ResNet.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><h1>ResNet para Classificação das Imagens GASF-GADF-MTF</h1></center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function  \n",
    "from keras.models import Model\n",
    "from keras.layers import Input, Dense, merge, Activation\n",
    "from keras.layers.merge import Concatenate\n",
    "from keras.utils import np_utils\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import keras\n",
    "import time\n",
    "import pickle\n",
    "from keras.callbacks import ReduceLROnPlateau\n",
    "np.random.seed(7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=\"red\"><b><h3>Configurações</h3></b></font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_epochs = 1000\n",
    "datafiles  = ['imagens_Coffee_16x16.pkl',\n",
    "              'imagens_OliveOil_16x16.pkl',\n",
    "              'imagens_Beef_16x16.pkl']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=\"Blue\"><b><h3>Estrutura da ResNet</h3></b></font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def build_resnet(input_shape, n_feature_maps, nb_classes):\n",
    "    print ('Construindo ResNet...')\n",
    "    x = Input(shape=(input_shape))\n",
    "    conv_x = keras.layers.normalization.BatchNormalization()(x)\n",
    "    conv_x = keras.layers.Conv2D(n_feature_maps, (8,1), padding='same')(conv_x)\n",
    "    conv_x = keras.layers.normalization.BatchNormalization()(conv_x)\n",
    "    conv_x = Activation('relu')(conv_x)     \n",
    "    conv_y = keras.layers.Conv2D(n_feature_maps, (5,1), padding='same')(conv_x)\n",
    "    conv_y = keras.layers.normalization.BatchNormalization()(conv_y)\n",
    "    conv_y = Activation('relu')(conv_y)     \n",
    "    conv_z = keras.layers.Conv2D(n_feature_maps, (3,1), padding='same')(conv_y)\n",
    "    conv_z = keras.layers.normalization.BatchNormalization()(conv_z)     \n",
    "    is_expand_channels = not (input_shape[-1] == n_feature_maps)\n",
    "    if is_expand_channels:\n",
    "        shortcut_y = keras.layers.Conv2D(n_feature_maps, (1,1), padding='same')(x)\n",
    "        shortcut_y = keras.layers.normalization.BatchNormalization()(shortcut_y)\n",
    "    else:\n",
    "        shortcut_y = keras.layers.normalization.BatchNormalization()(x)\n",
    "    y = Concatenate()([shortcut_y, conv_z]);\n",
    "    y = Activation('relu')(y)\n",
    "    \n",
    "    x1 = y\n",
    "    conv_x = keras.layers.Conv2D(n_feature_maps*2, (8,1), padding='same')(x1)\n",
    "    conv_x = keras.layers.normalization.BatchNormalization()(conv_x)\n",
    "    conv_x = Activation('relu')(conv_x)     \n",
    "    conv_y = keras.layers.Conv2D(n_feature_maps*2, (5,1), padding='same')(conv_x)\n",
    "    conv_y = keras.layers.normalization.BatchNormalization()(conv_y)\n",
    "    conv_y = Activation('relu')(conv_y)     \n",
    "    conv_z = keras.layers.Conv2D(n_feature_maps*2, (3,1), padding='same')(conv_y)\n",
    "    conv_z = keras.layers.normalization.BatchNormalization()(conv_z)     \n",
    "    is_expand_channels = not (input_shape[-1] == n_feature_maps*2)\n",
    "    if is_expand_channels:\n",
    "        shortcut_y = keras.layers.Conv2D(n_feature_maps*2, (1,1), padding='same')(x1)\n",
    "        shortcut_y = keras.layers.normalization.BatchNormalization()(shortcut_y)\n",
    "    else:\n",
    "        shortcut_y = keras.layers.normalization.BatchNormalization()(x1)        \n",
    "    y = Concatenate()([shortcut_y, conv_z])\n",
    "    y = Activation('relu')(y)\n",
    "     \n",
    "    x1 = y\n",
    "    conv_x = keras.layers.Conv2D(n_feature_maps*2, (8,1), padding='same')(x1)\n",
    "    conv_x = keras.layers.normalization.BatchNormalization()(conv_x)\n",
    "    conv_x = Activation('relu')(conv_x)     \n",
    "    conv_y = keras.layers.Conv2D(n_feature_maps*2, (5,1), padding='same')(conv_x)\n",
    "    conv_y = keras.layers.normalization.BatchNormalization()(conv_y)\n",
    "    conv_y = Activation('relu')(conv_y)     \n",
    "    conv_z = keras.layers.Conv2D(n_feature_maps*2, (3,1), padding='same')(conv_y)\n",
    "    conv_z = keras.layers.normalization.BatchNormalization()(conv_z)\n",
    "    is_expand_channels = not (input_shape[-1] == n_feature_maps*2)\n",
    "    if is_expand_channels:\n",
    "        shortcut_y = keras.layers.Conv2D(n_feature_maps*2, (1,1), padding='same')(x1)\n",
    "        shortcut_y = keras.layers.normalization.BatchNormalization()(shortcut_y)\n",
    "    else:\n",
    "        shortcut_y = keras.layers.normalization.BatchNormalization()(x1)\n",
    "    y = Concatenate()([shortcut_y, conv_z])\n",
    "    y = Activation('relu')(y)     \n",
    "    full = keras.layers.pooling.GlobalAveragePooling2D()(y)   \n",
    "    out = Dense(nb_classes, activation='softmax')(full)\n",
    "    print ('Modelo elaborado.')\n",
    "    return x, out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=\"Blue\"><b><h3>Leitura dos arquivos .pkl gerados pelo Imaging.py</h3></b></font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def unpickleimg(file):\n",
    "    with open(file, 'rb') as fo:\n",
    "        dict = pickle.load(fo, encoding='bytes')\n",
    "    y_train = dict[0][1]\n",
    "    x_train = dict[0][0]\n",
    "    y_test = dict[1][1]\n",
    "    x_test = dict[1][0]\n",
    "    return y_train, x_train, y_test, x_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=\"Green\"><b><h3>Execução da Resnet</h3></b></font><br/>\n",
    "<b>Os resultados serão salvos em arquivos .pkl para cada datafile</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Construindo ResNet...\n",
      "Modelo elaborado.\n",
      "Train on 28 samples, validate on 28 samples\n",
      "Epoch 1/1000\n",
      "28/28 [==============================] - 3s - loss: 0.5538 - acc: 0.6786 - val_loss: 0.6973 - val_acc: 0.4643\n",
      "Epoch 2/1000\n",
      "28/28 [==============================] - 0s - loss: 0.2357 - acc: 0.9643 - val_loss: 0.7007 - val_acc: 0.4643\n",
      "Epoch 3/1000\n",
      "28/28 [==============================] - 0s - loss: 0.1427 - acc: 0.9643 - val_loss: 0.7042 - val_acc: 0.4643\n",
      "Epoch 4/1000\n",
      "28/28 [==============================] - 0s - loss: 0.0603 - acc: 0.9643 - val_loss: 0.7135 - val_acc: 0.4643\n",
      "Epoch 5/1000\n",
      "28/28 [==============================] - 0s - loss: 0.0292 - acc: 1.0000 - val_loss: 0.7286 - val_acc: 0.4643\n",
      "Epoch 6/1000\n",
      "28/28 [==============================] - 0s - loss: 0.0157 - acc: 1.0000 - val_loss: 0.7504 - val_acc: 0.4643\n",
      "Epoch 7/1000\n",
      "28/28 [==============================] - 0s - loss: 0.0114 - acc: 1.0000 - val_loss: 0.7371 - val_acc: 0.4643\n",
      "Epoch 8/1000\n",
      "28/28 [==============================] - 0s - loss: 0.0100 - acc: 1.0000 - val_loss: 0.7214 - val_acc: 0.4643\n",
      "Epoch 9/1000\n",
      "28/28 [==============================] - 0s - loss: 0.0049 - acc: 1.0000 - val_loss: 0.7095 - val_acc: 0.4643\n",
      "Epoch 10/1000\n",
      "28/28 [==============================] - 0s - loss: 0.0037 - acc: 1.0000 - val_loss: 0.6872 - val_acc: 0.4643\n",
      "Epoch 11/1000\n",
      "28/28 [==============================] - 0s - loss: 0.0043 - acc: 1.0000 - val_loss: 0.6316 - val_acc: 0.4643\n",
      "Epoch 12/1000\n",
      "28/28 [==============================] - 0s - loss: 0.0067 - acc: 1.0000 - val_loss: 0.5637 - val_acc: 0.9643\n",
      "Epoch 13/1000\n",
      "28/28 [==============================] - 0s - loss: 0.0040 - acc: 1.0000 - val_loss: 0.5223 - val_acc: 0.9286\n",
      "Epoch 14/1000\n",
      "28/28 [==============================] - 0s - loss: 8.2293e-04 - acc: 1.0000 - val_loss: 0.5263 - val_acc: 0.5714\n",
      "Epoch 15/1000\n",
      "28/28 [==============================] - 0s - loss: 0.0019 - acc: 1.0000 - val_loss: 0.5809 - val_acc: 0.5357\n",
      "Epoch 16/1000\n",
      "28/28 [==============================] - 0s - loss: 0.0011 - acc: 1.0000 - val_loss: 0.6748 - val_acc: 0.5357\n",
      "Epoch 17/1000\n",
      "28/28 [==============================] - 0s - loss: 0.0027 - acc: 1.0000 - val_loss: 0.8247 - val_acc: 0.5357\n",
      "Epoch 18/1000\n",
      "28/28 [==============================] - 0s - loss: 4.6199e-04 - acc: 1.0000 - val_loss: 1.0387 - val_acc: 0.5357\n",
      "Epoch 19/1000\n",
      "28/28 [==============================] - 0s - loss: 0.0021 - acc: 1.0000 - val_loss: 1.3417 - val_acc: 0.5357\n",
      "Epoch 20/1000\n",
      "28/28 [==============================] - 0s - loss: 7.4452e-04 - acc: 1.0000 - val_loss: 1.6964 - val_acc: 0.5357\n",
      "Epoch 21/1000\n",
      "28/28 [==============================] - 0s - loss: 5.5894e-04 - acc: 1.0000 - val_loss: 2.0717 - val_acc: 0.5357\n",
      "Epoch 22/1000\n",
      "28/28 [==============================] - 0s - loss: 2.5301e-04 - acc: 1.0000 - val_loss: 2.4236 - val_acc: 0.5357\n",
      "Epoch 23/1000\n",
      "28/28 [==============================] - 0s - loss: 5.1401e-04 - acc: 1.0000 - val_loss: 2.7476 - val_acc: 0.5357\n",
      "Epoch 24/1000\n",
      "28/28 [==============================] - 0s - loss: 5.3345e-04 - acc: 1.0000 - val_loss: 3.0367 - val_acc: 0.5357\n",
      "Epoch 25/1000\n",
      "28/28 [==============================] - 0s - loss: 1.8540e-04 - acc: 1.0000 - val_loss: 3.3029 - val_acc: 0.5357\n",
      "Epoch 26/1000\n",
      "28/28 [==============================] - 0s - loss: 1.7287e-04 - acc: 1.0000 - val_loss: 3.5548 - val_acc: 0.5357\n",
      "Epoch 27/1000\n",
      "28/28 [==============================] - 0s - loss: 1.6813e-04 - acc: 1.0000 - val_loss: 3.7943 - val_acc: 0.5357\n",
      "Epoch 28/1000\n",
      "28/28 [==============================] - 0s - loss: 1.6522e-04 - acc: 1.0000 - val_loss: 4.0231 - val_acc: 0.5357\n",
      "Epoch 29/1000\n",
      "28/28 [==============================] - 0s - loss: 3.7883e-04 - acc: 1.0000 - val_loss: 4.2472 - val_acc: 0.5357\n",
      "Epoch 30/1000\n",
      "28/28 [==============================] - 0s - loss: 1.3924e-04 - acc: 1.0000 - val_loss: 4.4589 - val_acc: 0.5357\n",
      "Epoch 31/1000\n",
      "28/28 [==============================] - 0s - loss: 3.3385e-04 - acc: 1.0000 - val_loss: 4.6653 - val_acc: 0.5357\n",
      "Epoch 32/1000\n",
      "28/28 [==============================] - 0s - loss: 1.3545e-04 - acc: 1.0000 - val_loss: 4.8588 - val_acc: 0.5357\n",
      "Epoch 33/1000\n",
      "28/28 [==============================] - 0s - loss: 1.3357e-04 - acc: 1.0000 - val_loss: 5.0431 - val_acc: 0.5357\n",
      "Epoch 34/1000\n",
      "28/28 [==============================] - 0s - loss: 3.5009e-04 - acc: 1.0000 - val_loss: 5.2180 - val_acc: 0.5357\n",
      "Epoch 35/1000\n",
      "28/28 [==============================] - 0s - loss: 1.2795e-04 - acc: 1.0000 - val_loss: 5.3800 - val_acc: 0.5357\n",
      "Epoch 36/1000\n",
      "28/28 [==============================] - 0s - loss: 1.0782e-04 - acc: 1.0000 - val_loss: 5.5345 - val_acc: 0.5357\n",
      "Epoch 37/1000\n",
      "28/28 [==============================] - 0s - loss: 2.2882e-04 - acc: 1.0000 - val_loss: 5.6810 - val_acc: 0.5357\n",
      "Epoch 38/1000\n",
      "28/28 [==============================] - 0s - loss: 2.0452e-04 - acc: 1.0000 - val_loss: 5.8214 - val_acc: 0.5357\n",
      "Epoch 39/1000\n",
      "28/28 [==============================] - 0s - loss: 0.0012 - acc: 1.0000 - val_loss: 5.9538 - val_acc: 0.5357\n",
