Model_03-Adam-k=7-amsgrad-0001-p20.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Mandatory imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import log_loss\n",
    "from sklearn.model_selection import StratifiedKFold, StratifiedShuffleSplit\n",
    "from os.path import join as opj\n",
    "from matplotlib import pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import pylab\n",
    "plt.rcParams['figure.figsize'] = 10, 10\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = pd.read_json(\"input/train.json\")\n",
    "target_train=train['is_iceberg']\n",
    "test = pd.read_json(\"input/test.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "target_train=train['is_iceberg']\n",
    "test['inc_angle']=pd.to_numeric(test['inc_angle'], errors='coerce')\n",
    "train['inc_angle']=pd.to_numeric(train['inc_angle'], errors='coerce')#We have only 133 NAs.\n",
    "train['inc_angle']=train['inc_angle'].fillna(method='pad')\n",
    "X_angle=train['inc_angle']\n",
    "test['inc_angle']=pd.to_numeric(test['inc_angle'], errors='coerce')\n",
    "X_test_angle=test['inc_angle']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Generate the training data\n",
    "X_band_1=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in train[\"band_1\"]])\n",
    "X_band_2=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in train[\"band_2\"]])\n",
    "X_band_3=(X_band_1+X_band_2)/2\n",
    "#X_band_3=np.array([np.full((75, 75), angel).astype(np.float32) for angel in train[\"inc_angle\"]])\n",
    "X_train = np.concatenate([X_band_1[:, :, :, np.newaxis]\n",
    "                          , X_band_2[:, :, :, np.newaxis]\n",
    "                         , X_band_3[:, :, :, np.newaxis]], axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_band_test_1=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in test[\"band_1\"]])\n",
    "X_band_test_2=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in test[\"band_2\"]])\n",
    "X_band_test_3=(X_band_test_1+X_band_test_2)/2\n",
    "#X_band_test_3=np.array([np.full((75, 75), angel).astype(np.float32) for angel in test[\"inc_angle\"]])\n",
    "X_test = np.concatenate([X_band_test_1[:, :, :, np.newaxis]\n",
    "                          , X_band_test_2[:, :, :, np.newaxis]\n",
    "                         , X_band_test_3[:, :, :, np.newaxis]], axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "#Import Keras.\n",
    "from matplotlib import pyplot\n",
    "from keras.optimizers import RMSprop\n",
    "# from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPooling2D, Dense, Dropout, Input, Flatten, Activation\n",
    "from keras.layers import GlobalMaxPooling2D\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.layers.merge import Concatenate\n",
    "from keras.models import Model\n",
    "from keras import initializers\n",
    "from keras.optimizers import Adam\n",
    "from keras.optimizers import rmsprop\n",
    "from keras.layers.advanced_activations import LeakyReLU, PReLU\n",
    "from keras.optimizers import SGD\n",
    "from keras.optimizers import Adadelta\n",
    "from keras.callbacks import ModelCheckpoint, Callback, EarlyStopping\n",
    "\n",
    "from keras.datasets import cifar10\n",
    "from keras.applications.inception_v3 import InceptionV3\n",
    "from keras.applications.vgg16 import VGG16\n",
    "from keras.applications.xception import Xception\n",
    "from keras.applications.mobilenet import MobileNet\n",
    "from keras.applications.vgg19 import VGG19\n",
    "from keras.layers import Concatenate, Dense, LSTM, Input, concatenate\n",
    "from keras.preprocessing import image\n",
    "from keras.applications.vgg16 import preprocess_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Data Aug for multi-input\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "batch_size=64\n",
    "# Define the image transformations here\n",
    "gen = ImageDataGenerator(horizontal_flip = True,\n",
    "                         vertical_flip = True,\n",
    "                         width_shift_range = 0.,\n",
    "                         height_shift_range = 0.,\n",
    "                         channel_shift_range=0,\n",
    "                         zoom_range = 0.2,\n",
    "                         rotation_range = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Here is the function that merges our two generators\n",
    "# We use the exact same generator with the same random seed for both the y and angle arrays\n",
    "def gen_flow_for_two_inputs(X1, X2, y):\n",
    "    genX1 = gen.flow(X1,y,  batch_size=batch_size,seed=55)\n",
    "    genX2 = gen.flow(X1,X2, batch_size=batch_size,seed=55)\n",
    "    while True:\n",
    "            X1i = genX1.next()\n",
    "            X2i = genX2.next()\n",
    "            #Assert arrays are equal - this was for peace of mind, but slows down training\n",
    "            #np.testing.assert_array_equal(X1i[0],X2i[0])\n",
    "            yield [X1i[0], X2i[1]], X1i[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Finally create generator\n",
    "def get_callbacks(filepath, patience=2):\n",
    "    es = EarlyStopping('val_loss', patience=20, mode=\"min\")\n",
    "    msave = ModelCheckpoint(filepath, save_best_only=True)\n",
    "    return [es, msave]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getVggAngleModel():\n",
    "    input_2 = Input(shape=[1], name=\"angle\")\n",
    "    angle_layer = Dense(1, )(input_2)\n",
    "    base_model = VGG16(weights='imagenet', include_top=False, \n",
    "                 input_shape=X_train.shape[1:], classes=1)\n",
    "    x = base_model.get_layer('block5_pool').output\n",
    "    \n",
    "\n",
    "    x = GlobalMaxPooling2D()(x)\n",
    "    merge_one = concatenate([x, angle_layer])\n",
    "    merge_one = Dense(512, activation='relu', name='fc2')(merge_one)\n",
    "    merge_one = Dropout(0.3)(merge_one)\n",
    "    merge_one = Dense(512, activation='relu', name='fc3')(merge_one)\n",
    "    merge_one = Dropout(0.3)(merge_one)\n",
    "    \n",
    "    predictions = Dense(1, activation='sigmoid')(merge_one)\n",
    "    \n",
    "    model = Model(inputs=[base_model.input, input_2], outputs=predictions)\n",
    "    \n",
    "    adam = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-8, decay=1e-6, amsgrad=True)\n",
    "    model.compile(loss='binary_crossentropy',\n",
    "                    optimizer=adam,\n",
    "                    metrics=['accuracy'])\n",
    "    \n",
    "#     sgd = SGD(lr=1e-3, decay=1e-6, momentum=0.9, nesterov=True)\n",
    "#     model.compile(loss='binary_crossentropy',\n",
    "#                   optimizer=sgd,\n",
    "#                   metrics=['accuracy'])\n",
    "    return model\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "===================FOLD= 0\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 20s 840ms/step - loss: 0.6489 - acc: 0.6182 - val_loss: 0.4599 - val_acc: 0.7870\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 599ms/step - loss: 0.4007 - acc: 0.8082 - val_loss: 0.3415 - val_acc: 0.8217\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 600ms/step - loss: 0.3467 - acc: 0.8429 - val_loss: 0.2531 - val_acc: 0.8739\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.2897 - acc: 0.8689 - val_loss: 0.2477 - val_acc: 0.8739\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.2861 - acc: 0.8702 - val_loss: 0.2116 - val_acc: 0.9043\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2462 - acc: 0.8983 - val_loss: 0.2263 - val_acc: 0.9043\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.2670 - acc: 0.8877 - val_loss: 0.2550 - val_acc: 0.8783\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.2677 - acc: 0.8851 - val_loss: 0.2559 - val_acc: 0.8957\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 15s 609ms/step - loss: 0.2378 - acc: 0.8856 - val_loss: 0.2049 - val_acc: 0.8870\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.2249 - acc: 0.8994 - val_loss: 0.2321 - val_acc: 0.8870\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 15s 622ms/step - loss: 0.2340 - acc: 0.9000 - val_loss: 0.1760 - val_acc: 0.9217\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 531ms/step - loss: 0.2373 - acc: 0.9000 - val_loss: 0.1890 - val_acc: 0.9174\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 532ms/step - loss: 0.2417 - acc: 0.9000 - val_loss: 0.2065 - val_acc: 0.8957\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 15s 613ms/step - loss: 0.2052 - acc: 0.9074 - val_loss: 0.1752 - val_acc: 0.9217\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 532ms/step - loss: 0.1971 - acc: 0.9205 - val_loss: 0.1951 - val_acc: 0.9174\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.1986 - acc: 0.9133 - val_loss: 0.1811 - val_acc: 0.9217\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.1814 - acc: 0.9191 - val_loss: 0.1753 - val_acc: 0.9261\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.2129 - acc: 0.9152 - val_loss: 0.1919 - val_acc: 0.9087\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 15s 615ms/step - loss: 0.1939 - acc: 0.9204 - val_loss: 0.1675 - val_acc: 0.9348\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.1771 - acc: 0.9295 - val_loss: 0.2002 - val_acc: 0.9174\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.1887 - acc: 0.9223 - val_loss: 0.1875 - val_acc: 0.9217\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.1629 - acc: 0.9340 - val_loss: 0.1597 - val_acc: 0.9478\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.1559 - acc: 0.9379 - val_loss: 0.1998 - val_acc: 0.9348\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 15s 614ms/step - loss: 0.1587 - acc: 0.9367 - val_loss: 0.1534 - val_acc: 0.9391\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 15s 613ms/step - loss: 0.1452 - acc: 0.9407 - val_loss: 0.1512 - val_acc: 0.9435\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.1416 - acc: 0.9412 - val_loss: 0.1643 - val_acc: 0.9261\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.1530 - acc: 0.9400 - val_loss: 0.1875 - val_acc: 