Model_03-SGD-k=10_3e-4_10p.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Mandatory imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import log_loss\n",
    "from sklearn.model_selection import StratifiedKFold, StratifiedShuffleSplit\n",
    "from os.path import join as opj\n",
    "from matplotlib import pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import pylab\n",
    "plt.rcParams['figure.figsize'] = 10, 10\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_json(\"input/train.json\")\n",
    "target_train=train['is_iceberg']\n",
    "test = pd.read_json(\"input/test.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_train=train['is_iceberg']\n",
    "test['inc_angle']=pd.to_numeric(test['inc_angle'], errors='coerce')\n",
    "train['inc_angle']=pd.to_numeric(train['inc_angle'], errors='coerce')#We have only 133 NAs.\n",
    "train['inc_angle']=train['inc_angle'].fillna(method='pad')\n",
    "X_angle=train['inc_angle']\n",
    "test['inc_angle']=pd.to_numeric(test['inc_angle'], errors='coerce')\n",
    "X_test_angle=test['inc_angle']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Generate the training data\n",
    "X_band_1=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in train[\"band_1\"]])\n",
    "X_band_2=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in train[\"band_2\"]])\n",
    "X_band_3=(X_band_1+X_band_2)/2\n",
    "#X_band_3=np.array([np.full((75, 75), angel).astype(np.float32) for angel in train[\"inc_angle\"]])\n",
    "X_train = np.concatenate([X_band_1[:, :, :, np.newaxis]\n",
    "                          , X_band_2[:, :, :, np.newaxis]\n",
    "                         , X_band_3[:, :, :, np.newaxis]], axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_band_test_1=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in test[\"band_1\"]])\n",
    "X_band_test_2=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in test[\"band_2\"]])\n",
    "X_band_test_3=(X_band_test_1+X_band_test_2)/2\n",
    "#X_band_test_3=np.array([np.full((75, 75), angel).astype(np.float32) for angel in test[\"inc_angle\"]])\n",
    "X_test = np.concatenate([X_band_test_1[:, :, :, np.newaxis]\n",
    "                          , X_band_test_2[:, :, :, np.newaxis]\n",
    "                         , X_band_test_3[:, :, :, np.newaxis]], axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "#Import Keras.\n",
    "from matplotlib import pyplot\n",
    "from keras.optimizers import RMSprop\n",
    "# from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPooling2D, Dense, Dropout, Input, Flatten, Activation\n",
    "from keras.layers import GlobalMaxPooling2D\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.layers.merge import Concatenate\n",
    "from keras.models import Model\n",
    "from keras import initializers\n",
    "from keras.optimizers import Adam\n",
    "from keras.optimizers import rmsprop\n",
    "from keras.optimizers import SGD\n",
    "from keras.optimizers import Nadam\n",
    "from keras.optimizers import Adadelta\n",
    "from keras.layers.advanced_activations import LeakyReLU, PReLU\n",
    "from keras.callbacks import ModelCheckpoint, Callback, EarlyStopping\n",
    "\n",
    "from keras.datasets import cifar10\n",
    "from keras.applications.inception_v3 import InceptionV3\n",
    "from keras.applications.vgg16 import VGG16\n",
    "from keras.applications.xception import Xception\n",
    "from keras.applications.mobilenet import MobileNet\n",
    "from keras.applications.vgg19 import VGG19\n",
    "from keras.layers import Concatenate, Dense, LSTM, Input, concatenate\n",
    "from keras.preprocessing import image\n",
    "from keras.applications.vgg16 import preprocess_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data Aug for multi-input\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "batch_size=64\n",
    "# Define the image transformations here\n",
    "gen = ImageDataGenerator(horizontal_flip = True,\n",
    "                         vertical_flip = True,\n",
    "                         width_shift_range = 0.,\n",
    "                         height_shift_range = 0.,\n",
    "                         channel_shift_range=0,\n",
    "                         zoom_range = 0.2,\n",
    "                         rotation_range = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here is the function that merges our two generators\n",
    "# We use the exact same generator with the same random seed for both the y and angle arrays\n",
    "def gen_flow_for_two_inputs(X1, X2, y):\n",
    "    genX1 = gen.flow(X1,y,  batch_size=batch_size,seed=55)\n",
    "    genX2 = gen.flow(X1,X2, batch_size=batch_size,seed=55)\n",
    "    while True:\n",
    "            X1i = genX1.next()\n",
    "            X2i = genX2.next()\n",
    "            #Assert arrays are equal - this was for peace of mind, but slows down training\n",
    "            #np.testing.assert_array_equal(X1i[0],X2i[0])\n",
    "            yield [X1i[0], X2i[1]], X1i[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Finally create generator\n",
    "def get_callbacks(filepath, patience=2):\n",
    "    es = EarlyStopping('val_loss', patience=10, mode=\"min\")\n",
    "    msave = ModelCheckpoint(filepath, save_best_only=True)\n",
    "    return [es, msave]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getVggAngleModel():\n",
    "    input_2 = Input(shape=[1], name=\"angle\")\n",
    "    angle_layer = Dense(1, )(input_2)\n",
    "    base_model = VGG16(weights='imagenet', include_top=False, \n",
    "                 input_shape=X_train.shape[1:], classes=1)\n",
    "    x = base_model.get_layer('block5_pool').output\n",
    "    \n",
    "\n",
    "    x = GlobalMaxPooling2D()(x)\n",
    "    merge_one = concatenate([x, angle_layer])\n",
    "    merge_one = Dense(512, activation='relu', name='fc2')(merge_one)\n",
    "    merge_one = Dropout(0.3)(merge_one)\n",
    "    merge_one = Dense(512, activation='relu', name='fc3')(merge_one)\n",
    "    merge_one = Dropout(0.3)(merge_one)\n",
    "    \n",
    "    predictions = Dense(1, activation='sigmoid')(merge_one)\n",
    "    \n",
    "    model = Model(inputs=[base_model.input, input_2], outputs=predictions)\n",
    "    \n",
    "#     adadelta= Adadelta(lr=1.0, rho=0.95, epsilon=None, decay=0.0)\n",
    "#     model.compile(loss='binary_crossentropy',\n",
    "#                   optimizer=adadelta,\n",
    "#                   metrics=['accuracy'])\n",
    "    \n",
    "    sgd = SGD(lr=3e-4, 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 [==============================] - 17s 712ms/step - loss: 0.7270 - acc: 0.5847 - val_loss: 0.5378 - val_acc: 0.7716\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.5279 - acc: 0.7282 - val_loss: 0.3581 - val_acc: 0.8333\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.3888 - acc: 0.8065 - val_loss: 0.2912 - val_acc: 0.8642\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.3458 - acc: 0.8289 - val_loss: 0.2698 - val_acc: 0.8457\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.3150 - acc: 0.8599 - val_loss: 0.2580 - val_acc: 0.8951\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 12s 501ms/step - loss: 0.2684 - acc: 0.8773 - val_loss: 0.3275 - val_acc: 0.8642\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.2889 - acc: 0.8674 - val_loss: 0.3028 - val_acc: 0.8827\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 12s 501ms/step - loss: 0.2763 - acc: 0.8747 - val_loss: 0.2591 - val_acc: 0.8827\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.2487 - acc: 0.8952 - val_loss: 0.2677 - val_acc: 0.8889\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.2522 - acc: 0.8934 - val_loss: 0.2820 - val_acc: 0.8951\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.2381 - acc: 0.8882 - val_loss: 0.2575 - val_acc: 0.9074\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.2392 - acc: 0.8953 - val_loss: 0.2268 - val_acc: 0.9136\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.2388 - acc: 0.8993 - val_loss: 0.2279 - val_acc: 0.9074\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2324 - acc: 0.8905 - val_loss: 0.2346 - val_acc: 0.9198\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2174 - acc: 0.9052 - val_loss: 0.2448 - val_acc: 0.9074\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2311 - acc: 0.8995 - val_loss: 0.2377 - val_acc: 0.9074\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.2000 - acc: 0.9136 - val_loss: 0.2150 - val_acc: 0.9198\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2367 - acc: 0.9013 - val_loss: 0.2340 - val_acc: 0.9012\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.2066 - acc: 0.9106 - val_loss: 0.2376 - val_acc: 0.8951\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.2117 - acc: 0.9063 - val_loss: 0.2168 - val_acc: 0.9136\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2153 - acc: 0.9187 - val_loss: 0.2154 - val_acc: 0.9259\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.1977 - acc: 0.9188 - val_loss: 0.2107 - val_acc: 0.9136\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 13s 533ms/step - loss: 0.1964 - acc: 0.9195 - val_loss: 0.1978 - val_acc: 0.9198\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1964 - acc: 0.9129 - val_loss: 0.2309 - val_acc: 0.9136\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1886 - acc: 0.9239 - val_loss: 0.2022 - val_acc: 0.9136\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1792 - acc: 0.9239 - val_loss: 0.2171 - val_acc: 0.9074\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1732 - acc: 0.9291 - val_loss: 0.2140 - val_acc: 0.9321\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1914 - acc: 0.9205 - val_loss: 0.2013 - val_acc: 0.9198\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1765 - acc: 0.9241 - val_loss: 0.2030 - val_acc: 0.9198\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1779 - acc: 0.9228 - val_loss: 0.2112 - val_acc: 0.9074\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1663 - acc: 0.9294 - val_loss: 0.2000 - val_acc: 0.9136\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.1846 - acc: 0.9330 - val_loss: 0.1881 - val_acc: 0.9136\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.1630 - acc: 0.9343 - val_loss: 0.1827 - val_acc: 0.9259\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1696 - acc: 0.9354 - val_loss: 0.1858 - val_acc: 0.9259\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1830 - acc: 0.9284 - val_loss: 0.1852 - val_acc: 0.9259\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1448 - acc: 0.9391 - val_loss: 0.2067 - val_acc: 0.9259\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1550 - acc: 0.9371 - val_loss: 0.1910 - val_acc: 0.9136\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.1576 - acc: 0.9345 - val_loss: 0.1883 - val_acc: 0.9198\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.1804 - acc: 0.9320 - val_loss: 0.1863 - val_acc: 0.9074\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1395 - acc: 0.9480 - val_loss: 0.1845 - val_acc: 0.9198\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.1612 - acc: 0.9285 - val_loss: 0.1784 - val_acc: 0.9259\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1662 - acc: 0.9280 - val_loss: 0.1997 - val_acc: 0.9198\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1509 - acc: 0.9413 - val_loss: 0.2070 - val_acc: 0.9136\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.1454 - acc: 0.9427 - val_loss: 0.1885 - val_acc: 0.9198\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1429 - acc: 0.9422 - val_loss: 0.1895 - val_acc: 0.9198\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.1520 - acc: 0.9346 - val_loss: 0.1741 - val_acc: 0.9259\n",
      "Epoch 47/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1483 - acc: 0.9461 - val_loss: 0.2253 - val_acc: 0.9259\n",
      "Epoch 48/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1381 - acc: 0.9404 - val_loss: 0.2027 - val_acc: 0.9198\n",
      "Epoch 49/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1631 - acc: 0.9302 - val_loss: 0.2051 - val_acc: 0.9074\n",
      "Epoch 50/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1484 - acc: 0.9443 - val_loss: 0.1878 - val_acc: 0.9136\n",
      "Epoch 51/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1488 - acc: 0.9397 - val_loss: 0.1772 - val_acc: 0.9198\n",
      "Epoch 52/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1379 - acc: 0.9405 - val_loss: 0.1772 - val_acc: 0.9136\n",
      "Epoch 53/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1416 - acc: 0.9458 - val_loss: 0.1888 - val_acc: 0.9198\n",
      "Epoch 54/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1412 - acc: 0.9409 - val_loss: 0.1805 - val_acc: 0.9383\n",
      "Epoch 55/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.1036 - acc: 0.9603 - val_loss: 0.1902 - val_acc: 0.9321\n",
      "Epoch 56/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.1185 - acc: 0.9526 - val_loss: 0.1654 - val_acc: 0.9198\n",
      "Epoch 57/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1010 - acc: 0.9631 - val_loss: 0.2015 - val_acc: 0.9198\n",
      "Epoch 58/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1388 - acc: 0.9461 - val_loss: 0.1898 - val_acc: 0.9321\n",
      "Epoch 59/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1005 - acc: 0.9596 - val_loss: 0.2025 - val_acc: 0.9198\n",
      "Epoch 60/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1337 - acc: 0.9461 - val_loss: 0.1871 - val_acc: 0.9259\n",
      "Epoch 61/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1171 - acc: 0.9545 - val_loss: 0.1900 - val_acc: 0.9259\n",
      "Epoch 62/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1033 - acc: 0.9643 - val_loss: 0.2319 - val_acc: 0.9198\n",
      "Epoch 63/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1359 - acc: 0.9485 - val_loss: 0.1782 - val_acc: 0.9259\n",
      "Epoch 64/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1337 - acc: 0.9444 - val_loss: 0.1696 - val_acc: 0.9198\n",
      "Epoch 65/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1086 - acc: 0.9539 - val_loss: 0.1742 - val_acc: 0.9198\n",
      "Epoch 66/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1030 - acc: 0.9592 - val_loss: 0.2057 - val_acc: 0.9259\n",
      "Epoch 67/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1217 - acc: 0.9494 - val_loss: 0.2193 - val_acc: 0.9136\n",
      "Epoch 68/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1120 - acc: 0.9539 - val_loss: 0.2165 - val_acc: 0.9198\n",
      "Epoch 69/100\n",
      "24/24 [==============================] - 12s 492ms/step - loss: 0.1091 - acc: 0.9572 - val_loss: 0.1807 - val_acc: 0.9259\n",
      "Epoch 70/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1111 - acc: 0.9499 - val_loss: 0.2136 - val_acc: 0.9259\n",
      "Epoch 71/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1007 - acc: 0.9591 - val_loss: 0.1907 - val_acc: 0.9012\n",
      "Train loss: 0.0743423832604\n",
      "Train accuracy: 0.970180305132\n",
      "Test loss: 0.165411519072\n",
      "Test accuracy: 0.91975308642\n",
      "\n",
      "===================FOLD= 1\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 15s 633ms/step - loss: 0.7217 - acc: 0.5759 - val_loss: 0.5206 - val_acc: 0.8012\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.5417 - acc: 0.6946 - val_loss: 0.3909 - val_acc: 0.8137\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.4319 - acc: 0.7788 - val_loss: 0.2918 - val_acc: 0.8385\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.3637 - acc: 0.8280 - val_loss: 0.3044 - val_acc: 0.8509\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.3441 - acc: 0.8460 - val_loss: 0.2798 - val_acc: 0.8696\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.3224 - acc: 0.8539 - val_loss: 0.2478 - val_acc: 0.8696\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.3044 - acc: 0.8668 - val_loss: 0.2524 - val_acc: 0.8758\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.3057 - acc: 0.8667 - val_loss: 0.2585 - val_acc: 0.8571\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.2700 - acc: 0.8857 - val_loss: 0.2644 - val_acc: 0.8385\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.2774 - acc: 0.8715 - val_loss: 0.2298 - val_acc: 0.8634\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2691 - acc: 0.8811 - val_loss: 0.2404 - val_acc: 0.8944\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2492 - acc: 0.8908 - val_loss: 0.2323 - val_acc: 0.8820\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.2586 - acc: 0.8930 - val_loss: 0.2265 - val_acc: 0.8758\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.2323 - acc: 0.9066 - val_loss: 0.2162 - val_acc: 0.8820\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.2401 - acc: 0.8984 - val_loss: 0.2034 - val_acc: 0.9193\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2317 - acc: 0.8978 - val_loss: 0.2295 - val_acc: 0.8882\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2211 - acc: 0.9042 - val_loss: 0.2109 - val_acc: 0.8944\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2375 - acc: 0.8979 - val_loss: 0.2265 - val_acc: 0.9006\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.2175 - acc: 0.9105 - val_loss: 0.1981 - val_acc: 0.9193\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.2146 - acc: 0.9075 - val_loss: 0.1973 - val_acc: 0.8944\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.1944 - acc: 0.9192 - val_loss: 0.1890 - val_acc: 0.9193\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2290 - acc: 0.9092 - val_loss: 0.2110 - val_acc: 0.9317\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.2063 - acc: 0.9123 - val_loss: 0.1863 - val_acc: 0.9379\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2112 - acc: 0.9167 - val_loss: 0.2016 - val_acc: 0.9255\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1999 - acc: 0.9173 - val_loss: 0.1945 - val_acc: 0.9317\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1988 - acc: 0.9161 - val_loss: 0.1935 - val_acc: 0.9317\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2043 - acc: 0.9157 - val_loss: 0.2046 - val_acc: 0.9193\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1852 - acc: 0.9229 - val_loss: 0.1959 - val_acc: 