Model_03-Adam-k=10-amsgrad-0001-30p.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Mandatory imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import log_loss\n",
    "from sklearn.model_selection import StratifiedKFold, StratifiedShuffleSplit\n",
    "from os.path import join as opj\n",
    "from matplotlib import pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import pylab\n",
    "plt.rcParams['figure.figsize'] = 10, 10\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = pd.read_json(\"input/train.json\")\n",
    "target_train=train['is_iceberg']\n",
    "test = pd.read_json(\"input/test.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "target_train=train['is_iceberg']\n",
    "test['inc_angle']=pd.to_numeric(test['inc_angle'], errors='coerce')\n",
    "train['inc_angle']=pd.to_numeric(train['inc_angle'], errors='coerce')#We have only 133 NAs.\n",
    "train['inc_angle']=train['inc_angle'].fillna(method='pad')\n",
    "X_angle=train['inc_angle']\n",
    "test['inc_angle']=pd.to_numeric(test['inc_angle'], errors='coerce')\n",
    "X_test_angle=test['inc_angle']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Generate the training data\n",
    "X_band_1=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in train[\"band_1\"]])\n",
    "X_band_2=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in train[\"band_2\"]])\n",
    "X_band_3=(X_band_1+X_band_2)/2\n",
    "#X_band_3=np.array([np.full((75, 75), angel).astype(np.float32) for angel in train[\"inc_angle\"]])\n",
    "X_train = np.concatenate([X_band_1[:, :, :, np.newaxis]\n",
    "                          , X_band_2[:, :, :, np.newaxis]\n",
    "                         , X_band_3[:, :, :, np.newaxis]], axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_band_test_1=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in test[\"band_1\"]])\n",
    "X_band_test_2=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in test[\"band_2\"]])\n",
    "X_band_test_3=(X_band_test_1+X_band_test_2)/2\n",
    "#X_band_test_3=np.array([np.full((75, 75), angel).astype(np.float32) for angel in test[\"inc_angle\"]])\n",
    "X_test = np.concatenate([X_band_test_1[:, :, :, np.newaxis]\n",
    "                          , X_band_test_2[:, :, :, np.newaxis]\n",
    "                         , X_band_test_3[:, :, :, np.newaxis]], axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "#Import Keras.\n",
    "from matplotlib import pyplot\n",
    "from keras.optimizers import RMSprop\n",
    "# from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPooling2D, Dense, Dropout, Input, Flatten, Activation\n",
    "from keras.layers import GlobalMaxPooling2D\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.layers.merge import Concatenate\n",
    "from keras.models import Model\n",
    "from keras import initializers\n",
    "from keras.optimizers import Adam\n",
    "from keras.optimizers import rmsprop\n",
    "from keras.layers.advanced_activations import LeakyReLU, PReLU\n",
    "from keras.optimizers import SGD\n",
    "from keras.optimizers import Adadelta\n",
    "from keras.callbacks import ModelCheckpoint, Callback, EarlyStopping\n",
    "\n",
    "from keras.datasets import cifar10\n",
    "from keras.applications.inception_v3 import InceptionV3\n",
    "from keras.applications.vgg16 import VGG16\n",
    "from keras.applications.xception import Xception\n",
    "from keras.applications.mobilenet import MobileNet\n",
    "from keras.applications.vgg19 import VGG19\n",
    "from keras.layers import Concatenate, Dense, LSTM, Input, concatenate\n",
    "from keras.preprocessing import image\n",
    "from keras.applications.vgg16 import preprocess_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Data Aug for multi-input\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "batch_size=64\n",
    "# Define the image transformations here\n",
    "gen = ImageDataGenerator(horizontal_flip = True,\n",
    "                         vertical_flip = True,\n",
    "                         width_shift_range = 0.,\n",
    "                         height_shift_range = 0.,\n",
    "                         channel_shift_range=0,\n",
    "                         zoom_range = 0.2,\n",
    "                         rotation_range = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Here is the function that merges our two generators\n",
    "# We use the exact same generator with the same random seed for both the y and angle arrays\n",
    "def gen_flow_for_two_inputs(X1, X2, y):\n",
    "    genX1 = gen.flow(X1,y,  batch_size=batch_size,seed=55)\n",
    "    genX2 = gen.flow(X1,X2, batch_size=batch_size,seed=55)\n",
    "    while True:\n",
    "            X1i = genX1.next()\n",
    "            X2i = genX2.next()\n",
    "            #Assert arrays are equal - this was for peace of mind, but slows down training\n",
    "            #np.testing.assert_array_equal(X1i[0],X2i[0])\n",
    "            yield [X1i[0], X2i[1]], X1i[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Finally create generator\n",
    "def get_callbacks(filepath, patience=2):\n",
    "    es = EarlyStopping('val_loss', patience=30, mode=\"min\")\n",
    "    msave = ModelCheckpoint(filepath, save_best_only=True)\n",
    "    return [es, msave]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getVggAngleModel():\n",
    "    input_2 = Input(shape=[1], name=\"angle\")\n",
    "    angle_layer = Dense(1, )(input_2)\n",
    "    base_model = VGG16(weights='imagenet', include_top=False, \n",
    "                 input_shape=X_train.shape[1:], classes=1)\n",
    "    x = base_model.get_layer('block5_pool').output\n",
    "    \n",
    "\n",
    "    x = GlobalMaxPooling2D()(x)\n",
    "    merge_one = concatenate([x, angle_layer])\n",
    "    merge_one = Dense(512, activation='relu', name='fc2')(merge_one)\n",
    "    merge_one = Dropout(0.3)(merge_one)\n",
    "    merge_one = Dense(512, activation='relu', name='fc3')(merge_one)\n",
    "    merge_one = Dropout(0.3)(merge_one)\n",
    "    \n",
    "    predictions = Dense(1, activation='sigmoid')(merge_one)\n",
    "    \n",
    "    model = Model(inputs=[base_model.input, input_2], outputs=predictions)\n",
    "    \n",
    "    adam = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-8, decay=1e-6, amsgrad=True)\n",
    "    model.compile(loss='binary_crossentropy',\n",
    "                    optimizer=adam,\n",
    "                    metrics=['accuracy'])\n",
    "    \n",
    "#     sgd = SGD(lr=1e-3, decay=1e-6, momentum=0.9, nesterov=True)\n",
    "#     model.compile(loss='binary_crossentropy',\n",
    "#                   optimizer=sgd,\n",
    "#                   metrics=['accuracy'])\n",
    "    return model\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "===================FOLD= 0\n",
      "Epoch 1/200\n",
      "24/24 [==============================] - 18s 739ms/step - loss: 0.7866 - acc: 0.6113 - val_loss: 0.6131 - val_acc: 0.5309\n",
      "Epoch 2/200\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.6443 - acc: 0.6471 - val_loss: 0.4377 - val_acc: 0.7654\n",
      "Epoch 3/200\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.4866 - acc: 0.7754 - val_loss: 0.3048 - val_acc: 0.8642\n",
      "Epoch 4/200\n",
      "24/24 [==============================] - 14s 593ms/step - loss: 0.3709 - acc: 0.8482 - val_loss: 0.2637 - val_acc: 0.8827\n",
      "Epoch 5/200\n",
      "24/24 [==============================] - 14s 594ms/step - loss: 0.3860 - acc: 0.8401 - val_loss: 0.2458 - val_acc: 0.8765\n",
      "Epoch 6/200\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.3067 - acc: 0.8683 - val_loss: 0.2554 - val_acc: 0.9012\n",
      "Epoch 7/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2684 - acc: 0.8816 - val_loss: 0.2853 - val_acc: 0.8827\n",
      "Epoch 8/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2713 - acc: 0.8825 - val_loss: 0.2772 - val_acc: 0.9012\n",
      "Epoch 9/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2607 - acc: 0.8870 - val_loss: 0.2989 - val_acc: 0.8642\n",
      "Epoch 10/200\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2546 - acc: 0.8928 - val_loss: 0.2112 - val_acc: 0.9198\n",
      "Epoch 11/200\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2318 - acc: 0.9085 - val_loss: 0.2301 - val_acc: 0.9074\n",
      "Epoch 12/200\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2213 - acc: 0.8978 - val_loss: 0.2135 - val_acc: 0.9074\n",
      "Epoch 13/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2977 - acc: 0.8670 - val_loss: 0.2249 - val_acc: 0.9074\n",
      "Epoch 14/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2148 - acc: 0.9186 - val_loss: 0.2315 - val_acc: 0.8951\n",
      "Epoch 15/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2106 - acc: 0.9098 - val_loss: 0.2156 - val_acc: 0.9012\n",
      "Epoch 16/200\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2305 - acc: 0.9031 - val_loss: 0.1963 - val_acc: 0.9074\n",
      "Epoch 17/200\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1820 - acc: 0.9204 - val_loss: 0.2137 - val_acc: 0.9198\n",
      "Epoch 18/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2297 - acc: 0.9046 - val_loss: 0.2224 - val_acc: 0.9136\n",
      "Epoch 19/200\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.1916 - acc: 0.9248 - val_loss: 0.1833 - val_acc: 0.9321\n",
      "Epoch 20/200\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.2154 - acc: 0.9159 - val_loss: 0.2022 - val_acc: 0.9074\n",
      "Epoch 21/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2002 - acc: 0.9180 - val_loss: 0.1945 - val_acc: 0.9259\n",
      "Epoch 22/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1757 - acc: 0.9264 - val_loss: 0.2109 - val_acc: 0.9198\n",
      "Epoch 23/200\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1894 - acc: 0.9291 - val_loss: 0.2117 - val_acc: 0.9136\n",
      "Epoch 24/200\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1906 - acc: 0.9238 - val_loss: 0.2619 - val_acc: 0.8889\n",
      "Epoch 25/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1769 - acc: 0.9258 - val_loss: 0.2142 - val_acc: 0.9136\n",
      "Epoch 26/200\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1615 - acc: 0.9317 - val_loss: 0.2096 - val_acc: 0.9259\n",
      "Epoch 27/200\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.1538 - acc: 0.9343 - val_loss: 0.1833 - val_acc: 0.9321\n",
      "Epoch 28/200\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.1603 - acc: 0.9344 - val_loss: 0.2534 - val_acc: 0.9198\n",
      "Epoch 29/200\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1675 - acc: 0.9422 - val_loss: 0.2039 - val_acc: 0.9136\n",
      "Epoch 30/200\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1531 - acc: 0.9332 - val_loss: 0.2814 - val_acc: 0.9012\n",
      "Epoch 31/200\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1531 - acc: 0.9383 - val_loss: 0.2120 - val_acc: 0.9321\n",
      "Epoch 32/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1543 - acc: 0.9272 - val_loss: 0.2192 - val_acc: 0.9136\n",
      "Epoch 33/200\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1411 - acc: 0.9351 - val_loss: 0.1934 - val_acc: 0.9198\n",
      "Epoch 34/200\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1439 - acc: 0.9407 - val_loss: 0.1989 - val_acc: 0.9259\n",
      "Epoch 35/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1547 - acc: 0.9421 - val_loss: 0.2402 - val_acc: 0.9259\n",
      "Epoch 36/200\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1178 - acc: 0.9539 - val_loss: 0.2322 - val_acc: 0.9259\n",
      "Epoch 37/200\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.0995 - acc: 0.9564 - val_loss: 0.2825 - val_acc: 0.9198\n",
      "Epoch 38/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1497 - acc: 0.9338 - val_loss: 0.2010 - val_acc: 0.9198\n",
      "Epoch 39/200\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1831 - acc: 0.9339 - val_loss: 0.2052 - val_acc: 0.9136\n",
      "Epoch 40/200\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1493 - acc: 0.9456 - val_loss: 0.2473 - val_acc: 0.9074\n",
      "Epoch 41/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1380 - acc: 0.9463 - val_loss: 0.2194 - val_acc: 0.9321\n",
      "Epoch 42/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1207 - acc: 0.9534 - val_loss: 0.1950 - val_acc: 0.9259\n",
      "Epoch 43/200\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1333 - acc: 0.9467 - val_loss: 0.2353 - val_acc: 0.9074\n",
      "Epoch 44/200\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1019 - acc: 0.9571 - val_loss: 0.2262 - val_acc: 0.9136\n",
      "Epoch 45/200\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1627 - acc: 0.9401 - val_loss: 0.2020 - val_acc: 0.9259\n",
      "Epoch 46/200\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.1289 - acc: 0.9473 - val_loss: 0.2638 - val_acc: 0.9136\n",
      "Epoch 47/200\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1214 - acc: 0.9481 - val_loss: 0.2393 - val_acc: 0.9321\n",
      "Epoch 48/200\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1072 - acc: 0.9593 - val_loss: 0.2324 - val_acc: 0.9074\n",
      "Epoch 49/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1170 - acc: 0.9558 - val_loss: 0.2307 - val_acc: 0.9198\n",
      "Epoch 50/200\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1022 - acc: 0.9610 - val_loss: 0.1944 - val_acc: 0.9259\n",
      "Epoch 51/200\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.0801 - acc: 0.9671 - val_loss: 0.2368 - val_acc: 0.9321\n",
      "Epoch 52/200\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1107 - acc: 0.9658 - val_loss: 0.2342 - val_acc: 0.9012\n",
      "Epoch 53/200\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.0955 - acc: 0.9611 - val_loss: 0.2163 - val_acc: 0.9198\n",
      "Epoch 54/200\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.0884 - acc: 0.9655 - val_loss: 0.3160 - val_acc: 0.9136\n",
      "Epoch 55/200\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.0698 - acc: 0.9713 - val_loss: 0.2544 - val_acc: 0.9198\n",
      "Epoch 56/200\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.0732 - acc: 0.9753 - val_loss: 0.2303 - val_acc: 0.9136\n",
      "Epoch 57/200\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.0794 - acc: 0.9711 - val_loss: 0.2784 - val_acc: 0.9136\n",
      "Train loss: 0.101591617799\n",
      "Train accuracy: 0.95839112344\n",
      "Test loss: 0.183262157612\n",
      "Test accuracy: 0.932098765432\n",
      "\n",
      "===================FOLD= 1\n",
      "Epoch 1/200\n",
      "24/24 [==============================] - 19s 772ms/step - loss: 0.5613 - acc: 0.6873 - val_loss: 0.3726 - val_acc: 0.8137\n",
      "Epoch 2/200\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.4238 - acc: 0.7957 - val_loss: 0.3399 - val_acc: 0.7950\n",
      "Epoch 3/200\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.3516 - acc: 0.8219 - val_loss: 0.2456 - val_acc: 0.8820\n",
      "Epoch 4/200\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.3055 - acc: 0.8599 - val_loss: 0.2370 - val_acc: 0.8758\n",
