Model_03-Adamax-k=10.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "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": {},
   "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": {},
   "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": {},
   "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": {},
   "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 Adamax\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": {},
   "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": {},
   "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": {},
   "outputs": [],
   "source": [
    "# Finally create generator\n",
    "def get_callbacks(filepath, patience=2):\n",
    "    es = EarlyStopping('val_loss', patience=10, mode=\"min\")\n",
    "    msave = ModelCheckpoint(filepath, save_best_only=True)\n",
    "    return [es, msave]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "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",
    "    adamax = Adamax(lr=1e-3, beta_1=0.9, beta_2=0.999, epsilon=1e-6, decay=1e-6)\n",
    "    model.compile(loss='binary_crossentropy',\n",
    "                    optimizer=adamax,\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 [==============================] - 18s 737ms/step - loss: 0.7932 - acc: 0.5322 - val_loss: 0.7039 - val_acc: 0.5309\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.7047 - acc: 0.5122 - val_loss: 0.6998 - val_acc: 0.4691\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 13s 531ms/step - loss: 0.6899 - acc: 0.5440 - val_loss: 0.6880 - val_acc: 0.5309\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 11s 475ms/step - loss: 0.6995 - acc: 0.5141 - val_loss: 0.6924 - val_acc: 0.5309\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 11s 472ms/step - loss: 0.6992 - acc: 0.5094 - val_loss: 0.6930 - val_acc: 0.5309\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 11s 474ms/step - loss: 0.6948 - acc: 0.5089 - val_loss: 0.6921 - val_acc: 0.5309\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 11s 474ms/step - loss: 0.6964 - acc: 0.5210 - val_loss: 0.6940 - val_acc: 0.5309\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 11s 476ms/step - loss: 0.6966 - acc: 0.4981 - val_loss: 0.6920 - val_acc: 0.5309\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 11s 477ms/step - loss: 0.6983 - acc: 0.5001 - val_loss: 0.6924 - val_acc: 0.5309\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 11s 477ms/step - loss: 0.6964 - acc: 0.5229 - val_loss: 0.6920 - val_acc: 0.5309\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 11s 477ms/step - loss: 0.6950 - acc: 0.5252 - val_loss: 0.6918 - val_acc: 0.5309\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 11s 478ms/step - loss: 0.6939 - acc: 0.5109 - val_loss: 0.6918 - val_acc: 0.5309\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 11s 479ms/step - loss: 0.6936 - acc: 0.5238 - val_loss: 0.6941 - val_acc: 0.5309\n",
      "Train loss: 0.686826319949\n",
      "Train accuracy: 0.530513176144\n",
      "Test loss: 0.688020363266\n",
      "Test accuracy: 0.530864197531\n",
      "\n",
      "===================FOLD= 1\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 16s 677ms/step - loss: 1.1738 - acc: 0.5071 - val_loss: 0.6900 - val_acc: 0.5280\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 12s 493ms/step - loss: 0.8498 - acc: 0.5029 - val_loss: 0.6923 - val_acc: 0.5280\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.7830 - acc: 0.5074 - val_loss: 0.6875 - val_acc: 0.6211\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.7851 - acc: 0.5103 - val_loss: 0.6664 - val_acc: 0.5776\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.6801 - acc: 0.5926 - val_loss: 0.6417 - val_acc: 0.6460\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 13s 543ms/step - loss: 0.5661 - acc: 0.6921 - val_loss: 0.4053 - val_acc: 0.7826\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.4828 - acc: 0.7742 - val_loss: 0.4011 - val_acc: 0.8261\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.4538 - acc: 0.7931 - val_loss: 0.3375 - val_acc: 0.8571\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.3891 - acc: 0.8404 - val_loss: 0.3442 - val_acc: 0.8634\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.3812 - acc: 0.8438 - val_loss: 0.3276 - val_acc: 0.8758\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.3899 - acc: 0.8259 - val_loss: 0.2866 - val_acc: 0.8571\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.3597 - acc: 0.8416 - val_loss: 0.2978 - val_acc: 0.8758\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.3358 - acc: 0.8619 - val_loss: 0.2506 - val_acc: 0.9255\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.3112 - acc: 0.8680 - val_loss: 0.2022 - val_acc: 0.9317\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.2871 - acc: 0.8796 - val_loss: 0.1919 - val_acc: 0.9379\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2903 - acc: 0.8791 - val_loss: 0.2035 - val_acc: 0.9068\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2920 - acc: 0.8701 - val_loss: 0.2466 - val_acc: 0.8820\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.3009 - acc: 0.8688 - val_loss: 0.2239 - val_acc: 0.9130\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2648 - acc: 0.8894 - val_loss: 0.2076 - val_acc: 0.9193\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.2376 - acc: 0.9084 - val_loss: 0.1725 - val_acc: 0.9565\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2371 - acc: 0.9046 - val_loss: 0.1994 - val_acc: 0.9193\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.2396 - acc: 0.9058 - val_loss: 0.1678 - val_acc: 0.9379\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2400 - acc: 0.8986 - val_loss: 0.1918 - val_acc: 0.9255\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2284 - acc: 0.9102 - val_loss: 0.1991 - val_acc: 0.9317\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2323 - acc: 0.9001 - val_loss: 0.1804 - val_acc: 0.9255\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.2273 - acc: 0.9187 - val_loss: 0.1583 - val_acc: 0.9379\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2235 - acc: 0.9171 - val_loss: 0.1795 - val_acc: 0.9317\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2051 - acc: 0.9209 - val_loss: 0.1685 - val_acc: 0.9255\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2235 - acc: 0.9164 - val_loss: 0.1860 - val_acc: 0.9255\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2143 - acc: 0.9083 - val_loss: 0.1877 - val_acc: 0.9193\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1952 - acc: 0.9187 - val_loss: 0.1826 - val_acc: 0.9068\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2026 - acc: 0.9252 - val_loss: 0.1823 - val_acc: 0.9317\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2429 - acc: 0.8981 - val_loss: 0.2496 - val_acc: 0.8696\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2120 - acc: 0.9153 - val_loss: 0.2005 - val_acc: 0.9130\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2660 - acc: 0.8903 - val_loss: 0.2044 - val_acc: 0.9006\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2174 - acc: 0.9148 - val_loss: 0.1662 - val_acc: 0.9379\n",
      "Train loss: 0.184949330423\n",
      "Train accuracy: 0.927927927928\n",
      "Test loss: 0.158266305752\n",
      "Test accuracy: 0.937888198758\n",
      "\n",
      "===================FOLD= 2\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 14s 587ms/step - loss: 0.9982 - acc: 0.4864 - val_loss: 0.6941 - val_acc: 0.5280\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.8340 - acc: 0.5046 - val_loss: 0.6885 - val_acc: 0.5404\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.7270 - acc: 0.5454 - val_loss: 0.5308 - val_acc: 0.7019\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.5923 - acc: 0.6455 - val_loss: 0.4809 - val_acc: 0.7578\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.4800 - acc: 0.7724 - val_loss: 0.3530 - val_acc: 0.8509\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.4247 - acc: 0.8184 - val_loss: 0.3136 - val_acc: 0.8882\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.3793 - acc: 0.8289 - val_loss: 0.3403 - val_acc: 0.8696\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 13s 548ms/step - loss: 0.3265 - acc: 0.8445 - val_loss: 0.2556 - val_acc: 0.8944\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.3241 - acc: 0.8418 - val_loss: 0.2741 - val_acc: 0.8758\n",