0.9255\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2021 - acc: 0.9167 - val_loss: 0.2030 - val_acc: 0.9317\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.1895 - acc: 0.9298 - val_loss: 0.1995 - val_acc: 0.9317\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.1864 - acc: 0.9218 - val_loss: 0.1804 - val_acc: 0.9317\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1834 - acc: 0.9210 - val_loss: 0.2220 - val_acc: 0.9068\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1827 - acc: 0.9291 - val_loss: 0.2371 - val_acc: 0.8944\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1748 - acc: 0.9231 - val_loss: 0.2193 - val_acc: 0.8944\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1931 - acc: 0.9182 - val_loss: 0.2024 - val_acc: 0.9255\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1692 - acc: 0.9364 - val_loss: 0.2086 - val_acc: 0.9193\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1717 - acc: 0.9240 - val_loss: 0.2192 - val_acc: 0.9255\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1635 - acc: 0.9381 - val_loss: 0.1941 - val_acc: 0.9255\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1650 - acc: 0.9415 - val_loss: 0.2238 - val_acc: 0.8820\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1602 - acc: 0.9306 - val_loss: 0.2098 - val_acc: 0.9193\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1655 - acc: 0.9312 - val_loss: 0.1831 - val_acc: 0.9255\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1974 - acc: 0.9135 - val_loss: 0.1869 - val_acc: 0.9441\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.1596 - acc: 0.9373 - val_loss: 0.1769 - val_acc: 0.9255\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1681 - acc: 0.9325 - val_loss: 0.1850 - val_acc: 0.9255\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1672 - acc: 0.9298 - val_loss: 0.2156 - val_acc: 0.9130\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 12s 491ms/step - loss: 0.1604 - acc: 0.9353 - val_loss: 0.2021 - val_acc: 0.9379\n",
      "Epoch 47/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1481 - acc: 0.9379 - val_loss: 0.1848 - val_acc: 0.9255\n",
      "Epoch 48/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1513 - acc: 0.9428 - val_loss: 0.1902 - val_acc: 0.9317\n",
      "Epoch 49/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1435 - acc: 0.9363 - val_loss: 0.1869 - val_acc: 0.9317\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 50/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1599 - acc: 0.9286 - val_loss: 0.1809 - val_acc: 0.9379\n",
      "Epoch 51/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1416 - acc: 0.9408 - val_loss: 0.2184 - val_acc: 0.9006\n",
      "Epoch 52/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1539 - acc: 0.9358 - val_loss: 0.2495 - val_acc: 0.8820\n",
      "Epoch 53/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1475 - acc: 0.9412 - val_loss: 0.1816 - val_acc: 0.9317\n",
      "Epoch 54/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1363 - acc: 0.9429 - val_loss: 0.1779 - val_acc: 0.9317\n",
      "Epoch 55/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1331 - acc: 0.9446 - val_loss: 0.1795 - val_acc: 0.9317\n",
      "Epoch 56/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.1423 - acc: 0.9474 - val_loss: 0.1937 - val_acc: 0.9317\n",
      "Epoch 57/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.1498 - acc: 0.9398 - val_loss: 0.2083 - val_acc: 0.9193\n",
      "Epoch 58/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1486 - acc: 0.9418 - val_loss: 0.2042 - val_acc: 0.9255\n",
      "Train loss: 0.11273350785\n",
      "Train accuracy: 0.955647955648\n",
      "Test loss: 0.176940077418\n",
      "Test accuracy: 0.925465838509\n",
      "\n",
      "===================FOLD= 2\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 13s 548ms/step - loss: 0.7857 - acc: 0.5690 - val_loss: 0.4295 - val_acc: 0.7764\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.5117 - acc: 0.7474 - val_loss: 0.3351 - val_acc: 0.8758\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.3949 - acc: 0.8109 - val_loss: 0.3254 - val_acc: 0.8571\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.4024 - acc: 0.8171 - val_loss: 0.2892 - val_acc: 0.8509\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.3271 - acc: 0.8498 - val_loss: 0.2323 - val_acc: 0.8820\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.3303 - acc: 0.8477 - val_loss: 0.2384 - val_acc: 0.8820\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.3035 - acc: 0.8631 - val_loss: 0.2287 - val_acc: 0.8944\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.2815 - acc: 0.8715 - val_loss: 0.2172 - val_acc: 0.9068\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2582 - acc: 0.8934 - val_loss: 0.2214 - val_acc: 0.9006\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.2547 - acc: 0.8920 - val_loss: 0.2170 - val_acc: 0.9006\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.2817 - acc: 0.8757 - val_loss: 0.1999 - val_acc: 0.9130\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.2563 - acc: 0.8909 - val_loss: 0.2042 - val_acc: 0.9130\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.2189 - acc: 0.9089 - val_loss: 0.2087 - val_acc: 0.9130\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.2331 - acc: 0.9029 - val_loss: 0.2073 - val_acc: 0.9068\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.2372 - acc: 0.8921 - val_loss: 0.1980 - val_acc: 0.8882\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.2211 - acc: 0.9075 - val_loss: 0.1938 - val_acc: 0.9193\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2382 - acc: 0.8998 - val_loss: 0.2177 - val_acc: 0.8882\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2247 - acc: 0.9014 - val_loss: 0.2042 - val_acc: 0.9068\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.1952 - acc: 0.9088 - val_loss: 0.1934 - val_acc: 0.9193\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.2260 - acc: 0.9075 - val_loss: 0.1929 - val_acc: 0.9317\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2010 - acc: 0.9139 - val_loss: 0.2012 - val_acc: 0.9006\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1984 - acc: 0.9168 - val_loss: 0.1949 - val_acc: 0.9317\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 492ms/step - loss: 0.2103 - acc: 0.9153 - val_loss: 0.1969 - val_acc: 0.9379\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1981 - acc: 0.9201 - val_loss: 0.1973 - val_acc: 0.9193\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1863 - acc: 0.9191 - val_loss: 0.2001 - val_acc: 0.9193\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2067 - acc: 0.9126 - val_loss: 0.2125 - val_acc: 0.9255\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1761 - acc: 0.9201 - val_loss: 0.2022 - val_acc: 0.9193\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1868 - acc: 0.9261 - val_loss: 0.1988 - val_acc: 0.9193\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1796 - acc: 0.9299 - val_loss: 0.1966 - val_acc: 0.9130\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1754 - acc: 0.9261 - val_loss: 0.1998 - val_acc: 0.9130\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1803 - acc: 0.9243 - val_loss: 0.2224 - val_acc: 0.9006\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1744 - acc: 0.9230 - val_loss: 0.2222 - val_acc: 0.9130\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1800 - acc: 0.9339 - val_loss: 0.2045 - val_acc: 0.9130\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1805 - acc: 0.9255 - val_loss: 0.1997 - val_acc: 0.9255\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1627 - acc: 0.9306 - val_loss: 0.1938 - val_acc: 0.9255\n",
      "Train loss: 0.159304537592\n",
      "Train accuracy: 0.936243936244\n",
      "Test loss: 0.192922941461\n",
      "Test accuracy: 0.931677018634\n",
      "\n",
