359\n",
      "Epoch 389/400\n",
      "537/537 [==============================] - 0s 307us/step - loss: 0.0382 - acc: 0.9888 - val_loss: 1.2533 - val_acc: 0.7316\n",
      "Epoch 390/400\n",
      "537/537 [==============================] - 0s 226us/step - loss: 0.0373 - acc: 0.9888 - val_loss: 1.2936 - val_acc: 0.7229\n",
      "Epoch 391/400\n",
      "537/537 [==============================] - 0s 353us/step - loss: 0.0375 - acc: 0.9907 - val_loss: 1.2908 - val_acc: 0.7273\n",
      "Epoch 392/400\n",
      "537/537 [==============================] - 0s 282us/step - loss: 0.0395 - acc: 0.9888 - val_loss: 1.3052 - val_acc: 0.7316\n",
      "Epoch 393/400\n",
      "537/537 [==============================] - 0s 123us/step - loss: 0.0360 - acc: 0.9907 - val_loss: 1.3512 - val_acc: 0.7273\n",
      "Epoch 394/400\n",
      "537/537 [==============================] - 0s 286us/step - loss: 0.0381 - acc: 0.9907 - val_loss: 1.3177 - val_acc: 0.7229\n",
      "Epoch 395/400\n",
      "537/537 [==============================] - 0s 516us/step - loss: 0.0408 - acc: 0.9870 - val_loss: 1.3038 - val_acc: 0.7316\n",
      "Epoch 396/400\n",
      "537/537 [==============================] - 0s 236us/step - loss: 0.0352 - acc: 0.9907 - val_loss: 1.2734 - val_acc: 0.7316\n",
      "Epoch 397/400\n",
      "537/537 [==============================] - 0s 280us/step - loss: 0.0333 - acc: 0.9907 - val_loss: 1.2897 - val_acc: 0.7316\n",
      "Epoch 398/400\n",
      "537/537 [==============================] - 0s 332us/step - loss: 0.0330 - acc: 0.9907 - val_loss: 1.2727 - val_acc: 0.7446\n",
      "Epoch 399/400\n",
      "537/537 [==============================] - 0s 227us/step - loss: 0.0330 - acc: 0.9907 - val_loss: 1.3006 - val_acc: 0.7273\n",
      "Epoch 400/400\n",
      "537/537 [==============================] - 0s 161us/step - loss: 0.0330 - acc: 0.9907 - val_loss: 1.3114 - val_acc: 0.7316\n"
     ]
    }
   ],
   "source": [
    "run_hist_400 = nn_model_2.fit(X_train_norm, y_train, validation_data=(X_test_norm, y_test), epochs=400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_class_nn_400 = nn_model_2.predict_classes(X_test_norm)\n",
    "y_pred_prob_nn_400 = nn_model_2.predict(X_test_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c7b036fb38>"
      ]
     },
     "execution_count": 468,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the curve to check training and validation accuracy\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(run_hist_400.history[\"acc\"],'r', marker='.', label=\"Training Accuracy\")\n",
    "ax.plot(run_hist_400.history[\"val_acc\"],'b', marker='.', label=\"Validation Accuracy\")\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c7b03ee240>"
      ]
     },
     "execution_count": 469,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting curves for Training Loss and Validation loss\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(run_hist_400.history[\"loss\"],'r', marker='.', label=\"Training Loss\")\n",
    "ax.plot(run_hist_400.history[\"val_loss\"],'b', marker='.', label=\"Validation Loss\")\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy is 0.732\n",
      "roc-auc is 0.774\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Print model performance and plot the roc curve\n",
    "print('accuracy is {:.3f}'.format(accuracy_score(y_test,y_pred_class_nn_400)))\n",
    "print('roc-auc is {:.3f}'.format(roc_auc_score(y_test,y_pred_prob_nn_400)))\n",
    "\n",
    "plot_roc(y_test, y_pred_prob_nn_400, 'NN')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accuracy obtained for epochs = 400 is 73.2%\n",
    "\n",
    "We see that accuracy is slightly better than previous model. \n",
    "\n",
    "Now, let's check for epochs = 600 wherein model improves or not. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 537 samples, validate on 231 samples\n",
      "Epoch 1/600\n",
      "537/537 [==============================] - 0s 918us/step - loss: 4.6693e-04 - acc: 1.0000 - val_loss: 2.2036 - val_acc: 0.7100\n",
      "Epoch 2/600\n",
      "537/537 [==============================] - 0s 416us/step - loss: 4.6172e-04 - acc: 1.0000 - val_loss: 2.2038 - val_acc: 0.7100\n",
      "Epoch 3/600\n",
      "537/537 [==============================] - 0s 234us/step - loss: 4.6060e-04 - acc: 1.0000 - val_loss: 2.2041 - val_acc: 0.7100\n",
      "Epoch 4/600\n",
      "537/537 [==============================] - 0s 223us/step - loss: 4.6026e-04 - acc: 1.0000 - val_loss: 2.2040 - val_acc: 0.7100\n",
      "Epoch 5/600\n",
      "537/537 [==============================] - 0s 102us/step - loss: 4.6800e-04 - acc: 1.0000 - val_loss: 2.2045 - val_acc: 0.7100\n",
      "Epoch 6/600\n",
      "537/537 [==============================] - 0s 136us/step - loss: 4.6793e-04 - acc: 1.0000 - val_loss: 2.2032 - val_acc: 0.7100\n",
      "Epoch 7/600\n",
      "537/537 [==============================] - 0s 221us/step - loss: 4.6049e-04 - acc: 1.0000 - val_loss: 2.2042 - val_acc: 0.7100\n",
      "Epoch 8/600\n",
      "537/537 [==============================] - 0s 176us/step - loss: 4.6149e-04 - acc: 1.0000 - val_loss: 2.2042 - val_acc: 0.7100\n",
      "Epoch 9/600\n",
      "537/537 [==============================] - 0s 107us/step - loss: 4.5749e-04 - acc: 1.0000 - val_loss: 2.2043 - val_acc: 0.7100\n",
      "Epoch 10/600\n",
      "537/537 [==============================] - 0s 326us/step - loss: 4.5731e-04 - acc: 1.0000 - val_loss: 2.2056 - val_acc: 0.7056\n",
      "Epoch 11/600\n",
      "537/537 [==============================] - 0s 110us/step - loss: 4.5687e-04 - acc: 1.0000 - val_loss: 2.2046 - val_acc: 0.7100\n",
      "Epoch 12/600\n",
      "537/537 [==============================] - 0s 308us/step - loss: 4.5564e-04 - acc: 1.0000 - val_loss: 2.2047 - val_acc: 0.7100\n",
      "Epoch 13/600\n",
      "537/537 [==============================] - 0s 219us/step - loss: 4.5891e-04 - acc: 1.0000 - val_loss: 2.2057 - val_acc: 0.7100\n",
      "Epoch 14/600\n",
      "537/537 [==============================] - 0s 117us/step - loss: 4.5416e-04 - acc: 1.0000 - val_loss: 2.2050 - val_acc: 0.7100\n",
      "Epoch 15/600\n",
      "537/537 [==============================] - 0s 236us/step - loss: 4.5837e-04 - acc: 1.0000 - val_loss: 2.2060 - val_acc: 0.7100\n",
      "Epoch 16/600\n",
      "537/537 [==============================] - 0s 202us/step - loss: 4.5547e-04 - acc: 1.0000 - val_loss: 2.2056 - val_acc: 0.7100\n",
      "Epoch 17/600\n",
      "537/537 [==============================] - 0s 193us/step - loss: 4.5388e-04 - acc: 1.0000 - val_loss: 2.2050 - val_acc: 0.7100\n",
      "Epoch 18/600\n",
      "537/537 [==============================] - 0s 201us/step - loss: 4.5554e-04 - acc: 1.0000 - val_loss: 2.2068 - val_acc: 0.7100\n",
      "Epoch 19/600\n",
      "537/537 [==============================] - 0s 249us/step - loss: 4.5884e-04 - acc: 1.0000 - val_loss: 2.2065 - val_acc: 0.7100\n",
      "Epoch 20/600\n",
      "537/537 [==============================] - 0s 127us/step - loss: 4.5544e-04 - acc: 1.0000 - val_loss: 2.2070 - val_acc: 0.7100\n",
      "Epoch 21/600\n",
      "537/537 [==============================] - 0s 284us/step - loss: 4.6578e-04 - acc: 1.0000 - val_loss: 2.2060 - val_acc: 0.7100\n",
      "Epoch 22/600\n",
      "537/537 [==============================] - 0s 314us/step - loss: 4.6009e-04 - acc: 1.0000 - val_loss: 2.2059 - val_acc: 0.7100\n",
      "Epoch 23/600\n",
      "537/537 [==============================] - 0s 165us/step - loss: 4.6556e-04 - acc: 1.0000 - val_loss: 2.2085 - val_acc: 0.7056\n",
      "Epoch 24/600\n",
      "537/537 [==============================] - 0s 208us/step - loss: 4.6195e-04 - acc: 1.0000 - val_loss: 2.2060 - val_acc: 0.7100\n",
      "Epoch 25/600\n",
      "537/537 [==============================] - 0s 227us/step - loss: 4.5038e-04 - acc: 1.0000 - val_loss: 2.2084 - val_acc: 0.7100\n",
      "Epoch 26/600\n",
      "537/537 [==============================] - 0s 306us/step - loss: 4.5410e-04 - acc: 1.0000 - val_loss: 2.2078 - val_acc: 0.7100\n",
      "Epoch 27/600\n",
      "537/537 [==============================] - 0s 125us/step - loss: 4.5969e-04 - acc: 1.0000 - val_loss: 2.2068 - val_acc: 0.7100\n",
      "Epoch 28/600\n",
      "537/537 [==============================] - 0s 327us/step - loss: 4.5087e-04 - acc: 1.0000 - val_loss: 2.2083 - val_acc: 0.7056\n",
      "Epoch 29/600\n",
      "537/537 [==============================] - 0s 106us/step - loss: 4.5008e-04 - acc: 1.0000 - val_loss: 2.2089 - val_acc: 0.7100\n",
      "Epoch 30/600\n",
      "537/537 [==============================] - 0s 90us/step - loss: 4.5028e-04 - acc: 1.0000 - val_loss: 2.2075 - val_acc: 0.7100\n",
      "Epoch 31/600\n",
      "537/537 [==============================] - 0s 123us/step - loss: 4.5267e-04 - acc: 1.0000 - val_loss: 2.2089 - val_acc: 0.7100\n",
      "Epoch 32/600\n",
      "537/537 [==============================] - 0s 163us/step - loss: 4.5140e-04 - acc: 1.0000 - val_loss: 2.2082 - val_acc: 0.7100\n",
      "Epoch 33/600\n",
      "537/537 [==============================] - 0s 139us/step - loss: 4.5459e-04 - acc: 1.0000 - val_loss: 2.2085 - val_acc: 0.7056\n",
      "Epoch 34/600\n",
      "537/537 [==============================] - 0s 219us/step - loss: 4.4579e-04 - acc: 1.0000 - val_loss: 2.2099 - val_acc: 0.7100\n",
      "Epoch 35/600\n",
      "537/537 [==============================] - 0s 119us/step - loss: 4.5274e-04 - acc: 1.0000 - val_loss: 2.2098 - val_acc: 0.7100\n",
      "Epoch 36/600\n",
      "537/537 [==============================] - 0s 115us/step - loss: 4.4914e-04 - acc: 1.0000 - val_loss: 2.2105 - val_acc: 0.7056\n",
      "Epoch 37/600\n",
      "537/537 [==============================] - 0s 139us/step - loss: 4.4568e-04 - acc: 1.0000 - val_loss: 2.2102 - val_acc: 0.7100\n",
      "Epoch 38/600\n",
      "537/537 [==============================] - 0s 139us/step - loss: 4.4522e-04 - acc: 1.0000 - val_loss: 2.2104 - val_acc: 0.7100\n",
      "Epoch 39/600\n",
      "537/537 [==============================] - 0s 589us/step - loss: 4.4600e-04 - acc: 1.0000 - val_loss: 2.2103 - val_acc: 0.7100\n",
      "Epoch 40/600\n",
      "537/537 [==============================] - 0s 230us/step - loss: 4.4712e-04 - acc: 1.0000 - val_loss: 2.2096 - val_acc: 0.7100\n",
      "Epoch 41/600\n",
      "537/537 [==============================] - 0s 247us/step - loss: 4.4353e-04 - acc: 1.0000 - val_loss: 2.2083 - val_acc: 0.7100\n",
      "Epoch 42/600\n",
      "537/537 [==============================] - 0s 286us/step - loss: 4.4701e-04 - acc: 1.0000 - val_loss: 2.2105 - val_acc: 0.7056\n",
      "Epoch 43/600\n",
      "537/537 [==============================] - 0s 494us/step - loss: 4.4572e-04 - acc: 1.0000 - val_loss: 2.2106 - val_acc: 0.7100\n",
      "Epoch 44/600\n",
      "537/537 [==============================] - 0s 180us/step - loss: 4.4456e-04 - acc: 1.0000 - val_loss: 2.2098 - val_acc: 0.7100\n",
      "Epoch 45/600\n",
      "537/537 [==============================] - 0s 182us/step - loss: 4.4259e-04 - acc: 1.0000 - val_loss: 2.2103 - val_acc: 0.7100\n",
      "Epoch 46/600\n",
      "537/537 [==============================] - 0s 487us/step - loss: 4.4312e-04 - acc: 1.0000 - val_loss: 2.2103 - val_acc: 0.7100\n",
      "Epoch 47/600\n",
      "537/537 [==============================] - 0s 210us/step - loss: 4.4746e-04 - acc: 1.0000 - val_loss: 2.2120 - val_acc: 0.7056\n",
      "Epoch 48/600\n",
      "537/537 [==============================] - 0s 124us/step - loss: 4.4724e-04 - acc: 1.0000 - val_loss: 2.2114 - val_acc: 0.7100\n",
      "Epoch 49/600\n",
      "537/537 [==============================] - 0s 117us/step - loss: 4.4362e-04 - acc: 1.0000 - val_loss: 2.2103 - val_acc: 0.7100\n",
      "Epoch 50/600\n",
      "537/537 [==============================] - 0s 146us/step - loss: 4.4691e-04 - acc: 1.0000 - val_loss: 2.2123 - val_acc: 0.7100\n",
      "Epoch 51/600\n",
      "537/537 [==============================] - 0s 134us/step - loss: 4.4035e-04 - acc: 1.0000 - val_loss: 2.2115 - val_acc: 0.7100\n",
      "Epoch 52/600\n",
      "537/537 [==============================] - 0s 234us/step - loss: 4.4309e-04 - acc: 1.0000 - val_loss: 2.2124 - val_acc: 0.7100\n",
      "Epoch 53/600\n",
      "537/537 [==============================] - 0s 193us/step - loss: 4.4578e-04 - acc: 1.0000 - val_loss: 2.2125 - val_acc: 0.7100\n",
      "Epoch 54/600\n",
      "537/537 [==============================] - 0s 284us/step - loss: 4.4722e-04 - acc: 1.0000 - val_loss: 2.2117 - val_acc: 0.7100\n",
      "Epoch 55/600\n",
      "537/537 [==============================] - 0s 186us/step - loss: 4.5169e-04 - acc: 1.0000 - val_loss: 2.2137 - val_acc: 0.7056\n",
      "Epoch 56/600\n",
      "537/537 [==============================] - 0s 261us/step - loss: 4.4092e-04 - acc: 1.0000 - val_loss: 2.2124 - val_acc: 0.7100\n",
      "Epoch 57/600\n",
      "537/537 [==============================] - 0s 117us/step - loss: 4.5032e-04 - acc: 1.0000 - val_loss: 2.2124 - val_acc: 0.7100\n",
      "Epoch 58/600\n",
      "537/537 [==============================] - 0s 197us/step - loss: 4.3959e-04 - acc: 1.0000 - val_loss: 2.2131 - val_acc: 0.7100\n",
      "Epoch 59/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "537/537 [==============================] - 0s 206us/step - loss: 4.4240e-04 - acc: 1.0000 - val_loss: 2.2140 - val_acc: 0.7100\n",
      "Epoch 60/600\n",
      "537/537 [==============================] - 0s 182us/step - loss: 4.3932e-04 - acc: 1.0000 - val_loss: 2.2121 - val_acc: 0.7100\n",
      "Epoch 61/600\n",
      "537/537 [==============================] - 0s 158us/step - loss: 4.3847e-04 - acc: 1.0000 - val_loss: 2.2129 - val_acc: 0.7100\n",
      "Epoch 62/600\n",
      "537/537 [==============================] - 0s 225us/step - loss: 4.4233e-04 - acc: 1.0000 - val_loss: 2.2148 - val_acc: 0.7056\n",
      "Epoch 63/600\n",
      "537/537 [==============================] - 0s 161us/step - loss: 4.3761e-04 - acc: 1.0000 - val_loss: 2.2133 - val_acc: 0.7100\n",
      "Epoch 64/600\n",
      "537/537 [==============================] - 0s 587us/step - loss: 4.3836e-04 - acc: 1.0000 - val_loss: 2.2138 - val_acc: 0.7100\n",
      "Epoch 65/600\n",
      "537/537 [==============================] - 0s 245us/step - loss: 4.3878e-04 - acc: 1.0000 - val_loss: 2.2136 - val_acc: 0.7100\n",
      "Epoch 66/600\n",
      "537/537 [==============================] - 0s 253us/step - loss: 4.4361e-04 - acc: 1.0000 - val_loss: 2.2139 - val_acc: 0.7100\n",
      "Epoch 67/600\n",
      "537/537 [==============================] - 0s 154us/step - loss: 4.3740e-04 - acc: 1.0000 - val_loss: 2.2141 - val_acc: 0.7100\n",
      "Epoch 68/600\n",
      "537/537 [==============================] - 0s 372us/step - loss: 4.3849e-04 - acc: 1.0000 - val_loss: 2.2135 - val_acc: 0.7100\n",
      "Epoch 69/600\n",
      "537/537 [==============================] - 0s 229us/step - loss: 4.3679e-04 - acc: 1.0000 - val_loss: 2.2132 - val_acc: 0.7100\n",
      "Epoch 70/600\n",
      "537/537 [==============================] - 0s 217us/step - loss: 4.3649e-04 - acc: 1.0000 - val_loss: 2.2160 - val_acc: 0.7056\n",
      "Epoch 71/600\n",
      "537/537 [==============================] - 0s 228us/step - loss: 4.3814e-04 - acc: 1.0000 - val_loss: 2.2151 - val_acc: 0.7100\n",
      "Epoch 72/600\n",
      "537/537 [==============================] - 0s 184us/step - loss: 4.4470e-04 - acc: 1.0000 - val_loss: 2.2158 - val_acc: 0.7100\n",
      "Epoch 73/600\n",
      "537/537 [==============================] - 0s 565us/step - loss: 4.3807e-04 - acc: 1.0000 - val_loss: 2.2161 - val_acc: 0.7100\n",
      "Epoch 74/600\n",
      "537/537 [==============================] - 0s 243us/step - loss: 4.3270e-04 - acc: 1.0000 - val_loss: 2.2152 - val_acc: 0.7100\n",
      "Epoch 75/600\n",
      "537/537 [==============================] - 0s 247us/step - loss: 4.4149e-04 - acc: 1.0000 - val_loss: 2.2158 - val_acc: 0.7100\n",
      "Epoch 76/600\n",
      "537/537 [==============================] - 0s 117us/step - loss: 4.4369e-04 - acc: 1.0000 - val_loss: 2.2131 - val_acc: 0.7100\n",
      "Epoch 77/600\n",
      "537/537 [==============================] - 0s 160us/step - loss: 4.3231e-04 - acc: 1.0000 - val_loss: 2.2160 - val_acc: 0.7100\n",
      "Epoch 78/600\n",
      "537/537 [==============================] - 0s 193us/step - loss: 4.3404e-04 - acc: 1.0000 - val_loss: 2.2161 - val_acc: 0.7100\n",
      "Epoch 79/600\n",
      "537/537 [==============================] - 0s 205us/step - loss: 4.3651e-04 - acc: 1.0000 - val_loss: 2.2166 - val_acc: 0.7100\n",
      "Epoch 80/600\n",
      "537/537 [==============================] - 0s 277us/step - loss: 4.3154e-04 - acc: 1.0000 - val_loss: 2.2171 - val_acc: 0.7100\n",
      "Epoch 81/600\n",
      "537/537 [==============================] - 0s 165us/step - loss: 4.3203e-04 - acc: 1.0000 - val_loss: 2.2165 - val_acc: 0.7100\n",
      "Epoch 82/600\n",
      "537/537 [==============================] - 0s 234us/step - loss: 4.3299e-04 - acc: 1.0000 - val_loss: 2.2182 - val_acc: 0.7056\n",
      "Epoch 83/600\n",
      "537/537 [==============================] - 0s 249us/step - loss: 4.3467e-04 - acc: 1.0000 - val_loss: 2.2172 - val_acc: 0.7100\n",
      "Epoch 84/600\n",
      "537/537 [==============================] - 0s 189us/step - loss: 4.3422e-04 - acc: 1.0000 - val_loss: 2.2165 - val_acc: 0.7100\n",
      "Epoch 85/600\n",
      "537/537 [==============================] - 0s 176us/step - loss: 4.3379e-04 - acc: 1.0000 - val_loss: 2.2161 - val_acc: 0.7100\n",
      "Epoch 86/600\n",
      "537/537 [==============================] - 0s 353us/step - loss: 4.2789e-04 - acc: 1.0000 - val_loss: 2.2179 - val_acc: 0.7100\n",
      "Epoch 87/600\n",
      "537/537 [==============================] - 0s 291us/step - loss: 4.3437e-04 - acc: 1.0000 - val_loss: 2.2175 - val_acc: 0.7100\n",
      "Epoch 88/600\n",
      "537/537 [==============================] - 0s 254us/step - loss: 4.2845e-04 - acc: 1.0000 - val_loss: 2.2185 - val_acc: 0.7056\n",
      "Epoch 89/600\n",
      "537/537 [==============================] - 0s 232us/step - loss: 4.2973e-04 - acc: 1.0000 - val_loss: 2.2189 - val_acc: 0.7056\n",
      "Epoch 90/600\n",
      "537/537 [==============================] - 0s 260us/step - loss: 4.3457e-04 - acc: 1.0000 - val_loss: 2.2177 - val_acc: 0.7100\n",
      "Epoch 91/600\n",
      "537/537 [==============================] - 0s 429us/step - loss: 4.2837e-04 - acc: 1.0000 - val_loss: 2.2194 - val_acc: 0.7056\n",
      "Epoch 92/600\n",
      "537/537 [==============================] - 0s 134us/step - loss: 4.3262e-04 - acc: 1.0000 - val_loss: 2.2184 - val_acc: 0.7100\n",
      "Epoch 93/600\n",
      "537/537 [==============================] - 0s 132us/step - loss: 4.3458e-04 - acc: 1.0000 - val_loss: 2.2174 - val_acc: 0.7100\n",
      "Epoch 94/600\n",
      "537/537 [==============================] - 0s 243us/step - loss: 4.3029e-04 - acc: 1.0000 - val_loss: 2.2190 - val_acc: 0.7056\n",
      "Epoch 95/600\n",
      "537/537 [==============================] - 0s 149us/step - loss: 4.2947e-04 - acc: 1.0000 - val_loss: 2.2195 - val_acc: 0.7100\n",
      "Epoch 96/600\n",
      "537/537 [==============================] - 0s 286us/step - loss: 4.3022e-04 - acc: 1.0000 - val_loss: 2.2204 - val_acc: 0.7100\n",
      "Epoch 97/600\n",
      "537/537 [==============================] - 0s 162us/step - loss: 4.3054e-04 - acc: 1.0000 - val_loss: 2.2191 - val_acc: 0.7100\n",
      "Epoch 98/600\n",
      "537/537 [==============================] - 0s 193us/step - loss: 4.2402e-04 - acc: 1.0000 - val_loss: 2.2188 - val_acc: 0.7100\n",
      "Epoch 99/600\n",
      "537/537 [==============================] - 0s 165us/step - loss: 4.2403e-04 - acc: 1.0000 - val_loss: 2.2193 - val_acc: 0.7100\n",
      "Epoch 100/600\n",
      "537/537 [==============================] - 0s 552us/step - loss: 4.2507e-04 - acc: 1.0000 - val_loss: 2.2206 - val_acc: 0.7056\n",
      "Epoch 101/600\n",
      "537/537 [==============================] - 0s 219us/step - loss: 4.2489e-04 - acc: 1.0000 - val_loss: 2.2192 - val_acc: 0.7100\n",
      "Epoch 102/600\n",
      "537/537 [==============================] - 0s 156us/step - loss: 4.2697e-04 - acc: 1.0000 - val_loss: 2.2194 - val_acc: 0.7100\n",
      "Epoch 103/600\n",
      "537/537 [==============================] - 0s 169us/step - loss: 4.2806e-04 - acc: 1.0000 - val_loss: 2.2206 - val_acc: 0.7100\n",
      "Epoch 104/600\n",
      "537/537 [==============================] - 0s 409us/step - loss: 4.2801e-04 - acc: 1.0000 - val_loss: 2.2195 - val_acc: 0.7100\n",
      "Epoch 105/600\n",
      "537/537 [==============================] - 0s 106us/step - loss: 4.2379e-04 - acc: 1.0000 - val_loss: 2.2200 - val_acc: 0.7100\n",
      "Epoch 106/600\n",
      "537/537 [==============================] - 0s 141us/step - loss: 4.2572e-04 - acc: 1.0000 - val_loss: 2.2210 - val_acc: 0.7100\n",
      "Epoch 107/600\n",
      "537/537 [==============================] - 0s 282us/step - loss: 4.2735e-04 - acc: 1.0000 - val_loss: 2.2183 - val_acc: 0.7100\n",
      "Epoch 108/600\n",
      "537/537 [==============================] - 0s 186us/step - loss: 4.2260e-04 - acc: 1.0000 - val_loss: 2.2209 - val_acc: 0.7056\n",
      "Epoch 109/600\n",
      "537/537 [==============================] - 0s 123us/step - loss: 4.2425e-04 - acc: 1.0000 - val_loss: 2.2207 - val_acc: 0.7100\n",
      "Epoch 110/600\n",
      "537/537 [==============================] - 0s 248us/step - loss: 4.2268e-04 - acc: 1.0000 - val_loss: 2.2217 - val_acc: 0.7100\n",
      "Epoch 111/600\n",
      "537/537 [==============================] - 0s 186us/step - loss: 4.2323e-04 - acc: 1.0000 - val_loss: 2.2213 - val_acc: 0.7100\n",
      "Epoch 112/600\n",
      "537/537 [==============================] - 0s 204us/step - loss: 4.2272e-04 - acc: 1.0000 - val_loss: 2.2207 - val_acc: 0.7100\n",
      "Epoch 113/600\n",
      "537/537 [==============================] - 0s 110us/step - loss: 4.2256e-04 - acc: 1.0000 - val_loss: 2.2217 - val_acc: 0.7100\n",
      "Epoch 114/600\n",
      "537/537 [==============================] - 0s 180us/step - loss: 4.2141e-04 - acc: 1.0000 - val_loss: 2.2219 - val_acc: 0.7100\n",
      "Epoch 115/600\n",
      "537/537 [==============================] - 0s 243us/step - loss: 4.2234e-04 - acc: 1.0000 - val_loss: 2.2222 - val_acc: 0.7056\n",
      "Epoch 116/600\n",
      "537/537 [==============================] - 0s 113us/step - loss: 4.2180e-04 - acc: 1.0000 - val_loss: 2.2220 - val_acc: 0.7100\n",
      "Epoch 117/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "537/537 [==============================] - 0s 342us/step - loss: 4.2242e-04 - acc: 1.0000 - val_loss: 2.2230 - val_acc: 0.7100\n",
      "Epoch 118/600\n",
      "537/537 [==============================] - 0s 184us/step - loss: 4.1775e-04 - acc: 1.0000 - val_loss: 2.2224 - val_acc: 0.7100\n",
      "Epoch 119/600\n",
      "537/537 [==============================] - 0s 123us/step - loss: 4.2317e-04 - acc: 1.0000 - val_loss: 2.2230 - val_acc: 0.7100\n",
      "Epoch 120/600\n",
      "537/537 [==============================] - 0s 163us/step - loss: 4.1784e-04 - acc: 1.0000 - val_loss: 2.2228 - val_acc: 0.7056\n",
      "Epoch 121/600\n",
      "537/537 [==============================] - 0s 130us/step - loss: 4.2132e-04 - acc: 1.0000 - val_loss: 2.2234 - val_acc: 0.7100\n",
      "Epoch 122/600\n",
      "537/537 [==============================] - 0s 409us/step - loss: 4.1708e-04 - acc: 1.0000 - val_loss: 2.2234 - val_acc: 0.7100\n",
      "Epoch 123/600\n",
      "537/537 [==============================] - 0s 275us/step - loss: 4.2857e-04 - acc: 1.0000 - val_loss: 2.2240 - val_acc: 0.7056\n",
      "Epoch 124/600\n",
      "537/537 [==============================] - 0s 145us/step - loss: 4.1987e-04 - acc: 1.0000 - val_loss: 2.2225 - val_acc: 0.7100\n",
      "Epoch 125/600\n",
      "537/537 [==============================] - ETA: 0s - loss: 4.6723e-04 - acc: 1.000 - 0s 221us/step - loss: 4.2074e-04 - acc: 1.0000 - val_loss: 2.2240 - val_acc: 0.7100\n",
      "Epoch 126/600\n",
      "537/537 [==============================] - 0s 134us/step - loss: 4.2174e-04 - acc: 1.0000 - val_loss: 2.2231 - val_acc: 0.7100\n",
      "Epoch 127/600\n",
      "537/537 [==============================] - 0s 178us/step - loss: 4.1925e-04 - acc: 1.0000 - val_loss: 2.2238 - val_acc: 0.7100\n",
      "Epoch 128/600\n",
      "537/537 [==============================] - 0s 128us/step - loss: 4.1522e-04 - acc: 1.0000 - val_loss: 2.2252 - val_acc: 0.7056\n",
      "Epoch 129/600\n",
      "537/537 [==============================] - 0s 134us/step - loss: 4.2109e-04 - acc: 1.0000 - val_loss: 2.2241 - val_acc: 0.7100\n",
      "Epoch 130/600\n",
      "537/537 [==============================] - 0s 106us/step - loss: 4.1604e-04 - acc: 1.0000 - val_loss: 2.2247 - val_acc: 0.7056\n",
      "Epoch 131/600\n",
      "537/537 [==============================] - 0s 139us/step - loss: 4.1783e-04 - acc: 1.0000 - val_loss: 2.2251 - val_acc: 0.7100\n",
      "Epoch 132/600\n",
      "537/537 [==============================] - 0s 378us/step - loss: 4.2267e-04 - acc: 1.0000 - val_loss: 2.2237 - val_acc: 0.7100\n",
      "Epoch 133/600\n",
      "537/537 [==============================] - 0s 162us/step - loss: 4.1845e-04 - acc: 1.0000 - val_loss: 2.2244 - val_acc: 0.7100\n",
      "Epoch 134/600\n",
      "537/537 [==============================] - 0s 264us/step - loss: 4.1666e-04 - acc: 1.0000 - val_loss: 2.2251 - val_acc: 0.7100\n",
      "Epoch 135/600\n",
      "537/537 [==============================] - 0s 126us/step - loss: 4.1344e-04 - acc: 1.0000 - val_loss: 2.2240 - val_acc: 0.7100\n",
      "Epoch 136/600\n",
      "537/537 [==============================] - 0s 256us/step - loss: 4.1641e-04 - acc: 1.0000 - val_loss: 2.2249 - val_acc: 0.7056\n",
      "Epoch 137/600\n",
      "537/537 [==============================] - 0s 111us/step - loss: 4.1419e-04 - acc: 1.0000 - val_loss: 2.2262 - val_acc: 0.7100\n",
      "Epoch 138/600\n",
      "537/537 [==============================] - 0s 110us/step - loss: 4.1552e-04 - acc: 1.0000 - val_loss: 2.2255 - val_acc: 0.7100\n",
      "Epoch 139/600\n",
      "537/537 [==============================] - 0s 112us/step - loss: 4.1412e-04 - acc: 1.0000 - val_loss: 2.2260 - val_acc: 0.7100\n",
      "Epoch 140/600\n",
      "537/537 [==============================] - 0s 143us/step - loss: 4.1181e-04 - acc: 1.0000 - val_loss: 2.2268 - val_acc: 0.7056\n",
      "Epoch 141/600\n",
      "537/537 [==============================] - 0s 141us/step - loss: 4.1079e-04 - acc: 1.0000 - val_loss: 2.2258 - val_acc: 0.7100\n",
      "Epoch 142/600\n",
      "537/537 [==============================] - 0s 199us/step - loss: 4.1523e-04 - acc: 1.0000 - val_loss: 2.2252 - val_acc: 0.7100\n",
      "Epoch 143/600\n",
      "537/537 [==============================] - 0s 150us/step - loss: 4.1508e-04 - acc: 1.0000 - val_loss: 2.2268 - val_acc: 0.7056\n",
      "Epoch 144/600\n",
      "537/537 [==============================] - 0s 111us/step - loss: 4.1366e-04 - acc: 1.0000 - val_loss: 2.2254 - val_acc: 0.7100\n",
      "Epoch 145/600\n",
      "537/537 [==============================] - 0s 123us/step - loss: 4.1343e-04 - acc: 1.0000 - val_loss: 2.2268 - val_acc: 0.7100\n",
      "Epoch 146/600\n",
      "537/537 [==============================] - 0s 327us/step - loss: 4.1263e-04 - acc: 1.0000 - val_loss: 2.2275 - val_acc: 0.7056\n",
      "Epoch 147/600\n",
      "537/537 [==============================] - 0s 139us/step - loss: 4.0932e-04 - acc: 1.0000 - val_loss: 2.2249 - val_acc: 0.7100\n",
      "Epoch 148/600\n",
      "537/537 [==============================] - 0s 201us/step - loss: 4.1068e-04 - acc: 1.0000 - val_loss: 2.2274 - val_acc: 0.7100\n",
      "Epoch 149/600\n",
      "537/537 [==============================] - 0s 118us/step - loss: 4.1250e-04 - acc: 1.0000 - val_loss: 2.2277 - val_acc: 0.7100\n",
      "Epoch 150/600\n",
      "537/537 [==============================] - 0s 247us/step - loss: 4.1373e-04 - acc: 1.0000 - val_loss: 2.2269 - val_acc: 0.7100\n",
      "Epoch 151/600\n",
      "537/537 [==============================] - 0s 156us/step - loss: 4.1150e-04 - acc: 1.0000 - val_loss: 2.2281 - val_acc: 0.7056\n",
      "Epoch 152/600\n",
      "537/537 [==============================] - 0s 153us/step - loss: 4.1163e-04 - acc: 1.0000 - val_loss: 2.2276 - val_acc: 0.7100\n",
      "Epoch 153/600\n",
      "537/537 [==============================] - 0s 234us/step - loss: 4.0899e-04 - acc: 1.0000 - val_loss: 2.2286 - val_acc: 0.7100\n",
      "Epoch 154/600\n",
      "537/537 [==============================] - 0s 214us/step - loss: 4.1017e-04 - acc: 1.0000 - val_loss: 2.2287 - val_acc: 0.7056\n",
      "Epoch 155/600\n",
      "537/537 [==============================] - 0s 117us/step - loss: 4.0782e-04 - acc: 1.0000 - val_loss: 2.2283 - val_acc: 0.7100\n",
      "Epoch 156/600\n",
      "537/537 [==============================] - 0s 162us/step - loss: 4.1178e-04 - acc: 1.0000 - val_loss: 2.2284 - val_acc: 0.7100\n",
      "Epoch 157/600\n",
      "537/537 [==============================] - 0s 141us/step - loss: 4.0697e-04 - acc: 1.0000 - val_loss: 2.2281 - val_acc: 0.7100\n",
      "Epoch 158/600\n",
      "537/537 [==============================] - 0s 236us/step - loss: 4.0664e-04 - acc: 1.0000 - val_loss: 2.2293 - val_acc: 0.7056\n",
      "Epoch 159/600\n",
      "537/537 [==============================] - 0s 169us/step - loss: 4.1005e-04 - acc: 1.0000 - val_loss: 2.2287 - val_acc: 0.7100\n",
      "Epoch 160/600\n",
      "537/537 [==============================] - 0s 102us/step - loss: 4.0771e-04 - acc: 1.0000 - val_loss: 2.2300 - val_acc: 0.7056\n",
      "Epoch 161/600\n",
      "537/537 [==============================] - 0s 108us/step - loss: 4.0855e-04 - acc: 1.0000 - val_loss: 2.2296 - val_acc: 0.7100\n",
      "Epoch 162/600\n",
      "537/537 [==============================] - 0s 113us/step - loss: 4.0582e-04 - acc: 1.0000 - val_loss: 2.2291 - val_acc: 0.7100\n",
      "Epoch 163/600\n",
      "537/537 [==============================] - 0s 241us/step - loss: 4.0591e-04 - acc: 1.0000 - val_loss: 2.2294 - val_acc: 0.7056\n",
      "Epoch 164/600\n",
      "537/537 [==============================] - 0s 106us/step - loss: 4.0591e-04 - acc: 1.0000 - val_loss: 2.2309 - val_acc: 0.7100\n",
      "Epoch 165/600\n",
      "537/537 [==============================] - 0s 110us/step - loss: 4.0894e-04 - acc: 1.0000 - val_loss: 2.2299 - val_acc: 0.7056\n",
      "Epoch 166/600\n",
      "537/537 [==============================] - 0s 128us/step - loss: 4.0897e-04 - acc: 1.0000 - val_loss: 2.2300 - val_acc: 0.7100\n",
      "Epoch 167/600\n",
      "537/537 [==============================] - 0s 117us/step - loss: 4.0634e-04 - acc: 1.0000 - val_loss: 2.2296 - val_acc: 0.7100\n",
      "Epoch 168/600\n",
      "537/537 [==============================] - 0s 245us/step - loss: 4.0524e-04 - acc: 1.0000 - val_loss: 2.2306 - val_acc: 0.7100\n",
      "Epoch 169/600\n",
      "537/537 [==============================] - 0s 119us/step - loss: 4.1152e-04 - acc: 1.0000 - val_loss: 2.2318 - val_acc: 0.7100\n",
      "Epoch 170/600\n",
      "537/537 [==============================] - 0s 115us/step - loss: 4.0620e-04 - acc: 1.0000 - val_loss: 2.2298 - val_acc: 0.7100\n",
      "Epoch 171/600\n",
      "537/537 [==============================] - 0s 162us/step - loss: 4.0544e-04 - acc: 1.0000 - val_loss: 2.2307 - val_acc: 0.7056\n",
      "Epoch 172/600\n",
      "537/537 [==============================] - 0s 115us/step - loss: 4.0266e-04 - acc: 1.0000 - val_loss: 2.2312 - val_acc: 0.7056\n",
      "Epoch 173/600\n",
      "537/537 [==============================] - 0s 121us/step - loss: 4.0081e-04 - acc: 1.0000 - val_loss: 2.2318 - val_acc: 0.7100\n",
