Model_03-Adam-k=10-amsgrad-0001-15p-Copy1.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.layers.advanced_activations import LeakyReLU, PReLU\n",
    "from keras.optimizers import SGD\n",
    "from keras.optimizers import Adadelta\n",
    "from keras.callbacks import ModelCheckpoint, Callback, EarlyStopping\n",
    "\n",
    "from keras.datasets import cifar10\n",
    "from keras.applications.inception_v3 import InceptionV3\n",
    "from keras.applications.vgg16 import VGG16\n",
    "from keras.applications.xception import Xception\n",
    "from keras.applications.mobilenet import MobileNet\n",
    "from keras.applications.vgg19 import VGG19\n",
    "from keras.layers import Concatenate, Dense, LSTM, Input, concatenate\n",
    "from keras.preprocessing import image\n",
    "from keras.applications.vgg16 import preprocess_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data Aug for multi-input\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "batch_size=64\n",
    "# Define the image transformations here\n",
    "gen = ImageDataGenerator(horizontal_flip = True,\n",
    "                         vertical_flip = True,\n",
    "                         width_shift_range = 0.,\n",
    "                         height_shift_range = 0.,\n",
    "                         channel_shift_range=0,\n",
    "                         zoom_range = 0.2,\n",
    "                         rotation_range = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here is the function that merges our two generators\n",
    "# We use the exact same generator with the same random seed for both the y and angle arrays\n",
    "def gen_flow_for_two_inputs(X1, X2, y):\n",
    "    genX1 = gen.flow(X1,y,  batch_size=batch_size,seed=55)\n",
    "    genX2 = gen.flow(X1,X2, batch_size=batch_size,seed=55)\n",
    "    while True:\n",
    "            X1i = genX1.next()\n",
    "            X2i = genX2.next()\n",
    "            #Assert arrays are equal - this was for peace of mind, but slows down training\n",
    "            #np.testing.assert_array_equal(X1i[0],X2i[0])\n",
    "            yield [X1i[0], X2i[1]], X1i[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Finally create generator\n",
    "def get_callbacks(filepath, patience=2):\n",
    "    es = EarlyStopping('val_loss', patience=15, mode=\"min\")\n",
    "    msave = ModelCheckpoint(filepath, save_best_only=True)\n",
    "    return [es, msave]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getVggAngleModel():\n",
    "    input_2 = Input(shape=[1], name=\"angle\")\n",
    "    angle_layer = Dense(1, )(input_2)\n",
    "    base_model = VGG16(weights='imagenet', include_top=False, \n",
    "                 input_shape=X_train.shape[1:], classes=1)\n",
    "    x = base_model.get_layer('block5_pool').output\n",
    "    \n",
    "\n",
    "    x = GlobalMaxPooling2D()(x)\n",
    "    merge_one = concatenate([x, angle_layer])\n",
    "    merge_one = Dense(512, activation='relu', name='fc2')(merge_one)\n",
    "    merge_one = Dropout(0.3)(merge_one)\n",
    "    merge_one = Dense(512, activation='relu', name='fc3')(merge_one)\n",
    "    merge_one = Dropout(0.3)(merge_one)\n",
    "    \n",
    "    predictions = Dense(1, activation='sigmoid')(merge_one)\n",
    "    \n",
    "    model = Model(inputs=[base_model.input, input_2], outputs=predictions)\n",
    "    \n",
    "    adam = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-8, decay=1e-6, amsgrad=True)\n",
    "    model.compile(loss='binary_crossentropy',\n",
    "                    optimizer=adam,\n",
    "                    metrics=['accuracy'])\n",
    "    \n",
    "#     sgd = SGD(lr=1e-3, decay=1e-6, momentum=0.9, nesterov=True)\n",
    "#     model.compile(loss='binary_crossentropy',\n",
    "#                   optimizer=sgd,\n",
    "#                   metrics=['accuracy'])\n",
    "    return model\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "===================FOLD= 0\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 19s 811ms/step - loss: 0.6997 - acc: 0.6101 - val_loss: 0.4851 - val_acc: 0.7593\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.4994 - acc: 0.7411 - val_loss: 0.3702 - val_acc: 0.8333\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.4584 - acc: 0.8022 - val_loss: 0.3376 - val_acc: 0.8580\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.3839 - acc: 0.8394 - val_loss: 0.2862 - val_acc: 0.8889\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 14s 593ms/step - loss: 0.3448 - acc: 0.8565 - val_loss: 0.2684 - val_acc: 0.8951\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 14s 594ms/step - loss: 0.3231 - acc: 0.8616 - val_loss: 0.2634 - val_acc: 0.8765\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 14s 593ms/step - loss: 0.2637 - acc: 0.8877 - val_loss: 0.2428 - val_acc: 0.9198\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.2704 - acc: 0.8878 - val_loss: 0.2464 - val_acc: 0.9074\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 14s 597ms/step - loss: 0.2227 - acc: 0.9117 - val_loss: 0.2313 - val_acc: 0.9136\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.2326 - acc: 0.9070 - val_loss: 0.3403 - val_acc: 0.8704\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.2562 - acc: 0.8917 - val_loss: 0.2467 - val_acc: 0.9012\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 14s 597ms/step - loss: 0.2200 - acc: 0.9084 - val_loss: 0.2135 - val_acc: 0.9136\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 521ms/step - loss: 0.2658 - acc: 0.8858 - val_loss: 0.2313 - val_acc: 0.9198\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1902 - acc: 0.9244 - val_loss: 0.2561 - val_acc: 0.9074\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1963 - acc: 0.9162 - val_loss: 0.2413 - val_acc: 0.9136\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1903 - acc: 0.9139 - val_loss: 0.2838 - val_acc: 0.9136\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1817 - acc: 0.9337 - val_loss: 0.2206 - val_acc: 0.9198\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2182 - acc: 0.9111 - val_loss: 0.2380 - val_acc: 0.9074\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1759 - acc: 0.9304 - val_loss: 0.2182 - val_acc: 0.9321\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1846 - acc: 0.9245 - val_loss: 0.2430 - val_acc: 0.8951\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.1820 - acc: 0.9252 - val_loss: 0.2011 - val_acc: 0.9136\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.2137 - acc: 0.9094 - val_loss: 0.2141 - val_acc: 0.9198\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.1851 - acc: 0.9335 - val_loss: 0.2283 - val_acc: 0.9012\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1638 - acc: 0.9381 - val_loss: 0.2801 - val_acc: 0.9074\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1705 - acc: 0.9292 - val_loss: 0.2149 - val_acc: 0.9074\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1678 - acc: 0.9268 - val_loss: 0.2254 - val_acc: 0.8951\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1511 - acc: 0.9414 - val_loss: 0.2350 - val_acc: 0.9074\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1565 - acc: 0.9357 - val_loss: 0.3654 - val_acc: 0.8889\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1560 - acc: 0.9356 - val_loss: 0.2104 - val_acc: 0.9074\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1296 - acc: 0.9525 - val_loss: 0.2626 - val_acc: 0.9012\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1566 - acc: 0.9351 - val_loss: 0.2234 - val_acc: 0.9074\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1732 - acc: 0.9291 - val_loss: 0.2251 - val_acc: 0.9136\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1292 - acc: 0.9506 - val_loss: 0.2366 - val_acc: 0.9259\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1375 - acc: 0.9439 - val_loss: 0.2042 - val_acc: 0.9074\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1509 - acc: 0.9461 - val_loss: 0.3682 - val_acc: 0.8889\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1085 - acc: 0.9577 - val_loss: 0.2549 - val_acc: 0.9198\n",
      "Train loss: 0.132176520879\n",
      "Train accuracy: 0.946601941748\n",
      "Test loss: 0.20111233407\n",
      "Test accuracy: 0.913580246914\n",
      "\n",
      "===================FOLD= 1\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 18s 748ms/step - loss: 0.5941 - acc: 0.6658 - val_loss: 0.3484 - val_acc: 0.8261\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.3571 - acc: 0.8319 - val_loss: 0.2167 - val_acc: 0.9193\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.3114 - acc: 0.8559 - val_loss: 0.1953 - val_acc: 0.8882\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.2716 - acc: 0.8754 - val_loss: 0.2183 - val_acc: 0.9255\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2823 - acc: 0.8784 - val_loss: 0.3019 - val_acc: 0.8820\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2846 - acc: 0.8688 - val_loss: 0.2380 - val_acc: 0.9255\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2881 - acc: 0.8799 - val_loss: 0.2184 - val_acc: 0.9006\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2545 - acc: 0.8910 - val_loss: 0.2043 - val_acc: 0.9068\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.2126 - acc: 0.9070 - val_loss: 0.1618 - val_acc: 0.9503\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2480 - acc: 0.9001 - val_loss: 0.1691 - val_acc: 0.9317\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2313 - acc: 0.9012 - val_loss: 0.1694 - val_acc: 0.9317\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2078 - acc: 0.9101 - val_loss: 0.2208 - val_acc: 0.9068\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2442 - acc: 0.9037 - val_loss: 0.1703 - val_acc: 0.9255\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2297 - acc: 0.9084 - val_loss: 0.1742 - val_acc: 0.9317\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 15s 604ms/step - loss: 0.1952 - acc: 0.9219 - val_loss: 0.1471 - val_acc: 0.9441\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1948 - acc: 0.9226 - val_loss: 0.1730 - val_acc: 0.9565\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1757 - acc: 0.9237 - val_loss: 0.1911 - val_acc: 0.9193\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2000 - acc: 0.9242 - val_loss: 0.1896 - val_acc: 0.9317\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1817 - acc: 0.9201 - val_loss: 0.1920 - val_acc: 0.9255\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1545 - acc: 0.9377 - val_loss: 0.1756 - val_acc: 0.9379\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1571 - acc: 0.9363 - val_loss: 0.2114 - val_acc: 0.9255\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1788 - acc: 0.9324 - val_loss: 0.2450 - val_acc: 0.8882\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 13s 538ms/step - loss: 0.2138 - acc: 0.9102 - val_loss: 0.1768 - val_acc: 0.9068\n",
      "Epoch 24/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1905 - acc: 0.9214 - val_loss: 0.1768 - val_acc: 0.9193\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1645 - acc: 0.9297 - val_loss: 0.1819 - val_acc: 0.9379\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1529 - acc: 0.9410 - val_loss: 0.1698 - val_acc: 0.9255\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1298 - acc: 0.9418 - val_loss: 0.2462 - val_acc: 0.9130\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1222 - acc: 0.9468 - val_loss: 0.2470 - val_acc: 0.9379\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1390 - acc: 0.9397 - val_loss: 0.2624 - val_acc: 0.9130\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.1594 - acc: 0.9259 - val_loss: 0.1993 - val_acc: 0.9068\n",
      "Train loss: 0.166950000857\n",
      "Train accuracy: 0.925155925156\n",
      "Test loss: 0.147050012516\n",
      "Test accuracy: 0.944099378882\n",
      "\n",