      "Epoch 5/200\n",
      "24/24 [==============================] - 14s 600ms/step - loss: 0.2606 - acc: 0.8792 - val_loss: 0.2298 - val_acc: 0.8944\n",
      "Epoch 6/200\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2858 - acc: 0.8754 - val_loss: 0.1472 - val_acc: 0.9317\n",
      "Epoch 7/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2794 - acc: 0.8774 - val_loss: 0.1862 - val_acc: 0.9193\n",
      "Epoch 8/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2905 - acc: 0.8745 - val_loss: 0.1789 - val_acc: 0.9317\n",
      "Epoch 9/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2368 - acc: 0.8989 - val_loss: 0.1850 - val_acc: 0.9379\n",
      "Epoch 10/200\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.2570 - acc: 0.8889 - val_loss: 0.1730 - val_acc: 0.9379\n",
      "Epoch 11/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2220 - acc: 0.9081 - val_loss: 0.1945 - val_acc: 0.9193\n",
      "Epoch 12/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2037 - acc: 0.9119 - val_loss: 0.1819 - val_acc: 0.9317\n",
      "Epoch 13/200\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2046 - acc: 0.9037 - val_loss: 0.1410 - val_acc: 0.9565\n",
      "Epoch 14/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2241 - acc: 0.9066 - val_loss: 0.2009 - val_acc: 0.9255\n",
      "Epoch 15/200\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.2013 - acc: 0.9221 - val_loss: 0.1565 - val_acc: 0.9503\n",
      "Epoch 16/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2110 - acc: 0.9109 - val_loss: 0.1925 - val_acc: 0.9503\n",
      "Epoch 17/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1898 - acc: 0.9145 - val_loss: 0.1713 - val_acc: 0.9379\n",
      "Epoch 18/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1926 - acc: 0.9158 - val_loss: 0.1694 - val_acc: 0.9441\n",
      "Epoch 19/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1918 - acc: 0.9260 - val_loss: 0.1823 - val_acc: 0.9441\n",
      "Epoch 20/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1816 - acc: 0.9259 - val_loss: 0.1967 - val_acc: 0.9441\n",
      "Epoch 21/200\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1625 - acc: 0.9259 - val_loss: 0.1839 - val_acc: 0.9255\n",
      "Epoch 22/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1741 - acc: 0.9325 - val_loss: 0.1785 - val_acc: 0.9379\n",
      "Epoch 23/200\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1661 - acc: 0.9296 - val_loss: 0.1908 - val_acc: 0.9130\n",
      "Epoch 24/200\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1725 - acc: 0.9359 - val_loss: 0.1927 - val_acc: 0.9317\n",
      "Epoch 25/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1837 - acc: 0.9257 - val_loss: 0.1443 - val_acc: 0.9565\n",
      "Epoch 26/200\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.1601 - acc: 0.9392 - val_loss: 0.2088 - val_acc: 0.9317\n",
      "Epoch 27/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1322 - acc: 0.9436 - val_loss: 0.2734 - val_acc: 0.9379\n",
      "Epoch 28/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1593 - acc: 0.9319 - val_loss: 0.2540 - val_acc: 0.9193\n",
      "Epoch 29/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1482 - acc: 0.9401 - val_loss: 0.2320 - val_acc: 0.9503\n",
      "Epoch 30/200\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1376 - acc: 0.9429 - val_loss: 0.2141 - val_acc: 0.9255\n",
      "Epoch 31/200\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1380 - acc: 0.9461 - val_loss: 0.1633 - val_acc: 0.9565\n",
      "Epoch 32/200\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1293 - acc: 0.9365 - val_loss: 0.2881 - val_acc: 0.9255\n",
      "Epoch 33/200\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1452 - acc: 0.9389 - val_loss: 0.2136 - val_acc: 0.9255\n",
      "Epoch 34/200\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1534 - acc: 0.9446 - val_loss: 0.2378 - val_acc: 0.9255\n",
      "Epoch 35/200\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1700 - acc: 0.9270 - val_loss: 0.1740 - val_acc: 0.9379\n",
      "Epoch 36/200\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1410 - acc: 0.9436 - val_loss: 0.2335 - val_acc: 0.9193\n",
      "Epoch 37/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1136 - acc: 0.9540 - val_loss: 0.2523 - val_acc: 0.9255\n",
      "Epoch 38/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1653 - acc: 0.9326 - val_loss: 0.2036 - val_acc: 0.9255\n",
      "Epoch 39/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1291 - acc: 0.9429 - val_loss: 0.2609 - val_acc: 0.9006\n",
      "Epoch 40/200\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1320 - acc: 0.9445 - val_loss: 0.2187 - val_acc: 0.9503\n",
      "Epoch 41/200\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1316 - acc: 0.9481 - val_loss: 0.2374 - val_acc: 0.9441\n",
      "Epoch 42/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1333 - acc: 0.9425 - val_loss: 0.1958 - val_acc: 0.9379\n",
      "Epoch 43/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1148 - acc: 0.9566 - val_loss: 0.2352 - val_acc: 0.9317\n",
      "Train loss: 0.162886185462\n",
      "Train accuracy: 0.927927927928\n",
      "Test loss: 0.141034466601\n",
      "Test accuracy: 0.95652173913\n",
      "\n",
      "===================FOLD= 2\n",
      "Epoch 1/200\n",
      "24/24 [==============================] - 18s 766ms/step - loss: 0.7927 - acc: 0.5933 - val_loss: 0.4058 - val_acc: 0.7888\n",
      "Epoch 2/200\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.5347 - acc: 0.7592 - val_loss: 0.2503 - val_acc: 0.9068\n",
      "Epoch 3/200\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.4130 - acc: 0.8245 - val_loss: 0.2821 - val_acc: 0.8944\n",
      "Epoch 4/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.4801 - acc: 0.7931 - val_loss: 0.2786 - val_acc: 0.8323\n",
      "Epoch 5/200\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.3289 - acc: 0.8492 - val_loss: 0.2542 - val_acc: 0.8758\n",
      "Epoch 6/200\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.3471 - acc: 0.8332 - val_loss: 0.2282 - val_acc: 0.9006\n",
      "Epoch 7/200\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2731 - acc: 0.8846 - val_loss: 0.1867 - val_acc: 0.9068\n",
      "Epoch 8/200\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.2807 - acc: 0.8765 - val_loss: 0.2140 - val_acc: 0.8944\n",
      "Epoch 9/200\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.2807 - acc: 0.8808 - val_loss: 0.2203 - val_acc: 0.9006\n",
      "Epoch 10/200\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.2585 - acc: 0.9011 - val_loss: 0.1848 - val_acc: 0.9193\n",
      "Epoch 11/200\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2472 - acc: 0.8913 - val_loss: 0.1809 - val_acc: 0.9193\n",
      "Epoch 12/200\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2511 - acc: 0.9038 - val_loss: 0.1953 - val_acc: 0.9130\n",
      "Epoch 13/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2120 - acc: 0.9206 - val_loss: 0.2002 - val_acc: 0.9130\n",
      "Epoch 14/200\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.1986 - acc: 0.9192 - val_loss: 0.1848 - val_acc: 0.9130\n",
      "Epoch 15/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2280 - acc: 0.9032 - val_loss: 0.1967 - val_acc: 0.9193\n",
      "Epoch 16/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1992 - acc: 0.9197 - val_loss: 0.1924 - val_acc: 0.9193\n",
      "Epoch 17/200\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2119 - acc: 0.9214 - val_loss: 0.1784 - val_acc: 0.9255\n",
      "Epoch 18/200\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2160 - acc: 0.9067 - val_loss: 0.1839 - val_acc: 0.9317\n",
      "Epoch 19/200\n",
      "24/24 [==============================] - 14s 587ms/step - loss: 0.1831 - acc: 0.9244 - val_loss: 0.1578 - val_acc: 0.9130\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 20/200\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.2104 - acc: 0.9156 - val_loss: 0.2434 - val_acc: 0.9193\n",
      "Epoch 21/200\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1745 - acc: 0.9266 - val_loss: 0.2042 - val_acc: 0.8944\n",
      "Epoch 22/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1924 - acc: 0.9231 - val_loss: 0.1750 - val_acc: 0.9193\n",
      "Epoch 23/200\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1724 - acc: 0.9266 - val_loss: 0.2714 - val_acc: 0.9006\n",
      "Epoch 24/200\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1952 - acc: 0.9182 - val_loss: 0.2161 - val_acc: 0.9068\n",
      "Epoch 25/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1850 - acc: 0.9210 - val_loss: 0.1859 - val_acc: 0.9068\n",
      "Epoch 26/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2080 - acc: 0.9197 - val_loss: 0.1579 - val_acc: 0.9379\n",
      "Epoch 27/200\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.1530 - acc: 0.9335 - val_loss: 0.1506 - val_acc: 0.9130\n",
      "Epoch 28/200\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1580 - acc: 0.9339 - val_loss: 0.1639 - val_acc: 0.9130\n",
      "Epoch 29/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1493 - acc: 0.9370 - val_loss: 0.1787 - val_acc: 0.9193\n",
      "Epoch 30/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1721 - acc: 0.9332 - val_loss: 0.1687 - val_acc: 0.9193\n",
      "Epoch 31/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1527 - acc: 0.9398 - val_loss: 0.1552 - val_acc: 0.9379\n",
      "Epoch 32/200\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1534 - acc: 0.9359 - val_loss: 0.2716 - val_acc: 0.9255\n",
      "Epoch 33/200\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.2079 - acc: 0.9213 - val_loss: 0.1860 - val_acc: 0.9130\n",
      "Epoch 34/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1465 - acc: 0.9455 - val_loss: 0.1961 - val_acc: 0.8944\n",
      "Epoch 35/200\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1574 - acc: 0.9352 - val_loss: 0.2072 - val_acc: 0.9068\n",
      "Epoch 36/200\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1405 - acc: 0.9427 - val_loss: 0.1928 - val_acc: 0.9255\n",
      "Epoch 37/200\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1609 - acc: 0.9418 - val_loss: 0.1703 - val_acc: 0.9193\n",
      "Epoch 38/200\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1603 - acc: 0.9403 - val_loss: 0.1991 - val_acc: 0.9006\n",
      "Epoch 39/200\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1254 - acc: 0.9498 - val_loss: 0.1911 - val_acc: 0.9255\n",
      "Epoch 40/200\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1401 - acc: 0.9386 - val_loss: 0.1896 - val_acc: 0.9255\n",
      "Epoch 41/200\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1329 - acc: 0.9487 - val_loss: 0.2107 - val_acc: 0.8944\n",
      "Epoch 42/200\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1158 - acc: 0.9563 - val_loss: 0.1552 - val_acc: 0.9317\n",
      "Epoch 43/200\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1166 - acc: 0.9514 - val_loss: 0.2049 - val_acc: 0.9193\n",
      "Epoch 44/200\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1159 - acc: 0.9524 - val_loss: 0.1594 - val_acc: 0.9193\n",
      "Epoch 45/200\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1561 - acc: 0.9401 - val_loss: 0.1695 - val_acc: 0.9193\n",
      "Epoch 46/200\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1843 - acc: 0.9313 - val_loss: 0.2264 - val_acc: 0.9255\n",
      "Epoch 47/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1406 - acc: 0.9433 - val_loss: 0.1602 - val_acc: 0.9317\n",
      "Epoch 48/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1000 - acc: 0.9599 - val_loss: 0.2423 - val_acc: 0.9068\n",
      "Epoch 49/200\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1165 - acc: 0.9560 - val_loss: 0.1934 - val_acc: 0.8882\n",
      "Epoch 50/200\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1213 - acc: 0.9481 - val_loss: 0.1967 - val_acc: 0.9130\n",
      "Epoch 51/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1363 - acc: 0.9494 - val_loss: 0.1975 - val_acc: 0.9006\n",
      "Epoch 52/200\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.0892 - acc: 0.9608 - val_loss: 0.2310 - val_acc: 0.9068\n",
      "Epoch 53/200\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.0950 - acc: 0.9604 - val_loss: 0.2757 - val_acc: 0.8882\n",
      "Epoch 54/200\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.0962 - acc: 0.9661 - val_loss: 0.2016 - val_acc: 0.9193\n",
      "Epoch 55/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1043 - acc: 0.9605 - val_loss: 0.2249 - val_acc: 0.9193\n",
      "Epoch 56/200\n",
      "24/24 [==============================] - 14s 584ms/step - loss: 0.1378 - acc: 0.9481 - val_loss: 0.1388 - val_acc: 0.9193\n",
      "Epoch 57/200\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.0988 - acc: 0.9562 - val_loss: 0.2393 - val_acc: 0.9130\n",
      "Epoch 58/200\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1097 - acc: 0.9576 - val_loss: 0.2085 - val_acc: 0.9193\n",
      "Epoch 59/200\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.0875 - acc: 0.9597 - val_loss: 0.2165 - val_acc: 0.9255\n",
      "Epoch 60/200\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.0579 - acc: 0.9785 - val_loss: 0.1893 - val_acc: 0.9130\n",
      "Epoch 61/200\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.0625 - acc: 0.9794 - val_loss: 0.2263 - val_acc: 0.9379\n",
      "Epoch 62/200\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.0674 - acc: 0.9703 - val_loss: 0.1778 - val_acc: 0.9130\n",
      "Epoch 63/200\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.0995 - acc: 0.9676 - val_loss: 0.2033 - val_acc: 0.9255\n",
      "Epoch 64/200\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.0891 - acc: 0.9622 - val_loss: 0.2464 - val_acc: 0.9193\n",
      "Epoch 65/200\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.0544 - acc: 0.9818 - val_loss: 0.2256 - val_acc: 0.9379\n",
      "Epoch 66/200\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.0465 - acc: 0.9831 - val_loss: 0.2979 - val_acc: 0.9255\n",
      "Epoch 67/200\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.0794 - acc: 0.9700 - val_loss: 0.1841 - val_acc: 0.9379\n",
      "Epoch 68/200\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.0884 - acc: 0.9636 - val_loss: 0.2993 - val_acc: 0.9255\n",
      "Epoch 69/200\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.0992 - acc: 0.9611 - val_loss: 0.2382 - val_acc: 0.9193\n",
      "Epoch 70/200\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.0595 - acc: 0.9785 - val_loss: 0.2122 - val_acc: 0.9255\n",
      "Epoch 71/200\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.0586 - acc: 0.9792 - val_loss: 0.2306 - val_acc: 0.9441\n",
      "Epoch 72/200\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.0500 - acc: 0.9801 - val_loss: 0.3397 - val_acc: 0.9255\n",
      "Epoch 73/200\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.0806 - acc: 0.9695 - val_loss: 0.2740 - val_acc: 0.9317\n",
      "Epoch 74/200\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.0731 - acc: 0.9698 - val_loss: 0.2759 - val_acc: 0.8944\n",