      "Epoch 174/600\n",
      "537/537 [==============================] - 0s 93us/step - loss: 4.0618e-04 - acc: 1.0000 - val_loss: 2.2310 - val_acc: 0.7100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 175/600\n",
      "537/537 [==============================] - 0s 202us/step - loss: 4.0163e-04 - acc: 1.0000 - val_loss: 2.2318 - val_acc: 0.7056\n",
      "Epoch 176/600\n",
      "537/537 [==============================] - 0s 252us/step - loss: 4.0096e-04 - acc: 1.0000 - val_loss: 2.2320 - val_acc: 0.7056\n",
      "Epoch 177/600\n",
      "537/537 [==============================] - 0s 186us/step - loss: 4.0311e-04 - acc: 1.0000 - val_loss: 2.2322 - val_acc: 0.7100\n",
      "Epoch 178/600\n",
      "537/537 [==============================] - 0s 256us/step - loss: 4.0412e-04 - acc: 1.0000 - val_loss: 2.2313 - val_acc: 0.7100\n",
      "Epoch 179/600\n",
      "537/537 [==============================] - 0s 178us/step - loss: 4.1663e-04 - acc: 1.0000 - val_loss: 2.2335 - val_acc: 0.7056\n",
      "Epoch 180/600\n",
      "537/537 [==============================] - 0s 353us/step - loss: 4.1015e-04 - acc: 1.0000 - val_loss: 2.2309 - val_acc: 0.7100\n",
      "Epoch 181/600\n",
      "537/537 [==============================] - 0s 299us/step - loss: 4.0230e-04 - acc: 1.0000 - val_loss: 2.2329 - val_acc: 0.7056\n",
      "Epoch 182/600\n",
      "537/537 [==============================] - 0s 306us/step - loss: 3.9998e-04 - acc: 1.0000 - val_loss: 2.2331 - val_acc: 0.7100\n",
      "Epoch 183/600\n",
      "537/537 [==============================] - 0s 334us/step - loss: 4.0122e-04 - acc: 1.0000 - val_loss: 2.2331 - val_acc: 0.7056\n",
      "Epoch 184/600\n",
      "537/537 [==============================] - 0s 347us/step - loss: 4.0030e-04 - acc: 1.0000 - val_loss: 2.2323 - val_acc: 0.7100\n",
      "Epoch 185/600\n",
      "537/537 [==============================] - 0s 132us/step - loss: 4.0196e-04 - acc: 1.0000 - val_loss: 2.2333 - val_acc: 0.7056\n",
      "Epoch 186/600\n",
      "537/537 [==============================] - 0s 117us/step - loss: 3.9571e-04 - acc: 1.0000 - val_loss: 2.2332 - val_acc: 0.7100\n",
      "Epoch 187/600\n",
      "537/537 [==============================] - 0s 173us/step - loss: 4.0025e-04 - acc: 1.0000 - val_loss: 2.2334 - val_acc: 0.7100\n",
      "Epoch 188/600\n",
      "537/537 [==============================] - 0s 165us/step - loss: 4.0050e-04 - acc: 1.0000 - val_loss: 2.2333 - val_acc: 0.7100\n",
      "Epoch 189/600\n",
      "537/537 [==============================] - 0s 195us/step - loss: 3.9918e-04 - acc: 1.0000 - val_loss: 2.2343 - val_acc: 0.7056\n",
      "Epoch 190/600\n",
      "537/537 [==============================] - 0s 228us/step - loss: 4.0486e-04 - acc: 1.0000 - val_loss: 2.2329 - val_acc: 0.7100\n",
      "Epoch 191/600\n",
      "537/537 [==============================] - 0s 251us/step - loss: 3.9777e-04 - acc: 1.0000 - val_loss: 2.2346 - val_acc: 0.7056\n",
      "Epoch 192/600\n",
      "537/537 [==============================] - 0s 136us/step - loss: 3.9835e-04 - acc: 1.0000 - val_loss: 2.2343 - val_acc: 0.7100\n",
      "Epoch 193/600\n",
      "537/537 [==============================] - 0s 162us/step - loss: 3.9741e-04 - acc: 1.0000 - val_loss: 2.2341 - val_acc: 0.7056\n",
      "Epoch 194/600\n",
      "537/537 [==============================] - 0s 228us/step - loss: 3.9830e-04 - acc: 1.0000 - val_loss: 2.2338 - val_acc: 0.7100\n",
      "Epoch 195/600\n",
      "537/537 [==============================] - 0s 197us/step - loss: 3.9820e-04 - acc: 1.0000 - val_loss: 2.2344 - val_acc: 0.7056\n",
      "Epoch 196/600\n",
      "537/537 [==============================] - ETA: 0s - loss: 4.0135e-04 - acc: 1.000 - 0s 347us/step - loss: 3.9799e-04 - acc: 1.0000 - val_loss: 2.2352 - val_acc: 0.7100\n",
      "Epoch 197/600\n",
      "537/537 [==============================] - 0s 230us/step - loss: 3.9642e-04 - acc: 1.0000 - val_loss: 2.2346 - val_acc: 0.7100\n",
      "Epoch 198/600\n",
      "537/537 [==============================] - 0s 323us/step - loss: 3.9678e-04 - acc: 1.0000 - val_loss: 2.2347 - val_acc: 0.7100\n",
      "Epoch 199/600\n",
      "537/537 [==============================] - 0s 117us/step - loss: 3.9671e-04 - acc: 1.0000 - val_loss: 2.2355 - val_acc: 0.7056\n",
      "Epoch 200/600\n",
      "537/537 [==============================] - 0s 159us/step - loss: 4.0178e-04 - acc: 1.0000 - val_loss: 2.2342 - val_acc: 0.7100\n",
      "Epoch 201/600\n",
      "537/537 [==============================] - 0s 212us/step - loss: 3.9698e-04 - acc: 1.0000 - val_loss: 2.2360 - val_acc: 0.7056\n",
      "Epoch 202/600\n",
      "537/537 [==============================] - 0s 182us/step - loss: 3.9411e-04 - acc: 1.0000 - val_loss: 2.2350 - val_acc: 0.7056\n",
      "Epoch 203/600\n",
      "537/537 [==============================] - 0s 149us/step - loss: 3.9769e-04 - acc: 1.0000 - val_loss: 2.2348 - val_acc: 0.7056\n",
      "Epoch 204/600\n",
      "537/537 [==============================] - 0s 215us/step - loss: 3.9423e-04 - acc: 1.0000 - val_loss: 2.2362 - val_acc: 0.7056\n",
      "Epoch 205/600\n",
      "537/537 [==============================] - 0s 230us/step - loss: 3.9589e-04 - acc: 1.0000 - val_loss: 2.2365 - val_acc: 0.7056\n",
      "Epoch 206/600\n",
      "537/537 [==============================] - 0s 305us/step - loss: 3.9453e-04 - acc: 1.0000 - val_loss: 2.2358 - val_acc: 0.7056\n",
      "Epoch 207/600\n",
      "537/537 [==============================] - 0s 247us/step - loss: 3.9863e-04 - acc: 1.0000 - val_loss: 2.2356 - val_acc: 0.7100\n",
      "Epoch 208/600\n",
      "537/537 [==============================] - 0s 152us/step - loss: 3.9398e-04 - acc: 1.0000 - val_loss: 2.2367 - val_acc: 0.7056\n",
      "Epoch 209/600\n",
      "537/537 [==============================] - 0s 113us/step - loss: 3.9450e-04 - acc: 1.0000 - val_loss: 2.2383 - val_acc: 0.7056\n",
      "Epoch 210/600\n",
      "537/537 [==============================] - 0s 217us/step - loss: 3.9160e-04 - acc: 1.0000 - val_loss: 2.2373 - val_acc: 0.7100\n",
      "Epoch 211/600\n",
      "537/537 [==============================] - 0s 149us/step - loss: 3.9366e-04 - acc: 1.0000 - val_loss: 2.2365 - val_acc: 0.7100\n",
      "Epoch 212/600\n",
      "537/537 [==============================] - 0s 204us/step - loss: 3.9830e-04 - acc: 1.0000 - val_loss: 2.2358 - val_acc: 0.7100\n",
      "Epoch 213/600\n",
      "537/537 [==============================] - 0s 100us/step - loss: 3.9020e-04 - acc: 1.0000 - val_loss: 2.2391 - val_acc: 0.7056\n",
      "Epoch 214/600\n",
      "537/537 [==============================] - 0s 241us/step - loss: 3.9320e-04 - acc: 1.0000 - val_loss: 2.2390 - val_acc: 0.7056\n",
      "Epoch 215/600\n",
      "537/537 [==============================] - 0s 135us/step - loss: 3.9372e-04 - acc: 1.0000 - val_loss: 2.2383 - val_acc: 0.7100\n",
      "Epoch 216/600\n",
      "537/537 [==============================] - 0s 254us/step - loss: 3.9147e-04 - acc: 1.0000 - val_loss: 2.2369 - val_acc: 0.7100\n",
      "Epoch 217/600\n",
      "537/537 [==============================] - 0s 128us/step - loss: 3.9619e-04 - acc: 1.0000 - val_loss: 2.2364 - val_acc: 0.7100\n",
      "Epoch 218/600\n",
      "537/537 [==============================] - 0s 199us/step - loss: 3.8863e-04 - acc: 1.0000 - val_loss: 2.2377 - val_acc: 0.7100\n",
      "Epoch 219/600\n",
      "537/537 [==============================] - 0s 199us/step - loss: 3.9633e-04 - acc: 1.0000 - val_loss: 2.2393 - val_acc: 0.7056\n",
      "Epoch 220/600\n",
      "537/537 [==============================] - 0s 108us/step - loss: 3.8973e-04 - acc: 1.0000 - val_loss: 2.2377 - val_acc: 0.7100\n",
      "Epoch 221/600\n",
      "537/537 [==============================] - 0s 162us/step - loss: 3.9081e-04 - acc: 1.0000 - val_loss: 2.2388 - val_acc: 0.7056\n",
      "Epoch 222/600\n",
      "537/537 [==============================] - 0s 126us/step - loss: 3.8932e-04 - acc: 1.0000 - val_loss: 2.2376 - val_acc: 0.7100\n",
      "Epoch 223/600\n",
      "537/537 [==============================] - 0s 106us/step - loss: 3.9146e-04 - acc: 1.0000 - val_loss: 2.2382 - val_acc: 0.7056\n",
      "Epoch 224/600\n",
      "537/537 [==============================] - 0s 100us/step - loss: 3.8839e-04 - acc: 1.0000 - val_loss: 2.2404 - val_acc: 0.7056\n",
      "Epoch 225/600\n",
      "537/537 [==============================] - 0s 189us/step - loss: 3.8916e-04 - acc: 1.0000 - val_loss: 2.2396 - val_acc: 0.7056\n",
      "Epoch 226/600\n",
      "537/537 [==============================] - 0s 160us/step - loss: 3.8955e-04 - acc: 1.0000 - val_loss: 2.2404 - val_acc: 0.7056\n",
      "Epoch 227/600\n",
      "537/537 [==============================] - 0s 104us/step - loss: 3.8758e-04 - acc: 1.0000 - val_loss: 2.2401 - val_acc: 0.7056\n",
      "Epoch 228/600\n",
      "537/537 [==============================] - 0s 95us/step - loss: 3.8857e-04 - acc: 1.0000 - val_loss: 2.2388 - val_acc: 0.7100\n",
      "Epoch 229/600\n",
      "537/537 [==============================] - 0s 117us/step - loss: 3.9063e-04 - acc: 1.0000 - val_loss: 2.2394 - val_acc: 0.7056\n",
      "Epoch 230/600\n",
      "537/537 [==============================] - 0s 245us/step - loss: 3.8386e-04 - acc: 1.0000 - val_loss: 2.2391 - val_acc: 0.7100\n",
      "Epoch 231/600\n",
      "537/537 [==============================] - 0s 91us/step - loss: 3.8846e-04 - acc: 1.0000 - val_loss: 2.2382 - val_acc: 0.7100\n",
      "Epoch 232/600\n",
      "537/537 [==============================] - 0s 124us/step - loss: 3.8552e-04 - acc: 1.0000 - val_loss: 2.2401 - val_acc: 0.7056\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 233/600\n",
      "537/537 [==============================] - 0s 310us/step - loss: 3.8542e-04 - acc: 1.0000 - val_loss: 2.2402 - val_acc: 0.7100\n",
      "Epoch 234/600\n",
      "537/537 [==============================] - 0s 215us/step - loss: 3.9004e-04 - acc: 1.0000 - val_loss: 2.2420 - val_acc: 0.7056\n",
      "Epoch 235/600\n",
      "537/537 [==============================] - 0s 308us/step - loss: 3.9891e-04 - acc: 1.0000 - val_loss: 2.2398 - val_acc: 0.7100\n",
      "Epoch 236/600\n",
      "537/537 [==============================] - 0s 208us/step - loss: 3.8973e-04 - acc: 1.0000 - val_loss: 2.2413 - val_acc: 0.7056\n",
      "Epoch 237/600\n",
      "537/537 [==============================] - 0s 126us/step - loss: 3.8627e-04 - acc: 1.0000 - val_loss: 2.2410 - val_acc: 0.7100\n",
      "Epoch 238/600\n",
      "537/537 [==============================] - 0s 184us/step - loss: 3.8814e-04 - acc: 1.0000 - val_loss: 2.2415 - val_acc: 0.7100\n",
      "Epoch 239/600\n",
      "537/537 [==============================] - 0s 128us/step - loss: 3.9621e-04 - acc: 1.0000 - val_loss: 2.2396 - val_acc: 0.7056\n",
      "Epoch 240/600\n",
      "537/537 [==============================] - 0s 119us/step - loss: 3.8347e-04 - acc: 1.0000 - val_loss: 2.2428 - val_acc: 0.7056\n",
      "Epoch 241/600\n",
      "537/537 [==============================] - 0s 184us/step - loss: 3.8423e-04 - acc: 1.0000 - val_loss: 2.2427 - val_acc: 0.7056\n",
      "Epoch 242/600\n",
      "537/537 [==============================] - 0s 214us/step - loss: 3.8294e-04 - acc: 1.0000 - val_loss: 2.2424 - val_acc: 0.7056\n",
      "Epoch 243/600\n",
      "537/537 [==============================] - 0s 160us/step - loss: 3.8371e-04 - acc: 1.0000 - val_loss: 2.2420 - val_acc: 0.7056\n",
      "Epoch 244/600\n",
      "537/537 [==============================] - 0s 184us/step - loss: 3.8352e-04 - acc: 1.0000 - val_loss: 2.2421 - val_acc: 0.7056\n",
      "Epoch 245/600\n",
      "537/537 [==============================] - 0s 172us/step - loss: 3.8505e-04 - acc: 1.0000 - val_loss: 2.2431 - val_acc: 0.7056\n",
      "Epoch 246/600\n",
      "537/537 [==============================] - 0s 199us/step - loss: 3.8142e-04 - acc: 1.0000 - val_loss: 2.2423 - val_acc: 0.7056\n",
      "Epoch 247/600\n",
      "537/537 [==============================] - 0s 231us/step - loss: 3.8402e-04 - acc: 1.0000 - val_loss: 2.2422 - val_acc: 0.7100\n",
      "Epoch 248/600\n",
      "537/537 [==============================] - 0s 195us/step - loss: 3.8167e-04 - acc: 1.0000 - val_loss: 2.2415 - val_acc: 0.7100\n",
      "Epoch 249/600\n",
      "537/537 [==============================] - 0s 210us/step - loss: 3.8287e-04 - acc: 1.0000 - val_loss: 2.2422 - val_acc: 0.7056\n",
      "Epoch 250/600\n",
      "537/537 [==============================] - 0s 142us/step - loss: 3.8130e-04 - acc: 1.0000 - val_loss: 2.2444 - val_acc: 0.7056\n",
      "Epoch 251/600\n",
      "537/537 [==============================] - 0s 206us/step - loss: 3.8987e-04 - acc: 1.0000 - val_loss: 2.2425 - val_acc: 0.7056\n",
      "Epoch 252/600\n",
      "537/537 [==============================] - 0s 175us/step - loss: 3.8421e-04 - acc: 1.0000 - val_loss: 2.2441 - val_acc: 0.7056\n",
      "Epoch 253/600\n",
      "537/537 [==============================] - 0s 202us/step - loss: 3.8172e-04 - acc: 1.0000 - val_loss: 2.2442 - val_acc: 0.7100\n",
      "Epoch 254/600\n",
      "537/537 [==============================] - 0s 197us/step - loss: 3.8033e-04 - acc: 1.0000 - val_loss: 2.2432 - val_acc: 0.7100\n",
      "Epoch 255/600\n",
      "537/537 [==============================] - 0s 204us/step - loss: 3.8047e-04 - acc: 1.0000 - val_loss: 2.2434 - val_acc: 0.7056\n",
      "Epoch 256/600\n",
      "537/537 [==============================] - 0s 184us/step - loss: 3.8030e-04 - acc: 1.0000 - val_loss: 2.2442 - val_acc: 0.7056\n",
      "Epoch 257/600\n",
      "537/537 [==============================] - 0s 217us/step - loss: 3.7801e-04 - acc: 1.0000 - val_loss: 2.2434 - val_acc: 0.7100\n",
      "Epoch 258/600\n",
      "537/537 [==============================] - 0s 139us/step - loss: 3.7806e-04 - acc: 1.0000 - val_loss: 2.2445 - val_acc: 0.7056\n",
      "Epoch 259/600\n",
      "537/537 [==============================] - 0s 121us/step - loss: 3.8172e-04 - acc: 1.0000 - val_loss: 2.2449 - val_acc: 0.7056\n",
      "Epoch 260/600\n",
      "537/537 [==============================] - 0s 277us/step - loss: 3.8326e-04 - acc: 1.0000 - val_loss: 2.2433 - val_acc: 0.7100\n",
      "Epoch 261/600\n",
      "537/537 [==============================] - 0s 123us/step - loss: 3.7926e-04 - acc: 1.0000 - val_loss: 2.2451 - val_acc: 0.7056\n",
      "Epoch 262/600\n",
      "537/537 [==============================] - 0s 156us/step - loss: 3.7933e-04 - acc: 1.0000 - val_loss: 2.2455 - val_acc: 0.7056\n",
      "Epoch 263/600\n",
      "537/537 [==============================] - 0s 154us/step - loss: 3.8439e-04 - acc: 1.0000 - val_loss: 2.2462 - val_acc: 0.7056\n",
      "Epoch 264/600\n",
      "537/537 [==============================] - 0s 128us/step - loss: 3.7982e-04 - acc: 1.0000 - val_loss: 2.2456 - val_acc: 0.7056\n",
      "Epoch 265/600\n",
      "537/537 [==============================] - 0s 145us/step - loss: 3.7978e-04 - acc: 1.0000 - val_loss: 2.2458 - val_acc: 0.7056\n",
      "Epoch 266/600\n",
      "537/537 [==============================] - 0s 136us/step - loss: 3.7762e-04 - acc: 1.0000 - val_loss: 2.2455 - val_acc: 0.7056\n",
      "Epoch 267/600\n",
      "537/537 [==============================] - 0s 113us/step - loss: 3.7606e-04 - acc: 1.0000 - val_loss: 2.2456 - val_acc: 0.7100\n",
      "Epoch 268/600\n",
      "537/537 [==============================] - 0s 93us/step - loss: 3.7623e-04 - acc: 1.0000 - val_loss: 2.2464 - val_acc: 0.7056\n",
      "Epoch 269/600\n",
      "537/537 [==============================] - 0s 97us/step - loss: 3.7848e-04 - acc: 1.0000 - val_loss: 2.2456 - val_acc: 0.7056\n",
      "Epoch 270/600\n",
      "537/537 [==============================] - 0s 143us/step - loss: 3.7514e-04 - acc: 1.0000 - val_loss: 2.2461 - val_acc: 0.7100\n",
      "Epoch 271/600\n",
      "537/537 [==============================] - 0s 216us/step - loss: 3.7953e-04 - acc: 1.0000 - val_loss: 2.2476 - val_acc: 0.7056\n",
      "Epoch 272/600\n",
      "537/537 [==============================] - 0s 136us/step - loss: 3.7511e-04 - acc: 1.0000 - val_loss: 2.2449 - val_acc: 0.7100\n",
      "Epoch 273/600\n",
      "537/537 [==============================] - 0s 98us/step - loss: 3.7780e-04 - acc: 1.0000 - val_loss: 2.2458 - val_acc: 0.7100\n",
      "Epoch 274/600\n",
      "537/537 [==============================] - 0s 119us/step - loss: 3.7477e-04 - acc: 1.0000 - val_loss: 2.2467 - val_acc: 0.7056\n",
      "Epoch 275/600\n",
      "537/537 [==============================] - 0s 201us/step - loss: 3.7422e-04 - acc: 1.0000 - val_loss: 2.2479 - val_acc: 0.7056\n",
      "Epoch 276/600\n",
      "537/537 [==============================] - 0s 293us/step - loss: 3.7449e-04 - acc: 1.0000 - val_loss: 2.2476 - val_acc: 0.7056\n",
      "Epoch 277/600\n",
      "537/537 [==============================] - 0s 171us/step - loss: 3.7608e-04 - acc: 1.0000 - val_loss: 2.2478 - val_acc: 0.7056\n",
      "Epoch 278/600\n",
      "537/537 [==============================] - 0s 221us/step - loss: 3.7849e-04 - acc: 1.0000 - val_loss: 2.2468 - val_acc: 0.7100\n",
      "Epoch 279/600\n",
      "537/537 [==============================] - 0s 132us/step - loss: 3.7507e-04 - acc: 1.0000 - val_loss: 2.2460 - val_acc: 0.7100\n",
      "Epoch 280/600\n",
      "537/537 [==============================] - 0s 173us/step - loss: 3.7967e-04 - acc: 1.0000 - val_loss: 2.2475 - val_acc: 0.7056\n",
      "Epoch 281/600\n",
      "537/537 [==============================] - 0s 108us/step - loss: 3.7296e-04 - acc: 1.0000 - val_loss: 2.2462 - val_acc: 0.7100\n",
      "Epoch 282/600\n",
      "537/537 [==============================] - 0s 228us/step - loss: 3.7370e-04 - acc: 1.0000 - val_loss: 2.2470 - val_acc: 0.7056\n",
      "Epoch 283/600\n",
      "537/537 [==============================] - 0s 169us/step - loss: 3.7500e-04 - acc: 1.0000 - val_loss: 2.2491 - val_acc: 0.7056\n",
      "Epoch 284/600\n",
      "537/537 [==============================] - 0s 138us/step - loss: 3.7378e-04 - acc: 1.0000 - val_loss: 2.2480 - val_acc: 0.7056\n",
      "Epoch 285/600\n",
      "537/537 [==============================] - 0s 100us/step - loss: 3.7392e-04 - acc: 1.0000 - val_loss: 2.2484 - val_acc: 0.7056\n",
      "Epoch 286/600\n",
      "537/537 [==============================] - 0s 124us/step - loss: 3.7156e-04 - acc: 1.0000 - val_loss: 2.2485 - val_acc: 0.7056\n",
      "Epoch 287/600\n",
      "537/537 [==============================] - 0s 152us/step - loss: 3.7423e-04 - acc: 1.0000 - val_loss: 2.2490 - val_acc: 0.7056\n",
      "Epoch 288/600\n",
      "537/537 [==============================] - 0s 251us/step - loss: 3.7199e-04 - acc: 1.0000 - val_loss: 2.2476 - val_acc: 0.7100\n",
      "Epoch 289/600\n",
      "537/537 [==============================] - 0s 152us/step - loss: 3.7281e-04 - acc: 1.0000 - val_loss: 2.2487 - val_acc: 0.7056\n",
      "Epoch 290/600\n",
      "537/537 [==============================] - 0s 102us/step - loss: 3.7010e-04 - acc: 1.0000 - val_loss: 2.2487 - val_acc: 0.7056\n",
      "Epoch 291/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "537/537 [==============================] - 0s 102us/step - loss: 3.7463e-04 - acc: 1.0000 - val_loss: 2.2484 - val_acc: 0.7056\n",
      "Epoch 292/600\n",
      "537/537 [==============================] - 0s 208us/step - loss: 3.7254e-04 - acc: 1.0000 - val_loss: 2.2492 - val_acc: 0.7056\n",
      "Epoch 293/600\n",
      "537/537 [==============================] - 0s 159us/step - loss: 3.6905e-04 - acc: 1.0000 - val_loss: 2.2496 - val_acc: 0.7056\n",
      "Epoch 294/600\n",
      "537/537 [==============================] - 0s 178us/step - loss: 3.7023e-04 - acc: 1.0000 - val_loss: 2.2498 - val_acc: 0.7056\n",
      "Epoch 295/600\n",
      "537/537 [==============================] - 0s 184us/step - loss: 3.7378e-04 - acc: 1.0000 - val_loss: 2.2496 - val_acc: 0.7056\n",
      "Epoch 296/600\n",
      "537/537 [==============================] - 0s 239us/step - loss: 3.7433e-04 - acc: 1.0000 - val_loss: 2.2501 - val_acc: 0.7056\n",
      "Epoch 297/600\n",
      "537/537 [==============================] - 0s 223us/step - loss: 3.6785e-04 - acc: 1.0000 - val_loss: 2.2493 - val_acc: 0.7056\n",
      "Epoch 298/600\n",
      "537/537 [==============================] - 0s 208us/step - loss: 3.7415e-04 - acc: 1.0000 - val_loss: 2.2494 - val_acc: 0.7056\n",
      "Epoch 299/600\n",
      "537/537 [==============================] - 0s 152us/step - loss: 3.6771e-04 - acc: 1.0000 - val_loss: 2.2504 - val_acc: 0.7056\n",
      "Epoch 300/600\n",
      "537/537 [==============================] - 0s 206us/step - loss: 3.6974e-04 - acc: 1.0000 - val_loss: 2.2502 - val_acc: 0.7056\n",
      "Epoch 301/600\n",
      "537/537 [==============================] - 0s 236us/step - loss: 3.6907e-04 - acc: 1.0000 - val_loss: 2.2503 - val_acc: 0.7056\n",
      "Epoch 302/600\n",
      "537/537 [==============================] - 0s 199us/step - loss: 3.7059e-04 - acc: 1.0000 - val_loss: 2.2497 - val_acc: 0.7100\n",
      "Epoch 303/600\n",
      "537/537 [==============================] - 0s 299us/step - loss: 3.6938e-04 - acc: 1.0000 - val_loss: 2.2502 - val_acc: 0.7056\n",
      "Epoch 304/600\n",
      "537/537 [==============================] - 0s 238us/step - loss: 3.6689e-04 - acc: 1.0000 - val_loss: 2.2509 - val_acc: 0.7056\n",
      "Epoch 305/600\n",
      "537/537 [==============================] - 0s 174us/step - loss: 3.6606e-04 - acc: 1.0000 - val_loss: 2.2513 - val_acc: 0.7056\n",
      "Epoch 306/600\n",
      "537/537 [==============================] - 0s 245us/step - loss: 3.7048e-04 - acc: 1.0000 - val_loss: 2.2499 - val_acc: 0.7100\n",
      "Epoch 307/600\n",
      "537/537 [==============================] - 0s 199us/step - loss: 3.6485e-04 - acc: 1.0000 - val_loss: 2.2514 - val_acc: 0.7056\n",
      "Epoch 308/600\n",
      "537/537 [==============================] - 0s 143us/step - loss: 3.6550e-04 - acc: 1.0000 - val_loss: 2.2525 - val_acc: 0.7056\n",
      "Epoch 309/600\n",
      "537/537 [==============================] - 0s 210us/step - loss: 3.6585e-04 - acc: 1.0000 - val_loss: 2.2522 - val_acc: 0.7056\n",
      "Epoch 310/600\n",
      "537/537 [==============================] - 0s 163us/step - loss: 3.6793e-04 - acc: 1.0000 - val_loss: 2.2515 - val_acc: 0.7100\n",
      "Epoch 311/600\n",
      "537/537 [==============================] - 0s 100us/step - loss: 3.6771e-04 - acc: 1.0000 - val_loss: 2.2509 - val_acc: 0.7056\n",
      "Epoch 312/600\n",
      "537/537 [==============================] - 0s 113us/step - loss: 3.7313e-04 - acc: 1.0000 - val_loss: 2.2503 - val_acc: 0.7056\n",
      "Epoch 313/600\n",
      "537/537 [==============================] - 0s 277us/step - loss: 3.6503e-04 - acc: 1.0000 - val_loss: 2.2530 - val_acc: 0.7056\n",
      "Epoch 314/600\n",
      "537/537 [==============================] - 0s 214us/step - loss: 3.6926e-04 - acc: 1.0000 - val_loss: 2.2529 - val_acc: 0.7056\n",
      "Epoch 315/600\n",
      "537/537 [==============================] - 0s 193us/step - loss: 3.6568e-04 - acc: 1.0000 - val_loss: 2.2513 - val_acc: 0.7100\n",
      "Epoch 316/600\n",
      "537/537 [==============================] - 0s 95us/step - loss: 3.6485e-04 - acc: 1.0000 - val_loss: 2.2525 - val_acc: 0.7056\n",
      "Epoch 317/600\n",
      "537/537 [==============================] - 0s 618us/step - loss: 3.6923e-04 - acc: 1.0000 - val_loss: 2.2538 - val_acc: 0.7056\n",
      "Epoch 318/600\n",
      "537/537 [==============================] - 0s 119us/step - loss: 3.6831e-04 - acc: 1.0000 - val_loss: 2.2521 - val_acc: 0.7100\n",
      "Epoch 319/600\n",
      "537/537 [==============================] - 0s 121us/step - loss: 3.6355e-04 - acc: 1.0000 - val_loss: 2.2530 - val_acc: 0.7100\n",
      "Epoch 320/600\n",
      "537/537 [==============================] - 0s 118us/step - loss: 3.6765e-04 - acc: 1.0000 - val_loss: 2.2529 - val_acc: 0.7100\n",
      "Epoch 321/600\n",
      "537/537 [==============================] - 0s 204us/step - loss: 3.6284e-04 - acc: 1.0000 - val_loss: 2.2520 - val_acc: 0.7056\n",
      "Epoch 322/600\n",
      "537/537 [==============================] - 0s 217us/step - loss: 3.6345e-04 - acc: 1.0000 - val_loss: 2.2542 - val_acc: 0.7056\n",
      "Epoch 323/600\n",
      "537/537 [==============================] - 0s 219us/step - loss: 3.6329e-04 - acc: 1.0000 - val_loss: 2.2540 - val_acc: 0.7056\n",
      "Epoch 324/600\n",
      "537/537 [==============================] - 0s 212us/step - loss: 3.6125e-04 - acc: 1.0000 - val_loss: 2.2530 - val_acc: 0.7056\n",
      "Epoch 325/600\n",
      "537/537 [==============================] - 0s 216us/step - loss: 3.6202e-04 - acc: 1.0000 - val_loss: 2.2536 - val_acc: 0.7056\n",
      "Epoch 326/600\n",
      "537/537 [==============================] - 0s 126us/step - loss: 3.6110e-04 - acc: 1.0000 - val_loss: 2.2536 - val_acc: 0.7056\n",
      "Epoch 327/600\n",
      "537/537 [==============================] - 0s 152us/step - loss: 3.6421e-04 - acc: 1.0000 - val_loss: 2.2542 - val_acc: 0.7056\n",
      "Epoch 328/600\n",
      "537/537 [==============================] - 0s 91us/step - loss: 3.6046e-04 - acc: 1.0000 - val_loss: 2.2546 - val_acc: 0.7056\n",
      "Epoch 329/600\n",
      "537/537 [==============================] - 0s 121us/step - loss: 3.6324e-04 - acc: 1.0000 - val_loss: 2.2538 - val_acc: 0.7056\n",
      "Epoch 330/600\n",
      "537/537 [==============================] - 0s 121us/step - loss: 3.5957e-04 - acc: 1.0000 - val_loss: 2.2540 - val_acc: 0.7100\n",
      "Epoch 331/600\n",
      "537/537 [==============================] - 0s 98us/step - loss: 3.6037e-04 - acc: 1.0000 - val_loss: 2.2560 - val_acc: 0.7056\n",
      "Epoch 332/600\n",
      "537/537 [==============================] - 0s 95us/step - loss: 3.6778e-04 - acc: 1.0000 - val_loss: 2.2563 - val_acc: 0.7056\n",
      "Epoch 333/600\n",
      "537/537 [==============================] - 0s 102us/step - loss: 3.5715e-04 - acc: 1.0000 - val_loss: 2.2547 - val_acc: 0.7100\n",
      "Epoch 334/600\n",
      "537/537 [==============================] - 0s 249us/step - loss: 3.6040e-04 - acc: 1.0000 - val_loss: 2.2539 - val_acc: 0.7056\n",
      "Epoch 335/600\n",
      "537/537 [==============================] - 0s 132us/step - loss: 3.6261e-04 - acc: 1.0000 - val_loss: 2.2555 - val_acc: 0.7056\n",
      "Epoch 336/600\n",
      "537/537 [==============================] - 0s 149us/step - loss: 3.5690e-04 - acc: 1.0000 - val_loss: 2.2553 - val_acc: 0.7100\n",
      "Epoch 337/600\n",
      "537/537 [==============================] - 0s 212us/step - loss: 3.5888e-04 - acc: 1.0000 - val_loss: 2.2545 - val_acc: 0.7100\n",
      "Epoch 338/600\n",
      "537/537 [==============================] - 0s 238us/step - loss: 3.5824e-04 - acc: 1.0000 - val_loss: 2.2560 - val_acc: 0.7056\n",
      "Epoch 339/600\n",
      "537/537 [==============================] - 0s 167us/step - loss: 3.5651e-04 - acc: 1.0000 - val_loss: 2.2555 - val_acc: 0.7100\n",
      "Epoch 340/600\n",
      "537/537 [==============================] - 0s 154us/step - loss: 3.5403e-04 - acc: 1.0000 - val_loss: 2.2557 - val_acc: 0.7100\n",
      "Epoch 341/600\n",
      "537/537 [==============================] - 0s 299us/step - loss: 3.5401e-04 - acc: 1.0000 - val_loss: 2.2564 - val_acc: 0.7056\n",
      "Epoch 342/600\n",
      "537/537 [==============================] - ETA: 0s - loss: 3.4453e-04 - acc: 1.000 - 0s 128us/step - loss: 3.5392e-04 - acc: 1.0000 - val_loss: 2.2575 - val_acc: 0.7100\n",
      "Epoch 343/600\n",
      "537/537 [==============================] - 0s 193us/step - loss: 3.5297e-04 - acc: 1.0000 - val_loss: 2.2548 - val_acc: 0.7100\n",
      "Epoch 344/600\n",
      "537/537 [==============================] - 0s 260us/step - loss: 3.5037e-04 - acc: 1.0000 - val_loss: 2.2565 - val_acc: 0.7056\n",
      "Epoch 345/600\n",
      "537/537 [==============================] - 0s 206us/step - loss: 3.4993e-04 - acc: 1.0000 - val_loss: 2.2563 - val_acc: 0.7100\n",
      "Epoch 346/600\n",
      "537/537 [==============================] - 0s 334us/step - loss: 3.4921e-04 - acc: 1.0000 - val_loss: 2.2555 - val_acc: 0.7100\n",
      "Epoch 347/600\n",
      "537/537 [==============================] - 0s 143us/step - loss: 3.4661e-04 - acc: 1.0000 - val_loss: 2.2560 - val_acc: 0.7100\n",
      "Epoch 348/600\n",
      "537/537 [==============================] - 0s 193us/step - loss: 3.4856e-04 - acc: 1.0000 - val_loss: 2.2558 - val_acc: 0.7100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 349/600\n",
      "537/537 [==============================] - 0s 149us/step - loss: 3.4612e-04 - acc: 1.0000 - val_loss: 2.2573 - val_acc: 0.7100\n",
      "Epoch 350/600\n",
      "537/537 [==============================] - 0s 251us/step - loss: 3.4553e-04 - acc: 1.0000 - val_loss: 2.2564 - val_acc: 0.7100\n",
      "Epoch 351/600\n",
      "537/537 [==============================] - 0s 169us/step - loss: 3.4731e-04 - acc: 1.0000 - val_loss: 2.2559 - val_acc: 0.7100\n",
      "Epoch 352/600\n",
      "537/537 [==============================] - 0s 201us/step - loss: 3.4686e-04 - acc: 1.0000 - val_loss: 2.2579 - val_acc: 0.7100\n",
      "Epoch 353/600\n",
      "537/537 [==============================] - 0s 149us/step - loss: 3.4417e-04 - acc: 1.0000 - val_loss: 2.2574 - val_acc: 0.7100\n",
      "Epoch 354/600\n",
      "537/537 [==============================] - 0s 225us/step - loss: 3.4228e-04 - acc: 1.0000 - val_loss: 2.2574 - val_acc: 0.7100\n",
      "Epoch 355/600\n",
      "537/537 [==============================] - 0s 130us/step - loss: 3.4109e-04 - acc: 1.0000 - val_loss: 2.2568 - val_acc: 0.7100\n",
      "Epoch 356/600\n",
      "537/537 [==============================] - 0s 145us/step - loss: 3.4208e-04 - acc: 1.0000 - val_loss: 2.2572 - val_acc: 0.7100\n",
      "Epoch 357/600\n",
      "537/537 [==============================] - 0s 191us/step - loss: 3.4043e-04 - acc: 1.0000 - val_loss: 2.2584 - val_acc: 0.7056\n",
      "Epoch 358/600\n",
      "537/537 [==============================] - 0s 167us/step - loss: 3.3962e-04 - acc: 1.0000 - val_loss: 2.2581 - val_acc: 0.7100\n",
      "Epoch 359/600\n",
      "537/537 [==============================] - 0s 182us/step - loss: 3.3927e-04 - acc: 1.0000 - val_loss: 2.2579 - val_acc: 0.7100\n",
      "Epoch 360/600\n",
      "537/537 [==============================] - 0s 221us/step - loss: 3.4090e-04 - acc: 1.0000 - val_loss: 2.2597 - val_acc: 0.7056\n",
      "Epoch 361/600\n",
      "537/537 [==============================] - 0s 149us/step - loss: 3.3927e-04 - acc: 1.0000 - val_loss: 2.2580 - val_acc: 0.7100\n",
      "Epoch 362/600\n",
      "537/537 [==============================] - 0s 171us/step - loss: 3.3721e-04 - acc: 1.0000 - val_loss: 2.2576 - val_acc: 0.7056\n",
      "Epoch 363/600\n",
      "537/537 [==============================] - 0s 221us/step - loss: 3.3773e-04 - acc: 1.0000 - val_loss: 2.2584 - val_acc: 0.7056\n",
      "Epoch 364/600\n",
      "537/537 [==============================] - 0s 321us/step - loss: 3.4138e-04 - acc: 1.0000 - val_loss: 2.2582 - val_acc: 0.7100\n",
      "Epoch 365/600\n",
      "537/537 [==============================] - 0s 255us/step - loss: 3.4139e-04 - acc: 1.0000 - val_loss: 2.2598 - val_acc: 0.7056\n",
      "Epoch 366/600\n",
      "537/537 [==============================] - 0s 225us/step - loss: 3.3521e-04 - acc: 1.0000 - val_loss: 2.2598 - val_acc: 0.7100\n",
      "Epoch 367/600\n",
      "537/537 [==============================] - 0s 217us/step - loss: 3.3803e-04 - acc: 1.0000 - val_loss: 2.2583 - val_acc: 0.7100\n",
      "Epoch 368/600\n",
      "537/537 [==============================] - 0s 186us/step - loss: 3.3399e-04 - acc: 1.0000 - val_loss: 2.2596 - val_acc: 0.7056\n",
      "Epoch 369/600\n",
