Model_03-Adam-k=10-amsgrad-0001-15p-Copy5.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 810ms/step - loss: 0.8344 - acc: 0.6125 - val_loss: 0.3930 - val_acc: 0.8025\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.5746 - acc: 0.7654 - val_loss: 0.3571 - val_acc: 0.8519\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 587ms/step - loss: 0.4330 - acc: 0.8104 - val_loss: 0.2860 - val_acc: 0.8519\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.4494 - acc: 0.8266 - val_loss: 0.2788 - val_acc: 0.8519\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.3594 - acc: 0.8406 - val_loss: 0.2568 - val_acc: 0.8765\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 14s 593ms/step - loss: 0.3168 - acc: 0.8752 - val_loss: 0.2468 - val_acc: 0.9012\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.2897 - acc: 0.8799 - val_loss: 0.2627 - val_acc: 0.8951\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.2724 - acc: 0.8808 - val_loss: 0.2591 - val_acc: 0.9074\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.2468 - acc: 0.9004 - val_loss: 0.2757 - val_acc: 0.9136\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 14s 595ms/step - loss: 0.2914 - acc: 0.8870 - val_loss: 0.2226 - val_acc: 0.8951\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.2798 - acc: 0.8859 - val_loss: 0.2401 - val_acc: 0.8827\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 14s 594ms/step - loss: 0.2397 - acc: 0.8940 - val_loss: 0.2114 - val_acc: 0.9012\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.2992 - acc: 0.8767 - val_loss: 0.2425 - val_acc: 0.9136\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.2104 - acc: 0.9174 - val_loss: 0.2325 - val_acc: 0.9074\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.1952 - acc: 0.9228 - val_loss: 0.2433 - val_acc: 0.9198\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.2108 - acc: 0.9132 - val_loss: 0.2402 - val_acc: 0.9136\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.1894 - acc: 0.9262 - val_loss: 0.2290 - val_acc: 0.9136\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 14s 598ms/step - loss: 0.2156 - acc: 0.9190 - val_loss: 0.1901 - val_acc: 0.9198\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.1689 - acc: 0.9299 - val_loss: 0.2491 - val_acc: 0.9136\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.2030 - acc: 0.9232 - val_loss: 0.2112 - val_acc: 0.9074\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.1691 - acc: 0.9317 - val_loss: 0.2222 - val_acc: 0.9136\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.2181 - acc: 0.9122 - val_loss: 0.2218 - val_acc: 0.9259\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 13s 533ms/step - loss: 0.1788 - acc: 0.9274 - val_loss: 0.2418 - val_acc: 0.9074\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.1883 - acc: 0.9220 - val_loss: 0.2225 - val_acc: 0.9012\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.1592 - acc: 0.9420 - val_loss: 0.1957 - val_acc: 0.9136\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.1534 - acc: 0.9322 - val_loss: 0.2028 - val_acc: 0.9444\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.1486 - acc: 0.9376 - val_loss: 0.2014 - val_acc: 0.9259\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.1539 - acc: 0.9369 - val_loss: 0.2406 - val_acc: 0.9259\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1508 - acc: 0.9377 - val_loss: 0.2253 - val_acc: 0.9074\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1393 - acc: 0.9403 - val_loss: 0.2315 - val_acc: 0.9074\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1453 - acc: 0.9416 - val_loss: 0.2202 - val_acc: 0.9321\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1567 - acc: 0.9472 - val_loss: 0.2144 - val_acc: 0.9259\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1363 - acc: 0.9428 - val_loss: 0.2335 - val_acc: 0.9383\n",
      "Train loss: 0.129622049413\n",
      "Train accuracy: 0.956310679612\n",
      "Test loss: 0.190131148967\n",
      "Test accuracy: 0.91975308642\n",
      "\n",
      "===================FOLD= 1\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 18s 742ms/step - loss: 0.7480 - acc: 0.6045 - val_loss: 0.4505 - val_acc: 0.7826\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 600ms/step - loss: 0.6742 - acc: 0.6384 - val_loss: 0.4204 - val_acc: 0.7826\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 600ms/step - loss: 0.4996 - acc: 0.7592 - val_loss: 0.3333 - val_acc: 0.8509\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 14s 600ms/step - loss: 0.4237 - acc: 0.8106 - val_loss: 0.2861 - val_acc: 0.8571\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.3317 - acc: 0.8504 - val_loss: 0.2963 - val_acc: 0.8075\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.3230 - acc: 0.8533 - val_loss: 0.2439 - val_acc: 0.8944\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 14s 601ms/step - loss: 0.3237 - acc: 0.8525 - val_loss: 0.2159 - val_acc: 0.9068\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.3089 - acc: 0.8593 - val_loss: 0.2283 - val_acc: 0.8820\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.2852 - acc: 0.8820 - val_loss: 0.2281 - val_acc: 0.8944\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 14s 602ms/step - loss: 0.2986 - acc: 0.8727 - val_loss: 0.2043 - val_acc: 0.9006\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.2629 - acc: 0.8898 - val_loss: 0.2101 - val_acc: 0.9006\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 14s 602ms/step - loss: 0.2419 - acc: 0.8930 - val_loss: 0.1891 - val_acc: 0.9130\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.2358 - acc: 0.8990 - val_loss: 0.1904 - val_acc: 0.9317\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2292 - acc: 0.8998 - val_loss: 0.1933 - val_acc: 0.9379\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.2277 - acc: 0.9123 - val_loss: 0.1567 - val_acc: 0.9441\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.2244 - acc: 0.9115 - val_loss: 0.2342 - val_acc: 0.9006\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2445 - acc: 0.8956 - val_loss: 0.1972 - val_acc: 0.9193\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.2284 - acc: 0.9044 - val_loss: 0.2157 - val_acc: 0.9130\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1917 - acc: 0.9240 - val_loss: 0.1903 - val_acc: 0.9193\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1891 - acc: 0.9233 - val_loss: 0.2107 - val_acc: 0.9255\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.1878 - acc: 0.9187 - val_loss: 0.2367 - val_acc: 0.8944\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.2354 - acc: 0.9072 - val_loss: 0.2086 - val_acc: 0.9130\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.1952 - acc: 0.9295 - val_loss: 0.1679 - val_acc: 0.9255\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1921 - acc: 0.9253 - val_loss: 0.1974 - val_acc: 0.9255\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1919 - acc: 0.9134 - val_loss: 0.1727 - val_acc: 0.9565\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1745 - acc: 0.9371 - val_loss: 0.1599 - val_acc: 0.9379\n",
      "Epoch 27/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1558 - acc: 0.9405 - val_loss: 0.2571 - val_acc: 0.9255\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1575 - acc: 0.9319 - val_loss: 0.2043 - val_acc: 0.9379\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1548 - acc: 0.9345 - val_loss: 0.2350 - val_acc: 0.9130\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 521ms/step - loss: 0.1455 - acc: 0.9338 - val_loss: 0.3066 - val_acc: 0.9130\n",
      "Train loss: 0.158117664166\n",
      "Train accuracy: 0.936243936244\n",
      "Test loss: 0.156742053905\n",
      "Test accuracy: 0.944099378882\n",
      "\n",
      "===================FOLD= 2\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 16s 678ms/step - loss: 0.7010 - acc: 0.5746 - val_loss: 0.5068 - val_acc: 0.7205\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.5963 - acc: 0.6657 - val_loss: 0.4875 - val_acc: 0.7640\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.4581 - acc: 0.7700 - val_loss: 0.2908 - val_acc: 0.8571\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 14s 602ms/step - loss: 0.3814 - acc: 0.8263 - val_loss: 0.2883 - val_acc: 0.8696\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 14s 602ms/step - loss: 0.2972 - acc: 0.8611 - val_loss: 0.2220 - val_acc: 0.9006\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.3352 - acc: 0.8387 - val_loss: 0.2255 - val_acc: 0.8820\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.2708 - acc: 0.8761 - val_loss: 0.1966 - val_acc: 0.8944\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2657 - acc: 0.8814 - val_loss: 0.2284 - val_acc: 0.9255\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2743 - acc: 0.8786 - val_loss: 0.2379 - val_acc: 0.8882\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2391 - acc: 0.8880 - val_loss: 0.1988 - val_acc: 0.9255\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2570 - acc: 0.8814 - val_loss: 0.2627 - val_acc: 0.9006\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2643 - acc: 0.8935 - val_loss: 0.2119 - val_acc: 0.9130\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2209 - acc: 0.9110 - val_loss: 0.2128 - val_acc: 0.9130\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 15s 609ms/step - loss: 0.2141 - acc: 0.9109 - val_loss: 0.1908 - val_acc: 0.9130\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.2122 - acc: 0.9039 - val_loss: 0.2317 - val_acc: 0.9006\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.2101 - acc: 0.9132 - val_loss: 0.2107 - val_acc: 0.9068\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.2293 - acc: 0.8989 - val_loss: 0.1967 - val_acc: 0.9130\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 15s 608ms/step - loss: 0.2046 - acc: 0.9118 - val_loss: 0.1776 - val_acc: 0.9193\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.1966 - acc: 0.9148 - val_loss: 0.1778 - val_acc: 0.9193\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 532ms/step - loss: 0.1980 - acc: 0.9183 - val_loss: 0.2009 - val_acc: 0.9006\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 530ms/step - loss: 0.1974 - acc: 0.9143 - val_loss: 0.2135 - val_acc: 0.8882\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.2002 - acc: 0.9105 - val_loss: 0.1981 - val_acc: 0.8944\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1811 - acc: 0.9265 - val_loss: 0.1899 - val_acc: 0.9130\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.1840 - acc: 0.9294 - val_loss: 0.1964 - val_acc: 0.9068\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 15s 609ms/step - loss: 0.1665 - acc: 0.9333 - val_loss: 0.1768 - val_acc: 0.9068\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 15s 608ms/step - loss: 0.1900 - acc: 0.9243 - val_loss: 0.1618 - val_acc: 0.9255\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1488 - acc: 0.9401 - val_loss: 0.1861 - val_acc: 0.9193\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 530ms/step - loss: 0.1626 - acc: 0.9325 - val_loss: 0.1692 - val_acc: 0.9379\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 530ms/step - loss: 0.1540 - acc: 0.9365 - val_loss: 0.2224 - val_acc: 0.9130\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.1489 - acc: 0.9295 - val_loss: 0.1909 - val_acc: 0.9068\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.1534 - acc: 0.9321 - val_loss: 0.1809 - val_acc: 0.9130\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1594 - acc: 0.9359 - val_loss: 0.2371 - val_acc: 0.9193\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1752 - acc: 0.9308 - val_loss: 0.1940 - val_acc: 0.9006\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1378 - acc: 0.9402 - val_loss: 0.2009 - val_acc: 0.9068\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.1503 - acc: 0.9385 - val_loss: 0.2507 - val_acc: 0.9068\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 15s 608ms/step - loss: 0.1387 - acc: 0.9479 - val_loss: 0.1575 - val_acc: 0.9193\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1381 - acc: 0.9429 - val_loss: 0.2178 - val_acc: 0.9193\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1540 - acc: 0.9392 - val_loss: 0.1852 - val_acc: 0.9130\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1405 - acc: 0.9415 - val_loss: 0.1677 - val_acc: 0.9193\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1364 - acc: 0.9403 - val_loss: 0.2610 - val_acc: 0.9130\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1563 - acc: 0.9317 - val_loss: 0.2007 - val_acc: 0.8944\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1272 - acc: 0.9563 - val_loss: 0.1880 - val_acc: 0.9193\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.1792 - acc: 0.9320 - val_loss: 0.1933 - val_acc: 0.9193\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.1321 - acc: 0.9513 - val_loss: 0.2565 - val_acc: 0.9006\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.1208 - acc: 0.9506 - val_loss: 0.1989 - val_acc: 0.9255\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1192 - acc: 0.9485 - val_loss: 0.2390 - val_acc: 0.9068\n",
