Model_03-Adam-k=10-amsgrad-0001-15p.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Mandatory imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import log_loss\n",
    "from sklearn.model_selection import StratifiedKFold, StratifiedShuffleSplit\n",
    "from os.path import join as opj\n",
    "from matplotlib import pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import pylab\n",
    "plt.rcParams['figure.figsize'] = 10, 10\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = pd.read_json(\"input/train.json\")\n",
    "target_train=train['is_iceberg']\n",
    "test = pd.read_json(\"input/test.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "target_train=train['is_iceberg']\n",
    "test['inc_angle']=pd.to_numeric(test['inc_angle'], errors='coerce')\n",
    "train['inc_angle']=pd.to_numeric(train['inc_angle'], errors='coerce')#We have only 133 NAs.\n",
    "train['inc_angle']=train['inc_angle'].fillna(method='pad')\n",
    "X_angle=train['inc_angle']\n",
    "test['inc_angle']=pd.to_numeric(test['inc_angle'], errors='coerce')\n",
    "X_test_angle=test['inc_angle']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Generate the training data\n",
    "X_band_1=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in train[\"band_1\"]])\n",
    "X_band_2=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in train[\"band_2\"]])\n",
    "X_band_3=(X_band_1+X_band_2)/2\n",
    "#X_band_3=np.array([np.full((75, 75), angel).astype(np.float32) for angel in train[\"inc_angle\"]])\n",
    "X_train = np.concatenate([X_band_1[:, :, :, np.newaxis]\n",
    "                          , X_band_2[:, :, :, np.newaxis]\n",
    "                         , X_band_3[:, :, :, np.newaxis]], axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_band_test_1=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in test[\"band_1\"]])\n",
    "X_band_test_2=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in test[\"band_2\"]])\n",
    "X_band_test_3=(X_band_test_1+X_band_test_2)/2\n",
    "#X_band_test_3=np.array([np.full((75, 75), angel).astype(np.float32) for angel in test[\"inc_angle\"]])\n",
    "X_test = np.concatenate([X_band_test_1[:, :, :, np.newaxis]\n",
    "                          , X_band_test_2[:, :, :, np.newaxis]\n",
    "                         , X_band_test_3[:, :, :, np.newaxis]], axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "#Import Keras.\n",
    "from matplotlib import pyplot\n",
    "from keras.optimizers import RMSprop\n",
    "# from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPooling2D, Dense, Dropout, Input, Flatten, Activation\n",
    "from keras.layers import GlobalMaxPooling2D\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.layers.merge import Concatenate\n",
    "from keras.models import Model\n",
    "from keras import initializers\n",
    "from keras.optimizers import Adam\n",
    "from keras.optimizers import rmsprop\n",
    "from keras.layers.advanced_activations import LeakyReLU, PReLU\n",
    "from keras.optimizers import SGD\n",
    "from keras.optimizers import Adadelta\n",
    "from keras.callbacks import ModelCheckpoint, Callback, EarlyStopping\n",
    "\n",
    "from keras.datasets import cifar10\n",
    "from keras.applications.inception_v3 import InceptionV3\n",
    "from keras.applications.vgg16 import VGG16\n",
    "from keras.applications.xception import Xception\n",
    "from keras.applications.mobilenet import MobileNet\n",
    "from keras.applications.vgg19 import VGG19\n",
    "from keras.layers import Concatenate, Dense, LSTM, Input, concatenate\n",
    "from keras.preprocessing import image\n",
    "from keras.applications.vgg16 import preprocess_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Data Aug for multi-input\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "batch_size=64\n",
    "# Define the image transformations here\n",
    "gen = ImageDataGenerator(horizontal_flip = True,\n",
    "                         vertical_flip = True,\n",
    "                         width_shift_range = 0.,\n",
    "                         height_shift_range = 0.,\n",
    "                         channel_shift_range=0,\n",
    "                         zoom_range = 0.2,\n",
    "                         rotation_range = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Here is the function that merges our two generators\n",
    "# We use the exact same generator with the same random seed for both the y and angle arrays\n",
    "def gen_flow_for_two_inputs(X1, X2, y):\n",
    "    genX1 = gen.flow(X1,y,  batch_size=batch_size,seed=55)\n",
    "    genX2 = gen.flow(X1,X2, batch_size=batch_size,seed=55)\n",
    "    while True:\n",
    "            X1i = genX1.next()\n",
    "            X2i = genX2.next()\n",
    "            #Assert arrays are equal - this was for peace of mind, but slows down training\n",
    "            #np.testing.assert_array_equal(X1i[0],X2i[0])\n",
    "            yield [X1i[0], X2i[1]], X1i[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Finally create generator\n",
    "def get_callbacks(filepath, patience=2):\n",
    "    es = EarlyStopping('val_loss', patience=15, mode=\"min\")\n",
    "    msave = ModelCheckpoint(filepath, save_best_only=True)\n",
    "    return [es, msave]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getVggAngleModel():\n",
    "    input_2 = Input(shape=[1], name=\"angle\")\n",
    "    angle_layer = Dense(1, )(input_2)\n",
    "    base_model = VGG16(weights='imagenet', include_top=False, \n",
    "                 input_shape=X_train.shape[1:], classes=1)\n",
    "    x = base_model.get_layer('block5_pool').output\n",
    "    \n",
    "\n",
    "    x = GlobalMaxPooling2D()(x)\n",
    "    merge_one = concatenate([x, angle_layer])\n",
    "    merge_one = Dense(512, activation='relu', name='fc2')(merge_one)\n",
    "    merge_one = Dropout(0.3)(merge_one)\n",
    "    merge_one = Dense(512, activation='relu', name='fc3')(merge_one)\n",
    "    merge_one = Dropout(0.3)(merge_one)\n",
    "    \n",
    "    predictions = Dense(1, activation='sigmoid')(merge_one)\n",
    "    \n",
    "    model = Model(inputs=[base_model.input, input_2], outputs=predictions)\n",
    "    \n",
    "    adam = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-8, decay=1e-6, amsgrad=True)\n",
    "    model.compile(loss='binary_crossentropy',\n",
    "                    optimizer=adam,\n",
    "                    metrics=['accuracy'])\n",
    "    \n",
    "#     sgd = SGD(lr=1e-3, decay=1e-6, momentum=0.9, nesterov=True)\n",
    "#     model.compile(loss='binary_crossentropy',\n",
    "#                   optimizer=sgd,\n",
    "#                   metrics=['accuracy'])\n",
    "    return model\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "===================FOLD= 0\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 20s 818ms/step - loss: 0.7992 - acc: 0.5660 - val_loss: 0.5025 - val_acc: 0.7469\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.5715 - acc: 0.7185 - val_loss: 0.3889 - val_acc: 0.8210\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 594ms/step - loss: 0.4357 - acc: 0.7983 - val_loss: 0.3110 - val_acc: 0.8457\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 14s 594ms/step - loss: 0.3841 - acc: 0.8344 - val_loss: 0.2642 - val_acc: 0.8704\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 14s 597ms/step - loss: 0.3286 - acc: 0.8531 - val_loss: 0.2616 - val_acc: 0.8704\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.2771 - acc: 0.8818 - val_loss: 0.2711 - val_acc: 0.9136\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 14s 598ms/step - loss: 0.2925 - acc: 0.8707 - val_loss: 0.2257 - val_acc: 0.9136\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.2867 - acc: 0.8813 - val_loss: 0.2864 - val_acc: 0.9136\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.2413 - acc: 0.8890 - val_loss: 0.2644 - val_acc: 0.9136\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.2475 - acc: 0.9012 - val_loss: 0.2290 - val_acc: 0.9136\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 14s 599ms/step - loss: 0.2330 - acc: 0.8950 - val_loss: 0.2052 - val_acc: 0.9198\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.2376 - acc: 0.8977 - val_loss: 0.2253 - val_acc: 0.9074\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2663 - acc: 0.8891 - val_loss: 0.2382 - val_acc: 0.9136\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2159 - acc: 0.9155 - val_loss: 0.2206 - val_acc: 0.8951\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2003 - acc: 0.9195 - val_loss: 0.2329 - val_acc: 0.9198\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 15s 604ms/step - loss: 0.2337 - acc: 0.9002 - val_loss: 0.1895 - val_acc: 0.9136\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1894 - acc: 0.9163 - val_loss: 0.1954 - val_acc: 0.9074\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2284 - acc: 0.9119 - val_loss: 0.2682 - val_acc: 0.8889\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1916 - acc: 0.9227 - val_loss: 0.2220 - val_acc: 0.9136\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2076 - acc: 0.9187 - val_loss: 0.1909 - val_acc: 0.9074\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.1987 - acc: 0.9174 - val_loss: 0.1839 - val_acc: 0.9259\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1962 - acc: 0.9206 - val_loss: 0.2165 - val_acc: 0.9259\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 13s 539ms/step - loss: 0.1635 - acc: 0.9353 - val_loss: 0.2630 - val_acc: 0.8951\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1934 - acc: 0.9226 - val_loss: 0.2619 - val_acc: 0.8889\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1673 - acc: 0.9304 - val_loss: 0.1984 - val_acc: 0.9136\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1678 - acc: 0.9349 - val_loss: 0.1932 - val_acc: 0.9259\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1605 - acc: 0.9348 - val_loss: 0.1917 - val_acc: 0.9136\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1607 - acc: 0.9317 - val_loss: 0.2500 - val_acc: 0.9074\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1679 - acc: 0.9311 - val_loss: 0.1951 - val_acc: 0.9136\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1451 - acc: 0.9416 - val_loss: 0.2178 - val_acc: 0.8951\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1414 - acc: 0.9417 - val_loss: 0.2103 - val_acc: 0.9198\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1730 - acc: 0.9350 - val_loss: 0.2080 - val_acc: 0.9074\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1374 - acc: 0.9467 - val_loss: 0.2104 - val_acc: 0.9321\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1608 - acc: 0.9352 - val_loss: 0.2348 - val_acc: 0.9198\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1703 - acc: 0.9358 - val_loss: 0.2121 - val_acc: 0.9136\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1080 - acc: 0.9629 - val_loss: 0.2384 - val_acc: 0.9259\n",
      "Train loss: 0.132853211874\n",
      "Train accuracy: 0.947295423024\n",
      "Test loss: 0.183931951916\n",
      "Test accuracy: 0.925925925926\n",
      "\n",
      "===================FOLD= 1\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 18s 761ms/step - loss: 0.5474 - acc: 0.7012 - val_loss: 0.3534 - val_acc: 0.8385\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.4009 - acc: 0.8275 - val_loss: 0.2522 - val_acc: 0.8758\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.3218 - acc: 0.8552 - val_loss: 0.2275 - val_acc: 0.8882\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2806 - acc: 0.8753 - val_loss: 0.2448 - val_acc: 0.8758\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.2612 - acc: 0.8872 - val_loss: 0.1880 - val_acc: 0.9255\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2615 - acc: 0.8911 - val_loss: 0.2196 - val_acc: 0.8820\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2882 - acc: 0.8797 - val_loss: 0.2441 - val_acc: 0.8634\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2856 - acc: 0.8663 - val_loss: 0.2063 - val_acc: 0.9068\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.2228 - acc: 0.9045 - val_loss: 0.1624 - val_acc: 0.9503\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2586 - acc: 0.8916 - val_loss: 0.1894 - val_acc: 0.9255\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2371 - acc: 0.9008 - val_loss: 0.1869 - val_acc: 0.9130\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2054 - acc: 0.9093 - val_loss: 0.2140 - val_acc: 0.9130\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2169 - acc: 0.9029 - val_loss: 0.1727 - val_acc: 0.9193\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2456 - acc: 0.8988 - val_loss: 0.2494 - val_acc: 0.9068\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2207 - acc: 0.9097 - val_loss: 0.1812 - val_acc: 0.9130\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2118 - acc: 0.9066 - val_loss: 0.1892 - val_acc: 0.9317\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1701 - acc: 0.9333 - val_loss: 0.2085 - val_acc: 0.9006\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2070 - acc: 0.9178 - val_loss: 0.2255 - val_acc: 0.9130\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1862 - acc: 0.9144 - val_loss: 0.1997 - val_acc: 0.9379\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1624 - acc: 0.9281 - val_loss: 0.2045 - val_acc: 0.9317\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1515 - acc: 0.9350 - val_loss: 0.2453 - val_acc: 0.8944\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1994 - acc: 0.9234 - val_loss: 0.1872 - val_acc: 0.9255\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.1755 - acc: 0.9316 - val_loss: 0.1772 - val_acc: 0.9068\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1672 - acc: 0.9312 - val_loss: 0.2202 - val_acc: 0.9255\n",