      "537/537 [==============================] - 0s 180us/step - loss: 3.3442e-04 - acc: 1.0000 - val_loss: 2.2591 - val_acc: 0.7100\n",
      "Epoch 370/600\n",
      "537/537 [==============================] - 0s 201us/step - loss: 3.3412e-04 - acc: 1.0000 - val_loss: 2.2597 - val_acc: 0.7056\n",
      "Epoch 371/600\n",
      "537/537 [==============================] - 0s 262us/step - loss: 3.3367e-04 - acc: 1.0000 - val_loss: 2.2601 - val_acc: 0.7056\n",
      "Epoch 372/600\n",
      "537/537 [==============================] - 0s 212us/step - loss: 3.3273e-04 - acc: 1.0000 - val_loss: 2.2605 - val_acc: 0.7056\n",
      "Epoch 373/600\n",
      "537/537 [==============================] - 0s 126us/step - loss: 3.3165e-04 - acc: 1.0000 - val_loss: 2.2598 - val_acc: 0.7100\n",
      "Epoch 374/600\n",
      "537/537 [==============================] - 0s 122us/step - loss: 3.3235e-04 - acc: 1.0000 - val_loss: 2.2596 - val_acc: 0.7100\n",
      "Epoch 375/600\n",
      "537/537 [==============================] - 0s 104us/step - loss: 3.3319e-04 - acc: 1.0000 - val_loss: 2.2604 - val_acc: 0.7056\n",
      "Epoch 376/600\n",
      "537/537 [==============================] - 0s 143us/step - loss: 3.3071e-04 - acc: 1.0000 - val_loss: 2.2614 - val_acc: 0.7056\n",
      "Epoch 377/600\n",
      "537/537 [==============================] - 0s 117us/step - loss: 3.3073e-04 - acc: 1.0000 - val_loss: 2.2602 - val_acc: 0.7056\n",
      "Epoch 378/600\n",
      "537/537 [==============================] - 0s 102us/step - loss: 3.3227e-04 - acc: 1.0000 - val_loss: 2.2619 - val_acc: 0.7056\n",
      "Epoch 379/600\n",
      "537/537 [==============================] - 0s 158us/step - loss: 3.3243e-04 - acc: 1.0000 - val_loss: 2.2599 - val_acc: 0.7100\n",
      "Epoch 380/600\n",
      "537/537 [==============================] - 0s 191us/step - loss: 3.2962e-04 - acc: 1.0000 - val_loss: 2.2619 - val_acc: 0.7056\n",
      "Epoch 381/600\n",
      "537/537 [==============================] - 0s 114us/step - loss: 3.2978e-04 - acc: 1.0000 - val_loss: 2.2616 - val_acc: 0.7056\n",
      "Epoch 382/600\n",
      "537/537 [==============================] - 0s 113us/step - loss: 3.2969e-04 - acc: 1.0000 - val_loss: 2.2609 - val_acc: 0.7056\n",
      "Epoch 383/600\n",
      "537/537 [==============================] - 0s 110us/step - loss: 3.2747e-04 - acc: 1.0000 - val_loss: 2.2609 - val_acc: 0.7056\n",
      "Epoch 384/600\n",
      "537/537 [==============================] - 0s 121us/step - loss: 3.2701e-04 - acc: 1.0000 - val_loss: 2.2607 - val_acc: 0.7056\n",
      "Epoch 385/600\n",
      "537/537 [==============================] - 0s 111us/step - loss: 3.2907e-04 - acc: 1.0000 - val_loss: 2.2609 - val_acc: 0.7100\n",
      "Epoch 386/600\n",
      "537/537 [==============================] - 0s 191us/step - loss: 3.2858e-04 - acc: 1.0000 - val_loss: 2.2621 - val_acc: 0.7056\n",
      "Epoch 387/600\n",
      "537/537 [==============================] - 0s 119us/step - loss: 3.2656e-04 - acc: 1.0000 - val_loss: 2.2625 - val_acc: 0.7056\n",
      "Epoch 388/600\n",
      "537/537 [==============================] - 0s 121us/step - loss: 3.2713e-04 - acc: 1.0000 - val_loss: 2.2626 - val_acc: 0.7056\n",
      "Epoch 389/600\n",
      "537/537 [==============================] - 0s 134us/step - loss: 3.2683e-04 - acc: 1.0000 - val_loss: 2.2616 - val_acc: 0.7056\n",
      "Epoch 390/600\n",
      "537/537 [==============================] - 0s 110us/step - loss: 3.2507e-04 - acc: 1.0000 - val_loss: 2.2615 - val_acc: 0.7100\n",
      "Epoch 391/600\n",
      "537/537 [==============================] - 0s 397us/step - loss: 3.2434e-04 - acc: 1.0000 - val_loss: 2.2621 - val_acc: 0.7100\n",
      "Epoch 392/600\n",
      "537/537 [==============================] - 0s 189us/step - loss: 3.2480e-04 - acc: 1.0000 - val_loss: 2.2620 - val_acc: 0.7056\n",
      "Epoch 393/600\n",
      "537/537 [==============================] - 0s 113us/step - loss: 3.2677e-04 - acc: 1.0000 - val_loss: 2.2616 - val_acc: 0.7056\n",
      "Epoch 394/600\n",
      "537/537 [==============================] - ETA: 0s - loss: 2.2002e-04 - acc: 1.000 - 0s 134us/step - loss: 3.2329e-04 - acc: 1.0000 - val_loss: 2.2636 - val_acc: 0.7056\n",
      "Epoch 395/600\n",
      "537/537 [==============================] - 0s 195us/step - loss: 3.2313e-04 - acc: 1.0000 - val_loss: 2.2629 - val_acc: 0.7056\n",
      "Epoch 396/600\n",
      "537/537 [==============================] - 0s 152us/step - loss: 3.2317e-04 - acc: 1.0000 - val_loss: 2.2632 - val_acc: 0.7056\n",
      "Epoch 397/600\n",
      "537/537 [==============================] - 0s 115us/step - loss: 3.2394e-04 - acc: 1.0000 - val_loss: 2.2627 - val_acc: 0.7100\n",
      "Epoch 398/600\n",
      "537/537 [==============================] - 0s 130us/step - loss: 3.2288e-04 - acc: 1.0000 - val_loss: 2.2634 - val_acc: 0.7056\n",
      "Epoch 399/600\n",
      "537/537 [==============================] - 0s 100us/step - loss: 3.2338e-04 - acc: 1.0000 - val_loss: 2.2633 - val_acc: 0.7056\n",
      "Epoch 400/600\n",
      "537/537 [==============================] - 0s 112us/step - loss: 3.2338e-04 - acc: 1.0000 - val_loss: 2.2641 - val_acc: 0.7056\n",
      "Epoch 401/600\n",
      "537/537 [==============================] - 0s 104us/step - loss: 3.2165e-04 - acc: 1.0000 - val_loss: 2.2636 - val_acc: 0.7056\n",
      "Epoch 402/600\n",
      "537/537 [==============================] - 0s 115us/step - loss: 3.2147e-04 - acc: 1.0000 - val_loss: 2.2635 - val_acc: 0.7056\n",
      "Epoch 403/600\n",
      "537/537 [==============================] - 0s 109us/step - loss: 3.2379e-04 - acc: 1.0000 - val_loss: 2.2632 - val_acc: 0.7056\n",
      "Epoch 404/600\n",
      "537/537 [==============================] - 0s 113us/step - loss: 3.2122e-04 - acc: 1.0000 - val_loss: 2.2636 - val_acc: 0.7056\n",
      "Epoch 405/600\n",
      "537/537 [==============================] - 0s 121us/step - loss: 3.2339e-04 - acc: 1.0000 - val_loss: 2.2654 - val_acc: 0.7056\n",
      "Epoch 406/600\n",
      "537/537 [==============================] - 0s 115us/step - loss: 3.2156e-04 - acc: 1.0000 - val_loss: 2.2636 - val_acc: 0.7056\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 407/600\n",
      "537/537 [==============================] - 0s 112us/step - loss: 3.2207e-04 - acc: 1.0000 - val_loss: 2.2644 - val_acc: 0.7056\n",
      "Epoch 408/600\n",
      "537/537 [==============================] - 0s 401us/step - loss: 3.2405e-04 - acc: 1.0000 - val_loss: 2.2640 - val_acc: 0.7056\n",
      "Epoch 409/600\n",
      "537/537 [==============================] - 0s 100us/step - loss: 3.2019e-04 - acc: 1.0000 - val_loss: 2.2645 - val_acc: 0.7056\n",
      "Epoch 410/600\n",
      "537/537 [==============================] - 0s 106us/step - loss: 3.2193e-04 - acc: 1.0000 - val_loss: 2.2653 - val_acc: 0.7056\n",
      "Epoch 411/600\n",
      "537/537 [==============================] - 0s 156us/step - loss: 3.2215e-04 - acc: 1.0000 - val_loss: 2.2650 - val_acc: 0.7056\n",
      "Epoch 412/600\n",
      "537/537 [==============================] - 0s 145us/step - loss: 3.2075e-04 - acc: 1.0000 - val_loss: 2.2654 - val_acc: 0.7056\n",
      "Epoch 413/600\n",
      "537/537 [==============================] - 0s 180us/step - loss: 3.1988e-04 - acc: 1.0000 - val_loss: 2.2657 - val_acc: 0.7056\n",
      "Epoch 414/600\n",
      "537/537 [==============================] - 0s 160us/step - loss: 3.1886e-04 - acc: 1.0000 - val_loss: 2.2658 - val_acc: 0.7056\n",
      "Epoch 415/600\n",
      "537/537 [==============================] - 0s 154us/step - loss: 3.1978e-04 - acc: 1.0000 - val_loss: 2.2655 - val_acc: 0.7056\n",
      "Epoch 416/600\n",
      "537/537 [==============================] - 0s 254us/step - loss: 3.1842e-04 - acc: 1.0000 - val_loss: 2.2654 - val_acc: 0.7056\n",
      "Epoch 417/600\n",
      "537/537 [==============================] - 0s 108us/step - loss: 3.1809e-04 - acc: 1.0000 - val_loss: 2.2656 - val_acc: 0.7056\n",
      "Epoch 418/600\n",
      "537/537 [==============================] - 0s 269us/step - loss: 3.1822e-04 - acc: 1.0000 - val_loss: 2.2667 - val_acc: 0.7056\n",
      "Epoch 419/600\n",
      "537/537 [==============================] - 0s 253us/step - loss: 3.1814e-04 - acc: 1.0000 - val_loss: 2.2665 - val_acc: 0.7056\n",
      "Epoch 420/600\n",
      "537/537 [==============================] - 0s 175us/step - loss: 3.1765e-04 - acc: 1.0000 - val_loss: 2.2657 - val_acc: 0.7056\n",
      "Epoch 421/600\n",
      "537/537 [==============================] - 0s 220us/step - loss: 3.1766e-04 - acc: 1.0000 - val_loss: 2.2663 - val_acc: 0.7056\n",
      "Epoch 422/600\n",
      "537/537 [==============================] - 0s 152us/step - loss: 3.1842e-04 - acc: 1.0000 - val_loss: 2.2671 - val_acc: 0.7056\n",
      "Epoch 423/600\n",
      "537/537 [==============================] - 0s 282us/step - loss: 3.1661e-04 - acc: 1.0000 - val_loss: 2.2669 - val_acc: 0.7056\n",
      "Epoch 424/600\n",
      "537/537 [==============================] - 0s 191us/step - loss: 3.1918e-04 - acc: 1.0000 - val_loss: 2.2661 - val_acc: 0.7056\n",
      "Epoch 425/600\n",
      "537/537 [==============================] - 0s 158us/step - loss: 3.1629e-04 - acc: 1.0000 - val_loss: 2.2669 - val_acc: 0.7056\n",
      "Epoch 426/600\n",
      "537/537 [==============================] - 0s 308us/step - loss: 3.1706e-04 - acc: 1.0000 - val_loss: 2.2669 - val_acc: 0.7056\n",
      "Epoch 427/600\n",
      "537/537 [==============================] - 0s 409us/step - loss: 3.1637e-04 - acc: 1.0000 - val_loss: 2.2673 - val_acc: 0.7056\n",
      "Epoch 428/600\n",
      "537/537 [==============================] - 0s 223us/step - loss: 3.1566e-04 - acc: 1.0000 - val_loss: 2.2664 - val_acc: 0.7056\n",
      "Epoch 429/600\n",
      "537/537 [==============================] - 0s 158us/step - loss: 3.1874e-04 - acc: 1.0000 - val_loss: 2.2675 - val_acc: 0.7056\n",
      "Epoch 430/600\n",
      "537/537 [==============================] - 0s 212us/step - loss: 3.1505e-04 - acc: 1.0000 - val_loss: 2.2667 - val_acc: 0.7056\n",
      "Epoch 431/600\n",
      "537/537 [==============================] - 0s 234us/step - loss: 3.1793e-04 - acc: 1.0000 - val_loss: 2.2681 - val_acc: 0.7056\n",
      "Epoch 432/600\n",
      "537/537 [==============================] - 0s 305us/step - loss: 3.1596e-04 - acc: 1.0000 - val_loss: 2.2679 - val_acc: 0.7056\n",
      "Epoch 433/600\n",
      "537/537 [==============================] - 0s 165us/step - loss: 3.1711e-04 - acc: 1.0000 - val_loss: 2.2686 - val_acc: 0.7056\n",
      "Epoch 434/600\n",
      "537/537 [==============================] - 0s 234us/step - loss: 3.1608e-04 - acc: 1.0000 - val_loss: 2.2677 - val_acc: 0.7056\n",
      "Epoch 435/600\n",
      "537/537 [==============================] - 0s 258us/step - loss: 3.1418e-04 - acc: 1.0000 - val_loss: 2.2677 - val_acc: 0.7056\n",
      "Epoch 436/600\n",
      "537/537 [==============================] - 0s 152us/step - loss: 3.1486e-04 - acc: 1.0000 - val_loss: 2.2681 - val_acc: 0.7056\n",
      "Epoch 437/600\n",
      "537/537 [==============================] - 0s 223us/step - loss: 3.1418e-04 - acc: 1.0000 - val_loss: 2.2677 - val_acc: 0.7056\n",
      "Epoch 438/600\n",
      "537/537 [==============================] - 0s 188us/step - loss: 3.1438e-04 - acc: 1.0000 - val_loss: 2.2691 - val_acc: 0.7056\n",
      "Epoch 439/600\n",
      "537/537 [==============================] - 0s 325us/step - loss: 3.1516e-04 - acc: 1.0000 - val_loss: 2.2689 - val_acc: 0.7056\n",
      "Epoch 440/600\n",
      "537/537 [==============================] - 0s 154us/step - loss: 3.1230e-04 - acc: 1.0000 - val_loss: 2.2674 - val_acc: 0.7056\n",
      "Epoch 441/600\n",
      "537/537 [==============================] - 0s 121us/step - loss: 3.1601e-04 - acc: 1.0000 - val_loss: 2.2680 - val_acc: 0.7056\n",
      "Epoch 442/600\n",
      "537/537 [==============================] - 0s 243us/step - loss: 3.1361e-04 - acc: 1.0000 - val_loss: 2.2686 - val_acc: 0.7056\n",
      "Epoch 443/600\n",
      "537/537 [==============================] - 0s 119us/step - loss: 3.1326e-04 - acc: 1.0000 - val_loss: 2.2691 - val_acc: 0.7056\n",
      "Epoch 444/600\n",
      "537/537 [==============================] - 0s 134us/step - loss: 3.1465e-04 - acc: 1.0000 - val_loss: 2.2686 - val_acc: 0.7056\n",
      "Epoch 445/600\n",
      "537/537 [==============================] - 0s 212us/step - loss: 3.1214e-04 - acc: 1.0000 - val_loss: 2.2695 - val_acc: 0.7056\n",
      "Epoch 446/600\n",
      "537/537 [==============================] - 0s 104us/step - loss: 3.1305e-04 - acc: 1.0000 - val_loss: 2.2693 - val_acc: 0.7056\n",
      "Epoch 447/600\n",
      "537/537 [==============================] - 0s 124us/step - loss: 3.1221e-04 - acc: 1.0000 - val_loss: 2.2701 - val_acc: 0.7056\n",
      "Epoch 448/600\n",
      "537/537 [==============================] - 0s 167us/step - loss: 3.1271e-04 - acc: 1.0000 - val_loss: 2.2693 - val_acc: 0.7056\n",
      "Epoch 449/600\n",
      "537/537 [==============================] - 0s 121us/step - loss: 3.1056e-04 - acc: 1.0000 - val_loss: 2.2692 - val_acc: 0.7056\n",
      "Epoch 450/600\n",
      "537/537 [==============================] - 0s 111us/step - loss: 3.1192e-04 - acc: 1.0000 - val_loss: 2.2694 - val_acc: 0.7056\n",
      "Epoch 451/600\n",
      "537/537 [==============================] - 0s 108us/step - loss: 3.1112e-04 - acc: 1.0000 - val_loss: 2.2695 - val_acc: 0.7056\n",
      "Epoch 452/600\n",
      "537/537 [==============================] - 0s 117us/step - loss: 3.1242e-04 - acc: 1.0000 - val_loss: 2.2695 - val_acc: 0.7056\n",
      "Epoch 453/600\n",
      "537/537 [==============================] - 0s 97us/step - loss: 3.1167e-04 - acc: 1.0000 - val_loss: 2.2702 - val_acc: 0.7056\n",
      "Epoch 454/600\n",
      "537/537 [==============================] - 0s 125us/step - loss: 3.0930e-04 - acc: 1.0000 - val_loss: 2.2695 - val_acc: 0.7056\n",
      "Epoch 455/600\n",
      "537/537 [==============================] - 0s 342us/step - loss: 3.1002e-04 - acc: 1.0000 - val_loss: 2.2711 - val_acc: 0.7056\n",
      "Epoch 456/600\n",
      "537/537 [==============================] - 0s 129us/step - loss: 3.0941e-04 - acc: 1.0000 - val_loss: 2.2710 - val_acc: 0.7056\n",
      "Epoch 457/600\n",
      "537/537 [==============================] - 0s 146us/step - loss: 3.1000e-04 - acc: 1.0000 - val_loss: 2.2713 - val_acc: 0.7056\n",
      "Epoch 458/600\n",
      "537/537 [==============================] - 0s 139us/step - loss: 3.0974e-04 - acc: 1.0000 - val_loss: 2.2711 - val_acc: 0.7056\n",
      "Epoch 459/600\n",
      "537/537 [==============================] - 0s 117us/step - loss: 3.0897e-04 - acc: 1.0000 - val_loss: 2.2715 - val_acc: 0.7056\n",
      "Epoch 460/600\n",
      "537/537 [==============================] - 0s 122us/step - loss: 3.0886e-04 - acc: 1.0000 - val_loss: 2.2711 - val_acc: 0.7056\n",
      "Epoch 461/600\n",
      "537/537 [==============================] - 0s 135us/step - loss: 3.1022e-04 - acc: 1.0000 - val_loss: 2.2699 - val_acc: 0.7056\n",
      "Epoch 462/600\n",
      "537/537 [==============================] - 0s 455us/step - loss: 3.1123e-04 - acc: 1.0000 - val_loss: 2.2708 - val_acc: 0.7056\n",
      "Epoch 463/600\n",
      "537/537 [==============================] - 0s 156us/step - loss: 3.0938e-04 - acc: 1.0000 - val_loss: 2.2719 - val_acc: 0.7056\n",
      "Epoch 464/600\n",
      "537/537 [==============================] - 0s 127us/step - loss: 3.0941e-04 - acc: 1.0000 - val_loss: 2.2717 - val_acc: 0.7056\n",
      "Epoch 465/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "537/537 [==============================] - 0s 106us/step - loss: 3.0903e-04 - acc: 1.0000 - val_loss: 2.2718 - val_acc: 0.7056\n",
      "Epoch 466/600\n",
      "537/537 [==============================] - 0s 145us/step - loss: 3.0848e-04 - acc: 1.0000 - val_loss: 2.2710 - val_acc: 0.7056\n",
      "Epoch 467/600\n",
      "537/537 [==============================] - 0s 104us/step - loss: 3.0741e-04 - acc: 1.0000 - val_loss: 2.2725 - val_acc: 0.7056\n",
      "Epoch 468/600\n",
      "537/537 [==============================] - ETA: 0s - loss: 4.6411e-04 - acc: 1.000 - 0s 114us/step - loss: 3.0773e-04 - acc: 1.0000 - val_loss: 2.2727 - val_acc: 0.7056\n",
      "Epoch 469/600\n",
      "537/537 [==============================] - 0s 93us/step - loss: 3.0973e-04 - acc: 1.0000 - val_loss: 2.2714 - val_acc: 0.7056\n",
      "Epoch 470/600\n",
      "537/537 [==============================] - 0s 179us/step - loss: 3.0740e-04 - acc: 1.0000 - val_loss: 2.2730 - val_acc: 0.7056\n",
      "Epoch 471/600\n",
      "537/537 [==============================] - 0s 117us/step - loss: 3.0790e-04 - acc: 1.0000 - val_loss: 2.2723 - val_acc: 0.7056\n",
      "Epoch 472/600\n",
      "537/537 [==============================] - 0s 227us/step - loss: 3.0858e-04 - acc: 1.0000 - val_loss: 2.2726 - val_acc: 0.7056\n",
      "Epoch 473/600\n",
      "537/537 [==============================] - 0s 147us/step - loss: 3.0691e-04 - acc: 1.0000 - val_loss: 2.2732 - val_acc: 0.7056\n",
      "Epoch 474/600\n",
      "537/537 [==============================] - 0s 153us/step - loss: 3.0995e-04 - acc: 1.0000 - val_loss: 2.2720 - val_acc: 0.7056\n",
      "Epoch 475/600\n",
      "537/537 [==============================] - 0s 189us/step - loss: 3.1060e-04 - acc: 1.0000 - val_loss: 2.2742 - val_acc: 0.7056\n",
      "Epoch 476/600\n",
      "537/537 [==============================] - 0s 161us/step - loss: 3.1101e-04 - acc: 1.0000 - val_loss: 2.2733 - val_acc: 0.7056\n",
      "Epoch 477/600\n",
      "537/537 [==============================] - 0s 203us/step - loss: 3.0508e-04 - acc: 1.0000 - val_loss: 2.2737 - val_acc: 0.7056\n",
      "Epoch 478/600\n",
      "537/537 [==============================] - 0s 211us/step - loss: 3.0717e-04 - acc: 1.0000 - val_loss: 2.2737 - val_acc: 0.7056\n",
      "Epoch 479/600\n",
      "537/537 [==============================] - 0s 115us/step - loss: 3.0480e-04 - acc: 1.0000 - val_loss: 2.2741 - val_acc: 0.7056\n",
      "Epoch 480/600\n",
      "537/537 [==============================] - 0s 172us/step - loss: 3.0570e-04 - acc: 1.0000 - val_loss: 2.2730 - val_acc: 0.7056\n",
      "Epoch 481/600\n",
      "537/537 [==============================] - 0s 196us/step - loss: 3.0500e-04 - acc: 1.0000 - val_loss: 2.2731 - val_acc: 0.7056\n",
      "Epoch 482/600\n",
      "537/537 [==============================] - 0s 188us/step - loss: 3.0548e-04 - acc: 1.0000 - val_loss: 2.2743 - val_acc: 0.7056\n",
      "Epoch 483/600\n",
      "537/537 [==============================] - 0s 308us/step - loss: 3.0525e-04 - acc: 1.0000 - val_loss: 2.2742 - val_acc: 0.7056\n",
      "Epoch 484/600\n",
      "537/537 [==============================] - 0s 199us/step - loss: 3.0434e-04 - acc: 1.0000 - val_loss: 2.2742 - val_acc: 0.7056\n",
      "Epoch 485/600\n",
      "537/537 [==============================] - 0s 257us/step - loss: 3.0509e-04 - acc: 1.0000 - val_loss: 2.2747 - val_acc: 0.7056\n",
      "Epoch 486/600\n",
      "537/537 [==============================] - 0s 302us/step - loss: 3.0265e-04 - acc: 1.0000 - val_loss: 2.2743 - val_acc: 0.7056\n",
      "Epoch 487/600\n",
      "537/537 [==============================] - 0s 225us/step - loss: 3.0384e-04 - acc: 1.0000 - val_loss: 2.2742 - val_acc: 0.7056\n",
      "Epoch 488/600\n",
      "537/537 [==============================] - 0s 127us/step - loss: 3.0469e-04 - acc: 1.0000 - val_loss: 2.2750 - val_acc: 0.7056\n",
      "Epoch 489/600\n",
      "537/537 [==============================] - 0s 187us/step - loss: 3.0919e-04 - acc: 1.0000 - val_loss: 2.2735 - val_acc: 0.7056\n",
      "Epoch 490/600\n",
      "537/537 [==============================] - 0s 359us/step - loss: 3.0405e-04 - acc: 1.0000 - val_loss: 2.2744 - val_acc: 0.7056\n",
      "Epoch 491/600\n",
      "537/537 [==============================] - 0s 140us/step - loss: 3.0288e-04 - acc: 1.0000 - val_loss: 2.2750 - val_acc: 0.7056\n",
      "Epoch 492/600\n",
      "537/537 [==============================] - 0s 123us/step - loss: 3.0492e-04 - acc: 1.0000 - val_loss: 2.2748 - val_acc: 0.7056\n",
      "Epoch 493/600\n",
      "537/537 [==============================] - 0s 117us/step - loss: 3.0203e-04 - acc: 1.0000 - val_loss: 2.2751 - val_acc: 0.7056\n",
      "Epoch 494/600\n",
      "537/537 [==============================] - 0s 263us/step - loss: 3.0407e-04 - acc: 1.0000 - val_loss: 2.2761 - val_acc: 0.7056\n",
      "Epoch 495/600\n",
      "537/537 [==============================] - 0s 208us/step - loss: 3.0164e-04 - acc: 1.0000 - val_loss: 2.2762 - val_acc: 0.7056\n",
      "Epoch 496/600\n",
      "537/537 [==============================] - 0s 200us/step - loss: 3.0174e-04 - acc: 1.0000 - val_loss: 2.2760 - val_acc: 0.7056\n",
      "Epoch 497/600\n",
      "537/537 [==============================] - 0s 218us/step - loss: 3.0736e-04 - acc: 1.0000 - val_loss: 2.2737 - val_acc: 0.7100\n",
      "Epoch 498/600\n",
      "537/537 [==============================] - 0s 242us/step - loss: 3.0580e-04 - acc: 1.0000 - val_loss: 2.2758 - val_acc: 0.7056\n",
      "Epoch 499/600\n",
      "537/537 [==============================] - 0s 110us/step - loss: 3.0352e-04 - acc: 1.0000 - val_loss: 2.2748 - val_acc: 0.7056\n",
      "Epoch 500/600\n",
      "537/537 [==============================] - 0s 118us/step - loss: 3.0112e-04 - acc: 1.0000 - val_loss: 2.2750 - val_acc: 0.7056\n",
      "Epoch 501/600\n",
      "537/537 [==============================] - 0s 168us/step - loss: 3.0136e-04 - acc: 1.0000 - val_loss: 2.2759 - val_acc: 0.7056\n",
      "Epoch 502/600\n",
      "537/537 [==============================] - 0s 270us/step - loss: 3.0209e-04 - acc: 1.0000 - val_loss: 2.2766 - val_acc: 0.7056\n",
      "Epoch 503/600\n",
      "537/537 [==============================] - 0s 222us/step - loss: 3.0118e-04 - acc: 1.0000 - val_loss: 2.2759 - val_acc: 0.7056\n",
      "Epoch 504/600\n",
      "537/537 [==============================] - 0s 207us/step - loss: 3.0065e-04 - acc: 1.0000 - val_loss: 2.2758 - val_acc: 0.7056\n",
      "Epoch 505/600\n",
      "537/537 [==============================] - 0s 223us/step - loss: 3.0177e-04 - acc: 1.0000 - val_loss: 2.2759 - val_acc: 0.7056\n",
      "Epoch 506/600\n",
      "537/537 [==============================] - 0s 366us/step - loss: 3.0064e-04 - acc: 1.0000 - val_loss: 2.2769 - val_acc: 0.7056\n",
      "Epoch 507/600\n",
      "537/537 [==============================] - 0s 171us/step - loss: 2.9990e-04 - acc: 1.0000 - val_loss: 2.2773 - val_acc: 0.7056\n",
      "Epoch 508/600\n",
      "537/537 [==============================] - 0s 123us/step - loss: 3.0191e-04 - acc: 1.0000 - val_loss: 2.2777 - val_acc: 0.7056\n",
      "Epoch 509/600\n",
      "537/537 [==============================] - 0s 205us/step - loss: 3.0280e-04 - acc: 1.0000 - val_loss: 2.2763 - val_acc: 0.7056\n",
      "Epoch 510/600\n",
      "537/537 [==============================] - 0s 123us/step - loss: 2.9969e-04 - acc: 1.0000 - val_loss: 2.2765 - val_acc: 0.7056\n",
      "Epoch 511/600\n",
      "537/537 [==============================] - 0s 102us/step - loss: 3.0051e-04 - acc: 1.0000 - val_loss: 2.2776 - val_acc: 0.7056\n",
      "Epoch 512/600\n",
      "537/537 [==============================] - 0s 111us/step - loss: 2.9779e-04 - acc: 1.0000 - val_loss: 2.2770 - val_acc: 0.7056\n",
      "Epoch 513/600\n",
      "537/537 [==============================] - 0s 145us/step - loss: 2.9886e-04 - acc: 1.0000 - val_loss: 2.2769 - val_acc: 0.7056\n",
      "Epoch 514/600\n",
      "537/537 [==============================] - 0s 144us/step - loss: 3.0054e-04 - acc: 1.0000 - val_loss: 2.2776 - val_acc: 0.7056\n",
      "Epoch 515/600\n",
      "537/537 [==============================] - 0s 109us/step - loss: 2.9954e-04 - acc: 1.0000 - val_loss: 2.2770 - val_acc: 0.7056\n",
      "Epoch 516/600\n",
      "537/537 [==============================] - 0s 132us/step - loss: 2.9845e-04 - acc: 1.0000 - val_loss: 2.2771 - val_acc: 0.7056\n",
      "Epoch 517/600\n",
      "537/537 [==============================] - 0s 158us/step - loss: 2.9828e-04 - acc: 1.0000 - val_loss: 2.2780 - val_acc: 0.7056\n",
      "Epoch 518/600\n",
      "537/537 [==============================] - 0s 122us/step - loss: 3.0126e-04 - acc: 1.0000 - val_loss: 2.2770 - val_acc: 0.7056\n",
      "Epoch 519/600\n",
      "537/537 [==============================] - 0s 113us/step - loss: 2.9711e-04 - acc: 1.0000 - val_loss: 2.2777 - val_acc: 0.7056\n",
      "Epoch 520/600\n",
      "537/537 [==============================] - 0s 126us/step - loss: 2.9756e-04 - acc: 1.0000 - val_loss: 2.2783 - val_acc: 0.7056\n",
      "Epoch 521/600\n",
      "537/537 [==============================] - 0s 114us/step - loss: 2.9997e-04 - acc: 1.0000 - val_loss: 2.2773 - val_acc: 0.7056\n",
      "Epoch 522/600\n",
      "537/537 [==============================] - 0s 277us/step - loss: 2.9713e-04 - acc: 1.0000 - val_loss: 2.2783 - val_acc: 0.7056\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 523/600\n",
      "537/537 [==============================] - 0s 194us/step - loss: 2.9747e-04 - acc: 1.0000 - val_loss: 2.2788 - val_acc: 0.7056\n",
      "Epoch 524/600\n",
      "537/537 [==============================] - 0s 216us/step - loss: 2.9644e-04 - acc: 1.0000 - val_loss: 2.2777 - val_acc: 0.7056\n",
      "Epoch 525/600\n",
      "537/537 [==============================] - 0s 276us/step - loss: 2.9682e-04 - acc: 1.0000 - val_loss: 2.2784 - val_acc: 0.7056\n",
      "Epoch 526/600\n",
      "537/537 [==============================] - 0s 161us/step - loss: 2.9677e-04 - acc: 1.0000 - val_loss: 2.2785 - val_acc: 0.7056\n",
      "Epoch 527/600\n",
      "537/537 [==============================] - 0s 162us/step - loss: 2.9638e-04 - acc: 1.0000 - val_loss: 2.2789 - val_acc: 0.7056\n",
      "Epoch 528/600\n",
      "537/537 [==============================] - 0s 224us/step - loss: 2.9582e-04 - acc: 1.0000 - val_loss: 2.2782 - val_acc: 0.7056\n",
      "Epoch 529/600\n",
      "537/537 [==============================] - 0s 263us/step - loss: 2.9598e-04 - acc: 1.0000 - val_loss: 2.2782 - val_acc: 0.7056\n",
      "Epoch 530/600\n",
      "537/537 [==============================] - 0s 185us/step - loss: 2.9650e-04 - acc: 1.0000 - val_loss: 2.2787 - val_acc: 0.7056\n",
      "Epoch 531/600\n",
      "537/537 [==============================] - 0s 189us/step - loss: 2.9801e-04 - acc: 1.0000 - val_loss: 2.2792 - val_acc: 0.7056\n",
      "Epoch 532/600\n",
      "537/537 [==============================] - 0s 188us/step - loss: 2.9572e-04 - acc: 1.0000 - val_loss: 2.2792 - val_acc: 0.7056\n",
      "Epoch 533/600\n",
      "537/537 [==============================] - 0s 185us/step - loss: 2.9458e-04 - acc: 1.0000 - val_loss: 2.2783 - val_acc: 0.7056\n",
      "Epoch 534/600\n",
      "537/537 [==============================] - 0s 180us/step - loss: 2.9503e-04 - acc: 1.0000 - val_loss: 2.2797 - val_acc: 0.7056\n",
      "Epoch 535/600\n",
      "537/537 [==============================] - 0s 261us/step - loss: 2.9469e-04 - acc: 1.0000 - val_loss: 2.2802 - val_acc: 0.7056\n",
      "Epoch 536/600\n",
      "537/537 [==============================] - 0s 256us/step - loss: 2.9465e-04 - acc: 1.0000 - val_loss: 2.2795 - val_acc: 0.7056\n",
      "Epoch 537/600\n",
      "537/537 [==============================] - 0s 147us/step - loss: 2.9375e-04 - acc: 1.0000 - val_loss: 2.2795 - val_acc: 0.7056\n",
      "Epoch 538/600\n",
      "537/537 [==============================] - 1s 1ms/step - loss: 2.9412e-04 - acc: 1.0000 - val_loss: 2.2800 - val_acc: 0.7056\n",
      "Epoch 539/600\n",
      "537/537 [==============================] - 0s 257us/step - loss: 2.9422e-04 - acc: 1.0000 - val_loss: 2.2800 - val_acc: 0.7056\n",
      "Epoch 540/600\n",
      "537/537 [==============================] - 0s 215us/step - loss: 2.9451e-04 - acc: 1.0000 - val_loss: 2.2804 - val_acc: 0.7056\n",
      "Epoch 541/600\n",
      "537/537 [==============================] - 0s 119us/step - loss: 2.9494e-04 - acc: 1.0000 - val_loss: 2.2796 - val_acc: 0.7056\n",
      "Epoch 542/600\n",
      "537/537 [==============================] - 0s 185us/step - loss: 2.9283e-04 - acc: 1.0000 - val_loss: 2.2801 - val_acc: 0.7056\n",
      "Epoch 543/600\n",
      "537/537 [==============================] - 0s 234us/step - loss: 2.9395e-04 - acc: 1.0000 - val_loss: 2.2809 - val_acc: 0.7056\n",
      "Epoch 544/600\n",
      "537/537 [==============================] - 0s 195us/step - loss: 2.9396e-04 - acc: 1.0000 - val_loss: 2.2805 - val_acc: 0.7056\n",
      "Epoch 545/600\n",
      "537/537 [==============================] - 0s 245us/step - loss: 2.9318e-04 - acc: 1.0000 - val_loss: 2.2806 - val_acc: 0.7056\n",
      "Epoch 546/600\n",
      "537/537 [==============================] - 0s 146us/step - loss: 2.9250e-04 - acc: 1.0000 - val_loss: 2.2806 - val_acc: 0.7056\n",
      "Epoch 547/600\n",
      "537/537 [==============================] - 0s 325us/step - loss: 2.9346e-04 - acc: 1.0000 - val_loss: 2.2805 - val_acc: 0.7056\n",
      "Epoch 548/600\n",
      "537/537 [==============================] - 0s 254us/step - loss: 2.9376e-04 - acc: 1.0000 - val_loss: 2.2806 - val_acc: 0.7056\n",
      "Epoch 549/600\n",
      "537/537 [==============================] - 0s 290us/step - loss: 2.9195e-04 - acc: 1.0000 - val_loss: 2.2813 - val_acc: 0.7056\n",
      "Epoch 550/600\n",
      "537/537 [==============================] - 0s 226us/step - loss: 2.9203e-04 - acc: 1.0000 - val_loss: 2.2824 - val_acc: 0.7056\n",
      "Epoch 551/600\n",
      "537/537 [==============================] - 0s 217us/step - loss: 2.9229e-04 - acc: 1.0000 - val_loss: 2.2815 - val_acc: 0.7056\n",
      "Epoch 552/600\n",
      "537/537 [==============================] - 0s 186us/step - loss: 2.9317e-04 - acc: 1.0000 - val_loss: 2.2827 - val_acc: 0.7056\n",
      "Epoch 553/600\n",
      "537/537 [==============================] - 0s 351us/step - loss: 2.9461e-04 - acc: 1.0000 - val_loss: 2.2817 - val_acc: 0.7056\n",
      "Epoch 554/600\n",
      "537/537 [==============================] - 0s 230us/step - loss: 2.9465e-04 - acc: 1.0000 - val_loss: 2.2822 - val_acc: 0.7056\n",
      "Epoch 555/600\n",
      "537/537 [==============================] - 0s 275us/step - loss: 2.9162e-04 - acc: 1.0000 - val_loss: 2.2816 - val_acc: 0.7056\n",
      "Epoch 556/600\n",
      "537/537 [==============================] - 0s 206us/step - loss: 2.9095e-04 - acc: 1.0000 - val_loss: 2.2826 - val_acc: 0.7056\n",
      "Epoch 557/600\n",
      "537/537 [==============================] - 0s 98us/step - loss: 2.9240e-04 - acc: 1.0000 - val_loss: 2.2827 - val_acc: 0.7056\n",
      "Epoch 558/600\n",
      "537/537 [==============================] - 0s 137us/step - loss: 2.9133e-04 - acc: 1.0000 - val_loss: 2.2815 - val_acc: 0.7056\n",
      "Epoch 559/600\n",
      "537/537 [==============================] - 0s 108us/step - loss: 2.9064e-04 - acc: 1.0000 - val_loss: 2.2817 - val_acc: 0.7056\n",
      "Epoch 560/600\n",
      "537/537 [==============================] - 0s 342us/step - loss: 2.9099e-04 - acc: 1.0000 - val_loss: 2.2808 - val_acc: 0.7056\n",
      "Epoch 561/600\n",
      "537/537 [==============================] - 0s 265us/step - loss: 2.8950e-04 - acc: 1.0000 - val_loss: 2.2826 - val_acc: 0.7056\n",
      "Epoch 562/600\n",
      "537/537 [==============================] - 0s 154us/step - loss: 2.9056e-04 - acc: 1.0000 - val_loss: 2.2820 - val_acc: 0.7056\n",
      "Epoch 563/600\n",
      "537/537 [==============================] - 0s 292us/step - loss: 2.8821e-04 - acc: 1.0000 - val_loss: 2.2838 - val_acc: 0.7056\n",
      "Epoch 564/600\n",