      "Epoch 10/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24/24 [==============================] - 12s 496ms/step - loss: 0.3109 - acc: 0.8714 - val_loss: 0.2729 - val_acc: 0.8944\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2960 - acc: 0.8767 - val_loss: 0.2700 - val_acc: 0.8944\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2876 - acc: 0.8710 - val_loss: 0.2607 - val_acc: 0.9006\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.2568 - acc: 0.8976 - val_loss: 0.2227 - val_acc: 0.9068\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2287 - acc: 0.9096 - val_loss: 0.2266 - val_acc: 0.9068\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2278 - acc: 0.9075 - val_loss: 0.2495 - val_acc: 0.9255\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2514 - acc: 0.9054 - val_loss: 0.2262 - val_acc: 0.9068\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2611 - acc: 0.8909 - val_loss: 0.2284 - val_acc: 0.9317\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.2463 - acc: 0.8873 - val_loss: 0.1902 - val_acc: 0.9317\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2207 - acc: 0.9110 - val_loss: 0.1967 - val_acc: 0.9255\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2355 - acc: 0.9063 - val_loss: 0.2324 - val_acc: 0.9193\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2379 - acc: 0.8852 - val_loss: 0.2158 - val_acc: 0.9130\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2482 - acc: 0.9003 - val_loss: 0.2173 - val_acc: 0.9068\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 13s 542ms/step - loss: 0.2237 - acc: 0.9012 - val_loss: 0.1726 - val_acc: 0.9379\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2114 - acc: 0.9182 - val_loss: 0.1887 - val_acc: 0.9255\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1957 - acc: 0.9173 - val_loss: 0.1814 - val_acc: 0.9130\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2435 - acc: 0.8975 - val_loss: 0.1840 - val_acc: 0.9255\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2149 - acc: 0.9180 - val_loss: 0.1933 - val_acc: 0.9130\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1981 - acc: 0.9170 - val_loss: 0.2175 - val_acc: 0.9317\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2128 - acc: 0.9140 - val_loss: 0.1938 - val_acc: 0.9255\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2017 - acc: 0.9197 - val_loss: 0.1885 - val_acc: 0.9379\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 548ms/step - loss: 0.2120 - acc: 0.9197 - val_loss: 0.1682 - val_acc: 0.9379\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1957 - acc: 0.9199 - val_loss: 0.2151 - val_acc: 0.9068\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2126 - acc: 0.9074 - val_loss: 0.1841 - val_acc: 0.9503\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1893 - acc: 0.9281 - val_loss: 0.1696 - val_acc: 0.9317\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2024 - acc: 0.9118 - val_loss: 0.1730 - val_acc: 0.9441\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1784 - acc: 0.9240 - val_loss: 0.1763 - val_acc: 0.9441\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1888 - acc: 0.9276 - val_loss: 0.1902 - val_acc: 0.9379\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2036 - acc: 0.9135 - val_loss: 0.2721 - val_acc: 0.9006\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2005 - acc: 0.9164 - val_loss: 0.2137 - val_acc: 0.9068\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2186 - acc: 0.9049 - val_loss: 0.2228 - val_acc: 0.9006\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2131 - acc: 0.9195 - val_loss: 0.2170 - val_acc: 0.9068\n",
      "Train loss: 0.146031378208\n",
      "Train accuracy: 0.942480942481\n",
      "Test loss: 0.16819365665\n",
      "Test accuracy: 0.937888198758\n",
      "\n",
      "===================FOLD= 3\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 16s 666ms/step - loss: 1.2020 - acc: 0.4966 - val_loss: 0.6972 - val_acc: 0.4688\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.8141 - acc: 0.4932 - val_loss: 0.6993 - val_acc: 0.4688\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.7794 - acc: 0.5046 - val_loss: 0.7036 - val_acc: 0.4688\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.7557 - acc: 0.5121 - val_loss: 0.7208 - val_acc: 0.5312\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 13s 543ms/step - loss: 0.7515 - acc: 0.5256 - val_loss: 0.6800 - val_acc: 0.5500\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 13s 543ms/step - loss: 0.7358 - acc: 0.5195 - val_loss: 0.6468 - val_acc: 0.6000\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.6476 - acc: 0.5948 - val_loss: 0.5259 - val_acc: 0.7438\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 13s 543ms/step - loss: 0.5251 - acc: 0.7352 - val_loss: 0.3779 - val_acc: 0.8000\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.4505 - acc: 0.7984 - val_loss: 0.5191 - val_acc: 0.7812\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.4041 - acc: 0.8308 - val_loss: 0.3683 - val_acc: 0.8375\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.3567 - acc: 0.8653 - val_loss: 0.3198 - val_acc: 0.8750\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.3367 - acc: 0.8508 - val_loss: 0.2396 - val_acc: 0.9187\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.3122 - acc: 0.8803 - val_loss: 0.2380 - val_acc: 0.9000\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.2861 - acc: 0.8813 - val_loss: 0.2359 - val_acc: 0.9000\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.2843 - acc: 0.8927 - val_loss: 0.1727 - val_acc: 0.9375\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2721 - acc: 0.8938 - val_loss: 0.1787 - val_acc: 0.9125\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2685 - acc: 0.8930 - val_loss: 0.2227 - val_acc: 0.9187\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2660 - acc: 0.8808 - val_loss: 0.1935 - val_acc: 0.9187\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2487 - acc: 0.8928 - val_loss: 0.2115 - val_acc: 0.9062\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2287 - acc: 0.9034 - val_loss: 0.2650 - val_acc: 0.8688\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2556 - acc: 0.8860 - val_loss: 0.1889 - val_acc: 0.9313\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2350 - acc: 0.9125 - val_loss: 0.1813 - val_acc: 0.9187\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.2505 - acc: 0.9044 - val_loss: 0.2021 - val_acc: 0.9250\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2458 - acc: 0.8932 - val_loss: 0.3571 - val_acc: 0.8438\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2339 - acc: 0.9037 - val_loss: 0.2234 - val_acc: 0.9000\n",
      "Train loss: 0.206429071332\n",
      "Train accuracy: 0.912049861496\n",
      "Test loss: 0.17267473191\n",
      "Test accuracy: 0.9375\n",
      "\n",
      "===================FOLD= 4\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 14s 586ms/step - loss: 7.3640 - acc: 0.5252 - val_loss: 7.5554 - val_acc: 0.5312\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 12s 484ms/step - loss: 7.5761 - acc: 0.5300 - val_loss: 7.5554 - val_acc: 0.5312\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/100\n",
      "24/24 [==============================] - 12s 485ms/step - loss: 7.4395 - acc: 0.5384 - val_loss: 7.5554 - val_acc: 0.5312\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 12s 486ms/step - loss: 7.5655 - acc: 0.5306 - val_loss: 7.5554 - val_acc: 0.5312\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 12s 485ms/step - loss: 7.7937 - acc: 0.5165 - val_loss: 7.5554 - val_acc: 0.5312\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 12s 485ms/step - loss: 7.4606 - acc: 0.5371 - val_loss: 7.5554 - val_acc: 0.5312\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 12s 485ms/step - loss: 7.4475 - acc: 0.5379 - val_loss: 7.5554 - val_acc: 0.5312\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 12s 485ms/step - loss: 7.8147 - acc: 0.5152 - val_loss: 7.5554 - val_acc: 0.5312\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 12s 486ms/step - loss: 7.5235 - acc: 0.5332 - val_loss: 7.5554 - val_acc: 0.5312\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 486ms/step - loss: 7.5393 - acc: 0.5322 - val_loss: 7.5554 - val_acc: 0.5312\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 12s 485ms/step - loss: 7.5682 - acc: 0.5305 - val_loss: 7.5554 - val_acc: 0.5312\n",
      "Train loss: 7.56791471915\n",
      "Train accuracy: 0.530470914127\n",
      "Test loss: 7.5553571701\n",
      "Test accuracy: 0.53125\n",
      "\n",
      "===================FOLD= 5\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 15s 622ms/step - loss: 1.1711 - acc: 0.5225 - val_loss: 0.6904 - val_acc: 0.5000\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 13s 542ms/step - loss: 0.8980 - acc: 0.4982 - val_loss: 0.6743 - val_acc: 0.5312\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 13s 543ms/step - loss: 0.7737 - acc: 0.5824 - val_loss: 0.4537 - val_acc: 0.7875\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.5484 - acc: 0.7296 - val_loss: 0.5039 - val_acc: 0.7438\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.4830 - acc: 0.7712 - val_loss: 0.3603 - val_acc: 0.8625\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.4797 - acc: 0.7834 - val_loss: 0.3564 - val_acc: 0.8625\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.3867 - acc: 0.8344 - val_loss: 0.3565 - val_acc: 0.8688\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.3939 - acc: 0.8337 - val_loss: 0.3449 - val_acc: 0.8688\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.3732 - acc: 0.8292 - val_loss: 0.3634 - val_acc: 0.8500\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.3908 - acc: 0.8287 - val_loss: 0.3134 - val_acc: 0.8688\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.3224 - acc: 0.8673 - val_loss: 0.3438 - val_acc: 0.8500\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.2943 - acc: 0.8738 - val_loss: 0.2536 - val_acc: 0.9250\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.2774 - acc: 0.8860 - val_loss: 0.2501 - val_acc: 0.9000\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2978 - acc: 0.8700 - val_loss: 0.2763 - val_acc: 0.9000\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2516 - acc: 0.8941 - val_loss: 0.2696 - val_acc: 0.9187\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2716 - acc: 0.8762 - val_loss: 0.2518 - val_acc: 0.8875\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.2713 - acc: 0.8779 - val_loss: 0.2233 - val_acc: 