Model_03-Adam-k=10-amsgrad-0001-15p-Copy3.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=15, mode=\"min\")\n",
    "    msave = ModelCheckpoint(filepath, save_best_only=True)\n",
    "    return [es, msave]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getVggAngleModel():\n",
    "    input_2 = Input(shape=[1], name=\"angle\")\n",
    "    angle_layer = Dense(1, )(input_2)\n",
    "    base_model = VGG16(weights='imagenet', include_top=False, \n",
    "                 input_shape=X_train.shape[1:], classes=1)\n",
    "    x = base_model.get_layer('block5_pool').output\n",
    "    \n",
    "\n",
    "    x = GlobalMaxPooling2D()(x)\n",
    "    merge_one = concatenate([x, angle_layer])\n",
    "    merge_one = Dense(512, activation='relu', name='fc2')(merge_one)\n",
    "    merge_one = Dropout(0.3)(merge_one)\n",
    "    merge_one = Dense(512, activation='relu', name='fc3')(merge_one)\n",
    "    merge_one = Dropout(0.3)(merge_one)\n",
    "    \n",
    "    predictions = Dense(1, activation='sigmoid')(merge_one)\n",
    "    \n",
    "    model = Model(inputs=[base_model.input, input_2], outputs=predictions)\n",
    "    \n",
    "    adam = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-8, decay=1e-6, amsgrad=True)\n",
    "    model.compile(loss='binary_crossentropy',\n",
    "                    optimizer=adam,\n",
    "                    metrics=['accuracy'])\n",
    "    \n",
    "#     sgd = SGD(lr=1e-3, decay=1e-6, momentum=0.9, nesterov=True)\n",
    "#     model.compile(loss='binary_crossentropy',\n",
    "#                   optimizer=sgd,\n",
    "#                   metrics=['accuracy'])\n",
    "    return model\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "===================FOLD= 0\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 20s 813ms/step - loss: 0.6232 - acc: 0.6154 - val_loss: 0.6183 - val_acc: 0.7531\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 592ms/step - loss: 0.4786 - acc: 0.7444 - val_loss: 0.3305 - val_acc: 0.8395\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 593ms/step - loss: 0.3522 - acc: 0.8365 - val_loss: 0.2808 - val_acc: 0.8704\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 14s 595ms/step - loss: 0.3302 - acc: 0.8462 - val_loss: 0.2647 - val_acc: 0.8704\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 14s 597ms/step - loss: 0.3202 - acc: 0.8533 - val_loss: 0.2391 - val_acc: 0.8889\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 14s 598ms/step - loss: 0.2826 - acc: 0.8682 - val_loss: 0.2332 - val_acc: 0.9012\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 14s 598ms/step - loss: 0.2600 - acc: 0.8786 - val_loss: 0.2283 - val_acc: 0.9012\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.2620 - acc: 0.8852 - val_loss: 0.2752 - val_acc: 0.8951\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 14s 598ms/step - loss: 0.2236 - acc: 0.9083 - val_loss: 0.2179 - val_acc: 0.9136\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.2270 - acc: 0.9103 - val_loss: 0.2379 - val_acc: 0.9136\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 14s 601ms/step - loss: 0.2352 - acc: 0.8929 - val_loss: 0.2169 - val_acc: 0.9074\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 14s 599ms/step - loss: 0.2096 - acc: 0.9074 - val_loss: 0.1978 - val_acc: 0.9259\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2412 - acc: 0.9020 - val_loss: 0.2158 - val_acc: 0.9198\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2052 - acc: 0.9114 - val_loss: 0.2029 - val_acc: 0.9012\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1855 - acc: 0.9220 - val_loss: 0.2183 - val_acc: 0.9198\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2129 - acc: 0.9080 - val_loss: 0.2163 - val_acc: 0.9074\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1741 - acc: 0.9351 - val_loss: 0.2238 - val_acc: 0.9074\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2233 - acc: 0.9162 - val_loss: 0.2284 - val_acc: 0.8889\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.1644 - acc: 0.9343 - val_loss: 0.1955 - val_acc: 0.9198\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1911 - acc: 0.9271 - val_loss: 0.1984 - val_acc: 0.9259\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.2055 - acc: 0.9174 - val_loss: 0.1902 - val_acc: 0.9198\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.1761 - acc: 0.9290 - val_loss: 0.1889 - val_acc: 0.9321\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 13s 540ms/step - loss: 0.1722 - acc: 0.9342 - val_loss: 0.2248 - val_acc: 0.9136\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1711 - acc: 0.9322 - val_loss: 0.2118 - val_acc: 0.9259\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1579 - acc: 0.9336 - val_loss: 0.2128 - val_acc: 0.9198\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1647 - acc: 0.9284 - val_loss: 0.1992 - val_acc: 0.9136\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1578 - acc: 0.9344 - val_loss: 0.2260 - val_acc: 0.9198\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1616 - acc: 0.9310 - val_loss: 0.2166 - val_acc: 0.9383\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1626 - acc: 0.9336 - val_loss: 0.2016 - val_acc: 0.9259\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1422 - acc: 0.9429 - val_loss: 0.2376 - val_acc: 0.9074\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1532 - acc: 0.9324 - val_loss: 0.1987 - val_acc: 0.9136\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1722 - acc: 0.9304 - val_loss: 0.2010 - val_acc: 0.9259\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.1364 - acc: 0.9454 - val_loss: 0.1808 - val_acc: 0.9383\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1552 - acc: 0.9325 - val_loss: 0.1936 - val_acc: 0.9198\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1725 - acc: 0.9316 - val_loss: 0.2506 - val_acc: 0.9012\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1173 - acc: 0.9486 - val_loss: 0.1944 - val_acc: 0.9383\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1161 - acc: 0.9468 - val_loss: 0.2039 - val_acc: 0.9321\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.1641 - acc: 0.9319 - val_loss: 0.1759 - val_acc: 0.9321\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.1444 - acc: 0.9403 - val_loss: 0.1716 - val_acc: 0.9259\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1515 - acc: 0.9392 - val_loss: 0.1867 - val_acc: 0.9259\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.1147 - acc: 0.9551 - val_loss: 0.1706 - val_acc: 0.9444\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1126 - acc: 0.9546 - val_loss: 0.2064 - val_acc: 0.9198\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1261 - acc: 0.9545 - val_loss: 0.2215 - val_acc: 0.9136\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1194 - acc: 0.9551 - val_loss: 0.2073 - val_acc: 0.9136\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1413 - acc: 0.9445 - val_loss: 0.2159 - val_acc: 0.9012\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.1257 - acc: 0.9484 - val_loss: 0.2185 - val_acc: 0.9198\n",
      "Epoch 47/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1186 - acc: 0.9514 - val_loss: 0.2391 - val_acc: 0.9198\n",
      "Epoch 48/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.0976 - acc: 0.9585 - val_loss: 0.2146 - val_acc: 0.9198\n",
      "Epoch 49/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1111 - acc: 0.9630 - val_loss: 0.2391 - val_acc: 0.9198\n",
      "Epoch 50/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1024 - acc: 0.9573 - val_loss: 0.1887 - val_acc: 0.9383\n",
      "Epoch 51/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1559 - acc: 0.9312 - val_loss: 0.2154 - val_acc: 0.8951\n",
      "Epoch 52/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.0982 - acc: 0.9650 - val_loss: 0.2104 - val_acc: 0.9198\n",
      "Epoch 53/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.0768 - acc: 0.9669 - val_loss: 0.2111 - val_acc: 0.9259\n",
      "Epoch 54/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.0752 - acc: 0.9681 - val_loss: 0.2855 - val_acc: 0.9198\n",
      "Epoch 55/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1092 - acc: 0.9609 - val_loss: 0.2559 - val_acc: 0.9012\n",
      "Epoch 56/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.0905 - acc: 0.9714 - val_loss: 0.2004 - val_acc: 0.9198\n",
      "Train loss: 0.0585668475035\n",
      "Train accuracy: 0.97919556172\n",
      "Test loss: 0.170607180576\n",
      "Test accuracy: 0.944444444444\n",
      "\n",
      "===================FOLD= 1\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 18s 749ms/step - loss: 1.0556 - acc: 0.5085 - val_loss: 0.5285 - val_acc: 0.7267\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 15s 607ms/step - loss: 0.7702 - acc: 0.6168 - val_loss: 0.3792 - val_acc: 0.8075\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.6850 - acc: 0.6700 - val_loss: 0.4700 - val_acc: 0.8012\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 15s 607ms/step - loss: 0.5289 - acc: 0.7840 - val_loss: 0.3471 - val_acc: 0.8447\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.4011 - acc: 0.8310 - val_loss: 0.3014 - val_acc: 0.8323\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.3656 - acc: 0.8405 - val_loss: 0.2199 - val_acc: 0.9006\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.3260 - acc: 0.8507 - val_loss: 0.2733 - val_acc: 0.8882\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 15s 608ms/step - loss: 0.3205 - acc: 0.8584 - val_loss: 0.2160 - val_acc: 0.9006\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 15s 607ms/step - loss: 0.2880 - acc: 0.8728 - val_loss: 0.2084 - val_acc: 0.9193\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 15s 607ms/step - loss: 0.2831 - acc: 0.8809 - val_loss: 0.1799 - val_acc: 0.9068\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2636 - acc: 0.8891 - val_loss: 0.1917 - val_acc: 0.9193\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2683 - acc: 0.8893 - val_loss: 0.1800 - val_acc: 0.9193\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2482 - acc: 0.8906 - val_loss: 0.1945 - val_acc: 0.9317\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.2409 - acc: 0.8975 - val_loss: 0.1726 - val_acc: 0.9255\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 15s 608ms/step - loss: 0.2422 - acc: 0.8990 - val_loss: 0.1702 - val_acc: 0.9255\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 15s 608ms/step - loss: 0.2354 - acc: 0.8998 - val_loss: 0.1561 - val_acc: 0.9689\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2203 - acc: 0.9100 - val_loss: 0.1913 - val_acc: 0.9130\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2398 - acc: 0.9023 - val_loss: 0.2437 - val_acc: 0.8820\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2150 - acc: 0.9092 - val_loss: 0.1931 - val_acc: 0.9255\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2019 - acc: 0.9111 - val_loss: 0.2774 - val_acc: 0.9130\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1897 - acc: 0.9219 - val_loss: 0.1582 - val_acc: 0.9317\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2072 - acc: 0.9106 - val_loss: 0.1688 - val_acc: 0.9068\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 13s 541ms/step - loss: 0.2070 - acc: 0.9215 - val_loss: 0.1641 - val_acc: 0.9503\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1853 - acc: 0.9296 - val_loss: 0.1842 - val_acc: 0.9068\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 15s 608ms/step - loss: 0.1866 - acc: 0.9200 - val_loss: 0.1379 - val_acc: 0.9565\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1688 - acc: 0.9291 - val_loss: 0.1458 - val_acc: 0.9379\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1585 - acc: 0.9393 - val_loss: 0.3060 - val_acc: 0.9255\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1665 - acc: 0.9330 - val_loss: 0.1906 - val_acc: 0.9130\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1625 - acc: 0.9337 - val_loss: 0.2239 - val_acc: 0.9193\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1567 - acc: 0.9291 - val_loss: 0.1799 - val_acc: 0.9441\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1569 - acc: 0.9342 - val_loss: 0.1932 - val_acc: 0.9441\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1774 - acc: 0.9195 - val_loss: 0.2794 - val_acc: 0.9193\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1654 - acc: 0.9286 - val_loss: 0.1875 - val_acc: 0.9317\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1641 - acc: 0.9323 - val_loss: 0.1833 - val_acc: 0.9068\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.1644 - acc: 0.9333 - val_loss: 0.2062 - val_acc: 0.9255\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1366 - acc: 0.9496 - val_loss: 0.1739 - val_acc: 0.9255\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.1175 - acc: 0.9604 - val_loss: 0.2217 - val_acc: 0.9317\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.1570 - acc: 0.9319 - val_loss: 0.1727 - val_acc: 0.9193\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.1522 - acc: 0.9347 - val_loss: 0.1717 - val_acc: 0.9379\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1187 - acc: 0.9491 - val_loss: 0.2508 - val_acc: 0.9441\n",
      "Train loss: 0.146168453406\n",
      "Train accuracy: 0.939708939709\n",
      "Test loss: 0.137901776872\n",
      "Test accuracy: 0.95652173913\n",
      "\n",
      "===================FOLD= 2\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 16s 659ms/step - loss: 0.7552 - acc: 0.5799 - val_loss: 0.5432 - val_acc: 0.6832\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 15s 608ms/step - loss: 0.5469 - acc: 0.7346 - val_loss: 0.3306 - val_acc: 0.8634\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.4019 - acc: 0.8133 - val_loss: 0.2857 - val_acc: 0.9068\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.3901 - acc: 0.8229 - val_loss: 0.2783 - val_acc: 0.8758\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.3196 - acc: 0.8595 - val_loss: 0.2116 - val_acc: 0.9006\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.3289 - acc: 0.8563 - val_loss: 0.2413 - val_acc: 0.9006\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.3185 - acc: 0.8702 - val_loss: 0.2735 - val_acc: 0.8571\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.2577 - acc: 0.8821 - val_loss: 0.1940 - val_acc: 0.9193\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2658 - acc: 0.8800 - val_loss: 0.2082 - val_acc: 0.9006\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2297 - acc: 0.9105 - val_loss: 0.1962 - val_acc: 0.9317\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2423 - acc: 0.9042 - val_loss: 0.2197 - val_acc: 0.9068\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.2357 - acc: 0.9053 - val_loss: 0.1948 - val_acc: 0.9193\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2284 - acc: 0.9044 - val_loss: 0.2035 - val_acc: 0.9068\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.1958 - acc: 0.9218 - val_loss: 0.1837 - val_acc: 0.9255\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2152 - acc: 0.9095 - val_loss: 0.1952 - val_acc: 0.9193\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2118 - acc: 0.9145 - val_loss: 0.2419 - val_acc: 0.8944\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2293 - acc: 0.9006 - val_loss: 0.2500 - val_acc: 0.9130\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.2031 - acc: 0.9113 - val_loss: 0.1822 - val_acc: 0.9130\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1978 - acc: 0.9136 - val_loss: 0.2217 - val_acc: 0.8944\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1970 - acc: 0.9259 - val_loss: 0.2389 - val_acc: 0.8944\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 15s 604ms/step - loss: 0.1753 - acc: 0.9227 - val_loss: 0.1817 - val_acc: 0.9193\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.1915 - acc: 0.9187 - val_loss: 0.1654 - val_acc: 0.9317\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.1768 - acc: 0.9306 - val_loss: 0.1894 - val_acc: 0.9317\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1602 - acc: 0.9352 - val_loss: 0.1845 - val_acc: 0.9130\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1719 - acc: 0.9257 - val_loss: 0.1661 - val_acc: 0.9317\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.1914 - acc: 0.9190 - val_loss: 0.1424 - val_acc: 0.9317\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1499 - acc: 0.9394 - val_loss: 0.1871 - val_acc: 0.9068\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1618 - acc: 0.9358 - val_loss: 0.1804 - val_acc: 0.9379\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1515 - acc: 0.9450 - val_loss: 0.2112 - val_acc: 0.9068\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1518 - acc: 0.9429 - val_loss: 0.1839 - val_acc: 0.9130\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1753 - acc: 0.9276 - val_loss: 0.1694 - val_acc: 0.9317\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1705 - acc: 0.9294 - val_loss: 0.2350 - val_acc: 0.9068\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1830 - acc: 0.9256 - val_loss: 0.1807 - val_acc: 0.9130\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1687 - acc: 0.9276 - val_loss: 0.1714 - val_acc: 0.9193\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1180 - acc: 0.9535 - val_loss: 0.1867 - val_acc: 0.9130\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1615 - acc: 0.9375 - val_loss: 0.1670 - val_acc: 0.9130\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1343 - acc: 0.9470 - val_loss: 0.1691 - val_acc: 0.9379\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1501 - acc: 0.9412 - val_loss: 0.1758 - val_acc: 0.9193\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1289 - acc: 0.9480 - val_loss: 0.2013 - val_acc: 0.9255\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1297 - acc: 0.9452 - val_loss: 0.2526 - val_acc: 0.8944\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1409 - acc: 0.9434 - val_loss: 0.2560 - val_acc: 0.8820\n",
      "Train loss: 0.113215898133\n",
      "Train accuracy: 0.957726957727\n",
      "Test loss: 0.142415675132\n",
      "Test accuracy: 0.931677018634\n",
      "\n",
      "===================FOLD= 3\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 18s 734ms/step - loss: 0.8715 - acc: 0.5397 - val_loss: 0.6115 - val_acc: 0.6562\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 15s 607ms/step - loss: 0.6165 - acc: 0.6822 - val_loss: 0.3053 - val_acc: 0.8500\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.4793 - acc: 0.7720 - val_loss: 0.2696 - val_acc: 0.8875\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.3616 - acc: 0.8420 - val_loss: 0.3031 - val_acc: 0.8688\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.3354 - acc: 0.8484 - val_loss: 0.2732 - val_acc: 0.8625\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2987 - acc: 0.8704 - val_loss: 0.2967 - val_acc: 0.8750\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 15s 608ms/step - loss: 0.2720 - acc: 0.8757 - val_loss: 0.2465 - val_acc: 0.8812\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 15s 640ms/step - loss: 0.2641 - acc: 0.8809 - val_loss: 0.2407 - val_acc: 0.8938\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.2686 - acc: 0.8819 - val_loss: 0.4334 - val_acc: 0.8250\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.2416 - acc: 0.8927 - val_loss: 0.2265 - val_acc: 0.9125\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2222 - acc: 0.9063 - val_loss: 0.3024 - val_acc: 0.8688\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1852 - acc: 0.9235 - val_loss: 0.3133 - val_acc: 0.9000\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2272 - acc: 0.9068 - val_loss: 0.2995 - val_acc: 0.9125\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.2243 - acc: 0.9047 - val_loss: 0.2650 - val_acc: 0.8812\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2365 - acc: 0.9015 - val_loss: 0.2340 - val_acc: 0.9187\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.2117 - acc: 0.9148 - val_loss: 0.2129 - val_acc: 0.9062\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1956 - acc: 0.9153 - val_loss: 0.2425 - val_acc: 0.8875\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1870 - acc: 0.9220 - val_loss: 0.2225 - val_acc: 0.9062\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2033 - acc: 0.9140 - val_loss: 0.2254 - val_acc: 0.9000\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 15s 607ms/step - loss: 0.1803 - acc: 0.9275 - val_loss: 0.1972 - val_acc: 0.9250\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1693 - acc: 0.9293 - val_loss: 0.2057 - val_acc: 0.9062\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1886 - acc: 0.9248 - val_loss: 0.2031 - val_acc: 0.9062\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.1886 - acc: 0.9228 - val_loss: 0.2222 - val_acc: 0.9063\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1601 - acc: 0.9301 - val_loss: 0.2579 - val_acc: 0.9125\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1808 - acc: 0.9306 - val_loss: 0.2404 - val_acc: 0.9125\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1727 - acc: 0.9301 - val_loss: 0.2767 - val_acc: 0.9062\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1676 - acc: 0.9293 - val_loss: 0.2613 - val_acc: 0.9125\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1392 - acc: 0.9401 - val_loss: 0.2140 - val_acc: 0.9375\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1527 - acc: 0.9290 - val_loss: 0.2132 - val_acc: 0.9187\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1478 - acc: 0.9401 - val_loss: 0.2799 - val_acc: 0.9125\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1248 - acc: 0.9463 - val_loss: 0.2567 - val_acc: 0.8938\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1791 - acc: 0.9249 - val_loss: 0.3916 - val_acc: 0.8812\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1448 - acc: 0.9373 - val_loss: 0.2610 - val_acc: 0.9125\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1461 - acc: 0.9375 - val_loss: 0.2139 - val_acc: 0.9437\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1454 - acc: 0.9396 - val_loss: 0.2860 - val_acc: 0.8938\n",