      "Epoch 40/1000\n",
      "28/28 [==============================] - 0s - loss: 7.2045e-04 - acc: 1.0000 - val_loss: 6.0839 - val_acc: 0.5357\n",
      "Epoch 41/1000\n",
      "28/28 [==============================] - 0s - loss: 0.0011 - acc: 1.0000 - val_loss: 6.2204 - val_acc: 0.5357\n",
      "Epoch 42/1000\n",
      "28/28 [==============================] - 0s - loss: 2.3230e-04 - acc: 1.0000 - val_loss: 6.3544 - val_acc: 0.5357\n",
      "Epoch 43/1000\n",
      "28/28 [==============================] - 0s - loss: 1.0407e-04 - acc: 1.0000 - val_loss: 6.4761 - val_acc: 0.5357\n",
      "Epoch 44/1000\n",
      "28/28 [==============================] - 0s - loss: 2.2683e-04 - acc: 1.0000 - val_loss: 6.5898 - val_acc: 0.5357\n",
      "Epoch 45/1000\n",
      "28/28 [==============================] - 0s - loss: 9.1417e-05 - acc: 1.0000 - val_loss: 6.6933 - val_acc: 0.5357\n",
      "Epoch 46/1000\n",
      "28/28 [==============================] - 0s - loss: 2.2665e-04 - acc: 1.0000 - val_loss: 6.7905 - val_acc: 0.5357\n",
      "Epoch 47/1000\n",
      "28/28 [==============================] - 0s - loss: 8.6639e-04 - acc: 1.0000 - val_loss: 6.8721 - val_acc: 0.5357\n",
      "Epoch 48/1000\n",
      "28/28 [==============================] - 0s - loss: 8.4587e-04 - acc: 1.0000 - val_loss: 6.9382 - val_acc: 0.5357\n",
      "Epoch 49/1000\n",
      "28/28 [==============================] - 0s - loss: 1.7642e-04 - acc: 1.0000 - val_loss: 7.0013 - val_acc: 0.5357\n",
      "Epoch 50/1000\n",
      "28/28 [==============================] - 0s - loss: 8.7826e-04 - acc: 1.0000 - val_loss: 7.0880 - val_acc: 0.5357\n",
      "Epoch 51/1000\n",
      "28/28 [==============================] - 0s - loss: 1.9636e-04 - acc: 1.0000 - val_loss: 7.1849 - val_acc: 0.5357\n",
      "Epoch 52/1000\n",
      "28/28 [==============================] - 0s - loss: 6.4794e-04 - acc: 1.0000 - val_loss: 7.2774 - val_acc: 0.5357\n",
      "Epoch 53/1000\n",
      "28/28 [==============================] - 0s - loss: 7.0792e-04 - acc: 1.0000 - val_loss: 7.3629 - val_acc: 0.5357\n",
      "Epoch 54/1000\n",
      "28/28 [==============================] - 0s - loss: 7.1587e-04 - acc: 1.0000 - val_loss: 7.4116 - val_acc: 0.5357\n",
      "Epoch 55/1000\n",
      "28/28 [==============================] - 0s - loss: 6.7114e-05 - acc: 1.0000 - val_loss: 7.4428 - val_acc: 0.5357\n",
      "Epoch 56/1000\n",
      "28/28 [==============================] - 0s - loss: 1.5423e-04 - acc: 1.0000 - val_loss: 7.4643 - val_acc: 0.5357\n",
      "Epoch 57/1000\n",
      "28/28 [==============================] - 0s - loss: 6.5777e-05 - acc: 1.0000 - val_loss: 7.4733 - val_acc: 0.5357\n",
      "Epoch 58/1000\n",
      "28/28 [==============================] - 0s - loss: 1.7012e-04 - acc: 1.0000 - val_loss: 7.4774 - val_acc: 0.5357\n",
      "Epoch 59/1000\n",
      "28/28 [==============================] - 0s - loss: 0.0075 - acc: 1.0000 - val_loss: 7.1829 - val_acc: 0.5357\n",
      "Epoch 60/1000\n",
      "28/28 [==============================] - 0s - loss: 0.0079 - acc: 1.0000 - val_loss: 7.3616 - val_acc: 0.5357\n",
      "Epoch 61/1000\n",
      "28/28 [==============================] - 0s - loss: 0.0045 - acc: 1.0000 - val_loss: 7.4696 - val_acc: 0.5357\n",
      "Epoch 62/1000\n",
      "28/28 [==============================] - 0s - loss: 0.0047 - acc: 1.0000 - val_loss: 7.4834 - val_acc: 0.5357\n",
      "Epoch 63/1000\n",
      "28/28 [==============================] - 0s - loss: 0.0016 - acc: 1.0000 - val_loss: 7.4808 - val_acc: 0.5357\n",
      "Epoch 64/1000\n",
      "28/28 [==============================] - 0s - loss: 0.0019 - acc: 1.0000 - val_loss: 7.4671 - val_acc: 0.5357\n",
      "Epoch 65/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28/28 [==============================] - 0s - loss: 0.0010 - acc: 1.0000 - val_loss: 7.4171 - val_acc: 0.5357\n",
      "Epoch 66/1000\n",
      "28/28 [==============================] - 0s - loss: 0.0014 - acc: 1.0000 - val_loss: 7.3658 - val_acc: 0.5357\n",
      "Epoch 67/1000\n",
      "28/28 [==============================] - 0s - loss: 0.0302 - acc: 1.0000 - val_loss: 7.4690 - val_acc: 0.5357\n",
      "Epoch 68/1000\n",
      "28/28 [==============================] - 0s - loss: 0.0028 - acc: 1.0000 - val_loss: 7.4834 - val_acc: 0.5357\n",
      "Epoch 69/1000\n",
      "28/28 [==============================] - 0s - loss: 0.1215 - acc: 0.9643 - val_loss: 7.2615 - val_acc: 0.5357\n",
      "Epoch 70/1000\n",
      "28/28 [==============================] - 0s - loss: 0.0559 - acc: 1.0000 - val_loss: 4.2141 - val_acc: 0.5357\n",
      "Epoch 71/1000\n",
      "28/28 [==============================] - 0s - loss: 0.0358 - acc: 1.0000 - val_loss: 1.9023 - val_acc: 0.5357\n",
      "Epoch 72/1000\n",
      "28/28 [==============================] - 0s - loss: 0.0058 - acc: 1.0000 - val_loss: 1.7544 - val_acc: 0.5357\n",
      "Epoch 73/1000\n",
      "28/28 [==============================] - 0s - loss: 0.0147 - acc: 1.0000 - val_loss: 1.5473 - val_acc: 0.5357\n",
      "Epoch 74/1000\n",
      "28/28 [==============================] - 0s - loss: 0.0034 - acc: 1.0000 - val_loss: 1.1502 - val_acc: 0.5714\n",
      "Epoch 75/1000\n",
      "28/28 [==============================] - 0s - loss: 0.0128 - acc: 1.0000 - val_loss: 0.6393 - val_acc: 0.6786\n",
      "Epoch 76/1000\n",
      "28/28 [==============================] - 0s - loss: 0.0334 - acc: 1.0000 - val_loss: 0.0518 - val_acc: 1.0000\n",
      "Epoch 77/1000\n",
      "28/28 [==============================] - 0s - loss: 0.0017 - acc: 1.0000 - val_loss: 0.1087 - val_acc: 0.9643\n",
      "Epoch 78/1000\n",
      "28/28 [==============================] - 0s - loss: 0.0014 - acc: 1.0000 - val_loss: 0.1868 - val_acc: 0.9643\n",
      "Epoch 79/1000\n",
      "28/28 [==============================] - 0s - loss: 0.0047 - acc: 1.0000 - val_loss: 0.1372 - val_acc: 0.9643\n",
      "Epoch 80/1000\n",
      "28/28 [==============================] - 0s - loss: 9.5230e-04 - acc: 1.0000 - val_loss: 0.0842 - val_acc: 0.9643\n",
      "Epoch 81/1000\n",
      "28/28 [==============================] - 1s - loss: 7.9173e-04 - acc: 1.0000 - val_loss: 0.0491 - val_acc: 0.9643\n",
      "Epoch 82/1000\n",
      "28/28 [==============================] - 0s - loss: 0.0012 - acc: 1.0000 - val_loss: 0.0326 - val_acc: 1.0000\n",
      "Epoch 83/1000\n",
      "28/28 [==============================] - 0s - loss: 0.0010 - acc: 1.0000 - val_loss: 0.0254 - val_acc: 1.0000\n",
      "Epoch 84/1000\n",
      "28/28 [==============================] - 0s - loss: 0.0012 - acc: 1.0000 - val_loss: 0.0249 - val_acc: 1.0000\n",
      "Epoch 85/1000\n",
      "28/28 [==============================] - 0s - loss: 0.0060 - acc: 1.0000 - val_loss: 0.0269 - val_acc: 1.0000\n",
      "Epoch 86/1000\n",
      "28/28 [==============================] - 0s - loss: 6.5338e-04 - acc: 1.0000 - val_loss: 0.0320 - val_acc: 1.0000\n",
      "Epoch 87/1000\n",
      "28/28 [==============================] - 0s - loss: 0.0019 - acc: 1.0000 - val_loss: 0.0392 - val_acc: 1.0000\n",
      "Epoch 88/1000\n",
      "28/28 [==============================] - 0s - loss: 4.0958e-04 - acc: 1.0000 - val_loss: 0.0506 - val_acc: 1.0000\n",
      "Epoch 89/1000\n",
      "28/28 [==============================] - 0s - loss: 3.8796e-04 - acc: 1.0000 - val_loss: 0.0639 - val_acc: 0.9643\n",
      "Epoch 90/1000\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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      "Epoch 634/1000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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      "Epoch 697/1000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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      "Epoch 760/1000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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      "Epoch 873/1000\n",
      "28/28 [==============================] - 0s - loss: 2.7191e-04 - acc: 1.0000 - val_loss: 0.0117 - val_acc: 1.0000\n",
      "Epoch 874/1000\n",
      "28/28 [==============================] - 0s - loss: 2.4597e-05 - acc: 1.0000 - val_loss: 0.0118 - val_acc: 1.0000\n",
      "Epoch 875/1000\n",
      "28/28 [==============================] - 0s - loss: 6.3173e-04 - acc: 1.0000 - val_loss: 0.0120 - val_acc: 1.0000\n",
      "Epoch 876/1000\n",
      "28/28 [==============================] - 0s - loss: 1.0750e-05 - acc: 1.0000 - val_loss: 0.0119 - val_acc: 1.0000\n",
      "Epoch 877/1000\n",
      "28/28 [==============================] - 0s - loss: 1.1747e-05 - acc: 1.0000 - val_loss: 0.0119 - val_acc: 1.0000\n",
      "Epoch 878/1000\n",
      "28/28 [==============================] - 0s - loss: 2.2964e-05 - acc: 1.0000 - val_loss: 0.0120 - val_acc: 1.0000\n",
      "Epoch 879/1000\n",
      "28/28 [==============================] - 0s - loss: 1.1093e-05 - acc: 1.0000 - val_loss: 0.0120 - val_acc: 1.0000\n",
      "Epoch 880/1000\n",
      "28/28 [==============================] - 0s - loss: 8.1212e-06 - acc: 1.0000 - val_loss: 0.0119 - val_acc: 1.0000\n",
      "Epoch 881/1000\n",
      "28/28 [==============================] - 0s - loss: 1.9721e-05 - acc: 1.0000 - val_loss: 0.0119 - val_acc: 1.0000\n",
      "Epoch 882/1000\n",
      "28/28 [==============================] - 0s - loss: 1.0791e-05 - acc: 1.0000 - val_loss: 0.0119 - val_acc: 1.0000\n",
      "Epoch 883/1000\n",
      "28/28 [==============================] - 0s - loss: 1.0748e-05 - acc: 1.0000 - val_loss: 0.0119 - val_acc: 1.0000\n",
      "Epoch 884/1000\n",
      "28/28 [==============================] - 0s - loss: 2.5674e-05 - acc: 1.0000 - val_loss: 0.0118 - val_acc: 1.0000\n",
      "Epoch 885/1000\n",
      "28/28 [==============================] - 0s - loss: 8.2340e-06 - acc: 1.0000 - val_loss: 0.0118 - val_acc: 1.0000\n",
      "Epoch 886/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28/28 [==============================] - 0s - loss: 1.1380e-05 - acc: 1.0000 - val_loss: 0.0118 - val_acc: 1.0000\n",
      "Epoch 887/1000\n",
      "28/28 [==============================] - 0s - loss: 2.7250e-05 - acc: 1.0000 - val_loss: 0.0119 - val_acc: 1.0000\n",
      "Epoch 888/1000\n",
      "28/28 [==============================] - 0s - loss: 2.3781e-05 - acc: 1.0000 - val_loss: 0.0120 - val_acc: 1.0000\n",
      "Epoch 889/1000\n",
      "28/28 [==============================] - 0s - loss: 9.1217e-06 - acc: 1.0000 - val_loss: 0.0119 - val_acc: 1.0000\n",
      "Epoch 890/1000\n",
      "28/28 [==============================] - 0s - loss: 8.6704e-06 - acc: 1.0000 - val_loss: 0.0119 - val_acc: 1.0000\n",
      "Epoch 891/1000\n",
      "28/28 [==============================] - 0s - loss: 9.3836e-06 - acc: 1.0000 - val_loss: 0.0120 - val_acc: 1.0000\n",
      "Epoch 892/1000\n",
      "28/28 [==============================] - 0s - loss: 2.5668e-05 - acc: 1.0000 - val_loss: 0.0119 - val_acc: 1.0000\n",
      "Epoch 893/1000\n",
      "28/28 [==============================] - 0s - loss: 1.9921e-05 - acc: 1.0000 - val_loss: 0.0118 - val_acc: 1.0000\n",
      "Epoch 894/1000\n",
      "28/28 [==============================] - 0s - loss: 2.1663e-05 - acc: 1.0000 - val_loss: 0.0119 - val_acc: 1.0000\n",
      "Epoch 895/1000\n",
      "28/28 [==============================] - 0s - loss: 1.1185e-05 - acc: 1.0000 - val_loss: 0.0119 - val_acc: 1.0000\n",
      "Epoch 896/1000\n",