      "Train loss: 0.222477604223\n",
      "Train accuracy: 0.902286902287\n",
      "Test loss: 0.162431569241\n",
      "Test accuracy: 0.950310559006\n",
      "\n",
      "===================FOLD= 2\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 16s 660ms/step - loss: 0.6887 - acc: 0.5832 - val_loss: 0.5369 - val_acc: 0.6894\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.5094 - acc: 0.7070 - val_loss: 0.2666 - val_acc: 0.8758\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.4013 - acc: 0.8046 - val_loss: 0.2314 - val_acc: 0.8944\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.3109 - acc: 0.8554 - val_loss: 0.3597 - val_acc: 0.8075\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.3034 - acc: 0.8672 - val_loss: 0.1895 - val_acc: 0.9068\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2743 - acc: 0.8829 - val_loss: 0.2556 - val_acc: 0.8820\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2744 - acc: 0.8762 - val_loss: 0.2395 - val_acc: 0.9006\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.2506 - acc: 0.8895 - val_loss: 0.2355 - val_acc: 0.8758\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2531 - acc: 0.8883 - val_loss: 0.2194 - val_acc: 0.9006\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2251 - acc: 0.9102 - val_loss: 0.1973 - val_acc: 0.9130\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2520 - acc: 0.8979 - val_loss: 0.1934 - val_acc: 0.9255\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2240 - acc: 0.9055 - val_loss: 0.2079 - val_acc: 0.9068\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2045 - acc: 0.9149 - val_loss: 0.1963 - val_acc: 0.9255\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1944 - acc: 0.9206 - val_loss: 0.2003 - val_acc: 0.9068\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2067 - acc: 0.9108 - val_loss: 0.2370 - val_acc: 0.9130\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.2022 - acc: 0.9195 - val_loss: 0.2161 - val_acc: 0.9068\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2083 - acc: 0.9143 - val_loss: 0.2086 - val_acc: 0.9193\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1939 - acc: 0.9174 - val_loss: 0.2020 - val_acc: 0.9130\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 15s 639ms/step - loss: 0.1903 - acc: 0.9212 - val_loss: 0.1838 - val_acc: 0.9130\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1978 - acc: 0.9164 - val_loss: 0.1905 - val_acc: 0.9255\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1795 - acc: 0.9213 - val_loss: 0.1954 - val_acc: 0.9068\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1756 - acc: 0.9209 - val_loss: 0.2055 - val_acc: 0.9068\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.1599 - acc: 0.9389 - val_loss: 0.2024 - val_acc: 0.9317\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1639 - acc: 0.9368 - val_loss: 0.2191 - val_acc: 0.9006\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1727 - acc: 0.9285 - val_loss: 0.2304 - val_acc: 0.9068\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.1885 - acc: 0.9197 - val_loss: 0.1781 - val_acc: 0.9255\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1395 - acc: 0.9427 - val_loss: 0.2088 - val_acc: 0.9193\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1562 - acc: 0.9416 - val_loss: 0.1965 - val_acc: 0.9130\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1399 - acc: 0.9476 - val_loss: 0.2126 - val_acc: 0.9193\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1471 - acc: 0.9441 - val_loss: 0.1891 - val_acc: 0.9130\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1734 - acc: 0.9341 - val_loss: 0.1803 - val_acc: 0.9130\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1509 - acc: 0.9325 - val_loss: 0.2020 - val_acc: 0.9193\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.1343 - acc: 0.9450 - val_loss: 0.1698 - val_acc: 0.9130\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1172 - acc: 0.9574 - val_loss: 0.2079 - val_acc: 0.9006\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1257 - acc: 0.9437 - val_loss: 0.3019 - val_acc: 0.9006\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1550 - acc: 0.9414 - val_loss: 0.1840 - val_acc: 0.9317\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1481 - acc: 0.9424 - val_loss: 0.1921 - val_acc: 0.9130\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1343 - acc: 0.9463 - val_loss: 0.2461 - val_acc: 0.9068\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1185 - acc: 0.9487 - val_loss: 0.2140 - val_acc: 0.9193\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1119 - acc: 0.9514 - val_loss: 0.2448 - val_acc: 0.9068\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1291 - acc: 0.9475 - val_loss: 0.2170 - val_acc: 0.9130\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1084 - acc: 0.9552 - val_loss: 0.1971 - val_acc: 0.9193\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1171 - acc: 0.9439 - val_loss: 0.2516 - val_acc: 0.9130\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1438 - acc: 0.9505 - val_loss: 0.2414 - val_acc: 0.9006\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1395 - acc: 0.9446 - val_loss: 0.1762 - val_acc: 0.9193\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.1173 - acc: 0.9528 - val_loss: 0.2716 - val_acc: 0.9006\n",
      "Epoch 47/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1035 - acc: 0.9539 - val_loss: 0.2437 - val_acc: 0.9379\n",
      "Epoch 48/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1164 - acc: 0.9547 - val_loss: 0.2752 - val_acc: 0.8944\n",
      "Train loss: 0.0754658654384\n",
      "Train accuracy: 0.970893970894\n",
      "Test loss: 0.169764509297\n",
      "Test accuracy: 0.913043478261\n",
      "\n",
      "===================FOLD= 3\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 18s 745ms/step - loss: 0.6902 - acc: 0.5577 - val_loss: 0.4967 - val_acc: 0.7312\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 15s 607ms/step - loss: 0.5109 - acc: 0.7405 - val_loss: 0.3777 - val_acc: 0.8375\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.4096 - acc: 0.8187 - val_loss: 0.2754 - val_acc: 0.8750\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.3495 - acc: 0.8410 - val_loss: 0.3473 - val_acc: 0.8438\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.2843 - acc: 0.8708 - val_loss: 0.2639 - val_acc: 0.8750\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 15s 604ms/step - loss: 0.2785 - acc: 0.8761 - val_loss: 0.2472 - val_acc: 0.9000\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 15s 604ms/step - loss: 0.2621 - acc: 0.8897 - val_loss: 0.2183 - val_acc: 0.8875\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2592 - acc: 0.8892 - val_loss: 0.2462 - val_acc: 0.8812\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2412 - acc: 0.8971 - val_loss: 0.3503 - val_acc: 0.8625\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2142 - acc: 0.9098 - val_loss: 0.2237 - val_acc: 0.9187\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 11/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.2076 - acc: 0.9103 - val_loss: 0.2047 - val_acc: 0.9062\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2194 - acc: 0.9039 - val_loss: 0.2159 - val_acc: 0.9125\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1972 - acc: 0.9153 - val_loss: 0.2157 - val_acc: 0.9125\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1973 - acc: 0.9179 - val_loss: 0.2408 - val_acc: 0.8938\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.1982 - acc: 0.9205 - val_loss: 0.1874 - val_acc: 0.9187\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1977 - acc: 0.9223 - val_loss: 0.1930 - val_acc: 0.9000\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1646 - acc: 0.9288 - val_loss: 0.2386 - val_acc: 0.9125\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2034 - acc: 0.9108 - val_loss: 0.2215 - val_acc: 0.9125\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1810 - acc: 0.9235 - val_loss: 0.2749 - val_acc: 0.9000\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.1841 - acc: 0.9269 - val_loss: 0.1740 - val_acc: 0.9375\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1617 - acc: 0.9371 - val_loss: 0.1883 - val_acc: 0.9062\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.2045 - acc: 0.9195 - val_loss: 0.1640 - val_acc: 0.9313\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 13s 532ms/step - loss: 0.1843 - acc: 0.9298 - val_loss: 0.1828 - val_acc: 0.9250\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1609 - acc: 0.9314 - val_loss: 0.2220 - val_acc: 0.9062\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1571 - acc: 0.9391 - val_loss: 0.2241 - val_acc: 0.9062\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1621 - acc: 0.9326 - val_loss: 0.2518 - val_acc: 0.9313\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1559 - acc: 0.9417 - val_loss: 0.1969 - val_acc: 0.9313\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1288 - acc: 0.9472 - val_loss: 0.2642 - val_acc: 0.9125\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1248 - acc: 0.9516 - val_loss: 0.1910 - val_acc: 0.9313\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1437 - acc: 0.9371 - val_loss: 0.2606 - val_acc: 0.9125\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1295 - acc: 0.9497 - val_loss: 0.2273 - val_acc: 0.9250\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1841 - acc: 0.9236 - val_loss: 0.3404 - val_acc: 0.9375\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1333 - acc: 0.9436 - val_loss: 0.3087 - val_acc: 0.9000\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1502 - acc: 0.9425 - val_loss: 0.3247 - val_acc: 0.9250\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1207 - acc: 0.9578 - val_loss: 0.3567 - val_acc: 0.9000\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1170 - acc: 0.9563 - val_loss: 0.2983 - val_acc: 0.8938\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1197 - acc: 0.9559 - val_loss: 0.2795 - val_acc: 0.9000\n",
      "Train loss: 0.126475013318\n",
      "Train accuracy: 0.948060941828\n",
      "Test loss: 0.164010712504\n",
      "Test accuracy: 0.93125\n",
      "\n",
      "===================FOLD= 4\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 16s 675ms/step - loss: 0.7250 - acc: 0.6353 - val_loss: 0.6343 - val_acc: 0.7312\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 15s 604ms/step - loss: 0.5271 - acc: 0.7743 - val_loss: 0.2216 - val_acc: 0.9313\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.4376 - acc: 0.8036 - val_loss: 0.2953 - val_acc: 0.9125\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.3839 - acc: 0.8277 - val_loss: 0.4358 - val_acc: 0.7625\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.3421 - acc: 0.8515 - val_loss: 0.1706 - val_acc: 0.9500\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2843 - acc: 0.8702 - val_loss: 0.1760 - val_acc: 0.9375\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.4239 - acc: 0.8057 - val_loss: 0.1922 - val_acc: 0.9250\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.3162 - acc: 0.8521 - val_loss: 0.2199 - val_acc: 0.8938\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.2422 - acc: 0.8984 - val_loss: 0.1367 - val_acc: 0.9500\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2576 - acc: 0.8878 - val_loss: 0.2603 - val_acc: 0.9000\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2368 - acc: 0.8976 - val_loss: 0.3860 - val_acc: 0.8750\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2404 - acc: 0.9005 - val_loss: 0.1592 - val_acc: 0.9375\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2312 - acc: 0.9060 - val_loss: 0.1610 - val_acc: 0.9563\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2165 - acc: 0.9125 - val_loss: 0.1646 - val_acc: 0.9375\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2337 - acc: 0.9085 - val_loss: 0.2000 - val_acc: 0.9437\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2093 - acc: 0.9055 - val_loss: 0.2010 - val_acc: 0.9313\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1979 - acc: 0.9178 - val_loss: 0.3302 - val_acc: 0.8875\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1969 - acc: 0.9220 - val_loss: 0.2843 - val_acc: 0.9125\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2008 - acc: 0.9164 - val_loss: 0.1761 - val_acc: 0.9437\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2003 - acc: 0.9122 - val_loss: 0.1751 - val_acc: 0.9500\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2017 - acc: 0.9228 - val_loss: 0.2414 - val_acc: 0.9500\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1791 - acc: 0.9288 - val_loss: 0.2489 - val_acc: 0.9500\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.2069 - acc: 0.9097 - val_loss: 0.3181 - val_acc: 0.9438\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1828 - acc: 0.9218 - val_loss: 0.2401 - val_acc: 0.9375\n",
      "Train loss: 0.200579661081\n",
      "Train accuracy: 0.915512465374\n",
      "Test loss: 0.13667723462\n",
      "Test accuracy: 0.95\n",
      "\n",
