Model_03-SGD-k=10.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model_03-SGD-K=3\n",
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Mandatory imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import log_loss\n",
    "from sklearn.model_selection import StratifiedKFold, StratifiedShuffleSplit\n",
    "from os.path import join as opj\n",
    "from matplotlib import pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import pylab\n",
    "plt.rcParams['figure.figsize'] = 10, 10\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Input Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = pd.read_json(\"input/train.json\")\n",
    "target_train=train['is_iceberg']\n",
    "test = pd.read_json(\"input/test.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "target_train=train['is_iceberg']\n",
    "test['inc_angle']=pd.to_numeric(test['inc_angle'], errors='coerce')\n",
    "train['inc_angle']=pd.to_numeric(train['inc_angle'], errors='coerce')#We have only 133 NAs.\n",
    "train['inc_angle']=train['inc_angle'].fillna(method='pad')\n",
    "X_angle=train['inc_angle']\n",
    "test['inc_angle']=pd.to_numeric(test['inc_angle'], errors='coerce')\n",
    "X_test_angle=test['inc_angle']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate the training data\n",
    "Use band_1 as the first color dimension and band_2 as the second color dimension. I formatted the third color dimension using the average of band_1 and band_2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Generate the training data\n",
    "X_band_1=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in train[\"band_1\"]])\n",
    "X_band_2=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in train[\"band_2\"]])\n",
    "X_band_3=(X_band_1+X_band_2)/2\n",
    "#X_band_3=np.array([np.full((75, 75), angel).astype(np.float32) for angel in train[\"inc_angle\"]])\n",
    "X_train = np.concatenate([X_band_1[:, :, :, np.newaxis]\n",
    "                          , X_band_2[:, :, :, np.newaxis]\n",
    "                         , X_band_3[:, :, :, np.newaxis]], axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_band_test_1=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in test[\"band_1\"]])\n",
    "X_band_test_2=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in test[\"band_2\"]])\n",
    "X_band_test_3=(X_band_test_1+X_band_test_2)/2\n",
    "#X_band_test_3=np.array([np.full((75, 75), angel).astype(np.float32) for angel in test[\"inc_angle\"]])\n",
    "X_test = np.concatenate([X_band_test_1[:, :, :, np.newaxis]\n",
    "                          , X_band_test_2[:, :, :, np.newaxis]\n",
    "                         , X_band_test_3[:, :, :, np.newaxis]], axis=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transfer Learning"
   ]
  },
  {
   "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": "markdown",
   "metadata": {},
   "source": [
    "## Image Transformation\n",
    "Use the code below to perform the data augmentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Data Aug for multi-input\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "batch_size=64\n",
    "# Define the image transformations here\n",
    "gen = ImageDataGenerator(horizontal_flip = True,\n",
    "                         vertical_flip = True,\n",
    "                         width_shift_range = 0.,\n",
    "                         height_shift_range = 0.,\n",
    "                         channel_shift_range=0,\n",
    "                         zoom_range = 0.2,\n",
    "                         rotation_range = 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge two generators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Here is the function that merges our two generators\n",
    "# We use the exact same generator with the same random seed for both the y and angle arrays\n",
    "def gen_flow_for_two_inputs(X1, X2, y):\n",
    "    genX1 = gen.flow(X1,y,  batch_size=batch_size,seed=55)\n",
    "    genX2 = gen.flow(X1,X2, batch_size=batch_size,seed=55)\n",
    "    while True:\n",
    "            X1i = genX1.next()\n",
    "            X2i = genX2.next()\n",
    "            #Assert arrays are equal - this was for peace of mind, but slows down training\n",
    "            #np.testing.assert_array_equal(X1i[0],X2i[0])\n",
    "            yield [X1i[0], X2i[1]], X1i[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Finally create generator\n",
    "def get_callbacks(filepath, patience=2):\n",
    "    es = EarlyStopping('val_loss', patience=10, mode=\"min\")\n",
    "    msave = ModelCheckpoint(filepath, save_best_only=True)\n",
    "    return [es, msave]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VGG16 Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getVggAngleModel():\n",
    "    input_2 = Input(shape=[1], name=\"angle\")\n",
    "    angle_layer = Dense(1, )(input_2)\n",
    "    base_model = VGG16(weights='imagenet', include_top=False, \n",
    "                 input_shape=X_train.shape[1:], classes=1)\n",
    "    x = base_model.get_layer('block5_pool').output\n",
    "    \n",
    "\n",
    "    x = GlobalMaxPooling2D()(x)\n",
    "    merge_one = concatenate([x, angle_layer])\n",
    "    merge_one = Dense(512, activation='relu', name='fc2')(merge_one)\n",
    "    merge_one = Dropout(0.3)(merge_one)\n",
    "    merge_one = Dense(512, activation='relu', name='fc3')(merge_one)\n",
    "    merge_one = Dropout(0.3)(merge_one)\n",
    "    \n",
    "    predictions = Dense(1, activation='sigmoid')(merge_one)\n",
    "    \n",
    "    model = Model(inputs=[base_model.input, input_2], outputs=predictions)\n",
    "    \n",
    "#     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=1e-3, decay=1e-6, momentum=0.9, nesterov=True)\n",
    "    model.compile(loss='binary_crossentropy',\n",
    "                  optimizer=sgd,\n",
    "                  metrics=['accuracy'])\n",
    "    return model\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stratified K-Fold Cross Validation\n",
    "### K=10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "===================FOLD= 0\n",
      "Epoch 1/50\n",
      "24/24 [==============================] - 17s 703ms/step - loss: 0.8804 - acc: 0.5896 - val_loss: 0.4597 - val_acc: 0.7840\n",
      "Epoch 2/50\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.4435 - acc: 0.7991 - val_loss: 0.3330 - val_acc: 0.8272\n",
      "Epoch 3/50\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.3503 - acc: 0.8461 - val_loss: 0.2764 - val_acc: 0.8580\n",