      "Epoch 75/200\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.0550 - acc: 0.9806 - val_loss: 0.2776 - val_acc: 0.9255\n",
      "Epoch 76/200\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.0554 - acc: 0.9831 - val_loss: 0.2497 - val_acc: 0.9255\n",
      "Epoch 77/200\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.0498 - acc: 0.9799 - val_loss: 0.2240 - val_acc: 0.9255\n",
      "Epoch 78/200\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.0493 - acc: 0.9837 - val_loss: 0.2521 - val_acc: 0.9193\n",
      "Epoch 79/200\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.0579 - acc: 0.9778 - val_loss: 0.1866 - val_acc: 0.9503\n",
      "Epoch 80/200\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.0518 - acc: 0.9838 - val_loss: 0.2855 - val_acc: 0.9130\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 81/200\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.0732 - acc: 0.9696 - val_loss: 0.2484 - val_acc: 0.9317\n",
      "Epoch 82/200\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.0505 - acc: 0.9811 - val_loss: 0.3011 - val_acc: 0.9193\n",
      "Epoch 83/200\n",
      "24/24 [==============================] - 12s 501ms/step - loss: 0.0441 - acc: 0.9832 - val_loss: 0.3362 - val_acc: 0.9068\n",
      "Epoch 84/200\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.0411 - acc: 0.9837 - val_loss: 0.2990 - val_acc: 0.9130\n",
      "Epoch 85/200\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.0780 - acc: 0.9707 - val_loss: 0.3051 - val_acc: 0.8944\n",
      "Epoch 86/200\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1063 - acc: 0.9594 - val_loss: 0.2391 - val_acc: 0.8944\n",
      "Train loss: 0.0784934860391\n",
      "Train accuracy: 0.973665973666\n",
      "Test loss: 0.138775547469\n",
      "Test accuracy: 0.919254658385\n",
      "\n",
      "===================FOLD= 3\n",
      "Epoch 1/200\n",
      "24/24 [==============================] - 18s 762ms/step - loss: 0.8942 - acc: 0.6051 - val_loss: 0.4408 - val_acc: 0.7312\n",
      "Epoch 2/200\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.5547 - acc: 0.7593 - val_loss: 0.3900 - val_acc: 0.8375\n",
      "Epoch 3/200\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.4449 - acc: 0.8132 - val_loss: 0.2992 - val_acc: 0.8625\n",
      "Epoch 4/200\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.3352 - acc: 0.8682 - val_loss: 0.2895 - val_acc: 0.8625\n",
      "Epoch 5/200\n",
      "24/24 [==============================] - 14s 594ms/step - loss: 0.3244 - acc: 0.8523 - val_loss: 0.2597 - val_acc: 0.8875\n",
      "Epoch 6/200\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.3041 - acc: 0.8642 - val_loss: 0.2640 - val_acc: 0.9125\n",
      "Epoch 7/200\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.3261 - acc: 0.8518 - val_loss: 0.2178 - val_acc: 0.9000\n",
      "Epoch 8/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.3177 - acc: 0.8624 - val_loss: 0.2238 - val_acc: 0.8875\n",
      "Epoch 9/200\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.2755 - acc: 0.8896 - val_loss: 0.2586 - val_acc: 0.9062\n",
      "Epoch 10/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2675 - acc: 0.8871 - val_loss: 0.2468 - val_acc: 0.8938\n",
      "Epoch 11/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2260 - acc: 0.9072 - val_loss: 0.2297 - val_acc: 0.9062\n",
      "Epoch 12/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2260 - acc: 0.9060 - val_loss: 0.2437 - val_acc: 0.8938\n",
      "Epoch 13/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2413 - acc: 0.8951 - val_loss: 0.2274 - val_acc: 0.9125\n",
      "Epoch 14/200\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.2125 - acc: 0.9121 - val_loss: 0.1928 - val_acc: 0.9125\n",
      "Epoch 15/200\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2582 - acc: 0.8875 - val_loss: 0.2127 - val_acc: 0.9187\n",
      "Epoch 16/200\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.2228 - acc: 0.9091 - val_loss: 0.2078 - val_acc: 0.9062\n",
      "Epoch 17/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1811 - acc: 0.9262 - val_loss: 0.2090 - val_acc: 0.9125\n",
      "Epoch 18/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2177 - acc: 0.9099 - val_loss: 0.2546 - val_acc: 0.9187\n",
      "Epoch 19/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2055 - acc: 0.9145 - val_loss: 0.1966 - val_acc: 0.9062\n",
      "Epoch 20/200\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.2001 - acc: 0.9140 - val_loss: 0.1875 - val_acc: 0.9062\n",
      "Epoch 21/200\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1916 - acc: 0.9215 - val_loss: 0.1931 - val_acc: 0.9187\n",
      "Epoch 22/200\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1963 - acc: 0.9168 - val_loss: 0.2261 - val_acc: 0.8875\n",
      "Epoch 23/200\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1750 - acc: 0.9298 - val_loss: 0.2054 - val_acc: 0.9125\n",
      "Epoch 24/200\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1672 - acc: 0.9340 - val_loss: 0.2631 - val_acc: 0.9187\n",
      "Epoch 25/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1850 - acc: 0.9249 - val_loss: 0.2196 - val_acc: 0.9125\n",
      "Epoch 26/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1693 - acc: 0.9295 - val_loss: 0.3162 - val_acc: 0.8875\n",
      "Epoch 27/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1741 - acc: 0.9290 - val_loss: 0.2120 - val_acc: 0.9250\n",
      "Epoch 28/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1540 - acc: 0.9298 - val_loss: 0.1941 - val_acc: 0.9250\n",
      "Epoch 29/200\n",
      "24/24 [==============================] - 14s 585ms/step - loss: 0.1751 - acc: 0.9226 - val_loss: 0.1875 - val_acc: 0.9250\n",
      "Epoch 30/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1844 - acc: 0.9223 - val_loss: 0.2008 - val_acc: 0.9062\n",
      "Epoch 31/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1320 - acc: 0.9500 - val_loss: 0.2616 - val_acc: 0.9125\n",
      "Epoch 32/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1815 - acc: 0.9261 - val_loss: 0.3813 - val_acc: 0.8688\n",
      "Epoch 33/200\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1557 - acc: 0.9311 - val_loss: 0.1999 - val_acc: 0.9125\n",
      "Epoch 34/200\n",
      "24/24 [==============================] - 14s 587ms/step - loss: 0.1691 - acc: 0.9323 - val_loss: 0.1802 - val_acc: 0.9125\n",
      "Epoch 35/200\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1216 - acc: 0.9489 - val_loss: 0.2436 - val_acc: 0.9250\n",
      "Epoch 36/200\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1434 - acc: 0.9414 - val_loss: 0.2835 - val_acc: 0.8750\n",
      "Epoch 37/200\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1273 - acc: 0.9479 - val_loss: 0.2269 - val_acc: 0.9062\n",
      "Epoch 38/200\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1811 - acc: 0.9314 - val_loss: 0.1843 - val_acc: 0.9000\n",
      "Epoch 39/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1340 - acc: 0.9484 - val_loss: 0.2288 - val_acc: 0.9000\n",
      "Epoch 40/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1616 - acc: 0.9323 - val_loss: 0.2796 - val_acc: 0.9125\n",
      "Epoch 41/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1216 - acc: 0.9515 - val_loss: 0.2756 - val_acc: 0.9187\n",
      "Epoch 42/200\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1133 - acc: 0.9591 - val_loss: 0.3054 - val_acc: 0.8938\n",
      "Epoch 43/200\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1435 - acc: 0.9389 - val_loss: 0.2197 - val_acc: 0.9000\n",
      "Epoch 44/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1225 - acc: 0.9570 - val_loss: 0.1925 - val_acc: 0.9250\n",
      "Epoch 45/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1041 - acc: 0.9570 - val_loss: 0.3598 - val_acc: 0.8750\n",
      "Epoch 46/200\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.0993 - acc: 0.9570 - val_loss: 0.4135 - val_acc: 0.8938\n",
      "Epoch 47/200\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1170 - acc: 0.9471 - val_loss: 0.3913 - val_acc: 0.9062\n",
      "Epoch 48/200\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.1306 - acc: 0.9493 - val_loss: 0.1871 - val_acc: 0.9125\n",
      "Epoch 49/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1337 - acc: 0.9440 - val_loss: 0.3328 - val_acc: 0.9000\n",
      "Epoch 50/200\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.0949 - acc: 0.9603 - val_loss: 0.2351 - val_acc: 0.9187\n",
      "Epoch 51/200\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1120 - acc: 0.9608 - val_loss: 0.2696 - val_acc: 0.9062\n",
      "Epoch 52/200\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.1590 - acc: 0.9399 - val_loss: 0.1894 - val_acc: 0.9125\n",
      "Epoch 53/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.0955 - acc: 0.9656 - val_loss: 0.2649 - val_acc: 0.9125\n",
      "Epoch 54/200\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1053 - acc: 0.9580 - val_loss: 0.2691 - val_acc: 0.9000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 55/200\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1046 - acc: 0.9578 - val_loss: 0.2749 - val_acc: 0.9125\n",
      "Epoch 56/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1132 - acc: 0.9550 - val_loss: 0.2144 - val_acc: 0.9250\n",
      "Epoch 57/200\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1284 - acc: 0.9454 - val_loss: 0.2425 - val_acc: 0.9187\n",
      "Epoch 58/200\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1155 - acc: 0.9493 - val_loss: 0.2799 - val_acc: 0.8688\n",
      "Epoch 59/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.0996 - acc: 0.9627 - val_loss: 0.3917 - val_acc: 0.8938\n",
      "Epoch 60/200\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.0819 - acc: 0.9702 - val_loss: 0.4564 - val_acc: 0.9062\n",
      "Epoch 61/200\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.0965 - acc: 0.9616 - val_loss: 0.3303 - val_acc: 0.9250\n",
      "Epoch 62/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.0749 - acc: 0.9741 - val_loss: 0.4427 - val_acc: 0.8938\n",
      "Epoch 63/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1012 - acc: 0.9624 - val_loss: 0.2679 - val_acc: 0.9250\n",
      "Epoch 64/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.0628 - acc: 0.9772 - val_loss: 0.3523 - val_acc: 0.8938\n",
      "Train loss: 0.094363465286\n",
      "Train accuracy: 0.968836565097\n",
      "Test loss: 0.180214446783\n",
      "Test accuracy: 0.9125\n",
      "\n",
      "===================FOLD= 4\n",
      "Epoch 1/200\n",
      "24/24 [==============================] - 19s 796ms/step - loss: 0.8595 - acc: 0.5868 - val_loss: 0.9670 - val_acc: 0.6625\n",
      "Epoch 2/200\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.5886 - acc: 0.7076 - val_loss: 0.2827 - val_acc: 0.8812\n",
      "Epoch 3/200\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.4144 - acc: 0.8308 - val_loss: 0.2264 - val_acc: 0.8938\n",
      "Epoch 4/200\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.4022 - acc: 0.8309 - val_loss: 0.2523 - val_acc: 0.9187\n",
      "Epoch 5/200\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.3441 - acc: 0.8508 - val_loss: 0.2228 - val_acc: 0.8938\n",
      "Epoch 6/200\n",
      "24/24 [==============================] - 14s 593ms/step - loss: 0.3088 - acc: 0.8625 - val_loss: 0.1574 - val_acc: 0.9563\n",
      "Epoch 7/200\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.3568 - acc: 0.8498 - val_loss: 0.2591 - val_acc: 0.8875\n",
      "Epoch 8/200\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2803 - acc: 0.8788 - val_loss: 0.1630 - val_acc: 0.9500\n",
      "Epoch 9/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2509 - acc: 0.8989 - val_loss: 0.1984 - val_acc: 0.9313\n",
      "Epoch 10/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.3415 - acc: 0.8547 - val_loss: 0.2334 - val_acc: 0.8938\n",
      "Epoch 11/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2379 - acc: 0.9034 - val_loss: 0.2248 - val_acc: 0.9500\n",
      "Epoch 12/200\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.2649 - acc: 0.8972 - val_loss: 0.1735 - val_acc: 0.9437\n",
      "Epoch 13/200\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.2521 - acc: 0.8865 - val_loss: 0.1528 - val_acc: 0.9625\n",
      "Epoch 14/200\n",
      "24/24 [==============================] - 14s 587ms/step - loss: 0.2463 - acc: 0.8928 - val_loss: 0.1482 - val_acc: 0.9563\n",
      "Epoch 15/200\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.2437 - acc: 0.8915 - val_loss: 0.2273 - val_acc: 0.9313\n",
      "Epoch 16/200\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.2177 - acc: 0.9117 - val_loss: 0.1663 - val_acc: 0.9500\n",
      "Epoch 17/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2022 - acc: 0.9265 - val_loss: 0.2254 - val_acc: 0.9250\n",
      "Epoch 18/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2149 - acc: 0.9116 - val_loss: 0.2141 - val_acc: 0.9437\n",
      "Epoch 19/200\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2152 - acc: 0.9108 - val_loss: 0.1750 - val_acc: 0.9500\n",
      "Epoch 20/200\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.2031 - acc: 0.9127 - val_loss: 0.1436 - val_acc: 0.9625\n",
      "Epoch 21/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1790 - acc: 0.9337 - val_loss: 0.1581 - val_acc: 0.9563\n",
      "Epoch 22/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1690 - acc: 0.9313 - val_loss: 0.2130 - val_acc: 0.9500\n",
      "Epoch 23/200\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1929 - acc: 0.9195 - val_loss: 0.2622 - val_acc: 0.9563\n",
      "Epoch 24/200\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1652 - acc: 0.9290 - val_loss: 0.1746 - val_acc: 0.9500\n",
      "Epoch 25/200\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1842 - acc: 0.9252 - val_loss: 0.2152 - val_acc: 0.9375\n",
      "Epoch 26/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1740 - acc: 0.9259 - val_loss: 0.2024 - val_acc: 0.9250\n",
      "Epoch 27/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1786 - acc: 0.9189 - val_loss: 0.1931 - val_acc: 0.9437\n",
      "Epoch 28/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1399 - acc: 0.9453 - val_loss: 0.2003 - val_acc: 0.9313\n",
      "Epoch 29/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1812 - acc: 0.9254 - val_loss: 0.1982 - val_acc: 0.9313\n",
      "Epoch 30/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1495 - acc: 0.9388 - val_loss: 0.3657 - val_acc: 0.9187\n",
      "Epoch 31/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1483 - acc: 0.9334 - val_loss: 0.1736 - val_acc: 0.9437\n",
      "Epoch 32/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1294 - acc: 0.9516 - val_loss: 0.1910 - val_acc: 0.9437\n",
      "Epoch 33/200\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1365 - acc: 0.9463 - val_loss: 0.4018 - val_acc: 0.9062\n",
      "Epoch 34/200\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1629 - acc: 0.9264 - val_loss: 0.3092 - val_acc: 0.9313\n",