      "===================FOLD= 3\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 15s 621ms/step - loss: 0.7508 - acc: 0.5379 - val_loss: 0.5723 - val_acc: 0.7250\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.5658 - acc: 0.6922 - val_loss: 0.3970 - val_acc: 0.7937\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.4323 - acc: 0.7922 - val_loss: 0.3380 - val_acc: 0.8125\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.3826 - acc: 0.8184 - val_loss: 0.3154 - val_acc: 0.8438\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.3098 - acc: 0.8526 - val_loss: 0.2857 - val_acc: 0.8500\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.3000 - acc: 0.8541 - val_loss: 0.2592 - val_acc: 0.8812\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.2946 - acc: 0.8663 - val_loss: 0.2348 - val_acc: 0.9000\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.2741 - acc: 0.8739 - val_loss: 0.2654 - val_acc: 0.8750\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 12s 501ms/step - loss: 0.2610 - acc: 0.8819 - val_loss: 0.2919 - val_acc: 0.8688\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.2570 - acc: 0.8827 - val_loss: 0.2648 - val_acc: 0.8938\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 12s 501ms/step - loss: 0.2475 - acc: 0.8899 - val_loss: 0.2742 - val_acc: 0.8938\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.2245 - acc: 0.9072 - val_loss: 0.2934 - val_acc: 0.8750\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 501ms/step - loss: 0.2219 - acc: 0.9060 - val_loss: 0.2703 - val_acc: 0.8938\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.2236 - acc: 0.9052 - val_loss: 0.2403 - val_acc: 0.9000\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.2408 - acc: 0.9029 - val_loss: 0.2363 - val_acc: 0.9000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.2374 - acc: 0.9085 - val_loss: 0.2263 - val_acc: 0.8875\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2043 - acc: 0.9147 - val_loss: 0.2400 - val_acc: 0.9062\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.2123 - acc: 0.9065 - val_loss: 0.2413 - val_acc: 0.9062\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.2155 - acc: 0.9086 - val_loss: 0.2477 - val_acc: 0.8938\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.2090 - acc: 0.9184 - val_loss: 0.2581 - val_acc: 0.9000\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.1975 - acc: 0.9163 - val_loss: 0.2260 - val_acc: 0.9000\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2032 - acc: 0.9121 - val_loss: 0.2392 - val_acc: 0.9062\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.2057 - acc: 0.9110 - val_loss: 0.2360 - val_acc: 0.9063\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1896 - acc: 0.9191 - val_loss: 0.2566 - val_acc: 0.9062\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1942 - acc: 0.9267 - val_loss: 0.2920 - val_acc: 0.8688\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1790 - acc: 0.9265 - val_loss: 0.3437 - val_acc: 0.8562\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.1851 - acc: 0.9313 - val_loss: 0.2221 - val_acc: 0.9062\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1664 - acc: 0.9319 - val_loss: 0.2226 - val_acc: 0.9062\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1784 - acc: 0.9248 - val_loss: 0.2408 - val_acc: 0.9000\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1870 - acc: 0.9161 - val_loss: 0.2546 - val_acc: 0.9000\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1702 - acc: 0.9269 - val_loss: 0.2304 - val_acc: 0.9125\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1736 - acc: 0.9334 - val_loss: 0.2515 - val_acc: 0.8938\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1574 - acc: 0.9305 - val_loss: 0.2914 - val_acc: 0.9062\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1815 - acc: 0.9290 - val_loss: 0.3226 - val_acc: 0.8625\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.1524 - acc: 0.9339 - val_loss: 0.2290 - val_acc: 0.9000\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1785 - acc: 0.9205 - val_loss: 0.2499 - val_acc: 0.9187\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1543 - acc: 0.9368 - val_loss: 0.3014 - val_acc: 0.8875\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 12s 501ms/step - loss: 0.1655 - acc: 0.9327 - val_loss: 0.2401 - val_acc: 0.9062\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1391 - acc: 0.9420 - val_loss: 0.3028 - val_acc: 0.8812\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1640 - acc: 0.9336 - val_loss: 0.2261 - val_acc: 0.9313\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.1508 - acc: 0.9441 - val_loss: 0.2271 - val_acc: 0.9125\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 12s 501ms/step - loss: 0.1544 - acc: 0.9425 - val_loss: 0.2737 - val_acc: 0.9000\n",
      "Train loss: 0.129862672033\n",
      "Train accuracy: 0.945983379501\n",
      "Test loss: 0.222062005103\n",
      "Test accuracy: 0.90625\n",
      "\n",
      "===================FOLD= 4\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 14s 564ms/step - loss: 0.7871 - acc: 0.5824 - val_loss: 0.4310 - val_acc: 0.8875\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.5283 - acc: 0.7469 - val_loss: 0.3062 - val_acc: 0.9000\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.3997 - acc: 0.8145 - val_loss: 0.2583 - val_acc: 0.9313\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.3653 - acc: 0.8290 - val_loss: 0.4038 - val_acc: 0.8063\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.3636 - acc: 0.8288 - val_loss: 0.1805 - val_acc: 0.9812\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.3235 - acc: 0.8518 - val_loss: 0.1731 - val_acc: 0.9563\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.3090 - acc: 0.8524 - val_loss: 0.2008 - val_acc: 0.9250\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.2745 - acc: 0.8822 - val_loss: 0.1588 - val_acc: 0.9375\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 13s 547ms/step - loss: 0.2622 - acc: 0.8800 - val_loss: 0.1312 - val_acc: 0.9563\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2864 - acc: 0.8717 - val_loss: 0.1556 - val_acc: 0.9563\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2533 - acc: 0.8936 - val_loss: 0.2067 - val_acc: 0.9062\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.2542 - acc: 0.8977 - val_loss: 0.1736 - val_acc: 0.9250\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 501ms/step - loss: 0.2576 - acc: 0.8834 - val_loss: 0.1917 - val_acc: 0.9000\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.2411 - acc: 0.8853 - val_loss: 0.1357 - val_acc: 0.9563\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.2348 - acc: 0.8946 - val_loss: 0.1431 - val_acc: 0.9563\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.2418 - acc: 0.9008 - val_loss: 0.1227 - val_acc: 0.9688\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2365 - acc: 0.8985 - val_loss: 0.1693 - val_acc: 0.9500\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2009 - acc: 0.9068 - val_loss: 0.1389 - val_acc: 0.9625\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2345 - acc: 0.9011 - val_loss: 0.1720 - val_acc: 0.9250\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.2137 - acc: 0.9064 - val_loss: 0.1261 - val_acc: 0.9688\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.2050 - acc: 0.9080 - val_loss: 0.1388 - val_acc: 0.9375\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2023 - acc: 0.9166 - val_loss: 0.1297 - val_acc: 0.9625\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 493ms/step - loss: 0.2115 - acc: 0.9108 - val_loss: 0.1410 - val_acc: 0.9625\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.1869 - acc: 0.9248 - val_loss: 0.1437 - val_acc: 0.9437\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1828 - acc: 0.9169 - val_loss: 0.1829 - val_acc: 0.9437\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.2011 - acc: 0.9184 - val_loss: 0.1370 - val_acc: 0.9500\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.2042 - acc: 0.9121 - val_loss: 0.1832 - val_acc: 0.9437\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1639 - acc: 0.9347 - val_loss: 0.1435 - val_acc: 0.9625\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.2056 - acc: 0.9160 - val_loss: 0.1163 - val_acc: 0.9625\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1781 - acc: 0.9246 - val_loss: 0.1698 - val_acc: 0.9500\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1768 - acc: 0.9288 - val_loss: 0.1489 - val_acc: 0.9563\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.1638 - acc: 0.9336 - val_loss: 0.1332 - val_acc: 0.9500\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1723 - acc: 0.9239 - val_loss: 0.1724 - val_acc: 0.9500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 34/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.1733 - acc: 0.9309 - val_loss: 0.2630 - val_acc: 0.9000\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.1593 - acc: 0.9402 - val_loss: 0.1419 - val_acc: 0.9563\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.1886 - acc: 0.9207 - val_loss: 0.1304 - val_acc: 0.9625\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 12s 501ms/step - loss: 0.1557 - acc: 0.9288 - val_loss: 0.1420 - val_acc: 0.9563\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.1644 - acc: 0.9353 - val_loss: 0.1344 - val_acc: 0.9563\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1693 - acc: 0.9332 - val_loss: 0.1625 - val_acc: 0.9313\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 12s 501ms/step - loss: 0.1631 - acc: 0.9384 - val_loss: 0.1527 - val_acc: 0.9500\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.1560 - acc: 0.9339 - val_loss: 0.1552 - val_acc: 0.9563\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1700 - acc: 0.9293 - val_loss: 0.1463 - val_acc: 0.9625\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1391 - acc: 0.9419 - val_loss: 0.1754 - val_acc: 0.9437\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.1660 - acc: 0.9280 - val_loss: 0.1542 - val_acc: 0.9437\n",
      "Train loss: 0.131260706715\n",
      "Train accuracy: 0.94944598338\n",
      "Test loss: 0.116300611943\n",
      "Test accuracy: 0.9625\n",
      "\n",