0.9174\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.1707 - acc: 0.9249 - val_loss: 0.1593 - val_acc: 0.9261\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.1546 - acc: 0.9355 - val_loss: 0.2308 - val_acc: 0.9130\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 533ms/step - loss: 0.1557 - acc: 0.9419 - val_loss: 0.1716 - val_acc: 0.9261\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.1587 - acc: 0.9347 - val_loss: 0.2238 - val_acc: 0.8957\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.1486 - acc: 0.9347 - val_loss: 0.2036 - val_acc: 0.9348\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1545 - acc: 0.9358 - val_loss: 0.1929 - val_acc: 0.9130\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.1374 - acc: 0.9459 - val_loss: 0.2166 - val_acc: 0.9217\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.1607 - acc: 0.9315 - val_loss: 0.2080 - val_acc: 0.9174\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.1214 - acc: 0.9544 - val_loss: 0.1785 - val_acc: 0.9174\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.1161 - acc: 0.9517 - val_loss: 0.2068 - val_acc: 0.8957\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.0946 - acc: 0.9648 - val_loss: 0.1889 - val_acc: 0.9261\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.1298 - acc: 0.9511 - val_loss: 0.2615 - val_acc: 0.9174\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.1270 - acc: 0.9497 - val_loss: 0.2183 - val_acc: 0.9130\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.1011 - acc: 0.9595 - val_loss: 0.2110 - val_acc: 0.9174\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.1198 - acc: 0.9550 - val_loss: 0.1942 - val_acc: 0.9217\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 13s 533ms/step - loss: 0.0977 - acc: 0.9609 - val_loss: 0.1726 - val_acc: 0.9217\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.0812 - acc: 0.9680 - val_loss: 0.1954 - val_acc: 0.9261\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 13s 533ms/step - loss: 0.1726 - acc: 0.9273 - val_loss: 0.1973 - val_acc: 0.9087\n",
      "Train loss: 0.0904240009783\n",
      "Train accuracy: 0.966521106259\n",
      "Test loss: 0.15119020751\n",
      "Test accuracy: 0.94347826087\n",
      "\n",
      "===================FOLD= 1\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 16s 658ms/step - loss: 0.7820 - acc: 0.6326 - val_loss: 0.2807 - val_acc: 0.8652\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 13s 538ms/step - loss: 0.4673 - acc: 0.7995 - val_loss: 0.5543 - val_acc: 0.7609\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 15s 618ms/step - loss: 0.4859 - acc: 0.7729 - val_loss: 0.2191 - val_acc: 0.8913\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 15s 617ms/step - loss: 0.3936 - acc: 0.8235 - val_loss: 0.2013 - val_acc: 0.9043\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 15s 616ms/step - loss: 0.3574 - acc: 0.8480 - val_loss: 0.1999 - val_acc: 0.8957\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 15s 618ms/step - loss: 0.3354 - acc: 0.8493 - val_loss: 0.1945 - val_acc: 0.9000\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 13s 539ms/step - loss: 0.2946 - acc: 0.8728 - val_loss: 0.2044 - val_acc: 0.8913\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 15s 619ms/step - loss: 0.3075 - acc: 0.8642 - val_loss: 0.1881 - val_acc: 0.9174\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.2662 - acc: 0.8916 - val_loss: 0.2108 - val_acc: 0.9261\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.2479 - acc: 0.8974 - val_loss: 0.1931 - val_acc: 0.9087\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 530ms/step - loss: 0.3137 - acc: 0.8758 - val_loss: 0.2171 - val_acc: 0.8913\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.2801 - acc: 0.8812 - val_loss: 0.1937 - val_acc: 0.9174\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 15s 618ms/step - loss: 0.2488 - acc: 0.8910 - val_loss: 0.1728 - val_acc: 0.9391\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 15s 616ms/step - loss: 0.2219 - acc: 0.9021 - val_loss: 0.1693 - val_acc: 0.9348\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.2219 - acc: 0.9006 - val_loss: 0.2030 - val_acc: 0.9087\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.2580 - acc: 0.8917 - val_loss: 0.1871 - val_acc: 0.9348\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.2375 - acc: 0.8982 - val_loss: 0.1731 - val_acc: 0.9304\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.1989 - acc: 0.9113 - val_loss: 0.1942 - val_acc: 0.9130\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.2042 - acc: 0.9236 - val_loss: 0.1706 - val_acc: 0.9130\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.1937 - acc: 0.9158 - val_loss: 0.1901 - val_acc: 0.9261\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.2013 - acc: 0.9202 - val_loss: 0.2225 - val_acc: 0.9087\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.2184 - acc: 0.9156 - val_loss: 0.2205 - val_acc: 0.8957\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.2082 - acc: 0.9125 - val_loss: 0.3222 - val_acc: 0.8913\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.2012 - acc: 0.9145 - val_loss: 0.2379 - val_acc: 0.9174\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.1786 - acc: 0.9276 - val_loss: 0.2307 - val_acc: 0.9174\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.2026 - acc: 0.9204 - val_loss: 0.2278 - val_acc: 0.9043\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.2081 - acc: 0.9113 - val_loss: 0.2295 - val_acc: 0.9130\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.1849 - acc: 0.9268 - val_loss: 0.2715 - val_acc: 0.9000\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.1762 - acc: 0.9282 - val_loss: 0.2311 - val_acc: 0.9217\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.1529 - acc: 0.9340 - val_loss: 0.1782 - val_acc: 0.9217\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.1399 - acc: 0.9452 - val_loss: 0.2123 - val_acc: 0.9261\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.1693 - acc: 0.9360 - val_loss: 0.1966 - val_acc: 0.9174\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1637 - acc: 0.9305 - val_loss: 0.1921 - val_acc: 0.9217\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 532ms/step - loss: 0.1552 - acc: 0.9335 - val_loss: 0.1747 - val_acc: 0.9261\n",
      "Train loss: 0.156126690896\n",
      "Train accuracy: 0.927219797604\n",
      "Test loss: 0.169281424647\n",
      "Test accuracy: 0.934782608696\n",
      "\n",
      "===================FOLD= 2\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 16s 667ms/step - loss: 0.5656 - acc: 0.6951 - val_loss: 0.3745 - val_acc: 0.7565\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 15s 616ms/step - loss: 0.4143 - acc: 0.7826 - val_loss: 0.2664 - val_acc: 0.8826\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 15s 617ms/step - loss: 0.3050 - acc: 0.8670 - val_loss: 0.2480 - val_acc: 0.8696\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.3008 - acc: 0.8713 - val_loss: 0.3560 - val_acc: 0.8478\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 15s 619ms/step - loss: 0.2839 - acc: 0.8689 - val_loss: 0.2336 - val_acc: 0.8826\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 15s 617ms/step - loss: 0.2509 - acc: 0.8949 - val_loss: 0.2277 - val_acc: 0.9000\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 15s 616ms/step - loss: 0.2295 - acc: 0.9034 - val_loss: 0.1911 - val_acc: 0.9261\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.2341 - acc: 0.8994 - val_loss: 0.3251 - val_acc: 0.8826\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 13s 539ms/step - loss: 0.2663 - acc: 0.8902 - val_loss: 0.2217 - val_acc: 0.8913\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.2203 - acc: 0.9120 - val_loss: 0.2144 - val_acc: 0.9000\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 530ms/step - loss: 0.2088 - acc: 0.9122 - val_loss: 0.2363 - val_acc: 0.9130\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.2675 - acc: 0.8797 - val_loss: 0.2458 - val_acc: 0.8826\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 538ms/step - loss: 0.2171 - acc: 0.9098 - val_loss: 0.2008 - val_acc: 0.9174\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.1886 - acc: 0.9238 - val_loss: 0.2037 - val_acc: 0.9348\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.1878 - acc: 0.9197 - val_loss: 0.2003 - val_acc: 0.9130\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.2245 - acc: 0.9106 - val_loss: 0.1931 - val_acc: 0.9087\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.1933 - acc: 0.9192 - val_loss: 0.1962 - val_acc: 0.9087\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.1514 - acc: 0.9348 - val_loss: 0.2215 - val_acc: 0.9043\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.2039 - acc: 0.9144 - val_loss: 0.2542 - val_acc: 0.8957\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.1908 - acc: 0.9156 - val_loss: 0.2036 - val_acc: 0.9217\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.1715 - acc: 0.9249 - val_loss: 0.2013 - val_acc: 0.9000\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.1586 - acc: 0.9339 - val_loss: 0.1865 - val_acc: 0.9304\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.1800 - acc: 0.9250 - val_loss: 0.2440 - val_acc: 0.9174\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.1738 - acc: 0.9342 - val_loss: 0.2053 - val_acc: 0.9217\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.1825 - acc: 0.9261 - val_loss: 0.3268 - val_acc: 0.8609\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.2003 - acc: 0.9294 - val_loss: 0.2521 - val_acc: 0.8739\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.1840 - acc: 0.9191 - val_loss: 0.2766 - val_acc: 0.8826\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.1531 - acc: 0.9296 - val_loss: 0.2255 - val_acc: 0.9087\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.1368 - acc: 0.9458 - val_loss: 0.2191 - val_acc: 0.9000\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.1197 - acc: 0.9497 - val_loss: 0.2956 - val_acc: 0.8913\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.1571 - acc: 0.9374 - val_loss: 0.2390 - val_acc: 0.9174\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.1408 - acc: 0.9446 - val_loss: 0.2332 - val_acc: 0.9304\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1638 - acc: 0.9281 - val_loss: 0.2483 - val_acc: 0.9000\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.1459 - acc: 0.9452 - val_loss: 0.2081 - val_acc: 0.9130\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.1345 - acc: 0.9459 - val_loss: 0.2285 - val_acc: 0.9087\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.1452 - acc: 0.9341 - val_loss: 0.1932 - val_acc: 0.9087\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.1438 - acc: 0.9399 - val_loss: 0.2295 - val_acc: 0.9130\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.1150 - acc: 0.9543 - val_loss: 0.2440 - val_acc: 0.9043\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.1187 - acc: 0.9536 - val_loss: 0.2810 - val_acc: 0.9043\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 13s 533ms/step - loss: 0.1166 - acc: 0.9497 - val_loss: 0.2798 - val_acc: 0.9130\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 41/100\n",