      "Epoch 35/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1739 - acc: 0.9239 - val_loss: 0.1947 - val_acc: 0.9500\n",
      "Epoch 36/200\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1689 - acc: 0.9272 - val_loss: 0.2341 - val_acc: 0.9250\n",
      "Epoch 37/200\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1262 - acc: 0.9441 - val_loss: 0.1629 - val_acc: 0.9563\n",
      "Epoch 38/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1203 - acc: 0.9463 - val_loss: 0.2766 - val_acc: 0.9375\n",
      "Epoch 39/200\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1593 - acc: 0.9366 - val_loss: 0.1855 - val_acc: 0.9125\n",
      "Epoch 40/200\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1614 - acc: 0.9309 - val_loss: 0.2258 - val_acc: 0.9250\n",
      "Epoch 41/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1260 - acc: 0.9560 - val_loss: 0.2142 - val_acc: 0.9375\n",
      "Epoch 42/200\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.1339 - acc: 0.9461 - val_loss: 0.1945 - val_acc: 0.9500\n",
      "Epoch 43/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.0874 - acc: 0.9663 - val_loss: 0.2449 - val_acc: 0.9375\n",
      "Epoch 44/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1392 - acc: 0.9410 - val_loss: 0.2732 - val_acc: 0.9062\n",
      "Epoch 45/200\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.1224 - acc: 0.9479 - val_loss: 0.2580 - val_acc: 0.9250\n",
      "Epoch 46/200\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1285 - acc: 0.9524 - val_loss: 0.1686 - val_acc: 0.9500\n",
      "Epoch 47/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1147 - acc: 0.9528 - val_loss: 0.2327 - val_acc: 0.9187\n",
      "Epoch 48/200\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1422 - acc: 0.9428 - val_loss: 0.1632 - val_acc: 0.9313\n",
      "Epoch 49/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1133 - acc: 0.9565 - val_loss: 0.3069 - val_acc: 0.9250\n",
      "Epoch 50/200\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.0996 - acc: 0.9617 - val_loss: 0.2593 - val_acc: 0.9250\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train loss: 0.13430626905\n",
      "Train accuracy: 0.945983379501\n",
      "Test loss: 0.143599990755\n",
      "Test accuracy: 0.9625\n",
      "\n",
      "===================FOLD= 5\n",
      "Epoch 1/200\n",
      "24/24 [==============================] - 19s 809ms/step - loss: 0.7476 - acc: 0.6024 - val_loss: 0.6840 - val_acc: 0.7937\n",
      "Epoch 2/200\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.4803 - acc: 0.7912 - val_loss: 0.5126 - val_acc: 0.7063\n",
      "Epoch 3/200\n",
      "24/24 [==============================] - 14s 592ms/step - loss: 0.4563 - acc: 0.7940 - val_loss: 0.2910 - val_acc: 0.8938\n",
      "Epoch 4/200\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.3335 - acc: 0.8477 - val_loss: 0.3999 - val_acc: 0.8625\n",
      "Epoch 5/200\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.3640 - acc: 0.8349 - val_loss: 0.2686 - val_acc: 0.9000\n",
      "Epoch 6/200\n",
      "24/24 [==============================] - 14s 593ms/step - loss: 0.3352 - acc: 0.8396 - val_loss: 0.2241 - val_acc: 0.9313\n",
      "Epoch 7/200\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.2962 - acc: 0.8752 - val_loss: 0.2720 - val_acc: 0.8875\n",
      "Epoch 8/200\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.2970 - acc: 0.8741 - val_loss: 0.3083 - val_acc: 0.9062\n",
      "Epoch 9/200\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.2751 - acc: 0.8917 - val_loss: 0.2788 - val_acc: 0.9125\n",
      "Epoch 10/200\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2709 - acc: 0.8915 - val_loss: 0.2196 - val_acc: 0.9375\n",
      "Epoch 11/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2653 - acc: 0.8826 - val_loss: 0.2946 - val_acc: 0.9062\n",
      "Epoch 12/200\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.2327 - acc: 0.8943 - val_loss: 0.2087 - val_acc: 0.9062\n",
      "Epoch 13/200\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.2140 - acc: 0.9156 - val_loss: 0.1990 - val_acc: 0.9313\n",
      "Epoch 14/200\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.2303 - acc: 0.9041 - val_loss: 0.1993 - val_acc: 0.9375\n",
      "Epoch 15/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2045 - acc: 0.9147 - val_loss: 0.2060 - val_acc: 0.9250\n",
      "Epoch 16/200\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2177 - acc: 0.9111 - val_loss: 0.1958 - val_acc: 0.9437\n",
      "Epoch 17/200\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.2164 - acc: 0.9080 - val_loss: 0.2874 - val_acc: 0.8812\n",
      "Epoch 18/200\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.2208 - acc: 0.9031 - val_loss: 0.2662 - val_acc: 0.9062\n",
      "Epoch 19/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2621 - acc: 0.8878 - val_loss: 0.2237 - val_acc: 0.9313\n",
      "Epoch 20/200\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.1854 - acc: 0.9182 - val_loss: 0.1892 - val_acc: 0.9250\n",
      "Epoch 21/200\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.2139 - acc: 0.9068 - val_loss: 0.2111 - val_acc: 0.9250\n",
      "Epoch 22/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2014 - acc: 0.9228 - val_loss: 0.2063 - val_acc: 0.9313\n",
      "Epoch 23/200\n",
      "24/24 [==============================] - 14s 583ms/step - loss: 0.1718 - acc: 0.9281 - val_loss: 0.1877 - val_acc: 0.9250\n",
      "Epoch 24/200\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.2209 - acc: 0.9055 - val_loss: 0.2515 - val_acc: 0.9125\n",
      "Epoch 25/200\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1857 - acc: 0.9145 - val_loss: 0.2823 - val_acc: 0.9250\n",
      "Epoch 26/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2051 - acc: 0.9062 - val_loss: 0.1970 - val_acc: 0.9313\n",
      "Epoch 27/200\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1735 - acc: 0.9391 - val_loss: 0.2462 - val_acc: 0.9125\n",
      "Epoch 28/200\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1555 - acc: 0.9383 - val_loss: 0.2809 - val_acc: 0.9250\n",
      "Epoch 29/200\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1705 - acc: 0.9336 - val_loss: 0.2157 - val_acc: 0.9563\n",
      "Epoch 30/200\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1637 - acc: 0.9259 - val_loss: 0.2603 - val_acc: 0.9375\n",
      "Epoch 31/200\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1599 - acc: 0.9371 - val_loss: 0.3061 - val_acc: 0.9062\n",
      "Epoch 32/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1704 - acc: 0.9308 - val_loss: 0.3052 - val_acc: 0.9250\n",
      "Epoch 33/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1962 - acc: 0.9270 - val_loss: 0.2287 - val_acc: 0.9500\n",
      "Epoch 34/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1435 - acc: 0.9454 - val_loss: 0.2715 - val_acc: 0.9187\n",
      "Epoch 35/200\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1420 - acc: 0.9410 - val_loss: 0.1958 - val_acc: 0.9187\n",
      "Epoch 36/200\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.1660 - acc: 0.9313 - val_loss: 0.1740 - val_acc: 0.9437\n",
      "Epoch 37/200\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.1191 - acc: 0.9546 - val_loss: 0.3249 - val_acc: 0.8938\n",
      "Epoch 38/200\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.2029 - acc: 0.9103 - val_loss: 0.2668 - val_acc: 0.9062\n",
      "Epoch 39/200\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1585 - acc: 0.9337 - val_loss: 0.2379 - val_acc: 0.9500\n",
      "Epoch 40/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1454 - acc: 0.9366 - val_loss: 0.2691 - val_acc: 0.9625\n",
      "Epoch 41/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1094 - acc: 0.9546 - val_loss: 0.2295 - val_acc: 0.9563\n",
      "Epoch 42/200\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1190 - acc: 0.9523 - val_loss: 0.2566 - val_acc: 0.9313\n",
      "Epoch 43/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1603 - acc: 0.9342 - val_loss: 0.2491 - val_acc: 0.9313\n",
      "Epoch 44/200\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1370 - acc: 0.9430 - val_loss: 0.2325 - val_acc: 0.9375\n",
      "Epoch 45/200\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1216 - acc: 0.9505 - val_loss: 0.1811 - val_acc: 0.9313\n",
      "Epoch 46/200\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1360 - acc: 0.9477 - val_loss: 0.3583 - val_acc: 0.9313\n",
      "Epoch 47/200\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1190 - acc: 0.9552 - val_loss: 0.2558 - val_acc: 0.9313\n",
      "Epoch 48/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1197 - acc: 0.9586 - val_loss: 0.3230 - val_acc: 0.9000\n",
      "Epoch 49/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1081 - acc: 0.9536 - val_loss: 0.1929 - val_acc: 0.9500\n",
      "Epoch 50/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1197 - acc: 0.9534 - val_loss: 0.2453 - val_acc: 0.9187\n",
      "Epoch 51/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1241 - acc: 0.9511 - val_loss: 0.2053 - val_acc: 0.9437\n",
      "Epoch 52/200\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.0994 - acc: 0.9630 - val_loss: 0.1994 - val_acc: 0.9500\n",
      "Epoch 53/200\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.0978 - acc: 0.9599 - val_loss: 0.2822 - val_acc: 0.9437\n",
      "Epoch 54/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.0860 - acc: 0.9655 - val_loss: 0.2690 - val_acc: 0.9313\n",
      "Epoch 55/200\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1360 - acc: 0.9515 - val_loss: 0.3906 - val_acc: 0.8938\n",
      "Epoch 56/200\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1215 - acc: 0.9617 - val_loss: 0.1992 - val_acc: 0.9250\n",
      "Epoch 57/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1101 - acc: 0.9581 - val_loss: 0.2104 - val_acc: 0.9125\n",
      "Epoch 58/200\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1076 - acc: 0.9515 - val_loss: 0.2770 - val_acc: 0.9250\n",
      "Epoch 59/200\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.0679 - acc: 0.9715 - val_loss: 0.3137 - val_acc: 0.9313\n",
      "Epoch 60/200\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.0732 - acc: 0.9704 - val_loss: 0.4246 - val_acc: 0.9000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 61/200\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.0976 - acc: 0.9598 - val_loss: 0.3808 - val_acc: 0.9125\n",
      "Epoch 62/200\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.0971 - acc: 0.9593 - val_loss: 0.4961 - val_acc: 0.9000\n",
      "Epoch 63/200\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.0971 - acc: 0.9663 - val_loss: 0.4386 - val_acc: 0.8875\n",
      "Epoch 64/200\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.0975 - acc: 0.9655 - val_loss: 0.3082 - val_acc: 0.9125\n",
      "Epoch 65/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.0747 - acc: 0.9739 - val_loss: 0.3050 - val_acc: 0.9125\n",
      "Epoch 66/200\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.0683 - acc: 0.9739 - val_loss: 0.2452 - val_acc: 0.9375\n",
      "Train loss: 0.0942226873293\n",
      "Train accuracy: 0.961911357341\n",
      "Test loss: 0.173995134234\n",
      "Test accuracy: 0.94375\n",
      "\n",
      "===================FOLD= 6\n",
      "Epoch 1/200\n",
      "24/24 [==============================] - 20s 820ms/step - loss: 0.9323 - acc: 0.5645 - val_loss: 0.7018 - val_acc: 0.6000\n",
      "Epoch 2/200\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.6728 - acc: 0.6842 - val_loss: 0.5777 - val_acc: 0.6813\n",
      "Epoch 3/200\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.5470 - acc: 0.7620 - val_loss: 0.5048 - val_acc: 0.8063\n",
      "Epoch 4/200\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.4203 - acc: 0.8215 - val_loss: 0.7043 - val_acc: 0.7625\n",
      "Epoch 5/200\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.3726 - acc: 0.8412 - val_loss: 0.3320 - val_acc: 0.8688\n",
      "Epoch 6/200\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.3529 - acc: 0.8542 - val_loss: 0.4349 - val_acc: 0.8438\n",
      "Epoch 7/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.3209 - acc: 0.8721 - val_loss: 0.3789 - val_acc: 0.8438\n",
      "Epoch 8/200\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.2786 - acc: 0.8849 - val_loss: 0.2897 - val_acc: 0.8875\n",
      "Epoch 9/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2750 - acc: 0.8762 - val_loss: 0.2997 - val_acc: 0.8938\n",
      "Epoch 10/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2379 - acc: 0.9047 - val_loss: 0.3012 - val_acc: 0.8750\n",
      "Epoch 11/200\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.2245 - acc: 0.9031 - val_loss: 0.3037 - val_acc: 0.8875\n",
      "Epoch 12/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2365 - acc: 0.9049 - val_loss: 0.3853 - val_acc: 0.8313\n",
      "Epoch 13/200\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2218 - acc: 0.9098 - val_loss: 0.2684 - val_acc: 0.9125\n",
      "Epoch 14/200\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.2101 - acc: 0.9111 - val_loss: 0.2642 - val_acc: 0.9000\n",
      "Epoch 15/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2124 - acc: 0.9184 - val_loss: 0.2880 - val_acc: 0.9062\n",
      "Epoch 16/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2198 - acc: 0.9093 - val_loss: 0.3150 - val_acc: 0.8562\n",
      "Epoch 17/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2166 - acc: 0.9182 - val_loss: 0.2896 - val_acc: 0.8938\n",
      "Epoch 18/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1933 - acc: 0.9225 - val_loss: 0.2723 - val_acc: 0.8812\n",
      "Epoch 19/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1750 - acc: 0.9350 - val_loss: 0.3199 - val_acc: 0.8750\n",
      "Epoch 20/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1926 - acc: 0.9169 - val_loss: 0.3012 - val_acc: 0.9062\n",
      "Epoch 21/200\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.1834 - acc: 0.9226 - val_loss: 0.2302 - val_acc: 0.8938\n",
      "Epoch 22/200\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.1902 - acc: 0.9272 - val_loss: 0.2790 - val_acc: 0.8875\n",
      "Epoch 23/200\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1669 - acc: 0.9368 - val_loss: 0.2836 - val_acc: 0.9063\n",
      "Epoch 24/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1633 - acc: 0.9432 - val_loss: 0.2946 - val_acc: 0.8688\n",
      "Epoch 25/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1528 - acc: 0.9394 - val_loss: 0.3452 - val_acc: 0.8750\n",
      "Epoch 26/200\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.1581 - acc: 0.9410 - val_loss: 0.3059 - val_acc: 0.8875\n",
      "Epoch 27/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1656 - acc: 0.9399 - val_loss: 0.2834 - val_acc: 0.9000\n",
      "Epoch 28/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1554 - acc: 0.9417 - val_loss: 0.2522 - val_acc: 0.9062\n",
