Model_03-Adam-k=10-amsgrad-0001-25p.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=25, 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": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "===================FOLD= 0\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 19s 812ms/step - loss: 0.6592 - acc: 0.6636 - val_loss: 0.4287 - val_acc: 0.8148\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 585ms/step - loss: 0.5110 - acc: 0.7556 - val_loss: 0.3391 - val_acc: 0.8272\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 586ms/step - loss: 0.3736 - acc: 0.8354 - val_loss: 0.2679 - val_acc: 0.8580\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.3710 - acc: 0.8348 - val_loss: 0.2573 - val_acc: 0.8704\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 14s 592ms/step - loss: 0.3194 - acc: 0.8639 - val_loss: 0.2215 - val_acc: 0.9012\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.2719 - acc: 0.8805 - val_loss: 0.2678 - val_acc: 0.8765\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.2672 - acc: 0.8892 - val_loss: 0.2795 - val_acc: 0.8889\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.2702 - acc: 0.8846 - val_loss: 0.2284 - val_acc: 0.9136\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 14s 597ms/step - loss: 0.2225 - acc: 0.9057 - val_loss: 0.2208 - val_acc: 0.9136\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 14s 599ms/step - loss: 0.2221 - acc: 0.9064 - val_loss: 0.2118 - val_acc: 0.9198\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 14s 596ms/step - loss: 0.2207 - acc: 0.9069 - val_loss: 0.2111 - val_acc: 0.9074\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 14s 596ms/step - loss: 0.2108 - acc: 0.9100 - val_loss: 0.1995 - val_acc: 0.9198\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.3015 - acc: 0.8763 - val_loss: 0.2391 - val_acc: 0.9136\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.2195 - acc: 0.9089 - val_loss: 0.2238 - val_acc: 0.9259\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.1961 - acc: 0.9115 - val_loss: 0.2245 - val_acc: 0.9198\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.2175 - acc: 0.9066 - val_loss: 0.2446 - val_acc: 0.9259\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.1980 - acc: 0.9242 - val_loss: 0.2055 - val_acc: 0.9198\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 521ms/step - loss: 0.2276 - acc: 0.9079 - val_loss: 0.2054 - val_acc: 0.9136\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1831 - acc: 0.9300 - val_loss: 0.2320 - val_acc: 0.9198\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 14s 599ms/step - loss: 0.2028 - acc: 0.9187 - val_loss: 0.1987 - val_acc: 0.9198\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1836 - acc: 0.9251 - val_loss: 0.2375 - val_acc: 0.9074\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.2106 - acc: 0.9128 - val_loss: 0.2106 - val_acc: 0.9259\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 15s 617ms/step - loss: 0.2008 - acc: 0.9149 - val_loss: 0.1806 - val_acc: 0.9259\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.1729 - acc: 0.9265 - val_loss: 0.2162 - val_acc: 0.9074\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 521ms/step - loss: 0.1538 - acc: 0.9280 - val_loss: 0.2186 - val_acc: 0.8889\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1634 - acc: 0.9335 - val_loss: 0.2078 - val_acc: 0.9259\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1539 - acc: 0.9305 - val_loss: 0.2172 - val_acc: 0.9321\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1563 - acc: 0.9285 - val_loss: 0.3080 - val_acc: 0.9136\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1680 - acc: 0.9378 - val_loss: 0.2194 - val_acc: 0.9074\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1447 - acc: 0.9442 - val_loss: 0.2782 - val_acc: 0.8951\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1558 - acc: 0.9384 - val_loss: 0.2049 - val_acc: 0.9321\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1671 - acc: 0.9376 - val_loss: 0.2284 - val_acc: 0.9259\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1342 - acc: 0.9441 - val_loss: 0.1951 - val_acc: 0.9383\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1434 - acc: 0.9387 - val_loss: 0.2127 - val_acc: 0.9321\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1458 - acc: 0.9455 - val_loss: 0.2188 - val_acc: 0.9136\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1400 - acc: 0.9488 - val_loss: 0.2371 - val_acc: 0.9198\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1262 - acc: 0.9481 - val_loss: 0.2748 - val_acc: 0.9136\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1654 - acc: 0.9301 - val_loss: 0.2150 - val_acc: 0.9136\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 14s 599ms/step - loss: 0.1229 - acc: 0.9525 - val_loss: 0.1661 - val_acc: 0.9259\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1114 - acc: 0.9558 - val_loss: 0.2514 - val_acc: 0.9198\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1054 - acc: 0.9606 - val_loss: 0.2372 - val_acc: 0.9444\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1155 - acc: 0.9585 - val_loss: 0.2050 - val_acc: 0.9383\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1216 - acc: 0.9528 - val_loss: 0.2155 - val_acc: 0.9321\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1089 - acc: 0.9511 - val_loss: 0.2126 - val_acc: 0.9259\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1469 - acc: 0.9405 - val_loss: 0.2159 - val_acc: 0.9012\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.1242 - acc: 0.9428 - val_loss: 0.2096 - val_acc: 0.9259\n",
      "Epoch 47/100\n",
      "24/24 [==============================] - 12s 521ms/step - loss: 0.1089 - acc: 0.9514 - val_loss: 0.2265 - val_acc: 0.9321\n",
      "Epoch 48/100\n",
      "24/24 [==============================] - 12s 521ms/step - loss: 0.0954 - acc: 0.9635 - val_loss: 0.2742 - val_acc: 0.8951\n",
      "Epoch 49/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1356 - acc: 0.9480 - val_loss: 0.2251 - val_acc: 0.8889\n",
      "Epoch 50/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.0935 - acc: 0.9611 - val_loss: 0.2187 - val_acc: 0.9136\n",
      "Epoch 51/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1056 - acc: 0.9598 - val_loss: 0.2848 - val_acc: 0.8951\n",
      "Epoch 52/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.0845 - acc: 0.9651 - val_loss: 0.2370 - val_acc: 0.9012\n",
      "Epoch 53/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.0863 - acc: 0.9721 - val_loss: 0.2335 - val_acc: 0.9321\n",
      "Epoch 54/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.0942 - acc: 0.9656 - val_loss: 0.2764 - val_acc: 0.9074\n",
      "Epoch 55/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.0995 - acc: 0.9643 - val_loss: 0.2937 - val_acc: 0.9198\n",
      "Epoch 56/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.0854 - acc: 0.9683 - val_loss: 0.2751 - val_acc: 0.9074\n",
      "Epoch 57/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.0659 - acc: 0.9741 - val_loss: 0.2580 - val_acc: 0.9259\n",
      "Epoch 58/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.0975 - acc: 0.9663 - val_loss: 0.2303 - val_acc: 0.9259\n",
      "Epoch 59/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.0732 - acc: 0.9765 - val_loss: 0.2447 - val_acc: 0.9383\n",
      "Epoch 60/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.0639 - acc: 0.9727 - val_loss: 0.2931 - val_acc: 0.9198\n",
      "Epoch 61/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.0832 - acc: 0.9656 - val_loss: 0.4179 - val_acc: 0.8951\n",
      "Epoch 62/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.0923 - acc: 0.9648 - val_loss: 0.2621 - val_acc: 0.9074\n",
      "Epoch 63/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.0675 - acc: 0.9778 - val_loss: 0.3289 - val_acc: 0.9259\n",
      "Epoch 64/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.0802 - acc: 0.9644 - val_loss: 0.3977 - val_acc: 0.9012\n",
      "Train loss: 0.0630372517979\n",
      "Train accuracy: 0.977808599168\n",
      "Test loss: 0.166104819802\n",
      "Test accuracy: 0.925925925926\n",
      "\n",
      "===================FOLD= 1\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 18s 741ms/step - loss: 0.8360 - acc: 0.5980 - val_loss: 0.4562 - val_acc: 0.7826\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 15s 604ms/step - loss: 0.6139 - acc: 0.7268 - val_loss: 0.2998 - val_acc: 0.8820\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.4678 - acc: 0.8010 - val_loss: 0.2705 - val_acc: 0.9006\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.3559 - acc: 0.8503 - val_loss: 0.2370 - val_acc: 0.9130\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.3788 - acc: 0.8509 - val_loss: 0.3229 - val_acc: 0.8944\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.3216 - acc: 0.8723 - val_loss: 0.2020 - val_acc: 0.9255\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 15s 604ms/step - loss: 0.2752 - acc: 0.8865 - val_loss: 0.1977 - val_acc: 0.9130\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.2637 - acc: 0.8887 - val_loss: 0.1700 - val_acc: 0.9503\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2937 - acc: 0.8784 - val_loss: 0.2379 - val_acc: 0.8944\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2700 - acc: 0.8883 - val_loss: 0.1785 - val_acc: 0.9193\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2812 - acc: 0.8958 - val_loss: 0.1947 - val_acc: 0.9193\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2284 - acc: 0.9062 - val_loss: 0.1837 - val_acc: 0.9255\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2204 - acc: 0.9019 - val_loss: 0.1980 - val_acc: 0.9130\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2041 - acc: 0.9221 - val_loss: 0.1898 - val_acc: 0.9441\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 14s 594ms/step - loss: 0.2253 - acc: 0.9124 - val_loss: 0.1559 - val_acc: 0.9379\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.2467 - acc: 0.9040 - val_loss: 0.2209 - val_acc: 0.9006\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.2117 - acc: 0.9108 - val_loss: 0.1861 - val_acc: 0.9317\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2099 - acc: 0.9166 - val_loss: 0.2353 - val_acc: 0.9068\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2052 - acc: 0.9188 - val_loss: 0.1585 - val_acc: 0.9503\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1724 - acc: 0.9247 - val_loss: 0.1843 - val_acc: 0.9441\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1656 - acc: 0.9304 - val_loss: 0.2529 - val_acc: 0.9068\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2144 - acc: 0.9170 - val_loss: 0.1859 - val_acc: 