Model_03-Adam-k=10-amsgrad-0001.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Mandatory imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import log_loss\n",
    "from sklearn.model_selection import StratifiedKFold, StratifiedShuffleSplit\n",
    "from os.path import join as opj\n",
    "from matplotlib import pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import pylab\n",
    "plt.rcParams['figure.figsize'] = 10, 10\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = pd.read_json(\"input/train.json\")\n",
    "target_train=train['is_iceberg']\n",
    "test = pd.read_json(\"input/test.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "target_train=train['is_iceberg']\n",
    "test['inc_angle']=pd.to_numeric(test['inc_angle'], errors='coerce')\n",
    "train['inc_angle']=pd.to_numeric(train['inc_angle'], errors='coerce')#We have only 133 NAs.\n",
    "train['inc_angle']=train['inc_angle'].fillna(method='pad')\n",
    "X_angle=train['inc_angle']\n",
    "test['inc_angle']=pd.to_numeric(test['inc_angle'], errors='coerce')\n",
    "X_test_angle=test['inc_angle']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Generate the training data\n",
    "X_band_1=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in train[\"band_1\"]])\n",
    "X_band_2=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in train[\"band_2\"]])\n",
    "X_band_3=(X_band_1+X_band_2)/2\n",
    "#X_band_3=np.array([np.full((75, 75), angel).astype(np.float32) for angel in train[\"inc_angle\"]])\n",
    "X_train = np.concatenate([X_band_1[:, :, :, np.newaxis]\n",
    "                          , X_band_2[:, :, :, np.newaxis]\n",
    "                         , X_band_3[:, :, :, np.newaxis]], axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_band_test_1=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in test[\"band_1\"]])\n",
    "X_band_test_2=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in test[\"band_2\"]])\n",
    "X_band_test_3=(X_band_test_1+X_band_test_2)/2\n",
    "#X_band_test_3=np.array([np.full((75, 75), angel).astype(np.float32) for angel in test[\"inc_angle\"]])\n",
    "X_test = np.concatenate([X_band_test_1[:, :, :, np.newaxis]\n",
    "                          , X_band_test_2[:, :, :, np.newaxis]\n",
    "                         , X_band_test_3[:, :, :, np.newaxis]], axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "#Import Keras.\n",
    "from matplotlib import pyplot\n",
    "from keras.optimizers import RMSprop\n",
    "# from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPooling2D, Dense, Dropout, Input, Flatten, Activation\n",
    "from keras.layers import GlobalMaxPooling2D\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.layers.merge import Concatenate\n",
    "from keras.models import Model\n",
    "from keras import initializers\n",
    "from keras.optimizers import Adam\n",
    "from keras.optimizers import rmsprop\n",
    "from keras.layers.advanced_activations import LeakyReLU, PReLU\n",
    "from keras.optimizers import SGD\n",
    "from keras.optimizers import Adadelta\n",
    "from keras.callbacks import ModelCheckpoint, Callback, EarlyStopping\n",
    "\n",
    "from keras.datasets import cifar10\n",
    "from keras.applications.inception_v3 import InceptionV3\n",
    "from keras.applications.vgg16 import VGG16\n",
    "from keras.applications.xception import Xception\n",
    "from keras.applications.mobilenet import MobileNet\n",
    "from keras.applications.vgg19 import VGG19\n",
    "from keras.layers import Concatenate, Dense, LSTM, Input, concatenate\n",
    "from keras.preprocessing import image\n",
    "from keras.applications.vgg16 import preprocess_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Data Aug for multi-input\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "batch_size=64\n",
    "# Define the image transformations here\n",
    "gen = ImageDataGenerator(horizontal_flip = True,\n",
    "                         vertical_flip = True,\n",
    "                         width_shift_range = 0.,\n",
    "                         height_shift_range = 0.,\n",
    "                         channel_shift_range=0,\n",
    "                         zoom_range = 0.2,\n",
    "                         rotation_range = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Here is the function that merges our two generators\n",
    "# We use the exact same generator with the same random seed for both the y and angle arrays\n",
    "def gen_flow_for_two_inputs(X1, X2, y):\n",
    "    genX1 = gen.flow(X1,y,  batch_size=batch_size,seed=55)\n",
    "    genX2 = gen.flow(X1,X2, batch_size=batch_size,seed=55)\n",
    "    while True:\n",
    "            X1i = genX1.next()\n",
    "            X2i = genX2.next()\n",
    "            #Assert arrays are equal - this was for peace of mind, but slows down training\n",
    "            #np.testing.assert_array_equal(X1i[0],X2i[0])\n",
    "            yield [X1i[0], X2i[1]], X1i[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Finally create generator\n",
    "def get_callbacks(filepath, patience=2):\n",
    "    es = EarlyStopping('val_loss', patience=10, mode=\"min\")\n",
    "    msave = ModelCheckpoint(filepath, save_best_only=True)\n",
    "    return [es, msave]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getVggAngleModel():\n",
    "    input_2 = Input(shape=[1], name=\"angle\")\n",
    "    angle_layer = Dense(1, )(input_2)\n",
    "    base_model = VGG16(weights='imagenet', include_top=False, \n",
    "                 input_shape=X_train.shape[1:], classes=1)\n",
    "    x = base_model.get_layer('block5_pool').output\n",
    "    \n",
    "\n",
    "    x = GlobalMaxPooling2D()(x)\n",
    "    merge_one = concatenate([x, angle_layer])\n",
    "    merge_one = Dense(512, activation='relu', name='fc2')(merge_one)\n",
    "    merge_one = Dropout(0.3)(merge_one)\n",
    "    merge_one = Dense(512, activation='relu', name='fc3')(merge_one)\n",
    "    merge_one = Dropout(0.3)(merge_one)\n",
    "    \n",
    "    predictions = Dense(1, activation='sigmoid')(merge_one)\n",
    "    \n",
    "    model = Model(inputs=[base_model.input, input_2], outputs=predictions)\n",
    "    \n",
    "    adam = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-8, decay=1e-6, amsgrad=True)\n",
    "    model.compile(loss='binary_crossentropy',\n",
    "                    optimizer=adam,\n",
    "                    metrics=['accuracy'])\n",
    "    \n",
    "#     sgd = SGD(lr=1e-3, decay=1e-6, momentum=0.9, nesterov=True)\n",
    "#     model.compile(loss='binary_crossentropy',\n",
    "#                   optimizer=sgd,\n",
    "#                   metrics=['accuracy'])\n",