      "===================FOLD= 2\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 16s 664ms/step - loss: 0.7285 - acc: 0.5464 - val_loss: 0.5758 - val_acc: 0.6646\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 602ms/step - loss: 0.5531 - acc: 0.6878 - val_loss: 0.5111 - val_acc: 0.7081\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 601ms/step - loss: 0.5279 - acc: 0.7268 - val_loss: 0.4453 - val_acc: 0.7826\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 14s 601ms/step - loss: 0.4320 - acc: 0.8002 - val_loss: 0.3938 - val_acc: 0.7950\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 14s 602ms/step - loss: 0.3778 - acc: 0.8434 - val_loss: 0.2798 - val_acc: 0.8758\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 14s 599ms/step - loss: 0.3361 - acc: 0.8440 - val_loss: 0.2309 - val_acc: 0.8944\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.2874 - acc: 0.8670 - val_loss: 0.2483 - val_acc: 0.8944\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 14s 602ms/step - loss: 0.3009 - acc: 0.8628 - val_loss: 0.2260 - val_acc: 0.9255\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 14s 601ms/step - loss: 0.2736 - acc: 0.8810 - val_loss: 0.2049 - val_acc: 0.9130\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.2471 - acc: 0.8975 - val_loss: 0.2174 - val_acc: 0.9068\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2458 - acc: 0.8995 - val_loss: 0.2123 - val_acc: 0.9068\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.2396 - acc: 0.9001 - val_loss: 0.1985 - val_acc: 0.8820\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.2079 - acc: 0.9206 - val_loss: 0.2145 - val_acc: 0.9255\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2189 - acc: 0.9076 - val_loss: 0.2075 - val_acc: 0.9006\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2188 - acc: 0.9058 - val_loss: 0.1994 - val_acc: 0.9130\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2172 - acc: 0.9067 - val_loss: 0.2111 - val_acc: 0.9068\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2340 - acc: 0.8971 - val_loss: 0.2423 - val_acc: 0.8882\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.2118 - acc: 0.9071 - val_loss: 0.1737 - val_acc: 0.9006\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2181 - acc: 0.9075 - val_loss: 0.2191 - val_acc: 0.8944\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2071 - acc: 0.9149 - val_loss: 0.2535 - val_acc: 0.8882\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1856 - acc: 0.9169 - val_loss: 0.1959 - val_acc: 0.8944\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1991 - acc: 0.9132 - val_loss: 0.1945 - val_acc: 0.9006\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.1720 - acc: 0.9293 - val_loss: 0.1935 - val_acc: 0.9193\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 14s 602ms/step - loss: 0.1631 - acc: 0.9390 - val_loss: 0.1715 - val_acc: 0.9193\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1526 - acc: 0.9351 - val_loss: 0.1783 - val_acc: 0.9193\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.2131 - acc: 0.9158 - val_loss: 0.1399 - val_acc: 0.9565\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1597 - acc: 0.9355 - val_loss: 0.1659 - val_acc: 0.9130\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1681 - acc: 0.9379 - val_loss: 0.1629 - val_acc: 0.9255\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1625 - acc: 0.9307 - val_loss: 0.1885 - val_acc: 0.8944\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1477 - acc: 0.9364 - val_loss: 0.1869 - val_acc: 0.9130\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1682 - acc: 0.9347 - val_loss: 0.1769 - val_acc: 0.9379\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1720 - acc: 0.9371 - val_loss: 0.1891 - val_acc: 0.9068\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1899 - acc: 0.9208 - val_loss: 0.1737 - val_acc: 0.9068\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1671 - acc: 0.9334 - val_loss: 0.1837 - val_acc: 0.9317\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1528 - acc: 0.9416 - val_loss: 0.2210 - val_acc: 0.9068\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1499 - acc: 0.9407 - val_loss: 0.1995 - val_acc: 0.9317\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1518 - acc: 0.9373 - val_loss: 0.1789 - val_acc: 0.9317\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1465 - acc: 0.9423 - val_loss: 0.2271 - val_acc: 0.9193\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1541 - acc: 0.9343 - val_loss: 0.1595 - val_acc: 0.9255\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1497 - acc: 0.9313 - val_loss: 0.1685 - val_acc: 0.9193\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1362 - acc: 0.9467 - val_loss: 0.1823 - val_acc: 0.9317\n",
      "Train loss: 0.134916192838\n",
      "Train accuracy: 0.943866943867\n",
      "Test loss: 0.139855607604\n",
      "Test accuracy: 0.95652173913\n",
      "\n",
      "===================FOLD= 3\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 18s 742ms/step - loss: 0.8737 - acc: 0.5423 - val_loss: 0.5331 - val_acc: 0.7375\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.5798 - acc: 0.6982 - val_loss: 0.3021 - val_acc: 0.8562\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.3941 - acc: 0.8186 - val_loss: 0.2868 - val_acc: 0.8812\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.3903 - acc: 0.8270 - val_loss: 0.3678 - val_acc: 0.8562\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.4055 - acc: 0.8243 - val_loss: 0.3486 - val_acc: 0.8063\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.3284 - acc: 0.8593 - val_loss: 0.2846 - val_acc: 0.8812\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.2845 - acc: 0.8811 - val_loss: 0.2201 - val_acc: 0.8875\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.2611 - acc: 0.8801 - val_loss: 0.2110 - val_acc: 0.9187\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2607 - acc: 0.8917 - val_loss: 0.2886 - val_acc: 0.8812\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.2396 - acc: 0.8883 - val_loss: 0.2533 - val_acc: 0.9000\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2342 - acc: 0.9063 - val_loss: 0.2604 - val_acc: 0.9062\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2048 - acc: 0.9169 - val_loss: 0.2780 - val_acc: 0.8750\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2452 - acc: 0.8901 - val_loss: 0.2888 - val_acc: 0.8750\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.2543 - acc: 0.8912 - val_loss: 0.1978 - val_acc: 0.9125\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.2474 - acc: 0.8876 - val_loss: 0.1886 - val_acc: 0.9187\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2152 - acc: 0.9122 - val_loss: 0.2163 - val_acc: 0.9187\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1826 - acc: 0.9243 - val_loss: 0.2531 - val_acc: 0.9187\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1958 - acc: 0.9147 - val_loss: 0.2159 - val_acc: 0.9187\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1977 - acc: 0.9134 - val_loss: 0.2552 - val_acc: 0.9000\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2033 - acc: 0.9147 - val_loss: 0.2095 - val_acc: 0.9187\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1711 - acc: 0.9235 - val_loss: 0.2529 - val_acc: 0.9125\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1775 - acc: 0.9251 - val_loss: 0.1934 - val_acc: 0.9313\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 15s 610ms/step - loss: 0.2018 - acc: 0.9157 - val_loss: 0.1860 - val_acc: 0.9313\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1854 - acc: 0.9262 - val_loss: 0.1937 - val_acc: 0.9125\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1727 - acc: 0.9262 - val_loss: 0.2563 - val_acc: 0.8938\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.2015 - acc: 0.9207 - val_loss: 0.3447 - val_acc: 0.8562\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2261 - acc: 0.9104 - val_loss: 0.2209 - val_acc: 0.9187\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1660 - acc: 0.9335 - val_loss: 0.2090 - val_acc: 0.9250\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 14s 602ms/step - loss: 0.1712 - acc: 0.9262 - val_loss: 0.1818 - val_acc: 0.9125\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1853 - acc: 0.9230 - val_loss: 0.2029 - val_acc: 0.9062\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1346 - acc: 0.9456 - val_loss: 0.3155 - val_acc: 0.9250\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1469 - acc: 0.9386 - val_loss: 0.4134 - val_acc: 0.9000\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1483 - acc: 0.9340 - val_loss: 0.2148 - val_acc: 0.9125\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 12s 521ms/step - loss: 0.1532 - acc: 0.9388 - val_loss: 0.2220 - val_acc: 0.9125\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1420 - acc: 0.9435 - val_loss: 0.2062 - val_acc: 0.9062\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1519 - acc: 0.9381 - val_loss: 0.2822 - val_acc: 0.8875\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1279 - acc: 0.9526 - val_loss: 0.2383 - val_acc: 0.9062\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1601 - acc: 0.9375 - val_loss: 0.2198 - val_acc: 0.9125\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1379 - acc: 0.9492 - val_loss: 0.2534 - val_acc: 0.9125\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1861 - acc: 0.9283 - val_loss: 0.1898 - val_acc: 0.9062\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1389 - acc: 0.9515 - val_loss: 0.2280 - val_acc: 0.9250\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1245 - acc: 0.9578 - val_loss: 0.2232 - val_acc: 0.9313\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1108 - acc: 0.9524 - val_loss: 0.2915 - val_acc: 0.9250\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1220 - acc: 0.9505 - val_loss: 0.2318 - val_acc: 0.9062\n",
      "Train loss: 0.112436852751\n",
      "Train accuracy: 0.959141274238\n",
      "Test loss: 0.181846439838\n",
      "Test accuracy: 0.9125\n",
      "\n",
      "===================FOLD= 4\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 16s 686ms/step - loss: 0.6116 - acc: 0.6554 - val_loss: 0.5459 - val_acc: 0.6250\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.4195 - acc: 0.7704 - val_loss: 0.3043 - val_acc: 0.8438\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 602ms/step - loss: 0.3489 - acc: 0.8337 - val_loss: 0.2416 - val_acc: 0.8812\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 14s 602ms/step - loss: 0.3152 - acc: 0.8443 - val_loss: 0.2375 - val_acc: 0.9375\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.2846 - acc: 0.8668 - val_loss: 0.1955 - val_acc: 0.9187\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.2707 - acc: 0.8663 - val_loss: 0.1647 - val_acc: 0.9437\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.2787 - acc: 0.8709 - val_loss: 0.1863 - val_acc: 0.9563\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2220 - acc: 0.9031 - val_loss: 0.2031 - val_acc: 0.9187\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2185 - acc: 0.9189 - val_loss: 0.1735 - val_acc: 0.9187\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2705 - acc: 0.8860 - val_loss: 0.1969 - val_acc: 0.9437\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2198 - acc: 0.9080 - val_loss: 0.2377 - val_acc: 0.9187\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.2260 - acc: 0.9070 - val_loss: 0.1525 - val_acc: 0.9563\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1963 - acc: 0.9153 - val_loss: 0.2487 - val_acc: 0.8938\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2132 - acc: 0.9054 - val_loss: 0.1582 - val_acc: 0.9437\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2373 - acc: 0.9057 - val_loss: 0.1965 - val_acc: 0.9313\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2194 - acc: 0.9073 - val_loss: 0.1731 - val_acc: 0.9500\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2014 - acc: 0.9130 - val_loss: 0.2480 - val_acc: 0.9062\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1657 - acc: 0.9283 - val_loss: 0.2018 - val_acc: 0.9437\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1865 - acc: 0.9179 - val_loss: 0.1973 - val_acc: 0.9375\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2289 - acc: 0.9059 - val_loss: 0.1885 - val_acc: 0.9437\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1855 - acc: 0.9241 - val_loss: 0.2030 - val_acc: 0.9313\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1677 - acc: 0.9412 - val_loss: 0.2616 - val_acc: 0.9250\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.1726 - acc: 0.9234 - val_loss: 0.2619 - val_acc: 0.9438\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1562 - acc: 0.9326 - val_loss: 0.2530 - val_acc: 0.9125\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1428 - acc: 0.9399 - val_loss: 0.3018 - val_acc: 0.9062\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1701 - acc: 0.9236 - val_loss: 0.1886 - val_acc: 0.9437\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1733 - acc: 0.9290 - val_loss: 0.2499 - val_acc: 0.9375\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train loss: 0.175474767375\n",
      "Train accuracy: 0.925900277008\n",
      "Test loss: 0.15249427706\n",
      "Test accuracy: 0.95625\n",
      "\n",