      "Epoch 47/100\n",
      "24/24 [==============================] - 15s 607ms/step - loss: 0.1170 - acc: 0.9480 - val_loss: 0.1469 - val_acc: 0.9503\n",
      "Epoch 48/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1068 - acc: 0.9573 - val_loss: 0.2331 - val_acc: 0.9006\n",
      "Epoch 49/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1544 - acc: 0.9382 - val_loss: 0.1980 - val_acc: 0.9193\n",
      "Epoch 50/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1239 - acc: 0.9505 - val_loss: 0.1835 - val_acc: 0.9255\n",
      "Epoch 51/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1031 - acc: 0.9552 - val_loss: 0.1978 - val_acc: 0.9193\n",
      "Epoch 52/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.0897 - acc: 0.9646 - val_loss: 0.2036 - val_acc: 0.9130\n",
      "Epoch 53/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1132 - acc: 0.9522 - val_loss: 0.2866 - val_acc: 0.8944\n",
      "Epoch 54/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.0808 - acc: 0.9630 - val_loss: 0.2438 - val_acc: 0.9255\n",
      "Epoch 55/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.0990 - acc: 0.9545 - val_loss: 0.2668 - val_acc: 0.9068\n",
      "Epoch 56/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.0984 - acc: 0.9586 - val_loss: 0.2640 - val_acc: 0.9006\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 57/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1094 - acc: 0.9562 - val_loss: 0.2329 - val_acc: 0.9130\n",
      "Epoch 58/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.0973 - acc: 0.9617 - val_loss: 0.2349 - val_acc: 0.9068\n",
      "Epoch 59/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1068 - acc: 0.9519 - val_loss: 0.2156 - val_acc: 0.9193\n",
      "Epoch 60/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.0685 - acc: 0.9760 - val_loss: 0.2742 - val_acc: 0.8944\n",
      "Epoch 61/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.0712 - acc: 0.9721 - val_loss: 0.3162 - val_acc: 0.9255\n",
      "Epoch 62/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.0813 - acc: 0.9659 - val_loss: 0.3132 - val_acc: 0.9068\n",
      "Train loss: 0.0521078983055\n",
      "Train accuracy: 0.979902979903\n",
      "Test loss: 0.146851015757\n",
      "Test accuracy: 0.950310559006\n",
      "\n",
      "===================FOLD= 3\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 18s 748ms/step - loss: 0.6664 - acc: 0.5967 - val_loss: 0.4757 - val_acc: 0.7625\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 15s 610ms/step - loss: 0.4482 - acc: 0.7656 - val_loss: 0.3371 - val_acc: 0.8187\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 13s 530ms/step - loss: 0.3827 - acc: 0.8171 - val_loss: 0.3481 - val_acc: 0.8187\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 15s 611ms/step - loss: 0.3617 - acc: 0.8311 - val_loss: 0.3117 - val_acc: 0.8750\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 15s 607ms/step - loss: 0.3193 - acc: 0.8516 - val_loss: 0.2559 - val_acc: 0.8938\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.2837 - acc: 0.8656 - val_loss: 0.2633 - val_acc: 0.8750\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 13s 531ms/step - loss: 0.2849 - acc: 0.8673 - val_loss: 0.2955 - val_acc: 0.8750\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 15s 610ms/step - loss: 0.2688 - acc: 0.8863 - val_loss: 0.2228 - val_acc: 0.9000\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.2652 - acc: 0.8932 - val_loss: 0.2737 - val_acc: 0.9000\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.2318 - acc: 0.8946 - val_loss: 0.2511 - val_acc: 0.8875\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 531ms/step - loss: 0.2320 - acc: 0.9050 - val_loss: 0.2847 - val_acc: 0.8750\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 15s 610ms/step - loss: 0.2312 - acc: 0.9062 - val_loss: 0.2091 - val_acc: 0.9000\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 15s 608ms/step - loss: 0.2101 - acc: 0.9093 - val_loss: 0.2082 - val_acc: 0.9062\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2109 - acc: 0.9090 - val_loss: 0.2251 - val_acc: 0.9062\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 15s 609ms/step - loss: 0.2229 - acc: 0.9002 - val_loss: 0.2025 - val_acc: 0.8938\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2097 - acc: 0.9147 - val_loss: 0.2330 - val_acc: 0.8938\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.1805 - acc: 0.9204 - val_loss: 0.2464 - val_acc: 0.9000\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1927 - acc: 0.9111 - val_loss: 0.2253 - val_acc: 0.9062\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2033 - acc: 0.9083 - val_loss: 0.2413 - val_acc: 0.8875\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1865 - acc: 0.9217 - val_loss: 0.2134 - val_acc: 0.9125\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.1877 - acc: 0.9230 - val_loss: 0.2024 - val_acc: 0.9062\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1926 - acc: 0.9233 - val_loss: 0.2155 - val_acc: 0.8938\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 13s 533ms/step - loss: 0.1866 - acc: 0.9210 - val_loss: 0.2177 - val_acc: 0.9125\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1730 - acc: 0.9266 - val_loss: 0.2435 - val_acc: 0.9062\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1521 - acc: 0.9384 - val_loss: 0.2110 - val_acc: 0.9000\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1655 - acc: 0.9265 - val_loss: 0.2257 - val_acc: 0.9062\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 15s 607ms/step - loss: 0.1789 - acc: 0.9311 - val_loss: 0.1919 - val_acc: 0.9125\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.1527 - acc: 0.9291 - val_loss: 0.1885 - val_acc: 0.9187\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1802 - acc: 0.9200 - val_loss: 0.2016 - val_acc: 0.9000\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1807 - acc: 0.9236 - val_loss: 0.2179 - val_acc: 0.9187\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1458 - acc: 0.9397 - val_loss: 0.2372 - val_acc: 0.9000\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1615 - acc: 0.9321 - val_loss: 0.3514 - val_acc: 0.8938\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1414 - acc: 0.9404 - val_loss: 0.2439 - val_acc: 0.9187\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 15s 609ms/step - loss: 0.1543 - acc: 0.9386 - val_loss: 0.1844 - val_acc: 0.9187\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.1350 - acc: 0.9436 - val_loss: 0.1805 - val_acc: 0.9250\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1344 - acc: 0.9466 - val_loss: 0.3095 - val_acc: 0.8875\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1382 - acc: 0.9453 - val_loss: 0.2978 - val_acc: 0.9062\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1499 - acc: 0.9358 - val_loss: 0.2077 - val_acc: 0.9125\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1514 - acc: 0.9379 - val_loss: 0.2513 - val_acc: 0.9250\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1355 - acc: 0.9472 - val_loss: 0.2545 - val_acc: 0.9250\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1329 - acc: 0.9412 - val_loss: 0.2297 - val_acc: 0.9313\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.1303 - acc: 0.9510 - val_loss: 0.2887 - val_acc: 0.8875\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.1216 - acc: 0.9493 - val_loss: 0.2720 - val_acc: 0.9187\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1237 - acc: 0.9485 - val_loss: 0.2169 - val_acc: 0.9313\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1464 - acc: 0.9433 - val_loss: 0.4303 - val_acc: 0.8812\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1373 - acc: 0.9448 - val_loss: 0.2760 - val_acc: 0.8875\n",
      "Epoch 47/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1088 - acc: 0.9593 - val_loss: 0.3360 - val_acc: 0.8875\n",
      "Epoch 48/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1240 - acc: 0.9513 - val_loss: 0.2382 - val_acc: 0.9187\n",
      "Epoch 49/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1106 - acc: 0.9567 - val_loss: 0.3265 - val_acc: 0.9125\n",
      "Epoch 50/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.0917 - acc: 0.9643 - val_loss: 0.2801 - val_acc: 0.9000\n",
      "Train loss: 0.0861122916321\n",
      "Train accuracy: 0.968144044321\n",
      "Test loss: 0.18046746999\n",
      "Test accuracy: 0.925\n",
      "\n",
      "===================FOLD= 4\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 17s 692ms/step - loss: 0.7741 - acc: 0.5936 - val_loss: 0.6479 - val_acc: 0.7875\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 15s 608ms/step - loss: 0.5228 - acc: 0.7477 - val_loss: 0.3523 - val_acc: 0.8688\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 15s 609ms/step - loss: 0.4430 - acc: 0.7901 - val_loss: 0.3097 - val_acc: 0.9062\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/100\n",