0.9068\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.1679 - acc: 0.9338 - val_loss: 0.1912 - val_acc: 0.9130\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2168 - acc: 0.9162 - val_loss: 0.2160 - val_acc: 0.9193\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1712 - acc: 0.9295 - val_loss: 0.1780 - val_acc: 0.9317\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1652 - acc: 0.9285 - val_loss: 0.1597 - val_acc: 0.9441\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1570 - acc: 0.9326 - val_loss: 0.2466 - val_acc: 0.9193\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1360 - acc: 0.9480 - val_loss: 0.1944 - val_acc: 0.9565\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1645 - acc: 0.9376 - val_loss: 0.2344 - val_acc: 0.9441\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1465 - acc: 0.9377 - val_loss: 0.2497 - val_acc: 0.9317\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2189 - acc: 0.9039 - val_loss: 0.1604 - val_acc: 0.9752\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1559 - acc: 0.9337 - val_loss: 0.1762 - val_acc: 0.9379\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1480 - acc: 0.9420 - val_loss: 0.1961 - val_acc: 0.9317\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1306 - acc: 0.9476 - val_loss: 0.2140 - val_acc: 0.9379\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1804 - acc: 0.9295 - val_loss: 0.1930 - val_acc: 0.9317\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1318 - acc: 0.9489 - val_loss: 0.1826 - val_acc: 0.9255\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1399 - acc: 0.9444 - val_loss: 0.2697 - val_acc: 0.9193\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1361 - acc: 0.9492 - val_loss: 0.2393 - val_acc: 0.9255\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1254 - acc: 0.9557 - val_loss: 0.2735 - val_acc: 0.9193\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1215 - acc: 0.9536 - val_loss: 0.2391 - val_acc: 0.9193\n",
      "Train loss: 0.159015962484\n",
      "Train accuracy: 0.932778932779\n",
      "Test loss: 0.155854521021\n",
      "Test accuracy: 0.937888198758\n",
      "\n",
      "===================FOLD= 2\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 16s 655ms/step - loss: 0.7877 - acc: 0.5586 - val_loss: 0.5069 - val_acc: 0.7702\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.5663 - acc: 0.7113 - val_loss: 0.4197 - val_acc: 0.7764\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 15s 607ms/step - loss: 0.4097 - acc: 0.8047 - val_loss: 0.3279 - val_acc: 0.8820\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.3978 - acc: 0.8289 - val_loss: 0.4217 - val_acc: 0.7267\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.3306 - acc: 0.8505 - val_loss: 0.1962 - val_acc: 0.9130\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.3222 - acc: 0.8596 - val_loss: 0.2041 - val_acc: 0.9068\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2914 - acc: 0.8740 - val_loss: 0.2066 - val_acc: 0.9130\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.2517 - acc: 0.8962 - val_loss: 0.1837 - val_acc: 0.9255\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.2776 - acc: 0.8793 - val_loss: 0.2050 - val_acc: 0.9130\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2577 - acc: 0.8989 - val_loss: 0.1942 - val_acc: 0.9006\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2515 - acc: 0.8919 - val_loss: 0.1972 - val_acc: 0.9130\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2256 - acc: 0.9085 - val_loss: 0.1915 - val_acc: 0.9193\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2149 - acc: 0.9148 - val_loss: 0.1971 - val_acc: 0.9255\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2253 - acc: 0.9096 - val_loss: 0.1903 - val_acc: 0.9130\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2110 - acc: 0.9058 - val_loss: 0.2167 - val_acc: 0.9193\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2207 - acc: 0.9144 - val_loss: 0.1936 - val_acc: 0.9193\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2318 - acc: 0.8975 - val_loss: 0.2048 - val_acc: 0.9130\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.2204 - acc: 0.8995 - val_loss: 0.1789 - val_acc: 0.9317\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 15s 604ms/step - loss: 0.1924 - acc: 0.9180 - val_loss: 0.1741 - val_acc: 0.9193\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2056 - acc: 0.9200 - val_loss: 0.2103 - val_acc: 0.9193\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1999 - acc: 0.9149 - val_loss: 0.2159 - val_acc: 0.9193\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2147 - acc: 0.9075 - val_loss: 0.1917 - val_acc: 0.9193\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 521ms/step - loss: 0.2045 - acc: 0.9172 - val_loss: 0.1915 - val_acc: 0.9006\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1778 - acc: 0.9266 - val_loss: 0.1818 - val_acc: 0.9379\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.1553 - acc: 0.9359 - val_loss: 0.1610 - val_acc: 0.9255\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2187 - acc: 0.9072 - val_loss: 0.1773 - val_acc: 0.9317\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1583 - acc: 0.9414 - val_loss: 0.1832 - val_acc: 0.9255\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1720 - acc: 0.9273 - val_loss: 0.1961 - val_acc: 0.9193\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1537 - acc: 0.9371 - val_loss: 0.2009 - val_acc: 0.9130\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1628 - acc: 0.9343 - val_loss: 0.1771 - val_acc: 0.9193\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1549 - acc: 0.9352 - val_loss: 0.2191 - val_acc: 0.9068\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1779 - acc: 0.9208 - val_loss: 0.2344 - val_acc: 0.9006\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1697 - acc: 0.9359 - val_loss: 0.1873 - val_acc: 0.9317\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1556 - acc: 0.9392 - val_loss: 0.1876 - val_acc: 0.9130\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1757 - acc: 0.9226 - val_loss: 0.1939 - val_acc: 0.9317\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1692 - acc: 0.9343 - val_loss: 0.1926 - val_acc: 0.9441\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1700 - acc: 0.9372 - val_loss: 0.1677 - val_acc: 0.9193\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1599 - acc: 0.9356 - val_loss: 0.1884 - val_acc: 0.9255\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.1408 - acc: 0.9474 - val_loss: 0.1595 - val_acc: 0.9441\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1471 - acc: 0.9410 - val_loss: 0.1800 - val_acc: 0.9255\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1443 - acc: 0.9408 - val_loss: 0.2072 - val_acc: 0.9068\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1218 - acc: 0.9550 - val_loss: 0.1994 - val_acc: 0.9193\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1212 - acc: 0.9506 - val_loss: 0.1885 - val_acc: 0.9193\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1177 - acc: 0.9532 - val_loss: 0.1876 - val_acc: 0.9255\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1654 - acc: 0.9274 - val_loss: 0.1673 - val_acc: 0.9441\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.1315 - acc: 0.9465 - val_loss: 0.1954 - val_acc: 0.9193\n",
      "Epoch 47/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1171 - acc: 0.9565 - val_loss: 0.2044 - val_acc: 0.9130\n",
      "Epoch 48/100\n",
      "24/24 [==============================] - 12s 521ms/step - loss: 0.1027 - acc: 0.9609 - val_loss: 0.2416 - val_acc: 0.9193\n",
      "Epoch 49/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1229 - acc: 0.9476 - val_loss: 0.1756 - val_acc: 0.9255\n",
      "Epoch 50/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1038 - acc: 0.9584 - val_loss: 0.2020 - val_acc: 0.9193\n",
      "Epoch 51/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1139 - acc: 0.9534 - val_loss: 0.1922 - val_acc: 0.9255\n",
      "Epoch 52/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.0819 - acc: 0.9668 - val_loss: 0.2334 - val_acc: 0.9317\n",
      "Epoch 53/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.0873 - acc: 0.9629 - val_loss: 0.2407 - val_acc: 0.9193\n",
      "Epoch 54/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.0788 - acc: 0.9687 - val_loss: 0.2526 - val_acc: 0.9068\n",
      "Epoch 55/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.0979 - acc: 0.9571 - val_loss: 0.3921 - val_acc: 0.9193\n",
      "Epoch 56/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1172 - acc: 0.9508 - val_loss: 0.1689 - val_acc: 0.9379\n",
      "Epoch 57/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.0968 - acc: 0.9554 - val_loss: 0.2260 - val_acc: 0.9255\n",
      "Epoch 58/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.0787 - acc: 0.9707 - val_loss: 0.2297 - val_acc: 0.9317\n",
      "Epoch 59/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.0849 - acc: 0.9657 - val_loss: 0.2688 - val_acc: 0.9193\n",
      "Epoch 60/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.0800 - acc: 0.9695 - val_loss: 0.2065 - val_acc: 0.9317\n",
      "Epoch 61/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.0948 - acc: 0.9614 - val_loss: 0.2219 - val_acc: 0.9193\n",
      "Epoch 62/100\n",
      "24/24 [==============================] - 12s 521ms/step - loss: 0.0677 - acc: 0.9747 - val_loss: 0.2282 - val_acc: 0.9068\n",
      "Epoch 63/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1209 - acc: 0.9554 - val_loss: 0.2308 - val_acc: 0.9130\n",
      "Epoch 64/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1242 - acc: 0.9535 - val_loss: 0.2048 - val_acc: 0.9130\n",
      "Train loss: 0.0765285342415\n",
      "Train accuracy: 0.973665973666\n",
      "Test loss: 0.159518830524\n",
      "Test accuracy: 0.944099378882\n",
      "\n",