      "537/537 [==============================] - 0s 139us/step - loss: 2.9041e-04 - acc: 1.0000 - val_loss: 2.2829 - val_acc: 0.7056\n",
      "Epoch 565/600\n",
      "537/537 [==============================] - 0s 318us/step - loss: 2.9149e-04 - acc: 1.0000 - val_loss: 2.2828 - val_acc: 0.7056\n",
      "Epoch 566/600\n",
      "537/537 [==============================] - 0s 208us/step - loss: 2.8891e-04 - acc: 1.0000 - val_loss: 2.2825 - val_acc: 0.7056\n",
      "Epoch 567/600\n",
      "537/537 [==============================] - 0s 156us/step - loss: 2.9303e-04 - acc: 1.0000 - val_loss: 2.2838 - val_acc: 0.7056\n",
      "Epoch 568/600\n",
      "537/537 [==============================] - 0s 197us/step - loss: 2.8788e-04 - acc: 1.0000 - val_loss: 2.2824 - val_acc: 0.7056\n",
      "Epoch 569/600\n",
      "537/537 [==============================] - 0s 252us/step - loss: 2.8975e-04 - acc: 1.0000 - val_loss: 2.2839 - val_acc: 0.7056\n",
      "Epoch 570/600\n",
      "537/537 [==============================] - 0s 188us/step - loss: 2.8884e-04 - acc: 1.0000 - val_loss: 2.2835 - val_acc: 0.7056\n",
      "Epoch 571/600\n",
      "537/537 [==============================] - 0s 245us/step - loss: 2.9165e-04 - acc: 1.0000 - val_loss: 2.2839 - val_acc: 0.7056\n",
      "Epoch 572/600\n",
      "537/537 [==============================] - 0s 130us/step - loss: 2.8710e-04 - acc: 1.0000 - val_loss: 2.2831 - val_acc: 0.7056\n",
      "Epoch 573/600\n",
      "537/537 [==============================] - 0s 115us/step - loss: 2.8872e-04 - acc: 1.0000 - val_loss: 2.2834 - val_acc: 0.7056\n",
      "Epoch 574/600\n",
      "537/537 [==============================] - 0s 119us/step - loss: 2.8906e-04 - acc: 1.0000 - val_loss: 2.2841 - val_acc: 0.7056\n",
      "Epoch 575/600\n",
      "537/537 [==============================] - 0s 156us/step - loss: 2.8808e-04 - acc: 1.0000 - val_loss: 2.2846 - val_acc: 0.7056\n",
      "Epoch 576/600\n",
      "537/537 [==============================] - 0s 310us/step - loss: 2.8705e-04 - acc: 1.0000 - val_loss: 2.2849 - val_acc: 0.7056\n",
      "Epoch 577/600\n",
      "537/537 [==============================] - 0s 104us/step - loss: 2.8750e-04 - acc: 1.0000 - val_loss: 2.2843 - val_acc: 0.7056\n",
      "Epoch 578/600\n",
      "537/537 [==============================] - 0s 102us/step - loss: 2.8678e-04 - acc: 1.0000 - val_loss: 2.2845 - val_acc: 0.7056\n",
      "Epoch 579/600\n",
      "537/537 [==============================] - 0s 134us/step - loss: 2.8652e-04 - acc: 1.0000 - val_loss: 2.2843 - val_acc: 0.7056\n",
      "Epoch 580/600\n",
      "537/537 [==============================] - 0s 297us/step - loss: 2.8745e-04 - acc: 1.0000 - val_loss: 2.2852 - val_acc: 0.7056\n",
      "Epoch 581/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "537/537 [==============================] - 0s 117us/step - loss: 2.8791e-04 - acc: 1.0000 - val_loss: 2.2846 - val_acc: 0.7056\n",
      "Epoch 582/600\n",
      "537/537 [==============================] - 0s 108us/step - loss: 2.8825e-04 - acc: 1.0000 - val_loss: 2.2848 - val_acc: 0.7056\n",
      "Epoch 583/600\n",
      "537/537 [==============================] - 0s 115us/step - loss: 2.8650e-04 - acc: 1.0000 - val_loss: 2.2857 - val_acc: 0.7056\n",
      "Epoch 584/600\n",
      "537/537 [==============================] - 0s 290us/step - loss: 2.8639e-04 - acc: 1.0000 - val_loss: 2.2850 - val_acc: 0.7056\n",
      "Epoch 585/600\n",
      "537/537 [==============================] - 0s 201us/step - loss: 2.8602e-04 - acc: 1.0000 - val_loss: 2.2856 - val_acc: 0.7056\n",
      "Epoch 586/600\n",
      "537/537 [==============================] - 0s 409us/step - loss: 2.8622e-04 - acc: 1.0000 - val_loss: 2.2862 - val_acc: 0.7056\n",
      "Epoch 587/600\n",
      "537/537 [==============================] - 0s 191us/step - loss: 2.8584e-04 - acc: 1.0000 - val_loss: 2.2855 - val_acc: 0.7056\n",
      "Epoch 588/600\n",
      "537/537 [==============================] - 0s 147us/step - loss: 2.8625e-04 - acc: 1.0000 - val_loss: 2.2864 - val_acc: 0.7056\n",
      "Epoch 589/600\n",
      "537/537 [==============================] - 0s 238us/step - loss: 2.8447e-04 - acc: 1.0000 - val_loss: 2.2860 - val_acc: 0.7056\n",
      "Epoch 590/600\n",
      "537/537 [==============================] - 0s 206us/step - loss: 2.8465e-04 - acc: 1.0000 - val_loss: 2.2861 - val_acc: 0.7056\n",
      "Epoch 591/600\n",
      "537/537 [==============================] - 0s 167us/step - loss: 2.8562e-04 - acc: 1.0000 - val_loss: 2.2858 - val_acc: 0.7056\n",
      "Epoch 592/600\n",
      "537/537 [==============================] - 0s 291us/step - loss: 2.8423e-04 - acc: 1.0000 - val_loss: 2.2867 - val_acc: 0.7056\n",
      "Epoch 593/600\n",
      "537/537 [==============================] - 0s 197us/step - loss: 2.8540e-04 - acc: 1.0000 - val_loss: 2.2861 - val_acc: 0.7056\n",
      "Epoch 594/600\n",
      "537/537 [==============================] - 0s 284us/step - loss: 2.8632e-04 - acc: 1.0000 - val_loss: 2.2869 - val_acc: 0.7056\n",
      "Epoch 595/600\n",
      "537/537 [==============================] - 0s 165us/step - loss: 2.8611e-04 - acc: 1.0000 - val_loss: 2.2854 - val_acc: 0.7056\n",
      "Epoch 596/600\n",
      "537/537 [==============================] - 0s 256us/step - loss: 2.8732e-04 - acc: 1.0000 - val_loss: 2.2870 - val_acc: 0.7056\n",
      "Epoch 597/600\n",
      "537/537 [==============================] - 0s 174us/step - loss: 2.8438e-04 - acc: 1.0000 - val_loss: 2.2871 - val_acc: 0.7056\n",
      "Epoch 598/600\n",
      "537/537 [==============================] - 0s 306us/step - loss: 2.8403e-04 - acc: 1.0000 - val_loss: 2.2865 - val_acc: 0.7056\n",
      "Epoch 599/600\n",
      "537/537 [==============================] - 0s 115us/step - loss: 2.8433e-04 - acc: 1.0000 - val_loss: 2.2870 - val_acc: 0.7056\n",
      "Epoch 600/600\n",
      "537/537 [==============================] - 0s 164us/step - loss: 2.8442e-04 - acc: 1.0000 - val_loss: 2.2877 - val_acc: 0.7056\n"
     ]
    }
   ],
   "source": [
    "run_hist_600 = nn_model_2.fit(X_train_norm, y_train, validation_data=(X_test_norm, y_test), epochs=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_class_nn_600 = nn_model_2.predict_classes(X_test_norm)\n",
    "y_pred_prob_nn_600 = nn_model_2.predict(X_test_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 484,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c7c36880b8>"
      ]
     },
     "execution_count": 484,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the curve to check training and validation accuracy\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(run_hist_600.history[\"acc\"],'r', marker='.', label=\"Training Accuracy\")\n",
    "ax.plot(run_hist_600.history[\"val_acc\"],'b', marker='.', label=\"Validation Accuracy\")\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 485,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c7c36eff28>"
      ]
     },
     "execution_count": 485,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+t7/9jQcffJA333yTU089leuuu67N7bR2DquGU+9C7PS7iU65tLTNc845h4qKCpYtW8add97J+PHjufvuu1mzZg0rVqxg4cKFzJ49m5dffjmh/bSmzRG6mc01s11mtqGFx83MHjGzbWZWaWaJXwZERLqcZJ89t1evXowZM4bvfve7R30YWldXx+c+9zmysrJYuXIl77zzTqvbaXoK2w0bNlBZWQnETr3bs2dP+vbty3vvvdd4pSCA3r17s2fPnrjbeu6559i3bx979+5l8eLFXHzxxR3qZ0vbrKmpoUePHlxzzTXcdtttrF27lk8++YS6ujomTpzIrFmzknZR6kRG6L8FZgNPtvD4BODs6OcCYE50KyIZKtlnz502bRpXXXXVUUe8fPvb3+brX/86JSUlFBUV8cUvfrHVbdx4441cf/31FBYWUlRUxKhRo4DY1YdGjhzJsGHDjjn17owZM5gwYQKnnXYaK1eubFxfXFzMdddd17iNG264gZEjRyY8vQIwc+bMxg8+Aaqrq+Nu86WXXuL222+nW7duZGVlMWfOHPbs2cOkSZM4cOAA7s7DDz+c8H5bk9Dpc80sH3jB3QviPPY48Iq7L4iWtwBj3H1na9vU6XNFOodOn5u52nv63GR8KHo68G6T5eponYiIdKJkBHq8Y4ziDvvNbIaZlZtZeW1tbRJ2LSIiDZIR6NXAGU2WBwM18Rq6+xPuXuLuJQMGDEjCrkVEpEEyAn0JcG10tMuFQF1b8+ci0rnSdalJOX7H82/W5lEuZrYAGAP0N7Nq4B4gK9rhY8AyYCKwDdgHXN/uKkQkZXJycti9ezd5eXktfgtTuhZ3Z/fu3eTk5LTreW0Guru3ehYdj/0ZualdexWRTjN48GCqq6vR51aZJScnh8GDB7frOfqmqEjgsrKyGDJkSLrLkE6gc7mIiARCgS4iEggFuohIIBToIiKBUKCLiARCgS4iEggFuohIIBToIiKBUKCLiARCgS4iEggFuohIIBToIiKBUKCLiARCgS4iEggFuohIIBToIiKBUKCLiARCgS4iEggFuohIIBToIiKBUKCLiARCgS4iEggFuohIIBToIiKBUKCLiARCgS4iEggFuohIIBToIiKBUKCLiARCgS4iEggFuohIIBToIiKBUKCLiARCgS4iEoiEAt3MvmZmW8xsm5mVxXn8OjOrNbN10c8NyS9VRERa072tBmZ2EvAocBlQDbxpZkvcfVOzpk+5+49TUKOIiCQgkRH6KGCbu29390PAQmBSassSEZH2SiTQTwfebbJcHa1r7mozqzSzZ8zsjHgbMrMZZlZuZuW1tbXHUa6IiLQkkUC3OOu82fIfgHx3LwT+BPwu3obc/Ql3L3H3kgEDBrSvUhERaVUigV4NNB1xDwZqmjZw993ufjBa/A3wpeSUJyIiiUok0N8EzjazIWaWDUwFljRtYGanNVm8EticvBJFRCQRbR7l4u71ZvZj4CXgJGCuu280s/uAcndfAvzEzK4E6oEPgOtSWLOIiMRh7s2nwztHSUmJl5eXp2XfIiKZyswq3L0k3mP6pqiISCAU6CIigVCgi4gEQoEuIhIIBbqISCAU6CIigVCgi4gEQoEuIhIIBbqISCAU6CIigVCgi4gEQoEuIhIIBbqISCAU6CIigVCgi4gEQoEuIhIIBbqISCAU6CIigVCgi4gEQoEuIhIIBbqISCAU6CIigVCgi4gEQoEuIhIIBbqISCAU6CIigVCgi4gEQoEuIhIIBbqISCAU6CIigVCgi4gEQoEuIhIIBbqISCAU6CIigUgo0M3sa2a2xcy2mVlZnMdPNrOnosffMLP8ZBcqIiKt695WAzM7CXgUuAyoBt40syXuvqlJs+8BH7r7WWY2FfhfwJRUFMzq1fDAA/DWW3Dw4Gfrc3IgNxc+/PDo9Z1B++78fad7/yfqvtO9/xD2nZMDRUVwxx1QWpq8+kgg0IFRwDZ33w5gZguBSUDTQJ8E3BvdfwaYbWbm7p7EWmNhfvHF8OmnSd2siEinqqqCpUvhz39OaqgnMuVyOvBuk+XqaF3cNu5eD9QBec03ZGYzzKzczMpra2vbX+0rryjMRSQMhw/HMi2JEgl0i7Ou+cg7kTa4+xPuXuLuJQMGDEikvqONGQPdE3lTISLSxWVlxTItiRJJx2rgjCbLg4GaFtpUm1l3oC/wQVIqbKq0FF59VXPo2nf693+i7jvd+w9h32meQ38TONvMhgA7gKnA/2zWZgnwHWA1MBl4Oenz5w1KS2Hx4pRsWkQkk7UZ6O5eb2Y/Bl4CTgLmuvtGM7sPKHf3JcB/AP/HzLYRG5lPTWXRIiJyrIQmpN19GbCs2bq7m9w/APyP5JYmIiLtoW+KiogEQoEuIhIIBbqISCAU6CIigbBUHV3Y5o7NaoF3jvPp/YH3k1hOOqkvXZP60vWE0g/oWF/+0d3jfjMzbYHeEWZW7u4l6a4jGdSXrkl96XpC6Qekri+achERCYQCXUQkEJka6E+ku4AkUl+6JvWl6wmlH5CivmTkHLqIiBwrU0foIiLSjAJdRCQQGRfobV2wuqsxs7lmtsvMNjRZ18/M/mhmW6PbU6P1ZmaPRH2rNLPi9FV+NDM7w8xWmtlmM9toZrdE6zOxLzlmtsbM3o768q/R+iHRRc63Rhc9z47Wd/mLoJvZSWb2lpm9EC1nZF/MrMrM1pvZOjMrj9Zl3GsMwMxyzewZM/tr9P+mNNV9yahAb3LB6gnAUGCamQ1Nb1Vt+i3wtWbryoAV7n42sCJahli/zo5+ZgBzOqnGRNQD/+zu5wEXAjdFv/tM7MtB4J/cfQRQBHzNzC4kdnHzh6O+fEjs4ufQ5CLowMNRu67mFmBzk+VM7stYdy9qcpx2Jr7GAH4JLHf3LwIjiP37pLYv7p4xP0Ap8FKT5TuBO9NdVwJ15wMbmixvAU6L7p8GbInuPw5Mi9euq/0AzwOXZXpfgB7AWuACYt/c6978tUbsWgCl0f3uUTtLd+1N+jA4Cod/Al4gdknITO1LFdC/2bqMe40BfYC/Nf/dprovGTVCJ7ELVmeCf3D3nQDR7eei9RnRv+ht+kjgDTK0L9EUxTpgF/BH4L+Ajzx2kXM4ut6ELoKeRrOAO4Aj0XIemdsXB/6fmVWY2YxoXSa+xs4EaoF50VTY/zaznqS4L5kW6AldjDqDdfn+mVkvYBHwU3f/uLWmcdZ1mb64+6fuXkRsdDsKOC9es+i2y/bFzK4Adrl7RdPVcZp2+b5ELnL3YmJTEDeZ2SWttO3KfekOFANz3H0ksJfPplfiSUpfMi3QE7lgdSZ4z8xOA4hud0Xru3T/zCyLWJjPd/dno9UZ2ZcG7v4R8AqxzwVyLXaRczi63sa+WCovgn58LgKuNLMqYCGxaZdZZGZfcPea6HYXsJjYH9tMfI1VA9Xu/ka0/AyxgE9pXzIt0BsvWB19aj+V2AWqM03DRbWJbp9vsv7a6BPvC4G6hrdn6WZmRuzasZvd/aEmD2ViXwaYWW50/xTgK8Q+sFpJ7CLncGxfGvqY2ougt5O73+nug909n9j/h5fd/dtkYF/MrKeZ9W64D4wHNpCBrzF3/zvwrpmdG60aB2wi1X1J94cHx/Fhw0TgP4nNef5LuutJoN4FwE7gMLG/wt8jNme5Atga3faL2hqxo3j+C1gPlKS7/ib9GE3sLWAlsC76mZihfSkE3or6sgG4O1p/JrAG2Ab8X+DkaH1OtLwtevzMdPehhX6NAV7I1L5ENb8d/Wxs+P+dia+xqL4ioDx6nT0HnJrqvuir/yIigci0KRcREWmBAl1EJBAKdBGRQCjQRUQCoUAXEQmEAl1EJBAKdBGRQPw3Q0WtO2oLUBIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting curves for Training Loss and Validation loss\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(run_hist_600.history[\"loss\"],'r', marker='.', label=\"Training Loss\")\n",
    "ax.plot(run_hist_600.history[\"val_loss\"],'b', marker='.', label=\"Validation Loss\")\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 486,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy is 0.706\n",
      "roc-auc is 0.755\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Print model performance and plot the roc curve\n",
    "print('accuracy is {:.3f}'.format(accuracy_score(y_test,y_pred_class_nn_600)))\n",
    "print('roc-auc is {:.3f}'.format(roc_auc_score(y_test,y_pred_prob_nn_600)))\n",
    "\n",
    "plot_roc(y_test, y_pred_prob_nn_600, 'NN')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe that we reach an accuracy of 70.6% "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building more complex model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_4(model):\n",
    "    model.add(Dense(1024, input_dim=8, activation='relu'))\n",
    "    model.add(Dense(512, activation='relu'))\n",
    "    model.add(Dense(256, activation='relu'))\n",
    "    model.add(Dense(128, activation='relu'))\n",
    "    model.add(Dense(64, activation='relu'))\n",
    "    model.add(Dense(32, activation='relu'))\n",
    "    model.add(Dense(16, activation='relu'))\n",
    "    model.add(Dense(8, activation='relu'))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model = model_4(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compile the model\n",
    "    model.compile(\n",
    "        loss = 'binary_crossentropy',\n",
    "        optimizer = SGD(lr=1e-4, decay=1e-6, momentum=0.9),\n",
    "        metrics = ['accuracy']\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_103 (Dense)            (None, 1024)              9216      \n",
      "_________________________________________________________________\n",
      "dense_104 (Dense)            (None, 512)               524800    \n",
      "_________________________________________________________________\n",
      "dense_105 (Dense)            (None, 256)               131328    \n",
      "_________________________________________________________________\n",
      "dense_106 (Dense)            (None, 128)               32896     \n",
      "_________________________________________________________________\n",
      "dense_107 (Dense)            (None, 64)                8256      \n",
      "_________________________________________________________________\n",
      "dense_108 (Dense)            (None, 32)                2080      \n",
      "_________________________________________________________________\n",
      "dense_109 (Dense)            (None, 16)                528       \n",
      "_________________________________________________________________\n",
      "dense_110 (Dense)            (None, 8)                 136       \n",
      "_________________________________________________________________\n",
      "dense_111 (Dense)            (None, 1)                 9         \n",
      "=================================================================\n",
      "Total params: 709,249\n",
      "Trainable params: 709,249\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "mystr = model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 496,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 537 samples, validate on 231 samples\n",
      "Epoch 1/500\n",
      "10/10 [==============================] - 3s 300ms/step - loss: 0.6948 - acc: 0.3473 - val_loss: 0.6945 - val_acc: 0.3550\n",
      "Epoch 2/500\n",
      "10/10 [==============================] - 1s 80ms/step - loss: 0.6944 - acc: 0.3544 - val_loss: 0.6940 - val_acc: 0.3723\n",
      "Epoch 3/500\n",
      "10/10 [==============================] - 1s 51ms/step - loss: 0.6938 - acc: 0.4020 - val_loss: 0.6934 - val_acc: 0.4675\n",
      "Epoch 4/500\n",
      "10/10 [==============================] - 1s 84ms/step - loss: 0.6932 - acc: 0.5259 - val_loss: 0.6928 - val_acc: 0.5758\n",
      "Epoch 5/500\n",
      "10/10 [==============================] - 1s 61ms/step - loss: 0.6926 - acc: 0.6291 - val_loss: 0.6923 - val_acc: 0.6190\n",
      "Epoch 6/500\n",
      "10/10 [==============================] - 0s 49ms/step - loss: 0.6921 - acc: 0.6555 - val_loss: 0.6917 - val_acc: 0.6320\n",
      "Epoch 7/500\n",
      "10/10 [==============================] - 0s 44ms/step - loss: 0.6916 - acc: 0.6611 - val_loss: 0.6913 - val_acc: 0.6494\n",
      "Epoch 8/500\n",
      "10/10 [==============================] - 1s 52ms/step - loss: 0.6911 - acc: 0.6536 - val_loss: 0.6908 - val_acc: 0.6277\n",
      "Epoch 9/500\n",
      "10/10 [==============================] - 1s 72ms/step - loss: 0.6907 - acc: 0.6499 - val_loss: 0.6904 - val_acc: 0.6537\n",
      "Epoch 10/500\n",
      "10/10 [==============================] - 1s 77ms/step - loss: 0.6903 - acc: 0.6389 - val_loss: 0.6900 - val_acc: 0.6623\n",
      "Epoch 11/500\n",
      "10/10 [==============================] - 1s 69ms/step - loss: 0.6900 - acc: 0.6443 - val_loss: 0.6896 - val_acc: 0.6710\n",
      "Epoch 12/500\n",
      "10/10 [==============================] - 1s 120ms/step - loss: 0.6896 - acc: 0.6451 - val_loss: 0.6893 - val_acc: 0.6667\n",
      "Epoch 13/500\n",
      "10/10 [==============================] - 1s 135ms/step - loss: 0.6893 - acc: 0.6490 - val_loss: 0.6889 - val_acc: 0.6667\n",
      "Epoch 14/500\n",
      "10/10 [==============================] - 1s 62ms/step - loss: 0.6890 - acc: 0.6538 - val_loss: 0.6886 - val_acc: 0.6623\n",
      "Epoch 15/500\n",
      "10/10 [==============================] - 0s 49ms/step - loss: 0.6886 - acc: 0.6555 - val_loss: 0.6883 - val_acc: 0.6623\n",
      "Epoch 16/500\n",
      "10/10 [==============================] - 1s 56ms/step - loss: 0.6883 - acc: 0.6555 - val_loss: 0.6879 - val_acc: 0.6580\n",
      "Epoch 17/500\n",
      "10/10 [==============================] - 1s 52ms/step - loss: 0.6880 - acc: 0.6555 - val_loss: 0.6876 - val_acc: 0.6580\n",
      "Epoch 18/500\n",
      "10/10 [==============================] - 1s 51ms/step - loss: 0.6877 - acc: 0.6547 - val_loss: 0.6873 - val_acc: 0.6580\n",
      "Epoch 19/500\n",
      "10/10 [==============================] - 1s 56ms/step - loss: 0.6874 - acc: 0.6523 - val_loss: 0.6870 - val_acc: 0.6580\n",
      "Epoch 20/500\n",
      "10/10 [==============================] - 1s 51ms/step - loss: 0.6871 - acc: 0.6514 - val_loss: 0.6867 - val_acc: 0.6580\n",
      "Epoch 21/500\n",
      "10/10 [==============================] - 1s 55ms/step - loss: 0.6868 - acc: 0.6499 - val_loss: 0.6864 - val_acc: 0.6537\n",
      "Epoch 22/500\n",
      "10/10 [==============================] - 1s 64ms/step - loss: 0.6865 - acc: 0.6499 - val_loss: 0.6861 - val_acc: 0.6537\n",
      "Epoch 23/500\n",
      "10/10 [==============================] - 1s 58ms/step - loss: 0.6862 - acc: 0.6499 - val_loss: 0.6858 - val_acc: 0.6537\n",
      "Epoch 24/500\n",
      "10/10 [==============================] - 1s 66ms/step - loss: 0.6859 - acc: 0.6499 - val_loss: 0.6855 - val_acc: 0.6537\n",
      "Epoch 25/500\n",
      "10/10 [==============================] - 1s 57ms/step - loss: 0.6856 - acc: 0.6499 - val_loss: 0.6852 - val_acc: 0.6537\n",
      "Epoch 26/500\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 0.6853 - acc: 0.6499 - val_loss: 0.6849 - val_acc: 0.6494\n",
      "Epoch 27/500\n",
      "10/10 [==============================] - 1s 68ms/step - loss: 0.6850 - acc: 0.6499 - val_loss: 0.6847 - val_acc: 0.6494\n",
      "Epoch 28/500\n",
      "10/10 [==============================] - 1s 77ms/step - loss: 0.6847 - acc: 0.6499 - val_loss: 0.6844 - val_acc: 0.6494\n",
      "Epoch 29/500\n",
      "10/10 [==============================] - 1s 76ms/step - loss: 0.6844 - acc: 0.6499 - val_loss: 0.6841 - val_acc: 0.6494\n",
      "Epoch 30/500\n",
      "10/10 [==============================] - 1s 76ms/step - loss: 0.6841 - acc: 0.6499 - val_loss: 0.6838 - val_acc: 0.6494\n",
      "Epoch 31/500\n",
      "10/10 [==============================] - 1s 93ms/step - loss: 0.6838 - acc: 0.6507 - val_loss: 0.6836 - val_acc: 0.6494\n",
      "Epoch 32/500\n",
      "10/10 [==============================] - 1s 70ms/step - loss: 0.6835 - acc: 0.6518 - val_loss: 0.6833 - val_acc: 0.6494\n",
      "Epoch 33/500\n",
      "10/10 [==============================] - 1s 74ms/step - loss: 0.6833 - acc: 0.6518 - val_loss: 0.6830 - val_acc: 0.6494\n",
      "Epoch 34/500\n",
      "10/10 [==============================] - 1s 74ms/step - loss: 0.6830 - acc: 0.6518 - val_loss: 0.6827 - val_acc: 0.6494\n",
      "Epoch 35/500\n",
      "10/10 [==============================] - 1s 74ms/step - loss: 0.6827 - acc: 0.6518 - val_loss: 0.6825 - val_acc: 0.6494\n",
      "Epoch 36/500\n",
      "10/10 [==============================] - 1s 78ms/step - loss: 0.6824 - acc: 0.6518 - val_loss: 0.6822 - val_acc: 0.6494\n",
      "Epoch 37/500\n",
      "10/10 [==============================] - 1s 79ms/step - loss: 0.6822 - acc: 0.6518 - val_loss: 0.6819 - val_acc: 0.6494\n",
      "Epoch 38/500\n",
      "10/10 [==============================] - 1s 79ms/step - loss: 0.6819 - acc: 0.6518 - val_loss: 0.6817 - val_acc: 0.6494\n",
      "Epoch 39/500\n",
      "10/10 [==============================] - 1s 67ms/step - loss: 0.6816 - acc: 0.6518 - val_loss: 0.6814 - val_acc: 0.6494\n",
      "Epoch 40/500\n",
      "10/10 [==============================] - 1s 118ms/step - loss: 0.6813 - acc: 0.6518 - val_loss: 0.6811 - val_acc: 0.6494\n",
      "Epoch 41/500\n",
      "10/10 [==============================] - 1s 116ms/step - loss: 0.6811 - acc: 0.6518 - val_loss: 0.6809 - val_acc: 0.6494\n",
      "Epoch 42/500\n",
      "10/10 [==============================] - 1s 73ms/step - loss: 0.6808 - acc: 0.6518 - val_loss: 0.6806 - val_acc: 0.6494\n",
      "Epoch 43/500\n",
      "10/10 [==============================] - 1s 85ms/step - loss: 0.6805 - acc: 0.6518 - val_loss: 0.6803 - val_acc: 0.6494\n",
      "Epoch 44/500\n",
      "10/10 [==============================] - 1s 78ms/step - loss: 0.6802 - acc: 0.6518 - val_loss: 0.6801 - val_acc: 0.6494\n",
      "Epoch 45/500\n",
      "10/10 [==============================] - 1s 77ms/step - loss: 0.6800 - acc: 0.6518 - val_loss: 0.6798 - val_acc: 0.6494\n",
      "Epoch 46/500\n",
      "10/10 [==============================] - 1s 71ms/step - loss: 0.6797 - acc: 0.6518 - val_loss: 0.6796 - val_acc: 0.6494\n",
      "Epoch 47/500\n",
      "10/10 [==============================] - 1s 71ms/step - loss: 0.6795 - acc: 0.6518 - val_loss: 0.6793 - val_acc: 0.6494\n",
      "Epoch 48/500\n",
      "10/10 [==============================] - 1s 76ms/step - loss: 0.6792 - acc: 0.6518 - val_loss: 0.6791 - val_acc: 0.6494\n",
      "Epoch 49/500\n",
      "10/10 [==============================] - 1s 117ms/step - loss: 0.6789 - acc: 0.6518 - val_loss: 0.6788 - val_acc: 0.6494\n",
      "Epoch 50/500\n",
      "10/10 [==============================] - 1s 102ms/step - loss: 0.6787 - acc: 0.6518 - val_loss: 0.6786 - val_acc: 0.6494\n",
      "Epoch 51/500\n",
      "10/10 [==============================] - 1s 93ms/step - loss: 0.6784 - acc: 0.6518 - val_loss: 0.6783 - val_acc: 0.6494\n",
      "Epoch 52/500\n",
      "10/10 [==============================] - 1s 84ms/step - loss: 0.6782 - acc: 0.6518 - val_loss: 0.6781 - val_acc: 0.6494\n",
      "Epoch 53/500\n",
      "10/10 [==============================] - 1s 68ms/step - loss: 0.6779 - acc: 0.6518 - val_loss: 0.6778 - val_acc: 0.6494\n",
      "Epoch 54/500\n",
      "10/10 [==============================] - 1s 62ms/step - loss: 0.6777 - acc: 0.6518 - val_loss: 0.6776 - val_acc: 0.6494\n",
      "Epoch 55/500\n",
      "10/10 [==============================] - 1s 75ms/step - loss: 0.6774 - acc: 0.6518 - val_loss: 0.6773 - val_acc: 0.6494\n",
      "Epoch 56/500\n",
      "10/10 [==============================] - 1s 135ms/step - loss: 0.6772 - acc: 0.6518 - val_loss: 0.6771 - val_acc: 0.6494\n",
      "Epoch 57/500\n",
      "10/10 [==============================] - 1s 125ms/step - loss: 0.6769 - acc: 0.6518 - val_loss: 0.6769 - val_acc: 0.6494\n",
      "Epoch 58/500\n",
      "10/10 [==============================] - 1s 91ms/step - loss: 0.6767 - acc: 0.6518 - val_loss: 0.6766 - val_acc: 0.6494\n",
      "Epoch 59/500\n",
      "10/10 [==============================] - 1s 89ms/step - loss: 0.6764 - acc: 0.6518 - val_loss: 0.6764 - val_acc: 0.6494\n",
      "Epoch 60/500\n",
      "10/10 [==============================] - 1s 67ms/step - loss: 0.6762 - acc: 0.6518 - val_loss: 0.6761 - val_acc: 0.6494\n",
      "Epoch 61/500\n",
      "10/10 [==============================] - 2s 170ms/step - loss: 0.6759 - acc: 0.6518 - val_loss: 0.6759 - val_acc: 0.6494\n",
      "Epoch 62/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10/10 [==============================] - 1s 121ms/step - loss: 0.6757 - acc: 0.6518 - val_loss: 0.6757 - val_acc: 0.6494\n",
      "Epoch 63/500\n",
      "10/10 [==============================] - 1s 101ms/step - loss: 0.6755 - acc: 0.6518 - val_loss: 0.6754 - val_acc: 0.6494\n",
      "Epoch 64/500\n",
      "10/10 [==============================] - 1s 75ms/step - loss: 0.6752 - acc: 0.6518 - val_loss: 0.6752 - val_acc: 0.6494\n",
      "Epoch 65/500\n",
      "10/10 [==============================] - 1s 112ms/step - loss: 0.6750 - acc: 0.6518 - val_loss: 0.6750 - val_acc: 0.6494\n",
      "Epoch 66/500\n",
      "10/10 [==============================] - 1s 100ms/step - loss: 0.6748 - acc: 0.6518 - val_loss: 0.6747 - val_acc: 0.6494\n",
      "Epoch 67/500\n",
      "10/10 [==============================] - 1s 101ms/step - loss: 0.6745 - acc: 0.6518 - val_loss: 0.6745 - val_acc: 0.6494\n",
      "Epoch 68/500\n",
      "10/10 [==============================] - 1s 100ms/step - loss: 0.6743 - acc: 0.6518 - val_loss: 0.6743 - val_acc: 0.6494\n",
      "Epoch 69/500\n",
      "10/10 [==============================] - 1s 92ms/step - loss: 0.6740 - acc: 0.6518 - val_loss: 0.6741 - val_acc: 0.6494\n",
      "Epoch 70/500\n",
      "10/10 [==============================] - 1s 67ms/step - loss: 0.6738 - acc: 0.6518 - val_loss: 0.6738 - val_acc: 0.6494\n",
      "Epoch 71/500\n",
      "10/10 [==============================] - 1s 74ms/step - loss: 0.6736 - acc: 0.6518 - val_loss: 0.6736 - val_acc: 0.6494\n",
      "Epoch 72/500\n",
      "10/10 [==============================] - 1s 66ms/step - loss: 0.6734 - acc: 0.6518 - val_loss: 0.6734 - val_acc: 0.6494\n",
      "Epoch 73/500\n",
      "10/10 [==============================] - 1s 87ms/step - loss: 0.6731 - acc: 0.6518 - val_loss: 0.6732 - val_acc: 0.6494\n",
      "Epoch 74/500\n",
      "10/10 [==============================] - 1s 104ms/step - loss: 0.6729 - acc: 0.6518 - val_loss: 0.6730 - val_acc: 0.6494\n",
      "Epoch 75/500\n",
      "10/10 [==============================] - 1s 80ms/step - loss: 0.6727 - acc: 0.6518 - val_loss: 0.6727 - val_acc: 0.6494\n",
      "Epoch 76/500\n",
      "10/10 [==============================] - 1s 72ms/step - loss: 0.6725 - acc: 0.6518 - val_loss: 0.6725 - val_acc: 0.6494\n",
      "Epoch 77/500\n",
      "10/10 [==============================] - 1s 78ms/step - loss: 0.6722 - acc: 0.6518 - val_loss: 0.6723 - val_acc: 0.6494\n",
      "Epoch 78/500\n",
      "10/10 [==============================] - 1s 73ms/step - loss: 0.6720 - acc: 0.6518 - val_loss: 0.6721 - val_acc: 0.6494\n",
      "Epoch 79/500\n",
      "10/10 [==============================] - 1s 76ms/step - loss: 0.6718 - acc: 0.6518 - val_loss: 0.6719 - val_acc: 0.6494\n",
      "Epoch 80/500\n",
      "10/10 [==============================] - 1s 127ms/step - loss: 0.6716 - acc: 0.6518 - val_loss: 0.6717 - val_acc: 0.6494\n",
      "Epoch 81/500\n",
      "10/10 [==============================] - 1s 93ms/step - loss: 0.6714 - acc: 0.6518 - val_loss: 0.6715 - val_acc: 0.6494\n",
      "Epoch 82/500\n",
      "10/10 [==============================] - 1s 131ms/step - loss: 0.6711 - acc: 0.6518 - val_loss: 0.6713 - val_acc: 0.6494\n",
      "Epoch 83/500\n",
      "10/10 [==============================] - 1s 75ms/step - loss: 0.6709 - acc: 0.6518 - val_loss: 0.6710 - val_acc: 0.6494\n",
      "Epoch 84/500\n",
      "10/10 [==============================] - 1s 76ms/step - loss: 0.6707 - acc: 0.6518 - val_loss: 0.6708 - val_acc: 0.6494\n",
      "Epoch 85/500\n",
      "10/10 [==============================] - 1s 83ms/step - loss: 0.6705 - acc: 0.6518 - val_loss: 0.6706 - val_acc: 0.6494\n",
      "Epoch 86/500\n",
      "10/10 [==============================] - 1s 88ms/step - loss: 0.6703 - acc: 0.6518 - val_loss: 0.6704 - val_acc: 0.6494\n",
      "Epoch 87/500\n",
      "10/10 [==============================] - 2s 196ms/step - loss: 0.6701 - acc: 0.6518 - val_loss: 0.6702 - val_acc: 0.6494\n",
      "Epoch 88/500\n",
      "10/10 [==============================] - 2s 152ms/step - loss: 0.6699 - acc: 0.6518 - val_loss: 0.6700 - val_acc: 0.6494\n",
      "Epoch 89/500\n",
      "10/10 [==============================] - 1s 104ms/step - loss: 0.6697 - acc: 0.6518 - val_loss: 0.6698 - val_acc: 0.6494\n",
      "Epoch 90/500\n",
      "10/10 [==============================] - 1s 92ms/step - loss: 0.6695 - acc: 0.6518 - val_loss: 0.6696 - val_acc: 0.6494\n",
      "Epoch 91/500\n",
      "10/10 [==============================] - 1s 84ms/step - loss: 0.6693 - acc: 0.6518 - val_loss: 0.6694 - val_acc: 0.6494\n",
      "Epoch 92/500\n",
      "10/10 [==============================] - 1s 93ms/step - loss: 0.6690 - acc: 0.6518 - val_loss: 0.6692 - val_acc: 0.6494\n",
      "Epoch 93/500\n",
      "10/10 [==============================] - 1s 87ms/step - loss: 0.6688 - acc: 0.6518 - val_loss: 0.6690 - val_acc: 0.6494\n",
      "Epoch 94/500\n",
      "10/10 [==============================] - 1s 109ms/step - loss: 0.6686 - acc: 0.6518 - val_loss: 0.6688 - val_acc: 0.6494\n",
      "Epoch 95/500\n",
      "10/10 [==============================] - 1s 106ms/step - loss: 0.6684 - acc: 0.6518 - val_loss: 0.6686 - val_acc: 0.6494\n",
      "Epoch 96/500\n",
      "10/10 [==============================] - 1s 124ms/step - loss: 0.6682 - acc: 0.6518 - val_loss: 0.6684 - val_acc: 0.6494\n",
      "Epoch 97/500\n",
      "10/10 [==============================] - 1s 96ms/step - loss: 0.6680 - acc: 0.6518 - val_loss: 0.6682 - val_acc: 0.6494\n",
      "Epoch 98/500\n",
      "10/10 [==============================] - 1s 120ms/step - loss: 0.6678 - acc: 0.6518 - val_loss: 0.6681 - val_acc: 0.6494\n",
      "Epoch 99/500\n",
      "10/10 [==============================] - 1s 114ms/step - loss: 0.6676 - acc: 0.6518 - val_loss: 0.6679 - val_acc: 0.6494\n",
      "Epoch 100/500\n",
      "10/10 [==============================] - 1s 98ms/step - loss: 0.6674 - acc: 0.6518 - val_loss: 0.6677 - val_acc: 0.6494\n",
      "Epoch 101/500\n",
      "10/10 [==============================] - 1s 95ms/step - loss: 0.6672 - acc: 0.6518 - val_loss: 0.6675 - val_acc: 0.6494\n",
      "Epoch 102/500\n",
      "10/10 [==============================] - 1s 94ms/step - loss: 0.6671 - acc: 0.6518 - val_loss: 0.6673 - val_acc: 0.6494\n",
      "Epoch 103/500\n",
      "10/10 [==============================] - 1s 92ms/step - loss: 0.6669 - acc: 0.6518 - val_loss: 0.6671 - val_acc: 0.6494\n",
      "Epoch 104/500\n",
      "10/10 [==============================] - 1s 81ms/step - loss: 0.6667 - acc: 0.6518 - val_loss: 0.6669 - val_acc: 0.6494\n",
      "Epoch 105/500\n",
      "10/10 [==============================] - 1s 85ms/step - loss: 0.6665 - acc: 0.6518 - val_loss: 0.6667 - val_acc: 0.6494\n",
      "Epoch 106/500\n",
      "10/10 [==============================] - 1s 75ms/step - loss: 0.6663 - acc: 0.6518 - val_loss: 0.6666 - val_acc: 0.6494\n",
      "Epoch 107/500\n",
      "10/10 [==============================] - 1s 77ms/step - loss: 0.6661 - acc: 0.6518 - val_loss: 0.6664 - val_acc: 0.6494\n",
      "Epoch 108/500\n",
      "10/10 [==============================] - 1s 85ms/step - loss: 0.6659 - acc: 0.6518 - val_loss: 0.6662 - val_acc: 0.6494\n",
      "Epoch 109/500\n",
      "10/10 [==============================] - 1s 96ms/step - loss: 0.6657 - acc: 0.6518 - val_loss: 0.6660 - val_acc: 0.6494\n",
      "Epoch 110/500\n",
      "10/10 [==============================] - 1s 91ms/step - loss: 0.6655 - acc: 0.6518 - val_loss: 0.6658 - val_acc: 0.6494\n",
      "Epoch 111/500\n",
      "10/10 [==============================] - 1s 100ms/step - loss: 0.6653 - acc: 0.6518 - val_loss: 0.6656 - val_acc: 0.6494\n",
      "Epoch 112/500\n",
      "10/10 [==============================] - 1s 105ms/step - loss: 0.6651 - acc: 0.6518 - val_loss: 0.6655 - val_acc: 0.6494\n",
      "Epoch 113/500\n",
      "10/10 [==============================] - 1s 119ms/step - loss: 0.6650 - acc: 0.6518 - val_loss: 0.6653 - val_acc: 0.6494\n",
      "Epoch 114/500\n",
      "10/10 [==============================] - 1s 117ms/step - loss: 0.6648 - acc: 0.6518 - val_loss: 0.6651 - val_acc: 0.6494\n",