      "===================FOLD= 5\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 17s 688ms/step - loss: 0.6175 - acc: 0.6601 - val_loss: 0.4668 - val_acc: 0.7500\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.3898 - acc: 0.8099 - val_loss: 0.3833 - val_acc: 0.8375\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 601ms/step - loss: 0.3757 - acc: 0.8213 - val_loss: 0.3718 - val_acc: 0.8625\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.3471 - acc: 0.8358 - val_loss: 0.2220 - val_acc: 0.9000\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.3068 - acc: 0.8625 - val_loss: 0.2478 - val_acc: 0.9062\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.3077 - acc: 0.8603 - val_loss: 0.2152 - val_acc: 0.9437\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.2514 - acc: 0.8891 - val_loss: 0.2002 - val_acc: 0.9125\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.2405 - acc: 0.8953 - val_loss: 0.2952 - val_acc: 0.9000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2568 - acc: 0.8897 - val_loss: 0.2107 - val_acc: 0.9187\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2195 - acc: 0.9059 - val_loss: 0.2331 - val_acc: 0.9000\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.2285 - acc: 0.8935 - val_loss: 0.1777 - val_acc: 0.9500\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.2053 - acc: 0.9189 - val_loss: 0.1757 - val_acc: 0.9250\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2008 - acc: 0.9189 - val_loss: 0.2088 - val_acc: 0.9187\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2061 - acc: 0.9132 - val_loss: 0.2078 - val_acc: 0.9375\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1977 - acc: 0.9158 - val_loss: 0.1802 - val_acc: 0.9563\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2123 - acc: 0.9083 - val_loss: 0.1835 - val_acc: 0.9375\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1926 - acc: 0.9191 - val_loss: 0.2027 - val_acc: 0.9250\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2136 - acc: 0.9101 - val_loss: 0.2013 - val_acc: 0.9313\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.2023 - acc: 0.9142 - val_loss: 0.1655 - val_acc: 0.9625\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1613 - acc: 0.9376 - val_loss: 0.1690 - val_acc: 0.9437\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1813 - acc: 0.9140 - val_loss: 0.1982 - val_acc: 0.9125\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1837 - acc: 0.9285 - val_loss: 0.2367 - val_acc: 0.9250\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.1647 - acc: 0.9247 - val_loss: 0.1762 - val_acc: 0.9313\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2154 - acc: 0.9073 - val_loss: 0.2205 - val_acc: 0.9375\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1760 - acc: 0.9321 - val_loss: 0.2148 - val_acc: 0.9062\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2151 - acc: 0.9140 - val_loss: 0.2483 - val_acc: 0.9000\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1810 - acc: 0.9308 - val_loss: 0.1933 - val_acc: 0.9500\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1789 - acc: 0.9238 - val_loss: 0.2159 - val_acc: 0.9437\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1557 - acc: 0.9340 - val_loss: 0.2439 - val_acc: 0.9625\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1729 - acc: 0.9275 - val_loss: 0.2329 - val_acc: 0.9250\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1380 - acc: 0.9487 - val_loss: 0.2534 - val_acc: 0.9125\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1592 - acc: 0.9344 - val_loss: 0.2081 - val_acc: 0.9563\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1629 - acc: 0.9332 - val_loss: 0.2023 - val_acc: 0.9437\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1372 - acc: 0.9487 - val_loss: 0.2284 - val_acc: 0.9250\n",
      "Train loss: 0.125401890385\n",
      "Train accuracy: 0.951523545706\n",
      "Test loss: 0.165507046878\n",
      "Test accuracy: 0.9625\n",
      "\n",
      "===================FOLD= 6\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 17s 713ms/step - loss: 0.6628 - acc: 0.6168 - val_loss: 0.4970 - val_acc: 0.7063\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 15s 607ms/step - loss: 0.4712 - acc: 0.7718 - val_loss: 0.4712 - val_acc: 0.7188\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 15s 607ms/step - loss: 0.3751 - acc: 0.8189 - val_loss: 0.3566 - val_acc: 0.8313\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.3120 - acc: 0.8599 - val_loss: 0.3996 - val_acc: 0.8063\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.3236 - acc: 0.8599 - val_loss: 0.3495 - val_acc: 0.8812\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.3039 - acc: 0.8586 - val_loss: 0.2968 - val_acc: 0.8562\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2649 - acc: 0.8782 - val_loss: 0.3023 - val_acc: 0.8938\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 15s 607ms/step - loss: 0.2315 - acc: 0.8981 - val_loss: 0.2887 - val_acc: 0.8812\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 15s 604ms/step - loss: 0.2304 - acc: 0.8932 - val_loss: 0.2721 - val_acc: 0.8938\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2128 - acc: 0.9080 - val_loss: 0.3214 - val_acc: 0.8688\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1886 - acc: 0.9215 - val_loss: 0.2920 - val_acc: 0.8812\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2049 - acc: 0.9210 - val_loss: 0.4081 - val_acc: 0.8562\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 15s 608ms/step - loss: 0.2056 - acc: 0.9168 - val_loss: 0.2464 - val_acc: 0.9062\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1865 - acc: 0.9274 - val_loss: 0.2562 - val_acc: 0.8812\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2224 - acc: 0.9099 - val_loss: 0.2972 - val_acc: 0.8938\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2171 - acc: 0.9008 - val_loss: 0.2680 - val_acc: 0.8875\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1918 - acc: 0.9266 - val_loss: 0.2542 - val_acc: 0.8812\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1802 - acc: 0.9238 - val_loss: 0.2676 - val_acc: 0.8875\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1712 - acc: 0.9375 - val_loss: 0.3081 - val_acc: 0.8500\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1648 - acc: 0.9357 - val_loss: 0.3083 - val_acc: 0.9062\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1554 - acc: 0.9293 - val_loss: 0.2474 - val_acc: 0.9125\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1643 - acc: 0.9316 - val_loss: 0.2499 - val_acc: 0.9062\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1630 - acc: 0.9382 - val_loss: 0.2699 - val_acc: 0.8875\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.1894 - acc: 0.9239 - val_loss: 0.3681 - val_acc: 0.8375\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 15s 607ms/step - loss: 0.1901 - acc: 0.9138 - val_loss: 0.2409 - val_acc: 0.9062\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1649 - acc: 0.9365 - val_loss: 0.2654 - val_acc: 0.9187\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1595 - acc: 0.9362 - val_loss: 0.2552 - val_acc: 0.9000\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1386 - acc: 0.9423 - val_loss: 0.2686 - val_acc: 0.9000\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1436 - acc: 0.9450 - val_loss: 0.2606 - val_acc: 0.9000\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1375 - acc: 0.9487 - val_loss: 0.2756 - val_acc: 0.8625\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1347 - acc: 0.9433 - val_loss: 0.3030 - val_acc: 0.9062\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.1499 - acc: 0.9402 - val_loss: 0.2241 - val_acc: 0.9000\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1333 - acc: 0.9485 - val_loss: 0.2373 - val_acc: 0.9062\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1163 - acc: 0.9503 - val_loss: 0.2303 - val_acc: 0.9062\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 35/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1416 - acc: 0.9410 - val_loss: 0.3631 - val_acc: 0.8625\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1115 - acc: 0.9575 - val_loss: 0.2978 - val_acc: 0.9062\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1247 - acc: 0.9520 - val_loss: 0.2471 - val_acc: 0.9125\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1173 - acc: 0.9493 - val_loss: 0.2881 - val_acc: 0.8875\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1358 - acc: 0.9383 - val_loss: 0.3655 - val_acc: 0.8688\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1595 - acc: 0.9430 - val_loss: 0.2469 - val_acc: 0.9000\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1135 - acc: 0.9538 - val_loss: 0.2370 - val_acc: 0.9062\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1014 - acc: 0.9573 - val_loss: 0.3869 - val_acc: 0.8750\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1260 - acc: 0.9528 - val_loss: 0.3378 - val_acc: 0.8875\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.0842 - acc: 0.9676 - val_loss: 0.3356 - val_acc: 0.9062\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1051 - acc: 0.9616 - val_loss: 0.4005 - val_acc: 0.9000\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.1019 - acc: 0.9635 - val_loss: 0.2328 - val_acc: 0.9188\n",
      "Epoch 47/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.0878 - acc: 0.9643 - val_loss: 0.2573 - val_acc: 0.9000\n",
      "Train loss: 0.100680935117\n",
      "Train accuracy: 0.963988919668\n",
      "Test loss: 0.224096649885\n",
      "Test accuracy: 0.9\n",
      "\n",
      "===================FOLD= 7\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 17s 709ms/step - loss: 0.6439 - acc: 0.6293 - val_loss: 0.4783 - val_acc: 0.7688\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.4148 - acc: 0.7966 - val_loss: 0.3553 - val_acc: 0.8125\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.4392 - acc: 0.7977 - val_loss: 0.3677 - val_acc: 0.8375\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 15s 607ms/step - loss: 0.3438 - acc: 0.8410 - val_loss: 0.2850 - val_acc: 0.8250\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 15s 607ms/step - loss: 0.2895 - acc: 0.8751 - val_loss: 0.2613 - val_acc: 0.9250\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 15s 607ms/step - loss: 0.2799 - acc: 0.8764 - val_loss: 0.2462 - val_acc: 0.8812\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2523 - acc: 0.8935 - val_loss: 0.2561 - val_acc: 0.9000\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 15s 609ms/step - loss: 0.2921 - acc: 0.8594 - val_loss: 0.1975 - val_acc: 0.9125\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2422 - acc: 0.8932 - val_loss: 0.2102 - val_acc: 0.8875\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.2076 - acc: 0.9145 - val_loss: 0.2243 - val_acc: 0.9062\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2269 - acc: 0.9034 - val_loss: 0.2839 - val_acc: 0.8750\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2624 - acc: 0.8896 - val_loss: 0.3038 - val_acc: 0.8500\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2245 - acc: 0.9083 - val_loss: 0.2159 - val_acc: 0.8938\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2194 - acc: 0.9037 - val_loss: 0.2003 - val_acc: 0.9187\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.1915 - acc: 0.9238 - val_loss: 0.1903 - val_acc: 0.9062\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 15s 604ms/step - loss: 0.2101 - acc: 0.9143 - val_loss: 0.1762 - val_acc: 0.9313\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1742 - acc: 0.9254 - val_loss: 0.2961 - val_acc: 0.8688\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.2125 - acc: 0.9138 - val_loss: 0.1731 - val_acc: 0.9062\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1768 - acc: 0.9212 - val_loss: 0.2071 - val_acc: 0.9000\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1880 - acc: 0.9148 - val_loss: 0.1803 - val_acc: 0.9375\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1872 - acc: 0.9205 - val_loss: 0.2069 - val_acc: 0.9125\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2009 - acc: 0.9129 - val_loss: 0.2487 - val_acc: 0.8938\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 14s 600ms/step - loss: 0.1858 - acc: 0.9185 - val_loss: 0.1447 - val_acc: 0.9313\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1719 - acc: 0.9287 - val_loss: 0.2390 - val_acc: 0.8812\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1776 - acc: 0.9241 - val_loss: 0.1521 - val_acc: 0.9375\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.1571 - acc: 0.9402 - val_loss: 0.1352 - val_acc: 0.9375\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1377 - acc: 0.9485 - val_loss: 0.1704 - val_acc: 0.9062\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1683 - acc: 0.9321 - val_loss: 0.2578 - val_acc: 0.9062\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1894 - acc: 0.9285 - val_loss: 0.2389 - val_acc: 0.9000\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1696 - acc: 0.9278 - val_loss: 0.1899 - val_acc: 0.9313\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1401 - acc: 0.9474 - val_loss: 0.1524 - val_acc: 0.9375\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1393 - acc: 0.9454 - val_loss: 0.2035 - val_acc: 0.9000\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.1336 - acc: 0.9407 - val_loss: 0.1212 - val_acc: 0.9437\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1400 - acc: 0.9391 - val_loss: 0.2086 - val_acc: 0.9000\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.1331 - acc: 0.9467 - val_loss: 0.1102 - val_acc: 0.9625\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1221 - acc: 0.9487 - val_loss: 0.2559 - val_acc: 0.8750\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1264 - acc: 0.9432 - val_loss: 0.1929 - val_acc: 0.9125\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1749 - acc: 0.9262 - val_loss: 0.2033 - val_acc: 0.9313\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1484 - acc: 0.9360 - val_loss: 0.2244 - val_acc: 0.9062\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1336 - acc: 0.9490 - val_loss: 0.1899 - val_acc: 0.9187\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1554 - acc: 0.9456 - val_loss: 0.2072 - val_acc: 0.9000\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1315 - acc: 0.9451 - val_loss: 0.1551 - val_acc: 0.9250\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1437 - acc: 0.9401 - val_loss: 0.1754 - val_acc: 0.9375\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1390 - acc: 0.9523 - val_loss: 0.1510 - val_acc: 0.9437\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1411 - acc: 0.9454 - val_loss: 0.1751 - val_acc: 0.9187\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.1252 - acc: 0.9501 - val_loss: 0.1711 - val_acc: 0.9188\n",
