{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas\n",
    "import keras\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn import cross_validation\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "from keras.utils import np_utils\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.pipeline import Pipeline\n",
    "%matplotlib inline\n",
    "plt.rcParams['lines.linewidth'] = 2\n",
    "plt.rcParams['lines.markersize'] = 12\n",
    "plt.rcParams['xtick.labelsize'] = 24\n",
    "plt.rcParams['ytick.labelsize'] = 24\n",
    "plt.rcParams['legend.fontsize'] = 24\n",
    "plt.rcParams['axes.titlesize'] = 28\n",
    "plt.rcParams['axes.labelsize'] = 24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fix random seed for reproducibility\n",
    "seed = 7\n",
    "np.random.seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load dataset\n",
    "dataframe = pandas.read_csv(\"glass.data.csv\", header=None)\n",
    "dataset = dataframe.values\n",
    "X = dataset[:,0:10].astype(float)\n",
    "Y = dataset[:,10]\n",
    "columnNames = ['Id','RI', 'Na', 'Mg', 'Al', 'Si', 'K', 'Ca', 'Ba', 'Fe', 'Type of glass'] \n",
    "data = pandas.read_csv(\"glass.data.txt\",names = columnNames, header = None)\n",
    "X_train, X_test, y_train, y_test = cross_validation.train_test_split(data.loc[:, data.columns != 'Type of glass'], data['Type of glass'], \n",
    "test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# encode class values as integers\n",
    "encoder = LabelEncoder()\n",
    "encoder.fit(Y)\n",
    "encoded_Y = encoder.transform(Y)\n",
    "# convert integers to dummy variables (i.e. one hot encoded)\n",
    "dummy_y = np_utils.to_categorical(encoded_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# define baseline model\n",
    "def baseline_model():\n",
    "\t# create model\n",
    "\tmodel = Sequential()\n",
    "\tmodel.add(Dense(8, input_dim=10, activation='relu'))\n",
    "\tmodel.add(Dense(9, activation='relu'))\n",
    "\tmodel.add(Dense(8, activation='softmax'))\n",
    "\t# Compile model\n",
    "\tmodel.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\treturn model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 160 samples, validate on 54 samples\n",
      "Epoch 1/100\n",
      "160/160 [==============================] - 1s 4ms/step - loss: 5.4045 - acc: 0.3375 - val_loss: 3.5383 - val_acc: 0.5000\n",
      "Epoch 2/100\n",
      "160/160 [==============================] - 0s 647us/step - loss: 4.5632 - acc: 0.5563 - val_loss: 3.2708 - val_acc: 0.7593\n",
      "Epoch 3/100\n",
      "160/160 [==============================] - 0s 860us/step - loss: 4.4329 - acc: 0.6313 - val_loss: 3.1778 - val_acc: 0.7963\n",
      "Epoch 4/100\n",
      "160/160 [==============================] - 0s 869us/step - loss: 4.3814 - acc: 0.6313 - val_loss: 3.1232 - val_acc: 0.8148\n",
      "Epoch 5/100\n",
      "160/160 [==============================] - 0s 926us/step - loss: 4.3686 - acc: 0.6313 - val_loss: 3.1080 - val_acc: 0.8148\n",
      "Epoch 6/100\n",
      "160/160 [==============================] - 0s 886us/step - loss: 4.3189 - acc: 0.7063 - val_loss: 3.0950 - val_acc: 0.8148\n",
      "Epoch 7/100\n",
      "160/160 [==============================] - 0s 660us/step - loss: 4.2974 - acc: 0.7000 - val_loss: 3.0869 - val_acc: 0.8148\n",
      "Epoch 8/100\n",
      "160/160 [==============================] - 0s 927us/step - loss: 4.3024 - acc: 0.6813 - val_loss: 3.0822 - val_acc: 0.8148\n",
      "Epoch 9/100\n",
      "160/160 [==============================] - 0s 2ms/step - loss: 4.2838 - acc: 0.6875 - val_loss: 3.0756 - val_acc: 0.8148\n",
      "Epoch 10/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 4.2762 - acc: 0.7000 - val_loss: 3.0840 - val_acc: 0.8148\n",
      "Epoch 11/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 4.2734 - acc: 0.7063 - val_loss: 3.0836 - val_acc: 0.8148\n",
      "Epoch 12/100\n",
      "160/160 [==============================] - 0s 608us/step - loss: 4.2829 - acc: 0.6875 - val_loss: 3.1309 - val_acc: 0.7407\n",
      "Epoch 13/100\n",
      "160/160 [==============================] - 0s 614us/step - loss: 4.2707 - acc: 0.6938 - val_loss: 3.1313 - val_acc: 0.7407\n",
      "Epoch 14/100\n",
      "160/160 [==============================] - 0s 914us/step - loss: 4.2685 - acc: 0.7000 - val_loss: 3.0724 - val_acc: 0.8148\n",
      "Epoch 15/100\n",
      "160/160 [==============================] - 0s 981us/step - loss: 4.2748 - acc: 0.6813 - val_loss: 3.0587 - val_acc: 0.8148\n",
      "Epoch 16/100\n",
      "160/160 [==============================] - 0s 985us/step - loss: 4.2743 - acc: 0.6813 - val_loss: 3.0630 - val_acc: 0.8148\n",
      "Epoch 17/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 4.2588 - acc: 0.7000 - val_loss: 3.0571 - val_acc: 0.8148\n",
      "Epoch 18/100\n",
      "160/160 [==============================] - 0s 693us/step - loss: 4.2527 - acc: 0.7125 - val_loss: 3.0529 - val_acc: 0.8148\n",
      "Epoch 19/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 4.2435 - acc: 0.7125 - val_loss: 3.0745 - val_acc: 0.7963\n",
      "Epoch 20/100\n",
      "160/160 [==============================] - 0s 557us/step - loss: 4.2735 - acc: 0.6750 - val_loss: 3.0452 - val_acc: 0.8148\n",
      "Epoch 21/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 4.2510 - acc: 0.7063 - val_loss: 3.0735 - val_acc: 0.7963\n",
      "Epoch 22/100\n",
      "160/160 [==============================] - 0s 814us/step - loss: 4.3020 - acc: 0.6438 - val_loss: 3.0530 - val_acc: 0.8148\n",
      "Epoch 23/100\n",
      "160/160 [==============================] - 0s 752us/step - loss: 4.2625 - acc: 0.7000 - val_loss: 3.0408 - val_acc: 0.8148\n",
      "Epoch 24/100\n",
      "160/160 [==============================] - 0s 860us/step - loss: 4.2495 - acc: 0.7063 - val_loss: 3.0567 - val_acc: 0.8148\n",
      "Epoch 25/100\n",
      "160/160 [==============================] - 0s 847us/step - loss: 4.2877 - acc: 0.6688 - val_loss: 3.0999 - val_acc: 0.7593\n",
      "Epoch 26/100\n",
      "160/160 [==============================] - 0s 606us/step - loss: 4.2640 - acc: 0.6875 - val_loss: 3.0593 - val_acc: 0.8148\n",
      "Epoch 27/100\n",
      "160/160 [==============================] - 0s 672us/step - loss: 4.2700 - acc: 0.6813 - val_loss: 3.0622 - val_acc: 0.7963\n",
      "Epoch 28/100\n",
      "160/160 [==============================] - 0s 650us/step - loss: 4.2573 - acc: 0.6688 - val_loss: 3.0512 - val_acc: 0.8148\n",
      "Epoch 29/100\n",
      "160/160 [==============================] - 0s 637us/step - loss: 4.2383 - acc: 0.6938 - val_loss: 3.0322 - val_acc: 0.8148\n",
      "Epoch 30/100\n",
      "160/160 [==============================] - 0s 725us/step - loss: 4.2360 - acc: 0.7063 - val_loss: 3.0539 - val_acc: 0.8148\n",
      "Epoch 31/100\n",
      "160/160 [==============================] - 0s 788us/step - loss: 4.2328 - acc: 0.7000 - val_loss: 3.0287 - val_acc: 0.8148\n",
      "Epoch 32/100\n",
      "160/160 [==============================] - 0s 713us/step - loss: 4.2513 - acc: 0.7000 - val_loss: 3.0966 - val_acc: 0.7593\n",
      "Epoch 33/100\n",
      "160/160 [==============================] - 0s 676us/step - loss: 4.2936 - acc: 0.6563 - val_loss: 3.0326 - val_acc: 0.8148\n",
      "Epoch 34/100\n",
      "160/160 [==============================] - 0s 551us/step - loss: 4.2538 - acc: 0.7000 - val_loss: 3.0255 - val_acc: 0.8148\n",
      "Epoch 35/100\n",
      "160/160 [==============================] - 0s 698us/step - loss: 4.2204 - acc: 0.7250 - val_loss: 3.0277 - val_acc: 0.8148\n",
      "Epoch 36/100\n",
      "160/160 [==============================] - 0s 607us/step - loss: 4.2194 - acc: 0.7125 - val_loss: 3.0355 - val_acc: 0.8148\n",
      "Epoch 37/100\n",
      "160/160 [==============================] - ETA: 0s - loss: 4.5696 - acc: 0.700 - 0s 615us/step - loss: 4.2169 - acc: 0.7313 - val_loss: 3.0233 - val_acc: 0.8148\n",
      "Epoch 38/100\n",
      "160/160 [==============================] - 0s 621us/step - loss: 4.2121 - acc: 0.7250 - val_loss: 3.0157 - val_acc: 0.8148\n",
      "Epoch 39/100\n",
      "160/160 [==============================] - ETA: 0s - loss: 4.1714 - acc: 0.687 - 0s 544us/step - loss: 4.2606 - acc: 0.6813 - val_loss: 3.0153 - val_acc: 0.8148\n",
      "Epoch 40/100\n",
      "160/160 [==============================] - 0s 667us/step - loss: 4.2256 - acc: 0.7250 - val_loss: 3.0229 - val_acc: 0.8148\n",
      "Epoch 41/100\n",
      "160/160 [==============================] - 0s 648us/step - loss: 4.2220 - acc: 0.7000 - val_loss: 3.0125 - val_acc: 0.8148\n",
      "Epoch 42/100\n",
      "160/160 [==============================] - 0s 659us/step - loss: 4.2206 - acc: 0.7063 - val_loss: 3.0156 - val_acc: 0.8148\n",
      "Epoch 43/100\n",
      "160/160 [==============================] - 0s 704us/step - loss: 4.2239 - acc: 0.7188 - val_loss: 3.0277 - val_acc: 0.8148\n",
      "Epoch 44/100\n",
      "160/160 [==============================] - 0s 539us/step - loss: 4.2441 - acc: 0.6750 - val_loss: 3.0227 - val_acc: 0.8148\n",
      "Epoch 45/100\n",
      "160/160 [==============================] - 0s 595us/step - loss: 4.2280 - acc: 0.6938 - val_loss: 3.0096 - val_acc: 0.8148\n",
      "Epoch 46/100\n",
      "160/160 [==============================] - 0s 598us/step - loss: 4.2068 - acc: 0.7188 - val_loss: 3.0108 - val_acc: 0.8148\n",
      "Epoch 47/100\n",
      "160/160 [==============================] - 0s 888us/step - loss: 4.2247 - acc: 0.6938 - val_loss: 3.0173 - val_acc: 0.8148\n",
      "Epoch 48/100\n",
      "160/160 [==============================] - 0s 857us/step - loss: 4.2306 - acc: 0.6875 - val_loss: 3.0438 - val_acc: 0.7963\n",
      "Epoch 49/100\n",
      "160/160 [==============================] - 0s 631us/step - loss: 4.2215 - acc: 0.7125 - val_loss: 3.0302 - val_acc: 0.7963\n",
      "Epoch 50/100\n",
      "160/160 [==============================] - 0s 606us/step - loss: 4.2185 - acc: 0.7063 - val_loss: 3.0106 - val_acc: 0.8148\n",
      "Epoch 51/100\n",
      "160/160 [==============================] - 0s 562us/step - loss: 4.2207 - acc: 0.7188 - val_loss: 3.0856 - val_acc: 0.7407\n",
      "Epoch 52/100\n",
      "160/160 [==============================] - ETA: 0s - loss: 4.1930 - acc: 0.706 - 0s 660us/step - loss: 4.2439 - acc: 0.6875 - val_loss: 3.0065 - val_acc: 0.8148\n",
      "Epoch 53/100\n",
      "160/160 [==============================] - 0s 553us/step - loss: 4.2000 - acc: 0.7188 - val_loss: 3.0117 - val_acc: 0.8148\n",
      "Epoch 54/100\n",
      "160/160 [==============================] - 0s 585us/step - loss: 4.2330 - acc: 0.6875 - val_loss: 3.0075 - val_acc: 0.8148\n",
      "Epoch 55/100\n",
      "160/160 [==============================] - 0s 550us/step - loss: 4.2367 - acc: 0.6688 - val_loss: 3.0098 - val_acc: 0.8148\n",
      "Epoch 56/100\n",
      "160/160 [==============================] - 0s 549us/step - loss: 4.2115 - acc: 0.7188 - val_loss: 3.0044 - val_acc: 0.8148\n",
      "Epoch 57/100\n",
      "160/160 [==============================] - 0s 563us/step - loss: 4.2022 - acc: 0.7250 - val_loss: 3.0035 - val_acc: 0.8148\n",
      "Epoch 58/100\n",
      "160/160 [==============================] - 0s 567us/step - loss: 4.2003 - acc: 0.7250 - val_loss: 3.0049 - val_acc: 0.8148\n",
      "Epoch 59/100\n",
      "160/160 [==============================] - 0s 585us/step - loss: 4.2045 - acc: 0.7250 - val_loss: 3.0035 - val_acc: 0.8148\n",
      "Epoch 60/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "160/160 [==============================] - 0s 566us/step - loss: 4.1968 - acc: 0.7375 - val_loss: 3.0032 - val_acc: 0.8148\n",
      "Epoch 61/100\n",
      "160/160 [==============================] - 0s 544us/step - loss: 4.1939 - acc: 0.7313 - val_loss: 3.0155 - val_acc: 0.8148\n",
      "Epoch 62/100\n",
      "160/160 [==============================] - 0s 627us/step - loss: 4.2195 - acc: 0.6938 - val_loss: 3.0016 - val_acc: 0.8148\n",
      "Epoch 63/100\n",
      "160/160 [==============================] - 0s 564us/step - loss: 4.1964 - acc: 0.7375 - val_loss: 3.0016 - val_acc: 0.8148\n",
      "Epoch 64/100\n",
      "160/160 [==============================] - 0s 564us/step - loss: 4.1979 - acc: 0.7250 - val_loss: 3.0093 - val_acc: 0.8148\n",
      "Epoch 65/100\n",
      "160/160 [==============================] - 0s 591us/step - loss: 4.2011 - acc: 0.7188 - val_loss: 3.0007 - val_acc: 0.8148\n",
      "Epoch 66/100\n",
      "160/160 [==============================] - 0s 577us/step - loss: 4.2248 - acc: 0.6938 - val_loss: 3.0055 - val_acc: 0.8148\n",
      "Epoch 67/100\n",