      "Epoch 29/200\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1337 - acc: 0.9492 - val_loss: 0.2652 - val_acc: 0.9250\n",
      "Epoch 30/200\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1311 - acc: 0.9469 - val_loss: 0.3519 - val_acc: 0.8375\n",
      "Epoch 31/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1420 - acc: 0.9404 - val_loss: 0.3187 - val_acc: 0.8688\n",
      "Epoch 32/200\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1388 - acc: 0.9420 - val_loss: 0.2333 - val_acc: 0.9125\n",
      "Epoch 33/200\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1107 - acc: 0.9583 - val_loss: 0.4083 - val_acc: 0.8625\n",
      "Epoch 34/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1353 - acc: 0.9428 - val_loss: 0.2908 - val_acc: 0.8875\n",
      "Epoch 35/200\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1512 - acc: 0.9388 - val_loss: 0.2919 - val_acc: 0.8688\n",
      "Epoch 36/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1083 - acc: 0.9598 - val_loss: 0.3308 - val_acc: 0.9000\n",
      "Epoch 37/200\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.1505 - acc: 0.9440 - val_loss: 0.2276 - val_acc: 0.9125\n",
      "Epoch 38/200\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1128 - acc: 0.9554 - val_loss: 0.2969 - val_acc: 0.8875\n",
      "Epoch 39/200\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1222 - acc: 0.9531 - val_loss: 0.3677 - val_acc: 0.8875\n",
      "Epoch 40/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1492 - acc: 0.9414 - val_loss: 0.2602 - val_acc: 0.8938\n",
      "Epoch 41/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1330 - acc: 0.9503 - val_loss: 0.3565 - val_acc: 0.8875\n",
      "Epoch 42/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1032 - acc: 0.9581 - val_loss: 0.3967 - val_acc: 0.8938\n",
      "Epoch 43/200\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1280 - acc: 0.9523 - val_loss: 0.2805 - val_acc: 0.9125\n",
      "Epoch 44/200\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.0884 - acc: 0.9606 - val_loss: 0.3560 - val_acc: 0.8938\n",
      "Epoch 45/200\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1085 - acc: 0.9609 - val_loss: 0.2773 - val_acc: 0.9125\n",
      "Epoch 46/200\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1034 - acc: 0.9637 - val_loss: 0.2993 - val_acc: 0.9000\n",
      "Epoch 47/200\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.0865 - acc: 0.9661 - val_loss: 0.3497 - val_acc: 0.8625\n",
      "Epoch 48/200\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1460 - acc: 0.9518 - val_loss: 0.3769 - val_acc: 0.8938\n",
      "Epoch 49/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1025 - acc: 0.9637 - val_loss: 0.3329 - val_acc: 0.8812\n",
      "Epoch 50/200\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.0872 - acc: 0.9663 - val_loss: 0.4197 - val_acc: 0.8750\n",
      "Epoch 51/200\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.0946 - acc: 0.9621 - val_loss: 0.2821 - val_acc: 0.9000\n",
      "Epoch 52/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1023 - acc: 0.9634 - val_loss: 0.3225 - val_acc: 0.8875\n",
      "Epoch 53/200\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1272 - acc: 0.9528 - val_loss: 0.3544 - val_acc: 0.8750\n",
      "Epoch 54/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1067 - acc: 0.9599 - val_loss: 0.2810 - val_acc: 0.8938\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 55/200\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.0880 - acc: 0.9676 - val_loss: 0.4917 - val_acc: 0.8812\n",
      "Epoch 56/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.0768 - acc: 0.9676 - val_loss: 0.3509 - val_acc: 0.8812\n",
      "Epoch 57/200\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.0698 - acc: 0.9678 - val_loss: 0.4808 - val_acc: 0.8938\n",
      "Epoch 58/200\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.0699 - acc: 0.9717 - val_loss: 0.5383 - val_acc: 0.8688\n",
      "Epoch 59/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1160 - acc: 0.9616 - val_loss: 0.3285 - val_acc: 0.8812\n",
      "Epoch 60/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.0874 - acc: 0.9678 - val_loss: 0.3639 - val_acc: 0.9000\n",
      "Epoch 61/200\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.0789 - acc: 0.9728 - val_loss: 0.3813 - val_acc: 0.8500\n",
      "Epoch 62/200\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.0601 - acc: 0.9798 - val_loss: 0.4505 - val_acc: 0.8688\n",
      "Epoch 63/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.0694 - acc: 0.9728 - val_loss: 0.4856 - val_acc: 0.8688\n",
      "Epoch 64/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.0497 - acc: 0.9839 - val_loss: 0.4676 - val_acc: 0.8688\n",
      "Epoch 65/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.0693 - acc: 0.9775 - val_loss: 0.5123 - val_acc: 0.8750\n",
      "Epoch 66/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.0606 - acc: 0.9767 - val_loss: 0.3543 - val_acc: 0.8625\n",
      "Epoch 67/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.0515 - acc: 0.9774 - val_loss: 0.4748 - val_acc: 0.8750\n",
      "Train loss: 0.0765767953877\n",
      "Train accuracy: 0.969529085873\n",
      "Test loss: 0.227631270885\n",
      "Test accuracy: 0.9125\n",
      "\n",
      "===================FOLD= 7\n",
      "Epoch 1/200\n",
      "24/24 [==============================] - 20s 841ms/step - loss: 0.7228 - acc: 0.5550 - val_loss: 0.5774 - val_acc: 0.7438\n",
      "Epoch 2/200\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.4953 - acc: 0.7202 - val_loss: 0.4698 - val_acc: 0.8250\n",
      "Epoch 3/200\n",
      "24/24 [==============================] - 14s 592ms/step - loss: 0.3614 - acc: 0.8309 - val_loss: 0.2998 - val_acc: 0.8688\n",
      "Epoch 4/200\n",
      "24/24 [==============================] - 14s 592ms/step - loss: 0.3358 - acc: 0.8604 - val_loss: 0.2359 - val_acc: 0.8625\n",
      "Epoch 5/200\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.2856 - acc: 0.8744 - val_loss: 0.2641 - val_acc: 0.9250\n",
      "Epoch 6/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2882 - acc: 0.8756 - val_loss: 0.2604 - val_acc: 0.8438\n",
      "Epoch 7/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2613 - acc: 0.8839 - val_loss: 0.2563 - val_acc: 0.8625\n",
      "Epoch 8/200\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2757 - acc: 0.8796 - val_loss: 0.2035 - val_acc: 0.8875\n",
      "Epoch 9/200\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2260 - acc: 0.9086 - val_loss: 0.2288 - val_acc: 0.8750\n",
      "Epoch 10/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2217 - acc: 0.9078 - val_loss: 0.2442 - val_acc: 0.9000\n",
      "Epoch 11/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2418 - acc: 0.8949 - val_loss: 0.2821 - val_acc: 0.8625\n",
      "Epoch 12/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2652 - acc: 0.8811 - val_loss: 0.2867 - val_acc: 0.8938\n",
      "Epoch 13/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2066 - acc: 0.9117 - val_loss: 0.2197 - val_acc: 0.9125\n",
      "Epoch 14/200\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.2099 - acc: 0.9213 - val_loss: 0.2016 - val_acc: 0.9187\n",
      "Epoch 15/200\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.1960 - acc: 0.9246 - val_loss: 0.1976 - val_acc: 0.8875\n",
      "Epoch 16/200\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.2367 - acc: 0.8961 - val_loss: 0.1783 - val_acc: 0.9313\n",
      "Epoch 17/200\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.1667 - acc: 0.9339 - val_loss: 0.1606 - val_acc: 0.9313\n",
      "Epoch 18/200\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1889 - acc: 0.9192 - val_loss: 0.1740 - val_acc: 0.9187\n",
      "Epoch 19/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1810 - acc: 0.9238 - val_loss: 0.1886 - val_acc: 0.9000\n",
      "Epoch 20/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1881 - acc: 0.9290 - val_loss: 0.1992 - val_acc: 0.9313\n",
      "Epoch 21/200\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.1863 - acc: 0.9197 - val_loss: 0.1834 - val_acc: 0.9125\n",
      "Epoch 22/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1953 - acc: 0.9257 - val_loss: 0.1703 - val_acc: 0.9125\n",
      "Epoch 23/200\n",
      "24/24 [==============================] - 14s 585ms/step - loss: 0.1736 - acc: 0.9284 - val_loss: 0.1389 - val_acc: 0.9438\n",
      "Epoch 24/200\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.1888 - acc: 0.9147 - val_loss: 0.2588 - val_acc: 0.8938\n",
      "Epoch 25/200\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.1802 - acc: 0.9267 - val_loss: 0.1236 - val_acc: 0.9563\n",
      "Epoch 26/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1622 - acc: 0.9417 - val_loss: 0.1429 - val_acc: 0.9375\n",
      "Epoch 27/200\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1419 - acc: 0.9440 - val_loss: 0.1986 - val_acc: 0.9187\n",
      "Epoch 28/200\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1681 - acc: 0.9340 - val_loss: 0.1727 - val_acc: 0.9250\n",
      "Epoch 29/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1706 - acc: 0.9316 - val_loss: 0.2185 - val_acc: 0.9000\n",
      "Epoch 30/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1858 - acc: 0.9241 - val_loss: 0.1922 - val_acc: 0.9187\n",
      "Epoch 31/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1319 - acc: 0.9438 - val_loss: 0.1684 - val_acc: 0.9250\n",
      "Epoch 32/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1416 - acc: 0.9440 - val_loss: 0.1964 - val_acc: 0.9000\n",
      "Epoch 33/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1353 - acc: 0.9378 - val_loss: 0.1437 - val_acc: 0.9437\n",
      "Epoch 34/200\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1398 - acc: 0.9438 - val_loss: 0.2409 - val_acc: 0.9000\n",
      "Epoch 35/200\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1281 - acc: 0.9467 - val_loss: 0.1365 - val_acc: 0.9375\n",
      "Epoch 36/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1350 - acc: 0.9417 - val_loss: 0.2140 - val_acc: 0.9313\n",
      "Epoch 37/200\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1261 - acc: 0.9534 - val_loss: 0.2771 - val_acc: 0.9062\n",
      "Epoch 38/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1639 - acc: 0.9339 - val_loss: 0.1832 - val_acc: 0.9125\n",
      "Epoch 39/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1739 - acc: 0.9334 - val_loss: 0.2782 - val_acc: 0.8938\n",
      "Epoch 40/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1335 - acc: 0.9528 - val_loss: 0.1752 - val_acc: 0.9437\n",
      "Epoch 41/200\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1356 - acc: 0.9443 - val_loss: 0.3097 - val_acc: 0.8375\n",
      "Epoch 42/200\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1455 - acc: 0.9306 - val_loss: 0.1568 - val_acc: 0.9250\n",
      "Epoch 43/200\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1228 - acc: 0.9423 - val_loss: 0.2785 - val_acc: 0.8938\n",
      "Epoch 44/200\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1225 - acc: 0.9563 - val_loss: 0.2163 - val_acc: 0.9250\n",
      "Epoch 45/200\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.0953 - acc: 0.9630 - val_loss: 0.2115 - val_acc: 0.9250\n",
      "Epoch 46/200\n",
      "24/24 [==============================] - 12s 501ms/step - loss: 0.0993 - acc: 0.9655 - val_loss: 0.1840 - val_acc: 0.9375\n",
      "Epoch 47/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1481 - acc: 0.9411 - val_loss: 0.1601 - val_acc: 0.9250\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 48/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1022 - acc: 0.9534 - val_loss: 0.1829 - val_acc: 0.9437\n",
      "Epoch 49/200\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1058 - acc: 0.9585 - val_loss: 0.2258 - val_acc: 0.9000\n",
      "Epoch 50/200\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1163 - acc: 0.9575 - val_loss: 0.2177 - val_acc: 0.8688\n",
      "Epoch 51/200\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.0944 - acc: 0.9590 - val_loss: 0.1975 - val_acc: 0.9250\n",
      "Epoch 52/200\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.0986 - acc: 0.9580 - val_loss: 0.1960 - val_acc: 0.9437\n",
      "Epoch 53/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1660 - acc: 0.9336 - val_loss: 0.2705 - val_acc: 0.8625\n",
      "Epoch 54/200\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1337 - acc: 0.9539 - val_loss: 0.1856 - val_acc: 0.9313\n",
      "Epoch 55/200\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.0956 - acc: 0.9642 - val_loss: 0.1789 - val_acc: 0.9125\n",
      "Train loss: 0.145347349325\n",
      "Train accuracy: 0.939750692521\n",
      "Test loss: 0.123570086807\n",
      "Test accuracy: 0.95625\n",
      "\n",
      "===================FOLD= 8\n",
      "Epoch 1/200\n",
      "24/24 [==============================] - 20s 845ms/step - loss: 0.7118 - acc: 0.5562 - val_loss: 0.5698 - val_acc: 0.7000\n",
      "Epoch 2/200\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.6187 - acc: 0.6191 - val_loss: 0.4961 - val_acc: 0.7250\n",
      "Epoch 3/200\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.4520 - acc: 0.7777 - val_loss: 0.3666 - val_acc: 0.8187\n",
      "Epoch 4/200\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.3212 - acc: 0.8627 - val_loss: 0.3426 - val_acc: 0.8187\n",
      "Epoch 5/200\n",
      "24/24 [==============================] - 14s 594ms/step - loss: 0.3077 - acc: 0.8546 - val_loss: 0.2911 - val_acc: 0.8250\n",
      "Epoch 6/200\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.3010 - acc: 0.8770 - val_loss: 0.3046 - val_acc: 0.8438\n",
      "Epoch 7/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2619 - acc: 0.8816 - val_loss: 0.3847 - val_acc: 0.8000\n",
      "Epoch 8/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2544 - acc: 0.8866 - val_loss: 0.3140 - val_acc: 0.8500\n",
      "Epoch 9/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2373 - acc: 0.9020 - val_loss: 0.3874 - val_acc: 0.8375\n",
      "Epoch 10/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2335 - acc: 0.9059 - val_loss: 0.3470 - val_acc: 0.8375\n",
      "Epoch 11/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2510 - acc: 0.8941 - val_loss: 0.3582 - val_acc: 0.8313\n",
      "Epoch 12/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2076 - acc: 0.9103 - val_loss: 0.3244 - val_acc: 0.8438\n",
      "Epoch 13/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2212 - acc: 0.9111 - val_loss: 0.3234 - val_acc: 0.8313\n",
      "Epoch 14/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2047 - acc: 0.9166 - val_loss: 0.3366 - val_acc: 0.8438\n",
      "Epoch 15/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2099 - acc: 0.9155 - val_loss: 0.3945 - val_acc: 0.8187\n",
      "Epoch 16/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2068 - acc: 0.9143 - val_loss: 0.4074 - val_acc: 0.8438\n",
      "Epoch 17/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1885 - acc: 0.9186 - val_loss: 0.3810 - val_acc: 0.8375\n",