      "===================FOLD= 5\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 17s 691ms/step - loss: 0.7033 - acc: 0.6265 - val_loss: 0.4350 - val_acc: 0.7750\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.4995 - acc: 0.7625 - val_loss: 0.3626 - val_acc: 0.8688\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 602ms/step - loss: 0.4166 - acc: 0.8194 - val_loss: 0.3437 - val_acc: 0.8750\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.3453 - acc: 0.8480 - val_loss: 0.3280 - val_acc: 0.8562\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.3709 - acc: 0.8424 - val_loss: 0.3418 - val_acc: 0.8625\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 14s 602ms/step - loss: 0.3084 - acc: 0.8625 - val_loss: 0.2495 - val_acc: 0.9125\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.2794 - acc: 0.8835 - val_loss: 0.1982 - val_acc: 0.9313\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2687 - acc: 0.8853 - val_loss: 0.3692 - val_acc: 0.9062\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.2508 - acc: 0.8998 - val_loss: 0.4219 - val_acc: 0.8938\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2512 - acc: 0.8880 - val_loss: 0.2221 - val_acc: 0.9187\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 14s 602ms/step - loss: 0.2380 - acc: 0.9003 - val_loss: 0.1797 - val_acc: 0.9437\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.2309 - acc: 0.9036 - val_loss: 0.2316 - val_acc: 0.9187\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.2156 - acc: 0.9202 - val_loss: 0.1984 - val_acc: 0.9250\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2337 - acc: 0.9033 - val_loss: 0.2110 - val_acc: 0.9500\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2159 - acc: 0.9101 - val_loss: 0.1891 - val_acc: 0.9500\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.2139 - acc: 0.9075 - val_loss: 0.1868 - val_acc: 0.9313\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.2154 - acc: 0.9015 - val_loss: 0.3170 - val_acc: 0.9062\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2092 - acc: 0.9117 - val_loss: 0.2634 - val_acc: 0.9313\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 521ms/step - loss: 0.1992 - acc: 0.9117 - val_loss: 0.2300 - val_acc: 0.9313\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1671 - acc: 0.9298 - val_loss: 0.2398 - val_acc: 0.9125\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.2045 - acc: 0.9062 - val_loss: 0.1895 - val_acc: 0.9313\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 14s 602ms/step - loss: 0.1693 - acc: 0.9366 - val_loss: 0.1753 - val_acc: 0.9500\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 14s 594ms/step - loss: 0.1668 - acc: 0.9314 - val_loss: 0.1716 - val_acc: 0.9438\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.1872 - acc: 0.9209 - val_loss: 0.2385 - val_acc: 0.9313\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 521ms/step - loss: 0.1636 - acc: 0.9300 - val_loss: 0.2407 - val_acc: 0.9125\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.2111 - acc: 0.9114 - val_loss: 0.2444 - val_acc: 0.9187\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 521ms/step - loss: 0.1659 - acc: 0.9334 - val_loss: 0.2079 - val_acc: 0.9437\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.1925 - acc: 0.9280 - val_loss: 0.2229 - val_acc: 0.9313\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1916 - acc: 0.9257 - val_loss: 0.3040 - val_acc: 0.9250\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1969 - acc: 0.9143 - val_loss: 0.1743 - val_acc: 0.9375\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1606 - acc: 0.9391 - val_loss: 0.2563 - val_acc: 0.9250\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1459 - acc: 0.9414 - val_loss: 0.2159 - val_acc: 0.9500\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1573 - acc: 0.9485 - val_loss: 0.1997 - val_acc: 0.9500\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.1478 - acc: 0.9396 - val_loss: 0.3013 - val_acc: 0.9000\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.1235 - acc: 0.9552 - val_loss: 0.2184 - val_acc: 0.9187\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.2388 - acc: 0.8954 - val_loss: 0.2140 - val_acc: 0.9062\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1345 - acc: 0.9417 - val_loss: 0.2988 - val_acc: 0.9125\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1610 - acc: 0.9337 - val_loss: 0.3263 - val_acc: 0.9000\n",
      "Train loss: 0.127614643249\n",
      "Train accuracy: 0.943905817175\n",
      "Test loss: 0.171589575708\n",
      "Test accuracy: 0.94375\n",
      "\n",
      "===================FOLD= 6\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 17s 711ms/step - loss: 0.6290 - acc: 0.6510 - val_loss: 0.5150 - val_acc: 0.7812\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.4613 - acc: 0.7880 - val_loss: 0.5475 - val_acc: 0.7625\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.3636 - acc: 0.8277 - val_loss: 0.3581 - val_acc: 0.8500\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.2916 - acc: 0.8645 - val_loss: 0.3167 - val_acc: 0.8562\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2749 - acc: 0.8650 - val_loss: 0.3600 - val_acc: 0.8500\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2778 - acc: 0.8725 - val_loss: 0.3243 - val_acc: 0.8562\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.2701 - acc: 0.8734 - val_loss: 0.3142 - val_acc: 0.8875\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.2390 - acc: 0.9041 - val_loss: 0.2976 - val_acc: 0.8812\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2478 - acc: 0.8868 - val_loss: 0.3144 - val_acc: 0.8625\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.2010 - acc: 0.9125 - val_loss: 0.2935 - val_acc: 0.8875\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1961 - acc: 0.9101 - val_loss: 0.3283 - val_acc: 0.8625\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2023 - acc: 0.9173 - val_loss: 0.3499 - val_acc: 0.8562\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.2252 - acc: 0.9008 - val_loss: 0.2825 - val_acc: 0.9062\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.2119 - acc: 0.9098 - val_loss: 0.2795 - val_acc: 0.8688\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2206 - acc: 0.9023 - val_loss: 0.2956 - val_acc: 0.8625\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2426 - acc: 0.8901 - val_loss: 0.3242 - val_acc: 0.8313\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2073 - acc: 0.9085 - val_loss: 0.3474 - val_acc: 0.8562\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.1792 - acc: 0.9209 - val_loss: 0.2443 - val_acc: 0.8812\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1721 - acc: 0.9335 - val_loss: 0.2802 - val_acc: 0.8688\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1821 - acc: 0.9213 - val_loss: 0.2453 - val_acc: 0.9000\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1756 - acc: 0.9239 - val_loss: 0.2499 - val_acc: 0.9000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1598 - acc: 0.9396 - val_loss: 0.2765 - val_acc: 0.8562\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.1606 - acc: 0.9361 - val_loss: 0.3148 - val_acc: 0.8938\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1596 - acc: 0.9336 - val_loss: 0.2849 - val_acc: 0.8750\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1638 - acc: 0.9291 - val_loss: 0.2626 - val_acc: 0.8750\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1395 - acc: 0.9443 - val_loss: 0.3635 - val_acc: 0.8625\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1534 - acc: 0.9419 - val_loss: 0.2679 - val_acc: 0.9062\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.1287 - acc: 0.9441 - val_loss: 0.2400 - val_acc: 0.9125\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1414 - acc: 0.9406 - val_loss: 0.3063 - val_acc: 0.9187\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1317 - acc: 0.9500 - val_loss: 0.2499 - val_acc: 0.8938\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1307 - acc: 0.9472 - val_loss: 0.4071 - val_acc: 0.9000\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1400 - acc: 0.9381 - val_loss: 0.3244 - val_acc: 0.9125\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1425 - acc: 0.9414 - val_loss: 0.3269 - val_acc: 0.8875\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1239 - acc: 0.9427 - val_loss: 0.2570 - val_acc: 0.8875\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1468 - acc: 0.9456 - val_loss: 0.2986 - val_acc: 0.8562\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1452 - acc: 0.9456 - val_loss: 0.3162 - val_acc: 0.9187\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1433 - acc: 0.9459 - val_loss: 0.2429 - val_acc: 0.9187\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1396 - acc: 0.9477 - val_loss: 0.2938 - val_acc: 0.9062\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1146 - acc: 0.9550 - val_loss: 0.3429 - val_acc: 0.8688\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1260 - acc: 0.9476 - val_loss: 0.2550 - val_acc: 0.9187\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1050 - acc: 0.9581 - val_loss: 0.3268 - val_acc: 0.9000\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1069 - acc: 0.9606 - val_loss: 0.4058 - val_acc: 0.9062\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1188 - acc: 0.9560 - val_loss: 0.3040 - val_acc: 0.9000\n",
      "Train loss: 0.0926912830332\n",
      "Train accuracy: 0.966066481994\n",
      "Test loss: 0.23996142149\n",
      "Test accuracy: 0.9125\n",
      "\n",
      "===================FOLD= 7\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 18s 730ms/step - loss: 0.7666 - acc: 0.6063 - val_loss: 0.4294 - val_acc: 0.7375\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.5066 - acc: 0.7591 - val_loss: 0.4747 - val_acc: 0.7562\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.4366 - acc: 0.8044 - val_loss: 0.2627 - val_acc: 0.8500\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.3549 - acc: 0.8471 - val_loss: 0.2436 - val_acc: 0.8812\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.3487 - acc: 0.8495 - val_loss: 0.2712 - val_acc: 0.8875\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.3415 - acc: 0.8516 - val_loss: 0.2231 - val_acc: 0.8875\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.3028 - acc: 0.8715 - val_loss: 0.2988 - val_acc: 0.8500\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.3134 - acc: 0.8622 - val_loss: 0.2362 - val_acc: 0.9000\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2641 - acc: 0.8893 - val_loss: 0.2376 - val_acc: 0.9000\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2547 - acc: 0.8881 - val_loss: 0.3249 - val_acc: 0.8562\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2770 - acc: 0.8780 - val_loss: 0.2372 - val_acc: 0.8875\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2452 - acc: 0.8943 - val_loss: 0.2574 - val_acc: 0.8938\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2159 - acc: 0.9153 - val_loss: 0.2843 - val_acc: 0.8750\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.2377 - acc: 0.8945 - val_loss: 0.1848 - val_acc: 0.9125\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 15s 604ms/step - loss: 0.2050 - acc: 0.9093 - val_loss: 0.1799 - val_acc: 0.9313\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2344 - acc: 0.8989 - val_loss: 0.2079 - val_acc: 0.9000\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2015 - acc: 0.9156 - val_loss: 0.2782 - val_acc: 0.8812\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2277 - acc: 0.9020 - val_loss: 0.2105 - val_acc: 0.8938\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1970 - acc: 0.9225 - val_loss: 0.2282 - val_acc: 0.9187\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1944 - acc: 0.9181 - val_loss: 0.1948 - val_acc: 0.9125\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2016 - acc: 0.9202 - val_loss: 0.1928 - val_acc: 0.9125\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2280 - acc: 0.9039 - val_loss: 0.2440 - val_acc: 0.8875\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.1819 - acc: 0.9208 - val_loss: 0.1915 - val_acc: 0.9125\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.2244 - acc: 0.9132 - val_loss: 0.3444 - val_acc: 0.8625\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2577 - acc: 0.8982 - val_loss: 0.2250 - val_acc: 0.9125\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1760 - acc: 0.9402 - val_loss: 0.2089 - val_acc: 0.9000\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.1582 - acc: 0.9342 - val_loss: 0.1505 - val_acc: 0.9125\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1737 - acc: 0.9296 - val_loss: 0.2352 - val_acc: 0.9125\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1686 - acc: 0.9327 - val_loss: 0.2383 - val_acc: 0.8875\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1717 - acc: 0.9278 - val_loss: 0.2042 - val_acc: 0.9187\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1430 - acc: 0.9502 - val_loss: 0.2010 - val_acc: 0.9125\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1315 - acc: 0.9433 - val_loss: 0.1520 - val_acc: 0.9313\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.1452 - acc: 0.9383 - val_loss: 0.1253 - val_acc: 0.9437\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1492 - acc: 0.9401 - val_loss: 0.1734 - val_acc: 0.9250\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 14s 602ms/step - loss: 0.1319 - acc: 0.9422 - val_loss: 0.0980 - val_acc: 0.9563\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.1514 - acc: 0.9384 - val_loss: 0.2170 - val_acc: 0.9187\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 12s 521ms/step - loss: 0.1551 - acc: 0.9347 - val_loss: 0.2177 - val_acc: 0.9000\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.1507 - acc: 0.9334 - val_loss: 0.2166 - val_acc: 0.8938\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 39/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1761 - acc: 0.9236 - val_loss: 0.3679 - val_acc: 0.8438\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.1642 - acc: 0.9428 - val_loss: 0.1640 - val_acc: 0.9313\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1234 - acc: 0.9498 - val_loss: 0.1949 - val_acc: 0.9313\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.1210 - acc: 0.9438 - val_loss: 0.2200 - val_acc: 0.9313\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.1261 - acc: 0.9476 - val_loss: 0.2308 - val_acc: 0.9125\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.1179 - acc: 0.9531 - val_loss: 0.2671 - val_acc: 0.9250\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1440 - acc: 0.9410 - val_loss: 0.2299 - val_acc: 0.9000\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.1404 - acc: 0.9448 - val_loss: 0.2078 - val_acc: 0.9250\n",