      "160/160 [==============================] - 0s 775us/step - loss: 4.2064 - acc: 0.7188 - val_loss: 3.0031 - val_acc: 0.8148\n",
      "Epoch 68/100\n",
      "160/160 [==============================] - 0s 586us/step - loss: 4.1945 - acc: 0.7313 - val_loss: 2.9999 - val_acc: 0.8148\n",
      "Epoch 69/100\n",
      "160/160 [==============================] - 0s 556us/step - loss: 4.1955 - acc: 0.7125 - val_loss: 2.9996 - val_acc: 0.8148\n",
      "Epoch 70/100\n",
      "160/160 [==============================] - 0s 568us/step - loss: 4.1997 - acc: 0.7188 - val_loss: 3.0052 - val_acc: 0.8148\n",
      "Epoch 71/100\n",
      "160/160 [==============================] - 0s 544us/step - loss: 4.1946 - acc: 0.7188 - val_loss: 3.0014 - val_acc: 0.8148\n",
      "Epoch 72/100\n",
      "160/160 [==============================] - 0s 689us/step - loss: 4.1924 - acc: 0.7250 - val_loss: 3.0009 - val_acc: 0.8148\n",
      "Epoch 73/100\n",
      "160/160 [==============================] - 0s 560us/step - loss: 4.1946 - acc: 0.7250 - val_loss: 2.9988 - val_acc: 0.8148\n",
      "Epoch 74/100\n",
      "160/160 [==============================] - 0s 545us/step - loss: 4.2074 - acc: 0.7063 - val_loss: 2.9994 - val_acc: 0.8148\n",
      "Epoch 75/100\n",
      "160/160 [==============================] - 0s 533us/step - loss: 4.2049 - acc: 0.7188 - val_loss: 3.0311 - val_acc: 0.7963\n",
      "Epoch 76/100\n",
      "160/160 [==============================] - 0s 567us/step - loss: 4.2348 - acc: 0.7000 - val_loss: 3.0116 - val_acc: 0.8148\n",
      "Epoch 77/100\n",
      "160/160 [==============================] - 0s 587us/step - loss: 4.2116 - acc: 0.7063 - val_loss: 3.0083 - val_acc: 0.8148\n",
      "Epoch 78/100\n",
      "160/160 [==============================] - 0s 604us/step - loss: 4.2054 - acc: 0.7188 - val_loss: 3.0048 - val_acc: 0.8148\n",
      "Epoch 79/100\n",
      "160/160 [==============================] - 0s 591us/step - loss: 4.1902 - acc: 0.7313 - val_loss: 3.0102 - val_acc: 0.8148\n",
      "Epoch 80/100\n",
      "160/160 [==============================] - 0s 687us/step - loss: 4.2103 - acc: 0.6938 - val_loss: 2.9977 - val_acc: 0.8148\n",
      "Epoch 81/100\n",
      "160/160 [==============================] - 0s 541us/step - loss: 4.2104 - acc: 0.7125 - val_loss: 3.0027 - val_acc: 0.8148\n",
      "Epoch 82/100\n",
      "160/160 [==============================] - 0s 647us/step - loss: 4.1791 - acc: 0.7438 - val_loss: 3.0024 - val_acc: 0.8148\n",
      "Epoch 83/100\n",
      "160/160 [==============================] - 0s 619us/step - loss: 4.1861 - acc: 0.7313 - val_loss: 2.9970 - val_acc: 0.8148\n",
      "Epoch 84/100\n",
      "160/160 [==============================] - 0s 549us/step - loss: 4.1919 - acc: 0.7313 - val_loss: 3.0079 - val_acc: 0.8148\n",
      "Epoch 85/100\n",
      "160/160 [==============================] - 0s 658us/step - loss: 4.2548 - acc: 0.6688 - val_loss: 3.0033 - val_acc: 0.8148\n",
      "Epoch 86/100\n",
      "160/160 [==============================] - 0s 564us/step - loss: 4.1888 - acc: 0.7250 - val_loss: 2.9958 - val_acc: 0.8148\n",
      "Epoch 87/100\n",
      "160/160 [==============================] - 0s 573us/step - loss: 4.1814 - acc: 0.7375 - val_loss: 2.9964 - val_acc: 0.8148\n",
      "Epoch 88/100\n",
      "160/160 [==============================] - 0s 567us/step - loss: 4.1874 - acc: 0.7250 - val_loss: 2.9950 - val_acc: 0.8148\n",
      "Epoch 89/100\n",
      "160/160 [==============================] - 0s 550us/step - loss: 4.1778 - acc: 0.7313 - val_loss: 2.9950 - val_acc: 0.8148\n",
      "Epoch 90/100\n",
      "160/160 [==============================] - 0s 595us/step - loss: 4.1948 - acc: 0.7188 - val_loss: 3.0063 - val_acc: 0.8148\n",
      "Epoch 91/100\n",
      "160/160 [==============================] - 0s 590us/step - loss: 4.1993 - acc: 0.7250 - val_loss: 2.9960 - val_acc: 0.8148\n",
      "Epoch 92/100\n",
      "160/160 [==============================] - 0s 667us/step - loss: 4.1949 - acc: 0.7313 - val_loss: 2.9951 - val_acc: 0.8148\n",
      "Epoch 93/100\n",
      "160/160 [==============================] - 0s 590us/step - loss: 4.2056 - acc: 0.7125 - val_loss: 2.9943 - val_acc: 0.8148\n",
      "Epoch 94/100\n",
      "160/160 [==============================] - 0s 585us/step - loss: 4.1866 - acc: 0.7250 - val_loss: 3.0089 - val_acc: 0.8148\n",
      "Epoch 95/100\n",
      "160/160 [==============================] - 0s 989us/step - loss: 4.2032 - acc: 0.7125 - val_loss: 2.9957 - val_acc: 0.8148\n",
      "Epoch 96/100\n",
      "160/160 [==============================] - 0s 714us/step - loss: 4.1808 - acc: 0.7250 - val_loss: 2.9995 - val_acc: 0.8148\n",
      "Epoch 97/100\n",
      "160/160 [==============================] - 0s 627us/step - loss: 4.1906 - acc: 0.7313 - val_loss: 2.9951 - val_acc: 0.8148\n",
      "Epoch 98/100\n",
      "160/160 [==============================] - 0s 668us/step - loss: 4.1807 - acc: 0.7250 - val_loss: 2.9991 - val_acc: 0.8148\n",
      "Epoch 99/100\n",
      "160/160 [==============================] - 0s 565us/step - loss: 4.1921 - acc: 0.7125 - val_loss: 2.9945 - val_acc: 0.8148\n",
      "Epoch 100/100\n",
      "160/160 [==============================] - 0s 643us/step - loss: 4.1721 - acc: 0.7375 - val_loss: 2.9953 - val_acc: 0.8148\n"
     ]
    }
   ],
   "source": [
    "model = baseline_model()\n",
    "Y_train_k = keras.utils.np_utils.to_categorical(y_train)\n",
    "Y_test_k = keras.utils.np_utils.to_categorical(y_test)\n",
    "H = model.fit(X_train, Y_train_k, batch_size=5, epochs=100, verbose=1, validation_data=(X_test, Y_test_k))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "160/160 [==============================] - 0s 102us/step\n",
      "\n",
      "acc: 72.50%\n"
     ]
    }
   ],
   "source": [
    "scores = model.evaluate(X_train, Y_train_k)\n",
    "print(\"\\n%s: %.2f%%\" % (model.metrics_names[1], scores[1]*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZMGFCdt555xx00EF585vfnFtuuWWDc371q1/lda97XebMmZOJEydmypQp2Wef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\n1v8HVHLD5o5t6HYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2ab72dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 15))\n",
    "N = 100\n",
    "plt.plot(np.arange(0, N), H.history[\"loss\"], label=\"train_loss\")\n",
    "plt.plot(np.arange(0, N), H.history[\"val_loss\"], label=\"val_loss\")\n",
    "\n",
    "plt.title(\"Training Loss\")\n",
    "plt.xlabel(\"Epoch #\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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thz/8IZdddhkAv/3tbxk3bhwjRoygoKCAcePG8f3vf59PPvlkgA5CBNPOcZeh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\n3blVX91oQL0KhYxudPMCO5qi6mgigBlA/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aa1dkVr7dFJHpXkN9KN2lrpH5/q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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2b0402d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 15))\n",
    "N = 100\n",
    "plt.plot(np.arange(0, N), H.history[\"acc\"], label=\"train_acc\")\n",
    "plt.plot(np.arange(0, N), H.history[\"val_acc\"], label=\"val_acc\")\n",
    "\n",
    "plt.title(\"Training Accuracy\")\n",
    "plt.xlabel(\"Epoch #\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_noisy_x(X_train, stddev):\n",
    "\n",
    "    noise = np.random.normal(0, stddev / 100.0)\n",
    "    noisy_X = X_train * (1 + noise)\n",
    "    return noisy_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "noises = [x for x in range(0, 110, 20)]\n",
    "histories = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "Train on 160 samples, validate on 54 samples\n",
      "Epoch 1/100\n",
      "160/160 [==============================] - 0s 736us/step - loss: 4.1849 - acc: 0.7250 - val_loss: 2.9947 - val_acc: 0.8148\n",
      "Epoch 2/100\n",
      "160/160 [==============================] - 0s 644us/step - loss: 4.1802 - acc: 0.7313 - val_loss: 2.9951 - val_acc: 0.8148\n",
      "Epoch 3/100\n",
      "160/160 [==============================] - 0s 628us/step - loss: 4.1739 - acc: 0.7313 - val_loss: 2.9973 - val_acc: 0.8148\n",
      "Epoch 4/100\n",
      "160/160 [==============================] - 0s 627us/step - loss: 4.1787 - acc: 0.7313 - val_loss: 3.0005 - val_acc: 0.8148\n",
      "Epoch 5/100\n",
      "160/160 [==============================] - 0s 623us/step - loss: 4.1840 - acc: 0.7188 - val_loss: 2.9945 - val_acc: 0.8148\n",
      "Epoch 6/100\n",
      "160/160 [==============================] - 0s 868us/step - loss: 4.2639 - acc: 0.6875 - val_loss: 2.9931 - val_acc: 0.8148\n",
      "Epoch 7/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 4.2579 - acc: 0.6875 - val_loss: 3.0032 - val_acc: 0.8148\n",
      "Epoch 8/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 4.1817 - acc: 0.7250 - val_loss: 2.9923 - val_acc: 0.8148\n",
      "Epoch 9/100\n",
      "160/160 [==============================] - 0s 914us/step - loss: 4.1740 - acc: 0.7313 - val_loss: 2.9921 - val_acc: 0.8148\n",
      "Epoch 10/100\n",
      "160/160 [==============================] - 0s 664us/step - loss: 4.1872 - acc: 0.7250 - val_loss: 2.9950 - val_acc: 0.8148\n",
      "Epoch 11/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 4.1894 - acc: 0.7125 - val_loss: 2.9924 - val_acc: 0.8148\n",
      "Epoch 12/100\n",
      "160/160 [==============================] - 0s 2ms/step - loss: 4.1716 - acc: 0.7375 - val_loss: 3.0008 - val_acc: 0.8148\n",
      "Epoch 13/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 4.1936 - acc: 0.7188 - val_loss: 2.9946 - val_acc: 0.8148\n",
      "Epoch 14/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 4.1853 - acc: 0.7188 - val_loss: 2.9921 - val_acc: 0.8148\n",
      "Epoch 15/100\n",
      "160/160 [==============================] - 0s 618us/step - loss: 4.1838 - acc: 0.7188 - val_loss: 2.9965 - val_acc: 0.8148\n",
      "Epoch 16/100\n",
      "160/160 [==============================] - 0s 977us/step - loss: 4.1786 - acc: 0.7313 - val_loss: 2.9913 - val_acc: 0.8148\n",
      "Epoch 17/100\n",
      "160/160 [==============================] - 0s 939us/step - loss: 4.1714 - acc: 0.7438 - val_loss: 2.9914 - val_acc: 0.8148\n",
      "Epoch 18/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 4.1727 - acc: 0.7250 - val_loss: 2.9916 - val_acc: 0.8148\n",
      "Epoch 19/100\n",
      "160/160 [==============================] - 0s 846us/step - loss: 4.1698 - acc: 0.7375 - val_loss: 2.9994 - val_acc: 0.8148\n",
      "Epoch 20/100\n",
      "160/160 [==============================] - 0s 684us/step - loss: 4.2183 - acc: 0.7063 - val_loss: 2.9907 - val_acc: 0.8148\n",
      "Epoch 21/100\n",
      "160/160 [==============================] - 0s 679us/step - loss: 4.1713 - acc: 0.7375 - val_loss: 2.9964 - val_acc: 0.8148\n",
      "Epoch 22/100\n",
      "160/160 [==============================] - 0s 848us/step - loss: 4.1883 - acc: 0.7188 - val_loss: 2.9907 - val_acc: 0.8148\n",
      "Epoch 23/100\n",
      "160/160 [==============================] - 0s 922us/step - loss: 4.1906 - acc: 0.7188 - val_loss: 2.9947 - val_acc: 0.8148\n",
      "Epoch 24/100\n",
      "160/160 [==============================] - 0s 844us/step - loss: 4.1818 - acc: 0.7250 - val_loss: 2.9937 - val_acc: 0.8148\n",
      "Epoch 25/100\n",
      "160/160 [==============================] - 0s 614us/step - loss: 4.1818 - acc: 0.7250 - val_loss: 2.9905 - val_acc: 0.8148\n",
      "Epoch 26/100\n",
      "160/160 [==============================] - 0s 553us/step - loss: 4.1807 - acc: 0.7313 - val_loss: 2.9994 - val_acc: 0.8148\n",
      "Epoch 27/100\n",
      "160/160 [==============================] - 0s 586us/step - loss: 4.2076 - acc: 0.7063 - val_loss: 2.9954 - val_acc: 0.8148\n",
      "Epoch 28/100\n",
      "160/160 [==============================] - 0s 826us/step - loss: 4.1937 - acc: 0.7250 - val_loss: 2.9921 - val_acc: 0.8148\n",
      "Epoch 29/100\n",
      "160/160 [==============================] - 0s 571us/step - loss: 4.1809 - acc: 0.7313 - val_loss: 3.0040 - val_acc: 0.8148\n",
      "Epoch 30/100\n",
      "160/160 [==============================] - 0s 581us/step - loss: 4.1899 - acc: 0.7125 - val_loss: 3.0244 - val_acc: 0.8148\n",
      "Epoch 31/100\n",
      "160/160 [==============================] - 0s 577us/step - loss: 4.2224 - acc: 0.7125 - val_loss: 2.9928 - val_acc: 0.8148\n",
      "Epoch 32/100\n",
      "160/160 [==============================] - 0s 582us/step - loss: 4.1827 - acc: 0.7250 - val_loss: 2.9912 - val_acc: 0.8148\n",
      "Epoch 33/100\n",