0.9313\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2534 - acc: 0.8930 - val_loss: 0.2404 - val_acc: 0.9437\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.2410 - acc: 0.8994 - val_loss: 0.2097 - val_acc: 0.9375\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.2040 - acc: 0.9129 - val_loss: 0.2013 - val_acc: 0.9500\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2216 - acc: 0.9033 - val_loss: 0.2184 - val_acc: 0.9187\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2352 - acc: 0.9047 - val_loss: 0.2178 - val_acc: 0.9250\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 490ms/step - loss: 0.2366 - acc: 0.9073 - val_loss: 0.2029 - val_acc: 0.9313\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.2818 - acc: 0.8803 - val_loss: 0.1998 - val_acc: 0.9250\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.2122 - acc: 0.9011 - val_loss: 0.2204 - val_acc: 0.9125\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2302 - acc: 0.9013 - val_loss: 0.2193 - val_acc: 0.9375\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1903 - acc: 0.9212 - val_loss: 0.2159 - val_acc: 0.9125\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1952 - acc: 0.9161 - val_loss: 0.2143 - val_acc: 0.9375\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2128 - acc: 0.9054 - val_loss: 0.2136 - val_acc: 0.9437\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 547ms/step - loss: 0.2194 - acc: 0.9075 - val_loss: 0.1985 - val_acc: 0.9313\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.1819 - acc: 0.9226 - val_loss: 0.1946 - val_acc: 0.9313\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2054 - acc: 0.9134 - val_loss: 0.2294 - val_acc: 0.9250\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2283 - acc: 0.9135 - val_loss: 0.2903 - val_acc: 0.9062\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2313 - acc: 0.9091 - val_loss: 0.2054 - val_acc: 0.9187\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.2102 - acc: 0.9143 - val_loss: 0.1932 - val_acc: 0.9187\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2121 - acc: 0.9117 - val_loss: 0.2363 - val_acc: 0.9250\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1974 - acc: 0.9246 - val_loss: 0.2679 - val_acc: 0.9000\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2257 - acc: 0.9044 - val_loss: 0.2268 - val_acc: 0.9313\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.2001 - acc: 0.9207 - val_loss: 0.1852 - val_acc: 0.9437\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.1936 - acc: 0.9236 - val_loss: 0.1795 - val_acc: 0.9437\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1617 - acc: 0.9367 - val_loss: 0.2020 - val_acc: 0.9500\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1828 - acc: 0.9303 - val_loss: 0.1871 - val_acc: 0.9375\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.1985 - acc: 0.9235 - val_loss: 0.1780 - val_acc: 0.9437\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1853 - acc: 0.9300 - val_loss: 0.2075 - val_acc: 0.9313\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1790 - acc: 0.9303 - val_loss: 0.2609 - val_acc: 0.9250\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 12s 490ms/step - loss: 0.1780 - acc: 0.9280 - val_loss: 0.2093 - val_acc: 0.9250\n",
      "Epoch 47/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1517 - acc: 0.9391 - val_loss: 0.2344 - val_acc: 0.9062\n",
      "Epoch 48/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1761 - acc: 0.9254 - val_loss: 0.1995 - val_acc: 0.9187\n",
      "Epoch 49/100\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.1768 - acc: 0.9266 - val_loss: 0.1764 - val_acc: 0.9375\n",
      "Epoch 50/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1822 - acc: 0.9316 - val_loss: 0.2781 - val_acc: 0.8562\n",
      "Epoch 51/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2127 - acc: 0.9186 - val_loss: 0.2213 - val_acc: 0.9125\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 52/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1750 - acc: 0.9339 - val_loss: 0.2231 - val_acc: 0.9313\n",
      "Epoch 53/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1649 - acc: 0.9425 - val_loss: 0.2004 - val_acc: 0.9250\n",
      "Epoch 54/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1629 - acc: 0.9352 - val_loss: 0.1769 - val_acc: 0.9437\n",
      "Epoch 55/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1548 - acc: 0.9350 - val_loss: 0.2083 - val_acc: 0.9187\n",
      "Epoch 56/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1785 - acc: 0.9262 - val_loss: 0.2119 - val_acc: 0.9187\n",
      "Epoch 57/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1554 - acc: 0.9334 - val_loss: 0.1960 - val_acc: 0.9313\n",
      "Epoch 58/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1742 - acc: 0.9282 - val_loss: 0.3102 - val_acc: 0.9250\n",
      "Epoch 59/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1404 - acc: 0.9412 - val_loss: 0.2075 - val_acc: 0.9187\n",
      "Train loss: 0.145452324126\n",
      "Train accuracy: 0.943905817175\n",
      "Test loss: 0.176390650868\n",
      "Test accuracy: 0.9375\n",
      "\n",
      "===================FOLD= 6\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 16s 676ms/step - loss: 1.6814 - acc: 0.4992 - val_loss: 0.7533 - val_acc: 0.4688\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.7609 - acc: 0.5700 - val_loss: 0.6198 - val_acc: 0.5687\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.5601 - acc: 0.7088 - val_loss: 0.6361 - val_acc: 0.6687\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 13s 543ms/step - loss: 0.4830 - acc: 0.7733 - val_loss: 0.4719 - val_acc: 0.7625\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.4354 - acc: 0.8147 - val_loss: 0.4609 - val_acc: 0.7937\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.4531 - acc: 0.8111 - val_loss: 0.4535 - val_acc: 0.8000\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.4075 - acc: 0.8179 - val_loss: 0.3837 - val_acc: 0.8187\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.3351 - acc: 0.8720 - val_loss: 0.3423 - val_acc: 0.8562\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.3490 - acc: 0.8573 - val_loss: 0.3088 - val_acc: 0.8438\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 543ms/step - loss: 0.2907 - acc: 0.8826 - val_loss: 0.3074 - val_acc: 0.8688\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.3185 - acc: 0.8608 - val_loss: 0.2603 - val_acc: 0.8938\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2552 - acc: 0.8987 - val_loss: 0.2953 - val_acc: 0.8938\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2861 - acc: 0.8713 - val_loss: 0.2636 - val_acc: 0.8938\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2431 - acc: 0.8974 - val_loss: 0.2643 - val_acc: 0.8938\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2540 - acc: 0.8998 - val_loss: 0.4031 - val_acc: 0.8250\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.3099 - acc: 0.8624 - val_loss: 0.3492 - val_acc: 0.8562\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2260 - acc: 0.9158 - val_loss: 0.3104 - val_acc: 0.8875\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.2347 - acc: 0.9090 - val_loss: 0.2585 - val_acc: 0.8938\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.2082 - acc: 0.9116 - val_loss: 0.2393 - val_acc: 0.9125\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2228 - acc: 0.9081 - val_loss: 0.2776 - val_acc: 0.9125\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.2178 - acc: 0.9034 - val_loss: 0.2371 - val_acc: 0.9000\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.2104 - acc: 0.9134 - val_loss: 0.2337 - val_acc: 0.9250\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 13s 540ms/step - loss: 0.2193 - acc: 0.9055 - val_loss: 0.2274 - val_acc: 0.9125\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1983 - acc: 0.9174 - val_loss: 0.4814 - val_acc: 0.8187\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2272 - acc: 0.8995 - val_loss: 0.2434 - val_acc: 0.9187\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2138 - acc: 0.9205 - val_loss: 0.3291 - val_acc: 0.8688\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2474 - acc: 0.8985 - val_loss: 0.2453 - val_acc: 0.8875\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1886 - acc: 0.9215 - val_loss: 0.2546 - val_acc: 0.9062\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1796 - acc: 0.9254 - val_loss: 0.2781 - val_acc: 0.8938\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1832 - acc: 0.9272 - val_loss: 0.2281 - val_acc: 0.9000\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1871 - acc: 0.9184 - val_loss: 0.2569 - val_acc: 0.9125\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1871 - acc: 0.9182 - val_loss: 0.2625 - val_acc: 0.9062\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1925 - acc: 0.9187 - val_loss: 0.3356 - val_acc: 0.8938\n",
      "Train loss: 0.156934753845\n",
      "Train accuracy: 0.940443213296\n",
      "Test loss: 0.227433377504\n",
      "Test accuracy: 0.9125\n",
      "\n",