      "Epoch 4/50\n",
      "24/24 [==============================] - 12s 485ms/step - loss: 0.3243 - acc: 0.8526 - val_loss: 0.2810 - val_acc: 0.8704\n",
      "Epoch 5/50\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.3003 - acc: 0.8741 - val_loss: 0.2464 - val_acc: 0.8951\n",
      "Epoch 6/50\n",
      "24/24 [==============================] - 12s 487ms/step - loss: 0.2573 - acc: 0.8875 - val_loss: 0.2521 - val_acc: 0.9012\n",
      "Epoch 7/50\n",
      "24/24 [==============================] - 12s 489ms/step - loss: 0.2673 - acc: 0.8803 - val_loss: 0.2758 - val_acc: 0.8827\n",
      "Epoch 8/50\n",
      "24/24 [==============================] - 12s 491ms/step - loss: 0.2734 - acc: 0.8891 - val_loss: 0.2604 - val_acc: 0.8889\n",
      "Epoch 9/50\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.2331 - acc: 0.9032 - val_loss: 0.2402 - val_acc: 0.9136\n",
      "Epoch 10/50\n",
      "24/24 [==============================] - 12s 493ms/step - loss: 0.2206 - acc: 0.9175 - val_loss: 0.2531 - val_acc: 0.9074\n",
      "Epoch 11/50\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.2340 - acc: 0.9015 - val_loss: 0.2115 - val_acc: 0.9136\n",
      "Epoch 12/50\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.2166 - acc: 0.9172 - val_loss: 0.2256 - val_acc: 0.9198\n",
      "Epoch 13/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2221 - acc: 0.9064 - val_loss: 0.2120 - val_acc: 0.9259\n",
      "Epoch 14/50\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.1869 - acc: 0.9258 - val_loss: 0.2111 - val_acc: 0.9074\n",
      "Epoch 15/50\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1778 - acc: 0.9266 - val_loss: 0.2146 - val_acc: 0.9198\n",
      "Epoch 16/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1928 - acc: 0.9229 - val_loss: 0.2389 - val_acc: 0.9074\n",
      "Epoch 17/50\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.2012 - acc: 0.9221 - val_loss: 0.2130 - val_acc: 0.9136\n",
      "Epoch 18/50\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.2155 - acc: 0.9169 - val_loss: 0.1916 - val_acc: 0.9012\n",
      "Epoch 19/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1797 - acc: 0.9249 - val_loss: 0.2064 - val_acc: 0.9136\n",
      "Epoch 20/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1896 - acc: 0.9226 - val_loss: 0.2093 - val_acc: 0.9198\n",
      "Epoch 21/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1875 - acc: 0.9258 - val_loss: 0.2022 - val_acc: 0.9136\n",
      "Epoch 22/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1707 - acc: 0.9330 - val_loss: 0.2129 - val_acc: 0.9074\n",
      "Epoch 23/50\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.1669 - acc: 0.9396 - val_loss: 0.1882 - val_acc: 0.9259\n",
      "Epoch 24/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1673 - acc: 0.9342 - val_loss: 0.2118 - val_acc: 0.9136\n",
      "Epoch 25/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1613 - acc: 0.9336 - val_loss: 0.2114 - val_acc: 0.9136\n",
      "Epoch 26/50\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.1656 - acc: 0.9317 - val_loss: 0.1852 - val_acc: 0.9383\n",
      "Epoch 27/50\n",
      "24/24 [==============================] - 12s 493ms/step - loss: 0.1468 - acc: 0.9453 - val_loss: 0.1898 - val_acc: 0.9198\n",
      "Epoch 28/50\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1642 - acc: 0.9313 - val_loss: 0.2109 - val_acc: 0.9074\n",
      "Epoch 29/50\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1566 - acc: 0.9404 - val_loss: 0.2048 - val_acc: 0.9198\n",
      "Epoch 30/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1677 - acc: 0.9293 - val_loss: 0.2164 - val_acc: 0.9136\n",
      "Epoch 31/50\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1564 - acc: 0.9380 - val_loss: 0.1901 - val_acc: 0.9321\n",
      "Epoch 32/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1590 - acc: 0.9434 - val_loss: 0.1866 - val_acc: 0.9136\n",
      "Epoch 33/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1374 - acc: 0.9466 - val_loss: 0.2105 - val_acc: 0.9321\n",
      "Epoch 34/50\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1523 - acc: 0.9387 - val_loss: 0.2041 - val_acc: 0.9198\n",
      "Epoch 35/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1534 - acc: 0.9422 - val_loss: 0.1930 - val_acc: 0.9198\n",
      "Epoch 36/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1267 - acc: 0.9499 - val_loss: 0.1967 - val_acc: 0.9198\n",
      "Train loss: 0.0987686669033\n",
      "Train accuracy: 0.966712898752\n",
      "Test loss: 0.185240378669\n",
      "Test accuracy: 0.938271604938\n",
      "\n",
      "===================FOLD= 1\n",
      "Epoch 1/50\n",
      "24/24 [==============================] - 15s 634ms/step - loss: 0.6246 - acc: 0.6490 - val_loss: 0.3753 - val_acc: 0.7888\n",
      "Epoch 2/50\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.4519 - acc: 0.7752 - val_loss: 0.2625 - val_acc: 0.8820\n",
      "Epoch 3/50\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.3253 - acc: 0.8376 - val_loss: 0.2177 - val_acc: 0.9006\n",
      "Epoch 4/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2907 - acc: 0.8714 - val_loss: 0.2477 - val_acc: 0.8882\n",
      "Epoch 5/50\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.2865 - acc: 0.8770 - val_loss: 0.2358 - val_acc: 0.8509\n",
      "Epoch 6/50\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.2628 - acc: 0.8767 - val_loss: 0.1827 - val_acc: 0.9255\n",
      "Epoch 7/50\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.2410 - acc: 0.8889 - val_loss: 0.1810 - val_acc: 0.9255\n",
      "Epoch 8/50\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.2440 - acc: 0.8907 - val_loss: 0.2184 - val_acc: 0.9068\n",
      "Epoch 9/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2261 - acc: 0.9007 - val_loss: 0.2070 - val_acc: 0.9006\n",
      "Epoch 10/50\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.2411 - acc: 0.8924 - val_loss: 0.1788 - val_acc: 0.9441\n",
      "Epoch 11/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2229 - acc: 0.9009 - val_loss: 0.2063 - val_acc: 0.9255\n",
      "Epoch 12/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2114 - acc: 0.9111 - val_loss: 0.2096 - val_acc: 0.9006\n",
      "Epoch 13/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2129 - acc: 0.9005 - val_loss: 0.1870 - val_acc: 0.9441\n",
      "Epoch 14/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1947 - acc: 0.9266 - val_loss: 0.2380 - val_acc: 0.8758\n",