      "===================FOLD= 3\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 17s 721ms/step - loss: 0.9400 - acc: 0.5477 - val_loss: 0.8230 - val_acc: 0.5312\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.6911 - acc: 0.6506 - val_loss: 0.3048 - val_acc: 0.8500\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.4912 - acc: 0.7853 - val_loss: 0.2737 - val_acc: 0.8500\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 15s 604ms/step - loss: 0.3858 - acc: 0.8427 - val_loss: 0.2589 - val_acc: 0.8812\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.3179 - acc: 0.8645 - val_loss: 0.2734 - val_acc: 0.8688\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.3425 - acc: 0.8541 - val_loss: 0.2677 - val_acc: 0.9000\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.3052 - acc: 0.8715 - val_loss: 0.1975 - val_acc: 0.9187\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.3375 - acc: 0.8640 - val_loss: 0.2065 - val_acc: 0.9062\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2885 - acc: 0.8734 - val_loss: 0.2769 - val_acc: 0.8938\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2463 - acc: 0.8946 - val_loss: 0.2598 - val_acc: 0.9062\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2318 - acc: 0.9042 - val_loss: 0.2320 - val_acc: 0.9125\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2330 - acc: 0.9096 - val_loss: 0.2105 - val_acc: 0.8938\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2663 - acc: 0.8915 - val_loss: 0.2046 - val_acc: 0.9250\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2381 - acc: 0.9007 - val_loss: 0.2287 - val_acc: 0.9000\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2481 - acc: 0.9013 - val_loss: 0.2118 - val_acc: 0.9187\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2124 - acc: 0.9098 - val_loss: 0.1976 - val_acc: 0.9187\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 15s 604ms/step - loss: 0.1936 - acc: 0.9186 - val_loss: 0.1933 - val_acc: 0.9313\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1978 - acc: 0.9155 - val_loss: 0.2675 - val_acc: 0.9187\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2029 - acc: 0.9156 - val_loss: 0.2530 - val_acc: 0.9187\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1894 - acc: 0.9225 - val_loss: 0.2058 - val_acc: 0.9125\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.1887 - acc: 0.9248 - val_loss: 0.1819 - val_acc: 0.9313\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1741 - acc: 0.9301 - val_loss: 0.1905 - val_acc: 0.9313\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 13s 531ms/step - loss: 0.1743 - acc: 0.9294 - val_loss: 0.1936 - val_acc: 0.9438\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1742 - acc: 0.9262 - val_loss: 0.2058 - val_acc: 0.9250\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1715 - acc: 0.9300 - val_loss: 0.1976 - val_acc: 0.9437\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1711 - acc: 0.9233 - val_loss: 0.2941 - val_acc: 0.9000\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1616 - acc: 0.9428 - val_loss: 0.2108 - val_acc: 0.9250\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 14s 602ms/step - loss: 0.1642 - acc: 0.9309 - val_loss: 0.1765 - val_acc: 0.9437\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.1563 - acc: 0.9393 - val_loss: 0.1754 - val_acc: 0.9313\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1964 - acc: 0.9236 - val_loss: 0.1829 - val_acc: 0.9250\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1588 - acc: 0.9331 - val_loss: 0.2461 - val_acc: 0.9313\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1740 - acc: 0.9311 - val_loss: 0.2750 - val_acc: 0.9250\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1627 - acc: 0.9389 - val_loss: 0.2281 - val_acc: 0.9062\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1475 - acc: 0.9427 - val_loss: 0.2821 - val_acc: 0.9000\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1317 - acc: 0.9440 - val_loss: 0.2385 - val_acc: 0.9125\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1242 - acc: 0.9472 - val_loss: 0.2763 - val_acc: 0.8938\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1265 - acc: 0.9559 - val_loss: 0.2340 - val_acc: 0.8938\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 12s 521ms/step - loss: 0.1571 - acc: 0.9373 - val_loss: 0.1960 - val_acc: 0.9125\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1336 - acc: 0.9456 - val_loss: 0.2276 - val_acc: 0.9250\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1417 - acc: 0.9445 - val_loss: 0.1959 - val_acc: 0.9375\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1537 - acc: 0.9378 - val_loss: 0.2240 - val_acc: 0.9313\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1782 - acc: 0.9261 - val_loss: 0.2338 - val_acc: 0.9125\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1303 - acc: 0.9479 - val_loss: 0.2276 - val_acc: 0.9250\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1235 - acc: 0.9474 - val_loss: 0.2090 - val_acc: 0.9187\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1226 - acc: 0.9552 - val_loss: 0.3155 - val_acc: 0.8875\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.1275 - acc: 0.9537 - val_loss: 0.3240 - val_acc: 0.8812\n",
      "Epoch 47/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.0945 - acc: 0.9630 - val_loss: 0.3793 - val_acc: 0.8938\n",
      "Epoch 48/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1372 - acc: 0.9423 - val_loss: 0.2252 - val_acc: 0.9125\n",
      "Epoch 49/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1046 - acc: 0.9580 - val_loss: 0.4073 - val_acc: 0.9062\n",
      "Epoch 50/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.1066 - acc: 0.9648 - val_loss: 0.1908 - val_acc: 0.9563\n",
      "Epoch 51/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.1082 - acc: 0.9559 - val_loss: 0.2228 - val_acc: 0.9125\n",
      "Epoch 52/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.1111 - acc: 0.9554 - val_loss: 0.2528 - val_acc: 0.8875\n",
      "Epoch 53/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.0806 - acc: 0.9647 - val_loss: 0.2371 - val_acc: 0.9250\n",
      "Epoch 54/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.0967 - acc: 0.9651 - val_loss: 0.3382 - val_acc: 0.9187\n",
      "Train loss: 0.097901834015\n",
      "Train accuracy: 0.959833795014\n",
      "Test loss: 0.175389871001\n",
      "Test accuracy: 0.93125\n",
      "\n",
      "===================FOLD= 4\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 16s 676ms/step - loss: 0.7549 - acc: 0.5428 - val_loss: 0.5910 - val_acc: 0.6250\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.5092 - acc: 0.7374 - val_loss: 0.3083 - val_acc: 0.8688\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.3475 - acc: 0.8438 - val_loss: 0.3393 - val_acc: 0.8375\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.3430 - acc: 0.8458 - val_loss: 0.2266 - val_acc: 0.9375\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 14s 602ms/step - loss: 0.3052 - acc: 0.8529 - val_loss: 0.1685 - val_acc: 0.9313\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 14s 601ms/step - loss: 0.2876 - acc: 0.8668 - val_loss: 0.1414 - val_acc: 0.9563\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2825 - acc: 0.8645 - val_loss: 0.1683 - val_acc: 0.9625\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2937 - acc: 0.8625 - val_loss: 0.1504 - val_acc: 0.9313\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.2477 - acc: 0.9015 - val_loss: 0.1380 - val_acc: 0.9437\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2618 - acc: 0.8824 - val_loss: 0.1598 - val_acc: 0.9500\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2337 - acc: 0.8963 - val_loss: 0.1620 - val_acc: 0.9437\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2405 - acc: 0.8943 - val_loss: 0.1808 - val_acc: 0.9187\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2482 - acc: 0.8875 - val_loss: 0.1519 - val_acc: 0.9500\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.2429 - acc: 0.8954 - val_loss: 0.1315 - val_acc: 0.9500\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2300 - acc: 0.9039 - val_loss: 0.1610 - val_acc: 0.9437\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2248 - acc: 0.9070 - val_loss: 0.1713 - val_acc: 0.9375\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2041 - acc: 0.9210 - val_loss: 0.1798 - val_acc: 0.9187\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1766 - acc: 0.9270 - val_loss: 0.1520 - val_acc: 0.9563\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2167 - acc: 0.9047 - val_loss: 0.1949 - val_acc: 0.9250\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2115 - acc: 0.9096 - val_loss: 0.1561 - val_acc: 0.9437\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1987 - acc: 0.9187 - val_loss: 0.1673 - val_acc: 0.9313\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1861 - acc: 0.9217 - val_loss: 0.1926 - val_acc: 0.9313\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.2173 - acc: 0.9061 - val_loss: 0.1874 - val_acc: 0.9375\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.2024 - acc: 0.9191 - val_loss: 0.1342 - val_acc: 0.9625\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1759 - acc: 0.9254 - val_loss: 0.1526 - val_acc: 0.9563\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1878 - acc: 0.9259 - val_loss: 0.2320 - val_acc: 0.9187\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1808 - acc: 0.9287 - val_loss: 0.1469 - val_acc: 0.9563\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1473 - acc: 0.9448 - val_loss: 0.1437 - val_acc: 0.9625\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1854 - acc: 0.9179 - val_loss: 0.1651 - val_acc: 0.9375\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1468 - acc: 0.9407 - val_loss: 0.4111 - val_acc: 0.9125\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1788 - acc: 0.9236 - val_loss: 0.1430 - val_acc: 0.9500\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1491 - acc: 0.9430 - val_loss: 0.1741 - val_acc: 0.9500\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1549 - acc: 0.9371 - val_loss: 0.3683 - val_acc: 0.9000\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1639 - acc: 0.9295 - val_loss: 0.1798 - val_acc: 0.9500\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1618 - acc: 0.9318 - val_loss: 0.1730 - val_acc: 0.9375\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1518 - acc: 0.9388 - val_loss: 0.2934 - val_acc: 0.9125\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1432 - acc: 0.9389 - val_loss: 0.1625 - val_acc: 0.9500\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1465 - acc: 0.9394 - val_loss: 0.1978 - val_acc: 0.9375\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1670 - acc: 0.9337 - val_loss: 0.1627 - val_acc: 0.9313\n",
      "Train loss: 0.173137952994\n",
      "Train accuracy: 0.927977839335\n",
      "Test loss: 0.131529916078\n",
      "Test accuracy: 0.95\n",
      "\n",
