Model_03-SGD-k=10_3e-4_20p.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Mandatory imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import log_loss\n",
    "from sklearn.model_selection import StratifiedKFold, StratifiedShuffleSplit\n",
    "from os.path import join as opj\n",
    "from matplotlib import pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import pylab\n",
    "plt.rcParams['figure.figsize'] = 10, 10\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_json(\"input/train.json\")\n",
    "target_train=train['is_iceberg']\n",
    "test = pd.read_json(\"input/test.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_train=train['is_iceberg']\n",
    "test['inc_angle']=pd.to_numeric(test['inc_angle'], errors='coerce')\n",
    "train['inc_angle']=pd.to_numeric(train['inc_angle'], errors='coerce')#We have only 133 NAs.\n",
    "train['inc_angle']=train['inc_angle'].fillna(method='pad')\n",
    "X_angle=train['inc_angle']\n",
    "test['inc_angle']=pd.to_numeric(test['inc_angle'], errors='coerce')\n",
    "X_test_angle=test['inc_angle']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Generate the training data\n",
    "X_band_1=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in train[\"band_1\"]])\n",
    "X_band_2=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in train[\"band_2\"]])\n",
    "X_band_3=(X_band_1+X_band_2)/2\n",
    "#X_band_3=np.array([np.full((75, 75), angel).astype(np.float32) for angel in train[\"inc_angle\"]])\n",
    "X_train = np.concatenate([X_band_1[:, :, :, np.newaxis]\n",
    "                          , X_band_2[:, :, :, np.newaxis]\n",
    "                         , X_band_3[:, :, :, np.newaxis]], axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_band_test_1=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in test[\"band_1\"]])\n",
    "X_band_test_2=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in test[\"band_2\"]])\n",
    "X_band_test_3=(X_band_test_1+X_band_test_2)/2\n",
    "#X_band_test_3=np.array([np.full((75, 75), angel).astype(np.float32) for angel in test[\"inc_angle\"]])\n",
    "X_test = np.concatenate([X_band_test_1[:, :, :, np.newaxis]\n",
    "                          , X_band_test_2[:, :, :, np.newaxis]\n",
    "                         , X_band_test_3[:, :, :, np.newaxis]], axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "#Import Keras.\n",
    "from matplotlib import pyplot\n",
    "from keras.optimizers import RMSprop\n",
    "# from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPooling2D, Dense, Dropout, Input, Flatten, Activation\n",
    "from keras.layers import GlobalMaxPooling2D\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.layers.merge import Concatenate\n",
    "from keras.models import Model\n",
    "from keras import initializers\n",
    "from keras.optimizers import Adam\n",
    "from keras.optimizers import rmsprop\n",
    "from keras.optimizers import SGD\n",
    "from keras.optimizers import Nadam\n",
    "from keras.optimizers import Adadelta\n",
    "from keras.layers.advanced_activations import LeakyReLU, PReLU\n",
    "from keras.callbacks import ModelCheckpoint, Callback, EarlyStopping\n",
    "\n",
    "from keras.datasets import cifar10\n",
    "from keras.applications.inception_v3 import InceptionV3\n",
    "from keras.applications.vgg16 import VGG16\n",
    "from keras.applications.xception import Xception\n",
    "from keras.applications.mobilenet import MobileNet\n",
    "from keras.applications.vgg19 import VGG19\n",
    "from keras.layers import Concatenate, Dense, LSTM, Input, concatenate\n",
    "from keras.preprocessing import image\n",
    "from keras.applications.vgg16 import preprocess_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data Aug for multi-input\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "batch_size=64\n",
    "# Define the image transformations here\n",
    "gen = ImageDataGenerator(horizontal_flip = True,\n",
    "                         vertical_flip = True,\n",
    "                         width_shift_range = 0.,\n",
    "                         height_shift_range = 0.,\n",
    "                         channel_shift_range=0,\n",
    "                         zoom_range = 0.2,\n",
    "                         rotation_range = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here is the function that merges our two generators\n",
    "# We use the exact same generator with the same random seed for both the y and angle arrays\n",
    "def gen_flow_for_two_inputs(X1, X2, y):\n",
    "    genX1 = gen.flow(X1,y,  batch_size=batch_size,seed=55)\n",
    "    genX2 = gen.flow(X1,X2, batch_size=batch_size,seed=55)\n",
    "    while True:\n",
    "            X1i = genX1.next()\n",
    "            X2i = genX2.next()\n",
    "            #Assert arrays are equal - this was for peace of mind, but slows down training\n",
    "            #np.testing.assert_array_equal(X1i[0],X2i[0])\n",
    "            yield [X1i[0], X2i[1]], X1i[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Finally create generator\n",
    "def get_callbacks(filepath, patience=2):\n",
    "    es = EarlyStopping('val_loss', patience=20, mode=\"min\")\n",
    "    msave = ModelCheckpoint(filepath, save_best_only=True)\n",
    "    return [es, msave]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getVggAngleModel():\n",
    "    input_2 = Input(shape=[1], name=\"angle\")\n",
    "    angle_layer = Dense(1, )(input_2)\n",
    "    base_model = VGG16(weights='imagenet', include_top=False, \n",
    "                 input_shape=X_train.shape[1:], classes=1)\n",
    "    x = base_model.get_layer('block5_pool').output\n",
    "    \n",
    "\n",
    "    x = GlobalMaxPooling2D()(x)\n",
    "    merge_one = concatenate([x, angle_layer])\n",
    "    merge_one = Dense(512, activation='relu', name='fc2')(merge_one)\n",
    "    merge_one = Dropout(0.3)(merge_one)\n",
    "    merge_one = Dense(512, activation='relu', name='fc3')(merge_one)\n",
    "    merge_one = Dropout(0.3)(merge_one)\n",
    "    \n",
    "    predictions = Dense(1, activation='sigmoid')(merge_one)\n",
    "    \n",
    "    model = Model(inputs=[base_model.input, input_2], outputs=predictions)\n",
    "    \n",
    "#     adadelta= Adadelta(lr=1.0, rho=0.95, epsilon=None, decay=0.0)\n",
    "#     model.compile(loss='binary_crossentropy',\n",
    "#                   optimizer=adadelta,\n",
    "#                   metrics=['accuracy'])\n",
    "    \n",
    "    sgd = SGD(lr=3e-4, 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": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "===================FOLD= 0\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 13s 555ms/step - loss: 0.7154 - acc: 0.5850 - val_loss: 0.4938 - val_acc: 0.8148\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.5206 - acc: 0.7143 - val_loss: 0.3527 - val_acc: 0.8457\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.3999 - acc: 0.8124 - val_loss: 0.3268 - val_acc: 0.8395\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.3511 - acc: 0.8331 - val_loss: 0.2989 - val_acc: 0.8580\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.3276 - acc: 0.8518 - val_loss: 0.2611 - val_acc: 0.8704\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2834 - acc: 0.8720 - val_loss: 0.2983 - val_acc: 0.8704\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.2937 - acc: 0.8652 - val_loss: 0.3254 - val_acc: 0.8580\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2917 - acc: 0.8689 - val_loss: 0.2656 - val_acc: 0.8704\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2671 - acc: 0.8855 - val_loss: 0.2758 - val_acc: 0.8642\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2612 - acc: 0.8928 - val_loss: 0.2642 - val_acc: 0.8827\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2425 - acc: 0.8935 - val_loss: 0.2674 - val_acc: 0.9012\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2480 - acc: 0.8882 - val_loss: 0.2770 - val_acc: 0.8765\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.2358 - acc: 0.9011 - val_loss: 0.2572 - val_acc: 0.8827\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.2313 - acc: 0.9062 - val_loss: 0.2411 - val_acc: 0.9074\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2143 - acc: 0.9130 - val_loss: 0.2701 - val_acc: 0.8889\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.2298 - acc: 0.9021 - val_loss: 0.2384 - val_acc: 0.9136\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2148 - acc: 0.9125 - val_loss: 0.2666 - val_acc: 0.8827\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.2148 - acc: 0.9168 - val_loss: 0.2376 - val_acc: 0.9136\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2141 - acc: 0.9139 - val_loss: 0.2851 - val_acc: 0.8951\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.2111 - acc: 0.9084 - val_loss: 0.2305 - val_acc: 0.9012\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1950 - acc: 0.9206 - val_loss: 0.2372 - val_acc: 0.8951\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.2307 - acc: 0.9096 - val_loss: 0.2278 - val_acc: 0.9136\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.1904 - acc: 0.9264 - val_loss: 0.2303 - val_acc: 0.8951\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1914 - acc: 0.9188 - val_loss: 0.2441 - val_acc: 0.9074\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1880 - acc: 0.9186 - val_loss: 0.2346 - val_acc: 0.8765\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1921 - acc: 0.9239 - val_loss: 0.2278 - val_acc: 0.9012\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1680 - acc: 0.9326 - val_loss: 0.2429 - val_acc: 0.9012\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1968 - acc: 0.9123 - val_loss: 0.2370 - val_acc: 0.9136\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1746 - acc: 0.9305 - val_loss: 0.2413 - val_acc: 0.9012\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1797 - acc: 0.9278 - val_loss: 0.2406 - val_acc: 0.9136\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1875 - acc: 0.9222 - val_loss: 0.2359 - val_acc: 0.9136\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1844 - acc: 0.9284 - val_loss: 0.2281 - val_acc: 0.8951\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.1719 - acc: 0.9318 - val_loss: 0.2275 - val_acc: 0.9198\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.1869 - acc: 0.9247 - val_loss: 0.2240 - val_acc: 0.9136\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.1775 - acc: 0.9285 - val_loss: 0.2185 - val_acc: 0.9074\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1558 - acc: 0.9344 - val_loss: 0.2186 - val_acc: 0.9136\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1503 - acc: 0.9402 - val_loss: 0.2475 - val_acc: 0.9012\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1576 - acc: 0.9327 - val_loss: 0.2514 - val_acc: 0.9012\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1834 - acc: 0.9261 - val_loss: 0.2280 - val_acc: 0.9012\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1492 - acc: 0.9403 - val_loss: 0.2362 - val_acc: 0.9012\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1607 - acc: 0.9279 - val_loss: 0.2230 - val_acc: 0.9074\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1682 - acc: 0.9352 - val_loss: 0.2221 - val_acc: 0.9136\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1584 - acc: 0.9404 - val_loss: 0.2438 - val_acc: 0.9259\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1433 - acc: 0.9402 - val_loss: 0.2286 - val_acc: 0.9074\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1428 - acc: 0.9481 - val_loss: 0.2220 - val_acc: 0.9012\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 12s 489ms/step - loss: 0.1474 - acc: 0.9393 - val_loss: 0.2486 - val_acc: 0.9074\n",
      "Epoch 47/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1568 - acc: 0.9470 - val_loss: 0.2333 - val_acc: 0.9198\n",
      "Epoch 48/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1394 - acc: 0.9396 - val_loss: 0.2518 - val_acc: 0.8951\n",
      "Epoch 49/100\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.1604 - acc: 0.9372 - val_loss: 0.2060 - val_acc: 0.9012\n",
      "Epoch 50/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1619 - acc: 0.9363 - val_loss: 0.2321 - val_acc: 0.9012\n",
      "Epoch 51/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1432 - acc: 0.9467 - val_loss: 0.2273 - val_acc: 0.9198\n",
      "Epoch 52/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1281 - acc: 0.9481 - val_loss: 0.2331 - val_acc: 0.9198\n",
      "Epoch 53/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1256 - acc: 0.9487 - val_loss: 0.2231 - val_acc: 0.9074\n",
      "Epoch 54/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1314 - acc: 0.9493 - val_loss: 0.2282 - val_acc: 0.9198\n",
      "Epoch 55/100\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1059 - acc: 0.9584 - val_loss: 0.2871 - val_acc: 0.8889\n",
      "Epoch 56/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1202 - acc: 0.9506 - val_loss: 0.2195 - val_acc: 0.9198\n",
      "Epoch 57/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1102 - acc: 0.9549 - val_loss: 0.2570 - val_acc: 0.9136\n",
      "Epoch 58/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1347 - acc: 0.9499 - val_loss: 0.2207 - val_acc: 0.9136\n",
      "Epoch 59/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1044 - acc: 0.9597 - val_loss: 0.2447 - val_acc: 0.9012\n",
      "Epoch 60/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1258 - acc: 0.9487 - val_loss: 0.2500 - val_acc: 0.9074\n",
      "Epoch 61/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1144 - acc: 0.9559 - val_loss: 0.2065 - val_acc: 0.9198\n",
      "Epoch 62/100\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1029 - acc: 0.9622 - val_loss: 0.2592 - val_acc: 0.9074\n",
      "Epoch 63/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1227 - acc: 0.9499 - val_loss: 0.2443 - val_acc: 0.9074\n",
      "Epoch 64/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1285 - acc: 0.9483 - val_loss: 0.2270 - val_acc: 0.9012\n",
      "Epoch 65/100\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1038 - acc: 0.9631 - val_loss: 0.2319 - val_acc: 0.9136\n",
      "Epoch 66/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1244 - acc: 0.9493 - val_loss: 0.2463 - val_acc: 0.9136\n",
      "Epoch 67/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1308 - acc: 0.9441 - val_loss: 0.2163 - val_acc: 0.9074\n",
      "Epoch 68/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1017 - acc: 0.9592 - val_loss: 0.2450 - val_acc: 0.9074\n",
      "Epoch 69/100\n",
      "24/24 [==============================] - 12s 488ms/step - loss: 0.1154 - acc: 0.9559 - val_loss: 0.2146 - val_acc: 0.9136\n",
      "Train loss: 0.104272901128\n",
      "Train accuracy: 0.963245492372\n",
      "Test loss: 0.205990561161\n",
      "Test accuracy: 0.901234567901\n",
      "\n",
      "===================FOLD= 1\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 13s 559ms/step - loss: 1.0198 - acc: 0.5194 - val_loss: 0.5302 - val_acc: 0.7019\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.6919 - acc: 0.6548 - val_loss: 0.3788 - val_acc: 0.8012\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.4779 - acc: 0.7781 - val_loss: 0.2811 - val_acc: 0.8634\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.3995 - acc: 0.8167 - val_loss: 0.2529 - val_acc: 0.8882\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.3670 - acc: 0.8287 - val_loss: 0.2665 - val_acc: 0.8571\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.3243 - acc: 0.8561 - val_loss: 0.2124 - val_acc: 0.8820\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.3120 - acc: 0.8631 - val_loss: 0.2188 - val_acc: 0.9130\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.3155 - acc: 0.8631 - val_loss: 0.2177 - val_acc: 0.9068\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2758 - acc: 0.8920 - val_loss: 0.2312 - val_acc: 0.8882\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.2610 - acc: 0.8827 - val_loss: 0.1987 - val_acc: 0.9193\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2903 - acc: 0.8756 - val_loss: 0.2053 - val_acc: 0.9130\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2853 - acc: 0.8731 - val_loss: 0.2042 - val_acc: 0.9193\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.2558 - acc: 0.8919 - val_loss: 0.1935 - val_acc: 0.9068\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.2391 - acc: 0.9019 - val_loss: 0.1840 - val_acc: 0.9317\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.2440 - acc: 0.9046 - val_loss: 0.1633 - val_acc: 0.9441\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2492 - acc: 0.8938 - val_loss: 0.2206 - val_acc: 0.9130\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2257 - acc: 0.9079 - val_loss: 0.1840 - val_acc: 0.9503\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2295 - acc: 0.9049 - val_loss: 0.1740 - val_acc: 0.9317\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2245 - acc: 0.9167 - val_loss: 0.1956 - val_acc: 0.9193\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.2235 - acc: 0.9113 - val_loss: 0.1618 - val_acc: 0.9503\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1914 - acc: 0.9213 - val_loss: 0.1779 - val_acc: 0.9255\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2232 - acc: 0.9156 - val_loss: 0.1818 - val_acc: 0.9317\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2015 - acc: 0.9193 - val_loss: 0.1437 - val_acc: 0.9255\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.2063 - acc: 0.9186 - val_loss: 0.1753 - val_acc: 0.9255\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2057 - acc: 0.9130 - val_loss: 0.1505 - val_acc: 0.9627\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1895 - acc: 0.9238 - val_loss: 0.1497 - val_acc: 0.9565\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1874 - acc: 0.9287 - val_loss: 0.1588 - val_acc: 0.9317\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1851 - acc: 0.9201 - val_loss: 0.1547 - val_acc: 0.9627\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1950 - acc: 0.9206 - val_loss: 0.1471 - val_acc: 0.9565\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1952 - acc: 0.9174 - val_loss: 0.1614 - val_acc: 0.9441\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.1688 - acc: 0.9289 - val_loss: 0.1429 - val_acc: 0.9503\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1869 - acc: 0.9251 - val_loss: 0.1617 - val_acc: 0.9503\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1701 - acc: 0.9338 - val_loss: 0.1990 - val_acc: 0.9255\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1745 - acc: 0.9302 - val_loss: 0.1582 - val_acc: 0.9379\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1899 - acc: 0.9171 - val_loss: 0.1456 - val_acc: 0.9565\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1660 - acc: 0.9363 - val_loss: 0.1611 - val_acc: 0.9565\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1760 - acc: 0.9250 - val_loss: 0.1658 - val_acc: 0.9317\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1534 - acc: 0.9428 - val_loss: 0.1763 - val_acc: 0.9441\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1606 - acc: 0.9395 - val_loss: 0.1944 - val_acc: 0.9255\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1576 - acc: 0.9362 - val_loss: 0.2875 - val_acc: 0.8882\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1677 - acc: 0.9358 - val_loss: 0.1559 - val_acc: 0.9441\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1790 - acc: 0.9209 - val_loss: 0.1497 - val_acc: 0.9441\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1497 - acc: 0.9444 - val_loss: 0.1603 - val_acc: 0.9441\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1685 - acc: 0.9358 - val_loss: 0.1751 - val_acc: 0.9379\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1521 - acc: 0.9424 - val_loss: 0.1625 - val_acc: 0.9503\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 12s 489ms/step - loss: 0.1394 - acc: 0.9469 - val_loss: 0.1758 - val_acc: 0.9441\n",