      "Train loss: 0.121666241386\n",
      "Train accuracy: 0.94944598338\n",
      "Test loss: 0.197223505378\n",
      "Test accuracy: 0.925\n",
      "\n",
      "===================FOLD= 4\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 16s 678ms/step - loss: 0.6018 - acc: 0.6505 - val_loss: 0.3196 - val_acc: 0.8688\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.4981 - acc: 0.7586 - val_loss: 0.3118 - val_acc: 0.9062\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.3702 - acc: 0.8270 - val_loss: 0.2472 - val_acc: 0.9187\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.3044 - acc: 0.8671 - val_loss: 0.2604 - val_acc: 0.9062\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.2967 - acc: 0.8640 - val_loss: 0.1858 - val_acc: 0.9313\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2673 - acc: 0.8769 - val_loss: 0.2209 - val_acc: 0.9125\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.2918 - acc: 0.8697 - val_loss: 0.1816 - val_acc: 0.9437\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 15s 607ms/step - loss: 0.2360 - acc: 0.8943 - val_loss: 0.1692 - val_acc: 0.9437\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.2224 - acc: 0.9114 - val_loss: 0.1470 - val_acc: 0.9313\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2437 - acc: 0.8951 - val_loss: 0.1533 - val_acc: 0.9375\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2090 - acc: 0.9080 - val_loss: 0.2581 - val_acc: 0.9187\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2321 - acc: 0.8977 - val_loss: 0.1931 - val_acc: 0.9250\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2036 - acc: 0.9178 - val_loss: 0.1633 - val_acc: 0.9313\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.1933 - acc: 0.9147 - val_loss: 0.1396 - val_acc: 0.9625\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1838 - acc: 0.9200 - val_loss: 0.1939 - val_acc: 0.9375\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1939 - acc: 0.9134 - val_loss: 0.2153 - val_acc: 0.9250\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1890 - acc: 0.9264 - val_loss: 0.2917 - val_acc: 0.9000\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1780 - acc: 0.9262 - val_loss: 0.2507 - val_acc: 0.9250\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1928 - acc: 0.9161 - val_loss: 0.1495 - val_acc: 0.9563\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1816 - acc: 0.9187 - val_loss: 0.2151 - val_acc: 0.9563\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1718 - acc: 0.9252 - val_loss: 0.1968 - val_acc: 0.9437\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1506 - acc: 0.9371 - val_loss: 0.2154 - val_acc: 0.9563\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.1771 - acc: 0.9262 - val_loss: 0.2650 - val_acc: 0.9500\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1736 - acc: 0.9282 - val_loss: 0.1994 - val_acc: 0.9313\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1596 - acc: 0.9311 - val_loss: 0.2775 - val_acc: 0.9187\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1521 - acc: 0.9430 - val_loss: 0.2574 - val_acc: 0.9187\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1632 - acc: 0.9279 - val_loss: 0.2610 - val_acc: 0.9375\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1492 - acc: 0.9471 - val_loss: 0.2189 - val_acc: 0.9437\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1541 - acc: 0.9353 - val_loss: 0.3541 - val_acc: 0.8938\n",
      "Train loss: 0.15191492009\n",
      "Train accuracy: 0.93351800554\n",
      "Test loss: 0.139581429958\n",
      "Test accuracy: 0.9625\n",
      "\n",
      "===================FOLD= 5\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 17s 699ms/step - loss: 0.7592 - acc: 0.5244 - val_loss: 0.6440 - val_acc: 0.5437\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 15s 604ms/step - loss: 0.6468 - acc: 0.6267 - val_loss: 0.5604 - val_acc: 0.6813\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.6067 - acc: 0.6508 - val_loss: 0.4412 - val_acc: 0.8187\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.4316 - acc: 0.7932 - val_loss: 0.3701 - val_acc: 0.8688\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.3751 - acc: 0.8243 - val_loss: 0.2930 - val_acc: 0.9000\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.3606 - acc: 0.8279 - val_loss: 0.2731 - val_acc: 0.8812\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.3011 - acc: 0.8594 - val_loss: 0.2356 - val_acc: 0.9062\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.3069 - acc: 0.8643 - val_loss: 0.2570 - val_acc: 0.9000\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2797 - acc: 0.8805 - val_loss: 0.3243 - val_acc: 0.8938\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2929 - acc: 0.8594 - val_loss: 0.2458 - val_acc: 0.9313\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.2567 - acc: 0.8793 - val_loss: 0.2193 - val_acc: 0.9187\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.2332 - acc: 0.9026 - val_loss: 0.2027 - val_acc: 0.9125\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2558 - acc: 0.9034 - val_loss: 0.2259 - val_acc: 0.9062\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.3444 - acc: 0.8355 - val_loss: 0.2989 - val_acc: 0.8938\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2663 - acc: 0.8779 - val_loss: 0.2161 - val_acc: 0.9250\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2465 - acc: 0.8844 - val_loss: 0.2070 - val_acc: 0.9187\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2419 - acc: 0.8969 - val_loss: 0.2386 - val_acc: 0.9125\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.2566 - acc: 0.8798 - val_loss: 0.1894 - val_acc: 0.9313\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.2260 - acc: 0.9052 - val_loss: 0.1856 - val_acc: 0.9250\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2031 - acc: 0.9129 - val_loss: 0.1961 - val_acc: 0.9250\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2044 - acc: 0.9099 - val_loss: 0.2076 - val_acc: 0.9625\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2010 - acc: 0.9194 - val_loss: 0.2048 - val_acc: 0.9250\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 14s 599ms/step - loss: 0.1901 - acc: 0.9162 - val_loss: 0.1671 - val_acc: 0.9313\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2450 - acc: 0.8896 - val_loss: 0.1946 - val_acc: 0.9187\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1966 - acc: 0.9184 - val_loss: 0.2242 - val_acc: 0.9187\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1936 - acc: 0.9181 - val_loss: 0.1985 - val_acc: 0.9375\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1685 - acc: 0.9275 - val_loss: 0.1840 - val_acc: 0.9250\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1629 - acc: 0.9300 - val_loss: 0.1862 - val_acc: 0.9187\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1730 - acc: 0.9269 - val_loss: 0.2055 - val_acc: 0.9437\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.1985 - acc: 0.9101 - val_loss: 0.1633 - val_acc: 0.9500\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1969 - acc: 0.9221 - val_loss: 0.2628 - val_acc: 0.9250\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1755 - acc: 0.9272 - val_loss: 0.2380 - val_acc: 0.9375\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1894 - acc: 0.9264 - val_loss: 0.1874 - val_acc: 0.9563\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1569 - acc: 0.9428 - val_loss: 0.2190 - val_acc: 0.9187\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1524 - acc: 0.9345 - val_loss: 0.2426 - val_acc: 0.9437\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1673 - acc: 0.9287 - val_loss: 0.2510 - val_acc: 0.9375\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1373 - acc: 0.9415 - val_loss: 0.3060 - val_acc: 0.9250\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 38/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1871 - acc: 0.9195 - val_loss: 0.2739 - val_acc: 0.9000\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1635 - acc: 0.9350 - val_loss: 0.1857 - val_acc: 0.9375\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1491 - acc: 0.9441 - val_loss: 0.1960 - val_acc: 0.9313\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1301 - acc: 0.9454 - val_loss: 0.2077 - val_acc: 0.9500\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1579 - acc: 0.9339 - val_loss: 0.1827 - val_acc: 0.9437\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1415 - acc: 0.9415 - val_loss: 0.3256 - val_acc: 0.9187\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1444 - acc: 0.9492 - val_loss: 0.2159 - val_acc: 0.9375\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1282 - acc: 0.9536 - val_loss: 0.1776 - val_acc: 0.9500\n",
      "Train loss: 0.127407187942\n",
      "Train accuracy: 0.947368421053\n",
      "Test loss: 0.163309413195\n",
      "Test accuracy: 0.95\n",
      "\n",
      "===================FOLD= 6\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 17s 721ms/step - loss: 0.7457 - acc: 0.6573 - val_loss: 0.4223 - val_acc: 0.8125\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.4483 - acc: 0.8137 - val_loss: 0.5904 - val_acc: 0.7375\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.4104 - acc: 0.8324 - val_loss: 0.3767 - val_acc: 0.8250\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.3436 - acc: 0.8448 - val_loss: 0.3165 - val_acc: 0.8812\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2901 - acc: 0.8746 - val_loss: 0.4351 - val_acc: 0.8500\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.3299 - acc: 0.8606 - val_loss: 0.3383 - val_acc: 0.8313\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.3080 - acc: 0.8633 - val_loss: 0.3269 - val_acc: 0.8875\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 15s 608ms/step - loss: 0.2863 - acc: 0.8857 - val_loss: 0.2739 - val_acc: 0.8875\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2729 - acc: 0.8842 - val_loss: 0.2868 - val_acc: 0.8875\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2376 - acc: 0.9083 - val_loss: 0.3168 - val_acc: 0.9000\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2772 - acc: 0.8879 - val_loss: 0.3002 - val_acc: 0.8875\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2454 - acc: 0.8951 - val_loss: 0.3133 - val_acc: 0.8938\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.2400 - acc: 0.9059 - val_loss: 0.2696 - val_acc: 0.8875\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 15s 607ms/step - loss: 0.2001 - acc: 0.9195 - val_loss: 0.2632 - val_acc: 0.9125\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.2364 - acc: 0.9094 - val_loss: 0.2629 - val_acc: 0.9250\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 