    "    return model\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "===================FOLD= 0\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 19s 790ms/step - loss: 0.6828 - acc: 0.6072 - val_loss: 0.4271 - val_acc: 0.7840\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 578ms/step - loss: 0.4545 - acc: 0.7713 - val_loss: 0.3636 - val_acc: 0.8025\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 578ms/step - loss: 0.3774 - acc: 0.8312 - val_loss: 0.2705 - val_acc: 0.8457\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.3776 - acc: 0.8201 - val_loss: 0.2806 - val_acc: 0.8272\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 14s 582ms/step - loss: 0.3235 - acc: 0.8501 - val_loss: 0.2505 - val_acc: 0.8951\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 14s 583ms/step - loss: 0.2715 - acc: 0.8729 - val_loss: 0.2264 - val_acc: 0.8889\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.2575 - acc: 0.8791 - val_loss: 0.2389 - val_acc: 0.9012\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2792 - acc: 0.8651 - val_loss: 0.2843 - val_acc: 0.8827\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2851 - acc: 0.8704 - val_loss: 0.2600 - val_acc: 0.8704\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2336 - acc: 0.8979 - val_loss: 0.2609 - val_acc: 0.8951\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2372 - acc: 0.8933 - val_loss: 0.2382 - val_acc: 0.8889\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 14s 585ms/step - loss: 0.2168 - acc: 0.9085 - val_loss: 0.2249 - val_acc: 0.8951\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.2486 - acc: 0.9007 - val_loss: 0.2455 - val_acc: 0.9136\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2019 - acc: 0.9154 - val_loss: 0.2259 - val_acc: 0.9136\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1749 - acc: 0.9258 - val_loss: 0.2789 - val_acc: 0.9074\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 14s 585ms/step - loss: 0.2311 - acc: 0.8965 - val_loss: 0.2109 - val_acc: 0.9012\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.2013 - acc: 0.9273 - val_loss: 0.2253 - val_acc: 0.9074\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2007 - acc: 0.9182 - val_loss: 0.2173 - val_acc: 0.9012\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1866 - acc: 0.9273 - val_loss: 0.2224 - val_acc: 0.9136\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 14s 585ms/step - loss: 0.1849 - acc: 0.9226 - val_loss: 0.2090 - val_acc: 0.9383\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1822 - acc: 0.9251 - val_loss: 0.2176 - val_acc: 0.9198\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 14s 587ms/step - loss: 0.1775 - acc: 0.9278 - val_loss: 0.1860 - val_acc: 0.9321\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.1694 - acc: 0.9325 - val_loss: 0.2287 - val_acc: 0.9136\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1889 - acc: 0.9251 - val_loss: 0.1954 - val_acc: 0.9198\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 14s 586ms/step - loss: 0.1677 - acc: 0.9298 - val_loss: 0.1748 - val_acc: 0.9259\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1595 - acc: 0.9369 - val_loss: 0.1971 - val_acc: 0.9259\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1482 - acc: 0.9420 - val_loss: 0.1899 - val_acc: 0.9198\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1471 - acc: 0.9402 - val_loss: 0.2664 - val_acc: 0.9074\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1540 - acc: 0.9365 - val_loss: 0.1969 - val_acc: 0.9259\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1561 - acc: 0.9317 - val_loss: 0.2842 - val_acc: 0.8889\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1755 - acc: 0.9332 - val_loss: 0.2189 - val_acc: 0.9259\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1737 - acc: 0.9330 - val_loss: 0.2304 - val_acc: 0.9259\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1499 - acc: 0.9331 - val_loss: 0.1877 - val_acc: 0.9259\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1514 - acc: 0.9375 - val_loss: 0.2230 - val_acc: 0.9136\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1450 - acc: 0.9430 - val_loss: 0.2346 - val_acc: 0.9136\n",
      "Train loss: 0.108891924649\n",
      "Train accuracy: 0.955617198336\n",
      "Test loss: 0.174843915949\n",
      "Test accuracy: 0.925925925926\n",
      "\n",
      "===================FOLD= 1\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 17s 723ms/step - loss: 0.6822 - acc: 0.5870 - val_loss: 0.6553 - val_acc: 0.5901\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 587ms/step - loss: 0.5344 - acc: 0.7053 - val_loss: 0.3067 - val_acc: 0.8634\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 584ms/step - loss: 0.3767 - acc: 0.8168 - val_loss: 0.2491 - val_acc: 0.8696\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 14s 585ms/step - loss: 0.3199 - acc: 0.8524 - val_loss: 0.2423 - val_acc: 0.8758\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 14s 585ms/step - loss: 0.3131 - acc: 0.8634 - val_loss: 0.2127 - val_acc: 0.9006\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.2851 - acc: 0.8736 - val_loss: 0.2011 - val_acc: 0.9068\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.2783 - acc: 0.8633 - val_loss: 0.2440 - val_acc: 0.8882\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2653 - acc: 0.8835 - val_loss: 0.1972 - val_acc: 0.9255\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.2792 - acc: 0.8675 - val_loss: 0.2484 - val_acc: 0.8944\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.2698 - acc: 0.8868 - val_loss: 0.1840 - val_acc: 0.9255\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.2436 - acc: 0.8966 - val_loss: 0.1918 - val_acc: 0.9130\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2183 - acc: 0.9061 - val_loss: 0.1924 - val_acc: 0.9317\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 14s 585ms/step - loss: 0.2052 - acc: 0.9164 - val_loss: 0.1780 - val_acc: 0.9193\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2080 - acc: 0.9161 - val_loss: 0.1763 - val_acc: 0.9379\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.2132 - acc: 0.9093 - val_loss: 0.1849 - val_acc: 0.9068\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.2116 - acc: 0.9105 - val_loss: 0.2327 - val_acc: 0.8820\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.2066 - acc: 0.9070 - val_loss: 0.2065 - val_acc: 0.9068\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2179 - acc: 0.9137 - val_loss: 0.1911 - val_acc: 0.9379\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1985 - acc: 0.9195 - val_loss: 0.2043 - val_acc: 0.9006\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1833 - acc: 0.9246 - val_loss: 0.1814 - val_acc: 