      "Epoch 15/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2039 - acc: 0.9167 - val_loss: 0.1804 - val_acc: 0.9255\n",
      "Epoch 16/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1973 - acc: 0.9148 - val_loss: 0.2141 - val_acc: 0.9193\n",
      "Epoch 17/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1697 - acc: 0.9281 - val_loss: 0.2156 - val_acc: 0.9255\n",
      "Epoch 18/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1982 - acc: 0.9152 - val_loss: 0.1980 - val_acc: 0.9255\n",
      "Epoch 19/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1778 - acc: 0.9273 - val_loss: 0.2469 - val_acc: 0.9006\n",
      "Epoch 20/50\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.1820 - acc: 0.9157 - val_loss: 0.1671 - val_acc: 0.9565\n",
      "Epoch 21/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1602 - acc: 0.9296 - val_loss: 0.1877 - val_acc: 0.9255\n",
      "Epoch 22/50\n",
      "24/24 [==============================] - 12s 493ms/step - loss: 0.1782 - acc: 0.9311 - val_loss: 0.1913 - val_acc: 0.9317\n",
      "Epoch 23/50\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1670 - acc: 0.9302 - val_loss: 0.1744 - val_acc: 0.9193\n",
      "Epoch 24/50\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1683 - acc: 0.9312 - val_loss: 0.2493 - val_acc: 0.8944\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 25/50\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1685 - acc: 0.9257 - val_loss: 0.1822 - val_acc: 0.9503\n",
      "Epoch 26/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1518 - acc: 0.9351 - val_loss: 0.2181 - val_acc: 0.9379\n",
      "Epoch 27/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1543 - acc: 0.9329 - val_loss: 0.2200 - val_acc: 0.9193\n",
      "Epoch 28/50\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1432 - acc: 0.9436 - val_loss: 0.2134 - val_acc: 0.9441\n",
      "Epoch 29/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1755 - acc: 0.9219 - val_loss: 0.2214 - val_acc: 0.9255\n",
      "Epoch 30/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1487 - acc: 0.9397 - val_loss: 0.2297 - val_acc: 0.9379\n",
      "Train loss: 0.137933181347\n",
      "Train accuracy: 0.948717948718\n",
      "Test loss: 0.167097230415\n",
      "Test accuracy: 0.95652173913\n",
      "\n",
      "===================FOLD= 2\n",
      "Epoch 1/50\n",
      "24/24 [==============================] - 13s 544ms/step - loss: 0.6665 - acc: 0.6177 - val_loss: 0.4143 - val_acc: 0.8012\n",
      "Epoch 2/50\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.4015 - acc: 0.8000 - val_loss: 0.3072 - val_acc: 0.8509\n",
      "Epoch 3/50\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.3137 - acc: 0.8455 - val_loss: 0.2270 - val_acc: 0.8758\n",
      "Epoch 4/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.3167 - acc: 0.8459 - val_loss: 0.3039 - val_acc: 0.8447\n",
      "Epoch 5/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2728 - acc: 0.8726 - val_loss: 0.2284 - val_acc: 0.8820\n",
      "Epoch 6/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2732 - acc: 0.8773 - val_loss: 0.2657 - val_acc: 0.8634\n",
      "Epoch 7/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2499 - acc: 0.8900 - val_loss: 0.2300 - val_acc: 0.8944\n",
      "Epoch 8/50\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.2336 - acc: 0.9001 - val_loss: 0.2263 - val_acc: 0.8696\n",
      "Epoch 9/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2381 - acc: 0.8959 - val_loss: 0.2489 - val_acc: 0.8758\n",
      "Epoch 10/50\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.2267 - acc: 0.9066 - val_loss: 0.2254 - val_acc: 0.8820\n",
      "Epoch 11/50\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.2329 - acc: 0.9029 - val_loss: 0.2112 - val_acc: 0.9068\n",
      "Epoch 12/50\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.2255 - acc: 0.9110 - val_loss: 0.2342 - val_acc: 0.8882\n",
      "Epoch 13/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2004 - acc: 0.9175 - val_loss: 0.2297 - val_acc: 0.8634\n",
      "Epoch 14/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1874 - acc: 0.9225 - val_loss: 0.2745 - val_acc: 0.8758\n",
      "Epoch 15/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2185 - acc: 0.9062 - val_loss: 0.2233 - val_acc: 0.8944\n",
      "Epoch 16/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2026 - acc: 0.9145 - val_loss: 0.2254 - val_acc: 0.9006\n",
      "Epoch 17/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2080 - acc: 0.9097 - val_loss: 0.2305 - val_acc: 0.8944\n",
      "Epoch 18/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1979 - acc: 0.9164 - val_loss: 0.2203 - val_acc: 0.8944\n",
      "Epoch 19/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1934 - acc: 0.9206 - val_loss: 0.2115 - val_acc: 0.9006\n",
      "Epoch 20/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1893 - acc: 0.9190 - val_loss: 0.2175 - val_acc: 0.9006\n",
      "Epoch 21/50\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.1776 - acc: 0.9312 - val_loss: 0.2012 - val_acc: 0.9006\n",
      "Epoch 22/50\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1882 - acc: 0.9265 - val_loss: 0.2213 - val_acc: 0.8944\n",
      "Epoch 23/50\n",
      "24/24 [==============================] - 12s 489ms/step - loss: 0.1680 - acc: 0.9302 - val_loss: 0.2339 - val_acc: 0.9006\n",
      "Epoch 24/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1670 - acc: 0.9292 - val_loss: 0.2096 - val_acc: 0.9006\n",
      "Epoch 25/50\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.1602 - acc: 0.9294 - val_loss: 0.1925 - val_acc: 0.9130\n",
      "Epoch 26/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1829 - acc: 0.9250 - val_loss: 0.2017 - val_acc: 0.8944\n",
      "Epoch 27/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1510 - acc: 0.9382 - val_loss: 0.2094 - val_acc: 0.8944\n",
      "Epoch 28/50\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1464 - acc: 0.9384 - val_loss: 0.2326 - val_acc: 0.9068\n",
      "Epoch 29/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1676 - acc: 0.9315 - val_loss: 0.2224 - val_acc: 0.9006\n",
      "Epoch 30/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1411 - acc: 0.9371 - val_loss: 0.2385 - val_acc: 0.8944\n",
      "Epoch 31/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1452 - acc: 0.9380 - val_loss: 0.1986 - val_acc: 0.9255\n",