      "===================FOLD= 7\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 15s 629ms/step - loss: 0.9694 - acc: 0.5410 - val_loss: 0.6963 - val_acc: 0.5312\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.7891 - acc: 0.5225 - val_loss: 0.7156 - val_acc: 0.5312\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 12s 493ms/step - loss: 0.7921 - acc: 0.4873 - val_loss: 0.7611 - val_acc: 0.4688\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.7599 - acc: 0.4954 - val_loss: 0.7028 - val_acc: 0.4688\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.7450 - acc: 0.5042 - val_loss: 0.7135 - val_acc: 0.4688\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.7048 - acc: 0.5528 - val_loss: 0.7134 - val_acc: 0.5312\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 13s 543ms/step - loss: 0.7146 - acc: 0.5275 - val_loss: 0.5871 - val_acc: 0.6500\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 13s 543ms/step - loss: 0.5713 - acc: 0.6598 - val_loss: 0.5587 - val_acc: 0.6750\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 13s 542ms/step - loss: 0.5135 - acc: 0.7267 - val_loss: 0.5363 - val_acc: 0.7063\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 543ms/step - loss: 0.4749 - acc: 0.7593 - val_loss: 0.4699 - val_acc: 0.7812\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.4503 - acc: 0.7998 - val_loss: 0.4087 - val_acc: 0.8250\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.4360 - acc: 0.8029 - val_loss: 0.4111 - val_acc: 0.8313\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.3742 - acc: 0.8290 - val_loss: 0.2945 - val_acc: 0.8688\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.3255 - acc: 0.8568 - val_loss: 0.3677 - val_acc: 0.8063\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2959 - acc: 0.8712 - val_loss: 0.3327 - val_acc: 0.8688\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.3024 - acc: 0.8720 - val_loss: 0.2508 - val_acc: 0.9000\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.2450 - acc: 0.8998 - val_loss: 0.2453 - val_acc: 0.9000\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.2674 - acc: 0.8796 - val_loss: 0.2329 - val_acc: 0.9062\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2303 - acc: 0.9111 - val_loss: 0.2594 - val_acc: 0.9000\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.2456 - acc: 0.8935 - val_loss: 0.2300 - val_acc: 0.8938\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.2437 - acc: 0.8919 - val_loss: 0.2263 - val_acc: 0.8938\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 14s 576ms/step - loss: 0.2492 - acc: 0.8958 - val_loss: 0.2095 - val_acc: 0.9000\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 13s 538ms/step - loss: 0.2293 - acc: 0.9077 - val_loss: 0.2088 - val_acc: 0.9313\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.2395 - acc: 0.9011 - val_loss: 0.2031 - val_acc: 0.9062\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.2482 - acc: 0.8897 - val_loss: 0.1845 - val_acc: 0.9125\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.2300 - acc: 0.9024 - val_loss: 0.1928 - val_acc: 0.9313\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2049 - acc: 0.9166 - val_loss: 0.2405 - val_acc: 0.8875\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2420 - acc: 0.9039 - val_loss: 0.2128 - val_acc: 0.9062\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2374 - acc: 0.9080 - val_loss: 0.2185 - val_acc: 0.9000\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2432 - acc: 0.9006 - val_loss: 0.2188 - val_acc: 0.9125\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2021 - acc: 0.9124 - val_loss: 0.2248 - val_acc: 0.9062\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.1972 - acc: 0.9221 - val_loss: 0.1756 - val_acc: 0.9187\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2254 - acc: 0.9036 - val_loss: 0.2610 - val_acc: 0.8875\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2212 - acc: 0.9103 - val_loss: 0.1844 - val_acc: 0.9000\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1984 - acc: 0.9194 - val_loss: 0.2164 - val_acc: 0.9125\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1938 - acc: 0.9239 - val_loss: 0.2072 - val_acc: 0.9062\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2026 - acc: 0.9207 - val_loss: 0.2514 - val_acc: 0.8875\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2124 - acc: 0.9031 - val_loss: 0.2185 - val_acc: 0.8750\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2098 - acc: 0.9153 - val_loss: 0.2232 - val_acc: 0.8938\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.1967 - acc: 0.9143 - val_loss: 0.1624 - val_acc: 0.9313\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2062 - acc: 0.9163 - val_loss: 0.3191 - val_acc: 0.8438\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2304 - acc: 0.8987 - val_loss: 0.2033 - val_acc: 0.9187\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1840 - acc: 0.9244 - val_loss: 0.3068 - val_acc: 0.8625\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1818 - acc: 0.9291 - val_loss: 0.2424 - val_acc: 0.8812\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.1862 - acc: 0.9295 - val_loss: 0.1604 - val_acc: 0.9125\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 13s 540ms/step - loss: 0.2138 - acc: 0.9154 - val_loss: 0.1496 - val_acc: 0.9375\n",
      "Epoch 47/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1813 - acc: 0.9319 - val_loss: 0.2427 - val_acc: 0.8938\n",
      "Epoch 48/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2066 - acc: 0.9158 - val_loss: 0.2213 - val_acc: 0.8938\n",
      "Epoch 49/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1825 - acc: 0.9293 - val_loss: 0.2260 - val_acc: 0.9125\n",
      "Epoch 50/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1952 - acc: 0.9220 - val_loss: 0.1677 - val_acc: 0.9187\n",
      "Epoch 51/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1966 - acc: 0.9207 - val_loss: 0.1903 - val_acc: 0.9125\n",
      "Epoch 52/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1765 - acc: 0.9319 - val_loss: 0.1730 - val_acc: 0.9250\n",
      "Epoch 53/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2077 - acc: 0.9182 - val_loss: 0.2861 - val_acc: 0.8625\n",
      "Epoch 54/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1858 - acc: 0.9261 - val_loss: 0.1806 - val_acc: 0.9375\n",
      "Epoch 55/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1720 - acc: 0.9332 - val_loss: 0.1944 - val_acc: 0.9187\n",
      "Epoch 56/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1592 - acc: 0.9342 - val_loss: 0.1955 - val_acc: 0.9125\n",
      "Train loss: 0.145842337625\n",
      "Train accuracy: 0.943905817175\n",
      "Test loss: 0.149558009207\n",
      "Test accuracy: 0.9375\n",
      "\n",
      "===================FOLD= 8\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 15s 629ms/step - loss: 7.3000 - acc: 0.5184 - val_loss: 7.5554 - val_acc: 0.5312\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 12s 484ms/step - loss: 7.5103 - acc: 0.5340 - val_loss: 7.5554 - val_acc: 0.5312\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 12s 484ms/step - loss: 7.6680 - acc: 0.5243 - val_loss: 7.5554 - val_acc: 0.5312\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 12s 482ms/step - loss: 7.3241 - acc: 0.5456 - val_loss: 7.5554 - val_acc: 0.5312\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 12s 483ms/step - loss: 7.6993 - acc: 0.5223 - val_loss: 7.5554 - val_acc: 0.5312\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 12s 483ms/step - loss: 7.7020 - acc: 0.5222 - val_loss: 7.5554 - val_acc: 0.5312\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 12s 483ms/step - loss: 7.4448 - acc: 0.5381 - val_loss: 7.5554 - val_acc: 0.5312\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 12s 484ms/step - loss: 7.5261 - acc: 0.5331 - val_loss: 7.5554 - val_acc: 0.5312\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 12s 483ms/step - loss: 7.5998 - acc: 0.5285 - val_loss: 7.5554 - val_acc: 0.5312\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 483ms/step - loss: 7.5130 - acc: 0.5339 - val_loss: 7.5554 - val_acc: 0.5312\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 12s 485ms/step - loss: 7.5236 - acc: 0.5332 - val_loss: 7.5554 - val_acc: 0.5312\n",
      "Train loss: 7.56791469802\n",
      "Train accuracy: 0.530470914127\n",
      "Test loss: 7.55535726547\n",
      "Test accuracy: 0.53125\n",
      "\n",
      "===================FOLD= 9\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 16s 647ms/step - loss: 1.5523 - acc: 0.5086 - val_loss: 0.8025 - val_acc: 0.4688\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 13s 543ms/step - loss: 0.8182 - acc: 0.5419 - val_loss: 0.6004 - val_acc: 0.6937\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.6091 - acc: 0.6784 - val_loss: 0.5426 - val_acc: 0.7250\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.5356 - acc: 0.7463 - val_loss: 0.4900 - val_acc: 0.7125\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.4662 - acc: 0.7868 - val_loss: 0.4074 - val_acc: 0.8125\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.4292 - acc: 0.8205 - val_loss: 0.3909 - val_acc: 0.8125\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.3749 - acc: 0.8428 - val_loss: 0.3172 - val_acc: 0.8625\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.3266 - acc: 0.8598 - val_loss: 0.3354 - val_acc: 0.8375\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.2995 - acc: 0.8596 - val_loss: 0.2333 - val_acc: 0.9062\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2744 - acc: 0.8813 - val_loss: 0.2334 - val_acc: 0.9000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.2762 - acc: 0.8824 - val_loss: 0.2279 - val_acc: 0.8625\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.3322 - acc: 0.8678 - val_loss: 0.3290 - val_acc: 0.8562\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.3262 - acc: 0.8652 - val_loss: 0.2368 - val_acc: 