0.9379\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1787 - acc: 0.9180 - val_loss: 0.2929 - val_acc: 0.8758\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2139 - acc: 0.9171 - val_loss: 0.1872 - val_acc: 0.9130\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.1749 - acc: 0.9284 - val_loss: 0.1565 - val_acc: 0.9379\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1930 - acc: 0.9299 - val_loss: 0.1684 - val_acc: 0.9317\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1579 - acc: 0.9351 - val_loss: 0.1782 - val_acc: 0.9317\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1591 - acc: 0.9350 - val_loss: 0.1929 - val_acc: 0.9193\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1639 - acc: 0.9289 - val_loss: 0.2220 - val_acc: 0.9379\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1568 - acc: 0.9325 - val_loss: 0.2849 - val_acc: 0.8820\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1773 - acc: 0.9266 - val_loss: 0.2386 - val_acc: 0.9193\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1483 - acc: 0.9317 - val_loss: 0.2004 - val_acc: 0.9379\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1865 - acc: 0.9187 - val_loss: 0.1695 - val_acc: 0.9503\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1564 - acc: 0.9299 - val_loss: 0.2190 - val_acc: 0.9193\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1416 - acc: 0.9358 - val_loss: 0.2104 - val_acc: 0.9255\n",
      "Train loss: 0.123294639447\n",
      "Train accuracy: 0.950103950104\n",
      "Test loss: 0.156465880265\n",
      "Test accuracy: 0.937888198758\n",
      "\n",
      "===================FOLD= 2\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 15s 643ms/step - loss: 0.8098 - acc: 0.5635 - val_loss: 0.5073 - val_acc: 0.7143\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 584ms/step - loss: 0.5343 - acc: 0.7248 - val_loss: 0.2763 - val_acc: 0.8758\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.4787 - acc: 0.7795 - val_loss: 0.2955 - val_acc: 0.8634\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.4014 - acc: 0.8232 - val_loss: 0.2979 - val_acc: 0.8882\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.3497 - acc: 0.8408 - val_loss: 0.2236 - val_acc: 0.8944\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 14s 586ms/step - loss: 0.3253 - acc: 0.8620 - val_loss: 0.2191 - val_acc: 0.8820\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2967 - acc: 0.8805 - val_loss: 0.2588 - val_acc: 0.8820\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.2715 - acc: 0.8805 - val_loss: 0.2251 - val_acc: 0.8944\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.2863 - acc: 0.8713 - val_loss: 0.2258 - val_acc: 0.8944\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 14s 585ms/step - loss: 0.2501 - acc: 0.8949 - val_loss: 0.1728 - val_acc: 0.9255\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.2653 - acc: 0.8904 - val_loss: 0.1709 - val_acc: 0.9379\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.2479 - acc: 0.8969 - val_loss: 0.2114 - val_acc: 0.9068\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.2250 - acc: 0.9130 - val_loss: 0.1954 - val_acc: 0.9317\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 14s 586ms/step - loss: 0.2082 - acc: 0.9140 - val_loss: 0.1625 - val_acc: 0.9193\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.2139 - acc: 0.9153 - val_loss: 0.1839 - val_acc: 0.9379\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.2376 - acc: 0.9072 - val_loss: 0.1691 - val_acc: 0.9317\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.2054 - acc: 0.9169 - val_loss: 0.1799 - val_acc: 0.9068\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.2217 - acc: 0.9124 - val_loss: 0.1849 - val_acc: 0.9130\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1994 - acc: 0.9205 - val_loss: 0.1852 - val_acc: 0.9068\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2124 - acc: 0.9085 - val_loss: 0.1890 - val_acc: 0.9193\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1854 - acc: 0.9219 - val_loss: 0.1985 - val_acc: 0.9193\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.2158 - acc: 0.9079 - val_loss: 0.1862 - val_acc: 0.9193\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.1843 - acc: 0.9239 - val_loss: 0.1700 - val_acc: 0.9317\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1736 - acc: 0.9307 - val_loss: 0.2087 - val_acc: 0.9130\n",
      "Train loss: 0.153143773662\n",
      "Train accuracy: 0.941787941788\n",
      "Test loss: 0.162532693481\n",
      "Test accuracy: 0.919254658385\n",
      "\n",
      "===================FOLD= 3\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 17s 718ms/step - loss: 0.6288 - acc: 0.6432 - val_loss: 0.3603 - val_acc: 0.8438\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 587ms/step - loss: 0.4179 - acc: 0.8127 - val_loss: 0.3151 - val_acc: 0.8375\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.3199 - acc: 0.8555 - val_loss: 0.2509 - val_acc: 0.8625\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.3051 - acc: 0.8663 - val_loss: 0.2718 - val_acc: 0.8812\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.3008 - acc: 0.8585 - val_loss: 0.2154 - val_acc: 0.9000\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.2673 - acc: 0.8907 - val_loss: 0.2398 - val_acc: 0.8812\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2798 - acc: 0.8792 - val_loss: 0.2334 - val_acc: 0.9000\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.2729 - acc: 0.8836 - val_loss: 0.2143 - val_acc: 0.8938\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.2305 - acc: 0.9005 - val_loss: 0.3630 - val_acc: 0.8438\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2399 - acc: 0.8992 - val_loss: 0.2223 - val_acc: 0.9062\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.2140 - acc: 0.9155 - val_loss: 0.2144 - val_acc: 0.9062\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.1827 - acc: 0.9228 - val_loss: 0.2113 - val_acc: 0.9125\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.2148 - acc: 0.9119 - val_loss: 0.2139 - val_acc: 0.9062\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.1804 - acc: 0.9261 - val_loss: 0.1944 - val_acc: 0.9250\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 14s 586ms/step - loss: 0.2018 - acc: 0.9143 - val_loss: 0.1822 - val_acc: 0.9125\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1884 - acc: 0.9292 - val_loss: 0.2021 - val_acc: 0.9125\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1724 - acc: 0.9308 - val_loss: 0.3166 - val_acc: 0.8750\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1802 - acc: 0.9323 - val_loss: 0.2265 - val_acc: 0.9187\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2281 - acc: 0.9060 - val_loss: 0.2184 - val_acc: 0.9000\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1857 - acc: 0.9243 - val_loss: 0.2088 - val_acc: 0.9062\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1629 - acc: 0.9274 - val_loss: 0.2193 - val_acc: 0.9125\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1789 - acc: 0.9313 - val_loss: 0.1875 - val_acc: 0.9000\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.1701 - acc: 0.9337 - val_loss: 0.1928 - val_acc: 0.9188\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1666 - acc: 0.9288 - val_loss: 0.2961 - val_acc: 0.9000\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1507 - acc: 0.9384 - val_loss: 0.2435 - val_acc: 0.9000\n",