      "Epoch 115/500\n",
      "10/10 [==============================] - 1s 92ms/step - loss: 0.6646 - acc: 0.6518 - val_loss: 0.6649 - val_acc: 0.6494\n",
      "Epoch 116/500\n",
      "10/10 [==============================] - 1s 98ms/step - loss: 0.6644 - acc: 0.6518 - val_loss: 0.6648 - val_acc: 0.6494\n",
      "Epoch 117/500\n",
      "10/10 [==============================] - 1s 92ms/step - loss: 0.6642 - acc: 0.6518 - val_loss: 0.6646 - val_acc: 0.6494\n",
      "Epoch 118/500\n",
      "10/10 [==============================] - 1s 99ms/step - loss: 0.6640 - acc: 0.6518 - val_loss: 0.6644 - val_acc: 0.6494\n",
      "Epoch 119/500\n",
      "10/10 [==============================] - 1s 97ms/step - loss: 0.6638 - acc: 0.6518 - val_loss: 0.6642 - val_acc: 0.6494\n",
      "Epoch 120/500\n",
      "10/10 [==============================] - 1s 111ms/step - loss: 0.6637 - acc: 0.6518 - val_loss: 0.6640 - val_acc: 0.6494\n",
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      "Epoch 123/500\n"
     ]
    },
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     "text": [
      "10/10 [==============================] - 1s 90ms/step - loss: 0.6631 - acc: 0.6518 - val_loss: 0.6635 - val_acc: 0.6494\n",
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      "Epoch 184/500\n"
     ]
    },
    {
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     "text": [
      "10/10 [==============================] - 1s 78ms/step - loss: 0.6531 - acc: 0.6518 - val_loss: 0.6538 - val_acc: 0.6494\n",
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      "Epoch 222/500\n",
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      "Epoch 234/500\n",
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      "Epoch 237/500\n",
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      "Epoch 238/500\n",
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      "Epoch 241/500\n",
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      "Epoch 242/500\n",
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      "Epoch 243/500\n",
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      "Epoch 244/500\n",
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      "Epoch 245/500\n"
     ]
    },
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     "text": [
      "10/10 [==============================] - 1s 69ms/step - loss: 0.6452 - acc: 0.6518 - val_loss: 0.6462 - val_acc: 0.6494\n",
      "Epoch 246/500\n",
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      "Epoch 249/500\n",
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      "Epoch 250/500\n",
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      "Epoch 251/500\n",
      "10/10 [==============================] - 1s 73ms/step - loss: 0.6445 - acc: 0.6518 - val_loss: 0.6455 - val_acc: 0.6494\n",
      "Epoch 252/500\n",
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      "Epoch 253/500\n",
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      "Epoch 254/500\n",
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      "Epoch 255/500\n",
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      "Epoch 256/500\n",
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      "Epoch 257/500\n",
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      "Epoch 258/500\n",
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      "Epoch 259/500\n",
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      "Epoch 260/500\n",
      "10/10 [==============================] - 1s 74ms/step - loss: 0.6434 - acc: 0.6518 - val_loss: 0.6445 - val_acc: 0.6494\n",
      "Epoch 261/500\n",
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      "Epoch 262/500\n",
      "10/10 [==============================] - 1s 128ms/step - loss: 0.6432 - acc: 0.6518 - val_loss: 0.6443 - val_acc: 0.6494\n",
      "Epoch 263/500\n",
      "10/10 [==============================] - 1s 132ms/step - loss: 0.6431 - acc: 0.6518 - val_loss: 0.6442 - val_acc: 0.6494\n",
      "Epoch 264/500\n",
      "10/10 [==============================] - 1s 120ms/step - loss: 0.6430 - acc: 0.6518 - val_loss: 0.6440 - val_acc: 0.6494\n",
      "Epoch 265/500\n",
      "10/10 [==============================] - 1s 82ms/step - loss: 0.6429 - acc: 0.6518 - val_loss: 0.6439 - val_acc: 0.6494\n",
      "Epoch 266/500\n",
      "10/10 [==============================] - 1s 78ms/step - loss: 0.6427 - acc: 0.6518 - val_loss: 0.6438 - val_acc: 0.6494\n",
      "Epoch 267/500\n",
      "10/10 [==============================] - 1s 76ms/step - loss: 0.6426 - acc: 0.6518 - val_loss: 0.6437 - val_acc: 0.6494\n",
      "Epoch 268/500\n",
      "10/10 [==============================] - 1s 69ms/step - loss: 0.6425 - acc: 0.6518 - val_loss: 0.6436 - val_acc: 0.6494\n",
      "Epoch 269/500\n",
      "10/10 [==============================] - 1s 83ms/step - loss: 0.6424 - acc: 0.6518 - val_loss: 0.6435 - val_acc: 0.6494\n",
      "Epoch 270/500\n",
      "10/10 [==============================] - 1s 79ms/step - loss: 0.6423 - acc: 0.6518 - val_loss: 0.6434 - val_acc: 0.6494\n",
      "Epoch 271/500\n",
      "10/10 [==============================] - 2s 162ms/step - loss: 0.6422 - acc: 0.6518 - val_loss: 0.6433 - val_acc: 0.6494\n",
      "Epoch 272/500\n",
      "10/10 [==============================] - 1s 140ms/step - loss: 0.6421 - acc: 0.6518 - val_loss: 0.6432 - val_acc: 0.6494\n",
      "Epoch 273/500\n",
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      "Epoch 274/500\n",
      "10/10 [==============================] - 1s 115ms/step - loss: 0.6418 - acc: 0.6518 - val_loss: 0.6429 - val_acc: 0.6494\n",
      "Epoch 275/500\n",
      "10/10 [==============================] - 1s 126ms/step - loss: 0.6417 - acc: 0.6518 - val_loss: 0.6428 - val_acc: 0.6494\n",
      "Epoch 276/500\n",
      "10/10 [==============================] - 1s 89ms/step - loss: 0.6416 - acc: 0.6518 - val_loss: 0.6427 - val_acc: 0.6494\n",
      "Epoch 277/500\n",
      "10/10 [==============================] - 1s 100ms/step - loss: 0.6415 - acc: 0.6518 - val_loss: 0.6426 - val_acc: 0.6494\n",
      "Epoch 278/500\n",
      "10/10 [==============================] - 1s 85ms/step - loss: 0.6414 - acc: 0.6518 - val_loss: 0.6425 - val_acc: 0.6494\n",
      "Epoch 279/500\n",
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      "Epoch 280/500\n",
      "10/10 [==============================] - 1s 83ms/step - loss: 0.6412 - acc: 0.6518 - val_loss: 0.6423 - val_acc: 0.6494\n",
      "Epoch 281/500\n",
      "10/10 [==============================] - 1s 77ms/step - loss: 0.6410 - acc: 0.6518 - val_loss: 0.6422 - val_acc: 0.6494\n",
      "Epoch 282/500\n",
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      "Epoch 283/500\n",
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      "Epoch 284/500\n",
      "10/10 [==============================] - 1s 75ms/step - loss: 0.6407 - acc: 0.6518 - val_loss: 0.6419 - val_acc: 0.6494\n",
      "Epoch 285/500\n",
      "10/10 [==============================] - 1s 67ms/step - loss: 0.6406 - acc: 0.6518 - val_loss: 0.6417 - val_acc: 0.6494\n",
      "Epoch 286/500\n",
      "10/10 [==============================] - 1s 81ms/step - loss: 0.6405 - acc: 0.6518 - val_loss: 0.6416 - val_acc: 0.6494\n",
      "Epoch 287/500\n",
      "10/10 [==============================] - 1s 80ms/step - loss: 0.6404 - acc: 0.6518 - val_loss: 0.6415 - val_acc: 0.6494\n",
      "Epoch 288/500\n",
      "10/10 [==============================] - 1s 81ms/step - loss: 0.6403 - acc: 0.6518 - val_loss: 0.6414 - val_acc: 0.6494\n",
      "Epoch 289/500\n",
      "10/10 [==============================] - 1s 74ms/step - loss: 0.6401 - acc: 0.6518 - val_loss: 0.6413 - val_acc: 0.6494\n",
      "Epoch 290/500\n",
      "10/10 [==============================] - 1s 71ms/step - loss: 0.6400 - acc: 0.6518 - val_loss: 0.6412 - val_acc: 0.6494\n",
      "Epoch 291/500\n",
      "10/10 [==============================] - 1s 78ms/step - loss: 0.6399 - acc: 0.6518 - val_loss: 0.6411 - val_acc: 0.6494\n",
      "Epoch 292/500\n",
      "10/10 [==============================] - 1s 82ms/step - loss: 0.6398 - acc: 0.6518 - val_loss: 0.6410 - val_acc: 0.6494\n",
      "Epoch 293/500\n",
      "10/10 [==============================] - 1s 75ms/step - loss: 0.6397 - acc: 0.6518 - val_loss: 0.6409 - val_acc: 0.6494\n",
      "Epoch 294/500\n",
      "10/10 [==============================] - 1s 71ms/step - loss: 0.6396 - acc: 0.6518 - val_loss: 0.6408 - val_acc: 0.6494\n",
      "Epoch 295/500\n",
      "10/10 [==============================] - 1s 85ms/step - loss: 0.6395 - acc: 0.6518 - val_loss: 0.6407 - val_acc: 0.6494\n",
      "Epoch 296/500\n",
      "10/10 [==============================] - 1s 76ms/step - loss: 0.6393 - acc: 0.6518 - val_loss: 0.6406 - val_acc: 0.6494\n",
      "Epoch 297/500\n",
      "10/10 [==============================] - 1s 83ms/step - loss: 0.6392 - acc: 0.6518 - val_loss: 0.6404 - val_acc: 0.6494\n",
      "Epoch 298/500\n",
      "10/10 [==============================] - 1s 73ms/step - loss: 0.6391 - acc: 0.6518 - val_loss: 0.6403 - val_acc: 0.6494\n",
      "Epoch 299/500\n",
      "10/10 [==============================] - 1s 80ms/step - loss: 0.6390 - acc: 0.6518 - val_loss: 0.6402 - val_acc: 0.6494\n",
      "Epoch 300/500\n",
      "10/10 [==============================] - 1s 88ms/step - loss: 0.6389 - acc: 0.6518 - val_loss: 0.6401 - val_acc: 0.6494\n",
      "Epoch 301/500\n",
      "10/10 [==============================] - 1s 79ms/step - loss: 0.6388 - acc: 0.6518 - val_loss: 0.6400 - val_acc: 0.6494\n",
      "Epoch 302/500\n",
      "10/10 [==============================] - 1s 75ms/step - loss: 0.6387 - acc: 0.6518 - val_loss: 0.6399 - val_acc: 0.6494\n",
      "Epoch 303/500\n",
      "10/10 [==============================] - 1s 82ms/step - loss: 0.6385 - acc: 0.6518 - val_loss: 0.6398 - val_acc: 0.6494\n",
      "Epoch 304/500\n",
      "10/10 [==============================] - 1s 81ms/step - loss: 0.6384 - acc: 0.6518 - val_loss: 0.6397 - val_acc: 0.6494\n",
      "Epoch 305/500\n",
      "10/10 [==============================] - 1s 84ms/step - loss: 0.6383 - acc: 0.6518 - val_loss: 0.6396 - val_acc: 0.6494\n",
      "Epoch 306/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10/10 [==============================] - 1s 73ms/step - loss: 0.6382 - acc: 0.6518 - val_loss: 0.6395 - val_acc: 0.6494\n",
      "Epoch 307/500\n",
      "10/10 [==============================] - 1s 63ms/step - loss: 0.6381 - acc: 0.6518 - val_loss: 0.6394 - val_acc: 0.6494\n",
      "Epoch 308/500\n",
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      "Epoch 309/500\n",
      "10/10 [==============================] - 1s 70ms/step - loss: 0.6379 - acc: 0.6518 - val_loss: 0.6391 - val_acc: 0.6494\n",
      "Epoch 310/500\n",
      "10/10 [==============================] - 1s 75ms/step - loss: 0.6377 - acc: 0.6518 - val_loss: 0.6390 - val_acc: 0.6494\n",
      "Epoch 311/500\n",
      "10/10 [==============================] - 1s 73ms/step - loss: 0.6376 - acc: 0.6518 - val_loss: 0.6389 - val_acc: 0.6494\n",
      "Epoch 312/500\n",
      "10/10 [==============================] - 1s 68ms/step - loss: 0.6375 - acc: 0.6518 - val_loss: 0.6388 - val_acc: 0.6494\n",
      "Epoch 313/500\n",
      "10/10 [==============================] - 1s 72ms/step - loss: 0.6374 - acc: 0.6518 - val_loss: 0.6387 - val_acc: 0.6494\n",
      "Epoch 314/500\n",
      "10/10 [==============================] - 1s 69ms/step - loss: 0.6373 - acc: 0.6518 - val_loss: 0.6386 - val_acc: 0.6494\n",
      "Epoch 315/500\n",
      "10/10 [==============================] - 1s 77ms/step - loss: 0.6372 - acc: 0.6518 - val_loss: 0.6385 - val_acc: 0.6494\n",
      "Epoch 316/500\n",
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      "Epoch 317/500\n",
      "10/10 [==============================] - 1s 61ms/step - loss: 0.6369 - acc: 0.6518 - val_loss: 0.6383 - val_acc: 0.6494\n",
      "Epoch 318/500\n",
      "10/10 [==============================] - 1s 69ms/step - loss: 0.6368 - acc: 0.6518 - val_loss: 0.6382 - val_acc: 0.6494\n",
      "Epoch 319/500\n",
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      "Epoch 320/500\n",
      "10/10 [==============================] - 1s 72ms/step - loss: 0.6366 - acc: 0.6518 - val_loss: 0.6379 - val_acc: 0.6494\n",
      "Epoch 321/500\n",
      "10/10 [==============================] - 1s 70ms/step - loss: 0.6365 - acc: 0.6518 - val_loss: 0.6378 - val_acc: 0.6494\n",
      "Epoch 322/500\n",
      "10/10 [==============================] - 1s 76ms/step - loss: 0.6364 - acc: 0.6518 - val_loss: 0.6377 - val_acc: 0.6494\n",
      "Epoch 323/500\n",
      "10/10 [==============================] - 1s 69ms/step - loss: 0.6363 - acc: 0.6518 - val_loss: 0.6376 - val_acc: 0.6494\n",
      "Epoch 324/500\n",
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      "Epoch 325/500\n",
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      "Epoch 326/500\n",
      "10/10 [==============================] - 1s 87ms/step - loss: 0.6359 - acc: 0.6518 - val_loss: 0.6373 - val_acc: 0.6494\n",
      "Epoch 327/500\n",
      "10/10 [==============================] - 1s 87ms/step - loss: 0.6358 - acc: 0.6518 - val_loss: 0.6372 - val_acc: 0.6494\n",
      "Epoch 328/500\n",
      "10/10 [==============================] - 1s 79ms/step - loss: 0.6357 - acc: 0.6518 - val_loss: 0.6371 - val_acc: 0.6494\n",
      "Epoch 329/500\n",
      "10/10 [==============================] - 1s 75ms/step - loss: 0.6356 - acc: 0.6518 - val_loss: 0.6370 - val_acc: 0.6494\n",
      "Epoch 330/500\n",
      "10/10 [==============================] - 1s 118ms/step - loss: 0.6355 - acc: 0.6518 - val_loss: 0.6369 - val_acc: 0.6494\n",
      "Epoch 331/500\n",
      "10/10 [==============================] - 1s 105ms/step - loss: 0.6354 - acc: 0.6518 - val_loss: 0.6367 - val_acc: 0.6494\n",
      "Epoch 332/500\n",
      "10/10 [==============================] - 1s 91ms/step - loss: 0.6353 - acc: 0.6518 - val_loss: 0.6366 - val_acc: 0.6494\n",
      "Epoch 333/500\n",
      "10/10 [==============================] - 1s 83ms/step - loss: 0.6351 - acc: 0.6518 - val_loss: 0.6365 - val_acc: 0.6494\n",
      "Epoch 334/500\n",
      "10/10 [==============================] - 1s 76ms/step - loss: 0.6350 - acc: 0.6518 - val_loss: 0.6364 - val_acc: 0.6494\n",
      "Epoch 335/500\n",
      "10/10 [==============================] - 1s 80ms/step - loss: 0.6349 - acc: 0.6518 - val_loss: 0.6363 - val_acc: 0.6494\n",
      "Epoch 336/500\n",
      "10/10 [==============================] - 1s 79ms/step - loss: 0.6348 - acc: 0.6518 - val_loss: 0.6362 - val_acc: 0.6494\n",
      "Epoch 337/500\n",
      "10/10 [==============================] - 1s 101ms/step - loss: 0.6347 - acc: 0.6518 - val_loss: 0.6361 - val_acc: 0.6494\n",
      "Epoch 338/500\n",
      "10/10 [==============================] - 1s 125ms/step - loss: 0.6346 - acc: 0.6518 - val_loss: 0.6360 - val_acc: 0.6494\n",
      "Epoch 339/500\n",
      "10/10 [==============================] - 1s 144ms/step - loss: 0.6345 - acc: 0.6518 - val_loss: 0.6359 - val_acc: 0.6494\n",
      "Epoch 340/500\n",
      "10/10 [==============================] - 1s 84ms/step - loss: 0.6344 - acc: 0.6518 - val_loss: 0.6358 - val_acc: 0.6494\n",
      "Epoch 341/500\n",
      "10/10 [==============================] - 1s 91ms/step - loss: 0.6342 - acc: 0.6518 - val_loss: 0.6357 - val_acc: 0.6494\n",
      "Epoch 342/500\n",
      "10/10 [==============================] - 1s 127ms/step - loss: 0.6341 - acc: 0.6518 - val_loss: 0.6355 - val_acc: 0.6494\n",
      "Epoch 343/500\n",
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      "Epoch 344/500\n",
      "10/10 [==============================] - 1s 103ms/step - loss: 0.6339 - acc: 0.6518 - val_loss: 0.6353 - val_acc: 0.6494\n",
      "Epoch 345/500\n",
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      "Epoch 346/500\n",
      "10/10 [==============================] - 1s 134ms/step - loss: 0.6337 - acc: 0.6518 - val_loss: 0.6351 - val_acc: 0.6494\n",
      "Epoch 347/500\n",
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      "Epoch 350/500\n",
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      "Epoch 351/500\n",
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      "Epoch 356/500\n",
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      "Epoch 359/500\n",
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      "Epoch 360/500\n",
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      "Epoch 362/500\n",
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      "Epoch 363/500\n",
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      "Epoch 364/500\n",
      "10/10 [==============================] - 1s 79ms/step - loss: 0.6316 - acc: 0.6518 - val_loss: 0.6331 - val_acc: 0.6494\n",
      "Epoch 365/500\n",
      "10/10 [==============================] - 1s 81ms/step - loss: 0.6315 - acc: 0.6518 - val_loss: 0.6330 - val_acc: 0.6494\n",
      "Epoch 366/500\n",
      "10/10 [==============================] - 1s 97ms/step - loss: 0.6314 - acc: 0.6518 - val_loss: 0.6329 - val_acc: 0.6494\n",
      "Epoch 367/500\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "10/10 [==============================] - 1s 72ms/step - loss: 0.6312 - acc: 0.6518 - val_loss: 0.6328 - val_acc: 0.6494\n",
      "Epoch 368/500\n",
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      "Epoch 369/500\n",
      "10/10 [==============================] - 1s 77ms/step - loss: 0.6310 - acc: 0.6518 - val_loss: 0.6325 - val_acc: 0.6494\n",
      "Epoch 370/500\n",
      "10/10 [==============================] - 1s 76ms/step - loss: 0.6309 - acc: 0.6518 - val_loss: 0.6324 - val_acc: 0.6494\n",
      "Epoch 371/500\n",
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      "Epoch 417/500\n",
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      "Epoch 418/500\n",
      "10/10 [==============================] - 1s 110ms/step - loss: 0.6250 - acc: 0.6518 - val_loss: 0.6267 - val_acc: 0.6494\n",
      "Epoch 419/500\n",
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      "Epoch 420/500\n",
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      "Epoch 421/500\n",
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      "Epoch 422/500\n",
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      "Epoch 423/500\n",
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      "Epoch 424/500\n",
      "10/10 [==============================] - 1s 78ms/step - loss: 0.6243 - acc: 0.6518 - val_loss: 0.6260 - val_acc: 0.6494\n",
      "Epoch 425/500\n",
      "10/10 [==============================] - 1s 79ms/step - loss: 0.6241 - acc: 0.6518 - val_loss: 0.6258 - val_acc: 0.6494\n",
      "Epoch 426/500\n",
      "10/10 [==============================] - 1s 102ms/step - loss: 0.6240 - acc: 0.6518 - val_loss: 0.6257 - val_acc: 0.6494\n",
      "Epoch 427/500\n",
      "10/10 [==============================] - 1s 145ms/step - loss: 0.6239 - acc: 0.6518 - val_loss: 0.6256 - val_acc: 0.6494\n",
      "Epoch 428/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10/10 [==============================] - 1s 105ms/step - loss: 0.6237 - acc: 0.6518 - val_loss: 0.6255 - val_acc: 0.6494\n",
      "Epoch 429/500\n",
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      "Epoch 450/500\n",
      "10/10 [==============================] - 1s 88ms/step - loss: 0.6208 - acc: 0.6518 - val_loss: 0.6226 - val_acc: 0.6494\n",
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      "10/10 [==============================] - 1s 79ms/step - loss: 0.6169 - acc: 0.6518 - val_loss: 0.6187 - val_acc: 0.6494\n",
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      "Epoch 486/500\n",
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      "Epoch 487/500\n",
      "10/10 [==============================] - 1s 79ms/step - loss: 0.6153 - acc: 0.6518 - val_loss: 0.6172 - val_acc: 0.6494\n",
      "Epoch 488/500\n",
      "10/10 [==============================] - 1s 77ms/step - loss: 0.6152 - acc: 0.6518 - val_loss: 0.6170 - val_acc: 0.6494\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 489/500\n",
      "10/10 [==============================] - 1s 66ms/step - loss: 0.6150 - acc: 0.6518 - val_loss: 0.6169 - val_acc: 0.6494\n",
      "Epoch 490/500\n",
      "10/10 [==============================] - 1s 59ms/step - loss: 0.6148 - acc: 0.6518 - val_loss: 0.6167 - val_acc: 0.6494\n",
      "Epoch 491/500\n",
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      "Epoch 492/500\n",
      "10/10 [==============================] - 1s 69ms/step - loss: 0.6145 - acc: 0.6518 - val_loss: 0.6164 - val_acc: 0.6494\n",
      "Epoch 493/500\n",
      "10/10 [==============================] - 1s 71ms/step - loss: 0.6144 - acc: 0.6518 - val_loss: 0.6162 - val_acc: 0.6494\n",
      "Epoch 494/500\n",
      "10/10 [==============================] - 1s 68ms/step - loss: 0.6142 - acc: 0.6518 - val_loss: 0.6161 - val_acc: 0.6494\n",
      "Epoch 495/500\n",
      "10/10 [==============================] - 1s 73ms/step - loss: 0.6141 - acc: 0.6518 - val_loss: 0.6159 - val_acc: 0.6494\n",
      "Epoch 496/500\n",
      "10/10 [==============================] - 1s 68ms/step - loss: 0.6139 - acc: 0.6518 - val_loss: 0.6158 - val_acc: 0.6494\n",
      "Epoch 497/500\n",
      "10/10 [==============================] - 1s 75ms/step - loss: 0.6137 - acc: 0.6518 - val_loss: 0.6156 - val_acc: 0.6494\n",
      "Epoch 498/500\n",
      "10/10 [==============================] - 1s 66ms/step - loss: 0.6136 - acc: 0.6518 - val_loss: 0.6154 - val_acc: 0.6494\n",
      "Epoch 499/500\n",
      "10/10 [==============================] - 1s 74ms/step - loss: 0.6134 - acc: 0.6518 - val_loss: 0.6153 - val_acc: 0.6494\n",
      "Epoch 500/500\n",
      "10/10 [==============================] - 1s 67ms/step - loss: 0.6132 - acc: 0.6518 - val_loss: 0.6151 - val_acc: 0.6494\n"
     ]
    }
   ],
   "source": [
    "# Train the model for the given number of epochs\n",
    "    history = model.fit(\n",
    "        X_train_norm, y_train,\n",
    "        steps_per_epoch=10,\n",
    "        epochs=500,\n",
    "        verbose=1,\n",
    "        validation_data=(X_test, y_test),\n",
    "        validation_steps=10\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 498,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Bc73y0UA373zprmpVGP1hEHNpxJMDg8FbLMsYExhdu3ZlwoQJ/ymbMGECXbt2TdHrS5QocVGroSZOEDNmzKBQoUKpPl9G5tfxm6o6Q1Urq2pFVX3RK3tWVad536uq9lXVqqpaQ1UneOXHvLKqqlpPVQO608bt3YQH7zvBMPryUYdvIC4ukOEYk6117NiR6dOn888//wCwceNGtm3bRoMGDTh06BDXX389ERER1KhRg6lTp571+o0bN1K9enXALbndpUsXwsLC6Ny5M0ePHj1Tr3fv3meWCn/uuecAtwLrtm3baNKkCU2aNAGgXLly7Nq1C4Bhw4ZRvXp1qlevfmap8I0bN1KlShV69uxJtWrVaNGixX/e53ySOufhw4dp06bNmeW/J06cCED//v2pWrUqYWFhZ+2RkRpZZ0aHnw0bEcTqlUe577c3qNziHq5dPsoNdzImGwvEat9FixalTp06fP/997Rr144JEybQuXNnRISQkBCmTJlCgQIF2LVrF/Xq1aNt27bn3I951KhR5MmThxUrVrBixYr/LNf94osvUqRIEU6ePMn111/PihUrePjhhxk2bBhz5syhWLFi/znXkiVLGDt2LAsXLkRVqVu3Ltdddx2FCxdm7dq1jB8/nvfff59OnToxefJkunXrdt6fxbnOuX79ekqUKMG337o9bPbv38+ePXuYMmUKa9asQUTSpNnLZoClUFAQTPrmEsqUOMktG15nc4fH4NSpQIdlTLbk28zk27ykqjz11FOEhYXRrFkztm7dyt9//33O8/z6669nPqjDwsIICws7c2zSpElERERQq1YtoqOjz7sQ37x587jlllvImzcv+fLlo3379sydOxeA8uXLEx4eDiS/pHhKz1mjRg1++uknnnzySebOnUvBggUpUKAAISEh3HvvvXz11VfkyZMnRe+RHLuDuABFisC0n/JQLyIn7X7sw7y+A8k7/MVAh2VMwARqte+bb775zKqmR48ePfOX/+eff87OnTtZsmQJQUFBlCtXLsklvn0ldXexYcMGhg4dyuLFiylcuDB33XXXec+T3Lp2p5cKB7dceEqbmM51zsqVK7NkyRJmzJjBgAEDaNGiBc8++yyLFi1i1qxZTJgwgbfffpvZs2en6H3Oxe4gLlCVKjD+y2CWU5M73ozg1HvvBzokY7KdfPny0bhxY+6+++7/dE7v37+fSy+9lKCgIObMmcOmTZuSPY/vkturVq1ixYoVgFsqPG/evBQsWJC///77zE5uAPnz5+dgEvOiGjVqxNdff82RI0c4fPgwU6ZMoWHDhhd1nec657Zt28iTJw/dunXjf//7H0uXLuXQoUPs37+f1q1bM3z48BRvfZocu4NIhdZthNeHnKJvvw483vtN3qj4EzRrFuiwjMlWunbtSvv27f8zoun222/npptuIjIykvDwcK666qpkz9G7d2969OhBWFgY4eHh1KlTB3C7w9WqVYtq1aqdtVR4r169aNWqFVdccQVz5sw5Ux4REcFdd9115hz33nsvtWrVSnFzEsDgwYPPdEQDxMfHJ3nOmTNn0q9fP3LkyEFQUBCjRo3i4MGDtGvXjmPHjqGqvPHGGyl+33Ox5b5TSRUe6/MPb47KzeshT9N3aTd3e2FMFmfLfWdeGWq576xMBIa9nZuOrQ/z+LEXmXjdSNi+PdBhGWNMmrEEcRFy5IBPJ+elQc2D3LHzdX5p+Iyt2WSMyTIsQVykkBCYOjs/FUufoF3cUFY0fxyOHw90WMb4VVZpms5OUvM7swSRBooUge/m5iN/4SBaLBxE3K0DbI6EybJCQkLYvXu3JYlMRFXZvXs3ISEhF/Q666ROQ6tXQ6PII+Q9soP5939GyVHPBDQeY/zhxIkTxMfHn3degMlYQkJCKFWqFEFBQf8pT66T2oa5pqEqVeD7Xy6hybWX0fzd9vxa8l2KPXN/oMMyJk0FBQVRvnz5QIdh0oE1MaWx2pHCN98HsyFHRVoNrM2BsZMDHZIxxqSKJQg/uK5pTr6YBMukFu3uKcrRb34KdEjGGHPBLEH4yY0dcvPxe//wizai8y3/cOLX3wMdkjHGXBC/JggRaSkisSISJyL9z1Gnk4jEiEi0iIzzKX/NK1stIm/JudbrzcBu65mXt18+xDcn23BXsy2cXLo80CEZY0yK+a2TWkRyAiOB5ri9pxeLyDRVjfGpEwoMAOqr6l4RudQrvxaoj9tqFGAecB3ws7/i9ZcH+hdg/569PDWkE8H1x/PBsjzkuDI00GEZY8x5+fMOog4Qp6rrVfU4MAFol6hOT2Ckqu4FUNUdXrkCIUAwkBu3Bem5F3XP4Aa8Vpjn+uzio2Ndua92FKc2bQl0SMYYc17+TBAlAd9PwnivzFdloLKIzBeRBSLSEkBVf8ftUf2X95ipqqsTv4GI9BKRKBGJ2rlzp18uIq08N6IYT929nTGHu/Jg+Fx0R8aO1xhj/JkgkuozSDwrLxcQCjQGugJjRKSQiFQCqgClcEmlqYg0OutkqqNVNVJVI4sXL56mwac1ERg85nKeuC2eUftu45GqP6I7dwU6LGOMOSd/Joh4oLTP81LAtiTqTFXVE6q6AYjFJYxbgAWqekhVDwHfAfX8GGu6EIFXPivFYx02MWL3bfSt+j26a3egwzLGmCT5M0EsBkJFpLyIBANdgGmJ6nwNNAEQkWK4Jqf1wGbgOhHJJSJBuA7qs5qYMiMReP2LsjxyyyaG7+rGg1V+4tROSxLGmIzHbwlCVROAB4GZuA/3SaoaLSKDRKStV20msFtEYnB9Dv1UdTfwJbAOWAksB5ar6jf+ijW9icAbk8vSr+MG3tnVmfuq/MLJnXsCHZYxxvyHLdYXQKrw3O1xvDC+Et2LTOfDNfXJVbxwoMMyxmQjtqNcBiUCg8ZVYnD3WD7dcyO3XRnFie3W3GSMyRgsQWQAT39yJa/fE8MXe5tz65XLObZ5x/lfZIwxfmYJIoPoO6Yqbz+4mqkHmtKmyjoOxiYe8GWMMenLEkQG0mdEFT59Zg2/HLmaJmG72blkc6BDMsZkY5YgMphuL1zF1NfXEX28Eg3qnWDTzxsCHZIxJpuyBJEBtel7JT9+GM+Ok0Wpf31uYqbFBTokY0w2ZAkig2rQI5RfvtrDSXLS8OYiLPg4NtAhGWOyGUsQGVjYzRWY/+NRCuc8wPV3lWLma7afhDEm/ViCyOAqNC3HvIXBhObewk1PVuHzRxYFOiRjTDZhCSITuDyiBD+vvoxrC6yi21t1eLHdIrLIBHhjTAZmCSKTKFS+MDM3XsXtJebwzLQ63Fv7D04ctyxhjPEfSxCZSO7Cefh0QwMG1pzKh3/UonXFNezfczLQYRljsihLEJmMBAcx6I+2jG39BT/HV6J++a1sjj0a6LCMMVmQJYjMSIS7vr2V73tPI/5AAerWOMyCb22RP2NM2rIEkYld/04HfhuxlDwJB7juxnx89OLWQIdkjMlC/JogRKSliMSKSJyI9D9HnU4iEiMi0SIyzitrIiLLfB7HRORmf8aaWVV9sCmLfjxAg+BF9HimJI+130RCQqCjMsZkBX5LECKSExgJtAKqAl1FpGqiOqHAAKC+qlYDHgVQ1TmqGq6q4UBT4Ajwg79izeyKXh/OzDXleLjo5wyfUpbWYfHssQ3qjDEXyZ93EHWAOFVdr6rHgQlAu0R1egIjVXUvgKomtRFCR+A7VT3ix1gzvVzlS/PmhraMqT6cn1dfSt0KO4lZYbcSxpjU82eCKAls8Xke75X5qgxUFpH5IrJARFomcZ4uwPik3kBEeolIlIhE7dy5M02CztTy5+eePx5kTqd3ObD/FPUi/mHyRwcDHZUxJpPyZ4KQJMoSz+zKBYQCjYGuwBgRKXTmBCJXADWAmUm9gaqOVtVIVY0sXrx4mgSd6eXKRf2JDxM19BeqnIqmY4/89Oux0/oljDEXzJ8JIh4o7fO8FJB4m7R4YKqqnlDVDUAsLmGc1gmYoqon/BhnllT68U78+gv0zvMRQz8qzvU1d7J9e6CjMsZkJv5MEIuBUBEpLyLBuKaiaYnqfA00ARCRYrgmp/U+x7tyjuYlc365G9bhnbUt+KTC8yyOyUtE6EHm/Xoq0GEZYzIJvyUIVU0AHsQ1D60GJqlqtIgMEpG2XrWZwG4RiQHmAP1UdTeAiJTD3YH84q8Ys4USJege3Z8FN71E3kPbadL4FMNfOWaL/Rljzks0i3xSREZGalRUVKDDyLhU2ffKu9z1VAmm0o5bbzjA+xMLULBgoAMzxgSSiCxR1cikjtlM6uxChEIDevPVTwV4Ne8gvpqZh/DQQyxYEOjAjDEZlSWIbCbH9U14IvYe5oX1gZ07aVj/JK++lMAp65owxiRiCSI7KlmSelFv88cDY7jl1GT6P52Llo2P2SgnY8x/WILIroKCKDTyRSZOysHo3A8xb+4palb5h5lJzjgxxmRHliCyObm1Iz1XPERUpa5cuu9PWraEfo+f4vjxQEdmjAk0SxAGKlem6rJxLOo2gt68w9BhOahb6zjR0YEOzBgTSJYgjJM3L5d8Opp3PivI1JDObF29n9q1TjJ8ONaBbUw2ZQnC/Nftt9N25YusrHE7zU/M4LHHoEWzk8THBzowY0x6swRhzlapEpctns60x35mND35/Zfj1Kh2kokTAx2YMSY9WYIwSQsORoa9Ts/pN7O84HVceWgJXbrA7bcr+/YFOjhjTHqwBGGS16YNlVZOYV6DAfwfzzJx/ClqVDvFjz8GOjBjjL9ZgjDnV7IkuWb/wLMvXcJvORqSb8c6WrSA3r3h0KFAB2eM8RdLECZlcuaEAQOos+AtlpbrwOO8znvvniKshvKLrbdrTJZkCcJcmMhILln2O0Pvj+NXGpFj2xYaN4ZHH4Ujtmu4MVmKJQhz4fLmhVGjaDDtSZYXaMSDOUfx5psQHq78/nuggzPGpBW/JggRaSkisSISJyL9z1Gnk4jEiEi0iIzzKS8jIj+IyGrveDl/xmpS4aabyLtyASNumM4smnI8fgcNGihPPgnHjgU6OGPMxfJbghCRnMBIoBVQFegqIlUT1QkFBgD1VbUa8KjP4U+AIapaBagD7PBXrOYiXH45TJ9O07c7sOJUDe4J+pTXXoPatcH2bzImc/PnHUQdIE5V16vqcWAC0C5RnZ7ASFXdC6CqOwC8RJJLVX/0yg+pqrVwZ1Qi0KcPBZb+zOgqb/AdLdm/aS/16ikDB2IL/xmTSfkzQZQEtvg8j/fKfFUGKovIfBFZICItfcr3ichXIvKHiAzx7khMRla1KixYQMsnarLqcAVuz/M1gwdDnTqwdGmggzPGXCh/JghJoizxBti5gFCgMdAVGCMihbzyhsD/gKuBCsBdZ72BSC8RiRKRqJ07d6Zd5Cb1cueGV1+l0Oyv+LjQI0yVm/k77iB16ihPPGEjnYzJTPyZIOKB0j7PSwHbkqgzVVVPqOoGIBaXMOKBP7zmqQTgayAi8Ruo6mhVjVTVyOLFi/vlIkwqNWkCK1fS9q4irD5cmrsLTmbIEKhRA2bNCnRwxpiU8GeCWAyEikh5EQkGugDTEtX5GmgCICLFcE1L673XFhaR05/6TYEYP8Zq/KFgQfjwQwp98xmjgx9iTo7rybl/N82aQY8esGdPoAM0xiTHbwnC+8v/QWAmsBqYpKrRIjJIRNp61WYCu0UkBpgD9FPV3ap6Ete8NEtEVuKaq973V6zGz268EVatonHny1i+uxQDLh/Lp58qVarAxImgiRsejTEZgmgW+d8ZGRmpUTauMuP78kvo3Zvl+8tx72XfEBV/OTfeCO+8A6VLn//lxpi0JSJLVDUyqWM2k9qkr44dYdUqarYpxe/xpXi9zJvMnnWKqlVh+HBISAh0gMaY01KUIESkoojk9r5vLCIPe6ONjLlwl10GX31Frs8/oe+Rwaw6cRUNLl/LY4+5CXbz5wc6QGMMpPwOYjJwUkQqAR8A5YFxyb/EmGSIwG23QUwM5TtdzYy4ykwu9Qh7//6HBg1cJ/YOmztvTEClNEGc8jqdbwGGq+pjwBX+C8tkG8WLw+efI9On016msPrvojwZ8SOffaZceSWMGgUnTwY6SGOyp5QmiBMi0hW4E5julQX5JySTLbVpA9HR5PEM3PMAACAASURBVO1zF6/8cQMrCl1HRMntPPAA1K0L8+YFOkBjsp+UJogewDXAi6q6QUTKA5/5LyyTLeXPD2+/DQsXUqXcUX6KvoLxVV9ge/wJGjaETp1gw4ZAB2lM9pGiBKGqMar6sKqOF5HCQH5VfcXPsZns6uqrYcECZNQoumwbRuyuYjxf/0e+/Va56iro3x8OHAh0kMZkfSkdxfSziBQQkSLAcmCsiAzzb2gmW8uZE+6/H2Jjydu9Pc/Nb8GfBevQ5dpNvPoqhIbC6NHWP2GMP6W0iamgqh4A2gNjVbU20Mx/YRnjufRSGDsW5s6lZLF/+Pjnciy++gEqlzzMffdBrVrw/fc2G9sYf0hpgsglIlcAnfi3k9qY9NOggVszfMQIItdN5NdlBfii+WgO7T9Jq1bQtCksWhToII3JWlKaIAbh1k1ap6qLRaQCsNZ/YRmThFy54MEHYe1a5KEH6Tj7AdbsvYy3bp5NdLRSt66bqL1mTaADNSZrSGkn9ReqGqaqvb3n61W1g39DM+YcihSBN9+ElSsJbliXh76+nnX5a/F8lzXMnKlUrw49e8LWrYEO1JjMLaWd1KVEZIqI7BCRv0VksoiU8ndwxiSrShX49lv47jvy5z7OcxOqsC68I31u/ZuPP4ZKleDJJ21ZcWNSK6VNTGNxezmUwG0b+o1XZkzgtWwJy5fDiBFcuvoX3pxwObE3PEzHGw4yZAiUKwfPPQf79gU6UGMyl5QmiOKqOlZVE7zHR4Bt4WYyjqAg1z+xbh08/TTlZ43h02+LsLzzS7S47hiDBkH58jB4sM2hMCalUpogdolINxHJ6T26Abv9GZgxqVKwoMsCcXFwzz3U+OJZvvy5OH/c/x6Nrk1g4ECXKF55BQ4dCnSwxmRsKU0Qd+OGuG4H/gI64pbfSJaItBSRWBGJE5H+56jTSURiRCRaRMb5lJ8UkWXeI/FWpcYkr0QJePddiI6GG24g/N37mRpVksVPfEG9uqcYMMAliqFD4ciRQAdrTMaU6h3lRORRVR2ezPGcwJ9AcyAet890V1WN8akTCkwCmqrqXhG5VFV3eMcOqWq+lMZjO8qZZC1YAE88AXPnQqVKLOj2Ns/Nb8EPPwqXXQYDBkCvXnDJJYEO1Jj05a8d5fqe53gdIM4bEnscmAC0S1SnJzBSVfcCnE4OxqS5evXgl19g+nS45BLqPd+SmVur8+ugn6laVXn0UahQAYYNg8OHAx2sMRnDxSQIOc/xksAWn+fxXpmvykBlEZkvIgtEpKXPsRARifLKb04yAJFeXp2onTt3XvAFmGxGxC0rvmwZTJgAJ0/S8NkmzN5Xmzkv/U6VKsrjj7tRTy+/bJ3ZxlxMgjhf21RSCSTxa3IBoUBjoCswxmcr0zLebc9twHARqXjWyVRHq2qkqkYWL26DqkwK5cgBnTvDqlXw8cewfz+Nn7qW2UeuYd4bi4mMVJ56yiWK//s/2Ls30AEbExjJJggROSgiB5J4HMTNiUhOPFDa53kpYFsSdaaq6glV3QDE4hIGqrrN+7oe+BmoldKLMiZFcuWCO+5wa3OMHg3btlH/sTp8d7QJi0f/QaNG8PzzULYsPPUU2E2qyW6STRCqml9VCyTxyK+quc5z7sVAqIiUF5FgoAtusp2vr4EmACJSDNfktF5ECotIbp/y+kAMxvhDUJBbm2PtWhgxAmJjiewVwddHb2D5Zytp1coNiy1XDh5/HP76K9ABG5M+LqaJKVneHtYP4hb5Ww1MUtVoERkkIm29ajOB3SISA8wB+qnqbqAKECUiy73yV3xHPxnjF7lz/zvZbuhQWLqUsG5hTDzYmujPl9Ghg1sCqnx5uO8+N9XCmKws1cNcMxob5mrS3MGDbgvUYcNg1y5o1ox1d7/Iaz/X4aOPICEBOnRw6z3Vrh3oYI1JHX8NczUma8uf302Q2LjR3VGsXEnF2+ryXmxjNn02lyf6KTNnQmQkNGsGP/1kGxeZrMUShDHnkzev63zYsMG1Ma1dy+WdGvHy3AZs/vAnXn1FiYmB5s1dspg0ybZCNVmDJQhjUuqSS+Dhh10fxTvvwJYtFOzYnCcm12XD29/y/mjl0CE3gvbKK91KH8eOBTpoY1LPEoQxFyokBHr3dr3U778Pu3aRu8ON3PtOBDH/9wVfTjpJkSKuStmybtKdLTVuMiNLEMakVnAw3HsvxMbCRx/BkSPk7NqJDk9dxcJ732f298epVcvNoShTxrVSbdoU6KCNSTlLEMZcrKAguPNOiImByZOhUCHkvl406VGO768fwtJfD3Hjja77omJF6NIFFi0KdNDGnJ8lCGPSSs6c0L69+/SfNQuqV4cnnqDWTaUYV/5p1i/cyWOPwXffQd260LAhTJliHdom47IEYUxaE4GmTeGHHyAqClq0gJdfpkyDMgw50of4eRsZPhzi410+qVzZTeC2DYxMRmMJwhh/ql3bjXtdswa6d4cxY8hfqxKPLLqdtZNX8MUXcOmlbnBU6dLQv79LHMZkBJYgjEkPlSu7BQE3bIC+fWHaNHLVrknHMS35/YWf+G2+0qwZDBnilvK47TZYuDDQQZvszhKEMempRAl47TXYvBlefBGWL4fmzbnmgVp80fZT4mKO06eP29eoXj33GD8eTpwIdOAmO7IEYUwgFC7sxr9u3AgffugWdrrjDspfX4HhJV4jftU+3noLdu92dxPlysHgwbbkuElfliCMCaTcuaFHD1i50g1vuuoqePJJClQrzUMb+hL7wyamT4dq1WDgQNdPcffd7sbDGH+zBGFMRiACLVu6Ff/++ANuvhlGjCBHaEXafNaVH16KIjra5ZKJEyE8HBo3hq++smGyxn8sQRiT0YSHw6efwvr18NhjMGMGXH01VR9ozKg204nffIrXXnP93R06uMl3Q4fa1qgm7fk1QYhISxGJFZE4Eel/jjqdRCRGRKJFZFyiYwVEZKuIvO3POI3JkEqXdsOatmyB1193CeOmmyjcoBr9Cr3PuuhjTJ7s1nvq1w9KlYIHHnAjao1JC35LECKSExgJtAKqAl1FpGqiOqHAAKC+qlYDHk10mheAX/wVozGZQoECbmjsunXw+eeQJw/06kWuimVpv2oQv0zexdKl0KkTfPABVKkCN9zgbjxOnQp08CYz8+cdRB0gTlXXq+pxYALQLlGdnsBIVd0LoKo7Th8QkdrAZcAPfozRmMwjKMgNaYqKgtmz4eqr4bnnoEwZao3uzdgBf7JlCwwaBCtWQJs2Llm8/bbbHM+YC+XPBFES2OLzPN4r81UZqCwi80VkgYi0BBCRHMDrQL/k3kBEeolIlIhE7bTxfya7EIEmTdxkiehouP12N1T2qqu4tNfNDGw8l00blc8/h0KF4KGHXPPTY4+5VipjUsqfCUKSKEu8IWMuIBRoDHQFxohIIeABYIaqbiEZqjpaVSNVNbJ48eJpELIxmUzVqm5Pis2b4ZlnYO5caNSI4Eb1uC3XJBbOT+D336F1a3cnUakStGvnbkBse1RzPv5MEPFAaZ/npYBtSdSZqqonVHUDEItLGNcAD4rIRmAocIeIvOLHWI3J3C67zLUtbdnidrvbs8dtbRcaSr2FbzJ+9EE2bnRz8377Da6/HmrWdH0WR48GOniTUfkzQSwGQkWkvIgEA12AaYnqfA00ARCRYrgmp/WqeruqllHVcsD/gE9UNclRUMYYH3nyuK3s1qxxa4mXKgWPPgplylByRH8G997K5s0uMYDb76hMGXfzsS3xn28m2/NbglDVBOBBYCawGpikqtEiMkhE2nrVZgK7RSQGmAP0U9Xd/orJmGwjZ0432W7uXFiwAJo3P7MS4CX338ndkStYvtw1NV17Lbz0khsu260bLF4c6OBNRiGaRRoiIyMjNSoqKtBhGJNxrV/vtrX74AM4fNgljccfhxYtWLdeGDHC9XUfPOiSxqOPwi23QK5cgQ7c+JOILFHVyKSO2UxqY7KLChVcgtiyBV5+GVatcst71KxJxbkfMfzVf4iPh+HDYft2N6+iQgW3+OyePYEO3gSCJQhjspvChd3ORBs3wkcfubIePaB8eQqMfJlHuu/hzz9h6lQ36unJJ92k7t69YfXqQAZu0pslCGOyq+BguPNOtzTszJlQo4Yb5lS6NDkfe5i21dcze7Y73KULjB3rRtW2agXff2+ztLMDSxDGZHcibt/smTNdNrj1Vnj3XQgNhVtvJezIAj74wLVMvfACLFvmkkS1ajBqlOvOMFmTJQhjzL/Cwlyz08aN8MQTbvnxa66BBg0oPm8Kzww4yaZN8NlnkC+fWxywVClXdfPmQAdv0polCGPM2UqUcB3ZW7a4ju2tW6F9e7jqKoLHvMPttxxh0SKYN88Nhho2zHVod+oE8+fbLO2swhKEMebc8uWDhx+GtWth0iQoUgT69IEyZZBnB1K/0t9MmuRG0D7+OPz4IzRoAHXquIVnjx8P9AWYi2EJwhhzfrlyub6JBQvc5LuGDeHFF9007HvvpcyhGF59FeLjXb/EoUNu0p3tpZ25WYIwxqSciLtFmDIFYmPhnntg3DjXY926NXkXzub++5ToaLfFds2a/+6lfc89bhlyk3lYgjDGpE5oqFsYcPNmt1DgkiVuFcCrrybHl5No2SyB775zcyfuvhsmTHAJo2lTmDbN9tLODCxBGGMuTrFi7jZh0yYYPdqt1dG5M1x5JYwaxVVlj/LOO6756bXXIC7OLTl+5ZUuvxw5EugLMOdiCcIYkzZCQqBnT4iJga++guLF3TjYsmVh8GAK6x769XMd2pMmubzi9Xfz3HOwY8f538KkL0sQxpi0lTOnW+Xv99/h55/d1qgDB7pM0Lcvuf7awq23usNz57oujRdecIfvu891bZiMwRKEMcY/ROC66+Dbb13v9C23wFtvuQkTd96JRK+iQQP4+mvXT3HnnfDxx24f7ZtvdnMsbD5FYFmCMMb4X40a8OmnsG6da1f68ktXduONMHcuV1ZW3nvP9XcPHOiSQ8OGbtnxyZOtQztQ/JogRKSliMSKSJyIJLkjnIh0EpEYEYkWkXFeWVkRWSIiy7zy+/0ZpzEmnZQt69YTPz3yaeFCaNTIZYKvv+bSYqf4v/9zh0eOdPMnOnaEypXdc1v3KX35bcMgEckJ/Ak0x+09vRjoqqoxPnVCgUlAU1XdKyKXquoOb4tSUdV/RCQfsAq4VlXPuSmibRhkTCZ05IhbJvb112HDBje0qV8/N8sud25OnnRNUEOGuFxyeiJ3nz5uG25z8QK1YVAdIE5V16vqcWAC0C5RnZ7ASFXdC6CqO7yvx1X1H69Obj/HaYwJlDx53Kf9n3/C+PHu+b33un6KIUPIeWg/HTr826HdsKGbmV22rHvZxo2BvoCszZ8fvCWBLT7P470yX5WByiIyX0QWiEjL0wdEpLSIrPDO8WpSdw8i0ktEokQkaqfN5Tcm88qVy206sWQJ/PCD66l+4gk3tKl/f2T7X//p0O7WDd5/321odOedbmStSXv+TBCSRFni9qxcQCjQGOgKjBGRQgCqukVVw4BKwJ0ictYNpaqOVtVIVY0sXrx4mgZvjAkAEbc87E8/QVSU2xJ1yBC3qNP998OGDVx5JYwZ4+ZTPPSQ6++uVs0tNrt4caAvIGvxZ4KIB0r7PC8FJL4LiAemquoJVd0AxOISxhnenUM00NCPsRpjMpratWHiRNf8dPfdrq8iNNTdMqxZQ6lS8MYbbgL3wIEwZ45bRbZFC/e9DZG9eP5MEIuBUBEp73U6dwGmJarzNdAEQESK4Zqc1otIKRG5xCsvDNTHJQ9jTHZTsaJbInbDBrf0+Jdfur1PO3WCZcsoVswNiNq0yS3lsWKFW+/p2mvhm29sa9SL4bcEoaoJwIPATGA1MElVo0VkkIi09arNBHaLSAwwB+inqruBKsBCEVkO/AIMVdWV/orVGJMJlCjhdibauBEGDHBbpNaqBTfdBAsWUKCAGwC1caNb42n7dmjbFsLD3UKBNpfiwvltmGt6s2GuxmQz+/bB22+7eRW7d7uVZJ9+Gho3BhFOnHAtVC+/7Dqxq1RxTVGdOrnVQIwTqGGuxhjjP4UKwTPPuFuGoUMhOtq1LTVoADNmEJRL6dYNVq50iwPmyAG33QbVq7stLOyO4vwsQRhjMrd8+dx+pxs2uOnW8fHQpo1bJHD6dHKIcuutrm/iiy/ciNrbb3cjnz7/3BJFcixBGGOyhpAQt7x4XBx88AHs2eP6J+rWhRkzyCFKx46wfLnr5w4OdvMpqlWDzz6DhIRAX0DGYwnCGJO1BAW5YbGxsW7CxM6d7o6iXj347jtyiNKhAyxb5hYCzJ0bund3ieLTTy1R+LIEYYzJmoKC3EbYsbFup7vt26F1azf+deZMcojSvj388YdLFCEhcMcdLlFMnGjDY8EShDEmqwsOdjvdrV0L774LW7e6GdoNGsCPP56VKIKC3KofERFuHkUWGeiZKpYgjDHZQ3Cw27Ju7Vo38W7zZjftumFDmDXrTKJYvtz1SRw65OZRXHMNzJoV6OADwxKEMSZ7yZ3bresUF+dGPW3cCM2aufkT8+eTM6cb5bR6tWuZ2rrVHW7a1K0qm51YgjDGZE+5c/876umtt1xfRYMGbuTTihUEBf3bMjV8uJtmce21bhO8ZcsCHXz6sARhjMneQkLcsrDr1sFLL7mNJ8LD3RjY9esJCYFHHvn38Pz5boWPTp1gzZpAB+9fliCMMQYgb163xtP69W4vismT3Q53ffrA9u3ky+cOb9jgJnDPmOFGPPXsCdvOuddl5mYJwhhjfBUpAq+84m4Z7r3XdURUrAhPPQX79lGoELzwgksUDz0EH3/sViF/9lk4eDDQwactSxDGGJOUEiXcaKfVq6FdO7fqX4UK8OqrcOQIxYu7vonVq123xQsvuB3uRo2CEycCHXzasARhjDHJqVTJre63bJnrpe7f35W99x4kJFCxoltOfOFCuOoq1+9dvTpMmZL551BYgjDGmJSoWROmT3ed2BUruqGyNWq4MlXq1IGff4Zp09xy4u3buykWCxYEOvDU82uCEJGWIhIrInEi0v8cdTqJSIyIRIvIOK8sXER+98pWiEhnf8ZpjDEp1qAB/PorTJ3q1uO46Sa3F8XSpYicGSXLe++5boxrrnHzKrZsCXTgF85vCUJEcgIjgVZAVaCriFRNVCcUGADUV9VqwKPeoSPAHV5ZS2C4iBTyV6zGGHNBRNw061Wr3KZFK1e6PbTvuAO2bCFXLujVy82hePrpfwdE/d//wZEjgQ4+5fx5B1EHiFPV9ap6HJgAtEtUpycwUlX3AqjqDu/rn6q61vt+G7ADKO7HWI0x5sIFBblhsHFx8OSTbmeiypVdVjhwgHz5YPBgNwfvppvg+eddohg3LnP0T/gzQZQEfG+q4r0yX5WByiIyX0QWiEjLxCcRkTpAMLDOb5EaY8zFKFjQDY2NjYUOHdyMutBQtzhgQgJly7oVYufOhcsuc01O114LixYFOvDk+TNBSBJliXNmLiAUaAx0Bcb4NiWJyBXAp0APVT1r8V0R6SUiUSIStXPnzjQL3BhjUqVsWbfS36JFbkhT796uc/vHHwHXfbFoEXz4oVsCqm5d1yq1dWtgwz4XfyaIeKC0z/NSQOL5hvHAVFU9oaobgFhcwkBECgDfAs+oapLjAFR1tKpGqmpk8eLWAmWMySCuvtoNaZoyBY4dc6vG3nwzrFtHjhzQowf8+aebmT1pkmt2eu01OH480IH/lz8TxGIgVETKi0gw0AWYlqjO10ATABEphmtyWu/VnwJ8oqpf+DFGY4zxDxGXFGJi3CS7n36CqlVdVjh0iPz5XUvU6tXQvLnrwqhZ01XLKPyWIFQ1AXgQmAmsBiaparSIDBKRtl61mcBuEYkB5gD9VHU30AloBNwlIsu8R7i/YjXGGL/JndtNrvvzT+jc2fVVVK7s9jc9dYry5d2NxrffuhnYzZu7avHxgQ4cRDNDV3oKREZGalRUVKDDMMaY5C1YAA8/DIsXu32y33rLNUnhWqOGDHF3FjlzuvWdHn3U7XXkLyKyRFUjkzpmM6mNMSY91avnksSHH7oV/+rUcXtn79xJSAgMHOhapZo1+7fZKVA72lmCMMaY9ObbU92vH3zyieupfu+9M81OX3/tVvE4ftwli9tug7//Tucw0/ftjDHGnFGggBu+tHy5u1W4/363NseSJQC0aeN2snvuOTcbu0oVd+ORXj0DliCMMSbQqlaF2bPdHIpNm1yz00MPwb59hIS4GdjLlrlVYu+5x+2P/eef/g/LEoQxxmQEIm6K9Zo1bs3wd95xk+0++wxUqVLFTa0YPRr++APCwtwyHv6cO2EJwhhjMpJChWDECDfKqWxZ6N4dmjSBmBhy5HBbnK5Z46ZYDBzo9seeP98/oViCMMaYjCgiAn7/3XVcr1jh+iiefRaOHePyy90mRdOnw6FDbr3AU2ctRnTxLEEYY0xGlSOHWzc8Nha6dnX7moaHu1X/+LcT+8svXdU0f/u0P6Uxxpg0Vby4Gwo7cyb88w80auRGPO3fT758bgdUf7AEYYwxmUWLFm6Tor594f333einKVP89naWIIwxJjPJmxdefx0WLnR3Fu3bQ6dOfumEyJXmZzTGGON/kZFupNOwYXDggF86ISxBGGNMZhUU5BZs8hNrYjLGGJMkSxDGGGOSZAnCGGNMkvyaIESkpYjEikiciPQ/R51OIhIjItEiMs6n/HsR2Sci0/0ZozHGmKT5rZNaRHICI4HmQDywWESmqWqMT51QYABQX1X3isilPqcYAuQB7vNXjMYYY87Nn3cQdYA4VV2vqseBCUC7RHV6AiNVdS+Aqu44fUBVZwEH/RifMcaYZPgzQZQEtvg8j/fKfFUGKovIfBFZICItL+QNRKSXiESJSNTOnTsvMlxjjDG+/JkgJImyxPsg5QJCgcZAV2CMiBRK6Ruo6mhVjVTVyOLFi6c6UGOMMWfz50S5eKC0z/NSwLYk6ixQ1RPABhGJxSWMxRf6ZkuWLNklIptSGWsxYFcqX5tZ2TVnD3bN2cPFXHPZcx3wZ4JYDISKSHlgK9AFuC1Rna9xdw4fiUgxXJPT+tS8maqm+hZCRKJUNTK1r8+M7JqzB7vm7MFf1+y3JiZVTQAeBGYCq4FJqhotIoNEpK1XbSawW0RigDlAP1XdDSAic4EvgOtFJF5EbvBXrMYYY87m17WYVHUGMCNR2bM+3yvQ13skfm1Df8ZmjDEmeTaT2hkd6AACwK45e7Brzh78cs3i/og3xhhj/svuIIwxxiTJEoQxxpgkZfsEkZIFBTMjEflQRHaIyCqfsiIi8qOIrPW+FvbKRUTe8n4GK0QkInCRp46IlBaROSKy2lv48RGvPCtfc4iILBKR5d41/59XXl5EFnrXPFFEgr3y3N7zOO94uUDGfzFEJKeI/HF6Mc+sfs0islFEVorIMhGJ8sr8/m87WycInwUFWwFVga4iUjWwUaWZj4DES5f0B2apaigwy3sO7vpDvUcvYFQ6xZiWEoDHVbUKUA/o4/0us/I1/wM0VdWaQDjQUkTqAa8Cb3jXvBe4x6t/D7BXVSsBb3j1MqtHcMPnT8sO19xEVcN95jv4/9+2qmbbB3ANMNPn+QBgQKDjSsPrKwes8nkeC1zhfX8FEOt9/x7QNal6mfUBTMWtJJwtrhm38vFSoC5uRm0ur/zMv3HcvKNrvO9zefUk0LGn4lpLeR+ITYHpuGV9svo1bwSKJSrz+7/tbH0HQcoWFMxKLlPVvwC8r6eXV89SPwevGaEWsJAsfs1eU8syYAfwI7AO2Kduoir897rOXLN3fD9QNH0jThPDgSeAU97zomT9a1bgBxFZIiK9vDK//9v260S5TCAlCwpmB1nm5yAi+YDJwKOqekAkqUtzVZMoy3TXrKongXBvkcspQJWkqnlfM/01i8iNwA5VXSIijU8XJ1E1y1yzp76qbvP2zPlRRNYkUzfNrjm730GkZEHBrORvEbkCwPt6ev+NLPFzEJEgXHL4XFW/8oqz9DWfpqr7gJ9x/S+FROT0H3++13Xmmr3jBYE96RvpRasPtBWRjbg9Zpri7iiy8jWjqtu8rztwfwjUIR3+bWf3BHFmQUFv1EMXYFqAY/KnacCd3vd34trpT5ff4Y1+qAfsP33rmlmIu1X4AFitqsN8DmXlay7u3TkgIpcAzXAdt3OAjl61xNd8+mfREZitXiN1ZqGqA1S1lKqWw/1/na2qt5OFr1lE8opI/tPfAy2AVaTHv+1Ad74E+gG0Bv7Etd0+Heh40vC6xgN/ASdwf1Hcg2t7nQWs9b4W8eoKbjTXOmAlEBno+FNxvQ1wt9ErgGXeo3UWv+Yw4A/vmlcBz3rlFYBFQBxuwcvcXnmI9zzOO14h0NdwkdffGJie1a/Zu7bl3iP69OdUevzbtqU2jDHGJCm7NzEZY4w5B0sQxhhjkmQJwhhjTJIsQRhjjEmSJQhjjDFJsgRhzHmIyElvFc3TjzRb9VdEyonPirvGZCTZfakNY1LiqKqGBzoIY9Kb3UEYk0reGv2vensyLBKRSl55WRGZ5a3FP0tEynjll4nIFG//huUi/9/eHbtUGYVxHP/+khAhNMixoD0Q1GjoX3BskGiKllxsivwD3AXRpaAhChwdpYgIomhobZS2Ah1E7iIiv4ZzzLc6lwvJ1aHfZ7mH514O75me97zvPc+j23WqEUnPak+H1/VUNJIWJX2t82yc0zLjP5YEETHY2B+PmOY73+3bvgWsUWoCUccvbE8Br4DVGl8F3rv0b5ihnIqFUrd/3fYNYA+4U+NLwHSd5+GwFhfRT05SRwwgqWf7UiP+jdKwZ7sWCvxh+4qkXUr9/cMa/257UtIOcNX2QWeO68Abl6YvSHoCXLS9LGkL6AGbwKbt3pCXGvGb7CAiTsd9xv1+03LQGR9x8m5wjlJTZxb40qlWGnEmR9b1iQAAAKpJREFUkiAiTme+8/mpjj9SKo0C3AM+1PFbYAF+NfoZ7zeppAvANdvvKM1xLgN/7WIihil3JBGDjdWubce2bB//1XVU0mfKzdbdGlsEnkt6DOwA92v8EfBU0gPKTmGBUnG3ZQR4KWmCUp1zxaXnQ8SZyTuIiH9U30HctL173tcSMQx5xBQREU3ZQURERFN2EBER0ZQEERERTUkQERHRlAQRERFNSRAREdH0E2zdztgS3DJvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluate the losses of the model \n",
    "    epochs = range(1, len(loss)+1)\n",
    "    plt.plot(epochs, loss, color='red', label='Training Loss')\n",
    "    plt.plot(epochs, val_loss, color='blue', label='Validation Loss')\n",
    "    plt.title('Training and Validation Loss')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.legend()\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluate the accuracy of the model\n",
    "    plt.plot(epochs, acc, color='red', label='Training acc')\n",
    "    plt.plot(epochs, val_acc, color='green', label='Validation acc')\n",
    "    plt.title('Training and validation accuracy')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 500,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_class_nn_mod5 = model.predict_classes(X_test_norm)\n",
    "y_pred_prob_nn_mod5 = model.predict(X_test_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 501,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy is 0.649\n",
      "roc-auc is 0.843\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Print model performance and plot the roc curve\n",
    "print('accuracy is {:.3f}'.format(accuracy_score(y_test,y_pred_class_nn_mod5)))\n",
    "print('roc-auc is {:.3f}'.format(roc_auc_score(y_test,y_pred_prob_nn_mod5)))\n",
    "\n",
    "plot_roc(y_test, y_pred_prob_nn_mod5, 'NN')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accuracy obtained from this model is 64.9%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 537 samples, validate on 231 samples\n",
      "Epoch 1/700\n",
      "10/10 [==============================] - 1s 58ms/step - loss: 0.5281 - acc: 0.7732 - val_loss: 0.5371 - val_acc: 0.7619\n",
      "Epoch 2/700\n",
      "10/10 [==============================] - 1s 56ms/step - loss: 0.5277 - acc: 0.7745 - val_loss: 0.5367 - val_acc: 0.7619\n",
      "Epoch 3/700\n",
      "10/10 [==============================] - 1s 53ms/step - loss: 0.5272 - acc: 0.7713 - val_loss: 0.5363 - val_acc: 0.7706\n",
      "Epoch 4/700\n",
      "10/10 [==============================] - 0s 48ms/step - loss: 0.5268 - acc: 0.7709 - val_loss: 0.5359 - val_acc: 0.7749\n",
      "Epoch 5/700\n",
      "10/10 [==============================] - 1s 56ms/step - loss: 0.5263 - acc: 0.7719 - val_loss: 0.5355 - val_acc: 0.7749\n",
      "Epoch 6/700\n",
      "10/10 [==============================] - 1s 52ms/step - loss: 0.5258 - acc: 0.7736 - val_loss: 0.5351 - val_acc: 0.7749\n",
      "Epoch 7/700\n",
      "10/10 [==============================] - 1s 52ms/step - loss: 0.5254 - acc: 0.7750 - val_loss: 0.5347 - val_acc: 0.7792\n",
      "Epoch 8/700\n",
      "10/10 [==============================] - 1s 55ms/step - loss: 0.5249 - acc: 0.7752 - val_loss: 0.5343 - val_acc: 0.7792\n",
      "Epoch 9/700\n",
      "10/10 [==============================] - 1s 57ms/step - loss: 0.5245 - acc: 0.7747 - val_loss: 0.5339 - val_acc: 0.7792\n",
      "Epoch 10/700\n",
      "10/10 [==============================] - 1s 54ms/step - loss: 0.5240 - acc: 0.7747 - val_loss: 0.5336 - val_acc: 0.7792\n",
      "Epoch 11/700\n",
      "10/10 [==============================] - 1s 53ms/step - loss: 0.5236 - acc: 0.7752 - val_loss: 0.5332 - val_acc: 0.7792\n",
      "Epoch 12/700\n",
      "10/10 [==============================] - 1s 65ms/step - loss: 0.5231 - acc: 0.7765 - val_loss: 0.5328 - val_acc: 0.7792\n",
      "Epoch 13/700\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 0.5227 - acc: 0.7765 - val_loss: 0.5324 - val_acc: 0.7792\n",
      "Epoch 14/700\n",
      "10/10 [==============================] - 1s 55ms/step - loss: 0.5222 - acc: 0.7765 - val_loss: 0.5320 - val_acc: 0.7792\n",
      "Epoch 15/700\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 0.5218 - acc: 0.7765 - val_loss: 0.5316 - val_acc: 0.7792\n",
      "Epoch 16/700\n",
      "10/10 [==============================] - 1s 62ms/step - loss: 0.5213 - acc: 0.7750 - val_loss: 0.5313 - val_acc: 0.7792\n",
      "Epoch 17/700\n",
      "10/10 [==============================] - 1s 58ms/step - loss: 0.5209 - acc: 0.7747 - val_loss: 0.5309 - val_acc: 0.7792\n",
      "Epoch 18/700\n",
      "10/10 [==============================] - 1s 56ms/step - loss: 0.5204 - acc: 0.7747 - val_loss: 0.5305 - val_acc: 0.7879\n",
      "Epoch 19/700\n",
      "10/10 [==============================] - 1s 57ms/step - loss: 0.5200 - acc: 0.7760 - val_loss: 0.5301 - val_acc: 0.7879\n",
      "Epoch 20/700\n",
      "10/10 [==============================] - 1s 63ms/step - loss: 0.5195 - acc: 0.7765 - val_loss: 0.5297 - val_acc: 0.7879\n",
      "Epoch 21/700\n",
      "10/10 [==============================] - 1s 54ms/step - loss: 0.5191 - acc: 0.7765 - val_loss: 0.5294 - val_acc: 0.7922\n",
      "Epoch 22/700\n",
      "10/10 [==============================] - 1s 56ms/step - loss: 0.5187 - acc: 0.7765 - val_loss: 0.5290 - val_acc: 0.7922\n",
      "Epoch 23/700\n",
      "10/10 [==============================] - 1s 57ms/step - loss: 0.5182 - acc: 0.7765 - val_loss: 0.5286 - val_acc: 0.7922\n",
      "Epoch 24/700\n",
      "10/10 [==============================] - 1s 56ms/step - loss: 0.5178 - acc: 0.7765 - val_loss: 0.5282 - val_acc: 0.7922\n",
      "Epoch 25/700\n",
      "10/10 [==============================] - 1s 50ms/step - loss: 0.5173 - acc: 0.7765 - val_loss: 0.5279 - val_acc: 0.7922\n",
      "Epoch 26/700\n",
      "10/10 [==============================] - 1s 53ms/step - loss: 0.5169 - acc: 0.7752 - val_loss: 0.5275 - val_acc: 0.7965\n",
      "Epoch 27/700\n",
      "10/10 [==============================] - 1s 54ms/step - loss: 0.5165 - acc: 0.7747 - val_loss: 0.5271 - val_acc: 0.7965\n",
      "Epoch 28/700\n",
      "10/10 [==============================] - 1s 57ms/step - loss: 0.5160 - acc: 0.7747 - val_loss: 0.5267 - val_acc: 0.7965\n",
      "Epoch 29/700\n",
      "10/10 [==============================] - 1s 57ms/step - loss: 0.5156 - acc: 0.7747 - val_loss: 0.5264 - val_acc: 0.7922\n",
      "Epoch 30/700\n",
      "10/10 [==============================] - 1s 52ms/step - loss: 0.5151 - acc: 0.7747 - val_loss: 0.5260 - val_acc: 0.7922\n",
      "Epoch 31/700\n",
      "10/10 [==============================] - 1s 63ms/step - loss: 0.5147 - acc: 0.7743 - val_loss: 0.5256 - val_acc: 0.7922\n",
      "Epoch 32/700\n",
      "10/10 [==============================] - 1s 75ms/step - loss: 0.5143 - acc: 0.7728 - val_loss: 0.5253 - val_acc: 0.7922\n",
      "Epoch 33/700\n",
      "10/10 [==============================] - 1s 56ms/step - loss: 0.5138 - acc: 0.7728 - val_loss: 0.5249 - val_acc: 0.7922\n",
      "Epoch 34/700\n",
      "10/10 [==============================] - 1s 57ms/step - loss: 0.5134 - acc: 0.7728 - val_loss: 0.5245 - val_acc: 0.7922\n",
      "Epoch 35/700\n",
      "10/10 [==============================] - 1s 52ms/step - loss: 0.5129 - acc: 0.7728 - val_loss: 0.5242 - val_acc: 0.7922\n",
      "Epoch 36/700\n",
      "10/10 [==============================] - 1s 61ms/step - loss: 0.5125 - acc: 0.7730 - val_loss: 0.5238 - val_acc: 0.7922\n",
      "Epoch 37/700\n",
      "10/10 [==============================] - 1s 59ms/step - loss: 0.5121 - acc: 0.7747 - val_loss: 0.5234 - val_acc: 0.7922\n",
      "Epoch 38/700\n",
      "10/10 [==============================] - 1s 51ms/step - loss: 0.5116 - acc: 0.7728 - val_loss: 0.5231 - val_acc: 0.7922\n",
      "Epoch 39/700\n",
      "10/10 [==============================] - 1s 58ms/step - loss: 0.5112 - acc: 0.7728 - val_loss: 0.5227 - val_acc: 0.7879\n",
      "Epoch 40/700\n",
      "10/10 [==============================] - 1s 53ms/step - loss: 0.5107 - acc: 0.7709 - val_loss: 0.5224 - val_acc: 0.7879\n",
      "Epoch 41/700\n",
      "10/10 [==============================] - 1s 56ms/step - loss: 0.5103 - acc: 0.7702 - val_loss: 0.5220 - val_acc: 0.7879\n",
      "Epoch 42/700\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 0.5099 - acc: 0.7700 - val_loss: 0.5217 - val_acc: 0.7879\n",
      "Epoch 43/700\n",
      "10/10 [==============================] - ETA: 0s - loss: 0.5095 - acc: 0.769 - 1s 56ms/step - loss: 0.5094 - acc: 0.7691 - val_loss: 0.5213 - val_acc: 0.7879\n",
      "Epoch 44/700\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 0.5090 - acc: 0.7713 - val_loss: 0.5209 - val_acc: 0.7879\n",
      "Epoch 45/700\n",
      "10/10 [==============================] - 1s 57ms/step - loss: 0.5086 - acc: 0.7728 - val_loss: 0.5206 - val_acc: 0.7879\n",
      "Epoch 46/700\n",
      "10/10 [==============================] - 1s 59ms/step - loss: 0.5081 - acc: 0.7728 - val_loss: 0.5202 - val_acc: 0.7879\n",
      "Epoch 47/700\n",
      "10/10 [==============================] - 1s 51ms/step - loss: 0.5077 - acc: 0.7728 - val_loss: 0.5199 - val_acc: 0.7879\n",
      "Epoch 48/700\n",
      "10/10 [==============================] - 1s 57ms/step - loss: 0.5073 - acc: 0.7728 - val_loss: 0.5195 - val_acc: 0.7879\n",
      "Epoch 49/700\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 0.5068 - acc: 0.7728 - val_loss: 0.5192 - val_acc: 0.7879\n",
      "Epoch 50/700\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 0.5064 - acc: 0.7728 - val_loss: 0.5188 - val_acc: 0.7879\n",
      "Epoch 51/700\n",
      "10/10 [==============================] - 1s 63ms/step - loss: 0.5060 - acc: 0.7726 - val_loss: 0.5185 - val_acc: 0.7879\n",
      "Epoch 52/700\n",
      "10/10 [==============================] - 1s 68ms/step - loss: 0.5056 - acc: 0.7764 - val_loss: 0.5182 - val_acc: 0.7835\n",
      "Epoch 53/700\n",
      "10/10 [==============================] - 1s 69ms/step - loss: 0.5051 - acc: 0.7784 - val_loss: 0.5178 - val_acc: 0.7835\n",
      "Epoch 54/700\n",
      "10/10 [==============================] - 1s 59ms/step - loss: 0.5047 - acc: 0.7784 - val_loss: 0.5175 - val_acc: 0.7835\n",
      "Epoch 55/700\n",
      "10/10 [==============================] - 1s 53ms/step - loss: 0.5043 - acc: 0.7778 - val_loss: 0.5171 - val_acc: 0.7879\n",
      "Epoch 56/700\n",
      "10/10 [==============================] - 1s 51ms/step - loss: 0.5039 - acc: 0.7765 - val_loss: 0.5168 - val_acc: 0.7879\n",
      "Epoch 57/700\n",
      "10/10 [==============================] - 1s 70ms/step - loss: 0.5034 - acc: 0.7749 - val_loss: 0.5165 - val_acc: 0.7879\n",
      "Epoch 58/700\n",
      "10/10 [==============================] - 1s 56ms/step - loss: 0.5030 - acc: 0.7747 - val_loss: 0.5161 - val_acc: 0.7879\n",
      "Epoch 59/700\n",
      "10/10 [==============================] - 1s 66ms/step - loss: 0.5026 - acc: 0.7756 - val_loss: 0.5158 - val_acc: 0.7879\n",
      "Epoch 60/700\n",
      "10/10 [==============================] - 1s 64ms/step - loss: 0.5022 - acc: 0.7765 - val_loss: 0.5155 - val_acc: 0.7879\n",
      "Epoch 61/700\n",
      "10/10 [==============================] - 1s 63ms/step - loss: 0.5017 - acc: 0.7765 - val_loss: 0.5151 - val_acc: 0.7879\n",
      "Epoch 62/700\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10/10 [==============================] - 1s 53ms/step - loss: 0.5013 - acc: 0.7765 - val_loss: 0.5148 - val_acc: 0.7879\n",
      "Epoch 63/700\n",
      "10/10 [==============================] - 0s 47ms/step - loss: 0.5009 - acc: 0.7765 - val_loss: 0.5145 - val_acc: 0.7879\n",
      "Epoch 64/700\n",
      "10/10 [==============================] - 1s 51ms/step - loss: 0.5005 - acc: 0.7765 - val_loss: 0.5141 - val_acc: 0.7879\n",
      "Epoch 65/700\n",
      "10/10 [==============================] - 0s 49ms/step - loss: 0.5000 - acc: 0.7765 - val_loss: 0.5138 - val_acc: 0.7879\n",
      "Epoch 66/700\n",
      "10/10 [==============================] - 1s 50ms/step - loss: 0.4996 - acc: 0.7765 - val_loss: 0.5135 - val_acc: 0.7879\n",
      "Epoch 67/700\n",
      "10/10 [==============================] - 1s 57ms/step - loss: 0.4992 - acc: 0.7765 - val_loss: 0.5131 - val_acc: 0.7879\n",
      "Epoch 68/700\n",
      "10/10 [==============================] - 1s 52ms/step - loss: 0.4988 - acc: 0.7765 - val_loss: 0.5128 - val_acc: 0.7879\n",
      "Epoch 69/700\n",
      "10/10 [==============================] - 0s 49ms/step - loss: 0.4984 - acc: 0.7765 - val_loss: 0.5125 - val_acc: 0.7879\n",
      "Epoch 70/700\n",
      "10/10 [==============================] - 1s 54ms/step - loss: 0.4980 - acc: 0.7765 - val_loss: 0.5122 - val_acc: 0.7879\n",
      "Epoch 71/700\n",
      "10/10 [==============================] - 1s 53ms/step - loss: 0.4975 - acc: 0.7780 - val_loss: 0.5118 - val_acc: 0.7835\n",
      "Epoch 72/700\n",
      "10/10 [==============================] - 0s 49ms/step - loss: 0.4971 - acc: 0.7784 - val_loss: 0.5115 - val_acc: 0.7835\n",
      "Epoch 73/700\n",
      "10/10 [==============================] - 1s 58ms/step - loss: 0.4967 - acc: 0.7784 - val_loss: 0.5112 - val_acc: 0.7835\n",
      "Epoch 74/700\n",
      "10/10 [==============================] - 1s 65ms/step - loss: 0.4963 - acc: 0.7801 - val_loss: 0.5109 - val_acc: 0.7835\n",
      "Epoch 75/700\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 0.4959 - acc: 0.7832 - val_loss: 0.5106 - val_acc: 0.7835\n",
      "Epoch 76/700\n",
      "10/10 [==============================] - 1s 55ms/step - loss: 0.4955 - acc: 0.7840 - val_loss: 0.5102 - val_acc: 0.7835\n",
      "Epoch 77/700\n",
      "10/10 [==============================] - 1s 58ms/step - loss: 0.4951 - acc: 0.7845 - val_loss: 0.5099 - val_acc: 0.7879\n",
      "Epoch 78/700\n",
      "10/10 [==============================] - 0s 50ms/step - loss: 0.4947 - acc: 0.7858 - val_loss: 0.5096 - val_acc: 0.7879\n",
      "Epoch 79/700\n",
      "10/10 [==============================] - 1s 52ms/step - loss: 0.4942 - acc: 0.7844 - val_loss: 0.5093 - val_acc: 0.7922\n",
      "Epoch 80/700\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 0.4938 - acc: 0.7840 - val_loss: 0.5090 - val_acc: 0.7879\n",
      "Epoch 81/700\n",
      "10/10 [==============================] - 1s 54ms/step - loss: 0.4934 - acc: 0.7855 - val_loss: 0.5087 - val_acc: 0.7879\n",
      "Epoch 82/700\n",
      "10/10 [==============================] - 1s 53ms/step - loss: 0.4930 - acc: 0.7858 - val_loss: 0.5084 - val_acc: 0.7879\n",
      "Epoch 83/700\n",
      "10/10 [==============================] - 1s 55ms/step - loss: 0.4926 - acc: 0.7858 - val_loss: 0.5081 - val_acc: 0.7879\n",
      "Epoch 84/700\n",
      "10/10 [==============================] - 1s 54ms/step - loss: 0.4922 - acc: 0.7858 - val_loss: 0.5078 - val_acc: 0.7835\n",
      "Epoch 85/700\n",
      "10/10 [==============================] - 1s 74ms/step - loss: 0.4918 - acc: 0.7858 - val_loss: 0.5075 - val_acc: 0.7835\n",
      "Epoch 86/700\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 0.4914 - acc: 0.7858 - val_loss: 0.5072 - val_acc: 0.7835\n",
      "Epoch 87/700\n",
      "10/10 [==============================] - 1s 59ms/step - loss: 0.4910 - acc: 0.7858 - val_loss: 0.5069 - val_acc: 0.7835\n",
      "Epoch 88/700\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 0.4906 - acc: 0.7858 - val_loss: 0.5066 - val_acc: 0.7835\n",
      "Epoch 89/700\n",
      "10/10 [==============================] - 1s 59ms/step - loss: 0.4902 - acc: 0.7858 - val_loss: 0.5063 - val_acc: 0.7879\n",
      "Epoch 90/700\n",
      "10/10 [==============================] - 1s 59ms/step - loss: 0.4898 - acc: 0.7858 - val_loss: 0.5060 - val_acc: 0.7879\n",
      "Epoch 91/700\n",
      "10/10 [==============================] - 1s 58ms/step - loss: 0.4894 - acc: 0.7858 - val_loss: 0.5057 - val_acc: 0.7835\n",
      "Epoch 92/700\n",
      "10/10 [==============================] - 1s 65ms/step - loss: 0.4890 - acc: 0.7858 - val_loss: 0.5054 - val_acc: 0.7835\n",
      "Epoch 93/700\n",
      "10/10 [==============================] - 1s 59ms/step - loss: 0.4886 - acc: 0.7877 - val_loss: 0.5051 - val_acc: 0.7835\n",
      "Epoch 94/700\n",
      "10/10 [==============================] - 1s 58ms/step - loss: 0.4882 - acc: 0.7877 - val_loss: 0.5048 - val_acc: 0.7835\n",
      "Epoch 95/700\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 0.4878 - acc: 0.7877 - val_loss: 0.5045 - val_acc: 0.7835\n",
      "Epoch 96/700\n",
      "10/10 [==============================] - 1s 61ms/step - loss: 0.4874 - acc: 0.7877 - val_loss: 0.5042 - val_acc: 0.7879\n",
      "Epoch 97/700\n",
      "10/10 [==============================] - 1s 58ms/step - loss: 0.4870 - acc: 0.7877 - val_loss: 0.5040 - val_acc: 0.7879\n",
      "Epoch 98/700\n",
      "10/10 [==============================] - 1s 57ms/step - loss: 0.4866 - acc: 0.7870 - val_loss: 0.5037 - val_acc: 0.7879\n",
      "Epoch 99/700\n",
      "10/10 [==============================] - 1s 64ms/step - loss: 0.4862 - acc: 0.7858 - val_loss: 0.5034 - val_acc: 0.7879\n",
      "Epoch 100/700\n",
      "10/10 [==============================] - 1s 61ms/step - loss: 0.4858 - acc: 0.7858 - val_loss: 0.5031 - val_acc: 0.7879\n",
      "Epoch 101/700\n",
      "10/10 [==============================] - 1s 58ms/step - loss: 0.4854 - acc: 0.7858 - val_loss: 0.5028 - val_acc: 0.7922\n",
      "Epoch 102/700\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 0.4850 - acc: 0.7858 - val_loss: 0.5026 - val_acc: 0.7922\n",
      "Epoch 103/700\n",
      "10/10 [==============================] - 1s 59ms/step - loss: 0.4846 - acc: 0.7858 - val_loss: 0.5023 - val_acc: 0.7922\n",
      "Epoch 104/700\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 0.4842 - acc: 0.7858 - val_loss: 0.5020 - val_acc: 0.7922\n",
      "Epoch 105/700\n",
      "10/10 [==============================] - 1s 59ms/step - loss: 0.4838 - acc: 0.7858 - val_loss: 0.5017 - val_acc: 0.7922\n",
      "Epoch 106/700\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 0.4834 - acc: 0.7858 - val_loss: 0.5015 - val_acc: 0.7922\n",
      "Epoch 107/700\n",
      "10/10 [==============================] - 1s 58ms/step - loss: 0.4830 - acc: 0.7858 - val_loss: 0.5012 - val_acc: 0.7879\n",
      "Epoch 108/700\n",
      "10/10 [==============================] - 1s 58ms/step - loss: 0.4826 - acc: 0.7858 - val_loss: 0.5009 - val_acc: 0.7879\n",
      "Epoch 109/700\n",
      "10/10 [==============================] - 1s 62ms/step - loss: 0.4822 - acc: 0.7858 - val_loss: 0.5006 - val_acc: 0.7879\n",
      "Epoch 110/700\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 0.4818 - acc: 0.7858 - val_loss: 0.5004 - val_acc: 0.7879\n",
      "Epoch 111/700\n",
      "10/10 [==============================] - 1s 58ms/step - loss: 0.4815 - acc: 0.7858 - val_loss: 0.5001 - val_acc: 0.7879\n",
      "Epoch 112/700\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 0.4811 - acc: 0.7858 - val_loss: 0.4998 - val_acc: 0.7879\n",
      "Epoch 113/700\n",
      "10/10 [==============================] - 1s 58ms/step - loss: 0.4807 - acc: 0.7858 - val_loss: 0.4996 - val_acc: 0.7879\n",
      "Epoch 114/700\n",
      "10/10 [==============================] - 1s 65ms/step - loss: 0.4803 - acc: 0.7858 - val_loss: 0.4993 - val_acc: 0.7879\n",
      "Epoch 115/700\n",
      "10/10 [==============================] - 1s 59ms/step - loss: 0.4799 - acc: 0.7886 - val_loss: 0.4991 - val_acc: 0.7879\n",
      "Epoch 116/700\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 0.4795 - acc: 0.7896 - val_loss: 0.4988 - val_acc: 0.7879\n",
      "Epoch 117/700\n",
      "10/10 [==============================] - 1s 61ms/step - loss: 0.4791 - acc: 0.7914 - val_loss: 0.4985 - val_acc: 0.7879\n",
      "Epoch 118/700\n",
      "10/10 [==============================] - 1s 57ms/step - loss: 0.4787 - acc: 0.7914 - val_loss: 0.4983 - val_acc: 0.7879\n",
      "Epoch 119/700\n",
      "10/10 [==============================] - 1s 61ms/step - loss: 0.4784 - acc: 0.7914 - val_loss: 0.4980 - val_acc: 0.7835\n",
      "Epoch 120/700\n",
      "10/10 [==============================] - 1s 63ms/step - loss: 0.4780 - acc: 0.7914 - val_loss: 0.4978 - val_acc: 0.7835\n",
      "Epoch 121/700\n",
      "10/10 [==============================] - 1s 58ms/step - loss: 0.4776 - acc: 0.7914 - val_loss: 0.4975 - val_acc: 0.7792\n",
      "Epoch 122/700\n",
      "10/10 [==============================] - 1s 56ms/step - loss: 0.4772 - acc: 0.7914 - val_loss: 0.4973 - val_acc: 0.7749\n",
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     ]
    },
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      "Epoch 184/700\n"
     ]
    },
    {
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     "text": [
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      "Epoch 245/700\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "10/10 [==============================] - 0s 49ms/step - loss: 0.4367 - acc: 0.8063 - val_loss: 0.4783 - val_acc: 0.7792\n",
      "Epoch 246/700\n",
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      "Epoch 306/700\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Epoch 367/700\n"
     ]
    },
    {
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     "text": [
      "10/10 [==============================] - 1s 55ms/step - loss: 0.4048 - acc: 0.8240 - val_loss: 0.4766 - val_acc: 0.7662\n",
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      "Epoch 419/700\n",
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      "Epoch 427/700\n",
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      "Epoch 428/700\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10/10 [==============================] - 1s 60ms/step - loss: 0.3895 - acc: 0.8436 - val_loss: 0.4794 - val_acc: 0.7706\n",
      "Epoch 429/700\n",
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      "Epoch 489/700\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "10/10 [==============================] - 1s 58ms/step - loss: 0.3732 - acc: 0.8529 - val_loss: 0.4848 - val_acc: 0.7749\n",
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      "Epoch 498/700\n",
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      "Epoch 512/700\n",
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      "Epoch 513/700\n",
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      "Epoch 515/700\n",
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      "Epoch 517/700\n",
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      "Epoch 518/700\n",
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      "Epoch 519/700\n",
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      "Epoch 520/700\n",
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      "Epoch 521/700\n",
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      "Epoch 522/700\n",
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      "Epoch 523/700\n",
      "10/10 [==============================] - 1s 63ms/step - loss: 0.3634 - acc: 0.8585 - val_loss: 0.4894 - val_acc: 0.7706\n",
      "Epoch 524/700\n",
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      "Epoch 525/700\n",
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      "Epoch 527/700\n",
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      "Epoch 528/700\n",
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      "Epoch 529/700\n",
      "10/10 [==============================] - 1s 71ms/step - loss: 0.3617 - acc: 0.8585 - val_loss: 0.4903 - val_acc: 0.7706\n",
      "Epoch 530/700\n",
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      "Epoch 531/700\n",
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      "Epoch 532/700\n",
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      "Epoch 533/700\n",
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      "Epoch 535/700\n",
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      "Epoch 536/700\n",
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      "Epoch 537/700\n",
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      "Epoch 538/700\n",
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      "Epoch 539/700\n",
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      "Epoch 540/700\n",
      "10/10 [==============================] - 1s 70ms/step - loss: 0.3586 - acc: 0.8641 - val_loss: 0.4919 - val_acc: 0.7706\n",
      "Epoch 541/700\n",
      "10/10 [==============================] - 2s 153ms/step - loss: 0.3583 - acc: 0.8641 - val_loss: 0.4921 - val_acc: 0.7706\n",