0.9187\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2425 - acc: 0.9052 - val_loss: 0.2321 - val_acc: 0.8875\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2497 - acc: 0.8893 - val_loss: 0.2303 - val_acc: 0.9062\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2573 - acc: 0.8961 - val_loss: 0.2510 - val_acc: 0.8750\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2552 - acc: 0.8995 - val_loss: 0.2325 - val_acc: 0.8812\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2406 - acc: 0.8915 - val_loss: 0.2316 - val_acc: 0.9062\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.2394 - acc: 0.9034 - val_loss: 0.2022 - val_acc: 0.9062\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2723 - acc: 0.8853 - val_loss: 0.2281 - val_acc: 0.8938\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2545 - acc: 0.8936 - val_loss: 0.3110 - val_acc: 0.8438\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.2411 - acc: 0.9000 - val_loss: 0.1993 - val_acc: 0.9313\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 490ms/step - loss: 0.2135 - acc: 0.9051 - val_loss: 0.2162 - val_acc: 0.8750\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2298 - acc: 0.9047 - val_loss: 0.2418 - val_acc: 0.8812\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2145 - acc: 0.9137 - val_loss: 0.2710 - val_acc: 0.9125\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2191 - acc: 0.9108 - val_loss: 0.2402 - val_acc: 0.8875\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2074 - acc: 0.9116 - val_loss: 0.2227 - val_acc: 0.9125\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.2245 - acc: 0.9147 - val_loss: 0.1987 - val_acc: 0.9000\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2441 - acc: 0.9047 - val_loss: 0.2283 - val_acc: 0.9000\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2026 - acc: 0.9204 - val_loss: 0.2174 - val_acc: 0.9000\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2087 - acc: 0.9164 - val_loss: 0.3045 - val_acc: 0.8438\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2062 - acc: 0.9163 - val_loss: 0.2493 - val_acc: 0.8875\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2056 - acc: 0.9187 - val_loss: 0.2125 - val_acc: 0.8938\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2254 - acc: 0.9073 - val_loss: 0.2419 - val_acc: 0.8625\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.1934 - acc: 0.9189 - val_loss: 0.1967 - val_acc: 0.9062\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 13s 555ms/step - loss: 0.1921 - acc: 0.9208 - val_loss: 0.1961 - val_acc: 0.9250\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1806 - acc: 0.9274 - val_loss: 0.2019 - val_acc: 0.9125\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1792 - acc: 0.9277 - val_loss: 0.2279 - val_acc: 0.9187\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1919 - acc: 0.9213 - val_loss: 0.2564 - val_acc: 0.9062\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1786 - acc: 0.9274 - val_loss: 0.2024 - val_acc: 0.9000\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1832 - acc: 0.9233 - val_loss: 0.2003 - val_acc: 0.8938\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1776 - acc: 0.9223 - val_loss: 0.2136 - val_acc: 0.8812\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1991 - acc: 0.9169 - val_loss: 0.2105 - val_acc: 0.8938\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1898 - acc: 0.9226 - val_loss: 0.1990 - val_acc: 0.9187\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1921 - acc: 0.9217 - val_loss: 0.2211 - val_acc: 0.9187\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 12s 490ms/step - loss: 0.1682 - acc: 0.9263 - val_loss: 0.2141 - val_acc: 0.9375\n",
      "Train loss: 0.140260875555\n",
      "Train accuracy: 0.941135734072\n",
      "Test loss: 0.196135535836\n",
      "Test accuracy: 0.925\n",
      "\n",
      " Train Log Loss Validation=  0.271070528743\n",
      " Test Log Loss Validation=  3.42418115748\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": {},
   "outputs": [],
   "source": [
    "#preds=myAngleCV(X_train, X_angle, X_test)\n",
    "preds = y_test_pred_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Submission to kaggle\n",
    "submission = pd.DataFrame()\n",
    "submission['id']=test['id']\n",
    "submission['is_iceberg']=preds\n",
    "submission.to_csv('sub_Adamax_10folds.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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k7bI9w859ynUloYbiFwQjpkPn4TD7Onj3fPtjYOjvqsee8BPM+Z0tXU+ZAf0u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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b86aba160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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87ewHIKo9fHoblBSVLe6XEM2zVw9i474snnzlVdyf31khcXiLqvLmgh28OG8L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\n/wGLwwHXS9UWkQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b08878a58>"
      ]
     },
     "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": {},
   "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 0x7f3b086e8908>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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59+YqvDjxws/5Uux5O0dLP89o7NehlY/v/rDuVzbw68kKv2/LufsezGGeV+KHcCvDFwsf\nO750msOl79Kvz1nt+TnLHC9q31WtlV9nlWOcl34vlod+nc3Cx65q+P45numqWbfZoqXDyNtbZd6X\n8dz/hlrxWfl//+NPaRPxUqHPr9DpVKwndDrFS5JkZm8ys8fM7LH5fH7r1wkJCQnPiZfKTPGOEEJ4\nRLWnmnZ2dsJiVf/qjYK/IA+3ffvjM884XE388yr+Wh9CzL8q/Wc7L/0XtBn8d+X+zH/xFw2fES3k\ns4bZwtuZNX2f/hZmGXGK0AIzaUwwO+34cXPMyJjKXl3ztNf8ks+asnPUWRap13Hu11yAJ3JpoJr4\n1GKCWWCOmaWtfKZS9H1cOpiR9wY+gy1G9bHTytu+iay4su1j3h16X6betAJmUDr2GaFNfcz3G5jZ\nou9bg7q/nB3y+0XPr2cyPjzz2kpMMbqY8XJmpwbGpagPCJipHRX+HA6ayIidgsrO/IBp4WOe973t\nQGZT4PxjnwlmcY1lghlh0fOLaIGFdMfPnj/ZXWXgXny8VGaKH9Xp/NSHdDrvNSEhIeEFwUvlpfjT\nkj7RzF5jZk1Jf1i/co4jCQkJL2O8JOhzCGFpZn9etY9gLuk7Qgi3NZAsVemqagrVQfDgk8reyfao\ngaDLihHq+v99hHCLyocqgAJPQF/mpdOabOrn7Cy87QKBnuo+p4+jFmjQqqYlGRbFl/2zo+blsdNd\nUuMugiuTMRbgM2+ziUDPpFEf3JH3myFvRplbAx/DEgGF0HW6VSJI1On4sVb59fcq/3zVjtHv4LSz\nUXHJws+z7Pk19GZ+ntzQ9+DXNmycE7pveTuTGHXNYAizxDORNX3frd3tk+35xCl+C5HbSQFjGfPr\nLLrer+YinnOKe196e0suGfS3TrbH2KfEX3CBwJStON/x9scDfB67UiISVEy938VNf7Z6O37+ebwv\n1Way55fGS1GSQu2fdpbTb0JCQsILhpcKfU5ISEh4UfCSmSk+XxQh05VFTSHB0tQGJW4hEnij51Sh\nHyncag4KBFHvcsuPWx45lem1nVYaTpp1fP/VZd8ngHqulh51XNO2FiKOBlqTt0DN0XYbGsAK/Z2a\n09AigzZy6e0PYrSYbVdLUkB/VGby9lY7iCavfLscefT7g9XTJ9uvanoNMOoqQxRVt1seze01fXs0\ndx2nQKXn0Im28DivclB/0MpfLnwpYd727QfKOtK71/D7U4AflqDJFZYpZkOPci+3EArHfIN6T4gS\nVM7rPk63fd925tS8NfFrOC6xTIL7vIOQNwXblc7WtRYr36e9jO2Aaq8VG5KU4VkZ3zjyc1b1Mkm+\nOvscL3WkmWJCQkICkF6KCQkJCcDG0mdJWmtSsxnoVst/BzqIVnYQXR4ta6q0QvTTusWZx9kW0+wY\nrXVMm04zDofDk+3LbadK4dDp2ahYR/f8PE38fBWgzIwAlhCgN0Dfc+jCjwzRUgh8FSOgjalH0At3\niT8VfR0UTpMFKrea+fkLLE28Kt892e6uQHERFV3GSPdqieUId6LXjhCpL5Gqh3S2BsTm3SVSBG/4\nmF96uH+ybVNEpY/rvo+hlyZ9J5j7vL3v18b9uTSyWPmYXscygLVjH1s+hu0cY0shwNjpM9NPtyBG\nX0EkXmHpZz45PnP/tfh60vHnM0MCQmvs4zMIUBzE5IaQb+acajOvKiEhIeEukV6KCQkJCcDG0ufK\ngqatepqfI8d42XKqMM2c1vRH/vvwTFlHJZugl20EPyuEH3e7zrcawen2ceVUcozo465At8e+T7+D\ndro1rVms4KgCyxSwYeUQBi9adKbxvpNWthHFLguIk+MSw5geZX2ncnPkPu8c4bcUubS65BSXzjia\n0YYFu2C7jALrVQHRNVx0GIk9ZQu2RAQfSxxzrDc0Ci+J3Ibwvln6ufJp/aewggB72UMktg0ui6h5\nA8se2sXSAPLTA/KpLy19yWSdTz458ueAucztLpYMtnEPcc+3KorxvSvVAiL5/GwnpWbMQ+/gNTBf\nwpoHmE6dvleR7r8UbAfvBmmmmJCQkABs7EwxM1M3Birof9hAFVtk/GlW+K/e3qqetU1hPzaCTqwB\nLV97waCD/5o3oY1b0KyzjdlJ24/NkX43iN2drzCDgB5vjhQ6w+dHXb/OAVIbBzOklkFLeO3GDe9X\n/H/rsqdzGVxSCpxn2PbZUY7Z5A4iAxlmuQiLnHhVSlKxhM9fvP75yNvuz/z7Dmbki67/lh83febT\n5iR3fMr+1fdpYMyxR7VT72O4V1nb7wn9HAu4wyz3fIwWuV9zVXFmf7YXp2bRDQmBs4wTtZs+Ft2B\nt1Eu/FkcQldI/8UlZnz9ls84V9DYVvN6nxXYQ4aUPwZuAma+rXYdjDllcLxBSDPFhISEBCC9FBMS\nEhKAjaXPWlZaXa+Jm2Hxnr8CyGLSDEESi4ayey3XtFVwFxmunNZcnzk5ZFoYDHjUIN2C4W2OBfgK\n+sAslgzIh36eRs/bWMA0lCmMfWjQMvSFJRN4wwcPXvI2o2YwQ+pfDvPyFa6haU7Hxkj5Y/2ZAuUY\n9trer+mBawYbcKlpruk+9ZhYmShZiwTuNWCsyqANXKfQ1SeCS82cxWtQ1iD25UbmwZ3e2BvfxtIA\nS1cw0LUE9We/hh3/xxxpgatB3U63ibaZqtjCfTue4/Nz6s/gGYL3sY7wjLbm3n4WSzasWNIAbRho\n/RRLOWs33RRoSUhISHgZIL0UExISEoCNpc/B7CTSOwcNm8mpwjaNV6ArzGNKG91lpkibontOgd8V\nms8G0Ncl6ntYH5oxRCKbcKFZrCOXl5ByhaAlq8N1oVPLJqjdgr4vkUbWn6FaHijeKI7VCHVR5kde\niyQDHW1te9g+R5SZZVrpnmNwj7nU2znZpvntIt6XVtPHp8povoqoKJQCGQx8q4n3ZQRdZV9w28F9\nLhChX6sFdgO0liieEqDvm0Bfqj3fPxv4vWBNnTYMb0PX+9IN0W0Gxrp0IOLzt+qiUiGuuXmM8xgU\nrFg9uNmk25F/0YupnS2WncWSBsd8fwZnqEjZbTPZc5opJiQkJBDppZiQkJAAbCx9zoPUm8Vi30dO\ng1qgpNOGf95bPLu+BdPsmse+73gX5psQyQ4QlbRtp1Uz1NogZb6/RInVqdPWYXtNa3A9XZTMrFDi\nE0LuFmq3CALzcQ6B8463ukQq2CRSNRZXJ2Um5hlNU/0RKrkThL0cxxKuNmWJ80ehPMtm0qVHiD53\ngp+T1DTHNtP/PpKh3CsuaSUf8yIeuwWXmG3UuZlWLpieomC9Gn6eFpYPStxPOt+0qDiItWbmqCFT\not+NG07T26CyywFMi6FEmA59OWJY+Jhv7buxb4YlhnGMKIcC9XdQVjcM/FmZtb1fWby3IYm3ExIS\nEjYfF+qlaGbfYWZXzey/4rM9M3uPmf1i/P/u7dpISEhI+Fhw0ejzd0n6R5L+MT57i6QfCSF8g5m9\nJf77zc/VkMlO6pFUMEhtw7BlgbxdFt7oFTVtKhpOWWYorrE9QiQapR8z7JNBML4FU9DDlYe8RxBy\nNyEkzmNEcWTeWeh71UDOdAOUkfx1NPbzsFTqk7mLp1eg3tm0plV9GNsWVzzKPDVvvA8BdFiijfuQ\nTA4sQDevLV1IPGv69VcxujspvX+MRPfhGFNCSJzjXgh9yRH930VJ1jnuaQf1TZplvf+YCcQY2g4E\n6KtjjC1rxEBBYFsu/J8jPzl0sdwQ6/LkeCarJhIAYGCcI9PAGr7PnEa9KP2q6z6O2Ud8+aAx8HE8\njrS9msKcuPDvGxjbGwPkckdVfZVtZvj5Qs0UQwj/XtLNWz7+QkmPxu1HJb3+Re1UQkLCywoXbaZ4\nFq6EEJ6K209LunLejmb2JklvkqQunEESEhIS7hQvhZfiCUIIwex8yWgI4RFJj0jS/vZuWNdVmSPi\nWwbQwCOnJMwnXvTrz5djjyAuIHrNQMF6sAhrN2AjdezRyjACNUdEbwlD0SnyXCcxt7WA5RfNcffm\nGAJEcOeY91PU3ESNmgw1auZD72O33Y3XhjKpiE7zuCXGKke0ODByiijzcO5UjiU2W4jWr8tssu0K\nRrElqRryjVug1XZOzZA5IsQB21MsmQxjRL2F5YgheG0LUfaxM2O1UG71VP0ZLBk0en7PGV23