      "Epoch 47/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1335 - acc: 0.9406 - val_loss: 0.1959 - val_acc: 0.9187\n",
      "Epoch 48/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1155 - acc: 0.9529 - val_loss: 0.1626 - val_acc: 0.9375\n",
      "Epoch 49/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1013 - acc: 0.9567 - val_loss: 0.2518 - val_acc: 0.8938\n",
      "Epoch 50/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.1015 - acc: 0.9593 - val_loss: 0.1773 - val_acc: 0.9187\n",
      "Train loss: 0.0879920781113\n",
      "Train accuracy: 0.962603878116\n",
      "Test loss: 0.0980413496494\n",
      "Test accuracy: 0.95625\n",
      "\n",
      "===================FOLD= 8\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 18s 744ms/step - loss: 0.6584 - acc: 0.6866 - val_loss: 0.4346 - val_acc: 0.7937\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.4007 - acc: 0.8256 - val_loss: 0.5350 - val_acc: 0.7750\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.3756 - acc: 0.8327 - val_loss: 0.3142 - val_acc: 0.8125\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.3614 - acc: 0.8482 - val_loss: 0.3142 - val_acc: 0.8125\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.3175 - acc: 0.8674 - val_loss: 0.3085 - val_acc: 0.8187\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2722 - acc: 0.8868 - val_loss: 0.3881 - val_acc: 0.8250\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2704 - acc: 0.8956 - val_loss: 0.3680 - val_acc: 0.8250\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2719 - acc: 0.8852 - val_loss: 0.3670 - val_acc: 0.8438\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2658 - acc: 0.8862 - val_loss: 0.5109 - val_acc: 0.8187\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2495 - acc: 0.9037 - val_loss: 0.4396 - val_acc: 0.8125\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2435 - acc: 0.8949 - val_loss: 0.3202 - val_acc: 0.8562\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2326 - acc: 0.9008 - val_loss: 0.3987 - val_acc: 0.8187\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2107 - acc: 0.9192 - val_loss: 0.3491 - val_acc: 0.8313\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2077 - acc: 0.9143 - val_loss: 0.3415 - val_acc: 0.8562\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1952 - acc: 0.9161 - val_loss: 0.3809 - val_acc: 0.8438\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1875 - acc: 0.9290 - val_loss: 0.4846 - val_acc: 0.8375\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1913 - acc: 0.9192 - val_loss: 0.3967 - val_acc: 0.8375\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1956 - acc: 0.9236 - val_loss: 0.3200 - val_acc: 0.8438\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2071 - acc: 0.9070 - val_loss: 0.5306 - val_acc: 0.7875\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2086 - acc: 0.9181 - val_loss: 0.3341 - val_acc: 0.8562\n",
      "Train loss: 0.203214812188\n",
      "Train accuracy: 0.91135734072\n",
      "Test loss: 0.308517012\n",
      "Test accuracy: 0.81875\n",
      "\n",
      "===================FOLD= 9\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 18s 737ms/step - loss: 0.6985 - acc: 0.6067 - val_loss: 0.4537 - val_acc: 0.7562\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 602ms/step - loss: 0.4095 - acc: 0.7984 - val_loss: 0.2879 - val_acc: 0.8625\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.3653 - acc: 0.8345 - val_loss: 0.2280 - val_acc: 0.9187\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.3134 - acc: 0.8552 - val_loss: 0.2483 - val_acc: 0.8875\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2946 - acc: 0.8609 - val_loss: 0.2775 - val_acc: 0.8688\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.2912 - acc: 0.8647 - val_loss: 0.2142 - val_acc: 0.9062\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2788 - acc: 0.8884 - val_loss: 0.2317 - val_acc: 0.8938\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2368 - acc: 0.8982 - val_loss: 0.3131 - val_acc: 0.8375\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.2354 - acc: 0.9024 - val_loss: 0.2049 - val_acc: 0.9000\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2423 - acc: 0.8954 - val_loss: 0.2061 - val_acc: 0.9000\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1900 - acc: 0.9293 - val_loss: 0.2066 - val_acc: 0.9000\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2304 - acc: 0.9008 - val_loss: 0.2491 - val_acc: 0.8938\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.2441 - acc: 0.9018 - val_loss: 0.1969 - val_acc: 0.9250\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2184 - acc: 0.9062 - val_loss: 0.2574 - val_acc: 0.8938\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2259 - acc: 0.9062 - val_loss: 0.2008 - val_acc: 0.9187\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.2023 - acc: 0.9156 - val_loss: 0.1869 - val_acc: 0.9125\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2003 - acc: 0.9212 - val_loss: 0.2282 - val_acc: 0.9125\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1801 - acc: 0.9241 - val_loss: 0.3793 - val_acc: 0.8313\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.2018 - acc: 0.9163 - val_loss: 0.1672 - val_acc: 0.9187\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1894 - acc: 0.9288 - val_loss: 0.2036 - val_acc: 0.8875\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2016 - acc: 0.9101 - val_loss: 0.2364 - val_acc: 0.9000\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1558 - acc: 0.9422 - val_loss: 0.2039 - val_acc: 0.9000\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.1535 - acc: 0.9402 - val_loss: 0.2157 - val_acc: 0.8938\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1708 - acc: 0.9334 - val_loss: 0.2399 - val_acc: 0.8812\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1729 - acc: 0.9308 - val_loss: 0.2165 - val_acc: 0.8938\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1549 - acc: 0.9402 - val_loss: 0.2452 - val_acc: 0.9062\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1376 - acc: 0.9450 - val_loss: 0.2045 - val_acc: 0.9187\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1503 - acc: 0.9445 - val_loss: 0.2291 - val_acc: 0.9000\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1693 - acc: 0.9342 - val_loss: 0.2356 - val_acc: 0.9000\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1361 - acc: 0.9456 - val_loss: 0.1845 - val_acc: 0.9250\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1096 - acc: 0.9612 - val_loss: 0.2983 - val_acc: 0.8750\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1331 - acc: 0.9482 - val_loss: 0.2664 - val_acc: 0.8938\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1435 - acc: 0.9430 - val_loss: 0.2655 - val_acc: 0.9000\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1394 - acc: 0.9524 - val_loss: 0.2279 - val_acc: 0.9062\n",
      "Train loss: 0.117981522801\n",
      "Train accuracy: 0.951523545706\n",
      "Test loss: 0.167192131281\n",
      "Test accuracy: 0.91875\n",
      "\n",
      " Train Log Loss Validation=  0.121103524179\n",
      " Test Log Loss Validation=  0.180744845822\n"
     ]
    }
   ],
   "source": [
    "#def myAngleCV(X_train, X_angle, X_test):\n",
    "K=10\n",
    "folds = list(StratifiedKFold(n_splits=K, shuffle=True, random_state=16).split(X_train, target_train))\n",
    "y_test_pred_log = 0\n",
    "y_train_pred_log=0\n",
    "y_valid_pred_log = 0.0*target_train\n",
    "for j, (train_idx, test_idx) in enumerate(folds):\n",
    "    print('\\n===================FOLD=',j)\n",
    "    X_train_cv = X_train[train_idx]\n",
    "    y_train_cv = target_train[train_idx]\n",
    "    X_holdout = X_train[test_idx]\n",
    "    Y_holdout= target_train[test_idx]\n",
    "    \n",
    "    #Angle\n",
    "    X_angle_cv=X_angle[train_idx]\n",
    "    X_angle_hold=X_angle[test_idx]\n",
    "    #define file path and get callbacks\n",
    "    file_path = \"%s_aug_model_weights.hdf5\"%j\n",
    "    callbacks = get_callbacks(filepath=file_path, patience=5)\n",
    "    gen_flow = gen_flow_for_two_inputs(X_train_cv, X_angle_cv, y_train_cv)\n",
    "    galaxyModel= getVggAngleModel()\n",
    "    histr = galaxyModel.fit_generator(\n",
    "            gen_flow,\n",
    "            steps_per_epoch=24,\n",
    "            epochs=100,\n",
    "            shuffle=True,\n",
    "            verbose=1,\n",
    "            validation_data=([X_holdout,X_angle_hold], Y_holdout),\n",
    "            callbacks=callbacks)\n",
    "     #Getting the Best Model\n",
    "    galaxyModel.load_weights(filepath=file_path)\n",
    "    #Getting Training Score\n",
    "    score = galaxyModel.evaluate([X_train_cv,X_angle_cv], y_train_cv, verbose=0)\n",
    "    print('Train loss:', score[0])\n",
    "    print('Train accuracy:', score[1])\n",
    "    #Getting Test Score\n",
    "    score = galaxyModel.evaluate([X_holdout,X_angle_hold], Y_holdout, verbose=0)\n",
    "    print('Test loss:', score[0])\n",
    "    print('Test accuracy:', score[1])\n",
    "\n",
    "    #Getting validation Score.\n",
    "    pred_valid=galaxyModel.predict([X_holdout,X_angle_hold])\n",
    "    y_valid_pred_log[test_idx] = pred_valid.reshape(pred_valid.shape[0])\n",
    "\n",
    "    #Getting Test Scores\n",
    "    temp_test=galaxyModel.predict([X_test, X_test_angle])\n",
    "    y_test_pred_log+=temp_test.reshape(temp_test.shape[0])\n",
    "\n",
    "    #Getting Train Scores\n",
    "    temp_train=galaxyModel.predict([X_train, X_angle])\n",
    "    y_train_pred_log+=temp_train.reshape(temp_train.shape[0])\n",
    "\n",
    "y_test_pred_log=y_test_pred_log/K\n",
    "y_train_pred_log=y_train_pred_log/K\n",
    "\n",
    "print('\\n Train Log Loss Validation= ',log_loss(target_train, y_train_pred_log))\n",
    "print(' Test Log Loss Validation= ',log_loss(target_train, y_valid_pred_log))\n",
    "#return y_test_pred_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#preds=myAngleCV(X_train, X_angle, X_test)\n",
    "preds = y_test_pred_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Submission to kaggle\n",
    "submission = pd.DataFrame()\n",
    "submission['id']=test['id']\n",
    "submission['is_iceberg']=preds\n",
    "submission.to_csv('sub_Adam_10folds_amsgrad_0001_15p_copy.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['acc', 'val_loss', 'val_acc', 'loss'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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ROR14CrhGRKKBx4DBuH4PS3zn7glWecstcanLY3cbXXjfyEfgtRPh1+fc4LGK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\nFggirG0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f24abad2d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VWl5/Qny/U7d3vwHOvR9WTIQ//lv55fIQDQqFhdaCnn+zY/tTtnrvPtvmwKeX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2448044a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# list all data in history\n",
    "print(histr.history.keys())\n",
    "# summarize history for accuracy\n",
    "plt.plot(histr.history['acc'])\n",
    "plt.plot(histr.history['val_acc'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()\n",
    "# summarize history for loss\n",
    "plt.plot(histr.history['loss'])\n",
    "plt.plot(histr.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.image as mpimg\n",
    "\n",
    "min1 = np.min(X_holdout)\n",
    "max1 = np.max(X_holdout)\n",
    "diff1 = max1 - min1\n",
    "my_sum = lambda x: (x - min1)/diff1\n",
    "\n",
    "X_holdout2 = my_sum(X_holdout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13      1\n",
       "14      0\n",
       "38      1\n",
       "43      0\n",
       "44      0\n",
       "80      0\n",
       "104     1\n",
       "127     0\n",
       "137     0\n",
       "138     1\n",
       "141     1\n",
       "167     1\n",
       "195     1\n",
       "199     0\n",
       "207     1\n",
       "212     0\n",
       "213     1\n",
       "221     0\n",
       "240     0\n",
       "256     0\n",
       "267     1\n",
       "274     1\n",
       "276     1\n",
       "285     1\n",
       "290     0\n",
       "294     0\n",
       "300     1\n",
       "307     1\n",
       "311     1\n",
       "318     1\n",
       "       ..\n",
       "1363    1\n",
       "1364    0\n",
       "1373    1\n",
       "1380    1\n",
       "1394    0\n",
       "1411    0\n",
       "1435    0\n",
       "1440    1\n",
       "1445    1\n",
       "1447    1\n",
       "1460    1\n",
       "1470    0\n",
       "1488    0\n",
       "1495    0\n",