      "24/24 [==============================] - 13s 533ms/step - loss: 0.0988 - acc: 0.9576 - val_loss: 0.2831 - val_acc: 0.9130\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 13s 531ms/step - loss: 0.1286 - acc: 0.9517 - val_loss: 0.2490 - val_acc: 0.9087\n",
      "Train loss: 0.155706448131\n",
      "Train accuracy: 0.935953422752\n",
      "Test loss: 0.186535130506\n",
      "Test accuracy: 0.930434783645\n",
      "\n",
      "===================FOLD= 3\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 16s 674ms/step - loss: 0.6671 - acc: 0.6783 - val_loss: 0.4275 - val_acc: 0.7565\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 15s 618ms/step - loss: 0.4246 - acc: 0.8029 - val_loss: 0.1832 - val_acc: 0.9391\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.3495 - acc: 0.8504 - val_loss: 0.1908 - val_acc: 0.9391\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 15s 617ms/step - loss: 0.3499 - acc: 0.8327 - val_loss: 0.1820 - val_acc: 0.9435\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 15s 616ms/step - loss: 0.3100 - acc: 0.8518 - val_loss: 0.1628 - val_acc: 0.9391\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.3032 - acc: 0.8681 - val_loss: 0.2091 - val_acc: 0.9304\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.2849 - acc: 0.8715 - val_loss: 0.2384 - val_acc: 0.9174\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 15s 618ms/step - loss: 0.2814 - acc: 0.8850 - val_loss: 0.1429 - val_acc: 0.9522\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 15s 616ms/step - loss: 0.2688 - acc: 0.8870 - val_loss: 0.1360 - val_acc: 0.9391\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.2533 - acc: 0.8910 - val_loss: 0.2102 - val_acc: 0.9304\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.2540 - acc: 0.8960 - val_loss: 0.1511 - val_acc: 0.9609\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.2067 - acc: 0.9132 - val_loss: 0.1712 - val_acc: 0.9391\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 538ms/step - loss: 0.2197 - acc: 0.9001 - val_loss: 0.1400 - val_acc: 0.9565\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.2116 - acc: 0.9053 - val_loss: 0.1517 - val_acc: 0.9435\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.2160 - acc: 0.9139 - val_loss: 0.1646 - val_acc: 0.9522\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.1855 - acc: 0.9176 - val_loss: 0.2041 - val_acc: 0.9087\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.1900 - acc: 0.9178 - val_loss: 0.1630 - val_acc: 0.9478\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.1824 - acc: 0.9256 - val_loss: 0.2053 - val_acc: 0.9348\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.1582 - acc: 0.9348 - val_loss: 0.1990 - val_acc: 0.9348\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.1831 - acc: 0.9229 - val_loss: 0.1652 - val_acc: 0.9435\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.2027 - acc: 0.9122 - val_loss: 0.1894 - val_acc: 0.9435\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2005 - acc: 0.9168 - val_loss: 0.1704 - val_acc: 0.9304\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.2063 - acc: 0.9120 - val_loss: 0.1666 - val_acc: 0.9478\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.1863 - acc: 0.9217 - val_loss: 0.1969 - val_acc: 0.9348\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.1325 - acc: 0.9459 - val_loss: 0.3262 - val_acc: 0.9130\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.1558 - acc: 0.9392 - val_loss: 0.2525 - val_acc: 0.9304\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 15s 615ms/step - loss: 0.1774 - acc: 0.9300 - val_loss: 0.1286 - val_acc: 0.9652\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.1951 - acc: 0.9164 - val_loss: 0.1635 - val_acc: 0.9522\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.1246 - acc: 0.9504 - val_loss: 0.1968 - val_acc: 0.9348\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.1827 - acc: 0.9263 - val_loss: 0.1573 - val_acc: 0.9522\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.1256 - acc: 0.9466 - val_loss: 0.1623 - val_acc: 0.9609\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.1163 - acc: 0.9498 - val_loss: 0.2069 - val_acc: 0.9391\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1201 - acc: 0.9588 - val_loss: 0.1973 - val_acc: 0.9261\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.1212 - acc: 0.9543 - val_loss: 0.1558 - val_acc: 0.9435\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.1410 - acc: 0.9399 - val_loss: 0.2083 - val_acc: 0.9087\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.1259 - acc: 0.9504 - val_loss: 0.2259 - val_acc: 0.9261\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.1218 - acc: 0.9503 - val_loss: 0.2998 - val_acc: 0.9391\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 13s 532ms/step - loss: 0.1603 - acc: 0.9314 - val_loss: 0.2606 - val_acc: 0.9348\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 13s 531ms/step - loss: 0.1145 - acc: 0.9582 - val_loss: 0.1690 - val_acc: 0.9391\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 13s 531ms/step - loss: 0.1264 - acc: 0.9485 - val_loss: 0.2529 - val_acc: 0.9087\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.1182 - acc: 0.9498 - val_loss: 0.2007 - val_acc: 0.9261\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.1199 - acc: 0.9510 - val_loss: 0.2067 - val_acc: 0.9435\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 13s 532ms/step - loss: 0.1367 - acc: 0.9466 - val_loss: 0.2102 - val_acc: 0.9261\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.0990 - acc: 0.9660 - val_loss: 0.2038 - val_acc: 0.9391\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 13s 532ms/step - loss: 0.0982 - acc: 0.9588 - val_loss: 0.2083 - val_acc: 0.9435\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.1284 - acc: 0.9536 - val_loss: 0.2882 - val_acc: 0.9130\n",
      "Epoch 47/100\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.0985 - acc: 0.9589 - val_loss: 0.2550 - val_acc: 0.9217\n",
      "Train loss: 0.115036896505\n",
      "Train accuracy: 0.951965066196\n",
      "Test loss: 0.128644591311\n",
      "Test accuracy: 0.965217391823\n",
      "\n",
      "===================FOLD= 4\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 18s 752ms/step - loss: 0.6097 - acc: 0.6420 - val_loss: 0.7119 - val_acc: 0.6272\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 15s 617ms/step - loss: 0.3962 - acc: 0.8118 - val_loss: 0.3832 - val_acc: 0.7939\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 15s 619ms/step - loss: 0.3278 - acc: 0.8574 - val_loss: 0.3528 - val_acc: 0.8158\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 15s 617ms/step - loss: 0.2758 - acc: 0.8731 - val_loss: 0.3397 - val_acc: 0.8553\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 15s 618ms/step - loss: 0.2671 - acc: 0.8757 - val_loss: 0.3327 - val_acc: 0.8728\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 15s 617ms/step - loss: 0.2508 - acc: 0.8925 - val_loss: 0.3046 - val_acc: 0.8904\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 15s 616ms/step - loss: 0.2393 - acc: 0.8887 - val_loss: 0.2897 - val_acc: 0.8816\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.2421 - acc: 0.9011 - val_loss: 0.3036 - val_acc: 0.8772\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 15s 618ms/step - loss: 0.2271 - acc: 0.9089 - val_loss: 0.2652 - val_acc: 0.8816\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 538ms/step - loss: 0.1952 - acc: 0.9238 - val_loss: 0.3334 - val_acc: 0.8816\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 530ms/step - loss: 0.2159 - acc: 0.9076 - val_loss: 0.2831 - val_acc: 0.8728\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.1686 - acc: 0.9238 - val_loss: 0.4338 - val_acc: 0.8728\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.1893 - acc: 0.9231 - val_loss: 0.3430 - val_acc: 0.8772\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.2264 - acc: 0.9134 - val_loss: 0.2694 - val_acc: 0.9035\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 15s 617ms/step - loss: 0.1721 - acc: 0.9290 - val_loss: 0.2496 - val_acc: 0.9211\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.1502 - acc: 0.9414 - val_loss: 0.3780 - val_acc: 0.8728\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.1528 - acc: 0.9368 - val_loss: 0.3339 - val_acc: 0.9123\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.1625 - acc: 0.9323 - val_loss: 0.4423 - val_acc: 0.8816\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.1669 - acc: 0.9343 - val_loss: 0.3386 - val_acc: 0.8684\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.2303 - acc: 0.9082 - val_loss: 0.2975 - val_acc: 0.9035\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 538ms/step - loss: 0.1542 - acc: 0.9343 - val_loss: 0.2845 - val_acc: 0.9123\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 530ms/step - loss: 0.1612 - acc: 0.9311 - val_loss: 0.2896 - val_acc: 0.9035\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.1387 - acc: 0.9453 - val_loss: 0.3745 - val_acc: 0.8816\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.1899 - acc: 0.9218 - val_loss: 0.2835 - val_acc: 0.9035\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.1359 - acc: 0.9460 - val_loss: 0.3336 - val_acc: 0.8904\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.1641 - acc: 0.9446 - val_loss: 0.3264 - val_acc: 0.8816\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 538ms/step - loss: 0.1736 - acc: 0.9297 - val_loss: 0.3203 - val_acc: 0.8553\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 538ms/step - loss: 0.1439 - acc: 0.9414 - val_loss: 0.2827 - val_acc: 0.8816\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.1265 - acc: 0.9518 - val_loss: 0.3059 - val_acc: 0.8816\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.1365 - acc: 0.9472 - val_loss: 0.3182 - val_acc: 0.9079\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.1210 - acc: 0.9531 - val_loss: 0.3629 - val_acc: 0.8684\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.1431 - acc: 0.9460 - val_loss: 0.2820 - val_acc: 0.8860\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.1181 - acc: 0.9544 - val_loss: 0.3525 - val_acc: 0.8947\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.1444 - acc: 0.9414 - val_loss: 0.3207 - val_acc: 0.8816\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.1392 - acc: 0.9435 - val_loss: 0.2905 - val_acc: 0.9079\n",