      "24/24 [==============================] - 15s 612ms/step - loss: 0.3801 - acc: 0.8257 - val_loss: 0.1913 - val_acc: 0.9375\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 15s 610ms/step - loss: 0.3338 - acc: 0.8409 - val_loss: 0.1762 - val_acc: 0.9437\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 13s 530ms/step - loss: 0.3038 - acc: 0.8787 - val_loss: 0.2105 - val_acc: 0.9125\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.2952 - acc: 0.8739 - val_loss: 0.2261 - val_acc: 0.9313\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.2707 - acc: 0.8829 - val_loss: 0.1885 - val_acc: 0.9250\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 13s 530ms/step - loss: 0.2727 - acc: 0.8849 - val_loss: 0.1841 - val_acc: 0.9375\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 530ms/step - loss: 0.3052 - acc: 0.8617 - val_loss: 0.2046 - val_acc: 0.9187\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.2446 - acc: 0.8971 - val_loss: 0.1943 - val_acc: 0.9437\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 15s 609ms/step - loss: 0.2361 - acc: 0.9008 - val_loss: 0.1476 - val_acc: 0.9250\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2351 - acc: 0.9029 - val_loss: 0.1767 - val_acc: 0.9313\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.2330 - acc: 0.9026 - val_loss: 0.1550 - val_acc: 0.9437\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.2291 - acc: 0.9034 - val_loss: 0.1860 - val_acc: 0.9437\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2143 - acc: 0.9093 - val_loss: 0.1647 - val_acc: 0.9563\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.2053 - acc: 0.9140 - val_loss: 0.2518 - val_acc: 0.9125\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1895 - acc: 0.9124 - val_loss: 0.2208 - val_acc: 0.9437\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.2254 - acc: 0.9023 - val_loss: 0.1766 - val_acc: 0.9437\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.2313 - acc: 0.8976 - val_loss: 0.1995 - val_acc: 0.9500\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.1853 - acc: 0.9246 - val_loss: 0.1901 - val_acc: 0.9375\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1796 - acc: 0.9319 - val_loss: 0.2155 - val_acc: 0.9563\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.2026 - acc: 0.9141 - val_loss: 0.2283 - val_acc: 0.9438\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2171 - acc: 0.9112 - val_loss: 0.2029 - val_acc: 0.9375\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1711 - acc: 0.9235 - val_loss: 0.2544 - val_acc: 0.9313\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.1918 - acc: 0.9182 - val_loss: 0.4018 - val_acc: 0.9062\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1867 - acc: 0.9267 - val_loss: 0.2479 - val_acc: 0.9250\n",
      "Train loss: 0.173445293035\n",
      "Train accuracy: 0.925207756233\n",
      "Test loss: 0.147560883313\n",
      "Test accuracy: 0.925\n",
      "\n",
      "===================FOLD= 5\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 17s 713ms/step - loss: 0.7060 - acc: 0.6140 - val_loss: 0.4943 - val_acc: 0.7375\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 15s 609ms/step - loss: 0.5265 - acc: 0.7440 - val_loss: 0.3946 - val_acc: 0.8375\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.4449 - acc: 0.7932 - val_loss: 0.4695 - val_acc: 0.8500\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 15s 609ms/step - loss: 0.3868 - acc: 0.8261 - val_loss: 0.2477 - val_acc: 0.9125\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.3630 - acc: 0.8467 - val_loss: 0.2814 - val_acc: 0.8562\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 15s 609ms/step - loss: 0.3518 - acc: 0.8425 - val_loss: 0.2401 - val_acc: 0.9187\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 15s 609ms/step - loss: 0.2986 - acc: 0.8791 - val_loss: 0.2266 - val_acc: 0.9062\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.3082 - acc: 0.8634 - val_loss: 0.2573 - val_acc: 0.9000\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.2664 - acc: 0.8873 - val_loss: 0.3180 - val_acc: 0.8875\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 15s 611ms/step - loss: 0.2709 - acc: 0.8829 - val_loss: 0.2216 - val_acc: 0.9125\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 15s 609ms/step - loss: 0.2554 - acc: 0.8871 - val_loss: 0.2205 - val_acc: 0.9250\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 15s 607ms/step - loss: 0.2294 - acc: 0.9023 - val_loss: 0.1952 - val_acc: 0.9375\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2266 - acc: 0.9078 - val_loss: 0.2017 - val_acc: 0.9250\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 532ms/step - loss: 0.2391 - acc: 0.8985 - val_loss: 0.2004 - val_acc: 0.9437\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 530ms/step - loss: 0.2159 - acc: 0.9024 - val_loss: 0.1979 - val_acc: 0.9375\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 15s 608ms/step - loss: 0.2067 - acc: 0.9122 - val_loss: 0.1852 - val_acc: 0.9500\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2243 - acc: 0.9034 - val_loss: 0.3246 - val_acc: 0.9000\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 530ms/step - loss: 0.2045 - acc: 0.9147 - val_loss: 0.2432 - val_acc: 0.9250\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 530ms/step - loss: 0.2073 - acc: 0.9085 - val_loss: 0.2623 - val_acc: 0.9125\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 15s 608ms/step - loss: 0.1700 - acc: 0.9252 - val_loss: 0.1839 - val_acc: 0.9500\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.1792 - acc: 0.9191 - val_loss: 0.1726 - val_acc: 0.9563\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.1707 - acc: 0.9306 - val_loss: 0.1881 - val_acc: 0.9375\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1677 - acc: 0.9253 - val_loss: 0.1872 - val_acc: 0.9500\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1968 - acc: 0.9134 - val_loss: 0.2002 - val_acc: 0.9437\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1618 - acc: 0.9287 - val_loss: 0.2559 - val_acc: 0.9375\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2305 - acc: 0.9108 - val_loss: 0.1982 - val_acc: 0.9313\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1882 - acc: 0.9212 - val_loss: 0.2005 - val_acc: 0.9500\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1544 - acc: 0.9357 - val_loss: 0.1970 - val_acc: 0.9375\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1587 - acc: 0.9349 - val_loss: 0.2644 - val_acc: 0.9313\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1676 - acc: 0.9291 - val_loss: 0.2590 - val_acc: 0.9000\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1649 - acc: 0.9311 - val_loss: 0.1936 - val_acc: 0.9375\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1673 - acc: 0.9321 - val_loss: 0.1802 - val_acc: 0.9563\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1879 - acc: 0.9288 - val_loss: 0.2427 - val_acc: 0.9313\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1467 - acc: 0.9363 - val_loss: 0.2280 - val_acc: 0.9375\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1190 - acc: 0.9531 - val_loss: 0.3051 - val_acc: 0.9500\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1396 - acc: 0.9440 - val_loss: 0.2439 - val_acc: 0.9563\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train loss: 0.114420213115\n",
      "Train accuracy: 0.95567867036\n",
      "Test loss: 0.172631277144\n",
      "Test accuracy: 0.95625\n",
      "\n",
      "===================FOLD= 6\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 17s 717ms/step - loss: 0.6845 - acc: 0.5821 - val_loss: 0.6529 - val_acc: 0.5687\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 15s 604ms/step - loss: 0.5061 - acc: 0.7125 - val_loss: 0.4422 - val_acc: 0.7438\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 597ms/step - loss: 0.3777 - acc: 0.8062 - val_loss: 0.3518 - val_acc: 0.8063\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.3137 - acc: 0.8432 - val_loss: 0.3655 - val_acc: 0.8250\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.2709 - acc: 0.8728 - val_loss: 0.3806 - val_acc: 0.8562\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.3148 - acc: 0.8409 - val_loss: 0.3755 - val_acc: 0.8063\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.2506 - acc: 0.8873 - val_loss: 0.3351 - val_acc: 0.8625\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.2598 - acc: 0.8836 - val_loss: 0.2835 - val_acc: 0.8875\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.2469 - acc: 0.8987 - val_loss: 0.3064 - val_acc: 0.8938\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 521ms/step - loss: 0.2274 - acc: 0.9054 - val_loss: 0.3158 - val_acc: 0.8812\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1984 - acc: 0.9164 - val_loss: 0.3172 - val_acc: 0.8625\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.1936 - acc: 0.9151 - val_loss: 0.2845 - val_acc: 0.8750\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 14s 600ms/step - loss: 0.2450 - acc: 0.9016 - val_loss: 0.2783 - val_acc: 0.8562\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 14s 602ms/step - loss: 0.2341 - acc: 0.8948 - val_loss: 0.2642 - val_acc: 0.9000\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.2292 - acc: 0.9072 - val_loss: 0.2754 - val_acc: 0.8875\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.2020 - acc: 0.9127 - val_loss: 0.3080 - val_acc: 0.8562\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1899 - acc: 0.9209 - val_loss: 0.2905 - val_acc: 0.8500\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 14s 599ms/step - loss: 0.1764 - acc: 0.9285 - val_loss: 0.2435 - val_acc: 0.9125\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.1677 - acc: 0.9368 - val_loss: 0.2859 - val_acc: 0.8688\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.1579 - acc: 0.9362 - val_loss: 0.3022 - val_acc: 0.8875\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.1595 - acc: 0.9267 - val_loss: 0.2462 - val_acc: 0.8812\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.1653 - acc: 0.9326 - val_loss: 0.3017 - val_acc: 0.8938\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.1516 - acc: 0.9426 - val_loss: 0.3025 - val_acc: 0.8875\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.1572 - acc: 0.9406 - val_loss: 0.2838 - val_acc: 0.8812\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 14s 602ms/step - loss: 0.1526 - acc: 0.9357 - val_loss: 0.2379 - val_acc: 0.9062\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.1504 - acc: 0.9393 - val_loss: 0.3564 - val_acc: 0.8875\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1823 - acc: 0.9272 - val_loss: 0.3077 - val_acc: 0.8562\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.1681 - acc: 0.9287 - val_loss: 0.2587 - val_acc: 0.9125\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.1578 - acc: 0.9430 - val_loss: 0.2574 - val_acc: 0.8812\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 521ms/step - loss: 0.1467 - acc: 0.9441 - val_loss: 0.2530 - val_acc: 0.8750\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.1285 - acc: 0.9450 - val_loss: 0.3578 - val_acc: 0.8875\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1527 - acc: 0.9383 - val_loss: 0.2444 - val_acc: 0.8875\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 12s 521ms/step - loss: 0.1337 - acc: 0.9538 - val_loss: 0.2572 - val_acc: 0.9000\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 14s 602ms/step - loss: 0.1357 - acc: 0.9404 - val_loss: 0.2261 - val_acc: 0.9250\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.1364 - acc: 0.9500 - val_loss: 0.2832 - val_acc: 0.8875\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.1279 - acc: 0.9479 - val_loss: 0.2837 - val_acc: 0.9125\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.1435 - acc: 0.9453 - val_loss: 0.2795 - val_acc: 0.9000\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.1273 - acc: 0.9451 - val_loss: 0.2622 - val_acc: 0.8875\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.1374 - acc: 0.9466 - val_loss: 0.2466 - val_acc: 0.8875\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.1372 - acc: 0.9423 - val_loss: 0.2529 - val_acc: 0.8750\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.1131 - acc: 0.9492 - val_loss: 0.3509 - val_acc: 0.8812\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.1133 - acc: 0.9586 - val_loss: 0.2752 - val_acc: 0.8688\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.1257 - acc: 0.9552 - val_loss: 0.3271 - val_acc: 0.9187\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.0939 - acc: 0.9651 - val_loss: 0.3182 - val_acc: 0.8938\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.0998 - acc: 0.9570 - val_loss: 0.3946 - val_acc: 0.8750\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.0938 - acc: 0.9583 - val_loss: 0.2381 - val_acc: 0.9375\n",