      "Epoch 47/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1285 - acc: 0.9464 - val_loss: 0.1401 - val_acc: 0.9187\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 48/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1019 - acc: 0.9547 - val_loss: 0.1667 - val_acc: 0.9187\n",
      "Epoch 49/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1155 - acc: 0.9554 - val_loss: 0.1538 - val_acc: 0.9375\n",
      "Epoch 50/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1149 - acc: 0.9572 - val_loss: 0.1488 - val_acc: 0.9250\n",
      "Train loss: 0.0762335420883\n",
      "Train accuracy: 0.970914127424\n",
      "Test loss: 0.11023716256\n",
      "Test accuracy: 0.9625\n",
      "\n",
      "===================FOLD= 8\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 18s 754ms/step - loss: 0.6511 - acc: 0.6860 - val_loss: 0.4684 - val_acc: 0.7625\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 602ms/step - loss: 0.4063 - acc: 0.8220 - val_loss: 0.4492 - val_acc: 0.8000\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.3406 - acc: 0.8495 - val_loss: 0.3718 - val_acc: 0.7875\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.3539 - acc: 0.8458 - val_loss: 0.4219 - val_acc: 0.8125\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.2925 - acc: 0.8790 - val_loss: 0.3094 - val_acc: 0.8562\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2791 - acc: 0.8935 - val_loss: 0.4071 - val_acc: 0.8313\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2677 - acc: 0.8915 - val_loss: 0.4609 - val_acc: 0.8375\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2604 - acc: 0.8941 - val_loss: 0.4519 - val_acc: 0.8500\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2511 - acc: 0.8907 - val_loss: 0.4213 - val_acc: 0.8375\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2203 - acc: 0.9063 - val_loss: 0.4911 - val_acc: 0.8313\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2640 - acc: 0.8932 - val_loss: 0.4226 - val_acc: 0.8187\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2395 - acc: 0.9018 - val_loss: 0.3694 - val_acc: 0.8438\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1978 - acc: 0.9220 - val_loss: 0.3958 - val_acc: 0.8313\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2032 - acc: 0.9181 - val_loss: 0.3608 - val_acc: 0.8500\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1811 - acc: 0.9200 - val_loss: 0.4211 - val_acc: 0.8250\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1870 - acc: 0.9246 - val_loss: 0.4548 - val_acc: 0.8562\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1789 - acc: 0.9243 - val_loss: 0.4629 - val_acc: 0.8375\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2232 - acc: 0.9132 - val_loss: 0.3551 - val_acc: 0.8438\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2119 - acc: 0.9080 - val_loss: 0.4273 - val_acc: 0.8187\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2068 - acc: 0.9148 - val_loss: 0.3606 - val_acc: 0.8625\n",
      "Train loss: 0.192787480053\n",
      "Train accuracy: 0.919667590028\n",
      "Test loss: 0.309434086084\n",
      "Test accuracy: 0.85625\n",
      "\n",
      "===================FOLD= 9\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 18s 744ms/step - loss: 0.7058 - acc: 0.6078 - val_loss: 0.4523 - val_acc: 0.7375\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.4444 - acc: 0.7792 - val_loss: 0.3082 - val_acc: 0.8562\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.3458 - acc: 0.8397 - val_loss: 0.2849 - val_acc: 0.8625\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.3201 - acc: 0.8445 - val_loss: 0.2489 - val_acc: 0.8750\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.3017 - acc: 0.8646 - val_loss: 0.2734 - val_acc: 0.8750\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.3043 - acc: 0.8748 - val_loss: 0.2881 - val_acc: 0.8938\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.2984 - acc: 0.8762 - val_loss: 0.2043 - val_acc: 0.9187\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2407 - acc: 0.9086 - val_loss: 0.2070 - val_acc: 0.9062\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.2340 - acc: 0.9142 - val_loss: 0.1930 - val_acc: 0.9250\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.2661 - acc: 0.8857 - val_loss: 0.1921 - val_acc: 0.9187\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2109 - acc: 0.9217 - val_loss: 0.2917 - val_acc: 0.8562\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2331 - acc: 0.8992 - val_loss: 0.2167 - val_acc: 0.9000\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2241 - acc: 0.9075 - val_loss: 0.1982 - val_acc: 0.9125\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2149 - acc: 0.9148 - val_loss: 0.2651 - val_acc: 0.8688\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2083 - acc: 0.9221 - val_loss: 0.2058 - val_acc: 0.8938\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.1960 - acc: 0.9249 - val_loss: 0.1909 - val_acc: 0.9187\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2091 - acc: 0.9195 - val_loss: 0.2079 - val_acc: 0.9125\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1932 - acc: 0.9199 - val_loss: 0.3214 - val_acc: 0.8562\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.1790 - acc: 0.9323 - val_loss: 0.1635 - val_acc: 0.9375\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1945 - acc: 0.9254 - val_loss: 0.2097 - val_acc: 0.9000\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1948 - acc: 0.9173 - val_loss: 0.2210 - val_acc: 0.9125\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1667 - acc: 0.9318 - val_loss: 0.1991 - val_acc: 0.9000\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.1543 - acc: 0.9364 - val_loss: 0.2016 - val_acc: 0.9250\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1760 - acc: 0.9339 - val_loss: 0.2356 - val_acc: 0.9125\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1973 - acc: 0.9257 - val_loss: 0.2533 - val_acc: 0.9062\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1743 - acc: 0.9339 - val_loss: 0.2325 - val_acc: 0.9187\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1544 - acc: 0.9391 - val_loss: 0.2066 - val_acc: 0.8875\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1399 - acc: 0.9510 - val_loss: 0.2074 - val_acc: 0.9062\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1578 - acc: 0.9394 - val_loss: 0.2467 - val_acc: 0.8938\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1257 - acc: 0.9493 - val_loss: 0.2346 - val_acc: 0.9125\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1085 - acc: 0.9572 - val_loss: 0.3518 - val_acc: 0.8625\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1447 - acc: 0.9508 - val_loss: 0.2327 - val_acc: 0.8938\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1366 - acc: 0.9476 - val_loss: 0.2389 - val_acc: 0.9062\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1332 - acc: 0.9472 - val_loss: 0.2612 - val_acc: 0.8938\n",
      "Train loss: 0.119979362161\n",
      "Train accuracy: 0.94944598338\n",
      "Test loss: 0.163482812047\n",
      "Test accuracy: 0.9375\n",
      "\n",
      " Train Log Loss Validation=  0.123066677223\n",
      " Test Log Loss Validation=  0.178947531537\n"
     ]
    }
   ],
   "source": [
    "#def myAngleCV(X_train, X_angle, X_test):\n",
    "K=10\n",
    "folds = list(StratifiedKFold(n_splits=K, shuffle=True, random_state=16).split(X_train, target_train))\n",
    "y_test_pred_log = 0\n",
    "y_train_pred_log=0\n",
    "y_valid_pred_log = 0.0*target_train\n",
    "for j, (train_idx, test_idx) in enumerate(folds):\n",
    "    print('\\n===================FOLD=',j)\n",
    "    X_train_cv = X_train[train_idx]\n",
    "    y_train_cv = target_train[train_idx]\n",
    "    X_holdout = X_train[test_idx]\n",
    "    Y_holdout= target_train[test_idx]\n",
    "    \n",
    "    #Angle\n",
    "    X_angle_cv=X_angle[train_idx]\n",
    "    X_angle_hold=X_angle[test_idx]\n",
    "    #define file path and get callbacks\n",
    "    file_path = \"%s_aug_model_weights.hdf5\"%j\n",
    "    callbacks = get_callbacks(filepath=file_path, patience=5)\n",
    "    gen_flow = gen_flow_for_two_inputs(X_train_cv, X_angle_cv, y_train_cv)\n",
    "    galaxyModel= getVggAngleModel()\n",
    "    histr = galaxyModel.fit_generator(\n",
    "            gen_flow,\n",
    "            steps_per_epoch=24,\n",
    "            epochs=100,\n",
    "            shuffle=True,\n",
    "            verbose=1,\n",
    "            validation_data=([X_holdout,X_angle_hold], Y_holdout),\n",
    "            callbacks=callbacks)\n",
    "     #Getting the Best Model\n",
    "    galaxyModel.load_weights(filepath=file_path)\n",
    "    #Getting Training Score\n",
    "    score = galaxyModel.evaluate([X_train_cv,X_angle_cv], y_train_cv, verbose=0)\n",
    "    print('Train loss:', score[0])\n",
    "    print('Train accuracy:', score[1])\n",
    "    #Getting Test Score\n",
    "    score = galaxyModel.evaluate([X_holdout,X_angle_hold], Y_holdout, verbose=0)\n",
    "    print('Test loss:', score[0])\n",
    "    print('Test accuracy:', score[1])\n",
    "\n",
    "    #Getting validation Score.\n",
    "    pred_valid=galaxyModel.predict([X_holdout,X_angle_hold])\n",
    "    y_valid_pred_log[test_idx] = pred_valid.reshape(pred_valid.shape[0])\n",
    "\n",
    "    #Getting Test Scores\n",
    "    temp_test=galaxyModel.predict([X_test, X_test_angle])\n",
    "    y_test_pred_log+=temp_test.reshape(temp_test.shape[0])\n",
    "\n",
    "    #Getting Train Scores\n",
    "    temp_train=galaxyModel.predict([X_train, X_angle])\n",
    "    y_train_pred_log+=temp_train.reshape(temp_train.shape[0])\n",
    "\n",
    "y_test_pred_log=y_test_pred_log/K\n",
    "y_train_pred_log=y_train_pred_log/K\n",
    "\n",
    "print('\\n Train Log Loss Validation= ',log_loss(target_train, y_train_pred_log))\n",
    "print(' Test Log Loss Validation= ',log_loss(target_train, y_valid_pred_log))\n",
    "#return y_test_pred_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#preds=myAngleCV(X_train, X_angle, X_test)\n",
    "preds = y_test_pred_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Submission to kaggle\n",
    "submission = pd.DataFrame()\n",
    "submission['id']=test['id']\n",
    "submission['is_iceberg']=preds\n",
    "submission.to_csv('sub_Adam_10folds_amsgrad_0001_15p.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['val_acc', 'acc', 'val_loss', 'loss'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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8Hf6Y6PpryiInu2zngRuZ9dmVkJMJN/8MdaNdP8qJkJNVcg3yBKnSo6Gio6OJ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\nIqLV/wANMAGR4qZB2wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbda391d320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Y7raPYd8yWwWzOlK3w+c3wNRz4OA6OP9ZuG8tDLjl93Ht8ePtsMbt31f4cL1b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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbcc8410278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# list all data in history\n",
    "print(histr.history.keys())\n",
    "# summarize history for accuracy\n",
    "plt.plot(histr.history['acc'])\n",
    "plt.plot(histr.history['val_acc'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()\n",
    "# summarize history for loss\n",
    "plt.plot(histr.history['loss'])\n",
    "plt.plot(histr.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.image as mpimg\n",
    "\n",
    "min1 = np.min(X_holdout)\n",
    "max1 = np.max(X_holdout)\n",
    "diff1 = max1 - min1\n",
    "my_sum = lambda x: (x - min1)/diff1\n",
    "\n",
    "X_holdout2 = my_sum(X_holdout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13      1\n",
       "14      0\n",
       "38      1\n",
       "43      0\n",
       "44      0\n",
       "80      0\n",
       "104     1\n",
       "127     0\n",
       "137     0\n",
       "138     1\n",
       "141     1\n",
       "167     1\n",
       "195     1\n",
       "199     0\n",
       "207     1\n",
       "212     0\n",
       "213     1\n",
       "221     0\n",
       "240     0\n",
       "256     0\n",
       "267     1\n",
       "274     1\n",
       "276     1\n",
       "285     1\n",
       "290     0\n",
       "294     0\n",
       "300     1\n",
       "307     1\n",
       "311     1\n",
       "318     1\n",
       "       ..\n",
       "1363    1\n",
       "1364    0\n",
       "1373    1\n",
       "1380    1\n",
       "1394    0\n",
       "1411    0\n",
       "1435    0\n",
       "1440    1\n",
       "1445    1\n",
       "1447    1\n",
       "1460    1\n",
       "1470    0\n",
       "1488    0\n",
       "1495    0\n",
       "1504    1\n",
       "1516    0\n",
       "1525    0\n",
       "1533    0\n",
       "1537    0\n",
       "1539    0\n",
       "1541    0\n",
       "1542    0\n",
       "1549    0\n",
       "1552    0\n",
       "1559    0\n",
       "1566    0\n",
       "1567    0\n",
       "1577    0\n",
       "1590    0\n",
       "1602    0\n",