      "Train loss: 0.128100890511\n",
      "Train accuracy: 0.94459833795\n",
      "Test loss: 0.182208153605\n",
      "Test accuracy: 0.9125\n",
      "\n",
      "===================FOLD= 4\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "24/24 [==============================] - 16s 671ms/step - loss: 1.2117 - acc: 0.5252 - val_loss: 0.6851 - val_acc: 0.5312\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.9004 - acc: 0.6127 - val_loss: 0.4132 - val_acc: 0.8250\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 586ms/step - loss: 0.5528 - acc: 0.7682 - val_loss: 0.2174 - val_acc: 0.9187\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.5693 - acc: 0.7679 - val_loss: 0.2815 - val_acc: 0.8812\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.4036 - acc: 0.8342 - val_loss: 0.2812 - val_acc: 0.8625\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.3592 - acc: 0.8551 - val_loss: 0.1517 - val_acc: 0.9437\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.4109 - acc: 0.8296 - val_loss: 0.2491 - val_acc: 0.8938\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.3326 - acc: 0.8381 - val_loss: 0.1748 - val_acc: 0.9313\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 14s 585ms/step - loss: 0.3119 - acc: 0.8702 - val_loss: 0.1346 - val_acc: 0.9437\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.3042 - acc: 0.8710 - val_loss: 0.1716 - val_acc: 0.9563\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.2866 - acc: 0.8660 - val_loss: 0.2049 - val_acc: 0.9250\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.2785 - acc: 0.8796 - val_loss: 0.1436 - val_acc: 0.9500\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.2447 - acc: 0.9011 - val_loss: 0.2059 - val_acc: 0.9250\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.2318 - acc: 0.9033 - val_loss: 0.1426 - val_acc: 0.9500\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.2316 - acc: 0.9065 - val_loss: 0.1669 - val_acc: 0.9563\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.2464 - acc: 0.8919 - val_loss: 0.1679 - val_acc: 0.9375\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.2885 - acc: 0.8788 - val_loss: 0.2616 - val_acc: 0.9062\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.2300 - acc: 0.9003 - val_loss: 0.1829 - val_acc: 0.9500\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.2569 - acc: 0.8844 - val_loss: 0.1414 - val_acc: 0.9563\n",
      "Train loss: 0.227107554757\n",
      "Train accuracy: 0.907202216066\n",
      "Test loss: 0.134599365667\n",
      "Test accuracy: 0.94375\n",
      "\n",
      "===================FOLD= 5\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 16s 679ms/step - loss: 0.8412 - acc: 0.6246 - val_loss: 0.6227 - val_acc: 0.7688\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 587ms/step - loss: 0.5644 - acc: 0.7528 - val_loss: 0.3432 - val_acc: 0.8688\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 586ms/step - loss: 0.4564 - acc: 0.8060 - val_loss: 0.3315 - val_acc: 0.8750\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.3586 - acc: 0.8663 - val_loss: 0.3414 - val_acc: 0.8875\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.4067 - acc: 0.8313 - val_loss: 0.2692 - val_acc: 0.8938\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.3575 - acc: 0.8425 - val_loss: 0.2233 - val_acc: 0.9000\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.2745 - acc: 0.8813 - val_loss: 0.2136 - val_acc: 0.9187\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2867 - acc: 0.8835 - val_loss: 0.3193 - val_acc: 0.9062\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2610 - acc: 0.8966 - val_loss: 0.2197 - val_acc: 0.9313\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2430 - acc: 0.8902 - val_loss: 0.2479 - val_acc: 0.9125\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2549 - acc: 0.8883 - val_loss: 0.2394 - val_acc: 0.9187\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 14s 585ms/step - loss: 0.2550 - acc: 0.8979 - val_loss: 0.2094 - val_acc: 0.9313\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.2227 - acc: 0.9041 - val_loss: 0.2004 - val_acc: 0.9187\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.2468 - acc: 0.8914 - val_loss: 0.2155 - val_acc: 0.9313\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.2182 - acc: 0.9083 - val_loss: 0.2808 - val_acc: 0.8938\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.2127 - acc: 0.9109 - val_loss: 0.2020 - val_acc: 0.9125\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.2097 - acc: 0.9103 - val_loss: 0.2479 - val_acc: 0.9062\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.2439 - acc: 0.8972 - val_loss: 0.2361 - val_acc: 0.9062\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.2136 - acc: 0.9114 - val_loss: 0.2135 - val_acc: 0.9187\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1827 - acc: 0.9252 - val_loss: 0.2261 - val_acc: 0.9062\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.2056 - acc: 0.9156 - val_loss: 0.2634 - val_acc: 0.9250\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1976 - acc: 0.9331 - val_loss: 0.2049 - val_acc: 0.9250\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 14s 580ms/step - loss: 0.1729 - acc: 0.9390 - val_loss: 0.1650 - val_acc: 0.9438\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1992 - acc: 0.9116 - val_loss: 0.1728 - val_acc: 0.9500\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1876 - acc: 0.9200 - val_loss: 0.1882 - val_acc: 0.9313\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1949 - acc: 0.9199 - val_loss: 0.1815 - val_acc: 0.9375\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1568 - acc: 0.9358 - val_loss: 0.2013 - val_acc: 0.9187\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1452 - acc: 0.9381 - val_loss: 0.1802 - val_acc: 0.9313\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 501ms/step - loss: 0.1617 - acc: 0.9401 - val_loss: 0.1747 - val_acc: 0.9375\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1948 - acc: 0.9164 - val_loss: 0.2483 - val_acc: 0.9375\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1435 - acc: 0.9409 - val_loss: 0.3028 - val_acc: 0.8938\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 12s 501ms/step - loss: 0.1373 - acc: 0.9476 - val_loss: 0.1900 - val_acc: 0.9437\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1696 - acc: 0.9293 - val_loss: 0.2741 - val_acc: 0.9375\n",