      "Epoch 18/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1790 - acc: 0.9300 - val_loss: 0.3214 - val_acc: 0.8562\n",
      "Epoch 19/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1954 - acc: 0.9158 - val_loss: 0.4478 - val_acc: 0.8063\n",
      "Epoch 20/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1999 - acc: 0.9122 - val_loss: 0.3569 - val_acc: 0.8375\n",
      "Epoch 21/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2038 - acc: 0.9200 - val_loss: 0.3327 - val_acc: 0.8250\n",
      "Epoch 22/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1716 - acc: 0.9334 - val_loss: 0.3516 - val_acc: 0.8375\n",
      "Epoch 23/200\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1604 - acc: 0.9324 - val_loss: 0.3075 - val_acc: 0.8562\n",
      "Epoch 24/200\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1650 - acc: 0.9360 - val_loss: 0.3745 - val_acc: 0.8438\n",
      "Epoch 25/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1500 - acc: 0.9363 - val_loss: 0.3623 - val_acc: 0.8500\n",
      "Epoch 26/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1513 - acc: 0.9349 - val_loss: 0.3990 - val_acc: 0.8562\n",
      "Epoch 27/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1485 - acc: 0.9461 - val_loss: 0.4648 - val_acc: 0.8500\n",
      "Epoch 28/200\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1605 - acc: 0.9362 - val_loss: 0.3536 - val_acc: 0.8438\n",
      "Epoch 29/200\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1594 - acc: 0.9355 - val_loss: 0.3697 - val_acc: 0.8688\n",
      "Epoch 30/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1491 - acc: 0.9365 - val_loss: 0.4282 - val_acc: 0.8375\n",
      "Epoch 31/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1583 - acc: 0.9326 - val_loss: 0.3653 - val_acc: 0.8625\n",
      "Epoch 32/200\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1433 - acc: 0.9409 - val_loss: 0.4859 - val_acc: 0.8562\n",
      "Epoch 33/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1682 - acc: 0.9311 - val_loss: 0.3127 - val_acc: 0.8562\n",
      "Epoch 34/200\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1246 - acc: 0.9511 - val_loss: 0.3342 - val_acc: 0.8375\n",
      "Epoch 35/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1201 - acc: 0.9557 - val_loss: 0.4349 - val_acc: 0.8313\n",
      "Train loss: 0.240351545225\n",
      "Train accuracy: 0.887811634349\n",
      "Test loss: 0.291122749448\n",
      "Test accuracy: 0.825\n",
      "\n",
      "===================FOLD= 9\n",
      "Epoch 1/200\n",
      "24/24 [==============================] - 21s 858ms/step - loss: 0.7119 - acc: 0.5568 - val_loss: 0.6063 - val_acc: 0.6000\n",
      "Epoch 2/200\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.5057 - acc: 0.7365 - val_loss: 0.4171 - val_acc: 0.7750\n",
      "Epoch 3/200\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.4229 - acc: 0.8148 - val_loss: 0.3632 - val_acc: 0.8250\n",
      "Epoch 4/200\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.3411 - acc: 0.8466 - val_loss: 0.2697 - val_acc: 0.8625\n",
      "Epoch 5/200\n",
      "24/24 [==============================] - 14s 593ms/step - loss: 0.3404 - acc: 0.8534 - val_loss: 0.2603 - val_acc: 0.8750\n",
      "Epoch 6/200\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.3080 - acc: 0.8642 - val_loss: 0.3134 - val_acc: 0.8438\n",
      "Epoch 7/200\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.3087 - acc: 0.8585 - val_loss: 0.2468 - val_acc: 0.9000\n",
      "Epoch 8/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2611 - acc: 0.8831 - val_loss: 0.2629 - val_acc: 0.8625\n",
      "Epoch 9/200\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.2525 - acc: 0.8927 - val_loss: 0.2141 - val_acc: 0.9000\n",
      "Epoch 10/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2703 - acc: 0.8790 - val_loss: 0.3083 - val_acc: 0.8688\n",
      "Epoch 11/200\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.2312 - acc: 0.9028 - val_loss: 0.2672 - val_acc: 0.8750\n",
      "Epoch 12/200\n",
      "24/24 [==============================] - 14s 592ms/step - loss: 0.2578 - acc: 0.8963 - val_loss: 0.2116 - val_acc: 0.9125\n",
      "Epoch 13/200\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2420 - acc: 0.9013 - val_loss: 0.2060 - val_acc: 0.9000\n",
      "Epoch 14/200\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.2121 - acc: 0.9135 - val_loss: 0.3174 - val_acc: 0.9000\n",
      "Epoch 15/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2202 - acc: 0.9059 - val_loss: 0.2236 - val_acc: 0.8938\n",
      "Epoch 16/200\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.2020 - acc: 0.9143 - val_loss: 0.2443 - val_acc: 0.9000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 17/200\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.2209 - acc: 0.9158 - val_loss: 0.2696 - val_acc: 0.8875\n",
      "Epoch 18/200\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.1924 - acc: 0.9182 - val_loss: 0.2484 - val_acc: 0.9062\n",
      "Epoch 19/200\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.1945 - acc: 0.9267 - val_loss: 0.2170 - val_acc: 0.9313\n",
      "Epoch 20/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1889 - acc: 0.9235 - val_loss: 0.2095 - val_acc: 0.9187\n",
      "Epoch 21/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1987 - acc: 0.9150 - val_loss: 0.2163 - val_acc: 0.9250\n",
      "Epoch 22/200\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.1728 - acc: 0.9332 - val_loss: 0.1911 - val_acc: 0.9187\n",
      "Epoch 23/200\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1780 - acc: 0.9281 - val_loss: 0.2753 - val_acc: 0.8813\n",
      "Epoch 24/200\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1778 - acc: 0.9236 - val_loss: 0.3154 - val_acc: 0.8562\n",
      "Epoch 25/200\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1713 - acc: 0.9296 - val_loss: 0.2307 - val_acc: 0.9187\n",
      "Epoch 26/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1639 - acc: 0.9313 - val_loss: 0.2494 - val_acc: 0.8938\n",
      "Epoch 27/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1572 - acc: 0.9419 - val_loss: 0.2457 - val_acc: 0.8875\n",
      "Epoch 28/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1685 - acc: 0.9355 - val_loss: 0.2382 - val_acc: 0.8938\n",
      "Epoch 29/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1631 - acc: 0.9368 - val_loss: 0.2544 - val_acc: 0.8938\n",
      "Epoch 30/200\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1403 - acc: 0.9420 - val_loss: 0.2012 - val_acc: 0.9000\n",
      "Epoch 31/200\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1258 - acc: 0.9523 - val_loss: 0.4189 - val_acc: 0.8250\n",
      "Epoch 32/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1513 - acc: 0.9410 - val_loss: 0.2676 - val_acc: 0.9000\n",
      "Epoch 33/200\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1539 - acc: 0.9368 - val_loss: 0.1972 - val_acc: 0.9125\n",
      "Epoch 34/200\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1487 - acc: 0.9404 - val_loss: 0.2488 - val_acc: 0.8812\n",
      "Epoch 35/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1464 - acc: 0.9366 - val_loss: 0.2799 - val_acc: 0.8938\n",
      "Epoch 36/200\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1508 - acc: 0.9443 - val_loss: 0.2353 - val_acc: 0.8875\n",
      "Epoch 37/200\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1446 - acc: 0.9366 - val_loss: 0.2023 - val_acc: 0.9062\n",
      "Epoch 38/200\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1636 - acc: 0.9404 - val_loss: 0.2642 - val_acc: 0.9062\n",
      "Epoch 39/200\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1197 - acc: 0.9577 - val_loss: 0.2940 - val_acc: 0.9000\n",
      "Epoch 40/200\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1303 - acc: 0.9482 - val_loss: 0.1986 - val_acc: 0.9000\n",
      "Epoch 41/200\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1265 - acc: 0.9482 - val_loss: 0.2398 - val_acc: 0.8938\n",
      "Epoch 42/200\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1096 - acc: 0.9585 - val_loss: 0.2090 - val_acc: 0.9313\n",
      "Epoch 43/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1008 - acc: 0.9586 - val_loss: 0.2835 - val_acc: 0.8875\n",
      "Epoch 44/200\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1590 - acc: 0.9363 - val_loss: 0.2337 - val_acc: 0.8938\n",
      "Epoch 45/200\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1271 - acc: 0.9464 - val_loss: 0.2249 - val_acc: 0.8875\n",
      "Epoch 46/200\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1147 - acc: 0.9565 - val_loss: 0.2110 - val_acc: 0.9063\n",
      "Epoch 47/200\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1105 - acc: 0.9565 - val_loss: 0.3920 - val_acc: 0.8562\n",
      "Epoch 48/200\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1525 - acc: 0.9319 - val_loss: 0.2772 - val_acc: 0.8812\n",
      "Epoch 49/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.0755 - acc: 0.9702 - val_loss: 0.2403 - val_acc: 0.9062\n",
      "Epoch 50/200\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1465 - acc: 0.9402 - val_loss: 0.2965 - val_acc: 0.8812\n",
      "Epoch 51/200\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1057 - acc: 0.9570 - val_loss: 0.2465 - val_acc: 0.9062\n",
      "Epoch 52/200\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1072 - acc: 0.9599 - val_loss: 0.1960 - val_acc: 0.9187\n",
      "Train loss: 0.129060943743\n",
      "Train accuracy: 0.945290858726\n",
      "Test loss: 0.191064150631\n",
      "Test accuracy: 0.91875\n",
      "\n",
      " Train Log Loss Validation=  0.112609400652\n",
      " Test Log Loss Validation=  0.179382493428\n"
     ]
    }
   ],
   "source": [
    "#def myAngleCV(X_train, X_angle, X_test):\n",
    "K=10\n",
    "folds = list(StratifiedKFold(n_splits=K, shuffle=True, random_state=16).split(X_train, target_train))\n",
    "y_test_pred_log = 0\n",
    "y_train_pred_log=0\n",
    "y_valid_pred_log = 0.0*target_train\n",
    "for j, (train_idx, test_idx) in enumerate(folds):\n",
    "    print('\\n===================FOLD=',j)\n",
    "    X_train_cv = X_train[train_idx]\n",
    "    y_train_cv = target_train[train_idx]\n",
    "    X_holdout = X_train[test_idx]\n",
    "    Y_holdout= target_train[test_idx]\n",
    "    \n",
    "    #Angle\n",
    "    X_angle_cv=X_angle[train_idx]\n",
    "    X_angle_hold=X_angle[test_idx]\n",
    "    #define file path and get callbacks\n",
    "    file_path = \"%s_aug_model_weights.hdf5\"%j\n",
    "    callbacks = get_callbacks(filepath=file_path, patience=5)\n",
    "    gen_flow = gen_flow_for_two_inputs(X_train_cv, X_angle_cv, y_train_cv)\n",
    "    galaxyModel= getVggAngleModel()\n",
    "    histr = galaxyModel.fit_generator(\n",
    "            gen_flow,\n",
    "            steps_per_epoch=24,\n",
    "            epochs=200,\n",
    "            shuffle=True,\n",
    "            verbose=1,\n",
    "            validation_data=([X_holdout,X_angle_hold], Y_holdout),\n",
    "            callbacks=callbacks)\n",
    "     #Getting the Best Model\n",
    "    galaxyModel.load_weights(filepath=file_path)\n",
    "    #Getting Training Score\n",
    "    score = galaxyModel.evaluate([X_train_cv,X_angle_cv], y_train_cv, verbose=0)\n",
    "    print('Train loss:', score[0])\n",
    "    print('Train accuracy:', score[1])\n",
    "    #Getting Test Score\n",
    "    score = galaxyModel.evaluate([X_holdout,X_angle_hold], Y_holdout, verbose=0)\n",
    "    print('Test loss:', score[0])\n",
    "    print('Test accuracy:', score[1])\n",
    "\n",
    "    #Getting validation Score.\n",
    "    pred_valid=galaxyModel.predict([X_holdout,X_angle_hold])\n",
    "    y_valid_pred_log[test_idx] = pred_valid.reshape(pred_valid.shape[0])\n",
    "\n",
    "    #Getting Test Scores\n",
    "    temp_test=galaxyModel.predict([X_test, X_test_angle])\n",
    "    y_test_pred_log+=temp_test.reshape(temp_test.shape[0])\n",
    "\n",
    "    #Getting Train Scores\n",
    "    temp_train=galaxyModel.predict([X_train, X_angle])\n",
    "    y_train_pred_log+=temp_train.reshape(temp_train.shape[0])\n",
    "\n",
    "y_test_pred_log=y_test_pred_log/K\n",
    "y_train_pred_log=y_train_pred_log/K\n",
    "\n",
    "print('\\n Train Log Loss Validation= ',log_loss(target_train, y_train_pred_log))\n",
    "print(' Test Log Loss Validation= ',log_loss(target_train, y_valid_pred_log))\n",
    "#return y_test_pred_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#preds=myAngleCV(X_train, X_angle, X_test)\n",
    "preds = y_test_pred_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Submission to kaggle\n",
    "submission = pd.DataFrame()\n",
    "submission['id']=test['id']\n",
    "submission['is_iceberg']=preds\n",
    "submission.to_csv('sub_Adam_10folds_amsgrad_0001_30p.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['val_acc', 'val_loss', 'loss', 'acc'])\n"
     ]
    },
    {
     "data": {
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CuO7NeW7edsY/8zOz/zhYaX/G5oNHuffL9QxoH8O95Yy2qQuhQYE8PDmBnSnZvPfLbgC+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\ntQoZaVnAzQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcd62ec0e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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4LLOedP7etp2nOXK3Q5/R0G+cx66zU4pv/g6B4TDxZug/yWw7stq3MjWmNM+1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\nOScFMzPLOSmYmVnuPwyPCMScpcXJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcd5ad012b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# list all data in history\n",
    "print(histr.history.keys())\n",
    "# summarize history for accuracy\n",
    "plt.plot(histr.history['acc'])\n",
    "plt.plot(histr.history['val_acc'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()\n",
    "# summarize history for loss\n",
    "plt.plot(histr.history['loss'])\n",
    "plt.plot(histr.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.image as mpimg\n",
    "\n",
    "min1 = np.min(X_holdout)\n",
    "max1 = np.max(X_holdout)\n",
    "diff1 = max1 - min1\n",
    "my_sum = lambda x: (x - min1)/diff1\n",
    "\n",
    "X_holdout2 = my_sum(X_holdout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13      1\n",
       "14      0\n",
       "38      1\n",
       "43      0\n",
       "44      0\n",
       "80      0\n",
       "104     1\n",
       "127     0\n",
       "137     0\n",
       "138     1\n",
       "141     1\n",
       "167     1\n",
       "195     1\n",
       "199     0\n",
       "207     1\n",
       "212     0\n",
       "213     1\n",
       "221     0\n",
       "240     0\n",
       "256     0\n",
       "267     1\n",
       "274     1\n",
       "276     1\n",
       "285     1\n",
       "290     0\n",
       "294     0\n",
       "300     1\n",
       "307     1\n",
       "311     1\n",
       "318     1\n",
       "       ..\n",
       "1363    1\n",
       "1364    0\n",
       "1373    1\n",
       "1380    1\n",
       "1394    0\n",
       "1411    0\n",
       "1435    0\n",
       "1440    1\n",
       "1445    1\n",