      "Epoch 47/100\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1469 - acc: 0.9352 - val_loss: 0.1612 - val_acc: 0.9441\n",
      "Epoch 48/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1501 - acc: 0.9467 - val_loss: 0.1680 - val_acc: 0.9379\n",
      "Epoch 49/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1488 - acc: 0.9376 - val_loss: 0.1589 - val_acc: 0.9441\n",
      "Epoch 50/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1439 - acc: 0.9424 - val_loss: 0.1551 - val_acc: 0.9379\n",
      "Epoch 51/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1321 - acc: 0.9428 - val_loss: 0.1559 - val_acc: 0.9441\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train loss: 0.124029410614\n",
      "Train accuracy: 0.948024948025\n",
      "Test loss: 0.142915828891\n",
      "Test accuracy: 0.950310559006\n",
      "\n",
      "===================FOLD= 2\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 14s 563ms/step - loss: 0.9656 - acc: 0.5704 - val_loss: 0.4602 - val_acc: 0.7578\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.6021 - acc: 0.7295 - val_loss: 0.3333 - val_acc: 0.8447\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.4381 - acc: 0.8013 - val_loss: 0.2914 - val_acc: 0.8634\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.4208 - acc: 0.8069 - val_loss: 0.3119 - val_acc: 0.8509\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.3869 - acc: 0.8210 - val_loss: 0.2398 - val_acc: 0.8696\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.3410 - acc: 0.8524 - val_loss: 0.2719 - val_acc: 0.8758\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.3081 - acc: 0.8683 - val_loss: 0.2293 - val_acc: 0.9006\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.2804 - acc: 0.8774 - val_loss: 0.2559 - val_acc: 0.8944\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.3006 - acc: 0.8655 - val_loss: 0.2162 - val_acc: 0.8820\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2723 - acc: 0.8778 - val_loss: 0.2331 - val_acc: 0.8944\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.2825 - acc: 0.8743 - val_loss: 0.2103 - val_acc: 0.9130\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.2698 - acc: 0.8840 - val_loss: 0.2622 - val_acc: 0.9006\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.2376 - acc: 0.9077 - val_loss: 0.2276 - val_acc: 0.9006\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2279 - acc: 0.9032 - val_loss: 0.2133 - val_acc: 0.9255\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2378 - acc: 0.9026 - val_loss: 0.2160 - val_acc: 0.9130\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2354 - acc: 0.9007 - val_loss: 0.2128 - val_acc: 0.9006\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.2514 - acc: 0.8891 - val_loss: 0.2173 - val_acc: 0.9130\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2323 - acc: 0.9036 - val_loss: 0.2108 - val_acc: 0.9193\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1980 - acc: 0.9180 - val_loss: 0.2203 - val_acc: 0.8944\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2514 - acc: 0.8894 - val_loss: 0.2157 - val_acc: 0.9068\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2038 - acc: 0.9144 - val_loss: 0.2350 - val_acc: 0.9068\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2347 - acc: 0.9007 - val_loss: 0.2339 - val_acc: 0.9006\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 490ms/step - loss: 0.2137 - acc: 0.9128 - val_loss: 0.2335 - val_acc: 0.9068\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1898 - acc: 0.9247 - val_loss: 0.2165 - val_acc: 0.9130\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1875 - acc: 0.9164 - val_loss: 0.2226 - val_acc: 0.9068\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2127 - acc: 0.9080 - val_loss: 0.2146 - val_acc: 0.8944\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.1827 - acc: 0.9290 - val_loss: 0.2086 - val_acc: 0.9130\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1930 - acc: 0.9175 - val_loss: 0.2098 - val_acc: 0.9255\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1833 - acc: 0.9270 - val_loss: 0.2130 - val_acc: 0.9193\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1756 - acc: 0.9260 - val_loss: 0.2195 - val_acc: 0.9006\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.1921 - acc: 0.9204 - val_loss: 0.2020 - val_acc: 0.9130\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1982 - acc: 0.9165 - val_loss: 0.2179 - val_acc: 0.9255\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1943 - acc: 0.9192 - val_loss: 0.2387 - val_acc: 0.9130\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1861 - acc: 0.9294 - val_loss: 0.2130 - val_acc: 0.9193\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1792 - acc: 0.9210 - val_loss: 0.2115 - val_acc: 0.9068\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1626 - acc: 0.9272 - val_loss: 0.2248 - val_acc: 0.8882\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1794 - acc: 0.9325 - val_loss: 0.2266 - val_acc: 0.9068\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1768 - acc: 0.9253 - val_loss: 0.2108 - val_acc: 0.9130\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1509 - acc: 0.9408 - val_loss: 0.2409 - val_acc: 0.8882\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1872 - acc: 0.9260 - val_loss: 0.2823 - val_acc: 0.8820\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1728 - acc: 0.9221 - val_loss: 0.2236 - val_acc: 0.9130\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1497 - acc: 0.9408 - val_loss: 0.2040 - val_acc: 0.9317\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1692 - acc: 0.9274 - val_loss: 0.2156 - val_acc: 0.9130\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1629 - acc: 0.9352 - val_loss: 0.2050 - val_acc: 0.9193\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1563 - acc: 0.9334 - val_loss: 0.2221 - val_acc: 0.9006\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 12s 490ms/step - loss: 0.1672 - acc: 0.9310 - val_loss: 0.2602 - val_acc: 0.8882\n",
      "Epoch 47/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1455 - acc: 0.9423 - val_loss: 0.2110 - val_acc: 0.9068\n",
      "Epoch 48/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1620 - acc: 0.9369 - val_loss: 0.2488 - val_acc: 0.9130\n",
      "Epoch 49/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1689 - acc: 0.9285 - val_loss: 0.2174 - val_acc: 0.9193\n",
      "Epoch 50/100\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1524 - acc: 0.9410 - val_loss: 0.2112 - val_acc: 0.9006\n",
      "Epoch 51/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1568 - acc: 0.9350 - val_loss: 0.2231 - val_acc: 0.9006\n",
      "Train loss: 0.139136704239\n",
      "Train accuracy: 0.943173943174\n",
      "Test loss: 0.202036718034\n",
      "Test accuracy: 0.913043478261\n",
      "\n",
      "===================FOLD= 3\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 15s 641ms/step - loss: 0.9946 - acc: 0.5665 - val_loss: 0.4807 - val_acc: 0.7438\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.6006 - acc: 0.7098 - val_loss: 0.3242 - val_acc: 0.8500\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.4582 - acc: 0.7837 - val_loss: 0.2879 - val_acc: 0.8500\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.4044 - acc: 0.8138 - val_loss: 0.2581 - val_acc: 0.8750\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.3042 - acc: 0.8604 - val_loss: 0.2665 - val_acc: 0.8750\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.3376 - acc: 0.8497 - val_loss: 0.2398 - val_acc: 0.9000\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.2914 - acc: 0.8720 - val_loss: 0.2230 - val_acc: 0.9125\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2997 - acc: 0.8726 - val_loss: 0.2479 - val_acc: 0.8938\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2779 - acc: 0.8894 - val_loss: 0.2922 - val_acc: 0.8875\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2623 - acc: 0.8910 - val_loss: 0.2483 - val_acc: 0.9062\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2562 - acc: 0.8906 - val_loss: 0.2492 - val_acc: 0.8938\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2197 - acc: 0.9134 - val_loss: 0.2986 - val_acc: 0.9000\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2397 - acc: 0.9007 - val_loss: 0.2593 - val_acc: 0.9000\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2547 - acc: 0.8964 - val_loss: 0.2251 - val_acc: 0.9062\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.2472 - acc: 0.8840 - val_loss: 0.2177 - val_acc: 0.9125\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2435 - acc: 0.8977 - val_loss: 0.2329 - val_acc: 0.9062\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2078 - acc: 0.9121 - val_loss: 0.2215 - val_acc: 0.9062\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2124 - acc: 0.9090 - val_loss: 0.2269 - val_acc: 0.9187\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2145 - acc: 0.9106 - val_loss: 0.2182 - val_acc: 0.9125\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2076 - acc: 0.9199 - val_loss: 0.2180 - val_acc: 0.9062\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1980 - acc: 0.9176 - val_loss: 0.2318 - val_acc: 0.9187\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2134 - acc: 0.9151 - val_loss: 0.2196 - val_acc: 0.9062\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1905 - acc: 0.9280 - val_loss: 0.2219 - val_acc: 0.9188\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2045 - acc: 0.9132 - val_loss: 0.2739 - val_acc: 0.9125\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1915 - acc: 0.9189 - val_loss: 0.2688 - val_acc: 0.8875\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1901 - acc: 0.9296 - val_loss: 0.3271 - val_acc: 0.8750\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1818 - acc: 0.9303 - val_loss: 0.2253 - val_acc: 0.9062\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1714 - acc: 0.9251 - val_loss: 0.2288 - val_acc: 0.9187\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1871 - acc: 0.9269 - val_loss: 0.2626 - val_acc: 0.9125\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1781 - acc: 0.9223 - val_loss: 0.2300 - val_acc: 0.9125\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1756 - acc: 0.9235 - val_loss: 0.2391 - val_acc: 0.9125\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1841 - acc: 0.9267 - val_loss: 0.2951 - val_acc: 0.8812\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1488 - acc: 0.9378 - val_loss: 0.2858 - val_acc: 0.8812\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1849 - acc: 0.9226 - val_loss: 0.2852 - val_acc: 0.8812\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1523 - acc: 0.9347 - val_loss: 0.2488 - val_acc: 0.9125\n",
      "Train loss: 0.170159536035\n",
      "Train accuracy: 0.930747922438\n",
      "Test loss: 0.217682528496\n",
      "Test accuracy: 0.9125\n",
      "\n",
      "===================FOLD= 4\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 14s 577ms/step - loss: 0.7436 - acc: 0.5536 - val_loss: 0.5837 - val_acc: 0.6875\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.5356 - acc: 0.7199 - val_loss: 0.3583 - val_acc: 0.8562\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.4064 - acc: 0.8070 - val_loss: 0.2897 - val_acc: 0.8938\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.3533 - acc: 0.8394 - val_loss: 0.3522 - val_acc: 0.8562\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.3354 - acc: 0.8433 - val_loss: 0.1902 - val_acc: 0.9375\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.3092 - acc: 0.8414 - val_loss: 0.1809 - val_acc: 0.9563\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.3053 - acc: 0.8642 - val_loss: 0.1880 - val_acc: 0.9187\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.2800 - acc: 0.8749 - val_loss: 0.1735 - val_acc: 0.9125\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.2632 - acc: 0.8863 - val_loss: 0.1604 - val_acc: 0.9313\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2842 - acc: 0.8721 - val_loss: 0.1613 - val_acc: 0.9563\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2620 - acc: 0.8769 - val_loss: 0.2039 - val_acc: 0.9125\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2550 - acc: 0.8886 - val_loss: 0.1892 - val_acc: 0.9125\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2408 - acc: 0.8930 - val_loss: 0.1805 - val_acc: 0.9125\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2253 - acc: 0.8971 - val_loss: 0.1642 - val_acc: 0.9500\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.2314 - acc: 0.8971 - val_loss: 0.1554 - val_acc: 0.9250\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.2387 - acc: 0.9037 - val_loss: 0.1535 - val_acc: 0.9500\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2297 - acc: 0.9013 - val_loss: 0.1651 - val_acc: 0.9563\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2119 - acc: 0.9005 - val_loss: 0.1546 - val_acc: 0.9437\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.2389 - acc: 0.8915 - val_loss: 0.1529 - val_acc: 0.9313\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.2126 - acc: 0.9134 - val_loss: 0.1500 - val_acc: 0.9313\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2124 - acc: 0.9124 - val_loss: 0.1608 - val_acc: 0.9375\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.2029 - acc: 0.9094 - val_loss: 0.1346 - val_acc: 0.9625\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 490ms/step - loss: 0.2089 - acc: 0.9056 - val_loss: 0.1621 - val_acc: 0.9500\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2015 - acc: 0.9204 - val_loss: 0.1709 - val_acc: 0.9375\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2038 - acc: 0.9078 - val_loss: 0.1610 - val_acc: 0.9500\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1985 - acc: 0.9130 - val_loss: 0.1694 - val_acc: 0.9375\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2124 - acc: 0.9090 - val_loss: 0.1771 - val_acc: 0.9375\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1665 - acc: 0.9344 - val_loss: 0.1788 - val_acc: 0.9313\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1942 - acc: 0.9204 - val_loss: 0.1498 - val_acc: 0.9500\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1659 - acc: 0.9383 - val_loss: 0.1621 - val_acc: 0.9437\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1722 - acc: 0.9218 - val_loss: 0.1382 - val_acc: 0.9563\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1752 - acc: 0.9336 - val_loss: 0.1484 - val_acc: 0.9437\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1901 - acc: 0.9256 - val_loss: 0.1879 - val_acc: 0.9313\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 34/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1707 - acc: 0.9309 - val_loss: 0.2230 - val_acc: 0.8938\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1786 - acc: 0.9261 - val_loss: 0.1409 - val_acc: 0.9500\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1826 - acc: 0.9256 - val_loss: 0.1368 - val_acc: 0.9500\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1574 - acc: 0.9306 - val_loss: 0.1676 - val_acc: 0.9500\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1608 - acc: 0.9313 - val_loss: 0.1484 - val_acc: 0.9437\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.1651 - acc: 0.9288 - val_loss: 0.1345 - val_acc: 0.9500\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1627 - acc: 0.9363 - val_loss: 0.1515 - val_acc: 0.9500\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1566 - acc: 0.9422 - val_loss: 0.1588 - val_acc: 0.9313\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1636 - acc: 0.9303 - val_loss: 0.1511 - val_acc: 0.9563\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1393 - acc: 0.9456 - val_loss: 0.1698 - val_acc: 0.9437\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1730 - acc: 0.9308 - val_loss: 0.1891 - val_acc: 0.9250\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1380 - acc: 0.9376 - val_loss: 0.2196 - val_acc: 0.9125\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 12s 488ms/step - loss: 0.1538 - acc: 0.9391 - val_loss: 0.1779 - val_acc: 0.9500\n",