       "Name: is_iceberg, Length: 160, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_holdout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fbcc82d5128>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2HZCVjLbHT0Iw20NkFdRzCU+nQEuxpdPW3UZNJTuIRNoMNlugrDnyk/sVRN0Q\nL09ZEhTR0taWU1xFQfAM/dip0D9GditGgkEl8fEYSvIC7YwXyAk+5TIdNyDAFpYAGsgZXkAMXYI+\nT0unyZdAa3vycVnlsNrCPW3O6vPOM9BURnPhSL0ElWwhyp5B7Hy15cstR5mP6Se2LvuxUew9g8X5\nuIsSpCPk1WP+YlvMCXfMUBSsuUTeMpY7MmxXUZ0xO/JxG7R8rPgMG6Lvy26Mmt8k2v9Cx2ZeVUJC\nQsIdIr0UExISEoDNpc+V1J7UfK5aoOgRIoTDtlOVCjmvvZie24Xl0woR38ZVp0a0bqpgS9bU+dRz\n3nPqc2AQTyMPtxGLR7EoUQHBNu2dKtCavO/npwhaB4hcNxHFnZOG1+fPQZ8WyLHtPuZ9PewgEonx\nbOWoE40U4usZinvNkCs8ctpog7rvM78E9eCMvoJF2w3k4fYLP4BO0ZMVlyMQlUaBsjki+usgbu8Y\njukNUnm/P/kQIvW5t1H0kEUFYf49WNbIETmfV/XzN1z62LKYWQP3M4eT9wyJAQuMV7Xtz1wLgvA2\nnr/jHMXQoiN7H1H7KSh4x1ds1Gr5Usv6LmeiqmNzkGaKCQkJCUB6KSYkJCQAG0ufgzxfeYpc1eI6\nInoo+mRNCGgj3Zic9pI+QUaHa0TomMpboSbuEuLZHMWt+jggwMbrMNbppYvVoOP0pUQVqyVyso9h\nNVYUTmWbez4A9yL5tUSd4mlsM2sjZxmR4u4l/3zG9HNEto9gY9XIUSxrhFzpzK95jhrL3VXdZguP\n5Bw22XTp3j/GfcO4Ma96icA6bdTGKFxVdp9eA7qx5W33phBmIxJdbft4lojml1O/nj4E1gG598sF\nbM/iUkJv7t8/dg/c4CEkb6LWM93D+w3kfq+8j2MsMTQPkB+PZ2cSHeSzDlQLxyj+BSW9DWE5F+8/\nXeQ3CWmmmJCQkACkl2JCQkICsLH0WSZZjBgWoHih45fcRBSRVaKqQU0rZhgdCo1L2GjNKtaARl5x\ny8OCBupzfAO1lCEqXsJqqhWp16rpn00PUZQIBZsvdbdPtndWEOxOQZl2IFJH9FOoexyiUL2HCPJC\nTuWY19tAzvAUYu9+8PYaH/OIM4tuzQtEny95HrhivwqIzpsdRFMbvr1A1Jw4Rsj7shAJhmB/1oGQ\nHbv0ozl1A1F+1veuEM0d7/t4FohKF6W33UaOeQWndMMjt2a+hvvWOnATKC4B9HN0duR9earw5+ny\njheoCog5DIBmAAAgAElEQVSKr5AwQLu2ScxdJg3OYde2XPp5Kiz7zKKN3Uo3dfF7QSPNFBMSEhKA\njZ0pmkzNmEbVxML0AjOFEgvJs6lrv9YpXeMd6AHH/qvZzWAaihHk72bFNXzMDoq2HzufozYG07ji\nDIkVPict37eLGekEurIe0tYM6XKHRz6baGExvgWz0iwGEqjdG2D6vA7+SFIDWsZOx2cwE6Tc6bL3\npYX0tzlm6vOefz5Yu7dc8/P0OrBsaaE9lCFl2iRrpCzh4LJEZGKB7bbHDtSc1/d62UZwBYGzvAMN\nJuV52OaxU5RPHQSf8nUwyR3P4zOHIN/lvUv+/dLbWEKz2kb64yXoB0fX/T6zDGoBV6cebHp6s/qJ\nnWNGuMQsNCDoZDt+nztrc1xLOsWEhISEjUd6KSYkJCQAG0ufQwhaRNPXBlKksn2nXkMs+jdR6Kq1\nrgFCHR0WqJEppxwUNMB1RzAIJa1ioKeB4IqhBsfaXHSUgWtBJ9dY4LcMqW3XVl7KsoMUxR5cakQ3\nIHN6lpX1+Qs48Gz33IS23XQKRv2kEFBgKtx85tR4CRPVAtLPCnR3bQTcgE7yxtHByXa/733p4LFt\nYjmgBGW+IdcmNmYwf8W6xuyU93D9eRf301q4V2069uB+IqC1GvsSzO4EtWigXxzuUe/YjNeA4NLH\nvN9553w3nBbKlK4QACnu8cDVFMG4NoJr5SFSK5tP//MPqCHUQNBlAgPlbkwJhUR2o5BmigkJCQlA\neikmJCQkAJtLnxVOUu2yvlOC1TFqilwGfWvDRDbyqvtKdw95Mnda04EzTRsUojWBwwiisguUGDXQ\n1wWiiEvoHdeOMCtoA/dGcMMBrRMj66B41BV2YARbIkUvhzPPeiVh15wmT1dIFWt6GznaWCFa2Tpy\n+lhCy9epSPdR7nWMcqoxohlwng6WPRYjmOyC9jVauDYayC7gUgS3G9JnmtVOVNPKZtePo75vUvlS\nw66htk0XpU9x/VNoTOe4F4tTaYn15zOkR/b2/LnJVwH7wl0HpWevFX7OwdyXhgbeXTVB6yfQbHbi\nMs18CyoE/H0ILkmsot6K7j2WpehzQkJCwsbjQr0UzeylZvYTZvY+M/vvZva18fM9M3uXmf1y/P/u\n3e5rQkLCZuKi0eelpL8QQvh5M9uS9HNm9i5JXyHpx0MIbzKzN0p6o6Q33KqhrJJa00g/4JKzWDg9\nWME9xAoIn6M4u4fSnF3Wa0FZyRz1Sk6Zlo4hQt53EfIS9CmHe84Ckeg8Ou80Rk57whboICjjEuar\ny6lHFil2XpvWStICSnbW2MhipHEImmgQiVcQ+GZUqSM9MusjzfG6t9Ma4AbAVagNKptFKkcDWyES\nKqTcdQ9xzW2kR0IEzbooTRq3wm1HcKfpxGMPBRcbKrNRC+eo4VSeJsODbVDfCVIEUbxmBeqbDdfi\nbb8PTyHzsYm0wX6AUW3bafI2ov8TPHOG8rGzHpZSYNy7rnuzhTD8ou/HMf2vQh/nMQEiXKgp1XOH\nC3VZIYTHQwg/H7ePJb1f0kskvVrSI3G3RyS95u70MCEhYdNxoV6KhJk9KOnTJL1b0pUQwuPxqyck\nXbnJMa83s/eY2XuYupSQkJBwu7ho9FmSZGZ9Sf9U0p8LIQwNOZYhhGBm59pzhBAelvSwJO1s74R5\ndLvpjJ1uVYgy9zrkfhDtniNK3QJ9KScI7XW9jRz0bTqFMHrmNDgDrQkwoiVVWv9U5YiaM2rMXG5N\nQSXhHtOAGP2gA7U1Rq4VkIccjViboGMj5N52Zz4+ez3neAUStCdDUO8OzWQhksd4rei2sx37MnRq\nuoQz0GkBOmg/tmdYvtgv/TwVIsQl6LZcXCBFsXkLQu8KSxN0poFGXKsma7ogAQDKgin6BeMhzWOE\nGlemYoTlAzyT04bT7sXQaXK76dfJujwtLJlUC6gfMA9ard2eoFqAYY80933b7Fd8bm0zPWYv3kzR\nzArVL8TvDyH8s/jxk2Z2X/z+PklXb3Z8QkJCwseDC/VStHpK+GZJ7w8h/H189U5JD8XthyS94/nu\nW0JCwosDF40+/y5Jf1zSfzOzX4iffYOkN0l6m5m9TtKHJb32mRqqLGiSRRE0LKAKiINFWg5eWcZ8\nUhqOFqBSBuusCahRzpIuaPvG9eu+D0uSwkS1C6rSibVjjrZA+w+dgrcZ8iVl2nE+aDBFpQXZHJFQ\ngeL1mzX1K1AOluLlEpZrc4ihF4gKL/j5oVO8Vu5i5zHygOctUuK6k2HHx7aAeNjGiJpjWcFgqZVf\n99xvw9g2kWNdIUI/h11cr9GO1wAVAPPNB96vDGVa50fe3vAev5wuBj1DfroZKXF9f6d4tlpcmYH6\noQHz3wai/KTv22PkVWO6kyHiX7Wd8x6E+mTNws/ThPFwBjPlJSL4OrHR20yT2Qv1Ugwh/JR002Ky\nr3o++5KQkPDixIWizwkJCQl3GxdqpvhcIquk1qSe3vd2nL7N4Q5NqjhHKG3Zq4elceP8/FWj6Bdt\n0NX6EAw330F5UoS2F6R+qE1ikZbsHYKy5nCbBsUkTc+QV5zvIrQa0N8KZSsR3Wz262sON2CXhpor\nC+RsT1CXJR+DPrPOzL4LtungvQD1vJEjV3xVn2vVR0QczujMsWYZzgIKAkbr5wdO37ssSUs7rqaP\n6SrSVioCTgFtzHFvSStbcD5vQWBddREJLr3ve1bvs3oKruZtxqIdgdZloLUrlEylLR2j8hXueTV6\nungdj5Y6Y39WVkguGOMapmX9jKzCZnqHpZliQkJCApBeigkJCQnAxtJnmRTW+bxTpyclyo1uoYhU\nPkV0MwpV52BaJQtUIRTUgQt2CautApFdluqcw65qBupzrYFc4diX7cJ5TdimqhZ0DKLuOSyxuxAJ\nj/B5iwWysAywLoxUbPk5C4h+m6C9FQTjNnCavph6Ym0JGjzFWDCf1rCUMC7r6++P/PxN5pj3YGkG\n+6th8HG7tESJV7iAH4CmzxCtHSxJVes2V9f8Gvb7nrNOdcJ15BL3O379T5Vw+waVbq9QAAqKhtm6\n71f82iYBQm/Q12zskfUZHsBl5fsMpn7OJqLfFa6ZwvdOpPgtCLNL2JhlcI/vVXieI+3PN3ROtZlX\nlZCQkHCHSC/FhISEBGBj6XOogtdqRuRuCXowBZXLII8sYiS4BL1swBE5gLJOjp2ahyOncs2+Wz4y\nb/l46YJhoehRHzZaZRRNh6HTazAwzRApD4gsboHKNZBjTOfrau707EbTaeXlUF9fft37N8vPT27N\nEP3u5Mgl73pOdImxnZtfZwN+UwOsT6zF2Td6yBOHLVs7p3UW6CjYJqn01iUWuvJ7233Co+tVhXsR\nI9e9y06ZVygQtUTUmIW7VhCYb1vv3P1HqO89HjrFzqKDetVBkgCi6a0MzvDH3u8mRO+HGVy1YW93\nb/C+9OCb3Sy8/aOqbnOMC+rjnjQueRsrLB90j+rnJttM7XaaKSYkJCQQ6aWYkJCQAGwsfSZKBBkp\nqqV11gw1oI+iv9POEEWO6Fh9ijb4P5bbiDIj4r0wRqid4nTw+Qq1oXeOa4rTBX1cHiMnueXbjcLP\n2TiEvRVr+sLrqiW/pnsziNpj3nJj2yOheUAxLUQ5wbq1Qp3o7FRBK7/+/IrT6gCB+Ri0srEWEoPq\n8he7lNPE0YHT5BLjRrfxFhLeO0tEZTFeowoi9Nj3GYTMs3CM7xHBbWOpBX3M4N7OuttCUTLbxbMQ\nlRCja4cnn636Pp7trt+fDFS+iXzze7a3/fMB6lsPfR8uscDF7STf/XDh92p2yuYMQm8884cxf3q5\noVOqDb2shISEhDvDxs4UQ24n6XCLHDMl/AyEEuUmoVO83KpnS8fb59clmaPOi+AeUyBt7GDhC+r5\nkc+O9laor3LZ9x824bYTU8oqeuDCtLUDDd6KfrvXYLGCdL05ZsFLpvbJ21xLBoeoZZljKjBFcGeK\nwEnzwMei2/AOt1GXZYbBW2ImlmHWPu/W19/t++I+Z40lHHu2YKYbFj5u1H0uMeMxpF9qhrKtmP3l\nUcvXROyFLjE5SoxOcP1zpNbR6SjMMMtCKuQA85Aiq5+zj+762B6OhifbFXSKey2fNTJYJbCDcenX\n35n4mIfLfi9YnrYdg1s9GOtO8Dwv6KgE01ptZnbfCdJMMSEhIQFIL8WEhIQEYGPps8lOnEK2sOi9\nApWbntLhwZElUi87xmI19ixZJAV0wxDQGPSd7pR96ArBcNsw9Cwyp57TmPK3ZGZfE8EVrPkvR9Ay\n3uMHcDmg97hT+XC/UymjWWu8whzX3ATtzPD7OSGV2nW+PUf6mSFIpIlTVpqidgzGqbHWyCECKjAO\nUh9WLlPcjTZWMrb6uDYEmhYwea1wHaOun2AnLnGMB/5ZG6ltLFk6gptwH7VgBFo/hsNPC8skTQhO\nV5G+3ysPRLUROJqX3t6CBVFQZ6jxOIJO8u2BPGAmaE/nKCHbiCa21vL+7eOVkEEnucIzfzWrl2ZS\nidOEhISEFwHSSzEhISEB2Fj6XGqlj6nWmRWkqZjzM/1tMIV+LNbM6KEuxgjRYZa+bCFaWcJ8tUC5\nywGivF3UiGli+KcjlESNWsI2XGLAKtWCA8ocbR+g9Gp+DFebgZ9zGLyh/RGipbHuByO+YeL0rQJ9\nR0biKZNbprwJmr0GKGOONMODhUdad6ImbxelVGlieqNxdLJ9xZwmI+B7Sj9Jber6OZCkAssdg9wp\n5vFOTSu7pTfYQsR5BaejnYV/fpD7Pb+E6PsWqPcx6r7cmLsm8b5prAsz8nvSaKJkax/nWbmaoG9O\nd8srfs7LU78emtla15+RBkxmR1trKu3nacI0WWO/thIphO1L9Vhk1G5uENJMMSEhIQG4UC9FM2ub\n2c+a2X8xs/9uZn8tfv4JZvZuM/ugmf2QmTWfqa2EhISEO8FFo89zSa8MIYzMrJD0U2b2ryX9eUnf\nGkJ4q5l9p6TXSfpHt2oot0y7VlPYFUxRmw2ndV3Q47IFSlzWtGC+QtocDGR7TUSzO4hEo3aI4JgT\nGtgfJUnhQ6qAaO1cNVXpoDQn0/OOMpimVr6dZ77P6pKfsz30Pl6BIHuC6Ps41jTZpvEq0yO7vhww\nO3QqK6SQsRaNoXbJvI1SqV2Yn173sXhqWUfIB6CGAcsU+wvvTA9lRe1ev84F6u8YOP4AJTxPyQhw\n63pZ3U4JYXYFC55miaj0MYTse/75ccs/7ztjV+tjSJfs+vjfiAL/9rb3bwGT2bzt17aNVMUStXDm\nEKxXEz9/gVK6ewGlUvdxH6/dqK9B3r9JCw5EcOPpIHmgHNZ0P6w2U8V9oWaKocZaP1LE/4KkV0p6\ne/z8EUmvuQvdS0hIeBHgQr0UJcnMcjP7BUlXJb1L0q9IOgzh5Cf0MUkvucmxrzez95jZe0poxhIS\nEhJuFxeNPiuEsJL0W8xsR9IPS/qUZ3Hsw5IelqTB9nZoxShhp/Spfwsi4IDfhCkihIdZTUn4Yp3C\n5LO/784kFaKFxR7ydqE8XiHfdlmCnoFKzxFptKO6L0vQ2xki29fN+9Vv+hIAa3QUJUt5+mbVRSQe\n7bdjpLeR43tQ9urYzznoevS3QunTFXKFDRHNLUSUl4d+HUPkDefRUDdvoE9u3qIO8tS1A6ejAx/b\n+WXvb0BfGlgHCIjKlrCMmcflkRai5gu44cyWEMPjz2afqgX8NRmenQpLBssMZrWD+jrG1fl50h3U\ntjHUE2pBFdBmGVoI47faeBbt/LlP94H9+jwo6/rUyKPjoQfXHwi8W4/XfbSQos/PK0IIh5J+QtJn\nSdoxO1lge0DSR+9axxISEjYaF+qlaGaX4wxRZtaR9AWS3q/65fglcbeHJL3j7vQwISFh03HR6PN9\nkh4xs1z1C/ttIYQfMbP3SXqrmf1NSe+V9OZnaihbVmo9UUfJjNE9mKVmM9/uID93Pqh/KwqUGO3s\n+3aXfBRi10PknmZgRAUil0J0bwlaJURaG60YCYX91AqRvm05lWpCnZSDMmYoSbqABdUcNDjAlDXr\n1e1UdA5F7nM/g2B6ieg3ypQ2cc2tOZYDEJSfISo6wOO3uBJrxCwhHkfU+hjLFMOGn6g7RslQ9Kv9\nhG/30C/1YZfWokVsjQomuJbBRmt5fqSVFFvYn6V0me/dQlQ8n9dtGmrOjAqoCT7mVDa7x8e/1ffn\n2bB8AX292p7uriUVCgXq9Txdu612y5+tJYT2U4j+V1v1AadE/BuEC/VSDCH8V0mfds7nH5L0mc9/\njxISEl5suFD0OSEhIeFu40LNFJ9LhCJXFW2y8n2nHiuU3pw96bm3WxRYT5BoHJH3nFZUyMk9ZbUF\nF6nFDAJjlhjtgTJm3pdJwynOThRht5FjSyrbQFRYc+9L2PK2K9C3MGMeLKPLiBbHdgo4VFWwWRvD\nLgvd1hTUbAWKefnA297a87KhPdRIeUIeXt6qYj4tXMWXEM9niD7vzUE30a/OKddsP8+p4CsipuVV\nV1gv47XmsFMrD9Agyt2OUSMn23XBOvOtqwGswzj+WD7ZioJ43qsc9/N6g7VVkLNOyzUsRwSM/zFq\n50xHzqXbGNNGjG7nbeTjN1HPBn2ZYsVoPeYVXd83CGmmmJCQkACkl2JCQkICsLn0WeFEnNtE7nMG\n+lzAWThDFHk32n49iRKXnQksrZBjG0A3BHupbguCcdiVlYgE91wPrg5E5WutcdiHMBflJhuz80Wz\nDUScSwhvj7Z8u22glc7w1Tqqr2k58wh61Uf51BxCXuTh9lEmlpbgzQ4i14ji5xAkjx49ONme7YfY\nb1A5nDNDcaVww6nk5XvdtTrDdS4QzQ8o6MRSrer5PZpHrTMF3TnHB8/Kbtcpc0C+eYnE6inkB0NU\nw9rGn9z16AfXRJ70NoqfbfV8e7zwNijGNoi9VxOUjMWSTQ+q8sOu0+et9VIG8tfxaJ8SwLcxFs0o\nDP/ghs6pNvOqEhISEu4Q6aWYkJCQAGwsfc6CqRWFyNM5HKlZ/wfFnQSqthaljuU7b0EAHJCzO+uD\nVoIOdyGqDojSBVBMRm4LhK6PYu61Fn7+bRTfWuKcK9Q9XhYQgOdwFQd9yrCUQFjM+c0zP08Basg8\n6DlqIrXGyOtFDi3zoBtwCu9hXLYu755sj9v1PeLCwAp0NKBYE3/JV8gDpjs26y5X1fnWZYzcrmtY\nTZGH3Ci8r01Ev+eIxAbWscZ1wolOuxjHDyyv+uexuNQADutbsLZroEBYNUI0m4XLUMe7QH+7Q392\nql3kqg89Ev3U5Hrd3iVfgugjMaDCs1VhmahY1xdfpuhzQkJCwsYjvRQTEhISgI2lz5KUR8YRUICo\ngd+Bxq5ThfETHgk9juLsQce50Qh1fHv8KUG+a4Uc18XUaRjzUxn9DKB1DdQm3om2Xzb1z8BYNZGH\nAnPUN9YCkdOxLxm0mHsNGjTOnR7N4qU2QPU6EIyP6EINmjbu+fV0J36ey+jx1gLUC/fictst2JqR\nto1mvgYxRZGpBQZggaj8EiLlBbZhNi2DCDrAQX2JfOr2ot5mMbMFhOTUy+sQtakrcGnW0YYFWTHy\nMfo1W1dOtk+eBfRpIt9uN5+emy1JohUdro0WZRlyyFcrFJ3ad6p8f6hp9RJ9XeK5EZ5hbcHhPF7m\nhjqHpZliQkJCApFeigkJCQnAxtLnEILKeczRxDx/hVq1Swh1G3DN7qwpDKjmdOy0boEIZYYhZF7r\nHA7LyzkioSyTAGsmpNbKYkTROhAjg3ZOEdktEBXNELutIBIvkMNazpGTvefLA/PoeG2wKOsgx5iC\n4enc6SMLWhko61MQG3eRq1vABbsNGr5m+AcdH5NRAJUbwv4K11ygX8sC0W/83HcKP/8EQu7+0q+v\nPYnHYtwWHW/v1LPSdy6/NIzzUxBPo9BTgPCZf3GTqEqYoxAVBfg5qLQuO+2dw729MYaC4sj3v1Z5\nXvl+c+tk+yD4c9zq1DcM9dE0z3GdiP4b/hbW9chT3eeEhISEFwE2dqaozKRBPUMwGIdWH/PynNXA\npzYB5TnXMySWjETWmIYNzDamPoQNlKQUa51Av5chMGKYfVxHbYzycuwXfp1zpCfCvOaUU8nWobe3\nghvPZEDDV8wmx34dvRjUyfE7WW4j0AT7nBwGpqsDn5EscJ1bKCs6bcKIFkawg9LHvNmozzUvYWA7\n9RMFTKUbcDddYTaNCeGpYFQPgaYCMzi6Fy179T6YtKmHGWlZYGwRLGtihj9HYChHEI2OPW0E1PIY\nyMtX3tdygRkxDH8DdKosLWpwuNGxt7M3wp/2nm/u5s6IjiPjMRgsd+cwKkYN3jFSCNWtB7pS0ikm\nJCQkbDzSSzEhISEB2Fz6vFypeqI2kc2hvct2nT4Y0sKYCjiPC9wZvs9hjto/gtMN0qyWCC4EBFGa\noNJcmq6wz27hqVjDWDvmAIGGe5dONbuof3LcAx2Dw0yGVMRj1DRpV9DSgW614/VRp1iCys4RoBg3\nvd8DULwSVH4HY8c6Jgfgp/2roGQxjY8uRiU0cwx0bMtp/RL7U9e5B4Pe6gBmwi/fP9meQNc4nNTL\nAIOG0/4VS5YyOxL6xQAnocCSqdh/CcPhFrSvqxiAa0z8/hQdvz9zaGMXcw+QbCEVLyxxLJ7RAjWF\nqIfNCr+oZgwq0TS52YQeEfe5N/btVVwmMKVAS0JCQsLG40K+FM0sN7P3mtmPxH9/gpm928w+aGY/\nZAZXgYSEhITnEBeVPn+t6nrPa3HW35H0rSGEt5rZd0p6naR/dKsGqtw02q8vr5g7rWqC12RI8yra\noC0x0jldOH3rtJxWtaAZy68xKuf0ZY5SkqUQLURZ1QqR0yemnmY4KGvq88DSKRBp5WqAcquI+E5d\njnaiO5SkNiKKFfrS2sFSQlW3sxz6NR8hglsN/HdoZ4qIb0GXFqdT15tOTZdwj7ksP+f1XbjARHp+\njEgs66UsYG80x/1c4AnOofFrI0K9HMAU9sCvr78LYeVuvT91rHmF65m6aiFHWdX2EvVSYCzM9E86\nMy2QcrdO0QsBpXaH/n0Lc5bRDkLrcPcp6N6EKc4BUji3lv5cjlZ+/c3YvqGs6QjmuMUQLkW4nmYU\nNm4meb6AM0Uze0DSF0v67vhvk/RKSW+Puzwi6TV3p3cJCQmbjgv3UpT0bZK+TjqZ0uxLOgwhrH/6\nHpP0kvMONLPXm9l7zOw95aI8b5eEhISEW+JC0Wcz+/2SroYQfs7MXvFsjw8hPCzpYUna3t4Ovaq+\nvGmDxqFOPRl1CzDUbEZLlHnD6QPLgVYQ7y4vOwXLYbrZLCk29mNzmIiOph6t7rS8X+tUuHLkVHJ+\n6ELmftedVvJT6YFOaNqIIgtpYSWilS0sH6xLa84oQG44rcpBlqYTLypy2PU2tju+rECXnAbo7rjj\n57+eeUR1ntd0G6cUrWmW5vdqhuhvhvFkutwKpWen8u39lq8xlHA16qwixRxiOQQRfNa2UROiZQrz\n4VIUsJRAiXMXyoVVVAscIDo/Hnlfe0iJ7M78mg+6EKZjmaB34G23kPJ4jNTJAuL9PD5zp9IM8ayW\nsIMKiKA3R/VzG6rNFG9fqJeipN8l6Q+Y2e+T1Fa9pvjtknbMrBFniw9I+uhd7GNCQsIG40LR5xDC\n14cQHgghPCjpSyX9uxDCl0n6CUlfEnd7SNI77lIXExISNhwXbaZ4M7xB0lvN7G9Keq+kNz/TASY7\noTxDiFAfzyGIHvrllxyJSM8uoRjHfIQ8XERcJwNEcw9hLIuc1HkLUVbUaCmG3k6niyhmFPA2nY2q\nhej4ELYrBY7rL2CgC5oUKpS4BJVcgj4W7ZpKtY8hmB46vW3gPOW2LxlsI7LcRo7xouFjfpT7OZvH\nvmRA4fXa/HcOsjmHA02GwiR0wMlR4rMPistoOfVbsykE66gvs64Bc4x+51Aq7OW+HDBGbdgV7icp\nbolnhPVlWiiruoxi9wFqBQ3bnkt+YD5W+1MmEiB/Hvnx5Q7Ogxzr45FHzqcQcj/ZjwoBiL7vn/q9\nbYK+T2EmvIjLOhSFbxIu7EsxhPCTkn4ybn9I0mfezf4kJCS8OHCh6HNCQkLC3caFnSl+3DBprVkd\nHMNYlrm10O62kJ+cRePSFUK7iz4EszBtbSNYicCdMhjOGnKC5zAOrfZA90BFWjGKuVr5ccM2aqGA\n1rRAmVm7gxHFnKU3KdQFrZ1GxVMFNXAGNfAs+OcDuMmSQo0OnabNGn7924VTz6MMNVVQ4lUxh3wX\nudkNUOPpvvdlBdVAAXsr2nsxV31ofp7OjueYT1A75/HRDUnSpRls1i6jNCgE29BFqwMf3JKFdJYQ\n7PdBmbHLIl5HBZu3ouvi9umR52wPUTN1B3VhkG6tGVqfQni/b97mHMsn2+N6jFZYdliiXkwLrwc+\nz3m0yDNE/jcJaaaYkJCQAKSXYkJCQgKwsfS5CkGLaJe93Xb6kOc+5X+yRD5rhkh0tFMukT/cRfSv\nDdFqBYq5vOR0i7LWJfJdmxAkG6KIvSGcra/XkW7mRvcgBh92vb0OcolZ7vRwBUds1IW5lHtZUcMy\nQHP9KJSImkKMPocYeHmECDKE4QZa20PtkIafUntNF6kPEZWdxfZL0NEKVG7EnOWej3MTecCaMMkY\nyxGXnZKfWqbwy1B/Gp23aReH8VkM8LkPrWYsmQpX8YbRgg21W1AvaO3CnuGzCkkELTyTGezF5hDA\nF3ieuKzDWjiHwS+0jf3bx/V4rTA3upb7vjuZL5MwzrwVLyfbzOBzmikmJCQkEOmlmJCQkABsLH0O\nJpWRkh2CBm+jutH+yPNgVyjilI/r/TPk9VIwm62cJmWg41OQjAZzkmEXNh/4/rugbwUcj1cvr/uY\nIw92MATtRr8mKGK0mLvAnKLiHAW15mM/KQXR0xhSZSEsqp57U1hHYcmg3ceSAfrSgEhaYx+v0HZK\nNsOyQiPSusYMNB1jew+WCbIcCdLI8W6gXyvQUDtg3i7iv3BN3zpZesD1I915duRj20PhribayJGT\nzSx3+D8AACAASURBVLiswZukxDWvyrovZeV9KsFJmW/dRTnYgGdxijxsw7JOwFJGBuF/Btf2Ks6J\nSliX8T5Pe4hUoxBWtY5yh83MfU4zxYSEhAQgvRQTEhISgA2mz0HTmDs7h0h4BUp6ZcsL4jZRMCiP\ntXcNVIfWWbYH1TcpU4VILCgzLcUyRAhPuRwHUKi4Twe/WRnE4/2rfj2jbeQbg1Yzyt2AkNm2sA8i\ntFmkXhUo8BjO41WPNaBPNlVgXMoF1gzu8/5euuEfVyhaPcf2eFaHdJsNp8lhH+OMfqntbQ8x5l1E\nfFkPujUCrcd1lIjKlzdqTtzo+/dTCu2Ry7zYcSrbYk1p5BsH0nAIuXPUgJ6thdRDP89+yxXgsyZV\nDhSP+zZcwVSiNrbhPB1E/AWq3IxJ5F04xk9z7+vRgbvBH2Es2nEJCsbkG4U0U0xISEgA0ksxISEh\nAdhY+qxKakxrqtBg0R1Qxhmcn1nv1mKe57ouryQdILK73/SodU5ah+jzEDm+bVDsPgoR0uX4cOGK\n4Hmst1ysEP5tO61aQUjcwrVtI+DLvFTWAx6JSwKge1EczkhoP/hxK1znFqK/SzpMI8q9zh+XpMXL\ncH7UWl4it/kljZ26r7gPjISPWojawtKLFlkrUDwuGYx73vfuBPWL0f743vqaCkakA3KWcZ3LwGcI\n1mVNP2cDad2hBQfxLmh9dM3urlCjeYqIfNf/PFEfSwER9PZTEIxjKWOOpR/LYGO3wFJSHLtGl3Mj\nb6M38Od8CUu5+fXaeT3AR2CTkGaKCQkJCUB6KSYkJCQAG0ufTVKR3/rypkOnxNcKpwJb/Zo2tOBe\nvXvstGe85QLo1lXnrAF0p88cWkZoEfHMg+/fgNXW2il8NEZoEY7QV+VUewDL8N7Kz1nAkXldp1eS\nmkvYSyEqX0bq2aAdFCgwXb1RGlklcn/biIqKUWY4Qi9ZdxvRUq2ty0CHBcutNs4/gY1ZjqjwClZg\nFEkXY9jFofkxrNOKg3osesirnvdRiAw1pcclk6aRYywfz8sBNbt9b80WcE2PYnPq5UtYxLVhY5aD\ngq8ClxWe7l4uSQaVxVHlFmQlqXIco9YhCl49sHuyXXGpp/DzryPo2QeTdVhCQkLCxmNjZ4oNy7QT\nzVANOjFBvzZDut4uUspa8/q3IiDQkm25Zi5Duc3pfSixiUXsDtxYaCy6wucLBDoKzBTXwZhR4TOS\nram3cnnu58w4g4B+sCq9L00YyzJ1bI78s15Wn3OKWdj1pZ//XvMZ1DhDRAeBHkOQYk6NHWq95LgX\n9wopgnHGdQP1dPZRplQzv54BAjrFtgeg5tAszid+zqlQ68VbPKPlrGd2TbCDJWb4C5R+HQSWo/Wx\n7WFmNSy9DGyFWdYA965YxTYx9Z4bjIJxD1tom05P+SUU8sGzVZXOggzP8W7Ln+NGTNGkHjMgJTND\nv6iTzNaBQ9tMoWKaKSYkJCQAF26maGaPSjqWtJK0DCH8NjPbk/RDkh6U9Kik14YQDm7WRkJCQsKd\n4sK9FCM+P4TwFP79Rkk/HkJ4k5m9Mf77DbdqIFRBYVbThkbfKcNq6JQwZ4lPGn3GVKjrDddm7cOl\nxRg4QaCh2YBODQahXABvwgVm3HK6s2r5Pt1ZTY/6E6dmYRcaQAQgDHVcSGZKmL8upnBuzRE8WXkf\nDxo1Jab0bC/3a57gOnsNp6ytgOWDqVPWOdIWWxj/3hK1a6B9tDj+WwgcPbUFrR3SzzoIosxnfs4Z\nNJgsa5o/5fd8da9fk7D0kMclkcUSxsLuQawcyxsB+lKmuk1Q02er4RR7jOAW71cZr6lqwnUI9Drg\nuIDrD0hzDCV0l6jLU/WxNICATYk2D6Y1xS+Rtri9wPIBloOa0DquexKq5JJzN/FqSY/E7UckveYu\n9iUhIWGDcRFfikHSvzGznzOz18fProQQHo/bT0i6ct6BZvZ6M3uPmb1ngUXqhISEhNvFRaTPnx1C\n+KiZ3SPpXWb2S/wyhBCMNUNPf/ewpIclabC3E8b79eX1oIdbdVBTZUV68vRIWpE7rSgR/S1QC2Sw\n7TTJejBzBX3MEdFcYMQXiGI30WYVaX8ATaZ7jTFtETR9Yd5GGDqVXCL6nIMeTkA3+zGK2uz4OUdM\ng0TKYXME09zAMqWIoiIS3Fs53aYR6gJUTtFhp5giJW8XaYZYdlisEP2ewRwYCwgIxKp1xYvENKAi\nOEJ0fW342i5RWwW1THMsrzBtjymHo8ojvlnhkfXlFGVdRzjnffU+bWhaA8rULuFoE7DsUaEWTGuC\nC92CEeyO3/Nrc9e17g/9OnbK+p5WqD9U7kOF0fT71mA6ZRzcDQ0+X7yZYgjho/H/VyX9sKTPlPSk\nmd0nSfH/V+9eDxMSEjYZF+qlaGY9M9tab0v6PZJ+UdI7JT0Ud3tI0jvuTg8TEhI2HReNPl+R9MNW\nz8sbkn4ghPCjZvafJb3NzF4n6cOSXntbrUU6t4B4tw2218wRUUN62zKKqpukY84ktEC0kILpsnIq\nNwN93JdTtjYo+QJuK1O4wKzrlQxRpvTeCkJnCKBXBegW6HOriboch6CboPhLRCtHVkeo9xswUDVf\nGiDtm9A0FWN0jLS5K3CiXaFgTdki50JaWqSQR/t+PVs3YIgK0XnJ6wEaPVjJdFCyFqmAvC+so7OI\nyyooSyIDZZ1jqaXA8kEH4ed82+/zlHV8kC55jHTNe+OSRQNuRFULywQwJF4hzTDAdYnmyEss8eRw\n7LnccoH3FmoEKZrgzDAQbRjellBnjBHOLxZ1Hzcz9nzBXoohhA9J+tRzPr8u6VXPf48SEhJebLhQ\n9DkhISHhbuNCzRSfS1gVlMfcWQpvj1dwuEHtii7yYNfGsROIhBkpniI/tUJU8lR7cAU1RFQNVL4J\nGlrCVWZdQvRy7pT5ieBUugKVgf5a/bn/xhU9FymXPeStojxmDy497Xj+teBdkto9rBm0kbM9dNeV\nAGpKUbFA93oLRHSx9MCc5DxG/3eWfnEHcCNqQPS8xNIAQ6AN1MXZQbS6gHsPXXIC3IN28/rejTuI\nuFPV1Tz/T2WIZ2HY8P5ewb1jjvm9cheaPNLjEe5tH+J2Lk0sexC9I8d8ieevM4WT0KklE6zxQCFQ\n6ukmsQGuPxTXBzz/JwkAGxp+TjPFhISEBCC9FBMSEhKAjaXPkk6m9zMIqZcQuO4unSqQKs3ix03Q\nS5s4HWkOIGptw9h1cn487jpMYUcd0OSl/ya1hjhgTaURKNzteL0MW9B+zCnQ1Zaf50CeuLuufyJJ\nh0unh1uZ09BmNHGdzD1PulegNCZE16NTAl9weURrF4hc5itQNowjxdFhHYlH9LcHi6wVxqIH2itY\nWlnPdxr1vO0u7LKyEcTesCbLsvr8xRYE+zAePh3x99OroPzAz3m49HtxuefjX6C+znBR34vp1Ps0\nyn3b9vjniTKts/NzqYsuEgnwjFBsX0LsPuzW+7QKKA6w3cEyRQPR90lUGYQNjT+nmWJCQkICkF6K\nCQkJCcDG0mcLpla0eJpkqGMBepZD7JoZos+RFYwy1B9BvZf5yCmmXUfNjZL5sb7/ziWPBI9h42X4\nTWpAbDub1fsMdpwyhwXoS+lttBChvLflEc8SdGsJerZTguIWPhjDGF1kydbZ0CngCgFfRh2XcHVu\nF77T1Y6fszWEHRcEyXNQ/zJGxWm5VaGsKKOmYi47LNdKOEh3Wt7OFPd/lqP0rLNqLWJJ2ibC+eOl\nj3MOAXbOP5uJt1cgEk+7ONrLVacuqf4HlyBIb2nFliH6baDsg7E3eJR77vWpei1z5N53fTuLyx2z\na/BI20bU3D89pVpQ++mO4ZuENFNMSEhIANJLMSEhIQHYWPosc5Hr1tSjZG0IXDPQnTmcjSfHUYS7\n7TRljEhkHkBB8bOyQLA2wOB5+wYoFjSwBkEuI+TDSOXa17z40YwHonxpB7nUFaj5BNQY7EljRGir\nyq+pf1i330TEcdhBISS4tRUUuiNnnNHI3alfWwYhdY78ZKQzqxVzrpeIThuoac7fbziOlbDRWsKK\nrBzAXg1LIwGckEsMRbRgu4GCT4Mx6PAVp5W8F6tDOLnDtboBalk+5W0uWnz+4hhhaaKChV3eQ5Ep\n0NcMCvQF1Azd43PaltRF8kID1nGzaE3WuOy50TZCZLvEdV5GYbWqvnG0U9skpJliQkJCApBeigkJ\nCQnAxtLnsKw0O6jpZ7/jXHYBS6clossBlDRE26l8CPqGvN4F7K/ogt2GwniBfNtpHyLtzOkuI9Rz\nVIzqDmt6Mhx4GzNEMAdw8i5Bu8ulU+YOoux0iq4QxczgyJ3163aaIwhyR8jZ7jrXJTWjI3iG3OMZ\n+lKAMhcUCkMQvozC5xxtl8gZLuFCTVG7IWqvQx+Lq9eunWz3MOY9LJ80xhikB2uB9ZUG6ii3vD3W\n6B5A9D8RVP8Y5xbWBlYjjyKvWCc7PlP5nDcL4z/B53jmZljqyVBTutuDeJs1uMe+ZrBihDxGsduC\ndRnc3ssG3LaRmz9axiJnWH7YJKSZYkJCQgKQXooJCQkJwObSZwWtYjRytHD60ubvACy9KlCykxpN\nDVBjUM3FyqO8RxBS502PULIK8ySgiBQqKlUQVY9RjKmIEcL9hdP+TE6NDoaHfn5Q804XLsy4sxmo\n1BbC5SsqiU8EwSyK5JRpjpzosuuD1W94hLRAEa0M9bWLEVyot/38h4Z88nj+kfm+Kxb2AgXvImeb\ndbznyE8etFAsiznZbb/mYs/HNHTqazpVyrgFl3LQ9ww1tTPae8FJnQ01cF9yfD6Pz6eB0vamvHFw\nfrfzlxWIJShu1USOfe7X2V6xzXqfUxZlKOx2kPnYLvCsdqKaI1SJPickJCRsPDZ2pmhFrvwldWnL\nPrWJWFxfwJJlCPPZPM5E2qhnwjS7HoataPiMZNz2X+fOTUxOC5S7XGHxvkDKWblu/gDlMFFzZLpD\n3Zkf14CxazHw2cEKM5gxTVExy70nxOtAsGC9oC5Jx9AstrC43+Y1oC/tro9L/jHX6QX8DqOC5sns\nvAcLGkx8ZDDQDegXg1uTHQQJuj5Gq5mP+fDY7YjuW/lMvFB97BwzrIqavoqzfe9XAZ3mHALKFjrf\nRfDoGCxjHtMV9wbuooOYxylt6HHpz0J/ilQ99GU2gynvMXSaW54uuoCpkaKTzgr6zjFm530G1PAM\n92NZ1UaVdIoJCQkJG48L91I0sx0ze7uZ/ZKZvd/MPsvM9szsXWb2y/H/u8/cUkJCQsKzx0Wkz98u\n6UdDCF9iZk1JXUnfIOnHQwhvMrM3SnqjpDfcqhGT1AqxVKicsqjCoj9+EirQwFGo999CiU9UlVQG\nytSETi+fQDOIBf1l6ec8ZQqaIV1vAneaaOL6gT6LhPj2IMAIFLewhXTG+TECN9Am9uTHbpXQZsb0\nsgLauAL0sQp+/g7Sxli7ZT5HGVAYoeql2yebp1LknnJ3lnVWXNj3dYQFnX5Agenks1h4H/fMjx2B\n7oFJqgvNIjWO6zKwzY6PCQM0dMYpoMc06Esbe74eUCC18Bja2DGDE3FzMXbay1TFg5kvO+yU0D32\nz6+F08Y5FwPf50bh59+aI6IY6XsF41usaMhAnw3ppNl6CSa/cHOq5wQX6qrMbFvS50p6sySFEBYh\nhENJr5b0SNztEUmvuTs9TEhI2HRcqJeipE+QdE3S95rZe83su82sJ+lKCOHxuM8Tkq6cd7CZvd7M\n3mNm71nMF+ftkpCQkHBLXDT63JD06ZL+TAjh3Wb27aqp8glCCMHMwnkHhxAelvSwJO3s7qCupVOM\nYc+3d3InC73AKGpNIccZopxwV8mR/laiFknYRtocXFIyaOwa6HoG+hxQTrUbaXgPlIUmpwI1zR53\nzWJ4mS+12i40dqBsGSRuNNYdVfWSQbODdDKkjfWhk8wZIoVTygTRd6GUaV6Qhvs+AWsS61bC2H/M\nrudQBGDJorWABhDpj0dzN8XNkGZXnKqvckrA6e3EaHVgmh3mDF3ciybS3w5XTnEFPWaJCH0GxcE2\n5yFRn3iw9DYKfM/lDS7Z5MhzzLbwbLXRR1ybDZHmN4ZB8U4dfS+x78yQTgltJtNZQ7Mezw0NPl+4\nmeJjkh4LIbw7/vvtql+ST5rZfZIU/3/1LvUvISFhw3GhXoohhCckfcTMfl386FWS3ifpnZIeip89\nJOkdd6F7CQkJLwJcNPosSX9G0vfHyPOHJH2l6pf328zsdZI+LOm1z9iKmVYxolpuI0IbnHosp04P\nenQKibVDliyf2YWZa8kIItK/8HGGyFwTEULrO91hDRLW0agifabQfNn3/vF61HUB8i6ocWMOETJo\natkCfYXbSmNYf/5UgXoumactBqQ8VqizItRfacDJRYiyL2F+u0IZ0CbonsXlhgxt319AAI5ysHOk\nTVLU3EZqXwURehv0sJifL6ove3Eb92RB1cAhzG8xzv1dH/9jrE0sFjAWxvPSQErdOo10v+3i6nmF\nqD3MZ5ug7xT9L+FGNKp8LLpQGTSQlllB7H4U018RzGclVbVzf+b6SBU8uohvjecQF+7yQgi/IOm3\nnfPVq57vviQkJLz4cKHoc0JCQsLdxoWbKT5nWFayo5rybG+heAq4wvXxjZPtAlRJa6oKCtwCfVk2\nnDLOQRPDzQTbMLNtoqxqZwaKAxreCfVt6aBk6AqC6WNEc1FVVUPQysaR0z3myuZbTomuNpxuDZ6q\njx084FRu2oDrDES/zWP/nHVZiidRvnXHhdR5269jDIod4NiSRSrbptMOqN4cEd8mTHb7oIk08M3Q\nX7q5FIjihx5LzELUvG4bZVKPB+60NDny7a5vCsF8tTtYJoGTEcXmzRhFb2JpgM5Nh21QaZR1LVAj\naLXr9zOHkLxElNmwZNRCqd48CsUXzKtHcsMUBsY5Pt+KyQN019kkpJliQkJCApBeigkJCQnAxtJn\nk51EmpeT8+26Bi2YuEKPnMUc3u7Q6cNSbjmlPaeGNOUMyKI5bKK+CHJP72l7HnAX4cUmNLPTmKvd\nCxBMQ7DdO0T4E3Rw3kMuLyygDhpOpaqG96W9JGWs22xWMDxF7ncbYvRFz88fYEtmEBizVCdLsjbh\nkUZj3xAj7mMcZzo/T7zZgFIAUfFFG4JlCtYh9s4ZucX9yqLVGWvEHMwgBkfp1RxR61EH0XfkUi8g\ngu8iiisoHuZxKWEu/6wFFcTONe/fpI+xaGDZBY8C7b0y0GSa/67oohv5fgXaXS28wQpLQ1d7/vzd\nsz5OFLpvDtJMMSEhIQFIL8WEhIQEYGPp88qChs16yt8A9aI4uII78hx0Z7esaXXWh2AZPx8HsCLr\nHnh7R7nTnR2w7WLPnZUb4OklIs4LWEaVMbp9zZy+dcBUsh6ctxGV7o2c+szQ323kLWdwBJ9ByD1s\n1WHUNqLmzSNEikHBAhS+tL3qbPlyxAwO34Zry1FrJAP1v9GN+bQ4//4xbLwQQR0XcIHOUTsHjJ15\n06PgucV9OHjvoabO7Lge6xIK/NnCKfN25fctUMifg47DdmwOu7DWimVFMY7RQTxHDZcA8fhyy5da\neqDDB8GvbVj6M9LqeL8Gp4oO+WaBvOlVTCqYL0mZYUUGYX7fT6Ph/XW/E31OSEhIeBEgvRQTEhIS\ngI2lz1UmTaIrdA7rqC5m/I0xSmiibOY0Bkg74MzlCCU+UTJ1t+UUhzmxzH1uHPp5jgs/tmRJTFhN\nKRbJGiKvd9l3atqGk/apCCqoHIs+MW/5GKVUBUqsl9cUfwUrsCPQo84xcnkn4GMEXLu7iDhXcOSe\nTd1tuwkq17u3Hv9wFfZbLaesw5a3fSS/hgfE8YcYHAMwaPvY9SEkf/LIxfuNGNFtwnJsewlh+JN+\nL8KOL0eUOE+OcSnoq4VCW+Jyx6reXkHEziWYTuX7shDaBFOZAfYZQC2QQey9RPCb06BZTBUfIsd/\nr/IlhTxHLncLxd+GsY9Vos8JCQkJG4/0UkxISEgANpY+58E0iMWEx9cO/IueUykKf69VHqG8FGpK\nVo6cMpUonNTtu0XVCo7EOSLLE0Rfu6CBBagSbazGiOhapMfd4FRvjjzUzsLbM9hiZfR9ws9dCRfs\nZsMploFi5ZGGjZHj2gK9tTZqAOOc/eBi7Ax53WPkKl/tOg3bb/v+beQnd6/X+5dwQ58gN7lA+vql\nmfflyRzJx/9/e+cSI1lZxfH/ufXsrn5NDw+RQcBIUFwIxhiMxBCIiQ+iLoyBaEIMCS5cQKJRdG+C\nGx8LY2JAw8KoBDUaFyYGWbhCEUyIIBGJBIZhYIbu6e7qrvdxcb+a79eEDM6kme66nF8ymVvVVbfu\nV/f21/d855z/HxJdDfRHL0JSzGHGtdRBn/eoHDcVriedvI/x+/I+imM5FTsNgSWpS59mqLYvwTdt\njsskC+X1aTUYmLF/uZffuN5CZnkbium4LjTM3/NOAUk7yNIN8pDVSsre74BdFSXCWBm+ZjBCW+6k\n467mPVU1RxUEQXCOxKQYBEEAKhs+e00azpdzfvOipdPP2ylk1FBUvCSGh+Vr2DM7h+zv9koOX16C\nUvWVpxBKwgDo5Bg9tJQjQ/i+AP+jnRR6NxF2d3B8NUhHFSgGrg0ZSiEcb8G/l0Zbyq8fbJahWgPS\nXh1IWo0Rmo/RBzyELNZwGceFAud3Fjk8G0AdWlg+6KWorQezJEpqWTe/dgx/7cP0bs571gC93zuQ\nVBugh7vZyssT7ZS5biMaH+ch7OrfHl2SxzNCyDyGUvY8TKcmO3nMJyx/wAUpVGWh+6QP+TdokRWD\nPJ7uKorH+3n8vbX8OWxYaM7nkHiyzQqFcrvAdcaab/pBC+d8kIq9d5t8VYe4UwyCIAAxKQZBEIDK\nhs/mOXlmW8iyooe0jcLXbfQBTzPNmzWG1wi7EBodQk9sH9lSh0RUE0Xig6W8nz5C1Vo3f9Y4NavW\nkTWsD5FNRFQzQYhXx9h6nrPIA2SUOz301mL5oN4udzpB2NmH8jZDudYyDK024CMMf2v2WI8nOSSs\noff8aJGXFUbJXKmgbxP6cBscNKXIkJVt4BhZpD9kJh79zCMqlacQso6+8g7OmyFU3KQsGNTBOzhf\nQxRvb0LZuo3e761UnI/ErsbIjrehvD5CZnn4GtZaIO/VQjF+A40EI2S0HVUEp1uy0e+9M0SqnPJv\nMD9rbpQ7tyjefusxs6vN7B/4t2Fmd5vZqpn9ycz+nf4/9OZ7C4IgOHsO1J2iuz8j6VpJMrOapKOS\nfivpHkkPu/u9ZnZPevzNM+3L+mO1niularYb+S/hEFahvutvQt6eCsc2LP+lbhi9MPK7mqgxWzu1\nnl8Pgdo61EsMiilN3E1pLu9/KjRK0dYC3h71LdxVtviXH14gqBPc6kPJpsgJozosRqcCuS3cEYwh\n2jqe8K4pH3ZtEdabEPMdwq8F3XJqLuRkFOs0J6mu7hDqFAdIBO008+fPwU5nAJvWMa1n0dqJTQk1\nm61jOenRSIpINg/RXsjLFPheOqdwPeHOk0pLIyog4U69jtbJIp2vIX4L55jw6+Z9t3B9NmGrOh7k\n76j/2ubp7e15to3iTr0O75jUftmkIG0X7YFoIa0dye2Uk9Q+KyStqsSBulN8HTdL+o+7Py/ps5Ie\nSM8/IOlz+3ZUQRBUmgN1p/g6bpX0i7R9sbsfS9svS7r4jd5gZndKulOS5pqtN3pJEATBGbGDWGtk\nZk1JL0l6v7sfN7N1d1/Bz9fc/YzriitLy37j9TdIktZ2cljRRI3fYiOHaqwVG6X6LIZyrBPbgcVp\njb4cCLcmTIwgSUPxUe/m7QV6l7SSt8w4H1ODoTb8WjZyVKMhQmZ0E+qiWk6MNBAqjVB75inGZBti\nDwmS9X5e3F/s5P3R08UQvg4ReuskwtSVHPrRHnUtecdQOafGWkvY1BrGD1EfbcP6lQmIHajUrKJm\nst5A+2UKmiYIRzcnyICgvnS5ns9VbzOPbdRDuAtL3Npi/gO9Dt+X+bTC4Dv5ex6hNrA1l8fcgPVp\nvZOfZ4hfw/IJLWH7EO7tQGR3K9Xjst2zgzbYucX8XfWP5lbZbkpA/vXxx7SxuVm5GPqghs+flPS4\nux9Pj4+b2SWSlP5/Zd+OLAiCSnNQJ8XblENnSfq9pNvT9u2SfnfejygIgrcFB25N0cw6kj4u6St4\n+l5JD5rZHZKel/SFN9tPURRqt8owo9ZE8RsiLIqStqF2spXCie0CbVNo+WPNlhA+U8mmwLJEG7aV\nxYTZ4vwaLmMM+mUYNmSb2Tzeh4Bl8VR+ny1RVQdqK47aM9T10Z50+2SZOe8hgzpVcZGkRjOHjLv8\nZJA1d9Q1Nk/k7655JK907FqsQZ3b1JoEyXkNkAm+cJw/Z4RaQuvBHhQKQAOUCMxv5OPyHPnvaoWb\nCg8NG/kza0N8Jmr9TvTzckyD2WTUAG5D8Jc1rkvzOIAk+DtCaN6D1+3Jfq5muHw1L6OPmP3HqR2h\n7nW7yCH5GK2DLVx/y+maN4T6Qn2lYwmifWFep+nvlEspVGiqEgduUnT3rqTDr3vupMpsdBAEwVtK\nNaf6IAiCc+TA3SnuFSbLijRQAdmZwG7zFNr42jnTtpIKorvKWbt+wbY92HfCu6RGYU8UDPe6Odzq\nw56zjXa5IQqfx6k6mp4vdYR1Bdq5amgtNBag96iYArFWy6HSFouqLymT+0voiaPSTLeBJYjRG2d/\nKTo6XEboicw+W/QmKCqeJLHeIdrZVlD1XUP7W/8wlhJQJO3Hs/8LW96GaD80hLJDjG+6JkFvFUcm\nvI4i/SG2O9gfhW3Zrrc1yhnqRYTPO0mxaAuheRNWroaKhDXsowP/2gaWdRr4/hdwLeRvRSrayG5P\nfYmQzd8c5Hh8s5fPBe1gqyouO6XaowuCIDhLYlIMgiAAlQ2fBzbW0aLMkk0Y7sJuc9LJIfMORFRr\nKaqYR/jWQKa218yhbg8+Gk0UGDdhZTlCP6ltoXEXhdc6nI9lMiyzksXLOWQaw2K1sZoLbAsoSczh\nxgAAA65JREFUmYzY44qQuYki4ALhYWeA0DuFVUNkbccoQF8RMruQYFnAd9RDH/KJBpYVuhgHsqXN\nTg4Pm1MlI3zmq41cMH6ogIAvCtm7KLAuYC8yRlbYUWA9PpxfNFdH5jxl/2tI7U5QPO7omV6CR04D\n55OF/DL0SmNp5jgsXqcF42NUCjh9c1ZxTUAxaMK+5nbeZh+GYV1jAfa9DJ+3UimGoyRjRGUgXKrd\nOpaSUjH4QWz82AviTjEIggDEpBgEQQAOZO/zXmBmr0rqSjqx38dyHrhAMc4qMSvjvNzdL9zvg9hr\nKjspSpKZPebuH9rv43iriXFWi7fLOA8qET4HQRCAmBSDIAhA1SfFn+z3AZwnYpzV4u0yzgNJpdcU\ngyAIzpaq3ykGQRCcFTEpBkEQgMpOimb2CTN7xsyeTbaolcDMLjOzR8zsKTP7p5ndlZ6vnDe2mdXM\n7Akz+0N6fKWZPZrO6a+Sl8/MY2YrZvaQmf3LzJ42s49U8XzOCpWcFJNn9I9Uer1cI+k2M7tmf49q\nzxhJ+pq7XyPpeklfTWObemNfJenh9HjWuUvS03j8XUnfd/f3SFqTdMe+HNXe80NJf3T390r6gMox\nV/F8zgSVnBQlfVjSs+7+nLsPJP1SpXf0zOPux9z98bS9qfIX6FJVzBvbzI5I+rSk+9Jjk3STpIfS\nS2Z+jJJkZsuSPibpfkly94G7r6ti53OWqOqkeKmkF/D4xfRcpTCzKyRdJ+lR/Z/e2DPEDyR9Qzot\nX3RY0rr7aSOcqpzTKyW9KulnaangvuRTVLXzOTNUdVKsPGa2IOnXku529w3+zMs6q5mttTKzWyS9\n4u5/3+9jOQ/UJX1Q0o/d/TqV/fq7QuVZP5+zRlUnxaOSLsPjI+m5SmBmDZUT4s/d/Tfp6Sp5Y39U\n0mfM7L8qlz5uUrnutmJmU6HKqpzTFyW96O6PpscPqZwkq3Q+Z4qqTop/k3RVylY2Jd2q0jt65klr\na/dLetrdv4cfVcYb292/5e5H3P0Klefuz+7+RUmPSPp8etlMj3GKu78s6QUzuzo9dbOkp1Sh8zlr\nVLajxcw+pXJdqibpp+7+nX0+pD3BzG6Q9BdJTyqvt31b5brig5LepeSN7e6v7ctB7iFmdqOkr7v7\nLWb2bpV3jquSnpD0JXfvn+n9s4CZXasyodSU9JykL6u8Yanc+ZwFKjspBkEQnAtVDZ+DIAjOiZgU\ngyAIQEyKQRAEICbFIAgCEJNiEAQBiEkxCIIAxKQYBEEA/gc8rbnjVo+DpgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbd2be3b518>"
      ]
     },
     "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 0x7fbcc82b82e8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CDyy8kU7Pz9/O/fqzI98/w/58oe93vb98ufTr5h4t8SIOS+9ra+b7bu9se7fO+jWRNJVf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\nglC7FSk+SWiFZ6K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cMJtrlQye8PxNv8rKZx6r1ENJastnMO0d1+D1UXNlgWBMC+K7Fuuy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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbcc83a9b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[1], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[14])+ \",\"+\" iceberg_probability : \"+str(pred_valid[1])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fbcc82148d0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QrDD1RlK9enqtcratYXuhBH0skOSp6HEMHRmcsQNo4gzqzfO2X1GbIpY4OkEP\nm7gPtGLC+jorIMsGq+nTS3dM7/bcebtEzDgUytSGY/hqhjDF6AhOz4wxtvp6ajosGUvsnzfg9M+t\nmVXt7E8f0l6DNtS9YcFv4WcLNTcd9ZD8ClN0htSv+QINiNMipzM6DeI7TscbL0AKLQY6hOSYnZCQ\nkLC5SA/EhISEhIiNpcxZEdQ7rHgLEyE1kMZ2IKc4ButnKzpGt6BYTHJQIpbXjqBkDYtj81QsM2ji\n0o8ZH5tB6qvfu5lWl3BYzuGkOyFPhxzTfhPSYZB0WkD+i3HAqzzCfdAntruFmO0C1LyBWOoGLKTL\nrlNZe9+zJ8etT3JHaoOqdi1aLrsTr3MKI2uxi/IQ112HY3qzBUdmSnEhF/Ri4BfTct6InvGTAK6J\n5cJoAYt0B9sRcKruIRdyWeNmAtoLi24WrbScByVyPncH+NyrVLPuZSzgFVAP6Gfh7tsZ7mPGMYp1\nGcaqQGKrGsZZLUiUjSv6vHmEOa0QExISEk6QHogJCQkJERtLmZWZina1zG8i7+xx3WlyCXmt06go\nRomw6aOmU5M5rLbbsH5mdDQu/ZyyDoXtllvrduYYfhgua5GSzGGdzY79uiZoYgtWQRhtlUMNnBZs\nWhmHc0hHRUr0TOmyWQ/VPB543mEyI7fgYgdCO6B7i4WPV/mpF0+OadkWnNobMYlXa4R43IYPSt38\nugHkxGyMLQNsOwjJmnK0q6CSNHOFxW2ARZNE0M/dWzhl7xz7eA7h6D6B/Fu/8O2WGeZC3nG6O4nj\nf9aPcCjkAh9j2wMJ0agkvoRXwADUOBsgsRrUsZcrWo+c4xmCDgLmbQOh5MO4HXPKiX5DkFaICQkJ\nCRHpgZiQkJAQsbGUuSyDZjGhTxuWyy1Yzq6BHmVIHHUib8TkSDMfqucyODqDNmRI2mRwAF9JS0lS\nR4iDhlpUE7dildAnb8LiewkOwzPQZ1iz63OncrR+LtqgPnBA30KschmTZd03cao3bkFoCuxotvT6\nLzOBU3O9RBlYqgxUegLH58Zx9Xm24xQwpwM04nEbdADHO72JHQD41CuAVhddlAkP97xbjXWD8mgL\nKFm3EEsNabUe7lsBKa7xAEnLtr1Po7lPwJVj87TjfaCieQl18WeWXt4j2vN2wfsgjJgICl4JNap0\nOxYx1zPzOVPdm1s9JaZiZxbzhmeJMickJCRsLDZ2hWjyN1jRQ9gbwuFq3JDHZvdBfLvuDf1t2TpE\nWNwF9xMtjpGhAAAgAElEQVQcFP7Gb9ex5MOKpguLybjmb+7OAqtSLsGinxlD4XK8uXdbrmrDFQcN\nCUsYGDK892zLV4DLJVauJ0opXme75v2hr9ylgbe1sUVlWz/cOYDhATlNFhBarUNottyrVppc2bXg\n47hoQEkH/pbzY/cbXFBPF0YFjeFvOIAA7QMu+juL/ncZRGZHRxBFRfraEuM8G0MsOIdPJBbXOYxX\njfvdUHUUQ/P60/UefVjw6lLdV+LvL66fHG8h4cblnQsnx92pt/Gg6f2vjSl6W9W7t0QiHdqocM+p\npLNsVvMiCcQmJCQkbDDSAzEhISEhYmMpc6Fwkiuis420lWMoi+w69ST1XRkqjtsIi0JKytZ1L2MO\nH7ODpouybjUQfgfDyww+XzMoyFzqeUhbiJvdBYVVg1OWKSiYHTg3G8KxrgvjTQ30rejhHQh6dBiN\nBt05DEMwzGwJ2wH0dwQFy+BjWMBvcIbUq+SBrS0fo5VxaomxKvaP/Ny+x/EtQbsL0NcC7V3OvdO2\nhzwpCFPLQYkX8ZT6cL0aEMM/aVRpNn1cljB27ew4Hbdd7yfTtvZCdY/mU29HDeN2oeXztgtl4w7o\n+7XhNe/DB51K55906eR4C36ro5ZvMfRjzpT9Apo5YO97T/tc6G/7b2WV1KVGZZwNQVohJiQkJESk\nB2JCQkJCxMZSZmIxcZ52oeu+d4aQJjWc+syvV3lP2rvwsQPtm+wgreXAP79Qd5o02vcQuBZSePZa\neAfVYPFF2FceLc60iF9deHn5FD55sMruBqeVzB1TgAcdHjit77adyu2scoLC94y2z+Ox+8FtN7ye\nIVKy9koo0sCvs5E5fYd7ouojr6EWHQCfLzznzC6m5+x5p8+NPb8vfajHjEvaZR2LGe8z+ge1n37k\nzFnP+3YqPSfUXmgdHy69zh4HHbc5g8fBDOK601k1dnP6w4JSd874ebYpyHPJx6Lo+XwJ+35SiXC8\nHoRzhy9UYxr6SF878/n8Qt+PmxDZLdpVu8oN1LtJK8SEhISEiHP1QDSzh83sR83svWb2s2b21fHz\nPTP7YTP7xfj/7ouVlZCQkHCnOG+UeSnpz4cQ/puZ9SX9lJn9sKQvlfQjIYQ3m9mbJL1J0htvVVBm\n5kKaCAFbHjqVqMOR1pCq8+QzWHDbD7qVjeKfrZK5JpxCXLjoTrIHY6e7BQRihZCpOiyXZTyuQay1\nAZHXreswGxMIryoQIjeGaspWhpSsTzs9Vcz7EdoIIZw6HdxC/pGJbh4rSRqCYtZmSOGKvKkdKvzo\n5tCv3aZvOxzOnSbXrzgdb44mWodu3+kghWBzhCuOoGBUgAbXRlVb8gm2Iy75lsK1Bah8A2pDSGta\nQ36X+SHo+z4UfJ7FHsdD1Ziflc5zNHbrb15AhQb36CKcqptQOJq0vP4pHAQC5mt+uRc/g+M8RHMf\nbvscnkCIlso3m4ZztUIMITwbQvhv8Xgg6X2SHpT0WkmPx9Mel/S6u9PChISETcZ5WyGewMwekfSZ\nkt4t6UoIYaVD/5ykK2dc8wZJb5CkJny1EhISEm4H5/KBaGY9ST8o6c+EEI4NdDKEEMxsLccIITwm\n6TFJ2trZDlbefA6FXpugUoM2HG87FcegzfIixUdhKdxmWlEqzMBqvAUH2wXyYOaIqy2ffuHkOPu0\n+25uOByWw44/7NsFtgMCxEqhgkKncqK+C6v0XlUmU5lOOl5nBxS4Zl7nbOr0vYVY7uUxYnxR53h7\nvbJLI56PotXuQfxUtNT6SdnQ2zucgSbDeVsNiKVCILUND/NlTI86o+X02GfAfX33TmiB6hfwPphC\nYWaB7YsmKCbTprb3q2vnfS9v3jqV2PXk6GiAbQ/8bBkzbVDvKei8PvZ+MB/Pc9vVcePQy36462K+\nRK/0sRoOqy2gslzzA7vHca4osySZWV3Vw/B7Qwj/LH78vJndH7+/X9LVu9W+hISEzcW5eiBatRT8\nDknvCyH8XXz1LkmPxuNHJb3z4922hISEzcd5o8y/WdIfkfQ/zOyn42dfJ+nNkt5uZl8u6cOSXv9i\nBWVZpm6norPjmfOKHTgPLxHXS6HPE+sqJLcOBQo4Ax+C87QdIF8LJMcCKFszA4HcgozV025RHR5X\nFs36FtORwvqJ/VGGkwZasOGMXja9H5Qcm8NyWHu6cryuwyJfg5hqDvHZKWKmSfCmE6esW6DjS+Su\noYjssO1Xd4+q8muwlJZwnJ5QZPfIx20Gp2KDLFhWhwfB1K/NIFk1W9ycU6eEyCtv8+4CAdyYK2Pk\nq8lAjZcQfc3hlTBE2tj5KvYZtDdAFLi27/ewBXpfQLZtgPvchcWb2yd1KPQGOIlfnlZ1HcILYXTs\nDvi7c+RxYfd7sY0bKP91rh6IIYSf0DpfjAqv+Xi2JSEh4RMPm/eIT0hISHiJOFcrxI8pQpBFK3IH\nsZ+CN04TlIBxqysr6oLxqMiXMoI1szNEfo8tpzs07C4nzomWW245DbDStX7l5ZPjXrR0LkBfS1jE\nx6hfcOp9ATpfDTgdtyBplTW9fqibabRdnQ91LI0nkIUyqFfDEEuH8lbLKdsUJv5Fxor8UG3EPver\n4wL9aSKuerTkhX5OYwzK2nFeVwPFr8FaTYv3wdTpYTmJ9Bn5Z2qghIOZT6IZTOHHhY95HfU0cmzB\nUAYcexyDVTpP0PTdqV9n2z5Z6YrePnCqD2cCDeEXscSWwazp9W9jy2gWG/PybfdiawyxTTH0PueQ\nMFspZdtZXO4eRlohJiQkJESkB2JCQkJCxOZSZoAKy/OR0yRSpt29m/UiWnWnQMXIqVH3OhyqYTUV\nElHNt2FZHjvFK5HCVEiEVFx3SmgxiRVjXJtwwBasw0foAw2Byz75jB9PkUI0wEI+XsUbH0PRG4rV\nBayfvQc8xrWPWumMXlBm63mnck1Id2Wl96kYVucsc1JmH/89ZLCauxi0luj06KrHGy/A/S8iWdWg\n4W3chcP84GClNu300vacJlK2rHmELQjENR8c+L1oXPRkUg0oVs8gr9W/Wo11DWlKhRh0Ju1ikMEc\nY7Q1X89bF12/4BhbKRzzdoz9zgMfA0hr2vdxmxz42IbotUCL9aYgrRATEhISItIDMSEhISFiYylz\nXpp6UWpqBCfVF2DxZIzvAnHIF2J8siFH7zR3qrG4DPXkp5wmtTpOsVr7iCslJQJoOcxxztGgoied\nmVsZO4GEGPXvOn29fuBJehugddMG33tIRAX15BCdhwuEiXfrvo2w7COWFRJSnT5oJQzBJSzE/V0v\nZyLfMmiB7q1IdRv0zV7wEaKTNAzIyqdexvZFbHscOU0fFd7e9gRbDzUvqH+putawZZDN/Nw5qWQX\n9wLO3XuZj0WD6tlklpCcq12qJOUW13wO0Zq7yJBDnGsXONpPEHo8h8l5sHSafLmJJGsekn2C5+X1\n93CfM6jIjzDnO0+tl1/bBKQVYkJCQkJEeiAmJCQkRGwsZVZmnlCIuYCnTglbHacSdcgbLWY3Jyta\n1mEphmNw52V7J8f5kdOnHLQ2R3Gzhl+7DY2oztLfTZ1xVc4RaGLGoOUtpzIB2wFb7a7WYbnExbAc\nDhHDarG9tQ7kqSAnVQMdXvT9nGLo1GwCWtmAB/wc8cY9UNYFElHlWTUuJWhiC5bt2QRK53CM3kHC\nqxKq52PEks/hYLzANgjzRa9yRF+ARZoIuOfjhd/QWt1/Qss27jkSmBWg2Bni409UvaGAXcLyn8Pg\nHOApUa8jAABxzQ3Mswu5j4sVPs7HwU/qRMd/svv6MSXE/PMtKJ0PLlVfUDl+U5BWiAkJCQkRG7tC\nnJYL/dzseUmnN+p3t7AqfM43h+2Cvw6LTnX+DDlFFgiz273gq0JmFylhmDl+3g0c3Su+2R+ovJKv\nX420ltWbO2/5W34KIdbyjJwidYSxwR6jVtNXdAvUv4VV7PVO9fnFEgYgKJ8ECJTqKRy/wo06NJIE\nGK+yEZRysBrS3Mdr3qpWYA0os4xGviyt7bixocsQwQM/h6k3i6aX08aKKs+hdoMV4qotU4TZ0ZAi\nrGYXUOFZwBevDsfBEYRwaQ4T+le/EOcihGWXR96HsO39zHDfBsMzcspgnge01xD22YLva7nyeYSd\nadz19vURIshcK1kej4vkh5iQkJCwsUgPxISEhISIjaXMzSLTK48rynmUg+IhpK14wClpAxF1tRhq\nl7Wcd/awGT4/QphdFxv/2LAet7AJf+T0+SLSbI6OPByq2HEqP9ytqEhjCkqP8LtJ6Y3dDeDGI6RE\nheHllBEI/owLbAk0YjhYxtCxPgwjXZ8qed37oJnTve2uO7kFirvS4xKha/MtL7MeedtsSQsYaN8E\n/oHI3ZIjVesoc4o/Rw6aEmlQA/wjm/TtbFZtmT/tKWN1yedHPrpZTFY6narWIIrL1CyHpfd/x3xM\nV1sMI/OyRx2/cLdYb0iZjKFCAwNT2ffxbJAHA21I1KxsLdD7VbdE/hnjhpBjHg1565On3ttIK8SE\nhISEiPRATEhISIjYWMpMdBGCtdxB2BtEXwP8ueqtikoVSBlaw7mk0jPEZWUtp2C5s2RZj75/To+2\ntmFFhojs6jU1unro7b4MkVP6vsGCmO96u7IZRWEZjuXXTpZO5doxBI6inx1YLesdbA0gVekQaV2p\nF8SxLWAVpsJQbYjUr/H/LsLF5tgaaAfkeoGSzRypQmstb+N04X0bIm3pDizh/ZqP1yBalCcPIfXp\ndd/S2IEyzhZC4aagsgFjTiWjDmhoA2lTJ/FSKgbR92+JbQemGO213OI+gc/sEl4OHMcm0t3OkUJ3\nMa+2IebN9f6EDJccw9+1FfuTnZnt495FWiEmJCQkRJyrB6KZtczsv5jZz5jZz5rZX4ufv8LM3m1m\n7zezHzCz9Q58CQkJCR8Fzhtlnkn6vBDCMCas/wkz+9eS/pykt4QQ3mZmb5X05ZL+0a0KCiGcODMX\nl5wGTaZuoesbaAVSey4igRvDcbdLeg360sx8COkA3uq6JXYAy/YpyxyM3wzH08o5F2F27QzOxaC9\nM4QUduYIhSuR4hSOvwsc13ve5+NIMRtIpTrrgBKNqZDqh72u0/45nLcPoTBzDCsrjZ8lQuD69YoG\nzkCT+1NaSmm2RUpYKBYd4dqs643cnSMlLMoJyGXSjHsFC7ja9+vrVYpqCP87taTAx6T1BajvcQNb\nGUfVGBU7fh/qKJtqP+UucvGgzp0hUoWC1s6Pne5zi8fgiaA4pZow7A8b/v1yDjqOsmsxR09Ijtm/\nvAgVVptM9fgvSPo8Se+Inz8u6XV3oXkJCQkbjnP1QJQkM8tjkvqrkn5Y0gckHYZwopv/lKQHz7j2\nDWb2hJk9MVvO152SkJCQcCbOG2VWqORbPsPMdiT9kKRfdQfXPibpMUna3d4JurCyxiFPR8OpZ2vi\n74PJzGlYLVpF63CM3R8cnBzTGXivdIuf4LwdGn7cBA3dz72eXt9joscDt76ulHWaYKkHU6dAPUGI\ntgnHbKThzDM/nh67tbrFWw662ZtVxwW6Exb+UqEyTr2L+mE1HULVJvMqtQ3lm18wN7+/YtuToxyv\nct3QQT5DylQ4Yy9hFV3OaOX2eoageKcUYXre9hLqNEcxzSkVZhpwep7DYX3/go/bVrZeYWiWraeT\nbUjL1GLa2ozWccSptyB4u4972MD2ju0wH4vfoxKxzJMe4reRj2f7/dXWTH7FreaLbL27dR3jkkUn\ndSs3zzX73K0QVwghHEr6UUm/SdKOma1m0kOSnr5rDUtISNhYnKsHopldiitDmVlb0m+X9D5VD8Yv\njKc9Kumdd6eFCQkJm4zzRpnvl/S4meWqHtZvDyH8CzN7r6S3mdk3SXqPpO94sYKKUGoQLaekWzb3\nZf4kwKkaftHXyoqebEN+q3Nh++R4NnZas193Orw9gmURlCmHI/UlOT2pDan66giRiux3nKd0BFoF\nZ1w6jC8Qb7ycIXcJaTUoZg6n7mmnavsQscSd4NS0ufR6Bs867W1d8KQexYGPS6cDKgnJr5fBY2rc\n8vI/JX9AkvTCsW9NNHa8jP3r/rlwr/pwUi4ghJtdR4zxZS9nAVpZbzrd3I4xxvOhx6nPEeQ7aIMC\nj73sKeKtaxgvgyU8u+htJMksomPzsuafNuDErn0vY6f0z91PQqdcozNs5QgpbAvMBf7g81dESj6G\nmG15huUYjuaj3WoulPnmOWafqwdiCOG/S/rMNZ9/UNKrPv4tSkhI+ETCuaLMCQkJCXcT52qF+DFF\nZu7sfAinUspobSPXx9yPL8ZhOQiQ00Luy1YD1lxYoldK25KkqV9LWtsBfR9DXqkFq2wrvqdqkN9a\nIma6V/fPC1A2KkC3zdtiSC06aXt7M1p0I93sLPCOPPW69DL6F3ZOjkcopLvl1HA29v43QZnbiNv9\nzHDl5PihfiUd9hHIo9Ga+rOF942OzvtImNNYgpBuO33MsDVQR1x3eN4t+0U30sCLsNoiR87Fmm91\nHGZeZwlZssEL7glA7CC16LLpg3o1q7YY9kY+PvuHPhEubCM6HPHQnYn3fwrn+fK60/2862PXuAb6\nfr9LtA2j83ytA0k0PBKuw7OhM4Nl/UJVRp6vlxi7l5FWiAkJCQkR6YGYkJCQELGxlDkzU8sqetTf\ndspyXMABG0beBZx97Wplx4Pf7in5pbIOSxziUK8vnbLs0JqKhEvlcL0K8bLjlbUOqnN6UHfeN8SV\nIrFRExRwkXuHClgZS7S9gTjoEkmUunnVj8XIx2eJVJ5tULCZ+XVnkaYcqUrrSDL1qbknpZLBMTzK\naP2KzL9/3/Satw/q1gcNL68DZ+wCzvUZ0oDWtiGvBSpfYMtk5ZDdgrN6E07cGWjqxXAqbdQJDmuQ\nWYOT/ARx3SPQ99WdWMAjoYYY7HKIIGOkZy0DbdV+P8eX/druM27xn1xEe6fIJ7s6F3R80kRs/I5v\nX9SRvnelOh42z8icVogJCQkJK6QHYkJCQkLExlJmK6X6tKIiE1git6CSPEHgbAEaupzfnFDIEJt8\n3EZipbFT4D3kWT7OUQaoxRKxwm142DbHkAjr3UzJtmt+4YLtPkQeX1Ds4SGcpK+4VXg2h/UX8c6r\n7YOivv4dOYQ1uQ79KYNKeAsJlIYztAtW5sO509pPa97n9cdmDQ7c8ns/2ncIC3KtDcVsyJ/xHoUG\nJM8mfv+pGN6ECnfZurnfBaXKGOOL7Ygc978Fih1AzcVkXqCvtZijmf7NBTwSxpgrHTijE3UEGlyq\nOWUuUM+s9ItzKJ/3opJ8C7mihRhwJh3PmojrXg2hbR5nTivEhISEhIj0QExISEiI2FjKrGUpXato\n23ELeXkRnzyDkzJpbTvG0M5qTqMnYLEB5ul9qDTfN/Ph7MPKvF84fewhWVEGiy4pWS1aX0s48eaw\nMjL/MjGfIF80rN/7cLBttJHTuAYpquhgvgNq/pGGt7tZR6IuxMZq4sed1vrpVCBm/OGeS35NG+hz\ntNCGhQ/0L153UaNntnys9krkiwavo1V4AWpYIj63YbDKN5D3OHYjIB6aWycFYqDH8DLodbm9AVq9\n5w7eJfpfHvuYNjo3S4eVOaTqxn58BEt1E1sWfaiBaxu5s+E03cr8ngfk0Z4Nq7bAlq360ssYll5n\n2PUyZrGfpZJidkJCQsLGIj0QExISEiI2ljKXjUzzl1f0NDtymjKChXZ74bSmaEBhuFGRCOadRfph\nTTFqVKC2bSgZoy33X8cfcMydXYAZ8TpMzrEtzKdM1EANqfA8hWJ1L/e2lFB7HsMZewq6uXLeHh+5\nlbfsw7kY9GkLybQutDw2do544+2m08H7IHl2NTitv7DEmEdL6JMf+dDJZ8M6yZxjBif63cKp3FEG\nxWjEO8+OvM5s152NM8RV12J8eoC3cT6Hpbru53YGTt9nF+HQT6dr5vkuvJ/1OhS74/k1xMm3kQSM\nXgNdJBlbMq4bibJKWNNroMw1bL0I2yTzmPCqiRzOdLbOc1wHdpxF67ylvMwJCQkJm4uNXSFaEZQf\nV6vBYhfCmcgD0Zr5589DmHSFRsffnA0EqW3hjd/N/ZwpfPwayN1StJBfpY7P4UM3gMjKJFRv+j5u\nT458LW3kNBkNfUXXL2EwgCZsgfwajYm/6g2+lxSdXWHrQ+7LePiAl10yxSYMEzlWXKN9b9cYjnaX\n2i60u8BK5yNHV6v2dSGE24PxYuorp6vlkfcBbnNbbV+V9hBex54VUCHKserZP6hEbzu7vuK1A19Z\n1pD3pNzze96ByOwkIG0o7CuLOlKFmle6vazKHF13wd1TuOL9CbhX9WveB0NYqmFFy7jTbObXIqWL\nGjE96cTWh5PWkHr22LzOnXp1D7O0QkxISEjYXKQHYkJCQkLExlLmRRb0XKva/G4jH0WOZf5BzclU\nr+v0ZBl9wepj5EWB0YWpJBcI7+rA93AwdiNJgR3pLUYFguEY/PwaUVmkgPhovePUbEbfO9zC+RIp\nQeegb087xaxfckrYyiF0G/325l1K/PhhD758JfztRtdc0HTbvI272I6owdhzMHUq3UY/jpbVeP3i\nBfhMZt63y/Cl47hRcHf7mn9BQ8ExfC+pE1Of3JxG83jp920b4WrDLRpPoPYzX5+7hqF7i5K5TX0e\nDWrVtc2+j1u2B6MLxxy+j+UltBsuqfWmj7nlUDWaehszbHe0ZlU5s10aT5CLBVS/VmIs4jZNWazP\nCXQvI60QExISEiLO5QPRzHIze4+Z/Yv49yvM7N1m9n4z+wEzLMUSEhISPkY4r5T5q1XlY17xu78p\n6S0hhLeZ2Vslfbmkf3S7hfWhJNPYcZ84LvnnpdPn6fhmaZE9+hvCatsCNZlCoLRO6VRYCGdIZ8l8\nLKfSSR5Vbal1nIItIBB7XHMK1M6QBrPv74ltWHwP5JR5ArWZDGlAFzE0MFAxBmFs3YX386jmPG0C\nq+kWzt/PKajqlHWENKc/v7jq5cSxu4AYyYFH+ekIfb5S+j0cLbw8huvRb3Sn6efPp95/UtnZxaqN\nPaghBYR2NkElx03vWwPpTkdD36bo7bk1napCR9jKGGdVOT2EPHYbnAmYQ/AOmMA8PtvxazNY7Xsz\n0Gr4IRrmy+Gw2p6oT7zPGcZQ2FIJmNt5XEeZncv11EeFc9cjM3tI0u+W9O3xb5P0eZLeEU95XNLr\n7k7rEhISNhnn7oEo6VskfY3cN/6CpMMQwur195SkB9ddaGZvMLMnzOyJ5Xy9AEJCQkLCWThXlNnM\nfo+kqyGEnzKzV9/p9SGExyQ9Jkk7W9vh8rha8pdL5LRourNtu+FUoQUVFIvnzw793Gv3O5XpI1yq\nX8KjGmjgebyogQaBbpf8HBR3cql6F0yhkmJGS6FfNQN9MlgFC6QebT/i3HMJx+QZ6G6mVTiW48rY\nqW4NFLTVXj9tDvtwAIY9dz52yj6HxTmXlz9ZVGPdg9rN7hAO4AhjK3SzdViSxhCxNVDPDNZqwfH7\nVD6YcHOukekEeUlg5W1D1eh42+vZ63ja0CXu3Yw5dZpoS1SeWcKJen8Ip3M44zeXXg9T0gao8LRB\nzZc9WJnhGE9R5MblyqKNpqqOkL8pFHbqqH8aHblDWH8f7mWcqweipN8s6feZ2e+S1FK1h/itknbM\nrBZXiQ9JevoWZSQkJCS8JJwryhxC+NoQwkMhhEckfZGk/xBC+MOSflTSF8bTHpX0zrvUxISEhA3G\neVshnoU3SnqbmX2TpPdI+o7bucitYBD9hFLJtI2Ui3C2DtOKEjRaTumaEKMRRGoaoJLMNTKDBbsL\np9YMKUHpBD2deAWtGCsdQHWY/yNAHWUKq/EM+T3qdACn5RK0mjSovnIOhhBtsYX35THSd8IZmYos\nNaTwHC/QLjqvw2Gb9PXhvStVeU8f+/eHEGi9hLhy1FODw3YT4zw5dgdwIlzym/e0uRP4xW5lFaYy\nkG0hIPxpjzfudODcjm2PLlSAZvBUmLnAjsLMy7/Yr3LdGHKT7A8ojeTn1nqwlC8w/iiP48k0KZyj\nAfHJB82qnF2IAnMLhg7wkx49IiI1p2jxhuDcPhBDCD8m6cfi8QclveputichIWHzca4oc0JCQsLd\nxLldIX7UyOwknWdr4lSqb0g9+YLTmmLXh6K1XcU129S5RrvEUM29jAK0JoPT6whmWVqcc5zTGsFC\nXIO1OlrvijNyVpxFVJhHQ4i3DT3EFcNJnH61k0FlZT2GR/ODcmo4zd0BetbzCzOk5OxhbLt1p5sN\n9JOx5AaKt9ptmF+GMzocjbtHfjyHwzSFUwm77BRzirjyZfCbcX/m52hSjVcBCzpzqvR3fCwyCNtm\nuEULOGZnzC16zevfoYN/dCovLnif29vepuZVn5+hgIgZxoUrmoB6MtD0HHJxFPHtx72Mg8K3F1pL\nH885wseZ36W7VRXOubwp2LweJSQkJLxEpAdiQkJCQsTmUmZJeUwQUXS8m5R8au45PRnDWqsYz7tK\njSlJNNoaLMgGS9sCFKvs0xnWedWSFHuBNJxwqrVIfcME5kFIUS2RqrIGVe8pUrDU8a47hCLz3v0I\nEB7BSTdaH7chYdUeUObK+7NTwIkc1sdp4eUdmo/nZThgC3k6ygOnePMofT1t8VQ/99q2t+UyUm8O\nQYGpKj4aO31l/K7BQj9roP/ReTscebsNtJdO9FOM/wjzpr3t7TpGfpfLGToFTwDFLZkaFK23EUt9\nfHG943M4gDI75miGOVJrccy97dtdn/Mrx/wOYuqZprQ7hTM4vBPCQTW2VPHeFKQVYkJCQkJEeiAm\nJCQkRGw0ZT4BVYCR8CkHVVkGeKFG2pC3kKZ05Fa+fMc/ZzKjU0A6zwGcXWmJnsFEOa/RkTuL9SNR\nEtSwFyNYH7doZQU1bFA6yg9fGLrC9aXc6VPoVnXVDryfQyhNX5D3melOW4hnDS2vf6fm7dqfeXxu\nX/75bsPrr8fx74JRHkJmbCfz+kdL0GGqZyMevU6Lf4aTIFHWmIAG62bJtx6cvlcJyyRpvg3JLUhr\nzeJO8PMAACAASURBVLReQfqo52O0jWqej+NyeY7EY4iTb3W9/sGxO5GvUqZKUqu1PpZ+DMf0XgP3\njsLjMd59hACFbsspO7eMbOzzolhtE22eX3ZaISYkJCSskB6ICQkJCRGbS5lDOIm5pa2urDlNbaD7\nfVgC93sV9dmvOTW7CNpBh+lZE/HAsNbNoUZd6yBxENrSRq5hDZ0qLXbiARyd63BMpsV7BGp04cKF\nk+MAir2dg1YhJvvwstffi+pXrb4n2ypAk/YzH4s9BOfOBkhm1cfWBLYDaAkXHHznsEqvrKVz5i0u\n1m8HZD04Gh95h8aIMW8tkce67dSPlvAZ4pZbsRgqhg8hG9fcXb810kOWsRIJpOqg0oz5PdzD/kUU\nDD+Y+T3c7SNvNWKWF0vedUcOa3oDOb9zjPliAcd4UPZ5XvU/x7ZHhrj3wGRa5Qby4zVIK8SEhISE\niPRATEhISIjYWMpspdSITtgzxL7WJpACg8PqrHRK0olWv+bQ6dCsgVhSSF61hl7GeHazpVKSMshC\nZTlplX8+hlP3aFHRoK2Gc8CAREStjlPWHNbxozn4MF51e8hdPNnyPl2sQVKrcbOTbQHrLK3wxyVo\nMiTCMjjAC2rTHbDEgNjn8aFLfU0vOlX3An1sW7BOT7HtcdRDG0unuA+Zj9Fo6GrYdWxDUOkqa1aU\nuE51bRjqC0qoYd40YE0XZLzygd+Ladc/34K1fHKxGjuqmxucnZsw44ZmG+f4+ZRCw46NAjaKFrh3\np7wVomN4L+fWBG7Wrs+/TGuc1DeQRacVYkJCQkJEeiAmJCQkRGwsZZYki2v6FqxvGRyzxy84ZevC\nEnz4fBX7uyXEoPZg8YXVsoAzbgmaAmOpOlM6AMPBF21too0WncAHpdMu0rs2JahpzeY5kCsbN+iA\nDioFebOdaIkOc1BQqD5nfR+LWd3L2ILzcoHY7Cm2AzLkCK5fg4wYZl8R48PHSMjUwwnMlW0zH7ns\nAR+3K3Nv45OFK09vL+hNQG8BR60bzzn29jHWPHS9DDrmM7d3VvNzanUkyzrEOVCsbsT5ub/l9QzM\n+7a7gAI2VceRL5m0ns7TjPEuscXT2fa2r1qbIedzB/R5MvTymAu6NY9Xbl6OqbRCTEhISFhhc1eI\nZVAYV2/JKfbru/DJ29veOTkeLpFrJS40WlTzyGBgwcZ0ve5DuGj7++XSGBvVc3+VLrGBvsAycgEf\ntiKmpWT9C+QencDfrnnd3+K72GAXREexztVVpLlsIUzLok/eHOk2dQH+i9cRLrjjK5SxUTEHIWow\nkozhZ6ctX1EaxEpb0cBTwmCwRJ/bWKEusULcfgErWoQUXtzylKAG1aDagvfLmzWP68X5FpRhrq9f\n8Y8G3p9m4XVuN5GbBCGCC4Q0NrFCXAnklrgncyjpTLEqrcENcZhD7Qb3/JeaHt63PcbSEfbAWt+N\nTUVZtaWFFSdVfWowGM2QI2c1b8MGLhHTCjEhISEh4tytEM3sSUkDVVs8yxDCrzezPUk/IOkRSU9K\nen0I4eButTEhIWEzce4eiBG/NYTwAv5+k6QfCSG82czeFP9+4+0WliFErsp1X2GK1Jq9plO5Mq+o\nzy81nILc36YKiA/bCJv9c1CIMcRK66BJ2kVIFfNkrMmfMgOlrHWQoyT3+kvkzli2nOK0YcihWOvL\ngoeGlZObKU+GVKqnUoneBz84qActxFBICIoewiAEmjyZrldtmS+qe5HBl6+OfjKk7/DiqUwiJ0cd\nhLp1YJBZ1hFeh2Hun/JtrMppmH92hDC7OUxgDVDMvUNvy5Iiw22vc9U3SVpeQRhjDI3bLnx7ATsq\nOjh2o18bOWpqou+hX3A/yplBYafbciq/wHbDSmBpCvNSi2K+aEsbSkKdmMcm30CCea/06LWSHo/H\nj0t63V1sS0JCwobiPD4Qg6R/Z2Y/ZWZviJ9dCSE8G4+fk3Rl3YVm9gYze8LMnpidEQyfkJCQcBbO\nI2X+7BDC02Z2WdIPm9nP8csQQjCzteatEMJjkh6TpL3tnZDvVRQCLmFaQoCTAUvjwqnEvFO9Jx5e\nOL0cIoxtF5bKBkKqNPAyCvr+bTEGDH52CM0bwhR4NKvo5gW0cHQMVZNd58m1vtffYYggwtjasETS\nKtyRD8wqTwtzyyz3QJ8A+tuVMy9vCIqd01duDTWXpMmT17yNL79YtQmW7znoHUZQLYarFfChgy8p\ntzWGhq0J+FlOJt72vagyU4JT15CS8yrSyl6Hf2ir5yK3tNYuJt52ehPYoYcRNrpVXwM6Nzavp0Ba\n2bKGEElsAdgBrL+Y0BdanjZ1yV85xmU+q8alDUt5fYD64Z6Q2XlcO33sce56GUJ4Ov5/VdIPSXqV\npOfN7H5Jiv9fvXstTEhI2FScqweimXXNqqh8M+tK+h2S/qekd0l6NJ72qKR33p0WJiQkbDLOG2W+\nIumHrLI01iR9Xwjh35jZf5X