      "160/160 [==============================] - 0s 544us/step - loss: 4.1767 - acc: 0.7188 - val_loss: 2.9905 - val_acc: 0.8148\n",
      "Epoch 34/100\n",
      "160/160 [==============================] - 0s 596us/step - loss: 4.2181 - acc: 0.6875 - val_loss: 3.0045 - val_acc: 0.8148\n",
      "Epoch 35/100\n",
      "160/160 [==============================] - 0s 607us/step - loss: 4.1719 - acc: 0.7313 - val_loss: 2.9908 - val_acc: 0.8148\n",
      "Epoch 36/100\n",
      "160/160 [==============================] - 0s 588us/step - loss: 4.1807 - acc: 0.7188 - val_loss: 3.0022 - val_acc: 0.8148\n",
      "Epoch 37/100\n",
      "160/160 [==============================] - 0s 641us/step - loss: 4.1689 - acc: 0.7313 - val_loss: 2.9931 - val_acc: 0.8148\n",
      "Epoch 38/100\n",
      "160/160 [==============================] - 0s 682us/step - loss: 4.1660 - acc: 0.7375 - val_loss: 2.9961 - val_acc: 0.8148\n",
      "Epoch 39/100\n",
      "160/160 [==============================] - 0s 661us/step - loss: 4.1707 - acc: 0.7313 - val_loss: 3.0065 - val_acc: 0.7963\n",
      "Epoch 40/100\n",
      "160/160 [==============================] - 0s 614us/step - loss: 4.1951 - acc: 0.7125 - val_loss: 2.9930 - val_acc: 0.8148\n",
      "Epoch 41/100\n",
      "160/160 [==============================] - 0s 562us/step - loss: 4.2239 - acc: 0.7125 - val_loss: 3.0008 - val_acc: 0.8148\n",
      "Epoch 42/100\n",
      "160/160 [==============================] - 0s 544us/step - loss: 4.1766 - acc: 0.7250 - val_loss: 3.0012 - val_acc: 0.8148\n",
      "Epoch 43/100\n",
      "160/160 [==============================] - 0s 549us/step - loss: 4.1766 - acc: 0.7375 - val_loss: 2.9923 - val_acc: 0.8148\n",
      "Epoch 44/100\n",
      "160/160 [==============================] - 0s 564us/step - loss: 4.1906 - acc: 0.7188 - val_loss: 2.9939 - val_acc: 0.8148\n",
      "Epoch 45/100\n",
      "160/160 [==============================] - 0s 671us/step - loss: 4.2014 - acc: 0.7188 - val_loss: 2.9985 - val_acc: 0.8148\n",
      "Epoch 46/100\n",
      "160/160 [==============================] - 0s 636us/step - loss: 4.2109 - acc: 0.7063 - val_loss: 3.0085 - val_acc: 0.8148\n",
      "Epoch 47/100\n",
      "160/160 [==============================] - 0s 591us/step - loss: 4.1977 - acc: 0.7125 - val_loss: 2.9935 - val_acc: 0.8148\n",
      "Epoch 48/100\n",
      "160/160 [==============================] - 0s 800us/step - loss: 4.1707 - acc: 0.7250 - val_loss: 2.9907 - val_acc: 0.8148\n",
      "Epoch 49/100\n",
      "160/160 [==============================] - 0s 586us/step - loss: 4.1690 - acc: 0.7250 - val_loss: 2.9886 - val_acc: 0.8148\n",
      "Epoch 50/100\n",
      "160/160 [==============================] - 0s 580us/step - loss: 4.1700 - acc: 0.7250 - val_loss: 2.9894 - val_acc: 0.8148\n",
      "Epoch 51/100\n",
      "160/160 [==============================] - 0s 551us/step - loss: 4.1673 - acc: 0.7313 - val_loss: 2.9896 - val_acc: 0.8148\n",
      "Epoch 52/100\n",
      "160/160 [==============================] - 0s 617us/step - loss: 4.1654 - acc: 0.7438 - val_loss: 2.9889 - val_acc: 0.8148\n",
      "Epoch 53/100\n",
      "160/160 [==============================] - ETA: 0s - loss: 4.1428 - acc: 0.744 - 0s 605us/step - loss: 4.1666 - acc: 0.7313 - val_loss: 2.9980 - val_acc: 0.8148\n",
      "Epoch 54/100\n",
      "160/160 [==============================] - 0s 566us/step - loss: 4.1651 - acc: 0.7375 - val_loss: 2.9892 - val_acc: 0.8148\n",
      "Epoch 55/100\n",
      "160/160 [==============================] - 0s 611us/step - loss: 4.1656 - acc: 0.7375 - val_loss: 2.9981 - val_acc: 0.8148\n",
      "Epoch 56/100\n",
      "160/160 [==============================] - ETA: 0s - loss: 4.8745 - acc: 0.681 - 0s 576us/step - loss: 4.1728 - acc: 0.7188 - val_loss: 2.9891 - val_acc: 0.8148\n",
      "Epoch 57/100\n",
      "160/160 [==============================] - 0s 640us/step - loss: 4.1665 - acc: 0.7313 - val_loss: 3.0000 - val_acc: 0.8148\n",
      "Epoch 58/100\n",
      "160/160 [==============================] - 0s 646us/step - loss: 4.1990 - acc: 0.7125 - val_loss: 3.0042 - val_acc: 0.8148\n",
      "Epoch 59/100\n",
      "160/160 [==============================] - 0s 551us/step - loss: 4.1894 - acc: 0.7188 - val_loss: 2.9966 - val_acc: 0.8148\n",
      "Epoch 60/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "160/160 [==============================] - 0s 568us/step - loss: 4.1650 - acc: 0.7375 - val_loss: 2.9903 - val_acc: 0.8148\n",
      "Epoch 61/100\n",
      "160/160 [==============================] - 0s 556us/step - loss: 4.1849 - acc: 0.7063 - val_loss: 2.9914 - val_acc: 0.8148\n",
      "Epoch 62/100\n",
      "160/160 [==============================] - 0s 600us/step - loss: 4.1704 - acc: 0.7313 - val_loss: 2.9888 - val_acc: 0.8148\n",
      "Epoch 63/100\n",
      "160/160 [==============================] - 0s 586us/step - loss: 4.1667 - acc: 0.7438 - val_loss: 3.0599 - val_acc: 0.7407\n",
      "Epoch 64/100\n",
      "160/160 [==============================] - 0s 554us/step - loss: 4.3026 - acc: 0.6875 - val_loss: 2.9918 - val_acc: 0.8148\n",
      "Epoch 65/100\n",
      "160/160 [==============================] - 0s 609us/step - loss: 4.2627 - acc: 0.6750 - val_loss: 2.9985 - val_acc: 0.8148\n",
      "Epoch 66/100\n",
      "160/160 [==============================] - 0s 585us/step - loss: 4.1898 - acc: 0.7063 - val_loss: 2.9934 - val_acc: 0.8148\n",
      "Epoch 67/100\n",
      "160/160 [==============================] - 0s 595us/step - loss: 4.1731 - acc: 0.7250 - val_loss: 2.9884 - val_acc: 0.8148\n",
      "Epoch 68/100\n",
      "160/160 [==============================] - 0s 768us/step - loss: 4.1729 - acc: 0.7313 - val_loss: 2.9880 - val_acc: 0.8148\n",
      "Epoch 69/100\n",
      "160/160 [==============================] - 0s 555us/step - loss: 4.1736 - acc: 0.7188 - val_loss: 2.9887 - val_acc: 0.8148\n",
      "Epoch 70/100\n",
      "160/160 [==============================] - 0s 559us/step - loss: 4.1651 - acc: 0.7375 - val_loss: 2.9973 - val_acc: 0.8148\n",
      "Epoch 71/100\n",
      "160/160 [==============================] - 0s 572us/step - loss: 4.1819 - acc: 0.7125 - val_loss: 2.9894 - val_acc: 0.8148\n",
      "Epoch 72/100\n",
      "160/160 [==============================] - 0s 614us/step - loss: 4.1612 - acc: 0.7375 - val_loss: 2.9881 - val_acc: 0.8148\n",
      "Epoch 73/100\n",
      "160/160 [==============================] - 0s 638us/step - loss: 4.1614 - acc: 0.7438 - val_loss: 2.9875 - val_acc: 0.8148\n",
      "Epoch 74/100\n",
      "160/160 [==============================] - 0s 617us/step - loss: 4.1593 - acc: 0.7438 - val_loss: 2.9885 - val_acc: 0.8148\n",
      "Epoch 75/100\n",
      "160/160 [==============================] - 0s 574us/step - loss: 4.1685 - acc: 0.7313 - val_loss: 2.9881 - val_acc: 0.8148\n",
      "Epoch 76/100\n",
      "160/160 [==============================] - 0s 656us/step - loss: 4.1566 - acc: 0.7438 - val_loss: 2.9903 - val_acc: 0.8148\n",
      "Epoch 77/100\n",
      "160/160 [==============================] - 0s 651us/step - loss: 4.1651 - acc: 0.7375 - val_loss: 2.9914 - val_acc: 0.8148\n",
      "Epoch 78/100\n",
      "160/160 [==============================] - 0s 620us/step - loss: 4.1655 - acc: 0.7313 - val_loss: 2.9929 - val_acc: 0.8148\n",
      "Epoch 79/100\n",
      "160/160 [==============================] - 0s 566us/step - loss: 4.1725 - acc: 0.7313 - val_loss: 2.9886 - val_acc: 0.8148\n",
      "Epoch 80/100\n",
      "160/160 [==============================] - 0s 562us/step - loss: 4.1696 - acc: 0.7250 - val_loss: 3.0131 - val_acc: 0.7963\n",
      "Epoch 81/100\n",
      "160/160 [==============================] - 0s 536us/step - loss: 4.1761 - acc: 0.7250 - val_loss: 2.9909 - val_acc: 0.8148\n",
      "Epoch 82/100\n",
      "160/160 [==============================] - 0s 536us/step - loss: 4.1665 - acc: 0.7313 - val_loss: 2.9897 - val_acc: 0.8148\n",
      "Epoch 83/100\n",
      "160/160 [==============================] - 0s 566us/step - loss: 4.1603 - acc: 0.7375 - val_loss: 2.9877 - val_acc: 0.8148\n",
      "Epoch 84/100\n",
      "160/160 [==============================] - 0s 560us/step - loss: 4.1601 - acc: 0.7375 - val_loss: 2.9877 - val_acc: 0.8148\n",
      "Epoch 85/100\n",
      "160/160 [==============================] - 0s 559us/step - loss: 4.1623 - acc: 0.7250 - val_loss: 2.9905 - val_acc: 0.8148\n",
      "Epoch 86/100\n",
      "160/160 [==============================] - 0s 540us/step - loss: 4.1619 - acc: 0.7375 - val_loss: 2.9874 - val_acc: 0.8148\n",
      "Epoch 87/100\n",
      "160/160 [==============================] - 0s 547us/step - loss: 4.1597 - acc: 0.7313 - val_loss: 2.9872 - val_acc: 0.8148\n",
      "Epoch 88/100\n",
      "160/160 [==============================] - 0s 994us/step - loss: 4.1673 - acc: 0.7313 - val_loss: 2.9912 - val_acc: 0.8148\n",
      "Epoch 89/100\n",
      "160/160 [==============================] - 0s 566us/step - loss: 4.1876 - acc: 0.7125 - val_loss: 2.9897 - val_acc: 0.8148\n",
      "Epoch 90/100\n",
      "160/160 [==============================] - 0s 553us/step - loss: 4.1761 - acc: 0.7188 - val_loss: 3.0296 - val_acc: 0.7963\n",
      "Epoch 91/100\n",
      "160/160 [==============================] - 0s 574us/step - loss: 4.1773 - acc: 0.7250 - val_loss: 2.9877 - val_acc: 0.8148\n",
      "Epoch 92/100\n",
      "160/160 [==============================] - 0s 604us/step - loss: 4.1649 - acc: 0.7313 - val_loss: 2.9870 - val_acc: 0.8148\n",
      "Epoch 93/100\n",
      "160/160 [==============================] - 0s 596us/step - loss: 4.1739 - acc: 0.7250 - val_loss: 3.0658 - val_acc: 0.7593\n",
      "Epoch 94/100\n",
      "160/160 [==============================] - 0s 600us/step - loss: 4.1646 - acc: 0.7375 - val_loss: 2.9871 - val_acc: 0.8148\n",
      "Epoch 95/100\n",
      "160/160 [==============================] - ETA: 0s - loss: 4.4386 - acc: 0.700 - 0s 590us/step - loss: 4.1650 - acc: 0.7250 - val_loss: 2.9901 - val_acc: 0.8148\n",
      "Epoch 96/100\n",
      "160/160 [==============================] - 0s 638us/step - loss: 4.1609 - acc: 0.7375 - val_loss: 2.9877 - val_acc: 0.8148\n",
      "Epoch 97/100\n",
      "160/160 [==============================] - 0s 604us/step - loss: 4.2118 - acc: 0.7125 - val_loss: 2.9894 - val_acc: 0.8148\n",
      "Epoch 98/100\n",
      "160/160 [==============================] - 0s 612us/step - loss: 4.1744 - acc: 0.7188 - val_loss: 2.9878 - val_acc: 0.8148\n",
      "Epoch 99/100\n",
      "160/160 [==============================] - 0s 587us/step - loss: 4.1613 - acc: 0.7375 - val_loss: 2.9887 - val_acc: 0.8148\n",
      "Epoch 100/100\n",
      "160/160 [==============================] - 0s 605us/step - loss: 4.1672 - acc: 0.7313 - val_loss: 2.9994 - val_acc: 0.8148\n",
      "20\n",
      "Train on 160 samples, validate on 54 samples\n",
      "Epoch 1/100\n",
      "160/160 [==============================] - 0s 591us/step - loss: 4.1936 - acc: 0.7375 - val_loss: 2.9875 - val_acc: 0.8148\n",
      "Epoch 2/100\n",
      "160/160 [==============================] - ETA: 0s - loss: 4.3097 - acc: 0.727 - 0s 577us/step - loss: 4.1924 - acc: 0.7375 - val_loss: 2.9884 - val_acc: 0.8148\n",
      "Epoch 3/100\n",
      "160/160 [==============================] - 0s 604us/step - loss: 4.1946 - acc: 0.7438 - val_loss: 2.9886 - val_acc: 0.8148\n",
      "Epoch 4/100\n",
      "160/160 [==============================] - 0s 689us/step - loss: 4.1989 - acc: 0.7188 - val_loss: 2.9904 - val_acc: 0.8148\n",
      "Epoch 5/100\n",
      "160/160 [==============================] - 0s 578us/step - loss: 4.1928 - acc: 0.7438 - val_loss: 2.9889 - val_acc: 0.8148\n",
      "Epoch 6/100\n",
      "160/160 [==============================] - 0s 592us/step - loss: 4.1885 - acc: 0.7375 - val_loss: 2.9904 - val_acc: 0.8148\n",
      "Epoch 7/100\n",
      "160/160 [==============================] - 0s 688us/step - loss: 4.1963 - acc: 0.7250 - val_loss: 2.9869 - val_acc: 0.8148\n",
      "Epoch 8/100\n",
      "160/160 [==============================] - 0s 563us/step - loss: 4.2096 - acc: 0.7063 - val_loss: 3.0246 - val_acc: 0.7963\n",
      "Epoch 9/100\n",
      "160/160 [==============================] - ETA: 0s - loss: 4.0476 - acc: 0.742 - 0s 637us/step - loss: 4.1917 - acc: 0.7375 - val_loss: 2.9888 - val_acc: 0.8148\n",
      "Epoch 10/100\n",
      "160/160 [==============================] - 0s 675us/step - loss: 4.1899 - acc: 0.7438 - val_loss: 2.9890 - val_acc: 0.8148\n",
      "Epoch 11/100\n",
      "160/160 [==============================] - 0s 600us/step - loss: 4.1862 - acc: 0.7438 - val_loss: 2.9883 - val_acc: 0.8148\n",
