Model_03-Adadelta-k=3.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=10, mode=\"min\")\n",
    "    msave = ModelCheckpoint(filepath, save_best_only=True)\n",
    "    return [es, msave]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getVggAngleModel():\n",
    "    input_2 = Input(shape=[1], name=\"angle\")\n",
    "    angle_layer = Dense(1, )(input_2)\n",
    "    base_model = VGG16(weights='imagenet', include_top=False, \n",
    "                 input_shape=X_train.shape[1:], classes=1)\n",
    "    x = base_model.get_layer('block5_pool').output\n",
    "    \n",
    "\n",
    "    x = GlobalMaxPooling2D()(x)\n",
    "    merge_one = concatenate([x, angle_layer])\n",
    "    merge_one = Dense(512, activation='relu', name='fc2')(merge_one)\n",
    "    merge_one = Dropout(0.3)(merge_one)\n",
    "    merge_one = Dense(512, activation='relu', name='fc3')(merge_one)\n",
    "    merge_one = Dropout(0.3)(merge_one)\n",
    "    \n",
    "    predictions = Dense(1, activation='sigmoid')(merge_one)\n",
    "    \n",
    "    model = Model(inputs=[base_model.input, input_2], outputs=predictions)\n",
    "    \n",
    "    adadelta = Adadelta(lr=1e-3, rho=0.95, epsilon=1e-6, decay=1e-6)\n",
    "    model.compile(loss='binary_crossentropy',\n",
    "                    optimizer=adadelta,\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": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "===================FOLD= 0\n",
      "Epoch 1/50\n",
      "24/24 [==============================] - 16s 667ms/step - loss: 0.7706 - acc: 0.5513 - val_loss: 0.6114 - val_acc: 0.6486\n",
      "Epoch 2/50\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.6989 - acc: 0.5902 - val_loss: 0.5662 - val_acc: 0.7121\n",
      "Epoch 3/50\n",
      "24/24 [==============================] - 14s 587ms/step - loss: 0.6581 - acc: 0.6298 - val_loss: 0.5115 - val_acc: 0.7664\n",
      "Epoch 4/50\n",
      "24/24 [==============================] - 15s 628ms/step - loss: 0.6015 - acc: 0.6743 - val_loss: 0.4532 - val_acc: 0.8000\n",
      "Epoch 5/50\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.5677 - acc: 0.7057 - val_loss: 0.4255 - val_acc: 0.8037\n",
      "Epoch 6/50\n",
      "24/24 [==============================] - 14s 593ms/step - loss: 0.5306 - acc: 0.7239 - val_loss: 0.3806 - val_acc: 0.8150\n",
      "Epoch 7/50\n",
      "24/24 [==============================] - 14s 592ms/step - loss: 0.4725 - acc: 0.7717 - val_loss: 0.3633 - val_acc: 0.8318\n",
      "Epoch 8/50\n",
      "24/24 [==============================] - 14s 587ms/step - loss: 0.4749 - acc: 0.7651 - val_loss: 0.3373 - val_acc: 0.8355\n",
      "Epoch 9/50\n",
      "24/24 [==============================] - 14s 592ms/step - loss: 0.4144 - acc: 0.7850 - val_loss: 0.3267 - val_acc: 0.8318\n",
      "Epoch 10/50\n",
      "24/24 [==============================] - 14s 587ms/step - loss: 0.4085 - acc: 0.8087 - val_loss: 0.3239 - val_acc: 0.8318\n",
      "Epoch 11/50\n",
      "24/24 [==============================] - 14s 593ms/step - loss: 0.4078 - acc: 0.7923 - val_loss: 0.3056 - val_acc: 0.8430\n",
      "Epoch 12/50\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.3768 - acc: 0.8235 - val_loss: 0.3026 - val_acc: 0.8449\n",
      "Epoch 13/50\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.3853 - acc: 0.8236 - val_loss: 0.2962 - val_acc: 0.8523\n",
      "Epoch 14/50\n",
      "24/24 [==============================] - 14s 594ms/step - loss: 0.3570 - acc: 0.8198 - val_loss: 0.2832 - val_acc: 0.8523\n",
      "Epoch 15/50\n",
      "24/24 [==============================] - 13s 539ms/step - loss: 0.3305 - acc: 0.8575 - val_loss: 0.2914 - val_acc: 0.8579\n",
      "Epoch 16/50\n",
      "24/24 [==============================] - 14s 592ms/step - loss: 0.3693 - acc: 0.8334 - val_loss: 0.2684 - val_acc: 0.8766\n",
      "Epoch 17/50\n",
      "24/24 [==============================] - 14s 592ms/step - loss: 0.3284 - acc: 0.8505 - val_loss: 0.2613 - val_acc: 0.8748\n",
      "Epoch 18/50\n",
      "24/24 [==============================] - 14s 593ms/step - loss: 0.3387 - acc: 0.8403 - val_loss: 0.2596 - val_acc: 0.8785\n",
      "Epoch 19/50\n",
      "24/24 [==============================] - 14s 594ms/step - loss: 0.3268 - acc: 0.8521 - val_loss: 0.2548 - val_acc: 0.8766\n",
      "Epoch 20/50\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.3263 - acc: 0.8486 - val_loss: 0.2508 - val_acc: 0.8729\n",
      "Epoch 21/50\n",
      "24/24 [==============================] - 14s 593ms/step - loss: 0.2978 - acc: 0.8607 - val_loss: 0.2502 - val_acc: 0.8729\n",
      "Epoch 22/50\n",
      "24/24 [==============================] - 13s 538ms/step - loss: 0.2964 - acc: 0.8696 - val_loss: 0.2648 - val_acc: 0.8710\n",
      "Epoch 23/50\n",
      "24/24 [==============================] - 14s 593ms/step - loss: 0.2826 - acc: 0.8790 - val_loss: 0.2451 - val_acc: 0.8822\n",
      "Epoch 24/50\n",
      "24/24 [==============================] - 14s 593ms/step - loss: 0.2851 - acc: 0.8837 - val_loss: 0.2434 - val_acc: 0.8822\n",
      "Epoch 25/50\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.2986 - acc: 0.8576 - val_loss: 0.2306 - val_acc: 0.8879\n",
      "Epoch 26/50\n",
      "24/24 [==============================] - 14s 593ms/step - loss: 0.2629 - acc: 0.8834 - val_loss: 0.2300 - val_acc: 0.8860\n",
      "Epoch 27/50\n",
      "24/24 [==============================] - 13s 538ms/step - loss: 0.2665 - acc: 0.8927 - val_loss: 0.2325 - val_acc: 0.8897\n",
      "Epoch 28/50\n",