      "Epoch 542/700\n",
      "10/10 [==============================] - 2s 182ms/step - loss: 0.3580 - acc: 0.8641 - val_loss: 0.4922 - val_acc: 0.7706\n",
      "Epoch 543/700\n",
      "10/10 [==============================] - 2s 153ms/step - loss: 0.3577 - acc: 0.8659 - val_loss: 0.4924 - val_acc: 0.7706\n",
      "Epoch 544/700\n",
      "10/10 [==============================] - 1s 136ms/step - loss: 0.3575 - acc: 0.8659 - val_loss: 0.4926 - val_acc: 0.7706\n",
      "Epoch 545/700\n",
      "10/10 [==============================] - 1s 65ms/step - loss: 0.3572 - acc: 0.8659 - val_loss: 0.4927 - val_acc: 0.7706\n",
      "Epoch 546/700\n",
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      "Epoch 547/700\n",
      "10/10 [==============================] - 1s 116ms/step - loss: 0.3566 - acc: 0.8659 - val_loss: 0.4930 - val_acc: 0.7749\n",
      "Epoch 548/700\n",
      "10/10 [==============================] - 1s 98ms/step - loss: 0.3563 - acc: 0.8659 - val_loss: 0.4932 - val_acc: 0.7749\n",
      "Epoch 549/700\n",
      "10/10 [==============================] - 1s 65ms/step - loss: 0.3561 - acc: 0.8659 - val_loss: 0.4933 - val_acc: 0.7749\n",
      "Epoch 550/700\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10/10 [==============================] - 1s 79ms/step - loss: 0.3558 - acc: 0.8646 - val_loss: 0.4935 - val_acc: 0.7749\n",
      "Epoch 551/700\n",
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      "Epoch 552/700\n",
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      "Epoch 554/700\n",
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      "Epoch 555/700\n",
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      "Epoch 556/700\n",
      "10/10 [==============================] - 1s 50ms/step - loss: 0.3541 - acc: 0.8624 - val_loss: 0.4944 - val_acc: 0.7749\n",
      "Epoch 557/700\n",
      "10/10 [==============================] - 1s 64ms/step - loss: 0.3538 - acc: 0.8622 - val_loss: 0.4946 - val_acc: 0.7749\n",
      "Epoch 558/700\n",
      "10/10 [==============================] - 1s 53ms/step - loss: 0.3535 - acc: 0.8622 - val_loss: 0.4948 - val_acc: 0.7749\n",
      "Epoch 559/700\n",
      "10/10 [==============================] - 0s 49ms/step - loss: 0.3533 - acc: 0.8622 - val_loss: 0.4949 - val_acc: 0.7749\n",
      "Epoch 560/700\n",
      "10/10 [==============================] - 1s 56ms/step - loss: 0.3530 - acc: 0.8622 - val_loss: 0.4951 - val_acc: 0.7749\n",
      "Epoch 561/700\n",
      "10/10 [==============================] - 1s 54ms/step - loss: 0.3527 - acc: 0.8622 - val_loss: 0.4952 - val_acc: 0.7749\n",
      "Epoch 562/700\n",
      "10/10 [==============================] - 1s 51ms/step - loss: 0.3524 - acc: 0.8639 - val_loss: 0.4954 - val_acc: 0.7749\n",
      "Epoch 563/700\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 0.3522 - acc: 0.8641 - val_loss: 0.4956 - val_acc: 0.7749\n",
      "Epoch 564/700\n",
      "10/10 [==============================] - 1s 51ms/step - loss: 0.3519 - acc: 0.8641 - val_loss: 0.4957 - val_acc: 0.7749\n",
      "Epoch 565/700\n",
      "10/10 [==============================] - 1s 134ms/step - loss: 0.3516 - acc: 0.8641 - val_loss: 0.4959 - val_acc: 0.7749\n",
      "Epoch 566/700\n",
      "10/10 [==============================] - 1s 85ms/step - loss: 0.3514 - acc: 0.8641 - val_loss: 0.4961 - val_acc: 0.7749\n",
      "Epoch 567/700\n",
      "10/10 [==============================] - 1s 80ms/step - loss: 0.3511 - acc: 0.8641 - val_loss: 0.4962 - val_acc: 0.7749\n",
      "Epoch 568/700\n",
      "10/10 [==============================] - 1s 71ms/step - loss: 0.3508 - acc: 0.8641 - val_loss: 0.4964 - val_acc: 0.7749\n",
      "Epoch 569/700\n",
      "10/10 [==============================] - 0s 49ms/step - loss: 0.3505 - acc: 0.8641 - val_loss: 0.4966 - val_acc: 0.7749\n",
      "Epoch 570/700\n",
      "10/10 [==============================] - 1s 104ms/step - loss: 0.3503 - acc: 0.8641 - val_loss: 0.4967 - val_acc: 0.7749\n",
      "Epoch 571/700\n",
      "10/10 [==============================] - 1s 106ms/step - loss: 0.3500 - acc: 0.8641 - val_loss: 0.4969 - val_acc: 0.7749\n",
      "Epoch 572/700\n",
      "10/10 [==============================] - 0s 44ms/step - loss: 0.3497 - acc: 0.8659 - val_loss: 0.4971 - val_acc: 0.7749\n",
      "Epoch 573/700\n",
      "10/10 [==============================] - 0s 48ms/step - loss: 0.3495 - acc: 0.8659 - val_loss: 0.4973 - val_acc: 0.7749\n",
      "Epoch 574/700\n",
      "10/10 [==============================] - 0s 45ms/step - loss: 0.3492 - acc: 0.8659 - val_loss: 0.4974 - val_acc: 0.7749\n",
      "Epoch 575/700\n",
      "10/10 [==============================] - 1s 107ms/step - loss: 0.3489 - acc: 0.8659 - val_loss: 0.4976 - val_acc: 0.7749\n",
      "Epoch 576/700\n",
      "10/10 [==============================] - 1s 88ms/step - loss: 0.3486 - acc: 0.8659 - val_loss: 0.4978 - val_acc: 0.7706\n",
      "Epoch 577/700\n",
      "10/10 [==============================] - 1s 58ms/step - loss: 0.3484 - acc: 0.8659 - val_loss: 0.4979 - val_acc: 0.7706\n",
      "Epoch 578/700\n",
      "10/10 [==============================] - 1s 58ms/step - loss: 0.3481 - acc: 0.8659 - val_loss: 0.4981 - val_acc: 0.7706\n",
      "Epoch 579/700\n",
      "10/10 [==============================] - 1s 128ms/step - loss: 0.3478 - acc: 0.8659 - val_loss: 0.4983 - val_acc: 0.7706\n",
      "Epoch 580/700\n",
      "10/10 [==============================] - 1s 95ms/step - loss: 0.3476 - acc: 0.8659 - val_loss: 0.4984 - val_acc: 0.7706\n",
      "Epoch 581/700\n",
      "10/10 [==============================] - 1s 90ms/step - loss: 0.3473 - acc: 0.8659 - val_loss: 0.4986 - val_acc: 0.7706\n",
      "Epoch 582/700\n",
      "10/10 [==============================] - 1s 110ms/step - loss: 0.3470 - acc: 0.8659 - val_loss: 0.4988 - val_acc: 0.7706\n",
      "Epoch 583/700\n",
      "10/10 [==============================] - 1s 56ms/step - loss: 0.3467 - acc: 0.8659 - val_loss: 0.4989 - val_acc: 0.7706\n",
      "Epoch 584/700\n",
      "10/10 [==============================] - 1s 66ms/step - loss: 0.3465 - acc: 0.8678 - val_loss: 0.4991 - val_acc: 0.7706\n",
      "Epoch 585/700\n",
      "10/10 [==============================] - 1s 95ms/step - loss: 0.3462 - acc: 0.8680 - val_loss: 0.4993 - val_acc: 0.7706\n",
      "Epoch 586/700\n",
      "10/10 [==============================] - 1s 58ms/step - loss: 0.3459 - acc: 0.8696 - val_loss: 0.4994 - val_acc: 0.7706\n",
      "Epoch 587/700\n",
      "10/10 [==============================] - 1s 51ms/step - loss: 0.3457 - acc: 0.8696 - val_loss: 0.4996 - val_acc: 0.7706\n",
      "Epoch 588/700\n",
      "10/10 [==============================] - 1s 67ms/step - loss: 0.3454 - acc: 0.8706 - val_loss: 0.4998 - val_acc: 0.7706\n",
      "Epoch 589/700\n",
      "10/10 [==============================] - 1s 57ms/step - loss: 0.3451 - acc: 0.8715 - val_loss: 0.5000 - val_acc: 0.7706\n",
      "Epoch 590/700\n",
      "10/10 [==============================] - 1s 54ms/step - loss: 0.3448 - acc: 0.8715 - val_loss: 0.5001 - val_acc: 0.7706\n",
      "Epoch 591/700\n",
      "10/10 [==============================] - 1s 64ms/step - loss: 0.3446 - acc: 0.8715 - val_loss: 0.5003 - val_acc: 0.7706\n",
      "Epoch 592/700\n",
      "10/10 [==============================] - 1s 94ms/step - loss: 0.3443 - acc: 0.8715 - val_loss: 0.5005 - val_acc: 0.7706\n",
      "Epoch 593/700\n",
      "10/10 [==============================] - 1s 77ms/step - loss: 0.3440 - acc: 0.8715 - val_loss: 0.5006 - val_acc: 0.7706\n",
      "Epoch 594/700\n",
      "10/10 [==============================] - 1s 97ms/step - loss: 0.3438 - acc: 0.8715 - val_loss: 0.5008 - val_acc: 0.7706\n",
      "Epoch 595/700\n",
      "10/10 [==============================] - 1s 97ms/step - loss: 0.3435 - acc: 0.8715 - val_loss: 0.5010 - val_acc: 0.7706\n",
      "Epoch 596/700\n",
      "10/10 [==============================] - 1s 104ms/step - loss: 0.3432 - acc: 0.8715 - val_loss: 0.5011 - val_acc: 0.7706\n",
      "Epoch 597/700\n",
      "10/10 [==============================] - 1s 87ms/step - loss: 0.3430 - acc: 0.8715 - val_loss: 0.5013 - val_acc: 0.7706\n",
      "Epoch 598/700\n",
      "10/10 [==============================] - 1s 79ms/step - loss: 0.3427 - acc: 0.8715 - val_loss: 0.5015 - val_acc: 0.7706\n",
      "Epoch 599/700\n",
      "10/10 [==============================] - 1s 58ms/step - loss: 0.3424 - acc: 0.8715 - val_loss: 0.5016 - val_acc: 0.7706\n",
      "Epoch 600/700\n",
      "10/10 [==============================] - 1s 64ms/step - loss: 0.3421 - acc: 0.8732 - val_loss: 0.5018 - val_acc: 0.7706\n",
      "Epoch 601/700\n",
      "10/10 [==============================] - 1s 57ms/step - loss: 0.3419 - acc: 0.8734 - val_loss: 0.5020 - val_acc: 0.7706\n",
      "Epoch 602/700\n",
      "10/10 [==============================] - 1s 92ms/step - loss: 0.3416 - acc: 0.8734 - val_loss: 0.5021 - val_acc: 0.7706\n",
      "Epoch 603/700\n",
      "10/10 [==============================] - 1s 89ms/step - loss: 0.3413 - acc: 0.8734 - val_loss: 0.5023 - val_acc: 0.7706\n",
      "Epoch 604/700\n",
      "10/10 [==============================] - 1s 68ms/step - loss: 0.3411 - acc: 0.8734 - val_loss: 0.5025 - val_acc: 0.7706\n",
      "Epoch 605/700\n",
      "10/10 [==============================] - 1s 75ms/step - loss: 0.3408 - acc: 0.8734 - val_loss: 0.5026 - val_acc: 0.7706\n",
      "Epoch 606/700\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 0.3405 - acc: 0.8734 - val_loss: 0.5028 - val_acc: 0.7706\n",
      "Epoch 607/700\n",
      "10/10 [==============================] - 1s 64ms/step - loss: 0.3403 - acc: 0.8734 - val_loss: 0.5030 - val_acc: 0.7706\n",
      "Epoch 608/700\n",
      "10/10 [==============================] - 1s 71ms/step - loss: 0.3400 - acc: 0.8736 - val_loss: 0.5031 - val_acc: 0.7706\n",
      "Epoch 609/700\n",
      "10/10 [==============================] - 1s 68ms/step - loss: 0.3397 - acc: 0.8752 - val_loss: 0.5033 - val_acc: 0.7706\n",
      "Epoch 610/700\n",
      "10/10 [==============================] - 1s 62ms/step - loss: 0.3395 - acc: 0.8752 - val_loss: 0.5035 - val_acc: 0.7706\n",
      "Epoch 611/700\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "10/10 [==============================] - 1s 61ms/step - loss: 0.3392 - acc: 0.8752 - val_loss: 0.5037 - val_acc: 0.7706\n",
      "Epoch 612/700\n",
      "10/10 [==============================] - 1s 55ms/step - loss: 0.3389 - acc: 0.8752 - val_loss: 0.5038 - val_acc: 0.7706\n",
      "Epoch 613/700\n",
      "10/10 [==============================] - 1s 56ms/step - loss: 0.3387 - acc: 0.8752 - val_loss: 0.5040 - val_acc: 0.7706\n",
      "Epoch 614/700\n",
      "10/10 [==============================] - 1s 56ms/step - loss: 0.3384 - acc: 0.8752 - val_loss: 0.5042 - val_acc: 0.7706\n",
      "Epoch 615/700\n",
      "10/10 [==============================] - 1s 68ms/step - loss: 0.3381 - acc: 0.8752 - val_loss: 0.5043 - val_acc: 0.7706\n",
      "Epoch 616/700\n",
      "10/10 [==============================] - 1s 57ms/step - loss: 0.3379 - acc: 0.8752 - val_loss: 0.5045 - val_acc: 0.7706\n",
      "Epoch 617/700\n",
      "10/10 [==============================] - 1s 57ms/step - loss: 0.3376 - acc: 0.8752 - val_loss: 0.5047 - val_acc: 0.7706\n",
      "Epoch 618/700\n",
      "10/10 [==============================] - 1s 61ms/step - loss: 0.3373 - acc: 0.8752 - val_loss: 0.5048 - val_acc: 0.7706\n",
      "Epoch 619/700\n",
      "10/10 [==============================] - 1s 61ms/step - loss: 0.3371 - acc: 0.8752 - val_loss: 0.5050 - val_acc: 0.7706\n",
      "Epoch 620/700\n",
      "10/10 [==============================] - 1s 138ms/step - loss: 0.3368 - acc: 0.8752 - val_loss: 0.5052 - val_acc: 0.7706\n",
      "Epoch 621/700\n",
      "10/10 [==============================] - 2s 167ms/step - loss: 0.3365 - acc: 0.8764 - val_loss: 0.5053 - val_acc: 0.7706\n",
      "Epoch 622/700\n",
      "10/10 [==============================] - 0s 48ms/step - loss: 0.3363 - acc: 0.8771 - val_loss: 0.5055 - val_acc: 0.7706\n",
      "Epoch 623/700\n",
      "10/10 [==============================] - 1s 74ms/step - loss: 0.3360 - acc: 0.8771 - val_loss: 0.5056 - val_acc: 0.7706\n",
      "Epoch 624/700\n",
      "10/10 [==============================] - 1s 75ms/step - loss: 0.3358 - acc: 0.8771 - val_loss: 0.5058 - val_acc: 0.7706\n",
      "Epoch 625/700\n",
      "10/10 [==============================] - 0s 45ms/step - loss: 0.3355 - acc: 0.8771 - val_loss: 0.5060 - val_acc: 0.7706\n",
      "Epoch 626/700\n",
      "10/10 [==============================] - 0s 45ms/step - loss: 0.3352 - acc: 0.8771 - val_loss: 0.5061 - val_acc: 0.7706\n",
      "Epoch 627/700\n",
      "10/10 [==============================] - 1s 57ms/step - loss: 0.3350 - acc: 0.8771 - val_loss: 0.5063 - val_acc: 0.7706\n",
      "Epoch 628/700\n",
      "10/10 [==============================] - 1s 67ms/step - loss: 0.3347 - acc: 0.8771 - val_loss: 0.5064 - val_acc: 0.7706\n",
      "Epoch 629/700\n",
      "10/10 [==============================] - 0s 46ms/step - loss: 0.3344 - acc: 0.8771 - val_loss: 0.5066 - val_acc: 0.7706\n",
      "Epoch 630/700\n",
      "10/10 [==============================] - 0s 44ms/step - loss: 0.3342 - acc: 0.8771 - val_loss: 0.5067 - val_acc: 0.7706\n",
      "Epoch 631/700\n",
      "10/10 [==============================] - 0s 48ms/step - loss: 0.3339 - acc: 0.8771 - val_loss: 0.5069 - val_acc: 0.7706\n",
      "Epoch 632/700\n",
      "10/10 [==============================] - 0s 46ms/step - loss: 0.3337 - acc: 0.8771 - val_loss: 0.5070 - val_acc: 0.7706\n",
      "Epoch 633/700\n",
      "10/10 [==============================] - 0s 46ms/step - loss: 0.3334 - acc: 0.8771 - val_loss: 0.5072 - val_acc: 0.7706\n",
      "Epoch 634/700\n",
      "10/10 [==============================] - 1s 66ms/step - loss: 0.3331 - acc: 0.8771 - val_loss: 0.5073 - val_acc: 0.7706\n",
      "Epoch 635/700\n",
      "10/10 [==============================] - 0s 44ms/step - loss: 0.3329 - acc: 0.8771 - val_loss: 0.5075 - val_acc: 0.7706\n",
      "Epoch 636/700\n",
      "10/10 [==============================] - 0s 44ms/step - loss: 0.3326 - acc: 0.8771 - val_loss: 0.5076 - val_acc: 0.7706\n",
      "Epoch 637/700\n",
      "10/10 [==============================] - 1s 126ms/step - loss: 0.3324 - acc: 0.8771 - val_loss: 0.5078 - val_acc: 0.7706\n",
      "Epoch 638/700\n",
      "10/10 [==============================] - 1s 132ms/step - loss: 0.3321 - acc: 0.8771 - val_loss: 0.5079 - val_acc: 0.7706\n",
      "Epoch 639/700\n",
      "10/10 [==============================] - 1s 82ms/step - loss: 0.3318 - acc: 0.8771 - val_loss: 0.5081 - val_acc: 0.7706\n",
      "Epoch 640/700\n",
      "10/10 [==============================] - 1s 56ms/step - loss: 0.3316 - acc: 0.8771 - val_loss: 0.5083 - val_acc: 0.7706\n",
      "Epoch 641/700\n",
      "10/10 [==============================] - 1s 52ms/step - loss: 0.3313 - acc: 0.8771 - val_loss: 0.5084 - val_acc: 0.7706\n",
      "Epoch 642/700\n",
      "10/10 [==============================] - 1s 63ms/step - loss: 0.3311 - acc: 0.8771 - val_loss: 0.5086 - val_acc: 0.7706\n",
      "Epoch 643/700\n",
      "10/10 [==============================] - 1s 62ms/step - loss: 0.3308 - acc: 0.8771 - val_loss: 0.5087 - val_acc: 0.7706\n",
      "Epoch 644/700\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 0.3305 - acc: 0.8771 - val_loss: 0.5089 - val_acc: 0.7662\n",
      "Epoch 645/700\n",
      "10/10 [==============================] - 1s 88ms/step - loss: 0.3303 - acc: 0.8771 - val_loss: 0.5091 - val_acc: 0.7662\n",
      "Epoch 646/700\n",
      "10/10 [==============================] - 1s 89ms/step - loss: 0.3300 - acc: 0.8771 - val_loss: 0.5092 - val_acc: 0.7662\n",
      "Epoch 647/700\n",
      "10/10 [==============================] - 1s 93ms/step - loss: 0.3298 - acc: 0.8771 - val_loss: 0.5094 - val_acc: 0.7662\n",
      "Epoch 648/700\n",
      "10/10 [==============================] - 1s 67ms/step - loss: 0.3295 - acc: 0.8771 - val_loss: 0.5095 - val_acc: 0.7662\n",
      "Epoch 649/700\n",
      "10/10 [==============================] - 1s 66ms/step - loss: 0.3292 - acc: 0.8771 - val_loss: 0.5097 - val_acc: 0.7662\n",
      "Epoch 650/700\n",
      "10/10 [==============================] - 1s 58ms/step - loss: 0.3290 - acc: 0.8771 - val_loss: 0.5098 - val_acc: 0.7662\n",
      "Epoch 651/700\n",
      "10/10 [==============================] - 1s 65ms/step - loss: 0.3287 - acc: 0.8771 - val_loss: 0.5100 - val_acc: 0.7662\n",
      "Epoch 652/700\n",
      "10/10 [==============================] - 1s 96ms/step - loss: 0.3285 - acc: 0.8771 - val_loss: 0.5102 - val_acc: 0.7662\n",
      "Epoch 653/700\n",
      "10/10 [==============================] - 1s 104ms/step - loss: 0.3282 - acc: 0.8771 - val_loss: 0.5103 - val_acc: 0.7662\n",
      "Epoch 654/700\n",
      "10/10 [==============================] - 1s 63ms/step - loss: 0.3279 - acc: 0.8771 - val_loss: 0.5105 - val_acc: 0.7662\n",
      "Epoch 655/700\n",
      "10/10 [==============================] - 1s 64ms/step - loss: 0.3277 - acc: 0.8771 - val_loss: 0.5107 - val_acc: 0.7662\n",
      "Epoch 656/700\n",
      "10/10 [==============================] - 1s 58ms/step - loss: 0.3274 - acc: 0.8771 - val_loss: 0.5109 - val_acc: 0.7662\n",
      "Epoch 657/700\n",
      "10/10 [==============================] - 1s 62ms/step - loss: 0.3272 - acc: 0.8771 - val_loss: 0.5110 - val_acc: 0.7662\n",
      "Epoch 658/700\n",
      "10/10 [==============================] - 1s 83ms/step - loss: 0.3269 - acc: 0.8771 - val_loss: 0.5112 - val_acc: 0.7662\n",
      "Epoch 659/700\n",
      "10/10 [==============================] - 1s 96ms/step - loss: 0.3266 - acc: 0.8771 - val_loss: 0.5114 - val_acc: 0.7662\n",
      "Epoch 660/700\n",
      "10/10 [==============================] - 1s 86ms/step - loss: 0.3264 - acc: 0.8771 - val_loss: 0.5116 - val_acc: 0.7662\n",
      "Epoch 661/700\n",
      "10/10 [==============================] - 1s 108ms/step - loss: 0.3261 - acc: 0.8771 - val_loss: 0.5117 - val_acc: 0.7662\n",
      "Epoch 662/700\n",
      "10/10 [==============================] - 1s 76ms/step - loss: 0.3258 - acc: 0.8788 - val_loss: 0.5119 - val_acc: 0.7662\n",
      "Epoch 663/700\n",
      "10/10 [==============================] - 1s 72ms/step - loss: 0.3256 - acc: 0.8790 - val_loss: 0.5121 - val_acc: 0.7662\n",
      "Epoch 664/700\n",
      "10/10 [==============================] - 1s 85ms/step - loss: 0.3253 - acc: 0.8790 - val_loss: 0.5123 - val_acc: 0.7662\n",
      "Epoch 665/700\n",
      "10/10 [==============================] - 1s 97ms/step - loss: 0.3250 - acc: 0.8790 - val_loss: 0.5124 - val_acc: 0.7662\n",
      "Epoch 666/700\n",
      "10/10 [==============================] - 1s 67ms/step - loss: 0.3248 - acc: 0.8790 - val_loss: 0.5126 - val_acc: 0.7662\n",
      "Epoch 667/700\n",
      "10/10 [==============================] - 1s 86ms/step - loss: 0.3245 - acc: 0.8790 - val_loss: 0.5128 - val_acc: 0.7662\n",
      "Epoch 668/700\n",
      "10/10 [==============================] - 1s 62ms/step - loss: 0.3242 - acc: 0.8801 - val_loss: 0.5129 - val_acc: 0.7662\n",
      "Epoch 669/700\n",
      "10/10 [==============================] - 1s 67ms/step - loss: 0.3239 - acc: 0.8808 - val_loss: 0.5131 - val_acc: 0.7662\n",
      "Epoch 670/700\n",
      "10/10 [==============================] - 1s 76ms/step - loss: 0.3237 - acc: 0.8808 - val_loss: 0.5133 - val_acc: 0.7662\n",
      "Epoch 671/700\n",
      "10/10 [==============================] - 1s 62ms/step - loss: 0.3234 - acc: 0.8808 - val_loss: 0.5134 - val_acc: 0.7662\n",
      "Epoch 672/700\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10/10 [==============================] - 1s 64ms/step - loss: 0.3231 - acc: 0.8808 - val_loss: 0.5136 - val_acc: 0.7662\n",
      "Epoch 673/700\n",
      "10/10 [==============================] - 1s 56ms/step - loss: 0.3229 - acc: 0.8808 - val_loss: 0.5138 - val_acc: 0.7662\n",
      "Epoch 674/700\n",
      "10/10 [==============================] - 1s 91ms/step - loss: 0.3226 - acc: 0.8808 - val_loss: 0.5140 - val_acc: 0.7662\n",
      "Epoch 675/700\n",
      "10/10 [==============================] - 1s 85ms/step - loss: 0.3223 - acc: 0.8808 - val_loss: 0.5141 - val_acc: 0.7662\n",
      "Epoch 676/700\n",
      "10/10 [==============================] - 1s 64ms/step - loss: 0.3221 - acc: 0.8808 - val_loss: 0.5143 - val_acc: 0.7662\n",
      "Epoch 677/700\n",
      "10/10 [==============================] - 1s 56ms/step - loss: 0.3218 - acc: 0.8808 - val_loss: 0.5145 - val_acc: 0.7662\n",
      "Epoch 678/700\n",
      "10/10 [==============================] - 1s 62ms/step - loss: 0.3215 - acc: 0.8808 - val_loss: 0.5147 - val_acc: 0.7662\n",
      "Epoch 679/700\n",
      "10/10 [==============================] - 1s 58ms/step - loss: 0.3213 - acc: 0.8808 - val_loss: 0.5149 - val_acc: 0.7662\n",
      "Epoch 680/700\n",
      "10/10 [==============================] - 1s 56ms/step - loss: 0.3210 - acc: 0.8808 - val_loss: 0.5151 - val_acc: 0.7662\n",
      "Epoch 681/700\n",
      "10/10 [==============================] - 1s 53ms/step - loss: 0.3207 - acc: 0.8808 - val_loss: 0.5152 - val_acc: 0.7662\n",
      "Epoch 682/700\n",
      "10/10 [==============================] - 1s 55ms/step - loss: 0.3204 - acc: 0.8808 - val_loss: 0.5154 - val_acc: 0.7662\n",
      "Epoch 683/700\n",
      "10/10 [==============================] - 0s 49ms/step - loss: 0.3202 - acc: 0.8808 - val_loss: 0.5156 - val_acc: 0.7662\n",
      "Epoch 684/700\n",
      "10/10 [==============================] - 1s 57ms/step - loss: 0.3199 - acc: 0.8808 - val_loss: 0.5158 - val_acc: 0.7706\n",
      "Epoch 685/700\n",
      "10/10 [==============================] - 1s 59ms/step - loss: 0.3196 - acc: 0.8808 - val_loss: 0.5160 - val_acc: 0.7706\n",
      "Epoch 686/700\n",
      "10/10 [==============================] - 1s 57ms/step - loss: 0.3193 - acc: 0.8808 - val_loss: 0.5162 - val_acc: 0.7706\n",
      "Epoch 687/700\n",
      "10/10 [==============================] - 1s 55ms/step - loss: 0.3191 - acc: 0.8808 - val_loss: 0.5164 - val_acc: 0.7706\n",
      "Epoch 688/700\n",
      "10/10 [==============================] - 1s 56ms/step - loss: 0.3188 - acc: 0.8808 - val_loss: 0.5166 - val_acc: 0.7706\n",
      "Epoch 689/700\n",
      "10/10 [==============================] - 1s 63ms/step - loss: 0.3185 - acc: 0.8808 - val_loss: 0.5168 - val_acc: 0.7706\n",
      "Epoch 690/700\n",
      "10/10 [==============================] - 1s 53ms/step - loss: 0.3182 - acc: 0.8808 - val_loss: 0.5170 - val_acc: 0.7662\n",
      "Epoch 691/700\n",
      "10/10 [==============================] - 1s 59ms/step - loss: 0.3179 - acc: 0.8808 - val_loss: 0.5172 - val_acc: 0.7662\n",
      "Epoch 692/700\n",
      "10/10 [==============================] - 1s 55ms/step - loss: 0.3177 - acc: 0.8808 - val_loss: 0.5174 - val_acc: 0.7662\n",
      "Epoch 693/700\n",
      "10/10 [==============================] - 1s 61ms/step - loss: 0.3174 - acc: 0.8808 - val_loss: 0.5176 - val_acc: 0.7662\n",
      "Epoch 694/700\n",
      "10/10 [==============================] - 1s 51ms/step - loss: 0.3171 - acc: 0.8808 - val_loss: 0.5178 - val_acc: 0.7662\n",
      "Epoch 695/700\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 0.3168 - acc: 0.8808 - val_loss: 0.5180 - val_acc: 0.7662\n",
      "Epoch 696/700\n",
      "10/10 [==============================] - 1s 65ms/step - loss: 0.3165 - acc: 0.8821 - val_loss: 0.5182 - val_acc: 0.7662\n",
      "Epoch 697/700\n",
      "10/10 [==============================] - 1s 54ms/step - loss: 0.3163 - acc: 0.8827 - val_loss: 0.5184 - val_acc: 0.7662\n",
      "Epoch 698/700\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 0.3160 - acc: 0.8827 - val_loss: 0.5186 - val_acc: 0.7662\n",
      "Epoch 699/700\n",
      "10/10 [==============================] - 1s 57ms/step - loss: 0.3157 - acc: 0.8827 - val_loss: 0.5188 - val_acc: 0.7662\n",
      "Epoch 700/700\n",
      "10/10 [==============================] - 1s 65ms/step - loss: 0.3154 - acc: 0.8827 - val_loss: 0.5190 - val_acc: 0.7662\n"
     ]
    }
   ],
   "source": [
    "# Train the model for the given number of epochs\n",
    "    history_700 = model.fit(\n",
    "        X_train_norm, y_train,\n",
    "        steps_per_epoch=10,\n",
    "        epochs=700,\n",
    "        verbose=1,\n",
    "        validation_data=(X_test, y_test),\n",
    "        validation_steps=10\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_class_nn_mod6 = model.predict_classes(X_test_norm)\n",
    "y_pred_prob_nn_mod6 = model.predict(X_test_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluate the losses of the model \n",
    "    epochs = range(1, len(loss)+1)\n",
    "    plt.plot(epochs, loss, color='red', label='Training Loss')\n",
    "    plt.plot(epochs, val_loss, color='blue', label='Validation Loss')\n",
    "    plt.title('Training and Validation Loss')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluate the accuracy of the model\n",
    "    plt.plot(epochs, acc, color='red', label='Training acc')\n",
    "    plt.plot(epochs, val_acc, color='green', label='Validation acc')\n",
    "    plt.title('Training and validation accuracy')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy is 0.766\n",
      "roc-auc is 0.828\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Print model performance and plot the roc curve\n",
    "print('accuracy is {:.3f}'.format(accuracy_score(y_test,y_pred_class_nn_mod6)))\n",
    "print('roc-auc is {:.3f}'.format(roc_auc_score(y_test,y_pred_prob_nn_mod6)))\n",
    "\n",
    "plot_roc(y_test, y_pred_prob_nn_mod6, 'NN')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accuracy obtained from this model is 76.6%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, lets modify the model to accomodate new learning rate and then check the performance. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 502,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_1 = Sequential()\n",
    "model_1 = model_4(model_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 503,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compile the model\n",
    "    model_1.compile(\n",
    "        loss = 'binary_crossentropy',\n",
    "        optimizer = SGD(lr=0.05, decay=1e-6, momentum=0.9),\n",
    "        metrics = ['accuracy']\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 510,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 537 samples, validate on 231 samples\n",
      "Epoch 1/200\n",
      "20/20 [==============================] - 1s 68ms/step - loss: 2.8621e-05 - acc: 1.0000 - val_loss: 2.6161 - val_acc: 0.7576\n",
      "Epoch 2/200\n",
      "20/20 [==============================] - 1s 67ms/step - loss: 2.7863e-05 - acc: 1.0000 - val_loss: 2.6232 - val_acc: 0.7576\n",
      "Epoch 3/200\n",
      "20/20 [==============================] - 1s 68ms/step - loss: 2.7142e-05 - acc: 1.0000 - val_loss: 2.6242 - val_acc: 0.7576\n",
      "Epoch 4/200\n",
      "20/20 [==============================] - 1s 71ms/step - loss: 2.6454e-05 - acc: 1.0000 - val_loss: 2.6281 - val_acc: 0.7576\n",
      "Epoch 5/200\n",
      "20/20 [==============================] - 1s 68ms/step - loss: 2.5800e-05 - acc: 1.0000 - val_loss: 2.6266 - val_acc: 0.7576\n",
      "Epoch 6/200\n",
      "20/20 [==============================] - 1s 71ms/step - loss: 2.5175e-05 - acc: 1.0000 - val_loss: 2.6323 - val_acc: 0.7576\n",
      "Epoch 7/200\n",
      "20/20 [==============================] - 1s 72ms/step - loss: 2.4578e-05 - acc: 1.0000 - val_loss: 2.6334 - val_acc: 0.7576\n",
      "Epoch 8/200\n",
      "20/20 [==============================] - 1s 68ms/step - loss: 2.4007e-05 - acc: 1.0000 - val_loss: 2.6328 - val_acc: 0.7576\n",
      "Epoch 9/200\n",
      "20/20 [==============================] - 1s 68ms/step - loss: 2.3459e-05 - acc: 1.0000 - val_loss: 2.6365 - val_acc: 0.7576\n",
      "Epoch 10/200\n",
      "20/20 [==============================] - 1s 68ms/step - loss: 2.2934e-05 - acc: 1.0000 - val_loss: 2.6377 - val_acc: 0.7576\n",
      "Epoch 11/200\n",
      "20/20 [==============================] - 1s 71ms/step - loss: 2.2431e-05 - acc: 1.0000 - val_loss: 2.6386 - val_acc: 0.7576\n",
      "Epoch 12/200\n",
      "20/20 [==============================] - 1s 70ms/step - loss: 2.1948e-05 - acc: 1.0000 - val_loss: 2.6418 - val_acc: 0.7576\n",
      "Epoch 13/200\n",
      "20/20 [==============================] - 1s 71ms/step - loss: 2.1483e-05 - acc: 1.0000 - val_loss: 2.6438 - val_acc: 0.7576\n",
      "Epoch 14/200\n",
      "20/20 [==============================] - 1s 70ms/step - loss: 2.1035e-05 - acc: 1.0000 - val_loss: 2.6464 - val_acc: 0.7576\n",
      "Epoch 15/200\n",
      "20/20 [==============================] - 1s 69ms/step - loss: 2.0606e-05 - acc: 1.0000 - val_loss: 2.6472 - val_acc: 0.7576\n",
      "Epoch 16/200\n",
      "20/20 [==============================] - 1s 73ms/step - loss: 2.0192e-05 - acc: 1.0000 - val_loss: 2.6474 - val_acc: 0.7576\n",
      "Epoch 17/200\n",
      "20/20 [==============================] - 1s 71ms/step - loss: 1.9793e-05 - acc: 1.0000 - val_loss: 2.6508 - val_acc: 0.7576\n",
      "Epoch 18/200\n",
      "20/20 [==============================] - 2s 76ms/step - loss: 1.9408e-05 - acc: 1.0000 - val_loss: 2.6480 - val_acc: 0.7619\n",
      "Epoch 19/200\n",
      "20/20 [==============================] - 1s 68ms/step - loss: 1.9037e-05 - acc: 1.0000 - val_loss: 2.6522 - val_acc: 0.7619\n",
      "Epoch 20/200\n",
      "20/20 [==============================] - 1s 75ms/step - loss: 1.8679e-05 - acc: 1.0000 - val_loss: 2.6515 - val_acc: 0.7619\n",
      "Epoch 21/200\n",
      "20/20 [==============================] - 1s 70ms/step - loss: 1.8333e-05 - acc: 1.0000 - val_loss: 2.6518 - val_acc: 0.7619\n",
      "Epoch 22/200\n",
      "20/20 [==============================] - 2s 79ms/step - loss: 1.7999e-05 - acc: 1.0000 - val_loss: 2.6543 - val_acc: 0.7619\n",
      "Epoch 23/200\n",
      "20/20 [==============================] - 2s 84ms/step - loss: 1.7677e-05 - acc: 1.0000 - val_loss: 2.6582 - val_acc: 0.7619\n",
      "Epoch 24/200\n",
      "20/20 [==============================] - 1s 73ms/step - loss: 1.7364e-05 - acc: 1.0000 - val_loss: 2.6583 - val_acc: 0.7619\n",
      "Epoch 25/200\n",
      "20/20 [==============================] - 1s 73ms/step - loss: 1.7062e-05 - acc: 1.0000 - val_loss: 2.6565 - val_acc: 0.7619\n",
      "Epoch 26/200\n",
      "20/20 [==============================] - 2s 75ms/step - loss: 1.6770e-05 - acc: 1.0000 - val_loss: 2.6619 - val_acc: 0.7619\n",
      "Epoch 27/200\n",
      "20/20 [==============================] - 1s 75ms/step - loss: 1.6486e-05 - acc: 1.0000 - val_loss: 2.6620 - val_acc: 0.7619\n",
      "Epoch 28/200\n",
      "20/20 [==============================] - 1s 68ms/step - loss: 1.6212e-05 - acc: 1.0000 - val_loss: 2.6635 - val_acc: 0.7619\n",
      "Epoch 29/200\n",
      "20/20 [==============================] - 1s 72ms/step - loss: 1.5945e-05 - acc: 1.0000 - val_loss: 2.6659 - val_acc: 0.7619\n",
      "Epoch 30/200\n",
      "20/20 [==============================] - 1s 69ms/step - loss: 1.5685e-05 - acc: 1.0000 - val_loss: 2.6647 - val_acc: 0.7619\n",
      "Epoch 31/200\n",
      "20/20 [==============================] - 1s 70ms/step - loss: 1.5434e-05 - acc: 1.0000 - val_loss: 2.6646 - val_acc: 0.7619\n",
      "Epoch 32/200\n",
      "20/20 [==============================] - 1s 69ms/step - loss: 1.5190e-05 - acc: 1.0000 - val_loss: 2.6674 - val_acc: 0.7619\n",
      "Epoch 33/200\n",
      "20/20 [==============================] - 1s 72ms/step - loss: 1.4953e-05 - acc: 1.0000 - val_loss: 2.6676 - val_acc: 0.7619\n",
      "Epoch 34/200\n",