      "Epoch 12/100\n",
      "160/160 [==============================] - 0s 549us/step - loss: 4.1866 - acc: 0.7313 - val_loss: 2.9867 - val_acc: 0.8148\n",
      "Epoch 13/100\n",
      "160/160 [==============================] - 0s 595us/step - loss: 4.1894 - acc: 0.7313 - val_loss: 2.9865 - val_acc: 0.8148\n",
      "Epoch 14/100\n",
      "160/160 [==============================] - 0s 586us/step - loss: 4.1873 - acc: 0.7438 - val_loss: 2.9865 - val_acc: 0.8148\n",
      "Epoch 15/100\n",
      "160/160 [==============================] - 0s 580us/step - loss: 4.1902 - acc: 0.7250 - val_loss: 2.9863 - val_acc: 0.8148\n",
      "Epoch 16/100\n",
      "160/160 [==============================] - 0s 587us/step - loss: 4.1916 - acc: 0.7250 - val_loss: 2.9898 - val_acc: 0.8148\n",
      "Epoch 17/100\n",
      "160/160 [==============================] - 0s 656us/step - loss: 4.1882 - acc: 0.7375 - val_loss: 2.9889 - val_acc: 0.8148\n",
      "Epoch 18/100\n",
      "160/160 [==============================] - 0s 585us/step - loss: 4.1838 - acc: 0.7438 - val_loss: 2.9863 - val_acc: 0.8148\n",
      "Epoch 19/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "160/160 [==============================] - 0s 589us/step - loss: 4.1878 - acc: 0.7375 - val_loss: 2.9900 - val_acc: 0.8148\n",
      "Epoch 20/100\n",
      "160/160 [==============================] - 0s 567us/step - loss: 4.1882 - acc: 0.7313 - val_loss: 2.9943 - val_acc: 0.8148\n",
      "Epoch 21/100\n",
      "160/160 [==============================] - 0s 572us/step - loss: 4.1829 - acc: 0.7313 - val_loss: 2.9862 - val_acc: 0.8148\n",
      "Epoch 22/100\n",
      "160/160 [==============================] - ETA: 0s - loss: 4.1792 - acc: 0.711 - 0s 707us/step - loss: 4.1912 - acc: 0.7188 - val_loss: 2.9861 - val_acc: 0.8148\n",
      "Epoch 23/100\n",
      "160/160 [==============================] - 0s 585us/step - loss: 4.1893 - acc: 0.7375 - val_loss: 2.9892 - val_acc: 0.8148\n",
      "Epoch 24/100\n",
      "160/160 [==============================] - 0s 595us/step - loss: 4.1824 - acc: 0.7375 - val_loss: 2.9945 - val_acc: 0.8148\n",
      "Epoch 25/100\n",
      "160/160 [==============================] - 0s 574us/step - loss: 4.1892 - acc: 0.7250 - val_loss: 2.9881 - val_acc: 0.8148\n",
      "Epoch 26/100\n",
      "160/160 [==============================] - 0s 556us/step - loss: 4.1805 - acc: 0.7438 - val_loss: 2.9887 - val_acc: 0.8148\n",
      "Epoch 27/100\n",
      "160/160 [==============================] - 0s 648us/step - loss: 4.1837 - acc: 0.7438 - val_loss: 2.9861 - val_acc: 0.8148\n",
      "Epoch 28/100\n",
      "160/160 [==============================] - 0s 628us/step - loss: 4.1808 - acc: 0.7250 - val_loss: 3.0007 - val_acc: 0.8148\n",
      "Epoch 29/100\n",
      "160/160 [==============================] - 0s 553us/step - loss: 4.1851 - acc: 0.7188 - val_loss: 2.9867 - val_acc: 0.8148\n",
      "Epoch 30/100\n",
      "160/160 [==============================] - 0s 553us/step - loss: 4.1838 - acc: 0.7313 - val_loss: 2.9859 - val_acc: 0.8148\n",
      "Epoch 31/100\n",
      "160/160 [==============================] - 0s 604us/step - loss: 4.1880 - acc: 0.7313 - val_loss: 2.9872 - val_acc: 0.8148\n",
      "Epoch 32/100\n",
      "160/160 [==============================] - 0s 617us/step - loss: 4.1829 - acc: 0.7438 - val_loss: 2.9873 - val_acc: 0.8148\n",
      "Epoch 33/100\n",
      "160/160 [==============================] - 0s 556us/step - loss: 4.1817 - acc: 0.7375 - val_loss: 2.9924 - val_acc: 0.8148\n",
      "Epoch 34/100\n",
      "160/160 [==============================] - 0s 593us/step - loss: 4.1836 - acc: 0.7313 - val_loss: 2.9883 - val_acc: 0.8148\n",
      "Epoch 35/100\n",
      "160/160 [==============================] - 0s 572us/step - loss: 4.1802 - acc: 0.7438 - val_loss: 2.9859 - val_acc: 0.8148\n",
      "Epoch 36/100\n",
      "160/160 [==============================] - 0s 580us/step - loss: 4.1820 - acc: 0.7375 - val_loss: 2.9857 - val_acc: 0.8148\n",
      "Epoch 37/100\n",
      "160/160 [==============================] - 0s 627us/step - loss: 4.1839 - acc: 0.7313 - val_loss: 2.9909 - val_acc: 0.8148\n",
      "Epoch 38/100\n",
      "160/160 [==============================] - 0s 586us/step - loss: 4.1793 - acc: 0.7438 - val_loss: 2.9879 - val_acc: 0.8148\n",
      "Epoch 39/100\n",
      "160/160 [==============================] - 0s 615us/step - loss: 4.1863 - acc: 0.7250 - val_loss: 2.9865 - val_acc: 0.8148\n",
      "Epoch 40/100\n",
      "160/160 [==============================] - 0s 578us/step - loss: 4.1909 - acc: 0.7313 - val_loss: 2.9893 - val_acc: 0.8148\n",
      "Epoch 41/100\n",
      "160/160 [==============================] - 0s 562us/step - loss: 4.1953 - acc: 0.7063 - val_loss: 2.9891 - val_acc: 0.8148\n",
      "Epoch 42/100\n",
      "160/160 [==============================] - 0s 622us/step - loss: 4.1862 - acc: 0.7313 - val_loss: 2.9899 - val_acc: 0.8148\n",
      "Epoch 43/100\n",
      "160/160 [==============================] - 0s 582us/step - loss: 4.1765 - acc: 0.7313 - val_loss: 2.9856 - val_acc: 0.8148\n",
      "Epoch 44/100\n",
      "160/160 [==============================] - ETA: 0s - loss: 4.3150 - acc: 0.718 - 0s 568us/step - loss: 4.1837 - acc: 0.7313 - val_loss: 2.9862 - val_acc: 0.8148\n",
      "Epoch 45/100\n",
      "160/160 [==============================] - ETA: 0s - loss: 4.1567 - acc: 0.727 - 0s 592us/step - loss: 4.1870 - acc: 0.7250 - val_loss: 2.9874 - val_acc: 0.8148\n",
      "Epoch 46/100\n",
      "160/160 [==============================] - 0s 569us/step - loss: 4.1744 - acc: 0.7375 - val_loss: 2.9856 - val_acc: 0.8148\n",
      "Epoch 47/100\n",
      "160/160 [==============================] - 0s 632us/step - loss: 4.1931 - acc: 0.7125 - val_loss: 2.9864 - val_acc: 0.8148\n",
      "Epoch 48/100\n",
      "160/160 [==============================] - 0s 610us/step - loss: 4.1750 - acc: 0.7438 - val_loss: 2.9872 - val_acc: 0.8148\n",
      "Epoch 49/100\n",
      "160/160 [==============================] - 0s 567us/step - loss: 4.1763 - acc: 0.7438 - val_loss: 2.9866 - val_acc: 0.8148\n",
      "Epoch 50/100\n",
      "160/160 [==============================] - 0s 640us/step - loss: 4.1761 - acc: 0.7438 - val_loss: 2.9898 - val_acc: 0.8148\n",
      "Epoch 51/100\n",
      "160/160 [==============================] - 0s 559us/step - loss: 4.1763 - acc: 0.7438 - val_loss: 3.0097 - val_acc: 0.7963\n",
      "Epoch 52/100\n",
      "160/160 [==============================] - 0s 621us/step - loss: 4.1842 - acc: 0.7188 - val_loss: 2.9856 - val_acc: 0.8148\n",
      "Epoch 53/100\n",
      "160/160 [==============================] - 0s 576us/step - loss: 4.1915 - acc: 0.7188 - val_loss: 2.9855 - val_acc: 0.8148\n",
      "Epoch 54/100\n",
      "160/160 [==============================] - 0s 586us/step - loss: 4.1735 - acc: 0.7313 - val_loss: 3.0212 - val_acc: 0.7963\n",
      "Epoch 55/100\n",
      "160/160 [==============================] - 0s 582us/step - loss: 4.1894 - acc: 0.7188 - val_loss: 2.9914 - val_acc: 0.8148\n",
      "Epoch 56/100\n",
      "160/160 [==============================] - 0s 622us/step - loss: 4.1741 - acc: 0.7188 - val_loss: 2.9887 - val_acc: 0.8148\n",
      "Epoch 57/100\n",
      "160/160 [==============================] - 0s 660us/step - loss: 4.1799 - acc: 0.7250 - val_loss: 3.0098 - val_acc: 0.7963\n",
      "Epoch 58/100\n",
      "160/160 [==============================] - 0s 590us/step - loss: 4.1821 - acc: 0.7250 - val_loss: 2.9957 - val_acc: 0.8148\n",
      "Epoch 59/100\n",
      "160/160 [==============================] - 0s 557us/step - loss: 4.1793 - acc: 0.7313 - val_loss: 2.9882 - val_acc: 0.8148\n",
      "Epoch 60/100\n",
      "160/160 [==============================] - 0s 591us/step - loss: 4.1756 - acc: 0.7313 - val_loss: 2.9962 - val_acc: 0.8148\n",
      "Epoch 61/100\n",
      "160/160 [==============================] - 0s 554us/step - loss: 4.1753 - acc: 0.7250 - val_loss: 2.9856 - val_acc: 0.8148\n",
      "Epoch 62/100\n",
      "160/160 [==============================] - 0s 677us/step - loss: 4.1801 - acc: 0.7250 - val_loss: 2.9855 - val_acc: 0.8148\n",
      "Epoch 63/100\n",
      "160/160 [==============================] - 0s 702us/step - loss: 4.1721 - acc: 0.7313 - val_loss: 3.0160 - val_acc: 0.7963\n",
      "Epoch 64/100\n",
      "160/160 [==============================] - 0s 840us/step - loss: 4.2047 - acc: 0.7000 - val_loss: 3.0250 - val_acc: 0.7963\n",
      "Epoch 65/100\n",
      "160/160 [==============================] - 0s 586us/step - loss: 4.2082 - acc: 0.6938 - val_loss: 3.0538 - val_acc: 0.7963\n",
      "Epoch 66/100\n",
      "160/160 [==============================] - 0s 634us/step - loss: 4.2022 - acc: 0.7063 - val_loss: 2.9986 - val_acc: 0.8148\n",
      "Epoch 67/100\n",
      "160/160 [==============================] - 0s 558us/step - loss: 4.1724 - acc: 0.7438 - val_loss: 2.9854 - val_acc: 0.8148\n",
      "Epoch 68/100\n",
      "160/160 [==============================] - ETA: 0s - loss: 3.6355 - acc: 0.764 - 0s 636us/step - loss: 4.1725 - acc: 0.7313 - val_loss: 2.9925 - val_acc: 0.8148\n",
      "Epoch 69/100\n",
      "160/160 [==============================] - 0s 639us/step - loss: 4.1835 - acc: 0.7250 - val_loss: 2.9860 - val_acc: 0.8148\n",
      "Epoch 70/100\n",
      "160/160 [==============================] - 0s 635us/step - loss: 4.1716 - acc: 0.7438 - val_loss: 2.9863 - val_acc: 0.8148\n",
      "Epoch 71/100\n",
      "160/160 [==============================] - 0s 643us/step - loss: 4.1746 - acc: 0.7313 - val_loss: 2.9854 - val_acc: 0.8148\n",
      "Epoch 72/100\n",
      "160/160 [==============================] - 0s 596us/step - loss: 4.1728 - acc: 0.7375 - val_loss: 2.9868 - val_acc: 0.8148\n",
      "Epoch 73/100\n",
      "160/160 [==============================] - 0s 603us/step - loss: 4.1688 - acc: 0.7375 - val_loss: 2.9859 - val_acc: 0.8148\n",
      "Epoch 74/100\n",
      "160/160 [==============================] - 0s 620us/step - loss: 4.1705 - acc: 0.7375 - val_loss: 2.9857 - val_acc: 0.8148\n",
      "Epoch 75/100\n",
      "160/160 [==============================] - 0s 591us/step - loss: 4.1697 - acc: 0.7375 - val_loss: 2.9852 - val_acc: 0.8148\n",
      "Epoch 76/100\n",
      "160/160 [==============================] - 0s 682us/step - loss: 4.1738 - acc: 0.7438 - val_loss: 2.9858 - val_acc: 0.8148\n",
      "Epoch 77/100\n",
      "160/160 [==============================] - 0s 572us/step - loss: 4.1702 - acc: 0.7438 - val_loss: 2.9864 - val_acc: 0.8148\n",
      "Epoch 78/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "160/160 [==============================] - 0s 588us/step - loss: 4.1761 - acc: 0.7375 - val_loss: 2.9855 - val_acc: 0.8148\n",
      "Epoch 79/100\n",
      "160/160 [==============================] - 0s 591us/step - loss: 4.1698 - acc: 0.7313 - val_loss: 2.9855 - val_acc: 0.8148\n",
      "Epoch 80/100\n",
      "160/160 [==============================] - 0s 545us/step - loss: 4.1768 - acc: 0.7250 - val_loss: 2.9852 - val_acc: 0.8148\n",
      "Epoch 81/100\n",
      "160/160 [==============================] - 0s 601us/step - loss: 4.1663 - acc: 0.7313 - val_loss: 2.9972 - val_acc: 0.8148\n",
      "Epoch 82/100\n",
      "160/160 [==============================] - 0s 560us/step - loss: 4.1757 - acc: 0.7313 - val_loss: 2.9852 - val_acc: 0.8148\n",
      "Epoch 83/100\n",
      "160/160 [==============================] - 0s 571us/step - loss: 4.1858 - acc: 0.7250 - val_loss: 2.9859 - val_acc: 0.8148\n",
      "Epoch 84/100\n",
      "160/160 [==============================] - 0s 548us/step - loss: 4.1682 - acc: 0.7438 - val_loss: 2.9872 - val_acc: 0.8148\n",
      "Epoch 85/100\n",
      "160/160 [==============================] - 0s 599us/step - loss: 4.1657 - acc: 0.7438 - val_loss: 2.9851 - val_acc: 0.8148\n",
      "Epoch 86/100\n",
      "160/160 [==============================] - 0s 629us/step - loss: 4.1699 - acc: 0.7375 - val_loss: 2.9897 - val_acc: 0.8148\n",
      "Epoch 87/100\n",
      "160/160 [==============================] - 0s 632us/step - loss: 4.1664 - acc: 0.7375 - val_loss: 2.9851 - val_acc: 0.8148\n",
      "Epoch 88/100\n",
      "160/160 [==============================] - 0s 554us/step - loss: 4.1767 - acc: 0.7188 - val_loss: 2.9851 - val_acc: 0.8148\n",
      "Epoch 89/100\n",
      "160/160 [==============================] - 0s 567us/step - loss: 4.1723 - acc: 0.7313 - val_loss: 2.9859 - val_acc: 0.8148\n",
      "Epoch 90/100\n",
      "160/160 [==============================] - 0s 549us/step - loss: 4.1715 - acc: 0.7250 - val_loss: 3.0235 - val_acc: 0.7963\n",