      "28/28 [==============================] - 0s - loss: 2.4737e-05 - acc: 1.0000 - val_loss: 0.0118 - val_acc: 1.0000\n",
      "Epoch 897/1000\n",
      "28/28 [==============================] - 0s - loss: 2.0860e-05 - acc: 1.0000 - val_loss: 0.0118 - val_acc: 1.0000\n",
      "Epoch 898/1000\n",
      "28/28 [==============================] - 0s - loss: 8.1353e-05 - acc: 1.0000 - val_loss: 0.0119 - val_acc: 1.0000\n",
      "Epoch 899/1000\n",
      "28/28 [==============================] - 0s - loss: 2.3275e-05 - acc: 1.0000 - val_loss: 0.0120 - val_acc: 1.0000\n",
      "Epoch 900/1000\n",
      "28/28 [==============================] - 0s - loss: 2.8720e-05 - acc: 1.0000 - val_loss: 0.0119 - val_acc: 1.0000\n",
      "Epoch 901/1000\n",
      "28/28 [==============================] - 0s - loss: 2.4574e-05 - acc: 1.0000 - val_loss: 0.0119 - val_acc: 1.0000\n",
      "Epoch 902/1000\n",
      "28/28 [==============================] - 0s - loss: 3.2748e-05 - acc: 1.0000 - val_loss: 0.0118 - val_acc: 1.0000\n",
      "Epoch 903/1000\n",
      "28/28 [==============================] - 0s - loss: 3.5553e-05 - acc: 1.0000 - val_loss: 0.0118 - val_acc: 1.0000\n",
      "Epoch 904/1000\n",
      "28/28 [==============================] - 0s - loss: 9.7050e-06 - acc: 1.0000 - val_loss: 0.0118 - val_acc: 1.0000\n",
      "Epoch 905/1000\n",
      "28/28 [==============================] - 0s - loss: 2.0343e-05 - acc: 1.0000 - val_loss: 0.0118 - val_acc: 1.0000\n",
      "Epoch 906/1000\n",
      "28/28 [==============================] - 0s - loss: 1.1320e-04 - acc: 1.0000 - val_loss: 0.0119 - val_acc: 1.0000\n",
      "Epoch 907/1000\n",
      "28/28 [==============================] - 0s - loss: 2.6622e-05 - acc: 1.0000 - val_loss: 0.0118 - val_acc: 1.0000\n",
      "Epoch 908/1000\n",
      "28/28 [==============================] - 0s - loss: 9.9073e-06 - acc: 1.0000 - val_loss: 0.0118 - val_acc: 1.0000\n",
      "Epoch 909/1000\n",
      "28/28 [==============================] - 0s - loss: 9.7668e-06 - acc: 1.0000 - val_loss: 0.0119 - val_acc: 1.0000\n",
      "Epoch 910/1000\n",
      "28/28 [==============================] - 0s - loss: 9.2324e-06 - acc: 1.0000 - val_loss: 0.0119 - val_acc: 1.0000\n",
      "Epoch 911/1000\n",
      "28/28 [==============================] - 0s - loss: 1.0582e-05 - acc: 1.0000 - val_loss: 0.0118 - val_acc: 1.0000\n",
      "Epoch 912/1000\n",
      "28/28 [==============================] - 0s - loss: 7.7827e-06 - acc: 1.0000 - val_loss: 0.0118 - val_acc: 1.0000\n",
      "Epoch 913/1000\n",
      "28/28 [==============================] - 0s - loss: 1.1506e-05 - acc: 1.0000 - val_loss: 0.0118 - val_acc: 1.0000\n",
      "Epoch 914/1000\n",
      "28/28 [==============================] - 0s - loss: 2.9578e-04 - acc: 1.0000 - val_loss: 0.0117 - val_acc: 1.0000\n",
      "Epoch 915/1000\n",
      "28/28 [==============================] - 0s - loss: 1.0133e-05 - acc: 1.0000 - val_loss: 0.0117 - val_acc: 1.0000\n",
      "Epoch 916/1000\n",
      "28/28 [==============================] - 0s - loss: 1.0086e-05 - acc: 1.0000 - val_loss: 0.0117 - val_acc: 1.0000\n",
      "Epoch 917/1000\n",
      "28/28 [==============================] - 0s - loss: 1.1397e-04 - acc: 1.0000 - val_loss: 0.0118 - val_acc: 1.0000\n",
      "Epoch 918/1000\n",
      "28/28 [==============================] - 0s - loss: 1.6387e-05 - acc: 1.0000 - val_loss: 0.0118 - val_acc: 1.0000\n",
      "Epoch 919/1000\n",
      "28/28 [==============================] - 0s - loss: 2.7682e-05 - acc: 1.0000 - val_loss: 0.0118 - val_acc: 1.0000\n",
      "Epoch 920/1000\n",
      "28/28 [==============================] - 0s - loss: 9.6752e-06 - acc: 1.0000 - val_loss: 0.0118 - val_acc: 1.0000\n",
      "Epoch 921/1000\n",
      "28/28 [==============================] - 0s - loss: 2.2196e-05 - acc: 1.0000 - val_loss: 0.0118 - val_acc: 1.0000\n",
      "Epoch 922/1000\n",
      "28/28 [==============================] - 0s - loss: 3.2723e-05 - acc: 1.0000 - val_loss: 0.0119 - val_acc: 1.0000\n",
      "Epoch 923/1000\n",
      "28/28 [==============================] - 0s - loss: 3.3213e-05 - acc: 1.0000 - val_loss: 0.0119 - val_acc: 1.0000\n",
      "Epoch 924/1000\n",
      "28/28 [==============================] - 0s - loss: 2.9287e-05 - acc: 1.0000 - val_loss: 0.0120 - val_acc: 1.0000\n",
      "Epoch 925/1000\n",
      "28/28 [==============================] - 0s - loss: 3.3455e-05 - acc: 1.0000 - val_loss: 0.0121 - val_acc: 1.0000\n",
      "Epoch 926/1000\n",
      "28/28 [==============================] - 0s - loss: 2.9124e-05 - acc: 1.0000 - val_loss: 0.0120 - val_acc: 1.0000\n",
      "Epoch 927/1000\n",
      "28/28 [==============================] - 0s - loss: 8.2073e-05 - acc: 1.0000 - val_loss: 0.0119 - val_acc: 1.0000\n",
      "Epoch 928/1000\n",
      "28/28 [==============================] - 0s - loss: 2.1014e-05 - acc: 1.0000 - val_loss: 0.0118 - val_acc: 1.0000\n",
      "Epoch 929/1000\n",
      "28/28 [==============================] - 0s - loss: 7.1271e-04 - acc: 1.0000 - val_loss: 0.0120 - val_acc: 1.0000\n",
      "Epoch 930/1000\n",
      "28/28 [==============================] - 0s - loss: 9.0259e-06 - acc: 1.0000 - val_loss: 0.0119 - val_acc: 1.0000\n",
      "Epoch 931/1000\n",
      "28/28 [==============================] - 0s - loss: 3.2370e-05 - acc: 1.0000 - val_loss: 0.0118 - val_acc: 1.0000\n",
      "Epoch 932/1000\n",
      "28/28 [==============================] - 0s - loss: 1.4461e-04 - acc: 1.0000 - val_loss: 0.0119 - val_acc: 1.0000\n",
      "Epoch 933/1000\n",
      "28/28 [==============================] - 1s - loss: 9.1196e-06 - acc: 1.0000 - val_loss: 0.0119 - val_acc: 1.0000\n",
      "Epoch 934/1000\n",
      "28/28 [==============================] - 1s - loss: 1.8793e-05 - acc: 1.0000 - val_loss: 0.0119 - val_acc: 1.0000\n",
      "Epoch 935/1000\n",
      "28/28 [==============================] - 0s - loss: 1.5632e-05 - acc: 1.0000 - val_loss: 0.0118 - val_acc: 1.0000\n",
      "Epoch 936/1000\n",
      "28/28 [==============================] - 0s - loss: 1.4499e-04 - acc: 1.0000 - val_loss: 0.0117 - val_acc: 1.0000\n",
      "Epoch 937/1000\n",
      "28/28 [==============================] - 0s - loss: 1.1565e-04 - acc: 1.0000 - val_loss: 0.0116 - val_acc: 1.0000\n",
      "Epoch 938/1000\n",
      "28/28 [==============================] - 0s - loss: 9.2963e-06 - acc: 1.0000 - val_loss: 0.0117 - val_acc: 1.0000\n",
      "Epoch 939/1000\n",
      "28/28 [==============================] - 0s - loss: 2.0377e-05 - acc: 1.0000 - val_loss: 0.0117 - val_acc: 1.0000\n",
      "Epoch 940/1000\n",
      "28/28 [==============================] - 0s - loss: 2.9996e-05 - acc: 1.0000 - val_loss: 0.0117 - val_acc: 1.0000\n",
      "Epoch 941/1000\n",
      "28/28 [==============================] - 0s - loss: 8.4022e-06 - acc: 1.0000 - val_loss: 0.0117 - val_acc: 1.0000\n",
      "Epoch 942/1000\n",
      "28/28 [==============================] - 0s - loss: 2.9499e-05 - acc: 1.0000 - val_loss: 0.0118 - val_acc: 1.0000\n",
      "Epoch 943/1000\n",
      "28/28 [==============================] - 0s - loss: 3.4037e-05 - acc: 1.0000 - val_loss: 0.0118 - val_acc: 1.0000\n",
      "Epoch 944/1000\n",
      "28/28 [==============================] - 0s - loss: 2.5134e-05 - acc: 1.0000 - val_loss: 0.0118 - val_acc: 1.0000\n",
      "Epoch 945/1000\n",
      "28/28 [==============================] - 0s - loss: 1.4823e-04 - acc: 1.0000 - val_loss: 0.0119 - val_acc: 1.0000\n",
      "Epoch 946/1000\n",
      "28/28 [==============================] - 0s - loss: 1.0865e-05 - acc: 1.0000 - val_loss: 0.0119 - val_acc: 1.0000\n",
      "Epoch 947/1000\n",
      "28/28 [==============================] - 0s - loss: 8.0941e-05 - acc: 1.0000 - val_loss: 0.0119 - val_acc: 1.0000\n",
      "Epoch 948/1000\n",
      "28/28 [==============================] - 0s - loss: 9.4879e-06 - acc: 1.0000 - val_loss: 0.0119 - val_acc: 1.0000\n",
      "Epoch 949/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28/28 [==============================] - 0s - loss: 1.1755e-04 - acc: 1.0000 - val_loss: 0.0118 - val_acc: 1.0000\n",
      "Epoch 950/1000\n",
      "28/28 [==============================] - 0s - loss: 7.2984e-04 - acc: 1.0000 - val_loss: 0.0119 - val_acc: 1.0000\n",
      "Epoch 951/1000\n",
      "28/28 [==============================] - 0s - loss: 2.7161e-05 - acc: 1.0000 - val_loss: 0.0118 - val_acc: 1.0000\n",
      "Epoch 952/1000\n",
      "28/28 [==============================] - 0s - loss: 6.2287e-06 - acc: 1.0000 - val_loss: 0.0118 - val_acc: 1.0000\n",
      "Epoch 953/1000\n",
      "28/28 [==============================] - 0s - loss: 9.2814e-06 - acc: 1.0000 - val_loss: 0.0117 - val_acc: 1.0000\n",
      "Epoch 954/1000\n",
      "28/28 [==============================] - 0s - loss: 3.0554e-05 - acc: 1.0000 - val_loss: 0.0117 - val_acc: 1.0000\n",
      "Epoch 955/1000\n",
      "28/28 [==============================] - 0s - loss: 1.0290e-04 - acc: 1.0000 - val_loss: 0.0116 - val_acc: 1.0000\n",
      "Epoch 956/1000\n",
      "28/28 [==============================] - 0s - loss: 7.6060e-06 - acc: 1.0000 - val_loss: 0.0116 - val_acc: 1.0000\n",
      "Epoch 957/1000\n",
      "28/28 [==============================] - 0s - loss: 9.7136e-06 - acc: 1.0000 - val_loss: 0.0116 - val_acc: 1.0000\n",
      "Epoch 958/1000\n",
      "28/28 [==============================] - 0s - loss: 9.2516e-06 - acc: 1.0000 - val_loss: 0.0116 - val_acc: 1.0000\n",
      "Epoch 959/1000\n",
      "28/28 [==============================] - 0s - loss: 8.6449e-06 - acc: 1.0000 - val_loss: 0.0115 - val_acc: 1.0000\n",
      "Epoch 960/1000\n",
      "28/28 [==============================] - 0s - loss: 3.2443e-05 - acc: 1.0000 - val_loss: 0.0115 - val_acc: 1.0000\n",
      "Epoch 961/1000\n",
      "28/28 [==============================] - 0s - loss: 7.1015e-06 - acc: 1.0000 - val_loss: 0.0114 - val_acc: 1.0000\n",
      "Epoch 962/1000\n",
      "28/28 [==============================] - 0s - loss: 1.2160e-04 - acc: 1.0000 - val_loss: 0.0113 - val_acc: 1.0000\n",
      "Epoch 963/1000\n",
      "28/28 [==============================] - 0s - loss: 2.1146e-05 - acc: 1.0000 - val_loss: 0.0113 - val_acc: 1.0000\n",
      "Epoch 964/1000\n",
      "28/28 [==============================] - 0s - loss: 9.8945e-06 - acc: 1.0000 - val_loss: 0.0113 - val_acc: 1.0000\n",
      "Epoch 965/1000\n",
      "28/28 [==============================] - 0s - loss: 2.4376e-05 - acc: 1.0000 - val_loss: 0.0112 - val_acc: 1.0000\n",
      "Epoch 966/1000\n",
      "28/28 [==============================] - 0s - loss: 3.0152e-05 - acc: 1.0000 - val_loss: 0.0112 - val_acc: 1.0000\n",
      "Epoch 967/1000\n",
      "28/28 [==============================] - 0s - loss: 7.5357e-05 - acc: 1.0000 - val_loss: 0.0113 - val_acc: 1.0000\n",
      "Epoch 968/1000\n",
      "28/28 [==============================] - 0s - loss: 8.7826e-05 - acc: 1.0000 - val_loss: 0.0114 - val_acc: 1.0000\n",
      "Epoch 969/1000\n",
      "28/28 [==============================] - 0s - loss: 2.3903e-05 - acc: 1.0000 - val_loss: 0.0114 - val_acc: 1.0000\n",
      "Epoch 970/1000\n",
      "28/28 [==============================] - 0s - loss: 7.0653e-06 - acc: 1.0000 - val_loss: 0.0114 - val_acc: 1.0000\n",
      "Epoch 971/1000\n",
      "28/28 [==============================] - 0s - loss: 2.5106e-05 - acc: 1.0000 - val_loss: 0.0114 - val_acc: 1.0000\n",