      "Epoch 47/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1496 - acc: 0.9339 - val_loss: 0.1521 - val_acc: 0.9375\n",
      "Epoch 48/100\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1375 - acc: 0.9505 - val_loss: 0.1462 - val_acc: 0.9437\n",
      "Epoch 49/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1401 - acc: 0.9417 - val_loss: 0.2142 - val_acc: 0.9187\n",
      "Epoch 50/100\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.1703 - acc: 0.9233 - val_loss: 0.1311 - val_acc: 0.9563\n",
      "Epoch 51/100\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1377 - acc: 0.9436 - val_loss: 0.1531 - val_acc: 0.9563\n",
      "Epoch 52/100\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.1330 - acc: 0.9409 - val_loss: 0.1293 - val_acc: 0.9437\n",
      "Epoch 53/100\n",
      "24/24 [==============================] - 12s 493ms/step - loss: 0.1447 - acc: 0.9339 - val_loss: 0.1348 - val_acc: 0.9500\n",
      "Epoch 54/100\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1381 - acc: 0.9459 - val_loss: 0.1346 - val_acc: 0.9313\n",
      "Epoch 55/100\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1273 - acc: 0.9414 - val_loss: 0.1359 - val_acc: 0.9500\n",
      "Epoch 56/100\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.1400 - acc: 0.9428 - val_loss: 0.1245 - val_acc: 0.9563\n",
      "Epoch 57/100\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1301 - acc: 0.9492 - val_loss: 0.1463 - val_acc: 0.9437\n",
      "Epoch 58/100\n",
      "24/24 [==============================] - 12s 493ms/step - loss: 0.1344 - acc: 0.9445 - val_loss: 0.2078 - val_acc: 0.9313\n",
      "Epoch 59/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1444 - acc: 0.9432 - val_loss: 0.1531 - val_acc: 0.9500\n",
      "Epoch 60/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1267 - acc: 0.9502 - val_loss: 0.1865 - val_acc: 0.9250\n",
      "Epoch 61/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1095 - acc: 0.9577 - val_loss: 0.1653 - val_acc: 0.9500\n",
      "Epoch 62/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1165 - acc: 0.9510 - val_loss: 0.1510 - val_acc: 0.9375\n",
      "Epoch 63/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1243 - acc: 0.9480 - val_loss: 0.1610 - val_acc: 0.9313\n",
      "Epoch 64/100\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1135 - acc: 0.9541 - val_loss: 0.1875 - val_acc: 0.9437\n",
      "Epoch 65/100\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1226 - acc: 0.9482 - val_loss: 0.1274 - val_acc: 0.9688\n",
      "Epoch 66/100\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1088 - acc: 0.9603 - val_loss: 0.1391 - val_acc: 0.9500\n",
      "Epoch 67/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1126 - acc: 0.9570 - val_loss: 0.1525 - val_acc: 0.9250\n",
      "Epoch 68/100\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1197 - acc: 0.9505 - val_loss: 0.2966 - val_acc: 0.8750\n",
      "Epoch 69/100\n",
      "24/24 [==============================] - 12s 488ms/step - loss: 0.1191 - acc: 0.9549 - val_loss: 0.1468 - val_acc: 0.9500\n",
      "Epoch 70/100\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1155 - acc: 0.9493 - val_loss: 0.1951 - val_acc: 0.9187\n",
      "Epoch 71/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1111 - acc: 0.9577 - val_loss: 0.1420 - val_acc: 0.9437\n",
      "Epoch 72/100\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1228 - acc: 0.9477 - val_loss: 0.1939 - val_acc: 0.9250\n",
      "Epoch 73/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1045 - acc: 0.9560 - val_loss: 0.2130 - val_acc: 0.9313\n",
      "Epoch 74/100\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.0927 - acc: 0.9650 - val_loss: 0.1510 - val_acc: 0.9437\n",
      "Epoch 75/100\n",
      "24/24 [==============================] - 12s 493ms/step - loss: 0.1063 - acc: 0.9593 - val_loss: 0.1397 - val_acc: 0.9437\n",
      "Epoch 76/100\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.0945 - acc: 0.9661 - val_loss: 0.1747 - val_acc: 0.9375\n",
      "Train loss: 0.0796888588084\n",
      "Train accuracy: 0.971606648199\n",
      "Test loss: 0.12446148321\n",
      "Test accuracy: 0.95625\n",
      "\n",
      "===================FOLD= 5\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 14s 602ms/step - loss: 0.9771 - acc: 0.5663 - val_loss: 0.5797 - val_acc: 0.6375\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.6525 - acc: 0.6923 - val_loss: 0.2887 - val_acc: 0.8625\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.4568 - acc: 0.7962 - val_loss: 0.3064 - val_acc: 0.8812\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.3752 - acc: 0.8275 - val_loss: 0.4142 - val_acc: 0.8125\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.3509 - acc: 0.8370 - val_loss: 0.2216 - val_acc: 0.9062\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.3150 - acc: 0.8616 - val_loss: 0.2045 - val_acc: 0.9125\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.3012 - acc: 0.8633 - val_loss: 0.2329 - val_acc: 0.9000\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.3140 - acc: 0.8585 - val_loss: 0.2166 - val_acc: 0.9250\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2812 - acc: 0.8710 - val_loss: 0.2126 - val_acc: 0.9125\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.2589 - acc: 0.8871 - val_loss: 0.1860 - val_acc: 0.9250\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.2485 - acc: 0.9020 - val_loss: 0.1834 - val_acc: 0.9375\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2478 - acc: 0.8969 - val_loss: 0.1848 - val_acc: 0.9375\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2439 - acc: 0.9028 - val_loss: 0.2007 - val_acc: 0.9250\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2488 - acc: 0.8946 - val_loss: 0.1989 - val_acc: 0.9250\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2315 - acc: 0.8977 - val_loss: 0.2190 - val_acc: 0.9125\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2214 - acc: 0.9037 - val_loss: 0.1840 - val_acc: 0.9313\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2156 - acc: 0.9143 - val_loss: 0.2022 - val_acc: 0.9187\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2261 - acc: 0.9018 - val_loss: 0.2239 - val_acc: 0.9187\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2206 - acc: 0.9042 - val_loss: 0.2157 - val_acc: 0.9250\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1950 - acc: 0.9202 - val_loss: 0.2076 - val_acc: 0.9062\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1948 - acc: 0.9186 - val_loss: 0.2590 - val_acc: 0.8938\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1988 - acc: 0.9182 - val_loss: 0.1989 - val_acc: 0.9187\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1895 - acc: 0.9260 - val_loss: 0.1650 - val_acc: 0.9375\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2105 - acc: 0.9073 - val_loss: 0.2115 - val_acc: 0.9125\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1931 - acc: 0.9182 - val_loss: 0.1921 - val_acc: 0.9250\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1785 - acc: 0.9311 - val_loss: 0.1855 - val_acc: 0.9187\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1715 - acc: 0.9262 - val_loss: 0.2069 - val_acc: 0.9250\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1725 - acc: 0.9309 - val_loss: 0.1936 - val_acc: 0.9250\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1785 - acc: 0.9283 - val_loss: 0.1785 - val_acc: 0.9375\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1914 - acc: 0.9197 - val_loss: 0.2497 - val_acc: 0.9125\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1706 - acc: 0.9291 - val_loss: 0.2112 - val_acc: 0.9313\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1528 - acc: 0.9388 - val_loss: 0.1953 - val_acc: 0.9250\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1752 - acc: 0.9283 - val_loss: 0.2180 - val_acc: 0.9250\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1664 - acc: 0.9305 - val_loss: 0.2327 - val_acc: 0.9062\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1575 - acc: 0.9340 - val_loss: 0.1841 - val_acc: 0.9250\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1706 - acc: 0.9347 - val_loss: 0.1783 - val_acc: 0.9375\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1491 - acc: 0.9375 - val_loss: 0.2773 - val_acc: 0.9062\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1593 - acc: 0.9331 - val_loss: 0.1848 - val_acc: 0.9313\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1577 - acc: 0.9370 - val_loss: 0.1997 - val_acc: 0.9187\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1670 - acc: 0.9283 - val_loss: 0.2064 - val_acc: 0.9313\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1347 - acc: 0.9469 - val_loss: 0.1887 - val_acc: 0.9437\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1619 - acc: 0.9360 - val_loss: 0.1973 - val_acc: 0.9187\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1645 - acc: 0.9326 - val_loss: 0.2361 - val_acc: 0.9250\n",
      "Train loss: 0.148137006444\n",
      "Train accuracy: 0.939058171745\n",
      "Test loss: 0.165007358789\n",
      "Test accuracy: 0.9375\n",
      "\n",
      "===================FOLD= 6\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.8714 - acc: 0.5493 - val_loss: 0.6359 - val_acc: 0.6250\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.6442 - acc: 0.6668 - val_loss: 0.5122 - val_acc: 0.7562\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.4791 - acc: 0.7710 - val_loss: 0.4359 - val_acc: 0.8250\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.3707 - acc: 0.8293 - val_loss: 0.3728 - val_acc: 0.8438\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.3405 - acc: 0.8564 - val_loss: 0.4236 - val_acc: 0.8125\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.3422 - acc: 0.8453 - val_loss: 0.3131 - val_acc: 0.8625\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.3399 - acc: 0.8459 - val_loss: 0.3820 - val_acc: 0.8500\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.2920 - acc: 0.8687 - val_loss: 0.3065 - val_acc: 0.8688\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2709 - acc: 0.8819 - val_loss: 0.3892 - val_acc: 0.8562\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2659 - acc: 0.8845 - val_loss: 0.3431 - val_acc: 0.8875\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2490 - acc: 0.8884 - val_loss: 0.3833 - val_acc: 0.8625\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2593 - acc: 0.8842 - val_loss: 0.3549 - val_acc: 0.8750\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2494 - acc: 0.8915 - val_loss: 0.3181 - val_acc: 0.8875\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2456 - acc: 0.8928 - val_loss: 0.3426 - val_acc: 0.8688\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2288 - acc: 0.9002 - val_loss: 0.3137 - val_acc: 0.8938\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2480 - acc: 0.8875 - val_loss: 0.3231 - val_acc: 0.8875\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2210 - acc: 0.9034 - val_loss: 0.3111 - val_acc: 0.8750\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2268 - acc: 0.9122 - val_loss: 0.3099 - val_acc: 0.8938\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2162 - acc: 0.9068 - val_loss: 0.3169 - val_acc: 0.8875\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1992 - acc: 0.9125 - val_loss: 0.3124 - val_acc: 0.9062\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2068 - acc: 0.9103 - val_loss: 0.3325 - val_acc: 0.8875\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.2046 - acc: 0.9186 - val_loss: 0.3050 - val_acc: 0.9062\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 489ms/step - loss: 0.1950 - acc: 0.9195 - val_loss: 0.3582 - val_acc: 0.8688\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1961 - acc: 0.9171 - val_loss: 0.3092 - val_acc: 0.8750\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1891 - acc: 0.9244 - val_loss: 0.3378 - val_acc: 0.8750\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.1870 - acc: 0.9262 - val_loss: 0.2978 - val_acc: 0.9000\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1892 - acc: 0.9181 - val_loss: 0.3315 - val_acc: 0.8875\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1827 - acc: 0.9249 - val_loss: 0.3234 - val_acc: 0.8938\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1821 - acc: 0.9264 - val_loss: 0.3162 - val_acc: 0.8938\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1729 - acc: 0.9178 - val_loss: 0.3283 - val_acc: 0.8875\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1641 - acc: 0.9342 - val_loss: 0.3657 - val_acc: 0.8812\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1779 - acc: 0.9252 - val_loss: 0.3080 - val_acc: 0.8875\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1637 - acc: 0.9327 - val_loss: 0.3161 - val_acc: 0.8875\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1620 - acc: 0.9407 - val_loss: 0.3276 - val_acc: 0.8750\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 35/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1696 - acc: 0.9308 - val_loss: 0.3114 - val_acc: 0.8812\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1520 - acc: 0.9441 - val_loss: 0.3419 - val_acc: 0.8750\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1846 - acc: 0.9252 - val_loss: 0.3354 - val_acc: 0.8688\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1649 - acc: 0.9342 - val_loss: 0.3360 - val_acc: 0.8750\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1744 - acc: 0.9298 - val_loss: 0.3063 - val_acc: 0.8875\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1593 - acc: 0.9363 - val_loss: 0.3263 - val_acc: 0.8938\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1580 - acc: 0.9319 - val_loss: 0.3429 - val_acc: 0.8812\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1632 - acc: 0.9339 - val_loss: 0.3325 - val_acc: 0.8750\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.1685 - acc: 0.9290 - val_loss: 0.2899 - val_acc: 0.9187\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1472 - acc: 0.9347 - val_loss: 0.3300 - val_acc: 0.8938\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1515 - acc: 0.9357 - val_loss: 0.3197 - val_acc: 0.8875\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 12s 489ms/step - loss: 0.1467 - acc: 0.9422 - val_loss: 0.3216 - val_acc: 0.8688\n",
      "Epoch 47/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1470 - acc: 0.9402 - val_loss: 0.3202 - val_acc: 0.8938\n",
      "Epoch 48/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1504 - acc: 0.9430 - val_loss: 0.3486 - val_acc: 0.8812\n",
      "Epoch 49/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1402 - acc: 0.9430 - val_loss: 0.3921 - val_acc: 0.8562\n",
      "Epoch 50/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1653 - acc: 0.9277 - val_loss: 0.3654 - val_acc: 0.8688\n",
      "Epoch 51/100\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.1575 - acc: 0.9386 - val_loss: 0.2879 - val_acc: 0.9187\n",
      "Epoch 52/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1331 - acc: 0.9393 - val_loss: 0.2995 - val_acc: 0.9062\n",
      "Epoch 53/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1252 - acc: 0.9474 - val_loss: 0.3012 - val_acc: 0.8875\n",
      "Epoch 54/100\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.1286 - acc: 0.9484 - val_loss: 0.2776 - val_acc: 0.9000\n",
      "Epoch 55/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1287 - acc: 0.9482 - val_loss: 0.3660 - val_acc: 0.8750\n",
      "Epoch 56/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1303 - acc: 0.9445 - val_loss: 0.3229 - val_acc: 0.8875\n",
      "Epoch 57/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1298 - acc: 0.9541 - val_loss: 0.3189 - val_acc: 0.8875\n",
      "Epoch 58/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1091 - acc: 0.9500 - val_loss: 0.3756 - val_acc: 0.8812\n",
      "Epoch 59/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1355 - acc: 0.9472 - val_loss: 0.3459 - val_acc: 0.8750\n",
      "Epoch 60/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1204 - acc: 0.9529 - val_loss: 0.3148 - val_acc: 0.8812\n",
      "Epoch 61/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1167 - acc: 0.9529 - val_loss: 0.2944 - val_acc: 0.9000\n",
      "Epoch 62/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1258 - acc: 0.9493 - val_loss: 0.3352 - val_acc: 0.8688\n",
      "Epoch 63/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1317 - acc: 0.9453 - val_loss: 0.3366 - val_acc: 0.8875\n",
      "Epoch 64/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1177 - acc: 0.9541 - val_loss: 0.3269 - val_acc: 0.8812\n",
      "Epoch 65/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1120 - acc: 0.9550 - val_loss: 0.3957 - val_acc: 0.8750\n",
      "Epoch 66/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1016 - acc: 0.9567 - val_loss: 0.4175 - val_acc: 0.8812\n",
      "Epoch 67/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1127 - acc: 0.9542 - val_loss: 0.3850 - val_acc: 0.8562\n",
      "Epoch 68/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1223 - acc: 0.9506 - val_loss: 0.3429 - val_acc: 0.8938\n",
      "Epoch 69/100\n",
      "24/24 [==============================] - 12s 489ms/step - loss: 0.0965 - acc: 0.9661 - val_loss: 0.3411 - val_acc: 0.8625\n",
      "Epoch 70/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1068 - acc: 0.9531 - val_loss: 0.3454 - val_acc: 0.9062\n",
      "Epoch 71/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1083 - acc: 0.9554 - val_loss: 0.3754 - val_acc: 0.8688\n",
      "Epoch 72/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1173 - acc: 0.9526 - val_loss: 0.3263 - val_acc: 0.8938\n",
      "Epoch 73/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1132 - acc: 0.9544 - val_loss: 0.2877 - val_acc: 0.9250\n",
      "Epoch 74/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1194 - acc: 0.9546 - val_loss: 0.4753 - val_acc: 0.8625\n",