      "===================FOLD= 5\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 14s 566ms/step - loss: 0.7440 - acc: 0.5432 - val_loss: 0.5591 - val_acc: 0.6937\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 13s 553ms/step - loss: 0.5678 - acc: 0.6671 - val_loss: 0.4205 - val_acc: 0.7812\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.4380 - acc: 0.7774 - val_loss: 0.3575 - val_acc: 0.8250\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.3844 - acc: 0.8176 - val_loss: 0.3756 - val_acc: 0.8000\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.3382 - acc: 0.8329 - val_loss: 0.2747 - val_acc: 0.8875\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.3254 - acc: 0.8355 - val_loss: 0.2368 - val_acc: 0.8875\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.2818 - acc: 0.8668 - val_loss: 0.2271 - val_acc: 0.9062\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.3017 - acc: 0.8624 - val_loss: 0.2416 - val_acc: 0.8938\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 12s 501ms/step - loss: 0.2750 - acc: 0.8891 - val_loss: 0.2282 - val_acc: 0.9062\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.2579 - acc: 0.8700 - val_loss: 0.2027 - val_acc: 0.9125\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.2726 - acc: 0.8795 - val_loss: 0.2114 - val_acc: 0.9062\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.2407 - acc: 0.8925 - val_loss: 0.1865 - val_acc: 0.9187\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2330 - acc: 0.9000 - val_loss: 0.2002 - val_acc: 0.9250\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2513 - acc: 0.8963 - val_loss: 0.2275 - val_acc: 0.9062\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.2313 - acc: 0.9003 - val_loss: 0.2238 - val_acc: 0.9000\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2283 - acc: 0.8977 - val_loss: 0.1908 - val_acc: 0.9375\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.2220 - acc: 0.8959 - val_loss: 0.2078 - val_acc: 0.9125\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.2212 - acc: 0.9037 - val_loss: 0.2125 - val_acc: 0.9125\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.2170 - acc: 0.9041 - val_loss: 0.2066 - val_acc: 0.9187\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.1882 - acc: 0.9181 - val_loss: 0.2006 - val_acc: 0.9250\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.2142 - acc: 0.9108 - val_loss: 0.2141 - val_acc: 0.9125\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.2022 - acc: 0.9210 - val_loss: 0.1811 - val_acc: 0.9375\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.1992 - acc: 0.9157 - val_loss: 0.1792 - val_acc: 0.9188\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2068 - acc: 0.9129 - val_loss: 0.2259 - val_acc: 0.9062\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.1987 - acc: 0.9130 - val_loss: 0.1725 - val_acc: 0.9313\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1926 - acc: 0.9176 - val_loss: 0.1730 - val_acc: 0.9000\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.1919 - acc: 0.9225 - val_loss: 0.1731 - val_acc: 0.9313\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.1823 - acc: 0.9257 - val_loss: 0.1802 - val_acc: 0.9313\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1923 - acc: 0.9230 - val_loss: 0.1955 - val_acc: 0.9313\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.1927 - acc: 0.9150 - val_loss: 0.2181 - val_acc: 0.9062\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.1764 - acc: 0.9267 - val_loss: 0.2119 - val_acc: 0.9000\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1617 - acc: 0.9355 - val_loss: 0.1905 - val_acc: 0.9000\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1814 - acc: 0.9196 - val_loss: 0.2193 - val_acc: 0.9125\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1722 - acc: 0.9344 - val_loss: 0.2123 - val_acc: 0.9375\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.1744 - acc: 0.9257 - val_loss: 0.1615 - val_acc: 0.9437\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1771 - acc: 0.9264 - val_loss: 0.1818 - val_acc: 0.9062\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.1537 - acc: 0.9358 - val_loss: 0.2719 - val_acc: 0.8938\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1676 - acc: 0.9254 - val_loss: 0.1905 - val_acc: 0.9437\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1616 - acc: 0.9366 - val_loss: 0.2183 - val_acc: 0.9062\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1648 - acc: 0.9366 - val_loss: 0.2074 - val_acc: 0.9375\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1490 - acc: 0.9406 - val_loss: 0.1875 - val_acc: 0.9375\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1733 - acc: 0.9269 - val_loss: 0.1650 - val_acc: 0.9500\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1653 - acc: 0.9357 - val_loss: 0.2023 - val_acc: 0.9187\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.1366 - acc: 0.9472 - val_loss: 0.1660 - val_acc: 0.9437\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1562 - acc: 0.9334 - val_loss: 0.1667 - val_acc: 0.9375\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 12s 493ms/step - loss: 0.1487 - acc: 0.9379 - val_loss: 0.2237 - val_acc: 0.9000\n",
      "Epoch 47/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1438 - acc: 0.9386 - val_loss: 0.1895 - val_acc: 0.9375\n",
      "Epoch 48/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1446 - acc: 0.9375 - val_loss: 0.1914 - val_acc: 0.9250\n",
      "Epoch 49/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1500 - acc: 0.9355 - val_loss: 0.1860 - val_acc: 0.9437\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 50/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1402 - acc: 0.9376 - val_loss: 0.2077 - val_acc: 0.9250\n",
      "Train loss: 0.131540527469\n",
      "Train accuracy: 0.94459833795\n",
      "Test loss: 0.16147320047\n",
      "Test accuracy: 0.94375\n",
      "\n",
      "===================FOLD= 6\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 14s 575ms/step - loss: 0.7840 - acc: 0.5883 - val_loss: 0.5487 - val_acc: 0.6687\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.5377 - acc: 0.7337 - val_loss: 0.4242 - val_acc: 0.7688\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.4349 - acc: 0.7891 - val_loss: 0.3725 - val_acc: 0.8250\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.3486 - acc: 0.8368 - val_loss: 0.3705 - val_acc: 0.8500\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.3208 - acc: 0.8487 - val_loss: 0.3965 - val_acc: 0.8688\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.3122 - acc: 0.8673 - val_loss: 0.3753 - val_acc: 0.8313\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.2951 - acc: 0.8708 - val_loss: 0.4092 - val_acc: 0.8438\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.2909 - acc: 0.8679 - val_loss: 0.3190 - val_acc: 0.8812\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2549 - acc: 0.8827 - val_loss: 0.3458 - val_acc: 0.8688\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2506 - acc: 0.8993 - val_loss: 0.3417 - val_acc: 0.8750\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2739 - acc: 0.8783 - val_loss: 0.3410 - val_acc: 0.8688\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.2461 - acc: 0.9016 - val_loss: 0.3940 - val_acc: 0.8625\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.2532 - acc: 0.8852 - val_loss: 0.3093 - val_acc: 0.8812\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.2473 - acc: 0.8930 - val_loss: 0.3316 - val_acc: 0.8688\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2397 - acc: 0.8958 - val_loss: 0.3260 - val_acc: 0.8750\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2380 - acc: 0.8893 - val_loss: 0.3115 - val_acc: 0.8875\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2281 - acc: 0.8981 - val_loss: 0.3453 - val_acc: 0.8750\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.2212 - acc: 0.9062 - val_loss: 0.3099 - val_acc: 0.8812\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2139 - acc: 0.9173 - val_loss: 0.3281 - val_acc: 0.8750\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1988 - acc: 0.9179 - val_loss: 0.3385 - val_acc: 0.8750\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.2150 - acc: 0.9068 - val_loss: 0.2994 - val_acc: 0.8875\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1998 - acc: 0.9164 - val_loss: 0.3002 - val_acc: 0.8812\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 492ms/step - loss: 0.2035 - acc: 0.9167 - val_loss: 0.3222 - val_acc: 0.8750\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1887 - acc: 0.9166 - val_loss: 0.3045 - val_acc: 0.8812\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.2048 - acc: 0.9106 - val_loss: 0.2902 - val_acc: 0.8812\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1958 - acc: 0.9280 - val_loss: 0.3121 - val_acc: 0.8688\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1998 - acc: 0.9151 - val_loss: 0.3556 - val_acc: 0.8562\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1870 - acc: 0.9184 - val_loss: 0.3193 - val_acc: 0.8750\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.1851 - acc: 0.9262 - val_loss: 0.2866 - val_acc: 0.8938\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1761 - acc: 0.9269 - val_loss: 0.3340 - val_acc: 0.8812\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1709 - acc: 0.9337 - val_loss: 0.3448 - val_acc: 0.8812\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.1708 - acc: 0.9277 - val_loss: 0.2854 - val_acc: 0.8938\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1754 - acc: 0.9257 - val_loss: 0.3215 - val_acc: 0.8938\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.1648 - acc: 0.9349 - val_loss: 0.2826 - val_acc: 0.8750\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1868 - acc: 0.9238 - val_loss: 0.3516 - val_acc: 0.8562\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1531 - acc: 0.9321 - val_loss: 0.2987 - val_acc: 0.8750\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1815 - acc: 0.9309 - val_loss: 0.2994 - val_acc: 0.8750\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.1703 - acc: 0.9311 - val_loss: 0.2977 - val_acc: 0.8938\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1719 - acc: 0.9305 - val_loss: 0.3244 - val_acc: 0.8688\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1579 - acc: 0.9402 - val_loss: 0.3319 - val_acc: 0.8875\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1507 - acc: 0.9349 - val_loss: 0.3083 - val_acc: 0.8875\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1483 - acc: 0.9443 - val_loss: 0.3272 - val_acc: 0.8750\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.1746 - acc: 0.9244 - val_loss: 0.2780 - val_acc: 0.8938\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1436 - acc: 0.9435 - val_loss: 0.3226 - val_acc: 0.8875\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1507 - acc: 0.9433 - val_loss: 0.3038 - val_acc: 0.8812\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 12s 493ms/step - loss: 0.1473 - acc: 0.9412 - val_loss: 0.3491 - val_acc: 0.8813\n",
      "Epoch 47/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1443 - acc: 0.9360 - val_loss: 0.3115 - val_acc: 0.8688\n",
      "Epoch 48/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1512 - acc: 0.9443 - val_loss: 0.3361 - val_acc: 0.8938\n",
      "Epoch 49/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1454 - acc: 0.9443 - val_loss: 0.3919 - val_acc: 0.8625\n",
      "Epoch 50/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1454 - acc: 0.9384 - val_loss: 0.3560 - val_acc: 0.8562\n",
      "Epoch 51/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1473 - acc: 0.9386 - val_loss: 0.2900 - val_acc: 0.8875\n",
      "Epoch 52/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1241 - acc: 0.9467 - val_loss: 0.3225 - val_acc: 0.8750\n",
      "Epoch 53/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1295 - acc: 0.9506 - val_loss: 0.3366 - val_acc: 0.8688\n",
      "Epoch 54/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1209 - acc: 0.9516 - val_loss: 0.3315 - val_acc: 0.8562\n",
      "Epoch 55/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1166 - acc: 0.9547 - val_loss: 0.3336 - val_acc: 0.8938\n",
      "Epoch 56/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1313 - acc: 0.9443 - val_loss: 0.3087 - val_acc: 0.9000\n",
      "Epoch 57/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1302 - acc: 0.9508 - val_loss: 0.3897 - val_acc: 0.8688\n",
      "Epoch 58/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1119 - acc: 0.9487 - val_loss: 0.3967 - val_acc: 0.8625\n",
      "Train loss: 0.119572313043\n",
      "Train accuracy: 0.954293628809\n",
      "Test loss: 0.278009831905\n",
      "Test accuracy: 0.89375\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "===================FOLD= 7\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 14s 582ms/step - loss: 0.7313 - acc: 0.5992 - val_loss: 0.4200 - val_acc: 0.8187\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.5063 - acc: 0.7624 - val_loss: 0.2841 - val_acc: 0.8500\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.3923 - acc: 0.8093 - val_loss: 0.2569 - val_acc: 0.8688\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.3730 - acc: 0.8303 - val_loss: 0.2339 - val_acc: 0.8938\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.3524 - acc: 0.8440 - val_loss: 0.2220 - val_acc: 0.8812\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.3420 - acc: 0.8627 - val_loss: 0.2282 - val_acc: 0.8875\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.2977 - acc: 0.8655 - val_loss: 0.2326 - val_acc: 0.8938\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.2869 - acc: 0.8796 - val_loss: 0.2210 - val_acc: 0.8938\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2670 - acc: 0.8881 - val_loss: 0.2252 - val_acc: 0.9062\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2519 - acc: 0.8951 - val_loss: 0.2683 - val_acc: 0.9062\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2393 - acc: 0.8897 - val_loss: 0.2397 - val_acc: 0.9062\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.2751 - acc: 0.8813 - val_loss: 0.2343 - val_acc: 0.8750\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.2346 - acc: 0.9031 - val_loss: 0.2622 - val_acc: 0.9000\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.2355 - acc: 0.9013 - val_loss: 0.1970 - val_acc: 0.9187\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2202 - acc: 0.9010 - val_loss: 0.2169 - val_acc: 0.9000\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2508 - acc: 0.8912 - val_loss: 0.2302 - val_acc: 0.9125\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1966 - acc: 0.9280 - val_loss: 0.2243 - val_acc: 0.9000\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.2265 - acc: 0.9011 - val_loss: 0.2270 - val_acc: 0.8938\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.1965 - acc: 0.9148 - val_loss: 0.1952 - val_acc: 0.9125\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.2244 - acc: 0.9016 - val_loss: 0.1932 - val_acc: 0.9250\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2039 - acc: 0.9176 - val_loss: 0.2015 - val_acc: 0.9187\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.2026 - acc: 0.9200 - val_loss: 0.2111 - val_acc: 0.9187\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 493ms/step - loss: 0.1991 - acc: 0.9175 - val_loss: 0.2099 - val_acc: 0.9250\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.2096 - acc: 0.9109 - val_loss: 0.2786 - val_acc: 0.8875\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.2106 - acc: 0.9137 - val_loss: 0.2138 - val_acc: 0.9250\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.1954 - acc: 0.9291 - val_loss: 0.1767 - val_acc: 0.9313\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1755 - acc: 0.9278 - val_loss: 0.1903 - val_acc: 0.9187\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1902 - acc: 0.9186 - val_loss: 0.1994 - val_acc: 0.9125\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1696 - acc: 0.9332 - val_loss: 0.1985 - val_acc: 0.9313\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1862 - acc: 0.9239 - val_loss: 0.2206 - val_acc: 0.9187\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1650 - acc: 0.9365 - val_loss: 0.1843 - val_acc: 0.9250\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1638 - acc: 0.9278 - val_loss: 0.2088 - val_acc: 0.9125\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1826 - acc: 0.9205 - val_loss: 0.1783 - val_acc: 0.9437\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1697 - acc: 0.9321 - val_loss: 0.1861 - val_acc: 0.9062\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1735 - acc: 0.9267 - val_loss: 0.1886 - val_acc: 0.9313\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1696 - acc: 0.9274 - val_loss: 0.2130 - val_acc: 0.9062\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 12s 501ms/step - loss: 0.1714 - acc: 0.9295 - val_loss: 0.2045 - val_acc: 0.9000\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.1837 - acc: 0.9274 - val_loss: 0.1781 - val_acc: 0.9250\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1687 - acc: 0.9314 - val_loss: 0.2344 - val_acc: 0.8938\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.1565 - acc: 0.9414 - val_loss: 0.1686 - val_acc: 0.9313\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1477 - acc: 0.9384 - val_loss: 0.2164 - val_acc: 0.9125\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1506 - acc: 0.9401 - val_loss: 0.2044 - val_acc: 0.8875\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1537 - acc: 0.9332 - val_loss: 0.2197 - val_acc: 0.8750\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1466 - acc: 0.9357 - val_loss: 0.1819 - val_acc: 0.9062\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1486 - acc: 0.9358 - val_loss: 0.1965 - val_acc: 0.9125\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 12s 492ms/step - loss: 0.1590 - acc: 0.9364 - val_loss: 0.1759 - val_acc: 0.9250\n",
      "Epoch 47/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1507 - acc: 0.9384 - val_loss: 0.1806 - val_acc: 0.9187\n",
      "Epoch 48/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1610 - acc: 0.9340 - val_loss: 0.1766 - val_acc: 0.9250\n",
      "Epoch 49/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1458 - acc: 0.9399 - val_loss: 0.2040 - val_acc: 0.9187\n",
      "Epoch 50/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1407 - acc: 0.9433 - val_loss: 0.1741 - val_acc: 0.9250\n",
      "Epoch 51/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1282 - acc: 0.9466 - val_loss: 0.1844 - val_acc: 0.9250\n",