      "Train loss: 0.126287596956\n",
      "Train accuracy: 0.951523545706\n",
      "Test loss: 0.164966508746\n",
      "Test accuracy: 0.94375\n",
      "\n",
      "===================FOLD= 6\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 17s 689ms/step - loss: 0.7522 - acc: 0.6005 - val_loss: 0.6425 - val_acc: 0.6875\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.5581 - acc: 0.7086 - val_loss: 0.4172 - val_acc: 0.7562\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 587ms/step - loss: 0.3947 - acc: 0.8155 - val_loss: 0.3628 - val_acc: 0.8187\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 14s 592ms/step - loss: 0.3422 - acc: 0.8318 - val_loss: 0.3134 - val_acc: 0.8688\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.2831 - acc: 0.8663 - val_loss: 0.4662 - val_acc: 0.8187\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.3360 - acc: 0.8515 - val_loss: 0.3415 - val_acc: 0.8438\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2935 - acc: 0.8647 - val_loss: 0.3637 - val_acc: 0.8688\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8/100\n",
      "24/24 [==============================] - 14s 592ms/step - loss: 0.2654 - acc: 0.8845 - val_loss: 0.2927 - val_acc: 0.8812\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.2523 - acc: 0.8945 - val_loss: 0.3251 - val_acc: 0.9000\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 14s 587ms/step - loss: 0.2373 - acc: 0.9049 - val_loss: 0.2926 - val_acc: 0.8812\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.2200 - acc: 0.9003 - val_loss: 0.2663 - val_acc: 0.8938\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.2174 - acc: 0.9101 - val_loss: 0.3170 - val_acc: 0.8875\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.2215 - acc: 0.9129 - val_loss: 0.2765 - val_acc: 0.8938\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2005 - acc: 0.9189 - val_loss: 0.2729 - val_acc: 0.9000\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.2083 - acc: 0.9230 - val_loss: 0.2756 - val_acc: 0.9125\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2165 - acc: 0.9086 - val_loss: 0.2819 - val_acc: 0.8875\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1947 - acc: 0.9192 - val_loss: 0.2954 - val_acc: 0.9000\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1959 - acc: 0.9132 - val_loss: 0.2682 - val_acc: 0.9187\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1851 - acc: 0.9309 - val_loss: 0.2696 - val_acc: 0.8875\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1872 - acc: 0.9192 - val_loss: 0.3182 - val_acc: 0.9000\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 14s 583ms/step - loss: 0.1634 - acc: 0.9300 - val_loss: 0.2369 - val_acc: 0.9250\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1970 - acc: 0.9222 - val_loss: 0.3699 - val_acc: 0.8438\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.1804 - acc: 0.9152 - val_loss: 0.3021 - val_acc: 0.8938\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1609 - acc: 0.9353 - val_loss: 0.3798 - val_acc: 0.8562\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1852 - acc: 0.9274 - val_loss: 0.2944 - val_acc: 0.8812\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1662 - acc: 0.9366 - val_loss: 0.2773 - val_acc: 0.8938\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1759 - acc: 0.9267 - val_loss: 0.2769 - val_acc: 0.9000\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1559 - acc: 0.9371 - val_loss: 0.2452 - val_acc: 0.9000\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1358 - acc: 0.9497 - val_loss: 0.2371 - val_acc: 0.9062\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1361 - acc: 0.9415 - val_loss: 0.3333 - val_acc: 0.8688\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1451 - acc: 0.9401 - val_loss: 0.3319 - val_acc: 0.8938\n",
      "Train loss: 0.126212108911\n",
      "Train accuracy: 0.952908587258\n",
      "Test loss: 0.236920264363\n",
      "Test accuracy: 0.925\n",
      "\n",
      "===================FOLD= 7\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 17s 724ms/step - loss: 0.6346 - acc: 0.6290 - val_loss: 0.3140 - val_acc: 0.8562\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 585ms/step - loss: 0.3838 - acc: 0.7994 - val_loss: 0.2689 - val_acc: 0.8375\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.3482 - acc: 0.8464 - val_loss: 0.2460 - val_acc: 0.9062\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.3317 - acc: 0.8432 - val_loss: 0.2304 - val_acc: 0.8812\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2934 - acc: 0.8660 - val_loss: 0.2481 - val_acc: 0.8688\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2957 - acc: 0.8739 - val_loss: 0.2597 - val_acc: 0.8625\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 14s 587ms/step - loss: 0.2494 - acc: 0.9018 - val_loss: 0.2294 - val_acc: 0.9125\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.2610 - acc: 0.8936 - val_loss: 0.2076 - val_acc: 0.8938\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2699 - acc: 0.8787 - val_loss: 0.3127 - val_acc: 0.8375\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.2630 - acc: 0.8805 - val_loss: 0.2318 - val_acc: 0.9125\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2433 - acc: 0.8878 - val_loss: 0.3208 - val_acc: 0.8750\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2512 - acc: 0.8927 - val_loss: 0.2811 - val_acc: 0.8750\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2137 - acc: 0.9137 - val_loss: 0.2074 - val_acc: 0.8875\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.2069 - acc: 0.9103 - val_loss: 0.2334 - val_acc: 0.8875\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1963 - acc: 0.9197 - val_loss: 0.2138 - val_acc: 0.9062\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2230 - acc: 0.9070 - val_loss: 0.2107 - val_acc: 0.9125\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 14s 584ms/step - loss: 0.1669 - acc: 0.9344 - val_loss: 0.1960 - val_acc: 0.9250\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.2176 - acc: 0.8995 - val_loss: 0.2144 - val_acc: 0.9125\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 14s 586ms/step - loss: 0.1904 - acc: 0.9257 - val_loss: 0.1831 - val_acc: 0.9125\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1973 - acc: 0.9153 - val_loss: 0.2063 - val_acc: 0.8938\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2198 - acc: 0.9103 - val_loss: 0.2974 - val_acc: 0.8438\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.2223 - acc: 0.9127 - val_loss: 0.2169 - val_acc: 0.8812\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 14s 581ms/step - loss: 0.1889 - acc: 0.9262 - val_loss: 0.1651 - val_acc: 0.9375\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1729 - acc: 0.9259 - val_loss: 0.1823 - val_acc: 0.9125\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 14s 587ms/step - loss: 0.1702 - acc: 0.9270 - val_loss: 0.1603 - val_acc: 0.9313\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1710 - acc: 0.9305 - val_loss: 0.2066 - val_acc: 0.9125\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1398 - acc: 0.9383 - val_loss: 0.1626 - val_acc: 0.9250\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1767 - acc: 0.9230 - val_loss: 0.2160 - val_acc: 0.8938\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1648 - acc: 0.9324 - val_loss: 0.2321 - val_acc: 0.9000\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1514 - acc: 0.9332 - val_loss: 0.2315 - val_acc: 0.9062\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1487 - acc: 0.9502 - val_loss: 0.2140 - val_acc: 0.9000\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1474 - acc: 0.9409 - val_loss: 0.1906 - val_acc: 0.9250\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 14s 586ms/step - loss: 0.1412 - acc: 0.9375 - val_loss: 0.1409 - val_acc: 0.9313\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1391 - acc: 0.9393 - val_loss: 0.2338 - val_acc: 0.8875\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 14s 583ms/step - loss: 0.1248 - acc: 0.9489 - val_loss: 0.1320 - val_acc: 0.9437\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1331 - acc: 0.9430 - val_loss: 0.1904 - val_acc: 0.9125\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 37/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.1373 - acc: 0.9402 - val_loss: 0.2311 - val_acc: 0.9062\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1564 - acc: 0.9389 - val_loss: 0.1761 - val_acc: 0.9187\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1361 - acc: 0.9425 - val_loss: 0.2494 - val_acc: 0.8875\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1395 - acc: 0.9497 - val_loss: 0.1496 - val_acc: 0.9187\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1136 - acc: 0.9567 - val_loss: 0.2257 - val_acc: 0.9250\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1123 - acc: 0.9461 - val_loss: 0.1836 - val_acc: 0.9250\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1337 - acc: 0.9415 - val_loss: 0.2041 - val_acc: 0.9313\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1109 - acc: 0.9536 - val_loss: 0.1844 - val_acc: 0.9125\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.0978 - acc: 0.9583 - val_loss: 0.2380 - val_acc: 0.9187\n",
      "Train loss: 0.0628946275964\n",
      "Train accuracy: 0.974376731302\n",
      "Test loss: 0.132008890063\n",
      "Test accuracy: 0.94375\n",
      "\n",
      "===================FOLD= 8\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 17s 711ms/step - loss: 0.8173 - acc: 0.6026 - val_loss: 0.6000 - val_acc: 0.7812\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.6047 - acc: 0.7187 - val_loss: 0.5430 - val_acc: 0.7937\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 585ms/step - loss: 0.4543 - acc: 0.8239 - val_loss: 0.4346 - val_acc: 0.8187\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 14s 585ms/step - loss: 0.3603 - acc: 0.8519 - val_loss: 0.3457 - val_acc: 0.8000\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.3120 - acc: 0.8766 - val_loss: 0.4343 - val_acc: 0.7875\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.2886 - acc: 0.8774 - val_loss: 0.3295 - val_acc: 0.8625\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2862 - acc: 0.8770 - val_loss: 0.3559 - val_acc: 0.8063\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.2781 - acc: 0.8811 - val_loss: 0.4027 - val_acc: 0.8250\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.3084 - acc: 0.8671 - val_loss: 0.4649 - val_acc: 0.8250\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2316 - acc: 0.9054 - val_loss: 0.3489 - val_acc: 0.8562\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 14s 592ms/step - loss: 0.2314 - acc: 0.9068 - val_loss: 0.3294 - val_acc: 0.8438\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1970 - acc: 0.9186 - val_loss: 0.3471 - val_acc: 0.8750\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.2190 - acc: 0.9127 - val_loss: 0.3398 - val_acc: 0.8375\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1971 - acc: 0.9158 - val_loss: 0.3795 - val_acc: 0.8375\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.2011 - acc: 0.9147 - val_loss: 0.4281 - val_acc: 0.8438\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 501ms/step - loss: 0.2019 - acc: 0.9225 - val_loss: 0.3917 - val_acc: 0.8500\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.2055 - acc: 0.9181 - val_loss: 0.3829 - val_acc: 0.8562\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.2128 - acc: 0.9223 - val_loss: 0.3973 - val_acc: 0.8187\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.2162 - acc: 0.9093 - val_loss: 0.4120 - val_acc: 0.8187\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.2175 - acc: 0.9085 - val_loss: 0.3830 - val_acc: 0.8500\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 14s 583ms/step - loss: 0.1797 - acc: 0.9283 - val_loss: 0.3145 - val_acc: 0.8750\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1813 - acc: 0.9293 - val_loss: 0.3402 - val_acc: 0.8500\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 14s 579ms/step - loss: 0.1660 - acc: 0.9268 - val_loss: 0.3030 - val_acc: 0.8687\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1600 - acc: 0.9316 - val_loss: 0.3185 - val_acc: 0.8500\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1616 - acc: 0.9337 - val_loss: 0.3159 - val_acc: 0.8625\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1327 - acc: 0.9476 - val_loss: 0.3426 - val_acc: 0.8688\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1645 - acc: 0.9401 - val_loss: 0.3948 - val_acc: 0.8562\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1209 - acc: 0.9547 - val_loss: 0.3546 - val_acc: 0.8875\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1646 - acc: 0.9440 - val_loss: 0.3192 - val_acc: 0.8625\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1318 - acc: 0.9476 - val_loss: 0.3571 - val_acc: 0.8438\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1482 - acc: 0.9440 - val_loss: 0.3086 - val_acc: 0.8812\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 12s 501ms/step - loss: 0.1272 - acc: 0.9492 - val_loss: 0.5193 - val_acc: 0.8562\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1585 - acc: 0.9357 - val_loss: 0.3133 - val_acc: 0.8938\n",
