Model_03-Adam-k=10-amsgrad-0001-Copy1.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=10, mode=\"min\")\n",
    "    msave = ModelCheckpoint(filepath, save_best_only=True)\n",
    "    return [es, msave]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "===================FOLD= 0\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 20s 830ms/step - loss: 0.8361 - acc: 0.6461 - val_loss: 0.3036 - val_acc: 0.8457\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.5469 - acc: 0.7873 - val_loss: 0.3251 - val_acc: 0.8210\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.4113 - acc: 0.8514 - val_loss: 0.2509 - val_acc: 0.8765\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.4128 - acc: 0.8326 - val_loss: 0.3708 - val_acc: 0.8519\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.3512 - acc: 0.8512 - val_loss: 0.3258 - val_acc: 0.8457\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.3153 - acc: 0.8726 - val_loss: 0.2727 - val_acc: 0.8827\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.2944 - acc: 0.8837 - val_loss: 0.2654 - val_acc: 0.9198\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2678 - acc: 0.8846 - val_loss: 0.2574 - val_acc: 0.9074\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.2167 - acc: 0.9123 - val_loss: 0.2423 - val_acc: 0.9012\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.2376 - acc: 0.8975 - val_loss: 0.2563 - val_acc: 0.9198\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.2620 - acc: 0.8922 - val_loss: 0.2383 - val_acc: 0.8951\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 14s 584ms/step - loss: 0.2133 - acc: 0.9029 - val_loss: 0.2067 - val_acc: 0.9198\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.2829 - acc: 0.8824 - val_loss: 0.2695 - val_acc: 0.9012\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2041 - acc: 0.9056 - val_loss: 0.2465 - val_acc: 0.9012\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.2003 - acc: 0.9137 - val_loss: 0.2855 - val_acc: 0.9074\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.2364 - acc: 0.9075 - val_loss: 0.2255 - val_acc: 0.9136\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1741 - acc: 0.9300 - val_loss: 0.2217 - val_acc: 0.9198\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.2043 - acc: 0.9182 - val_loss: 0.2444 - val_acc: 0.9012\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1747 - acc: 0.9305 - val_loss: 0.2260 - val_acc: 0.9259\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.1739 - acc: 0.9252 - val_loss: 0.2015 - val_acc: 0.9012\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1994 - acc: 0.9225 - val_loss: 0.2076 - val_acc: 0.9136\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.2071 - acc: 0.9166 - val_loss: 0.2035 - val_acc: 0.9198\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1673 - acc: 0.9354 - val_loss: 0.2580 - val_acc: 0.9074\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1875 - acc: 0.9264 - val_loss: 0.2172 - val_acc: 0.9074\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1510 - acc: 0.9361 - val_loss: 0.2029 - val_acc: 0.9012\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1828 - acc: 0.9213 - val_loss: 0.2227 - val_acc: 0.9198\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1515 - acc: 0.9363 - val_loss: 0.2157 - val_acc: 0.9259\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1433 - acc: 0.9459 - val_loss: 0.3860 - val_acc: 0.9136\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 14s 586ms/step - loss: 0.1921 - acc: 0.9168 - val_loss: 0.1959 - val_acc: 0.9074\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1634 - acc: 0.9345 - val_loss: 0.2640 - val_acc: 0.8765\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1409 - acc: 0.9423 - val_loss: 0.2114 - val_acc: 0.9259\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1563 - acc: 0.9390 - val_loss: 0.2175 - val_acc: 0.9383\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1292 - acc: 0.9493 - val_loss: 0.2488 - val_acc: 0.9136\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1531 - acc: 0.9354 - val_loss: 0.2256 - val_acc: 0.8889\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1461 - acc: 0.9402 - val_loss: 0.2427 - val_acc: 0.8951\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1315 - acc: 0.9473 - val_loss: 0.2605 - val_acc: 0.9198\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1101 - acc: 0.9546 - val_loss: 0.2693 - val_acc: 0.9259\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1239 - acc: 0.9454 - val_loss: 0.2530 - val_acc: 0.9321\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1306 - acc: 0.9545 - val_loss: 0.2607 - val_acc: 0.9136\n",
      "Train loss: 0.114490326272\n",
      "Train accuracy: 0.95492371706\n",
      "Test loss: 0.195914586063\n",
      "Test accuracy: 0.907407407407\n",
      "\n",
      "===================FOLD= 1\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 20s 818ms/step - loss: 0.6500 - acc: 0.6143 - val_loss: 0.4402 - val_acc: 0.7764\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.3756 - acc: 0.8112 - val_loss: 0.2638 - val_acc: 0.8944\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.3525 - acc: 0.8246 - val_loss: 0.2680 - val_acc: 0.8571\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 14s 592ms/step - loss: 0.3079 - acc: 0.8485 - val_loss: 0.2612 - val_acc: 0.8758\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.2716 - acc: 0.8812 - val_loss: 0.2513 - val_acc: 0.8820\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2700 - acc: 0.8827 - val_loss: 0.2182 - val_acc: 0.8820\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 14s 586ms/step - loss: 0.2476 - acc: 0.8946 - val_loss: 0.2149 - val_acc: 0.9130\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.2366 - acc: 0.8936 - val_loss: 0.1809 - val_acc: 0.9255\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2139 - acc: 0.9117 - val_loss: 0.2040 - val_acc: 0.8758\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.2373 - acc: 0.8994 - val_loss: 0.1967 - val_acc: 0.9130\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2182 - acc: 0.9127 - val_loss: 0.2069 - val_acc: 0.9068\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2012 - acc: 0.9141 - val_loss: 0.2621 - val_acc: 0.9193\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2265 - acc: 0.9037 - val_loss: 0.2272 - val_acc: 0.9130\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2163 - acc: 0.9212 - val_loss: 0.1842 - val_acc: 0.9255\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.1977 - acc: 0.9206 - val_loss: 0.1687 - val_acc: 0.9317\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1958 - acc: 0.9186 - val_loss: 0.2707 - val_acc: 0.9006\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1992 - acc: 0.9182 - val_loss: 0.2080 - val_acc: 0.9006\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2026 - acc: 0.9059 - val_loss: 0.2322 - val_acc: 0.9006\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1770 - acc: 0.9253 - val_loss: 0.2330 - val_acc: 0.9130\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1603 - acc: 0.9273 - val_loss: 0.2750 - val_acc: 0.8882\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1618 - acc: 0.9355 - val_loss: 0.2506 - val_acc: 0.8944\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1842 - acc: 0.9261 - val_loss: 0.2007 - val_acc: 0.9255\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1743 - acc: 0.9276 - val_loss: 0.1726 - val_acc: 0.9379\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1942 - acc: 0.9266 - val_loss: 0.1976 - val_acc: 0.9068\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1560 - acc: 0.9295 - val_loss: 0.1878 - val_acc: 0.9379\n",
      "Train loss: 0.146616265085\n",
      "Train accuracy: 0.941787941788\n",
      "Test loss: 0.168713525364\n",
      "Test accuracy: 0.931677018634\n",
      "\n",