      "Epoch 91/100\n",
      "160/160 [==============================] - 0s 609us/step - loss: 4.1901 - acc: 0.7188 - val_loss: 2.9997 - val_acc: 0.7963\n",
      "Epoch 92/100\n",
      "160/160 [==============================] - 0s 681us/step - loss: 4.1693 - acc: 0.7313 - val_loss: 2.9852 - val_acc: 0.8148\n",
      "Epoch 93/100\n",
      "160/160 [==============================] - 0s 547us/step - loss: 4.1673 - acc: 0.7375 - val_loss: 2.9853 - val_acc: 0.8148\n",
      "Epoch 94/100\n",
      "160/160 [==============================] - 0s 634us/step - loss: 4.1652 - acc: 0.7438 - val_loss: 2.9865 - val_acc: 0.8148\n",
      "Epoch 95/100\n",
      "160/160 [==============================] - 0s 578us/step - loss: 4.1719 - acc: 0.7250 - val_loss: 2.9853 - val_acc: 0.8148\n",
      "Epoch 96/100\n",
      "160/160 [==============================] - 0s 673us/step - loss: 4.1711 - acc: 0.7375 - val_loss: 2.9893 - val_acc: 0.8148\n",
      "Epoch 97/100\n",
      "160/160 [==============================] - 0s 551us/step - loss: 4.1692 - acc: 0.7375 - val_loss: 2.9892 - val_acc: 0.8148\n",
      "Epoch 98/100\n",
      "160/160 [==============================] - 0s 571us/step - loss: 4.1755 - acc: 0.7313 - val_loss: 2.9909 - val_acc: 0.8148\n",
      "Epoch 99/100\n",
      "160/160 [==============================] - 0s 584us/step - loss: 4.1706 - acc: 0.7313 - val_loss: 2.9861 - val_acc: 0.8148\n",
      "Epoch 100/100\n",
      "160/160 [==============================] - 0s 574us/step - loss: 4.1733 - acc: 0.7313 - val_loss: 2.9874 - val_acc: 0.8148\n",
      "40\n",
      "Train on 160 samples, validate on 54 samples\n",
      "Epoch 1/100\n",
      "160/160 [==============================] - 0s 625us/step - loss: 4.1572 - acc: 0.7313 - val_loss: 2.9893 - val_acc: 0.8148\n",
      "Epoch 2/100\n",
      "160/160 [==============================] - ETA: 0s - loss: 4.6363 - acc: 0.652 - 0s 606us/step - loss: 4.8301 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 3/100\n",
      "160/160 [==============================] - 0s 603us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 4/100\n",
      "160/160 [==============================] - 0s 587us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 5/100\n",
      "160/160 [==============================] - 0s 635us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 6/100\n",
      "160/160 [==============================] - 0s 639us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 7/100\n",
      "160/160 [==============================] - 0s 550us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 8/100\n",
      "160/160 [==============================] - 0s 556us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 9/100\n",
      "160/160 [==============================] - 0s 600us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 10/100\n",
      "160/160 [==============================] - 0s 579us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 11/100\n",
      "160/160 [==============================] - 0s 582us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 12/100\n",
      "160/160 [==============================] - 0s 642us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 13/100\n",
      "160/160 [==============================] - 0s 596us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 14/100\n",
      "160/160 [==============================] - 0s 880us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 15/100\n",
      "160/160 [==============================] - 0s 653us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 16/100\n",
      "160/160 [==============================] - 0s 573us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 17/100\n",
      "160/160 [==============================] - 0s 551us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 18/100\n",
      "160/160 [==============================] - 0s 555us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 19/100\n",
      "160/160 [==============================] - 0s 601us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 20/100\n",
      "160/160 [==============================] - 0s 683us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 21/100\n",
      "160/160 [==============================] - 0s 677us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 22/100\n",
      "160/160 [==============================] - 0s 558us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 23/100\n",
      "160/160 [==============================] - 0s 554us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 24/100\n",
      "160/160 [==============================] - 0s 562us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 25/100\n",
      "160/160 [==============================] - 0s 683us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 26/100\n",
      "160/160 [==============================] - 0s 555us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 27/100\n",
      "160/160 [==============================] - 0s 565us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 28/100\n",
      "160/160 [==============================] - 0s 591us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 29/100\n",
      "160/160 [==============================] - 0s 576us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 30/100\n",
      "160/160 [==============================] - 0s 623us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 31/100\n",
      "160/160 [==============================] - 0s 621us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 32/100\n",
      "160/160 [==============================] - 0s 578us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 33/100\n",
      "160/160 [==============================] - 0s 590us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 34/100\n",
      "160/160 [==============================] - 0s 598us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 35/100\n",
      "160/160 [==============================] - 0s 745us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 36/100\n",
      "160/160 [==============================] - 0s 603us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 37/100\n",
      "160/160 [==============================] - 0s 617us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 38/100\n",
      "160/160 [==============================] - 0s 571us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 39/100\n",
      "160/160 [==============================] - 0s 645us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 40/100\n",
      "160/160 [==============================] - 0s 573us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 41/100\n",
      "160/160 [==============================] - 0s 570us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 42/100\n",
      "160/160 [==============================] - 0s 592us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 43/100\n",
      "160/160 [==============================] - 0s 607us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 44/100\n",
      "160/160 [==============================] - 0s 601us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 45/100\n",
      "160/160 [==============================] - 0s 630us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 46/100\n",
      "160/160 [==============================] - 0s 539us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 47/100\n",
      "160/160 [==============================] - 0s 560us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 48/100\n",
      "160/160 [==============================] - 0s 552us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 49/100\n",
      "160/160 [==============================] - 0s 565us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 50/100\n",
      "160/160 [==============================] - 0s 570us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 51/100\n",
      "160/160 [==============================] - 0s 571us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 52/100\n",
      "160/160 [==============================] - 0s 568us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 53/100\n",
      "160/160 [==============================] - 0s 627us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 54/100\n",
      "160/160 [==============================] - 0s 594us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 55/100\n",
      "160/160 [==============================] - 0s 652us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 56/100\n",
      "160/160 [==============================] - 0s 546us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 57/100\n",
      "160/160 [==============================] - 0s 577us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 58/100\n",
      "160/160 [==============================] - 0s 597us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 59/100\n",
      "160/160 [==============================] - 0s 552us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 60/100\n",
      "160/160 [==============================] - 0s 659us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 61/100\n",
      "160/160 [==============================] - 0s 645us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 62/100\n",
      "160/160 [==============================] - 0s 616us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 63/100\n",
      "160/160 [==============================] - 0s 555us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 64/100\n",
      "160/160 [==============================] - 0s 645us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 65/100\n",
      "160/160 [==============================] - 0s 683us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 66/100\n",
      "160/160 [==============================] - 0s 578us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 67/100\n",
      "160/160 [==============================] - 0s 577us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 68/100\n",
      "160/160 [==============================] - 0s 560us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 69/100\n",
      "160/160 [==============================] - 0s 588us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 70/100\n",
      "160/160 [==============================] - 0s 679us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 71/100\n",
      "160/160 [==============================] - 0s 631us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 72/100\n",
      "160/160 [==============================] - ETA: 0s - loss: 6.1542 - acc: 0.618 - 0s 558us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 73/100\n",
      "160/160 [==============================] - 0s 625us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 74/100\n",
      "160/160 [==============================] - 0s 622us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 75/100\n",
      "160/160 [==============================] - 0s 651us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 76/100\n",
      "160/160 [==============================] - 0s 573us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 77/100\n",
      "160/160 [==============================] - 0s 600us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 78/100\n",
      "160/160 [==============================] - 0s 550us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 79/100\n",
      "160/160 [==============================] - 0s 576us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 80/100\n",
      "160/160 [==============================] - 0s 595us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 81/100\n",
      "160/160 [==============================] - 0s 598us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 82/100\n",
      "160/160 [==============================] - 0s 608us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 83/100\n",
      "160/160 [==============================] - 0s 586us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 84/100\n",
      "160/160 [==============================] - 0s 583us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 85/100\n",
      "160/160 [==============================] - 0s 680us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 86/100\n",
      "160/160 [==============================] - 0s 566us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 87/100\n",
      "160/160 [==============================] - 0s 549us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 88/100\n",
      "160/160 [==============================] - 0s 555us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 89/100\n",