      "Train loss: 0.118817352883\n",
      "Train accuracy: 0.956371191136\n",
      "Test loss: 0.303008133173\n",
      "Test accuracy: 0.86875\n",
      "\n",
      "===================FOLD= 9\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 18s 751ms/step - loss: 0.7940 - acc: 0.5703 - val_loss: 0.5203 - val_acc: 0.7625\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.5402 - acc: 0.7205 - val_loss: 0.5384 - val_acc: 0.8125\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 586ms/step - loss: 0.4676 - acc: 0.7816 - val_loss: 0.2701 - val_acc: 0.8688\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.4031 - acc: 0.8132 - val_loss: 0.3374 - val_acc: 0.8313\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.3547 - acc: 0.8386 - val_loss: 0.2664 - val_acc: 0.8875\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.2965 - acc: 0.8611 - val_loss: 0.2246 - val_acc: 0.8875\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.3148 - acc: 0.8622 - val_loss: 0.2288 - val_acc: 0.8875\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.2628 - acc: 0.8871 - val_loss: 0.2085 - val_acc: 0.9250\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.2634 - acc: 0.8873 - val_loss: 0.2013 - val_acc: 0.9187\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.2592 - acc: 0.8896 - val_loss: 0.2341 - val_acc: 0.9062\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2375 - acc: 0.8977 - val_loss: 0.2736 - val_acc: 0.8625\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2535 - acc: 0.8865 - val_loss: 0.1854 - val_acc: 0.9125\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.2652 - acc: 0.8938 - val_loss: 0.2002 - val_acc: 0.9313\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2403 - acc: 0.9129 - val_loss: 0.2388 - val_acc: 0.8938\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2806 - acc: 0.8837 - val_loss: 0.3241 - val_acc: 0.8313\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2199 - acc: 0.9111 - val_loss: 0.1959 - val_acc: 0.9187\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.2188 - acc: 0.9135 - val_loss: 0.2442 - val_acc: 0.8938\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1954 - acc: 0.9109 - val_loss: 0.2621 - val_acc: 0.8875\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.1877 - acc: 0.9226 - val_loss: 0.1698 - val_acc: 0.9187\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1965 - acc: 0.9217 - val_loss: 0.2008 - val_acc: 0.9062\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2198 - acc: 0.9156 - val_loss: 0.2501 - val_acc: 0.8688\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1964 - acc: 0.9155 - val_loss: 0.2048 - val_acc: 0.9062\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.2149 - acc: 0.9126 - val_loss: 0.1971 - val_acc: 0.9000\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1938 - acc: 0.9184 - val_loss: 0.2932 - val_acc: 0.8375\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1883 - acc: 0.9239 - val_loss: 0.2256 - val_acc: 0.9250\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1710 - acc: 0.9396 - val_loss: 0.2146 - val_acc: 0.9187\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.1841 - acc: 0.9283 - val_loss: 0.2959 - val_acc: 0.8750\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.1960 - acc: 0.9202 - val_loss: 0.2091 - val_acc: 0.9062\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1692 - acc: 0.9291 - val_loss: 0.2299 - val_acc: 0.9062\n",
      "Train loss: 0.128232548545\n",
      "Train accuracy: 0.947368421053\n",
      "Test loss: 0.169820037484\n",
      "Test accuracy: 0.91875\n",
      "\n",
      " Train Log Loss Validation=  0.117314350908\n",
      " Test Log Loss Validation=  0.181701044989\n"
     ]
    }
   ],
   "source": [
    "#def myAngleCV(X_train, X_angle, X_test):\n",
    "K=10\n",
    "folds = list(StratifiedKFold(n_splits=K, shuffle=True, random_state=16).split(X_train, target_train))\n",
    "y_test_pred_log = 0\n",
    "y_train_pred_log=0\n",
    "y_valid_pred_log = 0.0*target_train\n",
    "for j, (train_idx, test_idx) in enumerate(folds):\n",
    "    print('\\n===================FOLD=',j)\n",
    "    X_train_cv = X_train[train_idx]\n",
    "    y_train_cv = target_train[train_idx]\n",
    "    X_holdout = X_train[test_idx]\n",
    "    Y_holdout= target_train[test_idx]\n",
    "    \n",
    "    #Angle\n",
    "    X_angle_cv=X_angle[train_idx]\n",
    "    X_angle_hold=X_angle[test_idx]\n",
    "    #define file path and get callbacks\n",
    "    file_path = \"%s_aug_model_weights.hdf5\"%j\n",
    "    callbacks = get_callbacks(filepath=file_path, patience=5)\n",
    "    gen_flow = gen_flow_for_two_inputs(X_train_cv, X_angle_cv, y_train_cv)\n",
    "    galaxyModel= getVggAngleModel()\n",
    "    histr = galaxyModel.fit_generator(\n",
    "            gen_flow,\n",
    "            steps_per_epoch=24,\n",
    "            epochs=100,\n",
    "            shuffle=True,\n",
    "            verbose=1,\n",
    "            validation_data=([X_holdout,X_angle_hold], Y_holdout),\n",
    "            callbacks=callbacks)\n",
    "     #Getting the Best Model\n",
    "    galaxyModel.load_weights(filepath=file_path)\n",
    "    #Getting Training Score\n",
    "    score = galaxyModel.evaluate([X_train_cv,X_angle_cv], y_train_cv, verbose=0)\n",
    "    print('Train loss:', score[0])\n",
    "    print('Train accuracy:', score[1])\n",
    "    #Getting Test Score\n",
    "    score = galaxyModel.evaluate([X_holdout,X_angle_hold], Y_holdout, verbose=0)\n",
    "    print('Test loss:', score[0])\n",
    "    print('Test accuracy:', score[1])\n",
    "\n",
    "    #Getting validation Score.\n",
    "    pred_valid=galaxyModel.predict([X_holdout,X_angle_hold])\n",
    "    y_valid_pred_log[test_idx] = pred_valid.reshape(pred_valid.shape[0])\n",
    "\n",
    "    #Getting Test Scores\n",
    "    temp_test=galaxyModel.predict([X_test, X_test_angle])\n",
    "    y_test_pred_log+=temp_test.reshape(temp_test.shape[0])\n",
    "\n",
    "    #Getting Train Scores\n",
    "    temp_train=galaxyModel.predict([X_train, X_angle])\n",
    "    y_train_pred_log+=temp_train.reshape(temp_train.shape[0])\n",
    "\n",
    "y_test_pred_log=y_test_pred_log/K\n",
    "y_train_pred_log=y_train_pred_log/K\n",
    "\n",
    "print('\\n Train Log Loss Validation= ',log_loss(target_train, y_train_pred_log))\n",
    "print(' Test Log Loss Validation= ',log_loss(target_train, y_valid_pred_log))\n",
    "#return y_test_pred_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#preds=myAngleCV(X_train, X_angle, X_test)\n",
    "preds = y_test_pred_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Submission to kaggle\n",
    "submission = pd.DataFrame()\n",