      "===================FOLD= 2\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 21s 857ms/step - loss: 1.0356 - acc: 0.5830 - val_loss: 0.3614 - val_acc: 0.8385\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 587ms/step - loss: 0.6927 - acc: 0.7411 - val_loss: 0.2898 - val_acc: 0.8634\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.4850 - acc: 0.8038 - val_loss: 0.2549 - val_acc: 0.8944\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.5817 - acc: 0.7736 - val_loss: 0.3450 - val_acc: 0.7950\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.4141 - acc: 0.8297 - val_loss: 0.2652 - val_acc: 0.8758\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.3817 - acc: 0.8356 - val_loss: 0.2111 - val_acc: 0.9317\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.3138 - acc: 0.8615 - val_loss: 0.2303 - val_acc: 0.9068\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.3289 - acc: 0.8621 - val_loss: 0.2700 - val_acc: 0.8696\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2990 - acc: 0.8739 - val_loss: 0.2110 - val_acc: 0.9255\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2628 - acc: 0.8941 - val_loss: 0.2270 - val_acc: 0.9193\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.2724 - acc: 0.8842 - val_loss: 0.1867 - val_acc: 0.9130\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2659 - acc: 0.8833 - val_loss: 0.1933 - val_acc: 0.9068\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2281 - acc: 0.9044 - val_loss: 0.1990 - val_acc: 0.9379\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2364 - acc: 0.9003 - val_loss: 0.1866 - val_acc: 0.9255\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.2349 - acc: 0.9002 - val_loss: 0.2127 - val_acc: 0.9006\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2377 - acc: 0.8997 - val_loss: 0.1859 - val_acc: 0.9130\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.2240 - acc: 0.9012 - val_loss: 0.1822 - val_acc: 0.9255\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2026 - acc: 0.9094 - val_loss: 0.2070 - val_acc: 0.9068\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.1921 - acc: 0.9208 - val_loss: 0.1795 - val_acc: 0.9317\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.2241 - acc: 0.9140 - val_loss: 0.1947 - val_acc: 0.8944\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2016 - acc: 0.9063 - val_loss: 0.2065 - val_acc: 0.8944\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2196 - acc: 0.9006 - val_loss: 0.2083 - val_acc: 0.9193\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1921 - acc: 0.9199 - val_loss: 0.2032 - val_acc: 0.9130\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 14s 587ms/step - loss: 0.1841 - acc: 0.9239 - val_loss: 0.1707 - val_acc: 0.9255\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 14s 585ms/step - loss: 0.1609 - acc: 0.9358 - val_loss: 0.1531 - val_acc: 0.9441\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 14s 586ms/step - loss: 0.2235 - acc: 0.9085 - val_loss: 0.1422 - val_acc: 0.9379\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1739 - acc: 0.9335 - val_loss: 0.1819 - val_acc: 0.9255\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1884 - acc: 0.9216 - val_loss: 0.1707 - val_acc: 0.9255\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1718 - acc: 0.9269 - val_loss: 0.1824 - val_acc: 0.9130\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1790 - acc: 0.9246 - val_loss: 0.1594 - val_acc: 0.9193\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1716 - acc: 0.9237 - val_loss: 0.1651 - val_acc: 0.9130\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1559 - acc: 0.9339 - val_loss: 0.1607 - val_acc: 0.9130\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1532 - acc: 0.9347 - val_loss: 0.1633 - val_acc: 0.9255\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1521 - acc: 0.9373 - val_loss: 0.1568 - val_acc: 0.9317\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1462 - acc: 0.9421 - val_loss: 0.1669 - val_acc: 0.9255\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1542 - acc: 0.9343 - val_loss: 0.1822 - val_acc: 0.9193\n",
      "Train loss: 0.119817990461\n",
      "Train accuracy: 0.952875952876\n",
      "Test loss: 0.142235262609\n",
      "Test accuracy: 0.937888198758\n",
      "\n",
      "===================FOLD= 3\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 21s 871ms/step - loss: 0.7537 - acc: 0.6173 - val_loss: 0.3631 - val_acc: 0.8187\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.4776 - acc: 0.7647 - val_loss: 0.3051 - val_acc: 0.8313\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.4108 - acc: 0.8194 - val_loss: 0.2632 - val_acc: 0.8438\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.3980 - acc: 0.8293 - val_loss: 0.2602 - val_acc: 0.8625\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.3114 - acc: 0.8671 - val_loss: 0.2614 - val_acc: 0.8562\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.3197 - acc: 0.8609 - val_loss: 0.2295 - val_acc: 0.8875\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.3013 - acc: 0.8669 - val_loss: 0.2254 - val_acc: 0.9062\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 14s 587ms/step - loss: 0.2908 - acc: 0.8699 - val_loss: 0.2042 - val_acc: 0.8938\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2799 - acc: 0.8796 - val_loss: 0.2753 - val_acc: 0.8875\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2563 - acc: 0.8980 - val_loss: 0.2732 - val_acc: 0.8875\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2479 - acc: 0.8989 - val_loss: 0.3257 - val_acc: 0.8688\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2328 - acc: 0.9024 - val_loss: 0.2245 - val_acc: 0.9187\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.2215 - acc: 0.9013 - val_loss: 0.1906 - val_acc: 0.9313\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2151 - acc: 0.9085 - val_loss: 0.1771 - val_acc: 0.9250\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2333 - acc: 0.9015 - val_loss: 0.1798 - val_acc: 0.9000\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.2236 - acc: 0.9156 - val_loss: 0.2035 - val_acc: 0.9000\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1971 - acc: 0.9119 - val_loss: 0.1887 - val_acc: 0.9313\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1941 - acc: 0.9186 - val_loss: 0.2091 - val_acc: 0.9125\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1914 - acc: 0.9210 - val_loss: 0.1973 - val_acc: 0.9250\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.1852 - acc: 0.9164 - val_loss: 0.1851 - val_acc: 0.9125\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.1829 - acc: 0.9280 - val_loss: 0.2054 - val_acc: 0.9125\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.1860 - acc: 0.9222 - val_loss: 0.1679 - val_acc: 0.9187\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.1722 - acc: 0.9320 - val_loss: 0.2082 - val_acc: 0.9063\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1809 - acc: 0.9257 - val_loss: 0.2284 - val_acc: 0.9062\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1758 - acc: 0.9246 - val_loss: 0.2156 - val_acc: 0.9000\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1938 - acc: 0.9133 - val_loss: 0.2026 - val_acc: 0.9125\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.1794 - acc: 0.9272 - val_loss: 0.2187 - val_acc: 0.9125\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1488 - acc: 0.9363 - val_loss: 0.1807 - val_acc: 0.9187\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1516 - acc: 0.9349 - val_loss: 0.1812 - val_acc: 0.9250\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1643 - acc: 0.9257 - val_loss: 0.3233 - val_acc: 0.9062\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1481 - acc: 0.9391 - val_loss: 0.1734 - val_acc: 0.9187\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1871 - acc: 0.9187 - val_loss: 0.3795 - val_acc: 0.8812\n",
      "Train loss: 0.130862172249\n",
      "Train accuracy: 0.945290858726\n",
      "Test loss: 0.167947658896\n",
      "Test accuracy: 0.91875\n",
      "\n",
      "===================FOLD= 4\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 21s 874ms/step - loss: 1.0115 - acc: 0.5059 - val_loss: 0.6559 - val_acc: 0.5938\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 587ms/step - loss: 0.7925 - acc: 0.5925 - val_loss: 0.4972 - val_acc: 0.7750\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 576ms/step - loss: 0.5382 - acc: 0.7471 - val_loss: 0.2661 - val_acc: 0.8875\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.4213 - acc: 0.8155 - val_loss: 0.3490 - val_acc: 0.8625\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.4010 - acc: 0.8135 - val_loss: 0.2165 - val_acc: 0.9250\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.3529 - acc: 0.8459 - val_loss: 0.1651 - val_acc: 0.9313\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.3450 - acc: 0.8546 - val_loss: 0.3004 - val_acc: 0.9000\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.3534 - acc: 0.8314 - val_loss: 0.1771 - val_acc: 0.9437\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.2817 - acc: 0.8919 - val_loss: 0.1669 - val_acc: 0.9500\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2746 - acc: 0.8754 - val_loss: 0.1893 - val_acc: 0.9437\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2598 - acc: 0.8893 - val_loss: 0.2574 - val_acc: 0.9187\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 14s 587ms/step - loss: 0.2706 - acc: 0.8884 - val_loss: 0.1417 - val_acc: 0.9625\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2537 - acc: 0.8958 - val_loss: 0.2243 - val_acc: 0.9000\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.2495 - acc: 0.9008 - val_loss: 0.1214 - val_acc: 0.9563\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.2803 - acc: 0.8829 - val_loss: 0.2090 - val_acc: 0.9375\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2447 - acc: 0.9020 - val_loss: 0.1627 - val_acc: 0.9437\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2177 - acc: 0.9135 - val_loss: 0.2346 - val_acc: 0.9250\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2072 - acc: 0.9125 - val_loss: 0.1878 - val_acc: 0.9437\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.2148 - acc: 0.9049 - val_loss: 0.1652 - val_acc: 0.9250\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2228 - acc: 0.9055 - val_loss: 0.1823 - val_acc: 0.9563\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1979 - acc: 0.9155 - val_loss: 0.2228 - val_acc: 0.9187\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.2072 - acc: 0.9114 - val_loss: 0.1989 - val_acc: 0.9500\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 501ms/step - loss: 0.2070 - acc: 0.9110 - val_loss: 0.2500 - val_acc: 0.9563\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1847 - acc: 0.9231 - val_loss: 0.2528 - val_acc: 0.9062\n",