      "160/160 [==============================] - 0s 551us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 90/100\n",
      "160/160 [==============================] - 0s 652us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 91/100\n",
      "160/160 [==============================] - 0s 570us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 92/100\n",
      "160/160 [==============================] - 0s 569us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 93/100\n",
      "160/160 [==============================] - 0s 552us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 94/100\n",
      "160/160 [==============================] - 0s 554us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 95/100\n",
      "160/160 [==============================] - 0s 628us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 96/100\n",
      "160/160 [==============================] - 0s 602us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 97/100\n",
      "160/160 [==============================] - 0s 568us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 98/100\n",
      "160/160 [==============================] - 0s 547us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 99/100\n",
      "160/160 [==============================] - 0s 598us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 100/100\n",
      "160/160 [==============================] - 0s 711us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "60\n",
      "Train on 160 samples, validate on 54 samples\n",
      "Epoch 1/100\n",
      "160/160 [==============================] - 0s 552us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 2/100\n",
      "160/160 [==============================] - 0s 572us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 3/100\n",
      "160/160 [==============================] - 0s 584us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 4/100\n",
      "160/160 [==============================] - 0s 567us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 5/100\n",
      "160/160 [==============================] - 0s 633us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 6/100\n",
      "160/160 [==============================] - 0s 547us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 7/100\n",
      "160/160 [==============================] - 0s 626us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 8/100\n",
      "160/160 [==============================] - 0s 592us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 9/100\n",
      "160/160 [==============================] - 0s 622us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 10/100\n",
      "160/160 [==============================] - 0s 670us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 11/100\n",
      "160/160 [==============================] - 0s 583us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 12/100\n",
      "160/160 [==============================] - 0s 575us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 13/100\n",
      "160/160 [==============================] - 0s 579us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 14/100\n",
      "160/160 [==============================] - 0s 621us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 15/100\n",
      "160/160 [==============================] - 0s 633us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 16/100\n",
      "160/160 [==============================] - ETA: 0s - loss: 5.7465 - acc: 0.643 - 0s 551us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 17/100\n",
      "160/160 [==============================] - ETA: 0s - loss: 6.0443 - acc: 0.625 - 0s 628us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 18/100\n",
      "160/160 [==============================] - 0s 628us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 19/100\n",
      "160/160 [==============================] - 0s 549us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 20/100\n",
      "160/160 [==============================] - ETA: 0s - loss: 5.5518 - acc: 0.655 - 0s 644us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 21/100\n",
      "160/160 [==============================] - 0s 633us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 22/100\n",
      "160/160 [==============================] - 0s 606us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 23/100\n",
      "160/160 [==============================] - 0s 598us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 24/100\n",
      "160/160 [==============================] - 0s 648us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 25/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 26/100\n",
      "160/160 [==============================] - 0s 698us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 27/100\n",
      "160/160 [==============================] - 0s 662us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 28/100\n",
      "160/160 [==============================] - 0s 915us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 29/100\n",
      "160/160 [==============================] - 0s 677us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 30/100\n",
      "160/160 [==============================] - 0s 637us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 31/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 32/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 33/100\n",
      "160/160 [==============================] - 0s 837us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 34/100\n",
      "160/160 [==============================] - 0s 557us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 35/100\n",
      "160/160 [==============================] - 0s 568us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 36/100\n",
      "160/160 [==============================] - 0s 580us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 37/100\n",
      "160/160 [==============================] - 0s 565us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 38/100\n",
      "160/160 [==============================] - 0s 542us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 39/100\n",
      "160/160 [==============================] - 0s 570us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 40/100\n",
      "160/160 [==============================] - 0s 611us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 41/100\n",
      "160/160 [==============================] - 0s 665us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 42/100\n",
      "160/160 [==============================] - 0s 583us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 43/100\n",
      "160/160 [==============================] - 0s 582us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 44/100\n",
      "160/160 [==============================] - 0s 928us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 45/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 46/100\n",
      "160/160 [==============================] - 0s 663us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 47/100\n",
      "160/160 [==============================] - 0s 578us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 48/100\n",
      "160/160 [==============================] - 0s 571us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 49/100\n",
      "160/160 [==============================] - 0s 574us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 50/100\n",
      "160/160 [==============================] - 0s 625us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 51/100\n",
      "160/160 [==============================] - ETA: 0s - loss: 5.2192 - acc: 0.676 - 0s 587us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 52/100\n",
      "160/160 [==============================] - 0s 594us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 53/100\n",
      "160/160 [==============================] - 0s 605us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 54/100\n",
      "160/160 [==============================] - 0s 608us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 55/100\n",
      "160/160 [==============================] - 0s 651us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 56/100\n",
      "160/160 [==============================] - 0s 581us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 57/100\n",
      "160/160 [==============================] - 0s 568us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 58/100\n",
      "160/160 [==============================] - 0s 561us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 59/100\n",
      "160/160 [==============================] - 0s 554us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 60/100\n",
      "160/160 [==============================] - 0s 613us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 61/100\n",
      "160/160 [==============================] - 0s 559us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 62/100\n",
      "160/160 [==============================] - 0s 554us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 63/100\n",
      "160/160 [==============================] - 0s 539us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 64/100\n",
      "160/160 [==============================] - 0s 538us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 65/100\n",
      "160/160 [==============================] - 0s 571us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 66/100\n",
      "160/160 [==============================] - 0s 581us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 67/100\n",
      "160/160 [==============================] - 0s 565us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 68/100\n",
      "160/160 [==============================] - 0s 546us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 69/100\n",
      "160/160 [==============================] - 0s 562us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 70/100\n",
      "160/160 [==============================] - 0s 560us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 71/100\n",
      "160/160 [==============================] - 0s 612us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 72/100\n",
      "160/160 [==============================] - 0s 604us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 73/100\n",
      "160/160 [==============================] - 0s 739us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 74/100\n",
      "160/160 [==============================] - 0s 783us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 75/100\n",
      "160/160 [==============================] - 0s 599us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 76/100\n",
      "160/160 [==============================] - 0s 559us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 77/100\n",
      "160/160 [==============================] - 0s 714us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 78/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 79/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 80/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 81/100\n",
      "160/160 [==============================] - 0s 729us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 82/100\n",
      "160/160 [==============================] - 0s 743us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 83/100\n",
      "160/160 [==============================] - 0s 854us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 84/100\n",
      "160/160 [==============================] - 0s 614us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 85/100\n",
      "160/160 [==============================] - 0s 916us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 86/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 87/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 88/100\n",
      "160/160 [==============================] - 0s 641us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 89/100\n",
      "160/160 [==============================] - 0s 604us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 90/100\n",
      "160/160 [==============================] - 0s 864us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 91/100\n",
      "160/160 [==============================] - 0s 954us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 92/100\n",
      "160/160 [==============================] - 0s 931us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 93/100\n",
      "160/160 [==============================] - 0s 691us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 94/100\n",
      "160/160 [==============================] - 0s 880us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 95/100\n",
      "160/160 [==============================] - 0s 877us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 96/100\n",