      "24/24 [==============================] - 14s 593ms/step - loss: 0.2659 - acc: 0.8842 - val_loss: 0.2299 - val_acc: 0.8897\n",
      "Epoch 29/50\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.2705 - acc: 0.8787 - val_loss: 0.2333 - val_acc: 0.8953\n",
      "Epoch 30/50\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.2675 - acc: 0.8889 - val_loss: 0.2197 - val_acc: 0.9084\n",
      "Epoch 31/50\n",
      "24/24 [==============================] - 13s 543ms/step - loss: 0.2439 - acc: 0.8950 - val_loss: 0.2280 - val_acc: 0.9028\n",
      "Epoch 32/50\n",
      "24/24 [==============================] - 13s 538ms/step - loss: 0.2643 - acc: 0.8841 - val_loss: 0.2225 - val_acc: 0.8991\n",
      "Epoch 33/50\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.2488 - acc: 0.8902 - val_loss: 0.2230 - val_acc: 0.9084\n",
      "Epoch 34/50\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.2390 - acc: 0.8986 - val_loss: 0.2249 - val_acc: 0.9047\n",
      "Epoch 35/50\n",
      "24/24 [==============================] - 14s 594ms/step - loss: 0.2552 - acc: 0.8752 - val_loss: 0.2189 - val_acc: 0.9047\n",
      "Epoch 36/50\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.2412 - acc: 0.8967 - val_loss: 0.2341 - val_acc: 0.8860\n",
      "Epoch 37/50\n",
      "24/24 [==============================] - 13s 539ms/step - loss: 0.2429 - acc: 0.8913 - val_loss: 0.2360 - val_acc: 0.8860\n",
      "Epoch 38/50\n",
      "24/24 [==============================] - 13s 543ms/step - loss: 0.2347 - acc: 0.9045 - val_loss: 0.2260 - val_acc: 0.9065\n",
      "Epoch 39/50\n",
      "24/24 [==============================] - 13s 539ms/step - loss: 0.2451 - acc: 0.8890 - val_loss: 0.2238 - val_acc: 0.9140\n",
      "Epoch 40/50\n",
      "24/24 [==============================] - 14s 593ms/step - loss: 0.2414 - acc: 0.8937 - val_loss: 0.2170 - val_acc: 0.9121\n",
      "Epoch 41/50\n",
      "24/24 [==============================] - 14s 593ms/step - loss: 0.2390 - acc: 0.8944 - val_loss: 0.2147 - val_acc: 0.9103\n",
      "Epoch 42/50\n",
      "24/24 [==============================] - 13s 543ms/step - loss: 0.2348 - acc: 0.9017 - val_loss: 0.2251 - val_acc: 0.9140\n",
      "Epoch 43/50\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.2100 - acc: 0.9065 - val_loss: 0.2355 - val_acc: 0.9028\n",
      "Epoch 44/50\n",
      "24/24 [==============================] - 13s 539ms/step - loss: 0.2396 - acc: 0.8924 - val_loss: 0.2207 - val_acc: 0.9121\n",
      "Epoch 45/50\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.2152 - acc: 0.9028 - val_loss: 0.2235 - val_acc: 0.9047\n",
      "Epoch 46/50\n",
      "24/24 [==============================] - 14s 595ms/step - loss: 0.2349 - acc: 0.8911 - val_loss: 0.2115 - val_acc: 0.9140\n",
      "Epoch 47/50\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.2174 - acc: 0.9029 - val_loss: 0.2061 - val_acc: 0.9252\n",
      "Epoch 48/50\n",
      "24/24 [==============================] - 13s 543ms/step - loss: 0.2159 - acc: 0.9061 - val_loss: 0.2129 - val_acc: 0.9159\n",
      "Epoch 49/50\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.2114 - acc: 0.9090 - val_loss: 0.2053 - val_acc: 0.9140\n",
      "Epoch 50/50\n",
      "24/24 [==============================] - 13s 543ms/step - loss: 0.2115 - acc: 0.9059 - val_loss: 0.2155 - val_acc: 0.9121\n",
      "Train loss: 0.165050524016\n",
      "Train accuracy: 0.929840973708\n",
      "Test loss: 0.205333414924\n",
      "Test accuracy: 0.914018692146\n",
      "\n",
      "===================FOLD= 1\n",
      "Epoch 1/50\n",
      "24/24 [==============================] - 16s 672ms/step - loss: 0.8659 - acc: 0.5573 - val_loss: 0.6509 - val_acc: 0.5589\n",
      "Epoch 2/50\n",
      "24/24 [==============================] - 14s 594ms/step - loss: 0.8451 - acc: 0.5384 - val_loss: 0.5985 - val_acc: 0.6449\n",
      "Epoch 3/50\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.7740 - acc: 0.5863 - val_loss: 0.5491 - val_acc: 0.7140\n",
      "Epoch 4/50\n",
      "24/24 [==============================] - 14s 593ms/step - loss: 0.6971 - acc: 0.6222 - val_loss: 0.4987 - val_acc: 0.7664\n",
      "Epoch 5/50\n",
      "24/24 [==============================] - 14s 587ms/step - loss: 0.6643 - acc: 0.6590 - val_loss: 0.4449 - val_acc: 0.8131\n",
      "Epoch 6/50\n",
      "24/24 [==============================] - 14s 592ms/step - loss: 0.5721 - acc: 0.7149 - val_loss: 0.3989 - val_acc: 0.8336\n",
      "Epoch 7/50\n",
      "24/24 [==============================] - 14s 593ms/step - loss: 0.5636 - acc: 0.7080 - val_loss: 0.3784 - val_acc: 0.8187\n",
      "Epoch 8/50\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.5261 - acc: 0.7421 - val_loss: 0.3540 - val_acc: 0.8467\n",
      "Epoch 9/50\n",
      "24/24 [==============================] - 14s 592ms/step - loss: 0.5182 - acc: 0.7459 - val_loss: 0.3192 - val_acc: 0.8654\n",
      "Epoch 10/50\n",
      "24/24 [==============================] - 14s 587ms/step - loss: 0.4971 - acc: 0.7642 - val_loss: 0.3139 - val_acc: 0.8617\n",
      "Epoch 11/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24/24 [==============================] - 14s 593ms/step - loss: 0.4601 - acc: 0.7760 - val_loss: 0.3061 - val_acc: 0.8766\n",
      "Epoch 12/50\n",
      "24/24 [==============================] - 14s 594ms/step - loss: 0.4223 - acc: 0.7937 - val_loss: 0.2807 - val_acc: 0.8897\n",
      "Epoch 13/50\n",
      "24/24 [==============================] - 13s 542ms/step - loss: 0.4094 - acc: 0.8132 - val_loss: 0.2835 - val_acc: 0.8785\n",
      "Epoch 14/50\n",