      "Train loss: 0.158674367577\n",
      "Train accuracy: 0.935595567867\n",
      "Test loss: 0.121355755627\n",
      "Test accuracy: 0.95625\n",
      "\n",
      "===================FOLD= 5\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 21s 891ms/step - loss: 0.5931 - acc: 0.6537 - val_loss: 0.3965 - val_acc: 0.8250\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 587ms/step - loss: 0.4529 - acc: 0.7899 - val_loss: 0.3057 - val_acc: 0.8688\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.3850 - acc: 0.8319 - val_loss: 0.3752 - val_acc: 0.8438\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.3236 - acc: 0.8519 - val_loss: 0.2987 - val_acc: 0.8750\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 14s 592ms/step - loss: 0.3293 - acc: 0.8652 - val_loss: 0.2421 - val_acc: 0.9000\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2996 - acc: 0.8678 - val_loss: 0.2577 - val_acc: 0.8875\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.2631 - acc: 0.8777 - val_loss: 0.2150 - val_acc: 0.9187\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2417 - acc: 0.9003 - val_loss: 0.2562 - val_acc: 0.9125\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2360 - acc: 0.9039 - val_loss: 0.1963 - val_acc: 0.9250\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2329 - acc: 0.8987 - val_loss: 0.2190 - val_acc: 0.9187\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2351 - acc: 0.9006 - val_loss: 0.2424 - val_acc: 0.8812\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2304 - acc: 0.9078 - val_loss: 0.2135 - val_acc: 0.9187\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2274 - acc: 0.9098 - val_loss: 0.2057 - val_acc: 0.9250\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 14s 587ms/step - loss: 0.2364 - acc: 0.8961 - val_loss: 0.1892 - val_acc: 0.9437\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.1893 - acc: 0.9218 - val_loss: 0.1870 - val_acc: 0.9187\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1944 - acc: 0.9209 - val_loss: 0.2089 - val_acc: 0.9313\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2009 - acc: 0.9200 - val_loss: 0.2469 - val_acc: 0.9000\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2187 - acc: 0.9055 - val_loss: 0.2186 - val_acc: 0.9062\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1968 - acc: 0.9174 - val_loss: 0.2052 - val_acc: 0.9250\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1713 - acc: 0.9285 - val_loss: 0.2008 - val_acc: 0.9125\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1942 - acc: 0.9075 - val_loss: 0.2094 - val_acc: 0.9437\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 22/100\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.1748 - acc: 0.9334 - val_loss: 0.1674 - val_acc: 0.9500\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.1659 - acc: 0.9325 - val_loss: 0.1678 - val_acc: 0.9375\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1874 - acc: 0.9256 - val_loss: 0.2096 - val_acc: 0.9125\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1521 - acc: 0.9352 - val_loss: 0.2119 - val_acc: 0.9313\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.2026 - acc: 0.9164 - val_loss: 0.2307 - val_acc: 0.9250\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1718 - acc: 0.9353 - val_loss: 0.2045 - val_acc: 0.9187\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1548 - acc: 0.9368 - val_loss: 0.2122 - val_acc: 0.9313\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1419 - acc: 0.9410 - val_loss: 0.1903 - val_acc: 0.9500\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1639 - acc: 0.9241 - val_loss: 0.1939 - val_acc: 0.9437\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1289 - acc: 0.9428 - val_loss: 0.2553 - val_acc: 0.9500\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1515 - acc: 0.9378 - val_loss: 0.1971 - val_acc: 0.9313\n",
      "Train loss: 0.118444997642\n",
      "Train accuracy: 0.952908587258\n",
      "Test loss: 0.167416767776\n",
      "Test accuracy: 0.95\n",
      "\n",
      "===================FOLD= 6\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 22s 907ms/step - loss: 0.7802 - acc: 0.6344 - val_loss: 0.5563 - val_acc: 0.6312\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 587ms/step - loss: 0.5609 - acc: 0.7551 - val_loss: 0.4008 - val_acc: 0.7750\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.4465 - acc: 0.8021 - val_loss: 0.3686 - val_acc: 0.8500\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.3503 - acc: 0.8568 - val_loss: 0.4178 - val_acc: 0.8562\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.3266 - acc: 0.8622 - val_loss: 0.3617 - val_acc: 0.8750\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.3590 - acc: 0.8537 - val_loss: 0.3474 - val_acc: 0.8125\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.3079 - acc: 0.8630 - val_loss: 0.3360 - val_acc: 0.8688\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.3124 - acc: 0.8722 - val_loss: 0.3348 - val_acc: 0.8562\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 14s 587ms/step - loss: 0.2574 - acc: 0.8922 - val_loss: 0.3241 - val_acc: 0.9062\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.2419 - acc: 0.9003 - val_loss: 0.2757 - val_acc: 0.9000\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.2374 - acc: 0.8969 - val_loss: 0.2544 - val_acc: 0.9062\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2182 - acc: 0.9068 - val_loss: 0.3514 - val_acc: 0.8812\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2633 - acc: 0.8948 - val_loss: 0.2693 - val_acc: 0.9125\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2513 - acc: 0.9020 - val_loss: 0.2908 - val_acc: 0.8812\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.2271 - acc: 0.9075 - val_loss: 0.2901 - val_acc: 0.8875\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2696 - acc: 0.8886 - val_loss: 0.3319 - val_acc: 0.8125\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2137 - acc: 0.9041 - val_loss: 0.3528 - val_acc: 0.8625\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2091 - acc: 0.9091 - val_loss: 0.2951 - val_acc: 0.9000\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1934 - acc: 0.9238 - val_loss: 0.2758 - val_acc: 0.8938\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1769 - acc: 0.9259 - val_loss: 0.2676 - val_acc: 0.8938\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1773 - acc: 0.9238 - val_loss: 0.2656 - val_acc: 0.9000\n",
      "Train loss: 0.182344811899\n",
      "Train accuracy: 0.919667590028\n",
      "Test loss: 0.254438212514\n",
      "Test accuracy: 0.90625\n",
      "\n",
      "===================FOLD= 7\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 22s 917ms/step - loss: 0.6715 - acc: 0.6239 - val_loss: 0.3729 - val_acc: 0.8313\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.4136 - acc: 0.8182 - val_loss: 0.3367 - val_acc: 0.8250\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.3699 - acc: 0.8280 - val_loss: 0.2750 - val_acc: 0.8438\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.3047 - acc: 0.8713 - val_loss: 0.2697 - val_acc: 0.8938\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.3131 - acc: 0.8674 - val_loss: 0.3316 - val_acc: 0.8812\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.3079 - acc: 0.8598 - val_loss: 0.2286 - val_acc: 0.8875\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2503 - acc: 0.8842 - val_loss: 0.2573 - val_acc: 0.9062\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.2534 - acc: 0.8959 - val_loss: 0.2046 - val_acc: 0.8938\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 14s 587ms/step - loss: 0.2225 - acc: 0.9091 - val_loss: 0.2026 - val_acc: 0.9000\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.2130 - acc: 0.9125 - val_loss: 0.2643 - val_acc: 0.8875\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.2037 - acc: 0.9210 - val_loss: 0.2046 - val_acc: 0.9125\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2278 - acc: 0.9051 - val_loss: 0.2061 - val_acc: 0.8938\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.2154 - acc: 0.9067 - val_loss: 0.2566 - val_acc: 0.8938\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2246 - acc: 0.9121 - val_loss: 0.2174 - val_acc: 0.8938\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1944 - acc: 0.9220 - val_loss: 0.2312 - val_acc: 