      "20/20 [==============================] - 1s 69ms/step - loss: 1.4722e-05 - acc: 1.0000 - val_loss: 2.6705 - val_acc: 0.7619\n",
      "Epoch 35/200\n",
      "20/20 [==============================] - 1s 72ms/step - loss: 1.4499e-05 - acc: 1.0000 - val_loss: 2.6698 - val_acc: 0.7619\n",
      "Epoch 36/200\n",
      "20/20 [==============================] - 1s 72ms/step - loss: 1.4281e-05 - acc: 1.0000 - val_loss: 2.6700 - val_acc: 0.7619\n",
      "Epoch 37/200\n",
      "20/20 [==============================] - 1s 71ms/step - loss: 1.4070e-05 - acc: 1.0000 - val_loss: 2.6763 - val_acc: 0.7619\n",
      "Epoch 38/200\n",
      "20/20 [==============================] - 1s 71ms/step - loss: 1.3864e-05 - acc: 1.0000 - val_loss: 2.6803 - val_acc: 0.7619\n",
      "Epoch 39/200\n",
      "20/20 [==============================] - 1s 70ms/step - loss: 1.3664e-05 - acc: 1.0000 - val_loss: 2.6763 - val_acc: 0.7619\n",
      "Epoch 40/200\n",
      "20/20 [==============================] - 1s 73ms/step - loss: 1.3469e-05 - acc: 1.0000 - val_loss: 2.6788 - val_acc: 0.7619\n",
      "Epoch 41/200\n",
      "20/20 [==============================] - 1s 73ms/step - loss: 1.3280e-05 - acc: 1.0000 - val_loss: 2.6825 - val_acc: 0.7619\n",
      "Epoch 42/200\n",
      "20/20 [==============================] - 1s 73ms/step - loss: 1.3095e-05 - acc: 1.0000 - val_loss: 2.6809 - val_acc: 0.7619\n",
      "Epoch 43/200\n",
      "20/20 [==============================] - 1s 68ms/step - loss: 1.2915e-05 - acc: 1.0000 - val_loss: 2.6849 - val_acc: 0.7619\n",
      "Epoch 44/200\n",
      "20/20 [==============================] - 2s 99ms/step - loss: 1.2740e-05 - acc: 1.0000 - val_loss: 2.6846 - val_acc: 0.7619\n",
      "Epoch 45/200\n",
      "20/20 [==============================] - 1s 73ms/step - loss: 1.2568e-05 - acc: 1.0000 - val_loss: 2.6847 - val_acc: 0.7619\n",
      "Epoch 46/200\n",
      "20/20 [==============================] - 1s 70ms/step - loss: 1.2402e-05 - acc: 1.0000 - val_loss: 2.6875 - val_acc: 0.7619\n",
      "Epoch 47/200\n",
      "20/20 [==============================] - 1s 69ms/step - loss: 1.2239e-05 - acc: 1.0000 - val_loss: 2.6886 - val_acc: 0.7619\n",
      "Epoch 48/200\n",
      "20/20 [==============================] - 2s 75ms/step - loss: 1.2081e-05 - acc: 1.0000 - val_loss: 2.6897 - val_acc: 0.7619\n",
      "Epoch 49/200\n",
      "20/20 [==============================] - 2s 75ms/step - loss: 1.1926e-05 - acc: 1.0000 - val_loss: 2.6905 - val_acc: 0.7619\n",
      "Epoch 50/200\n",
      "20/20 [==============================] - 1s 74ms/step - loss: 1.1774e-05 - acc: 1.0000 - val_loss: 2.6913 - val_acc: 0.7619\n",
      "Epoch 51/200\n",
      "20/20 [==============================] - 1s 72ms/step - loss: 1.1627e-05 - acc: 1.0000 - val_loss: 2.6906 - val_acc: 0.7619\n",
      "Epoch 52/200\n",
      "20/20 [==============================] - 1s 68ms/step - loss: 1.1482e-05 - acc: 1.0000 - val_loss: 2.6908 - val_acc: 0.7619\n",
      "Epoch 53/200\n",
      "20/20 [==============================] - 1s 74ms/step - loss: 1.1341e-05 - acc: 1.0000 - val_loss: 2.6933 - val_acc: 0.7619\n",
      "Epoch 54/200\n",
      "20/20 [==============================] - 1s 71ms/step - loss: 1.1203e-05 - acc: 1.0000 - val_loss: 2.6936 - val_acc: 0.7619\n",
      "Epoch 55/200\n",
      "20/20 [==============================] - 1s 70ms/step - loss: 1.1069e-05 - acc: 1.0000 - val_loss: 2.6956 - val_acc: 0.7619\n",
      "Epoch 56/200\n",
      "20/20 [==============================] - 1s 68ms/step - loss: 1.0936e-05 - acc: 1.0000 - val_loss: 2.6939 - val_acc: 0.7619\n",
      "Epoch 57/200\n",
      "20/20 [==============================] - 1s 71ms/step - loss: 1.0807e-05 - acc: 1.0000 - val_loss: 2.6955 - val_acc: 0.7619\n",
      "Epoch 58/200\n",
      "20/20 [==============================] - 1s 69ms/step - loss: 1.0681e-05 - acc: 1.0000 - val_loss: 2.6961 - val_acc: 0.7619\n",
      "Epoch 59/200\n",
      "20/20 [==============================] - 1s 72ms/step - loss: 1.0558e-05 - acc: 1.0000 - val_loss: 2.6983 - val_acc: 0.7619\n",
      "Epoch 60/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20/20 [==============================] - 1s 73ms/step - loss: 1.0437e-05 - acc: 1.0000 - val_loss: 2.7042 - val_acc: 0.7619\n",
      "Epoch 61/200\n",
      "20/20 [==============================] - 2s 77ms/step - loss: 1.0319e-05 - acc: 1.0000 - val_loss: 2.7035 - val_acc: 0.7619\n",
      "Epoch 62/200\n",
      "20/20 [==============================] - 1s 67ms/step - loss: 1.0202e-05 - acc: 1.0000 - val_loss: 2.7006 - val_acc: 0.7619\n",
      "Epoch 63/200\n",
      "20/20 [==============================] - 1s 70ms/step - loss: 1.0089e-05 - acc: 1.0000 - val_loss: 2.6998 - val_acc: 0.7619\n",
      "Epoch 64/200\n",
      "20/20 [==============================] - 1s 68ms/step - loss: 9.9777e-06 - acc: 1.0000 - val_loss: 2.6957 - val_acc: 0.7619\n",
      "Epoch 65/200\n",
      "20/20 [==============================] - 1s 67ms/step - loss: 9.8687e-06 - acc: 1.0000 - val_loss: 2.7049 - val_acc: 0.7619\n",
      "Epoch 66/200\n",
      "20/20 [==============================] - 1s 68ms/step - loss: 9.7624e-06 - acc: 1.0000 - val_loss: 2.7015 - val_acc: 0.7619\n",
      "Epoch 67/200\n",
      "20/20 [==============================] - 1s 68ms/step - loss: 9.6578e-06 - acc: 1.0000 - val_loss: 2.7067 - val_acc: 0.7619\n",
      "Epoch 68/200\n",
      "20/20 [==============================] - 1s 74ms/step - loss: 9.5550e-06 - acc: 1.0000 - val_loss: 2.7071 - val_acc: 0.7619\n",
      "Epoch 69/200\n",
      "20/20 [==============================] - 2s 88ms/step - loss: 9.4545e-06 - acc: 1.0000 - val_loss: 2.7058 - val_acc: 0.7619\n",
      "Epoch 70/200\n",
      "20/20 [==============================] - 1s 66ms/step - loss: 9.3556e-06 - acc: 1.0000 - val_loss: 2.7048 - val_acc: 0.7619\n",
      "Epoch 71/200\n",
      "20/20 [==============================] - 1s 67ms/step - loss: 9.2588e-06 - acc: 1.0000 - val_loss: 2.7066 - val_acc: 0.7619\n",
      "Epoch 72/200\n",
      "20/20 [==============================] - 1s 67ms/step - loss: 9.1638e-06 - acc: 1.0000 - val_loss: 2.7092 - val_acc: 0.7619\n",
      "Epoch 73/200\n",
      "20/20 [==============================] - 1s 70ms/step - loss: 9.0708e-06 - acc: 1.0000 - val_loss: 2.7090 - val_acc: 0.7619\n",
      "Epoch 74/200\n",
      "20/20 [==============================] - 1s 68ms/step - loss: 8.9793e-06 - acc: 1.0000 - val_loss: 2.7086 - val_acc: 0.7619\n",
      "Epoch 75/200\n",
      "20/20 [==============================] - 1s 70ms/step - loss: 8.8893e-06 - acc: 1.0000 - val_loss: 2.7142 - val_acc: 0.7619\n",
      "Epoch 76/200\n",
      "20/20 [==============================] - 1s 70ms/step - loss: 8.8018e-06 - acc: 1.0000 - val_loss: 2.7091 - val_acc: 0.7619\n",
      "Epoch 77/200\n",
      "20/20 [==============================] - 1s 68ms/step - loss: 8.7149e-06 - acc: 1.0000 - val_loss: 2.7134 - val_acc: 0.7619\n",
      "Epoch 78/200\n",
      "20/20 [==============================] - 1s 70ms/step - loss: 8.6303e-06 - acc: 1.0000 - val_loss: 2.7140 - val_acc: 0.7619\n",
      "Epoch 79/200\n",
      "20/20 [==============================] - ETA: 0s - loss: 8.5492e-06 - acc: 1.000 - 1s 66ms/step - loss: 8.5471e-06 - acc: 1.0000 - val_loss: 2.7107 - val_acc: 0.7619\n",
      "Epoch 80/200\n",
      "20/20 [==============================] - 1s 68ms/step - loss: 8.4655e-06 - acc: 1.0000 - val_loss: 2.7200 - val_acc: 0.7619\n",
      "Epoch 81/200\n",
      "20/20 [==============================] - 1s 71ms/step - loss: 8.3852e-06 - acc: 1.0000 - val_loss: 2.7121 - val_acc: 0.7619\n",
      "Epoch 82/200\n",
      "20/20 [==============================] - 1s 70ms/step - loss: 8.3064e-06 - acc: 1.0000 - val_loss: 2.7152 - val_acc: 0.7619\n",
      "Epoch 83/200\n",
      "20/20 [==============================] - 1s 69ms/step - loss: 8.2279e-06 - acc: 1.0000 - val_loss: 2.7196 - val_acc: 0.7619\n",
      "Epoch 84/200\n",
      "20/20 [==============================] - 1s 72ms/step - loss: 8.1520e-06 - acc: 1.0000 - val_loss: 2.7167 - val_acc: 0.7619\n",
      "Epoch 85/200\n",
      "20/20 [==============================] - 1s 70ms/step - loss: 8.0766e-06 - acc: 1.0000 - val_loss: 2.7204 - val_acc: 0.7619\n",
      "Epoch 86/200\n",
      "20/20 [==============================] - ETA: 0s - loss: 8.0054e-06 - acc: 1.000 - 1s 66ms/step - loss: 8.0036e-06 - acc: 1.0000 - val_loss: 2.7203 - val_acc: 0.7619\n",
      "Epoch 87/200\n",
      "20/20 [==============================] - 1s 69ms/step - loss: 7.9305e-06 - acc: 1.0000 - val_loss: 2.7178 - val_acc: 0.7619\n",
      "Epoch 88/200\n",
      "20/20 [==============================] - 1s 69ms/step - loss: 7.8591e-06 - acc: 1.0000 - val_loss: 2.7176 - val_acc: 0.7619\n",
      "Epoch 89/200\n",
      "20/20 [==============================] - 1s 68ms/step - loss: 7.7891e-06 - acc: 1.0000 - val_loss: 2.7198 - val_acc: 0.7619\n",
      "Epoch 90/200\n",
      "20/20 [==============================] - 1s 73ms/step - loss: 7.7203e-06 - acc: 1.0000 - val_loss: 2.7260 - val_acc: 0.7619\n",
      "Epoch 91/200\n",
      "20/20 [==============================] - 1s 71ms/step - loss: 7.6526e-06 - acc: 1.0000 - val_loss: 2.7193 - val_acc: 0.7619\n",
      "Epoch 92/200\n",
      "20/20 [==============================] - 1s 67ms/step - loss: 7.5859e-06 - acc: 1.0000 - val_loss: 2.7256 - val_acc: 0.7619\n",
      "Epoch 93/200\n",
      "20/20 [==============================] - 1s 68ms/step - loss: 7.5207e-06 - acc: 1.0000 - val_loss: 2.7186 - val_acc: 0.7619\n",
      "Epoch 94/200\n",
      "20/20 [==============================] - 1s 70ms/step - loss: 7.4554e-06 - acc: 1.0000 - val_loss: 2.7241 - val_acc: 0.7619\n",
      "Epoch 95/200\n",
      "20/20 [==============================] - 1s 69ms/step - loss: 7.3926e-06 - acc: 1.0000 - val_loss: 2.7222 - val_acc: 0.7619\n",
      "Epoch 96/200\n",
      "20/20 [==============================] - 1s 71ms/step - loss: 7.3295e-06 - acc: 1.0000 - val_loss: 2.7252 - val_acc: 0.7619\n",
      "Epoch 97/200\n",
      "20/20 [==============================] - 1s 70ms/step - loss: 7.2683e-06 - acc: 1.0000 - val_loss: 2.7263 - val_acc: 0.7619\n",
      "Epoch 98/200\n",
      "20/20 [==============================] - 1s 70ms/step - loss: 7.2076e-06 - acc: 1.0000 - val_loss: 2.7243 - val_acc: 0.7619\n",
      "Epoch 99/200\n",
      "20/20 [==============================] - 1s 67ms/step - loss: 7.1484e-06 - acc: 1.0000 - val_loss: 2.7308 - val_acc: 0.7619\n",
      "Epoch 100/200\n",
      "20/20 [==============================] - 1s 72ms/step - loss: 7.0892e-06 - acc: 1.0000 - val_loss: 2.7308 - val_acc: 0.7619\n",
      "Epoch 101/200\n",
      "20/20 [==============================] - 1s 71ms/step - loss: 7.0316e-06 - acc: 1.0000 - val_loss: 2.7281 - val_acc: 0.7619\n",
      "Epoch 102/200\n",
      "20/20 [==============================] - 1s 68ms/step - loss: 6.9748e-06 - acc: 1.0000 - val_loss: 2.7297 - val_acc: 0.7619\n",
      "Epoch 103/200\n",
      "20/20 [==============================] - 1s 70ms/step - loss: 6.9184e-06 - acc: 1.0000 - val_loss: 2.7289 - val_acc: 0.7619\n",
      "Epoch 104/200\n",
      "20/20 [==============================] - 1s 69ms/step - loss: 6.8634e-06 - acc: 1.0000 - val_loss: 2.7320 - val_acc: 0.7619\n",
      "Epoch 105/200\n",
      "20/20 [==============================] - 2s 77ms/step - loss: 6.8088e-06 - acc: 1.0000 - val_loss: 2.7301 - val_acc: 0.7619\n",
      "Epoch 106/200\n",
      "20/20 [==============================] - 2s 81ms/step - loss: 6.7547e-06 - acc: 1.0000 - val_loss: 2.7325 - val_acc: 0.7619\n",
      "Epoch 107/200\n",
      "20/20 [==============================] - 2s 82ms/step - loss: 6.7022e-06 - acc: 1.0000 - val_loss: 2.7320 - val_acc: 0.7619\n",
      "Epoch 108/200\n",
      "20/20 [==============================] - 1s 69ms/step - loss: 6.6506e-06 - acc: 1.0000 - val_loss: 2.7337 - val_acc: 0.7619\n",
      "Epoch 109/200\n",
      "20/20 [==============================] - 1s 67ms/step - loss: 6.5992e-06 - acc: 1.0000 - val_loss: 2.7329 - val_acc: 0.7619\n",
      "Epoch 110/200\n",
      "20/20 [==============================] - 1s 68ms/step - loss: 6.5480e-06 - acc: 1.0000 - val_loss: 2.7329 - val_acc: 0.7619\n",
      "Epoch 111/200\n",
      "20/20 [==============================] - 2s 75ms/step - loss: 6.4981e-06 - acc: 1.0000 - val_loss: 2.7320 - val_acc: 0.7619\n",
      "Epoch 112/200\n",
      "20/20 [==============================] - 1s 71ms/step - loss: 6.4500e-06 - acc: 1.0000 - val_loss: 2.7366 - val_acc: 0.7619\n",
      "Epoch 113/200\n",
      "20/20 [==============================] - 1s 67ms/step - loss: 6.4016e-06 - acc: 1.0000 - val_loss: 2.7358 - val_acc: 0.7619\n",
      "Epoch 114/200\n",
      "20/20 [==============================] - 1s 72ms/step - loss: 6.3538e-06 - acc: 1.0000 - val_loss: 2.7355 - val_acc: 0.7619\n",
      "Epoch 115/200\n",
      "20/20 [==============================] - 1s 72ms/step - loss: 6.3066e-06 - acc: 1.0000 - val_loss: 2.7336 - val_acc: 0.7619\n",
      "Epoch 116/200\n",
      "20/20 [==============================] - 1s 71ms/step - loss: 6.2595e-06 - acc: 1.0000 - val_loss: 2.7351 - val_acc: 0.7619\n",
      "Epoch 117/200\n",
      "20/20 [==============================] - 1s 71ms/step - loss: 6.2138e-06 - acc: 1.0000 - val_loss: 2.7383 - val_acc: 0.7619\n",
      "Epoch 118/200\n",
      "20/20 [==============================] - 1s 71ms/step - loss: 6.1685e-06 - acc: 1.0000 - val_loss: 2.7372 - val_acc: 0.7619\n",
      "Epoch 119/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20/20 [==============================] - 1s 68ms/step - loss: 6.1243e-06 - acc: 1.0000 - val_loss: 2.7371 - val_acc: 0.7619\n",
      "Epoch 120/200\n",
      "20/20 [==============================] - 1s 68ms/step - loss: 6.0800e-06 - acc: 1.0000 - val_loss: 2.7394 - val_acc: 0.7619\n",
      "Epoch 121/200\n",
      "20/20 [==============================] - 1s 65ms/step - loss: 6.0369e-06 - acc: 1.0000 - val_loss: 2.7329 - val_acc: 0.7619\n",
      "Epoch 122/200\n",
      "20/20 [==============================] - 1s 68ms/step - loss: 5.9940e-06 - acc: 1.0000 - val_loss: 2.7403 - val_acc: 0.7619\n",
      "Epoch 123/200\n",
      "20/20 [==============================] - 1s 67ms/step - loss: 5.9518e-06 - acc: 1.0000 - val_loss: 2.7383 - val_acc: 0.7619\n",
      "Epoch 124/200\n",
      "20/20 [==============================] - 1s 69ms/step - loss: 5.9101e-06 - acc: 1.0000 - val_loss: 2.7398 - val_acc: 0.7619\n",
      "Epoch 125/200\n",
      "20/20 [==============================] - 1s 70ms/step - loss: 5.8691e-06 - acc: 1.0000 - val_loss: 2.7374 - val_acc: 0.7619\n",
      "Epoch 126/200\n",
      "20/20 [==============================] - 1s 68ms/step - loss: 5.8281e-06 - acc: 1.0000 - val_loss: 2.7370 - val_acc: 0.7619\n",
      "Epoch 127/200\n",
      "20/20 [==============================] - 1s 69ms/step - loss: 5.7883e-06 - acc: 1.0000 - val_loss: 2.7405 - val_acc: 0.7619\n",
      "Epoch 128/200\n",
      "20/20 [==============================] - 1s 71ms/step - loss: 5.7486e-06 - acc: 1.0000 - val_loss: 2.7435 - val_acc: 0.7619\n",
      "Epoch 129/200\n",
      "20/20 [==============================] - 1s 71ms/step - loss: 5.7095e-06 - acc: 1.0000 - val_loss: 2.7450 - val_acc: 0.7619\n",
      "Epoch 130/200\n",
      "20/20 [==============================] - 1s 71ms/step - loss: 5.6710e-06 - acc: 1.0000 - val_loss: 2.7429 - val_acc: 0.7619\n",
      "Epoch 131/200\n",
      "20/20 [==============================] - 1s 68ms/step - loss: 5.6327e-06 - acc: 1.0000 - val_loss: 2.7381 - val_acc: 0.7619\n",
      "Epoch 132/200\n",
      "20/20 [==============================] - 1s 70ms/step - loss: 5.5954e-06 - acc: 1.0000 - val_loss: 2.7430 - val_acc: 0.7619\n",
      "Epoch 133/200\n",
      "20/20 [==============================] - 1s 72ms/step - loss: 5.5587e-06 - acc: 1.0000 - val_loss: 2.7439 - val_acc: 0.7619\n",
      "Epoch 134/200\n",
      "20/20 [==============================] - 1s 73ms/step - loss: 5.5218e-06 - acc: 1.0000 - val_loss: 2.7462 - val_acc: 0.7619\n",
      "Epoch 135/200\n",
      "20/20 [==============================] - 1s 68ms/step - loss: 5.4849e-06 - acc: 1.0000 - val_loss: 2.7472 - val_acc: 0.7619\n",
      "Epoch 136/200\n",
      "20/20 [==============================] - 1s 70ms/step - loss: 5.4498e-06 - acc: 1.0000 - val_loss: 2.7465 - val_acc: 0.7619\n",
      "Epoch 137/200\n",
      "20/20 [==============================] - ETA: 0s - loss: 5.4151e-06 - acc: 1.000 - 1s 70ms/step - loss: 5.4142e-06 - acc: 1.0000 - val_loss: 2.7476 - val_acc: 0.7619\n",
      "Epoch 138/200\n",
      "20/20 [==============================] - 1s 69ms/step - loss: 5.3794e-06 - acc: 1.0000 - val_loss: 2.7455 - val_acc: 0.7619\n",
      "Epoch 139/200\n",
      "20/20 [==============================] - 1s 71ms/step - loss: 5.3452e-06 - acc: 1.0000 - val_loss: 2.7489 - val_acc: 0.7619\n",
      "Epoch 140/200\n",
      "20/20 [==============================] - 1s 68ms/step - loss: 5.3108e-06 - acc: 1.0000 - val_loss: 2.7461 - val_acc: 0.7619\n",
      "Epoch 141/200\n",
      "20/20 [==============================] - 1s 69ms/step - loss: 5.2772e-06 - acc: 1.0000 - val_loss: 2.7476 - val_acc: 0.7619\n",
      "Epoch 142/200\n",
      "20/20 [==============================] - 1s 70ms/step - loss: 5.2439e-06 - acc: 1.0000 - val_loss: 2.7481 - val_acc: 0.7619\n",
      "Epoch 143/200\n",
      "20/20 [==============================] - 1s 71ms/step - loss: 5.2109e-06 - acc: 1.0000 - val_loss: 2.7480 - val_acc: 0.7619\n",
      "Epoch 144/200\n",
      "20/20 [==============================] - 1s 72ms/step - loss: 5.1791e-06 - acc: 1.0000 - val_loss: 2.7508 - val_acc: 0.7619\n",
      "Epoch 145/200\n",
      "20/20 [==============================] - 1s 66ms/step - loss: 5.1462e-06 - acc: 1.0000 - val_loss: 2.7479 - val_acc: 0.7619\n",
      "Epoch 146/200\n",
      "20/20 [==============================] - ETA: 0s - loss: 5.1157e-06 - acc: 1.000 - 1s 70ms/step - loss: 5.1149e-06 - acc: 1.0000 - val_loss: 2.7507 - val_acc: 0.7619\n",
      "Epoch 147/200\n",
      "20/20 [==============================] - 1s 71ms/step - loss: 5.0832e-06 - acc: 1.0000 - val_loss: 2.7526 - val_acc: 0.7619\n",
      "Epoch 148/200\n",
      "20/20 [==============================] - 1s 68ms/step - loss: 5.0522e-06 - acc: 1.0000 - val_loss: 2.7524 - val_acc: 0.7619\n",
      "Epoch 149/200\n",
      "20/20 [==============================] - 1s 72ms/step - loss: 5.0219e-06 - acc: 1.0000 - val_loss: 2.7544 - val_acc: 0.7619\n",
      "Epoch 150/200\n",
      "20/20 [==============================] - 1s 67ms/step - loss: 4.9914e-06 - acc: 1.0000 - val_loss: 2.7537 - val_acc: 0.7619\n",
      "Epoch 151/200\n",
      "20/20 [==============================] - 1s 72ms/step - loss: 4.9615e-06 - acc: 1.0000 - val_loss: 2.7524 - val_acc: 0.7619\n",
      "Epoch 152/200\n",
      "20/20 [==============================] - 1s 71ms/step - loss: 4.9318e-06 - acc: 1.0000 - val_loss: 2.7555 - val_acc: 0.7619\n",
      "Epoch 153/200\n",
      "20/20 [==============================] - 1s 69ms/step - loss: 4.9028e-06 - acc: 1.0000 - val_loss: 2.7509 - val_acc: 0.7619\n",
      "Epoch 154/200\n",
      "20/20 [==============================] - 2s 80ms/step - loss: 4.8731e-06 - acc: 1.0000 - val_loss: 2.7559 - val_acc: 0.7619\n",
      "Epoch 155/200\n",
      "20/20 [==============================] - 2s 82ms/step - loss: 4.8449e-06 - acc: 1.0000 - val_loss: 2.7564 - val_acc: 0.7619\n",
      "Epoch 156/200\n",
      "20/20 [==============================] - 2s 80ms/step - loss: 4.8169e-06 - acc: 1.0000 - val_loss: 2.7565 - val_acc: 0.7619\n",
      "Epoch 157/200\n",
      "20/20 [==============================] - 2s 114ms/step - loss: 4.7889e-06 - acc: 1.0000 - val_loss: 2.7537 - val_acc: 0.7619\n",
      "Epoch 158/200\n",
      "20/20 [==============================] - 2s 80ms/step - loss: 4.7605e-06 - acc: 1.0000 - val_loss: 2.7562 - val_acc: 0.7619\n",
      "Epoch 159/200\n",
      "20/20 [==============================] - 1s 72ms/step - loss: 4.7338e-06 - acc: 1.0000 - val_loss: 2.7576 - val_acc: 0.7619\n",
      "Epoch 160/200\n",
      "20/20 [==============================] - 1s 73ms/step - loss: 4.7064e-06 - acc: 1.0000 - val_loss: 2.7582 - val_acc: 0.7619\n",
      "Epoch 161/200\n",
      "20/20 [==============================] - 1s 67ms/step - loss: 4.6798e-06 - acc: 1.0000 - val_loss: 2.7608 - val_acc: 0.7619\n",
      "Epoch 162/200\n",
      "20/20 [==============================] - 1s 74ms/step - loss: 4.6533e-06 - acc: 1.0000 - val_loss: 2.7586 - val_acc: 0.7619\n",
      "Epoch 163/200\n",
      "20/20 [==============================] - 2s 101ms/step - loss: 4.6266e-06 - acc: 1.0000 - val_loss: 2.7581 - val_acc: 0.7619\n",
      "Epoch 164/200\n",
      "20/20 [==============================] - 1s 74ms/step - loss: 4.6004e-06 - acc: 1.0000 - val_loss: 2.7602 - val_acc: 0.7619\n",
      "Epoch 165/200\n",
      "20/20 [==============================] - 2s 77ms/step - loss: 4.5747e-06 - acc: 1.0000 - val_loss: 2.7590 - val_acc: 0.7619\n",
      "Epoch 166/200\n",
      "20/20 [==============================] - 2s 76ms/step - loss: 4.5500e-06 - acc: 1.0000 - val_loss: 2.7621 - val_acc: 0.7619\n",
      "Epoch 167/200\n",
      "20/20 [==============================] - 2s 90ms/step - loss: 4.5243e-06 - acc: 1.0000 - val_loss: 2.7627 - val_acc: 0.7619\n",
      "Epoch 168/200\n",
      "20/20 [==============================] - 1s 72ms/step - loss: 4.4996e-06 - acc: 1.0000 - val_loss: 2.7613 - val_acc: 0.7619\n",
      "Epoch 169/200\n",
      "20/20 [==============================] - 1s 74ms/step - loss: 4.4751e-06 - acc: 1.0000 - val_loss: 2.7588 - val_acc: 0.7619\n",
      "Epoch 170/200\n",
      "20/20 [==============================] - 1s 74ms/step - loss: 4.4510e-06 - acc: 1.0000 - val_loss: 2.7618 - val_acc: 0.7619\n",
      "Epoch 171/200\n",
      "20/20 [==============================] - 2s 95ms/step - loss: 4.4264e-06 - acc: 1.0000 - val_loss: 2.7640 - val_acc: 0.7619\n",
      "Epoch 172/200\n",
      "20/20 [==============================] - 2s 77ms/step - loss: 4.4018e-06 - acc: 1.0000 - val_loss: 2.7592 - val_acc: 0.7619\n",
      "Epoch 173/200\n",
      "20/20 [==============================] - 2s 80ms/step - loss: 4.3788e-06 - acc: 1.0000 - val_loss: 2.7646 - val_acc: 0.7619\n",
      "Epoch 174/200\n",
      "20/20 [==============================] - 2s 111ms/step - loss: 4.3555e-06 - acc: 1.0000 - val_loss: 2.7637 - val_acc: 0.7619\n",
      "Epoch 175/200\n",
      "20/20 [==============================] - 2s 92ms/step - loss: 4.3321e-06 - acc: 1.0000 - val_loss: 2.7655 - val_acc: 0.7619\n",
      "Epoch 176/200\n",
      "20/20 [==============================] - 1s 71ms/step - loss: 4.3089e-06 - acc: 1.0000 - val_loss: 2.7633 - val_acc: 0.7619\n",
      "Epoch 177/200\n",
      "20/20 [==============================] - 1s 75ms/step - loss: 4.2863e-06 - acc: 1.0000 - val_loss: 2.7636 - val_acc: 0.7619\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 178/200\n",
      "20/20 [==============================] - 1s 66ms/step - loss: 4.2636e-06 - acc: 1.0000 - val_loss: 2.7642 - val_acc: 0.7619\n",
      "Epoch 179/200\n",
      "20/20 [==============================] - 1s 74ms/step - loss: 4.2415e-06 - acc: 1.0000 - val_loss: 2.7657 - val_acc: 0.7619\n",
      "Epoch 180/200\n",
      "20/20 [==============================] - 2s 75ms/step - loss: 4.2195e-06 - acc: 1.0000 - val_loss: 2.7610 - val_acc: 0.7619\n",
      "Epoch 181/200\n",
      "20/20 [==============================] - 2s 81ms/step - loss: 4.1981e-06 - acc: 1.0000 - val_loss: 2.7656 - val_acc: 0.7619\n",
      "Epoch 182/200\n",
      "20/20 [==============================] - 1s 62ms/step - loss: 4.1763e-06 - acc: 1.0000 - val_loss: 2.7641 - val_acc: 0.7619\n",
      "Epoch 183/200\n",
      "20/20 [==============================] - 1s 48ms/step - loss: 4.1549e-06 - acc: 1.0000 - val_loss: 2.7666 - val_acc: 0.7619\n",
      "Epoch 184/200\n",
      "20/20 [==============================] - 1s 62ms/step - loss: 4.1335e-06 - acc: 1.0000 - val_loss: 2.7643 - val_acc: 0.7619\n",
      "Epoch 185/200\n",
      "20/20 [==============================] - 1s 52ms/step - loss: 4.1122e-06 - acc: 1.0000 - val_loss: 2.7678 - val_acc: 0.7619\n",
      "Epoch 186/200\n",
      "20/20 [==============================] - 1s 52ms/step - loss: 4.0913e-06 - acc: 1.0000 - val_loss: 2.7678 - val_acc: 0.7619\n",
      "Epoch 187/200\n",
      "20/20 [==============================] - 1s 56ms/step - loss: 4.0706e-06 - acc: 1.0000 - val_loss: 2.7699 - val_acc: 0.7619\n",
      "Epoch 188/200\n",
      "20/20 [==============================] - 2s 80ms/step - loss: 4.0501e-06 - acc: 1.0000 - val_loss: 2.7688 - val_acc: 0.7619\n",
      "Epoch 189/200\n",
      "20/20 [==============================] - 2s 91ms/step - loss: 4.0301e-06 - acc: 1.0000 - val_loss: 2.7691 - val_acc: 0.7619\n",
      "Epoch 190/200\n",
      "20/20 [==============================] - 2s 78ms/step - loss: 4.0100e-06 - acc: 1.0000 - val_loss: 2.7704 - val_acc: 0.7619\n",
      "Epoch 191/200\n",
      "20/20 [==============================] - 2s 79ms/step - loss: 3.9902e-06 - acc: 1.0000 - val_loss: 2.7688 - val_acc: 0.7619\n",
      "Epoch 192/200\n",
      "20/20 [==============================] - 2s 80ms/step - loss: 3.9711e-06 - acc: 1.0000 - val_loss: 2.7701 - val_acc: 0.7619\n",
      "Epoch 193/200\n",
      "20/20 [==============================] - 2s 105ms/step - loss: 3.9511e-06 - acc: 1.0000 - val_loss: 2.7705 - val_acc: 0.7619\n",
      "Epoch 194/200\n",
      "20/20 [==============================] - 2s 81ms/step - loss: 3.9318e-06 - acc: 1.0000 - val_loss: 2.7711 - val_acc: 0.7619\n",
      "Epoch 195/200\n",
      "20/20 [==============================] - 1s 67ms/step - loss: 3.9127e-06 - acc: 1.0000 - val_loss: 2.7705 - val_acc: 0.7619\n",
      "Epoch 196/200\n",
      "20/20 [==============================] - 2s 86ms/step - loss: 3.8940e-06 - acc: 1.0000 - val_loss: 2.7694 - val_acc: 0.7619\n",
      "Epoch 197/200\n",
      "20/20 [==============================] - 2s 78ms/step - loss: 3.8747e-06 - acc: 1.0000 - val_loss: 2.7720 - val_acc: 0.7619\n",
      "Epoch 198/200\n",
      "20/20 [==============================] - 1s 75ms/step - loss: 3.8560e-06 - acc: 1.0000 - val_loss: 2.7727 - val_acc: 0.7619\n",
      "Epoch 199/200\n",
      "20/20 [==============================] - 1s 67ms/step - loss: 3.8374e-06 - acc: 1.0000 - val_loss: 2.7707 - val_acc: 0.7619\n",
      "Epoch 200/200\n",
      "20/20 [==============================] - 1s 48ms/step - loss: 3.8197e-06 - acc: 1.0000 - val_loss: 2.7741 - val_acc: 0.7619\n"
     ]
    }
   ],
   "source": [
    "# Train the model for the given number of epochs\n",
    "    history = model_1.fit(\n",
    "        X_train_norm, y_train,\n",
    "        steps_per_epoch=20,\n",
    "        epochs=200,\n",
    "        verbose=1,\n",
    "        validation_data=(X_test, y_test),\n",
    "        validation_steps=10\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 512,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_class_nn_mod7 = model_1.predict_classes(X_test_norm)\n",
    "y_pred_prob_nn_mod7 = model_1.predict(X_test_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 513,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluate the losses of the model \n",
    "    epochs = range(1, len(loss)+1)\n",
    "    plt.plot(epochs, loss, color='red', label='Training Loss')\n",
    "    plt.plot(epochs, val_loss, color='blue', label='Validation Loss')\n",
    "    plt.title('Training and Validation Loss')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 514,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluate the accuracy of the model\n",
    "    plt.plot(epochs, acc, color='red', label='Training acc')\n",
    "    plt.plot(epochs, val_acc, color='green', label='Validation acc')\n",
    "    plt.title('Training and validation accuracy')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 516,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy is 0.762\n",
      "roc-auc is 0.769\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Print model performance and plot the roc curve\n",
    "print('accuracy is {:.3f}'.format(accuracy_score(y_test,y_pred_class_nn_mod7)))\n",
    "print('roc-auc is {:.3f}'.format(roc_auc_score(y_test,y_pred_prob_nn_mod7)))\n",
    "\n",
    "plot_roc(y_test, y_pred_prob_nn_mod7, 'NN')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accuracy obtained from this model is 76.2% and AUC-ROC value is 0.769"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "We used various machine learning and deep learning techniques to predict the outcome and evaluated the model based on the accuracy score and ROC-AUC curve obtained. During the model building and evaluation, we have calculated and plotted training and validation accuracy curve as well as training and validation losses curves. \n",
    "\n",
    "Here are the results:\n",
    "\n",
    "Random Forest Technique : Accuracy score : 72.3%   \n",
    "\n",
    "K-Nearest Neighbors     : Accuracy score : 75.3%        \n",
    "\n",
    "Gradient Boosting       : Accuracy score : 78.3%        \n",
    "\n",
    "ANN Model 1             : Best Accuracy score: 77.5%    AUC-ROC : 0.814\n",
    "\n",
    "ANN Model 2             : Best Accuracy score: 73.2%    AUC-ROC : 0.774\n",
    "\n",
    "ANN Model 3             : Best Accuracy score: 76.2%    AUC-ROC : 0.769\n",
    "\n",
    "If hyperparameters in deep learning are tuned more and regularization techniques are applied then model performance can be improved and this can give better accuracy then other machine learning models. \n",
    "\n",
    "We tried developing different models with different number of layers, learning rates and different activation functions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Contribution:\n",
    "\n",
    "85% of the code has been developed by me and the rest of the code has been referred from other sources like research paper, blogs and github. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Citation: \n",
    "\n",
    "https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/latest/install.html\n",
    "\n",
    "https://towardsdatascience.com/understanding-learning-rates-and-how-it-improves-performance-in-deep-learning-d0d4059c1c10\n",
    "\n",
    "https://www.geeksforgeeks.org/python-how-and-where-to-apply-feature-scaling/\n",
    "\n",
    "https://medium.com/machine-learning-101/chapter-5-random-forest-classifier-56dc7425c3e1\n",
    "\n",
    "https://towardsdatascience.com/machine-learning-basics-with-the-k-nearest-neighbors-algorithm-6a6e71d01761\n",
    "\n",
    "https://www.jeremyjordan.me/nn-learning-rate/\n",
    "\n",
    "https://keras.io/metrics/\n",
    "\n",
    "https://towardsdatascience.com/activation-functions-and-its-types-which-is-better-a9a5310cc8f\n",
    "\n",
    "https://machinelearningmastery.com/rectified-linear-activation-function-for-deep-learning-neural-networks/\n",
    "\n",
    "https://keras.io/optimizers/\n",
    "\n",
    "http://ruder.io/optimizing-gradient-descent/index.html#stochasticgradientdescent\n",
    "\n",
    "https://towardsdatascience.com/understanding-learning-rates-and-how-it-improves-performance-in-deep-learning-d0d4059c1c10\n",
    "\n",
    "https://www.kaggle.com/adhishthite/pima-dataset-prediction-model-with-keras-80\n",
    "\n",
    "https://www.kaggle.com/uciml/pima-indians-diabetes-database"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## License\n",
    "\n",
    "Copyright 2019 Ashmita Nigam\n",
    "\n",
    "Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n",
    "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n",
    "\n",
    "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}