15s 641ms/step - loss: 0.2326 - acc: 0.9088 - val_loss: 0.2513 - val_acc: 0.8875\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.1845 - acc: 0.9207 - val_loss: 0.2416 - val_acc: 0.9000\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1863 - acc: 0.9296 - val_loss: 0.2648 - val_acc: 0.9062\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1988 - acc: 0.9296 - val_loss: 0.3006 - val_acc: 0.8625\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1936 - acc: 0.9233 - val_loss: 0.2597 - val_acc: 0.9250\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1549 - acc: 0.9306 - val_loss: 0.2479 - val_acc: 0.9187\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1807 - acc: 0.9257 - val_loss: 0.3039 - val_acc: 0.8812\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.2133 - acc: 0.9046 - val_loss: 0.2469 - val_acc: 0.9063\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1482 - acc: 0.9419 - val_loss: 0.3125 - val_acc: 0.8625\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1568 - acc: 0.9342 - val_loss: 0.2730 - val_acc: 0.8938\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1534 - acc: 0.9365 - val_loss: 0.2596 - val_acc: 0.8812\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1648 - acc: 0.9319 - val_loss: 0.2860 - val_acc: 0.9250\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.1360 - acc: 0.9415 - val_loss: 0.2295 - val_acc: 0.9000\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1227 - acc: 0.9520 - val_loss: 0.2660 - val_acc: 0.9000\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1505 - acc: 0.9459 - val_loss: 0.2417 - val_acc: 0.8625\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1523 - acc: 0.9334 - val_loss: 0.2472 - val_acc: 0.9062\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1333 - acc: 0.9433 - val_loss: 0.2321 - val_acc: 0.8875\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1373 - acc: 0.9489 - val_loss: 0.4022 - val_acc: 0.8812\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.1431 - acc: 0.9393 - val_loss: 0.1945 - val_acc: 0.9000\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1440 - acc: 0.9492 - val_loss: 0.2816 - val_acc: 0.9125\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1080 - acc: 0.9577 - val_loss: 0.3525 - val_acc: 0.9062\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1526 - acc: 0.9339 - val_loss: 0.3234 - val_acc: 0.8938\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1296 - acc: 0.9476 - val_loss: 0.2564 - val_acc: 0.8875\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1303 - acc: 0.9448 - val_loss: 0.2872 - val_acc: 0.8750\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1334 - acc: 0.9456 - val_loss: 0.2262 - val_acc: 0.9062\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.0941 - acc: 0.9660 - val_loss: 0.3109 - val_acc: 0.8875\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1104 - acc: 0.9591 - val_loss: 0.3991 - val_acc: 0.8875\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1474 - acc: 0.9469 - val_loss: 0.2729 - val_acc: 0.9125\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.0934 - acc: 0.9629 - val_loss: 0.3704 - val_acc: 0.8875\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1104 - acc: 0.9598 - val_loss: 0.2398 - val_acc: 0.9125\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.1138 - acc: 0.9598 - val_loss: 0.2730 - val_acc: 0.9000\n",
      "Epoch 47/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.0978 - acc: 0.9573 - val_loss: 0.3085 - val_acc: 0.9000\n",
      "Epoch 48/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1268 - acc: 0.9542 - val_loss: 0.3516 - val_acc: 0.8750\n",
      "Epoch 49/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1101 - acc: 0.9515 - val_loss: 0.3682 - val_acc: 0.8375\n",
      "Train loss: 0.0842317453382\n",
      "Train accuracy: 0.971606648199\n",
      "Test loss: 0.194526548684\n",
      "Test accuracy: 0.9\n",
      "\n",
      "===================FOLD= 7\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 17s 714ms/step - loss: 0.7865 - acc: 0.6128 - val_loss: 0.3230 - val_acc: 0.8625\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 15s 607ms/step - loss: 0.5094 - acc: 0.7570 - val_loss: 0.2970 - val_acc: 0.8375\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.4041 - acc: 0.8220 - val_loss: 0.2730 - val_acc: 0.8313\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.3564 - acc: 0.8495 - val_loss: 0.2546 - val_acc: 0.8688\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.3086 - acc: 0.8689 - val_loss: 0.2473 - val_acc: 0.8812\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.3590 - acc: 0.8546 - val_loss: 0.2451 - val_acc: 0.8812\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2676 - acc: 0.8819 - val_loss: 0.3417 - val_acc: 0.8500\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 15s 608ms/step - loss: 0.3071 - acc: 0.8656 - val_loss: 0.2436 - val_acc: 0.9187\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 15s 607ms/step - loss: 0.2700 - acc: 0.8930 - val_loss: 0.1991 - val_acc: 0.8688\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2286 - acc: 0.9034 - val_loss: 0.2523 - val_acc: 0.8750\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2360 - acc: 0.8989 - val_loss: 0.2519 - val_acc: 0.8625\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.2424 - acc: 0.8928 - val_loss: 0.1890 - val_acc: 0.9187\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2230 - acc: 0.9072 - val_loss: 0.2004 - val_acc: 0.8938\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 15s 635ms/step - loss: 0.2288 - acc: 0.9078 - val_loss: 0.1775 - val_acc: 0.9187\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.1943 - acc: 0.9195 - val_loss: 0.1583 - val_acc: 0.9187\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2264 - acc: 0.9041 - val_loss: 0.1777 - val_acc: 0.9250\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1575 - acc: 0.9340 - val_loss: 0.1851 - val_acc: 0.9187\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.2268 - acc: 0.9049 - val_loss: 0.1463 - val_acc: 0.9250\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1942 - acc: 0.9270 - val_loss: 0.2162 - val_acc: 0.9062\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2205 - acc: 0.9101 - val_loss: 0.1733 - val_acc: 0.9313\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1691 - acc: 0.9290 - val_loss: 0.1764 - val_acc: 0.9062\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1839 - acc: 0.9220 - val_loss: 0.2034 - val_acc: 0.8750\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1650 - acc: 0.9348 - val_loss: 0.1726 - val_acc: 0.9500\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1993 - acc: 0.9158 - val_loss: 0.2870 - val_acc: 0.8938\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2104 - acc: 0.9091 - val_loss: 0.1881 - val_acc: 0.9125\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1828 - acc: 0.9226 - val_loss: 0.1886 - val_acc: 0.9125\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1458 - acc: 0.9344 - val_loss: 0.1772 - val_acc: 0.9125\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1714 - acc: 0.9303 - val_loss: 0.2001 - val_acc: 0.9125\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1542 - acc: 0.9397 - val_loss: 0.2085 - val_acc: 0.9187\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1827 - acc: 0.9215 - val_loss: 0.2102 - val_acc: 0.8812\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1489 - acc: 0.9383 - val_loss: 0.1994 - val_acc: 0.9125\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1407 - acc: 0.9415 - val_loss: 0.1502 - val_acc: 0.9250\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1385 - acc: 0.9401 - val_loss: 0.1710 - val_acc: 0.9062\n",
      "Train loss: 0.136393300921\n",
      "Train accuracy: 0.94459833795\n",
      "Test loss: 0.146349695325\n",
      "Test accuracy: 0.925\n",
      "\n",
      "===================FOLD= 8\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 17s 728ms/step - loss: 0.6703 - acc: 0.6734 - val_loss: 0.7206 - val_acc: 0.7375\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.4159 - acc: 0.8166 - val_loss: 0.4482 - val_acc: 0.7625\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 15s 607ms/step - loss: 0.3371 - acc: 0.8565 - val_loss: 0.3589 - val_acc: 0.8125\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.3029 - acc: 0.8598 - val_loss: 0.3786 - val_acc: 0.8250\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2889 - acc: 0.8796 - val_loss: 0.3649 - val_acc: 0.8375\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.3006 - acc: 0.8728 - val_loss: 0.3691 - val_acc: 0.8375\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2664 - acc: 0.8808 - val_loss: 0.3780 - val_acc: 0.8500\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2398 - acc: 0.8904 - val_loss: 0.3801 - val_acc: 0.8313\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.2441 - acc: 0.8958 - val_loss: 0.4604 - val_acc: 0.8187\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2187 - acc: 0.9125 - val_loss: 0.4327 - val_acc: 0.8250\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.2284 - acc: 0.9137 - val_loss: 0.3835 - val_acc: 0.8438\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2229 - acc: 0.9132 - val_loss: 0.4709 - val_acc: 0.8187\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 15s 608ms/step - loss: 0.2185 - acc: 0.9163 - val_loss: 0.3310 - val_acc: 0.8438\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1941 - acc: 0.9199 - val_loss: 0.4464 - val_acc: 0.8187\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2077 - acc: 0.9156 - val_loss: 0.4335 - val_acc: 0.8250\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.1700 - acc: 0.9329 - val_loss: 0.4744 - val_acc: 0.8438\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2089 - acc: 0.9111 - val_loss: 0.3792 - val_acc: 0.8438\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1885 - acc: 0.9178 - val_loss: 0.3352 - val_acc: 0.8625\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1798 - acc: 0.9238 - val_loss: 0.5315 - val_acc: 0.8438\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2038 - acc: 0.9148 - val_loss: 0.3520 - val_acc: 0.8438\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1701 - acc: 0.9272 - val_loss: 0.3806 - val_acc: 0.8500\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1751 - acc: 0.9285 - val_loss: 0.3892 - val_acc: 0.8250\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 14s 601ms/step - loss: 0.1592 - acc: 0.9254 - val_loss: 0.3247 - val_acc: 0.8687\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1656 - acc: 0.9355 - val_loss: 0.4253 - val_acc: 0.8313\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1573 - acc: 0.9313 - val_loss: 0.3436 - val_acc: 0.8375\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1480 - acc: 0.9461 - val_loss: 0.4386 - val_acc: 0.8500\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1880 - acc: 0.9176 - val_loss: 0.3819 - val_acc: 