      "===================FOLD= 5\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 17s 691ms/step - loss: 0.6808 - acc: 0.6195 - val_loss: 0.5242 - val_acc: 0.6813\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.4286 - acc: 0.7679 - val_loss: 0.2689 - val_acc: 0.8938\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.3533 - acc: 0.8363 - val_loss: 0.3392 - val_acc: 0.8375\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.3067 - acc: 0.8580 - val_loss: 0.3466 - val_acc: 0.8750\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.3591 - acc: 0.8340 - val_loss: 0.2431 - val_acc: 0.9062\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.2890 - acc: 0.8678 - val_loss: 0.1941 - val_acc: 0.9125\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2398 - acc: 0.8863 - val_loss: 0.1950 - val_acc: 0.9250\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2344 - acc: 0.9054 - val_loss: 0.2265 - val_acc: 0.9187\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.2284 - acc: 0.9148 - val_loss: 0.1922 - val_acc: 0.9375\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2250 - acc: 0.9034 - val_loss: 0.2028 - val_acc: 0.9250\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.2228 - acc: 0.9024 - val_loss: 0.1691 - val_acc: 0.9437\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2164 - acc: 0.9108 - val_loss: 0.1987 - val_acc: 0.9250\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2095 - acc: 0.9164 - val_loss: 0.2141 - val_acc: 0.9375\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2169 - acc: 0.9122 - val_loss: 0.2178 - val_acc: 0.9187\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1928 - acc: 0.9204 - val_loss: 0.1796 - val_acc: 0.9125\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1986 - acc: 0.9130 - val_loss: 0.1936 - val_acc: 0.9437\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1941 - acc: 0.9098 - val_loss: 0.2032 - val_acc: 0.9437\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1876 - acc: 0.9210 - val_loss: 0.2151 - val_acc: 0.9062\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1899 - acc: 0.9182 - val_loss: 0.1999 - val_acc: 0.9625\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1573 - acc: 0.9261 - val_loss: 0.2674 - val_acc: 0.9062\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1931 - acc: 0.9184 - val_loss: 0.2233 - val_acc: 0.9250\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1903 - acc: 0.9318 - val_loss: 0.2520 - val_acc: 0.9187\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.1522 - acc: 0.9338 - val_loss: 0.1903 - val_acc: 0.9375\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1852 - acc: 0.9210 - val_loss: 0.2446 - val_acc: 0.9125\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1784 - acc: 0.9324 - val_loss: 0.2545 - val_acc: 0.9187\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1659 - acc: 0.9386 - val_loss: 0.2571 - val_acc: 0.8938\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1568 - acc: 0.9365 - val_loss: 0.2199 - val_acc: 0.9187\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1487 - acc: 0.9357 - val_loss: 0.2365 - val_acc: 0.9375\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1424 - acc: 0.9404 - val_loss: 0.2809 - val_acc: 0.9375\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1510 - acc: 0.9357 - val_loss: 0.2136 - val_acc: 0.9500\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1344 - acc: 0.9428 - val_loss: 0.2795 - val_acc: 0.9187\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1437 - acc: 0.9365 - val_loss: 0.2641 - val_acc: 0.9187\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1714 - acc: 0.9331 - val_loss: 0.3737 - val_acc: 0.8938\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1548 - acc: 0.9373 - val_loss: 0.2417 - val_acc: 0.9187\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1283 - acc: 0.9533 - val_loss: 0.2809 - val_acc: 0.9375\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1518 - acc: 0.9466 - val_loss: 0.2404 - val_acc: 0.9500\n",
      "Train loss: 0.169193531792\n",
      "Train accuracy: 0.928670360111\n",
      "Test loss: 0.169116418809\n",
      "Test accuracy: 0.94375\n",
      "\n",
      "===================FOLD= 6\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 17s 708ms/step - loss: 0.5674 - acc: 0.6795 - val_loss: 0.4546 - val_acc: 0.7562\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.4079 - acc: 0.7974 - val_loss: 0.4020 - val_acc: 0.7875\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.3514 - acc: 0.8293 - val_loss: 0.3988 - val_acc: 0.8063\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 15s 608ms/step - loss: 0.3161 - acc: 0.8542 - val_loss: 0.3255 - val_acc: 0.8313\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 15s 607ms/step - loss: 0.2716 - acc: 0.8681 - val_loss: 0.3179 - val_acc: 0.8313\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.3222 - acc: 0.8513 - val_loss: 0.3590 - val_acc: 0.8313\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.2449 - acc: 0.8930 - val_loss: 0.3463 - val_acc: 0.8250\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 15s 609ms/step - loss: 0.2550 - acc: 0.8930 - val_loss: 0.2844 - val_acc: 0.8812\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2357 - acc: 0.8915 - val_loss: 0.3289 - val_acc: 0.8938\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.2164 - acc: 0.9145 - val_loss: 0.3157 - val_acc: 0.8812\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.1968 - acc: 0.9197 - val_loss: 0.3655 - val_acc: 0.8812\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2088 - acc: 0.9158 - val_loss: 0.3304 - val_acc: 0.8875\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 15s 607ms/step - loss: 0.2270 - acc: 0.9094 - val_loss: 0.2834 - val_acc: 0.8750\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.2160 - acc: 0.9182 - val_loss: 0.2501 - val_acc: 0.9000\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2027 - acc: 0.9184 - val_loss: 0.2714 - val_acc: 0.9062\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2307 - acc: 0.9023 - val_loss: 0.2768 - val_acc: 0.8812\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1997 - acc: 0.9192 - val_loss: 0.3137 - val_acc: 0.8438\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 15s 607ms/step - loss: 0.2048 - acc: 0.9142 - val_loss: 0.2368 - val_acc: 0.8938\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1834 - acc: 0.9329 - val_loss: 0.3083 - val_acc: 0.8375\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1752 - acc: 0.9265 - val_loss: 0.2466 - val_acc: 0.9062\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1723 - acc: 0.9264 - val_loss: 0.2497 - val_acc: 0.8938\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.1686 - acc: 0.9300 - val_loss: 0.3053 - val_acc: 0.8875\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1818 - acc: 0.9272 - val_loss: 0.2916 - val_acc: 0.8813\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1606 - acc: 0.9357 - val_loss: 0.2787 - val_acc: 0.8625\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1711 - acc: 0.9318 - val_loss: 0.3025 - val_acc: 0.8812\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1553 - acc: 0.9360 - val_loss: 0.3511 - val_acc: 0.8812\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1602 - acc: 0.9417 - val_loss: 0.2854 - val_acc: 0.8938\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1607 - acc: 0.9350 - val_loss: 0.2566 - val_acc: 0.9000\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1419 - acc: 0.9419 - val_loss: 0.2814 - val_acc: 0.9000\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1289 - acc: 0.9466 - val_loss: 0.2691 - val_acc: 0.8938\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1293 - acc: 0.9495 - val_loss: 0.3493 - val_acc: 0.9062\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1420 - acc: 0.9363 - val_loss: 0.2713 - val_acc: 0.8750\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.1413 - acc: 0.9406 - val_loss: 0.3332 - val_acc: 0.8750\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.1376 - acc: 0.9427 - val_loss: 0.2760 - val_acc: 0.8875\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.1533 - acc: 0.9373 - val_loss: 0.3092 - val_acc: 0.8750\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1254 - acc: 0.9497 - val_loss: 0.4401 - val_acc: 0.8625\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1588 - acc: 0.9322 - val_loss: 0.2382 - val_acc: 0.8812\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1154 - acc: 0.9554 - val_loss: 0.3389 - val_acc: 0.8938\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1245 - acc: 0.9479 - val_loss: 0.3112 - val_acc: 0.8812\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1259 - acc: 0.9401 - val_loss: 0.3375 - val_acc: 0.8688\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1155 - acc: 0.9541 - val_loss: 0.2873 - val_acc: 0.8875\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.0950 - acc: 0.9598 - val_loss: 0.3661 - val_acc: 0.8625\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1251 - acc: 0.9506 - val_loss: 0.3688 - val_acc: 0.8938\n",
      "Train loss: 0.163229722726\n",
      "Train accuracy: 0.932132963989\n",
      "Test loss: 0.236799073219\n",
      "Test accuracy: 0.89375\n",
      "\n",
      "===================FOLD= 7\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 17s 712ms/step - loss: 0.7320 - acc: 0.6254 - val_loss: 0.3089 - val_acc: 0.8625\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.4758 - acc: 0.7772 - val_loss: 0.2879 - val_acc: 0.8625\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.5246 - acc: 0.7723 - val_loss: 0.2876 - val_acc: 0.8250\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.3972 - acc: 0.8225 - val_loss: 0.3108 - val_acc: 0.8375\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.3360 - acc: 0.8533 - val_loss: 0.2445 - val_acc: 0.8875\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.3546 - acc: 0.8519 - val_loss: 0.2043 - val_acc: 0.9000\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.3147 - acc: 0.8555 - val_loss: 0.2213 - val_acc: 0.9250\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2937 - acc: 0.8710 - val_loss: 0.2055 - val_acc: 0.9125\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2517 - acc: 0.8911 - val_loss: 0.2107 - val_acc: 0.8938\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2504 - acc: 0.8923 - val_loss: 0.2485 - val_acc: 0.8938\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2620 - acc: 0.8906 - val_loss: 0.2068 - val_acc: 0.9250\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2489 - acc: 0.8946 - val_loss: 0.2101 - val_acc: 0.9125\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2254 - acc: 0.9140 - val_loss: 0.2082 - val_acc: 0.9062\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.2433 - acc: 0.8954 - val_loss: 0.1864 - val_acc: 0.9187\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.2053 - acc: 0.9111 - val_loss: 0.1843 - val_acc: 0.9125\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2351 - acc: 0.8923 - val_loss: 0.2085 - val_acc: 0.9000\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.1893 - acc: 0.9225 - val_loss: 0.1575 - val_acc: 0.9187\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2225 - acc: 0.9016 - val_loss: 0.2294 - val_acc: 0.8875\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2164 - acc: 0.9125 - val_loss: 0.2100 - val_acc: 0.9187\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1976 - acc: 0.9176 - val_loss: 0.1770 - val_acc: 0.9187\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1973 - acc: 0.9137 - val_loss: 0.1968 - val_acc: 0.9062\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2158 - acc: 0.9127 - val_loss: 0.1913 - val_acc: 0.9062\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 14s 598ms/step - loss: 0.1860 - acc: 0.9180 - val_loss: 0.1498 - val_acc: 0.9313\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1871 - acc: 0.9189 - val_loss: 0.3477 - val_acc: 0.8750\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2301 - acc: 0.9138 - val_loss: 0.1629 - val_acc: 0.9125\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1643 - acc: 0.9376 - val_loss: 0.1965 - val_acc: 0.9187\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2046 - acc: 0.9119 - val_loss: 0.2577 - val_acc: 0.8812\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2091 - acc: 0.9160 - val_loss: 0.2029 - val_acc: 0.9187\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1632 - acc: 0.9342 - val_loss: 0.2117 - val_acc: 0.9250\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1724 - acc: 0.9239 - val_loss: 0.1755 - val_acc: 0.9313\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1501 - acc: 0.9428 - val_loss: 0.1962 - val_acc: 0.9000\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1354 - acc: 0.9453 - val_loss: 0.1844 - val_acc: 0.9125\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.1460 - acc: 0.9321 - val_loss: 0.1309 - val_acc: 0.9500\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1724 - acc: 0.9272 - val_loss: 0.1884 - val_acc: 0.9125\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1568 - acc: 0.9389 - val_loss: 0.1480 - val_acc: 0.9375\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1358 - acc: 0.9415 - val_loss: 0.1610 - val_acc: 0.9125\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1400 - acc: 0.9379 - val_loss: 0.2238 - val_acc: 0.9062\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1705 - acc: 0.9248 - val_loss: 0.2137 - val_acc: 0.9000\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1502 - acc: 0.9316 - val_loss: 0.2307 - val_acc: 0.8812\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1393 - acc: 0.9469 - val_loss: 0.1351 - val_acc: 0.9500\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1368 - acc: 0.9378 - val_loss: 0.2112 - val_acc: 0.9250\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1330 - acc: 0.9401 - val_loss: 0.1711 - val_acc: 0.9187\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1253 - acc: 0.9482 - val_loss: 0.2876 - val_acc: 0.9187\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1349 - acc: 0.9432 - val_loss: 0.2576 - val_acc: 0.8875\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1539 - acc: 0.9461 - val_loss: 0.1966 - val_acc: 0.9000\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.1724 - acc: 0.9312 - val_loss: 0.1777 - val_acc: 0.9250\n",
      "Epoch 47/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1368 - acc: 0.9417 - val_loss: 0.1574 - val_acc: 0.9313\n",
      "Epoch 48/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1288 - acc: 0.9477 - val_loss: 0.1754 - val_acc: 0.9313\n",
      "Epoch 49/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1162 - acc: 0.9547 - val_loss: 0.2411 - val_acc: 0.9000\n",
      "Epoch 50/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1423 - acc: 0.9503 - val_loss: 0.2052 - val_acc: 0.8938\n",
      "Epoch 51/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1035 - acc: 0.9539 - val_loss: 0.1880 - val_acc: 0.9250\n",
      "Epoch 52/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1218 - acc: 0.9523 - val_loss: 0.2601 - val_acc: 0.9000\n",
      "Epoch 53/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1710 - acc: 0.9309 - val_loss: 0.2002 - val_acc: 0.9187\n",
      "Epoch 54/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1119 - acc: 0.9604 - val_loss: 0.1708 - val_acc: 0.9500\n",
      "Epoch 55/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.0862 - acc: 0.9700 - val_loss: 0.1481 - val_acc: 0.9250\n",
      "Epoch 56/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.0811 - acc: 0.9642 - val_loss: 0.1924 - val_acc: 0.9313\n",
      "Epoch 57/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1464 - acc: 0.9450 - val_loss: 0.1662 - val_acc: 0.9437\n",
      "Epoch 58/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1073 - acc: 0.9513 - val_loss: 0.1651 - val_acc: 0.9437\n",
      "Train loss: 0.100476062049\n",
      "Train accuracy: 0.958448753463\n",
      "Test loss: 0.130946443602\n",
      "Test accuracy: 0.95\n",
      "\n",
      "===================FOLD= 8\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 18s 740ms/step - loss: 0.7472 - acc: 0.5855 - val_loss: 0.4827 - val_acc: 0.7750\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.4767 - acc: 0.7831 - val_loss: 0.5519 - val_acc: 0.7438\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.4131 - acc: 0.8342 - val_loss: 0.4303 - val_acc: 0.8125\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.3373 - acc: 0.8622 - val_loss: 0.3530 - val_acc: 0.8187\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.3089 - acc: 0.8616 - val_loss: 0.3294 - val_acc: 0.8250\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.3185 - acc: 0.8715 - val_loss: 0.3226 - val_acc: 0.8438\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2734 - acc: 0.8865 - val_loss: 0.3402 - val_acc: 0.8375\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2467 - acc: 0.8925 - val_loss: 0.3519 - val_acc: 0.8500\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2677 - acc: 0.8822 - val_loss: 0.4164 - val_acc: 0.8125\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2200 - acc: 0.9111 - val_loss: 0.4410 - val_acc: 0.8313\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2235 - acc: 0.9176 - val_loss: 0.3660 - val_acc: 0.8562\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2336 - acc: 0.8966 - val_loss: 0.4080 - val_acc: 0.8375\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2180 - acc: 0.9060 - val_loss: 0.3447 - val_acc: 0.8250\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1954 - acc: 0.9204 - val_loss: 0.4110 - val_acc: 0.8250\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2048 - acc: 0.9109 - val_loss: 0.4278 - val_acc: 0.8125\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1909 - acc: 0.9199 - val_loss: 0.4473 - val_acc: 0.8375\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1841 - acc: 0.9220 - val_loss: 0.3819 - val_acc: 0.8500\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1811 - acc: 0.9196 - val_loss: 0.3951 - val_acc: 0.8375\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1881 - acc: 0.9228 - val_loss: 0.3822 - val_acc: 0.8125\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2011 - acc: 0.9137 - val_loss: 0.3497 - val_acc: 0.8438\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2231 - acc: 0.9098 - val_loss: 0.3482 - val_acc: 0.8375\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1772 - acc: 0.9305 - val_loss: 0.3499 - val_acc: 0.8313\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.1788 - acc: 0.9273 - val_loss: 0.3239 - val_acc: 0.8500\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1825 - acc: 0.9327 - val_loss: 0.3942 - val_acc: 0.8125\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1633 - acc: 0.9316 - val_loss: 0.3844 - val_acc: 0.8438\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1530 - acc: 0.9402 - val_loss: 0.3725 - val_acc: 0.8688\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1546 - acc: 0.9388 - val_loss: 0.4930 - val_acc: 0.8438\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1521 - acc: 0.9423 - val_loss: 0.3683 - val_acc: 0.8438\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1671 - acc: 0.9309 - val_loss: 0.3739 - val_acc: 0.8562\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1462 - acc: 0.9368 - val_loss: 0.3774 - val_acc: 0.8375\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1319 - acc: 0.9487 - val_loss: 0.3862 - val_acc: 0.8688\n",
      "Train loss: 0.195075951692\n",
      "Train accuracy: 0.920360110803\n",
      "Test loss: 0.322593274713\n",
      "Test accuracy: 0.84375\n",
      "\n",
      "===================FOLD= 9\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 18s 748ms/step - loss: 0.5996 - acc: 0.6861 - val_loss: 0.3971 - val_acc: 0.7937\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.3949 - acc: 0.8215 - val_loss: 0.3743 - val_acc: 0.8125\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 15s 604ms/step - loss: 0.3289 - acc: 0.8407 - val_loss: 0.2650 - val_acc: 0.8688\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.3031 - acc: 0.8528 - val_loss: 0.2645 - val_acc: 0.8750\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.2955 - acc: 0.8648 - val_loss: 0.2402 - val_acc: 0.8750\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.2575 - acc: 0.8919 - val_loss: 0.2098 - val_acc: 0.9125\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2547 - acc: 0.8888 - val_loss: 0.2160 - val_acc: 0.9125\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.2248 - acc: 0.9021 - val_loss: 0.1925 - val_acc: 0.9187\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2234 - acc: 0.9029 - val_loss: 0.2128 - val_acc: 0.9125\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2276 - acc: 0.8966 - val_loss: 0.1999 - val_acc: 0.9062\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2139 - acc: 0.9137 - val_loss: 0.3212 - val_acc: 0.8187\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 15s 608ms/step - loss: 0.2184 - acc: 0.9033 - val_loss: 0.1903 - val_acc: 0.9187\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 15s 607ms/step - loss: 0.2242 - acc: 0.9046 - val_loss: 0.1832 - val_acc: 0.9313\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.2145 - acc: 0.9091 - val_loss: 0.2389 - val_acc: 0.9250\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.2081 - acc: 0.9187 - val_loss: 0.2065 - val_acc: 0.9125\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2104 - acc: 0.9191 - val_loss: 0.2158 - val_acc: 0.9125\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.2009 - acc: 0.9186 - val_loss: 0.2018 - val_acc: 0.9125\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1682 - acc: 0.9311 - val_loss: 0.2979 - val_acc: 0.8500\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1725 - acc: 0.9288 - val_loss: 0.2317 - val_acc: 0.8938\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1927 - acc: 0.9228 - val_loss: 0.1862 - val_acc: 