      "Epoch 52/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1353 - acc: 0.9479 - val_loss: 0.2397 - val_acc: 0.9062\n",
      "Epoch 53/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1501 - acc: 0.9366 - val_loss: 0.2277 - val_acc: 0.9125\n",
      "Epoch 54/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1298 - acc: 0.9513 - val_loss: 0.1828 - val_acc: 0.9062\n",
      "Epoch 55/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1252 - acc: 0.9505 - val_loss: 0.2558 - val_acc: 0.8875\n",
      "Train loss: 0.104090516948\n",
      "Train accuracy: 0.954986149584\n",
      "Test loss: 0.16855455339\n",
      "Test accuracy: 0.93125\n",
      "\n",
      "===================FOLD= 8\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.7918 - acc: 0.5140 - val_loss: 0.6405 - val_acc: 0.6125\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.6247 - acc: 0.6344 - val_loss: 0.5009 - val_acc: 0.7750\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 13s 541ms/step - loss: 0.4814 - acc: 0.7506 - val_loss: 0.4168 - val_acc: 0.7937\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.3760 - acc: 0.8257 - val_loss: 0.3919 - val_acc: 0.8187\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.3340 - acc: 0.8440 - val_loss: 0.3719 - val_acc: 0.8250\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2980 - acc: 0.8642 - val_loss: 0.4511 - val_acc: 0.8000\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.2993 - acc: 0.8695 - val_loss: 0.3709 - val_acc: 0.8000\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2788 - acc: 0.8717 - val_loss: 0.4054 - val_acc: 0.8187\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.2798 - acc: 0.8766 - val_loss: 0.3594 - val_acc: 0.8313\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.2543 - acc: 0.8866 - val_loss: 0.3454 - val_acc: 0.8375\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.2586 - acc: 0.8894 - val_loss: 0.3499 - val_acc: 0.8250\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.2463 - acc: 0.8910 - val_loss: 0.3738 - val_acc: 0.8438\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2290 - acc: 0.9008 - val_loss: 0.4101 - val_acc: 0.7937\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2390 - acc: 0.8933 - val_loss: 0.3499 - val_acc: 0.8187\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2286 - acc: 0.9046 - val_loss: 0.3753 - val_acc: 0.8250\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.2289 - acc: 0.9031 - val_loss: 0.4367 - val_acc: 0.8187\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.2307 - acc: 0.8994 - val_loss: 0.3703 - val_acc: 0.8250\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2094 - acc: 0.9171 - val_loss: 0.3566 - val_acc: 0.8438\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.2095 - acc: 0.9067 - val_loss: 0.3732 - val_acc: 0.8375\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2086 - acc: 0.9111 - val_loss: 0.3716 - val_acc: 0.8250\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.2113 - acc: 0.9117 - val_loss: 0.3389 - val_acc: 0.8375\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2002 - acc: 0.9142 - val_loss: 0.3837 - val_acc: 0.8250\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 493ms/step - loss: 0.1930 - acc: 0.9206 - val_loss: 0.3505 - val_acc: 0.8375\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.2005 - acc: 0.9199 - val_loss: 0.3530 - val_acc: 0.8562\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1936 - acc: 0.9296 - val_loss: 0.3925 - val_acc: 0.8313\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1959 - acc: 0.9238 - val_loss: 0.3433 - val_acc: 0.8375\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1780 - acc: 0.9306 - val_loss: 0.4121 - val_acc: 0.8250\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1959 - acc: 0.9223 - val_loss: 0.3594 - val_acc: 0.8438\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1718 - acc: 0.9283 - val_loss: 0.3936 - val_acc: 0.8250\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1682 - acc: 0.9337 - val_loss: 0.3578 - val_acc: 0.8438\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1701 - acc: 0.9331 - val_loss: 0.3845 - val_acc: 0.8438\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1731 - acc: 0.9303 - val_loss: 0.4001 - val_acc: 0.8500\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1881 - acc: 0.9248 - val_loss: 0.3443 - val_acc: 0.8438\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.1643 - acc: 0.9389 - val_loss: 0.3870 - val_acc: 0.8375\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1716 - acc: 0.9308 - val_loss: 0.4143 - val_acc: 0.8313\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1776 - acc: 0.9257 - val_loss: 0.3670 - val_acc: 0.8313\n",
      "Train loss: 0.156569907891\n",
      "Train accuracy: 0.937673130194\n",
      "Test loss: 0.338898205757\n",
      "Test accuracy: 0.8375\n",
      "\n",
      "===================FOLD= 9\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 14s 595ms/step - loss: 0.6919 - acc: 0.5881 - val_loss: 0.5407 - val_acc: 0.6937\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.4852 - acc: 0.7410 - val_loss: 0.3950 - val_acc: 0.7625\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.3939 - acc: 0.8094 - val_loss: 0.3118 - val_acc: 0.8375\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.3285 - acc: 0.8461 - val_loss: 0.2775 - val_acc: 0.8875\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.3185 - acc: 0.8489 - val_loss: 0.2499 - val_acc: 0.9062\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.3006 - acc: 0.8663 - val_loss: 0.2778 - val_acc: 0.8875\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2958 - acc: 0.8596 - val_loss: 0.2729 - val_acc: 0.9062\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2521 - acc: 0.8868 - val_loss: 0.3024 - val_acc: 0.8500\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.2514 - acc: 0.8884 - val_loss: 0.2280 - val_acc: 0.9125\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.2666 - acc: 0.8671 - val_loss: 0.2219 - val_acc: 0.9125\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.2412 - acc: 0.8899 - val_loss: 0.2517 - val_acc: 0.9000\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.2520 - acc: 0.8927 - val_loss: 0.2179 - val_acc: 0.9125\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.2548 - acc: 0.8977 - val_loss: 0.2133 - val_acc: 0.9187\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2106 - acc: 0.9151 - val_loss: 0.2771 - val_acc: 0.8938\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.2395 - acc: 0.8961 - val_loss: 0.2243 - val_acc: 0.9125\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.2112 - acc: 0.9125 - val_loss: 0.1987 - val_acc: 0.9062\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2312 - acc: 0.9127 - val_loss: 0.2145 - val_acc: 0.9187\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.2236 - acc: 0.8992 - val_loss: 0.2074 - val_acc: 0.9187\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.2196 - acc: 0.9108 - val_loss: 0.2001 - val_acc: 0.9125\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2065 - acc: 0.9202 - val_loss: 0.2335 - val_acc: 0.8938\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.2284 - acc: 0.9034 - val_loss: 0.2325 - val_acc: 0.9125\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.2089 - acc: 0.9187 - val_loss: 0.2115 - val_acc: 0.9375\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 493ms/step - loss: 0.1882 - acc: 0.9210 - val_loss: 0.2005 - val_acc: 0.9125\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.2028 - acc: 0.9173 - val_loss: 0.2153 - val_acc: 0.8875\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.1929 - acc: 0.9264 - val_loss: 0.2141 - val_acc: 0.9000\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1935 - acc: 0.9221 - val_loss: 0.1987 - val_acc: 0.8938\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.1893 - acc: 0.9164 - val_loss: 0.1725 - val_acc: 0.9250\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1897 - acc: 0.9197 - val_loss: 0.2339 - val_acc: 0.8875\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1934 - acc: 0.9246 - val_loss: 0.2119 - val_acc: 0.9000\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1743 - acc: 0.9366 - val_loss: 0.1947 - val_acc: 0.9313\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1585 - acc: 0.9347 - val_loss: 0.2091 - val_acc: 0.8938\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1868 - acc: 0.9257 - val_loss: 0.2263 - val_acc: 0.8938\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1760 - acc: 0.9262 - val_loss: 0.2145 - val_acc: 0.9062\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 12s 501ms/step - loss: 0.1802 - acc: 0.9261 - val_loss: 0.1966 - val_acc: 0.9250\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1628 - acc: 0.9306 - val_loss: 0.1833 - val_acc: 0.9250\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1683 - acc: 0.9331 - val_loss: 0.2000 - val_acc: 0.9187\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1594 - acc: 0.9379 - val_loss: 0.1949 - val_acc: 0.9313\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1698 - acc: 0.9293 - val_loss: 0.1921 - val_acc: 0.9062\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1497 - acc: 0.9410 - val_loss: 0.2116 - val_acc: 0.9250\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1658 - acc: 0.9295 - val_loss: 0.1763 - val_acc: 0.9187\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1719 - acc: 0.9282 - val_loss: 0.1987 - val_acc: 0.9125\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.1564 - acc: 0.9324 - val_loss: 0.2197 - val_acc: 0.8875\n",