qCLI\nhjyPX8N038cw8H4ufZ9u4DOMewTBfIW8bbi1qYiJ81nflxc6yJ+fz/z538tcKbGO8ucXi2i+YHgp\nXNUzZvaAJMX/X73H/UlISNhgvBReiu+W9Ma4/UZJ77qHfUlISNhwXCj6bGb/RNJnS7pkZk9I+hpJ\n3yDpHWb2ZZI+LOkNd9JWaZWuNuvp/2mRKajETdSxQH5so6rpwTEspxYHTjVtz6lEk07NpdOgJcqN\nLuf++e7S64UsUQ/kwFOCVUTq2V85NRohJ/a4AwUy8mNb8IsqEHEOU+xDut/1yPnaAisMYVEFkXCF\nsajMx3OC/ODG2KnkTsPHaA8i6Dk8tVag6nmv3qdFcTWezmrmx7HmzAIO192u08AWXMMXKCsaIF7O\nIA4PcXmgY07p2/zzgF3Z1goR+uxsis/ZxmTuNJSWXuthLOBqzUh0PvXxnGHZJ8A9nOL19sj7uNj2\n53mGZY3tEvneN2MpXeSSi89h18dwgqWGtb1YtTpb3P9Sx4V6KYYQ/sg5X73uRe1IQkLCyxYvBfqc\nkJCQ8KLhQs0UX0hkMnUiVSb1miFvN4OoO0A8fXB8JEmaIw+4DSrbDshKRtvjZyCY7SGyCuq5hKdT\noKXY0mnrbqOmkh1EIm0Gmy1Q1hz5yf0Kom6Il6csCYpoaWvLKa6iIHiGfuxU6B8juxUjwaCS+HgM\nJXmBdsYL5ASfcpmOGxBgC0sADeQMLyCGLkGfp6XT5EugtT35uKxyWG3hnjZn9XnnGWgqo7lwpF6C\nSrYQZc8gdr7a8uWWo8zH9ONbl/3YKPaeweJ83EUJ0hHy6jF/sS3mhDtmKArWXCJvGcsdGbarqM6Y\nHfm4DVo+VnyGDdH3ZTdGzc+J9r/UsZlXlZCQkHCXSC/FhISEBGBz6XMltSc1n6sWKHqECOGw7VSl\nQs5rL6bndmH5tELEt3HVqRGtmyrYkjV1NvWc95z6HBjE08jDbcTiUSxKVECwTXunCrQm7/v5KYLW\nASLXTURx56Th9flz0KcFcmy7T3hfDzuIRGI8WznqRCOF+EaG4l4z5AqPnDbaoO77zC9BPTijr2DR\ndhN5uP3CD6BT9GTF5QhEpVGgbI6I/jqI2zuGY3qDVN7vTz6ESH3ubRQ9ZFFBmH8fljVyRM7nVf38\nDZc+tixm1sD9zOHkPUNiwALjVW37M9eCILyN5+84RzG06MjeR9R+Cgre8RUbtVq+1LK+y5mo6tgc\npJliQkJCApBeigkJCQnAxtLnIM9XniJXtbiBiB6KPlkTAtpINyanvaRPkNHhGhE6pvJWqIm7hHg2\nR3GrPg4IsPE6jHV66WI16Dh9KVHFaomc7GNYjRWFU9nmng/A/Uh+LVGneBrbzNrIWUakuHvJP58x\n/RyR7SPYWDVyFMsaIVc682ueo8Zyd1W32cIjOYdNNl26949x3zBuzKteIrBOG7UxCleV3WfXgG5s\nedu9KYTZiERX2z6eJaL55dSvpw+BdUDu/XIB27O4lNCb+/dP3Ac3eAjJm6j1TPfwfgO53yvv4xhL\nDM0D5Mfj2ZlEB/msA9XCMYp/QUlvQ1jOxftPF/lNQpopJiQkJADppZiQkJAAbCx9lkkWI4YFKF7o\n+CU3EUVklahqUNOKGUaHQuMSNlqzijWgkVfc8rCggfoc30QtZYiKl7CaakXqtWr6Z9NDFCVCweZL\n3e2T7Z0VBLtTUKYdiNQR/RTqHocoVO8hgryQUznm9TaQMzyF2LsfvL3Gkx5xZtGteYHo8yXPA1fs\nVwHRebODaGrDtxeImhPHCHlfFiLBEOzPOhCyY5d+NKduIMrP+t4VornjfR/PAlHpovS228gxr+CU\nbnjk1szXcN9aB24CxSWAfo7Ojrwv1wt/ni7veIGqgKj4CgkDtGubxNxl0uAcdm3LpZ+nwrLPLNrY\nrXSui99LGmmmmJCQkABs7EzRZGrGNKomFqYXmCmUWEieTV37tU7pGu9ADzj2X81uBtNQjCB/Nyuu\n4WN2ULTO/rE/AAAgAElEQVT92PkctTGYxhVnSKzwOWn5vl3MSCfQlfWQtmZIlzs88tlEC4vxLZiV\nZjGQQO3eANPndfBHkhrQMnY6PoOZIOVOl70vLaS/zTFTn/f888HaveWan6fXgWVLC+2hDCnTJlkj\nZQkHlyUiEwtstz12oOa8vtfLNoIrCJzlHWgwKc/DNo+donzqIPiUr4NJ7ngenzkE+S7vXfLvl97G\nEprVNtIfL0E/OLrh95llUAu4OvVg09Ob1U/sHDPCJWahAUEn2/H73Fmb41rSKSYkJCRsPNJLMSEh\nIQHYWPocQtAimr42kCKV7Tv1GmLRv4lCV611DRDq6LBAjUw55aCgAa47gkEoaRUDPQ0EVww1ONbm\noqMMXAs6ucYCv2VIbbu28lKWHaQo9uBSI7oBmdOzrKzPX8CBZ7vnJrTtplMw6ieFgAJT4eYzp8ZL\nmKgWkH5WoLtrI+AGdJI3jw5Otvt970sHj20TywElKPNNuTaxMYP5K9Y1Zqe8h+vPu7if1sK9atOx\nB/cTAa3V2JdgdieoRQP94nCPesdmvAYEl570fueds91wWihTukIApLjPA1dTBOPaCK6Vh0itbD77\nzz+ghlADQZcJDJS7MSUUEtmNQpopJiQkJADppZiQkJAAbC59VjhJtcv6TglWx6gpchn0rQ0T2cir\nHijdPeSZ3GlNB840bVCI1gQOI4jKLlBi1EBfF4giLqF3XDvCrKAN3BvBDQe0Toysg+JRV9iBEWyJ\nFL0czjzrlYRdc5o8XSFVrOlt5GhjhWhl68jpYwktX6ci3Ue51zHKqcaIZsB5Olj2WIxgsgva12jh\n2mggu4BLEdxuSJ9pVjtRTSubXT+O+r5J5UsNu4baNl2UPsX1T6ExneNeLE6lJdafz5Ae2dvz5yZf\nBewLdx2Unr1W+DkHc18aGnh31QStn0Cz2YnLNPMtqBDw9yG4JLGKeiu691iWos8JCQkJG48L9VI0\ns1ea2Y+Z2c+Z2X8zs6+In++Z2XvM7Bfj/3fvdV8TEhI2ExeNPi8l/dUQws+a2ZaknzGz90j645J+\nJITwDWb2FklvkfTm2zWUVVJrGukHXHIWC6cHK7iHWAHhcxRn91Cas8t6LSgrmaNeySnT0jFEyPsu\nQl6CPuVwz1kgEp1H553GyGlP2AIdBGVcwnx1OfXIIsXOa9NaSVpAyc4aG1mMNA5BEw0i8QoC34wq\ndaRHZn2kOd7wdloD3AC4CrVBZbNI5WhgK0RChZS77iGuuY30SIigWRelSeNWuO0I7jSdeOyh4GJD\nZTZq4Rw1nMrTZHiwDeo7QYogitesQH2z4Vq87ffhOjIfm0gb7AcY1badJm8j+j/BM2coHzvrYSkF\nxr3rujdbCMMv+n4c0/8q9HEeEyDChZpSvXC4UJcVQngqhPCzcftY0gckvULSF0p6NO72qKTX35se\nJiQkbDou1EuRMLOHJf1GSe+VdCWE8FT86mlJV8455k1m9piZPcbUpYSEhIQ7xUWjz5IkM+tL+meS\n/lIIYWjIsQwhBDM7054jhPCIpEckaWd7J8yj201n7HSrQpS51yH3g2j3DFHqFuhLOUFor+tt5KBv\n0ymE0TOnwRloTYARLanS+qcqR9ScUWPmcmsKKgn3mAbE6AcdqK0xcq2APORoxNoEHRsh97Y78/HZ\n6znHK5CgPRmCendoJguRPMZrRbed7diXoVPTJZyBTgvQQfuxPcPyxX7p56kQIS5Bt+XiAimKzVsQ\neldYmqAzDTTiWjVZ0wUJAFAWTNEvGA9pHiPUuDIVIywf4JmcNpx2L4ZOk9tNv07W5WlhyaRaQP2A\nedBq7fYE1QIMe6S579tmv+Jza5vpMXvxZopmVqh+IX5vCOGfx4+fMbMH4vcPSLp63vEJCQkJHwsu\n1EvR6inh2yR9IITw9/HVuyW9MW6/UdK7Xuy+JSQkvDxw0ejzp0v6Y5L+i5m9P3721ZK+QdI7zOzL\nJH1Y0hueq6HKgiZZFEHDAqqAOFik5eCVZcwnpeFoASplsM6agBrlLOmCtm/euOH7sCQpTFS7oCqd\nWDvmaAu0/9ApeJshX1KmHeeDBlNUWpDNEQkVKF6/WVO/AuVgKV4uYbk2hxh6gajwgp8fOsVr5S52\nHiMPeN4iJa47GXZ8bAuIh22MqDmWFQyWWvkNz/02jG0TOdYVIvRz2MX1Gu14DVABMN984P3KUKZ1\nfuTtDe/zy+li0DPkp5uREtf3d4pnq8WVGagfGjD/bSDKT/q+PUZeNaY7GSL+Vds570GoT9Ys/DxN\nGA9nMFNeIoKvExu9zTSZvVAvxRDCT0rnFpN93YvZl4SEhJcnLhR9TkhISLjXuFAzxRcSWSW1JvX0\nvrfj9G0Od2hSxTlCactePSyNm2fnrxpFv2iDrtaHYLj5DsqTIrS9IPVDbRKLtGTvEJQ1h9s0KCZp\neoa84nwXodWA/lYoW4noZrNfX3O4Cbs01FxZIGd7gros+Rj0mXVm9l2wTQfvBajnzRy54qv6XKs+\nIuJwRmeONctwFlAQMFo/P3D63mVJWtpxNX1MV5G2UhFwCmhjjntLWtmC83kLAuuqi0hw6X3fs3qf\n1XW4mrcZi3YEWpeB1q5QMpW2dIzKV7jn1ejZ4nU8WuqM/VlZIblgjGuYlvUzsgqb6R2WZooJCQkJ\nQHopJiQkJAAbS59lUljn806dnpQoN7qFIlL5FNHNKFSdg2mVLFCFUFAHLtglrLYKRHZZqnMOu6oZ\nqM+1BnKFY1+2C+c1YZuqWtAxiLrnsMTuQiQ8wuctFsjCMsC6MFKx5ecsIPptgvZWEIzbwGn6YuqJ\ntSVo8BRjwXxaw1LCuKyvvz/y8zeZY96DpRnsr4bBx+3SEiVe4QJ+AJo+Q7R2sCRVrdtcXfNr2O97\nzjrVCTeQS9zv+PVfL+H2DSrdXqEAFBQNs3Xfr/i1TQKE3qCv2dgj6zM8gMvK9xlM/ZxNRL8rXDOF\n751I8VsQZpewMcvgHt+r8DxH2p9v6JxqM68qISEh4S6RXooJCQkJwMbS51AFr9WMyN0S9GAKKpdB\nHlnESHAJetmAI3IAZZ0cOzUPR07lmn23fGTe8vHSBcNC0aM+bLTKKJoOQ6fXYGCaIVIeEFncApVr\nIMeYztfV3OnZzabTysuhvr78hvdvlp+d3Joh+t3JkUve9ZzoEmM7N7/OBvymBlifWIuzb/aQJw5b\ntnZO6yzQUbBNUumtSyx05fe2+7RH16sK9yJGrnuXnTKvUCBqiagxC3etIDDftt6Z+49Q33s8dIqd\nRQf1qoMkAUTTWxmc4Y+9302I3g8zuGrD3u7+4H3pwTe7WXj7R1Xd5hgX1Mc9aVzyNlZYPuge1c9N\ntpna7TRTTEhISCDSSzEhISEB2Fj6TJQIMlJUS+usGWpAH0V/p50hihzRsfoUbfB/LLcRZUbEe2GM\nUDvF6eDzFWpD7xzXFKcL+rg8Rk5yy7cbhZ+zcQh7K9b0hddVS35N92cQtce85ca2R0LzgGJaiHKC\ndWuFOtHZqYJWfv35FafVAQLzMWhlYy0kBtXlL3Ypp4mjA6fJJcaNbuMtJLx3lojKYrxGFUTose8z\nCJln4RjfI4LbxlIL+pjBvZ11t4WiZLaLZyEqIUbXDk8+W/V9PNtdvz8ZqHwT+eb3bW/75wPUtx76\nPlxigYvbSb774cLv1eyUzRmE3njmD2P+9HJDp1QbelkJCQkJd4eNnSmG3E7S4RY5Zkr4GQglyk1C\np3i5Vc+WjrfPrksyR50XwT2mQNrYwcIX1PMjnx3trVBf5bLvP2zCbSemlFX0wIVpawcavBX9dq/B\nYgXpenPMgpdM7ZO3uZYMDlHLMsdUYIrgzhSBk+aBj0W34R1uoy7LDIO3xEwsw6x93q2vv9v3xX3O\nGks49mzBTDcsfNyo+1xixmNIv9QMZVsx+8ujlq+J2AtdYnKUGJ3g+udIraPTUZhhloVUyAHmIUVW\nP2cf3fWxPRwNT7Yr6BT3Wj5rZLBKYAfj0q+/M/ExD5f9XrA8bTsGt3ow1p3geV7QUQmmtdrM7L4T\npJliQkJCApBeigkJCQnAxtJnk504hWxh0XsFKjc9pcODI0ukXnaMxWrsWbJICuiGIaAx6DvdKfvQ\nFYLhtmHoWWROPacx5W/JzL4mgitY81+OoGW8zw/gckDvKafy4UGnUkaz1niFOa65CdqZ4fdzQiq1\n63x7jvQzQ5BIE6esNEXtGIxTY62RQwRUYBykPqxcprgbbaxkbPVxbQg0LWDyWuE6Rl0/wU5c4hgP\n/LM2UttYsnQEN+E+asEItH4Mh58WlkmaEJyuIn2/Xx6IaiNwNC+9vQULoqDOUOMpBJ3k2wN5wEzQ\nns5RQrYRTWyt5f3bxyshg05yhWf+alYvzaQSpwkJCQkvA6SXYkJCQgKwsfS51EpPqtaZFaSpmPMz\n/W0whX4s1szooS7GCNFhlr5sIVpZwny1QLnLAaK8XdSIaWL4pyOURI1awjZcYsAq1YIDyhxtH6D0\nan4MV5uBn3MYvKH9EaKlse4HI75h4vStAn1HRuIpk1umvAmavQYoY440w4OFR1p3oiZvF6VUaWJ6\ns3F0sn3FnCYj4HtKP0lt6vo5kKQCyx2D3Cnm8U5NK7ulN9hCxHkFp6OdhX9+kPs9v4To+xao9zHq\nvtycuybxgWmsCzPye9JoomRrH+dZuZqgb053yyt+zstTvx6a2VrXn5EGTGZHW2sq7edpwjRZY7+2\nEimE7Uv1WGTUbm4Q0kwxISEhAbhQL0Uza5vZfzSz/2Rm/83M/lb8/DVm9l4z+6CZfb+ZNZ+rrYSE\nhIS7wUWjz3NJnxNCGJlZIeknzeyHJP0VSd8YQni7mX2rpC+T9C23ayi3TLtWU9gVTFGbDad1XdDj\nsgVKXNa0YL5C2hwMZHtNRLM7iESjdojgmBMa2B8lSeFDqoBo7Vw1VemgNCfT844ymKZWvp1nvs/q\nkp+zPfQ+XoEge4Lo+zjWNNmm8SrTI7u+HDA7dCorpJCxFo2hdsm8jVKpXZif3vCxuL6sI+QDUMOA\nZYr9hXemh7Kidr9f5wL1dwwcf4ASnqdkBLh1vaxup4Qwu4IFT7NEVPoYQvY9//y45Z/3nbGr9STS\nJbs+/jejwL+97f1bwGQ2b/u1bSNVsUQtnDkE69XEz1+glO5eQKnUfdzHazfra5D3b9KCAxHceDpI\nHiiHNd0Pq81UcV+omWKosdaPFPG/IOlzJL0zfv6opNffg+4lJCS8DHChXoqSZGa5mb1f0lVJ75H0\nS5IOQzj5CX1C0ivOOfZNZvaYmT1WQjOWkJCQcKe4aPRZIYSVpE8xsx1JPyDpk57HsY9IekSSBtvb\noRWjhJ3Sp/4tiIADfhOmiBAeZjUl4Yt1CpPP/r47k1SIFhZ7yNuF8niFfNtlCXoGKj1HpNGO6r4s\nQW9niGzfMO9Xv+lLAKzRUZQs5embVReReLTfjpHeRo7vQdmrYz/noOvR3wqlT1fIFTZENLcQUV4e\n+nUMkTecR0PdvIE+uXmLOshT1w6cjg58bOeXvb8BfWlgHSAgKlvCMmYel0daiJov4IYzW0IMjz+b\nfaoW8NdkeHYqLBksM5jVDurrGFdn50l3UNvGUE+oBVVAm2VoIYzfauNZtLPnPt2H9uvzoKzr9ZFH\nx0MPrj8QeLeeqvtoIUWfX1SEEA4l/ZikT5O0Y3aywPaQpI/es44lJCRsNC7US9HMLscZosysI+l3\nSfqA6pfjF8Xd3ijpXfemhwkJCZuOi0afH5D0qJnlql/Y7wgh/KCZ/Zykt5vZ10l6n6S3PVdD2bJS\n6+k6SmaM7sEsNZv5dgf5ufNB/VtRoMRoZ9+3u+SjELseIvc0AyMqELkUontL0Coh0tpoxUgo7KdW\niPRty6lUE+qkHJQxQ0nSBSyo5qDBAaasWa9up6JzKHKf+xkE00tEv1GmtIlrbs2xHICg/AxR0QEe\nv8WVWCNmCfE4otbHWKYYNvxE3TFKhqJf7ad9u4d+qQ+7tBYtYmtUMMG1DDZay7MjraTYwv4spct8\n7xai4vm8btNQc2ZUQE3wpFPZ7D4f/1bfn2fD8gX09Wp7uruWVCgUqNfzbO222i1/tpYQ2k8h+l9t\n1QecEvFvEC7USzGE8J8l/cYzPv+QpNe++D1KSEh4ueFC0eeEhISEe40LNVN8IRGKXFW0ycr3nXqs\nUHpz9ozn3m5RYD1BonFE3nNaUSEn95TVFlykFjMIjFlitAfKmHlfJg2nODtRhN1Gji2pbANRYc29\nL2HL265A38KMebCMLiNaHNsp4FBVwWZtDLssdFtTULMVKOblA297a8/LhvZQI+VpeXh5q4r5tHAV\nX0I8nyH6vDcH3US/Oqdcs/08p4KviJiWV11hvYzXmsNOrTxAgyh3O0aNnGzXBevMt64GsA7j+GP5\nZCsK4nmvctzPGw3WVkHOOi3XsBwRMP7HqJ0zHTmXbmNMGzG6nbeRj99EPRv0ZYoVo/WYV3R93yCk\nmWJCQkICkF6KCQkJCcDm0meFE3FuE7nPGehzAWfhDFHk3Wj79QxKXHYmsLRCjm0A3RDspbotCMZh\nV1YiEtxzPbg6EJWvtcZhH8JclJtszM4WzTYQcS4hvD3a8u22gVY6w1frqL6m5cwj6FUf5VNzCHmR\nh9tHmVhagjc7iFwjip9DkDx6/OBke7YfYr9B5XDODMWVwk2nkpfvd9fqDNe5QDQ/oKATS7Wq5/do\nHrXOFHTnHB88K7tdp8wB+eYlEqunkB8MUQ1rG39yN6IfXBN50tsofrbV8+3xwtugGNsg9l5NUDIW\nSzY9qMoPu06ft9ZLGchfx6N9SgDfxlg0ozD8gxs6p9rMq0pISEi4S6SXYkJCQgKwsfQ5C6ZWFCJP\n53CkZv0fFHcSqNpalDqW77wFAXBAzu6sD1oJOtyFqDogShdAMRm5LRC6Poq511r4+bdRfGuJc65Q\n93hZQACew1Uc9CnDUgJhMec3z/w8Bagh86DnqInUGiOvFzm0zINuwCm8h3HZurx7sj1u1/eICwMr\n0NGAYk38JV8hD5ju2Ky7XFVnW5cxcruuYTVFHnKj8L42Ef2eIxIbWMca1wknOu1iHH9+edU/j8Wl\nBnBY34K1XQMFwqoRotksXIY63gX62x36s1PtIld96JHo65MbdXuXfAmij8SACs9WhWWiYl1ffJmi\nzwkJCQkbj/RSTEhISAA2lj5LUh4ZR0ABogZ+Bxq7ThXGT3sk9DiKswcd50Yj1PHt8acE+a4VclwX\nU6dhzE9l9DOA1jVQm3gn2n7Z1D8DY9VEHgrMUd9YC0ROx75k0GLuNWjQOHd6NIuX2gDV60AwPqIL\nNWjauOfX0534eS6jx1sLUC/ci8ttt2BrRto2mvkaxBRFphYYgAWi8kuIlBfYhtm0DCLoAAf1JfKp\n24t6m8XMFhCSUy+vQ9SmrsClWUcbFmTFyMfo47aunGyfPAvo00S+3W4+OzdbkkQrOlwbLcoy5JCv\nVig6te9U+cFQ0+ol+rrEcyM8w9qCw3m8zA11DkszxYSEhAQivRQTEhISgI2lzyEElfOYo4l5/gq1\napcQ6jbgmt1ZUxhQzenYad0CEcoMQ8i81jkclpdzREJZJgHWTEitlcWIonUgRgbtnCKyWyAqmiF2\nW0EkXiCHtZwjJ3vPlwfm0fHaYFHWQY4xBcPTudNHFrQyUNbrEBt3katbwAW7DRq+ZvgHHR+TUQCV\nG8L+CtdcoF/LAtFv/Nx3Cj//BELu/tKvrz2Jx2LcFh1v79Sz0ncuvzSM83WIp1HoKUD4zL+4SVQl\nzFGIigL8HFRal532zuHe3hhDQXHk+1+rPK98v7l1sn0Q/Dludeobhvpomue4TkT/DX8L63rkqe5z\nQkJCwssAGztTVGbSoJ4hGIxDqye9PGc18KlNQHnO9QyJJSORNaZhA7ONqQ9hAyUpxVon0O9lCIwY\nZh83UBujvBz7hV/nHOmJMK855VSydejtreDGMxnQ8BWzybFfRy8GdXL8TpbbCDTBPieHgenqwGck\nC1znFsqKTpswooUR7KD0MW826nPNSxjYTv1EAVPpBtxNV5hNY0J4KhjVQ6CpwAyO7kXLXr0PJm3q\nYUZaFhhbBMuamOHPERjKEUSjY08bAbU8BvLylfe1XGBGDMPfAJ0qS4saHG507O3sjfCnveebu7kz\nouPIeAwGy905jIpRg3eMFEJ164GulHSKCQkJCRuP9FJMSEhIADaXPi9Xqp6uTWRzaO+yXacPhrQw\npgLO4wJ3hu9zmKP2j+B0gzSrJYILAUGUJqg0l6Yr7LNbeCrWMNaOOUCg4f6lU80u6p8c90DH4DCT\nIRXxGDVN2hW0dKBb7Xh91CmWoLJzBCjGTe/3ABSvBJXfwdixjskB+Gn/KihZTOOji1EJzRwDHdty\nWr/E/tR17sGgtzqAmfCr90+2J9A1Dif1MsCg4bR/xZKlzI6EfjHASSiwZCr2X8JwuAXt6yoG4BoT\nvz9Fx+/PHNrYxdwDJFtIxQtLHItntEBNIephs8IvqhmDSjRNbjahR8R97o19exWXCUwp0JKQkJCw\n8biQL0Uzy83sfWb2g/HfrzGz95rZB83s+83gKpCQkJDwAuKi0uevUF3veS3O+j8kfWMI4e1m9q2S\nvkzSt9yugSo3jfbryyvmTqua4DUZ0ryKNmhLjHROF07fOi2nVS1oxvJrjMo5fZmjlGQpRAtRVrVC\n5PTpqacZDsqa+jy0dApEWrkaoNwqIr5Tl6Od6A4lqY2IYoW+tHawlFDV7SyHfs1HiOBWA/8d2pki\n4lvQpcXp1I2mU9Ml3GMuy895YxcuMJGeHyMSy3opC9gbzXE/F3iCc2j82ohQLwcwhT3w6+vvQli5\nW+9PHWte4XqmrlrIUVa1vUS9FBgLM/2TzkwLpNytU/RCQKndoX/fwpxltIPQOtx9Cro3YYpzgBTO\nraU/l6OVX38ztm8oazqCOW4xhEsRrqcZhY2bSZ4v4EzRzB6S9HskfXv8t0n6HEnvjLs8Kun196Z3\nCQkJm44L91KU9A8kfaV0MqXZl3QYQlj/9D0h6RVnHWhmbzKzx8zssXJRnrVLQkJCwm1xoeizmf1e\nSVdDCD9jZp/9fI8PITwi6RFJ2t7eDr2qvrxpg8ahTj0ZdQsw1GxGS5R5w+kDy4FWEO8uLzsFy2G6\n2SwpNvZjc5iIjqYere60vF/rVLhy5FRyfuhC5n7XnVbyU+mBTmjaiCILaWElopUtLB+sS2vOKEBu\nOK3KQZamEy8qctj1NrY7vqxAl5wG6O644+e/kXlEdZ7XdBunFK1plub3aobob4bxZLrcCqVnp/Lt\n/ZavMZRwNeqsIsUcYjkEEXzWtlETomUK8+FSFLCUQIlzF8qFVVQLHCA6Px55X3tIiezO/JoPuhCm\nY5mgd+Btt5DyeIzUyQLi/Tw+c6fSDPGslrCDCoigN0f1cxuqzRRvX6iXoqRPl/T7zOwLJLVVryn+\nQ0k7ZtaIs8WHJH30HvYxISFhg3Gh6HMI4atCCA+FEB6W9Icl/WgI4Ysl/ZikL4q7vVHSu+5RFxMS\nEjYcF22meB7eLOntZvZ1kt4n6W3PdYDJTijPECLUp3IIood++SVHItKzSyjGMR8hDxcR18kA0dxD\nGMsiJ3XeQpQVNVqKobfT6SKKGQW8TWejaiE6PoTtSoHj+gsY6IImhQolLkEll6CPRbumUu1jCKaH\nTm8bOE+57UsG24gst5FjvGj4mB/lfs7msS8ZUHi9Nv+dg2zO4UCToTAJHXBylPjsg+IyWk791mwK\nwTrqy6xrwByj3zmUCnu5LweMURt2hftJilviGWF9mRbKqi6j2H2AWkHDtueSH5iP1f6UiQTIn0d+\nfLmD8yDH+njkkfMphNzP9KNCAKLvB6d+b5ug71OYCS/isg5F4ZuEC/tSDCH8uKQfj9sfkvTae9mf\nhISElwcuFH1OSEhIuNe4sDPFjxkmrTWrg2MYyzK3FtrdFvKTs2hcukJod9GHYBamrW0EKxG4UwbD\nWUNO8BzGodUe6B6oSCtGMVcrP27YRi0U0JoWKDNrdzCimLP0JoW6oLXTqHiqoAbOoAaeBf98ADdZ\nUqjRodO0WcOvf7tw6nmUoaYKSrwq5pDvIje7AWo83fe+rKAaKGBvRXsv5qoPzc/T2fEc8wlq5zw1\nuilJujSDzdpllAaFYBu6aHXgg1uykM4Sgv0+KDN2WcTrqGDzVnRd3D498pztIWqm7qAuDNKtNUPr\nUwjv983bnGP5ZHtcj9EKyw5L1Itp4fXA5zmPFnmGyP8mIc0UExISEoD0UkxISEgANpY+VyFoEe2y\nt9tOH/Lcp/zPlMhnzRCJjnbKJfKHu4j+tSFarUAxl5ecblHWukS+axOCZEMUsTeEs/WNOtLN3Oge\nxODDrrfXQS4xy50eruCIjbowl3IvK2pYBmiuH4USUVOI0ecQAy+PEEGGMNxAa3uoHdLwU2qv6SL1\nIaKys9h+CTpagcqNmLPc83FuIg9YEyYZYznislPyU8sUfhnqT6PzNu3iMD6LAT73odWMJVPhKt4w\nWrChdgvqBa1d2DN8ViGJoIVnMoO92BwC+ALPE5d1WAvnMPiFtrF/+7gerxXmRtdy33cn82USxpm3\n4uVkmxl8TjPFhISEBCK9FBMSEhKAjaXPwaQyUrJD0OBtVDfaH3ke7ApFnPJxvX+GvF4KZrOV06QM\ndHwKktFgTjLswuYD338X9K2A4/Hq1XUfc+TBDoag3ejXBEWMFnMXmFNUnKOg1nzsJ6UgehpDqiyE\nRdVzbwrrKCwZtPtYMkBfGhBJa+zjFdpOyWZYVmhEWteYgaZjbO/DMkGWI0EaOd4N9GsFGmoHzNtF\n/Beu6VsnSw+4fqQ7z458bHso3NVEGzlyshmXNXiTlLjmVVn3pay8TyU4KfOtuygHG/AsTpGHbVjW\nCVjKyCD8z+DaXsU5UQnrMt7naQ+RahTCqtZR7rCZuc9pppiQkJAApJdiQkJCArDB9DloGnNn5xAJ\nr49WWFkAACAASURBVEBJr2x5QdwmCgblsfaugerQOsv2oPomZaoQiQVlpqVYhgjhKZfjAAoV9+ng\nNyuDeLx/1a9ntI18Y9BqRrkbEDLbFvZBhDaL1KsCBR7DebzqsQb0yaYKjEu5wJrBA97fSzf94wpF\nq+fYHs/qkG6z4TQ57GOc0S+1ve0hxryLiC/rQbdGoPW4jhJR+fJmzYkbff9+SqE9cpkXO05lW6wp\njXzjQBoOIXeOGtCztZB66OfZb7kCfNakyoHicd+GK5hK1MY2nKeDiL9AlZsxibwLx/hp7n09OnA3\n+COMRTsuQcGYfKOQZooJCQkJQHopJiQkJAAbS59VSY1pTRUaLLoDyjiD8zPr3VrM81zX5ZWkA0R2\n95setc5J6xB9HiLHtw2K3UchQrocHy5cETyP9ZaLFcK/badVKwiJW7i2bQR8mZfKesAjcUkAdC+K\nwxkJ7Qc/boXr3EL0d0mHaUS51/njkrR4Fc6PWstL5Da/orFT9xX3gZHwUQtRW1h60SJrBYrHJYNx\nz/venaB+Mdof319fU8GIdEDOMq5zGfgMwbqs6edsIK07tOAg3gWtj67Z3RVqNE8Rke/6nyfqYykg\ngt6+DsE4ljLmWPqxDDZ2CywlxbFrdDk38jZ6A3/Ol7CUm9+ondcDfAQ2CWmmmJCQkACkl2JCQkIC\nsLH02SQV+e0vbzp0SnytcCqw1a9pQwvu1bvHTnvGWy6Abl11zhpAd/rMoWWEFhHPPPj+DVhtrZ3C\nR2OEFuEIfVVOtQewDO+t/JwFHJnXdXolqbmEvRSi8mWkng3aQYEC09UbpZFVIve3jaioGGWGI/SS\ndbcRLdXaugx0WLDcauP8E9iY5YgKr2AFRpF0MYZdHJofwzqtOKjHooe86nkfhchQU3pcMmkaOcby\n8bwcULPb99ZsAdf0KDanXr6ERVwbNmY5KPgqcFnh2e7lkmRQWRxVbkFWkirHMWodouDVQ7sn2xWX\nego//zqCnn0wWYclJCQkbDw2dqbYsEw70QzVoBMT9GszpOvtIqWsNa9/KwICLdmWa+YylNucPoAS\nm1jE7sCNhcaiK3y+QKCjwExxHYwZFT4j2Zp6K5fnfs6MMwjoB6vS+9KEsSxTx+bIP+tl9TmnmIXd\nWPr57zefQY0zRHQQ6DEEKebU2KHWS457cb+QIhhnXDdRT2cfZUo18+sZIKBTbHsAag7N4nzi55wK\ntV68xVu0nPXMrgl2sMQMf4HSr4PAcrQ+tj3MrIall4GtMMsa4N4Vq9gmpt5zg1Ew7mELbdPpKb+E\nQj54tqrSWZDhOd5t+XPciCma1GMGpGRm6Bd1ktk6cGibKVRMM8WEhIQE4MLNFM3scUnHklaSliGE\n32xme5K+X9LDkh6X9IYQwsF5bSQkJCTcLS7cSzHid4QQruPfb5H0IyGEbzCzt8R/v/l2DYQqKMxq\n2tDoO2VYDZ0S5izxSaPPmAp1o+HarH24tBgDJwg0NBvQqcEglAvgTbjAjFtOd1Yt36c7q+lRf+LU\nLOxCA4gAhKGOC8lMCfPXxRTOrTmCJyvv40GjpsSUnu3lfs0TXGev4ZS1FbB8MHXKOkfaYgvj31ui\ndg20jxbHfwuBo+tb0Noh/ayDIMp85uecQYPJsqb5db/nq/v9moSlhzwuiSyWMBZ2D2LlWN4I0Jcy\n1W2Cmj5bDafYYwS3eL/KeE1VE65DoNcBxwVcf0CaYyihu0RdnqqPpQEEbEq0eTCtKX6JtMXtBZYP\nsBzUhNZx3ZNQJZece4kvlPRo3H5U0uvvYV8SEhI2GBfxpRgk/Vsz+xkze1P87EoI4am4/bSkK2cd\naGZvMrPHzOyxBRapExISEu4UF5E+//YQwkfN7D5J7zGz/84vQwjBWDP09HePSHpEkgZ7O2G8X19e\nD3q4VQc1VVakJ8+OpBW504oS0d8CtUAG206TrAczV9DHHBHNBUZ8gSh2E21WkfYH0GS61xjTFkHT\nF+ZthKFTySWizzno4QR0sx+jqM2On3PENEikHDZHMM0NLFOKKCoiwb2V020aoS5A5RQddoopUvJ2\nkWaIZYfFCtHvGcyBsYCAQKxaV7xITAMqgiNE19eGr+0StVVQyzTH8grT9phyOKo84psVHllfTlHW\ndYRzPlDv04amNaBM7RKONgHLHhVqwbQmuNAtGMHu+D2/Nndd6/7Qr2OnrO9phfpD5T5UGE2/bw2m\nU8bB3dDg88WbKYYQPhr/f1XSD0h6raRnzOwBSYr/v3rvepiQkLDJuFAvRTPrmdnWelvS50r6r5Le\nLemNcbc3SnrXvelhQkLCpuOi0ecrkn7A6nl5Q9L3hRB+2Mx+WtI7zOzLJH1Y0hvuqLVI5xYQ77bB\n9po5ImpIb1tGUXWTdMyZhBaIFlIwXVZO5Wagj/tyytYGJV/AbWUKF5h1vZIhypTeX0HoDAH0qgDd\nAn1uNVGX4xB0ExR/iWjlyOoI9X4DBqrmSwOkfROapmKMjpE2dwVOtCsUrClb5FxIS4sU8mjfr2fr\nJgxRIToveT1AowcrmQ5K1iIVkPeFdXQWcVkFZUlkoKxzLLUUWD7oIPycb/t9nrKOD9Ilj5GueX9c\nsmjAjahqYZkAhsQrpBkGuC7RHHmJJZ4cjj2XWy7w3kKNIEUTnBkGog3D2xLqjDHC+cWi7uNmxp4v\n2EsxhPAhSZ98xuc3JL3uxe9RQkLCyw0Xij4nJCQk3GtcqJniCwmrgvKYO0vh7fEKDjeoXdFFHuza\nOHYCkTAjxVPkp1aISp5qD66ghoiqgco3QUNLuMqsS4hezp0yPx2cSlegMtBfqz/337ii5yLlsoe8\nVZTH7MGlpx3Pvxa8S1K7hzWDNnK2h+66EkBNKSoW6F5vgYgulh6Yk5zH6P/O0i/uAG5EDYiel1ga\nYAi0gbo4O4hWF3DvoUtOgHvQbl7fu3EHEXequppn/6kM8SwMG97fK7h3zDG/X+5Ck0d6PMK97UPc\nzqWJZQ+id+SYL/H8daZwEjq1ZII1HigESj3bJDbA9Yfi+oDn/yQBYEPDz2mmmJCQkACkl2JCQkIC\nsLH0WdLJ9H4GIfUSAtfdpVMFUqVZ/LgJemkTpyPNAUStbRi7Ts6Ox92AKeyoA5q89N+k1hAHrKk0\nAoW7Ha+XYQvajzkFutry8xzIE3fX9U8k6XDp9HArcxrajCauk7nnSfcKlMaE6Hp0SuALLo9o7QKR\ny3wFyoZxpDg6rCPxiP72YJG1wlj0QHsFSyvr+U6jnrfdhV1WNoLYG9ZkWVafv9iCYB/Gw6cj/n56\nFZQf+DkPl34vLvd8/AvU1xku6nsxnXqfRrlv2x7/PFGmdXZ2LnXRRSIBnhGK7UuI3Yfdep9WAcUB\ntjtYpmgg+j6JKoOwofHnNFNMSEhIANJLMSEhIQHYWPpswdSKFk+TDHUsQM9yiF0zQ/Q5soJRhvoj\nqPcyHznFtBuouVEyP9b337nkkeAxbLwMv0kNiG1ns3qfwY5T5rAAfSm9jRYilPe3POJZgm4tQc92\nSlDcwgdjGKOLLNk6GzoFXCHgy6jjEq7O7cJ3utrxc7aGsOOCIHkO6l/GqDgttyqUFWXUVMxlh+Va\nCQfpTsvbmeL+z3KUnnVWrUUsSdtEOH+89HHOIcDO+Wcz8fYKROJpF0d7uerUJdX/4BIE6S2t2DJE\nvw2UfTD2Bo9yz70+Va9ljtz7rm9ncbljdg0eaduImvunp1QLaj/bMXyTkGaKCQkJCUB6KSYkJCQA\nG0ufZS5y3Zp6lKwNgWsGujOHs/HkOIpwt52mjBGJzAMoKH5WFgjWBhg8b98ExYIG1iDIZYR8GKlc\n+5oXP5rxQJQv7SCXugI1n4Aagz1pjAhtVfk19Q/r9puIOA47KIQEt7aCQnfkjDMauTv1a8sgpM6R\nn4x0ZrVizvUS0WkDNc35+w3HsRI2WktYkZUD2KthaSSAE3KJoYgWbDdR8GkwBh2+4rSS92J1CCd3\nuFY3QC3L697mosXnL44RliYqWNjlPRSZAn3NoEBfQM3QPT6jbUldJC80YB03i9ZkjcueG20jRLZL\nXOdlFFar6htHO7VNQpopJiQkJADppZiQkJAAbCx9DstKs4OafvY7zmUXsHRaIrocQElDtJ3Kh6Bv\nyOtdwP6KLthtKIwXyLed9iHSzpzuMkI9R8Wo7rCmJ8OBtzFDBHMAJ+8StLtcOmXuIMpOp+gKUcwM\njtxZv26nOYIgd4Sc7a5zXVIzOoJnyD2eoS8FKHNBoTAE4csofM7Rdomc4RIu1BS1G6L2OvSxuHrt\n2sl2D2Pew/JJY4xBergWWF9poI5yy9tjje4BRP8TQfWPcW5hbWA18ijyinWy4zOVz3mzMP4TfI5n\nboalngw1pbs9iLdZg3vsawYrRshjFLstWJfB7b1swG0bufmjZSxyhuWHTUKaKSYkJCQA6aWYkJCQ\nAGwufVbQKkYjRwunL23+DsDSqwIlO6nR1AA1BtVcrDzKewQhdd70CCWrME8CikiholIFUfUYxZiK\nGCHcXzjtz+TU6GB46OcHNe904cKMO5uBSm0hXL6ikvhEEMyiSE6Z5siJLrs+WP2GR0gLFNHKUF+7\nGMGFetvPf2jIJ4/nH5nvu2JhL1DwLnK2Wcd7jvzkQQvFspiT3fZrLvZ8TEOnvqZTpYxbcCkHfc9Q\nUzujvRec1NlQA/clx+fz+HwaKG1vyhsH53c7e1mBWILiVk3k2Od+ne0V26z3OWVRhsJuB5mP7QLP\naieqOUKV6HNCQkLCxmNjZ4pW5MpfUZe27FObiMX1BSxZhjCfzeNMpI16Jkyz62HYiobPSMZt/3Xu\nnGNyWqDc5QqL9wVSzsp18wcoh4maI9Md6s78uAaMXYuBzw5WmMGMaYqKWe59IV4HggXrBXVJOoZm\nsYXF/TavAX1pd31c8iddpxfwO4wKmiez8x4saDDxkcFAN6BfDG5NdhAk6PoYrWY+5sNjtyN6YOUz\n8UL1sXPMsCpq+irO9r1fBXSacwgoW+h8F8GjY7CMeUxX3Bu4iw5iHqe0ocelPwv9KVL10JfZDKa8\nx9Bpbnm66AKmRopOOivoO8eYnfcZUMMz3I9lVRtV0ikmJCQkbDwu3EvRzHbM7J1m9t/N7ANm9mlm\ntmdm7zGzX4z/333ulhISEhKePy4iff6Hkn44hPBFZtaU1JX01ZJ+JITwDWb2FklvkfTm2zVikloh\nlgqVUxZVWPTHT0IFGjgK9f5bKPGJqpLKQJma0OnlE2gGsaC/LP2cp0xBM6TrTeBOE01cf77PIiG+\nPQgwAsUtbCGdcX6MwA20iT35sVsltJkxvayANq4AfayCn7+DtDHWbpnPUQYURqh65fbJ5qkUuevu\nzrLOigv7vo6woNMPKDCdfBYL7+Oe+bEj0D0wSXWhWaTGcV0GttnxMWGAhs44BfSYBn1pY8/XAwqk\nFh5DGztmcCJuLsZOe5mqeDDzZYedErrH/tm1cNo452Lg+9ws/Pxbc0QUI32vYHyLFQ0Z6LMhnTRb\nL8HkF25O9YLgQl2VmW1L+kxJb5OkEMIihHAo6QslPRp3e1TS6+9NDxMSEjYdF+qlKOk1kq5J+k4z\ne