       "1447    1\n",
       "1460    1\n",
       "1470    0\n",
       "1488    0\n",
       "1495    0\n",
       "1504    1\n",
       "1516    0\n",
       "1525    0\n",
       "1533    0\n",
       "1537    0\n",
       "1539    0\n",
       "1541    0\n",
       "1542    0\n",
       "1549    0\n",
       "1552    0\n",
       "1559    0\n",
       "1566    0\n",
       "1567    0\n",
       "1577    0\n",
       "1590    0\n",
       "1602    0\n",
       "Name: is_iceberg, Length: 160, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_holdout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fcd5ac5df98>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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J1IlUmdRrhrzdDEHdAcHTB8dHkqQ58oDboLLtgKxktD1+BgGzPXhWQT2X0HQK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\nEAQBiEUxCIIAxKIYBEEA/gcLQ8LB3IFDFwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcdca8c9c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[0], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[13])+ \",\"+\" iceberg_probability : \"+str(pred_valid[0])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fcd5a5c52e8>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wohrNcfzo3ryH6Y0Oo/OYgzhs+3l20UaiHtJtG1ZYsEurAylm0+esZ7x82CPo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\n2vbU0QypeDXKjc7ASrqFn0/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RPrY8u9ZK0cM6vqVyKASGDzYlYVswUM7n3ueHuM8CpklG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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcd5ad3cda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[1], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[14])+ \",\"+\" iceberg_probability : \"+str(pred_valid[1])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fcd5a5a0828>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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r87l6KYYQvviMn17zkjYkISHhZYt7gT4nJCQkvGQ4VyvFDydCZlrE2NXZADJa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\n9YO/5YEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcd5ac2ec18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[2], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[38])+ \",\"+\" iceberg_probability : \"+str(pred_valid[2])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fcd5a57dba8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UwGFC8aZ4DvQOaXTPON2GHsrrI8kSEv70F0gchCRPS5RTR6vgmby4QKfrluIA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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcd5ac094a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[3], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[43])+ \",\"+\" iceberg_probability : \"+str(pred_valid[3])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fcd5a4e21d0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0UENbuBcB1IxtL/N6JWkM+698zkjs+n+TkX9f3u9tP2Oee/sq/JnkWj3mC1TS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\n5Ad79qxUGgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcd5a536e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[6], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[104])+ \",\"+\" iceberg_probability : \"+str(pred_valid[6])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fcd5a443b00>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sdr1vpxO/6OsDn0ogeufdhbzRjoH3tH+D2zeV+H7D7/8CJUOFwl3a9nvX7IN7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\nBTkYMLgzl+iwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcd5a4a0d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[9], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[138])+ \",\"+\" iceberg_probability : \"+str(pred_valid[9])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fcd5a42a4e0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rZrwvU2bCkbpCfHB7QNkxJlpyirVMQlQiQxLTmmZoJgFf+BNWYUMceJPBt7Ci\nn5Cxatf789zpXTVzypxD0uuy+X4mT3q4dFp9AWlFM6QNPap8iaGERTmL/TVCalJa0MkUG6fE/HZQ\ndxji1WZyLwpyX6jHwvDg0PchZnyr7VR/OGT6Wi/82shH5u5i43i718OzLaGIvgl7fXTO78OhvAXK\nnpu3Zw6VeFsq1i9S7HNCQkLC2iO9FBMSEhKAtaXPyk1VzOW8CKR1sLSNnRIEOEorOrU2IPN0IUD+\nC4rIk3KC67CJOFThlMlTnnQoa3uZWxmcpiMlLqH8vYDHdKeBWGIk3yIF7152vrvYRQIu0KBpE5bj\nBTMCLwv3zTZifBeIj53BKj1Hci34fSsDPStyL+cAywO9yDcpfnZ+6v2Td7zPn+r6/TcHcExvOJ27\ngLjmxhCO9KDMz2ROWzeWiZ6gUm1w5J7Beb+87hRzs4uYYJi/21h6Gbfh5cC8ZbHPs9bqnyHzLneb\nPj6o1H0RY4jIL8NyjeRqM0i9NWPwAuPKw3mn4DaDFNvQB/HlzUmsx4oxswZIM8WEhIQEIL0UExIS\nEoC1pc9lFnR9s57eb12GxQ25iQ3qyAEKxvPowFrC+lqUyMdLa6JBUmvsFGNIWgmLruFa5vjNYHVc\nMrw5rb8dWAJBe0egg7vOzJVNQX0mlOtykDIvc0NnsFp3YfENxj7EUgPamaEvtkDrurByM947g4L4\nYZQ326y8nRso47Dn7bxPTvEOdr0uD2B/6Dl9vTz2e/YmmAeAeS6it/eJhGMBawBPu2M0fzQLOKmH\nHV8mGIInDxGHzPWBEMdUizshc9aGXNdehoRrI9wTDvb9OeTNtumYDrqPe03j+KoQ40wr/xzO8wvI\nuLVH9dpEtjqk+p5HmikmJCQkAOmlmJCQkACsLX2WmapIRUYF4kahiBxAN0YfcW3b/L7aqbYBSylj\nX48mTqUOc6ePDSa3asKR+tBNoQXoWafn9KjZhGRTdHxu+G0U4Dx9dQffMlDZASjTzgjJrWBxLEFZ\n56B+7SzWd0xLLaiU3/GE03N71ykrk0XlsPLn+PZS9mpv6s+iU9bnkDLynp1DLAeMvT0b97szvEHG\nbUKjMKQ1p+iLLp3Ko6V53qTXNZYPkIN7Csf84pxz8GUe55tRQLotr2Chj/3VaCAp1xy5xlGV7UO/\n7sYGl3rQS5BoY991m/6MRvs+qLI4znJY0Cs49xtyoeewRPfjskaW0VdgfZBmigkJCQnA2s4UQ6hU\nxnSVo4YyQrFrAAAgAElEQVRPs8K5525yGT/cBXzKJlBDubHwqUeOhWuktJDB0NHL/EtcYgYzQMhZ\nD36Q8xhGVmB2OsRsl3lOFqdIlVy5z68dYUaWYfbTwawkWwqGUl0Gs1Dm+WgxxShmsHvmC/q7yB6T\nI5XpFCGH21CPGZ+rO28KlZxWg2FzmLX0YblCXhwhF0xzH3l0Kj9/AZHdBnxTp1ExZsgGIYxtcR6G\nhkM8aMysSwjkCrPwLn5mJ4w3KzBfQCj3ht9/BgNM65mhVmGCHDWdB88dbw8gxbtocLzU9ZpjoC/o\nX4tZaNmC32Uci4vT9Z7vaaSZYkJCQgKQXooJCQkJwBrTZ2kR/fBy+AAacmA0F1iYf9BFNI+a9XXW\n4kK806QNGAvIXguIdc4hJitook4nTr3P9VysdojUp9mK6KkFqNyVhp97X+m0u8SFVVgdghUgllv1\nvR3jSLGmq24u6VzORXyvSzVyulk14AMKAdcxxG9HTdRxE+lmo0DrVvBnRVRt5JPZQJtJWT/iRoQJ\n/BQ3xnhIFDKCxu1yRaIofGfA8kGee72OnJmeCK2c7Pn9d7c9PTlJ5uwAeXmiYabIvOxxA7SbEkQo\nZavjA2o0874NUM+5cbCvVSjRve04jOYIJy2mqG3PT54tIDIcQ1KzbD3nVOvZqoSEhIQXiPRSTEhI\nSADWlj4rhGPR0/nUp/456QH8rLIGfa7q68bwaZxU8PwCNd4yt3gyxWULuT4q5DE5jxwhJ6x3sC7O\nj6J1Ebk7BqCmxdAp1jV5HbebXnaOtKrboPvVtlPJ+Z5fu4gBgE34TjJl6X7pluXtystrgPrt0sex\nB4Wfi1Bp+QjKkfsYHi1zkMwRwibHHCot7fudmjK3zcF9SCV6w/trgBDBEpS4gshwsajbXcBncLhw\nFR1S7ary8pg9tLHptH6GsdCDwk3WRYhopMflKoUiSQN4PGxinM2YvhTpUxdcykFY6hjUN4fC03KM\n5Ne8b4tdH0M2gNISFIiK6CFgSWQ2ISEhYf1xpl6KZvawmf24mf28mf2cmX1l3L9rZu8ys1+J/+88\nV1kJCQkJLwRnjT6Xkv5yCOG/mNmGpJ8xs3dJ+lOSfiyE8GYze6OkN0p6w+0KqkKlSVStsSEoE88B\nfR604YQ7rWlLTpoEdR2GfDENZG+B7oRDbMFvD8rMQXEy5H3JWzVVYsjZBpzEZ7Ag5gOv16REPNsC\nDt64fRNW1BzKO7OYx6McehnVDqgUaOURLNsdhMp1cojCIuRsB3I0W3CebyOMbBmI1oRzOZcsSJ8b\nl72O5Ras0gf+LAYT386QO6fVZvyfYxlmOUFI5pxpZ5GatI067uVer4sDb3Mblv0FHM83YTmezery\nm2OMidwfFnPeTALag3Out7xe5zLv5x7S47YmUDnGD2AYU+8WIwYGwDsA929BJGkW1aXCmZpSfexw\nppoVQngmhPBf4vaRpF+Q9KCkV0t6PJ72uKTX3J0aJiQkrDvO1EuRMLNHJH2KpPdIuhRCeCYeelbS\npVOueb2ZPWFmT0ym01WnJCQkJNwWZ40+S5LMrC/pn0j6qhDCoZnTjxBCMFsddBlCeEzSY5J0Yftc\nWArKIvRWLSq/wGnVroN6dm79VnQ7cBiGtTBHHPAM1jiDtS5D7pLqyF/WM+RLaZHjxqqUSDE5hCXy\n/BGcxHfcslnB8TqDkzZVchpQm6FITPOQuio18oaf29jy9jdhwZ8dgcryWijZLM57H21vuMVZW97m\nZhT2nYL29lpIvfkQLOiw1Gdwht6W99eNBfLvoF6jsde3j3jzScyjUiA16LTpfbJdOR2dgtZfAmU+\nMShhuS02EPu+YumhSXWdG/7cJrDg8zcAcSedm8IqjefVRmXaWDJ5poBiU1QyGmNpYgyn790Wog6A\nI9Vtq5Sszy8KrM4K9U8kfX8I4Z/G3ZfN7P54/H5JV+5W/RISEtYbZ+qlaPXn8Lsl/UII4Ztw6J2S\nHo3bj0p6x4tdt4SEhJcGzhp9/ixJf1LSfzez98V9XyvpzZLeZmavk/QhSa99roJCJs1iPG3zGqyI\nW97kBtJQ4gwtjmo60YTT7QxUDzqtmkBwtA+pqzasf2UJR2LwGoTNqg3rbqPTimU71Wsifvtw16/r\ngMEwv8bwnJ+/HdCOBqyotERHp94Aek9H5xJWVtLkBaj5dNvvf6nv1KuJHC2Uxmqgv0K0yjd7TpM3\nNl0ctYQleG/hDuDqeRmjK079KL5aMq8omOoYVDqr6rqPu85NWxAEDriuizjwBQIDJli+WPSRBrfw\nNjP1abWMrYfDOsWMx3P0OZZsWvKxZfBm2FygP+fIf3Nw4Nf2IPsWT7e+l9dAPp+AZYojqBwvY6Zt\nTXO0nKmXYgjhJ6VTBAKlV72YdUlISHhp4kzR54SEhIS7jTM1U/x1A1JPNp5y6hXgVKv7nHoUnZru\nlbCgMjJ6Dt7QhVxYE/k3DCSzgApzBrrTBT1sz6CUHWNVWwZLeeHbBXLHFKiZgWLNac3GZg+pKvOh\nnz+K+SrD9mrprsHU+62Jem2cc5rc7oAyov0jxOqS1m4e+f13N2oZtaq7eki2EWPdQ/KSGZzqF9fd\nsgpxN03OI3AZjvxBWAeJyxPtiT/DEt7JBSTnFkg9O4AzencBx3BQSxixNcr9nha9Dyr0p/qwyA+R\nIwjPdjb2JY7NtjvYz7qwVmPsTjvepsYepMZi8QHdI9YFtL6PZY1RXAKiGvo6Ic0UExISEoD0UkxI\nSEgA1pY+W5Ca0Tk1p/P3KfSsiyROh5EpkCZO5k5HmpDlmuSUckbK0LFvw79WhnSSNONOQKv2bBLr\n6pTxoiH5EqjRHIVkcCRvo+4zg1UUNGyDNPzZmh6HB53Sw/iqTh+pRME6x+CJOxhOJFZUFae8Ga3Y\nx/WGR/noyOnwCKb6Gay8PTjVH5x3isclhvMd3182b3VSl6TJh69LksJ93v5OAcV28uGcY8jrMu17\n3buHWGIBJT+piF7vnzf93AYDihFj3xutnr9MSsh7jfw+R13U9wjbUETPYpwzqXkbNHkZgy+dlLlr\nHdZPjuNgnZBmigkJCQlAeikmJCQkAGtLn4OkMjoK57BcTtpOMRjDO507DemMaI6r0YXT7YQqxHtu\nCZwjxnWeOTnsVHAYH/m1gw6s0mOndVvNmsLsg2BOq9XW7IO5U8ydiVPJAvJSA+S97uWoI62vUXm8\nMfKyDY7RU0hKDdqghnBwLxHD2zslAdU88/af2/DEXe28bvMU0mUNxKmXyMdM+bMPH3jEp214XRp0\nnsf9u+b0MBQg+a+s71VNV9NrYQmmcYrDfAHp6w7GXAV6uln5/msx3ppW3MnKRQWpv+HPIoPkXIWl\nnBHyPlOdfQRr+VbufTTcrsdI3vVx0xJyaqMrGkdeRnnrz2OtkGaKCQkJCUB6KSYkJCQAa0ufrQqy\nSU1bpkXFA8ebC1gLDd+H64tajuq+KayW8N6egMpqm/QJkmJtcAzEXi8g9dWeQIILdc+ig/GDLbf4\nzqH8fIRY2kaBxFl7Xgqp5LmZn5PDLtwCDV08UGd4aBj6ofClAUNSKjpSd5AnuYSUVEBO4D4k0FoB\nfT70doxbtcWdX2kuaTB8uRpBRq3hzuNU6j7E9iaSO+VwSB43vC+K6BxfTSCXduBlZOgX61OxHEsw\nWDGYQHm9h+WTOfI6T4+XNRDXjP6hkDupLOOwm1A4X6BfGkh01WNeaWfK6sfbD/HcKDPXHKJtH7lx\nvF29bDkuk3RYQkJCwtpjbWeKiyzooF1/9Xoz//rmN52zRAZDQhZzU4w2V4cxtaDyWUL1ZW/mgqcP\nLrb9HKjkaM8NI4uWlxPgh1dGpZJqAPVwpONsIlQxewiirTuYwUFw9XACYVGEq1WYfmzE8w8uwUCA\n2V7eQOpThDNmhbehl/k5XZzfGj93ONh8VLd1DqNAG2WEgGeFp7iAcWdKkV9mssVsDu6oaqNe0+gn\nesKgxTw38FMs0Le9wmfk47294+0SqkcLGJ3gYqndeO1RCTWcG0fH21ubbog6MStDqKhgoNtsQI0I\njGjKaTbKmcbKbPokUPkCIZlHq9Xrl/6r6zlPTDPFhISEhBNIL8WEhIQEYG3pMzEVRT59/2bDQ7pm\nDT/nQhQ//cWR+8C9PPdU02QjAaKc55HKs4RhRFtOn5pYSG9BONawwD+b1PTY9p2+5Ahha0GZ5mi2\nmuI0QRkZc9fCojsEfnQ1hr/1XI9URceHxxh+gg3Q2h4tUCfUViDgi1wj07FTxc0B/Pqiek95Cidj\nv+0b0rBClDWHDyCNSCWoZKv0ey5m8AONfqV5y9s8Ql7PYgr/TWashRAvhYUXwc+f41fWhC+jReo7\nP3DKTExH3s7ulo/VEQRfi0OoBDEUdcPHYgY1pKqNpaT4jMZ9qOEcYJmigWWKV5473g7LZiaVnISE\nhIT1R3opJiQkJABrS59tIbWjn1ULyiMZuCTTcHY2nfrsjWs6cxF+gtcPwSuB3aZT2dHYrcJMlVkg\n18UUNju4jKlJ5ZVoucyD76P/XIZvGUMVewJl3PFrdwIlVx3jqVPZpSvnCd84WM17oIZMzZEhzK2F\n+jK/xxxW3KwLv8bxrSF198v7cwGqOTaEqk1BcfFZz5F/ZIbcKVRJYu1bsGKPlqlVYeXuzKiuBGqM\nfDGC9ftycO+DC7m3Y4Yc5Abx32n0N21CjYbjoNt3RSE+K85krm3DQo4lmx0u0+z6WORIGKous4Ex\nVHVAmdFO5qLJls1ZrKf9Oc0UExISEoAz9VI0s7aZ/bSZ/Vcz+zkz+xtx/8eZ2XvM7P1m9kNmtuYh\n6QkJCXcLZ40+TyV9XghhYGYNST9pZv9K0l+S9M0hhLea2XdIep2kb79dQbll2oyKIFMIcRqpFCyN\nY1CcolfTGbf3SdX9yEvSdWp0MHTL4fRpOGkPnUr1zc/PwD0pKFoh/MyWVj+wtAAKylCsNozP0/uo\nTIPzW2gzlGEMYVyDIt4MVuYH225xtyYs5UiJ2UTOlw04Mg9gId3uwsp/1ftrA/lFdmJ+E6YjLRDP\nFmBlXnTgJI7vej936nfZfLljb+apTx9Amxroi+nl2vF6q4en7v73GhwcHm+PNpCGFOLAD7Q99+zs\nip9vEP+9IafB4/hprxAAsNnysVLNGTbq/T9cgI5DJaeP1KfziqGDq2luf1q3g7q3FDvmosP0Ge/D\ncxfO13UKyfr8645QY/k2acR/QdLnSXp73P+4pNfcheolJCS8BHCmXoqSZGa5mb1P0hVJ75L0AUn7\nIRzHfz0l6cFTrn29mT1hZk+MZ5NVpyQkJCTcFmeNPiuEsJD028xsW9IPS/qk53HtY5Iek6SL2+dC\nKOp3/gxOpgVinEeginNwiOJ6TTdGDzgdPJ85rSpRxm7hFPDgPHK0IMh2injSHJ6/iytOsfNtOH53\n6nPKNqypUFLJAizBzrZkV936vYCz9xA8PKMcDxR+QnRkvtB0i3tzA9ZPpJY53/D9C1jT2+YUbxNL\nDGGKZQWkap2C4k/iNinzlY7TRDpp9zJvm8ECWjR8qXkDcd227x/IrvtDa46V6YvdmivvzTxOvEIA\ndd7xk9uIPaZKDc3yjU1/nsOhlznH0kcWY+irEmNy5LS7fcljnzmGCiy7bDS8LzL60cOKzdjqHPOg\ndvx9MNcK/Nw13ULOnYfOo451+6v1ZM9nb6a4RAhhX9KPS/pMSdtmx5mYHpL09F2rWEJCwlrjTL0U\nzexCnCHKzDqSfr+kX1D9cvyieNqjkt5xd2qYkJCw7jhr9Pl+SY+bWa76hf22EMKPmNnPS3qrmX2D\npPdK+u7nLKmS8hiv2iFlQ36RcQmKBVvbIMaCtkFHp12nPTsGqrvnFFhbTqWud8hTndc0j5x6jUFf\nrUnrcs1nKsiJhQljqeEYPvNzKjgv5yfkpbzurS5SWMIhvB2XARroqwnilAUrMx2cH8SyQtNZoqZz\nWEghkTabIW4XVuRrUXaNodQnrO84twULdRuUeYbkKV2kUp3vet8tkLe1se83WIY2k+p2+v6cmRpW\nkFSboD0zptLFcgxjiBvo8yI2do6Y7QXGUHUA2gte2zAI9WL5IAeVH0FYuYU+KhEfnbXq+9KC3WYb\nIJRrCJQfNus2V9l6Om+fqZdiCOG/SfqUFfs/KOkzXvwaJSQkvNRwpuhzQkJCwt3GmZop/nohh0Xt\nsO/0pVei+YhhXYpJ72VOnxuQmWqDPg0gL6WJb/crqGqD+lagkp2uU1JaF4/rDVXrcRtxyIhVDU0/\nB6lbNANN2kBaS/tVd2oOsFBbvP1k6PXobrsFeWviFG93y6npKX7BKo/czLs/dCo9GsDi3sHFsSot\npOacbfrzeSD3exriqseN1c7ezLnTP7eF/Y7J2JWyFavY34aj+cDpawOm3fmBt62NdLPDLb9ngfSs\npLibeBadXt2mYcv7p4R3/14DSxBIWbs5QbpVlD2FjFwLat8sswEqPY/UvwvL+nyCe1InAJpuF6u6\nDbSCrxPWs1UJCQkJLxDppZiQkJAArC99XlTS0nqHV//5tlPGCeKdm7BuPlPV8bkFDI4kXteD06cy\ng2J3z4NlqepM1eYS8bns/e7UKxm2aqvnDNbxFpx0FwXSbcJCnuM+23C8zuhlew772TEx9eoMcbi7\nHSZOOgU9p2kH168fb1++fs3r28m1CgFSV+1o3QwPOn19GDHjxALO+JMF5c9A90B3maxr0QTF3oWD\n+5W6vvOG16nJ/oE1vyU6oLv1uX/F+252ARZiUN8Kz8sm9f7uJbdyP3V41euNmGmqpC+QmnWCGGc6\nZk9zrKVgiaXBZGhxHB3u+5JKcRHeBBj/FdZJbF29tiPSTDEhISEBSC/FhISEBGBt6XNoZZq/rKZf\nkxGcYEsqWDsNmCO2eCPU1rij4NdtQ7O4hwxNhthfsu1iExrHcBI2JIMqBkg6dAGW4BUW3QwO2AMk\n2VLXT96ZsT1OpbcMjs+wPtLZ++mFx9wuUaJFmzmcviEjNkZypeoc2uxM+oTzcqvj1tfWDOrc99e0\nvoJEWsnYW9TrqYknKr5ovhzApN7dAm2GFf/qntP68tAdtWcHcfs8HKAhOddDXYU6tkglQdkHc6fh\nh/BQ+IRL0COLc5L9gff9ds9jzw33mcPK3Zj7PasW5OIQk19gf4kkWiWWD6qYa3sLEm5DWNbzPp4n\nloPUi3Q/W08anWaKCQkJCUB6KSYkJCQAa0ufFXScWKdZrX73L7acYgXEtuZRGmm38uP9zGkV2W0D\nnC2Hw+4MSZmml6C7BYy3vV5MYjWL3ubNfdBrOH33kEh41nEKU17y/buHsFBjyQC5rWRw1O5Hh/RJ\n5fvKll+3P3HFbKqH24a3bQh16gVzXbex3IDYWoMTfC8aZVv3O73MILPGWOqLhVulWcbDbc9NzARc\nh1fgpH0ZAdpgh/0H62tH7//w8b7qnFP9UZtP3dvQK7yQ8dSpZwPn7FRezvXcl2R2Qju2gY7hkBmD\nXFpzgGe44fdsHCJ+H+rcfeRZq9pUZAetXjp+4+exBeXvy8Et5V0k7hov6mWFcJrn/j2ONFNMSEhI\nANJLMSEhIQFYW/psldQY1TxvCgmuHNJY7RIq2HCwbUfV5KN9JCuCkzYTNNEZmM7WpIk5rMzadCq5\n6UZUHWz5OXl0Nj7aBNWEBTEnfUNiQyYSOmh7XeaIg744hURaizJW9X7GOx9UoHpIinR17lS6QG7g\nsuV0KoMzunpex0bF/nfa2I9K3c356iHZNu9z5FbSOTiYV3CwbkB2jHb1kjmbsQywdLZv7Xj7Wzge\n6IDd9nofVswv7eezjkeoTBe8fhJvFTAOS3gh6NCXDMbnnfbODuEADsv6RSz3LIRY/bH3SwkrcutC\nvVTRgAWdicPOI8a+RGDAcWBClehzQkJCwtpjbWeKC1Xaz+svLTQ81eryO+BfvwwL9suZWAuGk8Mx\nfNoKn4Xtdt2vjF/ZOb7mLSyS9+EzKHzlt2/4F/woFsmZLIVF21DgGTWQGhWGiQkEQlu5z9RuDKH8\n00JdYrrXDKK1/Y77r+U0imDYDDGDJYrzPuPq93w7R5hjiZnQNCq2tDb9nhS81cj7fHPHjTG8exMR\nbNcRcvjUB37V64V7hs1b04dTxSjHDKqASswcQrFMsXrQ9GeIaEFtzzAA4adaxjBHKh0ZlJayDQj7\nlr5/CKtcnyluISzbbELw+EToIttc9/kWlHtoRiTbKUdQz1n6Q6aZYkJCQsL6I70UExISEoC1pc8h\nBM3G01v2D2EwOd+CIgh8GcO0phvM/3E4hf/Y1BfOn5pePt7OQLFI8UqEXFXM1wEfxwrpTDs3kPdl\nWfY2/P4mMGiAppUQXG2Q2uAp9xH+NjgholpTL9LX+RGo9qb3FTlrF4aeccGcLwgnRNxigdCwE7lL\ndmuKPcfyBo0l9A28USEUDUsGGQRSr4zcimV9UMYbfg7Tllo0HvRHfp8hRHANVhSG3wmKRTsF0pqC\nSpPi0wCzVL4BS1XF5QgahXAfRhZOsQJyPSeXhj8mxsgcSxJb0TBzGHy8hQ30FZZ3IEakql1XIKxn\nlF+aKSYkJCQQZ/KlaGa5mb3XzH4k/v1xZvYeM3u/mf2Qmd26Qp6QkJDwMcBZpc9fqTrf89K0+39J\n+uYQwlvN7DskvU7St9+ugCo3jXfr5jULp5737SP8DSoxtKNN5zVtGR4itK2PNJTgQP2OU6bRdfeI\na478nBN5MQag4RA5pQxMq13TmlBA0eSaU9kApRnmn8kgpkrx1w5y0RSgRy1nocc5Pulr2b3gSwDW\n8DLmoKw5coRQ5LUBi3uzgRwxc69wtwdKHq3eBehzgSWNzYbT+kNYYksYdkvsJxjl2bof+Vpg9d2M\nVP7GdX8+XfhRHsGncwTt250xpHlw+wyeCBUUbhYDp8TZ8vlD3cawpDCDutP4wJ//HMKy8/PeL1sB\nVmxw8k7DOXaBZ3fUPKGiXDcBPJlLIOoiPDAuSyX6/CLBzB6S9IclfVf82yR9nqS3x1Mel/Sau1O7\nhISEdceZeylK+hZJXy1fnz4naT+EsPwOPyXpwVUXmtnrzewJM3tiNr3VyJKQkJDwXDhT9NnM/oik\nKyGEnzGzz32+14cQHpP0mCSd29kN26pp20aAcyrSjhTPOlUYtp3iXI+pJTfOwUt2fMIUeoxpcApy\nYdtVWhqgm+EDbgnlV+io7/yDVGUQTcoZHLa148cXUOBpIywvGyCnC6hU2AINgiWyOAe1mWn9zcl6\nTrUaEMo94aINmk5r6kbl92nCctlEyN8CSwKoojrduq/7c6dpGzOGYeI+MM7fGLgcTDbzyszgIVAM\nff+JdJ+w/o8n9VpCAQHdBeh4FylDN5BidI6urUagxlhWOCHG2oe5ODqkV7BsV3Ce30LYnqGjDyqn\n0v2nfAxX9/mY62E8FcPVywrtqJ5zue0TiJ0AcV6+HuB0vsxrS4q+TjhTL0VJnyXpC83sD0lqq15T\n/PuSts2siLPFhyQ9fRfrmJCQsMY4U/Q5hPA1IYSHQgiPSPpiSf8uhPAlkn5c0hfF0x6V9I67VMWE\nhIQ1x1mbKZ6GN0h6q5l9g6T3Svru57ogL6WtyzVtmF2CdQ9OzXOoujxdwNk17t5AnHKnCQsqcndk\noCnTBhxsEUNbvAKpT0HDSXGqOURuo+goBWPm5se3uk7rc+TrUBdCuBDNncKpubnrdbnQ8nLmrZrK\nDWB9XKCMTajUbKAvZLdaMKWTeUGYkrOFeOu86dvdw/r80PHyIG6kHLR/cM2t/FmG8/GJ3y69L67I\n6SbbtDd16l1Gcd0W0rEa6tp/GrHvbTiYD5GjB3Hd7P8RVmG2sMSwzNdzBfXQKeHEW12YvPtuce4j\nsD