0.8938\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2145 - acc: 0.9068 - val_loss: 0.2090 - val_acc: 0.9000\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1694 - acc: 0.9329 - val_loss: 0.2570 - val_acc: 0.8938\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 14s 587ms/step - loss: 0.2209 - acc: 0.9080 - val_loss: 0.1813 - val_acc: 0.9062\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1888 - acc: 0.9169 - val_loss: 0.1854 - val_acc: 0.9313\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.1885 - acc: 0.9138 - val_loss: 0.1697 - val_acc: 0.9187\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1926 - acc: 0.9208 - val_loss: 0.2000 - val_acc: 0.9125\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1989 - acc: 0.9189 - val_loss: 0.2246 - val_acc: 0.8688\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1660 - acc: 0.9284 - val_loss: 0.1732 - val_acc: 0.9250\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1695 - acc: 0.9391 - val_loss: 0.2674 - val_acc: 0.8875\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2004 - acc: 0.9243 - val_loss: 0.1656 - val_acc: 0.9437\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1660 - acc: 0.9332 - val_loss: 0.2095 - val_acc: 0.9125\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1333 - acc: 0.9493 - val_loss: 0.1698 - val_acc: 0.9187\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1600 - acc: 0.9375 - val_loss: 0.1983 - val_acc: 0.9187\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.1582 - acc: 0.9393 - val_loss: 0.2455 - val_acc: 0.9062\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1694 - acc: 0.9319 - val_loss: 0.2053 - val_acc: 0.9062\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 14s 586ms/step - loss: 0.1232 - acc: 0.9528 - val_loss: 0.1626 - val_acc: 0.9375\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1257 - acc: 0.9500 - val_loss: 0.1966 - val_acc: 0.9125\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 14s 583ms/step - loss: 0.1421 - acc: 0.9399 - val_loss: 0.1623 - val_acc: 0.9125\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1453 - acc: 0.9376 - val_loss: 0.1910 - val_acc: 0.8938\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 14s 583ms/step - loss: 0.1364 - acc: 0.9397 - val_loss: 0.1425 - val_acc: 0.9375\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1202 - acc: 0.9441 - val_loss: 0.1776 - val_acc: 0.9187\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 12s 501ms/step - loss: 0.1331 - acc: 0.9474 - val_loss: 0.1866 - val_acc: 0.8938\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1834 - acc: 0.9228 - val_loss: 0.1831 - val_acc: 0.9250\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1540 - acc: 0.9428 - val_loss: 0.2286 - val_acc: 0.8875\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1451 - acc: 0.9464 - val_loss: 0.2102 - val_acc: 0.9313\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1326 - acc: 0.9456 - val_loss: 0.2532 - val_acc: 0.9187\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1483 - acc: 0.9358 - val_loss: 0.1680 - val_acc: 0.9187\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1206 - acc: 0.9487 - val_loss: 0.1956 - val_acc: 0.9187\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1227 - acc: 0.9490 - val_loss: 0.1968 - val_acc: 0.9125\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1154 - acc: 0.9590 - val_loss: 0.2635 - val_acc: 0.9187\n",
      "Train loss: 0.0821971228847\n",
      "Train accuracy: 0.96675900277\n",
      "Test loss: 0.142476756871\n",
      "Test accuracy: 0.9375\n",
      "\n",
      "===================FOLD= 8\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 22s 933ms/step - loss: 0.7972 - acc: 0.6142 - val_loss: 0.4994 - val_acc: 0.7562\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.4992 - acc: 0.7795 - val_loss: 0.4902 - val_acc: 0.7875\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.4080 - acc: 0.8247 - val_loss: 0.5633 - val_acc: 0.7688\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.3387 - acc: 0.8596 - val_loss: 0.4024 - val_acc: 0.8250\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 14s 592ms/step - loss: 0.3338 - acc: 0.8629 - val_loss: 0.3260 - val_acc: 0.8500\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2965 - acc: 0.8752 - val_loss: 0.4701 - val_acc: 0.8125\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2904 - acc: 0.8836 - val_loss: 0.4397 - val_acc: 0.8187\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2409 - acc: 0.9015 - val_loss: 0.4150 - val_acc: 0.8438\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2640 - acc: 0.8836 - val_loss: 0.4166 - val_acc: 0.8500\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2387 - acc: 0.9047 - val_loss: 0.3960 - val_acc: 0.8375\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2265 - acc: 0.9125 - val_loss: 0.3176 - val_acc: 0.8500\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2104 - acc: 0.9060 - val_loss: 0.4550 - val_acc: 0.8313\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.2267 - acc: 0.9101 - val_loss: 0.3860 - val_acc: 0.8500\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2407 - acc: 0.9020 - val_loss: 0.3662 - val_acc: 0.8313\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2212 - acc: 0.9205 - val_loss: 0.3611 - val_acc: 0.8625\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1782 - acc: 0.9296 - val_loss: 0.4704 - val_acc: 0.8438\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2165 - acc: 0.9086 - val_loss: 0.4108 - val_acc: 0.8500\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2096 - acc: 0.9166 - val_loss: 0.3820 - val_acc: 0.8000\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2010 - acc: 0.9150 - val_loss: 0.4026 - val_acc: 0.8500\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1921 - acc: 0.9220 - val_loss: 0.3946 - val_acc: 0.8500\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.2192 - acc: 0.9179 - val_loss: 0.3447 - val_acc: 0.8500\n",
      "Train loss: 0.143710861287\n",
      "Train accuracy: 0.941135734072\n",
      "Test loss: 0.317634996772\n",
      "Test accuracy: 0.85\n",
      "\n",
      "===================FOLD= 9\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 23s 944ms/step - loss: 0.7371 - acc: 0.5437 - val_loss: 0.5459 - val_acc: 0.6625\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 586ms/step - loss: 0.6308 - acc: 0.6225 - val_loss: 0.4900 - val_acc: 0.7438\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.4686 - acc: 0.7712 - val_loss: 0.4236 - val_acc: 0.7812\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 14s 587ms/step - loss: 0.4183 - acc: 0.8282 - val_loss: 0.3237 - val_acc: 0.8562\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.3924 - acc: 0.8327 - val_loss: 0.3156 - val_acc: 0.8500\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.3283 - acc: 0.8668 - val_loss: 0.3033 - val_acc: 0.8750\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.3109 - acc: 0.8609 - val_loss: 0.2894 - val_acc: 0.8812\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.2792 - acc: 0.8757 - val_loss: 0.2793 - val_acc: 0.8750\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.2550 - acc: 0.8845 - val_loss: 0.2081 - val_acc: 0.9000\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.2755 - acc: 0.8749 - val_loss: 0.2106 - val_acc: 0.9062\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2378 - acc: 0.9011 - val_loss: 0.4079 - val_acc: 0.8125\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2800 - acc: 0.8803 - val_loss: 0.2180 - val_acc: 0.9062\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.2447 - acc: 0.9033 - val_loss: 0.2142 - val_acc: 0.9000\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2293 - acc: 0.9073 - val_loss: 0.2700 - val_acc: 0.8688\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2464 - acc: 0.8906 - val_loss: 0.2054 - val_acc: 0.9062\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.2192 - acc: 0.9078 - val_loss: 0.2242 - val_acc: 0.9062\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2169 - acc: 0.9114 - val_loss: 0.2658 - val_acc: 0.8750\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1899 - acc: 0.9168 - val_loss: 0.3381 - val_acc: 0.8375\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 14s 582ms/step - loss: 0.2015 - acc: 0.9166 - val_loss: 0.1992 - val_acc: 0.9187\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.2229 - acc: 0.9081 - val_loss: 0.2320 - val_acc: 0.8875\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.2275 - acc: 0.9125 - val_loss: 0.2205 - val_acc: 0.9000\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.1882 - acc: 0.9182 - val_loss: 0.1865 - val_acc: 0.9187\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.1719 - acc: 0.9297 - val_loss: 0.2016 - val_acc: 0.9000\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1729 - acc: 0.9332 - val_loss: 0.2638 - val_acc: 0.8562\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1790 - acc: 0.9340 - val_loss: 0.2090 - val_acc: 0.9125\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1786 - acc: 0.9272 - val_loss: 0.2189 - val_acc: 0.9000\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1550 - acc: 0.9283 - val_loss: 0.2457 - val_acc: 0.9125\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1687 - acc: 0.9366 - val_loss: 0.2087 - val_acc: 0.8875\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1727 - acc: 0.9259 - val_loss: 0.2242 - val_acc: 0.9000\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1386 - acc: 0.9454 - val_loss: 0.2148 - val_acc: 0.9187\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1302 - acc: 0.9474 - val_loss: 0.2950 - val_acc: 0.8750\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1630 - acc: 0.9378 - val_loss: 0.2373 - val_acc: 0.9187\n",