0.9125\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1798 - acc: 0.9223 - val_loss: 0.2702 - val_acc: 0.8750\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1747 - acc: 0.9259 - val_loss: 0.2491 - val_acc: 0.8938\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.1638 - acc: 0.9293 - val_loss: 0.1997 - val_acc: 0.9313\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1569 - acc: 0.9384 - val_loss: 0.2263 - val_acc: 0.9125\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1709 - acc: 0.9318 - val_loss: 0.2338 - val_acc: 0.8938\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1704 - acc: 0.9308 - val_loss: 0.2848 - val_acc: 0.8812\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1673 - acc: 0.9368 - val_loss: 0.1850 - val_acc: 0.9062\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.1407 - acc: 0.9503 - val_loss: 0.1944 - val_acc: 0.9250\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1540 - acc: 0.9344 - val_loss: 0.2702 - val_acc: 0.8625\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1370 - acc: 0.9461 - val_loss: 0.2069 - val_acc: 0.9000\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1140 - acc: 0.9580 - val_loss: 0.3622 - val_acc: 0.8688\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1453 - acc: 0.9428 - val_loss: 0.2879 - val_acc: 0.8812\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1460 - acc: 0.9404 - val_loss: 0.2021 - val_acc: 0.9000\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1222 - acc: 0.9492 - val_loss: 0.2316 - val_acc: 0.9187\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1473 - acc: 0.9393 - val_loss: 0.2192 - val_acc: 0.9062\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1270 - acc: 0.9428 - val_loss: 0.2651 - val_acc: 0.8875\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1211 - acc: 0.9497 - val_loss: 0.1982 - val_acc: 0.9313\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1237 - acc: 0.9489 - val_loss: 0.2983 - val_acc: 0.8750\n",
      "Train loss: 0.144663553819\n",
      "Train accuracy: 0.93836565097\n",
      "Test loss: 0.18315731585\n",
      "Test accuracy: 0.93125\n",
      "\n",
      " Train Log Loss Validation=  0.11924128702\n",
      " Test Log Loss Validation=  0.183048155108\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=100,\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": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#preds=myAngleCV(X_train, X_angle, X_test)\n",
    "preds = y_test_pred_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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_25p.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['val_loss', 'acc', 'loss', 'val_acc'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZbzYypGMzosJOZvgcyy3ghveXszs1mw9uGkh8XLMqt09EuLhPDBc70naNMRzOzCPhYKb9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\nIiJ6vQNZbmmq03XdjQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3c2996fef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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XYfusqyeFlEkg/p5dyJafBQv/AR/+GtZ/6LnX9YR1s+wUzpHX298Thtv/92Ve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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b35c74dd8>"
      ]
     },
     "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": 15,
   "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": 16,
   "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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_holdout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f3b35b50e10>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1trj/+Y5z91IMIXzZTb56xXPakYSEhBckni/0OSEhIeE5wbmbKT5byGTqRKpM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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b990baf60>"
      ]
     },
     "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": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f3b35b32fd0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nx2gKRkfwiVAM1kcNtFdU2zaPMqcZYkJCQkJEeiAmJCQkRGwsZQ4Kmse85S5U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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b35b06b70>"
      ]
     },
     "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": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f3b35a93588>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VNJ64n3AtTiB2wz+bt/FD3fEyeP9p8DbYvl9bbuBGwNHuwfFx94Zf296J8f1tKElhws6a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\nIDqO4wT+B6iS8qiMONCbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b35ae67b8>"
      ]
     },
     "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": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f3b35a71a58>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AW/o2Zfy1Daf2w+djjJxG/ovabHga4fijnrJaDf3XuIMRUI1RSsebJSviKA26ZnHg21Zo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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b35c9b240>"
      ]
     },
     "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": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f3b359d55f8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MTxstyM5BGm0ep0Ib8eg2wJZB7nOFceIwsmsMp4DNE/+jC4my6TGc/bHFslwR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\nHge7oImBFrgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b35a200f0>"
      ]
     },
     "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": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f3b359bad30>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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D7/8CJUOFwl3a9nvX7IN74kY2YwGy2QSFq0CfSyjoXTzDU0RFFMhlHiFvfTLw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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b35991390>"
      ]
     },
     "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": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f3b35920748>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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K23PnBwc4do7c1/0TdUBBGVF0aHQNVlsPeanOLoTOgBzeZlRxx8iJzWbIq2a+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\ndBzHEf4DFIqInWHDvmYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b3596ab38>"
      ]
     },
     "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": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f3b35886048>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pl8nMjzG44tfZH/p5xv3ByXKDa7ZpfbI873fXMZvNTtZN+h5n3+64Y9yqhy/rCu1aeFva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\n/wHpajufGdHnQwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b358d20b8>"
      ]
     },
     "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": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f3b35869a58>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2LqZlLNvvScf4Jp4VLegrMM86nL07iEPPCwxzWsJh081ifWdI5sU0qT1QbcHi\nL6RMLXGfbu7Uv4SDdx0K79PYpwVSqTJB2RL9fNo4q01hAUawwTyHBNihp7XNca88rlmMCziGo5/r\nSNZWDJfYX43/LNtO+pxmigkJCQlAeikmJCQkAFtLny0z1WL88YKmUEM8MxIg0de5WFX0tTbz44Pg\nVKaJIN9yF0mZ4BhMSrQPR94laPiTey519QnXvZwi5qme7vrjob430x5TSbu1Qhz2OT5a0l3EXq/8\nnrOYp3nZxXfyAnJKl96Gw7HTsRbUw0mf66jxDMsAbQy5OeJsFfMqL1oFzoXydQsx2fiWt6DwPQAN\nNEinIbRY3TE8Dvq+Xc7j/SGX1sB9JgWWL+pQoYY3QwGKPUXO6gacnMPI27GMKuxjPMMCVn41vOZc\nvsh3sUwC5j2jVDeWGJrQTltgyWDeiuMMiuXDOmL8Qc3HcG24dx1knuhzQkJCwvYjvRQTEhISgK2l\nz3nItBMq+nWucGfjDGrGh5DmaoFilOHmmM7zC6dyed+365TfgrzWCmrKdt4p86SJsuEdPiidQvVi\noqtW35Mc1WBZvVLzc6egzNOC1lpUHu1pwSq6hLWyFrMr8bIZTOL1qbenOXNaN+U5oMxM0EUUoGTl\n4maLatN8SDZAfCfoz9o9HjM9R/tPJHgGZW3DEm0B1vdDb+16L+n4DHmpm3Ce7sBhnFhhirGAU/v0\nGc/vffmEIHhFvdlTTMo1vubeCVdzr8sOrelMtIWA5zpU4+twkmdQwSLGcFPhXDPEO+O3cgmK6OXx\ns030OSEhIWHrkV6KCQkJCcDW0meTVETn1KKJ/L6gBCtQr48cXTnezmICppf3LnqBzGF1wp7pWARY\nP+EkvLri0lGNmsc7NxHna7CEN3sV3a+PQHsQBnx+SWsiKCAoZhNO0FkbFP8UuadVq6rLCkmu1k7k\nkmSUy4LD9ArxsYbY7/HclyYacHaHwVsttK/VrZYYiiPQYVC9DpI/sQVWg2P+ERyvkQCKccjLttdx\nhRDiydpyi+PtEXKEr7zsnH0Lb4YjJJGaXUNO8ROpwWHxjlS+hr49xBLMAtZ8QV1sCm+KToGlHCyx\n2BRO5bh7Y4AY9rg5QP1aQ//jXBPSbrB+Z8t1GUk6LCEhIWHrsbUzRaJAyN11+OyFq27pGOzAZy76\nftHtqw1/tDlmgVdnPiNgTpN8wXApdyYbP3H5eLu/71/iJnzf5jF3RonF+saRt2G0wiwM+TUolDvJ\nMJvAVOWMWP6mAAAgAElEQVQQM8uduT/+VqhmJXkGgxKMK5Oe32eYwWez4/0ygg+eRqhviVwsmc8+\n631Mf6MxYNnCbAeGmMYFF+elKOpw7n1eh2INhXifRt7OOoRgR8gRk69na0zr2kNa1zkYBvr5AGo8\nwmx/AV9CRNypAJsYjKvpX6PlfUjlmZpBGafv53Trvt0Mm3/C9MEtn/ExOkfonuXV9vnSn8llzFQn\nmJG2MGs9in2+2mCQ3AakmWJCQkICkF6KCQkJCcDW0ueslLqTioqUWPRvQnwTa9e6r+4+gf2ioied\nsVOGMKY/nqMNWnGI/SWoXCggRAqfyRIL9iVom8VtQ8jXEXN35F5eQZqOey5Bqw/H3tIWfOyWKHO9\nqtACHTuoe19lEJ8NWP+fghvuMIQPCjc7SM+K6LtjEWBJKqMxaI5+6FD395mj4+3a/f6s6CpHYd2A\n9heg2yMsKyzh19eLikAFhGoDfU2hQHQd/q2G57/K4Bs58nsOZr5Mc8Kzb90tR15eL4Oi0Ym+RafD\nGIauVU4jDhR2lvs4CahFI1EooByF0b0Y+LIDA0XNtnsutd2tS0hISHieSC/FhISEBGBr6bPkVsIT\nYVRdp497sLrlQz+rVa++FaR3GfKMjOGbJ1CsWblZCLSYb1bjsYJpS52gtMIJx7bqXPhXzqHAQj+5\nHHQwhzU7g+9fCR9DIfxLkXpPQQGZl6SB7QxhcxS6WZ5YpuD3Fr5xyAFisDRrvSQAMdUFjhfwaZwg\n9egEorUriMJ2sUyQwWeR4X/Np5werp0JC6QALeEDOQwbLNWSAqzJ7Rl8OdtI/TqH5bYBi+6i2t9d\nQQ0JyyGCcf4QKkWXCvdayOGbeH3KBSFHF2lVDWlQs3ivCcZTF8/nink/M59R2dzO8L410kwxISEh\nAThTL0Uze5mZ/bSZ/bKZ/ZKZfX3cv29mP2Fmvx7/35yZPSEhIeGjxFmjz0tJfymE8J/NrCfpF83s\nJyT9SUk/GUJ4i5m9UdIbJb3hVgWVmTRpR+sap/uZfwdOUJUNaUfyOqypcIY9pNINviv5irTKKdsU\n1sIm8nssPuTqKYv7XElnESnkXuH8ieVRiLUxIn30WtGKWu44JQpMK4pWDKNVtg4rZx0F1kHTG2jn\nUROpWSGsa7TEIiXp0jZTr7Wlt9X2Nq9KtI0iwGhbD+bsg8z7mU7165wiknRpjGWQrvf5OuRz0fG2\nlQgD3YWK0gBOzTUIyx7Mnb4y14suwCHefHuxFiX2YaAZDMVP1p2+ftIAKUaxZNOCELIhDekzOUIO\n0XddWo7jY+kvsExgvkxwcYHUrA0sJcVHu60k+kzNFEMIHwkh/Oe4PZD0K5Lul/RqSY/E0x6R9Jo7\nU8OEhIRtx5l6KRJm9qCkz5b0HkmXQggfiYeelnTplGteb2aPmtmjUxpDEhISEm4TZ40+S5LMrCvp\nn0r6hhDCkTGvSgjBzDYGXYYQHpb0sCSd29kLxyKqiHfNkdZzAq3QPeQaCfGc0PTraogDbsEZ+Cqk\nVs63XAHncAYJFlgRc6TEbDWd7hw+4dSrvVstmTLFJzGZOK2aw6m4yODgC5rcNPAnpNg8EWcc+7h8\n3F3QV7veQTWUXQN97SNl7HXksdmpwcyNuOEc6i0LKNKEeP+rE3fSJvbMy6NK0UWoDuUwJi9nXq8R\nPv2rNtpxjX0RnymXINrIP7OAYg9OOmoiVvpDcNJ+0GO1uU6xgIN5M/78Mpxg6Nv9uT/b6dgb1254\nAABjtSG6pDYUi0bXnZ837r3gJ8Wggon5c1tR+Lfn4waZcZVHT4wsP7Nzqo8KZ65VZlZT9UL8wRDC\nP4u7nzGze+PxeyU9e6fql5CQsN04Uy9Fq6Yr3yfpV0II345D75b0UNx+SNK7Xuy6JSQkvDRw1ujz\n50v6Kkn/zczeF/d9s6S3SHqHmb1O0uOSXvt8Cm2imTOwuv7YaRDlldZMgf7HoYeI57nTnXtW9IZ2\n7HSc1i0gUFpM3bpH5+XdFryMYvGUjhoWsHhCcNUFP3XiE9eBE+4I8d4NOoznN9sPy/PO9RegdVAZ\nUx1O4gHO0A1YeUXn8evIXQN6VqNTe7x0B5bqOfqN7ux7SH2qsZ9zseF9/tTM07DWczieo7uakG4L\ng2r5oos60cp8sIJzfc7od0f3Fb7UPRk6lV7Bc2E1hezaMD7fU2KJz2PdJav5dSWWNRYUDYaXxRLO\n/h2Uw9jzyYzO67F+5s+cwQs7cECvx/Fvm1d37nqcqZdiCOHndLql/1UvZl0SEhJemjhT9DkhISHh\nTuNMzRQ/lsiCqbW2wBnjeTfH5AoW5TLGPhsstZNN3t2SFgvff/3qNb/PvW59bB5ArgrXNlGXna5T\nOYvO2zM46XZBR69BumpqXuK9SEMZUK8Wk3RAKbqB/B6TmINjjmDmDM7rU+ToaML6W7vu1Dy/BAfj\nGSTNwNmQ1VUlJLOauxXFW0Ixu91EbhdcNzx0C/Vuz/v58tSXQCZIsVqD8rqQryZAOXo/eg4czL2G\nlAhra7Ok2DnEzy9RywZSpbLyzUAZr+rZMa2pITXpqO4XFpmXN8EzbGPJgt4KTUiAreqI24b3Qz1a\nn8PQj2dwXm/Agr3EikEDYuPbiDRTTEhISADSSzEhISEB2Fr6LJME6ac1mHRohURLY0hQtaOjcgbF\n7MbIaeqhQb37ijvGzu6DJbp0y16txthjv38NlHiSe/ntVqRKU7/PgA62+JQZpKNmM/AamKvyzP+w\nltOwIazSq9nNywMlrdNMCnUZ0UIdWJmHi43755D0alL5u+/lP331ZtfT/fOw5sJ5edBHIrLJZW1C\nDfSxiRj3w8LLYWz5QUxPWsAS3J9RVdvLLhA/z+dJrLBk00KsOFY7tLxW0fPljvdJMYbCOgINnuwh\nJhn6b5Mjd/qvIfa8hJp2Buv7HFJn9YNqG4pnaiOW+oRzPazcV1dVHy5tc9vvdqSZYkJCQgKQXooJ\nCQkJwNbS59KkWfQunWdQx4YpsLnjFGMAR+E8Okcb5LpqoFVdOLI2zp3zMnCfToH8xlCTniJx0lFw\n6+aOPJ712Vrcn1Pmy7fHUIGmJXiMGOsdJEBawOB5OHILLa2ircgqGy1YfGHZHEPt+sB9pLWLfhmj\nbWVADuya1/187hZqQRps974qJpcWz/vq3reXu95XxPgJt/hT6q0Ba+1lyGjRieCg8DLDqurT9gGW\nUaCwPdrdPH9YQLV6gvzWTaiT23CzuXbUq8YfIpkV6kiEVsAqvPCxdWXpz7C9RBw66P6sCWs58ocX\ncLy3uKzCvOCUGVti/HcQP5/FwIB8S8XD0kwxISEhAUgvxYSEhARga+mzQvCkUnUmG3YqQbrXBA2x\nQUVbrrWhHl24Za+OJFbXQcF24Jh7wsG14d3cyWCVzUnrYcmLtIaxp9wkNe5lm+NwScPqsMIzH3Vx\nza3Itldx4hy0M0cbSlDpwcgtnr9ROjW9FwmVFqDMpFlDxNvmHS+zE+OJX7YLaSvgXqhDP7G4frxd\n78Ca3fC2kWzvjfycCcy/h0de995B1cG0wZ9I1gX6+nTLlxLaCB8+p81x8FnP7z9Ccq/G+Zi/Gurd\ndFLvTuBoDu+AAs+ieYD8zjWvTAMydoax25oiVn23qu/BCi71SPI1m/v46GDMT7pVP5dZos8JCQkJ\nW4/tnSkSU58ddKASU0I49dkVZh9R0PRc7kvgR/kC25hCUUB04l/WesNnDZ22GxcmBRa9F6fIjERf\nsnbJFKNe1xxhcwtMFEsosKzg97icI+Tvstf9sOOzhiyqwAT4JpYj+GOi/X0kTOmcMoR6SKu5PPAZ\n2aSL2RfEchsxXO+EUCrUaEYDn2HlBU5qwbgw9fIOJ/DfQ+gcQ/vqhjDGOLVvtL3ec/g3clZUnjKV\nWGI212DZCB0szsEaFidlcyi42sjLmCIotAH/TsJ2oaQ08n6uY5bHkL9mz2eQIc4Kmy0fq1eedb/P\nxpHXZXbBn/m6JlZu1Hq+65FmigkJCQlAeikmJCQkANtLn4MU4uJ40YCh4bovRhdQeNn7ZBd53R1U\n56+Qg6JVo7Cr04ocqi49GANaTfgJolpdUJmrNadV+dCpkkVa00YYGhf6SyquTuAnh0X8ydz395Ev\nZQ7fx3vru17fSKHGKOPZDMsEYEqtmlO5NvzxJg1QTBi06g2kOIXFqAmjzoWsWmIoELYYEGaZIzyx\nvsDyAf1Ln/H+rNdI7fwJBDDvOfw3l5HCrmZQ7unAcIKcN1RG6pT+jHIY1MZIa8u8OKuPIJ/pxWp5\n5nDhY7KDnDcFDFT0td3LQcFB2UcF4z+1EYdL76NmHNPM/xLwoBd9b0/twPulFn1Zs+1kz2mmmJCQ\nkECkl2JCQkICsLX0OVjQPFIoss3Zrje5V3d61J36/qej8xnpcA2hfa0jWAXJx5geE+FU2Qi+kbBE\n1uuwbkJVJWygPkuEk01h5R7BctlCKNYKCjiDiYeF9fadMvfg77gunTlcCrT//tzFXHPkhRkxDSt9\nM2nFhp9ogeWGHVjoJ5Fuhq5T7QbSyh4gPPEoc7pXf9ppXR2pbFtHXplZBzlSUJenGf4Xm3rf2JcG\nqPozQ2rSe+GVEJBilFMM2/G2jSCKO4YHZTm7mX8WDSyZIB0sUvSomW22vl/Ao5hdhRDxA7AcT7xN\ni7XyE3IF9Vvethna1oCvZX9UPdt8S+dU29mqhISEhBeIM/VSNLOmmf2Cmf0XM/slM/vrcf8rzOw9\nZvZ+M/thM9scxpGQkJDwUeKs0eeZpC8JIQzNrCbp58zsX0v6i5K+I4TwdjP7Hkmvk/TdtyrIgpTP\nK3qS7zl96FIRZOwUc+dDTsnu+4QqXG0C59lF3elgA1bGOSx++1CAoZV3OIRTLbp8XT9JKmEJteic\nTMo8lNd1jlC5BQVPoTpTILQv73ubRwN3am4gzDCPAqUjKOB8cn7P8fZk4vdstZzi2cLrVSBsbzx1\n+rbEfdTYLExatKr7B4ijXh457ayDjg+g2LMDNRhBJaeEQ/JoiVg8iMJ+0sqXEparqu9acBWgaGsL\nz3kCC3q+530xPfT7zK+g/bDiNiFZtJpU5dQueXgkf5KrAAs2MIFlvYCD+RzLKlkXoYAQruUST7hS\n9WPzkj/0J6duHb+4s+/n0v1grR5kKczv445QYf2rrcV/QdKXSHpn3P+IpNfcgeolJCS8BHCmXoqS\nZGa5mb1P0rOSfkLSByQdhBDW06YnJN1/yrWvN7NHzezR6XzLU44lJCR8XHDW6LNCCCtJn2Vmu5J+\nRNKnPY9rH5b0sCSd7+2FVkys0boM8VPM+A8g1ppdcKtbNq5ozawNp1uoh4R9CMhOnFYcQT2m1XUq\nfb3p9+8intT6Xs5qweQdMXdGwD6gfs7pTgcpKw1O6gdw0s2wv0Dc8BLqLONWVY4hxnoFp+PzuVPN\nRp35T06JyYW19hD09VLfKdlu09tRj/HJR4jfLVfetsfHSB9b+LA96sJyCv3cogclIRiZr06cklvd\n77+YVGNhAX/tc2CMEFHSEu4M5QjUHM7rdSQ+acmp7AJx6Mtz1RjJ4LBe0rm9gfhx5EOxuW9nWIJp\n7viYm638PnU80yk8Hq59akX9d5/eTNPr1/z30en676M8iGPrlPw0dzvO3ExxjRDCgaSflvR5knbN\njiPsH5D05B2rWEJCwlbjTL0UzexCnCHKzFqSfq+kX1H1cvyKeNpDkt51Z2qYkJCw7Thr9PleSY+Y\nWa7qhf2OEMKPmtkvS3q7mb1Z0nslfd9zFZRJqkfH6lXLm3l9PsRZTneOnkauj3MVVWCOkjqdsceg\ntebUZI51zKcmztn6U3x7QNPpulsgtjpEya7FVa/r8j5m8nDaUocFMIMlOtuHE3DudWyiLpSsWg2r\nezb7TsGWoO+1Ju/voHzUFI7cdHZ+eeP88fa5fc+7MguQNKtXVHEKa/YRLOUZzaantHnk/uUn0oMO\nZxBRBbKlt7+IztYl5NyuI8a9CSf99fJK9Qdila/7869DgmwBSbe1lV2S1l23mvh1lvH5gI5j/gIt\n4xP1KiiRhnjqGaz/tYKhDNV9L/vj0SccugZAB+OfjtzqR0+BfDutz2fqpRhC+K+SPnvD/g9KeuWL\nX6OEhISXGs4UfU5ISEi40zhTM8WPJVYKGq2DcUFP+j3QV9Cngy4cuWM86QmaDOkqtb3bltecmgXI\nZe1C9qtEvCudt+ctWLeRBnMxjucjrrWEY3DrnDv7rlZe9tEOnIRntGg6GKpcHniZy92qfb3rkChz\ng7NWTOUJJ+EB5MU6oNhdpAfNapsDkHpNp+rjqMI9gdN3FxJls4XfZ7Ly7YYLpmuJ55LD4pr13aS8\nA2ttAQnt7qq6tgbTMlPJ0nmZMebDBRTBuxgj15Al5pzT0Ml1N5GXkWJTDbzAPGUOC3JW9zJq8BrI\nkGL2KIMj/YmH7pvMS7Smv7sr7+fpLl8JiF+fYalpUT2rEzmEtghpppiQkJAApJdiQkJCArC19Dko\nHCf+6TSdyi4h0TWBha6ANJJFWt0vnfZNQc0CLJvaQdlDp0xtpky96A7LJWJ7R5DDonG1HuNWDTJb\n7R7Ulvkt24XyM62YoFhd91c+YbkM56gsXfXVvOftvNR1c24JS+MSFKyFtJ59SIIXTdC9hu83xBBT\nNbvIq/u2oMZ99dA9AqaIH2/X/VmVSGsaYOUdNr3vDut+n/MYCzrysbC6XlHCWseP1+BUvWxCBRzq\n2J3a5rE1fsD7kfHmFwooskdKSov0CCrhBdTYp0i3W0MZ14xLMz7mrvad2g7gPN8VnO03JJ5aBb9u\nAuf5FSzYy3UAQEpxmpCQkLD9SC/FhISEBGBr6bPyTKFXUbjrc8SnIsxzFzlw+yunMJ1oXRwjd24B\nFWgIUms1QqKjvltfaUUULJ4GaarOFLHHpVPfZbO6fwFH20mJnMLIh7yEJTifOd2qw+K7OpHPCMsH\nSFjU2Knq3oUsGGOfs1MMjZdGfp8dWPZXNQYI++YAibHCGHHAUcbtyvUrx/uYWKoNyTfmfS5r3jj4\n0WsCeTWBso/5AAKSa8W+m+6woX7d4czpe9MQJw/r7zj3Z0GH6fk+5eqQMztU28x13TyARBxF3Qc+\nPo4aiHcnhW25NX8H6yR1tH+GROWtSN9zJD8LUOae97DsgcRVrejobjez761AmikmJCQkAOmlmJCQ\nkABsLX3Ogqm7rJo3g8W3lzmtKGAhboDiLqPTdwbacbnhtKYDx+jFkurcXl5vCWsljNUr5AyunaK8\nrSgvFRCznCHedzaBxRFSZGBbJ+Ka23Ovy8HULd71HI7kzxxWGw+6x/YMslgLqHrTQtzKnD7PsZTQ\nggr3laVbXw+ODv2cxc3f5AxJsTqwyq5gKB+ch2M4+mg19Pr2Zr7/CDHc50u3vtbQL4pLFmMopo8R\nh91EbPoC8dFjUnA8ATBpdcHYM8ThT6JqeB2JzWrIkZ1fdco+rfnzPMy9PbTEz8YYF1gGacLxmnS7\niMrfdEyvwWtgeIQka5g/DZdV48qQnLcTEhISth7ppZiQkJAAbC19DqHUIko/1eq1jecUiM9dUwJJ\nWk6r7SnimoV8xIs9yGghxJU0KIf1NUdyoQyUVaAn7QecH86ipZmJreaI96Ued77r9KkL5+kccduM\nUd2F8+4IJfVjPujJoVM2Kn/fvwN9KSCADjYglzXBmsF45n1b7/g5g5G3/yg6sjewZMHlhRpyOs+n\nyJGdwRIL+jqA9ffe4FZZog6qehSTRF0eeOKmvXbvpmskaQWHcYP6dAnrf3fodaxDEXtA5+yYXCzD\n0kQNSu6L+6CFdtmDvBd4ns/O3TO/ibG4c7DZNFyHOvjaIb9u+B204J3Qx5rOodcxWzuyp8RVCQkJ\nCduP7Z0plkGz6IfXwnpwp+Nf7WzgX/aD0hfp14vKTYR8Md0pU4828V1pdBF+hhA9GkBmEKKd7fqR\ngBC9LPqH0e+ugZlHiYXxHH5ytQkaiv0FUrJScBcTJc3jonlALhAI4GhxzWeQn3je84ZRseUIorH0\nBzWE+T17ePV4e4gwx51p1Vgu+ucwhFwLPqssr0HRqOOzuXkbM0W//Qkx316OFKMICzy06vk38bTo\nm1pHXpqAHClTbc5v0mv4zJ+Ct12EVk7HVQdPoQBU0+bZ1+xlPm7PoY7PHngo5BIT4inuSUPbEv6u\nnej7OUPK2OJl8LVFVUooAK13W5opJiQkJGw/0ksxISEhAdha+pzVC/Xur4wDTaiahMkpaUMZUheN\nGlyALtvgmlCMWbT8u0Kavur7+ddXEHNFjzchkNpErNfsSkXJqJ6SByyGI1RtAT+5CXzQGCJXgAct\nEU6YM14xnjOZel33FkhlirSiiyM/x9CdYYb0mR0sTSCckAKxOXwZl2tFlprX6fIKAr6klTDoCLaw\nFobzxQPQ4HuQKhRhgRTx7T9Z+VLO0G/5jv9Bv0PBN9KgZMO65/C3tAwGoytuDRnF1Lcz+JSyneOl\nL1nkzKuKMdToOh3vjvzaRhP+oyi/APU+jEa1HGo4y0Pv0CYUmGyO/DtrI1bYzji/NFNMSEhIAM7k\nS9HMcjN7r5n9aPz7FWb2HjN7v5n9sJlt1rdPSEhI+ChxVunz16vK97xORvJ/S/qOEMLbzex7JL1O\n0nffqoAsmNox70YJZZBJjVTCvwkrUKJlpLJL2C2LFegbWGdnQloHxZShh7YtQMnKkdOtfAf+jh/x\n8/MoVmtIq0qa2tl3yoQ0I5rUYS4G3UeUoXIaiBHGVgyqfmmiwFl981LDwPw+BfroKViIa4cIJ+OS\nAXxDDX2xjBRziqUBLmlwOWB37vtrXYT5QbalgfScbD/7azBAyGOkinkNvqEIycyRI2WJcLr+nvsS\nrsfbjcgOvJxQx5grqz5vYEDNbXOfD0dOpbGqoXPBx8K5OoWIHU1Y0Q8Kf+YWLeHl0152hrDR0nzZ\no2yijnHJpkz0+cWBmT0g6Q9K+t74t0n6EknvjKc8Iuk1d6Z2CQkJ244z91KU9J2SvlGuwndO0kEI\nx+EVT0i6f9OFZvZ6M3vUzB6dzKabTklISEi4Jc4UfTazPyTp2RDCL5rZFz/f60MID0t6WJIu7p8P\n63woc6iaHII+tAtYF0Er17Q6IGxrgbwkO0Ooy+D+DVhC+blpDhGK1vZ7zqFCk+/7/lFMVdmEk7hl\nfk86+zJUjao3h/goNBAWl9OKjFC8YoPT8KX+uePtASjbVajeXAR9y0svY4oQwRWcnVcInVy1bx5+\nFLM9kTIWIsDQWNUCuTwztOHoxEneFytYhdV2Gh6Oqvr2sVx9CO/1McLzGvubaaqgnmTIf7KCtX5w\nj5e/zgFDrZk2VIcOJnBuh/dBcUJdCHlULvo59ete3xnSxi4usM+jwzxSsK7oqQAB4xoiCeplDE88\nxdH8bseZeilK+nxJX25mf0BSU9Wa4t+TtGtmRZwtPiDpyTtYx4SEhC3GmaLPIYRvCiE8EEJ4UNIf\nlfRTIYSvlPTTkr4invaQpHfdoSomJCRsOc7aTPE0vEHS283szZLeK+n7nuuCsFxpfqWiHwsogwg0\n8MCcHuxCRDSPVDUsnNjk+H4w/0YXYqYjxMEGKvNQ+QWsjpT9+pGrs2St6tq6wUl5irSiAy9kDlrH\n9KHTpVO/KZ5yD9Zf8rZldGS+VLjIrK2cArZAK+dQFHpi5fWuw8F5hVhuNWC5nMFyDfq6ihbNFe7Z\nHtKpGWvEXaevYeH9coQljqMa1GhmoO+w/jYv+/5edBFYNJHnBo75kxkc5umkPUCeGVBcigKv7vOg\n5NZVp9Krveqegwacuxd+HZ3nJ4U/cwre5oy3L709V2bIawtvicEUlDha9wuo/mTzzfHeNSyNlGdq\nKvWxx5l9KYYQfkbSz8TtD0p65Z2sT0JCwksDW/7OT0hISHh+OLMzxY8lMjhB12guHsFRFXGw67wf\nS8SsLuDIuwMn2SFkl+glPIMTbtaElRMxqVOIzNYgSpvFWy0g+NmEpW960SlTDrpDSbG9hjuGMz66\ngIW6sbxZfPcQNLWJtKKTA7c4TwPSlMKyjy5S4B9DPz/LKMqKcyKt/fDUpcUezNwxusiQF+dJLDXs\nwPrdxTIJNhstP2dRIr9Jy/txES2tefDnM5z58+ln6PMDL2MGDwHKy9XgLbDEPYs979NlPJ+5gggK\n+HZqfn/6TGdIfbqqg+LCsj+YunN2B5EErs+LWH7EQTOWfAVZtqdCXJYy2s23B2mmmJCQkACkl2JC\nQkICsLX0ucxM03415Sc1bS03U+L5Es6uMV9HB3lB9orN+TrqkG6aTtyy2GoiPhnWPUaL2h7Omfj9\n13lcCtR72d4cEyuoatehtjyB83SzsVk/A37Pxw7ru0unaVOokU9gCG/CKj5GvPXkqlPsE/dEHYU0\npOWR0+pxlLd6ea1/vK/INtNK7TQ37wfuhxW9jrj1Mcz/OeJ517JnV6BFxvGxhAN+c8fHwhIW9EnH\n75MjrWhA7DGXMtbK1bOB33PEbqtBug3X9eQUeIzljjmCDSYrLIMUjINHPH/0rsixvFMgxnuKOHAh\nj8zFrLKmF1s6p9rOViUkJCS8QKSXYkJCQgKwtfQ5mLQ26tHGynSfsx1vfg/pQRWtojyXhlpjLC9p\n6i6kwIARzhGstbUxnbCdqizXVB5Oug1D+lLQwXLszrhD0jeybZgrC8SwNpHcame83g+rOVJcHiDe\neQKvc4P1s9bz5YDRCrHClAADJaQ02MVIN4/gAM+ETwWSXzER1xwm/2JBtWsm8ULcLhJtLdl3s4rW\nN3Lvk6MelLkbbgmv77j3wTijJLe3+aDt998JaD/qO71WWXFpWQ9MPoa0unvyvmVcdQ5n/HI023gO\n80vVaPDvxuebMbjB21xDkis6qRfXq2dOz4dtQpopJiQkJADppZiQkJAAbC19lknrXFAr0MdlTofo\nzc3vr6k04o2zQ7fmPa3hjZdIki7BcgcRaK0Qb9pb3izdJEkFaJ3F2NoC8dNLA008QbsdJ+KKQaua\nARIqjJkAACAASURBVN8+xCHT0tmJca6s9zPInjzjEgCc1LuQImtCiq2EJZSWyzos6jTF53H/HuKd\nVz3IpUHVnIS1tkJ5U28/rfmTwusyRXz0CavsblV3Olq/YumUuVl6v5E0trD/MPMxUkee7stQsC4Q\nQxz2qvsXh/QUcMv6DhzGx+j/Pug4afIMdW9lfk4jZzYubK6t+0jEtkQsuQ3hEYFGlztVvUO+ndJh\naaaYkJCQAKSXYkJCQgKwtfTZFqUaz1Z0ZrlH+uDfASYMai/xfYi9koMe1Ao/t4XERe2uy0I1xs4x\nBqWTvBa+PQGxpWvKJp2UbFrnZZovnHZ1oHnGhFOjkTv+Tnf9Pn3EOI9KqHA33XK6l7lFM8R8yEPk\nhW4L8bZIxNWCVTxDzPhq7Nc24Pg+pwo3k0uB4s0iJ893vJ22RJw0YoxXcLoua/6M6iVideXIkCDL\nQDGv7iDON1q/68gRPofydgaaHK57GYPCz+lCum3O9NL+GE8kMbs/VE7g0y6cxGkRr/l21/zCp8bX\n/Pzr/vzr5/zZlkd+7QxOEQwIuJJXMdF0Bg8LSr5Bim6KQIfd6hmtEn1OSEhI2H6kl2JCQkICsLX0\nmbAn3Ypq93jc6onEVG2nkvXoQHtCiumqyy/V+04rh8hTXEMZnev+vTloO01bgh5O6GENI2ptUO1f\ngAQanKEXfTjY1vyetMtS1qnfcIqfIQ53svI2rRlep+7lHUFyihJhqwzbWA6A8LSmQ6d11kSuZwho\nl0iSlEVLKMtbQOF8MOcSgLdnhZhgwVpdexrJopBnKsf92yOn8lmvopANmMQpBTaBYvcBKHMTpvAS\nlv0MCbqyA7/pxUvnhSskSR0sRxziGQ4vXz/eXiBHeIYxlEHhvEYpNKiDj5HEjHm1a3FJZoIg+AYc\n48sATwBIoRXTqs8t5X1OSEhI2H5s7UzRyuD+fE8irSYMJqHnX9wSC8mjKDj6gdIFTz+14V94Q1hU\nq+ezFiF3yuA8QtFgMBDUeOqNzX59a5+wHowiq5KmA8cUoYo7M8zIENtlKHu/5b53BxMXa13PLKdI\nh2kIrRNme/SH1BwzxRoMSvDrDId+en7eZzwj5HrpxNSeC5Q9OOGF6eh1KNrrz7MD/71r9/j5HORL\n+Az2kcakGSdl47Efb6I983NQI0Ko3KCLEErm7qkxtM/7YoIZ7263ehYjMIaDHFPPPmNL4aeK3Cnt\ne/x58llwvDRx/mrgBrNanKGWmG0vGEKIMNCcoYinjMVtQZopJiQkJABnbqZoZo9JGqjyqliGED7X\nzPYl/bCkByU9Jum1IYTrp5WRkJCQ8EJx5l6KEb87hHAFf79R0k+GEN5iZm+Mf7/hVgVYnqkW0z8u\nEK6GKCsFLIx3YYw4iovtnzRzyjxBcBd9EycwgMyQUyWDwksHFggqvzCnyoqKIxvEVVlvGi5aCAXM\nG5v9xs5nblxqZU6l8hx5Nz74uCRpfM7Lo7oLpVZWMJCcYFJIsSmIr7aQI4X3FJYVpkXVF8g6qy7S\n3/Qv7Pt+0F4tSHa8Mnt9GIzmEI7F2U34D66PdNr+bGddL3u08DKWMFyUyP+jPpYvWujnc17mtYKh\nmDcLB2dIWZofemc08JxX6LfxNR/bFDaeIS5vNkM5EP+tx3SqUzySBR55Cb/TErR6J/o1MvfRNuFu\noc+vlvRI3H5E0mvuYF0SEhK2GGfxpRgk/biZ/aKZvT7uuxRC+EjcflrSpU0XmtnrzexRM3t0Op9t\nOiUhISHhljiL9Pl3hRCeNLOLkn7CzP4HD4YQgpltdJAKITws6WFJutDf83M+0fN1UHC2PYb4KCx3\nzejvVV53+lDfdVozm/t+Et3lyulQiRCo9gGUTHBBRiFQCKSWH6qswssHnPbWcHy5AE09D/FVfOPq\nJeVQvC5F2y3eS/hh2oWK4kFcRwbftNaQ2jC+fwjKWoeV03LkF8F2BhHT6dL569r1MWudSFLi5x65\nz+SJsuHXWJ53+kjfvBmvRSuGsNwXkVcXLSjGIDVr3to8f2iiXxjmlyPM7nAFn