0.8625\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1386 - acc: 0.9443 - val_loss: 0.3799 - val_acc: 0.8812\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1584 - acc: 0.9386 - val_loss: 0.4115 - val_acc: 0.8500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1301 - acc: 0.9489 - val_loss: 0.3528 - val_acc: 0.8562\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1344 - acc: 0.9505 - val_loss: 0.3392 - val_acc: 0.8625\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1367 - acc: 0.9520 - val_loss: 0.5273 - val_acc: 0.8562\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1699 - acc: 0.9298 - val_loss: 0.3717 - val_acc: 0.8500\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1289 - acc: 0.9511 - val_loss: 0.5353 - val_acc: 0.8125\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1570 - acc: 0.9352 - val_loss: 0.3947 - val_acc: 0.8500\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1442 - acc: 0.9391 - val_loss: 0.4554 - val_acc: 0.8500\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.0943 - acc: 0.9655 - val_loss: 0.4750 - val_acc: 0.8250\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.1331 - acc: 0.9489 - val_loss: 0.2942 - val_acc: 0.8688\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1449 - acc: 0.9417 - val_loss: 0.5184 - val_acc: 0.8562\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1197 - acc: 0.9497 - val_loss: 0.3577 - val_acc: 0.8313\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.0897 - acc: 0.9625 - val_loss: 0.5218 - val_acc: 0.8250\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.1883 - acc: 0.9326 - val_loss: 0.4863 - val_acc: 0.8375\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1272 - acc: 0.9541 - val_loss: 0.5502 - val_acc: 0.8562\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1053 - acc: 0.9617 - val_loss: 0.4034 - val_acc: 0.8438\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.0929 - acc: 0.9606 - val_loss: 0.5489 - val_acc: 0.8562\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.1373 - acc: 0.9527 - val_loss: 0.3598 - val_acc: 0.8563\n",
      "Epoch 47/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.0882 - acc: 0.9694 - val_loss: 0.3249 - val_acc: 0.8500\n",
      "Epoch 48/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.0907 - acc: 0.9656 - val_loss: 0.4394 - val_acc: 0.8438\n",
      "Epoch 49/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.0945 - acc: 0.9599 - val_loss: 0.3413 - val_acc: 0.8375\n",
      "Epoch 50/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.0839 - acc: 0.9656 - val_loss: 0.4456 - val_acc: 0.8562\n",
      "Epoch 51/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1069 - acc: 0.9568 - val_loss: 0.4531 - val_acc: 0.8187\n",
      "Epoch 52/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1442 - acc: 0.9456 - val_loss: 0.3739 - val_acc: 0.8375\n",
      "Epoch 53/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1015 - acc: 0.9586 - val_loss: 0.4643 - val_acc: 0.8688\n",
      "Train loss: 0.0867537063214\n",
      "Train accuracy: 0.970221606648\n",
      "Test loss: 0.294225972891\n",
      "Test accuracy: 0.86875\n",
      "\n",
      "===================FOLD= 9\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 18s 755ms/step - loss: 0.9963 - acc: 0.5262 - val_loss: 0.7933 - val_acc: 0.5312\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 15s 607ms/step - loss: 0.6791 - acc: 0.6633 - val_loss: 0.3623 - val_acc: 0.8375\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.4986 - acc: 0.7744 - val_loss: 0.4020 - val_acc: 0.8187\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 15s 607ms/step - loss: 0.4118 - acc: 0.8111 - val_loss: 0.2725 - val_acc: 0.8688\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.3597 - acc: 0.8414 - val_loss: 0.2734 - val_acc: 0.8438\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 15s 608ms/step - loss: 0.3228 - acc: 0.8547 - val_loss: 0.2612 - val_acc: 0.8812\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.3408 - acc: 0.8487 - val_loss: 0.2505 - val_acc: 0.8938\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.2779 - acc: 0.8751 - val_loss: 0.2133 - val_acc: 0.9000\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.2700 - acc: 0.8998 - val_loss: 0.2008 - val_acc: 0.9187\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.2667 - acc: 0.8842 - val_loss: 0.1875 - val_acc: 0.9187\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2391 - acc: 0.8984 - val_loss: 0.2134 - val_acc: 0.9000\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2495 - acc: 0.8963 - val_loss: 0.2048 - val_acc: 0.9125\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 15s 607ms/step - loss: 0.2782 - acc: 0.8925 - val_loss: 0.1840 - val_acc: 0.9125\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2383 - acc: 0.9054 - val_loss: 0.2634 - val_acc: 0.8812\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2414 - acc: 0.9020 - val_loss: 0.2306 - val_acc: 0.8938\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2192 - acc: 0.9130 - val_loss: 0.1978 - val_acc: 0.9375\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2336 - acc: 0.8950 - val_loss: 0.2297 - val_acc: 0.9125\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1960 - acc: 0.9148 - val_loss: 0.2021 - val_acc: 0.9187\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 15s 607ms/step - loss: 0.2032 - acc: 0.9186 - val_loss: 0.1792 - val_acc: 0.9250\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2205 - acc: 0.9085 - val_loss: 0.1963 - val_acc: 0.9187\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2087 - acc: 0.9138 - val_loss: 0.2277 - val_acc: 0.9062\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1903 - acc: 0.9235 - val_loss: 0.1832 - val_acc: 0.9437\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.1699 - acc: 0.9218 - val_loss: 0.1932 - val_acc: 0.9188\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1879 - acc: 0.9235 - val_loss: 0.2361 - val_acc: 0.8688\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1782 - acc: 0.9266 - val_loss: 0.2467 - val_acc: 0.8812\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1869 - acc: 0.9233 - val_loss: 0.2151 - val_acc: 0.9000\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1870 - acc: 0.9210 - val_loss: 0.1822 - val_acc: 0.9125\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.1703 - acc: 0.9303 - val_loss: 0.1569 - val_acc: 0.9250\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1591 - acc: 0.9311 - val_loss: 0.2183 - val_acc: 0.9000\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1679 - acc: 0.9293 - val_loss: 0.1902 - val_acc: 0.9437\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1461 - acc: 0.9448 - val_loss: 0.3290 - val_acc: 0.8750\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2385 - acc: 0.9104 - val_loss: 0.2178 - val_acc: 0.9250\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1842 - acc: 0.9236 - val_loss: 0.1882 - val_acc: 0.9125\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1636 - acc: 0.9324 - val_loss: 0.3011 - val_acc: 0.8375\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1661 - acc: 0.9332 - val_loss: 0.1916 - val_acc: 0.9250\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1493 - acc: 0.9394 - val_loss: 0.1901 - val_acc: 0.9000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 37/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1419 - acc: 0.9336 - val_loss: 0.2382 - val_acc: 0.9187\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1592 - acc: 0.9363 - val_loss: 0.2272 - val_acc: 0.9062\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1391 - acc: 0.9469 - val_loss: 0.2695 - val_acc: 0.9062\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1347 - acc: 0.9506 - val_loss: 0.2011 - val_acc: 0.9187\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1532 - acc: 0.9360 - val_loss: 0.1830 - val_acc: 0.9313\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1147 - acc: 0.9559 - val_loss: 0.1952 - val_acc: 0.9250\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1289 - acc: 0.9490 - val_loss: 0.2464 - val_acc: 0.8812\n",
      "Train loss: 0.114272840548\n",
      "Train accuracy: 0.961218836565\n",
      "Test loss: 0.156905585527\n",
      "Test accuracy: 0.925\n",
      "\n",
      " Train Log Loss Validation=  0.107999644928\n",
      " Test Log Loss Validation=  0.174257478678\n"
     ]
    }
   ],
   "source": [
    "#def myAngleCV(X_train, X_angle, X_test):\n",
    "K=10\n",
    "folds = list(StratifiedKFold(n_splits=K, shuffle=True, random_state=16).split(X_train, target_train))\n",
    "y_test_pred_log = 0\n",
    "y_train_pred_log=0\n",
    "y_valid_pred_log = 0.0*target_train\n",
    "for j, (train_idx, test_idx) in enumerate(folds):\n",
    "    print('\\n===================FOLD=',j)\n",
    "    X_train_cv = X_train[train_idx]\n",
    "    y_train_cv = target_train[train_idx]\n",
    "    X_holdout = X_train[test_idx]\n",
    "    Y_holdout= target_train[test_idx]\n",
    "    \n",
    "    #Angle\n",
    "    X_angle_cv=X_angle[train_idx]\n",
    "    X_angle_hold=X_angle[test_idx]\n",
    "    #define file path and get callbacks\n",
    "    file_path = \"%s_aug_model_weights.hdf5\"%j\n",
    "    callbacks = get_callbacks(filepath=file_path, patience=5)\n",
    "    gen_flow = gen_flow_for_two_inputs(X_train_cv, X_angle_cv, y_train_cv)\n",
    "    galaxyModel= getVggAngleModel()\n",
    "    histr = galaxyModel.fit_generator(\n",
    "            gen_flow,\n",
    "            steps_per_epoch=24,\n",
    "            epochs=100,\n",
    "            shuffle=True,\n",
    "            verbose=1,\n",
    "            validation_data=([X_holdout,X_angle_hold], Y_holdout),\n",
    "            callbacks=callbacks)\n",
    "     #Getting the Best Model\n",
    "    galaxyModel.load_weights(filepath=file_path)\n",
    "    #Getting Training Score\n",
    "    score = galaxyModel.evaluate([X_train_cv,X_angle_cv], y_train_cv, verbose=0)\n",
    "    print('Train loss:', score[0])\n",
    "    print('Train accuracy:', score[1])\n",
    "    #Getting Test Score\n",
    "    score = galaxyModel.evaluate([X_holdout,X_angle_hold], Y_holdout, verbose=0)\n",
    "    print('Test loss:', score[0])\n",
    "    print('Test accuracy:', score[1])\n",
    "\n",
    "    #Getting validation Score.\n",
    "    pred_valid=galaxyModel.predict([X_holdout,X_angle_hold])\n",
    "    y_valid_pred_log[test_idx] = pred_valid.reshape(pred_valid.shape[0])\n",
    "\n",
    "    #Getting Test Scores\n",
    "    temp_test=galaxyModel.predict([X_test, X_test_angle])\n",
    "    y_test_pred_log+=temp_test.reshape(temp_test.shape[0])\n",
    "\n",
    "    #Getting Train Scores\n",
    "    temp_train=galaxyModel.predict([X_train, X_angle])\n",
    "    y_train_pred_log+=temp_train.reshape(temp_train.shape[0])\n",
    "\n",
    "y_test_pred_log=y_test_pred_log/K\n",
    "y_train_pred_log=y_train_pred_log/K\n",