      "Epoch 32/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1565 - acc: 0.9328 - val_loss: 0.2042 - val_acc: 0.9130\n",
      "Epoch 33/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1519 - acc: 0.9373 - val_loss: 0.2675 - val_acc: 0.8944\n",
      "Epoch 34/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1553 - acc: 0.9363 - val_loss: 0.2072 - val_acc: 0.9255\n",
      "Epoch 35/50\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1377 - acc: 0.9399 - val_loss: 0.2066 - val_acc: 0.9068\n",
      "Train loss: 0.118729914073\n",
      "Train accuracy: 0.952875952876\n",
      "Test loss: 0.192499478781\n",
      "Test accuracy: 0.913043478261\n",
      "\n",
      "===================FOLD= 3\n",
      "Epoch 1/50\n",
      "24/24 [==============================] - 15s 619ms/step - loss: 0.6889 - acc: 0.5824 - val_loss: 0.4517 - val_acc: 0.7688\n",
      "Epoch 2/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.6562 - acc: 0.6158 - val_loss: 0.5398 - val_acc: 0.7375\n",
      "Epoch 3/50\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.5713 - acc: 0.6645 - val_loss: 0.3641 - val_acc: 0.8187\n",
      "Epoch 4/50\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.4239 - acc: 0.7888 - val_loss: 0.3098 - val_acc: 0.8375\n",
      "Epoch 5/50\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.3221 - acc: 0.8534 - val_loss: 0.2599 - val_acc: 0.8688\n",
      "Epoch 6/50\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.3107 - acc: 0.8518 - val_loss: 0.2460 - val_acc: 0.8688\n",
      "Epoch 7/50\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.2813 - acc: 0.8723 - val_loss: 0.2403 - val_acc: 0.8750\n",
      "Epoch 8/50\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.2822 - acc: 0.8796 - val_loss: 0.2321 - val_acc: 0.9062\n",
      "Epoch 9/50\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.2517 - acc: 0.8824 - val_loss: 0.2270 - val_acc: 0.8875\n",
      "Epoch 10/50\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.2497 - acc: 0.8906 - val_loss: 0.2160 - val_acc: 0.8938\n",
      "Epoch 11/50\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.2405 - acc: 0.8901 - val_loss: 0.2078 - val_acc: 0.9125\n",
      "Epoch 12/50\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.2145 - acc: 0.9085 - val_loss: 0.1992 - val_acc: 0.9187\n",
      "Epoch 13/50\n",
      "24/24 [==============================] - 12s 493ms/step - loss: 0.2158 - acc: 0.9151 - val_loss: 0.2236 - val_acc: 0.9000\n",
      "Epoch 14/50\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.2093 - acc: 0.9143 - val_loss: 0.2120 - val_acc: 0.9125\n",
      "Epoch 15/50\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.2305 - acc: 0.9013 - val_loss: 0.1871 - val_acc: 0.9187\n",
      "Epoch 16/50\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.2118 - acc: 0.9152 - val_loss: 0.1859 - val_acc: 0.9062\n",
      "Epoch 17/50\n",
      "24/24 [==============================] - 12s 493ms/step - loss: 0.1931 - acc: 0.9153 - val_loss: 0.2209 - val_acc: 0.8875\n",
      "Epoch 18/50\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.2107 - acc: 0.9142 - val_loss: 0.2002 - val_acc: 0.9187\n",
      "Epoch 19/50\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.2050 - acc: 0.9168 - val_loss: 0.1814 - val_acc: 0.9187\n",
      "Epoch 20/50\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1945 - acc: 0.9186 - val_loss: 0.1935 - val_acc: 0.9313\n",
      "Epoch 21/50\n",
      "24/24 [==============================] - 12s 493ms/step - loss: 0.1840 - acc: 0.9280 - val_loss: 0.1978 - val_acc: 0.9187\n",
      "Epoch 22/50\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1844 - acc: 0.9249 - val_loss: 0.2036 - val_acc: 0.9250\n",
      "Epoch 23/50\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.1850 - acc: 0.9285 - val_loss: 0.1982 - val_acc: 0.9063\n",
      "Epoch 24/50\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1832 - acc: 0.9221 - val_loss: 0.2323 - val_acc: 0.9062\n",
      "Epoch 25/50\n",
      "24/24 [==============================] - 12s 493ms/step - loss: 0.1736 - acc: 0.9319 - val_loss: 0.2598 - val_acc: 0.9000\n",
      "Epoch 26/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1774 - acc: 0.9261 - val_loss: 0.3412 - val_acc: 0.8750\n",
      "Epoch 27/50\n",
      "24/24 [==============================] - 12s 493ms/step - loss: 0.1666 - acc: 0.9344 - val_loss: 0.2232 - val_acc: 0.9187\n",
      "Epoch 28/50\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1496 - acc: 0.9417 - val_loss: 0.1918 - val_acc: 0.9187\n",
      "Epoch 29/50\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1706 - acc: 0.9254 - val_loss: 0.2370 - val_acc: 0.9125\n",
      "Train loss: 0.147803950013\n",
      "Train accuracy: 0.941828254848\n",
      "Test loss: 0.181395292282\n",
      "Test accuracy: 0.91875\n",
      "\n",
      "===================FOLD= 4\n",
      "Epoch 1/50\n",
      "24/24 [==============================] - 14s 567ms/step - loss: 0.6486 - acc: 0.6270 - val_loss: 0.3933 - val_acc: 0.8125\n",
      "Epoch 2/50\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.3780 - acc: 0.8072 - val_loss: 0.2422 - val_acc: 0.9250\n",
      "Epoch 3/50\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.3231 - acc: 0.8428 - val_loss: 0.2045 - val_acc: 0.9500\n",
      "Epoch 4/50\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.3279 - acc: 0.8438 - val_loss: 0.1926 - val_acc: 0.9500\n",
      "Epoch 5/50\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.2840 - acc: 0.8704 - val_loss: 0.1880 - val_acc: 0.9437\n",
      "Epoch 6/50\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.2730 - acc: 0.8805 - val_loss: 0.1639 - val_acc: 0.9625\n",
      "Epoch 7/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2500 - acc: 0.8920 - val_loss: 0.1971 - val_acc: 0.9375\n",
      "Epoch 8/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2458 - acc: 0.8899 - val_loss: 0.1829 - val_acc: 0.9313\n",
      "Epoch 9/50\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.2273 - acc: 0.9116 - val_loss: 0.1341 - val_acc: 0.9437\n",
      "Epoch 10/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2316 - acc: 0.9021 - val_loss: 0.1522 - val_acc: 0.9563\n",
      "Epoch 11/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2164 - acc: 0.9047 - val_loss: 0.1562 - val_acc: 0.9437\n",
      "Epoch 12/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2144 - acc: 0.9098 - val_loss: 0.1581 - val_acc: 0.9437\n",