      "Train loss: 0.0986341711887\n",
      "Train accuracy: 0.960526315789\n",
      "Test loss: 0.277634304762\n",
      "Test accuracy: 0.9\n",
      "\n",
      "===================FOLD= 7\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.7635 - acc: 0.5738 - val_loss: 0.4847 - val_acc: 0.7438\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.5249 - acc: 0.7317 - val_loss: 0.3697 - val_acc: 0.8063\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.4239 - acc: 0.7997 - val_loss: 0.2963 - val_acc: 0.8500\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.3810 - acc: 0.8252 - val_loss: 0.2547 - val_acc: 0.9062\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.3548 - acc: 0.8379 - val_loss: 0.2451 - val_acc: 0.9000\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.3219 - acc: 0.8651 - val_loss: 0.2271 - val_acc: 0.8938\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2816 - acc: 0.8782 - val_loss: 0.2767 - val_acc: 0.8875\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2828 - acc: 0.8749 - val_loss: 0.2490 - val_acc: 0.8750\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2566 - acc: 0.8897 - val_loss: 0.2396 - val_acc: 0.9062\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2493 - acc: 0.8899 - val_loss: 0.3003 - val_acc: 0.9062\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2427 - acc: 0.8853 - val_loss: 0.2455 - val_acc: 0.8688\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2517 - acc: 0.8863 - val_loss: 0.2323 - val_acc: 0.9000\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2408 - acc: 0.8951 - val_loss: 0.2530 - val_acc: 0.9000\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.2300 - acc: 0.9006 - val_loss: 0.2212 - val_acc: 0.8750\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.2132 - acc: 0.9091 - val_loss: 0.2206 - val_acc: 0.8875\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2467 - acc: 0.8958 - val_loss: 0.2413 - val_acc: 0.9187\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1943 - acc: 0.9254 - val_loss: 0.2272 - val_acc: 0.8875\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2478 - acc: 0.8992 - val_loss: 0.2273 - val_acc: 0.8938\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.1935 - acc: 0.9239 - val_loss: 0.2086 - val_acc: 0.9125\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2200 - acc: 0.9094 - val_loss: 0.2265 - val_acc: 0.9062\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2013 - acc: 0.9143 - val_loss: 0.2193 - val_acc: 0.8938\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2051 - acc: 0.9119 - val_loss: 0.2215 - val_acc: 0.8812\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1886 - acc: 0.9130 - val_loss: 0.1916 - val_acc: 0.9125\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1943 - acc: 0.9137 - val_loss: 0.2134 - val_acc: 0.9125\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1990 - acc: 0.9230 - val_loss: 0.2591 - val_acc: 0.8938\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1957 - acc: 0.9226 - val_loss: 0.1992 - val_acc: 0.9125\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1712 - acc: 0.9316 - val_loss: 0.1964 - val_acc: 0.8938\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1881 - acc: 0.9207 - val_loss: 0.2030 - val_acc: 0.9125\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1798 - acc: 0.9241 - val_loss: 0.2188 - val_acc: 0.9250\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1887 - acc: 0.9166 - val_loss: 0.1835 - val_acc: 0.9187\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1591 - acc: 0.9334 - val_loss: 0.2252 - val_acc: 0.9062\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1639 - acc: 0.9337 - val_loss: 0.1899 - val_acc: 0.9187\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1670 - acc: 0.9319 - val_loss: 0.2097 - val_acc: 0.9000\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1715 - acc: 0.9283 - val_loss: 0.2039 - val_acc: 0.8812\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1655 - acc: 0.9342 - val_loss: 0.1855 - val_acc: 0.9250\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1516 - acc: 0.9344 - val_loss: 0.1898 - val_acc: 0.8938\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1583 - acc: 0.9342 - val_loss: 0.2163 - val_acc: 0.9000\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.1796 - acc: 0.9334 - val_loss: 0.1744 - val_acc: 0.9125\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1564 - acc: 0.9386 - val_loss: 0.2422 - val_acc: 0.9000\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1543 - acc: 0.9386 - val_loss: 0.1766 - val_acc: 0.9187\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1545 - acc: 0.9397 - val_loss: 0.2843 - val_acc: 0.8750\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1588 - acc: 0.9327 - val_loss: 0.2073 - val_acc: 0.9062\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1424 - acc: 0.9404 - val_loss: 0.2354 - val_acc: 0.8750\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1404 - acc: 0.9435 - val_loss: 0.1962 - val_acc: 0.8938\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1413 - acc: 0.9441 - val_loss: 0.1959 - val_acc: 0.9125\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1463 - acc: 0.9387 - val_loss: 0.1714 - val_acc: 0.9312\n",
      "Epoch 47/100\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1389 - acc: 0.9436 - val_loss: 0.1724 - val_acc: 0.9062\n",
      "Epoch 48/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1578 - acc: 0.9336 - val_loss: 0.2019 - val_acc: 0.9062\n",
      "Epoch 49/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1410 - acc: 0.9493 - val_loss: 0.2118 - val_acc: 0.8938\n",
      "Epoch 50/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1436 - acc: 0.9466 - val_loss: 0.1747 - val_acc: 0.9125\n",
      "Epoch 51/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1269 - acc: 0.9531 - val_loss: 0.2340 - val_acc: 0.8875\n",
      "Epoch 52/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1278 - acc: 0.9506 - val_loss: 0.2071 - val_acc: 0.9062\n",
      "Epoch 53/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1316 - acc: 0.9511 - val_loss: 0.2029 - val_acc: 0.9187\n",
      "Epoch 54/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1250 - acc: 0.9506 - val_loss: 0.1775 - val_acc: 0.9062\n",
      "Epoch 55/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1331 - acc: 0.9467 - val_loss: 0.2003 - val_acc: 0.8938\n",
      "Epoch 56/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1146 - acc: 0.9578 - val_loss: 0.2169 - val_acc: 0.9125\n",
      "Epoch 57/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1113 - acc: 0.9599 - val_loss: 0.2101 - val_acc: 0.9125\n",
      "Epoch 58/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1422 - acc: 0.9422 - val_loss: 0.2027 - val_acc: 0.8938\n",
      "Epoch 59/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1302 - acc: 0.9547 - val_loss: 0.2009 - val_acc: 0.9000\n",
      "Epoch 60/100\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1137 - acc: 0.9560 - val_loss: 0.2107 - val_acc: 0.8938\n",
      "Epoch 61/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1132 - acc: 0.9506 - val_loss: 0.2245 - val_acc: 0.9000\n",
      "Epoch 62/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1207 - acc: 0.9503 - val_loss: 0.2401 - val_acc: 0.9062\n",
      "Epoch 63/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1184 - acc: 0.9516 - val_loss: 0.2094 - val_acc: 0.8812\n",
      "Epoch 64/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1165 - acc: 0.9505 - val_loss: 0.1850 - val_acc: 0.9250\n",
      "Epoch 65/100\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.0990 - acc: 0.9599 - val_loss: 0.2567 - val_acc: 0.9000\n",
      "Epoch 66/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1114 - acc: 0.9565 - val_loss: 0.2194 - val_acc: 0.8938\n",
      "Train loss: 0.0915702712978\n",
      "Train accuracy: 0.959833795014\n",
      "Test loss: 0.17143977955\n",
      "Test accuracy: 0.93125\n",
      "\n",
      "===================FOLD= 8\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 15s 611ms/step - loss: 0.9278 - acc: 0.5901 - val_loss: 0.5006 - val_acc: 0.7562\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.5328 - acc: 0.7513 - val_loss: 0.4263 - val_acc: 0.7688\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.4492 - acc: 0.8072 - val_loss: 0.3945 - val_acc: 0.7812\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.3497 - acc: 0.8479 - val_loss: 0.3554 - val_acc: 0.8000\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.3056 - acc: 0.8726 - val_loss: 0.4050 - val_acc: 0.8187\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2767 - acc: 0.8917 - val_loss: 0.3896 - val_acc: 0.8187\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2731 - acc: 0.8749 - val_loss: 0.3880 - val_acc: 0.8125\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2621 - acc: 0.8933 - val_loss: 0.3694 - val_acc: 0.8375\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.2771 - acc: 0.8725 - val_loss: 0.3381 - val_acc: 0.8187\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2599 - acc: 0.8886 - val_loss: 0.3403 - val_acc: 0.8250\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.2641 - acc: 0.9059 - val_loss: 0.3170 - val_acc: 0.8375\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2507 - acc: 0.8940 - val_loss: 0.3670 - val_acc: 0.8438\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2281 - acc: 0.9044 - val_loss: 0.3510 - val_acc: 0.8313\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2376 - acc: 0.8963 - val_loss: 0.3520 - val_acc: 0.8375\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2013 - acc: 0.9194 - val_loss: 0.3689 - val_acc: 0.8250\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1998 - acc: 0.9173 - val_loss: 0.4088 - val_acc: 0.8438\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2329 - acc: 0.9033 - val_loss: 0.3425 - val_acc: 0.8562\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2012 - acc: 0.9202 - val_loss: 0.3410 - val_acc: 0.8500\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2009 - acc: 0.9200 - val_loss: 0.3616 - val_acc: 0.8562\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1987 - acc: 0.9182 - val_loss: 0.3328 - val_acc: 0.8313\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2053 - acc: 0.9225 - val_loss: 0.3378 - val_acc: 0.8500\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1944 - acc: 0.9238 - val_loss: 0.3418 - val_acc: 0.8438\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 489ms/step - loss: 0.1855 - acc: 0.9254 - val_loss: 0.3220 - val_acc: 0.8625\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1741 - acc: 0.9231 - val_loss: 0.3583 - val_acc: 0.8688\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1813 - acc: 0.9300 - val_loss: 0.3578 - val_acc: 0.8500\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1684 - acc: 0.9329 - val_loss: 0.3559 - val_acc: 0.8500\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1744 - acc: 0.9246 - val_loss: 0.3605 - val_acc: 0.8562\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1658 - acc: 0.9410 - val_loss: 0.3569 - val_acc: 0.8562\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1733 - acc: 0.9288 - val_loss: 0.3613 - val_acc: 0.8438\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1573 - acc: 0.9396 - val_loss: 0.3780 - val_acc: 0.8313\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1459 - acc: 0.9456 - val_loss: 0.4174 - val_acc: 0.8375\n",
      "Train loss: 0.182323463075\n",
      "Train accuracy: 0.928670360111\n",
      "Test loss: 0.316960558295\n",
      "Test accuracy: 0.8375\n",
      "\n",
      "===================FOLD= 9\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 15s 620ms/step - loss: 0.7049 - acc: 0.5995 - val_loss: 0.4957 - val_acc: 0.7937\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.5103 - acc: 0.7296 - val_loss: 0.3381 - val_acc: 0.8250\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.3868 - acc: 0.8073 - val_loss: 0.2679 - val_acc: 0.9000\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.3325 - acc: 0.8428 - val_loss: 0.2714 - val_acc: 0.9000\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.3332 - acc: 0.8438 - val_loss: 0.2288 - val_acc: 0.9187\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.3002 - acc: 0.8699 - val_loss: 0.2215 - val_acc: 0.9187\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.2893 - acc: 0.8609 - val_loss: 0.2072 - val_acc: 0.9062\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2358 - acc: 0.9059 - val_loss: 0.2363 - val_acc: 0.8938\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2479 - acc: 0.8925 - val_loss: 0.2152 - val_acc: 0.9062\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.2472 - acc: 0.8912 - val_loss: 0.1924 - val_acc: 0.9375\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2353 - acc: 0.8979 - val_loss: 0.2451 - val_acc: 0.8750\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2250 - acc: 0.9080 - val_loss: 0.2080 - val_acc: 0.9187\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.2506 - acc: 0.8977 - val_loss: 0.1710 - val_acc: 0.9313\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2095 - acc: 0.9161 - val_loss: 0.2141 - val_acc: 0.9125\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2205 - acc: 0.9024 - val_loss: 0.1957 - val_acc: 0.9125\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1979 - acc: 0.9184 - val_loss: 0.1963 - val_acc: 0.9125\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2230 - acc: 0.8985 - val_loss: 0.2040 - val_acc: 0.9250\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2027 - acc: 0.9160 - val_loss: 0.2175 - val_acc: 0.9062\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2042 - acc: 0.9181 - val_loss: 0.1835 - val_acc: 0.9187\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1914 - acc: 0.9189 - val_loss: 0.1983 - val_acc: 0.9062\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2153 - acc: 0.9125 - val_loss: 0.2020 - val_acc: 0.9187\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1923 - acc: 0.9187 - val_loss: 0.1903 - val_acc: 0.9313\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 489ms/step - loss: 0.1740 - acc: 0.9280 - val_loss: 0.2005 - val_acc: 0.9188\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2003 - acc: 0.9226 - val_loss: 0.2024 - val_acc: 0.9125\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1862 - acc: 0.9184 - val_loss: 0.2018 - val_acc: 0.9187\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1812 - acc: 0.9252 - val_loss: 0.2091 - val_acc: 0.9125\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1777 - acc: 0.9243 - val_loss: 0.1866 - val_acc: 0.9250\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1835 - acc: 0.9231 - val_loss: 0.2188 - val_acc: 0.9062\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1790 - acc: 0.9298 - val_loss: 0.1985 - val_acc: 0.9125\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1639 - acc: 0.9329 - val_loss: 0.2353 - val_acc: 0.8812\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1389 - acc: 0.9397 - val_loss: 0.2440 - val_acc: 0.9000\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1811 - acc: 0.9308 - val_loss: 0.2369 - val_acc: 0.8938\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1597 - acc: 0.9384 - val_loss: 0.1940 - val_acc: 0.9125\n",
      "Train loss: 0.167908856392\n",
      "Train accuracy: 0.928670360111\n",
      "Test loss: 0.171002429724\n",
      "Test accuracy: 0.93125\n",
      "\n",
      " Train Log Loss Validation=  0.119905835512\n",
      " Test Log Loss Validation=  0.19948751864\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": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#preds=myAngleCV(X_train, X_angle, X_test)\n",
    "preds = y_test_pred_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Submission to kaggle\n",
    "submission = pd.DataFrame()\n",
    "submission['id']=test['id']\n",
    "submission['is_iceberg']=preds\n",
    "submission.to_csv('sub_SGD_10folds_3e-4_20p.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['loss', 'val_loss', 'val_acc', 'acc'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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QsU/N0V17y8RemKu6cXbtVBUV5Khu+Fa1tPTIr9UUlBSprpuhmpfp3/Fz/uH8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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7febb73d17b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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KpVT9oknBGwKCbTNS1h5Y+DevnlpEuHVYJ3YdymP+xoNePbenODW5KeUxmhS8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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fec15372b38>"
      ]
     },
     "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": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.image as mpimg\n",
    "\n",
    "min1 = np.min(X_holdout)\n",
    "max1 = np.max(X_holdout)\n",
    "diff1 = max1 - min1\n",
    "my_sum = lambda x: (x - min1)/diff1\n",
    "\n",
    "X_holdout2 = my_sum(X_holdout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(160, 75, 75, 3)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_holdout2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_holdout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fec151e7390>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Y92rLl0OOMh/TT2pd9mNjMPMMFtXjLko0jpDXjfmIbZ2tns5QdKm5RN4sliMy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\nTIeesiMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7febb73ca748>"
      ]
     },