    "\n",
    "print('\\n Train Log Loss Validation= ',log_loss(target_train, y_train_pred_log))\n",
    "print(' Test Log Loss Validation= ',log_loss(target_train, y_valid_pred_log))\n",
    "#return y_test_pred_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#preds=myAngleCV(X_train, X_angle, X_test)\n",
    "preds = y_test_pred_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Submission to kaggle\n",
    "submission = pd.DataFrame()\n",
    "submission['id']=test['id']\n",
    "submission['is_iceberg']=preds\n",
    "submission.to_csv('sub_Adam_10folds_amsgrad_0001_15p_3rd.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['val_loss', 'acc', 'val_acc', 'loss'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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OBTkV53fAJpvn/x2yUlx/KW6ZY//tehERFdVAKpr9tG9FFYLCervIMdrphEbE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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f797cfcad68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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GsuOoBgWllHfzwqBQx35PTThnc7uGoeyNP0Nmdm4RBymllHfwvqBQRP4jgPaNwsjONeyN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\nfBoUlFJK5dOgoJRSKt//AyBNQ4ElJql1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79195d6cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# list all data in history\n",
    "print(histr.history.keys())\n",
    "# summarize history for accuracy\n",
    "plt.plot(histr.history['acc'])\n",
    "plt.plot(histr.history['val_acc'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()\n",
    "# summarize history for loss\n",
    "plt.plot(histr.history['loss'])\n",
    "plt.plot(histr.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.image as mpimg\n",
    "\n",
    "min1 = np.min(X_holdout)\n",
    "max1 = np.max(X_holdout)\n",
    "diff1 = max1 - min1\n",
    "my_sum = lambda x: (x - min1)/diff1\n",
    "\n",
    "X_holdout2 = my_sum(X_holdout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13      1\n",
       "14      0\n",
       "38      1\n",
       "43      0\n",
       "44      0\n",
       "80      0\n",
       "104     1\n",
       "127     0\n",
       "137     0\n",
       "138     1\n",
       "141     1\n",
       "167     1\n",
       "195     1\n",
       "199     0\n",
       "207     1\n",
       "212     0\n",
       "213     1\n",
       "221     0\n",
       "240     0\n",
       "256     0\n",
       "267     1\n",
       "274     1\n",
       "276     1\n",
       "285     1\n",
       "290     0\n",
       "294     0\n",
       "300     1\n",
       "307     1\n",
       "311     1\n",
       "318     1\n",
       "       ..\n",
       "1363    1\n",
       "1364    0\n",
       "1373    1\n",
       "1380    1\n",
       "1394    0\n",
       "1411    0\n",
       "1435    0\n",
       "1440    1\n",
       "1445    1\n",
       "1447    1\n",
       "1460    1\n",
       "1470    0\n",
       "1488    0\n",
       "1495    0\n",
       "1504    1\n",
       "1516    0\n",
       "1525    0\n",
       "1533    0\n",
       "1537    0\n",
       "1539    0\n",
       "1541    0\n",
       "1542    0\n",
       "1549    0\n",
       "1552    0\n",
       "1559    0\n",
       "1566    0\n",
       "1567    0\n",
       "1577    0\n",
       "1590    0\n",
       "1602    0\n",
       "Name: is_iceberg, Length: 160, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_holdout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f7919442470>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GSzVTfC6RydSJVJnUa4a83Qyi7gDx9MHxkSRpjjzgNqhsOyArGW2Pn4BgtofI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\nmBSDIAjA/wAl+gobSaak9wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f797cfe0e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[0], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[13])+ \",\"+\" iceberg_probability : \"+str(pred_valid[0])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f7919425748>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xrHBPdLbgDM4oAyjY8w4KUeEmYQGtCoEZOd3TYzQFoyP4RCgG50cNtFdU29aP\nMqcZYkJCQkJEeiAmJCQkRKwtZQ4Kmse85S5UuQcOneKyHu0Ciusi5niOnBmdUZlbyJ/NQTunldMh\nBgaPct93iFxV2m8ZqJyiOr5AbvCC9X/hJ9ZCMnENusk33R7sukoENY9BNzvRVbmHvOdOyynTx4K7\nZHexZNBbQll/xgOZN+HMbJnT5BasqEZzHD+6N+9heaNDdR5rEEdtP88u+kjUQ7ptwwoLdml1IMVs\nxpz1jJevcgV9DD/q7QMujfjxF6CsEyTHLxB4LVhtrUT5eultt6D8lr3zIxJwO+nJwqMcMjhcU9mv\nOlxi8KWMIkYlPNDxpQbeN9MS7uYt1LM+btoIVbL/SkhISFhbpAdiQkJCQsTaUmbVHhBdIxi4C7uk\nJdTfo7bTt0Es8lTNkeuMNgx2VllwOrI5gev1lu+7O0eUNFjQ/tgp5ractuRRfrWut5d3nYJOEIC8\nk0OJbkFBnSDqufbLPEHtZrojD7u3Uk8qm9cGXpdayN9ewkKqs+28dg71PXvOx3Z84gWylqByWSdu\njwDgHsaZ51ygaFIfOc7FAuOMO7vCOVeoVz0DJQ/RFqv/sl0/BwTDd0sf581dH6slaDeM0XW8dGpa\ndrCswfvoY1Gh3vL2ZnAg76OG9QKO1UXpJ3el5cshhqUMOMSdiYrYYLB5pNg8JpVjasiLsZ9P6Dfn\nsIbuX2mGmJCQkLBCeiAmJCQkRKwtZS6yXDsxmJeFpU4GTg8OO6ipDC47i4GqOSjzJPf97ret088t\nUIwZ3KBnsFwqYTNWIcd3cwCajEBeZdEWCm108O6aQfGuaB0FisnM68kC+ckQBjMEaVs8jwUKL83B\nATOc53JBmzMEBve5rAA1exfrBCeg0ggwnmTxXFGcqUSu8XAESZ4qP3hbPULOMnJ5Sf3mUIIXCDYe\nRIfp5bFTw2XXj9ky3B8t/34G2j8qfd98hCuAvrThqq2XN+fKXPvJDMc/Ak0tzg+CpnXXArWYhdrN\nLKDWw5gex/4uEbjdwniOkKe/0UW+dYxEsBSYnZCQkLC+WNsZYgi1FtE1u8BbUYidak0xu8EidBZN\nOruIveoi3tDgGHLGUwQzpBYW/meHLiT0rsDQs3W+MWc2b/pVw6WmQrreJmqxzCvYsGBWJtRoyXDO\nbTrlwPlnld1mmPEtTpCLh5kbYx9vBI+P7GK2MoB4UOKYNWYuFFCmMRavhrNugZjAOQSwEjOaAqmA\nPbYNkehvf+2WAAAgAElEQVSM1oKJ2w5kkGoSZ0P4fdNPTdM9CFYYtwo1SCiYMYZ0B+48Mw9tVK/b\nCCIZzIGfLvygVxEfuBkQ15kj/RJxpXUbghFm+hPUlOFsdBX72kEsKUiGtvp+fNzOCquZeJ0MYhMS\nEhLWFumBmJCQkBCxvpS5qhUOG2q73HNaUSKeq544PSmusORkQzeWqEvRpngCYYT0bQNlPVsIA+xs\nuQgz3nf6bObHrNl+jFurAyj4EcSOXRjegna3K1Af1PFgitViy/cdbjklmoLineLATyKcODWf4K7Z\n24SJLvSCOZYPhsi7OzzxMqflji9JFDEOcWMCMYaFXLB8cOamhQsR6X7o8F0Po1u4vZQYl250lslQ\no6WGQW+x52O1LL0vO6Cyrdq/H7ZRkvQK7iOa9baaY+UQt1618HjPgCWACvu14PYzATVeokQMXXMC\nllLOpOP1mvFv4b4RxnCE5ZgcjkEbMeUvWz/GnGaICQkJCSukB2JCQkJCxNpSZpmpirSpggvMdah4\nnSHo5onTs5UxbBtpftZHGt0SSixKbJ7AhmSj75Spv0A61AYo5gHU6p63PxjHftEBB7F3Y9B0FmWl\n+tnJ+a4DTaRxKKlkpKzzJw5Ov2vvONUdFUhjG4GmYVlBF5iflJuufubmvO7kqRt+rI24fABqxnKj\nvQ5ccJByOMcAHOdwBIJAXm57m72WU9wc84HyqNm3QGofDnOmvk028PZQNVX3zf3cFqDPB1M3ke2i\nX+d52QxhCjxHuuJ4QccgH5cc92iB+zLf8fPogW4zBVRxGaaFuNJnYVRMFLhXBSPcdUOaISYkJCRE\nXKoHopk9ZGY/bma/YGbvM7Ovit/vmtm7zOwD8f8797qvCQkJ64fLRplLSX8xhPDvzWxD0s+a2bsk\n/XFJ7w4hvNXM3izpzZK+5nYN1Qqax7DpCulIOQKGOyghmSFNL4t0s0TgMINXF20ED2MID1swPIWC\n2Vv4MftjpH1tQtlegpJtN9SLivgJjmmob7JEulp34JSNTjIkZxXKTPKcFbfvXXVFPIM6mSNd7VcO\nnzz9vCN/Nw3hvPJ05WlfLBuawx1osOHK7QqTBdLVBCeb4J9LnFsHcekbUHwHwa9Lv+XjnKEM7HyE\nVLuoPjPNkssULZQ7zRCwXUFxnqMs53IGhRbXawoj4u5mc/8VKFNaIgB/jnsi78DYmOVW8RfM0jkt\nfK6L8//M2/HvwlBTZXPLx6rC+dCrtvvhUdwvGcT+qiKE8FQI4d/HzyeS3i/pQUmvl/RY3OwxSW+4\nNz1MSEhYZ1y2GeIpzOxVkn6LpJ+WdF8I4an409OS7rtgn0ckPSKdXYRPSEhIuBtcygeimQ0l/VNJ\nfz6EcExXjRBCMLNzQ0JDCI9KelSSdja2TrchTdtY+CnXqNORowbIYtlQ1RYm0JPifMpKYrpb+UOY\n1CygxOViCENX7LscIj85yrXVBCo0TD7Vc/pUbft+M5jCzqGK03nm2tj3XQqOPPHzRo4XCRjRyjRX\nknZ6SMhlv6CEvgwmpnRNoeJc4LqsrmjexzIGcny1hbxvGNcGXIsBaucUGP8KlPQ4d5pMF5gqGsSy\n9GwxhBFs37+vkMNbV2j7BFwa4P1bYMmmivQ8Q965ITeaZU2vzz2gvUJENHPDt7EExJurj6UHYckk\nREpMY+EMZW2XGzBQBtUvNlpn9l8nXCrKLElm1lLzMPzeEMI/i18/Y2YPxN8fkHT9XvUvISFhfXGp\nHojWvEq/Q9L7QwjfiJ/eKenh+PlhSe/4RPctISFh/XHZKPNnS/pjkt5rZv8hfve1kt4q6e1m9hWS\nPiLpTXdqKCtydXcbq60c+alI/dQzGYKNJ/65F98TH6u95sm14HQEHqPKUOKyqpyOnFEWYdeEipya\n5wyq9X0nq7ojpNpbvFTedgeXsA3qk4PKThE8PgIj7pzACm3QUG+Is2fKXe5XnoNNyykGN0+mMBRt\nu+Kcb/pBK/hrWRf0PQaMM85bO75faw4T07YvE2QsH4uL28I1n0GJZ92TWQfBzq2oMrdgwzb231li\nlsa6/RxLHUMsAYDKlpPzg51DLJObIYKBVJf+q1e3UOsFY8jSowso3l0EuNPEOEdNlTKuiSz38B0C\nysdCMDawiMs0Ib9U86kXBZfqgRhC+EmdXVojPv8T2ZeEhIRPPqzfIz4hISHhBeJSzRBfVAS3J8ow\n6STF2IOP1XTu9GBRNUrbp/TdimkBLjc2WCih7gWRD51id6imImA7Q47pfOEKZRnretRXnHZuzF2d\nBtM+ozgGBBX3CiiLoPWkxFn71vfhUQ57tEBq5tssAmqE1Odl5ErHU6fYoUTdkyEU8sWtVHJSoMQr\nAoo7sELLxn6tjmsEr8Mu7IGtvdPPJWhtOfVr16ugXEeZu5q4yjqegd6zk6Dm5RbGFksW/cKv1xj0\neQaP9VUJ2/kYyzVt3y8b+OfZBEHkuJ5nArP3UVMG9X0WCGTv43ZdRvsxQwB6gTlS9xBLQFgmWKnj\nCuvn/5VmiAkJCQkR6YGYkJCQELG2lDnUtZaxSBK9oAsqvsdOCRZL5NBGSnBkTvtIE4sBAnY3oPJu\neCBt1120VM6dyswR4DvpMhcUbV5tKF6OwOQaFk1dqJLVAAHYoGyGwOQuijWxtKnBbXoe2ylG5zhn\nSyqx1NBzMVmLaxjPZ1E2te9jUfSdsh5f3z/93ELgcxb7voHrw9KwDCKfb5Di+bLCDuqm7i/92u0a\ncqZRTtSgxGcxb/1MMSX8dRhswdpYjihR+rOPaxhgOTfseB9r3GcrK7ZJB7ZdE+amI4gfwfgl2s4Z\nSA0JvaQVHZYMRnu3zoFmM1+u4fISr8/0SVy3vSbfff0Ic5ohJiQkJJwiPRATEhISItaWMucyDWIY\nbgZquF84DV10oPgGp3j5NNINcII26vxWUIr7KCYkqMkzmArXsBFjsG3owF4K9HDl3t3OnWoVI8i8\nCMztIk953kYt3g6DvlG4KXOKVUDR3Z0351GCUk7mTu+65pTt+GV+zhs9V9Nb4XxDDbivqbPnedAB\n47Kij50MueYQsEsEuoex96uYoUYyxnMDVmjl3M+5jQJNYg7vqvgUAuRrBGl3z6y7+Mccxx+NPZC/\nv+v1t49nTt9VokZ2vF96SzboH+el36szfF4cwRkc+c6t3Cl22fXxqnBftlsIho+K+xKRCgOYohQs\nOr7p17la2YUllTkhISFhfZEeiAkJCQkRa0uZQx0UoqK6qJ0GdZATfBg8wLcPR+Z5ZAe0DWuBsQ4L\npxU1aQMCracY2QLJzy3kAe9CFQ1wNBtFrj5HYPRsE0oxag6HY1iEwVqqCxrGHOsuQ4yhsp5Et+8Z\navv2B06T9p94zo9PLgvKXIKCTxlIjGOyXrBAZfOoqM6h5i+xLd/crK2cYzmkgsP4CEHaHRy/wyBt\nBCxnUV3vwpm61XUKSlRQ/CtYd40ZTfCcq7LsfAdFrspu3LeLpQNcq9YNp8bLHigw6oKzRjPRgRJe\no1jZcob87HHT/hYiAnIE0VdYMhBU9lnMDLggJv8ljTRDTEhISIhY3xliJs3iRK401ECBgcdm5m89\nTAaVxZnB2JDGVfrnvQMYq2K2dmb2g3StgJVywyyuVVIo4fGbfpWVzzhW6YSS1JbPXNo7HmPXR42U\nBYSXFoLrWqyjAlGjHWPl9jAmUxyz+wBMyiE8LEc+4+Ds5hjuMJuYUffhN1NjAlY/0wQ31l2/JYcQ\nsqaI1VtgJjpC7F330MeWhrpLvPf3DXVvUIMlazdtDpBNyNKzBdLlasxij8eId8QJPdU9P6WTdj55\nFCdOlhhDpBZubXvqaIZUvBrlRmdgJd3Cz6d16Neu2sTNDXNbRVPiCdIJyWbavFaYIS5GTVnVkESV\nhISEhPVFeiAmJCQkRKwtZTaZOqf0D4vgEC96WBWuUIpx1I0xcQigY0hWue3UqAMjzglqh8zhiHKM\nmMD750iXa0OEQEpfZ9Z8phHp9sw/b8zPj/eram+jXDr1msCqpkLa20PPgUpHoaCEg23vKsqkYjE+\n0HB33xf+F/d5vzoQJM7Ug0E84xLiQNhttqeBL8xozqTZPTm+cfp5a+BxjfUOHIFQ3qQ+8b7vtZDG\nR3ocXWZOSt+xh7owLDfbhfPO1ZnTygr0/cHMy7nu5ygnylKh8ZRaiCUcYQlifMPrqBSo+aMNpmj6\nNaTYFFA7pTiECw4CYVcMfwlxazJ1qj/a9GP2kcc4XMX3Xmhd+tJFmiEmJCQkRKQHYkJCQkLE+lLm\nILUiz53jLJneFUAx7IaT4uG4oQJF1ynDJmLyiinocFTcpLMuKG2wxD7dUaDyIUtOIyjKWVQLh6DJ\nNvTPS5TVXA5QKjKAJi9QbnPHaSVLUp5c9b50J805LVBTo2AZVMacsXbJwMeo7YfXAA43J0sfo4+h\nTsd2hbSz/q0xfwvWJcHyxlblaXFMHxuA4pbIu6Sn0Ik5JWXp2bJs+tXFskN7BhUV40Jt1bCsMahA\nX2ukILY8uPNG4csqRaTeLMc6uLaD1nEkUN2qhEExohaWcMGxAe4dlk3l/Tdttj9p+34bXb9Xlgfe\n1wwmsiuXJEuUOSEhIWF9cakeiGbWNbN/Z2Y/b2bvM7Ovj9+/2sx+2sw+aGY/YGbtO7WVkJCQ8Hxx\n2SjzXNLvDSGMYsH6nzSzfyHpqyV9UwjhbWb2bZK+QtK33ralqlZ10CiGyyuY7sM1Jt9BetOe0zeL\nzioZ6EgOOijU2mB5zBr5fRUoxuIY7iSZU0Om6726clp1HJXezhRlSMEoDzZ9v6OlK5FUvNswsS0Q\nyFsjCHiC1+FTnYZib5jv14OYPYfOPoR7TgkqX0wYaI60ONDnGnQPseOn47hAAPIMyruB6l+ZoS7L\nFFIxrkuB5ZAKIQItlBZlgHMZDXJvyK/VyYl/3tvzMqALpFSS1WYoj8ptagRPb8JNJlspyqC6bZRs\nZQD8GLVjQg8XDrS+PfHFgfEWqDGWiTLQ83EMwm5jbLsdv9Fae77f1FD3ZRzbXr+47Ms1QwwNVqH/\nrfhfkPR7Jf1g/P4xSW+4B91LSEhYc1yqB6IkmVkei9Rfl/QuSb8s6TCE03ypJyQ9eMG+j5jZ42b2\n+LQ8vzh4QkJCwkW4bJRZIYRK0m82s21JPyTp05/Hvo9KelSSrmzthOUDDQ3OR664ZqAk++bfz6Ai\nDiL1RFqnNhcIej1x2tdH7u+zlbd3VLuauls5DVnMEKQLs86s7yr21ZhjO9tAsDjU0eeO3UmlB7oj\nmNJS8S5I6/EKrI+8LxsrtRhqcj3xY2aoxTLZc3rfAWUbF9jmzKvWadqUKjacWvJIXztT/50lS0nZ\nDYpzieDu/TaCyhf+/TZyfI82nYZS/VcMPM/b/l2OOjLPZaiFgqWOzhgGtdCzc8w12jC3zSbIPV6t\nSbBeCsxahTz