      "Epoch 972/1000\n",
      "28/28 [==============================] - 0s - loss: 7.7087e-05 - acc: 1.0000 - val_loss: 0.0113 - val_acc: 1.0000\n",
      "Epoch 973/1000\n",
      "28/28 [==============================] - 0s - loss: 2.0656e-05 - acc: 1.0000 - val_loss: 0.0113 - val_acc: 1.0000\n",
      "Epoch 974/1000\n",
      "28/28 [==============================] - 0s - loss: 2.0162e-05 - acc: 1.0000 - val_loss: 0.0113 - val_acc: 1.0000\n",
      "Epoch 975/1000\n",
      "28/28 [==============================] - 0s - loss: 2.4494e-05 - acc: 1.0000 - val_loss: 0.0113 - val_acc: 1.0000\n",
      "Epoch 976/1000\n",
      "28/28 [==============================] - 0s - loss: 2.3735e-05 - acc: 1.0000 - val_loss: 0.0113 - val_acc: 1.0000\n",
      "Epoch 977/1000\n",
      "28/28 [==============================] - 0s - loss: 8.0445e-05 - acc: 1.0000 - val_loss: 0.0114 - val_acc: 1.0000\n",
      "Epoch 978/1000\n",
      "28/28 [==============================] - 1s - loss: 1.5934e-05 - acc: 1.0000 - val_loss: 0.0113 - val_acc: 1.0000\n",
      "Epoch 979/1000\n",
      "28/28 [==============================] - 1s - loss: 2.0038e-05 - acc: 1.0000 - val_loss: 0.0114 - val_acc: 1.0000\n",
      "Epoch 980/1000\n",
      "28/28 [==============================] - 0s - loss: 1.3666e-04 - acc: 1.0000 - val_loss: 0.0113 - val_acc: 1.0000\n",
      "Epoch 981/1000\n",
      "28/28 [==============================] - 0s - loss: 9.1941e-06 - acc: 1.0000 - val_loss: 0.0113 - val_acc: 1.0000\n",
      "Epoch 982/1000\n",
      "28/28 [==============================] - 0s - loss: 3.5749e-05 - acc: 1.0000 - val_loss: 0.0112 - val_acc: 1.0000\n",
      "Epoch 983/1000\n",
      "28/28 [==============================] - 0s - loss: 2.3400e-05 - acc: 1.0000 - val_loss: 0.0112 - val_acc: 1.0000\n",
      "Epoch 984/1000\n",
      "28/28 [==============================] - 0s - loss: 3.2195e-05 - acc: 1.0000 - val_loss: 0.0113 - val_acc: 1.0000\n",
      "Epoch 985/1000\n",
      "28/28 [==============================] - 0s - loss: 2.0207e-05 - acc: 1.0000 - val_loss: 0.0112 - val_acc: 1.0000\n",
      "Epoch 986/1000\n",
      "28/28 [==============================] - 0s - loss: 1.0176e-04 - acc: 1.0000 - val_loss: 0.0113 - val_acc: 1.0000\n",
      "Epoch 987/1000\n",
      "28/28 [==============================] - 0s - loss: 2.3075e-05 - acc: 1.0000 - val_loss: 0.0112 - val_acc: 1.0000\n",
      "Epoch 988/1000\n",
      "28/28 [==============================] - 0s - loss: 2.2615e-05 - acc: 1.0000 - val_loss: 0.0112 - val_acc: 1.0000\n",
      "Epoch 989/1000\n",
      "28/28 [==============================] - 0s - loss: 1.3646e-04 - acc: 1.0000 - val_loss: 0.0111 - val_acc: 1.0000\n",
      "Epoch 990/1000\n",
      "28/28 [==============================] - 0s - loss: 8.3916e-06 - acc: 1.0000 - val_loss: 0.0111 - val_acc: 1.0000\n",
      "Epoch 991/1000\n",
      "28/28 [==============================] - 0s - loss: 8.7811e-06 - acc: 1.0000 - val_loss: 0.0111 - val_acc: 1.0000\n",
      "Epoch 992/1000\n",
      "28/28 [==============================] - 0s - loss: 2.1333e-05 - acc: 1.0000 - val_loss: 0.0112 - val_acc: 1.0000\n",
      "Epoch 993/1000\n",
      "28/28 [==============================] - 0s - loss: 6.9653e-06 - acc: 1.0000 - val_loss: 0.0112 - val_acc: 1.0000\n",
      "Epoch 994/1000\n",
      "28/28 [==============================] - 0s - loss: 1.6828e-05 - acc: 1.0000 - val_loss: 0.0111 - val_acc: 1.0000\n",
      "Epoch 995/1000\n",
      "28/28 [==============================] - 0s - loss: 2.0779e-05 - acc: 1.0000 - val_loss: 0.0111 - val_acc: 1.0000\n",
      "Epoch 996/1000\n",
      "28/28 [==============================] - 0s - loss: 1.0961e-04 - acc: 1.0000 - val_loss: 0.0112 - val_acc: 1.0000\n",
      "Epoch 997/1000\n",
      "28/28 [==============================] - 0s - loss: 4.0305e-04 - acc: 1.0000 - val_loss: 0.0113 - val_acc: 1.0000\n",
      "Epoch 998/1000\n",
      "28/28 [==============================] - 0s - loss: 4.0357e-04 - acc: 1.0000 - val_loss: 0.0113 - val_acc: 1.0000\n",
      "Epoch 999/1000\n",
      "28/28 [==============================] - 0s - loss: 1.4235e-05 - acc: 1.0000 - val_loss: 0.0114 - val_acc: 1.0000\n",
      "Epoch 1000/1000\n",
      "28/28 [==============================] - 0s - loss: 5.2989e-04 - acc: 1.0000 - val_loss: 0.0114 - val_acc: 1.0000\n",
      "----------- Resultados -----------\n",
      "Loss:  6.22871955004e-06\n",
      "Acc:  1.0\n",
      "Tempo de processamento: 872.88 segundos.\n",
      "Construindo ResNet...\n",
      "Modelo elaborado.\n",
      "Train on 30 samples, validate on 30 samples\n",
      "Epoch 1/1000\n",
      "30/30 [==============================] - 3s - loss: 1.4762 - acc: 0.2000 - val_loss: 1.3739 - val_acc: 0.4000\n",
      "Epoch 2/1000\n",
      "30/30 [==============================] - 0s - loss: 1.3194 - acc: 0.4333 - val_loss: 1.3747 - val_acc: 0.4000\n",
      "Epoch 3/1000\n",
      "30/30 [==============================] - 0s - loss: 1.2881 - acc: 0.4333 - val_loss: 1.3733 - val_acc: 0.4000\n",
      "Epoch 4/1000\n",
      "30/30 [==============================] - 0s - loss: 1.2859 - acc: 0.4333 - val_loss: 1.3715 - val_acc: 0.4000\n",
      "Epoch 5/1000\n",
      "30/30 [==============================] - 0s - loss: 1.2891 - acc: 0.4333 - val_loss: 1.3689 - val_acc: 0.4000\n",
      "Epoch 6/1000\n",
      "30/30 [==============================] - 0s - loss: 1.2938 - acc: 0.4333 - val_loss: 1.3661 - val_acc: 0.4000\n",
      "Epoch 7/1000\n",
      "30/30 [==============================] - 0s - loss: 1.2859 - acc: 0.4333 - val_loss: 1.3643 - val_acc: 0.4000\n",
      "Epoch 8/1000\n",
      "30/30 [==============================] - 0s - loss: 1.2754 - acc: 0.4333 - val_loss: 1.3624 - val_acc: 0.4000\n",
      "Epoch 9/1000\n",
      "30/30 [==============================] - 0s - loss: 1.2806 - acc: 0.4333 - val_loss: 1.3600 - val_acc: 0.4000\n",
      "Epoch 10/1000\n",
      "30/30 [==============================] - 0s - loss: 1.2790 - acc: 0.4333 - val_loss: 1.3575 - val_acc: 0.4000\n",
      "Epoch 11/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30/30 [==============================] - 0s - loss: 1.2862 - acc: 0.4333 - val_loss: 1.3542 - val_acc: 0.4000\n",
      "Epoch 12/1000\n",
      "30/30 [==============================] - 0s - loss: 1.2806 - acc: 0.4333 - val_loss: 1.3498 - val_acc: 0.4000\n",
      "Epoch 13/1000\n",
      "30/30 [==============================] - 0s - loss: 1.2755 - acc: 0.4333 - val_loss: 1.3452 - val_acc: 0.4000\n",
      "Epoch 14/1000\n",
      "30/30 [==============================] - 0s - loss: 1.2658 - acc: 0.4333 - val_loss: 1.3396 - val_acc: 0.4000\n",
      "Epoch 15/1000\n",
      "30/30 [==============================] - 0s - loss: 1.2703 - acc: 0.4333 - val_loss: 1.3331 - val_acc: 0.4000\n",
      "Epoch 16/1000\n",
      "30/30 [==============================] - 0s - loss: 1.2529 - acc: 0.4333 - val_loss: 1.3268 - val_acc: 0.4000\n",
      "Epoch 17/1000\n",
      "30/30 [==============================] - 0s - loss: 1.2440 - acc: 0.4333 - val_loss: 1.3273 - val_acc: 0.4000\n",
      "Epoch 18/1000\n",
      "30/30 [==============================] - 0s - loss: 1.2107 - acc: 0.4333 - val_loss: 1.3715 - val_acc: 0.4000\n",
      "Epoch 19/1000\n",
      "30/30 [==============================] - 0s - loss: 1.1634 - acc: 0.4333 - val_loss: 1.5633 - val_acc: 0.4000\n",
      "Epoch 20/1000\n",
      "30/30 [==============================] - 0s - loss: 1.1373 - acc: 0.4333 - val_loss: 2.3138 - val_acc: 0.4000\n",
      "Epoch 21/1000\n",
      "30/30 [==============================] - 0s - loss: 1.1616 - acc: 0.4333 - val_loss: 3.6599 - val_acc: 0.4000\n",
      "Epoch 22/1000\n",
      "30/30 [==============================] - 0s - loss: 1.1416 - acc: 0.4333 - val_loss: 6.2945 - val_acc: 0.4000\n",
      "Epoch 23/1000\n",
      "30/30 [==============================] - 0s - loss: 1.0769 - acc: 0.5000 - val_loss: 9.2030 - val_acc: 0.4000\n",
      "Epoch 24/1000\n",
      "30/30 [==============================] - 0s - loss: 1.1028 - acc: 0.4333 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 25/1000\n",
      "30/30 [==============================] - 0s - loss: 1.0793 - acc: 0.4667 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 26/1000\n",
      "30/30 [==============================] - 0s - loss: 1.1178 - acc: 0.4667 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 27/1000\n",
      "30/30 [==============================] - 1s - loss: 1.1115 - acc: 0.4667 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 28/1000\n",
      "30/30 [==============================] - 0s - loss: 1.0928 - acc: 0.4667 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 29/1000\n",
      "30/30 [==============================] - 0s - loss: 1.0715 - acc: 0.4333 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 30/1000\n",
      "30/30 [==============================] - 0s - loss: 1.0133 - acc: 0.5667 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 31/1000\n",
      "30/30 [==============================] - 0s - loss: 1.0010 - acc: 0.6000 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 32/1000\n",
      "30/30 [==============================] - 0s - loss: 0.9992 - acc: 0.5333 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 33/1000\n",
      "30/30 [==============================] - 0s - loss: 0.9793 - acc: 0.6333 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 34/1000\n",
      "30/30 [==============================] - 0s - loss: 0.9979 - acc: 0.5333 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 35/1000\n",
      "30/30 [==============================] - 0s - loss: 0.9233 - acc: 0.5667 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 36/1000\n",
      "30/30 [==============================] - 0s - loss: 0.9541 - acc: 0.6000 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 37/1000\n",
      "30/30 [==============================] - 0s - loss: 0.8310 - acc: 0.7000 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 38/1000\n",
      "30/30 [==============================] - 0s - loss: 0.8388 - acc: 0.7333 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 39/1000\n",
      "30/30 [==============================] - 0s - loss: 1.0802 - acc: 0.6667 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 40/1000\n",
      "30/30 [==============================] - 0s - loss: 0.9696 - acc: 0.7333 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 41/1000\n",
      "30/30 [==============================] - 0s - loss: 0.9068 - acc: 0.7000 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 42/1000\n",
      "30/30 [==============================] - 0s - loss: 0.9144 - acc: 0.7000 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 43/1000\n",
      "30/30 [==============================] - 0s - loss: 0.8975 - acc: 0.6667 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 44/1000\n",
      "30/30 [==============================] - 0s - loss: 0.9075 - acc: 0.7000 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 45/1000\n",
      "30/30 [==============================] - 0s - loss: 0.9677 - acc: 0.6333 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 46/1000\n",
      "30/30 [==============================] - 0s - loss: 1.1124 - acc: 0.4667 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 47/1000\n",