      "24/24 [==============================] - 14s 595ms/step - loss: 0.4035 - acc: 0.7997 - val_loss: 0.2802 - val_acc: 0.8804\n",
      "Epoch 15/50\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.3784 - acc: 0.8326 - val_loss: 0.2568 - val_acc: 0.8935\n",
      "Epoch 16/50\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.4154 - acc: 0.8086 - val_loss: 0.2770 - val_acc: 0.8822\n",
      "Epoch 17/50\n",
      "24/24 [==============================] - 14s 595ms/step - loss: 0.4006 - acc: 0.8046 - val_loss: 0.2549 - val_acc: 0.9047\n",
      "Epoch 18/50\n",
      "24/24 [==============================] - 13s 543ms/step - loss: 0.3760 - acc: 0.8212 - val_loss: 0.2613 - val_acc: 0.8897\n",
      "Epoch 19/50\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.3845 - acc: 0.8275 - val_loss: 0.2608 - val_acc: 0.8935\n",
      "Epoch 20/50\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.3689 - acc: 0.8280 - val_loss: 0.2387 - val_acc: 0.9065\n",
      "Epoch 21/50\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.3528 - acc: 0.8417 - val_loss: 0.2597 - val_acc: 0.8897\n",
      "Epoch 22/50\n",
      "24/24 [==============================] - 13s 539ms/step - loss: 0.3558 - acc: 0.8309 - val_loss: 0.3182 - val_acc: 0.8467\n",
      "Epoch 23/50\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.3367 - acc: 0.8426 - val_loss: 0.2868 - val_acc: 0.8766\n",
      "Epoch 24/50\n",
      "24/24 [==============================] - 14s 593ms/step - loss: 0.3419 - acc: 0.8421 - val_loss: 0.2261 - val_acc: 0.9178\n",
      "Epoch 25/50\n",
      "24/24 [==============================] - 13s 540ms/step - loss: 0.3399 - acc: 0.8429 - val_loss: 0.2606 - val_acc: 0.8935\n",
      "Epoch 26/50\n",
      "24/24 [==============================] - 13s 543ms/step - loss: 0.3437 - acc: 0.8431 - val_loss: 0.2774 - val_acc: 0.8860\n",
      "Epoch 27/50\n",
      "24/24 [==============================] - 14s 592ms/step - loss: 0.3558 - acc: 0.8475 - val_loss: 0.2258 - val_acc: 0.9159\n",
      "Epoch 28/50\n",
      "24/24 [==============================] - 13s 543ms/step - loss: 0.3133 - acc: 0.8581 - val_loss: 0.2407 - val_acc: 0.9121\n",
      "Epoch 29/50\n",
      "24/24 [==============================] - 14s 595ms/step - loss: 0.3140 - acc: 0.8503 - val_loss: 0.2245 - val_acc: 0.9121\n",
      "Epoch 30/50\n",
      "24/24 [==============================] - 13s 539ms/step - loss: 0.3110 - acc: 0.8582 - val_loss: 0.2427 - val_acc: 0.9028\n",
      "Epoch 31/50\n",
      "24/24 [==============================] - 14s 593ms/step - loss: 0.3175 - acc: 0.8597 - val_loss: 0.2224 - val_acc: 0.9196\n",
      "Epoch 32/50\n",
      "24/24 [==============================] - 13s 538ms/step - loss: 0.3004 - acc: 0.8634 - val_loss: 0.2670 - val_acc: 0.8972\n",
      "Epoch 33/50\n",
      "24/24 [==============================] - 13s 543ms/step - loss: 0.3227 - acc: 0.8530 - val_loss: 0.2623 - val_acc: 0.8935\n",
      "Epoch 34/50\n",
      "24/24 [==============================] - 14s 593ms/step - loss: 0.3031 - acc: 0.8594 - val_loss: 0.2221 - val_acc: 0.9121\n",
      "Epoch 35/50\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.3078 - acc: 0.8615 - val_loss: 0.2919 - val_acc: 0.8636\n",
      "Epoch 36/50\n",
      "24/24 [==============================] - 14s 592ms/step - loss: 0.2983 - acc: 0.8681 - val_loss: 0.2137 - val_acc: 0.9215\n",
      "Epoch 37/50\n",
      "24/24 [==============================] - 13s 539ms/step - loss: 0.3087 - acc: 0.8671 - val_loss: 0.2308 - val_acc: 0.9103\n",
      "Epoch 38/50\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.3046 - acc: 0.8612 - val_loss: 0.2317 - val_acc: 0.9196\n",
      "Epoch 39/50\n",
      "24/24 [==============================] - 13s 539ms/step - loss: 0.2980 - acc: 0.8690 - val_loss: 0.2429 - val_acc: 0.9103\n",
      "Epoch 40/50\n",
      "24/24 [==============================] - 13s 543ms/step - loss: 0.2953 - acc: 0.8672 - val_loss: 0.2379 - val_acc: 0.9159\n",
      "Epoch 41/50\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.2812 - acc: 0.8760 - val_loss: 0.2208 - val_acc: 0.9196\n",
      "Epoch 42/50\n",
      "24/24 [==============================] - 13s 538ms/step - loss: 0.2799 - acc: 0.8780 - val_loss: 0.2394 - val_acc: 0.9121\n",
      "Epoch 43/50\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.2850 - acc: 0.8765 - val_loss: 0.2148 - val_acc: 0.9196\n",
      "Epoch 44/50\n",
      "24/24 [==============================] - 13s 539ms/step - loss: 0.2868 - acc: 0.8780 - val_loss: 0.2244 - val_acc: 0.9271\n",
      "Epoch 45/50\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.2756 - acc: 0.8689 - val_loss: 0.2210 - val_acc: 0.9196\n",
      "Epoch 46/50\n",
      "24/24 [==============================] - 14s 594ms/step - loss: 0.2851 - acc: 0.8683 - val_loss: 0.2083 - val_acc: 0.9234\n",
      "Epoch 47/50\n",
      "24/24 [==============================] - 13s 538ms/step - loss: 0.2595 - acc: 0.8880 - val_loss: 0.2318 - val_acc: 0.9084\n",
      "Epoch 48/50\n",
      "24/24 [==============================] - 13s 543ms/step - loss: 0.2633 - acc: 0.8818 - val_loss: 0.2293 - val_acc: 0.9215\n",
      "Epoch 49/50\n",
      "24/24 [==============================] - 13s 539ms/step - loss: 0.2752 - acc: 0.8731 - val_loss: 0.2352 - val_acc: 0.9234\n",
      "Epoch 50/50\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.2858 - acc: 0.8668 - val_loss: 0.2441 - val_acc: 0.9084\n",
      "Train loss: 0.19673831786\n",
      "Train accuracy: 0.915809167446\n",
      "Test loss: 0.208307215293\n",
      "Test accuracy: 0.923364487318\n",
      "\n",
      "===================FOLD= 2\n",