0djP7ckkflvT6Fyso6DQVOfkcqjHFcrn2826kJEs4KV96DiogCLUa\nlU5ZWiB2RwO3EDZ24aS8dPpWQkFnZ+LXdu3m29KFlXE6BH2mxXnHadICNL30pig7wtbAnl9rUUS0\nmztNPnzeHZDbXYjcXvK21JdQZMHuQRFAPaEwkyFPR0GFmxgOybwkzAtzMPe2NJiddcvLWMAsWqdS\nDhkzmHxvBrmWcaTPsA7XGX5n3v8e1GOYBpR+AnU4ks8nTrGXEBRePl95jiFaUfkVp+Dw1T/lpF5g\ne8d2cQ9HXtBh8Hm5M3UuPQ83p7YdQunJOhggeBOUqH8+r+oPYfMcs8/VAzGE8EFJn77m8+uSXvPx\nb1FCQsInEs4VZU5ISEi4mzhXK8SPJUImLaOo57K+XslyQbkT0IZGTEM6B30bXPYydmmJ6/g7ZYD4\n1X7H6ev1pVsWKbTZnaxv14pCTdGm5RwUFA7YxdTPOT7ymNgAypiDzC2aiLFdOA293KqsrLTf78n7\nMAYdtiF0emB9DOhO7QAW9xYo5hYCseXjMhxWbcn6TtnHdb+uCYs4U630973/GQRNDyBiWx+tt1YX\nUBiaxO2R9tz7cwza2RpgDHe8lOXQz2l2IP566A7oJWK/ecdXvTPELDcwbtlFOONjG+UgQ50Yl9aO\n978BeZyr5m3pw0Mii2K9/BksJki3izSo9SnyCHWrssMGLqc2sEsJCQkJLw3pgZiQkJAQsbGU2ZZB\n+X61/A+QwqL1s00xTAhzrmKSS1jt+hBcHVzy4zqchPugVTPQ1L2GWw5POYAjxrUOodU8EqtDxKN2\noKBVDJwyNZAvJYM1dQlH8gWcjTPIlXURSzyN59NJd9ZBHo0jp6AreitJ7Qf3To4N4rvMIwKWrtnE\n287xb0YR2cM20qfCMZrx0wHCtoxTlvzzLu5XC0KvTYzR4RVsd8T7v5xjOwDCqksYXzvPeiEL5Ot5\npkD4Pbhxc4gBgKRbFqW2so73cwAL9jbaWsLj4TKEcIVAgxzyZ7iN6s99LKa4p40ii//jPsPK3oFD\nPbOQ1qIGgBWbZ2VOK8SEhISEiPRATEhISIjYXMpspqxRda+BnBFMIUl9oy4jm6PSdo78GzNYWVtz\nL6PACA5byDtR+Ltm1IJTLRW/4NQ7gLN1b6+y+HZgxutDdXmYO+0cIkdLk5JTS+b6QOwvKNscaVMb\nk+qcWdfbNDpyamxbcMaGRNT8yC3FzR04FdNhGbG8xY6Pcw5ZtpV1uz31Aeoj7hbDqf0GhTugur2A\n2jNUyjtb3i6mm+3BR3m/Xt3zgxoc52d+wl7L48cNseScTW1sa5SIsWbq0w62LFYG4inuVRcdvSr3\nqL8vd3X3ArSaGwaHiLEWxvbavueXeeDyfV5OvBXMubIFRfkS8zk7QH6VWP/NgnX3PtIKMSEhISEi\nPRATEhISIjaWMpdlqclofNPndJ4tRaknJz+reE5SAqpBlyOnXSUUsgwU/Grd6WgXFLOOZFXLhVOy\nrcwp6STSRtKxF5BYqAXGeAwl5weX3rcWqM8MFucuLLuH+/snx7WY5GlZrrccdp4DlXzAKSgVmJsY\nwxJmyTFcf/kGrkOlXPvVvWrDyDvZwx2AbFcrQ6KouQ/GUd23He7re5KpARWhS6fyszn2L2Isc97E\ntgODjIEFlLmXmCUtOEMXzfU/rRnGdxm3Emo04S7gnYD0YKOp960G2j2Dn/tB4edsH58hufaM3/Pm\nlShvhzZN2mfoiDZR3nT9KZuAtEJMSEhIiEgPxISEhISIjaXMwdyZdlk4ZZrO3Cpah2MyzXWtUXV+\nAcXoGmI5J6CpLdCaEtTsArSw5kfOMbIZkjIh+RKls7Yi9Ry1nab04IA9gSW0J5e/Go7x+cjfda2L\nTnEp/0VJq1WioSYs6N0td7pedCEtNfQxPIWyWPtx1naavoDjc9hHjG3cViggObYEpa0zyRbY2wK3\ncBv7F0+N3Em6i8RRffNzGujrTpws15qwLDcgLQapOMM6wuAYniOxGAS2Nc7xB+jpzKrxouNBH3Ju\ntT3IxkH+rIWg8fXR8FINuw0XDxDXvo0Bi/ScZQwPPB6+2YDTf7eB41hGbfPWU5vXo4SEhISXiPRA\nTEhISIjYWMpstUz1vUgVYRXsN5ybTkew3LVBSWOipQAKPGPCHVhCBx1wE0hhtRE/y9zFTNs0Qkww\nnYePi6r+1hJnw3m31/U+1GkURJKnaQ1Oz8dOca2B+GBQr+b1qqA55KiHiJ9tQn6qhr4ZaCLHqIlc\nwBT8Wk598LozKI9HpbHly+D03IE1GfHLOZIvNWC1FyTPHtz3sZsi3ngBR/YGnPFr8WMm6lpCC61O\nLwP0p7X0e57DEl4gxrh/AcrgQB4tx80MEl5t5PzGFsRWze/58Mi3GqjAvtfyOZTvYVxAvXXVPS+O\n4uSpYz7n2ALI0B/NodJ+obpZm7ia2sQ+JSQkJLwkbOwKsSxLjeJb7YL5GzKb+FuvPoJ/XO7v/V5c\nORwZjDE5xEoRftZZ4o2KpUOBHBRCnooF3uhwW9TRHD6TscgehWi3IDIKw8wCvoxqY4VKP7RTuTnQ\n5wYNL9XKbH7sq9ajka9E2m1foZwStu1hCg189TVDPpTO1OtpHKK9l/y+NOLgNZinw7yeISIrZ6WX\nMcfnXeTbLBDG2MAqFq6CQgSkpjEHTRsKRwNfcKnAzcrhN7ggc8BKb9HFyvGqr9DDFe/zVj2undFn\neg+2MSWYHrYBP8wcuYCWI59btb6vywusaWcPe4Nny2ppuDiVewjKQPDZZPjh7KkqF0w5X29Eu5eR\nVogJCQkJEefugWhmO2b2DjP7OTN7n5n9JjPbM7MfNrNfjP/vvnhJCQkJCXeG80iZv1XSvwkhfKGZ\nNVTtyX+dpB8JIbzZzN4k6U2S3vhiBdmyWv6Pc/rtOceaQSmlg3C81aZ9DflSGlCJCae21UGZMz+/\nfgj6Aj/Icc9pSBc5Ma8Vzo/uCxWtqvfg4zilt5qXVyKtZWvo7TLQsOM+33vYGoCyyrxenZ/DALAL\nVdQu/BrVRH4TDMXVDnKXHCANa83LXIJKCmK5R1k1/jtjtKkFan5GyktuWdQmN6vnSKdD4+Y5RE8h\naKsoojtF+lYqz1AxaI55M8O2yqxcH8Y4gmpMb+DnTKOCEc8dYkvleOmGjH7wMefdPLU1gLSpghEq\n6/ucb+N+bderbZD9ObZG4O/a6PM+Q/mmU+0l2C+cu/XUR41z1SMz25b0WyR9hySFEOYhhENJr5X0\neDztcUmvuzstTEhI2GScqweipFdIuibpu8zsPWb27WbWlXQlhPBsPOc5VQntb4KZvcHMnjCzJ+bz\nM4LUExISEs7AeaPMNUmfJekrQwjvNrNvVUWPTxBCCGa2lj+FEB6T9Jgk9Xq9MBpV1r0GTJGTni/9\naSWbIhyum1cWujbo2KREekZQY/ovUrd01gc3OfR6Osj1MYFleRfpJFdpLukzl4OyLGDBbtfcmlgW\nCKljTg+w7QmcAgNOKgZRYYcCphRhAdU9NfoYi70xrY6w7B56u2ihHd8Ha31M/4ldBNXgp1jrIecM\nrMw2Rh4V+JiOZk43F9hW6GRe5zhAKSeGxjXgh1iH7x/BfCXMizPBFkC/QEdAa5+e+lZCHs+5OGTc\nKCz/NW/rPtSOaPGtc6unhfDGKf1gHez/qr3bdd/SmC18slAlqF/6dcNQnVOesY1xL+O8rRCfkvRU\nCOHd8e93qHpAPm9m90tS/P/qXWpfQkLCBuNcPRBDCM9J+oiZ/cr40WskvVfSuyQ9Gj97VNI770Lz\nEhISNhznjTJL0ldK+t5oYf6gpC9T9eB+u5l9uaQPS3r9i5aSmWpRzaYL1ZIClGQxdGfX8RUoe0TF\nkQxhYd19hLmBshTGuCenFUvsYS7h9G1PuUBneyXQKZ0IlErSPFoiA6hW2HY61II1NSDvx0mooqQ5\nVGVmIE0ztAv+ylqRQ1pNm3XvTwmaPhepsfets+N8nMKtecfH9viFQ7/0BQjtRuWfFpyl23OkNd1C\nytDgfdvCO308cGq+2IPCDmjtrL6e5k1jExtUqUHOme7U738byi8TbGUskWL16R3v2wMFAgPqN//k\nym1su0xgHeZ6petldAy5eAze22g7Hcmp1DRb+hi14v1tIBSzhns1GrmT/sAwz7rVXDQ7S2vn3sW5\neyCGEH5a0q9f89VrPt5tSUhI+MTCuaLMCQkJCXcT526F+LFCZpnazWppT5fmFtRelp+0dXLcQ0Bn\nKCKFoA8xqDYLLCdOxwagGO0+xEUzp08tfD6Hs3VjG+ow0RK7v3BLabsBtRk4bM8LxPUilrXAMRV2\nJi4mo+nMzxlfqs5qFBATnTEXive/CYWXOZzXMwjuLoZO5Wjl7F3ydJpUysmjtbiE+O4MSrB24HS0\nc8mtv08GbEFAKaYBDwIq9WRDbGV04dQcqWJjeiqw189l/h14ozch6Jq1vP8PBagXQU6GArVFNNeP\nUfb2ASg41YOG2KaAw/TFmt/Q/ZrPF5LZHIEBzK9TXK3EYPMHXAh4XPPxaWILSEhVmkdvAjsj/869\njLRCTEhISIhID8SEhISEiM2lzEFqRqZS70I6C2ket2AgRgZPNSeRCoAOt4dOTY+caZ+CIfaWLr39\n3LnPQk5rAiSySoqRtqvzdxFLW0B8dnFKZ8wPh4iZ7UJyq1NzmtZGTpUJnK2vRrHcBoRdBwHO6s/B\n+gnJqWbu5+d4vfa2ncrNSJ/R9g7TWUZx1zlmZBvUfIZcIE04fT+8dfnk+IUj0Gc4IPOtn4P6jZCz\nZSX6uwDXzIxWXtyrqQ96DnJ6GVpgdUi0raTVJClA6Dfk1bUUEB7teJ093MMprOPNEamq35dtpHUd\nY57pGFspfW/vLHpWZIjBbufwCOh6GbuQVpuE6nNG9G8K0goxISEhISI9EBMSEhIiNpYylybNImua\nwVq7mznFy0B9KPU0jaOSH8OJGc7YtZFTjGf21lOWGS1wdaSNRLhxSRVmOnvHw+IsIx6cuA9riHGF\ng22nBdJOay4socyTcbmszi8QP1zPvYzjS35u143p0nI9cToeuqRUTj9uKFI/v+vt2o3pXLdmsGyC\nXtaf9z6PHoZ1+MD7fxlpVTNQWcaeL4O3t42thGmk9Q2oqNsFBH4vfVyYo4U5Te4f+c9pDoptU9jZ\nmbozOtXXQONLOHpP9+gf4JgyfhnBA+M6887A4g/DOR2/D5tVOZ2aU32m7DXcqxG8LGy1BZVtnmN2\nWiEmJCQkRKQHYkJCQkLExlLmzEydqP47gGP0DD2eMQ73ead4ulI51Q7gxGykKX2nGA+d8U4pQZMy\nWLYDLN4Z0kxSRmoVItqYU90ZtA90fOfAyxgh3ndhXmcjIN535BZfpvlUtHjW0FZ2bSXPJUnlll83\nw3ZA2PcxLArGUkNtGbS+e+SUvL9Sr+5h6wD5XieIpW08Cwfk3nqKXTbWq2RPYZavIQ3rxcHNP4Uc\nibraSP06hMV3DFXrZ3ve3j3QZ+Yba6KcclyNywJ7CiU8FeYTRAAgUVlrDuftmhfOOOxsx+spZj4X\nh4WXuaxVZe5DNu6CPHCAKUlrKFvNai5sHmFOK8SEhISEE6QHYkJCQkLExlLmIpQ6mlVUIMAatg8a\n0oWV8egCnKBDRSszUBOBdmnsNKkOuaTFwD+n1bhGcgGaHNZYHCWpiNe2kPBnxvrRrNGO17ONpFAF\nci4vnnOzMGzMKmAJXcXqMofvdOj9GSMiubmEejWsmU1Yk49b/sflKR2Gffx3YMVefqhyqg5NxJTf\n5/StZbCEIt6ZtM12vTwbII8xPAgMkmaCRTdc/P/bO7NQSa4yjv+/rq7q/fZdZrkxk00MkXnRiKgh\nQSRBcAnRB5EEhSCB+JCHBBRNfBfii8uDCEOi5EHUMAqKD4IkQfRlUBNBklGMgzETZsvMXXpfqj8f\n6vT9/nfunUkGJ3O7K98Phqmurqo+p6vu6fM/35ady+nHEvIaUHKcrw/tU1dhMctcZGyyQCm1JpcX\nlwm1KRraczDigliUimxAl6tS7HN6wPrPHhL88RxjXwpO+gVaOhnzkgI9wyMliz92j/fOAz5DdBzH\nCfiA6DiOE8itZL4UPfJXHp23gj+cxmkQLHdVEphC6aRA2au54BLLVM5YrEW7dpedodXkS48sjb1R\nkEf0c8UO3YOqaaB4QvHIZZOjKxPr6KBpn18ja3EaUez11vXJuZhimZtUcKlPT01jYt9RTBbSG6m9\nEaUuW07IdE8O471DmfSMztGyA8k+pazSY5JvvBwinD28Qrmzuhy0TksClMk8DgWdBk273phubpUS\nU2+S7N+WZqtC0pf2d0jil0ZUuzukSxuRTB7S80nJy6H0bC1wirImyXSqBc7u8oVl+/77lP6rEu6p\nUC+EHNCLlM6NgwQ2Q5o7rtWcF3yG6DiOE/AB0XEcJ5BbyRxBUEcmYTZ6pndIVSKhOrbpiOr7Butz\nkSRbvGTWxG3WYSIlaTT9bAAokIO1UNxohyx6tYLtn2Y1TkmyDckxGRuUybphUrJMaaHaVIy5ROm6\nWlUWeSR5KlmfOJa2WLU+9/pm5exE1pYi1TbukDP40j47d0TLB0mLCm5x7eZi1q7oPSap+1R/OaXC\nUpyZurBu7ZpaigFgTBZ/zuzM32n8JknfStau8op9/pD6MySH8c4G52A3ypQ7jGsWFyM7t5Vae5em\nVuyx9XNMDv0bDUr5RZneF4R9BYy4R6nNbDVmW4bzfsn61Ctm27RygIlaW+odSmFGfaiGOufs2J4X\nZmqGKCK3icjf6N+miDwmIssi8nsR+Vf4f2mv2+o4Tv4Q1UulVNlbRCQC8AaAjwJ4BMAFVX1SRB4H\nsKSq37zc+c3Fpt5x15079re7ttpfm9AEucDZUbJfw0KJwq/Ix2xEp41pZsk/mFybhGlFNgNYLtts\npEBZSNb6bVwM1wU5T8k/V3tUwpKOGTZ29xWLqV0TDnUL4Vj9dLTjHADbQsdo8ok2zYqLFCLIs45a\nxbLGVEiUTMiY0AlJStsJp8YxGpSglOu7aI36PKIZfYGOP09+o2WbaQ3JCBRdyDo1IUNTtJ+mWZz9\ndsNmeW1SHym1hZMCM5s0Q5wmp+GEtx0KeRyNqT91CvkjA8vBLpWKpfvZijnWzuBzNagBDqdM+8Md\n5wDYqk8EAFEwEj7/pz9gbWM9V9PEmZohXsQ9AP6tqq8B+ByAZ8L+ZwB8fs9a5ThObpnlNcT7Afws\nbB9U1VNh+zSAg7udICIPA3gYAMqV3X+hHcdxLsVMDogikgC4D8ATF7+nqioiu+p8VT0C4AgALCw1\nNQ2ZWBKSJFzCEySrxm3TgdOjhRLIFilLTdSz/aQ2MCaDxOkalZ5UWhAf2zb7f2mF/PmmmpxKb3aa\ndu2DVSvliQJJoy5JHwoRLJB/3IR0fSQ7/eZS8sfskyGlecq+n/aKHcNhXGxUGK+ZlOw1ySczteNL\n5Z0JUJtD+x76FC7Yr9m168XdE6emJBNjVt5keCqQrC/T8sGgnn3ugEqzCoVoJhSi2G/ScgSVLuGE\nw5JQqVL6M+vR0ks7GE3KXa53a5vb6qhw/R0ywpyN7R6VyNgXU3xfq04Zhop0z1s7l0eKZAAEPfNt\n8necls9Nd/8znGtmVTJ/GsCLqnomvD4jItcBQPj/7J61zHGc3DKrA+IDMLkMAL8B8GDYfhDAr695\nixzHyT0zJ5lFpAbgkwC+SrufBPCsiDwE4DUAX3zL60yAwiDTH5zBo5mYZO5TCcekyplNMrmxOTA9\nVCU5OqJkrtWaWSI7lO5lpUCJNreF99F2z67fJ/+vcghpq+w3aaxUowRU6iOuki9lzJlSbLtPoWb8\nE3gdxYn1GtkbEWV7aVP/2xRxt5zaY8Phaq2xWfBJmQGUYaicWOMHVAY0qiXhGpRhp0vxciRNkwP7\nt7aHXWtw6/y6XXvJvn/2BFhRyogjJiunSykRydEylTIdky9lhb7OzUU7pkilalNK6Nslf88SlRZt\nh+eyLSZdk0X7flbjph07sn4OS7R8QNb3apeWLMhbYIPK027A7tGN9cxXdJhyMmPOcGP9KaX2nO1u\nh84HMzcgqmoHwMpF+84jszo7juO8Y8yqZHYcx7nmzNwM8WoxxgRrmsmM1Xhha/+AUohMuIQmWQjH\noR5HSmFUp8YmWQ8lFijDoW6LFH6n5Gid1s1ax/KNqziWo13CsciAnCQm6SnBDZQciZMyOUCLHXSi\nd25re5Vk8qRCcmttp8Vx35B0L1nBp2F2F7elUieZmprGrVCIJFtRQZJ1s59JOSFL6VJi4X+k0jFZ\nt2u3huTETsll2Um8UTG9f2ZoGY5WC7Z/PTjDN6gPlBhoW1hkn+rSVCkskqXkJtWDaVJ6oGRibdwX\nHqmNjj1bBXpfKlRulcqNxn2S72OSyVXqPy0xHDhHSxMUgteLsn7EMZUspRBFUOhe/+zG1vZ/69m9\nGuruzt/zjM8QHcdxAj4gOo7jBHIrmYsoYGmSycz2mEynPMsnZ1f+ZagGudEjaVhvmOxud01ej6g6\nOQAAA9VJREFUppRwM+6YNEnISTmmco6UeARRTHG9KZUTDdZVbrawvm7ZsaVFyvwyoBohA5NsK2SJ\n7JTJsk7Hl0MS1/EmOfrWTYLz0gDL3g7VXamRrm3Q98W1QZSWKYRq1kwdvJXrkpClNunsHuNcq5rE\nZVldocS1bMFvxNanPsXtlsIhPaXMSGQqb1Hi1piS33KJ1zaVBB117VlYo0w9nFF2Jd0ZTTXNQAMA\nDVoOKfZsf2ds1xtSedyFLi3ZUCEVevxAihxxuMyY/iikb9sRyfSy2pd7k2bW7zM5rK3iM0THcZyA\nD4iO4ziBmU3/9f8iIucAdAC8uddtuQbsg/czT8xLP29S1f1vfdj8kNsBEQBE5C+q+uG9bsc7jfcz\nX7xb+jmLuGR2HMcJ+IDoOI4TyPuAeGSvG3CN8H7mi3dLP2eOXK8hOo7jXAl5nyE6juO8bXxAdBzH\nCeR2QBSRT4nIP0Xk1VC6NBeIyA0i8oKIvCIiL4vIo2F/7mpXi0gkIi+JyG/D61tE5Fi4p78ItXfm\nHhFZFJGjIvIPETkuInfk8X7OA7kcEENN5x8iq81yGMADInJ4b1t11RgD+JqqHgbwMQCPhL49DuA5\nVb0VwHPh9bzzKIDj9Po7AL6nqu8DsAbgoT1p1dXnBwB+p6rvB/ABZH3O4/2ceXI5IAL4CIBXVfWE\nqg4B/BxZbee5R1VPqeqLYbuF7I/neuSsdrWIHALwWQBPhdcC4G4AR8Mhc99HABCRJoCPA3gaAFR1\nqKrryNn9nBfyOiBeD+B1en0y7MsVInIzgNsBHMPbrF09R3wfwDeArYIkKwDWVbdS1+Tlnt4C4ByA\nn4TlgadCXaG83c+5IK8DYu4RkTqAXwJ4TFU3+T3NfKnm1p9KRO4FcFZV/7rXbbkGFAF8CMCPVPV2\nZPH32+TxvN/PeSKvA+IbAG6g14fCvlwgIjGywfCnqvqrsDtPtavvBHCfiPwH2XLH3cjW2RZFZJqY\nLy/39CSAk6p6LLw+imyAzNP9nBvyOiD+GcCtwSqZALgfWW3nuSespT0N4Liqfpfeyk3talV9QlUP\nqerNyO7d86r6JQAvAPhCOGyu+zhFVU8DeF1Ebgu77gHwCnJ0P+eJ3EaqiMhnkK1DRQB+rKrf3uMm\nXRVE5C4AfwTwd9j62reQrSM+C+BGhNrVqnphTxp5FRGRTwD4uqreKyLvRTZjXAbwEoAvq+rgcufP\nAyLyQWTGowTACQBfQTZZyd39nHVyOyA6juNcKXmVzI7jOFeMD4iO4zgBHxAdx3ECPiA6juMEfEB0\nHMcJ+IDoOI4T8AHRcRwn8D8wE5stD+VT0AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbcc82a3320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[2], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[38])+ \",\"+\" iceberg_probability : \"+str(pred_valid[2])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fbcc81f2c18>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CCBmUZ5EGd5EUjBbX7XZ/7XnySibL6kARfQEF9achh9bqQVLr6dVz0edYMvS6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\nQC6d4owWfv0MZvYBZMdmiIll/uQ6JmDKYIXfgZP0aI6y8VM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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbcc827ff60>"
      ]
     },
     "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 0x7fbcc8157860>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YztVM8SOJUuEkkVKGJD6DIzwyKNY24qCn0Vp8FVJcm1PQV1AcJp8KoLtPjt3d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\nEINiEAQBiEExCIIAxKAYBEEA/gdUZEiMcDy/bAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbcc81aa048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[6], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[104])+ \",\"+\" iceberg_probability : \"+str(pred_valid[6])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fbcc813f0b8>"
      ]
     },
     "execution_count": 22,
     "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 0x7fbcc810eb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[9], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[138])+ \",\"+\" iceberg_probability : \"+str(pred_valid[9])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fbcc809fa90>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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C4Nx1U7tHkwN7qCL/nMXI6NQo2lNo6cTIjPS9exhR/vMOI1gWAQEBK+F8hZFd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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbcc80fa048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[10], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[141])+ \",\"+\" iceberg_probability : \"+str(pred_valid[10])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fbcc80091d0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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C23SU+/p+rzvX3Pw6FzOk4a68L8YLPB9Hfryp/J4f5eivvrelvM+TWMoSDY4/ctxvgFcC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\nRQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbcc8057860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[11], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[167])+ \",\"+\" iceberg_probability : \"+str(pred_valid[11])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fbcbbfebc50>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pI2XnVdzzMhNuwRI+gBXxnNOzMcqZgAbWY1D0HCrhjSPcv3QqWcw3OwlncIan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\n0XEch/gvz0OD5DmiSsAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbcc8038358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[12], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[195])+ \",\"+\" iceberg_probability : \"+str(pred_valid[12])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fbcbbf4f550>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Yg92xH4PXP/dzjg79WcgL34Y/uIT1cZyya2Oo/Bj8UaiCr69z75edY9+mxnM7yLy/+CP7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\nghtFx3EcwY2i4ziO4EbRcRxH+BtsqHY27kTz1wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbcbbfa19e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[13], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[199])+ \",\"+\" iceberg_probability : \"+str(pred_valid[13])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}