      "30/30 [==============================] - 0s - loss: 1.0781 - acc: 0.4667 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 48/1000\n",
      "30/30 [==============================] - 0s - loss: 0.9868 - acc: 0.5667 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 49/1000\n",
      "30/30 [==============================] - 0s - loss: 0.9411 - acc: 0.6000 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 50/1000\n",
      "30/30 [==============================] - 0s - loss: 0.8616 - acc: 0.7000 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 51/1000\n",
      "30/30 [==============================] - 0s - loss: 0.8065 - acc: 0.7333 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 52/1000\n",
      "30/30 [==============================] - 0s - loss: 0.8618 - acc: 0.6667 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 53/1000\n",
      "30/30 [==============================] - 0s - loss: 0.8294 - acc: 0.7000 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 54/1000\n",
      "30/30 [==============================] - 0s - loss: 0.8861 - acc: 0.7000 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 55/1000\n",
      "30/30 [==============================] - 0s - loss: 0.7918 - acc: 0.7000 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 56/1000\n",
      "30/30 [==============================] - 0s - loss: 0.8662 - acc: 0.6667 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 57/1000\n",
      "30/30 [==============================] - 0s - loss: 0.7912 - acc: 0.7333 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 58/1000\n",
      "30/30 [==============================] - 0s - loss: 0.9659 - acc: 0.5667 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 59/1000\n",
      "30/30 [==============================] - 0s - loss: 1.0766 - acc: 0.6000 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 60/1000\n",
      "30/30 [==============================] - 0s - loss: 0.9352 - acc: 0.6333 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 61/1000\n",
      "30/30 [==============================] - 0s - loss: 0.9662 - acc: 0.6000 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 62/1000\n",
      "30/30 [==============================] - 0s - loss: 0.9175 - acc: 0.6333 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 63/1000\n",
      "30/30 [==============================] - 0s - loss: 0.8115 - acc: 0.7000 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 64/1000\n",
      "30/30 [==============================] - 0s - loss: 0.7839 - acc: 0.6667 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 65/1000\n",
      "30/30 [==============================] - 0s - loss: 0.8079 - acc: 0.6667 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 66/1000\n",
      "30/30 [==============================] - 0s - loss: 0.7709 - acc: 0.7333 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 67/1000\n",
      "30/30 [==============================] - 0s - loss: 0.7807 - acc: 0.7000 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 68/1000\n",
      "30/30 [==============================] - 0s - loss: 0.8903 - acc: 0.6667 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 69/1000\n",
      "30/30 [==============================] - 0s - loss: 0.8651 - acc: 0.6667 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 70/1000\n",
      "30/30 [==============================] - 0s - loss: 0.8351 - acc: 0.7000 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 71/1000\n",
      "30/30 [==============================] - 0s - loss: 0.7578 - acc: 0.7000 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 72/1000\n",
      "30/30 [==============================] - 0s - loss: 0.7146 - acc: 0.7000 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 73/1000\n",
      "30/30 [==============================] - 0s - loss: 0.7087 - acc: 0.7333 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 74/1000\n",
      "30/30 [==============================] - 0s - loss: 0.7744 - acc: 0.6667 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 75/1000\n",
      "30/30 [==============================] - 0s - loss: 0.9062 - acc: 0.7000 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 76/1000\n",
      "30/30 [==============================] - 0s - loss: 0.8350 - acc: 0.6333 - val_loss: 7.5240 - val_acc: 0.4000\n",
      "Epoch 77/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30/30 [==============================] - 0s - loss: 0.8597 - acc: 0.6667 - val_loss: 2.2375 - val_acc: 0.3000\n",
      "Epoch 78/1000\n",
      "30/30 [==============================] - 0s - loss: 0.9684 - acc: 0.7000 - val_loss: 2.5095 - val_acc: 0.4000\n",
      "Epoch 79/1000\n",
      "30/30 [==============================] - 0s - loss: 0.8597 - acc: 0.6667 - val_loss: 4.2919 - val_acc: 0.4000\n",
      "Epoch 80/1000\n",
      "30/30 [==============================] - 0s - loss: 0.7940 - acc: 0.7000 - val_loss: 1.0257 - val_acc: 0.5667\n",
      "Epoch 81/1000\n",
      "30/30 [==============================] - 0s - loss: 0.7348 - acc: 0.7000 - val_loss: 3.7475 - val_acc: 0.1333\n",
      "Epoch 82/1000\n",
      "30/30 [==============================] - 0s - loss: 0.8433 - acc: 0.6667 - val_loss: 4.4531 - val_acc: 0.1333\n",
      "Epoch 83/1000\n",
      "30/30 [==============================] - 0s - loss: 0.7769 - acc: 0.7333 - val_loss: 2.8403 - val_acc: 0.1333\n",
      "Epoch 84/1000\n",
      "30/30 [==============================] - 0s - loss: 0.8480 - acc: 0.7333 - val_loss: 4.1295 - val_acc: 0.1333\n",
      "Epoch 85/1000\n",
      "30/30 [==============================] - 0s - loss: 0.7655 - acc: 0.7333 - val_loss: 3.8192 - val_acc: 0.1333\n",
      "Epoch 86/1000\n",
      "30/30 [==============================] - 0s - loss: 0.8238 - acc: 0.7000 - val_loss: 3.3247 - val_acc: 0.1333\n",
      "Epoch 87/1000\n",
      "30/30 [==============================] - 0s - loss: 0.7413 - acc: 0.6667 - val_loss: 4.2148 - val_acc: 0.1333\n",
      "Epoch 88/1000\n",
      "30/30 [==============================] - 0s - loss: 1.1029 - acc: 0.5333 - val_loss: 3.8601 - val_acc: 0.1333\n",
      "Epoch 89/1000\n",
      "30/30 [==============================] - 0s - loss: 0.8418 - acc: 0.6333 - val_loss: 5.5669 - val_acc: 0.4000\n",
      "Epoch 90/1000\n",
      "30/30 [==============================] - 0s - loss: 0.8592 - acc: 0.6000 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 91/1000\n",
      "30/30 [==============================] - 0s - loss: 0.8211 - acc: 0.7000 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 92/1000\n",
      "30/30 [==============================] - 0s - loss: 0.7598 - acc: 0.7000 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 93/1000\n",
      "30/30 [==============================] - 0s - loss: 0.7648 - acc: 0.7000 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 94/1000\n",
      "30/30 [==============================] - 0s - loss: 0.7005 - acc: 0.7333 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 95/1000\n",
      "30/30 [==============================] - 0s - loss: 0.6938 - acc: 0.7000 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 96/1000\n",
      "30/30 [==============================] - 0s - loss: 0.8059 - acc: 0.7000 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 97/1000\n",
      "30/30 [==============================] - 0s - loss: 0.8332 - acc: 0.6333 - val_loss: 9.6709 - val_acc: 0.4000\n",
      "Epoch 98/1000\n",
      "30/30 [==============================] - 0s - loss: 0.7116 - acc: 0.7000 - val_loss: 9.5173 - val_acc: 0.4000\n",
      "Epoch 99/1000\n",
      "30/30 [==============================] - 0s - loss: 0.6770 - acc: 0.7333 - val_loss: 9.2743 - val_acc: 0.4000\n",
      "Epoch 100/1000\n",
      "30/30 [==============================] - 0s - loss: 0.6872 - acc: 0.7333 - val_loss: 9.2987 - val_acc: 0.4000\n",
      "Epoch 101/1000\n",
      "30/30 [==============================] - 0s - loss: 0.6428 - acc: 0.7000 - val_loss: 9.1626 - val_acc: 0.4000\n",
      "Epoch 102/1000\n",
      "30/30 [==============================] - 0s - loss: 0.6233 - acc: 0.7333 - val_loss: 7.5113 - val_acc: 0.4333\n",
      "Epoch 103/1000\n",
      "30/30 [==============================] - 0s - loss: 0.6561 - acc: 0.7333 - val_loss: 5.0412 - val_acc: 0.4667\n",
      "Epoch 104/1000\n",
      "30/30 [==============================] - 0s - loss: 0.8376 - acc: 0.6667 - val_loss: 0.9978 - val_acc: 0.4667\n",
      "Epoch 105/1000\n",
      "30/30 [==============================] - 0s - loss: 0.7553 - acc: 0.7333 - val_loss: 2.8321 - val_acc: 0.1333\n",
      "Epoch 106/1000\n",
      "30/30 [==============================] - 0s - loss: 0.7889 - acc: 0.7333 - val_loss: 4.5838 - val_acc: 0.2333\n",
      "Epoch 107/1000\n",
      "30/30 [==============================] - 0s - loss: 0.6722 - acc: 0.8000 - val_loss: 5.4013 - val_acc: 0.1000\n",
      "Epoch 108/1000\n",
      "30/30 [==============================] - 0s - loss: 0.7034 - acc: 0.7333 - val_loss: 5.9888 - val_acc: 0.1333\n",
      "Epoch 109/1000\n",
      "30/30 [==============================] - 0s - loss: 0.6098 - acc: 0.8000 - val_loss: 4.8133 - val_acc: 0.1667\n",
      "Epoch 110/1000\n",
      "30/30 [==============================] - 0s - loss: 0.7191 - acc: 0.7000 - val_loss: 4.7764 - val_acc: 0.1333\n",
      "Epoch 111/1000\n",
      "30/30 [==============================] - 0s - loss: 0.6242 - acc: 0.7667 - val_loss: 6.3941 - val_acc: 0.1333\n",
      "Epoch 112/1000\n",
      "30/30 [==============================] - 0s - loss: 0.6000 - acc: 0.7667 - val_loss: 7.6078 - val_acc: 0.1333\n",
      "Epoch 113/1000\n",
      "30/30 [==============================] - 0s - loss: 0.5804 - acc: 0.8000 - val_loss: 7.9884 - val_acc: 0.1333\n",
      "Epoch 114/1000\n",
      "30/30 [==============================] - 0s - loss: 0.6606 - acc: 0.7667 - val_loss: 7.5919 - val_acc: 0.1333\n",
      "Epoch 115/1000\n",
      "30/30 [==============================] - 0s - loss: 0.6478 - acc: 0.7667 - val_loss: 7.3027 - val_acc: 0.1333\n",
      "Epoch 116/1000\n",
      "30/30 [==============================] - 0s - loss: 0.8416 - acc: 0.6333 - val_loss: 7.1823 - val_acc: 0.1333\n",
      "Epoch 117/1000\n",
      "30/30 [==============================] - 0s - loss: 0.6626 - acc: 0.7333 - val_loss: 7.0342 - val_acc: 0.1333\n",
      "Epoch 118/1000\n",
      "30/30 [==============================] - 0s - loss: 0.5862 - acc: 0.7667 - val_loss: 5.0801 - val_acc: 0.1333\n",
      "Epoch 119/1000\n",
      "30/30 [==============================] - 0s - loss: 0.7987 - acc: 0.6667 - val_loss: 5.1967 - val_acc: 0.1333\n",
      "Epoch 120/1000\n",
      "30/30 [==============================] - 0s - loss: 0.6050 - acc: 0.7667 - val_loss: 6.6448 - val_acc: 0.1333\n",
      "Epoch 121/1000\n",
      "30/30 [==============================] - 0s - loss: 0.6216 - acc: 0.7667 - val_loss: 7.3037 - val_acc: 0.1333\n",
      "Epoch 122/1000\n",
      "30/30 [==============================] - 0s - loss: 0.6053 - acc: 0.7667 - val_loss: 7.6111 - val_acc: 0.1333\n",
      "Epoch 123/1000\n",
      "30/30 [==============================] - 0s - loss: 0.5882 - acc: 0.7667 - val_loss: 7.7785 - val_acc: 0.1333\n",
      "Epoch 124/1000\n",
      "30/30 [==============================] - 0s - loss: 0.5904 - acc: 0.7667 - val_loss: 7.8382 - val_acc: 0.1333\n",
      "Epoch 125/1000\n",