      "Epoch 1/50\n",
      "24/24 [==============================] - 16s 686ms/step - loss: 0.9446 - acc: 0.5169 - val_loss: 0.6379 - val_acc: 0.5861\n",
      "Epoch 2/50\n",
      "24/24 [==============================] - 14s 594ms/step - loss: 0.8419 - acc: 0.5624 - val_loss: 0.5806 - val_acc: 0.6723\n",
      "Epoch 3/50\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.7586 - acc: 0.6030 - val_loss: 0.5357 - val_acc: 0.7341\n",
      "Epoch 4/50\n",
      "24/24 [==============================] - 14s 593ms/step - loss: 0.6924 - acc: 0.6463 - val_loss: 0.5061 - val_acc: 0.7434\n",
      "Epoch 5/50\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.6408 - acc: 0.6662 - val_loss: 0.4613 - val_acc: 0.7697\n",
      "Epoch 6/50\n",
      "24/24 [==============================] - 14s 592ms/step - loss: 0.5904 - acc: 0.7022 - val_loss: 0.4264 - val_acc: 0.7809\n",
      "Epoch 7/50\n",
      "24/24 [==============================] - 14s 592ms/step - loss: 0.5390 - acc: 0.7366 - val_loss: 0.3954 - val_acc: 0.7978\n",
      "Epoch 8/50\n",
      "24/24 [==============================] - 14s 587ms/step - loss: 0.4765 - acc: 0.7610 - val_loss: 0.3734 - val_acc: 0.8090\n",
      "Epoch 9/50\n",
      "24/24 [==============================] - 14s 592ms/step - loss: 0.4573 - acc: 0.7833 - val_loss: 0.3698 - val_acc: 0.8165\n",
      "Epoch 10/50\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.4505 - acc: 0.7839 - val_loss: 0.3607 - val_acc: 0.8146\n",
      "Epoch 11/50\n",
      "24/24 [==============================] - 14s 593ms/step - loss: 0.4001 - acc: 0.8180 - val_loss: 0.3600 - val_acc: 0.8315\n",
      "Epoch 12/50\n",
      "24/24 [==============================] - 14s 592ms/step - loss: 0.3991 - acc: 0.8186 - val_loss: 0.3381 - val_acc: 0.8333\n",
      "Epoch 13/50\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.4197 - acc: 0.8202 - val_loss: 0.3354 - val_acc: 0.8408\n",
      "Epoch 14/50\n",
      "24/24 [==============================] - 14s 593ms/step - loss: 0.3499 - acc: 0.8455 - val_loss: 0.3330 - val_acc: 0.8446\n",
      "Epoch 15/50\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.3579 - acc: 0.8401 - val_loss: 0.3274 - val_acc: 0.8371\n",
      "Epoch 16/50\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.3260 - acc: 0.8646 - val_loss: 0.3345 - val_acc: 0.8296\n",
      "Epoch 17/50\n",
      "24/24 [==============================] - 14s 594ms/step - loss: 0.3522 - acc: 0.8447 - val_loss: 0.3199 - val_acc: 0.8521\n",
      "Epoch 18/50\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.3307 - acc: 0.8542 - val_loss: 0.3238 - val_acc: 0.8689\n",
      "Epoch 19/50\n",
      "24/24 [==============================] - 14s 594ms/step - loss: 0.3508 - acc: 0.8390 - val_loss: 0.3157 - val_acc: 0.8614\n",
      "Epoch 20/50\n",
      "24/24 [==============================] - 13s 540ms/step - loss: 0.3275 - acc: 0.8476 - val_loss: 0.3265 - val_acc: 0.8652\n",
      "Epoch 21/50\n",
      "24/24 [==============================] - 14s 593ms/step - loss: 0.3375 - acc: 0.8648 - val_loss: 0.3147 - val_acc: 0.8670\n",
      "Epoch 22/50\n",
      "24/24 [==============================] - 13s 541ms/step - loss: 0.3236 - acc: 0.8574 - val_loss: 0.3426 - val_acc: 0.8483\n",
      "Epoch 23/50\n",
      "24/24 [==============================] - 14s 592ms/step - loss: 0.3000 - acc: 0.8696 - val_loss: 0.3138 - val_acc: 0.8670\n",
      "Epoch 24/50\n",
      "24/24 [==============================] - 13s 543ms/step - loss: 0.2834 - acc: 0.8813 - val_loss: 0.3203 - val_acc: 0.8558\n",
      "Epoch 25/50\n",
      "24/24 [==============================] - 13s 542ms/step - loss: 0.3114 - acc: 0.8687 - val_loss: 0.3167 - val_acc: 0.8614\n",
      "Epoch 26/50\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.3019 - acc: 0.8621 - val_loss: 0.3153 - val_acc: 0.8727\n",
      "Epoch 27/50\n",
      "24/24 [==============================] - 13s 539ms/step - loss: 0.2898 - acc: 0.8768 - val_loss: 0.3344 - val_acc: 0.8521\n",
      "Epoch 28/50\n",
      "24/24 [==============================] - 13s 543ms/step - loss: 0.2889 - acc: 0.8722 - val_loss: 0.3173 - val_acc: 0.8745\n",
      "Epoch 29/50\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.2722 - acc: 0.8805 - val_loss: 0.3209 - val_acc: 0.8652\n",
      "Epoch 30/50\n",
      "24/24 [==============================] - 13s 540ms/step - loss: 0.2701 - acc: 0.8937 - val_loss: 0.3215 - val_acc: 0.8652\n",
      "Epoch 31/50\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.2534 - acc: 0.8911 - val_loss: 0.3317 - val_acc: 0.8577\n",
      "Epoch 32/50\n",
      "24/24 [==============================] - 13s 540ms/step - loss: 0.2833 - acc: 0.8890 - val_loss: 0.3145 - val_acc: 0.8783\n",
      "Epoch 33/50\n",
      "24/24 [==============================] - 14s 593ms/step - loss: 0.2715 - acc: 0.8850 - val_loss: 0.3125 - val_acc: 0.8708\n",
      "Epoch 34/50\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.2615 - acc: 0.8853 - val_loss: 0.3184 - val_acc: 0.8745\n",
      "Epoch 35/50\n",
      "24/24 [==============================] - 14s 593ms/step - loss: 0.2467 - acc: 0.9007 - val_loss: 0.3068 - val_acc: 0.8764\n",
      "Epoch 36/50\n",
      "24/24 [==============================] - 13s 543ms/step - loss: 0.2595 - acc: 0.8922 - val_loss: 0.3275 - val_acc: 0.8689\n",
      "Epoch 37/50\n",
      "24/24 [==============================] - 13s 539ms/step - loss: 0.2546 - acc: 0.8898 - val_loss: 0.3097 - val_acc: 0.8783\n",
      "Epoch 38/50\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.2551 - acc: 0.9063 - val_loss: 0.3353 - val_acc: 0.8689\n",