      "Epoch 47/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.1181 - acc: 0.9565 - val_loss: 0.2825 - val_acc: 0.8938\n",
      "Epoch 48/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.1181 - acc: 0.9495 - val_loss: 0.3213 - val_acc: 0.8812\n",
      "Epoch 49/100\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.1048 - acc: 0.9585 - val_loss: 0.4095 - val_acc: 0.8375\n",
      "Train loss: 0.0900395090725\n",
      "Train accuracy: 0.968144044321\n",
      "Test loss: 0.226136386395\n",
      "Test accuracy: 0.925\n",
      "\n",
      "===================FOLD= 7\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 17s 704ms/step - loss: 0.8580 - acc: 0.5743 - val_loss: 0.5300 - val_acc: 0.7438\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 598ms/step - loss: 0.5682 - acc: 0.7228 - val_loss: 0.4099 - val_acc: 0.7688\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 599ms/step - loss: 0.3985 - acc: 0.8213 - val_loss: 0.2546 - val_acc: 0.8500\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.3729 - acc: 0.8440 - val_loss: 0.2961 - val_acc: 0.8750\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.3279 - acc: 0.8534 - val_loss: 0.2561 - val_acc: 0.8938\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 14s 601ms/step - loss: 0.3270 - acc: 0.8705 - val_loss: 0.1984 - val_acc: 0.9062\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.2829 - acc: 0.8800 - val_loss: 0.2907 - val_acc: 0.8812\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 14s 600ms/step - loss: 0.2950 - acc: 0.8779 - val_loss: 0.1970 - val_acc: 0.9000\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.2410 - acc: 0.9042 - val_loss: 0.2254 - val_acc: 0.9000\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.2389 - acc: 0.8974 - val_loss: 0.2651 - val_acc: 0.8625\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 11/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.2358 - acc: 0.9020 - val_loss: 0.2068 - val_acc: 0.8812\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 14s 599ms/step - loss: 0.2314 - acc: 0.9005 - val_loss: 0.1888 - val_acc: 0.9187\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.2166 - acc: 0.9039 - val_loss: 0.2368 - val_acc: 0.9000\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.2444 - acc: 0.8980 - val_loss: 0.2092 - val_acc: 0.9250\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 14s 597ms/step - loss: 0.2099 - acc: 0.9130 - val_loss: 0.1870 - val_acc: 0.9125\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.2216 - acc: 0.9124 - val_loss: 0.1910 - val_acc: 0.9000\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.1644 - acc: 0.9409 - val_loss: 0.2312 - val_acc: 0.8875\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 14s 599ms/step - loss: 0.1942 - acc: 0.9166 - val_loss: 0.1561 - val_acc: 0.9187\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.1647 - acc: 0.9342 - val_loss: 0.2386 - val_acc: 0.9000\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.2012 - acc: 0.9138 - val_loss: 0.1864 - val_acc: 0.9125\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.1797 - acc: 0.9252 - val_loss: 0.2439 - val_acc: 0.8688\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 14s 598ms/step - loss: 0.2124 - acc: 0.9160 - val_loss: 0.1522 - val_acc: 0.9375\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.1953 - acc: 0.9205 - val_loss: 0.1738 - val_acc: 0.9250\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.1947 - acc: 0.9191 - val_loss: 0.2075 - val_acc: 0.9062\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.1807 - acc: 0.9252 - val_loss: 0.1987 - val_acc: 0.9250\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.1666 - acc: 0.9350 - val_loss: 0.2027 - val_acc: 0.9125\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.1578 - acc: 0.9352 - val_loss: 0.2699 - val_acc: 0.8812\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.1821 - acc: 0.9259 - val_loss: 0.2986 - val_acc: 0.8438\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.1777 - acc: 0.9262 - val_loss: 0.1934 - val_acc: 0.9187\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.1732 - acc: 0.9293 - val_loss: 0.2021 - val_acc: 0.9062\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.1567 - acc: 0.9396 - val_loss: 0.2243 - val_acc: 0.8875\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.1441 - acc: 0.9480 - val_loss: 0.1980 - val_acc: 0.9187\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.1331 - acc: 0.9406 - val_loss: 0.1857 - val_acc: 0.9250\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.1356 - acc: 0.9443 - val_loss: 0.1568 - val_acc: 0.9563\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.1367 - acc: 0.9428 - val_loss: 0.1603 - val_acc: 0.9375\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.1358 - acc: 0.9423 - val_loss: 0.1935 - val_acc: 0.9062\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.1276 - acc: 0.9471 - val_loss: 0.2354 - val_acc: 0.9125\n",
      "Train loss: 0.151323480702\n",
      "Train accuracy: 0.929362880886\n",
      "Test loss: 0.152209414542\n",
      "Test accuracy: 0.9375\n",
      "\n",
      "===================FOLD= 8\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 18s 736ms/step - loss: 0.7817 - acc: 0.5984 - val_loss: 0.4795 - val_acc: 0.7688\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.5126 - acc: 0.7539 - val_loss: 0.5542 - val_acc: 0.7500\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 599ms/step - loss: 0.4448 - acc: 0.8112 - val_loss: 0.4602 - val_acc: 0.8125\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 14s 601ms/step - loss: 0.3945 - acc: 0.8379 - val_loss: 0.3614 - val_acc: 0.8250\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.3504 - acc: 0.8559 - val_loss: 0.4007 - val_acc: 0.8187\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 13s 531ms/step - loss: 0.3273 - acc: 0.8676 - val_loss: 0.4304 - val_acc: 0.8063\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 13s 532ms/step - loss: 0.3202 - acc: 0.8621 - val_loss: 0.3998 - val_acc: 0.8125\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 13s 530ms/step - loss: 0.2713 - acc: 0.8871 - val_loss: 0.3909 - val_acc: 0.8250\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 15s 608ms/step - loss: 0.2615 - acc: 0.8845 - val_loss: 0.3459 - val_acc: 0.8375\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2295 - acc: 0.9137 - val_loss: 0.3630 - val_acc: 0.8438\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 15s 608ms/step - loss: 0.2614 - acc: 0.9034 - val_loss: 0.3238 - val_acc: 0.8562\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.2063 - acc: 0.9158 - val_loss: 0.3817 - val_acc: 0.8500\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 532ms/step - loss: 0.2003 - acc: 0.9257 - val_loss: 0.3249 - val_acc: 0.8500\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 530ms/step - loss: 0.1825 - acc: 0.9236 - val_loss: 0.3736 - val_acc: 0.8438\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.2329 - acc: 0.9085 - val_loss: 0.3666 - val_acc: 0.8562\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.1973 - acc: 0.9205 - val_loss: 0.3813 - val_acc: 0.8688\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 530ms/step - loss: 0.2324 - acc: 0.8981 - val_loss: 0.3395 - val_acc: 0.8562\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 15s 609ms/step - loss: 0.2142 - acc: 0.9132 - val_loss: 0.3195 - val_acc: 0.8438\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1885 - acc: 0.9256 - val_loss: 0.4287 - val_acc: 0.8250\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 15s 609ms/step - loss: 0.2063 - acc: 0.9063 - val_loss: 0.3078 - val_acc: 0.8625\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.1804 - acc: 0.9298 - val_loss: 0.3511 - val_acc: 0.8375\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.1730 - acc: 0.9285 - val_loss: 0.3222 - val_acc: 0.8625\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1599 - acc: 0.9299 - val_loss: 0.3082 - val_acc: 0.8875\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.1606 - acc: 0.9298 - val_loss: 0.3789 - val_acc: 0.8688\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.1642 - acc: 0.9332 - val_loss: 0.3424 - val_acc: 0.8625\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.1411 - acc: 0.9482 - val_loss: 0.3460 - val_acc: 0.8562\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.1458 - acc: 0.9414 - val_loss: 0.4392 - val_acc: 0.8688\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.1435 - acc: 0.9461 - val_loss: 0.3709 - val_acc: 0.8562\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.1594 - acc: 0.9370 - val_loss: 0.3423 - val_acc: 0.8688\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1262 - acc: 0.9456 - val_loss: 0.4637 - val_acc: 0.8500\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.1559 - acc: 0.9432 - val_loss: 0.3426 - val_acc: 0.8812\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1566 - acc: 0.9406 - val_loss: 0.4514 - val_acc: 0.8375\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.1547 - acc: 0.9339 - val_loss: 0.3203 - val_acc: 0.8500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1284 - acc: 0.9498 - val_loss: 0.4157 - val_acc: 0.8562\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.1517 - acc: 0.9436 - val_loss: 0.4794 - val_acc: 0.8187\n",
      "Train loss: 0.135388263395\n",
      "Train accuracy: 0.948060941828\n",
      "Test loss: 0.307810878754\n",
      "Test accuracy: 0.8625\n",
      "\n",
      "===================FOLD= 9\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 18s 753ms/step - loss: 0.7067 - acc: 0.5664 - val_loss: 0.6313 - val_acc: 0.6062\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 15s 609ms/step - loss: 0.5009 - acc: 0.7520 - val_loss: 0.4025 - val_acc: 0.8125\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 15s 611ms/step - loss: 0.4177 - acc: 0.8142 - val_loss: 0.3420 - val_acc: 0.8375\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 15s 609ms/step - loss: 0.3751 - acc: 0.8329 - val_loss: 0.3069 - val_acc: 0.8562\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 15s 608ms/step - loss: 0.3282 - acc: 0.8534 - val_loss: 0.2550 - val_acc: 0.8750\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.3066 - acc: 0.8646 - val_loss: 0.3728 - val_acc: 0.8187\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 13s 532ms/step - loss: 0.3185 - acc: 0.8591 - val_loss: 0.2728 - val_acc: 0.8625\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 13s 532ms/step - loss: 0.2799 - acc: 0.8700 - val_loss: 0.3059 - val_acc: 0.8313\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 15s 611ms/step - loss: 0.2631 - acc: 0.8818 - val_loss: 0.2221 - val_acc: 0.8938\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2666 - acc: 0.8800 - val_loss: 0.2353 - val_acc: 0.9000\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 15s 609ms/step - loss: 0.2275 - acc: 0.8997 - val_loss: 0.1958 - val_acc: 0.9250\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.2223 - acc: 0.8959 - val_loss: 0.2459 - val_acc: 0.8875\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 15s 611ms/step - loss: 0.2594 - acc: 0.8792 - val_loss: 0.1877 - val_acc: 0.9250\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.2368 - acc: 0.8992 - val_loss: 0.2482 - val_acc: 0.9062\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 532ms/step - loss: 0.2364 - acc: 0.8959 - val_loss: 0.2479 - val_acc: 0.8812\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 