      "30/30 [==============================] - 0s - loss: 0.6311 - acc: 0.7000 - val_loss: 7.7611 - val_acc: 0.1333\n",
      "Epoch 126/1000\n",
      "30/30 [==============================] - 0s - loss: 0.7934 - acc: 0.6667 - val_loss: 7.6038 - val_acc: 0.1333\n",
      "Epoch 127/1000\n",
      "30/30 [==============================] - 0s - loss: 0.5723 - acc: 0.7667 - val_loss: 7.6845 - val_acc: 0.1333\n",
      "Epoch 128/1000\n",
      "30/30 [==============================] - 0s - loss: 0.6027 - acc: 0.7667 - val_loss: 7.6220 - val_acc: 0.1333\n",
      "Epoch 129/1000\n",
      "30/30 [==============================] - 0s - loss: 0.6057 - acc: 0.8000 - val_loss: 7.5367 - val_acc: 0.1333\n",
      "Epoch 130/1000\n",
      "30/30 [==============================] - 0s - loss: 0.6080 - acc: 0.8000 - val_loss: 7.3226 - val_acc: 0.1333\n",
      "Epoch 131/1000\n",
      "30/30 [==============================] - 0s - loss: 0.6356 - acc: 0.7667 - val_loss: 7.2070 - val_acc: 0.1333\n",
      "Epoch 132/1000\n",
      "30/30 [==============================] - 0s - loss: 0.5788 - acc: 0.7667 - val_loss: 6.9858 - val_acc: 0.1333\n",
      "Epoch 133/1000\n",
      "30/30 [==============================] - 0s - loss: 0.7184 - acc: 0.6667 - val_loss: 6.7141 - val_acc: 0.1333\n",
      "Epoch 134/1000\n",
      "30/30 [==============================] - 0s - loss: 0.5909 - acc: 0.7667 - val_loss: 6.4867 - val_acc: 0.1333\n",
      "Epoch 135/1000\n",
      "30/30 [==============================] - 0s - loss: 0.5477 - acc: 0.8000 - val_loss: 6.3463 - val_acc: 0.1333\n",
      "Epoch 136/1000\n",
      "30/30 [==============================] - 0s - loss: 0.6495 - acc: 0.7333 - val_loss: 6.2028 - val_acc: 0.1333\n",
      "Epoch 137/1000\n",
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     ]
    },
    {
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     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "text": [
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      "Epoch 337/1000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "30/30 [==============================] - 0s - loss: 0.5070 - acc: 0.8333 - val_loss: 5.9897 - val_acc: 0.2000\n",
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      "Epoch 400/1000\n",
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      "Epoch 402/1000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "30/30 [==============================] - 0s - loss: 0.2863 - acc: 0.9333 - val_loss: 7.5668 - val_acc: 0.2000\n",
      "Epoch 403/1000\n",
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      "Epoch 467/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Epoch 532/1000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "30/30 [==============================] - 0s - loss: 0.1586 - acc: 0.9000 - val_loss: 7.0857 - val_acc: 0.1333\n",
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      "Epoch 539/1000\n",
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      "Epoch 540/1000\n",
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      "Epoch 541/1000\n",
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      "Epoch 585/1000\n",
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      "Epoch 586/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0419 - acc: 1.0000 - val_loss: 9.0116 - val_acc: 0.1000\n",
      "Epoch 587/1000\n",
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      "Epoch 588/1000\n",
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      "Epoch 589/1000\n",
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      "Epoch 591/1000\n",
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      "Epoch 593/1000\n",
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      "Epoch 595/1000\n",
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      "Epoch 596/1000\n",
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      "Epoch 597/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30/30 [==============================] - 0s - loss: 0.0447 - acc: 0.9667 - val_loss: 9.0188 - val_acc: 0.1000\n",
      "Epoch 598/1000\n",
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      "Epoch 608/1000\n",
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      "Epoch 620/1000\n",
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      "Epoch 622/1000\n",
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      "Epoch 658/1000\n",
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      "Epoch 660/1000\n",
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      "Epoch 662/1000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "30/30 [==============================] - 0s - loss: 0.0766 - acc: 0.9667 - val_loss: 7.9998 - val_acc: 0.1000\n",
      "Epoch 663/1000\n",
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      "Epoch 666/1000\n",
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      "Epoch 675/1000\n",
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      "Epoch 676/1000\n",
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      "Epoch 721/1000\n",
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      "Epoch 722/1000\n",
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      "Epoch 723/1000\n",
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      "Epoch 724/1000\n",
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      "Epoch 725/1000\n",
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      "Epoch 726/1000\n",
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      "Epoch 727/1000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "30/30 [==============================] - 0s - loss: 0.0237 - acc: 1.0000 - val_loss: 4.1006 - val_acc: 0.1667\n",
      "Epoch 728/1000\n",
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      "Epoch 737/1000\n",
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      "Epoch 750/1000\n",
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      "Epoch 792/1000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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      "Epoch 850/1000\n",
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      "Epoch 851/1000\n",
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      "Epoch 852/1000\n",
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      "Epoch 853/1000\n",
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      "Epoch 855/1000\n",
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      "Epoch 856/1000\n",
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      "Epoch 857/1000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "30/30 [==============================] - 0s - loss: 0.0120 - acc: 1.0000 - val_loss: 1.2228 - val_acc: 0.8667\n",
      "Epoch 858/1000\n",
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      "Epoch 859/1000\n",
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      "Epoch 860/1000\n",
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      "Epoch 861/1000\n",
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      "Epoch 862/1000\n",
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      "Epoch 863/1000\n",
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      "Epoch 864/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0310 - acc: 1.0000 - val_loss: 1.1412 - val_acc: 0.9000\n",
      "Epoch 865/1000\n",
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      "Epoch 866/1000\n",
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      "Epoch 867/1000\n",
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      "Epoch 868/1000\n",
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      "Epoch 869/1000\n",
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      "Epoch 870/1000\n",
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      "Epoch 876/1000\n",
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      "Epoch 878/1000\n",
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      "Epoch 879/1000\n",
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      "Epoch 880/1000\n",
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      "Epoch 881/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0122 - acc: 1.0000 - val_loss: 1.1016 - val_acc: 0.9000\n",
      "Epoch 882/1000\n",
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      "Epoch 883/1000\n",
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      "Epoch 889/1000\n",
      "30/30 [==============================] - 0s - loss: 0.1368 - acc: 0.9667 - val_loss: 1.3287 - val_acc: 0.8333\n",
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      "Epoch 893/1000\n",
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      "Epoch 900/1000\n",
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      "Epoch 901/1000\n",
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      "Epoch 905/1000\n",
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      "Epoch 908/1000\n",
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      "Epoch 909/1000\n",
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      "Epoch 912/1000\n",
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      "Epoch 913/1000\n",
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      "Epoch 914/1000\n",
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      "Epoch 916/1000\n",
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      "Epoch 917/1000\n",
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      "Epoch 918/1000\n",
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      "Epoch 919/1000\n",
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      "Epoch 920/1000\n",
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      "Epoch 921/1000\n",
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      "Epoch 922/1000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "30/30 [==============================] - 0s - loss: 0.0142 - acc: 1.0000 - val_loss: 1.3045 - val_acc: 0.8667\n",
      "Epoch 923/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0756 - acc: 0.9667 - val_loss: 1.2494 - val_acc: 0.8667\n",
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      "Epoch 925/1000\n",
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      "Epoch 926/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0147 - acc: 1.0000 - val_loss: 3.3626 - val_acc: 0.4000\n",
      "Epoch 927/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0124 - acc: 1.0000 - val_loss: 4.3028 - val_acc: 0.3333\n",
      "Epoch 928/1000\n",
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      "Epoch 929/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0059 - acc: 1.0000 - val_loss: 3.7469 - val_acc: 0.3667\n",
      "Epoch 930/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0161 - acc: 1.0000 - val_loss: 2.9948 - val_acc: 0.5333\n",
      "Epoch 931/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0130 - acc: 1.0000 - val_loss: 2.3477 - val_acc: 0.5667\n",
      "Epoch 932/1000\n",
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      "Epoch 933/1000\n",
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      "Epoch 934/1000\n",
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      "Epoch 935/1000\n",
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      "Epoch 936/1000\n",
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      "Epoch 937/1000\n",
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      "Epoch 938/1000\n",