      "Epoch 39/50\n",
      "24/24 [==============================] - 14s 587ms/step - loss: 0.2439 - acc: 0.8985 - val_loss: 0.3039 - val_acc: 0.8764\n",
      "Epoch 40/50\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.2453 - acc: 0.8991 - val_loss: 0.3481 - val_acc: 0.8558\n",
      "Epoch 41/50\n",
      "24/24 [==============================] - 13s 545ms/step - loss: 0.2546 - acc: 0.8961 - val_loss: 0.3304 - val_acc: 0.8539\n",
      "Epoch 42/50\n",
      "24/24 [==============================] - 13s 539ms/step - loss: 0.2474 - acc: 0.9001 - val_loss: 0.3111 - val_acc: 0.8708\n",
      "Epoch 43/50\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.2651 - acc: 0.8784 - val_loss: 0.3076 - val_acc: 0.8876\n",
      "Epoch 44/50\n",
      "24/24 [==============================] - 13s 540ms/step - loss: 0.2386 - acc: 0.8977 - val_loss: 0.3067 - val_acc: 0.8839\n",
      "Epoch 45/50\n",
      "24/24 [==============================] - 13s 543ms/step - loss: 0.2467 - acc: 0.8987 - val_loss: 0.3121 - val_acc: 0.8820\n",
      "Epoch 46/50\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.2320 - acc: 0.9091 - val_loss: 0.3082 - val_acc: 0.8783\n",
      "Epoch 47/50\n",
      "24/24 [==============================] - 13s 539ms/step - loss: 0.2132 - acc: 0.9096 - val_loss: 0.3521 - val_acc: 0.8577\n",
      "Epoch 48/50\n",
      "24/24 [==============================] - 14s 594ms/step - loss: 0.2426 - acc: 0.9017 - val_loss: 0.2997 - val_acc: 0.8783\n",
      "Epoch 49/50\n",
      "24/24 [==============================] - 13s 542ms/step - loss: 0.2434 - acc: 0.8943 - val_loss: 0.3015 - val_acc: 0.8858\n",
      "Epoch 50/50\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.2169 - acc: 0.9061 - val_loss: 0.3051 - val_acc: 0.8839\n",
      "Train loss: 0.159140078152\n",
      "Train accuracy: 0.93738317757\n",
      "Test loss: 0.299701513757\n",
      "Test accuracy: 0.878277154674\n",
      "\n",
      " Train Log Loss Validation=  0.180423004555\n",
      " Test Log Loss Validation=  0.23774211096\n"
     ]
    }
   ],
   "source": [
    "#def myAngleCV(X_train, X_angle, X_test):\n",
    "K=3\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=50,\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": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#preds=myAngleCV(X_train, X_angle, X_test)\n",
    "preds = y_test_pred_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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_Adadelta.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['loss', 'acc', 'val_acc', 'val_loss'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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+dsvGO53dHYYV3dmdmQ7f3QQ16jvBpTw+f8eLjnZ216gPq791vjCUU6AoCatZ\nVCInw+ddk5DGsHfmcfc5bYhPzWDSikR+ubv/8XsgpyfCB32hVmO4+ffCZ/dmpDpj7ht2c2oQhTwA\nsnLzGPneApL2ZzLt3gFE1wgl96e7CFjxBZdkP0NKZFdeuawr63am88zkGEZ1qc5LO29GajZy7n94\nGGdmOnx9tTPrePC/4Yw7nOPxS2HB27BuEkigM2pn4KNQ62itJTMnj/ELttIospoztDZ9p9Pc5NkE\nMf1xJ9j8a74TFE+EKqyf4tTO0uOdUUwbpjojns575sTyrkh2rYaPBkDPm2DY6wWnUYUfRjuL8d34\ny7F9BmVtbxy8fzq4cqBWM7jjr3Ltq7CahZuqntDiaieLkyXovzpjA7WqBXPzgFbk5il/rt/DYz+u\n5rtbzzg6b8GV51TPc7Pgss+KXgaiWmTB3/DzeX3mRtbtTOfTG3odWfwuaMgLsPl3JsjnDM/5N6M+\nWYQqDOpUn3+HfYhk7HOaagI9NgcKq+n0W/w42vnWvmedM0dg23wIrQVn3u3UhAroKwgLDjx2Dama\nDZ1O9hmPO7Wo+p2czs5uV594oAAn+HS8CFqfA3NecdrqazZ25j1UJg1OcXd2fwQdh0Hzfs4wYE8r\nv3K+xQ98vHwDBTirxPa92+nsHvJShe7U9uTXYCEiQ4C3cHbKG6uqL+c73wwYD0S604xR1aki0gJY\nB2xwJ12kqv8q6f3DwsJITk4mKiqqUgcMVSU5OZmwsLDyLkqRFm9NYdaGJMYM7XBkW83HL+zEg9+t\nZMLf27n28HaVc16FbfOcph73DOCcPBf7DmZTr2bJP+Oizcl8PGczV/dpxrkdPca4h9WCi94i/MvL\n+PXMhfwn+3L2Hcrh5S7xBHz7HZw1xnkQ5Rcc5gSxqQ856wnVagqDX3JG5YSWcOG9PrfC8i9g2iPO\nJDcJdB5ovhRS3alJ9LzRyb8sRv+UtbMfdZYZ+fxikACIbO7uq2oLkU2dyXct+kP/B8q7pI6BTzjz\nWXzxpaCM+HMP7kCcPbgHAfE4W6RepaoxHmk+Bpar6gci0gmYqqot3MFiiqp6vY9gQc1QOTk5xMfH\nk5mZecKfp6ILCwujSZMmBAeXfotMf1JVrvxoEVuSDzLnoYFUCwk8cvyasX+xOiGN3+8/i3r7lsF/\nL3T+R7rkYxBhy96D3DlhGRt372f8Tb05s433Q4nTM3MY+uZcQoIC+OXufgUvJ/7jbU7/xy1/QO3m\n8N7pzvDzaTGpAAAgAElEQVTWW/48/hvqsR/KaQKp1/HY2kdJbZkL44c5r/s/COc+Wfq8qrL0nc4A\nhyNDpd2rwuZmOJP6/jUPavp2/+rKoCI0Q/UG4lR1s7tAXwMjgBiPNAocXrugFpDoywIEBwfTsmXZ\nrARqijYndi9/b03h+RGdjwQKcJbCfvHiUxj85hyen7SKd/bd5XwrHPY6iPDj8nie+HENwUEBNKkd\nzq2fL+WbW8+gUyPvlrx4+ue17ErP5Pvbzix834kh/3aG2/58B9Tv4gy3vfqbogOFU3ho2NXbP0Hh\nWvZ3ZhFvnuXMkDalU7MhnHrlscdcLkhPcJYOr1G3fMpVSfhz1dnGwA6P9/HuY56eAa4VkXicvbrv\n8jjXUkSWi8hsESlw/QsRGS0iS0RkSVJSUkFJTAWgqrwyfT1NalfjytOOX5yuZXR17hrYhlrrJjjf\nBIe8xCGpxoPfreS+b1bSuVEtfr2nPxNu6UONsCBu/Oxv4vcVvxrv5JWJ/Lg8gbvPaUu3ppGFJ6xW\n21laYvcaZ3G7fvcWPBHMn0a8C3ctcfpEjO8EBDjNUBYoTlh5L1F+FfBfVW0CXAB8LiIBwE6gmap2\nB+4HJojIcf8XqerHqtpLVXvVrWv/GCqqaWt2sSYhnXvPa0dIUMH/5G49vS4PhPzAcunE0tDeDHtn\nHt8vi+fuc9sy4ZY+NKxVjYa1qvHfm3qTmZPHDeP+JvVQdoF5uVzK139v57EfVtO9WSR3DPRiWGKH\nC5w9FZqcBmc9ciIft3REyn4vCWNKwJ/NUAlAU4/3TdzHPP0T97ZPqrpQRMKAaFXdA2S5jy8VkU1A\nO2AJpmLJSGXLXz8zb+FCUtqPYtDpPY5pIspzKa/N3EibejW4uHv+iuVRIX+9S4imcWPWA6z8cBH1\nIkL58uY+nNn62P6J9g0i+OT6Xlz36d/cPH4JX9zc55hJfavj03jy5zWs2JHKaS1q88aV3bzfI3rY\nG04/RCUeDGFMafkzWCwG2opIS5wgMQq4Ol+a7cC5wH9FpCMQBiSJSF0gRVXzRKQV0BbY7MeympJI\n2eLsBLZhKrptAS1dubQEDq78gTeXXspD0ZczsmcLRnRrxNzYvcTtOcAH1/QgsLCNZNITnWGdnS9h\nYO2hNN1zgGeGdy50f+c+raJ448pu3PnVMu7+ajkfXNuT/Zk5vDJ9AxP+3k5U9VDeuPJURnZrXPJR\ncBYojCmQXyflicgFwJs4w2LHqeqLIvIcziqHk9wjoD4BauB0dj+sqjNE5FLgOSAHcAFPq+rkou5V\n0Ggo40OuPGem7N+fwB73GIW6HdgWfRb3rWjI9ef14oKEtwjZPJNtgc156ND1LKEjYcGBtK5bg0l3\n9i38wf3zHbDyG2dnsDreD0j4bP4Wnp0cw8D2dVmxI5X0zFyuP6M59w1qd2RorjGmaLY2lPGdhGXw\ny/3OujqNe0GXS6H9ELR2Sy56dx6ph3L444GzCQkUZ4bwr2MgbTtr6w7lpdyruXt4P3q3LGQZht1r\n4cN+0Oc2Z1RSCb00dR0fzdnMaS1q89yILnRsaB3ExpRERRg6a052GfucyUxLxkH1unDJWGf5aHcN\nYfqanaxJSOeVy7oe7bjucCG0GghzX6Xz/Lf5IngBJD0Jzf/h7IiW38ynISSi6KWyizBmaAcu7dmE\ntvVqVOqJl8aUt/IeDWUqIlVYMcFZnXTpZ84s47uWQNfLjwSKPJfy+syNtKpb/fiO65BwOPcpZ9P6\nRt1g6oPwyUBn/SRPm2c5y4gPeKDUu52JCO3qR1igMMbPrGZRlak6ncvJsUc3Btob6+yPnB7vDCO9\n8AdnGYp8Jq9MZOPuA7xzVffCRxvVbecss732B5j2GIw919n45dynnSXEZzzpLJXR+1Y/f1BjzImy\nYFFVHUqBcYNh78ajx4KrO4ucNesDbR6HrqMK3FAmJ8/Fm79tpEODCC48pYDNdTyJOH0cbQbBrJed\nnc3WTXaWEd+16tid4IwxFZYFi6pI1RmBlLLFWQCvfmdn0bWIhl4NHf1+aTxbkw/xyfW9jq4UW5yw\nmk4HdrernY2Lln/h1FhOufwEP4wxpixYsKiK/v7EGbU0+CU44/YSXZqVm8fbv8dyatNIzutYr+T3\nbtAFbvrVmadRr6P3W2EaY8qVBYuqZucqZ/+EtoOdbR1L6Ou/d5CYlsl/Luta+k7lgIBjd74zxlR4\n9rWuKsk6ABNvcpZrHvl+iWcrZ2Tn8e6fcfRpWYd+JVgm3Bhz8rOaRVXy68OQvAlumOzs1+Cl3emZ\nzIzZzaQViSTtz+L9a3rYUFVjqhgLFlXFqm9hxZfO5vAtnRXf1+1MJ2l/FtVDg4gIC6JGaBDVQ53f\ncXsOMDNmFzNjdrMyPg2A5lHhPDq0A6e1KN2cCGPMycuCRWWRlgCx06FmE2f4a61mEOj+z5u8Cabc\nB83OOLL8duqhbEa+N5+sXFeR2XZrGslDg9szqFN9myVtTBVmweJkl5cDi96HWf+BnINHjweGQO2W\nzpDYvRshIAguHXskgExetZOsXBdvjepGrWrBHMjK5UBmLgeyctmfmUv9mmGc17Feqfa8NsZUPhYs\nTmZb58EvD0LSOmg3FM55ArIPHJ2NfXhG9v5dcPFHUKvJkUt/WBZP+/oRDD+1kdUWjDHFsmBxMtq/\nG2Y+Cau+cZqbRn117FDUZqcXefnmpAMs357Ko0M7WKAwxnjFgsXJZtV3zgzonEPQ/wHo/6CzcF8J\n/LAsgQCBkUXsXGeMMZ4sWJxM5r/t1CianQHD33H6I0rI5VJ+XJ5Av7Z1qW/9EcYYL/l1Up6IDBGR\nDSISJyJjCjjfTET+FJHlIrLKvbPe4XOPuq/bICKD/VnOCk/VWaF15pPQaaSzkmspAgXAX1tSSEjN\n4NIeVqswxnjPbzULEQkE3gMGAfHAYhGZpKoxHsmeAL5V1Q/cW6xOBVq4X48COgONgN9EpJ2q5vmr\nvBVWXg5MuhtWToDTboah/1fwJkJe+mFZPDVCgzi/UwMfFtIYU9n5s2bRG4hT1c2qmg18DYzIl0aB\nw/tg1gIS3a9HAF+rapaqbgHi3PlVLdmH4OtrnEAx8HG44NUTChSHsnOZunonF5zSgGohpc/HGFP1\n+LPPojGww+N9PNAnX5pngBkichdQHTjP49pF+a6tWu0mh1JgwpWQsASGvQG9/nHCWc5Yu5uD2Xlc\n0qNJ8YmNMcZDeS8keBXwX1VtAlwAfC4iXpdJREaLyBIRWZKUlOS3Qpa53Cz4/GLYuQIuH++TQAHw\n/bJ4GkdWo7ct12GMKSF/BosEoKnH+ybuY57+CXwLoKoLgTAg2strUdWPVbWXqvaqW7euD4tezmY+\n5Q4U/4VOw32S5a60TObH7eXSHo2937DIGGPc/BksFgNtRaSliITgdFhPypdmO3AugIh0xAkWSe50\no0QkVERaAm2Bv/1Y1opj/VRn69HTb4cOF/os259WJOBSuNiaoIwxpeC3PgtVzRWRO4HpQCAwTlXX\nishzwBJVnQQ8AHwiIvfhdHbfqKoKrBWRb4EYIBe4o0qMhEpLgJ9vhwZd4bxnfJatqvL90nh6NIuk\nZXR1n+VrjKk6/DopT1Wn4gyH9Tz2lMfrGKBvIde+CLzoz/JVKK48+OEWyM2Gyz6DoFCfZb02MZ3Y\nPQd48eIuPsvTGFO12AzuimLOK7BtvrPgX3Qbn2Y9cWk8IYEBDDulkU/zNcZUHRYsKoKt82D2f6Dr\nKDh11Alnl+dSkvZnkZiWwc7UTCatTOS8TvWoFR7sg8IaY6oiCxbl7VAKfH8L1G4BF75a6mzW70rn\nmUlr2ZGSwe70THJdeuScCFzVu5kPCmuMqaosWJSnvFz46TY4mAQ3/wahEaXKxuVSHpm4iu0phxjY\nvh4NI8NoWKsajY78rkatalarMMaUngWL8pKTARP/ARunwdBXoFG3Umf1/bJ4Vsan8foVp9rsbGOM\nX1iwKA8ZqfDVVbB9obPeU+9bSp3V/swc/jNtA92bRTKyW9VaEcUYU3YsWJS19J3wxaXOvtiXjYMu\nl5xQdu/+GcfeA1l8ekMvm5ltjPEbCxZlaW+cs+ZTRgpc8x20HnhC2W3Ze5Bx87ZwWc8mnNo00keF\nNMaY41mwKCsJy+DLywCBGyZD4x4nnOULU2IIDQrk4SHtT7x8xhhThPJedbZq2BsH4y+CkOrwzxk+\nCRSzNuzh9/V7uOucNtSLsO1RjTH+ZTWLsjDvdWc5j5umQa0T74TOyXPx/JQYWkSFc2PfFidePmOM\nKYbVLPwtdQes+gZ63uCTQAHwv4Xb2JR0kCeHdSI0yHa8M8b4nwULf1v4nvP7jDt9kl3ygSze/G0j\nZ7Wryzkd6vkkT2OMKY4FC386mAzLxsMpV0Bk0+LTe+Gt32PJyM7jyWGdELGhssaYsmHBwp/+/ghy\nDkG/e32S3Z79mXy9eAeX9WxCm3o1fJKnMcZ4w4KFv2Tth78+gg7DoK5vhrZ+Nn8ruXkubj2rtU/y\nM8YYb1mw8Jel4yEzFfrd55Ps0jNz+GLhNoae0tB2uzPGlDm/BgsRGSIiG0QkTkTGFHD+DRFZ4f7Z\nKCKpHufyPM7l37u7YsvNgoXvQov+0KSXT7L8fOE29mflcpvVKowx5cCreRYi8gPwKfCrqrq8vCYQ\neA8YBMQDi0VkknsrVQBU9T6P9HcB3T2yyFDV0i/FWp5Wfg37d8LI932SXWZOHp/N38JZ7erSpXEt\nn+RpjDEl4W3N4n3gaiBWRF4WEW8a4XsDcaq6WVWzga+BEUWkvwr4ysvyVFyuPJj/FjQ8FVqd2NpP\nh327ZAd7D2Rz29lWqzDGlA+vgoWq/qaq1wA9gK3AbyKyQERuEpHCdtVpDOzweB/vPnYcEWkOtAT+\n8DgcJiJLRGSRiIws5LrR7jRLkpKSvPko/rduEqRsgn73O1vUnaCcPBcfzd5Mj2aR9GlZxwcFNMaY\nkvO6z0JEooAbgZuB5cBbOMFjpg/KMQqYqKp5Hseaq2ovnBrNmyJy3NdqVf1YVXupaq+6dev6oBgn\nSBXmvQFRbaDjRT7JcsqqRBJSM7j97DY2r8IYU268ChYi8iMwFwgHLlLV4ar6jareBRQ24D8B8JyJ\n1sR9rCCjyNcEpaoJ7t+bgVkc259RMa3+DnauhL73QMCJL8PhcikfzNpE+/oRNlvbGFOuvK1ZvK2q\nnVT1JVXd6XnC/e2/IIuBtiLSUkRCcALCcaOaRKQDUBtY6HGstoiEul9HA32BmPzXVhh5uTDzafjh\nFmjUHbpe6ZNsf1+/h427D3Db2a1tYyNjTLnyNlh0EpEju+u4H+a3F3WBquYCdwLTgXXAt6q6VkSe\nE5HhHklHAV+rqnoc6wgsEZGVwJ/Ay56jqCqU9ERn+fH5b0LPm5yVZYNCTzhbVeX9WXE0qV2NYV0b\n+qCgxhhTet4uUX6Lqr53+I2q7hORW3BGSRVKVacCU/Mdeyrf+2cKuG4BcIqXZSs/m/6A72+BnAy4\n5BPoeoXPsv5rSwrLt6fy/IjOBAXa3EljTPnyNlgEiogc/vbvnkMR4r9iVXCuPJj9fzD7P1C3A1wx\n3mdLehz24exNRNcI4fJevlmA0BhjToS3wWIa8I2IfOR+f6v7WNX0+7POXIpTr4ILX3N2wPOhXWmZ\nzN6YxF0D2xAWbPtVGGPKn7fB4hGcAHGb+/1MYKxfSnQyiJ3pTLgb+YFP5lLk99OKBFThkh5NfJ63\nMcaUhlfBwr3Exwfun6ot+yAkrXdWk/VDoFBVvl8aT8/mtWlhCwYaYyoIb+dZtBWRiSISIyKbD//4\nu3AV0q41oC5o5J9lq9YmphO75wCX9PDNFqzGGOML3g6z+QynVpELDAT+B3zhr0JVaDtXOL8b+WeO\n4PfL4gkJDGDYKY38kr8xxpSGt8Gimqr+DoiqbnMPd73Qf8WqwBJXQPV6EOH7uQ85eS4mrUjkvE71\nqBVe2JJbxhhT9rzt4M4SkQCcVWfvxFm2o2ru67lzhdME5Yf+ijkbk0g+mM0l3a1j2xhTsXhbs7gH\nZ12ou4GewLXADf4qVIWVfcjp3G7on/6KH5YlUKd6CGe1rwCLIhpjjIdiaxbuCXhXquqDwAHgJr+X\nqqLa7b/O7bRDOcxct5urezcj2GZsG2MqmGKfSu5lw/uVQVkqvsTlzm8/1CymrE4kO9fFZT2tCcoY\nU/F422ex3L0P9nfAwcMHVfUHv5SqokpcAdXrQk3fj1T6YVkC7erXoHOjmj7P2xhjTpS3wSIMSAbO\n8TimQNUKFjtXOLUKH3dub917kKXb9jFmaAfb4MgYUyF5O4O76vZTHHa4c7vDMJ9n/cPyBERgZDeb\niGeMqZi8ChYi8hlOTeIYqvoPn5eoovJT57bLpfywLJ5+baJpUCvMp3kbY4yveNsMNcXjdRhwMZDo\n++JUYInumds+7txesm0f8fsyeOD8dj7N1xhjfMmrMZqq+r3Hz5fAFUBh26keISJDRGSDiMSJyJgC\nzr8hIivcPxtFJNXj3A0iEuv+Kf85HTv907n9w7J4wkMCGdy5gU/zNcYYX/K2ZpFfW6BeUQnc8zPe\nAwYB8cBiEZnkuT2qqt7nkf4uoLv7dR3gaZyApMBS97X7SlneE5e43Oed25k5efyyaidDuzQkPKS0\n/ymMMcb/vF11dr+IpB/+ASbj7HFRlN5AnKpuVtVs4GtgRBHprwK+cr8eDMxU1RR3gJgJDPGmrH5x\nuHPbx/0VszYksT8rlxHdbNFAY0zF5u1oqIhS5N0Y2OHxPh7oU1BCEWkOtAT+KOLa44YKichoYDRA\ns2bNSlFELx3u3PZxf8WUVYnUqR7Cma2jfJqvMcb4mrc1i4tFpJbH+0gRGenDcowCJrpni3tNVT9W\n1V6q2qtuXT+up3S4c9uHNYtD2bn8vm4PQ7s0IMiW9zDGVHDePqWeVtW0w29UNRWnT6EoCUBTj/dN\n3McKMoqjTVAlvdb/dq6A8Gio6bt5EH+s30NGTh7DuloTlDGm4vM2WBSUrrgmrMVAWxFpKSIhOAFh\nUv5EItIBqA0s9Dg8HThfRGqLSG3gfPex8pG4wtnsyIed25NXJlI3IpTeLev4LE9jjPEXb4PFEhF5\nXURau39eB5YWdYGq5gJ34jzk1wHfqupaEXlORIZ7JB0FfK2q6nFtCvA8TsBZDDznPlb2cjJ83rm9\nPzOHPzckceEpDQkMsOU9jDEVn7fjNe8CngS+wRnKOhO4o7iLVHUqMDXfsafyvX+mkGvHAeO8LJ//\n7FoDmufTzu3f1u0mO9fFRaf6frc9Y4zxB29HQx0EjptUVyUcXpbchzWLKSt30qhWGN2b1vZZnsYY\n40/ejoaaKSKRHu9ri0j59SGUJR93bqcdymFObBIXdm1IgDVBGWNOEt72WUS7R0AB4J4oV+QM7koj\n0bd7bk9fu4ucPLVRUMaYk4q3wcIlIkdmvYlICwpYhbbSOdy57cP+ismrEmlWJ5yuTWoVn9gYYyoI\nbzu4HwfmichsQID+uGdOV2qHO7d91F+RfCCLBZuSuXVAK9vkyBhzUvG2g3uaiPTCCRDLgZ+ADH8W\nrELY6dtlyaet3UWey5qgjDEnH283P7oZuAdnJvUK4HScSXTnFHXdSS/R3bldq4lPspu8MpFWdavT\nsWFpltoyxpjy422fxT3AacA2VR2Is5R4atGXVAI7fde5vSc9k7+2pHBR10bWBGWMOel4GywyVTUT\nQERCVXU90N5/xaoAXC7YGwt1O/gku6mrd6KKTcQzxpyUvO3gjnfPs/gJmCki+4Bt/itWBZCeAHlZ\nENXaJ9lNWbWTDg0iaFPPmqCMMScfbzu4L3a/fEZE/gRqAdP8VqqKIGWT87vOiQeLHSmHWLJtHw/a\nPtvGmJNUiffyVNXZ/ihIhZOy2fldp9UJZ/XiL+sICw7g4h6+6Sg3xpiyZrvuFCZ5EwSFnfAyHzNj\ndjNt7S7uObcdjSOr+ahwxhhTtixYFCZlM9RuCQGl/xMdzMrl6Z/X0L5+BDf3b+nDwhljTNkqcTNU\nlZGyGaLanFAWb8zcSGJaJt9f3YNg2zrVGHMSsydYQVwuSNkCdUpfG1iTkMa4+Vu4pk8zeja3pciN\nMSc3vwYLERkiIhtEJE5ECtwPQ0SuEJEYEVkrIhM8jueJyAr3z3HbsfpVerwzbLaUI6HyXMpjP66m\nTvVQHh7im3kaxhhTnvzWDCUigcB7wCAgHlgsIpNUNcYjTVvgUaCvqu4TEc9lzzNU1XfLvZZEsnvY\nbCnnWHy+cCur4tN456ru1KoW7LtyGWNMOfFnzaI3EKeqm1U1G/gaGJEvzS3Ae+79MVDVPX4sj/dO\nYNjszrQMXp2xkbPa1WVYV5utbYypHPwZLBoDOzzex7uPeWoHtBOR+SKySESGeJwLE5El7uMjC7qB\niIx2p1mSlJTku5KnbHaGzUaUfHXYZyfFkOty8cLILrYGlDGm0ijv0VBBQFvgbJwVbeeIyCnuXfma\nq2qCiLQC/hCR1aq6yfNiVf0Y+BigV69evtuMKXmTU6so4bDZ2RuTmLZ2F48M6UDTOuE+K44xxpQ3\nf9YsEoCmHu+buI95igcmqWqOqm4BNuIED1Q1wf17MzALZ6XbspGyuVRNUD+vSKB2eLDNqTDGVDr+\nDBaLgbYi0lJEQoBRQP5RTT/h1CoQkWicZqnNIlJbREI9jvcFYigLrjzYt6XEwUJVmRu7l35t69qc\nCmNMpeO3ZihVzRWRO4HpQCAwTlXXishzwBJVneQ+d76IxAB5wEOqmiwiZwIfiYgLJ6C97DmKyq/S\n4iEvu8Qjodbv2k/S/iz6t432U8GMMab8+LXPQlWnAlPzHXvK47UC97t/PNMsAE7xZ9kKdWQkVMmC\nxdxYp4N9QNu6vi6RMcaUO2svye/I0uQla4aas3Ev7erXoEGtMD8UyhhjypcFi/ySN0NQNYjwfo5E\nRnYef29Nob/VKowxlZQFi/wOj4QqwbDZv7emkJ3rYkA7CxbGmMrJgkV+KZtKvIDgnI1JhAQF0LtF\nHT8VyhhjypcFC0+uPNi3tcQjoebGJtG7RR2qhQT6p1zGGFPOLFh4OjxstgQjoXalZbJx9wEGtLMh\ns8aYysuChadSjISa4x4ya53bxpjKzIKFp1IsTT43di91I0Lp0CDCT4UyxpjyZ8HCU8oWCA73eths\nnkuZF5tE/7bRtsKsMaZSs2DhKcW92qyXD/61iWnsO5Rjs7aNMZWeBQtPySUbNjs3di8A/Ww9KGNM\nJWfB4rC8XGfYbAlGQs3emETnRjWJrhHqv3IZY0wFYMHisPR4cOV43bl9ICuXZdv22SgoY0yVYMHi\nsOSSDZtdtCmZXJcywJqgjDFVgAWLw0q4NPmc2CSqBQfSs0VtPxbKGGMqBgsWh6Vsdg+bbeBV8rmx\nezm9VR1Cg2yJD2NM5efXYCEiQ0Rkg4jEiciYQtJcISIxIrJWRCZ4HL9BRGLdPzf4s5yAeySUd8Nm\nd6QcYsveg9ZfYYypMvy2U56IBALvAYOAeGCxiEzy3B5VRNoCjwJ9VXWfiNRzH68DPA30AhRY6r52\nn7/KS8omqNfJq6SHl/iwJcmNMVWFP2sWvYE4Vd2sqtnA18CIfGluAd47HARUdY/7+GBgpqqmuM/N\nBIb4raR5ubBvm9cjoWZtSKJRrTBa163utyIZY0xF4s9g0RjY4fE+3n3MUzugnYjMF5FFIjKkBNci\nIqNFZImILElKSip9SdN2OMNmvRgJlZGdx9zYJM7rVN+W+DDGVBnl3cEdBLQFzgauAj4RkUhvL1bV\nj1W1l6r2qlv3BJqEjqw2W3zNYm5sEpk5Ls7v5F1HuDHGVAb+DBYJQFOP903cxzzFA5NUNUdVtwAb\ncYKHN9f6TsoW57cXzVAzYnYTERZEn1a2K54xpurwZ7BYDLQVkZYiEgKMAiblS/MTTq0CEYnGaZba\nDEwHzheR2iJSGzjffcw/kjdBcHWoUb/IZLl5Ln5ft5tzO9QjOLC8K2XGGFN2/DYaSlVzReROnId8\nIDBOVdeKyHPAElWdxNGgEAPkAQ+pajKAiDyPE3AAnlPVFH+V1dvVZhdv3ce+QzkM7mxNUMaYqsVv\nwQJAVacCU/Mde8rjtQL3u3/yXzsOGOfP8h2Rshnqdy422YyYXYQEBdiQWWNMlWNtKV6uNquqzFi7\nm/5toqke6tcYa4wxFY4Fi4N7IDSi2GGzMTvTSUjN4PzORfdrGGNMZWRfkWs2gke2gstVZLIZa3cT\nIHBeRwsWxpiqx2oWhwUU/aeYvnYXvZrXIco2OjLGVEEWLLywPfkQ63fttyYoY0yVZcHCCzNidgEw\nqJMFC2NM1WTBwgszYnbToUEEzaNs4UBjTNVkwaIYyQeyWLI1hfNtIp4xpgqzYFGM39ftwaVwvjVB\nGWOqMAsWxZgRs4vGkdXo3KhmeRfFGGPKjQWLIhzMymVO7F4G2d4VxpgqzoJFEebGJpGd67KFA40x\nVZ4FiyLMWLubyPBgTmtRu7yLYowx5cqCRSHyXMofG/ZwTod6BNneFcaYKs6egoVYnZBG6qEczrLl\nyI0xxoJFYeZsTEIE+re1YGGMMX4NFiIyREQ2iEiciIwp4PyNIpIkIivcPzd7nMvzOJ5/O1a/mxub\nxCmNa1GnekhZ39oYYyocvy1RLiKBwHvAICAeWCwik1Q1Jl/Sb1T1zgKyyFDVbv4qX1HSM3NYtj2V\nf51V9B4XxhhTVfizZtEbiFPVzaqaDXwNjPDj/Xxm4aZk8lzKAGuCMsYYwL/BojGww+N9vPtYfpeK\nyCoRmSgiTT2Oh4nIEhFZJCIjC7qBiIx2p1mSlJTks4LP2ZhE9ZBAujezIbPGGAPl38E9GWihql2B\nmcB4j3PNVbUXcDXwpogct0m2qn6sqr1UtVfdur6rBcyN3csZraMJCSrvP48xxlQM/nwaJgCeNYUm\n7mNHqGqyqma5344FenqcS3D/3gzMArr7saxHbN17kO0phxjQLrosbmeMMScFfwaLxUBbEWkpIiHA\nKKQjjkIAAAgfSURBVOCYUU0i0tDj7XBgnft4bREJdb+OBvoC+TvG/WJurNOcZf0VxhhzlN9GQ6lq\nrojcCUwHAoFxqrpWRJ4DlqjqJOBuERkO5AIpwI3uyzsCH4mICyegvVzAKCq/mL1xL03rVKN5VHhZ\n3M4YY04KfgsWAKo6FZia79hTHq8fBR4t4LoFwCn+LFtBcvJcLNy0l5HdG9sqs8YY48F6cD0s27aP\ng9l5NmvbGGPysWDhYW7sXgIDhDPbRJV3UYwxpkKxYOFhTmwS3ZtGUjMsuLyLYowxFYoFC7eUg9ms\nTkhjgK0ya4wxx7Fg4fb/7d1/rFd1Hcfx50vERKluiJgBSQZb4UaYjlFoI6ykcmrLfvhr2Gr+o0u3\nWmqrtWj+UX9ktbklKxdNKoykqH+KyFD+SH5JKmJIpgtmcA2MaEmAr/44n5vfbsT3wr3nfu/9ntdj\nu+N7Pt9zzv28x/ne9/mc8z3vz7odL2DDxTPyfEVERH9JFsVD23vpOW0ss6b0dLorEREjTpIFYJuH\nn+5l3vSJjDkpX5mNiOgvyQLYvvsAu/cf5F25BBURcVRJFrxS4iPPV0REHF2SBbB2ey/TJ43nDT3j\nOt2ViIgRqfHJ4qVDR1j/p70pHBgRcQyNTxb7/3mIS897Pe+ZOanTXYmIGLFqLSQ4Gkx6zal86+ph\nmSojImLUavzIIiIi2kuyiIiItpIsIiKirVqThaSFkv4gaYek24/y/g2SeiVtKT+fanlvkaSny8+i\nOvsZERHHVtsNbkljgLuB9wI7gQ2SVh1letTltm/ut+0E4EvAhYCBTWXbfXX1NyIi/r86RxZzgB22\nn7H9L+BHwBUD3PZSYLXtvSVBrAYW1tTPiIhoo85kMRn4c8vyztLW34clPSZphaSpx7ltREQMg07f\n4P45MM32LKrRw9Lj2VjSjZI2StrY29tbSwcjIqLeh/J2AVNblqeUtv+w/deWxe8AX2vZdn6/bX/b\n/xfYXgIsASg3yp8bRH8nAi8MYvvRKnE3S+JuloHEfc5AdiTbg+/O0XYsnQxsBy6h+uO/AbjG9taW\ndc62/Xx5/SHgNttzyw3uTcDby6qbgQts762ls9Xv32j7wrr2P1Il7mZJ3M0ylHHXNrKwfVjSzcAv\ngTHAvba3SloMbLS9Cvi0pMuBw8Be4Iay7V5JX6FKMACL60wUERFxbLWNLEabnHk0S+JulsQ9eJ2+\nwT2SLOl0BzokcTdL4m6WIYs7I4uIiGgrI4uIiGgrySIiItpqfLJoV+ywm0i6V9IeSU+0tE2QtLoU\nbFwt6XWd7ONQkzRV0oOSnpS0VdItpb3b4z5V0npJvy9xf7m0v0nSI+V4Xy7plE73tQ6Sxkh6VNIv\nynJT4n5W0uOlMOvG0jYkx3qjk0VLscP3AzOBqyXN7GyvavU9/rfG1u3AGtszgDVluZscBj5jeyYw\nF7ip/B93e9wHgQW23wbMBhZKmgt8FbjL9nRgH/DJDvaxTrcA21qWmxI3wLttz275FtSQHOuNThYM\nrtjhqGP7IarnWVpdwStlVpYCVw5rp2pm+3nbm8vrv1P9AZlM98dt2wfK4tjyY2ABsKK0d13cAJKm\nAB+kqgqBJNGAuI9hSI71pieLFCyEs/qeogf+ApzVyc7USdI04HzgERoQd7kUswXYQ1V77Y/Ai7YP\nl1W69Xj/BvA54OWyfAbNiBuqE4JfSdok6cbSNiTHep21oWKUsW1JXfldaknjgZ8At9reX51sVro1\nbttHgNmSeoCVwFs63KXaSboM2GN7k6T5ne5PB1xke5ekScBqSU+1vjmYY73pI4u2xQ4bYLeks6Gq\n1UV1FtpVJI2lShTLbD9Qmrs+7j62XwQeBN4B9JS6bdCdx/s84HJJz1JdVl4AfJPujxsA27vKv3uo\nThDmMETHetOTxQZgRvmmxCnAx4FVHe7TcFsF9E1buwj4WQf7MuTK9ervAttsf73lrW6P+8wyokDS\nOKoZK7dRJY2rympdF7ftO2xPsT2N6vP8G9vX0uVxA0g6XdKr+14D7wOeYIiO9cY/wS3pA1TXOPuK\nHd7Z4S7VRtIPqUq/TwR2U01d+1PgfuCNwHPAR7upaKOki4CHgcd55Rr256nuW3Rz3LOobmaOoTop\nvN/2YknnUp1xTwAeBa6zfbBzPa1PuQz1WduXNSHuEuPKsngy8APbd0o6gyE41hufLCIior2mX4aK\niIgBSLKIiIi2kiwiIqKtJIuIiGgrySIiItpKsogYASTN76uQGjESJVlERERbSRYRx0HSdWWeiC2S\n7inF+g5IuqvMG7FG0pll3dmSfifpMUkr++YRkDRd0q/LXBObJb257H68pBWSnpK0TK0FrCI6LMki\nYoAkvRX4GDDP9mzgCHAtcDqw0fZ5wFqqJ+MBvg/cZnsW1RPkfe3LgLvLXBPvBPoqgp4P3Eo1t8q5\nVHWOIkaEVJ2NGLhLgAuADeWkfxxVUbaXgeVlnfuAByS9Fuixvba0LwV+XGr3TLa9EsD2SwBlf+tt\n7yzLW4BpwLr6w4poL8kiYuAELLV9x381Sl/st96J1tBprVV0hHw+YwTJZaiIgVsDXFXmCuib2/gc\nqs9RX0XTa4B1tv8G7JN0cWm/HlhbZuvbKenKso9XSTptWKOIOAE5c4kYINtPSvoC1UxkJwGHgJuA\nfwBzynt7qO5rQFUO+tslGTwDfKK0Xw/cI2lx2cdHhjGMiBOSqrMRgyTpgO3xne5HRJ1yGSoiItrK\nyCIiItrKyCIiItpKsoiIiLaSLCIioq0ki4iIaCvJIiIi2vo38fuksC0gpzAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd55c306048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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eZtuYkMKa/Rd/mO3rDUnc8OIi1u0/xd9u6cQbd/dk7JVN8PcTZm485Fp8xpiK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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd559db67b8>"
      ]
     },
     "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": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.image as mpimg\n",
    "\n",
    "min1 = np.min(X_holdout)\n",
    "max1 = np.max(X_holdout)\n",
    "diff1 = max1 - min1\n",
    "my_sum = lambda x: (x - min1)/diff1\n",
    "\n",
    "X_holdout2 = my_sum(X_holdout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9       0\n",
       "13      1\n",
       "14      0\n",
       "19      1\n",
       "26      1\n",
       "27      0\n",
       "28      1\n",
       "36      1\n",
       "38      1\n",
       "42      0\n",
       "43      0\n",
       "44      0\n",
       "52      0\n",
       "63      0\n",
       "73      1\n",
       "76      0\n",
       "77      1\n",
       "79      0\n",
       "80      0\n",
       "81      1\n",
       "83      1\n",
       "86      1\n",
       "89      0\n",
       "92      0\n",
       "93      0\n",
       "95      0\n",
       "96      1\n",
       "97      1\n",
       "100     0\n",
       "101     0\n",
       "       ..\n",
       "1533    0\n",
       "1537    0\n",
       "1539    0\n",
       "1541    0\n",
       "1542    0\n",
       "1543    0\n",
       "1545    0\n",
       "1548    0\n",
       "1549    0\n",
       "1552    0\n",
       "1553    0\n",
       "1559    0\n",
       "1561    0\n",
       "1566    0\n",
       "1567    0\n",
       "1569    0\n",
       "1576    0\n",
       "1577    0\n",
       "1579    0\n",
       "1585    0\n",
       "1587    0\n",
       "1588    0\n",
       "1590    0\n",
       "1591    0\n",
       "1596    0\n",
       "1597    0\n",
       "1599    0\n",
       "1601    0\n",
       "1602    0\n",
       "1603    0\n",
       "Name: is_iceberg, Length: 534, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_holdout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fd559ca2b70>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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C91Az7+h2B6+K2FchS7nPCQkJCVuP9FJMSEhIALaXPrfdiaJ6ANctWnoNSRWA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\nm2IQBIEQm2IQBIEQm2IQBIHwDynokxY9OQ/WAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd55c7dd898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[0], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[9])+ \",\"+\" iceberg_probability : \"+str(pred_valid[0])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fd5579a6cf8>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TEhISInaWMofg7ssDFOW57xiWXgg8XsHhehVzn2dwgmLeZ4n82Wzl7xSqrKzd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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd5579f0fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[2], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[14])+ \",\"+\" iceberg_probability : \"+str(pred_valid[2])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fd557985320>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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P5m2/98OZP0WT3Nvu4/mYhIWfx7ydfOjOa9XUda37bWpcG5Qjl3lleK4H8vYm0MZu1cXJ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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd557a0f3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[7], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[28])+ \",\"+\" iceberg_probability : \"+str(pred_valid[7])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fd5578e4748>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZbHbVxmyI7j44COsjb/jhGY7pm1UhOiHd3ztebrCPGvfqFPnbfUzNNDhn5rUb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\nX5xG/oFmLZMKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd5579695f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[15], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[73])+ \",\"+\" iceberg_probability : \"+str(pred_valid[15])\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
}