      "Train loss: 0.154563323964\n",
      "Train accuracy: 0.942520775623\n",
      "Test loss: 0.172506463528\n",
      "Test accuracy: 0.925\n",
      "\n",
      " Train Log Loss Validation=  0.120850432703\n",
      " Test Log Loss Validation=  0.199247749671\n"
     ]
    }
   ],
   "source": [
    "#def myAngleCV(X_train, X_angle, X_test):\n",
    "K=10\n",
    "folds = list(StratifiedKFold(n_splits=K, shuffle=True, random_state=16).split(X_train, target_train))\n",
    "y_test_pred_log = 0\n",
    "y_train_pred_log=0\n",
    "y_valid_pred_log = 0.0*target_train\n",
    "for j, (train_idx, test_idx) in enumerate(folds):\n",
    "    print('\\n===================FOLD=',j)\n",
    "    X_train_cv = X_train[train_idx]\n",
    "    y_train_cv = target_train[train_idx]\n",
    "    X_holdout = X_train[test_idx]\n",
    "    Y_holdout= target_train[test_idx]\n",
    "    \n",
    "    #Angle\n",
    "    X_angle_cv=X_angle[train_idx]\n",
    "    X_angle_hold=X_angle[test_idx]\n",
    "    #define file path and get callbacks\n",
    "    file_path = \"%s_aug_model_weights.hdf5\"%j\n",
    "    callbacks = get_callbacks(filepath=file_path, patience=5)\n",
    "    gen_flow = gen_flow_for_two_inputs(X_train_cv, X_angle_cv, y_train_cv)\n",
    "    galaxyModel= getVggAngleModel()\n",
    "    histr = galaxyModel.fit_generator(\n",
    "            gen_flow,\n",
    "            steps_per_epoch=24,\n",
    "            epochs=100,\n",
    "            shuffle=True,\n",
    "            verbose=1,\n",
    "            validation_data=([X_holdout,X_angle_hold], Y_holdout),\n",
    "            callbacks=callbacks)\n",
    "     #Getting the Best Model\n",
    "    galaxyModel.load_weights(filepath=file_path)\n",
    "    #Getting Training Score\n",
    "    score = galaxyModel.evaluate([X_train_cv,X_angle_cv], y_train_cv, verbose=0)\n",
    "    print('Train loss:', score[0])\n",
    "    print('Train accuracy:', score[1])\n",
    "    #Getting Test Score\n",
    "    score = galaxyModel.evaluate([X_holdout,X_angle_hold], Y_holdout, verbose=0)\n",
    "    print('Test loss:', score[0])\n",
    "    print('Test accuracy:', score[1])\n",
    "\n",
    "    #Getting validation Score.\n",
    "    pred_valid=galaxyModel.predict([X_holdout,X_angle_hold])\n",
    "    y_valid_pred_log[test_idx] = pred_valid.reshape(pred_valid.shape[0])\n",
    "\n",
    "    #Getting Test Scores\n",
    "    temp_test=galaxyModel.predict([X_test, X_test_angle])\n",
    "    y_test_pred_log+=temp_test.reshape(temp_test.shape[0])\n",
    "\n",
    "    #Getting Train Scores\n",
    "    temp_train=galaxyModel.predict([X_train, X_angle])\n",
    "    y_train_pred_log+=temp_train.reshape(temp_train.shape[0])\n",
    "\n",
    "y_test_pred_log=y_test_pred_log/K\n",
    "y_train_pred_log=y_train_pred_log/K\n",
    "\n",
    "print('\\n Train Log Loss Validation= ',log_loss(target_train, y_train_pred_log))\n",
    "print(' Test Log Loss Validation= ',log_loss(target_train, y_valid_pred_log))\n",
    "#return y_test_pred_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#preds=myAngleCV(X_train, X_angle, X_test)\n",
    "preds = y_test_pred_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Submission to kaggle\n",
    "submission = pd.DataFrame()\n",
    "submission['id']=test['id']\n",
    "submission['is_iceberg']=preds\n",
    "submission.to_csv('sub_SGD_10folds_3e-4_10p.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['val_loss', 'val_acc', 'acc', 'loss'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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0jL4D1s7Y/3d8qPlT/ajrDzAeWA9sAh7wsb878D2wCpgLdPHadz2w0fNzfU33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\nRERErRSLiIiolWIRERG1/gP28J3Hj8Jl/wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe22b6ffdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/eL39xtxpJJz9kA0GnS6y+3IzIX4V7F8KB5ZDj2uheSeGh+UTGeLPJ8v2Mbxr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe226913be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# list all data in history\n",
    "print(histr.history.keys())\n",
    "# summarize history for accuracy\n",
    "plt.plot(histr.history['acc'])\n",
    "plt.plot(histr.history['val_acc'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()\n",
    "# summarize history for loss\n",
    "plt.plot(histr.history['loss'])\n",
    "plt.plot(histr.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.image as mpimg\n",
    "\n",
    "min1 = np.min(X_holdout)\n",
    "max1 = np.max(X_holdout)\n",
    "diff1 = max1 - min1\n",
    "my_sum = lambda x: (x - min1)/diff1\n",
    "\n",
    "X_holdout2 = my_sum(X_holdout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(160, 75, 75, 3)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_holdout2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_holdout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fe2268520b8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/dmr0j8poTSUVVA8fTxEpXaCd6Qo5qSdcguMGAowFit5CzuoKwb4l6O28dBp7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\nllmPisK5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe22b6ff9b0>"
      ]
     },
     "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": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fe2268385c0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6nEaKCQkJCUB6KCYkJCQAG0ufg4LmMc+5i2jdg0dOd1nvdoFI7CLmhI6cJZ2L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\nrwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe226808fd0>"
      ]
     },
     "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": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(160, 1)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_valid.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(160,)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_holdout.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fe226794b70>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5UrbsCVgwoeTNGFM6trZAJV+SI/YUGLa8rstF5Zy8gHW0BUsxWL1KOBXPEJ96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\n+C9E765fUGB5TwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe226822fd0>"
      ]
     },
     "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": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fe226772fd0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ugCbWPURWe96Fk8qPvoVodgBl2e8gJxjFgKhIL44a3obgo0rkZtcd/+GwcorV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\nxKQYBEEg/AcTlTWMDkVgTAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe2267830f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[159], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[1602])+ \",\"+\" iceberg_probability : \"+str(pred_valid[159])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fe2266d97b8>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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h/N71erdjit9sQ+lzWikmJCQkAGlSTEhISAA2lz6HoElUsx6CPtNaXMGRWkgx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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe226772320>"
      ]
     },
     "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": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fe2266bae10>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7TBzwULZAyFgjlC/GLGbqy3mbM/7wfkAyhnab81aYhmchywxl03Och4qL1gDF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\nnCnAJ2PHcZwp4H8e0J4VdHYFRQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe226698438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[4], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[44])+ \",\"+\" iceberg_probability : \"+str(pred_valid[4])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fe226620898>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QtJpnIZ5Dik3IRNfaSP6F85lWx0oXcIKuw9maya2aiNvVAxWFXCA2vNGBozkf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\nYBdUFw+aBlxz6H1xdMPpe76z+tvf6tJ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u7JROTQLSY5LfT/c8dMxgaWzAwTufOz2qRbZl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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe22667e0f0>"
      ]
     },
     "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": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fe226587080>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vpxO/6GsDn0ogeufdhbzRjoH3tH+D2zeV+H7D7/8CJUOFwl3a9nvX7IN74kY2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\nwn8B2t8QqMd+Iv0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe2265d69e8>"
      ]
     },
     "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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}