      "Train loss: 0.129656635287\n",
      "Train accuracy: 0.946675900277\n",
      "Test loss: 0.186509889364\n",
      "Test accuracy: 0.91875\n",
      "\n",
      " Train Log Loss Validation=  0.123310131424\n",
      " Test Log Loss Validation=  0.186437469291\n"
     ]
    }
   ],
   "source": [
    "#def myAngleCV(X_train, X_angle, X_test):\n",
    "K=10\n",
    "folds = list(StratifiedKFold(n_splits=K, shuffle=True, random_state=16).split(X_train, target_train))\n",
    "y_test_pred_log = 0\n",
    "y_train_pred_log=0\n",
    "y_valid_pred_log = 0.0*target_train\n",
    "for j, (train_idx, test_idx) in enumerate(folds):\n",
    "    print('\\n===================FOLD=',j)\n",
    "    X_train_cv = X_train[train_idx]\n",
    "    y_train_cv = target_train[train_idx]\n",
    "    X_holdout = X_train[test_idx]\n",
    "    Y_holdout= target_train[test_idx]\n",
    "    \n",
    "    #Angle\n",
    "    X_angle_cv=X_angle[train_idx]\n",
    "    X_angle_hold=X_angle[test_idx]\n",
    "    #define file path and get callbacks\n",
    "    file_path = \"%s_aug_model_weights.hdf5\"%j\n",
    "    callbacks = get_callbacks(filepath=file_path, patience=5)\n",
    "    gen_flow = gen_flow_for_two_inputs(X_train_cv, X_angle_cv, y_train_cv)\n",
    "    galaxyModel= getVggAngleModel()\n",
    "    histr = galaxyModel.fit_generator(\n",
    "            gen_flow,\n",
    "            steps_per_epoch=24,\n",
    "            epochs=100,\n",
    "            shuffle=True,\n",
    "            verbose=1,\n",
    "            validation_data=([X_holdout,X_angle_hold], Y_holdout),\n",
    "            callbacks=callbacks)\n",
    "     #Getting the Best Model\n",
    "    galaxyModel.load_weights(filepath=file_path)\n",
    "    #Getting Training Score\n",
    "    score = galaxyModel.evaluate([X_train_cv,X_angle_cv], y_train_cv, verbose=0)\n",
    "    print('Train loss:', score[0])\n",
    "    print('Train accuracy:', score[1])\n",
    "    #Getting Test Score\n",
    "    score = galaxyModel.evaluate([X_holdout,X_angle_hold], Y_holdout, verbose=0)\n",
    "    print('Test loss:', score[0])\n",
    "    print('Test accuracy:', score[1])\n",
    "\n",
    "    #Getting validation Score.\n",
    "    pred_valid=galaxyModel.predict([X_holdout,X_angle_hold])\n",
    "    y_valid_pred_log[test_idx] = pred_valid.reshape(pred_valid.shape[0])\n",
    "\n",
    "    #Getting Test Scores\n",
    "    temp_test=galaxyModel.predict([X_test, X_test_angle])\n",
    "    y_test_pred_log+=temp_test.reshape(temp_test.shape[0])\n",
    "\n",
    "    #Getting Train Scores\n",
    "    temp_train=galaxyModel.predict([X_train, X_angle])\n",
    "    y_train_pred_log+=temp_train.reshape(temp_train.shape[0])\n",
    "\n",
    "y_test_pred_log=y_test_pred_log/K\n",
    "y_train_pred_log=y_train_pred_log/K\n",
    "\n",
    "print('\\n Train Log Loss Validation= ',log_loss(target_train, y_train_pred_log))\n",
    "print(' Test Log Loss Validation= ',log_loss(target_train, y_valid_pred_log))\n",
    "#return y_test_pred_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#preds=myAngleCV(X_train, X_angle, X_test)\n",
    "preds = y_test_pred_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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_10folds_amsgrad_0001_2nd.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['val_loss', 'acc', 'loss', 'val_acc'])\n"
     ]
    },
    {
     "data": {
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X/o8m9f1TzjozJ585G9KYu2YrI1b8nREF8VyuQsOO9zPworsqPJqpTkpeAB9cCLkZ0Dga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\nrS2aPVlMA/bmtruo4D8Z2T/U7yRtlXRf2cGcpcs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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0142372e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Ckc06t4JSqqFrPEEB4E9/Be8A+OmJSu3WpWUQ3p6iPZCUUg1e4woKAc1h+DTY9g3sW+H0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\n/sTBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0141bad68>"
      ]
     },
     "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": 21,
   "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": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13      1\n",
       "14      0\n",
       "38      1\n",
       "43      0\n",
       "44      0\n",
       "80      0\n",
       "104     1\n",
       "127     0\n",
       "137     0\n",
       "138     1\n",
       "141     1\n",
       "167     1\n",
       "195     1\n",
       "199     0\n",
       "207     1\n",
       "212     0\n",
       "213     1\n",
       "221     0\n",
       "240     0\n",
       "256     0\n",
       "267     1\n",
       "274     1\n",
       "276     1\n",
       "285     1\n",
       "290     0\n",
       "294     0\n",
       "300     1\n",
       "307     1\n",
       "311     1\n",
       "318     1\n",
       "       ..\n",
       "1363    1\n",
       "1364    0\n",
       "1373    1\n",
       "1380    1\n",
       "1394    0\n",
       "1411    0\n",
       "1435    0\n",
       "1440    1\n",
       "1445    1\n",
       "1447    1\n",
       "1460    1\n",
       "1470    0\n",
       "1488    0\n",
       "1495    0\n",
       "1504    1\n",
       "1516    0\n",
       "1525    0\n",
       "1533    0\n",
       "1537    0\n",
       "1539    0\n",
       "1541    0\n",
       "1542    0\n",
       "1549    0\n",
       "1552    0\n",
       "1559    0\n",
       "1566    0\n",
       "1567    0\n",
       "1577    0\n",
       "1590    0\n",
       "1602    0\n",
       "Name: is_iceberg, Length: 160, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_holdout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fa01409f780>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SBf6CmvOzf7zaLT/PaubXNtryZ7XX7pxs58e+z2Q6Odlu9Ot92llx8pnN/TqP535tx5m3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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa01421df98>"
      ]
     },
     "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": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fa014002940>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cRVTukUOnuKxHu0TEdRlzPMfOjM5EmVvIn81BO2eV0yEKg8e5bztErirttwxU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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa01405bc18>"
      ]
     },
     "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": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fa013fddeb8>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lv4U/WyjB6aiHhFqYojOklc0XaECcFjkd1mko33Fq3rgOGbUYDBGS83ZCQkLC\n5iO9FBMSEhKAjaXPWRHUO6w4DJMrNZAmdyCnOwaraCs6T7egfEyiUCL2146giA1LZPNU7DMo49KP\nGU+bQSas37uVYpdwas7hyDshZ4eU034TsmOQg1pAOoxxw6s8xX1QKba7hRjvAjS9gdjrBiyny67T\nWvvQcyfHrc9wZ2uDOnctWjS7E69zCuNrsYvyEAdeh/N6swVnZ8p4Idf0YuA306LeiN7zkwDeiSXD\naAFLdQdbE3C87iHXclnjxgLaC0tvFq23nAclckp3BzjvVapZ9zIW8BaoB/SzcBfvDM8x4xjFugxj\nVSBZVg3jrBbkzcYVld5M8pxWigkJCQmnkF6KCQkJCcDG0mdlpqJdLfmbyGt7XHfKXEKa6zQqulEi\nxPqo6TRlDmvuNqyiGZ2RS7+mrEOpu+VWvJ05hh8GzVqkJ3NYbbNjv68JytiCtRDGXOVQFadlm9bH\n4RyyU5EePVu65NYjNY8fnneYIMktu9iN0A6o32Lh41V+9sWTY1q8Bcf3RkwM1hohfrfhg1I3v28A\nKTIbY/sAWxBCAqgc7SqoSM38Y3FLYNEkKfRr9xZO3zvHPp5DOMNPIB3XL3zrZYa5kHec+k7i+J/1\nRzgUco2PsQWCJGtUJF/CW2AAmpwNkKwNKtvLFcVHTvMMgQkB87aB0PNh3Jo55Wi/QUgrxYSEhAQg\nvRQTEhISgI2lz2UZNItJgtqwaG7BonYNVClDMqoTaSQmXJr5UD2fwRkaFCJDIiiDk/hKlkqSOkLc\nNJSmmngUqyRBeROW4EtwKp6BSsPKXZ87raNVdNEGDYKT+hZim8uYgOuBidO+cQsiVWBKs6XXf5lJ\noZrr5c3AWGWg1RM4RzeOq/PZjtPBnE7SiN9t0Ekc3/UmdgPgd68Ail10USa84PNuNdYNSqstoIjd\nQuw1ZNl6eG4FZLzGAyRC2/Y+jeY+AVfOz9OO94HK6CVUyp9denmv0Z63C14JYcTkUvBWqFHt27GI\nuaSZL5oq4dz2KTEVO7OYlzxL9DkhISFh47GxK0WTf8mKHkLkEDpX4yY9NsAP4ld2b+hfzdYhQugu\nuB/hoPAvf7uOpR9WNl1YUcY1/4J3FlidcikW/dAYNpfjC77bcjUcrjxoXFjC6JDh22dbvhJcLrGC\nPVFY8TrbNe8PfekuDbytjS2q4/rhzgGMEciRsoBYax1iteVeteLkCq8FH8hFAwo88MecH7tf4YKa\nvDA0aAx94FmuAAAgAElEQVR/xAFEbB9y4eBZ9M/LIFQ7OoKwKlLjlhjn2RiCwzl8JrHIzmHQajzo\nxqujGMbXn673+MPCV5fqviL/cHHj5HgLiTsu71w4Oe5OvY0HTe9/bUzh3KrevSUS89BuhWdOBZ5l\ns5oXSWQ2ISEh4RWA9FJMSEhIADaWPhcKJ7knOttIiTmGIsmu01DS4JXx4riNECqku2zd8DLm8EE7\naLqw61YDoXowxszgEzaD8sylnoe/hbgBXlCcNTh9mYKO2YHztCEc77ow6NRA5YoevoOgSofRkNCd\nw1gEY82WsDVAf0jQsQw+iAX8CmdI60pO2NryMVoZrJYYq2L/yK/te8zfEhS8AJUt0N7l3Dtte8i7\ngpC2HPR4ES+pD9erCDFUlIaWZtPHZQkD2M6OU3Pb9X4yJWwvVM9oPvV21DBuF1o+b7tQR+6Ayl8b\nXvM+fNRpdf4Zl06Ot+DXOmr5dkM/5mDZL6C1Aya/94zPhf62/62sksTUqKizQUgrxYSEhAQgvRQT\nEhISgI2lz8Ri4pztQtd98wzhT2o4DZrfqPKotHfhgwcKONlBysyBn79Qd8o02vdwuRbSg/Za+A7V\nYAlGiFgeLdG0lF9deHn5FD57sNbuBqeYzEVTgBMdHjjF77ad1u2s8o3CN4020eOx+8ltN7yeIdK9\n9koo2cDvs5E5lYf7ouojr6EWHQRfKDyHzS6m5+wFp9KNPX8ufajOjEvaax2LGZ8z+geVoH7kz1nP\n+3Yq9SdUYmg1Hy69zh4HHY85gyfCDAK901k1dnP6y4Jed87482xTyOeSj0XR8/kS9v2iEqF7PYjv\nDq9XYxr6SI078/l8ve/HTQj1Fu2qXeWG6uSklWJCQkICcK5eimb2qJn9uJn9nJn9rJl9Qzy/Z2bv\nM7Nfiv/ffbGyEhISEu4G540+LyX9hRDCfzKzvqSfNrP3SfpaST8WQnirmb1F0lskvfl2BWVmLsaJ\ncLHlodOKOpxtDWlAT87Bstt+2K1vFBBtlcxd4XTiwkV3pD0YO/UtIDIrhFfVYdEs43ENgq8NCMVu\n3YA5mUAoVoFwujHUVrYypHt9xqmqYh6R0Ea44dSp4RbymUx061hJ0hB0szZDeljkZO1QGUi3hont\nNn0L4nDulLl+xal5czTROnT7Tg0pJpsjtHEE5aMClLg2qtqST7A1ccm3F64tQOsbUClCytQa8sXM\nD0Hl96H88xz2Ox6pxvysVKGjsVuF8wLqNXhGF+F43YQy0qTl9U/hOBAwX/PLvXgOzvUQ3n207XN4\nAjFbKuZsIs7VSjGE8FwI4T/F44GkD0l6WNJXSXoiXvaEpNffmxYmJCRsOs7bSvEEZvYaSZ8v6QOS\nroQQVpr2z0u6csY9b5L0JklqwpcrISEh4U5xLl+KZtaT9E8kfWMI4dhALUMIwczW8o0QwuOSHpek\nrZ3tYOWt11AstglaNWjDObdT8Q3aMi9SwBQWxG2mLKUyDazJW3DCXSDHZo443PKZ6yfH2ec8cGvD\n4dQcdvyF3y6wNRAgeAr1FDqeE/VdWKv3qjKZJnXS8To7oMM18zpnU6fyLcR+L48RE4w6x9vrFWEa\n8XoUrXYPAqqiBdcvyobe3uEMlBkO3mpAcBUiq214oS9j6tUZLarHPgMe6LvXQgu0v4BXwhTKNAts\nZTRBN5mStb1f3Tvve3nz1qmksSdHRwNsgeDPljHWBtWfgg7uY+8H8/s8v10dNw697Ee7LghM9Eof\nq+Gw2g4qyzV/YBuAc0WfJcnM6qpeiD8QQvin8fQLZvZg/P1BSVfvVfsSEhI2G+fqpWjVkvBtkj4U\nQvhb+Om9kh6Lx49Jes/L3baEhIRXBs4bff7Nkr5G0n81s5+J575F0lslvcvM3ijp45Le8GIFZVmm\nbqeituOZc4wdOBgvEQdMsdATqyvkug4FOjgDN4KDtR0g/wvkygLoWzMDmdyCBNYzbmkdHleWzvoW\nU53CKor9UoafBlq24bBeNr0flCubw6JYe6Zyzq7DUl+DIGsOAdspYqxJ9qYTp69boOZL5MKhEO2w\n7Xd3j6rya7CglnCunlCo98jHbQbHY4OkWFaHZ8HU780gdzVb3Jqjp4RQLB/z7gIB35grY+S/yUCT\nlxCOzeGtMERK2vkqVhoUOEBYuLbvz7AFql9A8m2A59yFJZxbKXWo/AY4kl+eVnUdwjthdOxO+rtz\n5IVh93uxjRsqHXauXoohhJ/QOj+NCq97OduSkJDwysRmvuoTEhIS7hLnaqX4KUUIsmhd7iBWVPDU\naYIeMM51ZV1dMH4V+VdGsHJ2hsgXsuXUhwbf5cT50XLLLaoB1rvWr7h8ctyLFtAFqGwJS/kY9QuO\nv9ehEdaAY3ILclhZ0+uHMppG29X1UNbSeAJJKYMKNgy0dDpvtZy+TWH6X2SsyA/VRqx0vzou0J8m\n4rBHS97o1zTGoK8d53g10P0arNi0hB9MnSqWk0ilkc+mBno4mPkkmsFEflz4mNdRTyPHdgzlxLHf\nMVilCgVl3536fbbtk5Xu6u0Dp/1wMtAQ/hJLbB/Mml7/NraPZrExr952D7fGEFsWQ+9zDvmzleK2\nncXp7nOklWJCQkICkF6KCQkJCcDm0meASs3zkVMm0qfdvVs1Jlp1p0PFyGlS9wacrmFNFZJbzbdh\ncR473SuRHlVIrlTccHpoMTEWY2KbcNIWrMZH6AMNhMs+uY0fT5GeNMByPl7FJx9DGRzK1wWsor2H\nPCa2j1rpsF5QousFp3VNyH5lpfepGFbXLHPSZx//PWTFmruotJbo9OiqxycvsA9wEQmwBg1v4y6c\n6gcHK9Vqp5q255SRkmfNI2xHIA764MCfReOiJ6hqQPl6Bmmu/tVqrGtIgSrErDMRGAMR5hijrfl6\nDrvo+g3H2FbhmLdjrHge+BpAytS+j9vkwMc2RG8GWrI3CWmlmJCQkACkl2JCQkICsLH0OS9NvShT\nNYIj63VYQhkTvEDc8oUYz2zIATzNnXYsLkOF+WmnTK2O063WPuJQSY8AWhRzXHM0qKhKZ+bWx04g\nOUb9u05lbxx4EuAGKN60wW8fkltBhTlEB+MCYeXdum8pLPuIfYX8VKcPigkDcQnLcX/Xy5nItw9a\noH4rgt0GlbPrPkJ0pIZhWfnUy9i+iC2QI6fso8Lb255gG6LmBfUvVfcatg+ymV87J63s4lnAAXwv\n87FoUIWbLBNydbVLlRzd4prPIVp5FxlylHP9Amf8CUKV5zBFD5ZOmS83kbjNQ7hP8IK8/h6ecwY1\n+hHmfOfp9dJtm4K0UkxISEgA0ksxISEhAdhY+qzMPEkRcw1PnR62Ok4r6pBGWsxuTYC0rMOCDOfh\nzqv2To7zI6dSOShujuJmDb93G/pSnaV/nzrjqpwjUMaMQc5bTmsCtga22l2tw3KJm2FRHCLm1WJ7\nax1IW0GKqgZqvOj7NcXQadoEFLMBL/k54pN7oK8LJLfKs2pcSlDGFizeswkU0+E8vYMkWiXU08eI\nPZ/DCXmBLRHmo17loL4ASzUR8MzHC3+gtbr/CS3beOZIilaAbmeIpz9RB4eSdgmPgByG6AAPinod\nQQKIg25gnl3IfVys8HE+Dn5RJwYHkOnXjyk/5ue3oJg+uFT9QAX6TUJaKSYkJCQAG7tSnJYL/fzs\nBUmnN+93t7A6fN43jO2CfxaLTnX9DDlKFgjJ273gq0NmKylhrDl+wY0e3StuAAhUbMnXr0pay+oL\nnrf8az+FmGt5Ro6SOkLeYKNRq+kruwXq38Jq9kanOn+xhFEIiikBIqd6GsevdUMPDScBBq1sBIUd\nrIo09/Gat6qVWAOKLqORL09rO26A6DKc8MCvYVrPounltLGyynOo5GCluGrLFCF5NK4Iq9oF1HsW\n8NWrw7FwBDFdmsiE/tUvxLkIcdrlkfchbHs/Mzy3wfCMHDWY5wHtNYSItuAbW658ImF7Gne9fX2E\nEzJ3S5bH4yL5KSYkJCRsPNJLMSEhIQHYWPrcLDJ91nFFP49y0D2EvxUPOT1tIPquFsPyspZz0B42\nyOdHCMnrwhiATexxCxvzR06lLyKF5+jIQ6eKHaf1w92KljSmoPcI1ZuU3tjdAJ48QrpVGGNOGYbg\n77jA9kAjho5lDDPrw1jS9amS170Pmjn12+66E1ygQCw9MhHmNt/yMuuRw82WtIqBAk7gP4hcMDnS\nwI4yp/tz5LQpkWI1wH+ySd/PZtWW+TOejlaXfH7ko1sFaaXTaXANwrpM9XJYev93zMd0td0wMi97\n1PEbd4v1xpXJGOo1MDqVfR/PBjkx0Ia0zcr+As1gdUvkszFuDjnm0bi3PjHr/Y+0UkxISEgA0ksx\nISEhAdhY+kx0Ea613EGIHIRjA/y96q2KVhVIR1rDtaTVM8RwZS2nY7kzZlmPvoFOlba2YV2GEO3q\nUzW6eujtvgyhVPrGwbKY73q7shmFZRm65fdOlk7r2jFcjsKhHVgz6x1sEyAN6hApY6kzxLEtYC2m\nMlFtiLSy8f9dhJbNsU3QDsgdAwWcOdKQ1lrexunC+zZEStQdWMj7NR+vQbQ0Tx5BWtUbvr2xA0Wd\nLYTNTUFrA8acCkgdUNIGUrJO4q1UGqJv4BJbEExf2mu5JX4Cn9olvB84jk2k0p0jPe9iXm1JzJvr\n/Q0ZWjmGP2wr9ic7M3PI/Y20UkxISEgAztVL0cxaZvYfzew/m9nPmtn/Gs+/1sw+YGYfNrMfMrP1\nDn4JCQkJnyTOG32eSfrSEMLQzOqSfsLMflTSn5f0nSGEd5rZ90h6o6S/d7uCQggnDs/FJadEk6lb\n7voGioG0oYtI5sZw7u2SaoPKNDMfQjqJt7puoR3A4n3KYgejOEP3tHLgRUheO4MDMijwDOGHnTnC\n5kqkT4Vz8ALH9Z73+TjSzQbStM46oEdjqqz6Ya/rWwBzOHgfQpnmGNZXGkVLhMv16xUlnIEy96e0\noNKci3SzUDo6wr1Z1xu5O0e6WZQTkBulGfcNFnDH79fXqxvVECp4almB06T4BWjwcQPbGkfVGBU7\n/hzqKJsqQeUucvugzp0h0pCC4s6Pnfpzu8fgoaA4pZow+A8b/vtyDmqOsmsx509IztuffoQKq02n\nevwvSPpSSe+O55+Q9Pp70LyEhIRXAM7VS1GSzCw3s5+RdFXS+yR9RNJhCCca/E9LeviMe99kZk+a\n2ZOz5XzdJQkJCQm3xXmjzwqV7MvnmdmOpB+W9Ctfwr2PS3pckna3d4IurKx0yPvRcBramvg3YTJz\nSlaL1tI6nGf3Bwcnx3QY3ivdEig4eIeGHzdBSfdzr6fX9xjq8cCtsitFniYY68HU6VBPELNtwnkb\nKT7zzI+nx27FbvGRg3r2ZtVxge6EhX9YqKhT76J+WFOHUMPJvEptQzHnF83N8q/d9mQrx6vcOXSi\nz5COFQ7bS1hLlzNav72eIejeKSWZnre9hKrNUUyhSmWaBhyj53Bq37/g47aVrVcmmmXrqWUbkjS1\nmBI3o9Ucce0tiObu4xk2sNVjO8zv4s+oROzzpId4b+T32f5wtU2TX3Fr+iJb75Jdx7hk0ZHdys10\n3z53K8UVQgiHkn5c0m+UtGNmq9n0iKRn7lnDEhISNhrn6qVoZpfiClFm1pb0OyR9SNXL8avjZY9J\nes+9aWFCQsKm47zR5wclPWFmuaoX9rtCCD9iZj8n6Z1m9m2SPijpbS9WUBFKDaJFldTL5r7knwQ4\nXsN3+lpZUZVtSHd1LmyfHM/GTnH2606Nt0ewOII+5XC2viSnKrUhlWMdIdKS/Y5zlo5AseCwS6fy\nBeKTlzPkQiHFBt3M4fg97VRtHyL2uBOcpjaXXs/gOafArQueJKQ48HHpdEArIRf2KnhTjVte/i/P\nH5IkXT/2bYrGjpexf8PPC8+qD0fmAmK62Q3EJF/2chagmPWmU8/tGJM8H3pc+xxBwYM26PDYy54i\nPruG8TJYyLOL3kYSziI6Py9rfrYBR3ftexk7pZ93/wmdcp/OsK0jpMctMBf4B5+/NtLzMQRxyzMs\nynBGH+1Wc6HMN9N5+1y9FEMI/0XS5685/1FJX/jytyghIeGVhnNFnxMSEhLuNc7VSvFTiszcIfoQ\njqeU4NpG7pC5H1+Mw3IQIMWFvJqtBqy8sFCvFLslSVO/lxS3Ayo/hjRTC9baVvxW1SDdtUSMda/u\n5wvQNypJt83bYkhbOml7ezNaeiP17CzwnTz1yfQy+hd2To5HKKS75TRxNvb+N0Gf24jz/fxw5eT4\nkX4lO/YJSKvRyvqzhfeNztD7SMDTWIKcbjuVzLBNUEcceHjBLf5FN1LCi7DmIufOxZpvexxmXmcJ\nSbPBdfcQIHaQtnTZ9EG9mlXbDXsjH5/9Q58IF7YRTY746c7E+z+Fg315w6l/3vWxa1wDlX/Q5d2G\n0cG+1oGcGl4JN+Dx0JnB4n6hKiPP18uT3e9IK8WEhIQEIL0UExISEoCNpc+ZmVpWUaX+ttOX4wJO\n2jD+LuAQbFcr+x58e09JN5V1WOgQt3pj6fRlh1ZWJHEqh+vVjJcdr6x1UF3Tg0r0viEOFcmSmqCD\ni9w7VMD6WKLtDcRNl0jM1M2rfixGPj5LpAltg47NzO87i0DlSINaR+Kqz8490ZUMzuNRguuXZf77\nh6bXvH1QyT5oeHkdOGwXcMDPkGK0tg1pLtD6AtsnK6ftFhzam3D0zkBZL4ZTqahOcFiDRBsc6SeI\nAx+Byq+exAKeCjXEbJdDBCUj9WsZaMP25zm+7Pd2n3VPgMlFtHeKXLWra0HNJ03E0u/4VkYdqYFX\n6uVhM43PaaWYkJCQQKSXYkJCQgKwsfTZSqk+rWjJBBbKLagtTxBoW4CSLue3JikyxDIft5Gsaex0\neA95nI9zlAGasURscRteuM0x5MV6t9Kz7ZrfuGC7D5EnGHR7eAhH6ituLZ7NYRVGfPRqK6Gor/9O\nDmFlrkO7yqA23kJSpuEM7YL1+XDuFPdzmg94/bFZgwO3CD+I9h3CslxrQ3kb0ml8RqEBubSJP38q\njzeh5l22bu13QZkzxgRjayLH82+BbgfQdDFBGKhsLeaApg90AU+FMeZKBw7rRB3BCJdqTp8L1DMr\n/eYcCuq9qEjfQi5qIWacSc2zJuLAV0Nom8mf00oxISEhAUgvxYSEhARgY+mzlqV0raJwxy3k/UU8\n8wyOzKS47RhzO6s5pZ6A0QaYrfeh9vzAzIezD+vzfuFUsocESBksvaRntWiVLeHom8P6yPzOxHyC\nfNSwiu/DCbfRRs7kGmSsohP6Dmj6Jxre7mYdyb8QS6uJH3da66dTgRjzR3suFzZtoM/RchsWPtC/\ndMPFkJ7d8rHaK5GPGhyP1uIFaGKJeN6GwVrfQF7l2I2A+GluoxSImR7D+6DX5VYHKPaeO4GX6H95\n7GPa6NwqO1bmkLkb+/ERLNhNbF/0oSqubeTmhmN1K/NnHpCnezas2gIbt+pLL2NYep1h18uYxX6W\nSsrbCQkJCRuP9FJMSEhIADaWPpeNTPNXV1Q1O3LKMoLldnvhFKdoQKm4UREK5rVFemNNMWpUsrZt\nKCKjLQ/ewD/gvDu7APPiDZiiY1uYr5mogSZSKXoK5ete7m0poRo9hsP2FNRz5eA9PnLrb9mHAzKo\n1BYSdF1oeSztHPHJ202nhg9ALu1qcIp/YYkxjxbSpz7xsZNzwzqJnWMGR/vdwmndUQblacRHz468\nzmzXHZIzxGHXYjx7gEdyPocFu+7XdgZO5WcX4fRPx2zmES+8n/U6lL/j9TXE1beRWIzeBF0kLlsy\nDhzJt0pY2WugzzVswwhbJvOYRKuJHNF0yM5z3AemnEWrvaW8zwkJCQmbj41dKVoRlB9Xq8JiF+Kb\nyCvRmvn5FyBuukKj41/QBgLatvDl7+Z+zRQ+gA3kgilayNdSx3n42A0gzjIJ1Re/j8eTI/9LGzlS\nRkNf2fVLGBGgK1sgX0dj4p98g28mhWtX2PqY+zoePuRll0zfCWNFjpXXaN/bNYYj3qW2i/UusOL5\nxNHVqn1diOn2YNCY+grqannkfYBb3VbbV6c9hOKxZwXUi3KsfvYPKuHczq6vfO3AV5g15FEp9/yZ\ndyBUOwlISQqby6KONKTmlW4vqzJHN1y09xSueH8CnlX9mvfBEMJqWNkyRjWb+b1IEaNGTH06sfWh\npzWktT02r3OnXj3DLK0UExISEjYf6aWYkJCQAGwsfV5kQc+3qg3xNvJb5FjyH9ScWPW6TlWW0Ves\nPkaeFRhimKZygVCwDnwTB2M3nBTYpd5iBCHYjsEPsBEVSQoImNY7TtNm9M3DI5wvkW50Dir3jNPN\n+iWnh60cYrnRr2/epTSQH/bg61fCH290zUVRt83buIutiRoMQAdTp9Vt9ONoWY3XL12AT2XmfbsM\nXzuOG0V7t6/5DzQeHMM3k/oy9cmtKTqPl/7cthHaNtyiQQUqQfP1uXAY5rcomTfV59GgVt3b7Pu4\nZXswxHDM4RtZXkK74bJab/qYWw41pKm3McPWR2tWlTPbpUEFuV1A+2slxiJu2ZTF+hxD9zvSSjEh\nISEBOJcvRTPLzeyDZvYj8d+vNbMPmNmHzeyHzLAkS0hISPgU4rzS529Qle95xfX+D0nfGUJ4p5l9\nj6Q3Svp7d1pYHwo0jR33mePyf146lZ6Ob5Uk2aM/Iqy5LdCUKURO65RfheVwhlSZzO9yKlXlUdWW\nWsfp2AIis8c1p0PtDCk2+/6t2IYl+EBOnydQqcmQYnQRwwgDlWYQ8tZdeD+Pas7ZJrCmbuH6/Zyi\nrE5fR0ih+guLq15OHLsLiKcceESgjtDnK6U/w9HCy2NoH/1Kd5p+/Xzq/SetnV2s2tiDilJAGGgT\ntHLc9L41kEp1NPQti96eW9mpRnSEbY1xVpXTQ3hkt8GZgDkEr4EJzOazHb83gzW/NwPFhp+iYb4c\nDqutivrE+5xhDIXtlYC5nce1lNm5XFN90jh3vTKzRyR9paTvjf82SV8q6d3xkickvf7etC4hIWHT\nce5eipK+S9I3yX3oL0g6DCGsPoNPS3p43Y1m9iYze9LMnlzO14smJCQkJNwO54o+m9nvlnQ1hPDT\nZvYlL/X+EMLjkh6XpJ2t7XB5XC3/yyVyZDTdIbfdcNrQgnqKxetnh37ttQed1vQRWtUv4XUNNPBO\nXtRAiUC9S54H