54Q0f2wPUoQ2L85cpRqjd0i/8xsjQr0F0eMrQv7yHZAUkDuQIGA/H8f6vqN+vBy7d\nDHGFEMKhpB+X9FmSts1OF8heLunJe9axhISEtcWleiCa2dU4M5SZ9SR9oaT3q3kwvjFu9rCkd9yb\nHiYkJKwzLhtlfkDSY2aWq3lYvz2E8MNm9guS3mZmb5H0c5K+404NlVnQQb+hJ5sl8k2huHXJTmDc\nWsTP88IpwXTkiuMAFGwMQ9UCCvKratQgQTAy64gsLwgeWjHPPnKpF+hfhdKbrNHRgsltG7VJxoVT\n/Jz0CEHVRaTvzPWmgr7RQr7tc07Hqhx0GMHg8+75ub+fWrta20Ie7jSyxwJLALso5TpDADSDxzcq\nBCD75qpqHxeDEWybsc7I992MwdNTbDCD4kzD1S0ccyTUSzmAtVgL0QQIMGcu8aokLvPk57CqY70W\nwVqtc+ifp0jIzlDHZRd/2hk+VzBLDtFmbhWNIUmGzwHjVhe4zjGXus7XLzD7Uj0QQwj/UdJvOef7\nD0l67Se+RwkJCZ9MuFSUOSEhIeFe4lLNEF9MZME0WESqtnl+XidRgkrMoxVYJ3dFerQB+oD9Wgje\nzlC7IgOtGi4QSIwg3E7u33dAZesY8VrCXbmDPNkB7KJyiIzFFGrmnHUzEbC76/3qIJm6E28Fu+75\nq6z/kYFK7+Z+fAZGF3231roBt/E2ncnRXwavn/Sb7WtscN+J77fR8bZPDjwYfRkQXnWm8AqOg7Ho\n4fglAr8Vy7naBSzQjmChhfzxPUj72RYcthmkXeNaQFFe1cxhskBFCtxBzR2MFWv9LBFe1jvydgz3\n4tEGatccIDA+nmwNZbuaIE8ajuIB7u6Tlf3YGk6n1vCUEhISEl4Y0gMxISEhIWJtKbPUFJqSXDWW\ndIbvzk9cFTQU91lcbWjNkZw+PjBFEC90weU2AlkRSAvDanX7KJYEhvdc7Ypem27Xq6sCerfYQqA1\n6LNGTocKBBUvdvyYcwbpkp5hGWAVJF5fRVlRFFZqo2hRyHy/BXKZ53DlGiDfeTBHgSZcCoNCvhED\n349afpxBy8/hSs8dqAsEtC+g/s+QM/7R0iXqCdTk3rZT7822t79y5O7h+hQd0GGovCzUtIAqHYzb\n+L3Vg/V3jfzkIuabL7Du0TdXp+lkTWU9G/vn/gDb47p0EAA+hLXaM5uIOIi538xf5r09R2G166i+\ndV+3sSjLsvWbT63fGSUkJCS8QKQHYkJCQkLE+lLmEE7zPKlsZgwY3oAL9ibyc6PKvAEK3IYt1WLq\ndGgOla+AyjeG5VNnuIXtQT2RLP1M8ADf7VbjWpyfse3ytkdQLTeQfPyxgdOhMRy471+CGiIYu3fO\n5c+HoNooTlUjlzlHkG7Vgv0XaOWM1BhWXBkC46kyLycNrX9N8cDpd7uvvnJL/yQpmztlHkEpX5Q+\n5vcd+bVdoLJTBYuyucGlPA41c5C3RhgfKrug1VO4hzMneImbLkM0QTeHk3Ys7FVs+JjPkOs+w/pK\nG9ZuGbbvoGgX06AXqCnNWc8mAuwnq/PHMlKOtiueKNTvxaL5HC6I2HgpI80QExISEiLSAzEhISEh\nYm0pc5C0iJS0gI1Tdh2U5L7zk4lX5GjL438V2sgrxrZLBLKqAyXYD6MngjdUIz90iODtHVCZ4jDW\nSB6ihvOBU+oAKj+BOp4j93gH77pRxzvThy/XBDJ271pD6w/lijQtzyoEMefhfNunaYAqCirJ/PEa\nNssZCkRlkXovYG9dgF62dnx85gfedg1uzjrOLBBWYVysh1sequysiGOBwPkWfmdBJdYjZsGxADfu\nAkWeeljWWCA/vhWvUY1xsAtY6AKWX22o/0tcwyW2YV3wIZYJlhCUV9exPUBuPDDHWHWu+/fFasmm\nSpQ5ISEhYW2RHogJCQkJEWtLmauq0ni/oard0nlCRhunY0ih266u1VEJrof+vjjJnEr1UYipgrXV\ns5XT2vt3XVnOkQdaFD7kBwj8LqCEtqJA2jlAQPUmXLdx1eZwcg413Zv9mBtT33fG0grIq70eXbjZ\nv1bpVGqJvOqw8ON0sWLQAsUqkIfbgk33DLROS/9cRBo4huv1ssKY+F7qos404sL13IE7id/AuNjA\nj9+DjdgRbNlWzteGYGMW9gp0Eh/7Sc87fg6byDEvxt73WcvHbo4IgVXt6N6xX6sull3qa34PZcvz\nlymW6MszM1+aKUCr876PV43ljirelzcWXjf7KPPx3ztCzekhAvZXrubr5/6VZogJCQkJK6ztDNEy\nUxZj7iaIVdtEOlg1xMwJs5H2oHlL820+m6D+ShsuMWN/+/cxE5oGn320kbpXQlTYDRAKZv6mDzHm\nrUZFymzub+4c6kHAzLZ7Ru3xmcD1fZ85ZUj1CnBt2V7NBi8ok5GjxGdmfI/6+bB2S8ZsMKAD5xvW\nSWnFPMsHt6+dfnetv41tEVeH2Mdx26/L1tBnQpMjn/U8A7eXEiltJeuTxtPbzH1WnMPAlnGIh0h5\n3Kzh/ONbqx/glITSq2UBA97VNUJq3YyxfwGmuJiht4/8fGqknz6Y0XnHz+MpmNgydXJz5XA09Z5v\nYZxzOOaUhzDLXT021k9TSTPEhISEhBXSAzEhISEhYo0pc6buIFIIlGqsB6h1Uk5u3q3ZZiUagBr3\nMhcJatDRHPUy2of+/WHuVGbrxL8vIbBsFk5r9npO96ooplj3/MszA5UflDAOBcVbwG6nf8UX55cn\nKKeJNpdRnYDZjMYjKCZ9mOwibm4DHGwB8cBafs6DJd67EIpqFLWZR4GnRP/qlh9/2PbxOThxOrz/\n1FOnn4+Pj08/T4berw0YnRri88j4qliqdtpHzZ0Z4ifpJZv5yC0Qe7gHyrrcRDnTnCmAiKGM8aRM\ns5zA8JalP1ud82Nmh7gvW1t+/Cmu7hDnz3YGx802BqFp4tqJFqDvbPu06Wz9VJU0Q0xISEiIuJQP\nRDPLzeznzOyH479fbWY/bWYfNLMfMLML6tUlJCQkvHBcVsr8VWrqMa8sR79B0jeFEN5mZt8m6Ssk\nfevtGjBJeQyUYm2K2YHTrQAHmzbiE7NI32hgmsNYddm/83tkunDKvLEDHoISki2opVUL5p5XG3q4\nRJ2X5QglPpFGV1eI26tpCurHzKGmdjYYB+n8eDRvlg+moOMdprnh+y2UdZ3AsSbMYWI6QbnTkrF3\n0GKR3riIMZ8H5l8OUQb0wZkvL7RB6xkHOYIqel8Oh6Gxj92yjXS8xa1mrfulx4beQJzoTsvHs4US\nr2EH9wJSPWmK20YQ42EblDmOV4G4zsEW5HkUeFnOmCKK8rHl+bVWih628T2VI0Jg+EBTErYFA+V8\n7mN+hPssYJlkVDXjWa+hzHzpZohm9nJJXyrp2+O/TdLvlfSDcZPHJL3h3vQuISFhnXHpHoiS/r6k\nvySPiNuTdBhCWE0/npD04Hk7mtkjZva4mT0+XyzO2yQhISHhQlwqymxmv1/S9RDCz5rZ657v/iGE\nRyU9Kkm72zuhiAHZnNq3WbUSquwS9TCqWJujg0BrmpwWLbiXULVEHZP7UTazhnHq1a2dc/tegIYd\nxHocgRQYdURaCCIfw2S2WzjdGpVOExnUu6idem2MsUwQlxdoGru/CYcVmJ+WdFKRU7Oy6+NVwaxV\ncJ6xgDQ+GNAOFs33ywL1RfqubH5s7MHlF4GB9BkcZogW5OIOgvTni2a8OgWiCXAOpNo5ateUuLZh\nwKBzRBZwKQH0OI/3VBsqb0D9FwZxM3OvhXvYNvyajxd+bY8wIejWWNbAkkW515xTjt+LBSg4nJxm\ncOmpFs04k0avCy7VA1HSZ0v6A2b2+yR11awhfrOkbTMr4izx5ZKevId9TEhIWFNcKsocQvjLIYSX\nhxBeJekPS/pXIYQvl/Tjkt4YN3tY0jvuURcTEhLWGJdthngRvkbS28zsLZJ+TtJ33GmHUvWpYkmj\n0zmcTwxUujW9dfpvDHQOTnv2l67EhYV/vwFltzyBKoj41Qk4ex+5qtXY2yxiactWy+lQDfFxd+K0\nZtBz9ZXByDXaOyhdWe9OQWWhRG7HwPDp3FXehxCYPEeg07aQZE2lFkM4RmBwBep14isJp/nLklRG\n49zRyPNuP/zsx7xtdPv4yOXco7krwQdL/zwsfWw74Xz6bPBFLWJfBh0/t/2Wj6FVzIeGse6Jn/T1\nAUu1etuTjnf+5VM44pyyUIwVHIuO5jDrRXA1c9xZEjaHU87s6NCP00aOc+Edm+8398UM1w0BBFri\n74MK9jrj0j4QQwjvkfSe+PlDkl57L/uTkJCw/rhUlDkhISHhXuLSzhA/XhTBtDtrKMSsDU4Aq6OD\nwunrLihWv46fYRBbQ2V9qPa8WpbkPJiCYuFds9CtCp0k1aCbajlXyWPANsuADs0586SHgOoCFmWg\nT6PCaWoLyqmOoFxDUcwiZR0UKFmKwPQBjo+hklWgeDOnu62l9+W5yqlsCzv3dqG4z5t2cqwv7F9/\n7vTzxsC5dsW79lmo5hmo8SYoJoa5A+u05Rxj12mu7zEr5oAlzsSgc9R9gc/Z5nMoG7vr49ijso6U\n4NYoqrUo51mh5s798vuMCF2YyF7g17aL0resAbNA5MIoL2/5vQV1Pmd0AO6trSh557Z+NDrNEBMS\nEhIi0gMxISEhIWJtKbNZdmp1xHocMwQPC/VFun04JUdHYjog532nD6ELyfcIpScRPG1Q83pQSDuT\n8ylOBkfqQadpf4GOT+G6zPoaAcHDE3MK2EWO69AZq/KlU7np1H9YRIl4e8nRcrRg4bXc9dtm2mXA\nNih45dt34AweZt5fexbKeqRnAVR3gVzzfdipMeZ6DPfqDvrYgzP3AksPVGU7bQRmjxu6v4e7ZdKD\nA/gN1F/p+X4ZVPvFfeDDBFj4DNEKre14z+27mtzl8gYCwGnLdgLamyPQPMeyTidHsDXq5GTFrX/y\nJfLkj7GK0UIUQhc2Y3m8Fc86p68H1u+MEhISEl4g0gMxISEhIWJtKbPqIIt0Ztx1ijlHsaiHZk59\n2mSK8R9UhxfmdOR46kGvD23unX42UON6igBs5MyGTd+mU4LunCCoN9JKlvvM2T/QvgFU02oKNRvl\nKQPoVgfB3u1tDySfr2jYHmgfAtOfQdBziQYZMF2gmNcSua/dJZynYUV2tGQwekNrN5Eb3Q/IDYbT\n9DHVbCxftJFLnGEsCpQWBZM/E0jeGsZrhPzcNvo62fI/lXri17a9AYqPkqQF7OTmldPtLpT9er85\njw6uwxnAsX2J/HEDHW7hhAJs1uolXOKXoP4DP4/D6L+GAALVkOQNSzNzeLX1V4XK8vWbT63fGSUk\nJCS8QKQHYkJCQkLE+lLmEBRi4DODp0lDFmPkp6JGcRZtmZZQBNtQh3tQIvcXB+cevn0CZfMBp0RV\nB/m7oMwGtXoZLb0CtjVQIFKmgNzXCvm2g66r5p3h+RUXStLwcXOuJxnocPdMYiv6AmqIlG3W8Q1M\n5gW12iycktv2re/j6RRqMooc0QqruwmOh+DxGaIGQkEbKziGm5/0CD5aK/9MOkrDFUsdUGAN+Gfj\n29PyD64AACAASURBVLcMQedQvCsUmZodQ9nfbGh6vsSY9xCAP/PlHeYptxawc5v6Ni0oyBM4tnew\n3kL1/zA6km+24UZ+iKUbmnfv+Tbj6NJ+UVD4SxlphpiQkJAQkR6ICQkJCRFrS5kr1TqJxXDk7lcq\nQB/H28g9zZw+DSI77XaYv+uUNUM+NAOgMzhp6wE/znHLKVuJ/NmrldOQ5TUETMe6v3T3rlDMyXCc\nDLnJe7W3x/zYMaJ6Kxy/DcVT7fhuZNz6HDWCWRDr0GlffgRX7T7Ga+g07UzAOkRsBkmvnJ9LOH3X\nUKdz1D/mGBIzFNxq1T7+baishgJhLCy2Wl6pYdtW76JQEwo7BdSozo4RDD+CbH3Da0RvgOLPYC82\niUsic/SvOnAF3Xb8es5mToFPoDhvIkr96evPeL8e8HCGIfLTR4gSv6pmKYfu3r2eXyA6hi8mXDOJ\n1239GHOaISYkJCSskB6ICQkJCRFrS5nzYNqMebn1A24dNQnIPWZSLJTTU/sv5BfTXZs5zrSIKllY\naYz6uyOnmBlU7iOqrDh+HotbLWCvVJygbaC9C4uoA6dVUwQJL2+4w3SOc2rtOK2adZp+UbUmBe2b\nU8YhaNUsd4pZHUIhBsULO759p+Ofl6DknaiQ7m4ich2R7vMjP04Jx2hapPVIk8d+vebI/S1Qa5ra\nezlozrse4Tj7CEbfgFLd9z4Wm05rDf2l//r42NdsDnZh71U3Fl0ZljeEaAdWjmQBq36OwlIIbu/u\neb/mWI5YoEBWB9LxLAbYD6C8VwiAHyG4fjLx46xQ1tUt373UkWaICQkJCRFrO0NUkSlcad7eofQ3\nWWfgb0jWDzljjBkdRCiSTCqfOVwv/G3Jt3Ub8WYrxxxJ2uihkMg2hIcTpHRhclXHui8lXlcBYpBh\n5lj+spuoTntIBcT8h6VCWQ6lg7i5aSyVyVKe0yXKUA59DLcxWyx7PkZTiE1bKM7RZXohUgo5M2pt\nxr6MMSssz6+tnY1QbnR4/judqW5HMNEtpy5ahC5de5rt7X6/PgWKrvQh8MzhcJP1UJKUTkobMHHt\n4N5CruPKdLbVQvodxIt6y8dwgNhXzn5b3fPT/tqY3TGelvf5MJ7TcRu/o9xuP8ANCHGgG/F+KmjI\nuyZIM8SEhISEiEs3QzSzD6sJlKkklSGE32Zmu5J+QNKrJH1Y0ptCCOeniCQkJCS8QFy6B2LE54UQ\nnsO/3yzp3SGEt5rZm+O/v+Z2DdR10DQu7BvcTgqk8S3hZjKoQEki9V0sUX8iONXbRHnOXh+xbKCy\ny23U9ABNKUAJ+7SuBZVtRwPSZ4Mv6s9yiAdY3969H/Fmc99mihog3SEMWn1XVUhBC3Espovza4oE\nUN0R0uu6V/z4GxBEOjPsPIC5KEhJMfI+Hj/xbNOnqzDqxfLGoSFFEePcRe2WEWJJN7Hc0Tcf3Kd6\n3vcrlX/fmcdlClDGHox1KwTdDZag2rXHTU5yjPk17zuJpYG+V5H6sl5JuQ1jW4haS6TRtWGc24bY\nZlecPg9QqhYfNWcOZkwB7OGSj1iydscFO45VHl2YshSHeM/wekmPxc+PSXrDPexLQkLCmuIyPhCD\npB8zs581s0fid/eFEJ6Kn5+WdN95O5rZI2b2uJk9Pl+evyCfkJCQcBEuI2X+3SGEJ83smqR3mdkv\n8scQQjCzcN6OIYRHJT0qSTtb26FqNdQiTJzuTeDmMpzDrBM0cPpgQw+qzIdnU17W8fBjz55+bi2g\nLLMeBsxdixm4BZTNGZTAokO1MrYHdXhv4TQpQPEmNTaozO2Zv+typIbVSDsT1NJuVAzrllNWxu+1\ncA5duKrYde+LwVx2jNTFPm6zCvSQMZkbnbgNxgGnrBwUuEIsZR8KNt/uBdxuMqih16a8dRD7GVP3\n5kOkE87hTATKOkf6WwZT3JD7vlMscZxhlnOkccYfllBwN1FIpyfQbqrc5m2PUNOmH3gkLE0ce39P\nEAepSL1ZGrfT9mO2EClRgh+X8Rzqev0486WbIYYQnoz/vy7phyS9VtIzZvaAJMX/X793PUxISFhX\nXKoHopkNzGxj9VnSF0n6T5LeKenhuNnDkt5xb3qYkJCwzrhslPk+ST8UA48LSd8XQvhRM/sZSW83\ns6+Q9BFJb7pTQ2Z2SkOXqIHRK5wSkKp0EFRcxYDpvOv0ZQniM7zmtRoLvFN6x2cKl/qnNmuD+DY0\nmzmqXK3cWjRt9pGtB6KjUd//NXvG67tsDDwAvG7DoBSUeWY+FgYHnf2oltZTp0lXOkgF24FjDJYS\nymPvN+O/Mxi3lkiva8+97/Uc9Uu6cZ0AQ1ghoJj1Z7JDBEbDLLcNZX+WgUoi6L21CYNaLGuEdvN5\nh3VpcH8cB5wn+tjB0oR2/N7qT7HEsfAdjnNvfxqV/QEsgKrcj1nAlLic+XWZIM6/zXsUSjxWg844\nJfVbPkbzGDlR5HDywdJECSefOWoEVRvN9Q/rF5d9uR6IIYQPSXrNOd/fkPT5n/geJSQkfDLhUlHm\nhISEhHuJSzVDfFFRhdM6JYOhc4wsO/8dML4GdTGqhYZc2o3c1dcStHfe8f1aw1uDuyWpvMBBZpSh\nZgvqXkzLhsr04EISQJlqmNWWqLuSwcSVywGj2jljDpW12nSqtMo9ziDtTjoIXWIUU4G6IzjnXs4l\nBuTbgvptwJFmBoX8eN5Em48RAM+yphmWPTYzDxKuEVxe0GgVCvmC5TKnXIdAgHc8bEYzWYzPJoLe\n56CmPZjiFge8L/zzeAAafII+xqD+Gur0Ce45sFsNA5x8YJBbjj1Kf4GcfYOTUaDijXA0z3GH8o64\n7ZOOfz9FdMIy3vPVGlLmNENMSEhIiEgPxISEhISI9aXMmWQxCDogf7mE+joBP6D9amvW/CtAcZ2A\nvswWTuvayPGsNzyXNEDZNATjtsBONjNvNENpyZUqmDPoF6qpobfX9q74fqCVLZQBXdbUqKG+HiMw\nvNf0hbmuvQ96XivriOgh7/fVzJcj2gG0CvSNd1mOZYAtSKGzuHwwhSI9OXE6uNH3se22YJAK89Uw\nZ/62X5dnSrf8ohVXB3nQirVxaCA7b/EcUAYU19CwTJHVCIYvUVoUzZSwf+uPmh9aCDQPoKwF2i4R\n5ZBhCaCFeyjH8o2OkW+eed8nCDBfBZKzlOwUQf8BdmY1FP9VPrqdiX1YD6QZYkJCQkJEeiAmJCQk\nRKwtZa5NmraiTZELxCoqBFgjP/RZBCzvTiLVRqTxIdyjd4eoqYGg62KCnGHQV0FFJE1rIaiW+bHH\nR00NlDHyh3d3dtEcgq6pECI3uMZ5dnec1rKOyZlinrGmS+uqn9ti22nnycS9sIb7CFLGuWkL9VJq\n5PuCWi2h3Bo6MI/1OeDOpaLv7dHpua4Q6L3lVLqCXRst33ZzBJhDfV+2ETBtzfXFioqGE//zqDe8\n350Zxpx0OCDQH39aXJpZwHKuikHarRqWY3tuucXA8aqk67WPJ8853Li17okk1eD4OUrCzlZ/H1gv\naiNYPkBZrzdxPlHlZqnbdUGaISYkJCREpAdiQkJCQsTaUmaZKUQK2WdRngDFGWe/C9ullQvxfMPf\nF52uy3MQcGUoH0ozJAatdmCjVLcQhAtFz2ALlkXlMIDqj6dOWSctHHPqNOm+odPqNoKOwTD1dBel\nReFq3Y4q942llyx9rnD++rKe258FBPcySN2Q1xx2fDzHUCiNRB2K7so9+qD08xyMfKBvYKxaoH35\nFhy28X5v0/4Kn2csXIVr1Ir52d0Loo1thHPGkoWdiWlGPjzssipcAMRr6ygG4G/0/BxabafmBZZD\nlvPzLeR4/82xHDFGAkAx8P52sZSwCgivUeRqjgiKEnQ8r/3+H0SVP7ekMickJCSsLdIDMSEhISFi\nbSlzHkybkRO3EOBK+lKR7kG5W0SKMYPNVhuBuSWciVFXSBnyZGeIsM1KBFUPXLktQJ8PF059u3UM\nEoYz9RxeYC1YYfVgMzXvIA8YBZICKNEAda2WCB6exmDwPpTVPVS+Gsnp80Mb294IHLCXy/PHc2vo\nymlVIw+7ptfXre7LBxv+3aa5Ul6NQJ/BwDfgWF5XPuYTKrGsUYy+rxTVOWhvF3Zqi6UfaAz6Psyc\nSla4L2rkG2coClXi/tuO92cbzNPGuHDIq2YKvsEijA7jBfLdu8grr2Dp1cL3q1ZmsCTrQ7Xvgo4v\nsTawKvKVArMTEhIS1hjpgZiQkJAQsbaUOSioXrk2IxhXCJ49yZG3C1qdr/J2ZwhiBn1uwTG6Ro7x\nAjnGSGvV6CEUXQYK1gBG7eCVO/SZkkFklxOGVMMx+iOeszvb8/aWyH09OnAVOQflKbYbWrtgruuC\nPUABpamryf2BUyzWjh4i2LiD5YYx1NobI89DLmMe7hA5y1cuUHznG/79oMC44Xa2CdYyziw9cLnB\nN8niPTLBuZ0s/XMNOrrRA5WGas2cdQastxHInaGO90knton7sOr7+XBppHvg29A2rUR7gkVYBbWY\nGCEBoL1qHwW5DMH9LM4lnOesbvpSKwVmJyQkJKwt1naGKPksbTH3t2UX9SP2jrg11ZFmxwLmp4uS\n+U0+bAhrVD7EAvvYRYhO5e+dfvA2R+ZtDuAUM48zLfiQqsKiNmt0nHHp2cFMlI4omK7ubHo84Ri5\nc+PQzABGmc8E6onPkHYz36/fwXHgarM7cPGExw/UAAYec1cgva+KM5ASQlONODc7gmC05eOcQTCY\nQWxpw+EnMI0SE6p2z1MDFWeuGa5tdeACB2McmXM4RAwh69UsIXxQPBqJM7emjzQt5rWiM1G14R3P\nzq/CqxI1ZXpzONzsH/tGHTChWF+oO+WYe//ID86YIp804xLKW4WwlzrSDDEhISEh4tI9EM1s28x+\n0Mx+0czeb2afZWa7ZvYuM/tA/P/OnVtKSEhIeH64jJT5myX9aAjhjWbWltSX9LWS3h1CeKuZvVnS\nmyV9ze0asTqoiKVFzyz+Yp0433S6Mwet7g2bhX2DYFIida6FND6CRrQdiBA0a80Q2zU8csJ71HNK\ncmQN3V7i6pRTxL6Bp+coq3mmdghQHIOmbYAynZPGdZ/cGaYkHZ36EkDVcVEhRxnQgsyU1Aypk0sY\nze6VPo5HTza07