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      "Epoch 939/1000\n",
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      "Epoch 940/1000\n",
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      "Epoch 941/1000\n",
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      "Epoch 942/1000\n",
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      "Epoch 943/1000\n",
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      "Epoch 944/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0093 - acc: 1.0000 - val_loss: 1.1748 - val_acc: 0.9000\n",
      "Epoch 945/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0051 - acc: 1.0000 - val_loss: 1.1738 - val_acc: 0.9000\n",
      "Epoch 946/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0036 - acc: 1.0000 - val_loss: 1.1880 - val_acc: 0.9000\n",
      "Epoch 947/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0885 - acc: 0.9667 - val_loss: 1.2116 - val_acc: 0.9000\n",
      "Epoch 948/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0041 - acc: 1.0000 - val_loss: 1.3435 - val_acc: 0.8667\n",
      "Epoch 949/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0062 - acc: 1.0000 - val_loss: 1.4351 - val_acc: 0.8667\n",
      "Epoch 950/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0973 - acc: 0.9667 - val_loss: 1.6224 - val_acc: 0.7667\n",
      "Epoch 951/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0186 - acc: 1.0000 - val_loss: 1.8355 - val_acc: 0.7000\n",
      "Epoch 952/1000\n",
      "30/30 [==============================] - 0s - loss: 0.1042 - acc: 0.9333 - val_loss: 1.9760 - val_acc: 0.7333\n",
      "Epoch 953/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0055 - acc: 1.0000 - val_loss: 2.0357 - val_acc: 0.7333\n",
      "Epoch 954/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0061 - acc: 1.0000 - val_loss: 2.1149 - val_acc: 0.7000\n",
      "Epoch 955/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0139 - acc: 1.0000 - val_loss: 2.1751 - val_acc: 0.6667\n",
      "Epoch 956/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0772 - acc: 0.9667 - val_loss: 2.1673 - val_acc: 0.6667\n",
      "Epoch 957/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0054 - acc: 1.0000 - val_loss: 2.0794 - val_acc: 0.7000\n",
      "Epoch 958/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0478 - acc: 0.9667 - val_loss: 2.0172 - val_acc: 0.7000\n",
      "Epoch 959/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0049 - acc: 1.0000 - val_loss: 2.0249 - val_acc: 0.7000\n",
      "Epoch 960/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0841 - acc: 0.9667 - val_loss: 2.1673 - val_acc: 0.6333\n",
      "Epoch 961/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0079 - acc: 1.0000 - val_loss: 2.6995 - val_acc: 0.5333\n",
      "Epoch 962/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0269 - acc: 1.0000 - val_loss: 3.7280 - val_acc: 0.3667\n",
      "Epoch 963/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0217 - acc: 1.0000 - val_loss: 4.2560 - val_acc: 0.3667\n",
      "Epoch 964/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0051 - acc: 1.0000 - val_loss: 4.0973 - val_acc: 0.3667\n",
      "Epoch 965/1000\n",
      "30/30 [==============================] - 0s - loss: 0.1076 - acc: 0.9667 - val_loss: 2.6657 - val_acc: 0.5000\n",
      "Epoch 966/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0050 - acc: 1.0000 - val_loss: 2.0033 - val_acc: 0.5333\n",
      "Epoch 967/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0050 - acc: 1.0000 - val_loss: 1.7555 - val_acc: 0.6000\n",
      "Epoch 968/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0158 - acc: 1.0000 - val_loss: 2.7573 - val_acc: 0.5667\n",
      "Epoch 969/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0089 - acc: 1.0000 - val_loss: 4.0675 - val_acc: 0.5000\n",
      "Epoch 970/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0206 - acc: 1.0000 - val_loss: 4.9388 - val_acc: 0.4667\n",
      "Epoch 971/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0109 - acc: 1.0000 - val_loss: 5.4687 - val_acc: 0.4333\n",
      "Epoch 972/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0261 - acc: 1.0000 - val_loss: 5.7943 - val_acc: 0.4333\n",
      "Epoch 973/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0974 - acc: 0.9333 - val_loss: 5.2109 - val_acc: 0.4333\n",
      "Epoch 974/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0070 - acc: 1.0000 - val_loss: 4.2045 - val_acc: 0.4333\n",
      "Epoch 975/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0059 - acc: 1.0000 - val_loss: 3.5426 - val_acc: 0.5333\n",
      "Epoch 976/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0080 - acc: 1.0000 - val_loss: 3.1321 - val_acc: 0.5333\n",
      "Epoch 977/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0130 - acc: 1.0000 - val_loss: 2.7465 - val_acc: 0.5667\n",
      "Epoch 978/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0259 - acc: 1.0000 - val_loss: 2.3476 - val_acc: 0.7000\n",
      "Epoch 979/1000\n",
      "30/30 [==============================] - 0s - loss: 0.1053 - acc: 0.9333 - val_loss: 1.9193 - val_acc: 0.7333\n",
      "Epoch 980/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0133 - acc: 1.0000 - val_loss: 1.6007 - val_acc: 0.7667\n",
      "Epoch 981/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0257 - acc: 1.0000 - val_loss: 1.3789 - val_acc: 0.8000\n",
      "Epoch 982/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0050 - acc: 1.0000 - val_loss: 1.2326 - val_acc: 0.7667\n",
      "Epoch 983/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0503 - acc: 0.9667 - val_loss: 1.2066 - val_acc: 0.8000\n",
      "Epoch 984/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0088 - acc: 1.0000 - val_loss: 1.4063 - val_acc: 0.7333\n",
      "Epoch 985/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0249 - acc: 1.0000 - val_loss: 1.6792 - val_acc: 0.6000\n",
      "Epoch 986/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0366 - acc: 1.0000 - val_loss: 1.8037 - val_acc: 0.6333\n",
      "Epoch 987/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30/30 [==============================] - 0s - loss: 0.0063 - acc: 1.0000 - val_loss: 1.8883 - val_acc: 0.6000\n",
      "Epoch 988/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0090 - acc: 1.0000 - val_loss: 1.8923 - val_acc: 0.5667\n",
      "Epoch 989/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0112 - acc: 1.0000 - val_loss: 1.6720 - val_acc: 0.6667\n",
      "Epoch 990/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0443 - acc: 1.0000 - val_loss: 1.4554 - val_acc: 0.7667\n",
      "Epoch 991/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0113 - acc: 1.0000 - val_loss: 1.4235 - val_acc: 0.8333\n",
      "Epoch 992/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0096 - acc: 1.0000 - val_loss: 1.3211 - val_acc: 0.8333\n",
      "Epoch 993/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0359 - acc: 0.9667 - val_loss: 1.2710 - val_acc: 0.8333\n",
      "Epoch 994/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0102 - acc: 1.0000 - val_loss: 1.2507 - val_acc: 0.8333\n",
      "Epoch 995/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0068 - acc: 1.0000 - val_loss: 1.2033 - val_acc: 0.8333\n",
      "Epoch 996/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0107 - acc: 1.0000 - val_loss: 1.1469 - val_acc: 0.8667\n",
      "Epoch 997/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0057 - acc: 1.0000 - val_loss: 1.1244 - val_acc: 0.9000\n",
      "Epoch 998/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0291 - acc: 1.0000 - val_loss: 1.1513 - val_acc: 0.9000\n",
      "Epoch 999/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0041 - acc: 1.0000 - val_loss: 1.2144 - val_acc: 0.9000\n",
      "Epoch 1000/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0108 - acc: 1.0000 - val_loss: 1.2742 - val_acc: 0.8667\n",
      "----------- Resultados -----------\n",
      "Loss:  0.00199284181775\n",
      "Acc:  0.899999992053\n",
      "Tempo de processamento: 927.34 segundos.\n",
      "Construindo ResNet...\n",
      "Modelo elaborado.\n",
      "Train on 30 samples, validate on 30 samples\n",
      "Epoch 1/1000\n",
      "30/30 [==============================] - 3s - loss: 1.5689 - acc: 0.4000 - val_loss: 1.5909 - val_acc: 0.2000\n",
      "Epoch 2/1000\n",
      "30/30 [==============================] - 0s - loss: 1.1156 - acc: 0.7000 - val_loss: 1.5848 - val_acc: 0.2000\n",
      "Epoch 3/1000\n",
      "30/30 [==============================] - 0s - loss: 0.9683 - acc: 0.8000 - val_loss: 1.5856 - val_acc: 0.2000\n",
      "Epoch 4/1000\n",
      "30/30 [==============================] - 0s - loss: 0.6923 - acc: 0.8667 - val_loss: 1.5879 - val_acc: 0.2000\n",
      "Epoch 5/1000\n",
      "30/30 [==============================] - 0s - loss: 0.5037 - acc: 0.9667 - val_loss: 1.5878 - val_acc: 0.2000\n",
      "Epoch 6/1000\n",
      "30/30 [==============================] - 0s - loss: 0.4171 - acc: 0.9000 - val_loss: 1.5827 - val_acc: 0.3333\n",
      "Epoch 7/1000\n",
      "30/30 [==============================] - 0s - loss: 0.2925 - acc: 0.9333 - val_loss: 1.5834 - val_acc: 0.3667\n",
      "Epoch 8/1000\n",
      "30/30 [==============================] - 0s - loss: 0.2554 - acc: 0.9667 - val_loss: 1.5912 - val_acc: 0.3000\n",
      "Epoch 9/1000\n",
      "30/30 [==============================] - 0s - loss: 0.1649 - acc: 0.9667 - val_loss: 1.5869 - val_acc: 0.3000\n",
      "Epoch 10/1000\n",
      "30/30 [==============================] - 0s - loss: 0.2248 - acc: 0.9333 - val_loss: 1.5829 - val_acc: 0.3333\n",
      "Epoch 11/1000\n",
      "30/30 [==============================] - 0s - loss: 0.1828 - acc: 0.9000 - val_loss: 1.6008 - val_acc: 0.2667\n",
      "Epoch 12/1000\n",
      "30/30 [==============================] - 0s - loss: 0.1493 - acc: 0.9667 - val_loss: 1.6019 - val_acc: 0.3333\n",
      "Epoch 13/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0920 - acc: 1.0000 - val_loss: 1.6243 - val_acc: 0.3000\n",
      "Epoch 14/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0991 - acc: 0.9667 - val_loss: 1.6987 - val_acc: 0.2667\n",
      "Epoch 15/1000\n",
      "30/30 [==============================] - 0s - loss: 0.1542 - acc: 0.9667 - val_loss: 1.7760 - val_acc: 0.2333\n",
      "Epoch 16/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0991 - acc: 0.9667 - val_loss: 1.7073 - val_acc: 0.2333\n",
      "Epoch 17/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0517 - acc: 1.0000 - val_loss: 1.6680 - val_acc: 0.2333\n",
      "Epoch 18/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0525 - acc: 1.0000 - val_loss: 1.6782 - val_acc: 0.2333\n",
      "Epoch 19/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0486 - acc: 1.0000 - val_loss: 1.7319 - val_acc: 0.3000\n",
      "Epoch 20/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0462 - acc: 0.9667 - val_loss: 1.8000 - val_acc: 0.3000\n",
      "Epoch 21/1000\n",
      "30/30 [==============================] - 1s - loss: 0.0449 - acc: 1.0000 - val_loss: 1.8969 - val_acc: 0.2333\n",
      "Epoch 22/1000\n",
      "30/30 [==============================] - 1s - loss: 0.0790 - acc: 0.9333 - val_loss: 2.0298 - val_acc: 0.2000\n",
      "Epoch 23/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0164 - acc: 1.0000 - val_loss: 2.1750 - val_acc: 0.2000\n",