      "Epoch 13/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2194 - acc: 0.9090 - val_loss: 0.1631 - val_acc: 0.9437\n",
      "Epoch 14/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2021 - acc: 0.9150 - val_loss: 0.1420 - val_acc: 0.9625\n",
      "Epoch 15/50\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.2052 - acc: 0.9091 - val_loss: 0.1409 - val_acc: 0.9563\n",
      "Epoch 16/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2302 - acc: 0.9021 - val_loss: 0.1375 - val_acc: 0.9625\n",
      "Epoch 17/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2000 - acc: 0.9108 - val_loss: 0.1928 - val_acc: 0.9313\n",
      "Epoch 18/50\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1827 - acc: 0.9228 - val_loss: 0.1790 - val_acc: 0.9313\n",
      "Epoch 19/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2061 - acc: 0.9103 - val_loss: 0.1660 - val_acc: 0.9437\n",
      "Train loss: 0.195056848827\n",
      "Train accuracy: 0.916897506925\n",
      "Test loss: 0.134090191126\n",
      "Test accuracy: 0.94375\n",
      "\n",
      "===================FOLD= 5\n",
      "Epoch 1/50\n",
      "24/24 [==============================] - 14s 565ms/step - loss: 0.8026 - acc: 0.6218 - val_loss: 0.4487 - val_acc: 0.7875\n",
      "Epoch 2/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.4760 - acc: 0.7964 - val_loss: 0.4563 - val_acc: 0.8438\n",
      "Epoch 3/50\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.3869 - acc: 0.8181 - val_loss: 0.2823 - val_acc: 0.8875\n",
      "Epoch 4/50\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.3216 - acc: 0.8645 - val_loss: 0.2307 - val_acc: 0.8875\n",
      "Epoch 5/50\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.3108 - acc: 0.8656 - val_loss: 0.2219 - val_acc: 0.9250\n",
      "Epoch 6/50\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.2761 - acc: 0.8824 - val_loss: 0.2027 - val_acc: 0.9062\n",
      "Epoch 7/50\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.2486 - acc: 0.8941 - val_loss: 0.1917 - val_acc: 0.9187\n",
      "Epoch 8/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2631 - acc: 0.8816 - val_loss: 0.1976 - val_acc: 0.9313\n",
      "Epoch 9/50\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.2412 - acc: 0.8941 - val_loss: 0.1895 - val_acc: 0.9250\n",
      "Epoch 10/50\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.2228 - acc: 0.9026 - val_loss: 0.1892 - val_acc: 0.9125\n",
      "Epoch 11/50\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.2123 - acc: 0.9052 - val_loss: 0.1499 - val_acc: 0.9375\n",
      "Epoch 12/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2079 - acc: 0.9106 - val_loss: 0.1666 - val_acc: 0.9313\n",
      "Epoch 13/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2111 - acc: 0.9235 - val_loss: 0.1742 - val_acc: 0.9375\n",
      "Epoch 14/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2197 - acc: 0.9117 - val_loss: 0.1671 - val_acc: 0.9437\n",
      "Epoch 15/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1835 - acc: 0.9261 - val_loss: 0.1882 - val_acc: 0.9250\n",
      "Epoch 16/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2019 - acc: 0.9182 - val_loss: 0.1661 - val_acc: 0.9500\n",
      "Epoch 17/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1941 - acc: 0.9095 - val_loss: 0.1801 - val_acc: 0.9375\n",
      "Epoch 18/50\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1919 - acc: 0.9204 - val_loss: 0.1921 - val_acc: 0.9187\n",
      "Epoch 19/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1917 - acc: 0.9155 - val_loss: 0.1999 - val_acc: 0.9187\n",
      "Epoch 20/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1652 - acc: 0.9285 - val_loss: 0.1979 - val_acc: 0.9062\n",
      "Epoch 21/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1808 - acc: 0.9256 - val_loss: 0.2096 - val_acc: 0.9125\n",
      "Train loss: 0.160503550992\n",
      "Train accuracy: 0.93351800554\n",
      "Test loss: 0.14993249625\n",
      "Test accuracy: 0.9375\n",
      "\n",
      "===================FOLD= 6\n",
      "Epoch 1/50\n",
      "24/24 [==============================] - 14s 586ms/step - loss: 0.6779 - acc: 0.6045 - val_loss: 0.4684 - val_acc: 0.7688\n",
      "Epoch 2/50\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.5343 - acc: 0.7472 - val_loss: 0.4664 - val_acc: 0.7625\n",
      "Epoch 3/50\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.3584 - acc: 0.8392 - val_loss: 0.3609 - val_acc: 0.8375\n",
      "Epoch 4/50\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.2846 - acc: 0.8730 - val_loss: 0.3434 - val_acc: 0.8812\n",
      "Epoch 5/50\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.2748 - acc: 0.8738 - val_loss: 0.3328 - val_acc: 0.8812\n",
      "Epoch 6/50\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.2606 - acc: 0.8863 - val_loss: 0.3003 - val_acc: 0.8875\n",
      "Epoch 7/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2467 - acc: 0.8971 - val_loss: 0.3372 - val_acc: 0.8688\n",
      "Epoch 8/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24/24 [==============================] - 12s 515ms/step - loss: 0.2276 - acc: 0.9108 - val_loss: 0.2831 - val_acc: 0.8875\n",
      "Epoch 9/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2227 - acc: 0.9054 - val_loss: 0.3015 - val_acc: 0.8812\n",
      "Epoch 10/50\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.2025 - acc: 0.9194 - val_loss: 0.2693 - val_acc: 0.8750\n",
      "Epoch 11/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2113 - acc: 0.9081 - val_loss: 0.2969 - val_acc: 0.8875\n",
      "Epoch 12/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1980 - acc: 0.9160 - val_loss: 0.3512 - val_acc: 0.8562\n",
      "Epoch 13/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2062 - acc: 0.9134 - val_loss: 0.2810 - val_acc: 0.8938\n",
      "Epoch 14/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1899 - acc: 0.9241 - val_loss: 0.2735 - val_acc: 0.9000\n",
      "Epoch 15/50\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.2049 - acc: 0.9127 - val_loss: 0.2446 - val_acc: 0.9062\n",
      "Epoch 16/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1922 - acc: 0.9161 - val_loss: 0.2635 - val_acc: 0.8875\n",
      "Epoch 17/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1811 - acc: 0.9264 - val_loss: 0.2682 - val_acc: 0.8750\n",
      "Epoch 18/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1691 - acc: 0.9311 - val_loss: 0.2712 - val_acc: 0.8938\n",