ughbyMrIRhxLCAYUATgVZkgVrAcoB2IHdOZLwnsxxjOrSnKcG45BV0gltFlH8lO\n/KRzxBuOUI+EqZMswzotmnuBRsRT0OHNBcS7ijGejjliTAssx7TM9+3DUHdq3s5JdDG+mvv1LCCk\nnYDezxG/WfSbcbEsxSH+qsLMtiR9jqTvkKQQwiKEcCjp9ZIei5s9JukN96aHCQkJ64xL9UCU9GpJ\nz0r6LjP7OTP7djMbSLovhPBU3OZpNQXtb4GZPWJmj5vZ47Pl4rxNEhISEi7EZaPMhaTPlPSVIYSf\nNrNvVkOPTxFCCGbny2whhEclPSpJu9s7YdlrnvdTFqqAckqDy7CAcWd0HFmAAs3gGNJZIC0LtHMB\nWtPqIXUKiucEAm1/gZQqpK6tKMns2YPT79oV0r9AmhgrlyN1rUfVGn1nuiLp2y5i2E7Pp+1LChzC\nKcxyh7lT9gli3DYg5zLesX7Spf3jiauf5QMNEd1DKlwHlH0+99TGFkqpzubeRo6YyAoxdDliTGso\nvksscfRi3OSzGdx2JqCEPW9vgoiDWf/8PyEw0zMlZPtPY/ni/uZcF23EsnZxazPGUfza22sdIXUP\nKXiLAjVdUF/GybE0iY44Yywd9M5cN8yXuDQRx9aS282vOp6Q9EQI4afjv39QzQPyGTN7QJLi/6/f\no/4lJCSsMS7VAzGE8LSkj5rZp8WvPl/SL0h6p6SH43cPS3rHPeheQkLCmuOyUWZJ+kpJ3xsV5g9J\n+hNqHtxvN7OvkPQRSW+6UyOVah3XjYp20oVZJ9w+2uPzTTfr6DgyBo0NsIk5QvByB4G0NSKQC7rI\nTpw/LWqnW3Oon1NEYXeiFN7JoGcyFZA0mZUyGaUNNT2jWSzLg6LM6Tw638zRYNF1Gt1tI4gZSw2r\nNC5J6oNu1QjrraFElz2MFyh7J16XLsqU8ubMOnTewbIHlXAa0Z7hmN5HK/z444W3073eXNP7HkC9\nHLQxh8p/Y9PHqMf6JqDGvY+6Kp9f8+tYvHrv9PNwHNtEHZnA5ZUe6PMhNkLCQLWPOipQ9geFH7NG\nSdJ55X3M49JMB2VKqwVTHhHcjr8Fi6mr60eYL+EDMYTwHyT9tnN++vxPdF8SEhI+uXCpKHNCQkLC\nvcSlmyG+WMiDaaNqqEB75DTg6T1QPNDkbeQ1H27cmqPZAb1s7Z+f76ktOMycgNahnCjriMwhRS5h\nQFrEQNrWFdcEWWukl7tBawtlI2k+egPqK7FylZGkbSjE09j1ckoK7H1t91z9JU3vL2k+6p+r2s/5\nODhlOzy+4TvjddyPymkFh5cMynpGFxZQQ+aM2wB1aY4YdgWzWNTJ2Wz5+c9eHuvY4NwYGD8fwL3m\nBOU5A3PcvY9hG3ntXdZ3AaJTzQyq/UYbbjdgzItr/v2UJrKbfpzjNoxwlz4uS+Rvbw6dSpfxHh2x\nlCxyyQ2Gx+0N1FSpmwazNSTNaYaYkJCQEJEeiAkJCQkRa0uZa5OmRUNtMgQVXzt0WkP7qxOoyK0Y\n1NyCbVd14hQkMGcYVlwCTWRZ0RoqZ7kJGyf0d9hxRXeyaI41R6x0PYXlFwJwSVO7oPX7UGKLqW9/\nv5wm1nuw0Vq1A0usGrZZDG7OoD7aDDmuEEI7U6jJsKWqrnqp1BJ0t71S5aECZ22MFXKGt3DbPueH\nVBu54TmiCeYoydnPvC9Z7u3Movo/h6Fawd9xDRcoA1ugjxUCtmssTZSHboS7uOb9tajWz1ButguV\nN2e5U0QqhAHuFajGO6Vfu30U1el3/QDMT96N9HmOPHVDLZoM9X/aAYHuq3z89XP/SjPEhISEhBXS\nAzEhISEhYm0pc6iDqljjwkaoWQEVrQua3B4xPbr5bAxMRQAw3yPFGGVNx3Cghp2XFf55E9u3Qeto\nlLwSpbsf8aDbK5vuRr08QBlOKKjM093e8+0nHSiux779YAZaG5XQKSTkdgd5xWiiDzuvAZRq+m/V\nKEnaR+0SumBTB6+faerBTB50FZQBw1kPtBtUuwcKbB0MIpYsrHe+XdsECdpFPNazM88f35s5Zeyj\nLkyr78fMkTN9MnNqfAL7sa0tjxYYY3y3Yt+HjL9ue582IXO3EU1QZCgbalhXQa2dvRpLBiMsn/R9\nLOoYyW+wpGvjdzoGBNYiqptzq9eQM6cZYkJCQkJEeiAmJCQkRKwtZc5l2qhiziWsuHIWiEIJx4MF\nHLGjckiVrwAd7oHfFlc993U6coqbU5VrITIW9KQFRW8Oq7FVTnC7e37gq206lTsokTNLKzL4QV50\nkZewkZq2GvpEBZ3VW8HGzzhpT6F4BwQmF6ByiwlUTIzj7MhJ84q917Ci6qAgWIclMWHFFRh0jfFC\njPIZSjjHSdFJfbRort0A/Z4Vvl+35d8XmEcY1H/LoMpimw7K1mZIEijmsbAWFOy9HV/qaCNIfQGl\nOiBRnap4B98HuKTDVFsBKxyrxIC67xvQBI70eoqc9Xy1TJGt33xq/c4oISEh4QUiPRATEhISItaW\nMlsdTp2V230qkaABcroz2HQlcGXjRRftDihTPj1fXesMEdRM9+y278siRwFBzV2q3P2G+oRtp0BU\nZ6ctBOnO4Oo9c5pMh23af5VQYg/aCIyOOck7xhJKDtblXUBxrMao47wE3d71dlpQgtu0FMudhj3b\nb9qZ97B0MELdZvqZYSWBRaYCioLxnKmQthDgXOD6WnSVLhBQfQRKX2I8bQhiifrLC4xtJtauxuaw\n9+rHe/EYlHl63ZPqWxuocw0YxmW45dtw+SJ7hdfRpi1cxRrVsXBWhqWb5aY/EgoUkaKT9qhulhc4\nruuCNENMSEhIiEgPxISEhISItaXMxAJu1FOoyRnq1XZAPYtJs/0E9C07RHEgWEuV90O2A2rk5Aa4\nVC/BMpZQVLNtzzedxRzrMiA3un3+parRdgvvtwDqN0Fccj52/kTrsCxS4vHU1cwlXKJRIlkPDDwf\nOR/CFgrWVeXc+9WB4r6s/PjDjSG+b2jYM8i1PUHE8tYSAfUVOoM6wjUCkMOOU/YzFRgxBYD2fxr4\nXGCct3bdZo3LJ9Mplgk2sZSCcxZobYYog2HhY7SqdZwfnl8hsurCzgv5ywvkqVcIgB8gZ7ldYvkC\n3dqHS3hrlauNe69V+PkPq/Pvudyaa54n+6+EhISE9UV6ICYkJCRErC1lrkwax2BjBgNzml+VKD4F\n+miRqbQmCK6FOt1+yJW92f7o9HNRcDihPrZAiea0CHPqxz5mUS1cVK4Ct2ZoG/1WBy7VoKOGoNpr\ntX8+6iFgF2r5ItK3sg01tY/lgEMPOi/gAN5BwSlG9XZxa5Wg3ieImN6v4PwccWWJ4GYGtOM4EFPP\nqO/za8jTveH9ZTsBFNMQYL5S5Wuq5tn50QT5Pqg5lkYM37eRsz7Z8c8ZeXo8p3LX+72c+/EnyGs+\no84Xfv/1g49XNuVyCHLsUTt6Y8+XAapI/ZcI4u5f9aWGrY4fhwkNN8rG9Tyz9ZtPXaozMrNPM7P/\ngP+OzezPm9mumb3LzD4Q/79zr/uakJCwfrhUM8QQwi9J+v/bO5cYOa4qDP+n+t097Z6HB+PEBgcR\nBXlDghACgRBKhMQjCiwQSgRShCKFBYsgBYWEPVLY8FggJCsBZYGAyIBALNiELFhZQLJAxCCFiCiO\nPPbYcc+Me6a7uqpPFnV7zm9nAjGaTHfdnG/jrpqu6nvrtm/f/57X7QAgIhUArwL4LYBHATyjqo+L\nyKPh+Nv/7V5JNUFttfiFS95k3q9t2C/jOLFf1zS8rlNJTKUVSpMsDMmKhe5llP1jAPt1r1F4l1JN\nlUmFw8iMyR7+XU0q2TmhFc+QrmySH+Q1ixva+z68QWU7x5TQ9GhxbYPLtA6pXkuPVsgpL3OMdMNW\nfHmDwiV5JbZpq6hFSkA6TZa6Q/6OjYxWhVsU8kf+i0zORqgelTNds5V2SjVQOOntMPgQ1inha5U+\nH9QH7ZkRonqZks8eNmPTZIWu3TAVwW1E8Akck1+pLNoz4e+c0GpszHGUNLYZlSfdWLB2NSvsH0kX\nTOukvEZlWsccrkifaVehFbL3JJW5Wk/tC/Pco7sA/FtVXwbwBQBPhfNPAfjizFrlOE60zNUK8Tru\nBfCL8PqIqp4Pr9cAHNnrAhF5EMCDANButfZ6i+M4zpsylxOiiNQB3APgsev/pqoqInvGDKnqKQCn\nAGB5eVmndUJYJrBBYCc1WTGi8Klxt3gsOxRzNSQDSI9cEjtKiTgTkzIDSj661ib5QnVaKmRguaZm\nRZAqKwMyTCza5zQoWWkmdu/hgAwJFC6Ys0wkw0uVyqY2+8EARVr7KjmwLaV2vxG7XtZJjlF2lpyk\nfDIkoxYZnhqbJNWCcYgeDyb0DHNqV0JSd5t+94Rqyky2bJA2jpIfIMxoMOqbxK9OJTGrZH7OLFMJ\nlt2c5Jf3QFrkn8klR9MgmRvvonBJui6nnYmcMhNVyKh2pWNbEFTSB5fVvgvtbRuX5a4ZVaaZfw7R\nNsEqGbUS+r8ypjoytfCcRdwP8aD4LIDnVPVCOL4gIkcBIPx7cWYtcxwnWuZ1QrwPJpcB4PcA7g+v\n7wfwuwNvkeM40TN3kllEOgA+DeDrdPpxAE+LyAMAXgbw5f91H1VFHjLOJOQGyBlBcgq7auy8scxi\ntkB+gpQMs0YSIxWTUufFJFiFQspWKiZD2J9rm8LkmlSGdCob+2QprE/Ix5CkaZ38zYYda2+XnAK3\nxiar2kdMnlESFCShBklKWwfblA1ooib1VqkWSzcxzToeky/lukm24U32nn5q5zmmLBnSPsTuhSRT\nu9YfrbFMp/A2tgQv2WemnO6F6rt0aJ95EMIoU9oCyOt7S0JO3DrhUrEDql1Cluhkh7IDkfTNV4v3\nNMkfdLRAyVopw1It4xQ/9v6M1zQkmW+a2Divp5ZB5+rl/u5rDdsUW6nd+1LXvnMrh+we1a09xieP\nL9vN3E2IqjoAsHLducsorM6O4zhvG/MqmR3HcQ6cuVsh7hcynkDOB4fYHplFyRTXTSjbDZUn3clC\nrQuSMpsdk2/rIAdkUjI75Nzd5aSwZEEWumenTfK1+kZ5VuEynKT6NsTky6Gu9a3TojAyyg6zXCPn\ncUoUs0mZZUYhXrFBWwMtTnLKOVG59CgpqRZJvMlhu8+QpHejTc7YFGqm4T1Nzl9KDstXmyRlSexn\nJJnzjMLuqAxpjTK/pOQMPyavgGoIjbsyMUmf0HWNbbuuT4PRprovbNmfUB0ZTquTUgLWUdi+aVId\nl4Vt+34MuawrWbk5ZC6jUD/NySpM9WWqC+RNMLbGTH016tvWn81Vksz28VAK/9M0tCU+I7OvEB3H\ncab4hOg4jhOIVjJnMsGVpNBzTbLQHW2aY2p9i6yoZP3TYJUcLlEMrto91qgWyfLIJEgvJTlEco/L\nULLKSMiKypJomnS1VzdpOszIPEo3GZMERMoxruTUTbVGcuX4WHv7NLlpm5KvVinuulU1/Vbl/lAi\n2sHI9POwwrG0tDVB1nchL+zd+hzkc5+R1Ty/YPVNBj2yxLbs3kr3Ti6Z9FWy7Gdtel7U/0Ot4lmv\nWtgxdihLT05bLc2LNv7Zuyn5bd2ef5vKpk4ow06PJGsWGsBDOF7mcqdGv2XfzyWy8qNj9xtMyCuB\ny7DS/XOKX59mZ6qesFwpS5ThZkSlRzlL0HEt3lOLcD0VX48cx3H+T3xCdBzHCUiMpQQBQETWAQwA\nXJp1Ww6Aw/B+xkRZ+vleVV2ddSP2k2gnRAAQkb+q6odn3Y63G+9nXLxT+jmPuGR2HMcJ+IToOI4T\niH1CPDXrBhwQ3s+4eKf0c+6Ieg/RcRznRoh9heg4jvOW8QnRcRwnEO2EKCKfEZF/iciLoXRpvd+C\nZAAAAm1JREFUFIjIcRF5VkReEJF/iMhD4Xx0tatFpCIiz4vIH8LxLSJyJozpr0LtndIjIosiclpE\n/ikiZ0XkYzGOZxmIckIMNZ1/jKI2y0kA94nIydm2at/IADysqicBfBTAN0LfprWrbwXwTDguOw8B\nOEvH3wPwA1V9P4ArAB6YSav2nx8B+KOqfgDAB1H0OcbxnHuinBABfATAi6r6kqqmAH6JorZz6VHV\n86r6XHi9heI/z82IrHa1iBwD8HkAT4RjAXAngNPhLaXvIwCISA/AJwE8CQCqmqpqH5GNZ1mIdUK8\nGcArdHwunIsKETkB4A4AZ/AWa1eXiB8CeARWKWQFQF91t6ZqLGN6C4B1AD8L2wNPhLpCsY1nKYh1\nQoweEVkA8GsA31TVTf6bFr5UpfWnEpG7AVxU1b/Nui0HQBXAhwD8RFXvQBF/f408Lvt4lolYJ8RX\nARyn42PhXBSISA3FZPhzVf1NOB1T7eqPA7hHRP6DYrvjThT7bIsiu4keYxnTcwDOqeqZcHwaxQQZ\n03iWhlgnxL8AuDVYJesA7kVR27n0hL20JwGcVdXv05+iqV2tqo+p6jFVPYFi7P6kql8B8CyAL4W3\nlbqPU1R1DcArInJbOHUXgBcQ0XiWiWgjVUTkcyj2oSoAfqqq351xk/YFEfkEgD8D+Dtsf+07KPYR\nnwbwHoTa1ar62kwauY+IyKcAfEtV7xaR96FYMS4DeB7AV1V1j4LB5UJEbkdhPKoDeAnA11AsVqIb\nz3kn2gnRcRznRolVMjuO49wwPiE6juMEfEJ0HMcJ+IToOI4T8AnRcRwn4BOi4zhOwCdEx3GcwOvf\n92/t7cdz4QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79194b8438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[1], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[14])+ \",\"+\" iceberg_probability : \"+str(pred_valid[1])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f7919382c50>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jn1u6c3an6w7MBWKcoX6lFpyjVzOE6R1HcPxlTKzV1tPIYcHYVz9fh+M7t1FW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\nnuM4c4UPeo7jzBU+6DmOM1f8D5MvQDuyNHy5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79193d5160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[2], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[38])+ \",\"+\" iceberg_probability : \"+str(pred_valid[2])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f79193650b8>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GlFiJZSkk/Qo7Jw9vaX/iOnw/wYpraG/iemChlpPHTLyk7NtbvIWVU4XVTcuqJZeF1R3s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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7919414e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[3], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[43])+ \",\"+\" iceberg_probability : \"+str(pred_valid[3])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f79192c6320>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5pNiETHStjeRfOJ9pdax0ASfoOpytmdyqibhdPVBRyAViwxsdOJrzMbDmNjEW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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f791935a278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[6], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[104])+ \",\"+\" iceberg_probability : \"+str(pred_valid[6])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f79192ab9b0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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y0PFcoNKQaox9fIkniON20JGLFbUM5o1WwUQEKa+honNn7uunzfWBrZszcEha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\nV/OhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7919287b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[9], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[138])+ \",\"+\" iceberg_probability : \"+str(pred_valid[9])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f7919210278>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qNmtqkSP5c4Y8z0WTlMjXNyNT3J47HTpA2zlyX/dP1AEFZUTRodE1SG094KU6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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f791931ecc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[10], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[141])+ \",\"+\" iceberg_probability : \"+str(pred_valid[10])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f79191f4b38>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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B2Gj6nM07mnMJGm0Fu2nEBqvXIA95GKkqArBbKJsZuHaGgG2IjLIj8BdQ6QFo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\n4ziO8D9sx86BXNgYKAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79191d0d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[11], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[167])+ \",\"+\" iceberg_probability : \"+str(pred_valid[11])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f79191584e0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7RzwrkjR10RVPr6rrpiunFTt1xLXWnY4a+RBwwkm5DbVpqGAfDZ1KI/+T2vWK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\nL+pJtZUqC5gFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79191ad668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[12], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[195])+ \",\"+\" iceberg_probability : \"+str(pred_valid[12])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f791913acf8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DPwavf+rnHBz4s5AXvg0/uIR1cZyybWNY+TH4UVgFX1/l3i9bR75Nhee2l3l/8SP7kebw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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79191177b8>"
      ]
     },
     "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
}