      "Train loss: 0.131488510776\n",
      "Train accuracy: 0.950581395349\n",
      "Test loss: 0.249572888049\n",
      "Test accuracy: 0.921052631579\n",
      "\n",
      "===================FOLD= 5\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 17s 699ms/step - loss: 0.6640 - acc: 0.5864 - val_loss: 0.5943 - val_acc: 0.6491\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 15s 615ms/step - loss: 0.5311 - acc: 0.7142 - val_loss: 0.5325 - val_acc: 0.7325\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.4379 - acc: 0.7968 - val_loss: 0.5767 - val_acc: 0.7456\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 15s 616ms/step - loss: 0.4047 - acc: 0.8164 - val_loss: 0.4140 - val_acc: 0.8070\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 15s 619ms/step - loss: 0.3251 - acc: 0.8535 - val_loss: 0.3589 - val_acc: 0.8114\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 15s 617ms/step - loss: 0.2996 - acc: 0.8658 - val_loss: 0.3244 - val_acc: 0.8333\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 15s 618ms/step - loss: 0.2935 - acc: 0.8711 - val_loss: 0.3096 - val_acc: 0.8421\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.2729 - acc: 0.8802 - val_loss: 0.3724 - val_acc: 0.8640\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 15s 618ms/step - loss: 0.2695 - acc: 0.8965 - val_loss: 0.2620 - val_acc: 0.8772\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 538ms/step - loss: 0.2490 - acc: 0.8965 - val_loss: 0.2622 - val_acc: 0.8728\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 15s 612ms/step - loss: 0.2309 - acc: 0.9062 - val_loss: 0.2508 - val_acc: 0.8860\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.2269 - acc: 0.9088 - val_loss: 0.2803 - val_acc: 0.8684\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.2243 - acc: 0.8991 - val_loss: 0.2832 - val_acc: 0.8684\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 538ms/step - loss: 0.2393 - acc: 0.9024 - val_loss: 0.2654 - val_acc: 0.8772\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.2212 - acc: 0.9082 - val_loss: 0.2536 - val_acc: 0.8860\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.1668 - acc: 0.9291 - val_loss: 0.3156 - val_acc: 0.8509\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 538ms/step - loss: 0.2318 - acc: 0.9042 - val_loss: 0.2596 - val_acc: 0.8728\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.1868 - acc: 0.9278 - val_loss: 0.2741 - val_acc: 0.8553\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.1781 - acc: 0.9206 - val_loss: 0.2569 - val_acc: 0.8816\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.1927 - acc: 0.9153 - val_loss: 0.3033 - val_acc: 0.8596\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.2014 - acc: 0.9187 - val_loss: 0.3308 - val_acc: 0.8377\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 15s 611ms/step - loss: 0.1973 - acc: 0.9218 - val_loss: 0.2496 - val_acc: 0.8860\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 15s 616ms/step - loss: 0.1728 - acc: 0.9316 - val_loss: 0.2286 - val_acc: 0.8860\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.1769 - acc: 0.9297 - val_loss: 0.2299 - val_acc: 0.8904\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 15s 616ms/step - loss: 0.1415 - acc: 0.9414 - val_loss: 0.2216 - val_acc: 0.9167\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.1793 - acc: 0.9220 - val_loss: 0.2522 - val_acc: 0.8816\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 538ms/step - loss: 0.1596 - acc: 0.9336 - val_loss: 0.2217 - val_acc: 0.8816\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.1455 - acc: 0.9441 - val_loss: 0.2425 - val_acc: 0.8816\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.1382 - acc: 0.9401 - val_loss: 0.2237 - val_acc: 0.8860\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.1511 - acc: 0.9486 - val_loss: 0.3085 - val_acc: 0.8640\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.1374 - acc: 0.9427 - val_loss: 0.3213 - val_acc: 0.8772\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.1538 - acc: 0.9342 - val_loss: 0.3025 - val_acc: 0.8684\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.2301 - acc: 0.9029 - val_loss: 0.2794 - val_acc: 0.8860\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.1944 - acc: 0.9245 - val_loss: 0.3604 - val_acc: 0.8377\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.1617 - acc: 0.9342 - val_loss: 0.2571 - val_acc: 0.8772\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 36/100\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.1469 - acc: 0.9446 - val_loss: 0.2316 - val_acc: 0.8816\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.1203 - acc: 0.9551 - val_loss: 0.2678 - val_acc: 0.8728\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 15s 616ms/step - loss: 0.1385 - acc: 0.9401 - val_loss: 0.2156 - val_acc: 0.9079\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.1179 - acc: 0.9492 - val_loss: 0.2308 - val_acc: 0.9035\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.1284 - acc: 0.9538 - val_loss: 0.2194 - val_acc: 0.8991\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.1169 - acc: 0.9551 - val_loss: 0.2635 - val_acc: 0.8947\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.1156 - acc: 0.9511 - val_loss: 0.2359 - val_acc: 0.8991\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.0947 - acc: 0.9609 - val_loss: 0.2486 - val_acc: 0.8991\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.0974 - acc: 0.9590 - val_loss: 0.3743 - val_acc: 0.8684\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.1340 - acc: 0.9415 - val_loss: 0.2672 - val_acc: 0.8947\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.1178 - acc: 0.9505 - val_loss: 0.2482 - val_acc: 0.8991\n",
      "Epoch 47/100\n",
      "24/24 [==============================] - 13s 533ms/step - loss: 0.0955 - acc: 0.9623 - val_loss: 0.3750 - val_acc: 0.8816\n",
      "Epoch 48/100\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.1111 - acc: 0.9589 - val_loss: 0.2748 - val_acc: 0.8904\n",
      "Epoch 49/100\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.1121 - acc: 0.9584 - val_loss: 0.2633 - val_acc: 0.8904\n",
      "Epoch 50/100\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.0962 - acc: 0.9642 - val_loss: 0.2221 - val_acc: 0.9167\n",
      "Epoch 51/100\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.0880 - acc: 0.9681 - val_loss: 0.2559 - val_acc: 0.8860\n",
      "Epoch 52/100\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.0913 - acc: 0.9603 - val_loss: 0.3364 - val_acc: 0.8904\n",
      "Epoch 53/100\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.0878 - acc: 0.9655 - val_loss: 0.2871 - val_acc: 0.8772\n",
      "Epoch 54/100\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.0801 - acc: 0.9694 - val_loss: 0.2957 - val_acc: 0.8991\n",
      "Epoch 55/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.0836 - acc: 0.9681 - val_loss: 0.3390 - val_acc: 0.8860\n",
      "Epoch 56/100\n",
      "24/24 [==============================] - 13s 532ms/step - loss: 0.1194 - acc: 0.9557 - val_loss: 0.2954 - val_acc: 0.8816\n",
      "Epoch 57/100\n",
      "24/24 [==============================] - 13s 533ms/step - loss: 0.0924 - acc: 0.9642 - val_loss: 0.3103 - val_acc: 0.8947\n",
      "Epoch 58/100\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.0811 - acc: 0.9603 - val_loss: 0.2784 - val_acc: 0.8860\n",
      "Train loss: 0.0833853549042\n",
      "Train accuracy: 0.970203488372\n",
      "Test loss: 0.215574614834\n",
      "Test accuracy: 0.907894736842\n",
      "\n",