5+ZfbuZ9SRdCSE8Ffd5WtKVsw42szeZ2WNm9thivjhrl4SEhITb4qLR54akT5X0F0II7zWzf6ia\nKp8ghBDMLJx1cAjhEUmPSNLO7g7qWjrFGPZ8eyd3stALjKLWFHKcIcoJd5Uc6W8lapGEbaTNwSUl\ng8auga5noM8B5VS7kYb3QFlocipQ0+wp1yyGV/lSq+1CYwfKlkHiRmPdUVUvGTQ7SCdD2lgfOsmc\nIVI4pUwQfRdKmeYFabjvE7AmsW4ljP3H7EYORQCWLFoLaACR/ng0d1PcDGl2xan6KqcEnN5OjFYH\nptlhztDFvWgi/e1w5RRX0GOWiNBnUBxscx4S9YkHS2+jwPdc3uCSTY48x2wLz1YbfcS12RBpfmMY\nFO/U0fcS+84M6ZTQZjKdNTTr8dzQ4POFmyk+IemJEMJ747/fqfol+YyZPSBJ8f9X71H/EhISNhwX\n6qUYQnha0kfM7FfHj14n6eckvVvSG+Nnb5T0rnvQvYSEhJcBLhp9lqS/IOl7Y+T5Q5K+VPXL+x1m\n9mWSPizpDc/ZiplWMaJabiNCG5x6LKdOD3p0Com1Q5Ysn9mFmWvJCCLSv/BxhshcExFC6zvdYQ0S\n1tGoIn2m0HzZ9/7xetR1AfIuqHFjDhEyaGrZAn2F20pjWH9+vUA9l8zTFgNSHivUWRHqrzTg5CJE\n2Zcwv12hDGgTdM/ickOGth8sIABHOdg50iYpam4jta+CCL0NeljMzxbVl724jXuyoGrgEOa3GOf+\nro//MdYmFgsYC+N5aSClbp1Gut92cfW8QtQe5rNN0HeK/pdwIxpVPhZdqAwaSMusIHY/iumvCOaz\nkqrauT9zfaQKHl3Et8YLiAt3eSGE90v6zWd89boXuy8JCQkvP1wo+pyQkJBwr3HhZoovGJaV7Kim\nPNtbKJ4CrnBjfPNkuwBV0pqqggK3QF+WDaeMc9DEcJ5gG2a2TZRV7cxAcUDDO6G+LR2UDF1BMH2M\naC6qqmoIWtk4crrHXNl8yynR1YbTrcH1+tjBQ07lpg24zkD02zz2z1mXpXgG5Vt3XEidt/06xqDY\nAY4tWaSybTrtgOrNEfFtwmS3D5pIA98M/aWbS4EofuixxCxEzeu2USb1eOBOS5Mj3+76phDMV7uD\nZRI4GVFs3oxR9CaWBujcdNgGlUZZ1wI1gla7fj9zCMlLRJkNS0YtlOrNo1B8wbx6JDdMYWCc4/Ot\nmDxAd51NQpopJiQkJADppZiQkJAAbCx9NtlJpHk5Oduua9CCiSv0yFnM4e0OnT4s5ZZT2nNqSFPO\ngCyawybqiyD39L625wF3EV5sQjM7jbnavQDBNATbvUOEP0EH5z3k8sIC6qDhVKpqeF/aS1LGus1m\nBcNT5H63IUZf9Pz8AbZkBoExS3WyJGsTHmk09g0x4j7Gcaaz88SbDSgFEBVftCFYpmAdYu+ckVvc\nryxanbFGzMEMYnCUXs0RtR51EH1HLvUCIvguoriC4mEelxLm8s9aUEHsXPP+TfoYiwaWXfAo0N4r\nA02m+e+KLrqR71eg3dXCG6ywNHS158/ffevjRKH75iDNFBMSEhKA9FJMSEhIADaWPq8saNisp/wN\nUC+Kgyu4I89Bd3bLmlZnfQiW8fNxACuy7oG3d5Q73dkB2y723Fm5AZ5eIuK8gGVUGaPb18zpWwdM\nJevBeRtR6d7Iqc8M/d1G3nIGR/AZhNzDVh1GbSNq3jxCpBgULEDhS9urzpYvR8zg8G24thy1RjJQ\n/5vdmE+L8+8fw8YLEdRxARfoHLVzwNiZNz0Knlvch4P3HmrqzI7rsS6hwJ8tnDJvV37fAoX8Oeg4\nbMfmsAtrrVhWFOMYHcRz1HAJEI8vt3yppQc6fBD82oalPyOtjvdrcKrokG8WyJtexaSC+ZKUGVZk\nEOb3/TQaPlj3O9HnhISEhJcB0ksxISEhAdhY+lxl0iS6Quewjupixt8Yo4QmymZOY4C0A85cjlDi\nEyVTd1tOcZgTy9znxqGf57jwY0uWxITVlGKRrCHyepd9p6ZtOGmfiqCCyrHoE/OWj1FKVaDEenVN\n8VewAjsCPeocI5d3Aj5GwLW7i4hzBUfu2dTdtpugcr376/EPV2G/1XLKOmx520fya3hIHH+IwTEA\ng7aPXR9C8meOXLzfiBHdJizHtpcQhj/j9yLs+HJEifPkGJeCvlootCUud6zq7RVE7FyC6VS+Lwuh\nTTCVGWCfAdQCGcTeSwS/OQ2axVTxIXL89ypfUshz5HK3UPxtGPtYJfqckJCQsPFIL8WEhIQEYGPp\ncx5Mg1hMeHztwL/oOZWi8Pda5RHKS6GmZOXIKVOJwkndvltUreBInCOyPEH0tQsaWIAq0cZqjIiu\nRXrcDU715shD7Sy8PYMtVkbfJ/zclXDBbjacYhkoVh5p2Bg5ri3QW2ujBjDO2Q8uxs6Q1z1GrvLV\nrtOw/bbv30Z+cvdGvX8JN/QJcpMLpK9fmnlfnsmRfAyLrgL50VuwFAsoxjXoIc97WV83Ha6rnrex\n+jXeRvaUh2LXFFiSxqzTDNf2AeqmdbhM0q+fT8tRwIz5yzM/8LCFyPIEjul4LlT6OE8zWNrBlm7h\nl6xWdPa+H+WqaBFGZfiBoRDadi/2ezPnVJt5VQkJCQl3ifRSTEhISAA2lj6HXCq79Tu/ed/g5HM7\nQkQNouKBSA/rfZgz20H0d7Lj9OVJOFW/5ghUEgWAbqyQQ0s7MtD3Puof/f/tnU2MZFUZht+vfm5V\nd01P9/SABBkEjASDC8EYg5EYAjHxh6gLYiCaEEOCCxeQaBTdm+DGn4UxMaBhYVSCGo0LE4MsXKEI\nJkaQiEQC4zAwMz3T3dX1X5+Le2rO04QMDumhqy7fk0zmVk31rXPq1py+7/l+3l6S3gVkdwfjq6N1\nVA3JwPURpRTkeAv+vTTaUn79cKuUak209uqgpdUE0nyCOuAR2mKNVjEuJDi/s5bl2RDdoYXtg35S\nbX2YJbGllnXzayfw1z5M7+Z8Zg1R+91DS7UhariLVt6eaKfIdRtqfJKnsKt+e3xpns8YknmCTtnL\nMJ2a9vKcT1h+g4uSVGWi+3SA9m/oRVYb5vl015E8Psjz72/k92HBQrGcJfF0hxkK5XEN3zPmfNMP\nWrjmw5TsvdvkqzrEnWIQBAGIRTEIggBUVj6b5+CZbSPKihrSNhJfd1AHPIs0b9UpryG7II0OoSZ2\ngGipo0VUgSTx4cF8ngGkar2b32uSilUbiBo2RogmQtVMIfEamFvfcxR5iIhyp4/aWmwfNNrlSaeQ\nnQN03qaUa63C0GoTPsLwt2aN9WSaJWEdtedHa3lbYZzMlWr0bUIdbpOTZisyRGWbGCOT9EeMxKOe\necxO5UlCNlBX3sF1M0jFLbYFQ3fwDq7XCMnbW+hs3Ubt93ZKzkdgVxNEx9vovD5GZHl0CnstaO/V\nQjJ+E4UEY0S0HVkEZ0uyUe/dGyFUzvZvMD8rNsuTWyRvX3jM7Boz+xv+bJrZvWa2bmZ/MLN/pb8P\nvfHZgiAIzp+5ulN092clXSdJZlaXdFTSryXdJ+lRd7/fzO5Lj79+rnPZYKLW82Wrmp1m/k04glWo\n7/qdkI9njWObln9TN41eGPmnCuSYbZw5nV+PBrUNdC8xdEwpcDelpXz+WaNRNm2twdujsY27yhZ/\n88MLBHmC2wN0sqnlgFEDFqOzBrkt3BFM0LR1MuVdUx52fQXWm2jmO4JfC6rlVBzIwSjmaU5TXt0h\n5CkOEQjqFfn9l2CnM4RN64TWsyjtxKGEnM3WsRz0aKaOSLaMpr1oL1PD59I5g+8T7jzZaWnMDki4\nU2+gdLKWrtcI/wuXGPDr5nO38P0sYKs6GebPaHBq6+zxzjLLRnGn3oB3TCq/LNiQtovyQJSQ1o/k\ncsppKp8VglZVYq7uFF/DLZL+7e4vSPqMpIfS8w9J+uy+jSoIgkozV3eKr+F2ST9Lx5e4+7F0/LKk\nS17vB8zsbkl3S9JS0Xq9lwRBEJwTm8dcIzMrJP1X0vvc/biZnXb3Nfz7hrufc19x7eCq33TDjZKk\njV6WFQVy/FaaWaoxV2yc8rMo5Zgn1oPFaZ2+HJBbUwZGEKRh81Hv5uMD9C5pJW+ZSR5Tk1Ibfi2b\nWdVoBMmMakK9o54DI01IpTFyzzxpTJYh9hEgOT3Im/srnXw+eroY5OsI0lsnIVPXsvSjPepG8o5h\n55w6cy1hU2uYP5r6aAfWrwxA9NClZh05k40myi+TaJpCjm5NEQFBfulqI1+r/lae27gPuQtL3PpK\n/gV9Gr4vy2mHwXv5cx4jN7C1lOfchPVpo5Ofp8SvY/uElrADNO7toMnudsrHZblnB2WwSyv5sxoc\nzaWy3RSA/POTT2hza6tyGnpe5fMnJD3p7sfT4+Nmdqkkpb9f2beRBUFQaeZ1UbxDWTpL0m8l3ZmO\n75T0m7d8REEQvC2Yuz1FM+tI+pikL+Hp+yU9bGZ3SXpB0ufe6Dy1Wk3tVikz6gWS36Cw2JS0jW4n\n20lO7NRQNoWSP+ZsCfKZnWxq2JZow7ayNmW0OL+G2xjDQSnDRiwzW8bPQbCsnMk/ZwfZVQfdVhy5\nZ8jroz3pzskyct5HBHXWxUWSmkWWjLv8ZBA1d+Q1FifyZ1ccyTsduzZrkOc2syZBcF5DRIIvnuT3\nGSOX0PqwB0UHoCFSBJY387g8K/9dpXCzxkOjZn7P+gjviVy/E4O8HdNkNBk5gDto+Msc14PLGEBq\n+DuGNO/D6/bkIGczXLGet9HHjP7j0o6R97pTy5J8gtLBFr5/q+k7b5D6Qn6lYwuifXHepxn0yq0U\ndmiqEnO3KLp7V9Lh1zx3UmU0OgiC4IJSzaU+CILgTTJ3d4p7hclyRxp0AelNYbd5BmV87RxpW0sJ\n0V3lqN2gxrI92HfCu6TOxp5IGO53s9wawJ6zjXK5ERKfJyk7mp4vDci6Gsq56igtNCag99kxBc1a\nLUulbSZVX1oG9w+iJo6dZrpNbEGMXz/6y6ajo1VIT0T2WaI3RVLxNDXrHaGcbQ1Z33WUvw0OYysB\nSdJ+PPu/sORthPJDg5QdYX6zPQl6qzgi4Q0k6Y9w3MH52NiW5Xrb4xyhXoF87qWORduQ5gWsXA0Z\nCRs4Rwf+tU1s6zTx+R/AdyF/KlKtjej2zJcI0fytYdbjW/18LWgHW9XmsjOqPbsgCILzJBbFIAgC\nUFn5PLSJjtbKKNmUchd2m9NOlsw9NFGtJ1WxDPnWRKS2X2Sp24ePRoEE4wJWlmPUk9o2CneReK3D\neSzTURmVrL2cJdMEFqvN9ZxgW0MnkzFrXCGZCyQB1yAPO0NI7ySrRojaTpCAviZEdtGC5QA+oz7q\nkE80sa3QxTwQLS06WR4Ws05GeM9Xmzlh/FANDXyRyN5FgnUN9iITRIUdCdaTw/lFSw1EzlP0R1Ba\nTQAAA09JREFUv47Q7hTJ446a6YPwyGniejKRX4ZaaWzNHIfF6yxhfIJMAadvzjq+E+gYNGVdczsf\nsw7DsK9xAPa9lM/bKRXDkZIxZmcgfFW7DWwlpWTweSz82AviTjEIggDEohgEQQDmsvZ5LzCzVyV1\nJZ3Y77G8BVykmGeVWJR5XuHuF+/3IPaayi6KkmRmT7j7B/d7HBeamGe1eLvMc14J+RwEQQBiUQyC\nIABVXxR/tN8DeIuIeVaLt8s855JK7ykGQRCcL1W/UwyCIDgvYlEMgiAAlV0UzezjZvasmT2XbFEr\ngZldbmaPmdnTZvYPM7snPV9Jb2wzq5vZU2b2u/T4KjN7PF3XXyQ/n4XGzNbM7BEz+6eZPWNmH67q\n9VwEKrkoJs/oH6j0erlW0h1mdu3+jmrPGEv6irtfK+kGSV9Oc5t5Y18t6dH0uArcI+kZPP62pO+6\n+3skbUi6a19Gtbd8X9Lv3f29kt6vcr5VvZ5zTyUXRUkfkvScuz/v7kNJP1fpHb3wuPsxd38yHW+p\n/A90mSrojW1mRyR9StID6bFJulnSI+klCz9PM1uV9FFJD0qSuw/d/bQqeD0XhaouipdJehGPX0rP\nVQozu1LS9ZIe1//pjb1gfE/S16SzLYwOSzrtftYMpwrX9SpJr0r6SdomeCD5FFXxei4EVV0UK4+Z\nHZD0S0n3uvsm/83LPKuFzrUys1slveLuf93vsVxgGpI+IOmH7n69ynr9XVK5CtdzkajqonhU0uV4\nfCQ9VwnMrKlyQfypu/8qPV01b+yPSPq0mf1H5fbHzSr33tbMbNassgrX9SVJL7n74+nxIyoXyapd\nz4WhqoviXyRdnSKVhaTbVXpHLzxpX+1BSc+4+3fwT5Xyxnb3b7j7EXe/UuX1+6O7f17SY5JuSy+r\nwjxflvSimV2TnrpF0tOq2PVcJCpb0WJmn1S5J1WX9GN3/9Y+D2lPMLMbJf1J0t+V99q+qXJf8WFJ\n71Lyxnb3U/syyD3GzG6S9FV3v9XM3q3yznFd0lOSvuDug3P9/LxjZtepDCYVkp6X9EWVNyyVvJ7z\nTmUXxSAIgjdDVeVzEATBmyIWxSAIAhCLYhAEAYhFMQiCAMSiGARBAGJRDIIgALEoBkEQgP8BnW2n\nyZYvydkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b0effbef0>"
      ]
     },
     "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 0x7f3b0864cb00>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rSjDJ/BxpWvYULJVQ4GbMKJ1WW6CPD+WIJQUGLa/rclE5Hs9hBW3BIgwmrxIO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\nAdFxHCfgA6LjOE7AB0THcZzA/wAJ7Mt+W09DMQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b086c79b0>"
      ]
     },
     "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": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f3b0862f080>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/PqMYfAdO63Ontf2eU98Oy8bCUquCCLsVaT2JZ4V7orMNZ3CqDBDBXnRQiAo3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\nCdFxHCfgE6LjOE7AJ0THcZzAfwC++wQ2KKcmTAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b086b6f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[1], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[14])+ \",\"+\" iceberg_probability : \"+str(pred_valid[1])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f3b086984a8>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JEemFmJCQkBCxuZQ5BE2iCvUQlJlW4QpO0kIK4DCmeFPcB3qHNLpnnG5DD+X1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\nCTEIgiARE2IQBEHiv8LTgKSaeQy4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b0861f208>"
      ]
     },
     "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 0x7f3b0856dac8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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052NgzW1iLAKoH8texlVL0hjybrU5PdWr/yYj/7x40Mt+3jz2+TX42tS0us8X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\n8wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b08549358>"
      ]
     },
     "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": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f3b0841b588>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gYXZaISYkJCSskR6ICQkJCREbS5mbXJput8/7HWcMCj0osSf4wjGsoHba/y+3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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b0846bb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[7], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[127])+ \",\"+\" iceberg_probability : \"+str(pred_valid[7])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f3ac15bceb8>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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y0PFcoNKQaox9fIkniON20JGLFbUM5o1WwUQEKa+honNn7uunzfWBrZszcEha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\nV/OhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3ac1596438>"
      ]
     },
     "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": null,
   "metadata": {},
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}