     "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": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fec15149630>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+N+g97b9PKQbfhdP6zGltv+fUt8OysbDUqiDCbkVaT+JZ4Z7o7MAZnCoDRLDn\nHRSiwk3CAloVhBk53dOjmoLqCD4RisF61UB7RbVt8yhzmiEmJCQkRKQHYkJCQkLExlLmoKB5zFvu\nIir3wJFTXNajXSDiuog5niNnRueizC3kz+agndPK6RCFwaPc9x0iV5X2WwYqpxgdXyA3eMH6v/AT\nayGZuAbd5JvuAHZdJUTNY9DNTnRV7iHvudNyyvTh4C7ZXSwZ9JaIrD/pQuZtODNb5jS5BSuq0RzH\nj+7NB1je6DA6jzWI47afZxd9JOoh3bZhhQW7tDqQYjZjznrGy5d4BH0MP+rdQy6N+PEXoKwTJMcv\nILwWrLZWQfl66W23EPkte+sVCbid9FjhKocMDteM7FcdLjH4UkYRVQkPdHypgffNtIS7eQv1rE+a\nNkKV7L8SEhISNhbpgZiQkJAQsbGUWbULomuIgbuwS1oi+nvcdvo2iEWeqjlyndGGwc4qC05Htidw\nvd7xfffnUEmDBd0cO8XcldOWPIZfrevt5V2noBMIkPdyRKJbiKBOoHqu/TJPULuZ7sjD7u3Uk5HN\nawOvSy3kby9hIdXZdV47R/Q9e9rHdnzqBbKWoHJZJ24PAXAP48xzLlA0qY8c52KBccadXeGcK9Sr\nnoGSh2iL1X/Bvp8DxPDd0sd5e9/HagnaDWN0nSydmpYdLGvwPvpwjFDveHszOJD3UcN6AcfqovST\nu9Ly5RDDUgYc4s6pIrYoNo8Um8dk5Jgx5MXYzyf0m3PYQPevNENMSEhIWCE9EBMSEhIiNpYyF1mu\nvSjmZWGp04HTg6MOaiqDy86iUDUHZZ7kvt/9tnP2uQWKMYMb9AyWSyVsxirk+G4PQJMh5FUWbaHQ\nRgfvrhki3hWto0AxmXk9WSA/GYHBDCJti+exQOGlOThghvNcLmhzBmFwn8sKiGbvY53gFFQaAuNJ\nFs8VxZlK5BoPRwjJM8oP3laPkLOMXF5SvzkiwQuIjQfRYXp54tRw2fVjtgz3R8u/n4H2j0rfNx/h\nCqAvbbhq60XNuTLXfjLD8Y9BU4v1Imhady1Qi1mo3cwCaj2M6Uns7xLC7RbGc4Q8/a0u8q2jEsGS\nMDshISFhc7GxM8QQai2ia3aBt6KgnWpNMbvBInQWTTq70F51oTc0OIac8xTBDKmFhf/ZkQcSeldg\n6Nlab8yZzZt+1XCpqZCut41aLPMKNiyYlQk1WjKcc5tOOXD+WWW3GWZ8i1Pk4mHmRu3jjeD6yC5m\nKwMED0qfMMRtAAAgAElEQVQcs8bMhQGUadTi1XDWLaAJnCMAVmJGUyAVsMe2ESQ6F2vBxG0PYZBq\nEmdD+H3bT03TAwSsMG4VapAwYEYN6R7ceWYubVSv2wREMpgDP1H4Qa9CH7gdoOvMkX4JXWndRsAI\nM/0JaspwNrrSvnagJQXJ0E7fj4/bWWE1E6+TQWxCQkLCxiI9EBMSEhIiNpcyV7XCUUNtlwdOK0ro\nueqJ05PiCktONnRjiboUbQZPEBghfdtCWc8WZICdHQ/CjG86fTbzY9ZsP+rW6gAKfoxgxz4Mb0G7\n2xWoD+p4MMVqseP7DnecEk1B8c5w6CcRTp2aT3DXHGzDRBfxgjmWD4bIuzs69TKn5Z4vSRRRh7g1\nQTCGhVywfHDupoULEel+6PBdD6NbuL2UGJdudJbJUKOlhkFvceBjtSy9L3ugsq3avx+2UZL0Cu4j\nmvW2mmPlCG69ZOF6z4AlgAr7teD2MwE1XqJEDF1zApZSzqXj9Zrxb+G+EcZwhOWYHI5BWzHlL9s8\nxpxmiAkJCQkrpAdiQkJCQsTGUmaZqYq0qYILzHVE8TpD0M1Tp2crY9g20vysjzS6JSKxKLF5ChuS\nrb5Tpv4C6VBboJiHiFb3vP3BOPaLDjjQ3o1B01mUldHPTs53HWgijUNJJSNlnT96ePZde8+p7qhA\nGtsINA3LCrrA/KTc9uhnbs7rTh+/4cfaissHoGYsN9rrwAUHKYdzDMBJDkcgBMjLXW+z13KKm2M+\nUB43+xZI7cNhztW3yQbeHqqm6r65n9sC9Plw6iayXfRrnZfNEKbAc6Qrjhd0DPJxyXGPFrgv8z0/\njx7oNlNAFZdhWtCVPgWjYqLAvSoY4W4a0gwxISEhIeJSPRDN7EEz+ykz+yUze4+ZvT5+v29mbzOz\nX43/37vXfU1ISNg8XDbKXEr630MI/97MtiT9gpm9TdJrJb09hPCNZvYGSW+Q9DXP1FCtoHmUTVdI\nR8ohGO6ghGSGNL0s0s0SwmGKVxdtiIcxhEctGJ4igtlb+DH7Y6R9bSOyvQQl222oFyPipzimob7J\nEulq3YFTNjrJkJxVKDPJc1bcvnfVI+IZopM50tX+29FjZ5/35O+mIZxXnqg87YtlQ3O4Aw22PHK7\nwmSBdDXBySb45xLn1oEufQsR30Hw69Jv+ThnKAM7HyHVLkafmWbJZYoWyp1mEGxXiDjPUZZzOUOE\nFtdrCiPi7nZz/xUoU1pCgD/HPZF3YGzMcqv4C2bpnBY+18X6P/N2/Lsw1FTZ3vGxqnA+9KrtfmAU\n90sGsR9ThBAeDyH8+/j5VNJ7Jb1Q0qskPRI3e0TSq+9NDxMSEjYZl22GeAYze4mk3yLp5yTdF0J4\nPP70hKT7LtjndZJeJ51fhE9ISEi4G1zKB6KZDSX9iKS/EEI4oatGCCGY2VpJaAjhYUkPS9Le1s7Z\nNqRpWws/5Rp1OnLUAFksG6rawgR6UqynrCSm+5U/hEnNAkpcLoYwdMW+yyHyk2O4tpogCg2TT/Wc\nPlW7vt8MprBzRMXpPHNt7PsuBUee+Hkrx4sEjGhlmitJez0k5LJfiIS+ACamdE1hxLnAdVld0byP\nZQzk+GoHed8wrg24FgPUzikw/hUo6UnuNJkuMFU0iGXp2WIII9i+f18hh7eu0PYpuDTA+7fAkk0V\n6XmGvHNDbjTLml6fu6C9giKaueG7WALizdXH0oOwZBIiJaaxcIaytsstGCiD6hdbrXP7bxIuFWWW\nJDNrqXkYfn8I4R/Hr580swfi7w9Iun6v+peQkLC5uFQPRGtepd8l6b0hhG/GTz8m6aH4+SFJb/14\n9y0hIWHzcdko82dL+nJJv2hm/yF+93WSvlHSW8zsKyR9UNJr7tRQVuTq7jdWWznyU5H6qScziI0n\n/rkX3xMfrr3mybXgdAQeo8pQ4rKqnI6ciyzCrgkVOTXPKar1fSeruiOk2ju8VN52B5ewDeqTg8pO\nIR4fgRF3TmGFNmioN4Kz58pd3qw8B5uWUxQ3T6YwFG17xDnf9oNW8NeyLuh7FIxT56093681h4lp\n25cJMpaPxcVt4ZrPEIln3ZNZB2LnVowyt2DDNvbfWWKWxrr9HEsdQywBgMqWk/Vi5xDL5GZQMJDq\n0n/16g5qvWAMWXp0gYh3FwJ3mhjnqKlSxjWR5QG+g6B8LIixgUVcpgn5pZpPfVRwqR6IIYR36vzS\nGvEFH8++JCQkfOJh8x7xCQkJCc8Rl2qG+FFFcHuiDJNOUowD+FhN504PFlUTafvkvlsxLcDlxgYL\nJdS9IPKhU+wOo6kQbGfIMZ0vPEJZxroe9RWnnVtzj06DaZ+LOAaIinsFIoug9aTEWfv29+FxDnu0\nQGrm2ywCaoTU6zJypZOpU+xQou7JEBHyxe1UclKgxCsExR1YoWVjv1YnNcTrsAt7YOfg7HMJWltO\n/dr1KkSuY5i7mniUdTwDvWcnQc3LHYwtliz6hV+vMejzDB7rqxK28zGWa9q+Xzbwz7MJROS4nueE\n2TdRUwb1fRYQsvdxuy6j/ZhBgF5gjtQ9whIQlglW0XGFzfP/SjPEhISEhIj0QExISEiI2FjKHOpa\ny1gkiV7QBSO+J04JFkvk0EZKcGxO+0gTiwEEu1uI8m65kLbrLloq505l5hD4TrrMBUWbVxuKl0OY\nXMOiqYuoZDWAABuUzSBM7qJYE0ubGtym57GdYrTGOVtSiaWGngeTtbiG8XwKZVP7PhZF3ynryfWb\nZ59bED5nse9buD4sDUsR+XyLFM+XFfZQN/Xm0q/dviFnGuVEDZH4LOatnyumhL8Ogy1YG8sRJUp/\n9nENAyznhh3vY437bGXFNunAtmvC3HSI+CHGL9F2TiE1QuglreiwZDA6uH0ONJv5cg2Xl3h9po/h\nuh00+e6bR5jTDDEhISHhDOmBmJCQkBCxsZQ5l2kQZbgZqOHNwmnoooOIb3CKl08j3QAnaKPOb4VI\ncR/FhIRo8gymwjVsxCi2DR3YS4Eerty727lTrWKEMC+EuV3kKc/bqMXboegbhZsyp1gFIrr78+Y8\nSlDKydzpXdecsp28wM95q+fR9FZYb6gB9zV1DjwPOmBcVvSxkyHXHAHsEkL3MPZ+FTPUSMZ4bsEK\nrZz7ObdRoEnM4V0Vn4JAvoZIu3tu3cU/5jj+aOxC/v6+198+mTl9V4ka2fF+6S3ZoH+cl36vzvB5\ncQxncOQ7t3Kn2GXXx6vCfdluQQwfI+5LKBUGMEUpWHR8269ztbILS1HmhISEhM1FeiAmJCQkRGws\nZQ51UIgR1UXtNKiDnOCj4ALfPhyZ55Ed0DasBcY6LJxW1KQNEFpPMbIFkp9byAPeR1Q0wNFsFLn6\nHMLo2TYixag5HE5gEQZrqS5oGHOsu5QYI8p6Gt2+Z6jt2x84Tbr56NN+fHJZUOYSFHxKITGOyXrB\nApXNY0R1jmj+Etvyzc3ayjmWQyo4jI8g0u7g+B2KtCFYzmJ0vQtn6lbXKShRIeJfwbprTDXB0x6V\nZec7KHJVduO+XSwd4Fq1bjg1XvZAgVEXnDWaiQ4i4TWKlS1nyM8eN+3vQBGQQ0RfYclAiLLPYmbA\nBZr85zXSDDEhISEhYnNniJk0ixO50lADBQYe25m/9TAZVBZnBmNDGlfpnw8OYayK2dq52Q/StQJW\nyg2zuFbJQAmP3/SrrHzGsUonlKS2fObS3nONXR81UhYIvLQgrmuxjgqCGu2olTvAmExxzO4DMClH\n4GE58hkHZzcncIfZxoy6D7+ZGhOw+slG3Fh3/ZYcIpA1hVZvgZnoCNq77pGPLQ11l3jv3zTUvUEN\nlqzdtDlANiFLzxZIl6sxiz0ZQ++IE3q8uz6lk3Y+eQxOnC4xhkgt3Nn11NEMqXg1yo3OwEq6hZ9P\n68ivXbWNmxvmtoqmxBOkE5LNtHmtMENcjJqyqiEFVRISEhI2F+mBmJCQkBCxsZTZZOqc0T8sgiN4\n0cOqcIVSjKNu1MRBQEdJVrnr1KgDI84JaofM4YhyAk3g/XOky7URhEBKX2fWfKYR6e7MP2/N1+v9\nqtrbKJdOvSawqqmQ9vbg06DSMVBQwsG2dxVlUrEYH2i4e9MX/hf3eb86CEicqwcDPeMSwYGw32xP\nA1+Y0ZxLs3tsfOPs887AdY31HhyBUN6kPvW+H7SQxkd6HF1mTkvfsYe6MCw324XzztWZ08oK9P2F\nmZdzvZmjnChLhcZTakFLOMISxPiG11EpUPNHW0zR9GvIYFNA7ZTiCC44EMKuGP4Swa3J1Kn+aNuP\n2Uce43Cl773QuvT5izRDTEhISIhID8SEhISEiM2lzEFqRZ47x1kyvSuAYtgNJ8XDcUMFiq5Thm1o\n8oop6HCMuEnnXVDaYIl9uqMgyocsOY0QUc5itHAImmxD/7xEWc3lAKUiA2jyAuU295xWsiTl6VXv\nS3fSnNMCNTUKlkGl5oy1SwY+Rm0/vAZwuDld+hh9GHU6diuknfVv1/wtWJcEyxs7lafFMX1sAIpb\nIu+SnkKn5pSUpWfLsulXF8sO7RmiqBgXxlYNyxqDCvS1Rgpiy8WdNwpfViki9WY51sG1PbSOI4Hq\nViUMiqFaWMIFxwa4d1g2lffftNn+tO37bXX9Xlkeel8zmMiuXJIsUeaEhISEzcWleiCaWdfM/p2Z\n/Ucze4+Z/bX4/UvN7OfM7H1m9kNm1r5TWwkJCQnPFpeNMs8l/e4QwigWrH+nmf0LSV8t6VtCCG82\nszdJ+gpJ3/GMLVW1qsMmYri8guk+XGPyPaQ3HTh9s+iskoGO5KCDQq0Nlseskd9XgWIsTuBOkjk1\nZLreSyunVScx0tuZogwpGOXhtu93vPRIJCPebZjYFhDy1hABT/A6fLzTUOwt8/16CGbPEWcfwj2n\nBJUvJhSaIy0O9LkG3YN2/GwcFxAgzxB5N1D9KzPUZZkiVIzrUmA5pIJEoIXSohQ4l9Eg94b8Wp2e\n+ueDAy8DukBKJVlthvKo3KaGeHobbjLZKqIMqttGyVYK4MeoHRN6uHCg9e2JLw6Md0CNsUyUgZ6P\nowi7jbHtdvxGax34flND3ZdxbHvzdNmXa4YYGqyk/634X5D0uyX9cPz+EUmvvgfdS0hI2HBcqgei\nJJlZHovUX5f0Nkm/JukohLN8qUclvfCCfV9nZu8ys3dNy/XFwRMSEhIuwmWjzAohVJJ+s5ntSvpR\nSS97Fvs+LOlhSbqysxeWDzQ0OB95xDUDJblp/v0MUcRBpJ5I69T2AqLXU6d9feT+PlV5e8e1R1P3\nK6chixlEujDrzPoexb4ac2xnWxCLIzr69Ik7qfRAdwRTWka8C9J6vALrY+/L1ipajGhyPfFjZqjF\nMjlwet8BZRsX2Obcq9Zp2pRRbDi15JG+dqb+O0uWkrIbIs4lxN032xCVL/z7XeT4Hm87DWX0X1F4\nnrf9uxx1ZJ7OUAsFSx2dMQxqEc/OMddow9w2myD3eLUmwXopMGsV8uSHN3xsD1GHNizWL1OMULul\nX/iNkaFfg+jwlKF/eQ/JCkgcyCEYDyfx/q8Yv98MXLoZ4gohhCNJPyXpsyTtmp0tkL1I0mP3rGMJ\nCQkbi0v1QDSzq3FmKDPrSfq9kt6r5sH4pXGzhyS99d70MCEhYZNx2SjzA5IeMbNczcP6LSGEHzez\nX5L0ZjN7o6R3S/quOzVUZkGH/YaebJfIN0XErUt2AuPWIn6eF04JpiOPOA5AwcYwVC0QQX5JjRok\nECOzjsjyAvHQinn2kUu9QP8qlN5kjY4WTG7bqE0yLpzi56RHEFUXkb4z15sR9K0W8m2fdjpW5aDD\nEIPPu+tzfz+t9mhtC3m408geCywB7KOU6wwCaIrHtyoIkH1zVbWPi8EItk2tM/J9t6N4eooNZog4\n03B1B8ccCfVSDmEt1oKaAAJz5hKvSuIyT34OqzrWaxGs1TpH/nmKhOwMdVz28aed4XMFs+QQbeZW\nagxJMnwOGLe6wHWOudR1vnnC7Ev1QAwh/CdJv2XN9++X9IqPf48SEhI+kXCpKHNCQkLCvcSlmiF+\nNJEF02ARqdr2+rxOogSVmEcrsE7uEenRFugD9mtBvJ2hdkUGWjVcQEgMEW4n9+87oLJ1VLyWcFfu\nIE92ALuoHEHGYopo5px1MyHY3fd+dZBM3Ym3gl33/FXW/8hApfdzPz6F0UXfrbVuwG28TWdy9Jfi\n9dN+s32NDe479f22Ot726aGL0ZcB8qpzhVdwHIxFD8cvIfxWLOdqF7BAO4aFFvLHDxDaz3bgsE2R\ndo1rgYjyqmYOkwUqUuAOau5grFjrZwl5We/Y2zHci8dbqF1zCGF8PNkake1qgjxpOIoHuLtPVvZj\nGzid2sBTSkhISHhuSA/EhISEhIiNpcxSU2hK8qixpHN8d37qUUFDcZ/F1YbWHMvp4wNTiHgRF1zu\nQsgKIS0Mq9Xto1gSGN7TtUf02nS7Xl0V0LvFDoTWoM8aOR0qICpe7Pkx5xTpkp5hGWAlEq+voqwo\nCiu1UbQoZL7fArnMc7hyDZDvPJijQBMuhSFCvhWF78ctP86g5edwpecO1AUE7QtE/2fIGf9Q6SHq\nCaLJvV2n3tttb3/lyN3D9Sk6oMOI8rJQ0wJR6WDcxu+tHqy/a+QnFzHffIF1j755dJpO1oysZ2P/\n3B9ge1yXDgTgQ1irPbkNxUHM/Wb+Mu/tOQqrXUf1rfu6jUVZlm3efGrzzighISHhOSI9EBMSEhIi\nNpcyh3CW58nIZkbB8BZcsLeRnxujzFugwG3YUi2mTofmiPIViPKNYfnUGe5ge1BPJEs/GVzgu9tq\nXIvzc7Zd3vYIUcstJB9/eOB0aAwH7vuXoIYQY/fWXP58CKqN4lQ1cplziHSrFuy/QCtnpMaw4sog\njGeUeTlpaP3LiwfOvtt/6ZXb+idJ2dwp8wiR8kXpY37fsV/bBSo7VbAomxtcyuNQMwd5Z4TxYWQX\ntHoK93DmBC9x02VQE3RzOGnHwl7Flo/5DLnuM6yvtGHtlmH7Dop2MQ16gZrSnPVsQ2A/WZ0/lpFy\ntF3xRBH9Xiyaz+ECxcbzGWmGmJCQkBCRHogJCQkJERtLmYOkRaSkBWycsuugJPetTyZekaMd1/8q\ntJFXjG2XELKqg0iwH0aPBm+oRn7oEOLtPVCZ4ijWSB6ihvOhU+oAKj9BdDxH7vEe3nWjjnemD1+u\nCcLYvWsNrT+SR6RpeVZBxJyH9bZP04CoKKgk88dr2CxnKBCVReq9gL11AXrZ2vPxmR962zW4Oes4\ns0BYhXGxHm55RGVnRRwLCOdb+J0FlViPmAXHAty4CxR56mFZY4H8+Fa8RjXGwS5goQtYfrUR/V/i\nGi6xDeuCD7FMsERAeXUd2wPkxgNzjFXnun9frJZsqkSZExISEjYW6YGYkJCQELGxlLmqKo1vNlS1\nWzpPyGjjdIJQ6K5H1+oYCa6H/r44zZxK9VGIqYK11VOV09r79z2ynCMPtCh8yA8h/C4QCW3FAGnn\nEILqbbhu46rN4eQcaro3+zG3pr7vjKUVkFd7Pbpws3+t0qnUEnnVYeHH6WLFoAWKVSAPtwWb7hlo\nnZb+uYg0cAzX62WFMfG91EWdaejC9fShO4nfwLjYwI/fg43YMWzZVs7XBrExC3sFOomP/aTnHT+H\nbeSYF2Pv+6zlYzeHQmBVO7p34teqi2WX+prfQ9ly/TLFEn15cuZLMwVodd738aqx3FHF+/LGwutm\nH2c+/gfHqDk9hGB/5Wq+ee5faYaYkJCQsMLGzhAtM2VRczeBVm0b6WDVEDMnzEbag+Ytzbf5bIL6\nK224xIz97d/HTGgafPbRRupeiaDCfkCgYOZv+hA1bzUqUmZzf3PniB4EzGy756I9PhO4ftNnThlS\nvQJcW3ZXs8ELymTkKPGZGd+jfj6s3ZIxGwzowPmGdVJaMc/yhbvXzr671t/FttDVQfs4bvt12Rn6\nTGhy7LOeJ+H2UiKlrWR90nh627nPinMY2FKHeISUx+0azj++tfoBTkkovVoWMOBdXSOk1s2o/Qsw\nxcUMvX3s51Mj/fSFGZ13/Dweh4ktUye3Vw5HU+/5DsY5h2NOeQSz3NVjY/NiKmmGmJCQkLBCeiAm\nJCQkRGwwZc7UHUQKgVKN9QC1TsrJrbs126yCBqDGvcyDBDXoaI56Ge0j//4odyqzc+rflwiwbBdO\naw56TveqGEyx7vrLMwOVH5QwDgXFW8Bup3/FF+eXpyiniTaXMToBsxmNR4iY9GGyC93cFjjYAsED\na/k5D5Z47yJQVKOozTwGeEr0r2758YdtH5/DU6fDNx9//OzzycnJ2efJ0Pu1BaNTgz6PjK+KpWqn\nfdTcmUE/SS/ZzEduAe3hASjrchvlTHOmAEJDGfWkTLOcwPCWpT9bnfWa2SHuy9aOH3+KqzvE+bOd\nwUmzjSHQNPHYiRag72z7rOls86IqaYaYkJCQEHEpH4hmlpvZu83sx+O/X2pmP2dm7zOzHzKzC+rV\nJSQkJDx3XFbK/Ho19ZhXlqN/S9K3hBDebGZvkvQVkr7jmRowSXkUSrE2xezQ6VaAg00b+sQs0jca\nmOYwVl327/wemS6cMm/tgYeghGQL0dKqBXPPqw09XKLOy3KEEp9Io6sr6PZqmoL6MXNEUztb1EE6\nPx7Nm+WDKeh4h2lu+H4HZV0ncKwJc5iYTlDutKT2DrFYpDcuoubz0PzLIcqAvnDmywtt0HrqIEeI\nit6Xw2Fo7GO3bCMdb3G7WevN0rWhN6AT3Wv5eLZQ4jXs4V5AqidNcdsQMR61QZnjeBXQdQ52EJ5H\ngZfljCmiKB9brq+1UvSwje+pHAqB4QNNSdgWDJTzuY/5Me6zgGWSUdWMZ72BYeZLN0M0sxdJ+hJJ\n3xn/bZJ+t6Qfjps8IunV96Z3CQkJm4xL90CU9K2S/pJcEXcg6SiEsJp+PCrphet2NLPXmdm7zOxd\n88Vi3SYJCQkJF+JSUWYz+x8lXQ8h/IKZvfLZ7h9CeFjSw5K0v7sXiijI5tS+zaqViMouUQ+jirU5\nOhBa0+S0aMG9hFFL1DG5H2UzaxinXt3ZW9v3AjTsMNbjCKTAqCPSgoh8DJPZbuF0a1Q6TaSod1E7\n9doaY5kgLi/QNPbmNhxWYH5a0klFTs3Kro9XBbNWwXnGAtL4YEA7WDTfLwvUF+l7ZPPDYxeXXwQK\n6TM4zBAthIs7EOnPF814dQqoCXAOpNo5ateUuLZhQNE5lAVcSgA9zuM91UaUN6D+C0XczNxr4R62\nLb/m44Vf22NMCLo1ljWwZFEeNOeU4/diAQoOJ6cZXHqqRTPOpNGbgkv1QJT02ZJ+v5l9saSumjXE\nb5O0a2ZFnCW+SNJj97CPCQkJG4pLRZlDCF8bQnhRCOElkv6wpH8VQvgyST8l6UvjZg9Jeus96mJC\nQsIG47LNEC/C10h6s5m9UdK7JX3XnXYoVZ9FLGl0OofziYFKt6a3T/+NQufgtOfm0iNxYeHfbyGy\nW54iKgj96gScvY9c1WrsbRaxtGWr5XSoRvBxf+K0ZtDz6CvFyDXaOyw9st6dgsoiErkbheHTuUd5\nH4QweQ6h066QZM1ILYZwDGFwBep16isJZ/nLklRG49zRyPNuP/DUh71tdPvk2MO5x3OPBB8u/fOw\n9LHthPX02eCLWsS+DDp+bjdbPoZWMR8axrqnftLXByzV6m1POt75F03hiHPGQjFWcCw6nsOsF+Jq\n5rizJGwOp5zZ8ZEfp40c58I7Nr/Z3BczXDcICLTE3wcj2JuMS/tADCG8Q9I74uf3S3rFvexPQkLC\n5uNSUeaEhISEe4lLO0P8SFEE0/6soRCzNjgBrI4OC6ev+6BY/Tp+hkFsjSjrg7Xn1bIk5+EUFAvv\nmoVuj9BJUg26qZZzlTwKtlkGdGjOmSc9CKoLWJSBPo0Kp6ktRE51jMg1IopZpKyDAiVLIUwf4PgY\nKlkFijdzuttael+erpzKtrBzbx8R93nTTo71hZvXnz77vDVwrl3xrn0KUfMM1HgbFBPD3IF12nKO\nses01/eEFXPAEmei6Bx1X+Bztv00ysbu+zj2GFlHSnBrFKO1KOdZoebO/fL7jAhdmMhe4Ne2j9K3\nrAGzgHJhlJe3/d5CdD6nOgD31k4Meee2eTQ6zRATEhISItIDMSEhISFiYymzWXZmdcR6HDOIh4X6\nIt0+nJKjIzEdkPO+04fQRcj3GKUnIZ42RPN6iJB2JuspTgZH6kGnaX+Bjk/husz6GgHi4Yk5Bewi\nx3XojFX50qncdOo/LGKIeHfJ0XK0YOG13PfbZtqlYBsUvPLtO3AGDzPvrz2FyHqkZwFUd4Fc85uw\nU6Pmegz36g762IMz9wJLD4zKdtoQZo8bun+Au2XSgwP4DdRf6fl+GaL2i/vAhwmw8BnUCq3deM/d\n9Ghyl8sbEIDTlu0UtDeH0DzHsk4nh9gadXKy4vY/+RJ58idYxWhBhdCFzVgeb8Xzzumbgc07o4SE\nhITniPRATEhISIjYWMqsOsginRl3nWLOUSzqwZlTnzaZYvwHo8MLczpyMnXR64PbB2efDdS4nkKA\njZzZsO3bdErQnVOIeiOtZLnPnP0D7RsgalpNEc1GecoAutWB2Lu960Ly+YqGHYD2QZj+JETPJRqk\nYLpAMa8lcl+7SzhPw4rseEkxekNrt5Eb3Q/IDYbT9Amj2Vi+aCOXOMNYFCgtCiZ/TkjeGsZrhPzc\nNvo62fE/lXri17a9BYqPkqQF7OTmldPtLiL79c3mPDq4DucAx/Yl8scNdLiFEwqwWauXcIlfgvoP\n/DyOov8aBASqEZI3LM3M4dXWXxUqyzdvPrV5Z5SQkJDwHJEeiAkJCQkRm0uZQ1CIwmeKp0lDFmPk\np6JGcRZtmZaICLYRHe4hEnlzcbj28O1TRDYfcEpUdZC/C8psiFYvo6VXwLYGCkTKFJD7WiHfdtD1\nqHlnuL7iQkkaPm7O9TQDHe6eS2xFX0ANkbLNOr6BybygVtuFU3Lbvf19PJ0imowiR7TC6m6D40E8\nPoqz0OYAACAASURBVINqIBS0sYJjuPlJj+CjtfLPpKM0XLHUAQXWgH82vn3LIDpHxLtCkanZCSL7\n2w1Nz5cY8x4E+DNf3mGecmsBO7epb9NCBHkCx/YO1lsY/T+KjuTbbbiRH2HphubdB77NOLq0XyQK\nfz4jzRATEhISItIDMSEhISFiYylzpVqnsRiO3P1KBejjeBe5p5nTp0Fkp90O83edsmbIh6YAOoOT\nth7w45y0nLKVyJ+9WjkNWV6DYDrW/aW7d4ViTobjZMhNPqi9PebHjqHqrXD8NiKeasd3I3Xrc9QI\nZkGsI6d9+TFctfsYr6HTtHOCdQSxKZJeOT+XcPquEZ3OUf+YY0jMUHCrVfv4txFlNRQIY2Gx1fJK\nDdu2eh+FmlDYKaBGdXYCMfwIYesbXiN6CxR/BnuxSVwSmaN/1aFH0G3Pr+ds5hT4FBHnbajUn7j+\npPfrAZczDJGfPoJK/KqapRy6e/d6foHoGL6YcM0kXrfNY8xphpiQkJCwQnogJiQkJERsLGXOg2k7\n5uXWD7h11CQg95hJsYicntl/Ib+Y7trMcaZFVMnCSmPU3x05xcwQ5T5mlBXHz2NxqwXslYpTtA20\n92ERdei0agqR8PKGO0znOKfWntOqWafpF6PWpKB9c8o4BK2a5U4xqyNEiEHxwp5v3+n45yUoeSdG\nSPe3oVyH0n1+7Mcp4RhNi7QeafLYr9ccub8Fak0z9l4OmvOuRzjOTYjRtxCp7nsfi22ntYb+0n99\nfOJrNof7sPeqG4uuDMsbgtqBlSNZwKqfo7AUxO3dA+/XHMsRCxTI6iB0PIsC+wEi7xUE8COI6ycT\nP84KZV3d9t3zHWmGmJCQkBCxsTNEFZnClebtHUp/k3UG/oZk/ZBzxpjRQYRBkknlM4frhb8t+bZu\nQ2+2csyRpK0eConsIvBwipQuTK7qWPelxOsqIBhkmDmWv+YmqtMeUgEx/2GpUJZD6UA3N42lMlnK\nc7pEGcqhj+EuZotlz8doimDTDopzdJleiJRCzoxa27EvY8wKy/W1tbMRyo0O17/Tmep2DBPdcupB\ni9Cla0+zvd3v16dA0ZU+AjxzONxkPZQkpZPSFkxcO7i3kOu4Mp1ttZB+h+BFveNjOID2lbPfVnd9\n2l8bszvqaXmfD+M5nbTxO8rt9gPcgKAD3Yr3U0FD3g1BmiEmJCQkRFy6GaKZfUCNUKaSVIYQfpuZ\n7Uv6IUkvkfQBSa8JIaxPEUlISEh4jrh0D8SIzw8hPI1/v0HS20MI32hmb4j//ppnaqCug6ZxYd/g\ndlIgjW8JN5NBBUoSqe9iifoTwaneNspz9vrQsoHKLndR0wM0pQAl7NO6FlS2HQ1Inwq+qD/LETzA\n+vb+/dCbzX2bKWqAdIcwaPVdVSEFLcSxmC7W1xQJoLojpNd1r/jxtxAQ6cyw8wDmoiAlxcj7ePLo\nU02frsKoF8sbR4YURYxzF7VbRtCSbmO5o28+uI/3vO9XKv++M4/LFKCMPRjrVhDdDZag2rXrJic5\nxvya953E0kDfq0h9Wa+k3IWxLYJaS6TRtWGc20awza44fR6gVC0+as4czJgC2MMlH7Fk7Z4H7DhW\neXRhypIO8Z7hVZIeiZ8fkfTqe9iXhISEDcVlfCAGST9pZr9gZq+L390XQng8fn5C0n3rdjSz15nZ\nu8zsXfPl+gX5hISEhItwGSnz54QQHjOza5LeZma/zB9DCMHMwrodQwgPS3pYkvZ2dkPVaqhFmDjd\nm8DNZTiHWSdo4PSFDT2oMh+ebXlZx6MPP3X2ubVAZJn1MGDuWszALRDZnCESWHQYrYztITp8sHCa\nFBDxJjU2RJnbM3/X5UgNq5F2JkRLuzFiWLecslK/18I5dOGqYte9LwZz2TFSF/u4zSrQQ2oytzpx\nG4wDTlk5KHAFLWUfEWy+3Qu43WSIhl6b8taB9jOm7s2HSCecw5kIlHWO9LcMprgh932nWOI4xyzn\nSOOMPywRwd1GIZ2eQLsZ5TZve4SaNv3AI2Fp4sT7ewodpCL1ZmncTtuP2YJSogQ/LuM51PXmceZL\nN0MMITwW/39d0o9KeoWkJ83sAUmK/79+73qYkJCwqbhUD0QzG5jZ1uqzpC+U9J8l/Zikh+JmD0l6\n673pYUJCwibjslHm+yT9aBQeF5J+IITwE2b285LeYmZfIemDkl5zp4bM7IyGLlEDo1c4JSBV6UBU\nXEXBdN51+rIE8Rle81qNBd4pvZNzhUv9U5u1QXwbms0cVx6t3Fk0bfaRrQeio1Hf/zV70uu7bA1c\nAF63YVAKyjwzHwuDg87NGC2tp06TrnSQCrYHxxgsJZQn3m/qvzMYt5ZIr2vPve/1HPVLunGdAENY\nQVDM+jPZEYTRMMttI7I/y0AlIXpvbcOgFssaod183mNdGtwfJwHniT52sDShPb+3+lMscSx8h5Pc\n25/GyP4AFkBV7scsYEpczvy6TKDzb/MeRSQeq0HnnJL6LR+jeVROFDmcfLA0UcLJZ44aQdVWc/3D\n5umyL9cDMYTwfkkvX/P9DUlf8PHvUUJCwicSLhVlTkhISLiXuFQzxI8qqnBWp2QwdI6RZevfAeNr\niC7GaKEhl3Yr9+hrCdo77/h+reHt4m5JKi9wkBllqNmCuhfTsqEyPbiQBFCmGma1JequZDBx5XLA\nqHbOmCPKWm07VVrlHmcI7U46kC5RxVSg7gjOuZdziQH5tqB+W3CkmSFCfjJv1OZjCOBZ1jTDssd2\n5iLhGuLygkariJAvWC5zynUICLzjYTOayWJ8tiF6n4Oa9mCKWxzyvvDP4wFo8Cn6GEX9NaLTp7jn\nwG41DHDygUFuOXaV/gI5+wYno8CIN+RonuOOyDt026cd/34KdcIy3vPVBlLmNENMSEhIiEgPxISE\nhISIzaXMmWRRBB2Qv1wi+joBP6D9amvW/Csg4joBfZktnNa1keNZb3kuaUBk0yDGbYGdbGfeaIbS\nkquoYE7RL6Kmht5eO7ji+4FWtlAGdFkzRo3o6wmE4b2mL8x17b3P81pZR0QPer+vZr4c0Q6gVaBv\nvMtyLAPsIBQ6i8sHU0SkJ6dOB7f6PrbdFgxSYb4a5szf9uvyZOmWX7Ti6iAPWrE2Dg1k5y2eA8qA\n4hoalimyGmL4EqVF0UwJ+7f+qPmhBaF5AGUt0HYJlUOGJYAW7qEcyzc6Qb555n2fQGC+EpKzlOwU\nov8AO7MaEf9VPrqd0z5sBtIMMSEhISEiPRATEhISIjaWMtcmTVvRpsgDxCoqCKyRH/oUBMv7k0i1\noTQ+gnv0/hA1NSC6LibIGQZ9FaKIpGktiGqZH3ty3NRAGSN/eH9vH81BdM0IIXKDa5xnd89pLeuY\nnCvmGWu6tK76uS12nXaeTtwLa3gTImWcm3ZQL6VGvi+o1RKRW0MH5rE+B9y5VPS9PTo91xWE3jtO\npSvYtdHybT+HwBzR92Ubgmlrri9WVDSc+J9HveX97sww5qTDAUJ//GlxaWYBy7kqirRbNSzHDtxy\ni8LxqqTrtY8nzzncuL3uiSTV4Pg5SsLOVn8fWC9qQywfEFmvt3E+McrNUrebgjRDTEhISIhID8SE\nhISEiI2lzDJTiBSyz6I8ARFnnP0+bJdWLsTzLX9fdLoenkMAV4byoTRDomi1AxulugURLiJ6Bluw\nLEYOA6j+eOqUddLCMadOk+4bOq1uQ3QMhqknuigtClfrdoxy31h6ydKnC+evL+i5/VmAuJcidUNe\nc9jz8RwjQmkk6ojortyjD0s/z8HIB/oGxqoF2pfvwGEb7/c27a/wecbCVbhGrZif3b1AbWwjnDOW\nLOycphn58LDLqnABoNfWcRTgb/X8HFptp+YFlkOW8/UWcrz/5liOGCMBoBh4f7tYSlgJwmsUuZpD\nQVGCjue13/+DGOXPLUWZExISEjYW6YGYkJCQELGxlDkPpu3IiVsQuJK+VKR7iNwtIsWYwWarDWFu\nCWdi1BVShjzZGRS2WQlR9cAjtwXo89HCqW+3jiJhOFPP4QXWghVWDzZT8w7ygFEgKYASDVDXagnx\n8DSKwfuIrB6g8tVITp8f3Nr1RuCAvVyuH8+doUdOqxp52DW9vm53Xz7c8u+2zSPl1Qj0GQx8C47l\ndeVjPmEkljWK0fdVRHUO2tuFndpi6Qcag74PM6eSFe6LGvnGGYpClbj/duP92QbztDEuHPKqmYJv\nsAijw3iBfPcu8sorWHq18P2qlRksyfqI2ndBx5dYG1gV+UrC7ISEhIQNRnogJiQkJERsLGUOCqpX\nrs0Q4wri2dMcebug1fkqb3cGETPocwuO0TVyjBfIMUZaq0YPougyULAGMGoHr9yhz5UMIrucUFIN\nx+gPes7u7MDbWyL39fjQo8g5KE+x29DaBXNdF+wBCihNPZrcHzjFYu3oIcTGHSw3jBGtvTHyPOQy\n5uEOkbN85YKI73zLvx8UGDfczjbBWsa5pQcuN/gmWbxHJji306V/rkFHt3qg0ohaM2edgvU2hNwZ\n6nifdmKbuA+rvp8Pl0a6h74NbdNKtCdYhFWIFhMjJAC0V+2jIJdB3M/iXMJ5zuqmL7WSMDshISFh\nY7GxM0TJZ2mLub8tu6gfcXDMrRkdaXYsYH66KJnf5MMGWaPyIRbYxx6E6FT+3ukHb3Nk3uYATjHz\nONOCD6kqLGqzRsc5l549zETpiILp6t626wnHyJ0bh2YGMMp8JlBPfIa0n/l+/Q6OA1eb/YEHT3j8\nwBjAwDV3BdL7qjgDKRFoqqFzs2MEjHZ8nDMEDGYItrTh8BOYRokJVbvnqYGKM9cM17Y69AAHNY7M\nORxCQ8h6NUsEPhg8Gokzt6aPNC3mtaIzUbXlHc/WV+FViZoyvTkcbm6e+EYdMKFYX6g75Zh7/8gP\nzpkinzbjEsrbA2HPd6QZYkJCQkLEpXsgmtmumf2wmf2ymb3XzD7LzPbN7G1m9qvx/3t3bikhISHh\n2eEyUuZvk/QTIYQvNbO2pL6kr5P09hDCN5rZGyS9QdLXPFMjVgcVsbToucVfrBPn20535qDVvWGz\nsG8ImJRInWshjY+gEW0HQQiatWbQdg2PnfAe95ySHFtDt5e4OuUU2jfw9BxlNc/VDgGKE9C0LVCm\nNWlc98mdYUrS0akvAVQdDyrkKANakJmSmiF1cgmj2YPSx/H4sYbWHT/obWQlAkYcCwQMGATgVZlA\nK1gO0A6CHdOZLwncjBrOnSnKcO44BV1Ay+hhH8lO/aRz6A1HqEfC1EmWYZ0Wzb1AI+Ip6PD2AsG7\nihpPxxwa0wLLMS3zffsw1J2at3MaXYyv5n49CwTSTkHv59BvFv1mXCxLOsSPKcxsR9LnSfouSQoh\nLEIIR5JeJemRuNkjkl59b3qYkJCwybhUD0RJL5X0lKR/YGbvNrPvNLOBpPtCCI/HbZ5QU9D+NpjZ\n68zsXWb2rtlysW6ThISEhAtx2ShzIekzJX1lCOHnzOzb1NDjM4QQgtn6MFsI4WFJD0vS/u5eWPaa\n5/2UhSoQOaXBZVjAuDM6jixAgWZwDOkskJYF2rkArWn1kDqFiOcEAdr+AilVSF1bUZLZU4dn37Ur\npH+BNFErlyN1rceoNfrOdEXSt31o2M7Op+1LChzCKcxyh7lT9gk0blsI51LvWD/mof2TiUc/ywca\nInqAVLgOKPt87qmNLZRSnc29jRyayAoauhwa0xoR3yWWOHpRN/lUBredCShhz9ubQHEw66//EwIz\nPVdCtv8Eli/ub8510YaWtYtbmxpH8Wtvr3WM1D2k4C0K1HRBfRknx9IkOuKMsXTQO3fdMF/i0kQc\nW0tuNx9zPCrp0RDCz8V//7CaB+STZvaAJMX/X79H/UtISNhgXKoHYgjhCUkfMrNPj199gaRfkvRj\nkh6K3z0k6a33oHsJCQkbjstGmSXpKyV9f4wwv1/SH1fz4H6LmX2FpA9Kes2dGqlU66RuominXZh1\nwu2jPV5vullHx5ExaGyATcwxxMsdCGlrKJALushOnD8taqdbc0Q/p1Bhd2IovJMhnslUQNJkVsqk\nShvR9IxmsSwPijKn8+h8M0eDRddpdLcNETOWGlZpXJLUB92qIeutEYkuexgvUPZOvC5dlCnlzZl1\n6LyDZQ9GwmlEe45jeh+t8OOPF95O93pzTe97APVy0MYcUf4b2z5GPdY3ATXufcij8vk1v47FSw/O\nPg/HsU3UkQlcXumBPh9hIyQMVDdRRwWR/UHhx6xRknReeR/zuDTTQZnSasGUR4jb8bdgMXV18wjz\nJXwghhD+g6TftuanL/h49yUhIeETC5eKMickJCTcS1y6GeJHC3kwbVUNFWiPnAY8cQCKB5q8i7zm\no63bczQ7oJetm+vzPbUDh5lT0DqUE2UdkTlCkUsYkBZRSNu64jFB1hrp5W7Q2kLZSJqP3kD0lVi5\nykjSLiLE09j1ckoK7H1t9zz6S5reX9J81D9XtZ/zSXDKdnRyw3fG67gfI6cVHF4yRNYzurCAGjJn\n3AaoS3NM2RXMYlEnZ7vl5z97Uaxjg3OjMH4+gHvNKcpzBua4ex/DLvLau6zvAkSnmhmi9lttuN2A\nMS+u+fdTmshu+3FO2jDCXfq4LJG/vT10Kl3Ge3TEUrLIJTcYHre3UFOlbhrMNpA0pxliQkJCQkR6\nICYkJCREbCxlrk2aFg21ySAqvnbktIb2V6eIIreiqLkF267q1ClIYM4wrLgEmsiyojWinOU2bJzQ\n32HHI7qTRXOsObTS9RSWXxDgkqZ2QetvIhJbTH37++U0sT6AjdaqHVhi1bDNorg5Q/TRZshxRSC0\nM0U0GbZU1VUvlVqC7rZXUXlEgbM2xgo5wzu4bZ/2Q6qN3PAcaoI5SnL2M+9Llns7sxj9n8NQreDv\nuIYLlIEt0McKgu0aSxPlkRvhLq55fy1G62coN9tFlDdnuVMoFcIA9wqixnulX7ubKKrT7/oBmJ+8\nH+nzHHnqhlo0Ger/tAOE7qt8/M1z/0ozxISEhIQV0gMxISEhIWJjKXOog6pY48JGqFmBKFoXNLk9\nYnp089koTIUAmO+RYoyypmM4UMPOywr/vI3t26B1NEpeBaW7H3TR7ZVtd6NeHqIMJyKozNPdPfDt\nJx1EXE98+8EMtDZGQqcIIbc7yCtGE33YeQ0Qqab/Vo2SpH3ULqELNuPg9ZNNPZjJCz0KSsFw1gPt\nBtXugQJbB4OIJQvrrbdrmyBBu4jHemrm+eMHM6eMfdSFafX9mDlypk9nTo1PYT+2s+NqgTHGdyf2\nfUj9ddv7tI0wdxtqgiJD2VDDugpq7RzUWDIYYfmk72NRRyW/wZKujd/pGBBYi6huzq3eQM6cZogJ\nCQkJEemBmJCQkBCxsZQ5l2mrijmXsOLKWSAKJRwPF3DEjpFDRvkK0OEe+G1x1XNfpyOnuDmjci0o\nY0FPWojozWE1tsoJbnfXC19t26ncYYmcWVqRwQ/yoou8hI3UtNXQJ0bQWb0VbPyck/YUEe8AYXIB\nKreYIIqJcZwdO2lesfcaVlQdFATrsCQmrLgCRdcYL2iUz1HCOU6KTuqjRXPtBuj3rPD9ui3/vsA8\nwhD9twxRWWzTQdnaDEkCxTwW1kIE+2DPlzraEKkvEKkOSFRnVLyD7wNc0mGqrYAVjlViQN33DWgC\nR3o9Rc56vlqmyDZvPrV5Z5SQkJDwHJEeiAkJCQkRG0uZrQ5nzsrtPiORoAFyujPY9kjgysaLLtod\nUKZ8uj661hlC1Ez37LbvyyJHAaLmLqPc/Yb6hF2nQIzOTlsQ6c7g6j1zmkyHbdp/lYjEHrYhjI45\nyXvGEkoO1uVdIOJYjVHHeQm6ve/ttBAJbtNSLHca9lS/aWfew9LBCHWb6WeGlQQWmQooCsZzZoS0\nBYFzgetr0VW6gKD6GJS+xHjaEMQS9ZcXGNtMrF2NzWHv1Y/34gko8/S6J9W3tlDnGjCMy3DHt+Hy\nRfZir6NNW7iKNapj4awMSzfLbX8kFCgiRSftUd0sL3BcNwVphpiQkJAQkR6ICQkJCREbS5mJBdyo\np4gmZ6hX2wH1LCbN9hPQt+wIxYFgLVXej7AdUCMnN8ClegmWsURENdv1fNNZzLEuA3Kj2+svVY22\nW3i/BVC/CXTJ+dj5E63DskiJx1OPZi7hEo0SyXpg4PnI+RC2ULCuKuferw4i7svKjz/cGuL7hoY9\niVzbUyiWd5YQ1FfoDOoI1xAghz2n7OcqMGIKgNj/mfC5wDjv7LvNGpdPplMsE2xjKQXnLNDaDCqD\nYeFjtKp1nB+trxBZdWHnhfzlBfLUKwjgB8hZbpdYvkC3bsIlvLXK1ca91yr8/IfV+nsut+aa58n+\nKyEhIWFzkR6ICQkJCREbS5krk8ZRbEwxMKf5VYniU6CPFplKawJxLaLT7Qc9sje7OTr7XBQcTkQf\nW6BEc1qEOfVjH7MYLVxUHgVuzdA2+q0OXKpBRw2i2mu1fz7uQbCLaPki0reyjWhqH8sBRy46L+AA\n3kHBKap6u7i1SlDvUyimb1Zwfo64soS4mYJ2HAfB1HPR9/k15One8P6ynQCKaRCYr6LyNaPm2Xo1\nQX4T1BxLI4bv28hZn+z9/+2dTYhkVxXH/+fVd3XXVHf1tOMkMzoRQ2Q2JiKiKCIJgh8huhBJiBAk\nEBcuElA0cS/EjR8LEYZEyULUMCqKC0FiFq4GNRHEjMEYEjJheqanM9XdU11Vr96r4+Ld6vOfSUcz\n0umqujk/GKZe9Xuv7n23+vb93/NlrxPW6aFPWcfaPRra5+9QXPNV1vmyff+aas8r6fN2CMXYU+3o\n1optA+RB+o/Iibu5alsN7Zp9Dgc0bGRF1vNE4ltPzVSPROQWEfkb/dsSkYdEpCMifxCRf4X/l6fd\nVsdx4mOmVoiq+jyAWwFAREoAXgXwawAPA3hKVR8VkYfD8Tf/272ScoLKavEXLnmDeb+yaX8ZR4n9\ndU3D6yqVxFRaodTJwpCsWOheRtk/erC/7hUK71KqqTIucRiZMd7Dv6tOJTvHtOIZ0JV18oO8anFD\ne9+HN6ls54gSmh4trq1xmdYB1Wtp0wo55WWOkW7aii+vUbgkr8S2bBW1RAlIJ8lS++TvWMtoVbhN\nIX/kv8jkbIRqUznTNVtpp1QDhZPeDoIPYZUSvpbp80F90LYZIcoblHz2sBmbxit07aapCG4jgk/g\niPxKZcmeCX/nhFZjI46jpLHNqDzp5qK1q15i/0i6YFIn5TUq0zricEX6TLsKjZC9JynN1HpqX5jl\nHt0B4N+q+jKAzwF4Irz/BIDPT61VjuNEy0ytEK/hbgA/C6+PqOr58HoNwJG9LhCRBwA8AADNRmOv\nUxzHcd6QmZwQRaQK4C4Aj1z7M1VVEdkzZkhVTwE4BQCdTkcndUJYJrBBoJ+arBhS+NSoVTyWPsVc\nDcgA0iaXxAWlRJyJSZkeJR9da5J8oTotJTKwXFWzIkiVlR4ZJpbsc2qUrDQTu/egR4YEChfMWSaS\n4aVMZVPr3WCAIq19hRzYllO735BdL6skxyg7S05SPhmQUYsMT7UtkmrBOESPB2N6hjm1KyGpu0N/\n94Rqyoy3bZA2j5IfIMxoMOyaxC9PJDGrZH7OLFMJlt2c5Jf3QBrkn8klR9MgmWvvoHBJui6nnYmc\nMhOVyKh2ecG2IKikDzbUvgvNHRuXTsuMKpPMP4dom2CVjFoJ/a6MqI5MJTxnEfdDPCg+DeAZVb0Q\nji+IyFEACP9fnFrLHMeJllmdEO+ByWUA+C2A+8Lr+wD85sBb5DhO9MycZBaRBQCfBPAVevtRAE+K\nyP0AXgbwxf91H1VFHjLOJOQGyBlBcgq7qvVfX2YxWyQ/QUqGWSGJkYpJqfNiEqxEIWUrJZMh7M+1\nQ2FydSpDOpGNXbIUVsfkY0jStEr+ZoMFa2+LnAK3RyarmkdMnlESFCShBklKWwc7lA1orCb1VqkW\nSysxzToakS/lukm2wQ12Tje19zmmLBnQPsTuhSRTW9YfrbBMp/A2tgQv22emnO6F6rss0D5zL4RR\nprQFkFf3loScuHXMpWJ7VLuELNFJn7IDkfTNV4tz6uQPOlykZK2UYamScYofOz/jNQ1J5hvGNs7r\nqWXQubLR3X2tYZtiO7V7X2rZd27lkN2jvL3H+OTxZbuZuQlRVXsAVq55bwOF1dlxHOctY1Yls+M4\nzoEzcyvE/UJGY8j54BDbJrMomeJaCWW7ofKk/SzUuiAps7Vg8m0d5IBMSqZPzt0tTgpLFmShey40\nSb6WXy/PSlyGk1Tfpph8OdSyvi00KIyMssN0KuQ8TolitiizzDDEK9Zoa6DBSU45JyqXHiUl1SCJ\nNz5s9xmQ9K41yRmbQs00nFPn/KXksHylTlKWxH5GkjnPKOyOypBWKPNLSs7wI/IKKIfQuMtjk/QJ\nXVfbseu6NBhNqvvClv0x1ZHhtDopJWAdhu2bOtVxWdyx78eAy7qSlZtD5jIK9dOcrMJUX6a8SN4E\nI2vMxFejumP92VolyWwfD6XwP01DW+IzMvsK0XEcZ4JPiI7jOIFoJXMmY1xOCj1XJwvd0bo5pla3\nyYpK1j8NVsnBMsXgqt1jjWqRdIYmQdopySGSe1yGklVGQlZUlkSTpKvtqknTQUbmUbrJiCQgUo5x\nJaduqjWSK8fH2umT5KZNSr5aprjrRtn0W5n7Q4loe0PTz4MSx9LS1gRZ34W8sHfrc5DPfUZW8/yC\n1TfptckS27B7K907uWTSV8mynzXpeVH/DzWKZ71qYcfoU5aenLZa6hdt/LN3UvLbqj3/JpVNHVOG\nnTZJ1iw0gIdw1OFyp0a3Yd/PZbLyY8Hu1xuTVwKXYaX75xS/PsnOVD5huVKWKcPNkEqPcpag41qc\nU4lwPRVfjxzHcf5PfEJ0HMcJSIylBAFARNYB9ABcmnZbDoDD8H7GxLz0892qujrtRuwn0U6IACAi\nf1HVD067HW813s+4eLv0cxZxyew4jhPwCdFxHCcQ+4R4atoNOCC8n3HxdunnzBH1HqLjOM71xfGK\nsgAAAo1JREFUEPsK0XEc503jE6LjOE4g2glRRD4lIs+LyAuhdGkUiMhxEXlaRJ4TkX+IyIPh/ehq\nV4tISUSeFZHfheObRORMGNNfhNo7c4+ILInIaRH5p4icFZGPxDie80CUE2Ko6fxDFLVZTgK4R0RO\nTrdV+0YG4GuqehLAhwF8NfRtUrv6ZgBPheN550EAZ+n4OwC+p6rvBXAZwP1TadX+8wMAv1fV9wF4\nP4o+xzieM0+UEyKADwF4QVVfVNUUwM9R1Haee1T1vKo+E15vo/jluRGR1a4WkWMAPgvgsXAsAG4H\ncDqcMvd9BAARaQP4OIDHAUBVU1XtIrLxnBdinRBvBPAKHZ8L70WFiJwAcBuAM3iTtavniO8D+Aas\nUsgKgK7qbk3VWMb0JgDrAH4StgceC3WFYhvPuSDWCTF6RGQRwC8BPKSqW/wzLXyp5tafSkTuBHBR\nVf867bYcAGUAHwDwI1W9DUX8/VXyeN7Hc56IdUJ8FcBxOj4W3osCEamgmAx/qqq/Cm/HVLv6owDu\nEpGXUGx33I5in21JZDfRYyxjeg7AOVU9E45Po5ggYxrPuSHWCfHPAG4OVskqgLtR1Haee8Je2uMA\nzqrqd+lH0dSuVtVHVPWYqp5AMXZ/VNV7ATwN4AvhtLnu4wRVXQPwiojcEt66A8BziGg854loI1VE\n5DMo9qFKAH6sqt+ecpP2BRH5GIA/Afg7bH/tWyj2EZ8E8C6E2tWq+tpUGrmPiMgnAHxdVe8Ukfeg\nWDF2ADwL4EuqukfB4PlCRG5FYTyqAngRwJdRLFaiG89ZJ9oJ0XEc53qJVTI7juNcNz4hOo7jBHxC\ndBzHCfiE6DiOE/AJ0XEcJ+ATouM4TsAnRMdxnMB/ACrPA+9NbcSDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fec1523a8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[1], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[14])+ \",\"+\" iceberg_probability : \"+str(pred_valid[1])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(160, 1)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_valid.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(160,)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_holdout.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fec151b9b70>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vEk7FU8Sn7rac+i5At8LEK0kV7MnVyiG3hq2GElSyQOKoosc+dGRw2A6gjFOo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\nD4qO4ziED4qO4zjEfwFNhNPxk3zo6wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fec15102b00>"
      ]
     },
     "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": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fec15086fd0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CbfnjetrXJW76Et+aooVrw+mHsV/e/S/epW3Dg0KZH9fZ9Kpn4rozMLM3mNnTZvb0fDG/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fec1511c0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[159], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[1602])+ \",\"+\" iceberg_probability : \"+str(pred_valid[159])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fec1506aac8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Tv+ZePNcMFJURAHSCvlH7AzqCzVSNAHPmyk4wtkeiG7T3d2O1ZIBLvoHjaG21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\n2FU3zZO5SQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fec15041208>"
      ]
     },
     "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": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fec14fd12b0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IauJu9BKm34BJI/E7Vm+5WTmL2UACdvb7192YPCrcfB1OwT5A0EW1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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fec15022e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[4], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[44])+ \",\"+\" iceberg_probability : \"+str(pred_valid[4])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fec14fb4b70>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UAN19auzulvnQaVi749Sr03QL4eW9q37N/VXMLXx6VTx95H9ABTvsOTVrQK6q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\nT47bTadyc+QDpnJTiZjYEk6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jRl2okjJN7OY2hHhn/kwalC5vg0pbVd+dey+enNseIVSw\nC6qLB00Drjn0vji65vQ931n929/q0meval+n4+M5pp8gxHnr89W0sQkxWdLwOgx9TL26DMusY27N\nMD6NMYSPIVRMo1cBw4jhmim6vwGKze2WLBrMGleRF6aDVwK2jHbbbqDCBSvO3f1IK8WEhIQE4Fy+\nFM2sZmbvM7N/G//+ODN7r5l9yMx+2Myaz1VGQkJCwu3gvNLnr1aV73m5Zv+7kr4zhPAWM/seSV8p\n6btvVoDJKWEDflpMG2mgdWUXiinjSDfQO0fIWVkHBcqhcFKHVbZW8/JolWWYH62C3bpbNycF76gw\na4I+43yOsLGLY1gid/zw2pW9k+OtDqzv296OUVY9s98EvZshtBC/n1Pz8zVsO2xse9ge1VjYRxR5\n7cKivXlxJ56DfyF/+qAodHzs4ZSP2+HJ8eUnnzw5bsJPs+datcrhP3h8ze/tx+eGKfw4O+hpLB/o\n9triJMF2SwblGY7zAuNYX24DjOE3gJwrdeRTaVwDlW4hnLOPFK9QOOrCx7KBMMONlodfTo6qfsx2\nvU/Gx15Gw7tHjYs+tovlI9eTPZ+/laKZPSDp90j63vi3SfoCSW+Llzwi6dV3pnYJCQnrjnP3UpT0\nXZK+Vh7vsCvpIISw/Al/QtJLVt1oZq8zs0fN7NF8nq+6JCEhIeGmOFf02cx+r6TLIYSfNrNXPN/7\nQwgPS3pYknY2t0KnXtGM9hBxVMfI1wEqvYWuOOhX1rgpwsMaEIdtZQwbRAXmTlOmC38OKdOijjAy\nhN+1msipEunmMSzYvRLUuLfaYTcLsCwXCFHsb50cjyGNc4DgrVZUe5mg3h1YeRdI8Rmw1VC04HiM\nXCgLKuPA5jyY4/o5wix7sc9hzR4gBeq4dMWaZ83pM2H3O8XLMOaHUO+ZH6xOZTr7SLXFUPuE3ZNz\nEwgI51DgacLiXYMjdQFlnMUEeXSggkPlmTxa1DuwZnfwWz6GOHDO3K8FlHGgwCTvLjWfcu+DbgeO\n/Eh32mpXc26CPLn9XZ8rzDnDAIRm3D+wNeXP5+qlKOmzJf1+M/sSSW1Ve4r/UNKWmdXjavEBSU/e\npIyEhISE28a5os8hhK8LITwQQnhQ0h+W9J9DCF8m6SckfWm87CFJb79DVUxISFhznLeV4ll4vaS3\nmNm3SHqfpDc+1w2ZZeo2Kufced9p2gwUEyGxWsCpen9e0bNNxpWesghDdQQUcDhxWtfYcivfM5lT\nlgslrLtMDwqKtYjqOf3ijOEBe5o2vIwt0LAMjtf0mA5Ivflshjjc6PjcHMPKXXfnZqZS1QRWaZhi\nD8fe/u2m07BTVUeseK8NwdtJ1Y42HO1zjBUtroO59+Hje8/686+6wzZTcjYwzWsNeAWA4mb3VdS7\ngJN6G/Qwr6ETGcsOC7pANzNsPTAHS1F4+fOozNNpwIkeArrKfd5M0W8zKu1gXm4cw5F9x7n0qGSK\nX8cs7rvn5mUXzFvTRl4e5txZKjatJ3s+vy/FEMK7Jb07Hn9Y0mfcyfokJCS8OHCu6HNCQkLCnca5\nXSl+1CiDwtIpFjHEY1AJxoSSvu5YN57y+5rQ+1yA1lBSSjsuyjqDgO2luseN9o+9zBqESyc1p15L\nq3AbVHZh5Pr+W9ZvOCE6RHNKxEqPnaUqR+rTDdDwsVV1aYIm7RfezgL0sYt25rTm7qAv6LwOS3QJ\nh+TW3CtcRIvqcIIUpJDUak79+fuPO2U+OoJnNoE46Nam978hr0ij29X1sGMf6P2Gz5UmthLopF3i\nWPchZWzdxz9gnEvExw9Y5vJayJkFzMmGwVOhv9r7YJp73en3XkOfD4OP11G06HdzxLjjRjrjt5Hn\nZzGsnpMVjGRfH6SVYkJCQgKQXooJCQkJwPrSZ1VpTq9HC/HJJSxtlMBqLOnGyClQOQOVhCNvA4wx\nIJba4GBbb8Iq3EQKSeSFOTzwQNPaZkWrem3QqyOnaYOe09enC88z0qo799nrI6fK5MZYakmqIb9H\nI1qRZ33vHyp/zxC0TdLZ2nTLafvIrx9e8Ha2YMUcQR98AzJdzRgrXBgstag3W9Ctwem+4bUpKW+2\n49Rvf+GUfAMpSTWGF8FmVd/pFiTK7EZ6LUnHaEMXWxnFIVLZzldTy62BU+ylQ7xBZyy75rS/gwk8\ngmO2daH8ve/bB+WcaVh97pQjKKj3vM9PHL9zv2/a976tcasJGiytk7S262l+TivFhISEBCC9FBMS\nEhKAtaXPpQWN2xUVqx+AVl10+pAhPeUxUoV2VXHiGSxxZYfyT067J4hPZVivMesVfLQXE3/OsIlE\nV6DKx9OKQhme+dJtp8wNyFJ1wurntJgdE47EDUhwUd7LokPwpAHnali2u8hxWZv4fZMc1mJYVhE2\nrEbb9xh26063m9jK6C6tq0NIjiEm9/gxtzjPQZOPYOXtQC6rQGc00P5rbTjyI7doKJn2q8Kp5FNI\nK6WRTC0AACAASURBVNvBnokh+RlTnBbbfk0NSbQKjL9i0q9O5tcuEI/O1LRM91qHZb+5vX1DvSVp\ncejj0u96nxvi9u+zaouhAyv3wR5UvTE/M8wLLSl2SlyVkJCQsP5IL8WEhIQEYG3ps4Jks0iPILXV\nguzTHEHBAerHl6eVJbhW8+6pn2FNZEIjWuMWUK2m2nG5BSp77BRHUGe+aBXdaWawPherY283Zl7H\nydypF2OV6z2oejcQtwyT7pIotUDHGddtwZ/fuOIWT8tRyCWn+OoyWRIoppzKTWjFjc7Bk6m3Ye/K\n5ZPjaXEjva0K9H6bw0LOmGyGkC9wTZh6mYetqh3byC9dg1W2w0RcBCz4BbwJ6NQ/mo61ClkcyGHw\n/mEyseHcrdkBWwakzy1uhwQ4jIMSBziycxZ3e9W8zOhZAIP7ZOix7LWG9+dSLs0s0eeEhISEtUd6\nKSYkJCQAa0ufg6R5pLZ1ZiYo/A8oUOkjM0/u9GC3yj08mTntqG04lT1aOK0xlseAU5QNv1upcLpd\no7wX6M5RTCK1hRzMkzaoCnI6zyApNQcFb4LVzqCmXQ6c7s1Aj1v7FfWjxXkZDy1JJfTKZj3/Ld1s\nOt0bQJhqipBkOqk/UXg/L7cJJGkvPouUmWAbjg/cYb1zCUrR8Ms+lVAKfVHPIcF11enhbnRqrnWo\nKo42wLOAz2nAYd3gPD2f+RiRnpag+4so+9XJfOLkiF+mb3QNFcgWGHNY0HNa0OmUgDa1ftXbvJjG\ney/5DKVCGhO0sf8X0fuAkmjrhLRSTEhISADSSzEhISEBWFv6vLBS+1lFf+uQRhohAdDsLOfTcUUV\nunA6Lut+X2O++reENH2r5ZSETuIzUMkMqs3zoVPyw1ZFgzr4HExOexeR6OgIqslQ8p7BWjiD5baJ\n2F86B2dLdS3IiW3QqXhORWqch/J3AdtmDbm2m0jKtXkNfe45ok4kw8Y1JMjah/I0ztcvuRQYLciC\npNa+/N5FHxb/qfdL7153fF5aVIt9p73TbS8P4eunxjm0oepNWosY7gW2LOjFsGoWBWyvbAZYwhEM\nkAck0cK9M1ift7CtUWzACX2KAV7WifMGMnuLBZTJQatr5XpKhi2RVooJCQkJwNquFGsyDZbher6w\nkOGXugfhzMHGvX48r7plXMdGMg7znhsUGkf+65xBILQFsVb+9IyQYhKXa77pv/kvLfrxcy9jhNUe\n06cWLYR/YdHUhI/lDL/4Bfwns7qvRCxWpo7QtgKCrDRcNLnpj5C3OTppo4ZV9tzPN5ved8NnXBno\n6nxYlQdlHq7jmx2/j2udfMf7zSCgO4CALQV/c6740L+KYY4t+AkW8Jk09L+1oSSDZzbhm1ggRBGR\nhafvjXOxMcLKD6l0Gw2/lmGLFMplGtbNIQw6iMs0GL2OtsEy4qI4z/3as7Klt4+xUq9VdbQkMpuQ\nkJCw/jh3K0Uze0zSsaoFwSKE8FvMbEfSD0t6UNJjkl4TQtg/q4yEhISE28W5eylG/I4QwlX8/QZJ\n7wohfJuZvSH+/fqbFWCWqRapCsORWtjIZjrPNowBRSRo/UO/dngvxTedSizo2AViN9lzXzqK2bZB\niQTBWYqQ1GKIXgYCuQ/Hv3vgymfwMVsgbI7Upou2lShznsPHMnIGZv+onXaUOzkMHa/38GDobSid\neExRl3t37zk5zkunlU8Ep8/tuN2wv+n13kAOncXCyxuDyubXvLzNDd8naaLup3wDIRw8X/h4tfpV\nfBuFWinmyi0ApimtIXdMDofIVhe0fgajC1R1msti2qu/hleC960wh/vYSqgfw6CEcFYqKe2V7pvY\nWbjvYf3ku4Cxxbw5tWWC0MKwTHEbEn2+k3iVpEfi8SOSXn0H65KQkLDGOI8vxSDpP5rZT5vZ6+K5\nSyGEp+PxM5IurbrRzF5nZo+a2aMzRgYkJCQk3CLOI33+nBDCk2Z2j6R3mtnP88MQQjBblX1FCiE8\nLOlhSdra3ApLS6shnG4MsROmLaVFtzOr6ATdsfjEvjkdfcKcJvfAduYQNu0ylSXKacN/Moz9+aOo\n2AIDqnotxA3CB0+gdbSmGhReWu3Vz68HhpzFFKMN+BdmUODB1kAHFvRRy8/3EE73spHTx/GBU//6\nwNuxufBrPlw7qMpGo8dd0Df07RBqQKzj4eGBtwd9PoWT56INP8ErPgGW1vcmLfIQJ76GFLTCFsg2\ntiYCaHqLy435GQo/cU3SRZjfGPsol+R5Zk6JICMUc7rhnV6Hx0GBHEHdgPGHL2W+TEV06OPTQDrY\n1hFCCBnaiOm3jjh3K8UQwpPx/8uSflTSZ0h61szuk6T4/+oA2YSEhISPEufqpWhmPbNKI93MepJ+\nt6T/Lekdkh6Klz0k6e13poYJCQnrjvNGny9J+tFoLa5L+sEQwo+b2f+Q9FYz+0pJH5H0mucqKAtS\nO3rqjuhIC8o8BiXoQDFlulNZIpGiQu2ZU6PLpdONndKpSUB6TPL76Z6HjhksjQ04eOdzp0e1yLZs\nx8umU+/8Ab9vCufdwT65OcVPka8Ev4MtWIsXoeqjOmj/HJZi5iVhbpnNDClOYRV9coBwPWxNtCCW\nOgOtvbCo+pwpUGdQCRrtgEpnLma7v7/aMysf+hh1Wk5D67DKLqAeE5ZhkcekqU5rhwgVDEiNespt\ngBZvhM7VkDZ0jm2IflQVyrE3017Qu9wPdwvfdjjA9gW9HxqwHFP7uD33MhkueBRdtblloD0v+2jT\nB4jBCLUY3KA1FZk9Vy/FEMKHJX3qivN7kl75wtcoISHhxYZzRZ8TEhIS7jTO1UrxY4nM7MQhm0oi\nAWlDNaZVEPHE0ak3hzNsmPnnW8i5IgjRjtv+GzMGDVx0QJlAgw+RzrKFMNx5jGfuHcJJGLSr3AXv\nBH0egr53wfAmh7CQ91bnGskiFULWUxnM74a2tfFbmkNVpTX0PpqBVtZBs45qdDD3Z5VxLHoDWFah\njJORVmJz4kLXqfTTRy5ga1AyIpUsoR5Ep/pFVLVpbztNXcCCvzH2+45QlWVqWEnKdn1e7E28z9sI\nEmD/z6IScNNWW417UPSZ5U5rM1D5MVLMGuZlc4h+huM9vfNrcRy5NZLB6XyMmGgGHZSDqt+YB2ad\nkFaKCQkJCUB6KSYkJCQAa0ufg53OpbEEF/xFF/Rp4b8PHxo+KUl6UB6zSwHXwRAxuV2/r1N3GtIY\nwSrZ8PjkCSyHzSakoZCDpRHTZuZgyR040nYOnBuHntO9xhgWX8R4dwpv5xD5TRp9p6p5VBHtDFfn\n3chBJbuQ/6r3IDV2zS34nFiTHnqdzsNIfdpoVX1RB6WeYkuhhrbRgjqFOO+Dc2TD4Z7J0PtrBsHV\nGqhiJ9Lj6ZRbGhDhxfqhNvHyrOP9P8bYcqLlsBBz82K5lbAN628TluL8CKlRsXzJJ7BsQ39uMvbr\nix5i9eHUP5kjv89R1dbujjtsc5XU6zgdN8SMZ7H/15M8p5ViQkJCwimkl2JCQkICsL70WdIiUl5a\n9BYbcGqGJbIewNUigwo51Yb92hGclyFwrBJxwC3QWubuUBP0DZJS9PZuRRMh03EGWLkNKsx9UJwZ\nLNQ5rL8GutVrgm4jB0gv3jrpwCJ/6M8MW87lGbMtqEbncIaeNtEgUHlaP49BvXci8w6I5W6j3xZw\nQA9X3TGbuWsEZ+MulL+nyCNTguIaHPmbMZ8p888cIk9sCUvr9saFk+PZkW8Z3LPpSWf2xi6L1gmg\n3lBq78adjKe3vJ9fbp6y1WDNLp728voLOP23vI4NxJUzF0xuGNPFjdsjiyuIfUbK2BL5b5pNxITH\nIhJ9TkhISHgRIL0UExISEoC1pc8qg7KYzpFvftIwIZ5zt+0WuEFMbUmLc62Ew2zLz49QOBTKXJ1Y\nkmAtbdfdKjiFTFcJitmPas60rDIPVgvxyYIltIYKjOCM3h4gidXcr9+eQ2osbg90sI3w7H004SLF\nKVKv1tG7I6hwNxuws2aICUf/D0ovZ9RbnkefM7EUOqABxfLpxKkn0602aLmm5RikbwD3hE5M2HXc\nhkQaaOqoBuXtiffthUurVcUvmVvCj+QW3xaSnh3tV5S4aU57A8YQOzPq3OdO6uGKl7fV9ucEpOTN\nkHTt1NYLNM260VtiXHp7Jledpre2vOw54rOba0ucK6SVYkJCQgKQXooJCQkJwNrSZ8tMWaeiRyWp\n7LNuLdy9x62F+cgpSTvGzV4NsL5C/mkfytcXSqc+BRIhZaA4zR2Xrpof+PNL0No6pL6OI63OTllz\nQQ1LyHhBhfqUVRjMd4T46I2FX7OAtXoZwsx44F3E7M5hKQ5I6BQOILXVdWvpdOzPrCPpEdW+EZ58\n8usMNTMVSBDWPLV/4G2owYIOhqsiwAt85HW0DFJbiENfEt8eVLBzBE13jqH2Xcd2RIAjtz/xlHTY\nDrYScvR5sVtR6QxW9nGBeQin7uMjf379kvdzHXmk6yNI5CF4YI5+rEHNPF+OKeTSJvQaOPa6LLb8\nOd3oWVGu6ZJqTZuVkJCQcHtIL8WEhIQEYG3pc6GgUahoQWZOZTLS1CN3Wu3VnB5ctoo2TJHvp418\nwF3QmhniWjfHsJzuOmU6noBKo45lDuoFhr+kitOB/2Y14QCcwxmduLJw62+t65XvIiZbUBgvYQkd\nxdjrDSTIGvX8mXSAH4Oyb7GMmW8rlNBCaxryJ0Nqi/mOF5HWZkdO5ShjFvrehgUo6ynOCiv3bAS6\nCRktTvjx/Uj6Fal0tuf3MTa62/Z+KbFNUsP1x3Dqr8PxvYFEZwzH78TgASqWdxBXPhoiWxek2xYF\nZO7mtHh7HaczH+eCjvy6EYyx70GKbbLv3w+NfNxmedVv9JhYJ6SVYkJCQgKwtitFleHEh6+EkWRB\nAwA2zA9LeIXFRU4XK5wcPnDN3H9v2YHcRG+hbIp47pn/gguhc50mFF4uVyuuLi6dQNc2YNU6RX7r\nAiu7rEkhWKTenPtKpITPpMXVzyz3VQizj4SGl9GDsWY/+OpwTM86rPI6WFnnXS/nWuEroTy2qYcV\nLlV/mkO0cxOGHlRyfoSVFTBnWtNN5NTB6teiAajYgBEHoXK1jvcVc5owFLQLt1LvRWmCucO8JrXl\n6rTuq+pZCcoAg9Z8gnmLPDPzqT/pCTx195LPp3HDz/cgvGMx5K/V4+TyNvcvbZ8cl0+5ulIthp9m\n67lQTCvFhISEBOLcvRTNbMvM3mZmP29mHzSzzzSzHTN7p5n9Uvx/+7lLSkhISHj+OI/0+R9K+vEQ\nwpeaWVNSV9LXS3pXCOHbzOwNkt4g6fU3LcWkckkbEVpm8HGjuSJg03+Zm2QBn7Yu8mWc8pmDqkuG\nsDSGszGVZ6+GjXQQ1AlCrTYH1Q3lNXCdOoweGLV6zt815EU59g34DH6C8x2vjIHjzaOvXF44TevK\nN+57YKYLGKCY+hLsUdsj0FCkmLWFH8/tRqNL81kvZZljR5IM7Rd8HelXmc0QthcQWom8Kwxp4/bA\nOAq01jgp4KdpUy+7DQWkOkwXpNhZ088fYFvhYo7tgfisEso1Y/ijkpoP2qC4wCQgJSyo9+PmdLeL\n/DIFQhcVabvhOYb4yNrTPv8aBUMu1xvnaqVoZpuSPk/SGyUphJCHEA4kvUrSI/GyRyS9+s7UMCEh\nYd1xrl6Kkj5O0hVJ329m7zOz7zWznqRLIYSn4zXPSLq06mYze52ZPWpmj+Z5vuqShISEhJvivNHn\nuqRPl/QXQgjvNbN/qIoqnyCEEMwsrLo5hPCwpIclaWtzM5TLFT/TRs4g0AlLaAkft8U80hBQqQLW\n5OLAaR992YZQz5kgbHCnjTA/+DU2ESeVocyJRYrZYZpOr+tx4XSsA2rcvOIcd+Pei96e3GlVmDp9\nnfTxmxhDxKxNT0rHHPlsxqDYNVhO75t7OwVjOsMfBRfDXeauiX1uL/dz9WfgjwdxXjOo7hQUogUN\nzHy8qFjURu6S0dTHaBrzre40QFPdZU+aedlzjEs+w9YAnw/LcQMW5wCFn058/Bh+tIF9heHhGFrm\n5dECfBywHYG9jG7N+/Qww/ZBzF1jHYRK0uNi0ytwEXtAS/HZbE3Vcs7bSvEJSU+EEN4b/36bqpfk\ns2Z2nyTF/y/fofolJCSsOc7VSzGE8Iykx83s18ZTr5T0AUnvkPRQPPeQpLffgeolJCS8CHDe6LMk\n/QVJb46W5w9L+gpVL++3mtlXSvqIpNc8VyEmUzMKpjbgD5uBbgWokxgsgPVI1dqwuBUIyTsawxQL\nA3Hjfs9vsRlgcr7mXKbXgsjsgL9JUE+JlssarKwGR+bBbLXFubkNOppBTLbnIYeXD/ZOjks0oxMF\nSgOsvAsIvp5sKeg07RwMXJx3DotrDYotc3QFU4LmI68AKeESs4ugbKCmC8jhNEGTt7po/9MHJ8eN\nLa/j0QShkKDh9VjdBcaEKWubm/AsQB1Lht9BfLYcQ5kH2xS8N+tV5bfmfu0QVPtUm6erLcQtrGsG\nfd++6CB08NrILdGC4/8s5qDZRvBhlkMZCEEHM/R5e7llY+tJn8/dSzGE8H5Jv2XFR698oeuSkJDw\n4sO5os8JCQkJdxrnbqX4sYJZplZ0/p1vgwbjmhriOUdt0IPoBLzooXtajIN2CtjM/HzzmtOgft2v\ngcHvlCJJG8bCKWjr0me8gJGdcc0NlgdjcXOBGOeWW1Gxe6DdTQ8Gmk3dij2KXstN0jeqoIApdaAY\n04KDcxv3zkvQelhCF6t9kN1RGdsbTYrcgppnTIPaYFw3qO+mb2Xk2AboQES2fgirdFa16QDXFjP/\nvMDMyeCkbeiYFsYlwJuhceBl5qV3xrxbXXMNwrIL5HlhmtaAlDd1pJUt4Ax/ccfHdh+x/BsdKDbN\n4dQd6XE2wzYSLOHTHNZ5SBZtLar2U0h3nZBWigkJCQlAeikmJCQkAGtLn8uy0ChaNzt1Ok8j10nf\nLXQ7Y3gVR0zhjJ3BErzVd69e5lzJ+4gPpRLZRacvzZqXQ3rcG97ojz4a+LWdIzj4Qii3QB25NZA9\n5dbXADkoCs4Oa+C1kcoH0PQ24ocRVqz2BJZ6OI/TtIru0jg4r2zMvaCBTgVRV20ApbWB0/T2hz31\npr3cqXEDMlo0+B/XICw7w5jPIPsWbrSedo7Qi9g+mUNktYQlvsD57i4s8aChhm2NAtR3Fkdsq9nD\nOZ+HeeHHUGvTEKKxW/DYXTy+f3LcesAt8Qe5W9zrCBgol/H5PXTcmM7jfm0L2wQnsdIrQyjufqSV\nYkJCQgKQXooJCQkJwNrSZ0lastPZGDSxSQkkWHwh+zWPtGYGQrolUgxY3ZpQZAb1O+X4DErUOKPL\nW8h1soyP3obCc418HI8/GnsejdDw9jzdc8r6wDVYznNYJTe8Tctn1mARD7CEbsxApdGEBXKETJCy\nNTAOuIX8Kn6rmuBfWXT2zpCLJYCylR/vlNlG3s95HSrk2BoZQ4W604CMGSzE0w7oc4wtnkGZ3aiY\nze2D0WreOIbyd3vglLgDrbejttc3X1TPXGDLYo7YdFqiDfMmx/lr29hiQb1Gc7ccGxy/ZxPkrulU\nfT2Z+PejgQHaankbMlDpbDdKjtXXc021nq1KSEhIuE2kl2JCQkICsLb02czUiNS233QrJq17jC09\nltOmduyWJp1au/77ASFp1a46HUEWUtlqo6ysRuuuHw/h4d0LFVcrIXm2GDh/Y4ztZuYUZ0nHJKkL\ny3EOitUGlc2uOsWaR6p4yrkdWwOUpWocgLL28Rw6e4Oa9ve9vrML/vxrkP5uLp29IYu2UcIBfezn\np03vUSpsX566Y3IfDuZMKBaQepWO3MMsUsi2169G2nkBqto9L6N9TEdyvyYghjhDDHEPXg6X+5E+\nz7xve3DYptp1gdSv2wGTa+jnF5B368LKjvxnGjThPR/l6AJmaA4n7QPE+O9e3D05nh9VYxHK9TQ/\np5ViQkJCApBeigkJCQnA2tLnWpapH+N/M0gttWF9LEFl///2zi3E1rKM4/9nnU8zs/fsg4jbykgM\nb9KIKIoIJegg1kWEUiAh2EUXCkVp94HddLiIQLTwIiqxougiCPOiK6k0iDTJJFPZ7vOembVmzTo+\nXXzv7Pc3m1FTt86sz+cH4re+/a213u8w73r/7/O8/6cNG6nRrJA1K1tI3t3KEbodMpF1gJAMTClT\ngzv1lKbhTrGaeWlWRJSXa1ka94dZBPWqWabtiCY2ssQeVLKUPtjNyeZj2FTpKIoo/TdJT1h4zQ11\nl3Gt+rB1buARalDWQVrRwXk0yO/lL7Kla9RETe3pFp238/46otKTQT7/FhykZzPank1xDCzBwUpa\nzzuBlXVvOUvNge2+DryKNfM75h4QmZ1gWuPsENkC6QrMsX59hoh3G4W7eo57i2eR96s2hgu5Yw31\nASTJ08E8RfEnWD+N3G7VUSOb9aUvuM3vYvdWBmKkGARBAKJTDIIgAKWVz/O5azQqpFXbciRyu+iO\nJFVRDGqM5Nzqtmw6g0xmWFoJSdK0kZoj8dbW8nsnXThFd7Ik6Y9hI4Ua09u1lCdM3kXBpXVImQ7W\nqo4RRbSVfPz5aY4yNzpZkm1OEOldLo5vIeLM9d5MGG73sqxnAm9/ks+Zcpf2WoKUhTpXY6m4LrXm\nyzySsPGq1fPN8pW83UTt7gnWDdMui3WSO4g+z7tFGzvV/Hk0xlrOqld9pB/MmGyN54JS2tAuX833\nv5HkrqOYWYVr8LkmGdRxz880Ud/7ZY7H8vgdmRDeK86/tp73rsxzW4e4bdtTSpJUS5ZhXFNfJmKk\nGARBAKJTDIIgAKWVz+a5YJVjbacQlWx1c3RxhqTuabL3Gh5iwnR+HwtK8VdltJqPb70IuXkWVlMI\n7y2j6NWgBmuwJNzmcFueMGoNyWrHsl1VA47UGqO+L/aPIfF8Ix+znlQdg6mM2o+3MB2AIl41JCZX\nqTeHdIfO20uWj9/IRtFaT/7gRypc+wxZifra7Qmi3NX8CA+gWVkDmsn7LUhm5EZru9Q27dw6jPLX\n8+d1xkw5QD3sOZLRLbdrejxnDrSuQPQ7NavNAmWIPo+RsD5Eu7wLXQu17YgGj1Asax3tunycr8W2\nmJ6hFjVd3TdhizfB9M0sPUO7OK+Vgn01UjSza8zsb/hv3czuMrNVM/uDmf0r/f/gq39aEATBa2df\njRTd/WlJ10mSmVUlvSjp15LulvSIu99rZnen1998pc+qzF2NQfGrW0ffP2dgBBPFLVyK4UYa2bWR\nX4jP7qHE6JnV/Ms+wdT8gSPL2g07P9x1fx0jnu1J7R6cRRsbGMli+d/pTg4irM7yJH4F5VuZPjcf\nYtSKYEQ9jWxYC0W4Pk0EYOjkMoHhahOn1pwgl7CTR9ATjIg743x+zVSq1JHHyIEIvY2myMdEfEpj\n3CUGqSoIzDDSMIVB7FK3GMHV4Za0icCRasiT7OcrOm/AiBU1YpyBCdT30dk8+hslU+LeSs4jdZTa\nHZ44fWHbWBcGxyy1spyYjhB0g7Evwy8Dz9/fS6PyFtTLAPmwDfzdbAwQrBumezXbPc920dlXI8WL\nuFHSv939OUmflfRg2v+gpM/tWauCICg1+2qkeBG3SPpZ2r7M3Y+n7ZckXbbbG8zsDkl3SFK30d7t\nkCAIgldkX3aKZtaQdLOkey7+N3d3M9s1Qcrd75N0nyQdXjrgngImBsuayRC1NpZyoGUOhbNtK1KH\nS86KYYIcE92Hz8H8FQPvYQ9Ly2bYD1NUQ07c5gxSLWnCEZbkVTChvwan1KPK7ZqyDOcSpPQAQRcs\nv5v38g9HPQnUEcpW1ikHUVumugqnFZQ1HVSyxKpi7VodQZfTqAvTOong1VJxHi0EMbaaWLaGZZPN\nzSxNaX7aQWLdFjUjpgFGzikDHlIcM8VzUMeSw+G57BgzRADKIbfVQi0W7J/N83nOEQyp14o/v/4g\nS9ZuE+Vje/k6rw1youQGjGpXOnxw87VoIPfQEVxsQvH6cnEMg3jNOZ6zSn4mmy0sCZ1v/12VM9Ky\nX+XzpyQ97u4n0usTZna5JKX/n3zZdwZBELwB9muneKuydJak30q6LW3fJuk3b3mLgiB4W7Dv5LOZ\ndSV9QtJXsPteSQ+Z2e2SnpP0hVf7HK+YtpLMrXHoj7Ka6/MsfatIWqslSUanmRGirAdh7DpbzTrN\njmeJxVzCLciMCuqFbCL6Od3KbdkWO40WpMwoSyykwOk89h9azplKNLxlziLT1Bo7XHFT++AWRDPX\nTSyPm2CpZLedJV6vna9L7RTObYzj8Tt8vpOl7HIydF3D0sP1Wv7+g8rR/AHWrXUGeXsLke3aiSzT\nWRJ2epDPQmY4K65Rk3l6I3wGTHMHo9zGCuZdRvh+Xhc7B7mP79xaLz6/xmmXyxD95rQ4czMxTbLW\nz7KaBsbY1OqA35rfu9kvnp0eckfHB/P5GJa27jBK3r6elf06pnpj7LtO0d0Hkg5dtO+Mimh0EATB\nm0o5u/ogCILXyb4bKV4qJprpxIXqFFkSbBkkG6KVNcjjyfZVQZB1rZ1lRx9ZyofrOfrLZU91lEHt\nL8M4tY+aLkh+bUBWb/9SbUzz9zTbWfadq+f9HSQy0wHIEJWsHMg6bKWfj3FI5fOt4rqMkIDOiDcr\nrLYqkKOYAmjies4QLaZMbcPEdQpT2M1U66V2ElJ7LR+7MctJ6obob3Mly+oW6qJsruJL4TxDt5cN\nhGJ7Keo+h2ScwQT4HJyGlg2usEyGPwonI9T86XWx5A8lVNUu9lew3HS6w5snU2/lzz4C82NG6EdY\nlqeVLIn7OTd8hwtPv128t1mH1IdO7tYw1XAKD0B6X2W2axLIwhMjxSAIAhCdYhAEASitfK6ocsFE\ndLqBRbltuIcgwXcV0T1La0gZXDtYw6VCmdAxZHd9GQ4wkL5YhqylGcQkZOj4QP78eZJV3XXUzoA0\nvhxrnNeg5AbzLM1WtuCkA1k5Qa2RKn4TeylpegSrm9kmk3fxRdhswoGntpTPp0qXHHznZCmfQIez\n1gAAA39JREFUZwurm+fd4r2zo/mzWc+mi6TiTdQosSHrzebz6aKR0x6MaDFNgaXlaqSo8xhrr/vI\nPliGTJ+dy8fUcf6d9bxNY9kpFmhPOJeQYrqooKIZTHiHDSTjQ1Y3kZUguAHVjyzl78Ha7wZWjle5\nnj0lS5w9MNz1fYdHuWWMZl8w/A2T2SAIgvITnWIQBAGwstZZMLNTKlYxn361Y0vAYcV5lolFOc93\nuvuRvW7Epaa0naIkmdlf3P0De92ON5s4z3LxdjnP/UrI5yAIAhCdYhAEASh7p3jfXjfgLSLOs1y8\nXc5zX1LqOcUgCILXStlHikEQBK+J6BSDIAhAaTtFM/ukmT1tZs+ksqilwMyuNLNHzexJM/uHmd2Z\n9peuNraZVc3sCTP7XXp9lZk9lu7pL1Itn4XHzA6Y2cNm9k8ze8rMPlzG+7kolLJTTDWjf6ii1su1\nkm41s2v3tlWXjKmkr7n7tZI+JOmr6dy2a2NfLemR9HrRuVPSU3j9HUnfc/f3SDon6fY9adWl5weS\nfu/u75X0PhXnXMb7uRCUslOU9EFJz7j7s+4+lvRzFbWjFx53P+7uj6ftDRV/QFeoZLWxzeyYpM9I\nuj+9Nkk3SHo4HbLw5yhJZrYi6WOSHpAkdx+7+3mV7H4uEmXtFK+Q9Dxev5D2lQoze5ek6yU9pv+z\nNvYC8X1J31AuWXNI0nn3CzVKy3JPr5J0StJP0lTB/alOUdnu58JQ1k6x9JhZT9IvJd3l7uv8Ny/y\nrBY218rMbpJ00t3/utdteQuoSXq/pB+5+/Uq1uvvkMqLfj8XjbJ2ii9KuhKvj6V9pcDM6io6xJ+6\n+6/S7jLVxv6IpJvN7D8qpj5uUDHvdsDsQi3DstzTFyS94O6PpdcPq+gky3Q/F4qydop/lnR1ilY2\nJN2ionb0wpPm1h6Q9JS7fxf/VJra2O5+j7sfc/d3qbh3f3T3L0p6VNLn02ELfY7buPtLkp43s2vS\nrhslPakS3c9Fo7QrWszs0yrmpaqSfuzu397jJl0SzOyjkv4k6e/K823fUjGv+JCkdyjVxnb3s3vS\nyEuImX1c0tfd/SYze7eKkeOqpCckfcndR6/0/kXAzK5TEVBqSHpW0pdVDFhKdz8XgdJ2ikEQBK+H\nssrnIAiC10V0ikEQBCA6xSAIAhCdYhAEAYhOMQiCAESnGARBAKJTDIIgAP8D5NzTyEGRNfoAAAAA\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fec14f90128>"
      ]
     },
     "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": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fec14f1a470>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AfbH09Y0SD/rqYZz652F/fLxsrbpvmvPhP+X5aHo316Z41HLffjqd+upG5ZY0GgyP15V1\nb1+t6edXw78svXCKq1vLz19kvpxPlsfLs/7olt1u5N6mXq15vNzBsvgytfXXv5zO/Tzorxyvxd4s\n180IDW/rOMyOlw+b3u5pzY/XmOP8C29XZ+HHbjdb3i68jcbFrW+U2dDvuQZ+/mbTrz+voT/xnA3N\nr7nZ83yFGn6UjzrV+fM5X4R+beGKJ6TsnN/zduNFeDQaHC93J+t/FO1S93h5NPZrKorqOePL3zp4\ntku/nnzs28ziY/a9//pNa893v+N+oc8P6WQq1lM6meIlSTKzV1nl7/bEePpblTyQkJCwybhfRop3\nhBDCY5Iek6TL3b2Qv+tAkmSXfXTYXfolTzBSM5CFPL+V1yxBwcatm4084n6gOIsCx8YIssAvcXbo\nIzFiouoXmr/abYxOZvs+Ogi4ntE5377AyDfHSGC58BHM8sTguBqhTAv0Q/PY31NT9E8x9B+cOkat\nM/koZ1nj9fuJ6n0ffZCgjefV+lmbI2JfXl71c3aQ0dvY89Exr3kwxyjPB3laZn79wki0iJx8Yt6+\ndhMZqdfR7ikOuHvrCFeSGtvYF4PQyRz7Tqv2GqZmOHoPXb/nIx/gquEEQs2un+d9Pf+glfl9afb9\neWnVvb+sX/XFvANqPvBncoHnvKiD7fSrvuV0wSbhfhkpvk8n81Mf1sm814SEhIQXBffLS/G/SvpI\nM/tQM6urypf8rXIcSUhI+CDGfUGfQwgLM/tzqnwEc0nfG0JY6wCyQlbU1L1cTU6Prh4cr5/IaUVr\nBxPQMxcaskjDSJJDy6kkFc9FE7Q6OMWaYpK6kzkNgkQhbftfNS9FoTyKFzMIQXPQ8c75Y89NzYLT\nrfYQlDVfL3pMS6dv40OnW7N4HeeD87QJ2BFFiSn0nPkCUwCF07CG/Hr401sHZS4hAM0b1bWGzD+f\nQ6CxHqYdIOjUCz/PYuH91cN1lDO/Zht7f8359MdTNWocJ0DZvejPynTi1zzInNa32n7ArPTzUP1t\nLiDMxLbwOmc4ZwHFf8l+2Ubf4jbvZOu/zsMS9BmzB+Ot6n7l13yqYQYNL8f0wQzSdq9R9e0JFX6D\ncF+8FCUphPCvdavLbkJCQsKLis181SckJCTcJe6bkeILRchNs52KZnTDzvF6azv1mIFW5QgezqMq\nt4AqNzoPpRIF5hago4yTm7JnR85ZctCXOShWvud0bxEP00Z83QRK7BxqcnMJWn+Cpvq+c1Cfwb5L\ntzZDcPDqMnrejr0xlG2on62an3MMNXcB6leDWFuHKlyC+o7KW9X3/MCPl+2AAmOa4EScJuOrGT+K\nPtobOd/PoKgbhONZWZ03Y580/HhLTIcs0JbmzK9nkPuzsDPDHAPmW5qguCGrGnCIKYgZnqfQ8emV\ncOjTO3NEJbQavk0d3TnGMXkfBzU//nVV27T3MI2BgP2tGeJ7A74L8XtTlqdFi9/fSCPFhISEBCC9\nFBMSEhKAjaXPeSn1Vmps3enGdAglcguBv6AHChWVWq7Jh5VO0tTuxLtwOfZjTzJX9Jo1UOMF5EIE\ndR8cumv6XlZtX6tDZRw6VZkgSHqU+zIDdo+Wfv5W7tcZkBbItLydmCfeIR3H9bcmoEqg0l2ccwbq\nVUOfG4LUGeBeW3rby0W1nLdBWev8zUbuNSkuqDT19lrAFMeWn6eO2Gmqwnmc+pgitc8QsCyo0p2h\nr++3vS278/XbM294WkJRj0kCtQ4Cqmfe1mzk9HXZRr8d+kUsun7VNfTRVokpBsSRd/p+L67H5s6h\n2rcLb0sT0yQGMX3V57ah6vNmXlVCQkLCXSK9FBMSEhKAjaXPpXkgamPs9IVUZQpFl6y2WVbb1KfO\nGQxKYH0ENbfvAdAZqOF5c86yOMWaiT9JWzO/FbMoUS+Zj412D80pUAO3cBB8PX/t5jecmu8Ufh1d\nKM0rFZezCKR9iDlWm8o28qCFSpSDPd+308U0wKFv38A8xBJRAcfrYJ1FJ5clHFsK0Oc6lP3yBPP2\nPu83nCqOjpBDvnJEwn6LkSu+vYbfz/q2B3KXM+RYn/g2+TkxI6EBcs+Prd5OGZrQ2m5riOkDBMnP\ny/U7TzPvozq81mbIbe9Mb903J2XveMPrUM03Ned5hTRSTEhISAA2dqQYQtB0UU1ILxhPBdvAEpPK\nBYaK5VY1+sgRa9d+ykcVix0fndQG/ss/Pee/MS0MIIrdLv5AG+HRR++YeUwjy5BauEAq2DYcczgx\nP0Mq2GTuo8Z2m3l5OBHcWeqxAQHpgYe7/niUOHa+7+3mr+qkjVjPHLGJFLdgopqNbxUSOMIJYzQW\no3ZqJYb4PZrZGkSUa7mPTrcKqA7AasR9rQmXGORkbuFK50zFa3jfHi28LYwxNcTANjDiWtnbjXGv\n8gJuNDve1vLI+4p9zhH0CDGOJ4yAOcqDM1AzjvgaHWcMtZn34bC+3iuzGS9z49xlI9JIMSEhIQFI\nL8WEhIQEYGPp8zIstT+tKG8TnHVoTjEuuv6gsAthJMZt5eieyR5477tvHC/uXwKtQLzXBJsXoIlh\n1znZGPRk8jScUyOykdOqehuOOn2nlSXs6+ugUpOOH2c8cS7faSPlEVb6Jdxj/Hh+bRPQ5wDHmtmR\nH3uBtMWy75Q1R4xlA6lzrEGzWMUsQhQ5FBxokMJXR35eQCwh4yTDCCQb4tGg7m3cGsG9Jgo25ws/\nzxjpke8Lfn8eLN20eDRxypwjNpFlEnooEzDFRMk8brNdwK2p5OSAI0CImiK+s7nwYzfq66cG5rh3\nnIYpYlNqJ06JuEcINBnrvGTV+e+oPtF9iDRSTEhISADSSzEhISEB2Fj6nClTV5UyeH3uyjHVv+vy\nGMMLh6gjEvnWLF9vTjollQYdmu74elZWmzL2D7QqB62ncWg7puU1kKrGlEPGEtahcmeoC7KL+MHa\nFqrJ1dbf8kVUf5cD328xRDU30P5lHSozFNKchrNXBtjG6WZAXCHLpjbi4sq5RToZg6mJt6WFuiQi\n68fmVHF3oASPp06J+3voyBh9UEfs3tWpPx+sbHcI+p6zDC4oe3OE+j5bru5O8ei0VobHOMQ2KjLW\n8HxMGQqBZ3GGKZsmaDgrReYwKF6Ceue16vzloT9ESzoddZHyR605PkN2SvXG+x1ppJiQkJAApJdi\nQkJCArCx9FllUBmVtl3BheTIKU6n57SuhoL1KzqTIzC7JB1B+lkO15kC6YSLxokKL34eUKxpHSlX\nDadYLEO5whLK6onAXJQEpatLvgWXFNDgElRqirS8UXQGshZTuBiwDZV116XtwcLpdnjGFdqCRrQD\n37cxRRDyrl/zLI+BxKC3U0Sa11sItEean5CKNlx433bhWLNcOGUcNxHgjftYi1Lq9YYfY2sMB562\nX3Md10aKf3TVoxLmHRi3LpHaB2eZRlS6aQI8mfm2bSYdnKgL5McoEN8+Q/nUWs37K5/j+cug3Meg\netb/maCvNESmw4Ipl9XxGD2wSUgjxYSEhATgTL0Uzex7zeyqmf0PrNszs580s1+P/+/eyzYmJCRs\nNs4aff5+Sf9I0g9g3Wsk/VQI4XVm9pr496uf90i5yboVPdlquiqX7ULFnd8asEwsQXUnMJRZnkPu\n87v3j5fLc6DALVdrWXpzARWz1mfGsyPE2jBIw1VzzxVX5jsTPFp/7opiD7nXrF0yRo2aFT3LQJ/A\n6jQDxRodeMlYwhAk3Ebgd2g6ZStByZdTnD8GJ2fM/UVA/eAENV6v+NY7jApAHnhACVFQ5kbmFHNS\nxPX4vISx617L+7C59LHECKaxPVDsozEceBDUXVCtjlMyBV2HMGWwxJRFuQXKftLKyNcjEiFQlsc9\nDQME28fnjF7KzLenUW5/6g9jd9Vvmyk+n62RYgjhP0m6cdPqL5b0eFx+XNLLP6CNSkhI+KDCmXop\nnoJLIYRn4vIVSZdO29DMXmVmT5jZE6Pp5LTNEhISEk7FWaPPt0UIIZidUjil+vwxSY9J0kPnL4Wd\nVqUunwgypYmo1qtnIZYknfdg/3SK0pZ9xDnfD6cp27RoQt0TtH5SenAwg6pX9k3NMQ74PqdS9oCr\n5mQw+1d8kD3dokLu1KeZeVsyWofFsq0BNDmjOgs2VvKpQRnMntaXEiUmY/+xmjf9+K1B1Zb5vtPO\nMUpvGqjcIYLq8x5s1GC+GlCLpG3elv4CHBMiv0Vz1QzTCzkMXJcw8OWsQwvq7xj53hmC8Rkwvazh\njsXnrAS9LUDZFw96A1k+t5z4SRsot2vIPV8ix5l1bJrbTvGvlaNbPq+xngwjHnA/y1Xwegrevmd4\n1swekKT4/9V73J6EhIQNxv3wUvwxSY/G5UclveUetiUhIWHDcabos5n9sKTPknTezJ6S9DckvU7S\nm8zslZLeI+kVd3q8MjLtArJsH1ZPzGftwrlr3lpDC2BpVc8RmGxwj6ZFEyygQo4gYNiI1UBJ6Oa8\nijtuwsYqy6E4guLOxsgVpsP38sjbAio1Gjt9nI6c7h11b30Uduag2gg63h76+eeYM2B+Nm20Zh1v\n+7Xzfv1byDM+ivVQEHKu4mm/VwVyrDNMGhwE0GGK2duw93rWIwTaKJuaQaFd5Ur3xshZRz+Poe1n\nKPc6uOJKfHPLqWkB260a3Llb+MpNoyo+Y90WTGnQuo6WZ02o1X0Ee3dKquLeGe2lbz9hjEJx65io\nAWX9RJ40pk+WvaqN4fSZrPsaZ+qlGEL4slM+etkHtCEJCQkftLgf6HNCQkLCBwxnaqT4YqJU0DRU\nVKE2W6+S1Ren5CdHKjVCQaEpihjVmRPaBeGbUWX035vfHLg2dKnYOl7OoGjnoFtDqyjRGG7bJ7Rc\n5LgucqiFQ6eSTeRB11mFFGolYsPVjpdEO68ZONPuiL+fUK1R6IgUb9SFqzZU3iYI8rjp27eX1UaH\nY7dD7yCvvAbWR/W7fugf7Ha9b6cTv+gbA59KIHrn3YW80Y6B97R/g9s3lfh+w+//AiVDhcJd2vZ7\n1+yDe+JGNmMBstkEhatAn0so6F08w1NERRTIZR4hb30y8H4ZT/z6e4UnGPT7VV+/ZPuiNwoWbU0U\nVps+iOyFVR70ZrLnNFJMSEhIINJLMSEhIQHYWPpMsB5y1+BsTNutNhXViu4c0pcJVHdcQ93f3/CA\naQZs28h50hZydUeFy9znutvHy6SYioriaO4UsNtw9bVZIsB34hRnXCDAfAmKi9++AstdUKlZPP/k\nlID2KYJ6acs1dJFXfXLcvgemZw1XZVncqnMIm7B4j2iLJY/jVm0fZH+HNmveL/MRJwQcy0ecVrcx\n89E7594iRXbrV4GWWwvmBzP3/GG/hyWmLDjaWCJvuTjA/YpR4HYORdOwX3tAfurnbM2hoHcQeI3M\ngABum018330UA+vtVdMHxbPeb/MuAuBRoKwO+j5e3c7NjN1OI8WEhIQEIr0UExISEoDNpc+LoHI/\nqrFwxA4o4sTgVap+88gg9qZOH27kTjs6DPTuwNUbbtddqKzThtMX5pnOERzbaHm7ypgfu7X0bduo\nfrVA8atW01XOg5nTINp1TUB9DYm7TSjNtVlFZWmR1pxAzcT6sklah2NA2R41sA0CxuuoUzzG01dE\nq7GXds77OkMd6XMIKh4jMB40sWyhBjTo5kOZU9z5DtR6OGLXY6B8AAU9nMH+C8H71kbEARIAchS0\naiFgujbytjx74IHkK7RxzVMo/lMo2wxYV0ARrxEs2obI926gL+a+fafjz8s89t0QBbwyuM0Pd1DT\nHEHvqyAD29DCz2mkmJCQkACkl2JCQkICsLH02fJMxYoqj10JHV/3QNYagnfzNhTNlZsz8o0Dau1O\nmDOLmr6NrlOTAPW3h1q+zCctkRNbD/771KtXaukcNls2pyM2Ar1Lp0xZDjUdweCzgsHWx4sagNbn\nMQh4DousgOmFMnNa1WIhKBSlap5zKbq+oIqN/GhQuToCjy9m1b7dMaYJQFmpipcoMjbHvaDDdvc6\nAunhZp2DYhvqGlukzQMo/tZAjjty5uvISe80kZMNOlmHEM6eaJ2HWh0vFc5yaswwTsG3s4ZAaqEG\n90Kkz0hAqCMIHTWoSyjX+42qXxao4708j6kB5GQPAxzOo/N32NDo7TRSTEhISAA2dqQoc8PU4QUY\nZCLlbacmEJm2AAAgAElEQVSG2hgQIFrNagQxPHBFxTDy4/yy4Rd81qGxKGpt4Jw0eW3A1SQg3s+i\nMNKeY9SEOi8zOKOwWkcHI6sR9KTmAtcJByCKCoOVqAGx5Dk40GxPff24WD/BfmOCGMy6X9DR0o/T\nxmimg1Kps7Aa2WF0jmMb7lUNo+oFYgYX6PMbF337nYm3vQFhKsdIcBFH8ByxH4z9ejJsy3KrHPln\naDtH50uIFCUclubZagzpY8kbTX+eLj1zvIj+kZq19V/bGVIUbwRnRxdQ32cMR/rV80VBp174MShW\nBcSAzmM8Jp+fTUIaKSYkJCQA6aWYkJCQAGwsfV6WpQbDikIsQcToNnPj0A1CL3f3jpcn02pSfY4a\nFTlKbzYwc05XGT3nFHeS+UZbKAZSNhAbicnwEill47yigWAyKjDPvoR7TN6HQSnozgxFVcIUxrYQ\nhmwMYaJdtfGqOe3aQyAhUxhzlNisXXbhYAupYCyxynohfVwTZgyO4/BGM6d31zq+3xbyIDPkws2R\ncnlU9+VzD3js33jonVfUQJ+P4CQT4wPHM283789pmgLTA3OIHnOUZM0Qv0jKaavYWAhaHzrxXplu\nIwYS55zdcFpfQNCZoUMXSBh8Ri4u7tClKLaRomA48gNuNXxbRleGYbwvZaLPCQkJCRuP9FJMSEhI\nADaWPhNNpPnNWqCvMAIdHg11MyxH/BZ+Puowf60PwaWZtgaFdomUOzqvsI7GFGaxnUi3h7lTIGuB\nGoMmdzt0ZmH8nlMs3uTlM06lOh2410QT1fOZX9to4bF5tQO4BMHR53IdTj9QUS8unabvQ32liryE\not6PynF9Td2Q6gPfbxicYvdRlmZv6X8YqF0L9VoaV52SL0BlVzGenZ73yfW5p/ltFa7gBhz7xtD7\nczvD1ASencGhbzPa8utrT6ptmkOU0j3yPm/z+cScQYm+6DOcAotZF25IAZQZ0xO9dnVNI6SHZjRb\nRhxpb+pTCdNIzTczyS+NFBMSEhJO4Ey9FM3sJWb202b2K2b2y2b2tXH9npn9pJn9evx/9/mOlZCQ\nkHA3OGv0eSHpL4cQ/puZ9ST9gpn9pKQ/LumnQgivM7PXSHqNpFff7kA1ZdrNKnpQIp0tQ+rc0py+\nLreRrhXVOMRWaxJo/4lUNai5JVTWCdXfsJ5iG36Tsplvsx+p7xLpdLUa9UfHFHRz+pxT5iaMdXPQ\nICFFbTRxpXUWyVDZgyIOClgr+Kj49U9zb/ceFOIcZrHzoV/HEA4/U5Q+rUclmMHQ7bbT4TmCtFnu\n1ZaYYoAzzIlogaPl+m2uO1VdPlzR5hqOfRk1fPaNUx1aixz3cAy6W0OwtfX9+cvLqq+zIz922XDK\nPEXa4rwBlx48RCWC7XErtAsj4Bznb7a8T8v4XPZgYFwytALq+xLrazGGw/IzNaZ60XCmriqE8EwI\n4b/F5b6kd0h6SNIXS3o8bva4pJffmxYmJCRsOs7US5Ews0ckfYKkt0q6FEJYJT1dkXTplH1eZWZP\nmNkTw+l43SYJCQkJt8VZo8+SJDPrSvpnkv5CCOHI7ES+bjCztVGjIYTHJD0mSQ+fvxyaMYd5BCeX\nGspWFqBH9bkvl+2KHtQQ1DqZw5AUgdTjBmpuLPAiBts+qvsfPGcrgFahhGbRqvgZjG60qIHKIvl6\nCrVwsQeHE2C879cfSpQeRXB6EdXVbIK6KQg05/WcrzkF2yZlPkWPtALB3qCBF2teO+Vod6KbMb+B\n+9aGow2O93DTj9HK/Pqv7Hup1D6o9/Z1v74JSoXWm5Eq4j404JzU3UcwNgyJrePnZ7saA7+eGfLq\neyWMiGOt1uWDrnhPr7riPWeOPZ6VOR7/Fuq9zlDidgJnoF7X1feyhXsRS68uGA3fWv9KKPH8zQ+q\nSI1yub6ez/2OMzdSNLNC1QvxB0MI/zyuftbMHoifPyDp6mn7JyQkJLw/OFMvRauGhK+X9I4Qwj/E\nRz8m6dG4/Kikt3yg25aQkPDBgbNGn3+PpK+U9N/N7O1x3TdIep2kN5nZKyW9R9IrnvdIZZAmFS2o\nI4czoKbGHIpybQrrsEgDJyLt9UMH0CTmfy5ANxnIvDd36ncEw9sRKE4nR370rDpZQNnVAqroYHEr\n1ZSkJpTVYd9pWGfq9HF+DjnE2LeMSuxR5v3QQd2YrpxKbmM6Qx67LUMpzSFMaUukEL/0OWafO93r\nxlKhVyaej66eK7E13MOdnlNWlnJt4XHekVPSEQKs5+cReY6ccMU8cNZf6eP5KGAXphGubQL1m9ME\n295f+RHq0mQeKL2INmUo56KjB71/doNfP3Ov53huZsj95qQSIweGBeq+QNFux3z2GqZM+iN/bmh5\nxtz7/OnqGeaUzybhTL0UQwg/q9MD5V/2gWxLQkLCByfOFH1OSEhIuNc4UyPFFxNLCzqsVVShMwCv\nQO7zonRaUUc50ZVaWCDodYR6FXBf0hhO1kvIxV3kikI41Nau881rS+RbX3cq2YiqeUAwsMCkei3k\nLEPl7MGpu/OsADhC5954WmMdRQfxrTqSiQHWEZkhr7uH+h8Cfc/Qt3SWnu75gS7OcFH96jgt2nUR\nCMaunfgtR4TAwPtwaFBikQfcgR9bGy7c7VrV5/05eCLuW7/l19BzUVyBCiw4DpXZHLVg6rBXO7pR\nKeRH6AabIkgaSj2V/QmiKfb6cGdHud0TyjDs5aY7vv31eRXsv7UPxR2RDYM2poYwZdK4EJ+R2maO\nqTbzqhISEhLuEumlmJCQkABsLH0mjiBWFqAn7brzlkHh61cs2PZdiXu2DnpbQ1lTuGqLdlEoCVmO\noGxvO/UNcL4e4/yrI3bqKLuK8yz7yNlGycoSNDW75DR4Cuq/hNVY2XcltNWLSjMoOwOm9zJXojmt\nMIUsz7TZgiVWTwSe04Pt1iTiFq7naNv3u9TwAGTmhBNzFHeq132aoAmad77t0xcsQDYfVtMQ7Zm3\n6erhc8fLO30oscg3LjCusEM4r2/5caZT72cb+zbtonr+7Nr6IPUJVOOeef+fM58CGMmf0fbE2zLo\nIHKB0zooYtbLqmdk0kbSwdDb14Srd0Au+1KbqTqvkEaKCQkJCUB6KSYkJCQAG0uflxa0HwNlGXjd\ngE0S4otFQjabVPu1zzsFvTgC7UT+8gh5owxeXk6c4gYopMNDpzusRtQzp02Nneq8AXR8Dpdq0mQW\nzpo1QVknzn0Oacl8iLq/Oz6vkMd83hYKVO3lTvVPAJ1FV/EB8qoP5efvzvyY+3AEb5/z6YFVkbAx\ngrT3Gvgc19xGjnMfhaYmcLVu2PM/2iw6NWtVlHSA/PX8Ohy+sV9b/lwM5ffZBt7GcrFeRTeo6NPo\nNt7swdUbRcnOo8wai68F2MV1t3xaoR+8z6dw0wYL1u7UzzWJ3diG23h+5M/ndAs0nS7wsb55qJ0W\nUnx/I40UExISEoD0UkxISEgANpY+Z8HUjrZKc+QwTyZOiTKoz3nmVKEZqQqp6QJKaTl0QrIK+pWk\nMXllE3R74NuPUayqO4PLNdq4soAqtpzqzKEg11GIyzKnMBl+4+oIUj4/8utsIXj7Otq1W4dEH7Eo\n4N6NqQHGZU9B2aeou0zGbggk3t7zShID9MUyJu7mEEJnoGxN0OFZ6X0xmuNEmA5hnerthtPDFu7X\ncOHXP5nemk/eaPi2VMXLgbc7R2C4aLWG/prDHXxYQi2ObenAI2x8DnZmrC9eY2C835dr8gSAGutO\nI8HgvPn1l+WtyvGo4cfrPuh0vDbzDh3CL+98Hm3mNnRMtZlXlZCQkHCX2NiRospSiiOhBUYWQrnH\nSQ3lLvHzsFVWv9bTOcQKTMC3WVsFo6ZFGzFrASpKRicX/IQjZlKoYzKJMYsG554FYvBKGN5mSL/K\nMQpsIF2ufslHCvNrPlLZRiqiZdU5ayjBerTApDsudK9EbCauxzCC2iv8nA2MYMoj1Mihq1Dsrjrc\naGzh/RNgiEtRrJZjRM5Sshjlj1Evp2AhEwhw83ps4wQlPpE2t3i3u/ewWk9HPsKuN9gvjv1r14+X\nu1DX6vH+t1CXpT5BPRWM9g0Fg6YYErdxz9/ZdDcgxpUuqLTg2Z2v4iebfowlUguPDvz+78Iladqs\nzk8BcZOQRooJCQkJQHopJiQkJAAbS59NpiJSlRkEgzlSvtr7zisGHcSYPVOlXdV2nQLW6qCAiM/K\nBqiFAWo0wCw5a4fQuPYcyk0GTHbPIz2eYV2j9GNPmpjEn/vyweG14+WLhdO62Y5v07zINEOWe62u\n6frYKRh5Yg4qOcK15aijstVyipVx0h9izBzGrss6hJxuRSFruCdlHXVJUBp1gXtRz50mruL+qqbj\n2H4ZOpi5MDFc+rn6B5VjTRtUFh64Wj7sAgRFJzWc4jZsfWxiu+f3eQmRrhvXs+wqRylLuMYuMWmw\nxPM3nfg1s/7PiVkaTBksEO9ai7GZ2/P1r4E91J8J4YSDbWxrilNMSEhI2Hikl2JCQkICsLH0uVTQ\n0CpqUUM6V28KMoWUqi3U3QixtOUSampj16lhUdBxFfGDUJx7oF61EmVNT4iffvw6SN4qbK0Yo7YG\nFOwpUvhYLyQDrbxWd2q8By7FdEHCYppfvoOynqBVdZyyhlS1gxaUfWCR+YXSgShs+7751K9p1aXL\nD3HKloEOHtW83Q/IqSxRR/naWub9ORw5xc/3vV+KYo3bCz43TLWwNO4CeaM3kGYYZgzg9G2miFPs\nYSplOZ7FttJRCOl8OP9whvjaMfNWfZuHDv25fK7waYIF6tjUen5/VxENMxxuB2bLGsDYdvfWqQFL\n9DkhISFh83GmXopm1jSznzezXzSzXzazvxnXf6iZvdXM3mlmP2Jm66u+JyQkJLyfOGv0eSrps0MI\nAzMrJP2smf0bSX9J0reFEN5oZt8l6ZWSvvN2B8pKqTdZUSjU8dhFsDMoVnnd+eHMIg2iyolj56CP\n1ltferSJtEGWQa3XXcXdh9I7A8U/3hrsZIHg5QUMP/fmKKW5h8Dfmre4DCiDietoFAyUrjjUduFq\n7hL1Showh2XwNIOHhzlNXlFKdenHubF0unm57jS4jFMZh6X3YfdE+6Am46f8XHfH2wW19nDovjZd\nlI8dIf+wdujUfztSdaqsVFcXBvUXwfMHYz9PH9EK5xAEfmLoAUW9Hs1fRwwo5w06xUyXjjmd56C4\nd/xZ3JmtH+9MUHdl1V2HU6fae+grdWDU22fUQNVIK1Pw9m85QoVVGH0R/wVJny3pzXH945Jefg+a\nl5CQ8EGAM/VSlCQzy83s7ZKuSvpJSe+SdBDCcWXypyQ9dMq+rzKzJ8zsidFsfcH4hISEhNvhrNFn\nhRCWkj7ezHYk/aikj3kB+z4m6TFJenD3YihiTueJcpNIcp6B7mnHg3bLWJuFpTRZVpTH26cpKbjP\nCHnTuxnMUsGqF8iVPZFPGw/59NLpdWfkx76Yowwp2M72BEG9Db+2DhTPGmjdHC40q2bZDf8x6ez6\neRpwCTotaPdqNl67nnVZXlp6jZSLNafPT/era+10oI5Cwe3BcHVnd736TAecBWj64hDtKmHyuoXl\nlfMQ6unMx34PR6iXsoS7UA0RAh1McZQdlLhFgH1ZIMog5j4XEPD3oebX4Z7TLjBNAe/fMZ4z6sPL\nHUyf4HnluebR7qgOB6LDzK+/iYSBRs2Xx1bdl7CZ4vPZGymuEEI4kPTTkj5N0o7Z8Z17WNL77lnD\nEhISNhpn6qVoZhfiCFFm1pL0OZLeoerl+CVxs0clveXetDAhIWHTcdbo8wOSHjezXNUL+00hhB83\ns1+R9EYze62kt0l6/fMeKTOFSGGKU979c3gqLRCE3Yj2Vd0J1Ol9ULBtcFb6ikKXzREwOy1Bjj2O\nWAXKUDLAexxV7L0FzoM7NR15W4qW0/5pC0HiIFMFVPYAKkXbsWUMMO7VYLw7RNA1aqQEBDKPYGPV\nRBe1QB/bLQ/Ivj53Oypabb00uyBJ2l94B+3JlfABzjOCIeylrT0/6RQUF+2dgSY3ELw/naMf4y2a\nsO4I7NcKKNXLtq/vIqid/TxDwDbNX49yp6e9yD/HfVd/lznO2fbpi2wChRrP3BSlVMdzmPIiCHvC\nQAhfVCtuw7zyHFEQGf17cwTaR8PbTQ3ePlMvxRDCL0n6hDXr3y3pUz7wLUpISPhgw5mizwkJCQn3\nGmdqpPhiogzlcb5o0XNKOIILdwO5zVnj1q5YZCiZCSoxgSN2u+kUcAJ36gVUwRksuliecjFyMjNE\ncPAqDZqqcQM0OUfu7bBBQuQUhzm0jRyO1Kz1QaYYrbs0hBUVcrnnwc8Zhn79xXC9+lmHEtuBctnd\nu7y2jeNQnXcnuPrMPOF6zxvbhMpsfZSSRS47lejp3Cn5GIHkNodavAraXtKuC0p9G33IYHzc/+WR\n94WhPOgYFnAtOKgvo9t6ec7v7XrzMWmOXPLsEA7nTd93hq9zwHUYKPECUzlFdFkvoGwzeJ1JAgF9\nVT+q7pUtU/B2QkJCwsYjvRQTEhISgI2lz0sLOoqRqg0UhcpaLO4EdXeGANdYDCmHOlsi0Lsx9m3H\nMGquT5jj7B8sECQ9nkGuhCjdani7+lEVbYGaNQtGaftyDiuwBm5nPmO+s7erRHGtJalSnBLogo4t\nc/BrqOMkTY0hLgLqL3OiGzgmC0OVUE6L2JatwrftXvByqFit2Q1XsEe4L7UDn6aYjZ0yXyu96BTR\nK28lq7zn430/T7PrtH6EqIU5rrm57Wp5seXbL6H4T5BptaL7PVh0lQVcyhEwDue0E8W9eDOY4x6g\nogeUNS1qfs3L1fRF39u0wBthjimlOnKfV1uXqXBVQkJCwuYjvRQTEhISgI2lz9k8qPtMRXNayHe1\nc04fOrBXMgQSj4+qYFrqujXUxh0i9Xj+9P7x8hQBrnVQqQmU2yW3QfcbFNrLIQY7o+7xiWXQ2gaC\nfZv0wDA/3gK06vDw8Hi55THDCvH4w4fhTA52xGJR21Nv93LP+61sIocWcwN15BDXur59awiX6aju\nDszVb3Szthue8HuwBTq679Zd2aHTXf7a7y69j/anoMRwO28fB8p7+64ERCcwfxzB27ru/dmAWs9n\np0Bt6sxPf0yfA6g5w6ED6i4LCnEGGzUW4hogKiFHPvMS1tqtbe/HIuZ70wrNQK8zJDcPof6PJ9X0\nDu3sNglppJiQkJAApJdiQkJCArC59NlMjVjDt3AWpNp1eCfBNZuBqCsFboSCRw0U7qEfMgv62BEC\niREYftR0+kh7sQDluADFzVqVNVbzCMHgAZZSUKXJcRfM28Xi0cR5NV3DD+qe+7u3qPpiiTrCo23v\nnzrsv0ZNqo7Iny5hO3XZHbHHqIfc0XqKaZOqLzpbTpo7DaeVJVTucra+WFYJ520hkLqFqYdWc0vr\n0IoB/gZrr+nCzzNowIV7ieJWDzgdnT3jdDfU/TrGNb+PPVLvGBA9wxPFwPgxHLlLJAaM2ggAn6x3\n555iyqaD54WB2rM8BmGTMo8QpA+X8BmSC1au7syd3ySkkWJCQkICkF6KCQkJCcDm0ucsUysqyiUC\npusoLkQH5UHTqVK/G2kYVEGBDqvr3dbNQE3OO2VqI4u1DsUzICB2COsqbp+vaBhcqBk8W+/4NVAV\nH4G+Moe1ZX6c8RSFo4Ifx9qxcBNqWrOkcx2/nyzixLrTrQvuqt1su/pOV/ElA9yhnK+KTm2bR2lP\nrrnzeB0u4BncrhdXXH2ugSaHLQS44/zMbW51vY2rGsvDqx5N0MY1DPd9/VLwSEMCuXUQPH9KUaey\nidbEc5YlCn4hT72L5X4Tkw2I/9eW33+DXVmBPPAlAvkz5DA369W+ixHs3NoI3sdpOnV/hlaWa5sZ\nup1GigkJCQknsLEjxdKkafwR3X7A08UoNHCCW5hUX81vN1AmtIZBI0dhOWqXIJtPZebbbNV9RNbP\n/Tx7pa8fYpQ3PF+NoM4f+TFqcDrJET82xQQ8S7YuYDhaTCE0jFg7xLdZHZGjuoB4yBKxcTPUe61h\n5FPOuLejhRTFHorFZpjIX3XvUR0lRjGqXlx77nj5ytTT9hoF+hnnpOHuIlsfG1qwhOisWl5CrHrm\nyff6MXbhhnNCX+DI0x8SxiYWs/WC2SzGrNaw7RzfSEPcp7BfsQ+XIrSk6MEZCKIT71FR+jhofr0a\n8VkX4ssYx+759QwXVCtjByShJSEhIWHzkV6KCQkJCcDG0meFoOWyoi1zTGTnuGSKFAeHSJeaVFSh\nCfrGSefTSnyWoF5LxB3OYBDaCuhy0rql/z61G9UE/3wPFOjAqekUbQ0ZSrOiPCfLs2Z0UiE9AyNa\nRvGI8W19GqKCp3VLmL/u+BRAuwsxJPfzd8y3n4N6byNmLm9WJ6DrDeNEwzZ2vOqLY5gGG8p61s3P\n/0zwVLx87p3xkqWXSh0eVYLNr7zv3cfrHrj8gO/np1QBZ5wSgsqJWELEoDJ+soH0u1Xp0wmmHVhP\nyGDyWiBt09rotxmMcCm64eayXk3BekGN6vihjzTMht/o0RHSDHvo/9W93Uz2nEaKCQkJCcSZfCma\nWW5mbzOzH49/f6iZvdXM3mlmP2KGoUdCQkLCi4izSp+/VlW955Wg+HclfVsI4Y1m9l2SXinpO+/0\nYHPUVGE633zg6VpL9kRkG9cnHgN3sekxeBni95YtmLbiELNyvRJLY05DXF0bKW2KpqRh6scY1FEj\nA0p0f+xUugst0lrgu4glnA2Ronbe4/Dm0Skl74PqocQnVdsGzk/3GkEVp4HKAkpwFzGWOUxeJ7Gm\nTbY+a02TZ11xzhGnuNfx+8L+onvQS/p+nUelX//1idPq/evXJUm7TafUA6Tz7bZ8fYH4wSnq74ix\niaCWNZQnHSMethYV/faSYxMcA6mNGevsLHH+bd8XWYHqweT4RDoeppJW98vO4x6y5g2iD0Yow1qL\nUQPhlFjM+x1nbqRoZg9L+kJJ3xP/NkmfLenNcZPHJb383rQuISFh03HmXoqSvl3S18n9As5JOgjh\nWAF4StJD63Y0s1eZ2RNm9gQLpickJCTcKc4UfTazPyjpagjhF8zss17o/iGExyQ9JkkP7VwInUn1\nzl8iLW1+imRWhxI7j0GwrQYoCDK7+FNSQ7nJEgppga4dwaz0hC0ny2ZC3myOqjZOsK5TQ/oVgq4z\nKqEwcJ0jGH2OwHD1EBA9Z3nS6qJGF/0a2i7+qvGxF4+XpwPvjPEWqPQMAcugb030IzED36tFw9nB\nMzeO141KP88CfVHP1z+2BYKnhzCcHYyc+jFd0SDFj7Yqit1FOttsxiBp3HQo6zlKqS5RC2U6Bw1F\ngH8BKhtiqHyYQKnuIvUR0z41qMycmCnH3pZyiRo5PY8EmIX1Uzl5jEKf0bS2jdo+iFJvy6cgZri2\nTcSZeilK+j2SvsjMvkBSU9Wc4ndI2jGzWhwtPizpffewjQkJCRuMM0WfQwhfH0J4OITwiKQvlfQf\nQghfIemnJX1J3OxRSW+5R01MSEjYcJy1keJpeLWkN5rZayW9TdLrn3ePUgqroFg4hiwmCGqFElcW\nDDaOFAbuInMcoz5AUDMCc4sT3qcIvKZaCSpTgIY1+n6uRX6rqjdy9qIcMbUlAsznlHzpNwuV+xAG\npS2UIV20q+MUCABeQkFnvvf25XPHy/UDXPQpubBjzO8WiKbKsN6uV1z9IDhlzqhuQgmlKjqH+evh\n0mly3sZ6BGxffsrbe1T369vtVoEOnHZot5xKz6HaszTuuOb3cIhgc6QYq0DodwvTMGWskZLVMKVB\nRxvfVP3SpzramI44Ec2AXHL2OXOoZ6iXs8qDXrVDkmjiM2WgP+7FblGp1bmdqTHVi4Yz+1IMIfyM\npJ+Jy++W9Cn3sj0JCQkfHNjMV31CQkLCXeLMjhTfXyxV6jCql4HSJWhwk4oy65vE+h6NLQ9qzSC4\njeukt77fZOEHZH2PBsqn5hnUwjFtpGANFoN2DXZVdoBAcxiFCvS53nBqGobICQYl3qLXFFDG3FqW\nOckedDOudsf5e6tPmg4ltg/FGzZWhikDGyAIHYa282bsRxioZiOoqS3QbpSDHU1hqYVmHS6cyp7b\n93t0hOkDlKjRIuYqd5pOmRdHoMwL7+cp4uwbWt+hzczXt6Fyl01vyyqouqQVHR7DOaZ6CkQWzJon\nbHOPsURfzBAwn2NqqHWAui+rWAjUpQnIww5jX55AWe/sxXo2Kfc5ISEhYfORXooJCQkJwMbSZ2Um\ni5Qj7DMK2bnPpOu/CeOS+dEVVeiOT4Ra+6FhR3GAXNrxwrlft+l0c4xw20YJqgLLpnLg1KcZy3zW\nj3wd1cwacmLrW5ClaSTdPuX3jrVmUG6zFYOnG6DJQreNh1BWkXu7g4DtOWrXBFinUaVkjZAZ1NrB\ns1UNlPIAAdtodg/u4UKubnvo1HMI27EWaPoU93k09G3GDQSPH1b3orzhbWqihgtzsguUez2CRVcO\nul8iqH0O67icJV4j/Vwg6Jru5TmiFmhXV0fJ2GLkx34u8+epmYPjMw8dCQa1eMzp0p+zDDWHwhRz\nKTjPYFLdIwarbxLSSDEhISEBSC/FhISEBGBz6TNQv+T2Uv0pOCHoiaHoUzMGNS+g4Na6TWwLx2z4\nTtQFxRW5qiyDSmumEaTeZfvWW7HEeQqUOM13ndblsKUS1MrJOb+eGqYBamDPxRUPdl4hoFgRg5Sz\nIz9GA2r2FIHmpHgHQy9PeqFw261R3zuMlm6rqOFJwbbC5g2B6XVMAUyf9tKjrQt+HkE5Hc5ge4Ug\n5A6U41Gce7g+8XY/hKDmBQKjb+21CrYHyoqA+Qlufw56Pomu4Q10A6n0dMf7swOLtuYhcvkL32YH\n5WFvDH0aooG5l0UdqnRUyFlqdgG39UutHT/PxK96Hr8rtqHycxopJiQkJADppZiQkJAAbCx9tjxT\n0Y68BSIaraEGct6yg2JMWcyVbdacVmTI8YXxtA6gbOZQVncCiijRaQkB1nVWg0KA7ahZnX/BgPIF\nzm22GGkAACAASURBVIPDLSAtUnE+nDtN20bA7om6zqiZXD9XUc8F6v42kfu7KrIkSePMKXAd1CxH\nMPY2ama/t//s8fKDtfVO2cthDJjv+DmXXW8f0r3VGa+3rupfdyftKdzKTtBDKq2w9KrHkIJGBy7Z\ncB7vDDCVASW2v8W621CFce8GCOqfbcG6LJqJH26hdrX51Ih1QO+Ry1xH7nPB9PC2r68z9/0Gcs+R\nqj6LUxW9p3HPP8TvzwxFwerbdGlHhP0GIo0UExISEoD0UkxISEgANpY+qwzSytGY+cF1OEtD3eQ2\ntRi8vQClzcDBrwQnc3OonDno9gjB4BlcqOugWMuZ05MBqmEdx+CCgi47cImG87bh/PWaU882qGzI\nne7MQLftklOibv1Wd+yDAy8WpW2XUAuv56Wt7p7/AQfnJejjrnm7rv3mM95GBF63VucfeVsnF6Hm\nIxLgaN+V0GlgFTFfrKEvCjp1z1kZCxZky1vdqfsDv9C843ng2cyPsX3IPWDRZqCY4LLXS297IwZ4\nN5Gbv2zBvRw1mLcQ2cBrmHAKBC0pFrg2uHn3Zn6uxopVf4ir9gzk5jzN4sinAOpRZc+yzRxTbeZV\nJSQkJNwl0ksxISEhAdhY+lyaNI3Uoqf1gczL5ik+WpHCLBGAO4Bs10K+bxO5zDPQ7W7LqWndUKAK\n+aR5AcID2nItr3jNDnKMRxOnYy0EHYNUaX/J/GRYkcEuLTtwWn90ziljb2VphXzoCShwE7WJ56BV\nh/vOHxuYmsih5h8eOQ0PcMoW6HMZc6XLnu9HJ+sM0wRqYlpj4Mcb1p1WbsHVPCx8fYZ62GPQcMXp\niRJTKkXHpxQCpi+EglbZdeS+I/B8gZrR4aIHXp/LfHmlKBuC0Rdw4d7LfAqgN/VOn+DaClDpqwg8\nJw3ewXFO1KOO6jonDpbIjYYxuUrkr89iAH5YptznhISEhI3Hxo4UM0n1GFA4lv9qnhg1YtRUIs3O\n4i/xCStZTCobUrE4atpaYOSJni0xMV9gJDBBHFiO4McPV1UD5Un5KIwySIlJ72uZ/5zv9Vz0OITq\nUOuhPOUOYglRBnMRR7NzjHYanGmHsWlrilSxkY+IhjhnvjxRsMbbPoH5LWISJ/Wqfxe4VywreoQy\nsW2IVZOet6WB/p+OvV2MTd2fuHjS3vX1rdVIGMLFEk9AwD2cwwFHMKUtn/X71cYDUKK/Fhgpt0JM\nl6ONzb7f20kPjCDzJ6CBmMnhzK+zg9HsFP04XDrLOCf0+UqkAiMJYAQlmRQMbEdF1RclHYk3CGmk\nmJCQkACcuZGimT0pqa8qwGIRQvhkM9uT9COSHpH0pKRXhBD2TztGQkJCwt3izL0UI35fCOEa/n6N\npJ8KIbzOzF4T/3717Q4QTJrH4b3R2HR/snb7DKLKtBOpEiblZ1OnTwtQVgo3Y6YTYka7D/NTUjIO\n0+swLl3t+uG5lxK90fN2N0HB2iM4+cz9GJdzl2AmNJzFhPkRDHLLUF1rZ4JpBMRuNgocD4LO4ALi\n6mDgOpg7DSTJ2i5A5UnJomDQhkA1PHDhoJj4sQNq1zQQnVcHC6UzUR8xoAVErzkEk0Ye647gGAEu\nRRQu2nUY8SKucH4ZbkAQiRqogTLFs7MStbIAQQvi23wE4+OW0+dhtn5qgs9QHXGKA6RLzopb992a\n8A75+ffHCEi94bG5Y6uuoUxCyz3FF0t6PC4/Lunl97AtCQkJG4yz+FIMkv6dmf2Cmb0qrrsUQlil\nQlyRdGndjmb2KjN7wsyeGE3WjwgTEhISboezSJ9/bwjhfWZ2UdJPmtmv8sMQQjBbL3uFEB6T9Jgk\nXdq7EFaxb7zKGdTFDGUzJz0YcV6r4tfGiG9r7LhSu4S7Cm02l6BjfdSvyDtO5cZQnDPQqjbiCjsr\nCoOMtF2oj2PUAWW5zxrcULItlNJEgZE6LXZquObIG+cjurH4NRumF7o9p4kzlHIdDV193V6ib2vr\naZahMXmMiayhFksL9WJC6de8hFEsXXp6qL9Ds9Q24jr3c6TZ9aEoR/U7NNgniCll/Rvcw7KO6QjQ\n1Fbwe15iAqEoEVcZ3WYabe8rQ5uU+baHcNrJMa2xwHN2rQYlusYYXEwfFL5vf1T1BacDxujnE3Vh\n4MzTjtNRtTM5pnr/ceauKoTwvvj/VUk/KulTJD1rZg9IUvz/6r1rYUJCwibjTL0UzaxjZr3VsqQ/\nIOl/SPoxSY/GzR6V9JZ708KEhIRNx1mjz5ck/Wis/VCT9EMhhJ8ws/8q6U1m9kpJ75H0ijs5WBki\nbYGZKctWTpsI/KXquFPRsA6U3TlKT7Ynvt8SFFQI8K3BpaZEKpah1scS9LxPrhzb3YGjSXnVad8C\n1Li561S2BE1voA5rFyrvETK+GBy9InjjulM9A5WeI2B8d8drd4QDVyUbObm5Y2/qfWFw0kE3ar6o\n+jfgnO2WX88cy4tTSmuOEMjcgMPQIDglJOUrkNJYHrfdj91Z+L2amd+fERRkarmkm0s442R9Bj77\nvp1ZbAuerQxmwhmCpwOes6sFaHIDiZ6o0VNr+vpLCP1fHEHRjo/UArVsm3uo/4N212Z+PaM41bKZ\nodtn7KUYQni3pI9bs/66pJd94FuUkJDwwYYzRZ8TEhIS7jXO1EjxxUVQiDnKF+e3GqhKkoGSTBdO\nK+pR3Sx7qIWB/UpQoBx1PLQFY1PQ1+XCty9nNKV1ujnG+Z9aViruhTFuD04TEG1UHvkfzHFmjPC0\njbzloX9wBH+UaSeqv3CggU/uiV/PG895XH27QB+hdkkNQeKTkVPvNvolIM86n0VFsw3VFu4+TQRd\nj6dOh6c4dobA5/7S14ORnqDVQg57Fst11hH0HlC+NR94W0cZAv3pOoMg7Hrw9h4V6+n7anEKBT+D\nIW6z/vxjljnacqGDkqT01cXzx2iJ5kEMwn7Ioww4i0P1e4GppppVfWQZj7Y5SCPFhISEBCC9FBMS\nEhKAjaXPFqR6tEGCiKgcvwM3pp7bWQzIG+K2UFmZM0tXrC5yhUfd9XSihoDdORTFDEpsDmLTrVX0\nZL/j/LU7pOUU8pNZZ6TvlHHeYL4taFAHebDgStkqzXgLaib2m4JLB9SWabO+iO95glZy6uH6Aio6\nphKKGGw8h/q7gJo/glJ/dOQ50Xtbfo8oh9octl904sUxaZI6i9ZoLDtimIIY7PgHS9TIac8Q7F34\nRTNgmwax15rej9O8On/HEIyNpp5Ge1sZ6qwcouZOA3v3/JhjJIWfn/j0QfHQBUlSH4HhQpD+GNMH\ngQbKMUje8s0cU23mVSUkJCTcJdJLMSEhIQHYWPqsUspHFVXq1xA8vWRJSCclLBXZi3nGGfJqrU8F\nEQCv2UWQ8gzUg4Hf2ehWmi5JRdOPuj+vAqINudGkLyNQzG3ULplDOR6TGhtya0/YiftiI15r56of\nuz51ZXuWQ4oGRrCiYinRq6VT3A76cVhD8DKo9yyGQaPMioag7HNcz4msXuSbF0sET2MqYXqJVmNo\nizA9EfPNaYc1R842IxUMUykBijOTBEo8ZwvQd9brUYw+aAS/olbu92qGGjJLOMOzzg9v4rW5TwcZ\naPKF4FEJBgf1LPZXY+rbHoFK19DW/tL7ahKTGhbl+mf5fkcaKSYkJCQA6aWYkJCQAGwufTZXnZfg\nW1kHVAVO1eUcuaqrvGWUngwo99nyVFHVodCWpEnM252iQNYBlL7LHjQ7znyb0YqqgJ002r7t1ghK\naAt0DIstOC8LzsqLgGBfFF0qohI+PaF/OjpzBiP7NpPgywtMMTBcflRbf8xxA1MZkW7eEBR0WJ41\n696fnZrTwQKFlrLlejrXPPL14zamFWCjVZtU/ZJDnc7hvD6oYToEec35CBEEUGNpL8bIAsRaH5dE\n3Wn79ZRPey55DSrzbOR9i9kIzba9X8633Km9D3s5LtYwxTCN9mqkzPk2kuOBFmzlV3WuVgHvm4Y0\nUkxISEgA0ksxISEhAdhY+lzm0nS7Gud3S1fXOlPk5ML4qQPn61WvzMdwIe45TanB1ZgmVidcuBHg\na1CWJw87VWrAxikHlVzl5xZQNmlLlSMnm+cnNTu2TZO0AK1slgi2Hvj1FTFveIkjTpl63XZa1Z2t\nL5y0AK1sDP04Rcf7/yCDgzb2XUUCdHEflnXfLwOVzNY7h52gldklt1QbgO6P8/UUu9OvDlqgnw1F\nzrIuXL1B62coCsa+raNYGupfqSk6qFfzMM/KKfPFXTiWP3XjeHm+4+s5fbNEQS+UdNbF0PE/2N6h\nX5PFqSS2ezyG/Rit6GjFFouShQ31DksjxYSEhAQgvRQTEhISgI2lz9k8qH21ogLleTo/01kZKm4X\nQbPRwXiI8r5aOu3r7jg1WcIFunYAaogg2Rx5yMzPHcBBO8zQrhiePAHteTpzilVH7jNzVXPQbQZ+\n85zDlv/RRqEnRfpYQ4z2bxYeDPxhoH3Npi9fR/74Qc1VzAd3trQOdVBixNQrj3W1cxSu4vIC94KW\nZgGceQLXcNLkDL/9k+DUvxdAVWNfG4OxEU3QuOH7bbWQb45zLpEUP8LzsoXk6wyc8yVZRfHLvq+7\nMff7zCD1fL2Ar62a38P6kW90teEcey+H2zxU9NqNapt54X1VDjDtgykLG8HyLq5eXz7u/kcaKSYk\nJCQAGztStDxTFgWJ7sJ/c3OaqMLVRBBGVqJKB2IBHWWOUAayi0IjTMUjaCy6gxg7xqENYERaZNVt\n2Z/4KGwfdUamS28rjVFOChe4TjjMGOLaAkYt5cqIFqOwhyfe1kXDj7Ho+2T9EEPLAoGSk4wjC3/M\nWgPE9c1vTVdbYFSdQaBqPufn5Ch83EL6HTpgjLS0Mvd7RMeaJWqDr8ZV9RpMcwOYBJ6bJeJOJ3hu\nGhDg5iixejjzdL3zucebtqLAMURf1TOMD5tQlLoQ9/DccACZ18kgvI3Xl37+9nV/jlb78pxhgjRH\nsIC86aPqYYz1LLPNHCqmkWJCQkICcOZeima2Y2ZvNrNfNbN3mNmnmdmemf2kmf16/H/3XrczISFh\nM3EW6fN3SPqJEMKXmFldUlvSN0j6qRDC68zsNZJeI+nVz3egLNYJmSGwrQmT1ykmxsGwjie16RIy\nAx1qTNa7lISxH2+6A5NP5FntBJ8YJ93bzp22jWKK2DlzQaeB+iMZBYjMz0kRaQ7D2xkoka47JWru\n+W/LtKwuuoOYNYFqjYXCMHBibYK/lVvet02UZ10gtZEUv4XUwZVRy6wDA1Wk3M22MQWC6+d9Gda9\nMSwJ2mPBEgwDCkyfNGK6XhelaWHiI80Q94naOjmcYkZQgLKpr+/WqRL54iJSb4OgMwMhLiG0CSVm\nFzACbpFuD/xZaNbR/6gBY3j+Vy43BmeggC+CNdBveOSLmPJnp8SL3u84UyNFM9uW9BmSXi9JIYRZ\nCOFA0hdLejxu9rikl9+bFiYkJGw6ztRLUdKHSnpO0veZ2dvM7HvMrCPpUgjhmbjNFUmX1u1sZq8y\nsyfM7IkRvAATEhIS7hRnjT7XJH2ipK8JIbzVzL5DFVU+RgghmK2PkAohPCbpMUm6vHchzGJM1gKx\naTOYfzItj1Qgj+peLXcK0pshpg2qZVaA+0AJzRHvVW4jzQ/GpXUovTlcerpRfc5hIFvCwHSGsp45\nymCWWGa4HZXTOuL6wiEo2aTqo0nPKTudbmqYMqDTzA6U/euYPmBd0fq208cJYvwGh07PQ3QbykFT\nxyiScjSGglv6FERAjF8N/cxExBJqdQvFYxrbUJpjE8kI8xtQp8ce90d3nz76gjvvwvlGUN9zFAxa\nxU8uoRTzoZyjrOg2LJhyRDAGGBhzGNCQP6/zprd3OvR7Xq6eC7j7ZOZ9cuJZRfnac0WloOd21sZU\nLw7O2lU9JempEMJb499vVvWSfNbMHpCk+P/Ve9S+hISEDceZeimGEK5Ieq+ZfXRc9TJJvyLpxyQ9\nGtc9Kukt96B5CQkJHwQ4a/RZkr5G0g9G5fndkr5K1cv7TWb2SknvkfSK5z1KkBRVtQC1kO41NZiM\nTkEVQnSVmYPqZR1Kkb58eNXTss7vuJq72IFC+ZxvM4GZ7OK8B/KWUyd8l+YVPZySjyGQWXBJaaA8\n5hLXE8DIFqCPNGsNa+rFwFBGGUxwDfMLTQR952jX+aH/xrJezLQNTkgVc9ep+qgf++iG07tDRKZ3\nur7t/BCB4aBwdQTbNxGE3QSVp8nueMR6JFW/GBTvCWjybOzrazRtRb/sIJ2vhbK2iwUCppHauQqw\nb2IKpgU6vIMUPqZzMsB+3MF0ANrF+jaCsewCpU/z6xXhZjD8soPPcQ11uARpvplB2yucuZdiCOHt\nkj55zUcv+0C3JSEh4YMPZ4o+JyQkJNxrnLmR4ouFEILKSJUzUA9bwNWEGiVqrUwjbabK10SQ7Gzs\nOl+94dRnvEWK7Sguu+HpcuIcZ//a9ePlh9bU12ixHCaCehsP7/ky6PPiipcVHYG+t/dBk8/7YgOO\nNepX29ShMtdaVDlRQwbTCgGGsww8zkpvVx31PWpt768l9l2Z6Abk7+7CDYgFVudbUJlHfowWpg+o\n7GegzHUa8WLKYtWWcNFpegYFVwu4KCEYOoOBbmPg5y9PGW7kiGgoW9W+4wWep8X6ryT3Y2nW4ZSR\nFQw58MUMX/ODOlyCzlXnp1tQQNB3f44ysag502zGe5hqtCQkJCRsPtJLMSEhIQHYWPqc13Jt7e5I\nkq7sP3e8vonc1tYQVBoKXCOvtskPnTIswLvqpLWwCyuhMheX3GSVdOvZwb7vi5+ko7lLynm9Ov8C\n5UProEYBVIoCda3hNKj7lAc706IsdNZT/GwVYA1Fer7vxzjscdrBG15r+PIAOeaIY1cJhXYy8D5q\nUQmPprg0sK1PEciNaxtDWT1AXnkLyvl87n1eL2EyDLpdwnB1ZTLcQD+znszQfL851GQd+mJ/D/0/\nYR/BAg21TmhQvAIzsbZ6Pu1iCLDWMz5N4vEB0riDwHw+W7iOIkcSQhSUzw2wMS5tgqiNlfGyJK3y\nGJYbmvycRooJCQkJQHopJiQkJAAbS59DCFosKj51vudB1YOh07c5qF+j58GpK+WwnCGbFEG9AaUf\nWW5z3nDqNXzm2tp27RR+ngLKcZHDGmvF6lAyVFBwsz5oPZTVGvKjW5d2vF3PuV2YDpC3DeV0HhcX\nqG1C66rGFVBJxPGOL/kfY+SECwHbYGFqB7hmTxHUHC3AagbFFxJq81k/9viC37eLc9BtSL4syTmF\ns3V7B9ZtmNZorKILQK9z0OQtfFX2O34v6DbeGyGHWMT6YOci5hPPoSYzT3068CmV2hZK4172ezt8\nxsug5gimyDFl0kBe/WGBaaA4TTPGugXctLNTxkzTWCY1LDcziDuNFBMSEhKA9FJMSEhIADaWPufB\n1F1W9GC2WF/EqNFxSmKHiPbdqahX3kZQ6wA2V1CcAwKcWZKyRH5wDapoXkOAL9oyg4xcRHV7ATqe\nI1A2m/u2DdDuJcsYISc5e8SnD6iilkNc8+ryUDiqPsbHF3zKYNZ3VbpxDRQTUxAQPFVALc9QKjNA\nXVecyjgwFAVrwCUaSnHnKdhfwYU6Q2A+n+xO4RTbQPky5E2vKGSB+9YIcGkH1S7qiGBA4bIR+raN\nfZcjdAbcxg8jbW7CIm6yC6rbgkUbaO144P0/RClV5sT3LnqAP+LOhQq3CtFfbtDGdMDQz1kgUsMQ\n1H38XQiJPickJCRsPNJLMSEhIQHYWPocylLzQUWzStgh7QkWVFAFm+c9UHYc6eEY6l+BPOF8ALWw\nBooLSyUqoU3mGAN9+Hs1USVpFKtBlbBhnnizdeEARYlYiGnHpwPmqA09a8HGDLd82Pbz16OKWoOC\nOdxe/5vZMe+r2ohqNdRsJt9i6kFQRVsokpXHqY7W0te9L/dIAYi8qiP3mVMWS6j1LRRxmgZXrtlH\ngk1XFk8wRxGnE7QX9LFzgAJhu37NtROX7Pt24PA9nKCD42EGcMbOqBrDums89GfR8FzkUNMXuM9D\nHLOJPHR+4S0eP+CaaVHGsuhZDTn+5OkbiDRSTEhISADSSzEhISEB2Fj6XJorinWIZCUoM6laQK7s\nKiAX4qsMRZZae57XrNxpxbB1q5O1JA0nCIJeOCfpjeEIDbrZjk15V8dVRoGy1/Lt42XEJWsnOE2j\n1VQ+c0qUoWbzXg3O19FGDUxLNThcl20/Xg2B7KxHTVaV7Xi/LBp+/vw578fGzq11rRegrOdz//y6\n+d0w/JaPu+tzuZkrXqt7v8xGoNKIBFiJwlNMO8BI+/ieSFKOdhcsKAaFvG7rv1qh5wftxzrV/dKP\nsYtbblDHF3CGZ8B+u+5tmULNb6KOeB1VzBgJMYwF3Yb/f3vnFyLXVcfx7/femdnZmWk2u0kaapLa\nFkslL7YioigiLYJ/SvVBpMVCkYI++NCCoq3vQn3xz4MIoVX6IGqJiuKDILUPPgVjK4hNi7Vamtqm\naZPN7s7c+Xfn58M9m/PduNQkbDIzJ78PhMy9M3PnnDl3z5zv+f0biReC7EfIRgNqUnxrOaske57o\nmirNXjmO41wmPik6juMIycpnkuflUSkxzrlYJSdifaTEAfeCIikXomWvLw627dMirCWlU3NffP1G\nJnpL8mg1z4l1U8x7Y5G4wyClbzwlFs92TJm9yih3MpFG/aGkiBL5mIkTeoNqr41yb6NVfX62Jo7u\nYolslJKFuxP7w7X/jaUFgIlYUcdllNuNXSrKIhtB12WteO23RI83u/pqsZD2xBlfnI1VBpZSJlyM\n/KDG+W7W2m7GN44yyWoufyq5ZrWWTOGTQupxS+4uG8U3DGWbJtuUpGLl7YpHwqJY89flftJUcE1x\nWF+SYl2L/dgurZJ+eihx8IHOYnxfJoHqI0ltRhlDdMIWQO6Zt684JG8j+Vf5t0byYZIrJP9A8h/h\n/+X/fzXHcZxLZ6ZWimb2IoDbAYBkDuA1AL8G8AiAp83sMZKPhONvXux1axuyeS7n+4y/fmc6sgEf\nNsm1XsguSVRa7BKjy3VSo0PrgjTjhnpdQvTe7sQlT7OMv0ndelwJ7O6H94o9R93+9pfij6gLP1mE\n5RK2SPGfHEmcV7+Mn3mmV60glksxnMhCQDfoa7IiM2n30GIj81VpcHRr3PIzbLLSYAhd1BX2otye\nRS2OT2so2WjEoNSQ71Pv7KHUlzG5AwZiGNlMqtOVVWhDjGLnJAnwLsl0VJSyshPDiCzgQTFuQXwC\n20X1ojOrkql2izqJq7qe1FbZvxxVg/qV5mviV7kiYySf2RJj2CAsEBs1CWeUe0L7UJObYZCH0sFI\nk5laKV7AXQD+aWavAPgsgCfD+ScBfG5qrXIcJ2lmeVK8F8DPwuP9ZvZ6ePwGgP3bvYHkl0keJ3m8\nNyi2e4njOM47MlPyeROSDQD3AHj0wufMzEhuu3I3syMAjgDAgeXrrRGSa04kq02hNS4RJW5LkouO\ngqHlXesStvavWI7UDkVdOxlLTROpWdEZSS2OIsqaFdnI7omU3bsQNeZ68JBsqR+ldPlcETVQJ4+a\nWctq2r54vi5lSylZUBoSFrc3q14/EX+4uvimlSKWSil3ibhHD8o2waAu/qCSSaYmEjOXML9eXulz\nzfozWo39XNToOAmt09EspUytRMih7Iq/pdTigRgP+sGoNJErrpoknJXsOr1ctibk83uSpWhPM45n\nQ2IUW13pSLjMDcvXx3bIZ240pF6LlCEdiT+iGtEG4le52IvfXSHJgmvb/MWX65ocWOqydMU3VNYY\nrWH1XWi2ppSY1ZXipwA8a2anwvEpkjcAQPj/zam1zHGcpJnVSfE+ROkMAL8F8EB4/ACA31z1FjmO\nc00wc/KZZBvAJwB8RU4/BuApkg8CeAXAFy7mWpuWRoqfWNYS/y3xDVyS+hr14JPXFNnT2BvrYpRS\nblP9yhri19aVMpyiMJGJDFsSX7JJHs/vnlTXr2vJzmx7W19XEo42JNOPhnZp4lgTyVOKPK0vVZ85\nkbKias3uivk5Fx9ILfFaq8f+LGyI36VYeVtbErKIhTr4kg4mGs6oVmapXSL+pXXRyaVIX7UsqyTM\nZMzV368RQgG7oygl24x6tA/1TojvKyUscEHqz0B9E8V1YGFL6F7VxrEksFWnQrV+F5L1huJ3O+pv\n779YSB0dLVs6aopfZ7i/SxlDShLgpjh7jmTLQB0eUmTmJkUz6wLYc8G5t1FZox3Hca4osyqfHcdx\npsLMrRR3CpJoLFTyZ30YZeLkrMgzlUHqbDusJElfJFhTrHyF1E6pDeI1NEuJhvDlUmJya10QkVVi\nic02Ha/bGp4Xn6fU+cilRsxYsvFoUtTyXOy/OjJrEtlakIGFlNvMxOJsIs1q4oysEg/j7bMEaVnV\ntSImjh0vxf616p3Qofi2gUi9eiFWca15I2GbSm1FsvRI5p1MzK+ttmTuDU3PFzvnT03G8YM6Ek5Y\nisTM1QArbS8kMKAt41X0oxm3tyt8j/J9apaisdxzbZHApdi8raHWfAknVem7IPelbMOsB2t5S0IS\ntS4RJ+Lcruc3a6nmaa6p0uyV4zjOZeKTouM4jpCsfDYz9AeVbMtEVuqvQMkoK8ZNkbXBgXnvUDKG\nqKwspBaGnNc41CKL5zsTDUqODyeSoHRDymOirNq9lEW7tVaTrLWjNDTJZKJJP8fDeL216+JrmlLv\nsilW6Y1QKjP7j1jTNSZWQqI100ytkO9WrM+5ZBXSWjOFZNhRK+bGqJKVEtaMusRsU+R7LlsQo6h2\n0YdYYiUOW7ceINb/0dko5TeT9U72xe+8L/dHS5LD6nbERL8LKck6Ecf4nsQToy0W9eDIXe6JpmIT\nPb4o2y7QMj/iQZCLxM3l5jLxLBitSLlbre8SPBRykcELUvSnHMk+hWyl9FqhtkuiS6pEu+U4jnN5\n+KToOI4j0CzNBEAkTwPoAnhr2m25CuyF9zMl5qWf7zazfdNuxE6T7KQIACSPm9kHpt2OK433LHoJ\nPgAAAqhJREFUMy2ulX7OKi6fHcdxBJ8UHcdxhNQnxSPTbsBVwvuZFtdKP2eSpPcUHcdxLpXUV4qO\n4ziXhE+KjuM4QrKTIslPknyR5EuhLGoSkDxE8hmSz5P8O8mHwvkka2OTzEk+R/J34fhmksfCuP4i\n1POZa0juJnmU5AskT5D8cKrjOQ8kOSmGmtE/RFXr5TCA+0genm6rdowxgK+Z2WEAHwLw1dC3zdrY\ntwJ4OhynwEMATsjxdwB8z8zeA+AsgAen0qqd5QcAfm9m7wXwPlT9TXU8Z54kJ0UAHwTwkpm9bGZD\nAD9HVTt67jGz183s2fB4HdUf0AEkWBub5EEAnwHweDgmgDsBHA0vmft+klwC8DEATwCAmQ3NbBUJ\njue8kOqkeADAq3J8MpxLCpI3AbgDwDFcZG3sOeP7AL6BWMl0D4BVs/PZfFMY15sBnAbwk7BN8Hio\nU5TieM4FqU6KyUOyA+CXAB42szV9zio/q7n2tSJ5N4A3zewv027LFaYG4P0AfmRmd6CK198ilVMY\nz3ki1UnxNQCH5PhgOJcEJOuoJsSfmtmvwunUamN/BMA9JP+NavvjTlR7b7tJbiYITGFcTwI4aWbH\nwvFRVJNkauM5N6Q6Kf4ZwK3BUtkAcC+q2tFzT9hXewLACTP7rjyVVG1sM3vUzA6a2U2oxu+PZvZF\nAM8A+Hx4WQr9fAPAqyRvC6fuAvA8EhvPeSLZiBaSn0a1J5UD+LGZfXvKTdoRSH4UwJ8A/A1xr+1b\nqPYVnwJwI0JtbDM7M5VG7jAkPw7g62Z2N8lbUK0cVwA8B+B+Mxu80/tnHZK3ozImNQC8DOBLqBYs\nSY7nrJPspOg4jnM5pCqfHcdxLgufFB3HcQSfFB3HcQSfFB3HcQSfFB3HcQSfFB3HcQSfFB3HcYT/\nAmpLOHDbX1i2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fec14fadc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[9], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[138])+ \",\"+\" iceberg_probability : \"+str(pred_valid[9])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}