      "160/160 [==============================] - 0s 888us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 97/100\n",
      "160/160 [==============================] - 0s 618us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 98/100\n",
      "160/160 [==============================] - 0s 638us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 99/100\n",
      "160/160 [==============================] - 0s 977us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 100/100\n",
      "160/160 [==============================] - 0s 977us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "80\n",
      "Train on 160 samples, validate on 54 samples\n",
      "Epoch 1/100\n",
      "160/160 [==============================] - 0s 654us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 2/100\n",
      "160/160 [==============================] - 0s 761us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 3/100\n",
      "160/160 [==============================] - 0s 980us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 4/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 5/100\n",
      "160/160 [==============================] - 0s 746us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 6/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 7/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 8/100\n",
      "160/160 [==============================] - 0s 874us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 9/100\n",
      "160/160 [==============================] - 0s 882us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 10/100\n",
      "160/160 [==============================] - 0s 912us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 11/100\n",
      "160/160 [==============================] - 0s 842us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 12/100\n",
      "160/160 [==============================] - 0s 888us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 13/100\n",
      "160/160 [==============================] - 0s 937us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 14/100\n",
      "160/160 [==============================] - 0s 908us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 15/100\n",
      "160/160 [==============================] - 0s 801us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 16/100\n",
      "160/160 [==============================] - 0s 815us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 17/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "160/160 [==============================] - 0s 795us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 18/100\n",
      "160/160 [==============================] - 0s 812us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 19/100\n",
      "160/160 [==============================] - 0s 834us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 20/100\n",
      "160/160 [==============================] - 0s 911us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 21/100\n",
      "160/160 [==============================] - 0s 861us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 22/100\n",
      "160/160 [==============================] - 0s 714us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 23/100\n",
      "160/160 [==============================] - 0s 591us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 24/100\n",
      "160/160 [==============================] - 0s 582us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 25/100\n",
      "160/160 [==============================] - 0s 604us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 26/100\n",
      "160/160 [==============================] - ETA: 0s - loss: 5.7686 - acc: 0.642 - 0s 611us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 27/100\n",
      "160/160 [==============================] - 0s 768us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 28/100\n",
      "160/160 [==============================] - 0s 864us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 29/100\n",
      "160/160 [==============================] - 0s 880us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 30/100\n",
      "160/160 [==============================] - 0s 711us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 31/100\n",
      "160/160 [==============================] - 0s 815us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 32/100\n",
      "160/160 [==============================] - 0s 824us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 33/100\n",
      "160/160 [==============================] - 0s 812us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 34/100\n",
      "160/160 [==============================] - 0s 731us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 35/100\n",
      "160/160 [==============================] - 0s 827us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 36/100\n",
      "160/160 [==============================] - 0s 835us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 37/100\n",
      "160/160 [==============================] - 0s 927us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 38/100\n",
      "160/160 [==============================] - 0s 909us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 39/100\n",
      "160/160 [==============================] - 0s 832us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 40/100\n",
      "160/160 [==============================] - 0s 818us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 41/100\n",
      "160/160 [==============================] - 0s 801us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 42/100\n",
      "160/160 [==============================] - 0s 929us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 43/100\n",
      "160/160 [==============================] - 0s 891us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 44/100\n",
      "160/160 [==============================] - 0s 859us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 45/100\n",
      "160/160 [==============================] - 0s 846us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 46/100\n",
      "160/160 [==============================] - 0s 803us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 47/100\n",
      "160/160 [==============================] - 0s 728us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 48/100\n",
      "160/160 [==============================] - 0s 851us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 49/100\n",
      "160/160 [==============================] - 0s 856us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 50/100\n",
      "160/160 [==============================] - 0s 908us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 51/100\n",
      "160/160 [==============================] - 0s 879us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 52/100\n",
      "160/160 [==============================] - 0s 933us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 53/100\n",
      "160/160 [==============================] - 0s 895us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 54/100\n",
      "160/160 [==============================] - 0s 835us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 55/100\n",
      "160/160 [==============================] - 0s 732us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 56/100\n",
      "160/160 [==============================] - 0s 889us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 57/100\n",
      "160/160 [==============================] - 0s 920us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 58/100\n",
      "160/160 [==============================] - 0s 859us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 59/100\n",
      "160/160 [==============================] - 0s 834us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 60/100\n",
      "160/160 [==============================] - 0s 740us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 61/100\n",
      "160/160 [==============================] - 0s 791us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 62/100\n",
      "160/160 [==============================] - 0s 744us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 63/100\n",
      "160/160 [==============================] - 0s 794us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 64/100\n",
      "160/160 [==============================] - 0s 883us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 65/100\n",
      "160/160 [==============================] - 0s 854us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 66/100\n",
      "160/160 [==============================] - 0s 857us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 67/100\n",
      "160/160 [==============================] - 0s 961us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 68/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 69/100\n",
      "160/160 [==============================] - 0s 731us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 70/100\n",
      "160/160 [==============================] - 0s 765us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 71/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 72/100\n",
      "160/160 [==============================] - 0s 645us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 73/100\n",
      "160/160 [==============================] - 0s 698us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 74/100\n",
      "160/160 [==============================] - 0s 607us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 75/100\n",
      "160/160 [==============================] - 0s 589us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 76/100\n",
      "160/160 [==============================] - 0s 879us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 77/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "160/160 [==============================] - 0s 730us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 78/100\n",
      "160/160 [==============================] - 0s 586us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 79/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 80/100\n",
      "160/160 [==============================] - 0s 637us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 81/100\n",
      "160/160 [==============================] - 0s 629us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 82/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 83/100\n",
      "160/160 [==============================] - 0s 806us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 84/100\n",
      "160/160 [==============================] - 0s 581us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 85/100\n",
      "160/160 [==============================] - 0s 951us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 86/100\n",
      "160/160 [==============================] - 0s 868us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 87/100\n",
      "160/160 [==============================] - 0s 716us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 88/100\n",
      "160/160 [==============================] - 0s 581us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 89/100\n",
      "160/160 [==============================] - 0s 593us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 90/100\n",
      "160/160 [==============================] - 0s 606us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 91/100\n",
      "160/160 [==============================] - 0s 626us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 92/100\n",
      "160/160 [==============================] - 0s 882us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 93/100\n",
      "160/160 [==============================] - 0s 702us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 94/100\n",
      "160/160 [==============================] - 0s 635us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 95/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 96/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 97/100\n",
      "160/160 [==============================] - 0s 778us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 98/100\n",
      "160/160 [==============================] - 0s 625us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 99/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 100/100\n",
      "160/160 [==============================] - 0s 852us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "100\n",
      "Train on 160 samples, validate on 54 samples\n",
      "Epoch 1/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 2/100\n",