3Z1cqr4HU6irmNGC6HfNQKUM1sICaU3br3EeuoTz8gzUN9MqdMtxZey0twY62mqv\nnzaHfTgJw847Hzt9n8MSncvLnyyqse5BJWd3CCdxhLwVutVqLEljCOEaaGgGK7bgHH4qv0y4NXfJ\ndII8J7D+tqGGdLzt9ex1PCXpEs9uxhw9TbQlKtYs4Wi9P4RjOhz2m0uvh+luA9R72qDpyx6sz3Ce\np7By43Jl6UZTVUd44BTKPHXUP43O3iGsfw73O87VS1HSb5b0e8zsKyS1VO0p/m1JO2ZWi6vFRyQ9\nc5syEhISEu4a54o+hxC+OYTwSAjhNZL+gKR/E0L4w5J+XNJXx8sek/See9TEhISEDcd5WymehTdL\neqeZfZukD0p6253c5NYxCIdC4WTaRjpHOGSHaUUPGi2nd02I2AjiNg3QSuYumcGy3YXja4Z0o3SU\nnk68glaMrQ6gPcwnEqCqMoU1eYZ8IXU6idOiCYpNSlRfORBDzLbYwjfzGKlB4bBMJZca0oOOF2gX\nHdzh1E0q++jelaq8Z47990OIvF5CHDrqqcGpu4lxnhy7kzgRLvnDe8bcUfxit7IWU1HIthBA/ozH\nJ3c6cIDHFkgX6kEzeDDMXJhHYeblX+xXuXMMuU72B5RU8mtrPVjQFxh/lMfxZNoVztGAeOaDZlXO\nLoSFuR1DJ/lJj54SkaZT+HiDcG5fiiGE90t6fzz+qKQvvJftSUhIeGXgXNHnhISEhHuNc7tS/KSR\n2Umq0NbEaVXfkNbyulOcYteHorVdxUHb1HlHu8RQzb2MAhQng2PsCOZaWqJzXNMawXJcgxU7WvWK\nM3JgnEVamJdDiM8NPcQhw5GcvreTQWV9PYbX88NymjjN3Ul61vMbM6T77GFsu3Wnng30k7HnBrq3\n2nmYX4bDOpyRu0d+PIdTNcVXCbvsdHOKOPRl8IfxYObXaFKNVwHLOnO09Hd8LDKI42Z4RAs4b2fM\nW3rN699hEEB0PC8ueJ/b296m5lWfn6GAABrGhauagHoyUPYcUnMUAu7HfY2DwrcaWksfzznCzZkv\nprtVFc65vEnYzF4lJCQk3CXSSzEhISEB2Fz6LCmPCSeKjneTclHNPacqY1hxFeN/V2k3JYnGXINl\n2WCBW4BulX06zDrHWpJuL5DiE463FmlwmMBsCBmrJdJg1qAOPkVKlzq+d4dQdt57EAHFIzjyRqvk\nNuSv2gNKZHl/dgo4msMqOS28vEPz8bwMJ20h70d54HRvHiW0py1e6tde2/a2XEZazyHoMNXJR2On\nsoz3NVjuZw30Pzp4hyNvt4EC09F+ivEfYd60t71dx8gXczlDp+AhoLg9U4My9jZir48vrneODgdQ\neMcczTBHai2Oubd9u+tzfuW830EMPlOgdqdwGIfXQjioxpZq4JuEtFJMSEhIANJLMSEhIQHYaPp8\nAqoJI4lUDtqyDPBUjRQibyEF6sitf/mOn2eCpFNAqtABHGJpoZ7BdDmv0dk7i/Uj+RJUtRcjWCW3\naH0FTWxQdsoPrw9dKftS7lQqdKu6agfezyEUqy/I+8xUqi3Ev4aW179T83btzzyety8/v9vw+utx\n/Ltgl4eQKNvJvP7REtSYKtyIX6/TEyDDRZA3a0xAiXWrXFwPjuGrJGiSNN+GXBdkuWZar0R91PMx\n2kY1L8RxuTxHMjPE1be6Xv/g2B3NV+lYJanVWh97P4bzeq+BZ0cB8xgfP0IQQ7fl9J3bRzb2eVGs\ntow203c7rRQTEhISiPRSTEhISAA2lz6HcBKjSxteWXPK2kD3+7AQ7vcqGrRfc5p2ERSETtWzJuKH\nYSLgzkwAACAASURBVMWbQ9W61kEyIrSljVzGGjptWuzEAzhD1+G8TEv4CDTpwoULJ8cBdHs7B8VC\nDPfhZa+/F5WzWn1P4FWAMu1nPhZ7COadDZAgq49tCmwN0EIuOAHPYa1eWVHnzItcrN8ayHpwRj7y\nDo0Rk95aIk9222kgLeQzxDm3YjFUHh9Ccq65u36bpIfMZSWSUtVBqxkjfLiHvYwoPH4w82e420de\nbMQ4L5Z86o4cVvYGcornGPPFAs7zoO/zvOp/ji2QDHHygQm6yg3lymuQVooJCQkJQHopJiQkJAAb\nS5+tlBrRUXuGWNnaBDJicGqdlU5POtEa2Bw6NZo1EHsKuazW0MsYz261YEpSBkmpLCfF8vNjOH6P\nFhUl2mo4HwxIbtTqOH3NYTU/moMb43O3h9zIky3v08Ua5LgatzriFrDa0jp/XIIyQ14sg5O8oFrd\nAWMMiJUeH7pM2PSi03Yv0Me2Bav1FFsgRz20sXS6+4j5GI2Grqpdx5YEVbKyZkWP61TphgG/oPwa\n5k0DVnZBAiwf+LOYdv38Fqzok4vV2FEl3eAQ3YR5NzTbuMavp4wadm8UsGm0wLM75cUQncd7Obcp\n8LB2ff5lWuPIvqGMOq0UExISEoD0UkxISEgANpY+S5LF9X0LVrkMztvj607furAQH75QxQpvCTGr\nPViCYc0s4LBbgrLAiKrOlE7CcAJGW5too0VH8UHpFIxUr00pa1q5eQ2kzsYNOqmDVkEabSdaqMMc\ndBTq0Vnfx2JW9zK24OBcIJZ7iq2BDDmI69cgQYbZV8R48jGSPPVwAXNx28xHLnvIx+3K3Nv4VOEK\n1tsLehnQi8BR68Zrjr19jE0PXS+DzvvMHZ7V/JpaHQm4DnENlK8bcX7ub3k9A/O+7S6gpE31cuRj\nJsWngzVjwkts93S2ve2r1mbIKd0BlZ4MvTzmmm7N452bmbcqrRQTEhISiM1dKZZBYVx9LafYw+/C\nZ29ve+fkeLhE7pa44GhRBSSD0QWb1fW6D+Gi7d+YS2NsXs/9k7rEpvoCy8kFfNyKmPKS9S+Q13QC\nf7zmDf+a72LTXRAuxXpXV5FCs4WQLos+e3Ok8tQF+DfeQGjhjq9UxkalHYSzwXAyhh+etnxlaRA8\nbUWjTwkjwhJ9bmOlusRKcfs6VrYIP7y45elGDWpDtQWflzdrHteN8y0oytxYv/IfDbw/zcLr3G4i\n1wnCCRcIf2xipbgS2S3xTOZQ4JlidVqDm+Iwh0oOnvl/b3oo4PYYS0jYCGt9N0AVZdWWFlaeVAOq\nwYg0Q86d1bwNG7pUTCvFhISEBODcrRTN7ClJA1VbPssQwq81sz1JPyTpNZKekvSGEMLBvWpjQkLC\n5uLcvRQjflsI4Tr+/RZJPxZCeKuZvSX++813WliGcLoQnEpNkbaz13RaV+YVDfrvDacjD7apHuLD\nNoIBYA46MYbgaR2USbsIv2LejTX5WGagl7UOcp7kXn+JXBzLltOdNow7FHx9VfAwsnJyK/3JkKb1\nVJrSB+AnB9WhhRg2CVHSQxiJQJkn0/VqL/NF9Swy+PrV0U+G/x1ePJWZ5OSog7C4Dow0yzpC8TDM\n/VO+j1U5DfNzRwjJm8Ms1gDd3Dv0tiwpVNz2Old9k6TlFYQ8xjC67cK3GrC7ooNjNwS2kfOmJvom\n+g0PopwZlHm6Laf1C2w9rISZpjA5tSgIjLa0oUDUiXlx8g0lmvdLr75K0hPx+AlJr7+HbUlISNhg\nnMeXYpD0r8zsp83sTfHclRDCc/H4eUlX1t1oZm8ysyfN7MnZGQH0CQkJCbfDeaTPvyWE8IyZXZb0\nPjP7ef4YQghmttbsFUJ4XNLjkrS3vRPyvYpOwGVMS4h4MrhpXDitmHeqb8WjC6eaQ4S87cKC2UD4\nlQZeRkHfwC3Gi8EPD2F8Q5gIj2YV9byAFo6OoYay65y51vf6OwwnRMhbGxZKWos78oFZ5X1hrprl\nHqgUQH+8cublDUG3c/rSraHpkjR56pq38dUXqzbBIj4H1cMIqsXQtgI+dvA15RbH0LBNAT/MycTb\nvhfVaUrw6xrSfV5Fytob8B9t9Vwol1bcxcTbTi8DO/SQw0a36mtA58bm9RRIWVvWEE6J7QA7gFUY\nE/pCy1OyLvlXjnGZz6pxacOCXh+gfrgtZHYe10+fHpy7noYQnon/vyrphyV9oaQXzOxBSYr/v3rv\nWpiQkLDJOFcvRTPrmlWR/GbWlfQ7Jf03Se+V9Fi87DFJ77k3LUxISNh0nDf6fEXSD1tlgaxJ+sEQ\nwr80s5+S9C4ze6Okj0t6w4sVFHSalpych9pMsVyuPd+N9GQJR+ZLz0M9BGFZo9LpSwsk72jglsPG\nLhyZl07lSijv7Ez83q7d+li6sD5Oh6DStETvOGVagLKX3hRlR9gm2PN7LQqRdnOnzIcvuJNyuwuh\n3EvelvoSSi7YSSgCaCiUaTLk/SiojBNDJ5nnhHlmDubelgYzv255GQuYS+tU2CF7BqvvzSDzMo5U\nGlbjOkP1zPvfg+oMU4zSf6AOZ/P5xOn2EqLEyxcqrzJENiq/4nQc/vynHNkLbPXYLp7hyAs6DD4v\nd6bOq+fh1rS5QyhEWQcDBC+DEvXP51X9IWym8/a5eimGED4q6XPXnL8h6XUvf4sSEhJeaThX9Dkh\nISHhXuNcrRQ/lQiZtIzCoMv6ejXMBWVSQCEaMcXpHFRucNnL2KWFruPflQHiXfsdp7I3lm5xpFhn\nd7K+XSs6NUWblnPQUThpF1O/5vjIY2gD6GMOYrdoIiZ34ZT0cquyvtKuvyfvwxjU2IbQ94FVMqA7\ntQNY4lugm1sI3JaPy3BYtSXrO30f1/2+JizlTN3S3/f+ZxBFPYAQbn203opdQJloErdK2nPvzzEo\naGuAMdzxUpZDv6bZgYDsoTupl4gV5xNf9c4Q49zAuGUX4bCPLZWDDHViXFo73v8GZHWumrelD8+J\nLAr+8s9gMUEqX6RYrU+Rl6hblR02dEm1od1KSEhIuDukl2JCQkICsLH02ZZB+X5FBQJktGgVbVNQ\nE+KeqxjmEta8PkRbB5f8uA5H4j4o1gyUda/hFsVTTuKIia1DrDWPJOsQ8asdqG8VA6dPDeRfyWBl\nXcLZfAGH5AxSZ13EHk/j9XTknXWQl+PI6eiK6kpS++G9k2ODgC/zkoCxazbxtnP8m1GI9rCN1Kxw\nnma8dYA4LuOaJT/fxfNqQSy2iTE6vIKtj/j8l3NsDUCcdQmjbOc5L2SB/D/PFgjXB09uDjEAkIPL\nokxX1vF+DmDZ3kZbS3hCXIaYrhCMkEM6DY9R/bmPxRTPtFFk8f94zrC+d+B0zwyntagZYMVmWp/T\nSjEhISEBSC/FhISEBGBz6bOZskbVvQZyUDA9JbWRuoyEjordOfJ5zGB9bc29jAIjOGwhj0Xh35tR\nC463VAuD4+8ADtm9vcoS3IF5rw/15mHuFHSInC9NylUtmTsEscKgb3OkZG1MqmtmXW/T6Mhpsm3B\nYRvyUvMjtyA3d+B4TKdmxP4WOz7OOSTdVlbv9tQHqI84XQyn9hsU+4B69wKq0VA772x5u5jKtgc/\n5v169cwPanCun/kFey2PNzfEnnM2tbHFUSImm2lVO9i+WBmOp3hWXXT0qtzr/oHcVeILUGxuHhwi\nJlsY22v7nq/mocsPeDnxUTCHyxaU6UvM5+wA+Vpi/beK3W0G0koxISEhAUgvxYSEhARgY+lzWZaa\njMa3nKeDbSnKRDkRWsV/kh5QVbocOQUroa5loONX605Nu6CbdSTAWi6cnm1lTk8nkUKSml1HsqIW\n2OMxFKEfXnrfWqBBM1iiu7D4Hu7vnxzXYuKoZbneoth5HrTyIaejVHJuYgxLmCvHcA/mV7gOtXPt\nV8+qDePvZA9PAJJfrQzJp+Y+GEd134J4oO+JqwZUli6d1s/m2MuIsc95E1sQDEoGFlD4XmKWtOAw\nXTTX/2nNML7LuK1Qo2l3Aa8FpBwbTb1vNVDwGXzhDwq/Zvv4DLm2Z/2ZN69EaTy0adI+Q4e0ifKm\n6y/ZFKSVYkJCQgKQXooJCQkJwMbS52DucLssnD5NZ24trcN5mWa81qi6voDydA2xnxNQ1hYoTgma\ndgE6WvMj5xvZDImekNCJsltbkYaO2k5ZenDSnsBC2pNLZw3HOD/y713rotNdSodRDmuVvKgJy3p3\nyx2zF13IUg19DE+hLNaeztpO2Rdwjg77iMmNWwwF5MqWoLd1Ju4Ck1vgEW5jL+PpkTtSd5GMqm9+\nTQN93YmT5VoTFucGZMkgM2dYSxicx3MkK4NQt8Y5/gGqOrNqvOiQ0IcUXG0PknOQTmshyHx99LxU\nw87DxQPEwW9jwCJVZxnDA4+fbzYQGNBt4DiWUdvMNdVm9iohISHhLpFeigkJCQnAxtJnq2Wq70Xa\nCGthv+E8dTqCRa8NehqTNwXQ4RmT+MBCOuiAp0BGq414W+ZGZiqoEWKI6WB8XFT1t5a4Gg6+va73\noU5jIRJHTWtwjD52umsNxBODhjVvVAXNIWs9RLxtE9JVNfTNQBk5Rk3kGqZY2HLqg9edQcE8qpQt\nXwXH6A6szIh3zpHQqQFrviCX9vC+j90U8ckLOLs34LBfi6eZ/GsJHbU6vQ/Qn9bSn3kOC3mBmOT+\nBSiMA3m0KDczyH+1kVMc2xFbNX/mwyPfdqCS+17L51C+h3EBDddV98g4ipOnjvmcYzsgQ380h9r7\nhephbeqKalP7lZCQkHBX2NiVYlmWGsWv2wXzL2U28a9ffQT/udy//724gjgyGGhyCJ4iVK2zxJcV\nS4gCOS2EvBcLfNnh1qijOXwqY5E9itluQagUxpoFfB3VxkqVfmqncn2gzw0aY6oV2vzYV69HI1+R\ntNu+UjkljtvDFBr4KmyG/CqdqdfTOER7L/lzacTBazDvh3k9Q0RhzkovY47zXeTyLBDy2MBqFq6E\nQrSkpjGnTRvKSANfeKnAw8rhV7ggg8CKb9HFCvKqr9TDFe/zVj2uodFnehe2MSWYerYBP80cuYWW\nI59btb6vzwusbWePeoNny2qJuDiVywiKQvDpZKji7Okqt0w5X29Yu9+RVooJCQkJwLl7KZrZjpm9\n28x+3sw+ZGa/0cz2zOx9ZvZL8f+7L15SQkJCwkvHeaTPf1vSvwwhfLWZNVTt03+LpB8LIbzVzN4i\n6S2S3vxiBdmyogLjnH59zrdmUFjpIHRvtZFfQ/6VBtRlwqmtdtDnzK+vH4LKwE9y3HNK0kW+zWuF\nc6UHQkWx6j34QE7pzebllUiZ2Rp6uwyU7LjPbx+2CaDIMq9X1+cwCuxCWbULv0c1kS8FQ3G1g1wo\nB0jxWvMyl6CVguDuUVaN/84YbWqBpp+RTpPbF7XJrao70ukwunkO4VSI4ioK8U6RGpaKNVQammPe\nzLDFMivXhzyOoDbTG/g106h8xGuH2F45Xrpxox98zPk0T20TICWrYJjK+j7n23he2/VqS2R/jm0S\n+MM2+nzOUMzpVPsK9ovnbk31KcG56pWZbUv6rZLeJkkhhHkI4VDSV0l6Il72hKTX35sWJiQkbDrO\n1UtR0mslXZP0/Wb2QTP7XjPrSroSQnguXvO8pCvrbjazN5nZk2b25Hx+RmB7QkJCwm1w3uhzTdIX\nSPozIYQPmNnfVkWVTxBCCGa2lkuFEB6X9Lgk9Xq9MBpVVr8GTJSTntMAWs+mCJ3r5pXlrg1qNimR\n+hE0mf6N1D6d9cFTDr2eDnKHTGBx3kWqylUKTfrU5aAvC1i22zW3MpYFwu+YIwTMewKnwYCLikFU\n5qEIKsVbQHtPjT7GYm9MayQsvofeLlpuxw/Aih9Ti2JHQTX4MdZ6yGED67ONkZcFPqijmVPPBbYY\nOpnXOQ5Q2IlhdA34KdbhG0gw/wnz7EywHdAv0BFQ3Gemvq2Qx2suDhljCo+Amrd1HypJtATXue3T\nQijklH6yDvZ/1d7tum9vzBY+Wagu1C/9vmGorinP2NK433HeVopPS3o6hPCB+O93q3pJvmBmD0pS\n/P/Ve9S+hISEDce5eimGEJ6X9Akz+xXx1Osk/Zyk90p6LJ57TNJ77kHzEhISXgE4b/RZkv6MpB+I\nluePSvo6VS/vd5nZGyV9XNIbXrSUzFSLKjhdqJ0UoCeLoTvEjq9AESQqlWQIIevuIyQO9KUwxkg5\nxVhiT3MJx3B72kU+2yuRT+lE5FSS5tFCGUC7wrZToxasrAF5RE7CGiXNoUYzA4GaoV3wadaKKNKa\n2qx7f0pQ9rlIk71vnR3n5hR/zTs+tsfXD/3W6xDrjYpBLThUt+dImbqFdKTB+7aF7/p44DR9sQdl\nHlDcWX095ZvGJjaoboMcNt2pP/82FGMm2NZYIn3rMzvet4cKBA/Ub/2TK7exBTOB1Zhrlq6X0THk\n9jF4eKPtdDanwtNs6WPUis+3gbDNGp7VaOSO/APDPOtWc9HsLI2e+xvn7qUYQvgZSb92zU+ve7nb\nkpCQ8MrDuaLPCQkJCfca526l+KlCZpnazWqZT7fnFlRilp+xdXLcQwBoKCKdoJ8xaDcLLCdOzQag\nG+0+BEozp1ItnJ/DIbuxDVWZaKHdX7gFtd2ASg2cuucF4oAR+1rgmMo8Exeh0XTm14wvVVc1CgiS\nzphbxfvfhDLMHA7uGUR7F0OndbR+9i55qk4q7OTRilxCwHcGNVk7cGraueRW4acCtiOgMNOAZwEV\nfrIhtjW6cHyOtLExPRUI7Ncynw881psQhc1a3v9HAlSPIENDkdsimvHHKHv7AHScqkNDbFnAqfpi\nzR/ofs3nC4ltjuAB5usprlaCsvlDLiY8rvn4NLEdJKRBzaOXgZ2Rz+d+R1opJiQkJADppZiQkJAA\nbC59DlIzspZ6F7JbSCG5BcMxsoOqOYm0ANS4PXSaeuSs+xQMsbp0++3nzoMWcooTIK9VUtC0XV2/\ni9jbAgK2i1MaZX44RIxtF3JdnZpTtjZytEzgkH01Cu42IA47CHBofx5WUchVNXO/PscntrfttG5G\nKo22d5gqMwrEzjEj26DpM+QWacIx/NGtyyfH149ApeGkzC9/Dho4Qg6YlXDwArwzM1p/8aymPug5\niOpl6IjVIe+2kmWTpACx4JBX91KEeLTjdfbwDKewmjdHpK3+XLaRMnaMeaZjbKv0vb2z6HGRIWa7\nncNToOtl7EKWbRKq81QA2CSklWJCQkICkF6KCQkJCcDG0ufSpFlkUDNYcXczp3sZaBBloqZxVPJj\nODrDYbs2crrx7N56+jKjZa6OlJQITy6p5kyH8HhYnGXcg6P3YQ0xsXDC7bRA4GnlhYWUeTcul9X1\nBeKN67mXcXzJr+26kV1aridRx0OXo8rp6w1l6xd2vV27MVXs1gwWT1DN+gve59GjsBofeP8vI2Vr\nBlrLWPVl8Pa2sa0wjRS/ATV2u4BA8aWPC3O+MEfKgyP/c5qDbtsU9nemBY2O9zVQ+hLO4NM9+g04\npox3RoDBuM48NvAEgEGdzuGHzaqcTs1pP9MBG57VCN4XttqOyjbTeTutFBMSEhKA9FJMSEhIADaW\nPmdm6kQV4QGcp2fo8Yxxuy843dOVyvF2AEdnI2XpO9145IzvSgnKlMHiHWAJz5DCkhJUq5DSxpwq\n0aCAoOY7B17GCPHBC/M6GwHxwSO3BDOFqKIltIa2smsraS9JKrf8vhm2BsK+j2FRMPYaqs2g+N0j\np+f9lQp2D9sIyCU7Qext4zk4KffW0+2ysV5tewpzfQ0pXi8Obv1TyJH8q420skNYgsdQx36u5+3d\nA5VmDrMmyinH1bgssL9QwoNhPkGUAJKfteZw8K554Yzbzna8nmLmc3FYeJnLWlXmPiTnLsiDC5ju\ntIay1azmwmaS57RSTEhISDiF9FJMSEhIADaWPheh1NGsogUBVrJ9UJIurI9HF+AoHSqKmYGmCBRM\nY6dMdUgtLQZ+ntbkGokGKHNYY4mUpCLe20ISoRnrR7NGO17PNhJNFcjpvHjezcWwPauAhXQV28sc\nwdOh92eMCObmEirYsHI2YWU+bvk/Lk/pVOzjvwPr9vJjleN1aCIG/QGnci2DhRTx0aRwtuvl2QB5\nkuFZYJBDEyy94WJ1L6XLGvAmCHCu78291gfkMc5MXFZuQY6rvD3RbKBN+dznwYJJtiBjNkNxHcRK\nF5e9//ScYPWMyW9GR/4M2yhLbi9gDi8CPAG0Pj58U5BWigkJCQlAeikmJCQkABtLn8/CBD7Nixue\nRIgSULNo0euAbBqkqAQVbCZxImWl8nH4/9s7t1C5rjKO/7+Z2fvM7WTOJWlSm6atWCp50YqopUWk\nRfBSqg8iLQpFCvWhDy0o2vou1BcvDyKEVumDqCUKig+CtEX0JaitIG0Ua7A2Jbcm5zL3y57Ph73m\nfP+Tc0xtTTIzK98PQvbs2bPPWnvvWbP+67uV7NwddphWkzJdskB2h0Eq0U8WO333q6aHkjHFL5dN\nmq6OraP9hv39GlmRsyLFam+dnxyQKfa5QUWcevTULI7tGiVkOT1E7S1S2rOVlEz65FTePZjL0OI5\nWoIgCaiUnXpEUo6XRoSzkFco71aHg9xpeYAyoiehSFS/Yecb0c2tUoLrTVoC2Jaiq0IymPa3Se4v\nDKk2eEi1NiTJPKDnk5KgQ+nZ2sPpzRok2anWOLvUF1bs+vcodVgl3FOhXgg5qZcoFRwHEmyGFHlc\nCzomfKboOI5D+KDoOI5DRCufixDUkcuZja5pH1KYSKlObjak+sHBKl0i+ZYsm5Vxm9WYyEgmTf42\nABTICVsozrRNlr5awfZPsiNnJN8G5LyMDcqIvWiyskwppVpU7HmBUn01qyz4SP5U8j5x7G2pan3u\n9sz62S5aW0pUO7lNDuPLe+2zQ1pKSJtUxItrQ5fydhXfZfK6R/WdMypWxRmuC+vWrokFGQBG5AnA\nGaL5miZvkgyu5O0qr9rfH1B/BuRU3t7gXO5GmfKOcU3kUtE+28ysvcsT6/bI+jkip/+NRUoXRhnj\n9wj7EBhJl9Ki2crMtkzpvQXrU7eUb9MqAsZqbam3Kf0Z9aEa6qiz83tMzNRMUURuE5G/0L9NEXlM\nRFZE5Lci8o/w//K02+o4TpyI6n9LxTJdRKQI4A0AHwbwCIALqvqkiDwOYFlVv36pzzeWGnrHXXfu\n2N/qmAWgNqaJcoGzquS/ioUFCtUiH7QhfWxEM0z+4eRaJ0yzaDOBlbLNSgqUvWSt18LFcJ2R85RA\n9ECXymPSMYPF3X3JEmrXmMPiQuhWLxvu+AyAbWFmNAlFi2bHJQon5NlHrWLZZiokTsZkYGiHRKet\nlFPqGIuU5JTrxWiN+jykmX2Bjj9PfqVlm3ENyDBUvJB3akzGp+I+mm5xBt0Nm+21SIVk1BZOLMxs\n0kxxktSGk+a2KTxyOKL+1Ck8kIwu+ztUhpbuZzPhuDyDP6tBFXDoZdYb7PgMgK16RwBQDIbD5//w\nO6xtrEc3XZypmeJF3APgn6r6GoDPAHgm7H8GwGen1irHcaJmltcU7wfwk7C9X1VPhe3TAPbv9gER\neRjAwwBQruz+S+04jnMpZnJQFJEUwH0Anrj4PVVVEdlV86vqEQBHAGDPckOzkMElJXnC5UFBEmvU\nMk04OVooCW2JstsUu7aflAdGZKQ4XaOylkqL5CPbZv8wrZC/30SfU1nPdsPOvb9qZUJRIJnUIRlE\n4YQF8p8bk8Yvyk6/uoz8NXtkXGmcsuvTWrVjOOSLDQ2jNZOV3Qb5bGZ2/EJ5ZxLVxsCuQ49CC3s1\nO3e9tHvy1YwkY8IqnIxRBZL4ZVpK6Nfzv9unsq9C4ZwphTP2GrQ0QaVQOGmxpFQGlb5mXVqGaQVD\nSrnDtXRtc1tdFq7nQ4aZs4ndowUyACYUC9isU2aiEt3z5s6lkhIZBUHPfIv8ISelebPdv4Zzz6zK\n508CeFFVz4TXZ0TkegAI/5+dWsscx4maWR0UH4BJZwD4FYAHw/aDAH551VvkOM41wczJZxGpAfg4\ngC/T7icBPCsiDwF4DcDn3/I8Y6DQz7UIZ/5opCafe1QeMq1yRpRcemz2TRtVSZoOKSFstWYWyjal\niVktULLObaGAtN218/fIP6wcwt8q+0wmK9U8AZUOSarka5lwhhXb7lFYGv8MXk8xZd3F/I0iZYlp\nUf9bFJ23ktljw6FtzZFZ9kmlAZSZqJxa4/tUYrRYS8M5KDNPh2LrSKam1+3b2h50rMHN8+t27mW7\n/uwhsKqUSUdMYk6WVYokTctUJnVEvpYVupybS3ZMicrgZpQUuEP+oAtUtrQVnsuWmIxNl+z6HEga\nduzQ+jlYoKUEsspXO7R8QV4EG1T6dgN2jw7Vc1/SQcYJkTkzjvVnIbPnbHf7dDzM3KCoqm0Aqxft\nO4/cGu04jnNFmVX57DiOMxVmbqZ4uRhhjDXNJceBZM/W/j6lHhlzeU6yHI5CfY+MQq5OjUy+Hkwt\noIbD4pYoVE/JGTurmxWPpRxXiCwXdwndIsNympq8p8Q4UHI2TsvkJC120Inuua3tAySZxxWSXms7\nLZF7B6SByTo+Ccm7uC2VOknWzPRuhcIp2boKkq+bvVzWCVlQl1MLFSTFjvG6nbs5IEd3SlDLjuSL\nFdP+ZwaWGelAwfavB4f5ReoDJRTaFkLZozo3VQqhZFm5SfVlGpRWKB1bG/eGR2qjbc9Wgd6XCpVy\npVKmSY+k/Igkc5X6T8sN152jZQoK1+sW834kCZVDpXBGUJhf7+zG1va/6/m9GujuDuLzjs8UHcdx\nCB8UHcdxiGjlcwkFLI9zydkakUmVZ/zkEMu/DtUgPbokE+uLJsFbHZOaGSXtTNomU1JyZE6oVCQl\nLEExoTjgjEqVBqsrN1tYazft2IUlyhjTp5ojfZNvq2ShbJfJ4k7Hl0Mi2NEmOQPXTY7zMgFL4DbV\ncamRxl2k68W1RpSWLIRq4EycwJXrnJAFN23vHhNdq5rcZYldoeS3bNlfTKxPPYrzXQiHdJUylcDB\nhAAAA0xJREFUKpEJvUnJXxNKoMvlY1tUbnTYsWdhjTL8cFba1Wxn1NUkcw0ALNLSSKlr+9sjO9+A\nSu/u6dDyDRVmoccPpM6RhNOM6EshPdsukmQvq13cmzS3ip+JtFaLzxQdx3EIHxQdx3GImU0d9v8i\nIucAtAG8Oe22XAX2wvsZE/PSz5tUdd9bHzZfRDsoAoCI/ElVPzjtdlxpvJ9xca30c1Zx+ew4jkP4\noOg4jkPEPigemXYDrhLez7i4Vvo5k0S9pug4jvN2iX2m6DiO87bwQdFxHIeIdlAUkU+IyN9F5NVQ\nFjUKRORGEXlBRF4RkZdF5NGwP8ra2CJSFJGXROTX4fUtInIs3NefhXo+c42ILInIURH5m4gcF5E7\nYr2f80CUg2KoGf195LVeDgN4QEQOT7dVl40RgK+o6mEAHwHwSOjb4wCeU9VbATwXXsfAowCO0+tv\nAfiOqr4HwBqAh6bSqsvL9wD8RlXfC+B9yPsb6/2ceaIcFAF8CMCrqnpCVQcAfoq8dvTco6qnVPXF\nsN1E/gW6ARHWxhaRgwA+DeCp8FoA3A3gaDhk7vspIg0AHwXwNACo6kBV1xHh/ZwXYh0UbwDwOr0+\nGfZFhYjcDOB2AMfwP9bGnjO+C+BrwFaRk1UA66pbaW9iuK+3ADgH4EdhmeCpUKcoxvs5F8Q6KEaP\niNQB/BzAY6q6ye9p7mc1175WInIvgLOq+udpt+UKUwLwAQA/UNXbkcfrb5PKMdzPeSLWQfENADfS\n64NhXxSISIJ8QPyxqv4i7I6tNvadAO4TkX8hX/64G/na25KITJL7xXBfTwI4qarHwuujyAfJ2O7n\n3BDroPhHALcGS2UK4H7ktaPnnrCu9jSA46r6bXorqtrYqvqEqh5U1ZuR37/nVfULAF4A8LlwWAz9\nPA3gdRG5Ley6B8AriOx+zhPRRrSIyKeQr0kVAfxQVb855SZdFkTkLgC/B/BX2FrbN5CvKz4L4BBC\nbWxVvTCVRl5mRORjAL6qqveKyLuRzxxXALwE4Iuq2r/U52cdEXk/cmNSCuAEgC8hn7BEeT9nnWgH\nRcdxnHdCrPLZcRznHeGDouM4DuGDouM4DuGDouM4DuGDouM4DuGDouM4DuGDouM4DvEf4Hu/YQWy\ndIQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0140e4668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[2], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[38])+ \",\"+\" iceberg_probability : \"+str(pred_valid[2])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fa013f41390>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GkLRQAthPaN4U3gO9QxndU0G3vof2+iiyhII//QUKB6HI0xLtNEEVPFUXFyi6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\nCBIxIQZBECT+B+VmHjaYvIV3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa013fdd3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[3], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[43])+ \",\"+\" iceberg_probability : \"+str(pred_valid[3])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fa013f25080>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+QKanjSZk5yCNNo9DoYV4dOtjyaDmY4Vx4jCyawyngMGJ/9GBRNn0GM7+WGJZ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\nED4oOo7jEP8DP1Rc/zcA0jsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa013f7acc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[6], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[104])+ \",\"+\" iceberg_probability : \"+str(pred_valid[6])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fa013e86940>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2v+H3f4GSikJhJG37vWv2wQ1xI5uxwNNsgsJAoLclFOgunuEpogrqyKUdIW96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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa013ee28d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[9], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[138])+ \",\"+\" iceberg_probability : \"+str(pred_valid[9])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fa013e6c320>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BMbzNaMUdIyY2myGumvG+TJkJR+oK8cHtAWXHmGjJKdYyCVGJDElMa5qhmQR8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\n1wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa013e41630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[10], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[141])+ \",\"+\" iceberg_probability : \"+str(pred_valid[10])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fa013dd0b00>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jO9nyoOadHmzRYE1WQiFex73TmbyunBoHLFnkE+/zEgp2D9SX98mAfTpS1wMU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\n/gcDpkqTkWPPngAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa013e23c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[11], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[167])+ \",\"+\" iceberg_probability : \"+str(pred_valid[11])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fa013db2470>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Yx5mAptVjUu4cLs+NfZy/dKpXzDcHwWYI9mZAdg9K8n5jszNvMS3jsf2cDPxu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\nd3qO48wV/wM0YSfEA70u4gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa013d83f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[12], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[195])+ \",\"+\" iceberg_probability : \"+str(pred_valid[12])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fa013d14dd8>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kfBWxjrswny37GNojlpIGxtdaH/HtRGWEI8UG+83hX6wjv05D/Zs1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\nhP8AH1MAE34uh9YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa013d71518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[13], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[199])+ \",\"+\" iceberg_probability : \"+str(pred_valid[13])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "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
}