      "Epoch 19/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1780 - acc: 0.9283 - val_loss: 0.2891 - val_acc: 0.8875\n",
      "Epoch 20/50\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1544 - acc: 0.9389 - val_loss: 0.3221 - val_acc: 0.9062\n",
      "Epoch 21/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1678 - acc: 0.9290 - val_loss: 0.2648 - val_acc: 0.9000\n",
      "Epoch 22/50\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1584 - acc: 0.9376 - val_loss: 0.2762 - val_acc: 0.8750\n",
      "Epoch 23/50\n",
      "24/24 [==============================] - 12s 489ms/step - loss: 0.1772 - acc: 0.9281 - val_loss: 0.3855 - val_acc: 0.8625\n",
      "Epoch 24/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1491 - acc: 0.9464 - val_loss: 0.3612 - val_acc: 0.8750\n",
      "Epoch 25/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1657 - acc: 0.9379 - val_loss: 0.3312 - val_acc: 0.8313\n",
      "Train loss: 0.173735600058\n",
      "Train accuracy: 0.919667590028\n",
      "Test loss: 0.2446318537\n",
      "Test accuracy: 0.90625\n",
      "\n",
      "===================FOLD= 7\n",
      "Epoch 1/50\n",
      "24/24 [==============================] - 14s 580ms/step - loss: 0.9854 - acc: 0.5853 - val_loss: 0.4405 - val_acc: 0.7625\n",
      "Epoch 2/50\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.4913 - acc: 0.7747 - val_loss: 0.2724 - val_acc: 0.8812\n",
      "Epoch 3/50\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.3459 - acc: 0.8415 - val_loss: 0.2939 - val_acc: 0.8438\n",
      "Epoch 4/50\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.3528 - acc: 0.8410 - val_loss: 0.2706 - val_acc: 0.8625\n",
      "Epoch 5/50\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.2963 - acc: 0.8694 - val_loss: 0.2342 - val_acc: 0.8938\n",
      "Epoch 6/50\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.2887 - acc: 0.8818 - val_loss: 0.2571 - val_acc: 0.8812\n",
      "Epoch 7/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2558 - acc: 0.8893 - val_loss: 0.2699 - val_acc: 0.8938\n",
      "Epoch 8/50\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.2404 - acc: 0.8966 - val_loss: 0.2146 - val_acc: 0.8812\n",
      "Epoch 9/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2463 - acc: 0.8979 - val_loss: 0.2784 - val_acc: 0.8938\n",
      "Epoch 10/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2260 - acc: 0.9010 - val_loss: 0.2483 - val_acc: 0.8938\n",
      "Epoch 11/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2096 - acc: 0.9189 - val_loss: 0.2263 - val_acc: 0.8875\n",
      "Epoch 12/50\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.2395 - acc: 0.8990 - val_loss: 0.2122 - val_acc: 0.9062\n",
      "Epoch 13/50\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.2104 - acc: 0.9153 - val_loss: 0.2004 - val_acc: 0.9250\n",
      "Epoch 14/50\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.2126 - acc: 0.9148 - val_loss: 0.1922 - val_acc: 0.9000\n",
      "Epoch 15/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1963 - acc: 0.9217 - val_loss: 0.2025 - val_acc: 0.9125\n",
      "Epoch 16/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2285 - acc: 0.9093 - val_loss: 0.2165 - val_acc: 0.9187\n",
      "Epoch 17/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1663 - acc: 0.9402 - val_loss: 0.2205 - val_acc: 0.9125\n",
      "Epoch 18/50\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.2146 - acc: 0.9124 - val_loss: 0.2378 - val_acc: 0.8750\n",
      "Epoch 19/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1665 - acc: 0.9358 - val_loss: 0.2047 - val_acc: 0.9125\n",
      "Epoch 20/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1990 - acc: 0.9199 - val_loss: 0.2083 - val_acc: 0.9000\n",
      "Epoch 21/50\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.1841 - acc: 0.9202 - val_loss: 0.1808 - val_acc: 0.9250\n",
      "Epoch 22/50\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.1845 - acc: 0.9308 - val_loss: 0.1779 - val_acc: 0.9062\n",
      "Epoch 23/50\n",
      "24/24 [==============================] - 12s 490ms/step - loss: 0.1608 - acc: 0.9368 - val_loss: 0.2162 - val_acc: 0.9063\n",
      "Epoch 24/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1896 - acc: 0.9261 - val_loss: 0.2246 - val_acc: 0.9000\n",
      "Epoch 25/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1811 - acc: 0.9269 - val_loss: 0.2119 - val_acc: 0.9125\n",
      "Epoch 26/50\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1746 - acc: 0.9344 - val_loss: 0.1972 - val_acc: 0.9125\n",
      "Epoch 27/50\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.1472 - acc: 0.9425 - val_loss: 0.1743 - val_acc: 0.9250\n",
      "Epoch 28/50\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1666 - acc: 0.9300 - val_loss: 0.2041 - val_acc: 0.9062\n",
      "Epoch 29/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1540 - acc: 0.9406 - val_loss: 0.1930 - val_acc: 0.9187\n",
      "Epoch 30/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1575 - acc: 0.9399 - val_loss: 0.2176 - val_acc: 0.9000\n",
      "Epoch 31/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1363 - acc: 0.9459 - val_loss: 0.1942 - val_acc: 0.9250\n",
      "Epoch 32/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1394 - acc: 0.9500 - val_loss: 0.1904 - val_acc: 0.9125\n",
      "Epoch 33/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1510 - acc: 0.9412 - val_loss: 0.1985 - val_acc: 0.9187\n",
      "Epoch 34/50\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1353 - acc: 0.9459 - val_loss: 0.1879 - val_acc: 0.9125\n",
      "Epoch 35/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1381 - acc: 0.9482 - val_loss: 0.1767 - val_acc: 0.9250\n",
      "Epoch 36/50\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1248 - acc: 0.9479 - val_loss: 0.2018 - val_acc: 0.9000\n",
      "Epoch 37/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1455 - acc: 0.9494 - val_loss: 0.2218 - val_acc: 0.8812\n",
      "Train loss: 0.105302518198\n",
      "Train accuracy: 0.961911357341\n",
      "Test loss: 0.174281066656\n",
      "Test accuracy: 0.925\n",
      "\n",
      "===================FOLD= 8\n",
      "Epoch 1/50\n",
      "24/24 [==============================] - 14s 578ms/step - loss: 0.7484 - acc: 0.6194 - val_loss: 0.4445 - val_acc: 0.7750\n",
      "Epoch 2/50\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.4254 - acc: 0.7920 - val_loss: 0.3719 - val_acc: 0.8125\n",