      "===================FOLD= 6\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 17s 724ms/step - loss: 0.7350 - acc: 0.5774 - val_loss: 0.6028 - val_acc: 0.7325\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 15s 618ms/step - loss: 0.7071 - acc: 0.6088 - val_loss: 0.5153 - val_acc: 0.8289\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 15s 617ms/step - loss: 0.5017 - acc: 0.7526 - val_loss: 0.3953 - val_acc: 0.7939\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 15s 616ms/step - loss: 0.4190 - acc: 0.8020 - val_loss: 0.3655 - val_acc: 0.8158\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 15s 617ms/step - loss: 0.3425 - acc: 0.8607 - val_loss: 0.2925 - val_acc: 0.8509\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.3213 - acc: 0.8457 - val_loss: 0.3190 - val_acc: 0.8640\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 15s 618ms/step - loss: 0.3229 - acc: 0.8516 - val_loss: 0.2893 - val_acc: 0.8684\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 15s 618ms/step - loss: 0.2640 - acc: 0.8789 - val_loss: 0.2689 - val_acc: 0.8640\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 15s 617ms/step - loss: 0.2724 - acc: 0.8660 - val_loss: 0.2622 - val_acc: 0.8772\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 15s 616ms/step - loss: 0.2579 - acc: 0.8945 - val_loss: 0.2619 - val_acc: 0.8947\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 15s 608ms/step - loss: 0.2190 - acc: 0.9154 - val_loss: 0.2608 - val_acc: 0.8904\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.2332 - acc: 0.9037 - val_loss: 0.2937 - val_acc: 0.8728\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 15s 616ms/step - loss: 0.2321 - acc: 0.9010 - val_loss: 0.2602 - val_acc: 0.8947\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.2368 - acc: 0.8919 - val_loss: 0.2691 - val_acc: 0.8860\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.1980 - acc: 0.9135 - val_loss: 0.2702 - val_acc: 0.9035\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 15s 616ms/step - loss: 0.2102 - acc: 0.9160 - val_loss: 0.2561 - val_acc: 0.8991\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.2139 - acc: 0.9043 - val_loss: 0.3132 - val_acc: 0.8596\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.2059 - acc: 0.9160 - val_loss: 0.2809 - val_acc: 0.8728\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.2120 - acc: 0.9141 - val_loss: 0.3972 - val_acc: 0.8421\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.2148 - acc: 0.9108 - val_loss: 0.2901 - val_acc: 0.8947\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.1908 - acc: 0.9212 - val_loss: 0.3620 - val_acc: 0.8289\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.3042 - acc: 0.8893 - val_loss: 0.2868 - val_acc: 0.8728\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.2000 - acc: 0.9225 - val_loss: 0.2961 - val_acc: 0.8640\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.2001 - acc: 0.9193 - val_loss: 0.2586 - val_acc: 0.8816\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.1729 - acc: 0.9297 - val_loss: 0.2730 - val_acc: 0.8947\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.1753 - acc: 0.9348 - val_loss: 0.2630 - val_acc: 0.8860\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.1530 - acc: 0.9427 - val_loss: 0.2664 - val_acc: 0.8947\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.1764 - acc: 0.9264 - val_loss: 0.2735 - val_acc: 0.8860\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.1590 - acc: 0.9427 - val_loss: 0.2665 - val_acc: 0.8860\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.1742 - acc: 0.9284 - val_loss: 0.2798 - val_acc: 0.8772\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.1430 - acc: 0.9453 - val_loss: 0.2591 - val_acc: 0.9079\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 15s 615ms/step - loss: 0.1603 - acc: 0.9336 - val_loss: 0.2522 - val_acc: 0.8904\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1441 - acc: 0.9460 - val_loss: 0.2694 - val_acc: 0.8947\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.1501 - acc: 0.9401 - val_loss: 0.3527 - val_acc: 0.8684\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.1644 - acc: 0.9388 - val_loss: 0.3058 - val_acc: 0.8947\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.1409 - acc: 0.9433 - val_loss: 0.3325 - val_acc: 0.8816\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.1746 - acc: 0.9277 - val_loss: 0.2941 - val_acc: 0.8904\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 38/100\n",
      "24/24 [==============================] - 13s 536ms/step - loss: 0.1501 - acc: 0.9408 - val_loss: 0.2774 - val_acc: 0.8816\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 13s 537ms/step - loss: 0.1297 - acc: 0.9427 - val_loss: 0.2759 - val_acc: 0.8772\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 13s 533ms/step - loss: 0.1116 - acc: 0.9531 - val_loss: 0.3323 - val_acc: 0.8904\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.1302 - acc: 0.9466 - val_loss: 0.3654 - val_acc: 0.8509\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.1393 - acc: 0.9446 - val_loss: 0.3004 - val_acc: 0.8509\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.1225 - acc: 0.9512 - val_loss: 0.2536 - val_acc: 0.9035\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1297 - acc: 0.9466 - val_loss: 0.3818 - val_acc: 0.8640\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 13s 535ms/step - loss: 0.1301 - acc: 0.9486 - val_loss: 0.2910 - val_acc: 0.8947\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.1015 - acc: 0.9661 - val_loss: 0.3324 - val_acc: 0.9035\n",
      "Epoch 47/100\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.1146 - acc: 0.9551 - val_loss: 0.4231 - val_acc: 0.8596\n",
      "Epoch 48/100\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.1144 - acc: 0.9544 - val_loss: 0.3717 - val_acc: 0.8728\n",
      "Epoch 49/100\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.0885 - acc: 0.9668 - val_loss: 0.3443 - val_acc: 0.8904\n",
      "Epoch 50/100\n",
      "24/24 [==============================] - 13s 532ms/step - loss: 0.0998 - acc: 0.9642 - val_loss: 0.3810 - val_acc: 0.8728\n",
      "Epoch 51/100\n",
      "24/24 [==============================] - 13s 532ms/step - loss: 0.0957 - acc: 0.9629 - val_loss: 0.3850 - val_acc: 0.8816\n",
      "Epoch 52/100\n",
      "24/24 [==============================] - 13s 532ms/step - loss: 0.0953 - acc: 0.9610 - val_loss: 0.3485 - val_acc: 0.8860\n",
      "Train loss: 0.110579074295\n",
      "Train accuracy: 0.960029069767\n",
      "Test loss: 0.252150628389\n",
      "Test accuracy: 0.890350877193\n",
      "\n",
      " Train Log Loss Validation=  0.113610347708\n",
      " Test Log Loss Validation=  0.193107086096\n"
     ]
    }
   ],
   "source": [
    "#def myAngleCV(X_train, X_angle, X_test):\n",
    "K=7\n",
    "folds = list(StratifiedKFold(n_splits=K, shuffle=True, random_state=16).split(X_train, target_train))\n",
    "y_test_pred_log = 0\n",
    "y_train_pred_log=0\n",
    "y_valid_pred_log = 0.0*target_train\n",
    "for j, (train_idx, test_idx) in enumerate(folds):\n",
    "    print('\\n===================FOLD=',j)\n",
    "    X_train_cv = X_train[train_idx]\n",
    "    y_train_cv = target_train[train_idx]\n",
    "    X_holdout = X_train[test_idx]\n",
    "    Y_holdout= target_train[test_idx]\n",
    "    \n",
    "    #Angle\n",
    "    X_angle_cv=X_angle[train_idx]\n",
    "    X_angle_hold=X_angle[test_idx]\n",
    "    #define file path and get callbacks\n",
    "    file_path = \"%s_aug_model_weights.hdf5\"%j\n",
    "    callbacks = get_callbacks(filepath=file_path, patience=5)\n",
    "    gen_flow = gen_flow_for_two_inputs(X_train_cv, X_angle_cv, y_train_cv)\n",
    "    galaxyModel= getVggAngleModel()\n",
    "    histr = galaxyModel.fit_generator(\n",
    "            gen_flow,\n",
    "            steps_per_epoch=24,\n",
    "            epochs=100,\n",
    "            shuffle=True,\n",
    "            verbose=1,\n",
    "            validation_data=([X_holdout,X_angle_hold], Y_holdout),\n",
    "            callbacks=callbacks)\n",
    "     #Getting the Best Model\n",
    "    galaxyModel.load_weights(filepath=file_path)\n",
    "    #Getting Training Score\n",
    "    score = galaxyModel.evaluate([X_train_cv,X_angle_cv], y_train_cv, verbose=0)\n",
    "    print('Train loss:', score[0])\n",
    "    print('Train accuracy:', score[1])\n",
    "    #Getting Test Score\n",
    "    score = galaxyModel.evaluate([X_holdout,X_angle_hold], Y_holdout, verbose=0)\n",
    "    print('Test loss:', score[0])\n",
    "    print('Test accuracy:', score[1])\n",
    "\n",
    "    #Getting validation Score.\n",
    "    pred_valid=galaxyModel.predict([X_holdout,X_angle_hold])\n",
    "    y_valid_pred_log[test_idx] = pred_valid.reshape(pred_valid.shape[0])\n",
    "\n",
    "    #Getting Test Scores\n",
    "    temp_test=galaxyModel.predict([X_test, X_test_angle])\n",
    "    y_test_pred_log+=temp_test.reshape(temp_test.shape[0])\n",
    "\n",
    "    #Getting Train Scores\n",
    "    temp_train=galaxyModel.predict([X_train, X_angle])\n",
    "    y_train_pred_log+=temp_train.reshape(temp_train.shape[0])\n",
    "\n",
    "y_test_pred_log=y_test_pred_log/K\n",
    "y_train_pred_log=y_train_pred_log/K\n",
    "\n",
    "print('\\n Train Log Loss Validation= ',log_loss(target_train, y_train_pred_log))\n",
    "print(' Test Log Loss Validation= ',log_loss(target_train, y_valid_pred_log))\n",
    "#return y_test_pred_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#preds=myAngleCV(X_train, X_angle, X_test)\n",
    "preds = y_test_pred_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Submission to kaggle\n",
    "submission = pd.DataFrame()\n",
    "submission['id']=test['id']\n",
    "submission['is_iceberg']=preds\n",
    "submission.to_csv('sub_Adam_7folds_amsgrad_0001_20p.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['val_acc', 'acc', 'val_loss', 'loss'])\n"
     ]
    },
    {
     "data": {
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YYPl8I+AtIv52jkVEZojIVhHZmpBg/imKc+DUOb7ecpKpA1vTtknp1c2KE+Dl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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4d50323f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WNkqOm6GWHuPMsImNf5AZtt31deOnHuftNtOcfa07CMePBLQJPPXJ/MVc52qS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\negAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4d5020b860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# list all data in history\n",
    "print(histr.history.keys())\n",
    "# summarize history for accuracy\n",
    "plt.plot(histr.history['acc'])\n",
    "plt.plot(histr.history['val_acc'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()\n",
    "# summarize history for loss\n",
    "plt.plot(histr.history['loss'])\n",
    "plt.plot(histr.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.image as mpimg\n",
    "\n",
    "min1 = np.min(X_holdout)\n",
    "max1 = np.max(X_holdout)\n",
    "diff1 = max1 - min1\n",
    "my_sum = lambda x: (x - min1)/diff1\n",
    "\n",
    "X_holdout2 = my_sum(X_holdout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13      1\n",
       "14      0\n",
       "26      1\n",