      "160/160 [==============================] - 0s 620us/step - loss: 5.8282 - acc: 0.6313 - val_loss: 4.1579 - val_acc: 0.7222\n",
      "Epoch 3/100\n",
      "160/160 [==============================] - 0s 589us/step - loss: 5.4767 - acc: 0.6500 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 4/100\n",
      "160/160 [==============================] - 0s 988us/step - loss: 5.5062 - acc: 0.6438 - val_loss: 4.2404 - val_acc: 0.7037\n",
      "Epoch 5/100\n",
      "160/160 [==============================] - 0s 857us/step - loss: 5.5777 - acc: 0.6500 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 6/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 5.4462 - acc: 0.6563 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 7/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 5.4450 - acc: 0.6563 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 8/100\n",
      "160/160 [==============================] - 0s 981us/step - loss: 5.4406 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 9/100\n",
      "160/160 [==============================] - 0s 810us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 10/100\n",
      "160/160 [==============================] - 0s 583us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 11/100\n",
      "160/160 [==============================] - 0s 588us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 12/100\n",
      "160/160 [==============================] - 0s 611us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 13/100\n",
      "160/160 [==============================] - 0s 873us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 14/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 15/100\n",
      "160/160 [==============================] - 0s 939us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 16/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 17/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 18/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 19/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 20/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 21/100\n",
      "160/160 [==============================] - 0s 910us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 22/100\n",
      "160/160 [==============================] - 0s 818us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 23/100\n",
      "160/160 [==============================] - 0s 744us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 24/100\n",
      "160/160 [==============================] - 0s 701us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 25/100\n",
      "160/160 [==============================] - 0s 795us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 26/100\n",
      "160/160 [==============================] - 0s 653us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 27/100\n",
      "160/160 [==============================] - 0s 879us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 28/100\n",
      "160/160 [==============================] - 0s 685us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 29/100\n",
      "160/160 [==============================] - 0s 962us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 30/100\n",
      "160/160 [==============================] - 0s 2ms/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 31/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 32/100\n",
      "160/160 [==============================] - 0s 740us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 33/100\n",
      "160/160 [==============================] - 0s 700us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 34/100\n",
      "160/160 [==============================] - 0s 868us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 35/100\n",
      "160/160 [==============================] - 0s 861us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 36/100\n",
      "160/160 [==============================] - ETA: 0s - loss: 3.2236 - acc: 0.800 - ETA: 0s - loss: 4.7406 - acc: 0.705 - 0s 741us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 37/100\n",
      "160/160 [==============================] - 0s 862us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 38/100\n",
      "160/160 [==============================] - 0s 927us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 39/100\n",
      "160/160 [==============================] - 0s 960us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 40/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 41/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 42/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 43/100\n",
      "160/160 [==============================] - 0s 946us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 44/100\n",
      "160/160 [==============================] - 0s 909us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 45/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 46/100\n",
      "160/160 [==============================] - 0s 899us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 47/100\n",
      "160/160 [==============================] - 0s 715us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 48/100\n",
      "160/160 [==============================] - 0s 713us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 49/100\n",
      "160/160 [==============================] - 0s 592us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 50/100\n",
      "160/160 [==============================] - 0s 626us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 51/100\n",
      "160/160 [==============================] - 0s 631us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 52/100\n",
      "160/160 [==============================] - 0s 689us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 53/100\n",
      "160/160 [==============================] - 0s 920us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 54/100\n",
      "160/160 [==============================] - 0s 872us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 55/100\n",
      "160/160 [==============================] - 0s 809us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 56/100\n",
      "160/160 [==============================] - 0s 709us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 57/100\n",
      "160/160 [==============================] - 0s 652us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407  \n",
      "Epoch 58/100\n",
      "160/160 [==============================] - 0s 584us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 59/100\n",
      "160/160 [==============================] - 0s 851us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 60/100\n",
      "160/160 [==============================] - 0s 648us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 61/100\n",
      "160/160 [==============================] - 0s 928us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 62/100\n",
      "160/160 [==============================] - 0s 742us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 63/100\n",
      "160/160 [==============================] - 0s 685us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 64/100\n",
      "160/160 [==============================] - 0s 596us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 65/100\n",
      "160/160 [==============================] - 0s 712us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 66/100\n",
      "160/160 [==============================] - 0s 697us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 67/100\n",
      "160/160 [==============================] - 0s 680us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 68/100\n",
      "160/160 [==============================] - 0s 711us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 69/100\n",
      "160/160 [==============================] - 0s 806us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 70/100\n",
      "160/160 [==============================] - 0s 909us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 71/100\n",
      "160/160 [==============================] - 0s 717us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 72/100\n",
      "160/160 [==============================] - 0s 644us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 73/100\n",
      "160/160 [==============================] - 0s 593us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 74/100\n",
      "160/160 [==============================] - 0s 691us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 75/100\n",
      "160/160 [==============================] - 0s 709us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 76/100\n",
      "160/160 [==============================] - 0s 592us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 77/100\n",
      "160/160 [==============================] - 0s 693us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 78/100\n",
      "160/160 [==============================] - 0s 754us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 79/100\n",
      "160/160 [==============================] - 0s 819us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 80/100\n",
      "160/160 [==============================] - ETA: 0s - loss: 5.2384 - acc: 0.675 - 0s 731us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 81/100\n",
      "160/160 [==============================] - 0s 662us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 82/100\n",
      "160/160 [==============================] - 0s 608us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 83/100\n",
      "160/160 [==============================] - 0s 691us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 84/100\n",
      "160/160 [==============================] - 0s 588us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 85/100\n",
      "160/160 [==============================] - 0s 664us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 86/100\n",
      "160/160 [==============================] - 0s 601us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 87/100\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 88/100\n",
      "160/160 [==============================] - 0s 790us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 89/100\n",
      "160/160 [==============================] - 0s 782us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 90/100\n",
      "160/160 [==============================] - 0s 619us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 91/100\n",
      "160/160 [==============================] - 0s 660us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 92/100\n",
      "160/160 [==============================] - 0s 814us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 93/100\n",
      "160/160 [==============================] - 0s 646us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 94/100\n",
      "160/160 [==============================] - 0s 693us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 95/100\n",
      "160/160 [==============================] - 0s 886us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 96/100\n",
      "160/160 [==============================] - 0s 700us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 97/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "160/160 [==============================] - 0s 700us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 98/100\n",
      "160/160 [==============================] - 0s 639us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 99/100\n",
      "160/160 [==============================] - 0s 745us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n",
      "Epoch 100/100\n",
      "160/160 [==============================] - 0s 637us/step - loss: 5.4399 - acc: 0.6625 - val_loss: 4.1788 - val_acc: 0.7407\n"
     ]
    }
   ],
   "source": [
    "for noise in noises:\n",
    "    print(noise)\n",
    "    histories.append(model.fit(get_noisy_x(X_train, stddev=noise), Y_train_k, batch_size=5, epochs=100, verbose=1, validation_data=(X_test, Y_test_k)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2mnZXpqq0KJd5XUtKQXhwp1IuBgWEsbGZGBOzcVob67hlvVlWUimriwXCWFhT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PBhhg8fXu+YSUlJJCUlcfz4cfr378+0adMYNmwY3bp1qzMj6cCBAwAcOnSo3sDbuXPnADh1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\nJEmSJEmSBsnCmCRJkiRJkgbp/wEOKn1kxpTc8wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2b6147d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 15))\n",
    "i = 0\n",
    "for history in histories:  \n",
    "    plt.plot(history.history['acc'], label=\"Noise =\" + str(noises[i]/ 100.0))\n",
    "    i+= 1\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.title('Model accuracy')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}