531ms/step - loss: 0.2095 - acc: 0.9143 - val_loss: 0.2142 - val_acc: 0.9313\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 530ms/step - loss: 0.2039 - acc: 0.9169 - val_loss: 0.1974 - val_acc: 0.9250\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.1795 - acc: 0.9189 - val_loss: 0.2788 - val_acc: 0.8562\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 15s 609ms/step - loss: 0.2144 - acc: 0.9091 - val_loss: 0.1725 - val_acc: 0.9500\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.1808 - acc: 0.9262 - val_loss: 0.1888 - val_acc: 0.9062\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.2044 - acc: 0.9121 - val_loss: 0.2226 - val_acc: 0.9000\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.1806 - acc: 0.9195 - val_loss: 0.1864 - val_acc: 0.9125\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1760 - acc: 0.9268 - val_loss: 0.1889 - val_acc: 0.9250\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.1987 - acc: 0.9148 - val_loss: 0.3520 - val_acc: 0.8125\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.1901 - acc: 0.9233 - val_loss: 0.2069 - val_acc: 0.9125\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 530ms/step - loss: 0.1854 - acc: 0.9226 - val_loss: 0.2492 - val_acc: 0.8938\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.1724 - acc: 0.9259 - val_loss: 0.2300 - val_acc: 0.9125\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.1673 - acc: 0.9349 - val_loss: 0.1993 - val_acc: 0.9062\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.1655 - acc: 0.9326 - val_loss: 0.2348 - val_acc: 0.8938\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.1554 - acc: 0.9352 - val_loss: 0.2073 - val_acc: 0.9125\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.1299 - acc: 0.9451 - val_loss: 0.3253 - val_acc: 0.8562\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1671 - acc: 0.9358 - val_loss: 0.2381 - val_acc: 0.8938\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1519 - acc: 0.9352 - val_loss: 0.1886 - val_acc: 0.9187\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1401 - acc: 0.9448 - val_loss: 0.2106 - val_acc: 0.9125\n",
      "Train loss: 0.152872461237\n",
      "Train accuracy: 0.939058171745\n",
      "Test loss: 0.172539100051\n",
      "Test accuracy: 0.95\n",
      "\n",
      " Train Log Loss Validation=  0.113085269173\n",
      " Test Log Loss Validation=  0.185272178405\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_5nd.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['val_acc', 'loss', 'acc', 'val_loss'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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DDQHijDG7jDG5wCfApBJjegNLHK+XOu0/H1hojDlijDkKLMRaKUp9JS0BVr9u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\nmnKyMDOzpv4HjQ9GpT184AMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f610bd85c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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sR1fL5TpxyHajRQ+yLQVx1KsuJI8lBRHxAqYAFwHdgKtL+dD/1BjT0xjTB3gJ\n+Jen4qlWwyZDm3Nh0eNw8pjr5yWsAr9GhTM7An29efGyXmx7bhRvXdefQW3DkYJaSO3Ot7dFCvOJ\nCE+P787RzBxeWbi9+GunbodXe8P8h87kJ1MNWeHMo1KSQuZhOJZcM3FVt8S19jZ6MAQ1sS2GbfNq\nNqYq5MmWwiAgzhizyxiTDcwAxhc9wBhT9BMzCKgflxoOLxj5rP1DcWdf572rIGqgPb8IKVoUr0DL\nPuAfdtp6ha4tGnHDkBimr9rD70lH7YOZR2DG1ZB1FGK/bnjTB1XVSN9n/w+V1lKAhjOukLASxAta\n9bP3O4+2P/vhPTUbVxXxZFJoBRTt3E50PlaMiNwlIjuxLYV7PBhP9WrVD7pcDCvecK1gWOYRSNlc\n9vqEkhxe0HaoXa9Qotl+/4WdaBxoayuZvFz44s9wOB7Ouc/+Ucf/5P7Po1ThzKPOxR9v1t3eNpik\nsBpa9ALfIHu/yxh7u+3bmoupCtX4QLMxZooxpj3wMPBYaceIyCQRWSsia1NTU0s7pHYa/qjdPnOZ\nC0XzktYC5vTKqOVpP9zOGz+4o9jDoQE+PDyqC+v2HGb7Zw9B3Pcw+mUY9gj4BsPmOe79HErB6TOP\nCviFQOO2DWOwOS/Hdh8VvXiLaG9bT/WkC8mTSSEJiC5yP8r5WFlmAJeU9oQxZqoxZoAxZkBkZGQV\nhuhhzbpBz8th1TsV72W7d5UdsGrV3/XXb+cspV1K1dTL+0fxl8gNdI6bRnbfm2HAn2zZ744j7RS6\n/Dw3fhClsDOPAsJtP3pJBYPN9d3+TZCbeXqLvvNoiF9mu4zrOE8mhTVARxFpKyK+wFVAsUtUEelY\n5O4YoPglb30w7BHIy7bL4cuTsMrWkfELcf21w9vamval1EFy7N/I/SdeY1V+F/4pN596ots4uxJz\n7wrX30cpODXzqLQxruY94dBu2zKuz8oqa99lDJg82L6w+mOqYh5LCsaYXOBuYAGwBZhpjIkVkWdE\nZJzzsLtFJFZENgIPADd6Kp4aE9Ee+l4La6fBkTLWD+Tlnt4kdVW74RD/S/H6K+kHYMa1OIKbsqjH\nS7y/MontB5x/rB0uBG9/t8txqAbOmOKF8Epq3hMwcGBztYZV7RJWQWg0hJYYHm3ZD4Kb14suJI+O\nKRhj5htjOhlj2htjnnc+9oQxZo7z+3uNMd2NMX2MMcONMfWzeM/Qv9nbn14q/fmUWLuTVVmVUcvT\nfritRVPTpbsvAAAgAElEQVQwTS43C2Zebwe3r/qUO8cMIdjPm6fmxGKMAb9gaD/CJoWGMq9cnbnj\nKXDyyKlCeCU162Fv6/O4gjG2m7e0umQOB3QeBXGL6/xaoBofaG4QwqKh/82wYXrpdZH2FjRJyymC\nV5a2Q+1YRMEspPkP2quZS96EFr0ID/LlwZGdWL4zjfmbnPs2dB1rB6iT1lf+Z1INS6pzY52yWgqh\nUXaKdH0eVziaCOnJdn1CabpcbMvo767bs/s0KVSX8/4KXr6w9MXTn0tYCSEtbbPUXQGN7Q5tO5fA\nmvdg/Uf2vXpMKDzkmrPa0K1FI56bt5kT2bn2isbhDVt0FpJHLX0Rvri1pqOoGoUzj7qU/rxI/R9s\nTqjg4q3tUDu7b+s31ReTB2hSqC4hzewWnpv+d3q/a8JqOxW1tAE8V7Qbbqe0fjcZOl0Ew4vP7PVy\nCM+M786+oyf50wdr+DEhFxMz1CYF7ULyjLwcWPU2/P6Fe6vaa6vUrbYlENys7GOa97T/t+vrzLaE\nVeATdKqrrCRvP+gwwq5XqMPb5WpSqE7n3GtnFy15/tRjR5PgaELlBpkLtB8OJh/C28OEqbZ/s4QB\nMeE8NbYbcSnHuXHaav6d1BkO7eJ4Qv0p5FWrxP9spyeavPox0ytla9kzjwo062Gna1aidHydsHcl\nRA0AL++yj+k8Bo4fgOS62zWrSaE6BYbDkLts87KgP7+sKW7uaD0Ehv0drvkc/BuVedhN57Rl2eTz\n+c9VfdgUfA75Rvjgvdd47OtNp2YnuSArN093eatI7Ne2K8HLr873MduZR1vKHk8oUFDuoj4ONmcd\nt9WPK/o77XihLYGxte7OQion5SmPGHynXcy25Hm47gtnkzTw1B9UZTi8YNjDLh3q5+3F+D6tGN+n\nFcffHsiEw+sZtjaRT1buZUi7CG48uw2D2kaw72gmyUdOknT4BMlHT5J0OJOkI/YrNT2LIF8vvrnn\nPNo2Cap83PVVXq5N/J1GQfp+22qoyzIO2lZPWTOPCkR2AYePHVfocVn1xFZdktba1nhFFQcCwyHm\nHJsULniyemKrYpoUqpt/Izj3Plj0BOxZYZukrfqDl0+1hxLc+1KCFzzC6ttj+DTOh+kr93L7J6c3\ne329HbQKC6BVWADDO0fSMiyA93/ZzcOzfmPGpME4HJUcC6mv4n+GE2nQbbytZ7X0RfuhGtC4piOr\nnIpmHhXw9rXH1Mdd2PauAsQWrKxI5zHw3cO2Gy2ivcdDq2qaFGrCwFthxRRY+Ji9qjr3vpqJo+tY\nWPAIYfELuHPYfUw6rx0/bE1h76ETtHQmgZZhATQJ9j2tUmvLsAD+Nus3Plm1hxuGxNRM/LXV5tl2\nQLLjhRAYAbxgLwC6jK7pyCqnoplHRTXvWeoK+zovYRU07WZ3W6tIl9E2KWydB+fUvRqfOqZQE3wD\n7UY8SWvtQGRZ8549LSzaTmd1Tk319nIwsntzbjmvHaN7tqB3dBiRIX6llu6+on8U53VswovfbiXh\n0AmPhZiXb7jt47X8+YM1HC5lz+paJy/XLgzs9EfwCbADk97+dbsLKXUr+IVCSIuKj23WA47vL7b5\nU52XnweJa1xfRxTWGpr1hG11c+MdTQo1pd8Np9YlRLvQJPWUrmMhaZ1dmOMGEeGFCT0R4JEvN9nV\n0h7w2uIdLIg9wNLtqYyb8gtb9tXy6Z17ltnaUt2dtR29/ezg5O66nBS22W4hV6ZM18fB5tStkHXM\nvYoDXUbbruE6mBw1KdQUbz8Y97otmFeTfc1dnfsebXF/wU1U40Amj+7KL3EHPbIv9LK4g7z2ww4m\n9GvFrNuHkJ2bz4Q3lzPvt31V/l5VZvPXduJAhwtPPdb2PPsh6cq+GrVRigszjwrUxw139q60t+5U\nHOg8GjCw/TuPhORJOqZQk9oPt181qUkHWx9/y1wYfLvbp187qDXf/JrMc99s4Q+dmtI81L9KwkpJ\nP8m9MzbSPjKY5y7pQaCvN3P/ci53fLKeuz5dT2xye/46sjNeBYPch3bZfSJE7JRAcdhZWYW3zu+D\nm9ry4ZVdKFie/Dz7e+w40nYRFogZam/jf7FVauuSjIO25VPRzKMCgeHQqJXrg80FpVkCwm2Xqrdv\n5WP1lITVENTU7hnhqha9oVGU7ULqd73nYvMATQrKdiH9/E/b1A12b78Kh0P4x2W9GPWfn3j0q028\nd+OA0rcPLU1+PvzwDHQZC1Gn9pHIyzfcN2Mjx7NymH7LWQT62v+mTUP8+fTWs3hqTixvLt3Jln3H\nePWqvoQG+MA3D5S6r0SpRjxhS4FUtT3LICP1VNdRgVb9bOsh/ue6lxQKB5ldbCmAe+UuNk635VkA\ndiyAy96HJh3LP6e6Jay0rQR3LiREbBfS+o8h+0Txi4RaTruPlP2gMvmVLvsb0ySIB0d2ZvHWFOb8\n6sbm7Zu/gl/+bWdqFBmTeOOHOJbvTOOZcT3o3Lz4/hJ+3l68MKEXz1/ag593HOSSKcvYu3mVTQjD\nH4O/J8PkBHg4Hh7aBQ/ugAe2wv2b4T7n/PnFz8KORZX6Wcv/eWaDd4BtKRTl5WP7o+viuELhdFQX\nZh4VaNYDDm6HnJPlH3c8BRY8aidaXPmJLS3/9nmw9r+1p/xK+gG7lW1lKhh3Hm1XeO/8ocrD8iRN\nCsr+ETeOOaM9Fm4+py19W4fx5JxYUtNdKB2cm20/nL387MwOZymI5TsP8uri7Uzo24orBkSVefq1\nZ7Xhs0mDST+Zy7rP/488rwAY+Ge7b65/IztOExRhu4satbD178Naw7g37M/7xZ+rthxDfp7tvuo0\n8tTevUXFnGc/YOvawGPqNvANsV1Crmre086qK0goZfluMuScgHGv2dbqHcvth+8398GMayEj7cxi\nrwpnUnEg5lw7a6uOzULS7iNlm7pdx8LKtyHzCASEVXxOfn6xGkteDuHly3sx+j+/8NScWKZc26/8\n89d/CId3wxUfwrwH4JdXSQ3vz70zNtKuSRDPXtKjwm6ogTHhzLu5A+Hv/sInWcOJnZ9A4yDntqcG\nCq41jTGFF56hAT7cctmHBPx3hP3guWWRe7vdlWXvCshIgW6l7ihrK2iC7UIqUsG21ivYWMedrpPC\nwebf7ZTn0mxfYIsFDnvkVNdUoxZw3Ze2kOD3T8JbQ+CSt2yRuZqSsMpeuLTo7f65Xj72ImHbfFsm\nwy+46uPzAG0pKKvreMjPsX+sZTHGLkz67xh4ub1tVhfRoWkI917QkXmb9vHtpnJmCGWlw4//gDbn\n2lW/Z90OOxbwyidfcSwzhynX9iPIz7XrlWbbPsGbPA50vZnZG5P577J4PlgWzwfL4/lohf36ZOVe\npq/ay6er9/LKou3c+GUKJ8a/Bwe3wdd3Vk1XRezXdj1Cya6jAi362CvuurZeoaAQnjsat7WL98oa\nV8hKt2NAkV3g3PuLP+dwwJA74dYldvD5kwnw3SMVd0W5av/vMHUY7PjeteMTVtnE5u1XufcbNMmu\nZl8xpXLn1wBtKSirVX+7OGnLHOh9ZfHnjIEdC+Gnl21XT0gLWxr66zvhxm+KtRgmDW3Ht7/v4/HZ\nsQxuF0HjoFJmk6yYYgdkr55hr0AH3kLOj68wMPkT+o5/ky7Nyy7qV0z2CVjzPtJ5NH+7egx/c+GU\nb35L5r4ZG7lmcSif/eFJApY+CT+/AkMfdO09S5OfZ39vHS8s+2rQyxvaDKlb4wonDtnWT1M3k4LD\nAc26l50UfnjObvL0pwVlf9g27wGTlsCiJ2Hlm7DrR7jsPWjWzb1Yijp5DGbeAId2woxr4JoZ0P78\nso/POQnJG22SqqzoQbYVvuw/MOBm251Zy2lLQVkOh/3PG7cYsjPsY/n5tp/8naHw6UQ76DbmX3Dv\nr3DRi3a2zco3i72Mj5eDly7rzZET2Tz7TSn79R5PgeWvQ9dxdrUvsGKf4ZPsYVzitZyJ7kw8+fUz\nyDwEZ9/t8ikX92rJm9f2Y3PyMS7b2I+srhPsh9SZbLiesMqWSy6r66hAzHmQtgOO1eJ1FkW5U96i\npOY9bVXRkq2wxLW2IOTAP1dcXM4nAEa/BNf8zyanaaPsmonKMAbm3mNbt1dOtzOcPru6/Aq2yRts\n6/lMKhgDjHgKck/a1nEdoElBndJ1rJ0tsX0BbJoFb51t93vOPg7jp8A96+0fs7cf9LnWbuiz+Bnb\nxVBEt5aNuHN4B77ckET/ZxdxxdvLmfzFb0z9aScJXz+Nyckkd5jdCOjg8SzunbGBBaGX43AIsvIt\n12LNz7cJqWVfWzrcDSO7N2fqDf3ZeTCDyxKvJieyO3xxS+UHngu6jjqNKv+4tufZ2/hfKvc+1c3V\nQnilad7DrgI+sufUY7nZMOce29Ic4UYF0U4j4dYfwMcfpl9RuaS65j2I/QpGPA5dL4YbZtturk+v\nhPhlpZ9TFWXtwa4F6n+TnVV1MO7MXqsaaFJQp7Q+2/bjfnmrnZ2DgQnvwV1roO91xSu5isDY/9iZ\nNl/dZruTivjL+R149pIeXNitGYKwaPMBPvn2R5rv+IxPc4bR9bWdjHhlKZe/tZyjmTk8ed0fkR6X\nw7oPXVv5u2MhpMXBkLsrtRBtWOem/Pfmgew6ms/1GfeQJ162SyHL9X0lAJuctsyBDhdUPJDYvJct\nqBbvgf0VcrNg3oOwa2nVvWbqNrsnRGW2iW3ey94WXcS2/D+QEgtjXil3349ShbWGa2ba/xufXuHe\nv1PSOjsu0WkUnH2vfSyoCdw4x+4tPf2KU/ukF5Wwym5cFdTEvVhLM2yyvXBY/PSZv5aHaVJQp3h5\n2/0eWvaDiR/BHSug1xVl7zQV0gzGvgr7Ntp++SJ8vBxcP7gNL17Wi5m3D2Hd4xfyfe+fcPj40mjU\nY9xyXjs6NA0mxN+Hf17Rm64tGtmKkjkZsOb9imNd8YadJtltfKV/3LPbN+HjPw8iNiOMB/LvxRzc\nDl/f4d7Ac8IqSN9XcdcR2JXVbc7xzLjCt3+DNe/aPvPDeyo+3hWpW6FJp8qt/m7aza4gLxhXOBgH\nP75s/70qWy22ZR/7//LAZph542kXIqXKPAz/uwlCmtuZTEV3JQxuCjfOtc99cpnt2ipgjP23rcz6\nhNIEN7X/v7fMsSukazFNCqq4Pzxkp2l2G1/qtp6n6TYeek60g9BJ5WxBmLwB361f4TXkLsae24+H\nR3XhnesHMPcv5zK2d0t7TLPudvbOqrchJ7Ps19r3q53Fc9ZtZ7wPRf824Uy/9SyW5nTjNccNdq3G\nz/90/QU2f22nLHauoOuoQMx5diqumwUIy7X2v7DuA9uaMwZm3Wy7as5U6rbKjSeAXcEb3t6OK+Tn\nw9x7bffPRS+fWUwdL7AXIjsX2/UM5SVwY+CrO2x30xUf2hIcJYU0t4khKAI+nmDHEcB2JZ5Ic6/e\nUUWG3G3LZSx6ovYsziuFJgV15ka/ZP+zf3V72R/m3z9lu6Yqqi9/zn221s7G6WUfs2KK7dbod2Ol\nQy6qV1QYMyYN5iMzhvkyFPPD8/DLq7YMdnny8+0q5g4XuL7WoWBcoUhr4YwqzCashvkPQfsRMPY1\nW2Qxad2Zd1NkHrEtIHdnHhXVvCfs/w02fAx7foELn7WtyzPV7wYY+jfY8An8+FLZxy1/HbZ/C398\nvlgZldOEtrKz6AJC4aNLYN9vtrQFVG1Ze79g2420d0WtXtCmSUGduYDGMP51O+//h+dOf37nD7av\ne+hDFW9S0uZsaDXA/kGX9qF8LNkueup7vWuL7FzUtUUjPr99CC943c5SBsD3T5I55Txy95bT1E9c\nYz84S9Y6Kk/T7jY5OtcrvLk0jsEvLGaHG3tkF0rfD59fbz/ULnvPdk91vwQG3mK717adQYXOM5l5\nVKB5Dziy124m1eZc+2FeVYb/HXpfDUv/DzaUcgGxZ4W9EOk23q4VqEhYtE0MvsHw0Xj4dQb4h9nu\ns6rU7waI6Ghjq+iio6iTR203WGVnX7lBk4KqGh0ugAF/slfxRWfX5Ofbueahre3MpYqI2J3oDsfD\nltmnP796qq3TdNZtVRZ6gQ5NQ/j49mH8O+JJbsu+nyNp+3G8P5IfXrmej5b+xubkY+TnF7mqL+g6\nqmjWUVEOh93Dd/fPzP01mZe+20ZKeha3fLTWvU2EcrNtv3rWMTvFsmjXyMjn7VX617dXvpsq1Tmj\nrDIzjwoUDDbnZtlJCVVZmVbEtozaDbNTTeMWn3ou46DtQmvcxracXH3fxm3gprl2dl38z7bryJUu\nVHd4+di9mw9uty0oVxzcAe+OsF2bB2KrNp5SeDQpiMgoEdkmInEiMrmU5x8Qkc0i8puILBaRNp6M\nR3nYhc/aGkpf33Fqdkjsl7YL4fzHXF8V2nkMRHSwXThFu1ayjsPaadDlYgh3o4yxG2KaBDHnL+fx\n3COPsGHsQlZEXs4f0r9h1JKxTHnjZfo/u5A7p6/j4+W7yIv92pZgcHcmTcxQOLqXV/+3iIExjfn0\nlsHsO3KSuz5dT05evmuvseAR28Ux/g17RQ4kHDrByZw823d/xYd2IHbWn10bkC0pdaut7Bra2v1z\nC7Tsa5Pm8EfstMyq5u1rB56bdLYJcv8mu5Dwy1vtLKWJH7m2fWZR4e1si6FJZ1s80RO6XGynuS59\n4dSaoLLsWGQTQuZhO/bR83LPxFSEx5KCiHgBU4CLgG7A1SJScjniBmCAMaYXMAsop4NQ1Xp+wXDp\n27ba5YJHnUXvnrFbE/a8wvXXcTjg7HtsMik6xfLXz2wzeojri9UqKzLEj9EDOnLO3e/hNekHwpq1\nZorva3wW9AoH9mzjq7mz8UpPZn+rMspalONAE7vT3gWB23j7uv4MaR/BCxN6snxnWukL/kpa/7Gd\nd3/2PYUfXDPXJjDsn0u5ZMoyuz1qRHt7dZ6wEpb8n9sxFs48OpMr5aAm8OD200tZVCX/ULj2f3ZM\nZ/oVtsjezh9g9MunajC5q0kHuHs19L6qamMtIGIvoI4fKLv8hTH2omj6FbYFM2mp7VqtBp5sKQwC\n4owxu4wx2cAMoNj8QWPMEmNMwQa/K4Gyy2KquqH1YDuYvP5Du9bhyB648Cn3P1x6XwXBzWHZq/Z+\nfp79A4oaWPFK2KrWsi++ty2BUS/SJTuWWfkP8HHLL8jGmwk/hLEm3vUd1TKycrlpbjppJpS7YvYR\nEWxbT5f1j2LS0HZ8tGIPn6wsZ0pp4jpbQLDdMBjxJMYY3lwax99m/Uaf6DCSj2Qy7o1fWL7zoL2q\n7Hcj/PIviHOx1k+BCmYeGWM4nJFd8SB5FY77lCm0FVw3y151r55qxxqqcvzCE1qfZVsMy/5zeuXc\nnEzb2vn+Seh+qS0HElaJtSKV5Mmk0AooukdjovOxsvwZ+La0J0RkkoisFZG1qal1rPRwQzT8UTtP\nfcscWx20fSWqXHr7weA7bEsheQNs+9ZO5RxyV5WH6xIvbxvPXauRDiMISttEXtvz8Q9uzLXvreK7\n3yteZZuXb7h3xka2HUgnr805NNq3slj32MOjujC8cyRPzYm1H+olHU+Bz6+z0ygv/y/54sWz32zh\npe+2Ma53Sz67dTBf33UO4UG+XP/+aj5aEY8Z9YL9t/jyNtdXAp88amsTlTGekH4yh7s/3UDfZxdx\n4b9/4vXFO9iTVkE3iKc16w7XfG6T4JhXPLOzXlW74CmbAIqWvziaaMt5bJplN4O6fFq1b9BTKwaa\nReQ6YABQ6iRmY8xUY8wAY8yAyEj3dgZTNcDbDyZMtYvgRj5f+T/QATeDXyNY9pptJYS2tru01aTQ\nVnDVdPjTAgIufY1Zd5xN95aNuGP6ej5aEV/uqS99t5XvtxzgybHdadpzBKQn221EnbwcwmtX9yWm\nSRB3Tl9f/IM2L8f2m2cehiunk+0bxv0zNzJt2W5uOjuGV6/sg6+3g3aRwXx91zkM6xTJE7NjeWTu\nTrInTLP7Fnxxi21xVSR1u70tZQvOrfuPMf6NZXz7+z5uGNKG8CBfXlm0nT+8bLuu/rtsNynpVVTR\n1F1tzrZ7M5S2n0Vt1KQj9L8R1v3XrovYuxKmDrffX/2Z3R2wBpKbJ5NCElC0zRPlfKwYEbkAeBQY\nZ4xxYXcWVSc072mrXLboVfnX8A+1iSH2K9i73O4hXdbq6urWejA0akF4kC+f3jKYEV2a8cTsWF76\nbmupXSqfr9nLOz/t4vrBbbjx7JhT+zaXKMgW4u/DezcMwBi45cO1pJ90DhIveNT+Dsa9TkZ4N275\naC2zNybz0B878+TYbjgcUuw1pt4wgLuGt2fGmgSu+eowx0b8w64VqKgoW3bGqZo/JVoKs9YlcsmU\nZaRn5fLprYN5ZnwPZt42hGWTz+eRi7qQnZvP03M3M/j/FnPde6uYuTaBYycrMcjdkPxhsh2M//x6\n+OBiOy5362LofFGNhSRntHCmvBcW8Qa2AyOwyWANcI0xJrbIMX2xA8yjjDE7XHndAQMGmLVr11Z8\noKof0vfDqz1t3Zj7Y92f6VNNcvPyeXx2LJ+t3suEfq34x2W98PGy11wrdqZx/furGNI+gv/eNBBv\nL4ftNnqls92d6/Jpp73e8riDXD9tNRPa5fNS8GfI1m9gyN0cOvdJbv5gDZsSj/DChJ5cObD82UFz\nf03moVm/0jjQl3mtPyN8xyw7xz8/13ZHZaQ6b1Ns33aOs3XiGwKT94DDi5M5eTw5O5bP1yYwuF04\nr13dl6Yh/qW+X1xKOnM2JjP712T2pJ3Ax0toERpA4yBfIoJ8aRzoS0Sw8zbIl/AgXxoH+dK1RUjh\nXtwNzpIX4McXbRnvy6fZdT8eICLrjDEDKjzOU0nBGcRo4FXAC5hmjHleRJ4B1hpj5ojI90BPoKCz\nc68xptydzTUpNEBrp9npkZ6aDVJFjDG8/kMc/1q0nfM6NuGt6/pzMD2LS95cRkSQL1/eeQ6hAUXK\ncsz6s20pPLj99G6C3Gw2fP4sXba/jbeX4DP8YRK73coNH6wn6XAmr1/dl5Hdm7sUV2zyUSZ9tI6M\n40f5MfwFQo9tA8SubQhqCsGRztumEBRpv1r0hha92H0wgzunr2fLvmPcNbw991/QySY1F34XvyYe\nZUHsfvYdyeTQiRwOZWRxOCOHtIwsTuYUn3rbJNiXO4d14JqzWuPv4+XSz1Vv5OXY/SLaDfNoS7hW\nJAVP0KSgaruZaxJ45KtNdG0RwonsPA5nZPP1XefQJqJEX/e6D2xNoLtWF++q2bnElq5I20Fs6FBu\nPXA5Ey8Ywmer93IiO4/3bxzIoLal1PEpx8HjWdz5yXrWxadySUc/Ytq0pmPzxnRpHkJ0eCBejtP7\nrr/dtI+HZv2Gt5fw74l9GN6l6jaIOZGdy6GMbA5lZLPv6Ek+WhHPsrg0WoT6c++IjlzWP6qwpaWq\nhiYFpWrQkq0p3Dl9Pbn5+Xzy57M4q13E6Qel7YTX+8Hof8KgW+FoEix81I6hNG4Lo18mp90Irn9/\nFSt3HaJpiB8f/XmQ6zvTlZCdm88/F27ju9/3s/fQicLH/X0cdGoWQudmIXRubr+WbE1l2rLd9IkO\nY8q1/WgVFlDZX4XLlscd5OWF29iw9wgxEYHcf2EnxvZqWWy8RFWeJgWlalhcSjpHM3Pp36aMPmJj\n4N/d7f7NrQfD0hfB5MG5D8A5zqqiwOGMbKb+vItrBrUmOrxqpidmZOWyI+U42/ens3V/OtsOHGPb\n/uMcPH5qrsdNZ8fw99Fd8fWuvit2YwyLt6Twz4Xb2Lo/nS7NQ/jryM5c0LUpUhemmdZimhSUqgu+\nvA1+m2G/7/hHuOgfHivh4Yq041lsO5COn7dX2cmsGuTnG77ZtI9/L9rO7oMZ9I4O4/ah7RjSPoKw\nwFL2/VYV0qSgVF2w+yf4/mk7J72ym8/UY7l5+XyxPpH/fL+D5KN2/UPnZiEMahvOwLbhDIoJp3lo\n6TOhagtjTK1o5WhSUErVG9m5+WzYe5g18YdYHX+YdfGHyMi2C/FahwcyyJkgurVsRL4xZOXmczIn\nj6yc/FPf5+aTlZuHMTCscyTtIivYPrUKvPTdVv63LpHHxnRlXO+WNZocNCkopeqt3Lx8tuxLZ9Xu\nNNbEH2JN/GEOuVN6HBgY05grB7ZmdM/mHlkjsSzuINe+t4rGgT4cPpHDsM6RPHdJD6IaV2/ZigKa\nFJRSDYYxhp2px9lx4Di+3g78vL3w83Hg77z183bg7+OFn7eDE9l5zN6YzMy1Cew+mEGwnzdje7fk\nyoHR9I4KrZKr+aMncvjjqz8R5OfFnLvPZebaBF5eYDcueuiPnblhSEyp04A9SZOCUkqVwxjDmvjD\nfL4mgXmbkjmZk0/nZiFMHBjNpX1bER5U+QHtv3y2gW837eOrO8+hZ5Td0yHx8Ake/ep3ftyeSp/o\nMP5xWS86N3dxG9cqoElBKaVcdOxkDnN/TWbmmgR+TTyKr5eDv47sxG1/aO/2a83emMS9Mzby4MhO\n3H1+x2LPGWOYvTGZp+fGcjwrlzv+0J67zu+An7fnV3FrUlBKqUrYuv8Y/160nQWxB3jgwk7cM6Jj\nxSc5JR3JZNSrP9GpWQifTxpcZkmQtONZPDdvC19tSKJ9ZBAvTOjl9ip1d7maFHQduVJKFdGleSPe\nvLY/E/q14l+LtvOvRdsr3kwIu7biwZm/kp9v+NfE3uXWiIoI9uPfV/bhg5sHcjInn4nvrGDs67/w\n3s+7OHCshkqPOzXQsoRKKVU2L4fw8uW98XYIry3eQV5+Pg+O7FzuIPT7v+xmxa40/nFZz9PrXJVh\nWOemLLx/KJ+t3svsjck8N28Lz8/fwtntIxjfuxWjejankb9PxS9UhbT7SCmlypCfb3j069/5bPVe\nbhvajskXdSk1MWzZZzcfGtY5kneu71/pGUxxKceZ82syszcmsSftBL7eDs7v3JRL+rZkWOemZ1RB\n1tXuI20pKKVUGRwO4flLeuDtEN75aRc5eYbHL+5a7EM/KzeP+z/fSKMAH16Y0POMprR2aBrMAxd2\n4muzZbMAAAdrSURBVP4LOvJr4lG+3pDEN78l813sfkL8vXlmfHcu7evZrew1KSilVDkcDuGZ8d3x\n9hKmLdtNXn4+T43rXvjh/8rC7Wzdn860mwYQEexXJe8pIvSJDqNPdBiPjenK8p1pzN6Y7HK31JnQ\npKCUUhUQEZ64uBveDuHdn3eTm294dnwPVu0+xLs/7+Las1pzfpdmHnlvby8HQztFMrRT9exPr0lB\nKaVcICL8fXRXvL0cvLV0Jydz8lmx8yAxEUE8OqZrTYdXZTQpKKWUi0SEv/2xM94O4fUf4vByCF/c\ncXa92l+6/vwkSilVDUSEv47sTPNQf4J8vekTHVbTIVUpTQpKKVUJ157VpqZD8Ahd0ayUUqqQJgWl\nlFKFNCkopZQqpElBKaVUIU0KSimlCnk0KYjIKBHZJiJxIjK5lOeHish6EckVkcs9GYtSSqmKeSwp\niIgXMAW4COgGXC0i3Uocthe4CfjUU3EopZRynSfXKQwC4owxuwBEZAYwHthccIAxJt75XL4H41BK\nKeUiTyaFVkBCkfuJwFmVeSERmQRMct49LiLbKhlTE+BgJc+tSXU1bqi7sWvc1Uvj9jyXVtvViRXN\nxpipwNQzfR0RWevKJhO1TV2NG+pu7Bp39dK4aw9PDjQnAdFF7kc5H1NKKVVLeTIprAE6ikhbEfEF\nrgLmePD9lFJKnSGPJQVjTC5wN7AA2ALMNMbEisgzIjIOQEQGikgicAXwjojEeioepzPugqohdTVu\nqLuxa9zVS+OuJcQYU9MxKKWUqiV0RbNSSqlCmhSUUkoVajBJoaKSG7WViMSLyCYR2Sgia2s6nrKI\nyDQRSRGR34s8Fi4ii0Rkh/O2cU3GWJoy4n5KRJKcv/ONIjK6JmMsjYhEi8gSEdksIrEicq/z8Vr9\nOy8n7lr9OxcRfxFZLSK/OuN+2vl4WxFZ5fxc+dw5qaZOaxBjCs6SG9uBC7GL6NYAVxtjNpd7Yi0g\nIvHAAGNMrV4gIyJDgePAR8aYHs7HXgIOGWNedCbixsaYh2syzpLKiPsp4Lgx5p81GVt5RKQF0MIY\ns15EQoB1wCXYsjG19ndeTtwTqcW/cxERIMgYc1xEfIBfgHuBB4AvjTEzRORt4FdjzFs1GeuZaigt\nhcKSG8aYbKCg5IaqIsaYn4BDJR4eD3zo/P5D7B9/rVJG3LWeMWafMWa98/t07Ay/VtTy33k5cddq\nxjruvOvj/DLA+cAs5+O17vddGQ0lKZRWcqPW/0d0MsBCEVnnLPdRlzQzxuxzfr8faFaTwbjpbhH5\nzdm9VKu6YEoSkRigL7CKOvQ7LxE31PLfuYh4ichGIAVYBOwEjjin30Pd+lwpU0NJCnXZucaYfthq\ns3c5uzvqHGP7KetKX+VbQHugD7APeKVmwymbiAQDXwD3GWOOFX2uNv/OS4m71v/OjTF5xpg+2OoM\ng4AuNRySRzSUpFBnS24YY5KctynAV9j/jHXFAWcfckFfckoNx+MSY8wB5wdAPvAutfR37uzb/gKY\nboz50vlwrf+dlxZ3XfmdAxhjjgBLgCFAmIgU1JCrM58r5WkoSaFOltwQkSDnYBwiEgSMBH4v/6xa\nZQ5wo/P7G4HZNRiLywo+VJ0u/f/27t41iiiM4vDvGEGiAYMQG0El2qgQBcHCKAT8Byz8QE0KKwsb\nO1EUIWCrNoIpFCLGjyjGwtIUixaiogHBWIlCGm1EiKBIfC3u3SEmxg0bkh3Y81S7s8Nw58LsO3Nn\n5lxK2Of5xud1YDwiLk37qdR9Ple7y97nkjoktefPraSHVsZJxaE6QVjp+rseTfH0EUB+xO0K0ALc\niIiLDW5STZI6SVcHkBJtb5e13ZLuAD2kKOHPwAXgETAMrAc+AYciolQ3dedodw9pGCOAj8CJaeP0\npSBpD/AUeAtU5yM5SxqfL22f/6fdRyhxn0vqIt1IbiGdTA9HRH8+Ru8Ca4A3QG9E/GxcSxeuaYqC\nmZnV1izDR2ZmNg8uCmZmVnBRMDOzgouCmZkVXBTMzKzgomC2hCT1SHrc6HaYzcVFwczMCi4KZv8g\nqTfn549JGshhaJOSLuc8/VFJHXndHZKe5zC3kWqYm6TNkp7kDP7XkjblzbdJeiDpvaSh/JavWSm4\nKJjNIGkLcBjozgFoU8AxYBXwKiK2ARXS288AN4HTEdFFelO3unwIuBoR24HdpKA3SMmgp4CtQCfQ\nveg7ZTZPy2uvYtZ09gE7gZf5JL6VFCz3G7iX17kFPJS0GmiPiEpePgjcz5lV6yJiBCAifgDk7b2I\niIn8fQzYSJq0xazhXBTMZhMwGBFn/loonZ+xXr0ZMdOzcabwcWgl4uEjs9lGgQOS1kIx7/EG0vFS\nTcQ8CjyLiG/AV0l78/I+oJJnFZuQtD9vY4WklUu6F2Z18BmK2QwR8U7SOdKMd8uAX8BJ4DuwK//2\nhXTfAVJk8rX8p/8BOJ6X9wEDkvrzNg4u4W6Y1cUpqWbzJGkyItoa3Q6zxeThIzMzK/hKwczMCr5S\nMDOzgouCmZkVXBTMzKzgomBmZgUXBTMzK/wB8IBL/O6nN2cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6294b53cf8>"
      ]
     },
     "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 0x7f6294a147f0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GW/6s9tqdk+382PeZTCcn241+vU87K04+s7lf5/Hcr+048zauLNu+P/76yjb+VnL/o2/k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\nCEBMikEQBCAmxSAIAvA/jgS9sAJfwHcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f62f7f8de10>"
      ]
     },
     "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 0x7f62949f79b0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TwSOhmvq5jczPoT1s9vEPf/zH1rbjxY4XC31+qc6nYj0ePzsHM3ujmT1mZo/Nlotbv05I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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6294a6b588>"
      ]
     },
     "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 0x7f6294952f28>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5M8aUjq0tUMmX5Ig9BQYtr+tyUTknz2EdbcFSDFavEk7FU8SnXmg59V2AboWJ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\ns94XTxCpOmwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f62949dfb38>"
      ]
     },
     "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 0x7f6294935390>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5LsbWeOznt70uO2VzDl/aI/SVevhN4AGFsumr9/yn92oTcbfQ5/t0NhTr4bjvDMzsdWb2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\nIAgC4b+wXygn/rudYgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f62949474a8>"
      ]
     },
     "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 0x7f6294899128>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eRm/xM5Di2pyCvoLisPhUAN19YuyhlvnQaVi749Sr03QP4dW9a37MvVXOLWJ6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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f62948ec710>"
      ]
     },
     "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 0x7f629487cc50>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HY7dD7yCvvAbWR/W7fugf7Ha9b6cTv+jrA59KIHrn3YW80Y6B97R/g9s3lfh+\nw+//AiVDhcJd2vZ71+yDe+JGNmMBstkEhatAn0so6F08w1NERRTIZR4hb30y8H4ZT/z6e4UnGPT7\nVV+/YPuiNwoWbU0UVpveh+yFVR70ZrLnNFJMSEhIINJLMSEhIQHYWPpMsB5y1+BsTNutNhXViu4c\n0pcJVHdcQ93fP/CAaQZs28h50hZydUeFy9znutvHy6SYioriaO4UsNtw9bVZIsB34hRnXCDAfAmK\ni9++AstdUKlZPP/klID2KYJ6acs1dJFXfXLcvgemZw1XZVncqnMIm7B4j2iLJY/jVm0fZH+HNmve\nL/MRJwQcywedVrcx89E7574iRXbzV4GWWwvmBzP3/AG/hyWmLDjaWCJvuTjA/YpR4HYORdOwX3tA\nfurnbM2hoHcQeI3MgABum018330UA+vtVdMHxVPeb/MuAuBRoKwO+j5e3c7NjN1OI8WEhIQEIr0U\nExISEoDNpc+LoHI/qrFwxA4o4sTgVap+88gg9qZOH67nTjs6DPTuwNUbbtddqKzThtMX5pnOERzb\naHm7ypgfu7X0bduofrVA8atW01XOg5nTINp1TUB9DYm7TSjNtVlFZWmR1pxAzcT6sklah2NA2R41\nsA0CxuuoUzzG01dEq7EXds77OkMd6XMIKh4jMB40sWyhBjTo5v2ZU9z5DtR6OGLXY6B8AAU9nMH+\nC8H71kbEARIAchS0aiFgujbytjx14IHkK7RxzVMo/lMo2wxYV0ARrxEs2obI926gL+a+fafjz8s8\n9t0QBbwyuM0Pd1DTHEHvqyAD29DCz2mkmJCQkACkl2JCQkICsLH02fJMxYoqj10JHV/zQNYagnfz\nNhTNlZsz8o0Dau1OmDOLmr6NrlOTAPW3h1q+zCctkRNbD/771KtXaukcNls2pyM2Ar1Lp0xZDjUd\nweCzgsHWx4sagNbnMQh4DousgOmFMnNa1WIhKBSlap5zKbq+oIqN/GhQuToCjy9m1b7dMaYJQFmp\nipcoMjbHvaDDdvcaAunhZp2DYhvqGlukzQMo/tZAjjty5uvISe80kZMNOlmHEM6eaJ2HWh0vFc5y\naswwTsG3s4ZAaqEG90Kkz0hAqCMIHTWoSyjX+42qXxao4708j6kB5GQPAxzOo/N32NDo7TRSTEhI\nSAA2dqQoc8PU4QUYZCLlbaeG2hgQIFrNagQxPHBFxTDy4/yy4Rd81qGxKGpt4Jw0eW3A1SQg3s+i\nMNKeY9QUiTKqAAAgAElEQVSEOi8zOKOwWkcHI6sR9KTmAtcJByCKCoOVqAGx5Gk40GxPff24WD/B\nfn2CGMy6X9DR0o/Txmimg1Kps7Aa2WF0jmMb7lUNo+oFYgYX6PPrF337nYm3vQFhKsdIcBFH8Byx\nH4z9ejJsy3KrHPlnaDtH50uIFCUclubZagzpY8nrTX+eLj15vIj+kZq19V/bGVIUrwdnRxdQ32cM\nR/rV80VBp174MShWBcSAzmM8Jp+fTUIaKSYkJCQA6aWYkJCQAGwsfV6WpQbDikIsQcToNnP90A1C\n7+nuHS9PptWk+hw1KnKU3mxg5pyuMnraKe4k8422UAykbCA2EpPhJVLKxnlFA8FkVGCefQn3mLwP\ng1LQnRmKqoQpjG0hDNkYwkS7auMVc9q1h0BCpjDmKLFZu8eFgy2kgrHEKuuF9HFNmDE4jsMbzZze\nXe34flvIg8yQCzdHyuVR3ZfP3euxf+Ohd15RA30+gpNMjA8cz7zdvD+naQpMD8whesxRkjVD/CIp\np61iYyFofdDEe2W6jRhInHN23Wl9AUFnhg5dIGHwSbm4uEOXothGioLhyA+41fBtGV0ZhvG+lIk+\nJyQkJGw80ksxISEhAdhY+kw0keY3a4G+wgh0eDTUjbAc8Vv4+ajD/LU+BJdm2hoU2iVS7ui8wjoa\nU5jFdiLdHuZOgawFagya3O3QmYXxe06xeJOXTzqV6nTgXhNNVM9nfm2jhcfm1Q7gEgRHn3vqcPqB\ninpx6TR9H+orVeQlFPV+VI7ra+qGVB/4fsPgFLuPsjR7S//DQO1aqNfSuOKUfAEqu4rx7PS8T67N\nPc1vq3AFN+DY14fen9sZpibw7AwOfZvRll9fe1Jt0xyilO6R93mbzyfmDEr0RZ/hFFjMunBDCqDM\nmJ7otatrGiE9NKPZMuJIe1OfSphGar6ZSX5ppJiQkJBwAmfqpWhmLzCzXzCzt5nZb5nZ18X1e2b2\ns2b2+/H/3Wc7VkJCQsLt4KzR54WkvxVC+A0z60n6dTP7WUlfJunnQwivNrNXSnqlpFc804FqyrSb\nVfSgRDpbhtS5pTl9XW4jXSuqcYit1iTQ/hOpalBzS6isE6q/YT3FNvwmZTPfZj9S3yXS6Wo16o+O\nKejm9GmnzE0Y6+agQUKK2mjiSusskqGyB0UcFLBW8FHx65/m3u49KMQ5zGLnQ7+OIRx+pih9Wo9K\nMIOh222nw3MEabPcqy0xxQBnmBPRAkfL9dtcc6q6fKCizTUc+x7U8Nk3TnVoLXLcwzHobg3B1tb3\n5y8vq77OjvzYZcMp8xRpi/MGXHrwEJUItset0C6MgHOcv9nyPi3jc9mDgXHJ0Aqo70usr8UYDsvP\n1JjqecOZuqoQwpMhhN+Iy31Jvy3pfkmfK+mRuNkjkl5yZ1qYkJCw6ThTL0XCzB6U9LGS3izpUghh\nlfR0WdKlU/Z5uZk9amaPDqfjdZskJCQkPCPOGn2WJJlZV9K/lfT1IYQjsxP5usHM1kaNhhAelvSw\nJD1w/p7QjDnMIzi51FC2sgA9qs99uWxX9KCGoNbJHIakCKQeN1BzY4EXMdj2Ud3/4DlbAbQKJTSL\nVsXPYHSjRQ1UFsnXU6iFiz04nADjfb/+UKL0KILTi6iuZhPUTUGgOa/nfM0p2DYp8yl6pBUI9gYN\nvFjz2ilHuxPdiPl13Lc2HG1wvAeafoxW5td/ed9LpfZBvbev+