       "38      1\n",
       "43      0\n",
       "44      0\n",
       "52      0\n",
       "80      0\n",
       "104     1\n",
       "127     0\n",
       "137     0\n",
       "138     1\n",
       "141     1\n",
       "167     1\n",
       "180     0\n",
       "186     0\n",
       "195     1\n",
       "199     0\n",
       "206     1\n",
       "207     1\n",
       "212     0\n",
       "213     1\n",
       "217     0\n",
       "221     0\n",
       "234     1\n",
       "240     0\n",
       "256     0\n",
       "267     1\n",
       "274     1\n",
       "276     1\n",
       "       ..\n",
       "1417    0\n",
       "1435    0\n",
       "1440    1\n",
       "1445    1\n",
       "1447    1\n",
       "1460    1\n",
       "1465    0\n",
       "1466    0\n",
       "1468    0\n",
       "1470    0\n",
       "1488    0\n",
       "1489    1\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",
       "1597    0\n",
       "1602    0\n",
       "Name: is_iceberg, Length: 228, 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 0x7f4d50f6c0f0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OPOA2qGw7ICsZbY+fhmC2h8gqqOcSnk6BlmJLp627jZpKdhCJtBlstkBZc+Qn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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4e604af8d0>"
      ]
     },
     "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 0x7f4d501ac7b8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Rbds8ypxmiAkJCQkR6YGYkJCQELGxlDkoaB7zlrtQ5R44dorLerQLKK6LmOM5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\nHcdxAj4hOo7jBN4AZpu/Z56nuZwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4d501fdba8>"
      ]
     },
     "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 0x7f4d501111d0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wIPKFpsFJ+Napl94kq9r2EOtreLHvFf2T7VXBPzg8/IXf2m63d7JdFn5Ozf0lvsjbY/NB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\nq9XU6VkdFdgZ7be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A8z5oXYnk6xFY1hyUnA7Wu5E2LvvIsYXNGcoRy+rNqicDBGoqx6j1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\nWIERYVS8jAUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4d50165e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[2], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[26])+ \",\"+\" 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 0x7f4d500f3978>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hhCdUystoa6Mfaq3yizgt/AXY3d4+Om4OQZmwMmo9V34BR6/0r/6B+SqiXTvp2+DlzWr+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\nOM5M4ZOe4zgzhU96juPMFD7pOY4zU/wP+je9jxsoJNIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4d500d0940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[3], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[38])+ \",\"+\" 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 0x7f4d5005c1d0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EiDQhJiQkJERsLmUOQZOoQj0EZaZVuIKTtJACOIwp3hQ/A71DGt0zTrehh/L6\nSLKEhD/9ORIHIcnTAuXU0Sp4Ji8u0Om6pTiAdnewHTCjU+/Q7zmX07o+FL5nITomt5ErumAArZd3\nDFrX62T43C2rl9oev9vGVgK3D06Q4XgWqW8XCbxoWR22+2s/J5dkYqsOlMwXUD5/GpJmrR5ksZ5e\n3Rd9juVCr+tU2lhHWIJn4PtdOHJzy2Y68TZ3471moL30VKAC+NXKB+gQUmAVnOsZezxB345EFXCv\n79ZqGwJNvorrKD+2UzgFXznmW6LMCQkJCZuLNCEmJCQkRGwsZS4s007Mq1wjjzDjTYuuvw8CaN0q\nuVO/7Za1o8xphx1C/mngtKIErZzKqUxrAQfkCWJJURs+iLrf0JoK3+egwwH3acEb2nodfI7806Q2\nSJZ0MHfTaXdVztTrvdx1mlgE1huyXT2v134FmStsMRi2KUjxiSo6fl9D4PW9mecr7sCazuRf85nf\npwfJs26JOHFIvlnuz6sdvF4ng9imsbdtCfm1Gn1ucN4eZOjzgXsWkPpym+Y4c+qbx3FRzNfH2gu0\nf4B+myJ58nYLOcexH1FgzFGK7Mw2SWwTJcn4A2He6vmRO9cXUFrfNKQVYkJCQkJEmhATEhISIjaW\nMgeT6hifW+879c1APbOp05P+gVPi4SBa1BC/OcC7Y77tVKKElVXIY9yGNY8U9yppKqy4XTi+zkYN\nrWrhnv3cqVle0UmbZlZsAfQQh4ocwRUs4YOpt6NbN8cwtmv5uOc/biPncLHtZexOvQ10Ei8R+hpA\nwyawUOc1KGGkcnuQFjsBfW7BMZ3O0EUXUmRjP39Sw5qO+G2BntbY1lg5ftM6W+OeCzhXb0NyLF/6\n5+3S2zPKIMUFWr8Hx/AWs4xFXIVF2FD22VhmNKdYvwVhyB09BK2n8vkkNO2ng3qBraMpyp7u+PF9\nV5sxHJJidkJCQsLmYnNXiPKNer5dKYBJuRmuNGZx5ZBN/A2Y9ZGqcRdSLgNszFfuh0fkRwjX2/O3\ndTlaHzLVGTQb5TT06AQrG6iXLBAuVg58g70187r3IPczxoo2h1hqFvOnDNEpJztY8cEAtYVmMkeJ\nwScu2/G6zLAqpOdadua4+WuLoX1YIQaq0NAABiHeCfwgD2EEu4+rMhieOBROZs3aeLGPVKpYoZ1J\nH4pcKzqGEQaKQAuEC3YyiNgy1DMKAC8RolnBAFSgt1rI+YPFtxaoynTm7e/C93GSc6V9o1JQC8ZD\nlV4XMpgTTMgkAAAgAElEQVQlwlw/oqMb2rIpSCvEhISEhIg0ISYkJCREbCxlziS1Iv0L8K0Lc4ie\nUn8T+UtWNKQDOtQaIeYMtHIO9nxXBUFZKLIcwW9rgOQYRRecbUAi1GBKf8Mt5GKBeskEgp4F0mP2\nkO6zA+NJBR/KOUIHV2KsFBO1XfjYoV5ZB1QajmsL3H9AAxPoZglDRQEjxDAaVQr4VXYK788pfOym\noOCCiGlv6bU8hHGC6jQUiB0jZG4l3BtgaDOIuXZgdJt20Wbk5elBcHhf7pM5GbsP3wihk6u+a9Ov\nD8arAJWiYopj9GeBsW1QBKpgBKoCtmyYBjUq77TRTnlVzxi9ssrPGa0Mdil0LyEhIWFzkSbEhISE\nhIiNpcx1HbScNla3nmhldCo5gUUttPFuiDQ0h2WV9rQAZZrOMSggRHLoKdgX01kiBA/WRfowrqhy\n3WOKTVi84f7VhcjrDH51gaKgUNsRfBJP7gGVtGYoFB90ztRrQXllz+9DxZrl1BvdR26QAItrB+Fi\nE4y4BRpSXmv8/4o9V1VZjt1qOpYfZ9uwpuOdbgM//gx5OW3kXaHSbQ/dko3iM/ePdLDj9LmGIXYK\n2ru3ZLpRHGM8LTuwFi+hjhOfF9PRzpGydgs/z+IpCMeCpmct5EjBdkcFC3VnhK0R+JCujhbwjuDY\nCmdGsaMXXTXP0Sy+o5FWiAkJCQkRF2pCNLP7zewnzexXzez9Zvb18fN9M3unmf1W/H/vdtc1ISFh\n83DRKHMp6X8OIfwnMxtK+gUze6ekr5H0rhDCm83sjZLeKOkNz1VQXVU6GTUhRu2hq6bksIR2EN5W\ngrLm0al5PAAdgaWOqSp3SRMhrrqcgQ4zDyqoTI0wumIKBZW7GifcCpR1CqfjNp2uQcfbUHsZ1czR\n4uWMdkGZGHkVPy7ucqqZwTEZEW9qwWqbof1Ho2O/Z9tp7RwKO9wa6ILKzi43dbRjb+eyj5SdcNge\ng0q3+wjLg6rMDM95UfAZwcH8Caf7KwWfacefw5bgxE2NYTgyj0ce7NiClbvYAseGItJ87vesYw4U\niuLuIRigh/DPAsEAE+SraWH7IsP6pprSKwLU+8D7pd5q6rtEnp15z59Vf+7Hkw62N6InQNg8I/PF\nWiGGEJ4IIfyneDyS9GuS7pX0GkkPx9MelvTa21PDhISETcZFWyGewsxeJulzJL1X0pUQwhPxqycl\nXTnnmgclPSidDTtKSEhIuBlcyAnRzLYk/QtJfyWEcMzcDSGEYGZr7VshhIckPSRJe1vbYXuvocoz\nOA8fOZPTfhupLVFOuWgoTH7sdOgE0iTtM8k2QMdookS8bwBNnHSgjjJz6lPueF1CVCQhBerA/lnC\nKljCSXcJR25afxctp369MWOiYS1fxXtXVINxdI9pZ4cFGTsDtD6PoRqTVV6XHlR95pX36TzS8HoX\n9A79M6X5E/lqxnDAJsWtEO/MrYx2x/v56MXIQRNv1TpErDHFcpFKtQSVv6d2KtuCwswEfUTquRDr\nHikzHKqLLVinmWcHTvQl0o3O8ZTKY/cQ6Oz61gfj2s8gbmVksLxXiB+vsKVBwd2TbrSOJ8r8Ow8z\na6mZDH8whPAv48dPmdk98ft7JD19u+qXkJCwubhQE6I1S8Hvk/RrIYR/gK9+RNID8fgBSe/4RNct\nISFh83HRKPMXSPpqSb9iZr8YP/smSW+W9DYze72kD0t63fMVFEJQtWiW9uPMqVkB+jiDAOYMck3V\nQSNvVMM62ytAx0hZ5k5fW4hDrWDNLOGAXUKgtkYcbmsC61+kJ3lAvhZQ4LGHyUqt9e+0rnxvYIm0\nqm3IX2VDSJ5FGa0zDuWwINvIy8gQ910i70gvc/q4io2+HuURqDT6sRtzg5z0QFOx77Al5LeBxX1/\nQZFdOEajJQGW20kbcdC0vi+bMrv7EJzls7oGaj6CLBZi2UuMpwJrDQrN0pv5rs5ucx8w2jOCWgHp\nRhFvXKLsAKt8fgn9D08Ayn8F9FE3Uubqqo+91ha2LLC9UCB+fDdy5UKbx5kv1IQYQniPdG4vv/oT\nWZeEhIRPPlwoypyQkJBwO3GhVogfT2SVW1SpKjxuO/UJUKymFe241XCYSx13+g2t9V1VIG1l69jp\n4HIL8augHiXijTNY/3IwueypkSRp+nLnxh0aWQ3lQQE7BKdjI/MCL3W9HXm1/h140mvOZ46O/pF/\nHy6BguM65jeph25lzZAGM4eMlXWQawZ+z5128wf8olW1vc+fOb52eszUmy3E757Unq+mPiffR34I\n520oTF/Km3stW3TQR2WQvjRscdvFT5mP/f6DDtwZYI5lrpeV94Qh1nyB7ZUh4u7paVFgO2A0XC8L\nh65VG1svGSW7YjFUQ1e9PkC5xvZKNl2vDL8JSCvEhISEhIg0ISYkJCREbCxlVp5JO41lsgXn3T7i\nkPsFdKDxatjvNE6tFegd04pO4IE8oFMtnJ47oB60ZhdIs1kjJnUB2afWoHEo7/2m08Tsnu3T4xJ8\naKfy8nI42I5yp+8nCz8ejuCwfAVxwLEdO1CsDkxxiXhbvkXn20hmRAVumE77sNZOQfdoUS2iM3xx\nxlne67o39v5vIUzYYDVdHrgj/WIXccVIJ5sjKHsLsmRlpISULcsxPqZI5tWC03c9gpUXTu3z3voE\nTJQCO44O9sN6/c+Q44zOBPRRFzwI6E2xNXD9kw6s1eWB0/oyju8c6VvbA+/cEnHX8/ma9iT5r4SE\nhITNRZoQExISEiI2ljLbslL+WEMPtguf96/dD5oEZ9c2rK8hUp826NX08YPT4/09lxM7S/y8jPaQ\nVkaoVNeQ1Qbda+eIiR419Cy8xGlyhny6O1BpLh53U/C873RsHw7Tc+TULZDf2fZhuVxZ0WHxXEIK\nygbr44RPDBJaATR5st7bmOrhGe41j2rXFLdmDu3issfmCjHDJeKUA+rLfNEBztD9IyiPD8G9I2rE\nSRutzIjZPhi7zFkr82c+hMzbAhJlJXJ3t+Dg36+a+tZHTmOZH5yx7EyIVmD7glZ+DHMdB79/EXws\nkuWeRIv31tC3TnqIu348g7TZfA0/DpvHmdMKMSEhISEiTYgJCQkJERtLmetWptm9DVXoPeV8p3sE\ny+4OnFQRe7vy3V6WVB12nEwgs7TvVK6z7RRsCskre8atvP2+0z2mFxYtupHCZKBGvQVof+lU5rDv\nx1sDr0sXOY+32lDBvg+0Djmq89hFlJYqMTpmyEWcGykgYmNxgWXe/i76NocKN8tc9fUY8lMDDM8S\n6uIdKkMzZhl920F/UUZruuPl9EA3s+g8XR56f1ag5gFbEMNzfjajQx8XrQyJxWAtN1hry+i8XoFS\nd2HxFZKMlbDaTwfwZlg6xd6G1fz4Sd9KWSCWOZ9gG2CvuW+FcbBEve9qe2BAia2eVVx/YdS72wyk\nFWJCQkJCxMauEGV2usu8vOKbytWJrwC2S/+ca8E8+sLNaQ3owOgBp7ASfmvPBi+7hqBorw+hV+TX\nKJHUpJrhDRwXGi2Ev5Woy27m9c5oPGmt9/FbwG8xgxEiX8IIEXOQLA59g39xxVe8LfN7LtC2M10E\nwwPXDnOkXs3PcV47DRlEiOQzCHProez2gqtsX920oNpCVJ/iKx1D3pWS4ZLx9gVWvC36ZPYQugfB\nXwrHZn3EiMLYksMPcwFaEMpV6lMYhhAil6MvOhDzpXpOG1F0NBLlly/5+cgvM4OIrJ5p/jvGLHB5\ny/0XA/pqAV/OHorYNKQVYkJCQkJEmhATEhISIjaXMoegOgrEziHWusxBQ6/Bz+qy06qVCkobKiXV\nPpRHzpFsvDZ3LkH1HEN4Hzewp4UfL8DfFnEDuwVfvr3dXa83XmMtOKidLKG2g3r1pl5OB6lKGUZW\njptzWtvwWYPIaKiRuwOUbTT3PtxHTCHTY7IufAV34FvZWTR1CQitLLZRHu5pSGtagCZWO37M8DaD\nuG7GdoBKWryWwrIl1IvaLR8fS7YBf5xANaYDIeKVX6kkdbmtEDumBaqdg7LTJ5RbIO0RaPoA/q5A\ngFMi1Z5ml71ex9G5chspXg+h0nRs2D5ButW83dDqKkt+iAkJCQkbizQhJiQkJERsLGWuzanCsoSV\nrfJjZm5uI2dIHpniGUst/MAW4GMtCMR2QHEK5hSBBXmUr1dBUQ85OKLoad4CvYV/YA3Lcg5LaAuP\nkxSX6UGXY6eBx6WbKHvRotgGBdzOYJ6+6sT3yW0/HtZQlcF9MqgDzWFlD9htqD/kIXCLnVh3UL0W\n/DcN3TbZBR2FL2EAlWYopBCiOYZXAK2lhzFPSwBl3kcqzwVHw5QN9bK30BfBkI9n4NstdX2jhXoG\nxZpy7m3u8ecJn0TtOk0e4+MBhxZoupjHZscvOM0BBIWhCiKzfbStxTZ8MPo4Ls4Zy3cw0goxISEh\nIeJCTYhm1jWznzOzXzKz95vZt8TPX25m7zWzD5jZD5lZ+/nKSkhISLhVXDTKPJf0h0IIJzFh/XvM\n7N9I+gZJ3x5CeKuZfY+k10v67pstdEYP3NxpVa/vNGB2xmLWnLOcQlUECjPMV0GtTgqaMg1lCRWa\nHGlAuxCLHR47bapXp1C+BCyNKUlXlnRJGsCCfK2FtKqgVTvHTnO24Pi7slz3O25xNOR8mcEBeufp\n9SGNTE+6QB1P0P/M37G3w568kX7t7LtlPUfZz85Hp8ddyONUEGtFFlZluZ9DGjjb4+kxdA9PdDpz\nqpkfeRsqpOo0KMVwLLQPvf2zwfoQtxDHIvOstCewpuP5Fyh9fuTtPxigbwuvC1Pf1gOMIygV7Vex\nT72ZEnL0MA1rO3gbRi9pxkh1cKHWUx8XXKgWhQYrW38r/guS/pCkt8fPH5b02ttQvYSEhA3HhZoQ\nJcnM8pik/mlJ75T025IOQwirZcmjku4959oHzewRM3tksVysOyUhISHhXFw0yqwQQiXps81sV9IP\nS3rFLVz7kKSHJGmvPwy9UUMzMjRzjrwXR1D9zPFumEcrcg/xm104QFM9ZXFOrpECccoGFRZSDzoB\n1z2vYx3jVg0WvwViY3ndCazmA8Q+985519GKWs2prNLcf4G2tc4xInI7gDR5lkGUFdsHGRRudhDl\nzJjcQdySqNBXBSzonZmXtztwJ+kKWwZq+/FTc1d7eXHhgr6dsT+XgJw58xhjTHI7gtm8gzyw7Sec\nss7hPM4tjnzg7VhMwUkRy96K6VlzPDfmfzmBk/QuVJpI67sVYrPR/3xGXBpsz5gzp/k/w9gqRz6e\nGdfNOPl27KXzAhTuZFy4FeIKIYRDST8p6fMl7ZrZ6uncJ+mx21axhISEjcWFmhDN7HJcGcrMepL+\nsKRfUzMxfkU87QFJ77g9NUxISNhkXDTKfI+kh80sVzNZvy2E8KNm9quS3mpmb5L0Pknf9/xF2amj\nbht5PFpwdj5+5vD0mBbicrs5p4N8l3RYJmVZdv26OWjyNqyZXVCPkiwDjtwnIDaXo5l5KkpFOR1q\nZ+sfW4CDbxdmVkpULWZeZg3rZ3/aUKIa9yyYIwSO5hmEU405RRBXS1pvc8hIwal9gOeyYputZ5Cj\n5aVuNV2iXtsTp50zOp3DMXsLTuVLWLDbcOS2Ezjjt5prKzzbDM7yFP/tdN07YfFBH0Pah2kbTt1b\nhV97AuvvyqmbDu3lHlwC4MR9tOdtG5TeL11sRxiDxrHUWeKZt7pMp9tc2wFNbsMBvAa979IJO3pf\nZPXmxTJfqAkxhPDLkj5nzecflPTKT3yNEhISPplwoShzQkJCwu3EhVohflyR6dRySpkiKly3dp36\nVCfu1NuJ3bKESvFyAbfbNgNyveztnlMZQVWZ98xpFcTnLwENyiKVms8hT4WUmDW8tHtwbl4iTtpm\nfm21BRqWg+6ifdNIGbszb8/BFuJakT7zjOwTqFROR3bED+9NfZg9egnSVa4opRAVyZcvgqUWStsF\ntjrG2Go4LN2CO4bXQAsK51PQd3/iZ8vMtht6ODl2ySvKmXWRa2SO7Y3OFuW3vM3Z0964g9zH1mDL\nqXQ4burb2fFazWH57iI17gBW9hyydEu4l9Wt9VZfKm+Pwc9XR/3dvtYhQ+D5Eg7d2erZWrIyJyQk\nJGws0oSYkJCQELGxlNmqoDzGBy8ouQWHWVrJuhADm1xt6A7jbp+CuvQ2kjwNe06BiCXSZpaw/hZz\nqFcjxnYGGrzKYWWgfZMW5LwQs9wCfcqXcN6FhzGpt5A4KEPc6jTKTh2Nnd7VoNftjscVU1rMmFhp\nAEssrl3UTh/vC6CYOJyWTR/t7HmA8S6UnJ+s3Bn6aXPdrgBnZIZDT2CVvgKiXHa8nws4lWftpq8D\nqGGL3QaZtxrPjdbvyQQK7KDSXYyRAh4C1XZzLzo4X5pjfOL5k+sHbBlkUP0OFbYvoCQeoAzfg2Xf\nIsVfYmxRaXz5jG8fBNLxKO0WwuZZmdMKMSEhISEiTYgJCQkJERtLmctcOtxulvRHC6eBbVBC0of+\njlvahtGiO4K6MHuqnDo1uTb1mNkZKODe0v9o9SEXBSnHckmLn3OVSUw+1UI2qZOO05M+rKZF18vr\nMKfwM04rqx3cH9bfDJbzvNO0n2rch7DajjNQ+rafc0aYEgyqXq4PhKaqNa2rL790tyTpUnfby5j4\nVsPV3zg4Pb6C53Z1AjVoWL+Xc+bIRn9d9vJJVeuoMN1Gzm2D0vkcx4s2kmwNKPrl9epAOq0Ny3EJ\n5fM8OlUXyPlcoRNbGGdQn9Nh7W0uhuhPOKPbHLHHoO8LBA+szmjBobuqzwlgf9S3LBbReTxU9fpz\n72CkFWJCQkJCRJoQExISEiI2ljIrM5WR/vVqpxIdxOFud6B2jNzN1bC5roUYYJBn2ZHTsS6u24Yj\n76K73nk7zKik7OecZIxlbup10HJ6vw2d7hq5nWeghhVpEuuLmF3GWw92nT4u5829qOL9opZTraKA\n9ZPSXrByC/dZHnrdw4ucGo8KP+dSyz/f3m7qstt3azazJt3/zJXT4yeuPuPXFV7Hx2rfvuhgaB/n\n3l93w0ILJTbN4pZAOEasN5z151D97rfdajxo+/1zbEHYrvfXpISXwQTJwlbXIU7aEJs8O/Jtj+Ul\nqos7GO/eRf7vgOc8LRATDYm0y3VT9yUV5YEC7Dm/4hJq5UoXLk+O2QkJCQkbizQhJiQkJERsLGXO\na2ln2nCiCR1sYeVcMsaYitmRbnTHUJeGo3V9yWnSLvIaIfRUHcgUM0cxvYc7TyN+GjHJx+0Y44rY\n2C5y546RwImpEpg7d1x72SfZesvh8nGXrsojDW71QZMRv0sJqeMu4rdbvu0wRU7hDFTOkNN6e8vV\nrl+64zR4ldhLeD6GGNy7rtx9ejyaIqEyZNF6Ha/7JWxyzEZ+PuPHM1h/x9F5vUXrOL4XaCXHQoY6\nnlFlowM8vJ1z3ahqTXm2EvHb9SXkfIacXJbT4wCydLh/jp92WUDVHfU6isEAVFcvsaXTR6z/eORO\n2tvLpm/zkChzQkJCwsZiY1eIkk6NGQWMAAGKLEsYO06gApLFjfdiiTcgUjkyPWSAX1kbRg3mSKGq\nSoGVlg1h+qCYThRjrRAiR1WZ3ra/uYP5yjLAwNE99Dq2K9yHOUv6CAeMy5sSqw/6Ro4hiGKG1TR8\n8pYQi+0c+/mDEVbanXM28OMzOpwiXAw5Xz7ylGeNyOG3J3ePU7/txomtMepyghUQ6svRf7nXGA0C\n8p+UWM2N7/eyS6i89FAchIzUMf8jRxzlYsvrfhwNWdmBU5jssj/bSYn0pBgKPawWryKFqaZuYMtg\nqCE6SO9yKrBE8V88wwqrxd6GTxUrpBViQkJCQkSaEBMSEhIiNnYdHCQt42Z2q+M0xU6csuXw+epB\n+SSPG/vHPd+M715lKkkohiDvRHWCcLkTP38BBZXBZfezyyH6Okd+kzJusmclKDio/sHwnLC4jreh\n34dvG7YGpqCBFA5dp1vClKV9CJQGbAdUCN9qIWFM3aXcDpKGPOYc96P1R0+Pj/cajt0dODcfwEjC\nXCzP/rbT53rX27w98vtPYUhq3+v+lvTPnNIgUzbPlDaVWeV93ll4f+Z4LtkS/ok9+AoeIFUpDDLl\nPX7O/soPk2lUYKTrZfCTRZhnDqPOsPSLR5T7edzv37rshqwh/Fmr46bMKUIBt6AwxOUS3FC1WK7f\n9tgEpBViQkJCQsSFnBDNLDez95nZj8a/X25m7zWzD5jZDxkVEhISEhI+TriolPnr1eRjXnGdb5P0\n7SGEt5rZ90h6vaTvfq4CQl2rjOKZnaGHWpXbTqtmtdOQbaRiLGPOlBqUagkVmB7Sbc6gQjO5n+FV\noCZUODlyTnQmBA4+h8ed5r6XYUGegzKHhZfXQbrLvSXFWhECBuqXgfouz6jDNBbFTg9UGz6BNfpq\nJbwrSQaOudimCo63Z4o+OkaI4j3BafC1q9ckST2I2S63/NzjsVPA0cKtqZ2n/fzRjj+XDuirQTVm\nAh/KJUR/Z+OGNs6RR2YJq/0A/Z9jayJA2LfCORkUjoRwwMExNidiXYouykM4XE41JKREZYrXFtSO\nugcIlwRNpk9kK3i9VtR/iTKWyAXE9KgziM/2d5oxl+X0r90MXLgVopndJ+mPSfre+LdJ+kOS3h5P\neVjSa29P7RISEjYZF25ClPQdkr5R7pl3SdJhCGH1vnpU0r3rLjSzB83sETN7ZIFVWUJCQsLN4EJR\nZjP745KeDiH8gpm96lavDyE8JOkhSdrrboX+SUMJKqimFLA4L6Y+aVIFZBZZI8PvNEboFuib7nfK\nuoRTcwFLZBcG6iUtdKCb1Y7TjxXdJGWliOg2nHRzlD3rIjcGLaEVHMyh9lId4uI4EgzhekxrSXo/\n2YZjOt+p6M/ZHqyVoGE7T3q9Drf9/vsxwcrs8Wunn30kc2syLd6t3IdtDYqZZetDyRiuNzsTrokt\nhmg5L6Ze1yX94luguhSlBW0MrfXrC9LaACfoZaShUyi0Uo2oC6NxC2OxQJ8zB0p5Cfl94FmQP+5U\nurziz3fVpx0881Hh5xq8I/o11Hvy5jlXa30T7mxcqAlR0hdI+hNm9kclddXsIX6npF0zK+Iq8T5J\njz1HGQkJCQkvCBeKMocQ/noI4b4Qwssk/UlJ/y6E8FWSflLSV8TTHpD0jttUxYSEhA3GRVshnoc3\nSHqrmb1J0vskfd+tXNw+dErShvVvvu3WvWsQLl2peJyAgg7hmcrdyf6TbvHcRW8GpJAcwbKaIQdG\nC5bozhgx0Z0bxUAXcO61a07ZlvL75/e5iCfziLRrxPXikVddp7WzqE7TQi6Q0PHvl7ByzkeukjOu\nvG17Q+RDGcHD+By8dOJxu1vRov448rgMIHg6btEZfb1jcDZmnLg/rzme2HgMcV84OOeRH+fHfv+T\ny/6stmrEpjPwfEyHaeTLgQW/QqrW5QJbLytvhkOo8cABukL7mfekRoyxQMfb6PIFVHMq5Aayx1xE\nd5WPp4bIbUvPbzluxZj9bPMY88WdEEMI75b07nj8QUmvvJ31SUhI2HxcKMqckJCQcDtxYVeIHyuy\nPNdgr6FwU+QumSAfydaBr/lNtC42NGiJ+GLmy6AQZ9UGHcH9DSbKAURXK1vPM5YT5zsrJ+Eh4kqL\nfchPDZkTFY60zyA9JSTHurSEwvpYIJ2oIpVjas6caTBBmXcLaIGBmk8gxJp1/P7tgdf9nt3LXq8J\nrj1uZL/uHril9ACm1fnYaXq95WX3kKpVXbTnKZcRyzt+zh4sy4snvL535Q19zy/ddfpZOYeGGVBC\nwq2NWHbGW1M6bXkV4qpd77ssOsGzbw0ybzN4JCxBZa3r51PkiylpBWq+QF3Crl8RljeORdabMdjd\ngW+HnObR2Tx92LRCTEhISFghTYgJCQkJERtLmatcGsVwzskIjtQTyDX1nb7koCQhWgtzOC6TmU36\nkMIC1T1jCcR9ukjhWIBKHkPtmDR8sKLKKK89Qi4QyG+dCHHFu07NESasJayvbD/v2YsaVEtISJ1k\nbkEdwjF3CgpqGEJ7OMegvDzF9sG1zK281vdzZtExvn/oVtARU3/2QDXZHj4XWnavrFeMphV1CO+D\n0y0GOHfvDDwe+HABaTfkQKlRgRoeDC1vpnb6vg3A7YNlpKeUOZuMQdPPWa7kcO62ArHJ2NZZwAGd\nKXYxLKSo2F7guu6ZLSBsaUBJfBXvXifKnJCQkLC5SBNiQkJCQsTGUmaVtcpnG+veAPHLY/DQKRIO\n9XNIMEVWc3gPLLXMIASJqg4syAaLLylz/pTzp8Elp8/5jjsmV4WXM41UtXsNtAdxpbRgt8ZOmc+6\nLjOxE+KTkVpzCQJVjJpyFjBUtnK/7smDZ06PGVfc3kLaUtCtORSrW3jvMskX6WkWafg48y0I+JMr\nQJmbatyTCSg4niGl23oI2TbEZy/7Xs4splAdwgrdRsz0sOfP6ukBnifa1oa/dLuHcYGkXGO7sV6j\nGvwaMnOLZ9w63e34lkGrxXaCvs/8eIqtlBzJt3KmM41dWiOZ2Rz35HObd73fRlE2rQ6b55mdVogJ\nCQkJEWlCTEhISIjYXMost3QyX/IS6sQlPEspddVfmZSXTi8qxObu3uW0t4J69bx2Ori34+f0UI4g\nLzWAQ+4CUmDzSJlnUKAuyIfhXG2ImZ4jfrUFE2CFPNKMsV3CCXgeJZ1qqGEXXad9O3vumDtG/l9i\nnMFqu/D7DBArG3BPUrW81dA6Q8w4Byeto0xmtZX581yOQBmxHTKDg30PtJYW9XZU2C7hWbCASngJ\n5/6doVuf51MfF71yvfdBCbqfwzF61Sg+q0MkfOpc9j7PRSs/4uHnWNO0ne7vHPvnTx+TBntf9OPz\n5ZgY597mXeTz7iK+/mSl5J0oc0JCQsLmIk2ICQkJCREbS5mtDmqfNEv7eQdxpZBXakPSqZ7AwTla\n8Tozxjr7hcuj9ZSR+YfrfXfGrWo4fYM+BSTuobxVN1o6K9BoC6BjUImmxXUBR+9WfU68M+jzGRGt\nGLKjmvsAACAASURBVAfMOOHWHOrK4KxDWDzHc6d4PVjK2wWSZk2cso2XTiXvytxy248yWgHx1Yz7\nbh942wx9eDKASjicwTPknL4W/J4D3LMLirmynC9gHcduyJm82Eeg0gP08xkvAyZ4BkrE0neKZpy1\nJv4820f+/XgHbUNcc97xNlAZveNdpDEUvlEttdZUq515G3a2fDugjWiEM+NpsbnyX2mFmJCQkBCR\nJsSEhISEiI2lzCE3zXdi4qCx84StGSS9HvUY5/weqE1HLjAEBazgjK1z6FAG6jFD7GcNC10PVr4K\n9L2AdVXRElsgOVSFV1eF2NQRY1aPkDQLFu8uLs6hwpyD1uaRbnYQ11ojZpn5mjPE0m7BkTlAPdoG\nqDCl0HA+8wuHSOVJO6ndvNyiY7wftkCxT2DltmPv/3uWfk4X0lnlmsyM0wLtR8y4kCN59wTbF3hs\nrLBBFq1ksi70+XTWUPmTpW/BnHSwkQGZsZ2tvdPj+RKm8p5XYNb2uvcWcCpf+vgrsJVQRCftGn2e\n4Z7Vtj/z8eLGbaINZMxphZiQkJCwwsauEGu5YCcb2X8Wb8Ah/ONwTieGrIXz1EZaeMtjJbbAm7tT\n+1t5NvdN/QorwQD/yI7527iKIW3zGa4b+7Kofa+vFjpY/dkSK8EOrUcI+4MPY69zoyKMQeR0PPNV\nVg6fvSVylAxxnwq77AVCBPtyI8B4AXUghJRVcRVV1+vXHctD9AVWrgwv67R89VkgX44O/NqA1X2g\niGtcUVZY2ZZY8rWwEuOqjKFwGtMwhzHS8Xt2kA+liqv7Bf0ka++rPQjxtsByGH5pMJLx+AAhkIM2\ncvcwp86oGVMV/BrzPvx0YUjqIIxxtcrNNlAhNq0QExISEiIu3ArRzD4kaaRGq6AMIfxeM9uX9EOS\nXibpQ5JeF0K4dl4ZCQkJCS8EF25CjPjiEMKz+PuNkt4VQnizmb0x/v2G5yrAzNSKfmaDHeS9uNsX\nxU9VblR5cQs5SyKBPkOveqTJTl8PMz9eVE5TXiz45KGbFyDnvW33VcyYm+SgqdcMG/xCKsniw55f\nRFvr/RM7oOY1Pp/C2FMwdC76OTKnSoH0mVOkGy1R1xlUVYYFKKvX8IxBhj5vJQxCq1weVUCfg5HV\nUIGhkYz+jvycmyD1Jaeec5CiEvdaRoHYGbYMctD3gL5i+JvR7rKNdKPwG+yX8MksfLuhF/1Tt6Be\ncwyfSSozdZh35QCpVFs+znLkzimG3uYeRWzpKxlpPYcZtz1azLkCJZ2w8/ypSu9U3CmU+TWSHo7H\nD0t67W2sS0JCwobiIk6IQdJPmNkvmNmD8bMrIYQn4vGTkq6su9DMHjSzR8zsERo4EhISEm4GF5Ey\nf2EI4TEzu1vSO83s1/llCCGYrc/lGUJ4SNJDkrQ7GIYs+tSNC6ceAXSnA3WSGXJ5rCy3Kz9GSYIr\nm+agoD1QYw2cpmZQYcmXCM0C9QqIDasQjtbqNxSzNISIjb3JoysIBYQvXRehVktYpc8AxtLDClbk\n6J+2V8Ov0MBZERanqb9sujWtshSrhZV5tt4aOWM+lDoK1EKxhxZR5mipEV5Xw7Jbwlo6xrZGr/Ct\niRF8/kal09PuoHleMNRrL4f47THGEMI8D/sIi0R9y6nff/isP6N8hz6MMY8N6HBvyvwn2AJA+0tQ\n8/nUy+7TKn4I1SJu2eBeIeaAGdc4F36oAWpLW/Wa0NVq8zwRL9wKMYTwWPz/aUk/LOmVkp4ys3sk\nKf7/9O2rYUJCwqbiQk2IZjYws+HqWNKXSvrPkn5E0gPxtAckveP21DAhIWGTcdEo8xVJP2wNVSsk\nvSWE8ONm9vOS3mZmr5f0YUmve76C6kyaRMazPHZrcncBztgFJUNqS4vCnH04Hc9BX3oIRTvYd9qw\nXyLU79gp0xw5XeYtmPQCaBW47CI6Pj8bPPXlQLAajryMCZqzRFiaEF43qOBUm4OS9hhrFp3BkXp0\nDjpOtZs2Ul8uIJxLWruFPYYlRWyhFDND/pYQ69sjBZ+up/3FkB4BDoYCdo+8nMnI+/GMACsclk/i\ns7h7DqttD1sqoOlP9r28PWyZUCB3B07irGUXeWqq2C90um6h/UXXy5iVoLU9/7zCuJwhXJHIW0wb\ni/6NDvt58OczLaleBCduhPz1iybMNXt086zNF2pCDCF8UNJnrfn8QNKrP/E1SkhI+GTChaLMCQkJ\nCbcTF2qF+DuFQc/pZg5at4RjsO5xS+Q8OgnDfqyCAq2w7HUP4QDMHB2gQZMW6A5iQosTL+dg7iKq\nT+cNbfldxaXTzyrEu2ag1wbB1TPpJBFj3UYeE6YKZSzvym5/AgXRgJwZfXOaNmmBqO4jxSVo9Qy5\nY9Tzcqi20xl5v7QjDSwQGzyhkzQcsCtY+ceV0+oaSi09OJi3kGvFI4WlEqx2K2/aEWZo/whlw2H5\nrqW3eY4C7859DHV2nI7P4STN6PEqUvwpYt0z/CS7GZzrd+HBgL4oe17fE+Z38UNN4U2whPP2fHpG\nIripEyz15cTbX0HMeLbb/DLq9c4edzTSCjEhISEhIk2ICQkJCREbS5mzWtqKxsAS1LCcOQ0Iu7Di\njWF9jIKudBKelqAX25Cfmq+Pn+3AslzBebga3mjlk6TsyMvZjbJkTJO5NcZ1FcRfcffR4fHpcXuA\nOGTExOYHTs0DBVovN7SuBQHXDBZR5gHtY9j04DD+xNLvr12nj3tI/SrkANEOrPLxf4T6Kse5C6Y+\n3YFsGCzhTOfJtLKLobdpN/OcITnOz8c30j/GWp+Ja8Y5O3wCkPzi9kUH1FdHzmXn82Zc2MLH3u4M\ncd8Ynxkkx0rEGFeszb735+hZf87TFqzfM1iOV72OelPAt4vtixm8D7orqn2OVNudjLRCTEhISIhI\nE2JCQkJCxOZSZtmpla6aUOiBNBlW1oHTuizKMi1g5Zx0cS6o3M4U1mekIT2GSvLRnp8zgbM1c3Bc\nBsVtx5wpS6o00yTKBCtwjO5DTmxJqzTiUwtQyaPctw+68V4lKFgfqSeXkMqqsU0QQIdfNPLja7Bg\nXqucSu8Ntk+P59i+WEYn7ClypBjdruFcnE+87C3InJV0gK7QuXCwn6Ab9w7gIRDHCLcRauwT0FLL\nnDZd+CJUC2yrgI4zJruASvqq9b2BW6EDtgOYO6a45mN4irSuJ/fA+oyAgQzbEZr4mMv3/V4da/ro\n5PGD089msOALcm7c6jgsotJ2sjInJCQkbC7ShJiQkJAQsbGU2WSnTtgVYpYDZLGsjxhfOPKW0frW\nKv37FsooYbUt4bB7JiUlsANJp0HpJ43AiZaZ378bqWq3cAo6gpwVX2MlYqPbsGZmkBYT6HiNdnZR\nZB3TbO6eQwGXfSh9D2DNRF9sv9jpcGfktM4mXsfFwulbC47pZVTkPrnPG7fX8y2AgtZpHNfLs9HM\nK8x3Qb1pIYaT+gTbCuPoHL1feZ8zUVR/29s2GnvbjnHMMbSVezlLbA1QbbwXrfhLOD3P4cTf3naL\neAXPgiX2Wsprbn3v9/259HLIuEFyrELfrVLrtrdAr+E0MW1DWqzNGOymn8N6Vbc7GmmFmJCQkBCR\nJsSEhISEiI2lzHVhmu41zZsgJncLRl7BKnwydyvicNBQtZPM6UsLFteWgT6AVggW0hwfByToqZHa\nIINV9KDltGo/Uv3WkdOhNiSfalhci0tQMh4jQRHkonqQd4J/tbqgvlV05K1hHRficfOR12Uyczk1\nWvCvbnkdtyA9PRuCemIbYpE7DSwifbQjxA9DgVxITiUk/6J0VsXTR5Bz84/Vg4V4uoB6dozPLrFN\nsNVCDDxkudo7/jk9Dtj/E2xxZF0kf0Le48lqXCCfcoAz+KxyOk4YtleGBZKZYYz2JkhahmCARUCf\nx77o0fMCPvTtMb0zvF6D+HHGwbQhSCvEhISEhIg0ISYkJCREbC5lDrWmi2hFg/zT0WXIKyGp7mDp\ntGGVfKg3Bh2taf1zmjJf+Oc1nJFzOAMfL6F83WG+3BzHjv2yOWcEyp7DGTpHLOsUcl2ke204TNNa\n3G5DxgsWYttvzi/hmAvDsuZ3e3kF6KM+gvbPvbxnoDY9RLzvnM7WSKx1FB2fOxPQO+S8rhjkjNsv\nB97+7QW2LGZIlgQf7Rksui2oim/FpGBh6I0uJ1D3Rr1bSCC2QN/SKp0fYGsEse+UnBvsNNph8znM\n/dheYSx1D7JpjMEOzKON5zKHevfuCHHdGHPj+ICZ/3vKfNIou4NYe201YzvbPL/stEJMSEhIWGFj\nV4ihrFU/26zMsqeQkvIl7k8m5Mngm7s/bN7cCwrI4s06myCMi4oxCIvrIfXoNjbnszMCrY792lcd\n7ZhONMyxWmqtX60IQqBcfbVFw4+Xc7Dwjfphy/38QlzdFUjLMb6PRiIIrtLFcX9X64AFt46XXmgL\n4qYG49DKV/Qq2lBMPeSv1/M1dF3QqACBWrZZN4qfStKs7ashCNLIps2zDh2sEbb9mQyoXoRwycHA\nFWJDD76qs0O/J4xKwxqKONEntoYxJkP+mbzv91xgsPRg6yjARCr+mmFUWsAgEuB7248rWoYrdgU/\nTK6KL8EYtvrdZJvniJhWiAkJCQkRF25CNLNdM3u7mf26mf2amX2+me2b2TvN7Lfi/3u3u54JCQmb\nh4tImb9T0o+HEL7CzNqS+pK+SdK7QghvNrM3SnqjpDc8VyFmpiz67tl9ULJ5BnR3m45ruDbmTGmB\nmh3Uk3Wnnob5SdJdFUPkoDwCI8gZRVdQxjEMEu1IW1i/iWDUQYpPuPWpA1q7ot2StDjxNm9jo3wq\n/3xw0tQxQ86X7tTrbYjTKlFXhhxmBkUabDHsIARvMYdvZ8fP2T1p7mtbvr0wH3jZx48/e3rc2/Py\nBIPApHTqWcJJrsSWQStbH9J3Tc3z7eABlRCfpeBvByFyFbZJuMGSXXYqbTBUHPRgeIvFX77b3+/0\nMRwj/LH1S8+cHodPv9vvCRWmBfYADmo35PURAjrA6C3iM2oZQgED+9O3mq4izHSQcRBvFi7UCtHM\ndiT9QUnfJ0khhEUI4VDSayQ9HE97WNJrb08NExISNhkXakKU9HJJz0j6J2b2PjP7XjMbSLoSQngi\nnvOkmoT2N8DMHjSzR8zskUW5flM9ISEh4TxcNMpcSPpcSX8phPBeM/tONfT4FCGEYLZemTKE8JCk\nhyRpdzAMK2uogSZWA1AmKI/QYlZEk1517PRusQv/MYRIdRH2NEa8Xo8hgghB68IqXODYYH0d1c0X\nI9CXGQhZAUslrb81rX5T+LCR4tES+hGnodl2Q1UzhAjmoFKMfytglRwtvBEBlK2Yw5q65e0/qZAq\nk35u0aI778PyDCWb7ctOKysq+QDz2j+fLiF+2wZ9NVB8+A3utqJnAVJvLveRXwQ/lRIv29kzbgkv\n9hDSh3oFUG+D6G8n5iyp0Z6i6/ekX+Pyc5wm5wuMZ6N/qt/zrsLb3J2jNhT3jXljub1RwzsgtPH8\nUcdsFa6Y/BB/x/GopEdDCO+Nf79dzQT5lJndI0nx/6dvU/0SEhI2GBdqQgwhPCnpo2b2GfGjV0v6\nVUk/IumB+NkDkt5xG6qXkJCw4bholFmS/pKkH4wW5g9K+rNqJu63mdnrJX1Y0uuet5QqKMSUj9We\nU8bpFtJ5IvZovwPLZbWiEo4BlD+GsDhS8WOMP7IuwrVwXDLXRgknZVDmWaepl0HVZackrYE6CurY\nxvuthMJJhhBFg/JKfq87qS/Hy1gG2oPUn8zL0oGldkbndoSg7XacsmlCC/p6Z96VUowdI5Xr/Tt+\nHfLfLK75feawfpIytlHHIkffocfoyN6LSkVdmIoLqBotK9BeCs7yWVxzq302dEtst4vUonM+seaY\nHgRd7AacBNB30PS7kJemxDYFgwS2mPoUYZT2hFuui25zzgyKTZNz4vEsw9ppA1VuVrhwE2II4Rcl\n/d41X736E12XhISETy5cKMqckJCQcDtx4VaIHy/UmdPjLmhFi1ZUUAxaJVfCKi2kdbxnuH96vOzC\neXbitHKAVKElnIpJved9fM4AVdA3i/e1c6jJecKcOazMweiADS4JtRchT8yKbpVjOA4X/v0UDtAZ\n+q3TgsMyWrqcQu0F8baMg2Y+lFMrOjprm/G4sHLmoMAZ0o1az4+rGmlQD676/bEG6LbcYTnE2PO8\n63WdwaG6hHVeiHGnBwMdubtQyqmxTbFsIya8ao4Dnsl06lb4HPlfxl3v8w9M19sUP6W4dHq8QDmC\ntbp9yel7HbdkSsRvzxF3zvjxFijzSRQLrsPmcee0QkxISEiISBNiQkJCQsTGUmZlphCdsIvaaVC5\nJPVzTDtOScZRWPYu2FypiVqd45GaM+9JDw7Gx6BBYK8jOHIXjHeOl9Ky2EXujKKF9sCYyNwtixa2\nAygQizQdOSzbu1lTftnxc8cQv6Vt+Krc63x/6rSzB4fxGay8+QGoZwEB1suQxY0CvHS6rrDVkYOd\ndXKkW0Xs9QTO67SK5i0/ZzTzLQ5S5hWttHOs4IdjzyOzg063q7AsX0HMNq7t42fG59yaNo2q8RyK\nDPQajt7VkcuJbY/wsz3xvp3fD7HaoY/FnkFGrLrRqX0CAdv8BG3DOS14Vkxjvh5uy2wK0goxISEh\nISJNiAkJCQkRm0uZgWuggQWMrPMc9AUOszsxr0fVp0M1uC4MeCeQnMoQP1wHWFZhZR0sKbWEmODc\nSVYVrY6Urcrw7poHWKThdC1YQrsoO0Oq0BpWTlqILZZPatQB1ZxWXsb+3M9pg5pSYbzlzFSCCrQq\n78dsBIfk2C1jyrDhuoxK1lfdI2AyRtA4cqT0kfemwnZIuwOafAC5sK2mX+ZwTO4ce3s68EausNUx\nuMudxxn7W3GLBaH3rQBH7lFDt7MdWH6xTbCgNX/HHd2ztrehBU/yOZznwzm/7AxpU58Zrq71z/KS\nStuUKvM+78f7Z4kyJyQkJGwu0oSYkJCQELGxlNnMTtWR+8jymOMdYDOnTAbrc73b0K2AmNUKVIqf\nD5Aesh7AQggLbYBT82AMiyMUo9s4XsYqtmC1vTpzK2f2lPPR4n5P8tRDwqPpoZ9PDHvbaz9fMUKw\nYY2Ct4EDhQm5dIJYYiRFWmK7IYOT8hhJntpwsC76DQ1rIdZ7wtSv4KDIxyVdZT/z/Q6VbDi9d6HI\nXW3Bwb7T1KvN9LHbcACHxwEdtncP4LWA9nSwlXBtm6lPsWURreyHR0enn510vLwXHbnVuoW2hfF6\n+bMWLPHz0vulj62RDLbjduy7xWUk6sIzbI8Qp46UsfnKob5e721xJyOtEBMSEhIi0oSYkJCQELGx\nlDlf1Nr+aMx7i1ZOEEtsk9n1l0mSjqNTc29vePpZhTjV3kecstrLXMk5QyxxC3HNy6dcVfmI+ZVB\nOYq+091BaKjUHMmhum0k9nk5chSXTmtOFn7+DOb0AaTDZohxZX7jOiZLymBt34YD9MkcZeM1GuAx\nHTLEG4PVdWD93ZpA7blDq2hTX1rkK8T4HpTuUb6NfNV2ZX3Co+URLMhwBhckytrbTp9bcVujLsE7\nYalfYsukBcf4CfS6WmM4aQ/8+ffdp1pPdN0qfm/WbF/0Ot4GY6A6DttQRp/A4s6kXTM44+9OEVcP\nC/EEvvB5p2l/G/Js3FJivuYWHMpPoptFHRJlTkhISNhYpAkxISEhIWJjKbPyTPl2Q0WWcCqeIslR\nDUvoGHRvd9jwCgN9GhzBCo3btMdI/oQ41DEsoUxWNJo4ZRoI+aJncOqODr5dOBGXtOxO4TwLakiZ\nsS4csKcdlI143xYe/yqGF+LJZ1SyB0OnlzPQzvkQzrncAqDkFVhtf+hbA0tY+atoFe11/D4UaS5g\nfX6y49ftw2reK7yfGddtsOwWjDeH/FgRneqXkBBbqXg3dUFl+v5caiQQo2J6b+R9NEX7B/AjXw6a\ne1boqxaeoh15/cohnhVkuSrkzm5ja6JmUizk1M4wLlZO2gXUyLOrvh1UQul9toUkaFEqr9pA6ey0\nQkxISEiISBNiQkJCQsTmUuY6SFFxOAOtEWka6NEl5O6tjyJ9k1/XLkBfdyCXBCfZgIRLAfRluoAa\nN1Sou7D+dg792kUnxrj2/J4dOJfPT3wLgNbcNsrO4DD+LCyhGXIt7yz9nJNoiZxBKqyF8oqFXwcG\npiUoeAnKeJJ7P18CrZ2CbvfgMLwzbfqr/Ihb5M+8rS/74ZUlTKW0ymI0t3b9ec5H8Ao49vtniNVe\nxrj2Jcqb0aFejBNHoi5UsYTz9PHA+2W7gOMzrPjjOBa7be+HCpb/JSTkDHH3XVBc9c6xssN5PLQh\nlxao2B3viW2E4l4vLyCuuYctiKEa74vfPNP6zcCFWiGa2WeY2S/i37GZ/RUz2zezd5rZb8X/956/\ntISEhIRbw4VaIYYQfkPSZ0uSmeWSHpP0w5LeKOldIYQ3m9kb499veM7CMjvdrG5hE7zb9jftkO8D\nrLRa8Y1O3y+bQnx1x7vtKny8AowdA6xidhBrtsAKqdj1z08Qd5VbU6/ZyFdZvTPLH38znyAN513b\nvhJr4+3dR7jWFO1nqpV+FNH9/9s7mxC7zjKO/565nzN3JjPJNA3SpLZiVbLRioiiiLQIfpTqQqRF\noUihLlxUUIS6F3Tjx0KE0CpdiFqioLhwo124Cmq7KDYKtbQkta1JzHzdmfv9uDjv8P6TDDbBdObe\nt89vk3POPfec9z3vnTfn/z5f2+KzZ+JLt1XXOiLSFqnBcrSXz791mM/511x+Q7tlko0m7U5+RqNh\n1VdNVqqGhONdMZhIedbuML8tDzr5GWq51ZE4ovZ0zHX4e1U/5iXh7UhKeaqxadDLlpGJZLhpyltp\nWzIfqbdevZ+Przar0Ly6ZBjaGOZn1Z+Ij+MlTTmr7ZLkt1IPhVVJKCxZadoDSVyc3u5rkgGH5t4Z\nbBqSPajWqf4+7Fy8Ie4n9wL/dPeXgc8CT6bjTwKfO7BWBUFQLFP1hngVDwA/T9vH3P3VtP0acGyv\nL5jZI8AjAAuaHj4IguA6mMoJ0cyawP3AY1d/5u5uZnvGDLn7KeAUwJGlZSfVlRiJoaAji9BiA8Ek\nfMlSmJI3snzoy/y6pVpTFtKXGvnafblnbSwyVNq7vSEFTlZkcTxds97JN92RxKFr/Syrlg5lmTZW\nX0XxiVzZlNA5iVbsLYtP2qDabojUHUsGoGY/b1+UMLrbyQlS69LP7lyWeCf6uY2Ki4FjuFTJwJ0r\nstRktHysS8nY+XVJkCppjdZE4qsPY1eMQKsS3tZM9+3VRaaKTG7LEsRYfEZ3OnuLLJP2NjfEOCHL\nBLvZcdbXsiFpJKGQNc1MM5ZfjihVLRXbEMPfUJ7taD6PS0+us5CWEkaS4WYgtW+bG5JkeEGmin56\nMBG6t298CnjG3V9P+6+b2dsA0r97F6YNgiD4P5jWCfFBslwG+C3wUNp+CPjNvrcoCILimTrJbGYd\n4BPAV+Twd4CnzOxh4GXgC290ndF4zKXNtWuOH27lhKoN8c8aiWRm129xQUTbRpapR9YlFEwshFsi\nZZFksdbSspUSUqYudJLo1I42rvl8JH5lHQl/uzjJlsVbyXJsUUPwRHqPxCeyJ/55i830XZFgLbnG\nUKymhySksCvSdH5eraX5Pm2pR6OlRcdqlZ+rLK6HB/lZqSDbqkuyWslYVJc+dy7ne5rUUelJItj2\nSN4BJKRv1+e0LTJ1LGOIeBnUF/PyRnuoclSWXSRTj8ktu/L8Jyk0cqumz0HKh4oE3hEL+uS5LJAG\nZKv9leTzRxJS2RV3Ck+lbTXrEpKxibZ4Waxny/ogLTtoNqJSmLoJ0d27wOpVxy5RWZ2DIAjeNKZV\nMgdBEOw7U/eGeLOYw+gkK9qcSlb1S17LkrG1cG0I1ESTZUpIVf9ylqne2tsx18WaWRc1bkdy0lmt\n46LyePJKZcUdtve2uE6W8t6xWr5eQ+SOSwnPoRwfS9aWxe18Ti055I5Eag3Fyr5dV9mZ79+TfraG\ne0soDUdrN7PE6/ZzLRHSkkVHrNxbkqVHw+zqmsNVZN1wZe/n1Rbn8VpPy4lqPZTqhzEnVljXML6W\n1ELZEUvsVr52d0Oy4yzmeihNcQzvy/KBp0xFYwkF3ZafodZfaUoS2d67cqBWfV0CA8RJfH4+P2eT\nZQCJ1mS3Iu4V1nxZAthYklo44iS+ulZe+dFd4g0xCIIgERNiEARBoljJXLM5lhuVhNWymZOByDC1\nKEpCz1GSR72eyJttiSVdziJjR0plttRjVso2DrtiIV2RMphSpwOxvtaOVnJLQlDpi3w7NBZLrMjU\nnlgQe5a3d0Rj6sKALgPspPMbnv+PXOtnB2yV9K16vv/hUbaEan80n2qvJm0cSwLSjsjQlGjXRBp2\nZKmh184N2BYneUnkQ11ktWl9m02p3SK261ZbHOnTs2tcysshNUngO5KlgR15zguH8jm1y1kyD8Qq\nu9DJMeatYX4wm+l3NBEZ6yKT1yROvS3JitWzYbCc+1MTTwATKW2H1RIt10mnj3r59zlu5me4JA1b\n2cntvtCo+jbeOz5ipok3xCAIgkRMiEEQBAnzAuMRAczsAtAFLh50W/aBW4h+lsSs9PPt7n70jU+b\nHYqdEAHM7C/u/oGDbsebTfSzLN4q/ZxGQjIHQRAkYkIMgiBIlD4hnjroBuwT0c+yeKv0c+ooeg0x\nCILgRij9DTEIguC6iQkxCIIgUeyEaGafNLN/mNkLqXRpEZjZCTN72syeN7O/mdmj6XhxtavNrGZm\nz5rZ79L+nWZ2Jo3pL1PtnZnHzFbM7LSZ/d3MzprZh0scz1mgyAkx1XT+EVVtlpPAg2Z28mBbddMY\nAV9395PAh4Cvpr7t1q6+C/hD2p91HgXOyv53ge+7+zuBy8DDB9Kqm88Pgd+7+3uA91L1ucTxnHqK\nnBCBDwIvuPuL7j4AfkFV23nmcfdX3f2ZtL1J9cdzG4XVrjaz48BngMfTvgH3AKfTKTPfRwAzzMO2\nGQAAAZ1JREFUWwY+BjwB4O4Dd1+jsPGcFUqdEG8Dzsn++XSsKMzsDuBu4AzXWbt6hvgB8E1yepZV\nYM3dd1PNlDKmdwIXgJ+m5YHHU12h0sZzJih1QiweM1sEfgV8zd039DOvfKlm1p/KzO4D/u3ufz3o\ntuwDdeD9wI/d/W6q+Psr5PGsj+csUeqE+ApwQvaPp2NFYGYNqsnwZ+7+63S4pNrVHwHuN7OXqJY7\n7qFaZ1sxs92EfaWM6XngvLufSfunqSbIksZzZih1QvwzcFeySjaBB6hqO888aS3tCeCsu39PPiqm\ndrW7P+bux939Dqqx+6O7fxF4Gvh8Om2m+7iLu78GnDOzd6dD9wLPU9B4zhLFRqqY2aep1qFqwE/c\n/dsH3KSbgpl9FPgT8Bx5fe1bVOuITwG3k2pXu/t/DqSRNxEz+zjwDXe/z8zeQfXGeAR4FviSu/f/\n1/dnATN7H5XxqAm8CHyZ6mWluPGcdoqdEIMgCG6UUiVzEATBDRMTYhAEQSImxCAIgkRMiEEQBImY\nEIMgCBIxIQZBECRiQgyCIEj8F22C8iDgZxmWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4d500a4828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[4], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[43])+ \",\"+\" iceberg_probability : \"+str(pred_valid[4])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f4d5003dcc0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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F89gZGQ8DN2ULSHs2CDroMOc8zeqYDaK81xkBzBxxjFkAdFHefAKTGUyJPIAV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\nfF4GL9qk8X7uYrO1wPNcXvAw6QbuqzmU7eqhN6x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DcMcS+gkVggvCZWdNoBgF6BQUON6M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\njIMgCJaAmIyDIAiWgJiMgyAIloD/AQda2W8OGytoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4d50016048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[5], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[44])+ \",\"+\" iceberg_probability : \"+str(pred_valid[5])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f4d4ffa3588>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QU5mq6LTpKG4DZdkmGCpQnBaO2YfmFJQ1nS+PkTMMF+zJCZRDBLluAspLBC6v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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4d4fff2a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[6], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[52])+ \",\"+\" iceberg_probability : \"+str(pred_valid[6])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f4d4ff07e80>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bBjTFFAk6Bf1IoITIYSc6xP1fTFF3MFZvmcHHZI5kmcuVUxP3nnOTVSbxFLD2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\nx3HmAF+MHcdx5oD/AfV9WeIxG/M4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4d4ff5e160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[7], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[80])+ \",\"+\" iceberg_probability : \"+str(pred_valid[7])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f4d4feee668>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vVPdUFG+O3OR6G4HUPA3U0AbuRQA1Y9vLvF5JGsH+K58xErv633jo35f3etvP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\ns91yAei8Ic30EhISLhTuiJeemeVm9l4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91ZFLxcDvUbvpNLEGVXMO95GwTNs7Io102jlA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4d4ff68b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[8], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[104])+ \",\"+\" iceberg_probability : \"+str(pred_valid[8])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f4d4fe4ef28>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aTdvrNd3BNcRWRweB2Q1yxhvkoBu2YFYL7/zgBA7jUOKLXe9jHbu1QNDArPD2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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4d4fea8630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[9], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[127])+ \",\"+\" iceberg_probability : \"+str(pred_valid[9])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}