9c8Tp2tnGfAvQZuX6WY6qLwIEpnmd74p0+myGVa0yfyzSy64Qz+1IMIbxb0rvj9gclfcbdrE9C\nQsJLA2eKPickJCTcbZzZmeJHC8tNxVZtPV2MnDKMQZnVdlp33wLiotEqOYGlMsCCmDdX52s58YWB\nKGkOiyOdl7MJLaF+/nBc07195PPY3dw53p6AynTaXu8Cca17pVOpi8H5m4E+QTdW262aErVnEIGF\n9bMJh3ES5gaGEJ2KM8iutTN4WLP9M/Lj+pwOHMq5ZDAuvS84aCdYyhByhoxKXzLo0nQNi/P+DAG9\nEY0elgbQV7Nd0H4Irg4hI7cArRZo8uZq46+a8dLNjj/DBeKdGeM+g3RXi07V2Oz0/Tm3DHl8EBMf\nkJdnHp2wixxx9ViDmN/YP97O4SS+FLkNi9Wycfc60kwxISEhAUgvxYSEhARgbekzsYAZszwCZYK1\ndqvl9GB0vbYGdttOpaY0uJKCwmpcLHx/uM9pGklas+NUJQMNo9XPolL2rlY74Da6qBeCnwfmd6Is\nl8Awu5AO25r64+/G+OxW0/cZlgwoudZGLO2cdHiExQQ4L897kDTbhOUcyxCdYV3fcuH0LsM3e4qY\n7Cf7HpN+ferb28h5QxRwPZ/MKC927Xj74zoX6g14BzD2fA45t2tdlAdquotlitPS4BZIN9rIY2z+\nzK3jhwYl9VNUzbeuMvUt6tKCLBscq+kwP4R1fzGur+0iTr2gP3bbn/PRAh4X8XdzSubgex5r2qyE\nhISEF4b0UkxISEgA1pY+z8NCz85rJ9+DhZs0H77qTR693OnbidSXyzKO/LpigXOblErGdwVm2Tac\nekcNWGWNKtxwas6dqiwdZU8kTqqcMo3aIGewshqoed6AxRdO0gUssQukPlU8nRbsAaSlpqCJ+Rye\n1ECHnBFpTect7yPS8zlo7TCeXg29PSXkso4GTl9nXdQb1tf9mVvcG9egKo5HO+l5XR4u3KK/tIob\nHJmvmJc37nqbN+DIng28vhmcpFuwuOdTpK/tQy4uNr8LZ/jD0sfcs03ffllj93i7wLJLDimyBeLN\nj3LEfkMbpY2xMI4U2xYIOsAzPOj4fRibvxyXdoqj+b2ONFNMSEhIANJLMSEhIQFYW/psZsqWVA0O\nwQjOHKwAACAASURBVOX9bmXehmryxCDHtbQ6Mx3ufHWMb9WFBRf5lXM4Zhew1jJLUQnZMSpeLyIv\nySHHbRuIw4a1sNxE7CsSZHUq0GA5f9oC36QT8iyGRO+bd1YbDsgNUDDKb/Wueb2rFuTSsDQgxAGX\nQ6e1Buftq/s1PZ4963HAG4z3BfIPeexziWTPdNjeKtHP+PR3B5TDwlJGtBdfA9duyI8fwNF/iIRo\n55DTuoLz+CmGY03mXv4yB/YQ8eZDtIHSXUNQ2W08i/GWj+E5LNdMaCYkVCvh7N5pRStyH3nEm+DE\nkEIrFt7+buyXTOspHZZmigkJCQlAeikmJCQkAGtLnzOZelEtubN5/nh/B1bBG8FpS8d9gI+ddovg\n34wJqMkU+Y3noFUHcKTuzJDE6dDvM9uCsvNitaTTOFp9t1su89WghbDnZR9CSpuK3AKV3TpCTmt4\nch9C3qxzUNPXYQd0GO00lMcEWSXifQ30cYH47GrmbRuUTn37blxeiauHe8fbo7n34WDT7zMFxaPC\n9WIHsmT73o7DHeSJnt7qcdCD5Ngh9Mo24X3QgUQYAwOoRE2n7mITceNMujWveXCWQYpsA5btOWXJ\nvI6H4Mb75ly6B8f8bAJJNSSdKq54m2dV3ad515eUmAhtE3ms5wNfVplGN4tQpdjnhISEhLVHeikm\nJCQkAGtLnxWkpShzbw/Wz/NOJbqZU6IbPXfU3YzUd3yaDPIpCXvKnE7Vvn0Ieak2KGNg75PKRstp\nQQsiPl/5PtS7N/y6DBbnjZY7CXd24eFMZ1+070aUKZuzbQunZjlzExdemU6GOGgsB4wGyB88hFUU\nTboavC7TSX1+64rTtAxUckqqCzrYRHxuGyrklHr7SAeq6lixMLS/EcsxLBn0ZuCsuffnbA6HbXg7\nH4HW9go/fz6hUjuWG6L1f38L8cuoU0Z6ipjtLvKV07OiQuByDkq+OYFXwM6mn3+jHoyXj5DfHMsk\nhv6ktN0sLiWlvM8JCQkJLwGs70wRGEOBJofRo9/1r3kzg09cnHyMMSMImBFsTX0GcRAgRAojxgRh\ndhPcs0kZEkMKT4S35d1WLNuv28gw84BvWoHF+ACFGeYigUiLmlu+eD7BtOnYqAKh2Fyrc450IGA6\nGSEUEn6SFWZHYQyxVIQrDhGKVkYDhN3v9WsPuOoPwdsxlV68jDk+8RlTlXJGSOMZwiUXS4PVFOF8\nc+/zBdSVmhs+875WId/qaXYHpF7ldog5UBZ4DgjOVIXUtAsWDqPbbh85e4k2FJuecXqSb3mbjuJ4\n7Wy4oWUKA9kCM9ICz7zdWs6q13NOtZ6tSkhISHiBOHMzRTN7UtKRanmFMoTw6Wa2K+mHJD0i6UlJ\nrw0hnKKel5CQkPDCceZeihG/N4RwDX+/UdKPhRDebGZvjH+/4bkKKZe+hVC1mUIssyWnEg1DyN/y\nHFDmplanc2yAppWgnrN9p5U7U5AiKKZ0C6ctzS2I3A5rw0AFdZmrHa/39szL24c1ZgfGgNHE799C\nDg4aQxpQxClj6FoLwroFcoR0Jt6Hsw6sFUiDOT5ygwbDHAcIYWxCILeD+LvlUv9iz6neDPlSKtD3\nAuF5FIVlN+dwuOvC6DGAko1xeWBSxnP9nuNn3aeysePPh2lyKdpbQuQ1HMF/cI6xld0aLrqx73Ud\nn3fK3Bx7efsQ1zmC/2QB+5NB2HdrDuNajuc18Gu70egyB2HM+42Vl9EQeJr9cV1wr9DnV0t6PG4/\nLuk1d7EuCQkJa4yz+FIMkv6Nmf2Mmb0+7rsUQngmbj8r6dKqC83s9Wb2hJk9MYYLQ0JCQsKd4izS\n588OITxtZhclvcvMfpEHQwjBbLW8ZQjhMUmPSdKlrXOhX9VUYACrcNV0GjAw+uw5jiO9IIiaHSI8\ni/6IsJB2p6sTns47vt1vu3W1P/X9I9CjXqemakcI4WPqz6rr1+003Po4C6tzaQ5uuPJMF/6Ll+e+\nLNuKlvjOAURrEZ6WI4Qvu+GW2KulW1+nhffFJSxNFFhiWKZvldyPVJLKg7qOCyrXvN/pa/6Kc8fb\nAWGDAT6LHVhDp1Agagwg8rqNPCY3vK2NmC9mfARrsrv0qTn1eocGnmcDeVlgFS/5y4K/aaPttHYW\nlZnaGCutA5qwYWXPvcDDvp9zBJZ84QrCDHHTzXMbXi8sZVgcx/RBLIZYAsJywGDkSyP9OIbCmtLo\nMzdTDCE8Hf+/IumHJX2GpMtmdr8kxf+v3L0aJiQkrDPO1EvRzHpmtrHclvQHJP2spHdKejSe9qik\nd9ydGiYkJKw7zhp9viTph6PaSCHpB0IIP2pm/1nS28zsdZI+JOm1z1VQHkxbUSR1NnG6F8DZStC3\nBijObKem2E1YpAMcdvOSDs6O8RwUC9yi3XEq2YKH8fyU3h/Oaq6c73ulKI7b7ED9FuGEDItjaJ0h\nR8yV/esr77lckCgbfp/WERRVGBK4QKwixHdbsFaXfYScQbGFwrYBdLfbr+87hnfA/BW+NDAd+j3b\nsJC3kCMnIBSx2/HOvVH485/C2b0F6/4yZC3r+HNu4emOIc66hbC5CvsNSzNLB3xJCjhntO8hdYtL\nNfU+oTaDFZAK/VPgObewlEKKPcCwaB0ivw5SyVbwPsjjKSWWA+DYoAzKTSe0Z6fRO2JxqyV9HXCm\nXoohhA9K+uQV+69LetWLX6OEhISXGs4UfU5ISEi42zhTM8WPKRZBipbUrR23vl2Z7B9vZ3OnLSXo\n7jzmDilbEI0NzitaLSizgKa0QD2PYK0rETebnXfh2GZwuvXs0G1HebemO62CkbCO4RR5VPpu/exQ\n1YT6qRBFbcJCfoRw741GpFiwuJ9wakJfNdAXBSy75VW33C7OgT6D7gc4e9NCO41W0c6Gt4dW22oH\nlQWtY6wunaoP0P8NOIFPg9PwAtR73q+3W4w+xn2Yf2cOr+Y5HCECvAIWiD3udJCj54NeryoqCc2a\nuC7z6wrkmTFYn9vT1Wo4cySGaTZg5YbCUMi8vuMY704vAJsgHr6HvvAhrDz+rugsvk5IM8WEhIQE\nIL0UExISEoD1pc/ADHGwEzi7kpw2KI0UhVabiFktQEENDs6N66tln7TllM1AcUh3jhCffK7pFH8/\nElfGCSPDpNqwPg4rxBuDjzYRq7xAHHaXsc2wli5zhwRYMOkiT6vwYua8dorUsCCJGsNcWV71JYth\nDuksxF4rWlo7sHjbBaQPRYzzghWDY/zsMmKV0f9hiFSdsNb2UeMlO5wifekO4odL0M5nNxCHzXEB\nKt9Eftw5ZL+GL6f47DTWyZ9Vq4UHAPqet7wuvCct+CfGH3LEZEglOzZYlPO6/XmFuHaYv2fz1THr\nyur7J+mwhISEhJcA0ksxISEhAVhb+lw1TMOLNeUoQZlasISSkmVQwW5F1rDInHYwHelR02lFvuOk\nJcBhuAXL4RSU5PrQ45ApR/WRme9/2fWYmnXjVnopSVOYCzcQqtukjBcorvVO6Dn7fuY02aqpFPN8\nlPuIU0YJU1g5DbHK0weQhhUUd38DVtyRl0/n361ouW8hZep0sZqmLm5AXgz0OZx3ato35I6BCvgm\npMHyzMts3KjP4VKHqJIO74QA+Td1scSCMdS55v2fVb4dtuH4HRXc6TDehpzavAtPgCmXD7y8fgPL\nBHBeP0A0QpGvnvssa850qPQmqPC7yS7BAXxlaeuDNFNMSEhIANJLMSEhIQFYX/qscExbM1j3Okc+\n+c97ULum0+xhTVUaI983eMDpGC3Bsx2nFQ2oTVsBC6FBhXruXrD5kdOTbcREj1s1VYOamCaInz0R\nS93wRzieelxtCepZwvHa2khhiZjbjUZt/Z5DcsuQJrQ6QL1hlSyxxGCQ8dLC91dQbW4skMoVjuR5\npLgUPyta7sg9g1zb/Jw/i+kJuTQ8GCQOy9u4/56vN5SgoYv4jArMEyawoM+R0GqrDav40WrdznHD\n69KDRJzgHN3M6mfRKGHBLjE+meQKv9RNpF6l7NiIy0QjjDkopfcXUGEP0csB9cu4fAAl+fKa91u2\nDBRPztsJCQkJ64/0UkxISEgA1pY+K8hz7Pac4k76sDIfulNz1veumEcLaAFLYHsEqoAYV/giq5P5\nfYbX4LAMGS1+hrI9UC/Ifi2Vna/aaikyM1BDWFBJmQebcN6GhZRSX3M4+Dau1o7PM+TI7iPGuQKV\nMlozYYmm1NTRoTtSM6FWsQ0Xbyh1TwfRkRlW5inil0/E4cL82cAyBaXLFpSIQx21jUUJLAksrkU5\nrIddriwg+DpDDuYM8mdbfXe6P7zuz7yARXkKD4UJnOCX/dVD9Zjwa9jxNvQ2XQY8p9cAlk+Ka1CY\nh9Rbv0LEAvZ3tdzvz43W76MNWPw3vA3dG7FehnG9RkgzxYSEhAQgvRQTEhISgPWlzyaFaHUsT0lU\nO4dzNC3UzVB/K0o4Y5OCt1BeAXXuG0fugE3C0giw7iHOtIJ1s4AidHNU07YAyjihitPUaXc59iWA\n0cgthBXkpQpYS5uIrd2AmvP1ZpRZm3h7Rkj41W877Z3B4qwhnMTRFxvbTkPFeGZQ7HEPdYwW9XAE\nyopnUsESTsXyFhTGmQNZlMvahIM9kl5Z39u0vXFfXTac9Ie0he871e70vT8LeBB0IEiuEQMD/P5j\nrB50i3qUNCAhd9jx+0yGeLbB++XihYvH20czP+eg7+Niwx0RTkjM55gHVbGP+iLVh9X8ht/z0FcJ\n1Ik5yrNTnMLvdaxnqxISEhJeINZ2phhMmkfftgW+soNivvL8cwMsWEfjxbSDlJFtiKyuLkKGRfwx\n/Lo6hzAANJFiE9cOcp9Z7MYv95xqNDB0DOF31kZ+jSZ8zNpd+DLuwHgAIdbqwGcZzb36/ocwIu1C\nUqiExusIRoyMvokwdAgzsuauX8yl+RZCAeexX+YbEFxFGTlUagJ8+SaYebXhzEclnQDx23wLIrZ0\ncVw2A+yggdk7bRUBqkNMl1JsQQEHajt21Q1GWw+gI+MEseRMrucNIiPhrGxw4EasAgK2zRzPAv3Y\nQK6ZBcosY1dMMNs1nJsjxe3OHsb/ThHPTYaWhISEhLXHmXspmtm2mb3dzH7RzH7BzD7TzHbN7F1m\n9ivx/527Xc+EhIT1xFmkz39f0o+GEL7IzJqSupK+VtKPhRDebGZvlPRGSW+4bSlBskh/KOKJ9Xr1\nj5xKdPB9mJ2vuZIHAUqjG+6DtoB/4Y37QCVBcZrwO9R1P7858wr8WtdpVRs+gZPNWx+L5cjLAcWc\nauwUu9H0MsoWjBQnUrx6mYsDp5XtmANkY7J6SCwQ5sb4wwZyh8zGvroPfVZVM79/rwnxVYQLLm/b\nQgwlNH5lbCcEV8fBlx324TTaplhrx5/kAgYTg4/fYF4T4SNQ9i34BmrkZY+xfNADZT+Aw2GBpYxZ\nz7c34e86jwo/lzvets0c6Vs3nerP4VPbHiPPCpZd5qhXB2GOzHXTr/C8qigsjJDMEVYXxFBFtHMe\nwwnDKQbMex1naqZoZluSfrek75akEMIshLAv6dWSHo+nPS7pNXenhgkJCeuOM/VSlPRxkq5K+l4z\ne6+ZfZeZ9SRdCiE8E895VtKlVReb2evN7Akze2KC2UlCQkLCneKs0edC0qdK+gshhPeY2d9XTZWP\nEUIIZrZy3h5CeEzSY5J0YftcWIbGLYb+gtzBu7JXMKuIF9mKYVTzttOHrY5bDbP7nI7lH3bKOHjE\nuUcATw8XfP+kdHp23uDLByzDvyYIs0KUn3JYRU/41e25o1xvTiunl1OBBnehjlJF1pa3oaJCi/fC\nO66ZOX+eIeauhTSk8yF8JudexwN5HadGH7/6vm3QO0NdJvCZnARfjqggrEungAoKM/zyd1DHMZSH\nbHKrJbV1HY6PeJ4llmNG27DW4tr9ltfxZeCkPOcjVd1HGcbkURf5ZBBOSMWafN/LXpxYJoBALZYm\ndszHK1OcLkV16A+b4beSY3mhpODuUnUoS9bnFwNPSXoqhPCe+PfbVb8kL5vZ/ZIU/79yyvUJCQkJ\nHxXO1EsxhPCspA+b2W+Iu14l6eclvVPSo3Hfo5LecReql5CQ8BLAWaPPkvQXJH1/tDx/UNKXqX55\nv83MXifpQ5Je+5ylmKTo2NxqgibDKjrPV1O1EC2dMwiyZk10FSyhzS2nJj0I2M6bvn00p4uvg5bY\nVsvruIwKWwxGN19SH8d2fgha1ffyGhtwJC4pfguraNupVC/edALlnLK6lWpJ0vjQreadLaejgxwh\neujPrFotXNos/JxmFFotEAZZDiBmmzsFz2ag/UjT2su8DzMouEzRjyccn3veX4dl/Yw297yqnS2M\nD+YuQV6a68j/guZrk4+8S+dpx9a0Lv86+HMBL4TxHCpK4N1c9LFDnEMlWqy3jDqrow2WyxdNPJ8m\nchiNDge3XCNJWXTwr6r1zNZy5l6KIYT3Sfr0FYde9WLXJSEh4aWHM0WfExISEu42ztxM8WMFk5TH\nlJPTDvKFIFaYVKUa3JprI4diS4V0k0LOk8GW07T2R+BIe8FJzgnBzxyxraT14dbNjDkwoC4j5Dxp\nbdHF3DEbgZIVcNiFg3MGenxU1RSzLZdDWdD/N8AZHNbsgH7jYJrh2gZYVqvya2eMw44WzQrOzRkE\ndAt4nSMkW70WPMlhraWRtYL1fzRFv+y750Ar8tPZ5mqLaoDHweHM6fjQQOvR5hyW2WLqXNpA64dx\niSUDN66grtPF8krnAKK5OeKQpxzb7GjfPBg7DW60vfOa0VG+wbkR+q214f12Lfd+a8WUrcl5OyEh\nIeElgPRSTEhISADWlj5XVdA0irHOwaWKBvKoIG62gOBsM9LNJlJwTpCmVH3wN8h7HbwCTtpPuxmT\nJGMMZ2uBPtJavBjX9RqBDhWwLBObQ6djpGYNCMjOCt9uL2B9pi000r2DAxfKbQxR14ecVvNLSqs9\nhXJ7SGV64suLEZfNbk0xOoTwbAnL6iH02pqCU/fcn2HDkCYWbSthXW1jO4CeL33QDTHL0x6oPpzX\n220I9WKMDM3rMq7gJH9iGcBv2YuO6lymyMdwaP+5a8fbTTidt2A1H0ydylPKi+lmO4gnn469HcsQ\n6gzSaq1LHlCQId6+Qhz0OGquraftOc0UExISEk4gvRQTEhISgLWlzyZPPzrFRL9E7o4CtKKx45Sk\nH51q6TCbj5zKzYLTyum2d2ELbDTfdRoy2HNKOtt1WrNZQeoL5R9ms1vvz9hsyI8tQGvg96wZ9tOK\nPSy8klQBX8ZTdyd0NPZ29q779vyi91VRgrLhEztHn3cy76MRrLXMu7KMya0WdID38qgePUcqV3I4\nO8HrsT0pV57EVDPjLKYbheTWGIrtLI/W5zKA7iLFbQsWevidn0hJO2jU1zZwPKCuk4fd4t2EHzVj\n8octKGzDK6IJaz2txFTnXsrBzS8gvS4c84neeXhqxPKy9TQ+p5liQkJCApFeigkJCQnA2tLnSkHD\nyFu6cNLOkFxpDqvfYuq0ZRG/FdmhU+0FKHO16zSpA4srU39uQV6r/3K33M5Lp2QBElBMktSOVmkm\nwmrBapoduTMwLYCLHffY7UC2elF53TdGSMYFpfClmvIAXO8CUnlmpJ1zph6FszVYLXy9ddW8vlyy\nICXtRKdtKmYf4vl0UOBhdYpWpq3+o0OHcUi3BTrBX41levZQhTmWGnBd3vHnX/X8J0Q2Wez7tQ08\nu2njVpstyzPQa1qQFyO/LiDRVWeB5GKQV8snOB/SYIwHsLjEMocS3yHH8HUsgVzxc27cFx3t11M5\nLM0UExISEoj0UkxISEgA1pY+F3mu873aAjyBRS2Hcy6djVtgZFn7Vl6QI2aUQkwTqDf3YLgLUM3u\noJvBWHUQkDgK36duv7Y6zpFfOGvi/ptOhw4yv/82LJcZLJoZqGQJkte5AQfnXr1/Y+L1GDW87DYs\nq5S0asGROcd9JovZyv10Am6Bhjc3aupfwWG4jwxZjFnehll6vwcpMtSlXaIdY7cWM87XpqCh/XqJ\nA49TFPqiCnl1zWOm2/dtH28XM4wnJIqeoy9aY6h2X4jSdggoMMQsk7Ivmqh3Sad739zcco+HdoOJ\nqn1zCo+DYaxXNnOavN10i/fovD/ntvkY7h7E6xbraX5OM8WEhIQEIL0UExISEoC1pc9VCMfUtrft\n+XvpsBuQ6IlEYKkonMFqVyH2+WDqPJkxvhNQvM6G8yDSt2EFum1OcXKoGBeR+pUX3Po7zSGzdQWe\nvFDOug55Jyuh5A0n6fmh16W57VSp9VRs0znf12lAhXni13UCrNyMycZoakzhbI0EVdOBLwlMIcc2\n/f/bO7cQu646jH/f2ftc5szkzC1JNZloK5ZKXmxFRFFEWgQvpfog0tJCkYI++NCCoq3vQn3x8iBC\naJU+iFqiovggSO2DT8HYCmLTYi2WJs3NZC5nZs79/H3Y62R9SafVxknOOSv/H5ScvWfP2Wvttbtm\nfet/Q9H2umTPziQtVl2eVcdiP9XKr9saVZF77EnNYomVLr0e2zJ6uuUD8V1pronTvbw49WWJD5YY\n81omMljqJ2dNqbstFu9FFM9OFXumMeti/dZs5xqGrx4KrUF8AjVJMb/FeL4rsfcj63ouBapyeW5V\nke+asT0PNc01Tjwl0uyV4zjOVeKTouM4jpCsfC6RqJULKabxqRWJoc0kBVO/HS1tayGIuaaplIWK\nFJmCFDSq1yULtmTKLm2Jk/asxKSKZi+JhNmqh9hScZgeaOzz3ijxNFa1JNKnIpb1/pz0eUnaqNnQ\nVvYW3yGSSGVXVhEHYDXOi9N7pR6fZ1fiprtSS1jTm/VFB/aDVVR2A1AS5/bLXJ7FgsptTdElKbUk\n1ddQCnRBUp1pOjQLVu+O1E7eGuiaQRywL0Trc3dZLMFS9Gs4Iw7jB6KUbVyQ60NwQK7x6y2R9xIP\nr4njBlV9LyK9Wjx/chjbmGvotzwvhljpTDJst+XiUlWmB7U0p2l0vsRErRRJ3kbyr/LfBslHSC6R\n/APJf4R/F8fdVsdx0mSiVopm9hKA2wGAZAbgFIBfA3gUwDNm9jjJR8PxN9/qu/owrKLYSO9041/f\n/eW4ytLSj5QN6CwkMe2Kq9dQMrP0ZOUzZ5JhRLLaKIOFuLKsnRXjzlJcF3XED44bxV9wXXmVJSGt\nSabSPRKKt7UZV41lKd8qCwVUZ+M9K7LD3wrPKNdypLpQFgfLjqw8c1ntlaW+SC5hZizH6/uyKjKp\nNVIPtWs6sgwpLe+cTFXLoNYqO9fCUdoS5liSTDI9MYzVRrVupBxsYy6uJDNZKWWyImuLz2arJ2Mr\n4Yo9ebc2JavSqNaKnYvjNid1WdQQ1tuUGimicNYl65AOdFsMPY1KPMjFr7Ub/C2bzbiqzPbH90l9\nU6sSHtsO2YOMaS4ZJ2qleAV3Afinmb0K4HMAngrnnwLw+bG1ynGcpJnkSfFeAD8Ln28ys9Ph8xkA\nN+30CyS/TPI4yeOdzpskDXAcx3kLJko+jyBZAXAPgMeu/JmZGbnzut3MjgA4AgB7F5asvFXIkzmJ\nndqaE5kqjm3rM/H8vmFxfdaSkpkiB9UHzDTTTlcy6Vi8Psslq4qUyqyfi1KGYphprRRyR8O/NFRu\ncyDlWGUzfHEY5V47lw178Y1Tg4XK2pGzXFtqi1AMTT2R7H3x9RtK9qCZYexbpyxhcXL/koTW1cRi\nMwwyNKvGPq9DEvtqu0TK16SWpz7DvmT70dC+banLU5Hz7Xbwk5wVo4P4V+7pqXVJav7U4/0v1mIb\nG+vxPg3ZV9leFCPRaEtkf7wnt2ObMjG+4VAMJ1SqsmVyqhT9VyvYOeQR4lfaDmmNTA2RsqXSk2xE\nXSkNrElxU2RSV4qfBvCcmZ0Nx2dJvhMAwr/nxtYyx3GSZlInxfsQpTMA/BbAg+HzgwB+c91b5DjO\nDcHEyWeSswA+CeArcvpxAE+TfAjAqwC++N++JwMxG8ynfRGN1c34eVUzrEjdk4sVkaeBg30pQSql\nObvqa5e9MYEoAECk9FBkiO2PcrP9+uqlz71mYcWsiT/ksCJZd3pVuTbqnfW+WChF+nYlKW19Rize\nq9HqWApSui+lXtWa3hX/vbkNyWSzEa9p1yUzjiRFxYaUMhWpqP6j5XCzfi9em8vfbA2FE2WOodTc\n6YtXQEUstD2xPs+KlBzOiF/nfHG9SnaFVcku1Ivf0azH8/VM/F73yi+fj32aG8Z7jkIHNSFvX+7T\nh2bmESm7EMf2TBblc6Ulu0rtOOYtseLPNqUMbbAom2wBULY6BlIOVUujdhvF9w0ndUn1fzJxk6KZ\nbQFYvuLcBRTWaMdxnGtKonO94zjO1TFxK8Vdo0RgrrBk8kzUeHYgOqc2TELrxPN1VBp1PotSc0vC\nn7RGhuq6XELImt0oX7RU6P4sZljpagnTd0Sn8k6wnHab0RlYZbcmyoWEzQ0k085mO1oiFxvRcllq\nSuieWLdVysb2RalVFtmZS/nOfkXrusaP7XmRvlsi68RyzTz+wqgMZ18cC9RSXd+WtsxHuafJVyFJ\nfjvrkkloXhLh1sUjX9rbCadrnfhOtDQ5rDgyWytK7EYcZmyLvsm25X1aiF4B6pWQhy2GnoR+DmrS\nKIkDtRmpXdOLHR05vQNAJuVrF4fx+pK8aFqqNxvVIlqVtsrj2dgnXgPyfXmruA/fZLdo2vGVouM4\njuCTouM4jpCsfDYzDDpBKi7O7HiNGAKRi9NwZbOQB51cspSIZU+zkTS68iVilWxLqUqtanmpTQBm\nB5IINY/SuxWchtuzUsNEZHpHLLQdcWoeiDTbsxhlek+cjRuSEcVOx22FbLl4Rhv9KNnF/xkDSdPS\nVmu+yF2Vw+i9UY4DQEdKvDbE4l9qF9d3RQKqOivNx/G5LCRbs7c04jiX5P4d/SZJlropccD1UenX\ndYmEki2AtWp8GPMq2TPdJhAtLd4C+UaU8lV5/gw1dcriKdCpxPvkEr9eFleAJU2sK54QMwMpZv1P\nRwAAAwRJREFU5UvdVpCgAtl6YJDnmXhkdPL4eWlNPDIakqUp1MLRjEcp4StFx3EcwSdFx3EcgWZp\npv8heR6FPfLf427LdWAvvJ8pMS39fLeZ7Rt3I3abZCdFACB53Mw+OO52XGu8n2lxo/RzUnH57DiO\nI/ik6DiOI6Q+KR4ZdwOuE97PtLhR+jmRJL2n6DiO83ZJfaXoOI7ztvBJ0XEcR0h2UiT5KZIvkXw5\nlEVNApKHSD5L8gWSfyf5cDifZG1skhnJ50n+LhzfQvJYGNdfhHo+Uw3JBZJHSb5I8gTJj6Q6ntNA\nkpNiqBn9QxS1Xg4DuI/k4fG2atfoA/iamR0G8GEAXw19G9XGvhXAM+E4BR4GcEKOvwPge2b2XgCr\nAB4aS6t2lx8A+L2ZvQ/A+1H0N9XxnHiSnBQBfAjAy2b2ipl1AfwcRe3oqcfMTpvZc+FzE8X/QAeR\nYG1skisAPgvgiXBMAHcCOBoumfp+kpwH8HEATwKAmXXNbA0Jjue0kOqkeBDAa3J8MpxLCpI3A7gD\nwDH8j7Wxp4zvA/gGYsKcZQBrZpfqqqYwrrcAOA/gJ2Gb4IlQpyjF8ZwKUp0Uk4fkHIBfAnjEzDb0\nZ1b4WU21rxXJuwGcM7O/jLst15gcwAcA/MjM7kARr3+ZVE5hPKeJVCfFUwAOyfFKOJcEJMsoJsSf\nmtmvwunUamN/FMA9JP+FYvvjThR7bwskR4kGUxjXkwBOmtmxcHwUxSSZ2nhODalOin8GcGuwVFYA\n3IuidvTUE/bVngRwwsy+Kz9Kqja2mT1mZitmdjOK8fujmd0P4FkAXwiXpdDPMwBeI3lbOHUXgBeQ\n2HhOE8lGtJD8DIo9qQzAj83s22Nu0q5A8mMA/gTgb4h7bd9Csa/4NIB3IdTGNrOLY2nkLkPyEwC+\nbmZ3k3wPipXjEoDnATxgZjsXa54SSN6OwphUAfAKgC+hWLAkOZ6TTrKTouM4ztWQqnx2HMe5KnxS\ndBzHEXxSdBzHEXxSdBzHEXxSdBzHEXxSdBzHEXxSdBzHEf4DEZY8puntzE8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcd5a47f0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[10], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[141])+ \",\"+\" iceberg_probability : \"+str(pred_valid[10])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fcd5a38dc50>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9QEGtD3X6XfsS7/d44v2mtVwPNlotCp61yKXO5yjJWqHoGCzVxv3ung4RmL7C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\nCD4oOo7jCD4oOo7jCP8D1YYKO89ojU8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcd5a3ed2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[11], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[167])+ \",\"+\" iceberg_probability : \"+str(pred_valid[11])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fcd5a372438>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XiiQkJLxgcTfQ54SEhITnDWdqpvjRxDKUujqLVKGDGN854lmRpKmLrnh6VV03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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcd5a3428d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[12], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[195])+ \",\"+\" iceberg_probability : \"+str(pred_valid[12])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fcd5a2d3e10>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XBVBS9GOweE6cMkCfjDBn0zJ4HUMZOr9ncBhvmFSwgo0ab4ZImw1B99O+9+Ex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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcd5a32feb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[13], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[199])+ \",\"+\" iceberg_probability : \"+str(pred_valid[13])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}