      "Epoch 3/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.3559 - acc: 0.8440 - val_loss: 0.3811 - val_acc: 0.8000\n",
      "Epoch 4/50\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.2953 - acc: 0.8772 - val_loss: 0.3467 - val_acc: 0.8500\n",
      "Epoch 5/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2783 - acc: 0.8819 - val_loss: 0.3486 - val_acc: 0.8500\n",
      "Epoch 6/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2476 - acc: 0.8969 - val_loss: 0.4044 - val_acc: 0.8562\n",
      "Epoch 7/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2699 - acc: 0.8878 - val_loss: 0.3531 - val_acc: 0.8500\n",
      "Epoch 8/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2413 - acc: 0.9034 - val_loss: 0.3806 - val_acc: 0.8562\n",
      "Epoch 9/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2383 - acc: 0.8984 - val_loss: 0.3523 - val_acc: 0.8625\n",
      "Epoch 10/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2020 - acc: 0.9254 - val_loss: 0.3512 - val_acc: 0.8688\n",
      "Epoch 11/50\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.1998 - acc: 0.9220 - val_loss: 0.3222 - val_acc: 0.8500\n",
      "Epoch 12/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2092 - acc: 0.9181 - val_loss: 0.3340 - val_acc: 0.8688\n",
      "Epoch 13/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1914 - acc: 0.9233 - val_loss: 0.3333 - val_acc: 0.8375\n",
      "Epoch 14/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1881 - acc: 0.9269 - val_loss: 0.3439 - val_acc: 0.8375\n",
      "Epoch 15/50\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1788 - acc: 0.9257 - val_loss: 0.3614 - val_acc: 0.8688\n",
      "Epoch 16/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1853 - acc: 0.9254 - val_loss: 0.3864 - val_acc: 0.8562\n",
      "Epoch 17/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2146 - acc: 0.9075 - val_loss: 0.3228 - val_acc: 0.8688\n",
      "Epoch 18/50\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.1738 - acc: 0.9366 - val_loss: 0.3214 - val_acc: 0.8562\n",
      "Epoch 19/50\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.1739 - acc: 0.9283 - val_loss: 0.3222 - val_acc: 0.8750\n",
      "Epoch 20/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1777 - acc: 0.9221 - val_loss: 0.3761 - val_acc: 0.8375\n",
      "Epoch 21/50\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.1704 - acc: 0.9301 - val_loss: 0.3086 - val_acc: 0.8562\n",
      "Epoch 22/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1610 - acc: 0.9388 - val_loss: 0.3160 - val_acc: 0.8562\n",
      "Epoch 23/50\n",
      "24/24 [==============================] - 12s 490ms/step - loss: 0.1608 - acc: 0.9351 - val_loss: 0.3164 - val_acc: 0.8687\n",
      "Epoch 24/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1623 - acc: 0.9355 - val_loss: 0.3445 - val_acc: 0.8625\n",
      "Epoch 25/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1599 - acc: 0.9365 - val_loss: 0.3470 - val_acc: 0.8438\n",
      "Epoch 26/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1472 - acc: 0.9381 - val_loss: 0.3571 - val_acc: 0.8250\n",
      "Epoch 27/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1529 - acc: 0.9368 - val_loss: 0.3764 - val_acc: 0.8500\n",
      "Epoch 28/50\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.1482 - acc: 0.9480 - val_loss: 0.2951 - val_acc: 0.8750\n",
      "Epoch 29/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1435 - acc: 0.9397 - val_loss: 0.3488 - val_acc: 0.8500\n",
      "Epoch 30/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1325 - acc: 0.9461 - val_loss: 0.3788 - val_acc: 0.8438\n",
      "Epoch 31/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1330 - acc: 0.9534 - val_loss: 0.3710 - val_acc: 0.8688\n",
      "Epoch 32/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1420 - acc: 0.9458 - val_loss: 0.3846 - val_acc: 0.8500\n",
      "Epoch 33/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1429 - acc: 0.9463 - val_loss: 0.3295 - val_acc: 0.8688\n",
      "Epoch 34/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1257 - acc: 0.9524 - val_loss: 0.3819 - val_acc: 0.8438\n",
      "Epoch 35/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1377 - acc: 0.9456 - val_loss: 0.3771 - val_acc: 0.8500\n",
      "Epoch 36/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1362 - acc: 0.9412 - val_loss: 0.3299 - val_acc: 0.8500\n",
      "Epoch 37/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1142 - acc: 0.9531 - val_loss: 0.5468 - val_acc: 0.8250\n",
      "Epoch 38/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1309 - acc: 0.9463 - val_loss: 0.3325 - val_acc: 0.8625\n",
      "Train loss: 0.106090346934\n",
      "Train accuracy: 0.960526315789\n",
      "Test loss: 0.295113873482\n",
      "Test accuracy: 0.875\n",
      "\n",
      "===================FOLD= 9\n",
      "Epoch 1/50\n",
      "24/24 [==============================] - 14s 599ms/step - loss: 0.7029 - acc: 0.6099 - val_loss: 0.4742 - val_acc: 0.7500\n",
      "Epoch 2/50\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.4229 - acc: 0.8028 - val_loss: 0.3617 - val_acc: 0.8063\n",
      "Epoch 3/50\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.3246 - acc: 0.8554 - val_loss: 0.2404 - val_acc: 0.9062\n",
      "Epoch 4/50\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.3150 - acc: 0.8655 - val_loss: 0.2547 - val_acc: 0.8750\n",
      "Epoch 5/50\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.3078 - acc: 0.8594 - val_loss: 0.2404 - val_acc: 0.8938\n",
      "Epoch 6/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2829 - acc: 0.8858 - val_loss: 0.2601 - val_acc: 0.8875\n",
      "Epoch 7/50\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.2695 - acc: 0.8824 - val_loss: 0.2400 - val_acc: 0.9000\n",
      "Epoch 8/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2325 - acc: 0.8893 - val_loss: 0.2416 - val_acc: 0.8938\n",
      "Epoch 9/50\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.2390 - acc: 0.8976 - val_loss: 0.2081 - val_acc: 0.9125\n",
      "Epoch 10/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2397 - acc: 0.9003 - val_loss: 0.2193 - val_acc: 0.9125\n",
      "Epoch 11/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2219 - acc: 0.9046 - val_loss: 0.2904 - val_acc: 0.8562\n",
      "Epoch 12/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2213 - acc: 0.9106 - val_loss: 0.2558 - val_acc: 0.9000\n",
      "Epoch 13/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2291 - acc: 0.9041 - val_loss: 0.2287 - val_acc: 0.8875\n",