0Vcu4xrBYcNHzfj\nmffJvQUcLMH7V/BBnSNscQ6KX0PuoIKDFApLeT3mAqI3AdpQNDbnwplEJZ1yO9nz2ZsphhCejP8/\nK+lHJL1S0jNmdq8kxf+fvXM1TEhI2GacqZeimXXMrLfelvT7JP13Se+W9FA87SFJ77ozNUxISNh2\nnDX6fEnSj0TH40LSD4UQ/o2Z/SdJ7zCz10l6XNJrn7MkM6kV6ReEM6mMs5g4xVnuwLoaqUp+3q2G\nzG2yhAPsErRqAcWWE4ZF5DppIOSLTuCIvlP+QEX3yxNypnCqhfinNbENKolMrjoCTV2e93MORm65\nHMc12AJCsYsCijYtWFbBx3YCQssGNOdu5lZL1hG07ihSSaajbZTet2UbajgzCLh2Iawa0Be4f6vh\nVHKFPp0fURQ23gd9q663ubFgHhPQdITI2dEpzuY5qGyNSxJVOyYrL+MclYngZdBFOOdy5ssBh23O\na7xf9qGqtMQyyHIXp8flifLAfwcsjSlmpzWvYz2OEY7fbcKZeimGED4o6TM37L8q6VUvfo0SEhJe\najhT9DkhISHhTuNMzRQ/1sgim1juOZX4SBhtPPfCE4h5jZY7Q8rOwRwWTHxKmKZ01nb60q1D2RPU\nu7bv+5cjp2/NDtKDxs0a402ve/3mTactsyUcietOvdpwaqZ6y5VDFyjN4Eg9iblLajApZisv43rd\n6dM+LNuzGmj9Dqg0LPFzeHjPEB/cpJN6PP3gqcvHu/bucef5A9Bk5iLRGA7zoPWku0yxSkswY4vn\nkdbPlk6Bl4gJ7jT9uXWQl2aM1K/Xun5+9xAKM20/P2ApZxF3dyde1xGc5zsQf6ViTQ/0nTHJ9D6g\ne8aMLhcrxGrH/+s9WLmfdM/4edfv2ckR42/r/7aTPqeZYkJCQgKQXooJCQkJwNbS5zKTJtERN0Mz\nL86cBgxXcM69CEpSgwNxxAKSXws4bzdBWRtHTp9Gfae79YZTr2mTOUr8WjoKN9fxqTA+r3pwpEWO\nll7u+2ugzKQ2C9BNZlidDhE3G3O61GB9Jkirr0J+qw762gg3x2xL0iLfLEY6hYjrQbSoXrxw7njf\nfOF9vo92zvugzMi/MoXDdgP5Z8q5l9OEEOsSj3kYqjZdv4LlFRiTO/JnOIOMVxfybn14Fhy0/Z5t\nWH8Nzu6rg4rKX+t73zZn/gyP5FS/X3i9V5C5o3v1rLm5nwnmyMmi1XlcQMz4opfIVLor9mF8zklk\nNiEhIeElgPRSTEhISAC2lj6HstRsraKMeFsD9eggDecK9rrpWjIKzqnzgdO0GqzJtGzOkMekAT0K\npqpk7s0p8ngQ9cvV+QFO30vEp3aWJyJoj7coUUWpr4AUo6Mjt5aGy75d3BMdxifePwXkqgo4GM9g\nrCRmB16ewSo6J2UEw54jVWytWx1YgjJfzJ2yHmH5ojZF/C4sweUMSxwdWLk7TgkzONiPhtAgi8X0\nd1zJOh95eczRkyMm2+DUTjLZHyKuvMEYd5wUcwFlcBKfQI18T/BIwFJPebh53HA9gKrZ1kN6XngU\nLMLN0mVcaqkjTr698rJbz1R1yRaJPickJCRsPdJLMSEhIQHYWvpcrEz7hxWFOVpudtheuTKXrI74\n0EjVdpZOk5p7bhU9GjpNzDpOMZqw+NrEaUiBREtjaEBN6k4/mki9OV8raIMyUv5rBcmxYse57BSO\nx6RPo4ZTSSbaWoCGhlhmp3Ca1IW02rABiTBYmVdozxxxwJk2U6vTHH73F9V9d2GR5xe7PwXtbSF9\n59CfbbeFVK5UMkcW2BxB4ZbDwX1ePa8G6LhRSfsaea+DsdQYLsdK4pJUg+U2tLGUc2/17KalW/N3\nIINdY8pW3POphnsNFPBK2Akwp8NJu4WQdFrcO1GCbXbkmcUYe72CHkDexBi+UI2F8gPJeTshISFh\n65FeigkJCQnA1tLnkJnKKL3UltOqQQEZsR0kKRqBY4wruhNgZW7C4ntPy/WXVmAQcyQ6Wk6cYudj\nyFshDlaId67Xb6YiBgpElWMmqDoaD7UJJSzhI4RhNxG3bFATr6+d0OGMvcR9anBoHy28rwpQ8DrO\nOTpwSrYDWjuD5bR3sX9zxR9DkrF7veLzDuKtQeWLFvJIX/eyA4Y2cjgJamQqurSQRzBHN+n1BV+m\nyKCqTYfxOfqrAStvCXmvDPS9HitTR6WWoOOruY8bOl3XkHxqr7mhDyUtIYvHZFTtOeoVPQp6dbgT\nYNlhJ/jS0Byx17O4NLCleavSTDEhISGBSC/FhISEBGBr6XOZSZN+9c7PEHvaMUSLgikVXdCwmDxo\nhVjWWgumajitrmAV1gxOxUy6hFs2w+bv0NXc79Wfx4RCLacvxSmOsr3SqdQMztDTfTjbzumkC6kv\nJMCy6Mi7gGV7lLnVNGNyxDpoJ6TASqhq5/d4fx3C8Vkd9pcXeqFZOU3nn+HO08OBU2nSt5Z5vxiU\nrFeIIR7DqX2cI4Z66s+FclytSP2XqF9rspkfBngTCM7ujQm8CXpexwBKzv5ai8DvIE7bTuTXxk3h\n9N/BgVHpfUgn+bkhPh/9lfe87vWDqi7Lpj/PBpZ6htd8CaR3Yc/LmFcVT3mfExISEl4C2NqZopVB\nRQzNW8JnLIdKSij9qz2AwWItGjKGYExj6sYFa/ssLMACEjCDUsau9fuM517OAsadnQZC+mLY1wpf\nfubrqI+Q1pN32fUyoAmrSc/rW4NliKGIs3gKw/kICriuxpCP6TMlKVJ84tqw9PpmSEbzQPeiX9q6\nWZ1n0UCYHfwhT6TWxOyUwrasQNby2dHlFgwZFG7N6AlYYdzG8Se9s4YtGKMQohdgmCngpyqoyXB2\ntYoqyKOV92c3Qz+AedC40q75OU+OfDaXdZBi9qbWVGAfLXerTgowygm+kRp4m4+gqFTG3Dbl8uYw\nwW1AmikmJCQkAGfupWhmu2b2TjP7H2b2K2b2eWa2b2Y/YWa/Hv/fe+6SEhISEp4/ziJ9/nuS/k0I\n4SvMrC6pLembJf1kCOEtZvZGSW+U9IZblrIKPv3fBzWFCkuOHCW7CG8aR0US+gnSEKIcuTuQepJp\nTWvgbyfuie9QE5StDrWdWgyjm0JdZTR1KmMtiuB6efX55pCzHHlBCnDP1QZN2CFTy8AQQ3tSvk9p\nUwfpaCP40OrDl64Of7vxytvUjGF+UwjolliOmJEPQt2lDmHdHO3vNGG8mPg5E4QIDuT3qkfFmhnY\nK8MmpxdYAaRbncBYg+d5hLWJ9hx5b0CJ1+1bIofNZOZLKhSw7dQgsrvy8bSL9KkLpGdl2OiiRH4h\nhIUuY8ghhwHz0ixhrLqGvujvV2WURTK0fNxhZjuSvlDS90lSCGEeQjiQ9GpJj8TTHpH0mjtTw4SE\nhG3HmXopSnqFpMuS/pGZvdfMvtfMOpIuhRA+Es95WtKlTReb2evN7FEze5Rf+YSEhITbxVmjz4Wk\nz5H050II7zGzv6eKKh8jhBDMbKPTXgjhYUkPS9K5vf0we6AKLwuHziWK3KknqSRTRa4VRsYZLYi+\nWYNmyfLAQ8tqBtFa0KQmaPisi9BBKNbQfW1taDWI47Zh/b0+dUt5x+CnCLrdGIHWoWimRM2YxyX6\nNe5ARmUMRdSADgjwe2TK0hJt7iE9LKl8iXwgtLQ+s6oonsHvTzRu9vz5jCfe5ysK7sISXT+Ax8F5\nXxOwgX8sz5kP/+5aj/iaH4dnqn71kvfbPXNvW5+qPhhPF9DrTzWcvp7Lvc2Tw8qiW3Zgte4i5A5+\njwuIDAueCBp7/5/4UeCcYrjSJvTjgxlB2JbhlIZxvoA/5LWYz2Z1ihLS3Y6zNlN8QtITIYT3xL/f\nqeol+YyZ3StJ8f9n71D9EhISthxn6qUYQnha0ofN7FPjrldJ+mVJ75b0UNz3kKR33YHqJSQkvARw\n1uizJP05ST8YLc8flPQ1ql7e7zCz10l6XNJrn6uQVVlqOK5oy945Dx2j+GhjAQsx3KA77UhhYAkM\noHXNoVOZGnOEIJwsw/eGwrL12ubwrxXywYznFVVrzWBBBVO5IKdYDGcjmRkjXI1WdoNVUrAu145i\nOVDx6V1DbhX0TwYB2xVyoRRwDJ8XTrc6SJs6WCANaB2ViUXWoDpzVd7/90ARuIA1ncSQ7RwiRLH1\nrOdisa5Twr5gao73b4G+Xl76MsV5mKVrOXKqQCj43JG3n+Op2/B61Z9Getj481u1ER4IdZ1l18sb\noLzahA8RTYBQ8hjBBgVC+3brNyfY4fPJsRZ/GeLEM/xumu04LrPttD6fuZdiCOF9kj53w6FXvdh1\nSUhIeOnhTNHnhISEhDuNMzdT/FghM1OrWVkGD5EDYw+UaYIA4QasmCHGrXahRjIABV6CvpE+B3g4\nL2AJrbURbz1yesI42FEOi25Uj6Hw7bnz54+3wRJVIH47Ry6QKZywc8YEU7EHht5WZGQFeHoOC3J2\n6PWewHm9VSKPSQFafcUtxNMe+mLgN71W+DnNuKywggX9gSmViXyz2/T9M/Oyj3KkgYVT8/y69yMj\nnKl2U4uW4yOkEg2w/jOWfGHen32Y1kdNP+kq8wKB43e7bt0voyP5OXgB5IxrB2VdgfZP0f8UqK0j\nrnxJNSYI2zKQYJ2qdzn2/lnV/Nx9eV2vIP/O+p6bfUDufqSZYkJCQgKQXooJCQkJwNbSZy1L6XJF\nz1p7TqU+UkPulInTkP0j/z60o/XZkNaxifwfMzjbTnf9nAzW5KzcEFgsKbQgiso4WOwvy4oGZpDx\nmkJeatny+y+GbuXN4IBegqY3W25x7CEO2OAQbPH+M8QVt0Cf5rlzpTnkpVpsJ+hzse99Prvs8lZL\nnH5Pw3PdzJcbUojWvLxew9uwgERZHbJcnVOCmIodr0un8O2CIqlx6SMD7cwguFp0GRvvNxpOfVwQ\ndYwdBgmMdrFMc+wv4G3YQaog3n8QYM2H10Rmm8dZD4Htq6Zvs5+na4dsFNEawhkcywu7EGeuxQAI\nptHdJmxnqxISEhJeINJLMSEhIQHYXvqcmSxO/xeQQ+oeOj2YQIFrUEfazBjPO0FMaN5nTKiDKTP5\nhQlXnQeV97gpmJbDDPG8ZQZTXoyPbYJWTeCkXQOtnbW8Nj3kFLmv9LjqAJpTosw6nK3XpKqwzVbL\nIgPHmoA+Ipb6Qt9lLudwNrauW/zbM+S3QXx2iDJU9SVkucDryp5vg8lryfZA7TyDiT4DxZ5O/Lm0\n24hhj94CzHnSQHlhwvHh9+dj6yGX6CAgbrx/s6q4JF2LDu41WI1LOFp3DNJh8CwQLMjMi0OKv2ga\nzoGHAu6fx4CErAkleaR9ncCRO8eVzCOzjUgzxYSEhAQgvRQTEhISgK2lz1ZKtSiZZLCWNhH7mc+d\n+tGIupxFeoLeCaBa9RwSYXDYnsABW/c4ZWocuoWy03Lr51HTzx8h/nUZHWWbsEhnkD87Kp3W7Lhh\nV60GPLPhpUyH8UYNVAm0LkTJKC4ZaOp1qjFXVdOpeYmkVEwEVkPq0RkkyUeIfQ4zxISvHeVBe5lQ\nyeZI34lEZLSmZiO/Tw6neiFxVQvyblTqHsWUoIxrXiJB1XFsuKQcDs69hvfFxLzvahg8Y1j5A+KF\nz82qtrbhUU9ndK7TdETnbcQkNzFuKOPGVKlYPqqDhc+nVb3Kc1Ryx5IGHNYDKLjF4IUQttN7O80U\nExISEoD0UkxISEgAtpY+y6QQnX9rSAq1hCKxUbUZXMGmMfYZMlJ06l2ViA+GNbWoOx199vDa8Xa3\n5YmbGCt9bgl1ZOQJtkjJp+a0qxxB4btGLW1IlEEpe15zynS972WfO+WR91Y3fx8ncFKut/2epE15\n29u8gIU0XPfY33EOx2MklJpDjq0Wrc4ZcieHzJ/J3Hz7qaVLgREXYZWnUvj6eUrSGHmaG5DaWjue\nL64cHO/aaXuM9XTPz82xHDIVHOARK/1YzfMkd+Gx3lnASX9dRyzd1GDxrmFsqeYnNbxozZigjO0Z\n+DJFDWNkQjX5KBPXHJOC++ExqHRzg5O4nZpd+u5GmikmJCQkAOmlmJCQkABsLX0OmbRox3d+CSsn\nmlwD9asfOsXb36mUurPhhnhcSQdNWEXbTtkY+3wOiYsWsJB2EYc8gxUzHzsNG0Xv5CYsxYtLTsFH\nsDjWIV2VMblVgHQVKTYs5L0ZHKJjcq3eiQTL3j+XZ05Za3O/f3uCPqzBKo+42S7ksEgPG+fc2buM\nuY+HyPvcQVjxWkVdkhp975cLS9D6FmLFYYgvIHuW4flfXXmZtehIvjNG7DNWKdgrU8S+1695fa+2\nMV5qfkUTFm8Dre/Oq/43OGNPoXa9gNW8E5Cv+hyc2uuMA8fSEJKi0XNAcLbvhFg+8lI/sTrQRsBr\nIttS2vz/t3duIZJdVRj+V526dFV1VVdf5kZmYiKGyLyYiIiiiCQIXkL0QSRBIUhAH3xIQNHEdyG+\neHkQYUiUPIgaRkXxQZCYB58Gx0QQMwnGkJAeMjOdvtb9unw4u3r9Pd0mTuiZqtqzPhjmnFN1ztn7\n7NO79tpr7fWP8ZGi4zgO4Z2i4zgOEa35LCrIB4+qFNjjTNrIfVoTTNmxx043ziRd75LHkbNKn6Dz\niErRzMoRpcBqkem7SRnBQSJFGsrYoUDeAq1PXaSUUnlKF9aj1jxOZu2erFyUNTxLAentTHqvOQpG\n3+ibeclZltkc64NSlxXserwmPLdAwd7k5efg7bHXOaE11ps10s4mfeWFEblc6T7Zun2/Q+my2stU\nzzWLCuDpg8xKOj3BweCjTZsyaFQoO3bnYB3lEmVqr3RJU5qEoUCB1ONq5CnygLW0Gj3KiE0CWQUy\nd1sUsK3UtuU5m6Zp9y1dXp68yONg+2zB3q2FDmk9c1kpkF3HOuYevH39EZE7ReQf9G9HRB4VkSUR\n+bOI/Dv8v/jOV3Mcx7l2pmqkqKovA7gLAEQkAXARwO8APAbgWVV9QkQeC/vfebtrJdkElaXUYdIh\nfYkdGvEVevarOT/a/wu5STobZXJ6FOdNMjVHo8YtO4y1oY0yqnlzkvRJNjVHMZND+pUf68FUOyzB\nSku1aKK7QMvPMrQUrE0T8JmLB8f11Y+Tw0TS+g0bdp9Sdk9Old2tTsYm6+dqlgGIs6fwiHBAcZ0s\nj5qhpYDZfvqdPsmuZslxRaque+InC227dpNGM50RjeZoZMvl5WWM+Xr6HPuU9qZHmYlwxS5Sp5Fn\nvmJDuyZlWjraJwuCE9zQIDd7KX13OuSUSsokpcpZevaMTu34QoOSI5M1kVBi22LZRuqttr2v40w6\nLYqH7VepgNu0tJKWKg5Dkt9RpCItUzVSvIp7AfxHVV8H8HkAT4fjTwP4wsRK5ThO1Exzp/gAgF+G\n7WOq+mbYvgTg2EEniMjXROS8iJxvdw8WDHccx3k7psp8HiMieQD3A3j86s9UVUUOHrer6hkAZwDg\nxPJRrYaMOJ2RTTRX1UyMLGVh6ZJ5Nja31stkPpFpWqSJ8+ySmZXLWxwbaPd5Ldnc3S4ndm6R1mix\n3kVt3Cy05I0ntat0jdyK3Uda9kPQppbNrFAmF0p+ynKe1a30WXCNS0O7T4OSr/ZJl6ZEJjCfm+yY\nuZUtU/JTKtdQ2fTf//vcVLtGpUNOMQqTG1T4FbYPMiTryplpOMSOl98Vl9IlfYUdcn7R0rorOZuC\nqJTsZWmx9imZ7NvkjaolVn/OXtOj57hbvP8xTOmTLk+Jpno4M9CwyU4vO7dLSz6blAVpMBhnY7Ly\nceLh7qIdn6fpk15wAIprtNxQPgPgeVW9HPYvi8gJAAj/X5lYyRzHiZpp7RQfhJnOAPAHAA+F7YcA\n/P6Gl8hxnJuCqTOfRaQM4FMAvk6HnwDwjIg8DOB1AF96p+uMBiN011OzmVbiQSgjSHdEiUPpUayW\nUhNqYcvMlC5JWcoixXoNzRwtFsxm6ZIxeSIx7/Ooy0YmeWjFjg/KoSwFM2VkSOXukV4LecV5mV+X\nJDETkgRt0LnZPUF+++MEt0dmXg/JK5vboew1Ryi7EHmLFyjhrXDmF7I26/SMuiFOsk3L/I7nzFOc\nZA5+VVt1M3ebFOvZJJfzPAVwDsmLv7Fgz65VSO+7Mm9t1VizJW+SO/j+ZTLr18TKMqhbRUukl5It\n2j1zlXTpXIfqXIddQ1r8rhi9IumlUIBAwu1fsnt2aLZpQNme8mH6oE+eZY6jrdKKv1HNrl0KywMz\nGudyv6nrFFW1CWD5qmPrSL3RjuM415VpNZ8dx3EmwtSNFA8LHY3Qa6SmSI/M5Dlazsc5ZrvklT2K\n1FvbqlDAKpngrdX13e1KmYKXOag6bycMKVnqiAK2s2QdZbLk9RvLoJL+CUuP1ufJlKWg71GTvMkF\nM3d2CnadIzlLnNoivZR2yPzSGpD3lSiRY728bFHq/Q27Rpbii1tkvi4u2QIk1rTpDazsjbngCc3Q\ng6aiVChqoDE0c1PJQ9+jbEicWJWtvFN5kmHlaYVQ+LfopnPkfZUNq8+gZffvkWm8vGbt0qLY7cs7\n9r4cVbt/vZtepzvH7nTbVDL1l4d2wRwv26TEtmtdi7JYHFg7cxBDuWc7O400W22T9YRIYrZFJnNt\n3erfG7/Pw4OXO846PlJ0HMchvFN0HMchojWfhzLCdtAGyZDJlqEsKAmFgA/rJO25lD6WHGW66TbM\n+1w7RuZgg84T0t+Ys9+bhIKHW1le22unZmjNbyUcz7QpuHyRgpdJ4nMtMXOPvVM9yrBTHVLgeZ5M\nQsoCNAzJT0uUOShHptTcrRQA3qLsrxVKLEumdK1o5lubytKjeOWCWFkaSK+ZIe9sdWSfd0ijpUMW\ndpaC5IUiAXKU8PYYLZwuko5OkczQcRLbOiVhTTr050FTGXtGEjQdUlqxaYU8ecWzR5as7FsksDI+\nj0zabZ7qoDX2Rd0f6A0AQ5qCaJPtrSOS1aX2r+f2e7TzDYpIqNKzYsGWGpn4O9T+EeIjRcdxcvRE\nMwAAAuZJREFUHMI7RcdxHEI00kSRIrIGoAngrUmX5QawAq9nTMxKPd+jqkcmXYjDJtpOEQBE5Lyq\nfmjS5bjeeD3j4map57Ti5rPjOA7hnaLjOA4Re6d4ZtIFuEF4PePiZqnnVBL1nKLjOM61EvtI0XEc\n55rwTtFxHIeItlMUkU+LyMsi8kqQRY0CETklIs+JyIsi8i8ReSQcj1IbW0QSEXlBRP4Y9m8XkXOh\nXX8d9HxmGhGpichZEXlJRC6IyEdjbc9ZIMpOMWhG/wSp1stpAA+KyOnJlurQGAD4pqqeBvARAN8I\ndRtrY98B4NmwHwOPALhA+98H8ENVfR+ATQAPT6RUh8uPAfxJVd8P4ANI6xtre049UXaKAD4M4BVV\nfVVVewB+hVQ7euZR1TdV9fmwXUf6B3QLItTGFpGTAD4H4MmwLwDuAXA2fGXm6ykiCwA+AeApAFDV\nnqpuIcL2nBVi7RRvAfAG7a+GY1EhIrcBuBvAOfyf2tgzxo8AfBsmZrMMYEt1Vxs1hna9HcAagJ+H\naYIng05RjO05E8TaKUaPiMwD+A2AR1V1hz/TNM5qpmOtROQ+AFdU9e+TLst1JgvggwB+qqp3I12v\nv8dUjqE9Z4lYO8WLAE7R/slwLApEJIe0Q/yFqv42HI5NG/tjAO4XkdeQTn/cg3TurSaym7gyhnZd\nBbCqqufC/lmknWRs7TkzxNop/g3AHcFTmQfwAFLt6JknzKs9BeCCqv6APopKG1tVH1fVk6p6G9L2\n+4uqfhnAcwC+GL4WQz0vAXhDRO4Mh+4F8CIia89ZItoVLSLyWaRzUgmAn6nq9yZcpENBRD4O4K8A\n/gmba/su0nnFZwDciqCNraobEynkISMinwTwLVW9T0Tei3TkuATgBQBfUSWR6xlERO5C6kzKA3gV\nwFeRDliibM9pJ9pO0XEc590Qq/nsOI7zrvBO0XEch/BO0XEch/BO0XEch/BO0XEch/BO0XEch/BO\n0XEch/gvpIsTjHa/cR8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b358b4c50>"
      ]
     },
     "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": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f3b357cf278>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7NjWe20Hm/cUf2Y+1R6fLlzK/pmWva2Nd4aV54surlV/n8JI/2xZ/LH7kX71X24jnC31+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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b35823828>"
      ]
     },
     "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
}