fVNUCq03oxUEfehAeek7j6CsWFI\nbB0/P9vVGPj1zJBX3ythRBxrtS7vc8V7esUV7zlz7PGszPH4t1DvdYYStxM4A/W6rr6XLdyLWHp1\nwWj41vpXQonnb35QRWqUy/X1fO52nLmRopkVql6IPxRC+Hdx9VNmdm/8/F5JV07bPyEhIeF9wZl6\nKVo1JHyNpN8OIXwbPnqjpIfi8kOS3vD+bltCQsIHBs4aff5kSV8i6X+Y2Vvjum+S9GpJrzOzl0l6\nt6SXPuuRyiBNKlpQRw5nQE2NORTl2hTWYZEGTkTa64cOoEnM/1yAbjKQeW/u1O8IhrcjUJxOjvzo\nWXWygLKrBVTRweJmqilJTSirw77TsM7U6eP8HHKIsW8ZldijzPuhg7oxXTmV3MZ0hjx2W4ZSmkOY\n0pZIIX7h08w+d7rXjaVCL088H109V2JruIc7PaesLOXawuO8I6ekIwRYz88j8hw54Yp54Ky/0sfz\nUcAuTCNc2wTqN6cJtr2/8iPUpck8UHoRbcpQzkVH93n/7Aa/fuZez/HczJD7zUklRg4MC9R9gaLd\njvnsNUyZ9Ef+3NDyjLn3+RPVM8wpn03CmXophhB+WacHyr/4/dmWhISED0ycKfqckJCQcKdxpkaK\nzyeWFnRYq6hCZwBegdznRem0oo5yoiu1sEDQ6wj1KuC+pDGcrJeQi7vIFYVwqK1d55tXl8i3vuZU\nshFV84BgYIFJ9VrIWYbK2YNTd+cpAXCEzr3xtMY6ig7iW3UkEwOsIzJDXncP9T8E+p6hb+ksPd3z\nA12c4aL61XFatOsiEIxdO/FbjgiBgffh0KDEIg+4Az+2Nly427Wqz/tz8ETct37Lr6HnorgCFVhw\nHCqzOWrB1GGvdnS9UsiP0A02RZA0lHoq+xNEU+z14c6OcrsnlGHYy013fPtr8yrYf2sfijsiGwZt\nTA1hyqRxIT4jtc0cU23mVSUkJCTcJtJLMSEhIQHYWPpMHEGsLEBP2nXnLYPC169YsO27EvdUHfS2\nhrKmcNUW7aJQErIcQdneduob4Hw9xvlXR+zUUXYV51n2kbONkpUlaGp2yWnwFNR/Cauxsu9KaKsX\nlWZQdgZM72WuRHNaYQpZnmmzBUusngg8pwfbzUnELVzP0bbvd6nhAcjMCSfmKO5Ur/s0QRM073zb\npy9YgGw+rKYh2jNv05XDp4+Xd/pQYpFvXGBcYYdwXt/y40yn3s829m3aRfX82dX1QeoTqMY98/4/\nZz4FMJI/o+2Jt2XQQeQCp3VQxKyXVc/IpI2kg6G3rwlX74Bc9qU2U3VeIY0UExISEoD0UkxISEgA\nNpY+Ly1oPwbKMvC6AZskxBeLhGw2qfZrn3cKenEE2on85RHyRhm8vJw4xQ1QSIeHTndYjahnTpsa\nO9V5A+j4HC7VpMksnDVrgrJOnPsc0pL5EHV/d3xeIY/5vC0UqNrLneqfADqLruID5FUfys/fnfkx\n9+EI3j7n0wOrImFjBGnvNfA5rrmNHOc+Ck1N4GrdsGd/tFl0ataqKOkA+ev5NTh8Y7+2/LkYyu+z\nDbyN5WK9im5Q0afRbbzZg6s3ipKdR5k1Fl8LsIvrbvm0Qj94n0/hpg0WrN2pn2sSu7ENt/H8yJ/P\n6RZoOl3gY33zUDstpPjuRhopJiQkJADppZiQkJAAbCx9zoKpHW2V5shhnkycEmVQn/PMqUIzUhVS\n0wWU0nLohGQV9CtJY/LKJuj2wLcfo1hVdwaXa7RxZQFVbDnVmUNBrqMQl2VOYTL8xtURpHx+5NfZ\nQvD2NbRrtw6JPmJRwL0bUwOMy56Csk9Rd5mM3RBIvL3nlSQG6ItlTNzNIYTOQNmaoMOz0vtiNMeJ\nMB3COtXbDaeHLdyv4cKvfzK9OZ+80fBtqYqXA293jsBw0WoN/TWHO/iwhFoc29KBR9j4HOzMWF+8\nxsB4vy9X5QkANdadRoLBefPrL8ubleNRw4/Xvc/peG3mHTqEX975PNrMbeiYajOvKiEhIeE2sbEj\nRZWlFEdCC4wshHKPkxrKXeLnYausfq2nc4gVmIBvs7YKRk2LNmLWAlSUjE4u+AlHzKRQx2QSYxYN\nzj0LxOCVMLzNkH6VYxTYQLpc/ZKPFOZXfaSyjVREy6pz1lCC9WiBSXdc6F6J2Excj2EEtVf4ORsY\nwZRHqJFDV6HYXXW40djC+yfAEJeiWC3HiJylZDHKH6NeTsFCJhDg5vXYxglKfCJtbvFOd+9htZ6O\nfIRdb7BfHPtXrx0vd6Gu1eP9b6EuS32CeioY7RsKBk0xJG7jnr+96W5AjCtdUGnBsztfxU82/RhL\npBYeHfj934VL0rRZnZ8C4iYhjRQTEhISgPRSTEhISAA2lj6bTEWkKjMIBnOkfLX3nVcMOogxe7JK\nu6rtOgWs1UEBEZ+VDVALA9RogFly1g6hce05lJsMmOyeR3o8w7pG6ceeNDGJP/flg8Orx8sXC6d1\nsx3fpnmRaYYs91pd07WxUzDyxBxUcoRry1FHZavlFCvjpD/EmDmMXZd1CDndikLWcE/KOuqSoDTq\nAveinjtNXMX9VU3Hsf0ydDBzYWK49HP1DyrHmjaoLDxwtXzABQiKTmo4xW3Y+tjEds/v8xIiXTeu\nZ9lVjlKWcI1dYtJgiedvOvFrZv2fE7M0mDJYIN61FmMzt+frXwN7qD8TwgkH29jWFKeYkJCQsPFI\nL8WEhIQEYGPpc6mgoVXUooZ0rt4UZAopVVuouxFiacsl1NTGrlPDoqDjKuIHoTj3QL1qJcqanhA/\n/fh1kLxV2FoxRm0NKNhTpPCxXkgGWnm17tR4D1yK6YKExTS/fAdlPUGr6jhlDalqBy0o+8Ai8wul\nA1HY9n3zqV/TqkuXL3LKloEOHtW83ffKqSxRR/naWub9ORw5xc/3vV+KYo3bCz43TLWwNO4CeaPX\nkWYYZgzg9G2miFPsYSplOZ7FttJRCOl8OP9whvjaMfNWfZv7D/25fLrwaYIF6tjUen5/VxENMxxu\nB2bLGsDYdvfmqQFL9DkhISFh83GmXopm1jSzXzWz/2Zmv2Vm/zCu/yAze7OZvd3MftTM1ld9T0hI\nSHgfcdbo81TSp4cQBmZWSPplM/sPkv6mpG8PIbzWzL5H0sskffczHSgrpd5kRaFQx2MXwc6gWOU1\n54czizSIKieOnYM+Wm996dEm0gZZBrVedxV3H0rvDBT/eGuwkwWClxcw/Nybo5TmHgJ/a97iMqAM\nJq6jUTBQuuJQ24WruUvUK2nAHJbB0wweHuY0eUUp1aUf5/rS6eY9dafBZZzKOCy9D7sn2gc1GT/l\n57o73i6otYdD97XponzsCPmHtUOn/tuRqlNlpbq6MKi/CJ4/GPt5+ohWOIcg8BNDDyjq9Wj+OmJA\nOW/QKWa6dMzpPA3FvePP4s5s/Xhngrorq+46nDrV3kNfqQOj3j6jBqpGWpmCt//QESqswuiL+C9I\n+nRJr4/rH5H0kjvQvISEhA8AnKmXoiSZWW5mb5V0RdLPSnqHpIMQjiuTPybp/lP2fbmZPWpmj45m\n6wvGJyQkJDwTzhp9VghhKemPm9mOpB+T9OHPYd+HJT0sSfftXgxFzOk8UW4SSc4z0D3teNBuGWuz\nsJQmy4ryePs0JQX3GSFvejeDWSpY9QK5sifyaeMhn1g6ve6M/NgXc5QhBdvZniCot+HX1oHiWQOt\nm8OFZtUsu+4/Jp1dP08DLkGnBe1eycZr17MuywtLr5Fyseb0+Yl+da2dDtRRKLg9GK7u7K5Xn+mA\nswBNXxyiXSVMXrewvHIeQj2d+djv4Qj1UpZwF6ohQqCDKY6ygxK3CLAvC0QZxNznAgL+PtT8Otxz\n2gWmKeD9O8ZzRn14uYPpEzyvPNc82h3V4UB0mPn1N5Ew0Kj58tiq+xI2U3w+eyPFFUIIB5J+QdIn\nSdoxO75zD0h6/I41LCEhYaNxpl6KZnYhjhBlZi1Jf17Sb6t6OX5+3OwhSW+4My1MSEjYdJw1+nyv\npEfMLFf1wn5dCOEnzOxtkl5rZt8i6S2SXvOsR8pMIVKY4pR3/xyeSgsEYTeifVV3AnV6HxRsG5yV\nvqLQZXMEzE5LkGOPI1aBMpQM8B5HFXtvgfPgTk1H3pai5bR/2kKQOMhUAZU9gErRdmwZA4x7NRjv\nDhF0jRopAYHMI9hYNdFFLdDHdssDsq/N3Y6KVlsvzC5IkvYX3kF7ciV8gPOMYAh7aWvPTzoFxUV7\nZ6DJDQTvT+fox3iLJqw7Avu1Akr1su3ruwhqZz/PELBN89ej3OlpL/LPcd/V32WOc7Z9+iKbQKHG\nMzdFKdXxHKa8CMKeMBDCF9WK2zCvPEcUREb/3hyB9tHwdlODt8/USzGE8N8lfeya9e+U9Inv/xYl\nJCR8oOFM0eeEhISEO40zNVJ8PlGG8jhftOg5JRzBhbuB3OascXNXLDKUzASVmMARu910CjiBO/UC\nquAMFl0sT7kYOZkZIjh4lQZN1bgBmpwj93bYICFyisMc2kYOR2rW+iBTjNZdGsKKCrnc8+DnDEO/\n/mK4Xv2sQ4ntQLns7t2zto3jUJ13J7j6zDzhes8b24TKbH2UkkUuO5Xo6dwp+RiB5DaHWrwK2l7S\nrgtKfRt9yGB83P/lkfeFoTzoGBZwLTioL6PbennO7+168zFpjlzy7BAO503fd4avc8B1GCjxAlM5\nRXRZL6BsM3idSQIBfVU/qu6VLVPwdkJCQsLGI70UExISEoCNpc9LCzqKkaoNFIXKWizuBHV3hgDX\nWAwphzpbItC7MfZtxzBqrk+Y4+wfLBAkPZ5BroQo3Wp4u/pRFW2BmjULRmn7cg4rsAZuZz5jvrO3\nq0RxrSWpUpwS6IKOLXPwa6jjJE2NIS4C6i9zohs4JgtDlVBOi9iWrcK37V7wcqhYrdl1V7BHuC+1\nA5+mmI2dMl8tvegU0StvJqu85+N9P0+z67R+hKiFOa65ue1qebHl2y+h+E+QabWi+z1YdJUFXMoR\nMA7ntBPFvXgzmOMeoKIHlDUtan7Ny9X0Rd/btMAbYY4ppTpyn1dbl6lwVUJCQsLmI70UExISEoCN\npc/ZPKj7ZEVzWsh3tXNOHzqwVzIEEo+PqmBa6ro11MYdIvV4/sT+8fIUAa51UKkJlNslt0H3GxTa\ne0IMdkbd4xPLoLUNBPs26YFhfrwFaNXh4eHxcstjhhXi8YcPwJkc7IjForan3u7lnvdb2UQOLeYG\n6sghrnV9+9YQLtNR3R2Yq9/oZm03POH3YAt0dN+tu7JDp7v8td9deh/tT0GJ4XbePg6U9/ZdDohO\nYP44grd1zfuzAbWez06B2tSZn/6YPgdQc4ZDB9RdFhTiDDZqLMQ1QFRCjnzmJay1W9vej0XM96YV\nmoFeZ0huHkL9H0+q6R3a2W0S0kgxISEhAUgvxYSEhARgc+mzmRqxhm/hLEi1a/BOgms2A1FXCtwI\nBY8aKNxDP2QW9LEjBBIjMPyo6fSR9mIBynEBipu1Kmus5hGCwQMspaBKk+MumLeLxaOJ82q6hh/U\nPfd3b1H1xRJ1hEfb3j912H+NmlQdkT9dwnbqHnfEHqMeckfrKaZNqr7obDlp7jScVpZQucvZ+mJZ\nJZy3hUDqFqYeWs0trUMrBvgbrL2mCz/PoAEX7iWKW93rdHT2pNPdUPfrGNf8PvZIvWNA9AxPFAPj\nx3DkLpEYMGojAHyy3p17iimbDp4XBmrP8hiETco8QpA+XMJnSC5Yubozd36TkEaKCQkJCUB6KSYk\nJCQAm0ufs0ytqCiXCJiuo7gQHZQHTadK/W6kYVAFBTqsrndbNwM1Oe+UqY0s1joUz4CA2CGsq7h9\nvqJhcKFm8Gy949dAVXwE+soc1pb5ccZTFI4Kfhxrx8JNqGnNks51/H6yiBPrTrcuuKt2s+3qO13F\nlwxwh3K+Kjq1bR6lPbnqzuN1uIBncLteXHb1uQaaHLYQ4I7zM7e51fU2rmosD694NEEb1zDc9/VL\nwSMNCeTWQfD8KUWdyiZaE89Zlij4hTz1Lpb7TUw2IP5fW37/DXZlBfLAlwjkz5DD3KxX+y5GsHNr\nI3gfp+nU/RlaWa5tZuh2GikmJCQknMDGjhRLk6bxR3T7Xk8Xo9DACW5hUn01v91AmdAaBo0cheWo\nXYJsPpWZb7NV9xFZP/fz7JW+fohR3vB8NYI6f+THqMHpJEf82BQT8CzZuoDhaDGF0DBi7RDfZnVE\njuoC4iFLxMbNUO+1hpFPOePejhZSFHsoFpthIn/VvUd1lBjFqHpx9enj5ctTT9trFOhnnJOGu4ts\nfWxowRKis2p5CbHqyXe914+xCzecE/oCR57+kDA2sZitF8xmMWa1hm3n+EYa4j6F/Yp9uBShJUUP\nzkAQnXiPitLHQfNr1YjPuhBfxjh2z69nuKBaGTsgCS0JCQkJm4/0UkxISEgANpY+KwQtlxVtmWMi\nO8clU6Q4OES61KSiCk3QN046n1biswT1WiLucAaD0FZAl5PWLf33qd2oJvjne6BAB05Np2hryFCa\nFeU5WZ41o5MK6RkY0TKKR4xv69MQFTytW8L8dcenANpdiCG5n79jvv0c1HsbMXN5szoBXW8YJxq2\nseMVXxzDNNhQ1rNufv4ng6fi5XPvjBcsvVTq8KgSbN72+DuP1917z72+n59SBZxxSggqJ2IJEYPK\n+MkG0u9WpU8nmHZgPSGDyWuBtE1ro99mMMKl6Iaby3o1BesFNarjhz7SMBt+o0dHSDPsof9X93Yz\n2XMaKSYkJCQQZ/KlaGa5mb3FzH4i/v1BZvZmM3u7mf2oGYYeCQkJCc8jzip9/jpV9Z5XguI/lvTt\nIYTXmtn3SHqZpO++1YPNUVOF6XzzgadrLdkTkW1cm3gM3MWmx+BliN9btmDaikPMyvVKLI05DXF1\nbaS0KZqShqkfY1BHjQwo0f2xU+kutEhrge8ilnA2RIraeY/Dm0enlLwPqocSn1RtGzg/3WsEVZwG\nKgsowV3EWOYweZ3EmjbZ+qw1TZ5yxTlHnOJex+8L+4vuQS/o+3UelX791yZOq/evXZMk7TadUg+Q\nzrfb8vUF4genqL8jxiaCWtZQnnSMeNhaVPTbS45NcAykNmass7PE+bd9X2QFqgeT4xPpeJhKWt0v\nO497yJo3iD4YoQxrLUYNhFNiMe92nLmRopk9IOmzJX1v/Nskfbqk18dNHpH0kjvTuoSEhE3HmXsp\nSvoOSd8g9ws4J+kghGMF4DFJ96/b0cxebmaPmtmjLJiekJCQcKs4U/TZzP6SpCshhF83s099rvuH\nEB6W9LAk3b9zIXQm1Tt/ibS0+SmSWR1K7DwGwbYaoCDI7OJPSQ3lJksopAW6dgSz0hO2nCybCXmz\nOaraOMG6Tg3pVwi6zqiEwsB1jmD0OQLD1UNA9JzlSauLGl30a2i7+KvGR188Xp4OvDPGW6DSMwQs\ng7410Y/EDHyvFg1nB09eP143Kv08C/RFPV//2BYInh7CcHYwcurHdEWDFD/aqih2F+lssxmDpHHT\noaznKKW6RC2U6Rw0FAH+BahsiKHyYQKluovUR0z71KAyc2KmHHtbyiVq5PQ8EmAW1k/l5DEKfUbT\n2jZq+yBKvS2fgpjh2jYRZ+qlKOmTJX2OmX2WpKaqOcV/IWnHzGpxtPiApMfvYBsTEhI2GGeKPocQ\nvjGE8EAI4UFJf1XSfwohfJGkX5D0+XGzhyS94Q41MSEhYcNx1kaKp+EVkl5rZt8i6S2SXvOse5RS\nWAXFwjFkMUFQK5S4smCwcaQwcBeZ4xj1AYKaEZhbnPA+ReA11UpQmQI0rNH3cy3ym1W9kbMX5Yip\nLRFgPqfkS79ZqNyHMChtoQzpol0dp0AA8BIKOvO9t+85d7xcP8BFn5ILO8b8boFoqgzr7VrF1Q+C\nU+aM6iaUUKqic5i/Hi6dJudtrEfA9j2PeXuP6n59u90q0IHTDu2WU+k5VHuWxh3X/B4OEWyOFGMV\nCP1uYRqmjDVSshqmNOho45uqX/pURxvTESeiGZBLzj5nDvUM9XJWedCrdkgSTXymDPTHvdgtKrU6\ntzM1pnrecGZfiiGEN0l6U1x+p6RPvJPtSUhI+MDAZr7qExISEm4TZ3ak+L5iqVKHUb0MlC5Bg5tU\nlFnfJNb3aGx5UGsGwW1cJ731/SYLPyDrezRQPjXPoBaOaSMFa7AYtGuwq7IDBJrDKFSgz/WGU9Mw\nRE4wKPEWvaaAMubWssxJdp+bcbU7zt9bfdJ0KLF9KN6wsTJMGdgAQegwtJ03Yz/CQDUbQU1tgXaj\nHOxoCkstNOtw4VT23L7foyNMH6BEjRYxV7nTdMq8OAJlXng/TxFn39D6Dm1mvr4NlbtseltWQdUl\nrejwGM4x1VMgsmDWPGGbe4wl+mKGgPkcU0OtA9R9WcVCoC5NQB52GPvyBMp6Zy/Ws0m5zwkJCQmb\nj/RSTEhISAA2lj4rM1mkHGGfUcjOfSZd/00Yl8yPrqhCd3wi1NoPDTuKA+TSjhfO/bpNp5tjhNs2\nSlAVWDaVA6c+zVjms37k66hm1pATW9+CLE0j6fYpv3esNYNym60YPN0ATRa6bTyEsorc2x0EbM9R\nuybAOo0qJWuEzKDWDp6qaqCUBwjYRrN7cA8XcnXbQ6eeQ9iOtUDTp7jPo6FvM24gePywuhfldW9T\nEzVcmJNdoNzrESy6ctD9EkHtc1jH5SzxGunnAkHXdC/PEbVAu7o6SsYWIz/205k/T80cHJ956Egw\nqMVjTpf+nGWoORSmmEvBeQaT6h4xWH2TkEaKCQkJCUB6KSYkJCQAm0ufgfolt5fqT8EJQU8MRZ+a\nMah5AQW31m1iWzhmw3eiLiiuyFVlGVRaM40g9S7bN9+KJc5ToMRpvuu0LoctlaBWTs759dQwDVAD\ney4ue7DzCgHFihiknB35MRpQs6cINCfFOxh6edILhdtujfreYbR0W0UNTwq2FTZvCEyvYwpg+oSX\nHm1d8PMIyulwBtsrBCF3oByP4tzDtYm3+34ENS8QGH1zr1WwPVBWBMxPcPtz0PNJdA1voBtIpac7\n3p8dWLQ1D5HLX/g2OygPe33o0xANzL0s6lClo0LOUrMLuK1fau34eSZ+1fP4XbENlZ/TSDEhISEB\nSC/FhISEBGBj6bPlmYp25C0Q0WgNNZDzlh0UY8pirmyz5rQiQ44vjKd1AGUzh7K6E1BEiU5LCLCu\nsxoUAmxHzer8CwaUL3AeHG4BaZGK8+Hcado2AnZP1HVGzeT6uYp6LlD3t4nc31WRJUkaZ06B66Bm\nOYJJCzcAACAASURBVIKxt1Ez+739p46X76utd8peDmPAfMfPuex6+5Durc54vXVV/5o7aU/hVnaC\nHlJphaVXPYYUNDpwyYbzeGeAqQwosf0t1t2GKox7N0BQ/2wL1mXRTPxwC7WrzadGrAN6j1zmOnKf\nC6aHt319nbnv15F7jlT1WZyq6D2Be/4ivz8zFAWrb9OlHRH2G4g0UkxISEgA0ksxISEhAdhY+qwy\nSCtHY+YH1+EsDXWT29Ri8PYClDYDB78cnMzNoXLmoNsjBINncKGug2ItZ05PBqiGdRyDCwq67MAl\nGs7bhvPXa04926CyIXe6MwPdtktOibr1m92xDw68WJS2XUItvJ6Xtrp7/gccnJegj7vm7br6nie9\njQi8bq3OP/K2Ti5CzUckwNG+K6HTwCpivlhDXxR06p6zMhYsyJY3u1P3B36hecfzwLOZH2P7kHvA\nos1AMcFlr5Xe9kYM8G4iN3/Zgns5ajBvIbKB1zDhFAhaUixwbXDz7s38XI0Vq36Rq/YM5OY8zeLI\npwDqUWXPss0cU23mVSUkJCTcJtJLMSEhIQHYWPpcmjSN1KKn9YHMy+YpPlqRwiwRgDuAbNdCvm8T\nucwz0O1uy6lp3VCgCvmkeQHCA9pyNa94zQ5yjEcTp2MtBB2DVGl/yfxkWJHBLi07cFp/dM4pY29l\naYV86AkocBO1ieegVYf7zh8bmJrIoeYfHjkND3DKFuhzGXOly57vRyfrDNMEamJaY+DHG9adVm7B\n1TwsfH2Gethj0HDF6YkSUypFx6cUAqYvhIJW2TXkviPwfIGa0eGiB16fy3x5pSgbgtEXcOHey3wK\noDf1Tp/g2gpQ6SsIPCcN3sFxTtSjjuo6Jw6WyI2GMblK5K/PYgB+WKbc54SEhISNx8aOFDNJ9RhQ\nOJb/ap4YNWLUVCLNzuIv8QkrWUwqG1KxOGraWmDkiZ4tMTFfYCQwQRxYjuDHD1ZVA+Vd8lEYZZAS\nk95XM/853+u56HEI1aHWQ3nKHcQSogzmIo5m5xjtNDjTDmPT1hSpYiMfEQ1xznx5omCNt30C81vE\nJE7qVf8ucK9YVvQIZWLbEKsmPW9LA/0/HXu7GJu6P3HxpL3r61urkTCEiyWegIB7OIcDjmBKWz7l\n96uNB6BEfy0wUm6FmC5HG5t9v7eTHhhB5k9AAzGTw5lfZwej2Sn6cbh0lnFO6POVSAVGEsAISjIp\nGNiOiqovSjoSbxDSSDEhISEBOHMjRTN7l6S+qgCLRQjhE8xsT9KPSnpQ0rskvTSEsH/aMRISEhJu\nF2fupRjxaSGEq/j7lZJ+PoTwajN7Zfz7Fc90gGDSPA7vjcam+5O122cQVaadSJUwKT+bOn1agLJS\nuBkznRAz2n2Yn5KScZheh3HpatcPzr2U6PWet7sJCtYewcln7se4J3cJZkLDWUyYH8EgtwzVtXYm\nmEZA7GajwPEg6AwuIK4OBq6DudNAkqztAlSelCwKBm0IVMMDFw6KiR87oHZNA9F5dbBQOhP1EQNa\nQPSaQzBp5LHuCI4R4FJE4aJdhxEv4grn98ANCCJRAzVQpnh2VqJWFiBoQXybj2B83HL6PMzWT03w\nGaojTnGAdMlZcfO+WxPeIT///hgBqdc9Nnds1TWUSWi5o/hcSY/E5UckveQOtiUhIWGDcRZfikHS\nfzSzXzezl8d1l0IIq1SIy5IurdvRzF5uZo+a2aOjyfoRYUJCQsIz4SzS5z8dQnjczC5K+lkz+x1+\nGEIIZutlrxDCw5IelqRLexfCKvaNVzmDupihbOakByPOq1X82hjxbY0dV2qXcFehzeYSdKyP+hV5\nx6ncGIpzBlrVRlxhZ0VhkJG2C/VxjDqgLPdZgxtKtoVSmigwUqfFTg3XHHnjfEQ3Fr9mw/RCt+c0\ncYZSrqOhq6/bS/RtbT3NMjQmjzGRNdRiaaFeTCj9mpcwiqVLTw/1d2iW2kZc536ONLs+FOWofocG\n+wQxpax/g3tY1jEdAZraCn7PS0wgFCXiKqPbTKPtfWVokzLf9hBOOzmmNRZ4zq7WoETXGIOL6YPC\n9+2Pqr7gdMAY/XyiLgycedpxOqp2JsdU7zvO3FWFEB6P/1+R9GOSPlHSU2Z2ryTF/6/cuRYmJCRs\nMs7US9HMOmbWWy1L+guSflPSGyU9FDd7SNIb7kwLExISNh1njT5fkvRjsfZDTdIPhxB+2sx+TdLr\nzOxlkt4t6aW3crAyRNoCM1OWrZw2EfhL1XGnomEdKLtzlJ5sT3y/JSioEOBbg0tNiVQsQ62PJeh5\nn1w5trsDR5PyitO+Bahxc9epbAma3kAd1i5U3iNkfDE4ekXwxnWnegYqPUfA+O6O1+4IB65KNnJy\nc8fe1PvC4KSDbtR8UfVvwDnbLb+eOZYXp5TWHCGQuQGHoUFwSkjKVyClsTxuux+7s/B7NTO/PyMo\nyNRySTeXcMbJ+gx89n07s9gWPFsZzIQzBE8HPGdXCtDkBhI9UaOn1vT1lxD6vziCoh0fqQVq2Tb3\nUP8H7a7N/HpGcaplM0O3z9hLMYTwTkkfs2b9NUkvfv+3KCEh4QMNZ4o+JyQkJNxpnKmR4vOLoBBz\nlC/ObzZQlSQDJZkunFbUo7pZ9lALA/uVoEA56nhoC8amoK/LhW9fzmhK63RzjPM/tqxU3Atj3B6c\nJiDaqDzyP5jjzBjhaRt5y0P/4Aj+KNNOVH/hQAOf3BO/ntef9rj6doE+Qu2SGoLEJyOn3m30S0Ce\ndT6LimYbqi3cfZoIuh5PnQ5PcewMgc/9pa8HIz1Bq4Uc9iyW66wj6D2gfGs+8LaOMgT603UGQdj1\n4O09KtbT99XiFAp+BkPcZv3ZxyxztOVCByVJ6auL54/REs2DGIR9v0cZcBaH6vcCU001q/rIMh5t\nc5BGigkJCQlAeikmJCQkABtLny1I9WiDBBFROX4Hrk89t7MYkDfEbaGyMmeWrlhd5AqPuuvpRA0B\nu3MoihmU2BzEplur6Ml+x/lrd0jLKeQns85I3ynjvMF8W9CgDvJgwZWyVZrxFtRM7DcFlw6oLdNm\nfRHf8wSt5NTDtQVUdEwlFDHYeA71dwE1fwSl/ujIc6L3tvweUQ61OWy/6MSLY9IkdRat0Vh2xDAF\nMdjxD5aokdOeIdi78ItmwDYNYq82vR+neXX+jiEYG009jfa2MtRZOUTNnQb27vkxx0gKPz/x6YPi\n/guSpD4Cw4Ug/TGmDwINlGOQvOWbOabazKtKSEhIuE2kl2JCQkICsLH0WaWUjyqq1K8heHrJkpBO\nSlgqshfzjDPk1VqfCiIAXrOLIOUZqAcDv7PRzTRdkoqmH3V/XgVEG3KjSV9GoJjbqF0yh3I8JjU2\n5NaesBP3xUa81s4VP3Z96sr2LIcUDYxgRcVSoldKp7gd9OOwhuBlUO9ZDINGmRUNQdnnuJ4TWb3I\nNy+WCJ7GVML0Eq3G0BZheiLmm9MOa46cbUYqGKZSAhRnJgmUeM4WoO+s16MYfdAIfkWt3O/VDDVk\nlnCGZ50f3sSrc58OMtDkC8GjEgwO6lnsr8bUtz0Cla6hrf2l99UkJjUsyvXP8t2ONFJMSEhIANJL\nMSEhIQHYXPpsrjovwbeyDqgKnKrLOXJVV3nLKD0ZUO6z5amiqkOhLUmTmLc7RYGsAyh993jQ7Djz\nbUYrqgJ20mj7tlsjKKEt0DEstuC8LDgrLwKCfVF0qYhK+PSE/unozBmM7NtMgi8vMMXAcPlRbf0x\nxw1MZUS6eV1Q0GF51qx7f3ZqTgcLFFrKluvpXPPI14/bmFaAjVZtUvVLDnU6h/P6oIbpEOQ15yNE\nEECNpb0YIwsQa31cEnWn7ddTPuG55DWozLOR9y1mIzTb9n4533Kn9j7s5bhYwxTDNNqrkTLn20iO\nB1qwlV/VuVoFvG8a0kgxISEhAUgvxYSEhARgY+lzmUvT7Wqc3y1dXetMkZML46cOnK9XvTIfw4W4\n5zSlBldjmlidcOFGgK9BWZ484FSpARunHFRylZ9bQNmkLVWOnGyen9Ts2DZN0gK0slki2Hrg11fE\nvOEljjhl6nXbaVV3tr5w0gK0sjH04xQd7/+DDA7a2HcVCdDFfVjWfb8MVDJb7xx2glZml9xSbQC6\nP87XU+xOvzpogX42FDnLunD1Bq2foSgY+7aOYmmof6Wm6KBezcM8JafMF3fhWP7Y9ePl+Y6v5/TN\nEgW9UNJZF0PH/2B7h35NFqeS2O7xGPZjtKKjFVssShY21DssjRQTEhISgPRSTEhISAA2lj5n86D2\nlYoKlOfp/ExnZai4XQTNRgfjIcr7aum0r7vj1GQJF+jaAaghgmRz5CEzP3cAB+0wQ7tiePIEtOeJ\nzClWHbnPzFXNQbcZ+M1zDlv+RxuFnhTpYw0x2u8pPBj4j4D2NZu+fA354wc1VzHv29nSOtRBiRFT\nrzzW1c5RuIrLC9wLWpoFcOYJXMNJkzP89k+CU/9eAFWNfW0MxkY0QeO677fVQr45zrlEUvwIz8sW\nkq8zcM4XZBXFL/u+7vrc7zOD1PP1Ar62an4P60e+0ZWGc+y9HG7zUNFr16tt5oX3VTnAtA+mLGwE\ny7u4en35uLsfaaSYkJCQAGzsSNHyTFkUJLoL/83NaaIKVxNBGFmJKh2IBXSUOUIZyC4KjTAVj6Cx\n6A5i7BiHNoARaZFVt2V/4qOwfdQZmS69rTRGOSlc4DrhMGOIawsYtZQrI1qMwh6YeFsXDT/Gou+T\n9UMMLQsESk4yjiz8MWsNENc3vzldbYFRdQaBqvm0n5Oj8HEL6XfogDHS0src7xEda5aoDb4aV9Vr\nMM0NYBJ4bpaIO53guWlAgJujxOrhzNP1zuceb9qKAscQfVXPMD5sQlHqQtzDc8MBZF4ng/A2Xlv6\n+dvX/Dla7ctzhgnSHMEC8qaPqocx1rPMNnOomEaKCQkJCcCZeyma2Y6Zvd7MfsfMftvMPsnM9szs\nZ83s9+P/u3e6nQkJCZuJs0if/4Wknw4hfL6Z1SW1JX2TpJ8PIbzazF4p6ZWSXvFsB8pinZAZAtua\nMHmdYmIcDOt4UpsuITPQocZkvUtJGPvxpjsw+USe1U7wiXHSve3cadsopoidMxd0Gqg/klGAyPyc\nFJHmMLydgRLpmlOi5p7/tkzL6qI7iFkTqNZYKAwDJ9Ym+Fu55X3bRHnWBVIbSfFbSB1cGbXMOjBQ\nRcrdbBtTILh+3pdh3RvDkqA9FizBMKDA9Ekjput1UZoWJj7SDHGfqK2TwylmBAUom/r6bp0qkS8u\nIvU2CDozEOISQptQYnYBI+AW6fbAn4VmHf2PGjCG53/lcmNwBgr4IlgD/YZHvogpf3ZKvOjdjjM1\nUjSzbUl/RtJrJCmEMAshHEj6XEmPxM0ekfSSO9PChISETceZeilK+iBJT0v6fjN7i5l9r5l1JF0K\nITwZt7ks6dK6nc3s5Wb2qJk9OoIXYEJCQsKt4qzR55qkj5P0NSGEN5vZv1BFlY8RQghm6yOkQggP\nS3pYku7ZuxBmMSZrgdi0Gcw/mZZHKpBHda+WOwXpzRDTBtUyK8B9oITmiPcqt5HmB+PSOpTeHC49\n3ag+5zCQLWFgOkNZzxxlMEssM9yOymkdcX3hEJRsUvXRpOeUnU43NUwZ0GlmB8r+NUwfsK5ofdvp\n4wQxfoNDp+chug3loKljFEk5GkPBLX0KIiDGr4Z+ZiJiCbW6heIxjW0ozbGJZIT5dajTY4/7o7tP\nH33BnXfhfCOo7zkKBq3iJ5dQivlQzlFWdBsWTDkiGAMMjDkMaMif13nT2zsd+j0vV88F3H0y8z45\n8ayifO25olLQcztrY6rnB2ftqh6T9FgI4c3x79erekk+ZWb3SlL8/8odal9CQsKG40y9FEMIlyW9\n18z+aFz1Yklvk/RGSQ/FdQ9JesMdaF5CQsIHAM4afZakr5H0Q1F5fqekL1f18n6dmb1M0rslvfRZ\njxIkRVUtQC2ke00NJqNTUIUQXWXmoHpZh1KkLx9e8bSs8zuu5i52oFA+7dtMYCa7OO+BvOXUCd+l\neUUPp+RjCGQWXFIaKI+5xPUEMLIF6CPNWsOaejEwlFEGE1zD/EITQd852nV+6L+xrBczbYMTUsXc\ndao+6sc+uu707hCR6Z2ubzs/RGA4KFwdwfZNBGE3QeVpsjsesR5J1S8GxXsCmjwb+/oaTVvRLztI\n52uhrO1igYBppHauAuybmIJpgQ7vIIWP6ZwMsB93MB2AdrG+jWAsu0Dp0/xaRbgZDL/s4HNcQx0u\nQZpvZtD2CmfupRhCeKukT1jz0Yvf321JSEj4wMOZos8JCQkJdxpnbqT4fCGEoDJS5QzUwxZwNaFG\niVor00ibqfI1ESQ7G7vOV2849RlvkWI7invc8HQ5cY6zf/Xa8fL9a+prtFgOE0G9jQf2fBn0eXHZ\ny4qOQN/b+6DJ532xAcca9att6lCZay2qnKghg2mFAMNZBh5npberjvoetbb31xL7rkx0A/J3d+EG\nxAKr8y2ozCM/RgvTB1T2M1DmOo14MWWxaku46DQ9g4KrBVyUEAydwUC3MfDzl6cMN3JENJStat/x\nAs/TYv1XkvuxNOtwysgKhhz4Yoav+UEdLkHnqvPTLSgg6Ls/R5lY1JxpNuM9TDVaEhISEjYf6aWY\nkJCQAGwsfc5rubZ2dyRJl/efPl7fRG5rawgqDQWukVfb5IdOGRbgXXXSWtiFlVCZi0tuskq69dRg\n3/fFT9LR3CXlvF6df4HyoXVQowAqRYG61nAa1H3Mg51pURY66yl+tgqwhiI93/djHPY47eANrzV8\neYAcc8Sxq4RCOxl4H7WohEdTXBrY1qcI5Ma1jaGsHiCvvAXlfD73Pq+XMBkG3S5huLoyGW6gn1lP\nZmi+3xxqsg59sb+H/p+wj2CBhlonNChegZlYWz2fdjEEWOtJnybx+ABp3EFgPp8tXEeRIwkhCsrn\nBtgYlzZB1MbKeFmSVnkMyw1Nfk4jxYSEhAQgvRQTEhISgI2lzyEELRYVnzrf86DqwdDp2xzUr9Hz\n4NSVcljOkE2KoN6A0o8stzlvOPUaPnl1bbt2Cj9PAeW4yGGNtWJ1KBkqKLhZH7QeymoN+dGtSzve\nrqfdLkwHyNuGcjqPiwvUNqF1VeMyqCTieMeX/I8xcsKFgG2wMLUDXLOnCGqOFmA1g+ILCbX5lB97\nfMHv28U56DYkX5bknMLZur0D6zZMazRW0QWg1zlo8ha+Kvsdvxd0G++NkEMsYn2wcxHziedQk5mn\nPh34lEptC6Vx7/F7O3zSy6DmCKbIMWXSQF79YYFpoDhNM8a6Bdy0s1PGTNNYJjUsNzOIO40UExIS\nEoD0UkxISEgANpY+58HUXVb0YLZYX8So0XFKYoeI9t2pqFfeRlDrADZXUJwDApxZkrJEfnANqmhe\nQ4Av2jKDjFxEdXsBOp4jUDab+7YN0O4lyxghJzl70KcPqKKWQ1zz6vJQOKo+xscXfMpg1ndVunEV\nFBNTEBA8VUAtz1AqM0BdV5zKODAUBWvAJRpKcecx2F/BhTpDYD6f7E7hFNtA+TLkTa8oZIH71ghw\naQfVLuqIYEDhshH6to19lyN0BtzGDyNtbsIibrILqtuCRRto7Xjg/T9EKVXmxPcueoA/4s6FCrcK\n0V9u0MZ0wNDPWSBSwxDUffxdCIk+JyQkJGw80ksxISEhAdhY+hzKUvNBRbNK2CHtCRZUUAWb5z1Q\ndhzp4RjqX4E84XwAtbAGigtLJSqhTeYYA334ezVRJWkUq0GVsGGeeLN14QBFiViIacenA+aoDT1r\nwcYMt3zY9vPXo4pag4I53F7/m9kx76vaiGo11Gwm32LqQVBFWyiSlcepjtbS1z2ee6QARF7VkfvM\nKYsl1PoWijhNgyvX7CPBpiuLJ5ijiNMJ2gv62DlAgbBdv+baiUv2fTtw+B5O0MHxMAM4Y2dUjWHd\nNR76s2h4LnKo6Qvc5yGO2UQeOr/wFo8fcM20KGNZ9KyGHH/y9A1EGikmJCQkAOmlmJCQkABsLH0u\nzRXFOkSyEpSZVC0gV3YVkAvxVYYiS609z2tW7rRi2LrZyVqShhMEQS+ck/TGcIQG3WzHpryj4yqj\nQNlr+fbxMuKStROcptFqKp85JcpQs3mvBufraKMGpqUaHK7Lth+vhkB21qMmq8p2vF8WDT9//rT3\nY2Pn5rrWC1DW87l/fs38bhh+y8fd9bnczBWv1b1fZiNQaUQCrEThKaYdYKR9fE8kKUe7CxYUg0Je\nt/VfrdDzg/Zjnep+6cfYxS03qOMLOMMzYL9d97ZMoeY3UUe8jipmjIQYxoJuwzmiEDAfgYkG1VB8\nazerKHu+oWOqzbyqhISEhNtEeikmJCQkABtLn83smB4tkeOcQ5UsoT4a8oBHkZEsG67sTRBg23ka\nxBqWTs0Lvv0gA9+Cj1bzEOom5L0FKO4sUukXPgXFs+OW2QfmdOf/b+/sQuS8yjj+/8878+7s7HQ3\nu9k01CTaiqWSG1sRURSRFsGPUr0QaVEoUtALL1pQtPVeqDd+XIgQWqUXopaoKF4IUnvhVTC2gthY\nrNXS1H6kTTa7O/PO1zuPF++Znv/GpSZhk5k5eX4QMu87M++cM+fdM+c5z8e/JqZRbyAlosR8rEkQ\nek7110Zzb7tVfX5tUwLdxROZl1KFux37w83/zaUFgLF4UUdlNLfzZTXKItvBrqu14rVfF3u82dFX\ni4e0K8H4EmysZmApMuHi5Ac1z3eitd2MbxzWpKq5/KlkWtVaKoWPC9HjltpdNoxvGMg2TW1ikoqX\ntyMRCYvizd+S+0lLwTUlYH1FxLoWe7FdqpJ+ZiB58IH2YnxfTRLVh1LajDKGaIctgMwrb19xSN5C\n8i/yb5PkAyTXSP6e5D/C/6v//2qO4ziXzkytFM3sWQC3AgDJDMBLAH4F4EEAT5jZwyQfDMffuNjr\n1rdl81zO9xh//c62ZQM+bJKrXsiyFCotlsXpcp1odKguSDNuqDckRe+NdlzyNMv4m9RpxJXAvl54\nr/hzNOzvYCnxiLrwk0VYJmmLlPjJoeR59cr4mWe71QpitRTHiSwEdIO+Lisyk3YPLDYy25AGx7DG\nHT/DJisNhtRFXWEvyu1Z1OP4tAZSjUYcSrl8n3pnD0RfxuQO6ItjZFJUpyOr0FycYuelCPCyVDoq\nSlnZiWNEFvCgOLcgMYFLRfWisxtSqXaHdRJXdV3RVjm4Gq0GjSvNNiWuck3GSD6zJc6wflgg5nVJ\nZ5R7QvtQl5uhnwXpYKTJTK0UL+AOAP80sxcAfBrAY+H8YwA+M7VWOY6TNLM8Kd4N4Kfh8UEzezk8\nfgXAwd3eQPJLJE+SPNntF7u9xHEc5y2ZKfN5AskcwF0AHrrwOTMzkruu3M3sGIBjAHBo9XrLQ3HN\nsVS1KVTjEtHEbUlx0WFwtLxtS9LW/hXlSO1ItGvHI9E0Ec2K9lC0OIpo1qzJRnZXTNn1hWhjboUI\nyZbGUUqXzxfRBmpn0WZWWU07EM83RLaUUgUll7S49Vr1+rHEwzUkNq0UY6kUuUvEPXpQtgn6DYkH\nlUoydTExM0nz62aVfa5Vf4YbsZ+Lmh0nqXU6mqXI1EqGHMqOxFuKFg/EedALTqWxXHHDpOCsVNfp\nZrI1IZ/flSpF+5txPHPJUWx1pCPhMjesXh/bIZ+5nYtei8iQDiUeUZ1ofYmrXOzG766QYsH1Xf7i\nyy0tDiy6LB2JDZU1RmtQfRdarSklZnWl+AkAT5nZq+H4VZI3AED4/7WptcxxnKSZ1UnxHkTTGQB+\nA+De8PheAL++6i1yHOeaYObMZ5JLAD4G4Mty+mEAj5O8D8ALAD53MdeaeBopcWK1lsRvSWzgiuhr\nNEJMXlPMnnw96mKUIrepcWW5xLV1RIZTLEzUxAxbkViycRbP7xtX12+oZGdtd19fRwqO5lLpR1O7\ntHCsiclTinnaWKk+cyyyourN7oj7OZMYSJV4rTdifxa2Je5SvLytHQVZxEMdYkn7Y01nVC+zaJdI\nfGlD7ORSTF/1LKtJWJMx13i/PKQCdobRlFxitEd70OiE+L5S0gIXRH8GGpsooQMLO1L3qjaOpICt\nBhWq97uQqjeUuNthb/f4xUJ0dFS2dNiUuM5wf5cyhpQiwE0J9hzKloEGPKTIzE2KZtYBsP+Cc2+g\n8kY7juNcUWbVfHYcx5kKM7dS3CtIIl+ozJ+tQTQTx+fEPFMzSINtB5VJ0hMTrClevkK0U+r9eA2t\nUqIpfJlITO7UBRGzSjyxtUng9ZKm58XnKTofmWjEjKQajxZFLc/H/msgsxaRrQczsBC5zZp4nE1M\ns7oEI6uJh9HuVYJUVnWziIVjRyuxf61GO3Qovq0vpl6jEK+4at5I2qZSX5MqPVJ5pybu19aSVO4N\nTc8W22+eGo/iB7UlnbAUEzNTB6y0vZDEgCUZr6IX3bjd5fA9yvepVYpGcs8tiQlcis/bcvXmSzqp\nmr4Lcl/KNsxW8Ja3JCVRdYk4luB2PT/RUs3SXFOl2SvHcZzLxCdFx3EcIVnz2czQ61dmW03MSv0V\nKBnNilFTzNoQwLw+kIohalYWooUh5zUPtajF8+2xJiXHh2MpULot8pgoq3av1KLfWtUk60vRNDSp\nZKJFP0eDeL3N6+JrmqJ32RSv9HaQyqz9R7zpmhMrKdFaaaZeyHcr3udMqgqp1kwhFXbUi7k9rMxK\nSWtGQ3K2KeZ7JlsQw2jtogfxxEoetm49QLz/w3PRlJ8U6x0fiN95T+6PlhSH1e2IsX4XIsk6lsD4\nruQTY0k86iGQu9wfXcUm9viibLtAZX4kgiATEzeTm8sksmC4JnK3qu8SIhQyMYMXRPSnHMo+hWyl\ndFtB2yXRJVWi3XIcx7k8fFJ0HMcRaJZmASCSZwB0ALw+7bZcBdbh/UyJeennO8zswLQbsdckOykC\nAMmTZva+abfjSuP9TItrpZ+zipvPjuM4gk+KjuM4QuqT4rFpN+Aq4f1Mi2ulnzNJ0nuKjuM4yAre\nHwAAAnxJREFUl0rqK0XHcZxLwidFx3EcIdlJkeTHST5L8rkgi5oEJI+QfJLkMyT/RvL+cD5JbWyS\nGcmnSf42HN9E8kQY158HPZ+5huQ+ksdJ/p3kKZIfTHU854EkJ8WgGf0DVFovRwHcQ/LodFu1Z4wA\nfNXMjgL4AICvhL5NtLFvBvBEOE6B+wGckuNvA/iumb0LwDkA902lVXvL9wH8zszeDeA9qPqb6njO\nPElOigDeD+A5M3vezAYAfoZKO3ruMbOXzeyp8HgL1R/QISSojU3yMIBPAXgkHBPA7QCOh5fMfT9J\nrgD4CIBHAcDMBma2gQTHc15IdVI8BOBFOT4dziUFyRsB3AbgBC5SG3vO+B6AryMqme4HsGH2ZjXf\nFMb1JgBnAPw4bBM8EnSKUhzPuSDVSTF5SLYB/ALAA2a2qc9ZFWc117FWJO8E8JqZ/XnabbnC1AG8\nF8APzew2VPn6O0zlFMZznkh1UnwJwBE5PhzOJQHJBqoJ8Sdm9stwOjVt7A8BuIvkv1Ftf9yOau9t\nH8lJgcAUxvU0gNNmdiIcH0c1SaY2nnNDqpPinwDcHDyVOYC7UWlHzz1hX+1RAKfM7DvyVFLa2Gb2\nkJkdNrMbUY3fH8zs8wCeBPDZ8LIU+vkKgBdJ3hJO3QHgGSQ2nvNEshktJD+Jak8qA/AjM/vWlJu0\nJ5D8MIA/Avgr4l7bN1HtKz4O4O0I2thmdnYqjdxjSH4UwNfM7E6S70S1clwD8DSAL5hZ/63eP+uQ\nvBWVMykH8DyAL6JasCQ5nrNOspOi4zjO5ZCq+ew4jnNZ+KToOI4j+KToOI4j+KToOI4j+KToOI4j\n+KToOI4j+KToOI4j/BdgQTKfHywiCAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6294858470>"
      ]
     },
     "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 0x7f62947e2400>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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suXl75lCJt6Vi/SLFPickJCSsPdJLMSEhIQFYW/qs3FTFXM6LQFoHS9vYKUGA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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f629482d898>"
      ]
     },
     "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 0x7f6294746e10>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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saVn652WGh6zyJ3RVV2f2JSx8f3Ovp4FWle/vNd6XQejaX6s58/ynXiZzb2Nwxa+zP/Tz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f62947a2b00>"
      ]
     },
     "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 0x7f629472e4a8>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2czLwu4l7RQV6BWZYRzBzB3nQeYHHmkoyNNEs9neGYkksI9kDFRYUc6GkZInz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\npvBOz3GcmcI7PcdxZor/AfQG7I5yjqqsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f629477c5c0>"
      ]
     },
     "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 0x7f629468dfd0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1AyusJXNPoT4O0OXFoVOvFoWYqmgXhjhv5ZD0hvd7veJqF7IlKHMD6rWGijmD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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f62946e6ac8>"
      ]
     },
     "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
}