      "Epoch 14/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1999 - acc: 0.9223 - val_loss: 0.2486 - val_acc: 0.8938\n",
      "Epoch 15/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.2219 - acc: 0.9065 - val_loss: 0.2282 - val_acc: 0.9000\n",
      "Epoch 16/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1881 - acc: 0.9228 - val_loss: 0.2174 - val_acc: 0.9187\n",
      "Epoch 17/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1959 - acc: 0.9217 - val_loss: 0.2225 - val_acc: 0.9125\n",
      "Epoch 18/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1912 - acc: 0.9186 - val_loss: 0.2464 - val_acc: 0.9062\n",
      "Epoch 19/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1949 - acc: 0.9179 - val_loss: 0.2444 - val_acc: 0.8875\n",
      "Train loss: 0.181820382423\n",
      "Train accuracy: 0.927977839335\n",
      "Test loss: 0.208122503757\n",
      "Test accuracy: 0.9125\n",
      "\n",
      " Train Log Loss Validation=  0.131724694341\n",
      " Test Log Loss Validation=  0.193213699335\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=50,\n",
    "            shuffle=True,\n",
    "            verbose=1,\n",
    "            validation_data=([X_holdout,X_angle_hold], Y_holdout),\n",
    "            callbacks=callbacks)\n",
    "     #Getting the Best Model\n",
    "    galaxyModel.load_weights(filepath=file_path)\n",
    "    #Getting Training Score\n",
    "    score = galaxyModel.evaluate([X_train_cv,X_angle_cv], y_train_cv, verbose=0)\n",
    "    print('Train loss:', score[0])\n",
    "    print('Train accuracy:', score[1])\n",
    "    #Getting Test Score\n",
    "    score = galaxyModel.evaluate([X_holdout,X_angle_hold], Y_holdout, verbose=0)\n",
    "    print('Test loss:', score[0])\n",
    "    print('Test accuracy:', score[1])\n",
    "\n",
    "    #Getting validation Score.\n",
    "    pred_valid=galaxyModel.predict([X_holdout,X_angle_hold])\n",
    "    y_valid_pred_log[test_idx] = pred_valid.reshape(pred_valid.shape[0])\n",
    "\n",
    "    #Getting Test Scores\n",
    "    temp_test=galaxyModel.predict([X_test, X_test_angle])\n",
    "    y_test_pred_log+=temp_test.reshape(temp_test.shape[0])\n",
    "\n",
    "    #Getting Train Scores\n",
    "    temp_train=galaxyModel.predict([X_train, X_angle])\n",
    "    y_train_pred_log+=temp_train.reshape(temp_train.shape[0])\n",
    "\n",
    "y_test_pred_log=y_test_pred_log/K\n",
    "y_train_pred_log=y_train_pred_log/K\n",
    "\n",
    "print('\\n Train Log Loss Validation= ',log_loss(target_train, y_train_pred_log))\n",
    "print(' Test Log Loss Validation= ',log_loss(target_train, y_valid_pred_log))\n",
    "#return y_test_pred_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#preds=myAngleCV(X_train, X_angle, X_test)\n",
    "preds = y_test_pred_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Submission to kaggle\n",
    "submission = pd.DataFrame()\n",
    "submission['id']=test['id']\n",
    "submission['is_iceberg']=preds\n",
    "submission.to_csv('sub_SGD_10folds.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['val_acc', 'loss', 'val_loss', 'acc'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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f9bvz+GTNLsoqDpVs28VFVpVCTmjfmj15xcxcvo3cglLax0dzy7ieXDY8hb4N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\nLD7ENWb/3Xv8Q29by3FgOyx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C1htKVctE5E7gIyAcmK6qq0RkKpCmqrO8feeKyGqgHPi5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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f11ea01f978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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9F4/kpWsnkBgTzs+e+5KLH13OxuwjzR+sfHNkH7x/B0TEwwd3wIvf16sGFRCu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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f11e9f69748>"
      ]
     },
     "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": "markdown",
   "metadata": {},
   "source": [
    "## Prediction Result\n",
    "### Compare actual and predicted values with some validation set images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.image as mpimg\n",
    "\n",
    "min1 = np.min(X_holdout)\n",
    "max1 = np.max(X_holdout)\n",
    "diff1 = max1 - min1\n",
    "my_sum = lambda x: (x - min1)/diff1\n",
    "\n",
    "X_holdout2 = my_sum(X_holdout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(160, 75, 75, 3)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_holdout2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f11e9e52eb8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XyE7bVcZx6nScuBeXuJ52YtKxbU/a5nl4zdkbzj/ndnh0dCWE8HHP576TeLYokHyupPeF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\nCEBMikEQBCAmxSAIAhCTYhAEAfg/ueh9eJ4cGxkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f11ef0bc860>"
      ]
     },
     "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": 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": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f11e9e3a198>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NaoL2mnbbdtLnNFJMSEhIANJDMSEhIQHYWvocFLSIec5dROsePna6y3q3S0Ri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\nP47u4ONmaW/kAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f11e9e06fd0>"
      ]
     },
     "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 0x7f11e9d96748>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8Q3zqhZZT3yXoVph6I6mCPb1WOeTWsNVQgkoWSBxV9DiGjgwO2wGUcQYV6Hnb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\nHMchfFB0HMch/gv3DYpeaKVglwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f11e9e2c780>"
      ]
     },
     "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 0x7f11e9d789b0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DlA6nVKrrTlQvoYIYmeq0gHUYtPhdOMNX21huc1on2uPhNRAg7h4jUZ2C7q1Y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\nMQiCIPEfGKSubX8dEx4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f11e9df76d8>"
      ]
     },
     "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 0x7f11e9cde320>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iY3IbuaILBtB6eSegdb1Ohs/dsnq57fG7bWwlcPtgjAzH80h9u0jgRcvqsN3f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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f11e9d2efd0>"
      ]
     },
     "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)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}