       "1504    1\n",
       "1516    0\n",
       "1525    0\n",
       "1533    0\n",
       "1537    0\n",
       "1539    0\n",
       "1541    0\n",
       "1542    0\n",
       "1549    0\n",
       "1552    0\n",
       "1559    0\n",
       "1566    0\n",
       "1567    0\n",
       "1577    0\n",
       "1590    0\n",
       "1602    0\n",
       "Name: is_iceberg, Length: 160, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_holdout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f2443f91668>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3eC7Q54SEhIRnDedqpvihRCZTJ1JlUq8Z8nYziLoDxNMHx0eSpDnygNugsu2A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\nEAQBiEUxCIIAxKIYBEEA/geVWVlGcca2dAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f24abad2860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[0], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[13])+ \",\"+\" iceberg_probability : \"+str(pred_valid[0])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f2443f747b8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HbuSjPnKmoWwe7vFdivKdyEmeTvwfWyjsGqYo+gT21Ynq/gKlOX996orrNlTW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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2443f8e8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[1], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[14])+ \",\"+\" iceberg_probability : \"+str(pred_valid[1])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f2443ed0d30>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DyGjBwdeE+GBa6EYVPZhmfo6ULXsKFkwoeTPGlI6tLVDJR3LEngLDltd1uaic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\nQ/ig6DiOQ/ig6DiOQ/wXXDhNu/zOTYMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2443f66be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[2], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[38])+ \",\"+\" iceberg_probability : \"+str(pred_valid[2])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f2443eb4208>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AvkIwnfHJhrHAvphD1VtIW3pX8czA9Q5+hktY0JlwixsWCyYcwzPPSsTY77uF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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f244803ecf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[3], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[43])+ \",\"+\" iceberg_probability : \"+str(pred_valid[3])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f2443e15f60>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uzClZE5JWsyzEfUixCZnoWhvJv7A/0/JY6QJO0HU4WzO5VRNxu7qvopBzxIY3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\nBrIFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2443e6b400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[6], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[104])+ \",\"+\" iceberg_probability : \"+str(pred_valid[6])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f2443dfa9b0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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v3ymqjQ4nbofeRV55DayP6nfj0D/Y7XnfzqZ+0TeGPpVA9C+6C3mzEwPvaf8G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\nx3EcwSdFx3EcwSdFx3Ec4X+zK6ee38y03AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2443dc6160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[9], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[138])+ \",\"+\" iceberg_probability : \"+str(pred_valid[9])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f2443d60278>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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C4Nx1U7tHkwN7qCL/nMXI6NQo2lNo6cTIjPS9exhR/vMOI1gWAQEBK+F8hZFd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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2443db5630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[10], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[141])+ \",\"+\" iceberg_probability : \"+str(pred_valid[10])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f2443cc4a20>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PWuRS53OUZK1QdAyWauN+d0+HCExf4TlsxyzBCzr8rI/FbOzbBSIHBojB1wXk\nk4OSb0XX9gLLJHO4t/fxbA1R5Cw/6s6ZnR4H/6JHmikmJCQkAOmlmJCQkABsNH3O5h3NuQSNtoLd\nNGKD1WuQhzyMVBX8oIWymYFrZwjYhsgoOwJ/AZUegIYdBbh5g+6uxdUBaiA3Q9A+epEhrzVDkHgf\nAcNHE1cfryPAebqEpVZUFLdLV1azE6fx86wYr44lht0bCHDecro/x9JDH4lGzXUPCB6V3bXWoO8N\nguSNOdNwzza4d+egpoOrCCSH+rqEXVuzg1DyX98lSqHelFoo2CiXfKJ2dYYlA0HNty06nLMG9XOj\nD5oBLNcQcRAQvL9gcAKswGbXfGlkdP99fixqYIcG1uNwW+9He7f6Sc937r9673h7bigWBvpu6yJr\n2WbKz2mmmJCQkACkl2JCQkICsLn0WU7/SqiVdM2uUI+3hdVWFR2FaZHFur81qEmGz/MW6mdO6y7k\n3h4i8BtK4IK2X1HFbFB3egmLrPqqq7I5KPD2RbeoYvDuNpydR8g+HiBgvYjbVeFtzxHnXj7gtLqd\nw+rqAFZfKGgl7FNAlZ+OfEy3EUg9iPZVgXnCCFhvkIf+84sr3sYRFH+4cNsDrhwXCE5ett7f/QGU\n4GXMA2ZuPO+/X5nybSjbS6wH4Flo8IwMYfVWwvl8HdQfEAA+7iFn/dDv7bxFVTCM86WJ3/N2hmcE\n1xxQG3zU+uf9uEy0/CQEhl9xKl1MYHOGutuLuMTRKOU+JyQkJGw80ksxISEhAdhY+myS8picuUJA\n8GKMwOvWKVYPAb6LqDoPkIdrFZRA0M4ahbAWC9Rx7oFuIqh3AYqVIbd3CPfjct7190rt7RGjXQ8w\nZuBtBTozK/yCtigXQ63uwXZqFesKL6Gaj3GeuYuS6qGOdI6YcrEoGOgWz9ODoj1DQasqjktGRjZH\nwPKWU+NX77gtV4BTOB2mG9ioVblvL0o/wfYN3Md1XWXYYQ1Q/Km+CIuwArRfCEYHNV4i8P6wQf1w\n1l2OFHuE6IAW+cY1nOFLWtSxFBvt6kDDazxzYyQpGFT0iuEFEattb291BLswLGtMym6fzdSe00wx\nISEh4QQ2dqaYZZkGo2621uujjggWnVcwRW1n/hPan9AKNe6L+LEhho2OMdUQYgzMTIvS9x8hdSyH\n0MDF8zbO/i7uI2avB7EGwXRTWO00BWPTUDuGbeOUK5x/FmPZaObaICVvZ4rfT8xCTpS55MQDs6ka\nXxQwxT2AmJtEAAAgAElEQVRc+Sxv1etmSCdKuVKsEdL5OMPl7BDlZvcLpOLx3pWeWqk9P9duLD1b\nQqBo9zDOiPVrUBp2AQnmCLPQFXS2jM4/2D6Kx95ofEY2hh1QjVKyZYF4WJSPbRBLGxDv2CvhMIRn\n+wZKwKxnuTM8QwXmSUPzMW8venvrJzts6FQxzRQTEhISgPRSTEhISAA2lj6HEFRF49RcTgkD4t0q\nLBVXoCTZQUdnrkycAt9XuOiAdXsdzg6Pt3daLMYjfnC659vLBm4vEHcyxEluRYeVE8vgKAfax2J5\nxlQwiASMq6uYLge620KYGcf4RdYZoQnKAv0e0MoF43mEgWH5jgF+e23iY3RhTkrYnW0FsaICTb9y\n5I4+GdqYjCAiYAxbc5Gqv++jUVTeMawOSHW3T4uVkxYph1kNWh1g5su/oMqvf4j0R5ayrVHidDLr\nPj9CBuM24khX7Avia/u4hqsLd+ChbjLDQ9KHuMMlkTqKYSODgSxiQw0CJcXK3aiu5dlmzqk286oS\nEhIS7hLppZiQkJAAbC59llRHPkGFsA9aM2xQxwPHttFEdbLwfQ/kNLkCsZwhFc7ApXLUIqkr58nN\nClQOfSngQvLsqKNHLPmyDdWSbTAVrXd6JpgCzn/Yg5NMgVKZkZL1INUuQI0zOPPUUFZbGM6y3GmD\neMcVVNEScZUl1O8suuQEuM5UU6ejox4MgdFHKrElaOUe4j6FGMcpYknBGlXHNMollikqpGcGlDWd\noM6ODRiJ4J9TmaVx7uHS6e59/W5JZnjk9zAggsAQcbBYIaYT0QfGGFCkOfbwABS4jgO0s4xK++AZ\nrAfBqLfBvaVp8fVp58zDkqqbhDRTTEhISADO1UvRzB4ys58ws/eZ2c+b2dfHz/fM7EfN7Bfj/y/c\n674mJCRsJs4bfa4l/fkQws+Z2ZaknzWzH5X0VZJ+LITwFjN7s6Q3S3rTrRrKLNM4pqOVKJERCrjU\nINi2QBC2onEpS5NmoCyDgdMkCJQ6mjo1y0ZI4YMqXVz3dnI4zBjSqy4ddX1pSpaydIrF0pusEbM2\n1ZWkGaTIGYx1Szi29JCiZqHrC51WSiwHVLW3kW9jzQBYIBhbW37sYOn9XR04x59j8JaxSawo6NII\nkcYHHjy9EILucf4K6m818mvb2ka9mNJr9MxbBGFn3bEDUOCRx1TL0LE5AsN7UOINauwKbjuzpY/L\nVu5jN6+768Bt1rVDV9l7NWrRYKmBCnk99RG4AIVc2x6YPkVJ3OK6j1Evnji76OPMkrktlom41LTU\nZtLmNc7VTDGE8GQI4efi9qGk90t6uaQvk/RY3O0xSa+7Nz1MSEjYdJyrlyJhZg9L+g2S3i3pcgjh\nyfjVU5Iun3HMG83scTN7fAlxISEhIeF2cd7osyTJzCaS/omkPxNCOCCNDSEEMzvV3TKE8KikRyVp\nd2snrOKLsYb6SEVtNnN+dGHoFGJtRNqglCVzduva6VsFlbksUXoUriKs+1EM6dwKtRblSZeR+lXI\nsc1ZshPuOhmWAAxqbQ+/dw36ZaBhJfrYMto7gqVhbYz87RnUfCSF9/ue111DFa7GUKhR7rSCC8tR\n3fV9G7Y7DHQvoWBT9aQbjlAXpdfz/u5PPXJgt4ZLDa8v1nGhi8yYBVNAcQvU89EcQfXIq+/hfs6R\nbm0ImG+iKl3jAWm3vX9HN3zdh0sdxdifoa2Bj+cCiwkl1OIRzI8rLEms4k2vYE1EpZxFh8bIPR/E\n1waNjDcJ5+6qzKxU90L8vhDCP40fP21mD8bvH5T0zL3qX0JCwmbjXL0UrZsSvlXS+0MIfxtfvUvS\nI3H7EUnvfKH7lpCQ8NLAeaPPnyvpKyX9BzN7b/zsmyS9RdLbzewNkj4s6fXP11AbWi2jNVhRoxYK\nrJZWR64Wz2CiGuL+YB0nAp1HMAJl3ugRaFIfQ0sj2GJISgZONkVQbaRBOUU+qL811E9SmBpUnuUn\nK8Qxz1BWdRtaY7nb0aMKua+sP5LR5gwUsAB9zDDOATSMax20NGMZ1u2o4hYQnFcMUobi3wvoyxyR\nAP3TVfES/ToAZaYd11pRDQhS7+P+FCgfW4Fj0xR3tuvnv9KHme2Bn6f3rCvB9YVI2UH1w4I5y66a\n81lZYGzn6O/anFiS6uu+ZFDwOUKweRGvf3ndaTqXgJgHPUc0wzCWaeWy1ibhXL0UQwg/pbMNfV/7\nQvYlISHhpYlzRZ8TEhIS7jXO1UzxEwkrc5WXY6AulND8SQ+OvQR6ch20Mo/2TluZ06F+hqHahRKI\nc4YFSqYiePgGgo0zlCrNqGjPnMo/mMV+oS7MEurrCYUQHTDUSOlRTYZd2AT1OmgHtYo1UvIRcqwR\ngFwcol4IAsD3M1wbf2NBCdurTuVmoMc5bLQGsY5MgM0WLbfmkMdpr1XCXixDeysosXkArb5y7Xjz\nmcqfhfvLbsy3C1e/Myx1zIfgybAiK8e+PxX8bSy3LMeoi3PNlyTWec59WshlKLWLMqlNdUp4gKSc\nVmBQv8MC58F9KfDArEvpGhINrh0c+Dmv+r3du3jR+xWXadqQSpwmJCQkbDzSSzEhISEB2Fj6LDOp\n11GLDCVJbc/5W50j3xhBvfNozTXLUD5zzIJTKC6EkqWkewKVuYgCQDUCspc3nDJfmvv5Fw927QSo\nhiWodlk+t7CWJK2EXGr83vUC++WbLFJky+4LQzD4h4ZOn7ahbO6CGjYY2xJ+WRQ82x2Xv3dyKN4I\nQl9LpKsZLMd2cA3ITTe4mrdHyF+Gyt4gvp+6+eR+DzMYVigVO+vabFFK1FoGMvvm4RDLHsL9XMKW\nbe5n3UPhtCsP0h2827+BPs/lAyrOXL4JKH06nyB5AMHrfRTgCgsU8QIlX99qxqJvo3ztbATFG8/W\npGGG9OYhzRQTEhISgPRSTEhISAA2lj5bkMrIbOqSubdQIhGEjRjYYwfnCpShR/ds2HhpF3nICPDN\nQStDDVoLWtfbdkrOGsejGMg8RcAs7ZpWsKIiTV+A7kwRPFwssI2cXHE7Ujjm737SIXLG4cg8h4K+\nXqKQpAVoYInPjzIfrwz1sBvUI167XuU0A8O+K6jMBi5b417ksNEqQdPpFE63c6FI1noskDKs1RhO\n2qDpDN4egRoPEFS+2PeA6EHpvH7LfHvadPdxiRzwDHn692VYskGK98e2/ZpZg7yAC/dy4c8THcUg\n/qsXKXmOBYZFH0XOWMQMf0PrAPj2ZGm1jUGaKSYkJCQA6aWYkJCQAGwsfc7aoH6kjaSsNdyhs12n\nJ4jdVn69o0flE+4SXe369wbn6dkR6CBoSkDe7gjKLa2hyh4U7x0ESq8lYgRjr6CszpHMuosiSrsI\nUgZjVoP9e3CHPjKnbQfz7lovX4edGPJk55CtB1C/ERd/QpU8RGD4Duo7a+ntzDMfu1m1iPv6IG4P\nPZncEKQ8b+DCjWsrUGgLta00RBA8KXn7pAcqLx7qiOUQtLtAQanpln8+nmGZACpvDXu1duD7XKv8\nmbvQghKvun1GuIcNqOwRHNNbhCJsIXg9g+JvUO7pJF9jGWbeYkkoXmuNwPAl9i0mcHWXY7XOw9/M\n2O00U0xISEggNnamaMFdZgqkIy2HiP26BsGij/SqSfcrm3+ypzZlEAW4Vs90qpy/nJjZVIxlxAI4\njUPHGRbg4+znv6Bm6YXcl8hRpVSHjV/D4BDOOIiZY8zexFwY2MH+42e7/z/5CsykC1wDtKUZBmCC\nGiUZpsr3oxZJw4ni2Btqln59o2jyy9mewVg3Q/wgx3kHs+kS9VXmgTNS364qnx1Xn+qztnX6Y8Ds\nkOJSDjsc62OGjzjBae1tTzg7xZ/ZldxjU6fr/D7MzvpIDx3CxWcLVkeHK28jx8x/OUTfEY/Yx72g\nYHOj7dopMIbZCZ3LzzOBoDQadNtZtplzqs28qoSEhIS7RHopJiQkJAAbS5/b3LSMZUOHSLO7fOSX\nfLiF+DkYwWaDSFWgIoRn3FGlQdxZATPXHBQ8Kxjj5Z8vgvOXQxieDlAPJMQF8/vvQxwj62Egzau4\n4m18bBsOPFiM3wq+PV04xaMY1H9lpyTtHTml/Zjc3YaOQj1cD+u4NMHbvp9lTWFWytKfTF0bxJS2\ngvl0M8Qpgr5SoBnB9aeP+jc5Ak/nKKtaw6z2AoSpUdV9PispyuH56OHeNlyCgdAzRtoieOiASwyI\nmexFkYaxmYMF6sy0XJrA9fT88yuz/ePti4ULU6WhLzRVgontPDovrZC2yLo4SywNaeXXPIxprskl\nJyEhIeElgPRSTEhISAA2lj6radXud+rZHCl3DYxDmX7FGgjLVUdP51OnXYP7nY7YAYxV73eK2+8h\nmBDoQTktIJ22SLM7aL3Npu72L9DvHOVLs6dcFWRc2fZw2/ffhlkqS8SiFky18u2DsmvzvrGr3JcQ\nX5nPkdo2wPIBrnlR+jU8s3IqTfPVHmgYldM8UnJec6+P81BNR/rd/sAbX8HVqETKWwE3nq389JjJ\n9ZXmoMz13MdZoIoZogZy1K7JINE3qAvT4uk6UVZ23TRiJ2s4APVyv/6A8xfIReyjRhDNhNkm+5ij\n/XVG3wnPG5xnS/4M0VEpj45C2WZm+aWZYkJCQgJxrl6KZjYws58xs39vZj9vZn81fv5qM3u3mX3A\nzH7AzE43FExISEj4OHHe6PNS0heGEI7MrJT0U2b2w5L+nKRvCyG8zcy+Q9IbJP2DWzUUgtRGKlQh\n/WrVOmXsoTznsvHPLSq9457TB6Y0hW2nQIMpA1+h4hVwr0Ga10Hw7SWU2AeDK4drBXA4B+2FOr68\n6LRqCPqab8EDBf1lKc8wQanQmX9exjKX13acdvVRr0QwKr2KoiJLlIwdVXCvgXvQwE53cllA6Wyj\nuow4a2VQWZuJX2dGZZdB2qhzk7tJjVZDPOaghzVSF+exvk5g+VY/SlUDmo7as5MVTGlhINxHJAL5\naVUhtTGq6IYA8KKkIu8H5geILMC9HWIflqFtsdxQQ34uMQ8axFozSyjuJVX+3M85HEDZP71czMbg\nXM0UQ4f141zG/4KkL5T0jvj5Y5Jedw+6l5CQ8BLAuXopSpKZ5Wb2XknPSPpRSb8k6UYIx1OCJyS9\n/Ixj32hmj5vZ4yv4HyYkJCTcLs4bfVYIoZH0GWa2K+kHJf2aOzj2UUmPStKFyXY4VtqQ71yDHgSY\nezYjH4q9WNqUtLNCMPBRjznGUJPh2JLj2D5+e7a4DfXzWut9Ga1vC0urzP0lXzdIft5zWlnewBIA\nFO9l5arw4QLOPzC2VaTVLJ86DqDaOzDnBX0cYnkXFWFPuPFQ2Rf2GUDctfl6LJBLvMQOKDGa8/du\n5ddQ1chlx4lKBEEzb70CbV3FvOEhHHAa9PUics9nMPn9YM+ddh7a8vovJerYaB+lb5FPfinmEDcF\nns8B6qKAJm+hhlBJQ2LsFLB6QmE4RxTBCqVy8xg8voRzToaA+ku5nzMwguN4mSgFb7+gCCHckPQT\nkn6zpF2z44WpV0j66D3rWEJCwkbjXL0Uzey+OEOUmQ0l/Q5J71f3cvzyuNsjkt55b3qYkJCw6Thv\n9PlBSY+ZWa7uhf32EMIPmdn7JL3NzL5F0nskvfX5GqqzoOujqK5BCR7AfXQF1S2HZVV9Sh5ui8BY\n8sHrsGti230ECdcljEADlGDQ5y0ovYt+LH2Jmi/jgQeJF8jJbqakj84rA2ukoI4JhkLDHOdc/zyi\nFseCNVZhnZazdgnMXEm3aV3G8TcEgZcHyK2Nec4zSJv7e96Xi7A8q1GvhhEEl6/69uGroNwzOhmB\n7Ly/RQwqH0IprxEkXay87d3SeeonwZR2htotBcauN/YljnrmyxfrQPVt2MatcJ4l1sWnyAsIMLZd\nFLhfM5fcx0Pv4whUflb787KKy0C9kUc+DGHXxkBvqu/rhIHNJM/n7KUYQvj/JP2GUz7/oKTPfuF7\nlJCQ8FLDuaLPCQkJCfca52qm+ImEZZkslovcmTqVOZqA+0KKLGGp1Jbddp1DFYSe10D9zVB6cwWa\nNocLM0tI9hrff1Q4J2JQ72LZbWegUhlymcegbIsG5THR3/aM/NgcLssMVB5G67JRoGM4Ap2Rs82x\nylCjZuHiqwT6LNBd2nGxDOhaCafD+HDbaZ3s9HzjHq5ZD/oYzZ+67ufcgXI7OL0kbRuXMhiM3oPD\nNu9tQJD2gVxZ7i1hP7fydm4MfJtRAf2YB92A0raos9JAHe8jZ7xCBMWkRbD3FK7lcFsPSEwokWAQ\ntrvnb4Ac8xzHrWb+bHEJJovLHiFZhyUkJCRsPtJLMSEhIQHYWPocTKoi5au2/d0/Rjxwi0JLA6iY\nWRmV0BZFho6c6vV3XAneWsBhu386Ne9BZO1B/awRKN2HpdS86vpSwO15VaORQ+/XeMcdsQMU8gK0\nNq/82hgQHS463RwcxrKuULMz5C8bLNcMbuPacuq1nHrbFSjmCUdy2qjd730ftjuSpNk+7Nqgzvee\n8bYznHMxId2Hq/flnePtpmZCNYLKWW42Uun5oZ8/h/0XKq9qgRs6M287Lzzaew5btrLnnx+i6tiV\nSJuLaz7myws+VttDvz85loBORB9g2UXIm27gWl7hPis/YRTW9em6u3eTEu/2fSljG4HcTw07it+e\niMrfHKSZYkJCQgKQXooJCQkJwMbS56KWLl3v5vfVEAWAIJjZCOrv2ClJfdBRtRaq6fii07F26dQk\nwG25b1DxQH0b0HeIiOod+LFHoGFVZCp90CEWCbo2BDUG3UTsuHpQaK0H67SJ06AxVMcyUu/2Cly9\nodRXWF4IUHwL5HiTGvcR+DsFrWWA+RDLF+2o69eF4ONMZX+J3POGSjiuIUNg/BGCpA2Fs3LkHrNu\n8bjszt+7hPrbc28DAQQyRBBcap1isljZ4j7cuxuuKPdLH/82BkFPUSxsSWsvpKb3h67EV3T4Zi4/\nArkN+dR8RnP4mK3d1AOWfViga4Z7VWFZZRAHY1NnVJt6XQkJCQl3hfRSTEhISAA2lj6H3NRudVTF\nEMi8Ak0VnJ9z1FUejjpKFKA+CoWj4KKkFnnNAU7RNejWHHbSgwr7jHynAr5bO03X74C6u4jRVT72\nfVvUYG4WTnfmCALOMtQSHvo21dpBpE3z+/D9lp90e4WAadD+Fq7m11FEqrjotHICVXx54AHB1Ymi\nEt2g2sDbG9W+PUQw9BTK7hJtL5DLbgg8bz/iNbuzi05DaywrtHFJoKDl2pZf82zh4/nKxtvIV6cv\nE4xRj/rG0KmnwQJO8VzFA15wbAAKbsifr64i0B3O4yzKxXz3BucpsfTRLFEz/PjPH0st6F6YYdmD\nSzOx7rVtZux2mikmJCQkEOmlmJCQkABsLH1uFHSQdVzgrBrAC+SEDuBO3Ft09GCO72dQCC/uXDje\nJsWZI6/3oHYVt49hHhgcobedYh4hqDeLRaJo7XV4gtf4JnOSx1AWG+wUELxN0+4WKvLBqutv/4IH\nppcING+h/k7HqMEMt+t2C7nHONEWuH9xwalis/Llga2m6/u0hM0blOI+8sB7CIy/AYXUsJQgKNdb\n237ODNR/NcE1Ral3gnvVg5F3hedjOncqmwU6Zfs+Ezh/9/u+fSB/jkZ1t38Jt+ucdaFBWZtL3kYD\nGy+D/Vp+RJUZywewxQuYBs1j8kCO+G8mMTBnf4EiX2vn7Q1lz2mmmJCQkEBs7Ewxa6VRLD/aQyzb\nfkCpSMxEhJS7uh9/K7C6nO34LGiJ+MUS7i38Zc+RTlWsTs+HqiGGzJDGt7UWNfBTXBV0nfETsUxq\nD7OW0Pffu8OcLilwBkIfjxfNkULWxwymRU5XBqPYBpOzjO45EEYCzFzpEqMec+e6/31S43GKQ6hV\nq4sQPTCFmy+veh+fdUHlUs9TCBEOqdUeDHIbzKaiC9IR0jkpSgyf9Zn/wQj3E3Gfw7HP/Jcot8qp\nB58XxeePcY9LOOZwRtyDsWwOB6L5zPcvx/6MWg2D2AnEIzCSInaswb1aQCwqYI6bN4jpvXLYbbSb\nOVdMM8WEhIQEIL0UExISEoCNpc9tFnQQzT0vYZZPx5pli5om4L5z63aqIbSwtsdqB7QSjjEZYhB7\nWDC/MgI1XviCeQ4BaIKF/MVWd+whDE8biDiTCsJFhbooKD3aoFRmuQT1QpDlMoOQFPlru3Qh4FUr\nj8cbwKh2xVQ0UnyyKYzLEpRMKLHJsQuR446m/tkWYzonPm5HiNPLA4UJjHnlNHgH9W0GMIIdgJ63\ng44efqzw44aZj+3klff5cUibq5FCalh6QLdOxEyOweXz+EzNW6fAC1D2/cpjOu8XYiOvwjEITkZr\n01jpZAzhYY00QnzRj7R9WflyRIlVmmLq19ZMfCzWwpFlmzmn2syrSkhISLhLnMuXopnlZvYeM/uh\n+O9Xm9m7zewDZvYDZtZ7vjYSEhIS7gbnlT5/vbp6z+sAs/9F0reFEN5mZt8h6Q2S/sGtGjCZBhZT\n18Dxnr1+eLw96blaN4O6O461U0qo0zWMSluUAZ0h5WwEV5MBjGgvg7JcRU2PbZbTBMM8WHR0ZwRz\n0hFjEA+gbA6dMjEVEMz7hMlsxSUBOLaMy24sLCe9hktOizorULaNVA7Kar+ALA2T2UPWjgE9ziMV\nK+Bc1GthcovlBZYPzbAccN/Sx2tGHggwTrWFk89y0d28IdTkHCmER6hXMgPdtL4/QxUGfQ9qbcH6\nMnNfSjmKcZIUcScDlHI99PMEpIpOH0C9U1DmPurrNEe4z1CX+/tYP4quOpOBj1vRO/2VkF9F7Zje\nuZxLfcJw7q7OzF4h6UslfWf8t0n6QknviLs8Jul196Z3CQkJm45z91KU9HckfYN0XD7voqQbIRwH\n5D0h6eWnHWhmbzSzx83s8QozuISEhITbxbmiz2b2uyU9E0L4WTN7zZ0eH0J4VNKjkrSztR3yw+7F\nOC1OD0jWAoHPLGcZqdTVLVBq0NfhNaeMWwjwLh70dLIATpTzPEjdExx7bMcpzNbqub9V07mfczhF\noPkQ5U5pxIrg4SFqfQxL356CBvZi/tcAKWllifQwOAYNQQenu35Om8OBBSrrokB5zClSLnGdx+lt\nHv98Qk1uELCcHzoFLMvTA8PZ9x6C1HsIfG9Qtna9wlEhiD5rEFkAmt6bYNkFq9v9BcYFtXC2aj9/\ni/s1jo5FBXLvKqTwZTAZLvAbzxS+aoFnYeX3s3jKFef6AvbH47cVn4uqZHIByqpieaXEqyLEJYBM\npyclvNhxrl6Kkj5X0u8xsy+RNFC3pvjtknbNrIizxVdI+ug97GNCQsIG41zR5xDCN4YQXhFCeFjS\nH5D04yGEr5D0E5K+PO72iKR33qMuJiQkbDjO20zxLLxJ0tvM7FskvUfSW5/vgDoLutbvpvnDGwxq\nBmWDq8oKppxNr6MFfeSkTgegWsh9La+iXsvU6cui55Tll4vrx9v/dXO/nwdpsKFC/ml0JJlDZLww\nADWvXEFfwvyU9Uf6I1e/GchLU9TdA/+83OuoVIOAXMZir5BjS6eV8djPM4Nyey3zcaHbkK4xJ9gv\ncBjNZWcIZCY5q/pw+sESyKh0vr0o/dgVFOoSHTbkcBucdNZPAlLgT6jsJSIVCtRFuRz8GSphCvws\n6HM9gBKPMrDjSM+p1Neg78UQywFwHQpYSsiOWO7UqTfv3QRLJvUYywpxySLg2V8KpWnn3l655cHj\nIdJt1urZJJzbl2II4Scl/WTc/qCkz76X/UlISHhp4FzR54SEhIR7jXM7U/x4YTIV0QCWlEAIcA1D\n3x4WTgPrqqMwM9ARIcSngfrLPGBd8bzZHMaeD19yU9qPtk59M/Da7SWMaGOwM6kcg66rCzC2hSp9\nH3y8BlDLZ8Fp5axCHvY2jEtjbvVy6de8uOp91SWnqQXqvJwwHwVpM9D0y4fe3/Hug8fbNdTfwUFH\nCQ/k13MjA9UGaMi7RND9auh0Lp/jviAnedpgiQO54uvg+B5s3mrmOCNgv14w0t7vOaPnJxN/nlZY\nBrGFj9FhvC81bL6Immo+VPMK47bcg0LNErsf9ntXwgjZYEEWqu6ZGjIfHIHcDZYgFpX3JV+r/Bs6\npdrQy0pISEi4O6SXYkJCQgKwsfS5CKaLVUcnjlCGE3HE6u2DPiFQt4mOx6zbso+g14Cg2gWsoxrU\nESmQezpa+MGD3d3j7ZYlVLehkMac42YJygyqfbB0Cng5c1qb4/xUtjOomzmuc45c3cOmo6oD5G+P\nQAH5qOTIyab63UPbhhRbOH2dCMiGgbnqqIC2yKu9AjX5AEsA+Yw1d/z6ByjDOmj9mqdwHr9aOCXf\nW+Dxj833t+CknZ8+Z5gjkvpgyx+o4T52QqnWVeXnnIC+rgO5G36GZyL0vC9z5GELanYLhXzew/hf\nQKla1LHJaTUXb0Afge4l/j4CllKyGWzR1rVtGmrcm4M0U0xISEgA0ksxISEhAdhY+qzMZLEwUo0c\n48qQwwsH6wzq4rqg1Wru1HCAINlVHwHQudM3qq/r/OmbsQWFuEYg8dEK+0dLp7WFmSTdQE7speB0\nfNwgwHcEagQa2oC/BgTkDkFD+zEIetH37+dUx2m5hevpjZxK29Q58wjCcb30z9e2aJJU9kADo5t5\nvotgaJxoDJfyHMHT9T6KcsHGS7ifPVDpywhEmLYozxrV3Rwh4+3Ud55DqWYu9eXGlxj2d33/Fmrt\nRSR0h6H3fR7t2Br4xg1QZG0Fu7aqwLIDgvQ5qxkH0O1L/nmJ0qcBeeMWn4sDqPlruz1JKuAeP9/y\nM43W9y3bzODtNFNMSEhIANJLMSEhIQHYWPrcKmgRqXIf9XN3WjgiwzKpBVU8Zrgv89rBiL9VgX3D\ns3BhHtFt2mlV2EfAsEFlBg0agco20R18jsDcrT3vi1DreQlat0KxKjooB9Aw9nE1A5Xa6ajXdSw1\nMKC8BX0De1VNMRnjuQBNtBHcrKHcrxAQH2J/QwvZGvngJehzDwHQLRTiDMS+oJ01aHCOJYvJ2PPJ\nPz7KUJYAACAASURBVBaux7b9/M3Kt/ulPwEZtitcZ8G07hMG194vqu+rqNwHLLtQ/RXynSsUWSvg\nZD5m4W08C0PU1A6IShBqObfx76NEQH+NkIAGSxl7pT/P6wiOTbUOSzPFhISEBCC9FBMSEhKAjaXP\nCpJiwGsfQbAsyLsAraHjchVzW/vbTq/2USM523dlcXwfCheVUEVhwbU8hLIcYB01Zx6q86b9nW57\nkPv3dt1VW6JA8SW6fTOflm7T14dwIUfRq1Gv276veW5urCQ1J+ylYFeFIHEw02N7KUkqQbMq0LN2\n5I9fFQtaWYAVG5zEVYCO414N5Pf2YO5jFJDDbKCbJRzWS9DznUFnjTXH+Ydbrhr3EABfofgWA9Dn\nsDfL8MgZCkfRdm1tGWawAjOsTeTsN4tq454vsKpSYJ8WNnZC4Hcf0QqTuusMamlphXzrxZ4fdzBF\n1EJ0Z29DCt5OSEhI2Hikl2JCQkICsLH0OZdpO2rGDEJe9Z1WTcF9wszpQT/SkBlsubYK50MF3J4D\n8j9JJuoW7Y1R6Aj1g69AruyhMNIo0pM+ztkDAx9WoIagRsbcW5o99xFUDbl4D1ZT62YYaF1AWW9g\ns9Zi2aExKN6gdQVoao/WVQsf8yeC090ti0HgoOML5OyOWiwl7Pn4LzH+vb67Q7dTDBjY7ol83jlU\n35ir3C/8XjHduMXdXSD33K779uiin39ffv4KNZu3kDAwyLsxakawNoPK3G75vg1obwOLuiELp/F5\nmdNhHA8DgvfraBfXTDA3GvnYjvE30cDHroo5+YHLCBuENFNMSEhIADZ2phgyaRknQhUD65B+N0D6\nU4lZg2JtjLxiCU6UksxOX2CewNh0dM3bfvp+387hiDLhLzhmrct6XULS26PJ6BxCgyEeLaeIBGeW\neYHfPswU5ii3WcV4PwohGVIFTwgEMCLNMJ4VZpMUSQxxcD2k9l0I3s66xGlz4O1tI6ZTA6a2YRbE\neDzclqzAzAoiUQ1T2Ox+N//NYr2eJWrrzJGqN8UMmimEGQx/Rzd8zPMBRBcY7g5R02YQxa164f3b\nrzE7hwNRCbFw+4L3u38dMYaYZQr1choISjlr8MQZfAsRi2LRDmrowMtX43iZjPncJKSZYkJCQgJw\n7maKZvYhSYfqpk51COE3mdmepB+Q9LCkD0l6fQjh+lltJCQkJNwtzt1LMeILQghX8O83S/qxEMJb\nzOzN8d9vulUDjYJuZB39GcMlBb6qJ+bJNUaijWJMWTP9CnF/SC2bwvy0cQ1FAwTwDa45DZqNIXo0\nSB2D40gTa2DULWpxYLvFQn+BOL3elosENnZqOgDdWw18APaR0rcWjBrUXBkhTlKg4HYImgZB6QDl\nSS/A/HYBI9Tr5uLKA8Gp5DL2ZQr6WheIr0Nqo4Ea95HCxnoxFUp5rrZ9jCa7PkYB6YLXY22YPkqu\n9EHf6z4NaXEv8DwtUSNmgD+tPu5tb4A0u/VzhJDCEZ5VaDXKsOxyIjYUprjad/re4nlZ+KOgAZZh\n+stu22BsO4NL0vXSn9XdGss08do2kzy/eOjzl0l6LG4/Jul197AvCQkJG4zz+FIMkv6lmf2smb0x\nfnY5hPBk3H5K0uXTDjSzN5rZ42b2eAUr/4SEhITbxXmkz781hPBRM7tf0o+a2X/ilyGEYGanyr8h\nhEclPSpJW7s7YU2Vc5ifDg7hKrOLUp19FhWJ7dHYE/F9Q7xve4gZbGEWWkHZ3R54/FoBKtOA4ugI\njiyxBgeZFGvLtNtOAeegwEV+upMNo8lI65l+towmtgWWDOyMMLRnh87rJlOk80HBr1DHxPiY4fqX\nrEETVe8patVcKnzcMqqmyEtrUH9Hzzp9tBFoYg+UlXwX9HkS108MqZKrxs/TYjCoZl/AvTXEbJK+\nFqiBcnjj4Hh7HiVdlg9dxy5KJyk4S9ku6L6Lei07FQktjGMR42iIt1zGUzEec6dFbReMVTmDe058\nojYzye8czhRDCB+N/39G0g9K+mxJT5vZg5IU///MvethQkLCJuNcvRTNbGxmW+ttSb9T0n+U9C5J\nj8TdHpH0znvTw4SEhE3HeaPPlyX9YKQhhaTvDyH8iJn9O0lvN7M3SPqwpNc/b0shqI1pTBmUs9VF\np1KknrOZS4CTvON46zQoScoyDhUoDgxH85ZOKlBLQavGfeePK+wDP1EN2+5cU6SH7Q+RkgiW1INs\nzlofqzOcXPqowbFTuEK8Zk3TGZxmoDg3EDlbBJ3TZJYqf8ufWyi3e6Dve6hHYgcdJUW5EM0QjNwz\nUECUDxVoXTGCFTAVYoQcBER4rxC0vPZnzSs/rpz4vWLTMDTSArSWJr/EEM+LYFxbROV8gmD4HMso\nQvnc0ciXTAoo/oIb0NWJj9elfQSYIxWRzkTr5aFeiecWlzDM/HPb82s4jOenCr5JOFcvxRDCByV9\n+imfX5X02he+RwkJCS81nCv6nJCQkHCvca5mip9IZMG0XXfT/z4UuhYhpwUSendQqvPgyjVJEkQ7\nHbrfrAagrIhv1byB4roNWo1z5jdgVguFsgLFHEeJ2k6Unjy9jkcD5XKFnGhijsqfNVxiBsyJXqvl\noKZL0PEaedCDlW8PsawQQKcClPAB3FvaHJQVjjEWqXKGwPCPlNeOtx86QjD6y/aOtwusJWSg9QMs\nWcyZ+wu+f+L6IsW0C64m90HvhdoxS+Rh9zCv2MKagYGG0+2mhbq+puoZnIMaKNWzeol9kUsN2Xcg\n1lHxGz1A8HqBsr4BUnMWlxgqGNJmF7G8A9NkIRIjNPEabDPDt9NMMSEhIQFIL8WEhIQEYHPpcxvU\nP+joUTVBUC1owABUKhvC/HO7o1BHN9y1dQLKGECfpqC4DeylSpRUCaB4DfKmV8iVXTH3OuZTby+c\ndlKJnldOd5ZQaK2hd5ZvHjzl17F90W2nnoFzbRmVcKq/K9RoKfCobAWnbA0CfA95MNBf+XgtYU31\ndOHJ4kW/OxczkT4l875OM983u+H93s+RnwsrshO5wlhWyLFMMkAJ12qno54rCsXmfVmVp1PcDPnR\nDORfodbL0dQTqnug2Fv9Tv2fI5d9CcbeG3pfraRFnO/DiqgjGM62iD6gyl4OEJwd6+hcnXtA+c5T\n3tdyF+OJJag6hkow0H2TkGaKCQkJCUB6KSYkJCQAG0uflZlCtFWim3SJWh9TUKJQOT0bDToqNbq0\n6+1BTWWQ7tXgFKu/68N5wRmJmi2nHgsEErfoVwN7qXkTA4JB6Y9AnwOsnooGyjZyotX367wIC7Ah\nkq/LGksC466PtA4Lc6d1pI8r1Ggps9NzlQvQyhN5u+w7g6CjpE7VmvnouztOpWeg9SUCo6dzWpr5\nZmC+L5cE9pyrrmvqtOifoE4vYT1N+7l67Pf2ACp3ufTrHwcPksdtPHZKD6CpLWr4jJZ45qD+F7if\nJdyv57h3rN2To9ZKBZ+y1c5zIw5WKIE7QonVBvdlHf8eNlN8TjPFhISEBCK9FBMSEhKAzaXPZlJU\nendBGVn06MrCOW6N4kqzGJTagHYxlbhG+cwW9LnM4XyNIk4nVGG0WcGOqkQOaz+qmPPGJWyq48XE\n6Vi/hyJOUANnSKbOhgieho3a/MipVN75cJwItB71t463n6xcwabiOjEf2x4cvpFurl7hNGy1cHq4\nwv7DdR9rBqwjOgABzn0oqEvkrPdBK3uwBWPRrQbq734Ouh3HK0dhr6aELRrKtGZwYe/fAB9GXvVZ\necFUkdf1rMrSr8ewjHKIoOqjxrf3an/OhkKZWijXOcYixzW3N1BW9nq3z9VLoOk7UJmh7Lek7/G+\nJPqckJCQ8BJAeikmJCQkABtLn0MmVaPunT9ASnCAQsj81Aou1/2o1rHWbQOa2qP6N/DiSz0UBaLV\nFtXvpU4P9h7D8XidczuFEkkFtZ2Am4FiMj+2B25zLbCglN/yHAWgskgbe3t+PYZrvgSauEQubYbz\nsE6ywYUb9ad0HxTqeUV37m7/DGO4hJp6HQHGk9b7uB1APccITN5mrjB4HhTq7eugu5ES50jgzpmz\nDZV/hopSixwRDKTpWG9hzW4WicrjnKSPKIAGyzursX9+sfYHdIgc74AliD763sLfrMY5e3AhX1/9\n1qH3u2U+PPpySFd5Wx+fgrcTEhISNh7ppZiQkJAAbCx9tjpocL2jp3XtlGmBYkTLPmhg4dRjq+0U\n1YCiSC0CsE8ASiStu6oT1NCBsrpaIqi210fg9XrfLVeZG1iHtbSagspMF2zD7x1pcgVKXGbg+KNu\ne4bo4jFynNXDkgGcmldwgV7AOm0EKlegLwHWZRPQxnV+bgXKPDRSacch/wWaSjLXIHg94B6Nof5W\nDShmDEjPIRu3R4gyYBGzCeYSaM+wNFPjvuToC5dpwrzbp2igwmN548TzhCWLPk8/hRUcVlVmeF6W\njDZnhMK4u6YxPPJ2sByxwvUwqD0fdhQ829A51WZeVUJCQsJdYmNnik0m7Q/iLzFizMoGdTeueszW\nFDFes6iwjAv/sI+UK84g6F4jxCZi/V2GsqFZ6X0ZXrp4vM0ZxDAG+bVwN6lyn+0w7nE6hdMM4gHz\nBUQcb1rNirNZGI72un7VMDZdwo6lOIJLz+j0sq42wSxnAAHgEGIQZirLpaf/ZbNYr2SK69zz2XOL\n+i9zjNWRfHY6WSCWjk4ymE0+XR9hH99pp+xm5XXPP1tgdjbBs6Brrkz04WTTnJWKt4KxLtLy6kl3\nIQGpirw/S5gQjytvu2JqKWZ5K7jPHvUw44Z7Th/CjKbRmQjxiNW2X0+NGW5x1fexyLxOxt9uDtJM\nMSEhIQE4dy9FM9s1s3eY2X8ys/eb2W82sz0z+1Ez+8X4/wvP31JCQkLCneM80udvl/QjIYQvN7Oe\npJGkb5L0YyGEt5jZmyW9WdKbbtlKCFKc3h/VTjHHQziWXHJ6NoZ4cb3fUcjFyj/bRpxYDgPTDAvt\n14KnYvUgaIC9q1g4JVrmqK+C9C4bdX08YTyLWD/+gxQX/q0q+k7JZlNfJtjOnB5NLvhYHETLmsXM\nKW0NeruEQFWYH0cj0iHiJBcwi622KCL5RX0s99TB+/e7cSwfdLJ/5KdR7yqEIywN5Bg3BQgdoLIB\n6Xq74N5DGAfnMcjwROol0u96iFns7e74eZAquah8vHKYCTN1L0DQW4s38LrV0Pw8l3IvDNQMfWwp\nNLVYmqkhruQQqVooMw3On8VblB/gnoOCrwUVSepd8uPqj3b3zUKiz7/iMLMdSb9N0lslKYSwCiHc\nkPRlkh6Luz0m6XX3pocJCQmbjnP1UpT0aknPSvpuM3uPmX2nmY0lXQ4hPBn3eUrS5dMONrM3mtnj\nZvZ4VZ1e2S4hISHhVjhv9LmQ9JmSvi6E8G4z+3Z1VPkYIYRgZqfO20MIj0p6VJJ2d3ZCf63G0vAU\nMYtbiMkaYCj6kSrTtHM/Q70OKMGXzZ1ktqCQwodV1RFMVq+jeMtFp5WFOVUx686LbC61cE1dspQp\n2GOLeLwWJVOzsbfd5n7Nc8TStbPu+gaIHZyCsk3htLODWDqDq0y1wja4/DO1q7W7M6dhD19BGl08\n7TSHapo7f154VVMND6HE7vvSSP0qH89yhPHkIA1Bq5m6FmlwQDxiBiorxFfmiCBYgpoOnobJLJZm\nljAlnkPRzmfd/VrAbZfPAZ1uGjxQOZTo/aG3N2q9Xz0o3uUS6Z9YEqpjJECNZ6WBAxSf8wCqvP6z\nSi45LwyekPRECOHd8d/vUPeSfNrMHpSk+P9n7lH/EhISNhzn6qUYQnhK0kfM7FfHj14r6X2S3iXp\nkfjZI5LeeQ+6l5CQ8BLAeaPPkvR1kr4vKs8flPTV6l7ebzezN0j6sKTXP18jZpkGZUdFVlTrQBlX\ncKnZHjoNrqMCSXplzzoFLkGZZhNXH7eGrpzOEFTMQO4S6XyhdOX2CFS9F2nwpAKtAe1ZQf0O7Rm/\na1MsE8AZpQeD1qkhUDvSo96O92kHRq0QghUWrAUCaoa+jKbOrfYHoKmk/hCl16x5AGqYzf2at3Z8\nbCsEJs9RvvZa5UsToxlqwWD5YIBA7gVskIroKtND6mMxQwon6sxM3ehHuzDZHbz6QW8bZUubmY/z\nBOmSiyeuSTqZBnoE2j3se9uCgl0geaAPx6TGMM4Lv/8ZgtqFZ26w1Y1pte8ORA3cdbQPqo+Pi5g2\nemJZYoNw7l6KIYT3SvpNp3z12he6LwkJCS89nCv6nJCQkHCvce5mip8oBIVjB5cB1NIBIqlNyPlE\nrY+1C8yFDArifS4Fl2Pk5IJuHCEnVVAxwwWnLIsWCiVU8Za1OWI9jl4NBfXI226h7BpU4ZNqIBRa\nBHhrDnWx8e152VHFEY5bDEAv58hJhvq6MDjjYJ926du7OfKDkfvLsqkadW22KJm6wP3ZnjPH18d8\nFXx75wbqshR+Pw9WqOOy4zL2EscuR92xAc457cjv+RIB4ENGe8GZhi5BNBNmvnsftXiq+zsevmjR\nIIKnFzDtLY/odIMypFhKMSyZtLvI1b7fn5EaQeh2o9t/NPTnuQE1N7S9xDO3jH1sN5M9p5liQkJC\nApFeigkJCQnAxtJnScey3hCUQYcwnEWws5DnurbRamEFlmVOpSpQDNZZme4i8Be5ryx9Cecs1aBY\n48zpzo1ZF+x8QJNP1isBrbxv4arsIECtpkEuzl/PkcOMYOtetMxaVh4MXQ68T4sVaBXcbA+PPDC7\nnHhOcLaDwOx9H3Mazo6gxK9Tyxn0vkKkwMHMLb+WMLYtsezQG3l7DHw+EALm90Glca5pHK8G+dDF\niXol3pcZpNgayxoHUNlZKnaAeq99YRkmRkEvEJjPZZQ5Pqff7xBRDn3cIxuB4mJpoD2Rk+4q+io+\n34tDH58eIisYgL8oEQwe88TDhk6pNvSyEhISEu4O6aWYkJCQAGw0fT5WY8FZV+BnhwdO/YoH3KZp\nLRYO4Ii8hC3VtQqWVwhrnsxPd16uEYR8beVUZTCkpRYoYZQ0y22ogqhAcgn1MkoK3lA2S2xr4Le5\n3kZOtKCKr92XpyzBCtqL4F3SqmwLgeF4nLC7whYUT/SXqvgsdOfvoZToLsq+UnFdtT62MwzhDdTc\nGUOKv//Q+1gfOA3Xtn9eRKfwGve5ffrG8fboAbfwtAbPUN+XI1ZTp+a7SFxnTRUq/hYV9z1EseeY\np8wQ9d+gTm8FW/E+c9wZTA31v4C9GER5zXtdm+XInwPrwRkeY1gikWHFmi8biDRTTEhISADSSzEh\nISEB2Fj63CjoIHK1BrZPvTN+BwoEUq+toZjXW0CpHsz884Pc1bwJ6KiBM9Ujp3ujgedYFz2nWMUV\nqKKxYJDB1RvMUGCYyredYi4+6nQvQ95sDepzBHfoGjUxt5qunQzB01SKmz2nmtXCFeoBhN1q7OOy\nDdtwFmtaHPgBiwwO2pOufS4TnAgORiRAwL3K4BS+g1Kh1oeKu4USo8j9LuGCPoo2ctZHwPZ9KA0L\n+7ccAfODno9ziWDnAtEEhXk7165f8/2X3T4lllHmUPmLlfdviMGo9ryPYebrEUc4NsN1MG96BRV9\nfe8WEx/zefB70keUQYH48l5s73QDvxc/0kwxISEhAUgvxYSEhARgY+mztUH5rKONTy5dcbzUc/o6\ngB1VjSDsIkalInZYvSe8jb0HXam+Onclutpx+tRD4agMObEMmM5xgjBCberrkaqB6haw5RqASmWl\n93t+v19bC4W0hPp+oe/7TKGETqedEr8z9u8b8KPlwem2XBlyyenOzGDfFY4V8nwHoPjreOyAgPVr\nu6g1DFqP9GDlQ+S1U3E/9HZsjBxec1rZtFCCr0GWjRhO4Fi+BZUXkQWGeztBtEKJwlEVli9oL3fU\ndsfeaP0+CwHbW1CwWyx1MBIA9dQ0RjA8l35IiWc9UPLozl0d+jjsHiFnGrg68Osss3W96s1Emikm\nJCQkAOmlmJCQkABsLH3OLNNW2dGJXViAQaDULDhtCftOIepeR72Oek4Qhg8gABnBy3s9D+ot5qiZ\ni9rEAjW/1rhyGxiEveu0Kov8EHHBCqCyqC2lqmAgt9PHBiqj8bcPDJNK74WsC0Kfr5wmDeFCPa5Q\nRxh0nIWrEPes7AJ4Hah8w8JQW1B6Fx19HaAu8XCB60HAcsUAaziSN6jdvYIj9V7l5xmB4tKQuhl2\nfeyhyJOBJgvbFYPaEdlQG2p9I984G6BmOGzEhvF+2czbmEFlXyJqgehd8+e2us+XIEaoac764iu0\nbzlU7GgXN1jCvRt55aH0AdoJLKzWXUO2oc7baaaYkJCQAKSXYkJCQgKwsfTZ5AWjqNy1oLIBtO7/\nb+9sQiS7qjj+/9erV9VVXema7pnJEDLRRBIiszEREUURSRD8CNGFSIJCCIG4cJGAool7IW78WIgw\nJEoWooZRUVwIErNwNaiJIGYMxmDIZCbJZNI93V3VXVWv3nHxbvX9zzj5mNDTVXXn/GCY916/fnU/\nXt+6555zzx+L0cQpwl7RpmTsHqgEsAg+rQwkkFb2OA9kv28pDr1aFk/GYioVqxK8vVKZQWKBoZSM\n3cNRrMM5EcjqjMUcFX1rFcBSypaYP0HjuL4pe1xFFKouqbDGurF5f6xPRwKWN8XjXDQkC7l45SWm\nGTYJWN+U7NlSPPXglsNY/4EEaZ9rxzZcNgmIzmM/NyU7uKb3GoUy9jqx3ahubvGaF9K2mQTYZ20R\nBZPlk1o/tsXy0r6d43owP9eHEhguweC6xbiU5ZNSlj1Up7sUL/JIUprVZSNBJv2bhc/vd+PPm9Im\ni4M3mTOF6INamab/eaZmiiRvJvl3+bdO8kGSKyT/SPLf4f/lt3+a4zjOpTNTM0Uzew7ALQBAMgPw\nMoDfAHgIwJNm9gjJh8L5t97yWaVhvFV9W/blmzWXhfQO48ymeZWIeE4SxMo3r23IYrjEvWmGkXI5\nfsf05NufkqWkwfhNPNySb3OJSePw/7OQlDJradTjvUuy5a0uU6umdG1d6j8UJ8GiOB3aIfnpahkd\nQQOJNSwocZzymZBMN7qdsiVJe1+vxRlcTZwkkzg5ANgIzx9mcSa3UsTZXl3i7sqWzE5FvrW7JXGF\nG/Eztw7KDH5BZo3SLxPngWapUYdSIdPjbZl5nqf5I46zxXp8L7YQY1x7hczmwnPypfju6UytJ1o0\n2rfcH+/XMq4PY99lYoXk4jCBhERasI5asj1RJ38bsrcvk7jL1kSYhu5o2WtuB/AfM3sRwOcBPB6u\nPw7gC1MrleM4STNTM8ULuAvAz8PxITM7HY5fAXDoYr9A8n4A9wMXCIk7juO8Q2ZyUCTZAHAngIcv\n/JmZGXnx/BxmdhTAUQDodrtW7OiUiFkjZp1JGNhYQ8LC4+uyiF5TU0sW/Qsxx0aSCPVcLZpJXdla\nlq+Lo0c+c9Pi/c1RNYHPRT9Ti1eIKV1vxp8synLAUORW9XM0ca1uS+uHzDcaL6kKpLk4V7a2ow12\nbjHWeQmSqFe2JR6oR3OvJ/omul1uf1Hdsyam5nAs5pts88tlqaFdxldYJWbFhYaVbVmaEDNck8WW\n4fpYMsr0JB51LE8sRNZ2YSzme1PM4Ga8bvJe6PLEYpB+rWsWm4HIzmpqGtGLacr7x1F8tm7bLGWJ\nY3lbMvl0JLFwaN9MljFaDYnjlPfmjXF0Fi01qndoXHNHy17yGQBPm9mr4fxVktcAQPj/tamVzHGc\npJnVQfFuRNMZAH4H4J5wfA+A3+55iRzHuSKYOfOZ5CKATwH4qlx+BMATJO8D8CKAL73dc0oz9IOn\nL8/zi96jW7E2t6Ney0JoFopncXyeQRYZ9KIpqdueDqj3V6wMk3hIbEdzryle7IlZORpd3EwyNQGl\nLAWljA2xfeSmvng0V7Noyh3KKpNIPc4tkebcLkQasxvr0KnHem5I1p8FTdorCU/bY8k2I3KzY6vK\nfrCM5v1IMtMM32yJWLIOaVafTMrVkrUR9W5rkt2JKTgSnZVc5E5rPUlIO4gN2lqWZ0g85hric1oi\nvTqQd85C4mI1+zOJFOj24ueflvjJVl2WONqiPyN7WJu65VTel5q0VzGuyjhqxcZtyTbEuixftKUt\nzhTr1e+XaWq1zNygaGY9APsvuHYWlTfacRznsjKr5rPjOM5UmLmZ4m5RM6AVzJy2VLMhuhO6jUqs\nKgwRzAZJeDqSrWWmweBiGrIrW/hy2dqmwdhyXO+KtKXKsAazRQ32bh7vbUvw8qlTp2JZ2tH7mS9G\nk6gvgdcDSYraHojp26nKVVdzWEzt9VIT5Ur9JRtL0Y5tofKlqm+jgexDCYIvQsA8z0YzfXUQPZ5b\nEhjdkawumnD1KhPtlE5si7M1MU/l/lykUheCx78hiVoHkjGoLe0PXQERE/Js7+zO8VIeP38kWjQa\nHV2Ed6SmgfuS6Wcs2/MOiTIrVmQJoiWB/LKdczSO5ntftFsaI1lLCUsslKWOUhMfy/vfaUeTvdyo\n+jMzD952HMdJHh8UHcdxhGTNZ5JoBK9zJu5X3UO8JfbztpgKedBAGUiiUtaimbK2HqVECwmwzcX7\neQ7x+pIGG0tAeL4ZzZqJ9xUANkMWnqbsyhln4s0Va+zqg1fvHKuOigZblyLr2TDxLoqE5tpkr7DI\nd2rA+sowXs9EmlXlM0cb0fRtSD0pGWZM6gkxD8ug71LTYHRJYDsQ77N6mRckrN3ENO0NYlk0+kAz\nH22Ld3UiyJNFixm5LAdsicQpJSFxoxvN5Gva3ViWrfjsTfFENxckyiAUty+JXddzTWAryxSyHLPc\nlzbsx2drYHpD2rG21Nk57vXiO1KGv4tM+ln3sqtMqr6f3X1VPTNNwpsQPlN0HMcRfFB0HMcRqLKU\nKUHyDIAegNenXZY94AC8nikxL/V8r5kdnHYhdptkB0UAIPlXM/vQtMtxufF6psWVUs9Zxc1nx3Ec\nwQdFx3EcIfVB8ei0C7BHeD3T4kqp50yS9Jqi4zjOpZL6TNFxHOeS8EHRcRxHSHZQJPlpks+RapM1\n0AAAAmVJREFUfD7IoiYByetIPkXyWZL/JPlAuJ6kNjbJjOQzJH8fzm8geTz06y+Dns9cQ3IfyWMk\n/0XyBMmPptqf80CSg2LQjP4RKq2XIwDuJnlkuqXaNQoAXzezIwA+AuBroW4TbeybADwZzlPgAQAn\n5Py7AL5vZjcCWAVw31RKtbv8EMAfzOz9AD6Aqr6p9ufMk+SgCODDAJ43sxfMbAjgF6i0o+ceMztt\nZk+H4w1Uf0DXIkFtbJKHAXwOwKPhnABuA3As3DL39STZBfAJAI8BgJkNzWwNCfbnvJDqoHgtgJfk\n/GS4lhQkrwdwK4DjeIfa2HPGDwB8EzHf7n4Aa2Y2SSuTQr/eAOAMgJ+GZYJHg05Riv05F6Q6KCYP\nyQ6AXwF40MzW9WdWxVnNdawVyTsAvGZmf5t2WS4zdQAfBPBjM7sV1X7980zlFPpznkh1UHwZwHVy\nfjhcSwKSOaoB8Wdm9utwOTVt7I8BuJPkf1Etf9yGau1tH8lJUsQU+vUkgJNmdjycH0M1SKbWn3ND\nqoPiXwDcFDyVDQB3odKOnnvCutpjAE6Y2ffkR0lpY5vZw2Z22MyuR9V/fzKzLwN4CsAXw20p1PMV\nAC+RvDlcuh3As0isP+eJZHe0kPwsqjWpDMBPzOw7Uy7SrkDy4wD+DOAfiGtt30a1rvgEgPcgaGOb\n2RtTKeQuQ/KTAL5hZneQfB+qmeMKgGcAfMXMBm/1+7MOyVtQOZMaAF4AcC+qCUuS/TnrJDsoOo7j\nvBtSNZ8dx3HeFT4oOo7jCD4oOo7jCD4oOo7jCD4oOo7jCD4oOo7jCD4oOo7jCP8DTzoLOvEFMqQA\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2443d141d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[11], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[167])+ \",\"+\" iceberg_probability : \"+str(pred_valid[11])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f2443caa470>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZGwPU+PjFAcbXHNofOzkkBJCNiYrlMe6/EvWAgICHDOfekezkxKBeH4Jz4PYt\nwjjYQM7f566Ztm4hnz6H1bA/sy8INSvjBWmzlgwfX907We5t25s7R+5+7rURKwTJGn108irx1YZ+\nIgV8JhG+Pvi0HcMS2ZjbLWm6+isWRwjkIqg56dp5hhE4J22blxE4BDLCeCtobUZmrWQ9mEs+CFc0\n8XVEALRxwUSDKNYynNucZ6gApUDQM9C3zyBQM4IG6EnHM7Y/6KL9wRwWKeb5CNWtAutwAS4EmNeS\nwPocjGtzodG0OWQlZ6qoNO3ZNp0M3enc6n9W5BBVz9ozOgeFW+N6ebeye7IHy2YCC6YJK6fv57xc\nkQi4UwiWRUBAwFoIL4uAgIC1cK5uSFSJdHxj3QrBthyiIIgZySOZcRp6SW3mtcdmerkx+QSGFsyz\nY6yvYBK7BAIp4HxUCJRVMH/VLyuov31qM8Z2PDa1pRlewD05HtuVNsERKHDMpXfWhFl7lNlcRRDF\ncYi7TWFjb5DKjYrRDbQxAAv7RJxIRKTyQdg55qFNPaJnrRlw+qjdK6b6KfjjcP0J3JYR3LMCvISu\nr7BNIKDDTmGKit5D8HMU979E8+jWyM7JRtKnmAnLaenb8boRKoRPzS0mHUFoTK3EDJ6iYrXYxkZA\n6oOzDg3AYzzdi4G5bywYUL373/1gWQQEBKyF8LIICAhYC+eeDVlGrU/RaTtmhm8hChwPbatmVr/X\naCZH0JEcg1vAxryzarVASTJfXd2qCeX92YnrVGK+3hb8kDkqGpnnZ6euGNmVCNyFChwJAQ1YvAsz\nhSlN3Ul2NYvYAJquwil3j98G5Pah8ajIfMjStYLIywK/J+BkTCDRP4GYTgmxmg7crQicC9LA86fQ\nBdhnjBJI5VfgcAzdisyJiDhkN1ozcFFaaJEwRyahgQyDb0XWKVFdDLdSkCw6RtXvpcSyaDG4FYdT\nOtaGDtoPKNoFRP5cEzxPbAx9XW2eqVdb5XeP5n0ytrt+hoCAgAcC6zRGfomq/qaqvltV/1hVX+vX\nh+bIAQEPEdZxQwoR+Sbn3DtUtSsiv6+qvyYiXym32By5ikQmLR/tpdkU2TvrlMm3QlYyzhDdB8nl\nmJWjeAfGJc1TM32niF7n0G9c/JlVIy4escrUhTfFtxKzQ3k8CsQ0RjTDbVSM6lcb6FRF+X1cxdBn\nCTJE3TMcMIO708B19nO0MIDgjzIzAOn+QlebsMvMQ7Nl11yi21dJcSJcWxfplaPI5plkuaVmpIjI\npTHcyY7d4rTGAAAgAElEQVTN+ZL6v2izibSdfxNVyewClkLw5mhubgC1POUCiG5qy4ulWJI9BjJD\n4uLJzNyADx9Aih+ubxMCTQq5/mdjUM8xdx1mMvxt6aEz8xBdyC4u0MKgAZfc39p73Rj5aefcO/zy\nQETeIyKPSmiOHBDwUOGWYhaq+riIfIKIvE3WbI6sqq9R1ber6tunDEIGBAS8qLB2NkRVOyLysyLy\njc65vlI38zmaI7Mx8s7GljsRd0E9QAz5+wk0TLagJen8Ni63/VLUSTRB8tlH6eJu0ypKj2coaURU\nO4Z0fDM3s/H4qpmwLd+EmFL4xGRi5ukcZKEEzYCZ6cgVdiik6E/VYfg5rj5o1LJyE92+cGx2/uqh\ntcIhdEo3UqRdUFcRoxpygQpP58+/PzHyFbGldjxW/V5EFW+M5EYxQ8NofKbKFq7jgHPh7ylduRb0\nRdGkuo2N+jlqSf4M5KvHrZaF/t4CxLHc/5OIsIFibrfndm+nY7u4VsOIfaxlQRGztFABPDo0P6dx\n5YJt5MmCE3ReKylIhEbf6CAhsc8MRvHdy1msdWRVTaV+UbzFOfdzfnVojhwQ8BBhnWyIisgbReQ9\nzrnvwU+hOXJAwEOEddyQTxOR/1ZE/lBV3+XXfavcZnPkJXKcegbruDdGY1yU8S4tLvKKXBcVIXMz\nGy+zJRaw0TbzeAHhlGRq0WaSkjabyAb7w7NEeZggAg8hGBMikVOv4zbINSPUwzRIBItvjmdXu+Yz\nLWAeo5pdMpC/HEhODWQdhKSwQ2iTwsxNSVbzu24gczLHvJGmtoUWATK2bS42bM6fmlm7giwGoQzT\nlUMiwA1qN7CDMTHrcYTG2c2Y1UGGzsstlDZBg+MSmbRyivL+ob+/Z9Ra7MJ/jdDirYJ7uKCYEbJ+\nBUh8bRyHtTmTGUlpfnxouk1S4gaIZZl//nW1l3xHsE5j5N+RszMyoTlyQMBDgsDgDAgIWAvnW6Lu\nVJrLiLCy3mF1zYIgw1H52hBF5mCyirUlIouFrT/cP7DzXLFoeH6Esmjsm2MsGx0ziZdNcGcg33Rg\n1h+gRJrdpK5Art1hXE2KMELZqQH9xmXHK3YJi0BKm1bsyIaMwqG5OPElEIfYYBe2L7ofSIXS7Hyz\nNpULKFy1cmh3Yr/hsWVMNrs2z3tTcyUnaEWQQilNoEfqoPS07TNZR3MbIUvRW7K6dH0H9UUFRtlA\nSwEOPncsF6/vHeX/FRL+owwNiyM73gT3sAXX71QjaZSal+gyxmxc5rMhbmi/RyClNZBRKeB5NSAO\ndrcQLIuAgIC1EF4WAQEBa+F8S9RVRFBivATFVksIzI5R6tzyBKQICleNkZn7xwq1retGeJk9gsxI\nZZHmNGVtBjprwbWYxHb8VtObnOiINSBxBq9dRYnyDN2kGCaO0cBWm2bODpElKWc3u1kVsyUUw90D\nO7aNrMdwsXL9HKXjOZW6enb8Z/Zvps5s7yK7AFLSoAcB5smerEIKMzxHDdBxYsdh7c2Rl/FPkJno\nzaiCZcdOUF/E+0mUcH2bqKWB1yjFQe3mFBs2J8kYimggED7ZRc0GdAYmfSPzpajNqaB+FSEbNEdJ\nfXZUL6OyXlqoNTlFmkPWZb+s57DQ1dd+JxAsi4CAgLUQXhYBAQFr4VzdkEpFZp41Mo+gZoXQdL5h\nptqAHac86YlNdFOYpx0QVBo7O3YMnKedoD8G1J+mEIztO4u2b4jx/a+lfn3McnJbHkO1iZmJMWpQ\nNiD8ip7OcjxCpyxE6ZveOm802T3MlsdQpzoy7pNsYl7GuLbKoYdKamPfjS1jIihB33ykrllgBP6R\nzOZ2r2NzRYyvWgaKkgINZA/2UK7NpNZRYsd0ZT2nrSO4o1DEGm2u/tYtoDI1QX+UnM2Yh6vTB6Nu\n/fyh0kNcBgHoBFmKhT1b1wu7hy00bKbbNEMXPof+MwkIderdU/aVYTl7gee/jfqiyBP+4tAYOSAg\n4F4jvCwCAgLWwvlmQ5wzMd2MzSrMJKPZnMOc00Ft/h20oPaUWKQ5g3jvIUzZDRBuThFX0DKuHbFN\nId0jRJa9eUhuPhfpYnSj1XUKNGczZIXYzyQ5QFPjrdq3iGG+x7iGCi7JYGQR+D+tzMS/AiHZBVwP\nmqtD1CPEbTtm29dbvGQTJdTAFag5XV0cnixnbWRXGnZtdFq2RrbNBOmI476NvXtUTzBzQqdEiuEG\nPNM0l6yF8oodWV0nFHXt/CM2jN71/U+gtkXyWWcCAhmyVQnuRX6E/iApGnBDLkHx7DanqOXZrMd7\nVIIqB3Hj2dyejzae+UmnnucqCm5IQEDAPcY9a4wsU7aotzdkBUGXayW+Vl5oZSe20FM/XmAZn1wK\nm0zsTZw17CvTbllQb5Ig2LQ4o2zP58JbFaX4bawx6NMLGBYVKhpL8DYKdKdq7tnYj9v2lYl8VaUD\nt6IagU+C6+9BELN9xm3tQn6+OLIv+KSDrzVEfBqetn1KwAXVnaOBfZFjdAGTJoJ6Uzve8QT8A1Co\nSfHOFHR2bwo2WjbuOfgZ/IpWZ3z2Cnz9Gzw2KOTJDqLQ/iM+h7KMjuwYUxQHNMBPIXQTlckjm+cM\nVgGp33nXLA7nrYgcjZmvXzPeSqNvY5ldsHu+HIlWKzWo7giCZREQELAWwssiICBgLZxzgFPE+aBU\n0kCA79CCQAkqJrdeYeIzm4N6+xIag82UgjNmnsWokuwiCNfMwXPAsDowCfdTM0/jIRsj1+NugY7M\nAFtFJZgJ8vwInk3mtr4HPcw5uBtX0Ay66U3RMY5xLYK7BYuzmZpJ3AKfYNKAqY5ActZAKwBEanME\nUy9EtauWgL7OhtIxaOrZAm4Y+THP2nxmKU1kuwNs6jwH/6TwrkA5QyVsGwFLaJqy0rhd2T2KEcge\no/0DdU/Lp6H7f7F2c48X9ky2oWmaIDBMrtBWDFcGrs8oYR2ArMRxYXOU+2ea+p4ON3rRQ6e0I5uX\n1HNxorvnhQTLIiAgYD2El0VAQMBaOFc3xKmTuTdFabXPNm0Y3czMzM7U1j/jk+d0K1JQvJt9RKlp\n11JGHrTaCF3DxoiMZxmi7ahSdCtMyAK04imyLiNE0pug5JaoKB1MjB7c3TbXowu+xvLo1OhMcP2P\nxiYyE0P3c8R2BeSWMKsCnksCt20DGaOJN9tdx1yWBtovHIGm3o/MbM6eMfM4Q8uHZt8GM2tDAxNj\neYY0cH+pj4zRUBlVtDNI+F9BlsyxGTa9gA27thHEesZggFSzm+34pAHXE20TIMEqebQ6G3QBt2K2\nD4Gkx5DJmNg1LZaV1NCC7TXt2tjouwGuSG9U39v4Ln7/g2UREBCwFtZpBZCr6n9S1T/wjZH/J7/+\n5ar6NlV9n6r+tKqupi0GBAQ8EFjHDZmJyGc654a+2dDvqOqviMg/EJHvdc79lKr+sIh8jYj8i+c6\nkDqReF6befGWmWEdVtiNzVTf+DMzbR95WU1bnoAUs8jMrG4g6j1HBHobFZXMOgyHIMtgGpbjExGp\nEJlXTzqi6zEUG+sclOkFhVhQxZmA4h337JpHAyMrNUA3j71wyggVpa+IL58sTyZ2zmbTTGVFm8gE\n9O3x1MzgAueRxmrBlMR3VnMQbdkbmfmewa0ZoAJ2A9WVgqrTCkSjUQFONsRqPrw0l6zwTZCbSF1R\nTKaJ+zxBRifesrmYHtt55tdx/cgq5CgBLn1j5PSS0eT5z6R0q1twTpDpSUAcm8M9jTqghENQh66y\nu17PY37JbvqTU8vWXNzYtm2ZDltW457R5PpOYJ3GyM45t3yaU/+fE5HPFJG3+vWhMXJAwAOOddsX\nxr7B0DUR+TUReb+IHDl30sP+qtSd1Vfta42R5+cgQRwQEHBXsFY2xDlXisjHq+qmiPy8iHzUuidg\nY+Td7pZreuHE5h5EWWA5HUFEJrpgUeBoXJuHsxbINKjGc9sQtpmYedZHNWazYy7JIZrndsC3154d\n51RDWk9omjmsA7IdMxvbkHZXkM+OQL6JsD5BXUWBasdx03eZQg1KCTLRbmwmeyOjvuUZNQvIHhzD\nDbjUM9N2M7fryHz9Rh/1DVVp1/bBMdosJPYo9TuI5EPXJ+miMhdJDzZe1gxd4yb1s7AAD2sHljeK\nkqVAeq0awcUBKS2DsGUTHdQWqNMpdupnJAIRrSJprYH6GuhdKhpNR3Bl8w175mbooJbhnk6RgTv4\nyNqF2nxmtbuTHdi/j3bH/n1UR/7ZOkN/9E7glrIhzrkjEflNEflUEdlUPanMeUxEnrzDYwsICLiP\nsE425IK3KERVmyLyWSLyHqlfGl/qNwuNkQMCHnCs44ZcEZE3q2os9cvlZ5xzv6yq7xaRn1LV7xCR\nd0rdaf05EYlI5glTZdNOfTgfYiszG/vPQMtxpza5qEHJZsARmvEKmhfPESd5amK2b2+K9yTcHVJy\nEtSeOF8avti3sRaPUKnRzL8MEekImZFoG+Se2MaYYywsjS6H9TnznpmyBdygNOf5DSxTnoKgRRLT\nSxu7J8s726arOXMonc9qk3uK7EofmZuIYfwzrnlkvLFTMvrDGfugGSI0D048iaqCbMAhaoBykO+W\nbmr9B2o50AA6Q6n7AtIBy6yPiMhy6soJmmVHvD9wa/CtZVMzjithKT7qTWbIRqVsjO1ZdHt2e+Rl\nx1Yj1Wa3M5LPej5ztaKx9p3COo2R/18R+YQV6z8gIp9yNwYVEBBw/yEwOAMCAtbCudaGlOJktCxW\ngJnX68INgBl61AFBy/PtT7kbKJGWll1KcWAmrkNZ9ibKyyvUA5CUNW8i2wK5+MXYbw/efwXCT3PH\nSDxlacfub4D8M2OE3cBSjurIjlls1tfXPUQpvCVApKTkPcg/A5Sxt+GqdCCjH6WrCbfd3FyesVfN\nmoDM1UEp/Gxh55mUaO5sAmdS4L7EyABEPUtxbCB7kEDyquO7hqVIdbDlAklJrMEZLqDg1cEzcgAV\n0B00oD60lE3lXRWqd7FJ8hwZjSizY6TIYkVoxdCPQJA7ddNtkbqzSzdis7R5nm7ynynqe2Zw2Rf1\nvTqlEXuHESyLgICAtRBeFgEBAWvhfEvUxZ0InrZzcwkKlIJPEDFOUIKr3j3pVWY+T2HiOkTaZQPH\nHprpyU5RetGISGxYO2J3MASWM8/rV5Rzt7pQR+J7dxNKTYyqw1TtGA/pVCTd7VAJqp6redeu81LH\n0gtsklzAlG1C/r4HCa8kh9ncsPWKGguqXCW+eW8T6ln7x5ahmqK+ppXZvaog/++QdRjmNnfHmZ1n\nF8+C9NHx7bA2rdO2/Z6CLFXkUO2CmlU7Xf1sjR9DNzNkdS4kUFDzpj0zJCOoeiVQT5uiLUWKYxwo\nXVx75vbRPHoAUlxHQKJbIbhbogvZBKS4EhmVYknsC60AAgIC7jXCyyIgIGAtnK9gbxyJ69am8OEc\n/H3Q4DfRQ6FXmim4bAI7Ru+FBKpNEJCScgSB155lAxjVFkTgFSXQ7SlqM9BUt8jr8ycg0Ewq9KRA\nP40CmYkYDZgzZCDKUzqucMMg1NrYqMfeQfk5a0OiMwLfl0Z2ng1kmsqUBRS2OIAgsBujTsLLBVw/\nvH6yjoK6LUgLsG8IO6yBHycTlPELXJ8xb4CDqLCfu+kGL9T2O56ZG5Qr6ojYGDq2e0Ei1Hybsgjo\nueLqZfZKyY8gRUARtoE9H/0G6oHoCjQtu8TueBmun43Bm94NiiH67KCkNe/CfYRgb9MT2PRmL+aO\nIVgWAQEBayG8LAICAtbCubohkVPpFPUpZ8hAdCMzzxJkLBpwFQpP5opgvu010CQWhKdFQTUtO163\nQPQcyZMSPSfSM5SyZL5sJQhiEeohZhNEwFHyDqv1VN1Ha25jOZpaBiaLQRB79rheeNyYWDOUXy+g\nwsWMRZONnuGSNaGadb2wbMBR/9i2Wdz8/YggBtxGlqBE4mawC8IX5qgc2ni7M1vfR43LbmXZgBTz\nIt71G0PhbIw6lRy1OwvUj4zpyrDFJNzdDjyfCHVKE6/ylUHQOUWPlXjfXJ9pavfzGK0pmRmajfFc\nwJ3MQaii25J4pS4SzlJksYZ9iEvjWz8s6ourXCBlBQQE3GOEl0VAQMBaOF9Slqtk4UuM0yxduU2C\n+oWlaSUiUkzr5SnqPgT9LBZbKNdGCQDNyRjZgBiiqhFMf4GZ13rM7OyZz3xQ0HeOegjqZ8WbZoZ2\nQIqKUddCDv8mSDkjHKnn+4lMjs30pVLXoxuoYwYczGp2IJ/A92Irv6xt2wxGdv19T1BrwPWjm5ai\nJ8h8ih4rETIDcAMGyEZccZYlINhWse/FcfcGJli71eretI+ISAkimEItqkI2qjNEt3YoWA1IuvKi\nyhFcvBTKa4tHUHO/Z0UwC9zPa3Nj3OV4FjeOVqcqMraY9EQ7dpMfNpEt68E3PrYxRkuC2r0U7A0I\nCAgQOW/LonIy8zyCJuIw7ba95aOBfQmOKnS58u+1HNRftgWgRH+Od2CjAxoyqNoMPM4gkDPbtF8c\nqNqRz2+TN9DAl6pCQCpGnj+d4EKxPkHrAgoB4cMqcx+sctB6ZF/kxYFZHB+2a3rJrIDsQ8yGfBYF\n3fva8f7J8hB0941pfbEMtsUIQB44s0KqA1QIt+3rP2/BsrDTnxIZ6saQ4gc9/Fjr+5/jbpFbk0F3\n1EEDcyqr9Su76LZGIZ4OKPbTcT3BU1TUpmd0NJ69xJ7bHYzx2pFR4gsYUFOckwHuAnydtueuzNBa\nIXkJuEIYSoWK2uVqDZZFQEDAvUZ4WQQEBKyF8+VZZIl0H62DcjmqBN3kDHl9Uqt9MJGBn6oFmx0V\nmAs0laW7U/Zs+8MSIjOYhRzCLTk4v7PrtWnLasTYIQgFyvICef4JcuikSiewJwvQymPy1v02k6mN\ndWsByX/I7y/6to1iOh0a/R604eKBVk7hmhhcjGVHMEEntb0SwkI0zxFIFcSgm3jELh7BnbgMSX3Q\nwyku1Huy5oLMMG/xhv1B3oSA26GoDOXY2TxaIwRq0als5FtEzMCJ4XWOC3P9YvYfwDPU6KC59whd\n89BNjZybBC7MsQ9mx6guLY5tQnNUNOsc+qrL4LG7e3zvYFkEBASshbVfFr4r2TtV9Zf936ExckDA\nQ4RbcUNeK3W/kKXY5D+VW2yMHDmVltdVrFBpN0lpktn7q4RpWXiXoEAcPSlhBsN6b09oHqMCcWgU\nZ/TClWpkZmu8Ab7G07Z97EV0FO0HaO63t830hIykTDKkL+A2gW0uMRMWbGo8qOclxwFn2WqXbaB2\nngRz9BQyFukxaMV0vcBtUcxF4U31KVwsuoZ0qzbntj7tgO6NMsgGZOx5/ZyvwQDUd29yx2g6PAc1\nP4YGZgFadW/LuBDL5+1GREd2HJfhmfNd7hp4oOa6es6H6NQG71B2nD0LOxkFkgw5sjpHid1z9ZmZ\n6hk7doTygUrNfazQgHruXd/qXrshqvqYiHyBiLzB/60SGiMHBDxUWNcN+T4R+WYxFYQduY3GyJPZ\ndNUmAQEBLwI8rxuiql8oItecc7+vqq+81ROwMfLF7V231Luco0rwGGZYK0G0G+b50j1xoO8uoDu5\nMUS1Js7fYOMrFjQOQUlu2TnnqOqMt239yEu65yB/KbpNkcRDyjKrSI/xsmyAHh0zqwFKdrKCDHSp\nZ93DBjB991FFehFmcIyu01NQxUuQmEpQ6MvWzY8ERXZOtVaAOBG0X2QBzXt2Leuf2sjmokSWQlrm\nzrh+Pd4ewmHHYKWNQdNubK829wXVyOzUViJ7NLhsx19qfLJ2s4Uq3qMJSGvIhiWnqnWhk3nRtskO\nbbwztFdYXOCceyIcWhWUzJxBWCkFQzCrPE39DALZncA6MYtPE5EvUtXPF5Fc6pjF94tvjOyti9AY\nOSDgAcfzuiHOudc75x5zzj0uIl8mIr/hnPubEhojBwQ8VHghpKxvkVtsjOyKUubXazNugUo7gTl9\npGZmbULcJPYmv1uYgRjjXUd9xQ5EVkaoE3CsdGUlJaxjuj6Hfat2jHz3s0xBPppCfn9gB5nDPKbM\n/rQwE3qKme8iG0H7t/AEpUuJid9oyS5cIPmgQvdqaePOQFxis19pIJI+QyYFbkDpI+wlztkakqyE\nGFTH3AC3sHnpw1Xsp6junMENQjYi37P1XZ+yWuTQMQXhbjIDEY7kqwF0ROEqUKyofMSKNpr75pKU\nW/U5Bw2Qtha2H0lxk8TuOYV4YtYjVXY912fo/4Ds3WAK18JnmxJU0Ubz1fUwKVzM6hwYU7f0snDO\n/ZaI/JZfDo2RAwIeIgQGZ0BAwFo431YAQARyU8r0xQgEFNQJLHUdC3D6FyDobID8MkR5L9k/M5Br\nohxRd3D2pxC/SSGWE/lTLSBEkiPyPL1opmcMs5Gl61sNI3yxfiRBxqRR3CwKdAxzP4f8/uTIMiBT\nBzl/ZJowReL4Bzq1RRHFYrCNdw8+NLUS9scjIzwlEXRPn4TLtoFsTAfuJhYbTXQBq6Bf2bR5XPjI\nf+zs/gxndn96Eeb8yI4xY8NoyBikyF4VOGeyZXNa+O2pBUtQWKiNJtHkQkVoEVBmcBWQaRpMjXTV\nBkPQdINQ64Q6EdbalCj/f8p5916Zx7mzCJZFQEDAWggvi4CAgLVwrm5IFalMe7XpRBO/Wax2LeYF\nSCxej7EN3cetZLUeY4YS4enEIt3NHPUbbN6LfXUL26AT1FKnM8G4i9bqmgGBClYGdaQJSFF5Y3Xd\nHfhMJ0S0zcLM3SnUwyZIzOTI0oxRjzLZN1fl1DkxRoFcf9U392Tsy6hfmvZO1iXRavNcNvLV64FH\nkdXJUNczRjoqRr3Dsrz+Omre+XwUINblG/YsFMjoTNp2nhjy+47d7OASLpWmZgM754jThq5yOfbr\nirkSY7iNc5AIJyXcyYR1Qqh38tm+GG5yghqYKepkBDqhF6M6u5Pcxe9/sCwCAgLWQnhZBAQErIXz\nFexVkWWQmTF/yuLPNmxIXcjoi4/Sc1smDpS1DjT3N1FyDoywjSB7kI5JrkJHtKVLBPJNQyHzD7O6\nGhvJZkgzmF4LwucJOP45RH03xsv1yOJACv4I9SATsMnYsDftmls1KlFLwVJzmNYsQb/ozfY+iG0U\nuk0g+ksB4jlSUMmC6lQUL0ZdAwSGC86db6TciG1O+l0oaTUsM5NtWDZsHFFCy675qGXn33C4fox3\nelBnFZjpcRRdRvuJLbG5Zd1JDJJdNZqt3Ia6uikTUB1/f9lcGV31Uoj7knyWHNb3nJm4O41gWQQE\nBKyF8LIICAhYC+dLylKRpQZuCTO8iEl0Wj2k3tIlQT1GdGzR5WdkeOMuIiJyCZFkiDZJCT5+t7i5\nRFhEJIF5rL72IEF9SaEwt0+5L4ZTdRcwT3OH9zTqNBh5b/s6AI77WXTfYHcyks86KHnPUfLP7lyM\npGfI8DA1tOzgtoV6kLKLsnyokNHwT0scb2rXz+zSJLGxTFE/cipLsFmPnQSqlxfmeuSVzRuN7ybW\nH0doTIw+L3tQnEpQY+G26vMnx8xcWaZnA0SwMea/B7eG7sYMY2fz6kZMFWIsLrNNEKAuUGujQ2To\ncNHVRj1uF9+9EvVgWQQEBKyF8LIICAhYC+fqhuiiksa12iwstmiGod0gbLJWgXeZH2kMMytNbNsm\nBFtbHSs/bozNVhtUZiw38Z504N4vTV+R06XBSz3a+cLM1zZq6ym0O0Jz4emmnaeHGpBRBdWs3CL5\nW5FF2J3vpzFEX5EWmig7CBA3kaWJUFNTjm3fBhsZUzWLorowlWfet4k37Dq1QB0JajBKkKmq1O5R\nVqGWQQwRhIEVpvr+BuogfDYmQ4+ZOZSyIrgb7tCOMUhsmw4kAtAxUJp2G0+JNz/qanLXtAPyFzM0\nqS131HZ8aoyWhYd2/7Mdu7dV3/adIUlHot/1uK4ZIcnLLSgtAMkDtHqcb9b3qAxuSEBAwL1GeFkE\nBASshXtWoq5PWlRfLxuv/5Qgb8tM8swTY06V/O5bmW/WM/OcrQRTHKN9aO/Goxbaw8HMnpA5haB+\n6ru7L2BMK0hOix6IM6mdk3kClg/3GugcjzqFSWnXtLSU25kdr4/SZpailxGW4VZBKEqmQzOPNUev\nEAheVRCHjXxknsdbQJFsMKcrZddTomZCkD1Jn4FILvR1Y5y/NTKXKOrWpngDKRqWnE+gsHUE1yNH\naqZCpimCMHF0ZCe9eGlXsIeIiLTh1h3jHg73rL/lAj1mIjxDERTJUpbcQ81rDPFm9mVJvWs7QZFQ\nA4S3yiEzhZL7ZFrPud7rviEBAQEB5xvgrJzxEZ6E/DwCla5rb+gKAZyRF0J5f2VCLB/ZsC+Cgh7b\n7NpXTqCNOdgFJRmBOkF1a9ZYzUtY5rS7CEaWFUN2hiko6xszfMHB8VUce7tp3IGjiYnILC2RKWTj\nFRRrgXVAPoegYa5D02EHXoo7ts3jXftCjqDl2fYS+Asce3CKRWLotikmZPezDf7BwWXbng9eAc5D\nDzKVyybVYzRxznE98x1U94IyPeiASk9t1pQUb5uLCSykzU59L0awMI/YgbnHGgPwbKCN2bps95P3\ngs9Lju3LgQWqU2/RVLDOFqSSoxwgJiX9jGfxTiJYFgEBAWthLctCVZ8QkYHU2a/COffJqrotIj8t\nIo+LyBMi8mrn3OFZxwgICHhx41bckL/mnLuOv18nIr/unPsuVX2d//tbnusAGkeSepn0BWjLYNuK\nQ0CqgyBg3we5PnxmrscEJF9yKyYIPM6gmRmhYrKNyB8rKamZya5dskL0heNmwLAJSnjcWJ333o0s\nqNtEx6sYzaCf+sAHRURkvGPHY7UkSxfZteqURQopenb7akIDk+cUuGfTpJ4LdGeQDuRNexe2bT3c\nBzmjO9dWD4HaOQRtsHUO/sPyl3bL7u2sY8ceLewYBQKGFfRdpQc3sIl53rFjHiSk5N8saBRB2j8+\ntslo4D6XmLfxgT3bFFyagZ89m+E47GDn2w5McUsWuOUVeDMV3JMNz8ugtu2dxgtxQ75Y6obIIqEx\nck2jYskAAByHSURBVEDAA491XxZORP6dqv6+qr7Gr7vknHvaLz8jIpdW7cjGyNP5bNUmAQEBLwKs\n64b8Fefck6p6UUR+TVX/hD8655yqrkzwsjHyhd6WbfNhpsdIIZzWGKIoiCTnPl9dHaIB8aaZh7O5\nrafDUJRmVlagwraOUBmIHSIKlEC4pfqzOktRPGbuQ4rfC3RKk12IwuB9nFUsL7SxJGjMXIBHohdq\nU5kNy9iMuTlkraWtH8L0ZwcrjaEfieUI4irTwvyAJXUjap4SobRt+8b5OHVs8DKqXTPDyS2YcV9c\nxRCZpMT7J0kTFZhoYRA3V3/rcswL6d4x6NbHJTgX2LfwPtdxw56b8czm5EoCggj8pxIcmjno63O4\nSim0YRM+pKhYjjOv9crsFq4haazWOp34ytTq7nkh61kWzrkn/f+vicjPS92J7FlVvSIi4v9/7W4N\nMiAg4N7jeV8WqtpW1e5yWUQ+W0T+SER+SeqGyCKhMXJAwAOPddyQSyLy855QlIjITzjnflVVf09E\nfkZVv0ZEPigir37eI6mK+AbDAkEPVpouu1CJiBQbiPZ7ky/etSg2tSsLEFsKmKcLVECeCnRDy7IB\n6i/JXWBhS/xY7TZVp2RWQJaBKInmWIZJjo4H0oe5X+zaNkcji6SPfYwngYDNIkGFKLpjKezaDQeK\n8YDphdU2asExwjzue5OcbRsa6MBbtVBdOoOwTAeCLw5zgfM3G2aSl5jTeZ9iNf48mFvpoHsbGhYL\nKPFjUKW1fwaJLIZLkNK1q69jUtoxdljpi6xXB7T+YmZu1XGL32Cbl21UKRfsjraJzb2bVx3ZvwMe\nja0YpikaJvtnhM/vncbzvix8A+SPW7F+X0RedTcGFRAQcP8hMDgDAgLWwrlXnS77GhdbZpI97UYr\nt71wFTUBPpKskLYfzBFRx2uPcv6zlpmBnQyKI3Bh0m1bX4zQNaoNGX2/mJKPf2jjm+dm/s0KEIQy\nM2FbICuxGvL6sQmnRCBITbw2ZYoQd1TaMQ4zM0O3kWmZoWtVjE5hC2SG5mBuzVA/kZN85jc/emrv\nZNXWZSPFHcHdoNakjEGEg3tEt4GtCJiZYO3F3LtHs8JciQI1E+3c7lsbuqNjtEg46Nj2nWNUbKJJ\nsYNLvPCrOxMb6wikuDZEaVgB2oUbxJoNZsOYLpwxBViilsX/P+si6/KkMd7mHTtnO0YNlC7/d3+S\nsgICAh4ihJdFQEDAWjjnxsgiE0+wiXDqizMzp4ZoHhtfhGmHrllLLFBavgApK4fp3+ibGTrqmduQ\nNcyEnebUoLR9SQA6aSqMZEjZBUEGGpzd2NancD1oIi5gtrMTwXSIugKv2ZkiG0LQPdlHmXcGN6Dh\nVjcyXsSrRVKmEJc58hH+ixd2TtbN0Zh4G9c578H1gL7mFESsBvRFq7kdJ4dATIHbPHT1NR1eh5uK\n5EZb7B7OUC7egYxAD5muo5ads4VshILEVh7VLtFBz+Y2n6EjGpo09xIbdwk5BdKmZvnqeSaogRr5\nLMg4gcjSRTsiW06UnEN/n4P4TUBAwD1HeFkEBASshfNtjFxVMluqHqEeQWHCtSFXXyJ+PF2WJoN0\nMh+YuZsiu8FI+ww6lQ3UsVHSnRr1U+g0Etlevb0DmasAf79dnKowOFliKTRLyh2k+Ed9i967PVtO\nLnsiGDp5sSNaAuLQDMFzYnZkx1NE6dkMuISnMkdLhbRT/1DA9bgYm+nfhxuYTlHfgMxENYOr2EbW\npW2mdQTi3GiIWnd/mN6GKU/FIzseNVhj1KwoyGo0yntD1N00WAOEjbzWawTy1wTqYWyGPILLXB2v\nfm7oV1HlStHguYkM18LdXCJPlzVDHVGrtGM3n63HEi2CGxIQEHCPEV4WAQEBa+Fc3ZCkVNk+rk3B\nfrGaiFVaBbhoBv68N3k3CjM38y2L0vfRnStqm6mWIwOhEzPnEgjMjlFrPMnQpBkS9fOl4hVMb5aZ\nlyhtTzbMJ5iCUEQzdNQwk5wCwwuY884fs41mwR2U8A8bKEVH1qPE9cxRJxHJahP1LCLP9qI+7yYy\nRPy69KZwH9AFbTy0e9tpouUBlcfQLSFG0YyiO101r+9XA26NUvnqgP6DgbUmeFxOlL9ERFJkElwL\nLvGV+t5NK8subUC2KmVrA5zzqYZlsRJkyTYc0jsgX7EjGjNAbV/qP+ubojJrU0rUS8U5nuEL9bNQ\nvT+QsgICAu4xwssiICBgLZxvNiRSqXyJb0vMPB0kKFffgDjrCLbauDYbHbIeOTIQl5tW51vCEptD\n4LWYmKsSj1FGjToBQT1Ilt1s0ilMSaoSUZi3Px7KKlTIzIxQppKjrkOh/pUtyWUgWRU4Twqi2ggN\nmxO4Mhm26R+ZabsB92CGSH73Yu/mgT8BceUrNvB5G/UocImSJvqQoEmww+MG7VpB1bskHWZsPNjj\nhW7KBXP3IqhgkQg2x3w1kHWoUEYewQ3K/GAyDKqAW1POVzeUTiG6u5WvmEMRKSC/QBHeFjo2Vz7D\n1c2Q3oL7tuHMxZ6jNmXmXay7qNcbLIuAgID1EF4WAQEBa+H8a0N69fspAje/rWDTw+JMOjBnvWhq\nCa5/2kTqBGSUElkKmYEsRLFZnDJ3q9+Z++jY25t7IdWmmYHJGQSYbmUm6Qwkp+k2SDRzkm9QUg7h\nX/UEHbavG0UWxY8olo4mwXOUnFdQwYrRgPoYhCZpc77soBfymgwVf6yRooYDc0loBjfV5kWhPFWi\nxmIMsto4Ro3J1O4Ly76b3oUqML7mZLWd7ZDdEpDYGhNkt7o2RgfXhvO1FG3bQB2LnurPgpOCzNfG\nD6PK5pDkt7mifgnzFXdt7NlRPZYit/vZgMs8PDBXsnthy44xrwd+v/YNCQgIeIhw/o2RPUW7QM47\nRtWhQwetAQKFyyK8MQowG1ML6mnLvtoOkUeHL65EvFx0kJrbcRYIqm6gSXLh6b8lvhTUY8xG7GqG\ns2zaMaBVIxM0gE4RkSUlfeY3Ia2boLBMiU5V0qN0P6Twsa8rbLwRxEYf61y0XZs3V7suGqBbg89x\nSoIe1gwFdziAqGlf070mAogUlInIZKgxbuH3J22yhug8V4Cq7RAQTcCzEVRn8mtcenWmUWnz2Ykw\nD7BUGdRspbbNkyP7+kdttGK46WpqcI6KzXqSHILhAm6HDOya+6hQrrx2aVXcTBe/UwiWRUBAwFpY\n62Whqpuq+lZV/RNVfY+qfqqqbqvqr6nqf/b/33r+IwUEBLxYsa4b8v0i8qvOuS9V1UxEWiLyrXKL\njZGldGZGbcPER1Ujm/RuguY69hV+5DkwACkxtBkh0U75/xR28Klz4p2Zw/TNUL2aejr1FNWKo6mZ\nhGy6y3dwNl9NPY6h+5jAhi9XaNUMKR2KACjjuPH26k5VNOsbaAbdAxcgA19gXNo15Z7uPYWwTwW3\nbka7GtWSGQR/Ylx/G02CdWLbTEAVH4idK/MVoDN4AaTPTy9wAGhLMEGQFPezDx+vNYeuKVyL5fUV\n0CidoCMZhXXaKcR/0Ix7E20GFmhjwPKBRQX9WJQHFJ56fqqrHnRHCwSJDzAXve36GFVyDwOcqroh\nIp8hIm8UEXHOzZ1zRxIaIwcEPFRYxw15uYjsiciPqeo7VfUNvjPZrTdGLlY3fAkICLj/sY4bkojI\nJ4rINzjn3qaq3y+1y3GCdRsj72xtu9ljNc3YHZtNlqCpLk1ySqovK/bGESPatpiiBrA4QjNahZgO\nzM0c7sysAwo5KkCZfl8G/hWiPS1kIw6nlrlpK3gWcFsaI5jHODRbB0TU6fS8jA2UJY6h1OIwAQ68\nDUr7V7jmLtoo0CWqoPfIyP+zZW0qK3gLwmB71+7PeGJzXlIICJmRDM2o413zrXRgH5EdRcevpU7S\ngf0OZo2895LN2+W5XVuPVbJ4ni5g1p9qmBuwE9s1T47rDEPVRhalA+o1eBsLNi9GZkzGNv+n/lFg\nm2RYyir0/I0ZQXCHtHrFc74An+PA65WWZ1QW3wmsY1lcFZGrzrm3+b/fKvXLIzRGDgh4iPC8Lwvn\n3DMi8iFV/Ui/6lUi8m4JjZEDAh4qrJsN+QYReYvPhHxARL5K6hfNLTVGLqtKhuPa/NvaMQoxRVEa\nC2QsQG9qt7wpiMi0g3mcD80kTKkBCVpxhHcjBW+ydDUNuITe53hem7zNGSL6sPguiJmqpDXTKByD\ntsysjyJKLsh2pH1/HFTFdg+gnYn5iSCsU0LrMgHha56Y2dpGe4HBAnL5GQbjD5miinNfbP4vQ6ko\nQXaHBjavcwiqevOaaW1qx0zrHjp+Lc/fhBuwh25ju0iTpDE0MyFgtNO36+fz1EFHuOwZtFHw/yRK\ndLLLUa1adOx4AxwvnfAm4hIg4DQGiTABxXszu1lAlfcnRqxvD6JJM/y7yVv+ubyXjZFFRJxz7xKR\nT17xU2iMHBDwkCAwOAMCAtbCudaGRKrSzOtI9TE0Drdgek5QQNFAVN15Xn8H1X0DuBIFzGC6IQ7M\npQUi82kL9SgjM/NYJzCKkWHw1ZgU5NnZtSbBsLYlQX1LDK3HKchVMWsmWAGLxEPTW7YJ/J0YGY3o\n2MY9ASmtWUGnMoF7ct0yFtMu5mJgJz1IbJvcu2clMjqPTVnpa4ud3NazO1g/RrsEkJXmaCrNChBW\njy47rvUhue+QjWKtzUJtPntI9Yxy22ifuq/wlTodyzZVniC2g6xUzLofmP4l3Kcp5p/CORnqbgpW\nN0NwhwTBZUuLYmzzU6LR9bbYWK9DX3V5ztU5yTuDYFkEBASshfCyCAgIWAvn6oZIUYns1WZuc8tM\n0qdTaGNOzJzb7tu7rOWzIQr58xz6jjOQaKabtk2E7EZUrSi8EBGHDlYkblVYX1W1OR2hXHyKMuai\nia5SQ8s6RCCWVXB38qZFwLuok1AQfZadtWaou2jCDJ2jufEcZcxNXifckGTb5ny2Z2XUBTa/3DAt\n03mxQmo/teN1G3YNC5TCZyj/bp9B2k02bCztxJYTird4FzKC+R5BCCbpsHbITjScntFVDs8OyX+j\nTbi7J/kru4YNSMHy/AOH7BKyeJGufs66KPwpc1vmPE+XRCscojkEyQtu2iZEo1JPbGS7iTuNYFkE\nBASshfCyCAgIWAvn64ZEKurNqAXKbjvHZmZNUOk9yCAv7+sd2KQ27pEzb6C0PN+Gbt/syeqypSYY\nyY5Q71BFCC37+oEc5ukE5KsU7sGsaaPpQjPykcrqThzMxQrHzECiWhqnia6OoicRbNUJzHDUmlzo\nmczIHCQi7aDB7gz6pahfcb7cOStQ/g37uEJzX3hEUvB6oE4WIWUUwVWZTuy+tFqo8fHZK2paNnA8\nN+HzYefnbetCc3/gUFfTu1kFTETkwBPXUmQxKhCo2ooSdWS6BBkN6p7SVVrkim2QMcP5Y080jHIo\nv6E9woQd7rAndULvFoJlERAQsBbCyyIgIGAtnLNgr0jqS3MV0fsc3Ph4biY0g/rFzJt5GLGDyZrF\nKEUHEWsCYpVcNtOzgS5c7aZF4/u5bT9CfUDhCTA5MiQRyuz7lZmHG5ZokGYDjCuwj0gEa6BrGM1j\n50uT6XrJlM19bXUvNxenghgvBZBTSPTPICE2Qm2Im6FmZkmAg/tAIVmdQ+YeAsyM7kcjO08MspxA\nsLcJGQEqa428dD7rPgoI857UzohIDOJSt2FzMVGbuxQPzxhZJ4d6ip1Zfa0tMOVIMqO/2xaSslCz\nkeO5oVwAWwrADc/gzcyn9biqHSqvwTUEEc3BlVFPSnTu3paoBwQEBISXRUBAwHo432yIijhP6kkh\nhltAQUipsgSbS6e+NgTlyiTrlOjaFSO6n2Rm1l87PjhZ7jRNsJa1JDsF1IzQZ0K9azNFV6lqBEWu\nlNpXKIWHstU8NdPzsGfH3jnjNnTLm9/lE5CPspadk+Zn3LJrXiBi7w6tNmIcg1AEId05yv5TnwWJ\n0HvDoaPxXG35qcJKzomLyBJR2Wt5P0VExujz0UBJ95JQtrh+dLJqo2U1KNMt2zaGWzkVENtQS/JE\nan02OmCitRcg3y3HCBc4RQYmxbMlqW3UsEPLjMLMvJ4BuunhGZlQ/c3LEeRsho2xjOGS5CvIX3pm\nd5IXjmBZBAQErIXwsggICFgL5+qGuEhk0fLvpwpRdwwjhQmdHZupvL1RK2tFwxX1CiJylCNKj1aG\nrA3ZgWDrAhH7Duo0Zoiqx2gJOPKsoxyZi8Ulc2VGiIBnKJFmi8FDhxJpuirI2HRnIDp5UeHuqQYd\nNj97MzP907mdvzXBHKbIEqGuoIOya5rZjR0jcVW+d8YQfUPaKLtYqp6JiDTQMvFCAfcIrQnZjDpB\neX2E+79f2jFTTxDbGKM2BN4eZ2WK2qDswMa738LzktoeOTIwCveo4xsMK0hWU6hTLZDFaTv0O9kB\nWS1jnQxcbIhBM5MlING1nT8++ppcLY9kJZDFi+6i+2HnCAgICFgD4WUREBCwFs6XlOVUMh/h1wYz\nIOitgdZ3FdSslkFgKj8NZoiAUwXqyupWfl10Ba9Qaj2GC3EIBS+BOKvzY5yCoNMAf38LpcsZytLn\nmOHLE7ZsxMDYyhBEs0lUnysHyexggbZ3DMzDrF2gBWDUsOOxZibdAIkLWSeSspZZkBg1KIeb6L2C\n/hwbFVIAOE8ysO2nKMue7OA69yxLRTcs2q3dPJK8qkNzvYZdqFlNV/fhaEFZrTtDTxII4goIUsvL\nyNhxHZsO51CwgjBwA27DGEQsh3vbRuvDycJkGTJkNZYkugQd2jem6BXCsYKg5pZ9cO4lKUtVP1JV\n34X/+qr6jaExckDAw4XntSycc+8VkY8XEVHVWESeFJGfl7or2S01Ro6TWLrbdaByCv3APiyExtze\nsp3q5jfqIXQU2wg2NjvWWiCFlXFkq2WvtK9SL7Pg5ALtBVJwPkp8FZZ6n70pWxWAsosAUwM05AiU\n4AkCX9GTq3kJg8sIVPrOZiW6V7ELGskA08iCZPkmun2BYkwLogAvhW0EIlDCk0W9zQLtCRIEjIWN\nusD/aKBr1whfvymaDQubeWG8pLNng3oeFygjnbMl2jU7yACWStY1U2CEyuWLC1icLBiFUZQ8Uz87\nUwSD4zZaDrDq9ZQ1Y+s3hhBtgvUZQ3Cn2TbLjt3clpWpY/B5Fj0M8BgUe1DWSy8+VN1FEc5bjVm8\nSkTe75z7oITGyAEBDxVu9WXxZSLyk375lhsjT2arG7EEBATc/1g7wOm7kX2RiLz+xt/WbYx8Zeei\n6/kK02llAZ6eQ2NkVDXO2KjJW1z7bXbmReUiAlbJtpnnO0fkNth5nogPT5bbaIzbBFeXeoaby6kC\n9ZnBpB6Oke7aeXRsL8gJZjvaRWUkRFkoe987queCV9wq7TxDiMIsoDvagivBfeO+ma1JG6IsGFfp\n6ELd/C0ZOTQpniIYjTR/0eVjZT9EaH/ASk9SBEjDbm7X1O5GH0FnUKyvpebKdVvoCMceAXB9jhEF\n3ozt+lkNOsc8ngzvjE/qArqrLbjMrLQtRww2274zUP/ZBLkoltXNNj4KIs22bH0HbujcB971PtHg\n/DwReYdz7ln/d2iMHBDwEOFWXhZfLuaCiITGyAEBDxXWckNUtS0inyUifwerv0tusTFyVVQy26/d\nDzCyRVFhN6sgaILhXW3VpujGkZl7M0i+6xZy1SUa8DbM9pvBKL8SWzakmtFYR8YAXa6Kth9Lw0xC\nLTHuOfQ4kaUh3XsG6fgY0vlD7JucIinczHM4rsxNKZElSPuoBr2Aal1kLzYgxKOspITVPsAczTzP\nYwK69+XUMhdxtPrxGQ/MbRiBqzJCCqQDAkqJrNLBhs3duFGfd7dj92q4Z9RnTVefvw33aE9tLMXA\nLrQFPcykaedMfce3Ka55gGbQOuazYpg3oYeJhFXM+9+yc07htbNTWebdsAUyHeQB9cD8rjbt2C1P\nE2dHvTuNdRsjj0Rk54Z1+xIaIwcEPDQIdO+AgIC1cL5Vp1Ul82Ft0s3hbuSgdVP7ZoYswUWpswfj\nLogocGXGV/dPlrttkJJIlspshxIiLhWIWAmszChBFHrZLgD6lpToH3TgEoDMVY2Q3WiY2dhv2HEu\npCboMoYe5sRXUo4LZAOAFhI97R1jny0O7BgJeENjuAFb20a4pWbpvLCxD31T4TzCRGMoXWSxhqWZ\n7Q4Zozmqiyn4Qmv5JRnaFdA984O/jpPmyAboATrCje38c7gYO3t2X8bgZD3bt+florPzD5YNsHOm\nd2zRwWXaKdERjPR9CO7szSzrt1XYfWZSrT23P/rDWkVnRL1YtGIYw/XY3Lfrny+f53I17f1OIFgW\nAQEBayG8LAICAtbCubohpVZy7LUfI5i+EaoK2dmqHEACf7seaorK0dnQsiGbl2BWozGxKPQVc3s3\nxiAFjRPWPtiuEWoiun59hC5Ysy2QkiCFvxeb2cyo8BwVq70ShLIMpjWqaksvytJCJW4KkzR/KYhd\nY6jSoINWCZdks2lm8ARjmYOH1ECz3aHUx4yQLehV9vsUGpxTeCoJyG+KzFQKIZ5LKCxpQie1CXN+\nKa4zgDhMPMUjC5fw1FcPbmVr19yzDFma5MK2jf0IAprL/eAaHNNlRA1S091M4BIRKeHKsWudq9B+\nAvd/kN6cYcmGyJD1MFcU5NyEq9Rf3Qz6TiJYFgEBAWshvCwCAgLWgt7NDkY3nUx1T0RGInL93E56\n77Ar4TofJLxYrvNlzrkLd+PA5/qyEBFR1bc75z75XE96DxCu88HCw3Kdz4XghgQEBKyF8LIICAhY\nC/fiZfEj9+Cc9wLhOh8sPCzXeSbOPWYREBDw4kRwQwICAtZCeFkEBASshXN9Wajq56rqe1X1fb59\nwAMBVX2Jqv6mqr5bVf9YVV/r1z9wvVVUNVbVd6rqL/u/X66qb/P39Ke9VuuLHqq6qapvVdU/UdX3\nqOqnPoj381Zwbi8L33PkB6XW8vwYEflyVf2Y8zr/XUYhIt/knPsYEfnLIvJ1/tqWvVU+QkR+3f/9\nYsdrReQ9+Pufisj3OudeISKHIvI192RUdx7fLyK/6pz7KBH5OKmv+UG8n2vjPC2LTxGR9znnPuCc\nm4vIT0nde+RFD+fc0865d/jlgdQP1qPygPVWUdXHROQLROQN/m8Vkc8Ukbf6TV701ygioqobIvIZ\nIvJGERHn3Nw5dyQP2P28VZzny+JREfkQ/r7q1z1QUNXHReQTRORtsmZvlRcRvk9EvllMqHRHRI6c\nO+kf8KDc05eLyJ6I/Jh3ud7gdWgftPt5SwgBzjsIVe2IyM+KyDc65071J3R1jvpFm6dW1S8UkWvO\nud+/12M5ByQi8oki8i+cc58gdT3TKZfjxX4/bwfn+bJ4UkRegr8f8+seCKhqKvWL4i3OuZ/zqx+k\n3iqfJiJfpKpPSO1CfqbUfv2m6oloyINyT6+KyFXn3Nv832+V+uXxIN3PW8Z5vix+T0Q+wkfPM6lb\nIf7SOZ7/rsH77m8Ukfc4574HPz0wvVWcc693zj3mnPv/27tjFIWBKIzj/4fgATyAiI2tB7AQ7MRy\nO228g5W919gDeAi38ABbLFja2FjtFT6LyaLlQ9ZIwvcrh0AmPPgm80iYAaV2B0lL4Av4qC5r9DP+\nkXQFLhExqoZmwIkW1fMZdf+iPqfsezvAp6RdbTd/oYiYAEfgh/t+fkvpW+yBPtXZKpJ+3zLJfxQR\nU2AjaRERQ8qbRg/4BlbSwwEpDRURY0ojtwucgTVlcW1dPbP8ubeZpbjBaWYpDgszS3FYmFmKw8LM\nUhwWZpbisDCzFIeFmaXcAIJjuJvNLar1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2443cfae80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[12], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[195])+ \",\"+\" iceberg_probability : \"+str(pred_valid[12])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f2443c0abe0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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f9r0PjxHcPsn9+Ssjb755HMgzG2mkmJCQkACkl2JCQkICsLX0OZdpHKlFzdrA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\nyfYQygAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2443c68b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[13], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[199])+ \",\"+\" iceberg_probability : \"+str(pred_valid[13])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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
}