      "Epoch 24/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0738 - acc: 0.9667 - val_loss: 2.2985 - val_acc: 0.2000\n",
      "Epoch 25/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0370 - acc: 1.0000 - val_loss: 2.3674 - val_acc: 0.2000\n",
      "Epoch 26/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0332 - acc: 1.0000 - val_loss: 2.4435 - val_acc: 0.2000\n",
      "Epoch 27/1000\n",
      "30/30 [==============================] - 0s - loss: 0.1629 - acc: 0.9000 - val_loss: 2.5443 - val_acc: 0.2000\n",
      "Epoch 28/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0156 - acc: 1.0000 - val_loss: 2.6486 - val_acc: 0.2333\n",
      "Epoch 29/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0561 - acc: 0.9667 - val_loss: 2.7583 - val_acc: 0.2000\n",
      "Epoch 30/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0398 - acc: 1.0000 - val_loss: 2.8495 - val_acc: 0.2000\n",
      "Epoch 31/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0420 - acc: 1.0000 - val_loss: 2.9382 - val_acc: 0.2000\n",
      "Epoch 32/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0188 - acc: 1.0000 - val_loss: 3.0411 - val_acc: 0.2000\n",
      "Epoch 33/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0129 - acc: 1.0000 - val_loss: 3.1631 - val_acc: 0.2000\n",
      "Epoch 34/1000\n",
      "30/30 [==============================] - 1s - loss: 0.0133 - acc: 1.0000 - val_loss: 3.3088 - val_acc: 0.2000\n",
      "Epoch 35/1000\n",
      "30/30 [==============================] - 1s - loss: 0.0562 - acc: 0.9667 - val_loss: 3.5068 - val_acc: 0.2000\n",
      "Epoch 36/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0102 - acc: 1.0000 - val_loss: 3.6958 - val_acc: 0.2000\n",
      "Epoch 37/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0252 - acc: 1.0000 - val_loss: 3.8974 - val_acc: 0.2000\n",
      "Epoch 38/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0317 - acc: 1.0000 - val_loss: 4.0966 - val_acc: 0.2000\n",
      "Epoch 39/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0334 - acc: 1.0000 - val_loss: 4.1941 - val_acc: 0.2000\n",
      "Epoch 40/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0127 - acc: 1.0000 - val_loss: 4.1498 - val_acc: 0.2000\n",
      "Epoch 41/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0170 - acc: 1.0000 - val_loss: 4.1718 - val_acc: 0.2000\n",
      "Epoch 42/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0447 - acc: 0.9667 - val_loss: 4.3036 - val_acc: 0.2000\n",
      "Epoch 43/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0140 - acc: 1.0000 - val_loss: 4.4153 - val_acc: 0.2000\n",
      "Epoch 44/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0624 - acc: 0.9667 - val_loss: 4.4264 - val_acc: 0.2000\n",
      "Epoch 45/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0079 - acc: 1.0000 - val_loss: 4.3384 - val_acc: 0.2000\n",
      "Epoch 46/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0142 - acc: 1.0000 - val_loss: 4.2555 - val_acc: 0.2000\n",
      "Epoch 47/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0360 - acc: 1.0000 - val_loss: 4.2529 - val_acc: 0.2333\n",
      "Epoch 48/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0175 - acc: 1.0000 - val_loss: 4.3059 - val_acc: 0.3000\n",
      "Epoch 49/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0049 - acc: 1.0000 - val_loss: 4.3934 - val_acc: 0.2667\n",
      "Epoch 50/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0075 - acc: 1.0000 - val_loss: 4.4512 - val_acc: 0.2667\n",
      "Epoch 51/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30/30 [==============================] - 0s - loss: 0.0113 - acc: 1.0000 - val_loss: 4.4763 - val_acc: 0.2667\n",
      "Epoch 52/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0154 - acc: 1.0000 - val_loss: 4.5933 - val_acc: 0.2333\n",
      "Epoch 53/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0080 - acc: 1.0000 - val_loss: 4.7635 - val_acc: 0.2000\n",
      "Epoch 54/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0063 - acc: 1.0000 - val_loss: 4.9219 - val_acc: 0.2000\n",
      "Epoch 55/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0063 - acc: 1.0000 - val_loss: 5.0747 - val_acc: 0.2000\n",
      "Epoch 56/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0061 - acc: 1.0000 - val_loss: 5.1551 - val_acc: 0.2000\n",
      "Epoch 57/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0019 - acc: 1.0000 - val_loss: 5.1895 - val_acc: 0.2000\n",
      "Epoch 58/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0036 - acc: 1.0000 - val_loss: 5.2044 - val_acc: 0.2000\n",
      "Epoch 59/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0112 - acc: 1.0000 - val_loss: 5.1597 - val_acc: 0.2000\n",
      "Epoch 60/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0037 - acc: 1.0000 - val_loss: 5.0415 - val_acc: 0.2000\n",
      "Epoch 61/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0045 - acc: 1.0000 - val_loss: 4.9377 - val_acc: 0.2000\n",
      "Epoch 62/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0024 - acc: 1.0000 - val_loss: 4.8764 - val_acc: 0.2000\n",
      "Epoch 63/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0032 - acc: 1.0000 - val_loss: 4.8262 - val_acc: 0.2000\n",
      "Epoch 64/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0014 - acc: 1.0000 - val_loss: 4.7933 - val_acc: 0.2000\n",
      "Epoch 65/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0158 - acc: 1.0000 - val_loss: 4.9143 - val_acc: 0.2000\n",
      "Epoch 66/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0025 - acc: 1.0000 - val_loss: 5.1545 - val_acc: 0.2000\n",
      "Epoch 67/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0019 - acc: 1.0000 - val_loss: 5.3302 - val_acc: 0.2000\n",
      "Epoch 68/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0023 - acc: 1.0000 - val_loss: 5.4503 - val_acc: 0.2000\n",
      "Epoch 69/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0056 - acc: 1.0000 - val_loss: 5.5087 - val_acc: 0.2000\n",
      "Epoch 70/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0020 - acc: 1.0000 - val_loss: 5.5209 - val_acc: 0.2000\n",
      "Epoch 71/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0038 - acc: 1.0000 - val_loss: 5.4928 - val_acc: 0.2000\n",
      "Epoch 72/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0014 - acc: 1.0000 - val_loss: 5.4132 - val_acc: 0.2000\n",
      "Epoch 73/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0023 - acc: 1.0000 - val_loss: 5.3101 - val_acc: 0.2000\n",
      "Epoch 74/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0018 - acc: 1.0000 - val_loss: 5.2021 - val_acc: 0.2000\n",
      "Epoch 75/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0021 - acc: 1.0000 - val_loss: 5.0992 - val_acc: 0.2000\n",
      "Epoch 76/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0020 - acc: 1.0000 - val_loss: 5.0138 - val_acc: 0.2000\n",
      "Epoch 77/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0035 - acc: 1.0000 - val_loss: 4.9549 - val_acc: 0.2000\n",
      "Epoch 78/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0021 - acc: 1.0000 - val_loss: 4.9078 - val_acc: 0.2000\n",
      "Epoch 79/1000\n",
      "30/30 [==============================] - 0s - loss: 0.0012 - acc: 1.0000 - val_loss: 4.8605 - val_acc: 0.2000\n",
      "Epoch 80/1000\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
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     "text": [
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     ]
    },
    {
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     "text": [
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     ]
    },
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     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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      "30/30 [==============================] - 0s - loss: 0.0011 - acc: 1.0000 - val_loss: 2.6724 - val_acc: 0.5333\n",
      "Epoch 991/1000\n",
      "30/30 [==============================] - 0s - loss: 1.0214e-04 - acc: 1.0000 - val_loss: 2.6702 - val_acc: 0.5333\n",
      "Epoch 992/1000\n",
      "30/30 [==============================] - 0s - loss: 1.1003e-04 - acc: 1.0000 - val_loss: 2.6685 - val_acc: 0.5333\n",
      "Epoch 993/1000\n",
      "30/30 [==============================] - 0s - loss: 1.2852e-04 - acc: 1.0000 - val_loss: 2.6664 - val_acc: 0.5333\n",
      "Epoch 994/1000\n",
      "30/30 [==============================] - 0s - loss: 1.4579e-04 - acc: 1.0000 - val_loss: 2.6650 - val_acc: 0.5333\n",
      "Epoch 995/1000\n",
      "30/30 [==============================] - 0s - loss: 1.1000e-04 - acc: 1.0000 - val_loss: 2.6638 - val_acc: 0.5333\n",
      "Epoch 996/1000\n",
      "30/30 [==============================] - 0s - loss: 2.5171e-04 - acc: 1.0000 - val_loss: 2.6635 - val_acc: 0.5667\n",
      "Epoch 997/1000\n",
      "30/30 [==============================] - 0s - loss: 1.0675e-04 - acc: 1.0000 - val_loss: 2.6622 - val_acc: 0.5667\n",
      "Epoch 998/1000\n",
      "30/30 [==============================] - 0s - loss: 6.1391e-04 - acc: 1.0000 - val_loss: 2.6630 - val_acc: 0.5667\n",
      "Epoch 999/1000\n",
      "30/30 [==============================] - 0s - loss: 1.5559e-04 - acc: 1.0000 - val_loss: 2.6619 - val_acc: 0.5667\n",
      "Epoch 1000/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30/30 [==============================] - 0s - loss: 1.1133e-04 - acc: 1.0000 - val_loss: 2.6611 - val_acc: 0.5667\n",
      "----------- Resultados -----------\n",
      "Loss:  6.38791670402e-05\n",
      "Acc:  0.533333333333\n",
      "Tempo de processamento: 919.19 segundos.\n"
     ]
    }
   ],
   "source": [
    "for datafile in datafiles:\n",
    "    y_train, x_train, y_test, x_test = unpickleimg(datafile);\n",
    "    \n",
    "    nb_classes = len(np.unique(y_test))\n",
    "    batch_size = min(x_train.shape[0], 16)     \n",
    "    y_train = (y_train - y_train.min())/(y_train.max()-y_train.min())*(nb_classes-1)\n",
    "    y_test = (y_test - y_test.min())/(y_test.max()-y_test.min())*(nb_classes-1)     \n",
    "    Y_train = np_utils.to_categorical(y_train, nb_classes)\n",
    "    Y_test = np_utils.to_categorical(y_test, nb_classes)     \n",
    "    x_train_mean = x_train.mean()\n",
    "    x_train_std = x_train.std()\n",
    "    x_train = (x_train - x_train_mean)/(x_train_std)      \n",
    "    x_test = (x_test - x_train_mean)/(x_train_std) \n",
    "     \n",
    "    x , y = build_resnet(x_train.shape[1:], 64, nb_classes)\n",
    "    model = Model(outputs=y, inputs=x)\n",
    "    start = time.time()\n",
    "    model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])      \n",
    "    reduce_lr = ReduceLROnPlateau(monitor='loss', factor=0.5,patience=50, min_lr=0.0001) \n",
    "    hist = model.fit(x_train,Y_train,batch_size=batch_size,epochs=num_epochs,verbose=1,validation_data=(x_test, Y_test),callbacks = [reduce_lr])\n",
    "\n",
    "    log = pd.DataFrame(hist.history);\n",
    "    log['time'] =(time.time() - start);\n",
    "    print('----------- Resultados -----------');\n",
    "    print('Loss: ', log.loc[log['loss'].idxmin]['loss'])\n",
    "    print('Acc: ', log.loc[log['loss'].idxmin]['val_acc']);\n",
    "    print('Tempo de processamento: %.2f segundos.' % (time.time() - start))\n",
    "    log.to_pickle('Resultados_'+datafile);\n"
   ]
  }
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