    "submission['id']=test['id']\n",
    "submission['is_iceberg']=preds\n",
    "submission.to_csv('sub_Adam_10folds_amsgrad_0001.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['val_loss', 'acc', 'val_acc', 'loss'])\n"
     ]
    },
    {
     "data": {
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Drb2iXHQd6+Y33zjr+OU7fqm6CRwDKGDBQkSCgVeAMUBX4EoRyZ9g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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0e8c1c2668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RlFLKqzwWFIwx2cBEYC6wBZhhjNkkIs+IyEjHbnOBRBHZDMwHHjLGJHqqTGcTFRHMqYxs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\nu3UHuPi1RSzcnsD+pDSe+X4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d385KP2lrCm2GuS0wnUjL4pnvN/P9ugNk5tglRdvUr0Xv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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0db0c97208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# list all data in history\n",
    "print(histr.history.keys())\n",
    "# summarize history for accuracy\n",
    "plt.plot(histr.history['acc'])\n",
    "plt.plot(histr.history['val_acc'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()\n",
    "# summarize history for loss\n",
    "plt.plot(histr.history['loss'])\n",
    "plt.plot(histr.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.image as mpimg\n",
    "\n",
    "min1 = np.min(X_holdout)\n",
    "max1 = np.max(X_holdout)\n",
    "diff1 = max1 - min1\n",
    "my_sum = lambda x: (x - min1)/diff1\n",
    "\n",
    "X_holdout2 = my_sum(X_holdout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13      1\n",
       "14      0\n",
       "38      1\n",
       "43      0\n",
       "44      0\n",
       "80      0\n",
       "104     1\n",
       "127     0\n",
       "137     0\n",
       "138     1\n",
       "141     1\n",
       "167     1\n",
       "195     1\n",
       "199     0\n",
       "207     1\n",
       "212     0\n",
       "213     1\n",
       "221     0\n",
       "240     0\n",
       "256     0\n",
       "267     1\n",
       "274     1\n",
       "276     1\n",
       "285     1\n",
       "290     0\n",
       "294     0\n",
       "300     1\n",
       "307     1\n",
       "311     1\n",
       "318     1\n",
       "       ..\n",
       "1363    1\n",
       "1364    0\n",
       "1373    1\n",
       "1380    1\n",
       "1394    0\n",
       "1411    0\n",
       "1435    0\n",
       "1440    1\n",
       "1445    1\n",
       "1447    1\n",
       "1460    1\n",
       "1470    0\n",
       "1488    0\n",
       "1495    0\n",
       "1504    1\n",
       "1516    0\n",
       "1525    0\n",
       "1533    0\n",
       "1537    0\n",
       "1539    0\n",
       "1541    0\n",
       "1542    0\n",
       "1549    0\n",
       "1552    0\n",
       "1559    0\n",
       "1566    0\n",
       "1567    0\n",
       "1577    0\n",
       "1590    0\n",
       "1602    0\n",
       "Name: is_iceberg, Length: 160, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_holdout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f0db0b4df60>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WTlt3GzWV7CASaTPYbIGy5shP7lcQdUO8PGVJUERLW1tOcRUFwTP0Y6dC/xjZ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\ngiAA/wMoIayeJVy7pQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0e146dabe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[0], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[13])+ \",\"+\" iceberg_probability : \"+str(pred_valid[0])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f0db0b34240>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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78NDpLuvdLhCJXcSc0JGzpDPR5xbybXNQ0Gnl1Iji4VHu2w6R20rrLgOtU4ya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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0db0b4d2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[1], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[14])+ \",\"+\" iceberg_probability : \"+str(pred_valid[1])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f0db0a917f0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TZ1Bvnrf9jtoUscTRCXrYxHOgFRPW11kBWTZYTZ9aumN6t+fO2yVixqFQpjYc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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0db0b2f6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[2], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[38])+ \",\"+\" iceberg_probability : \"+str(pred_valid[2])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f0db0a6fbe0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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oNvTQXh9FllDwpz9H4SAUeVqgnTqqgmfq4gKdrivFAbS7g+WAGYN6h37OuZzW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\nCIIgERNiEARBIibEIAiCxH8BshyqIzLuvQgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0db0af3748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[3], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[43])+ \",\"+\" iceberg_probability : \"+str(pred_valid[3])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f0db09d17f0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3y3zoNKzdcerVabqF8OredT/n/irmFj69Kp469j+ggh32nJo1IFdV30S895xy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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0db0a27a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[6], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[104])+ \",\"+\" iceberg_probability : \"+str(pred_valid[6])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f0db09b91d0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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y0PFcoNKQaox9fIkniON20JGLFbUM5o1WwUQEKa+honNn7uunzfWBrZszcEha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\nV/OhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0db098e518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[9], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[138])+ \",\"+\" iceberg_probability : \"+str(pred_valid[9])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f0db091d9b0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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C4Nx1U7tHkwN7qCL/nMXI6NQo2lNo6cTIjPS9exhR/vMOI1gWAQEBK+F8hZFd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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0db09791d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[10], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[141])+ \",\"+\" iceberg_probability : \"+str(pred_valid[10])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f0db0882358>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yyCc+5iUU7B6oL6+TAft0pK4HKKj1oU6/a1/i/R5PvN+0luvBRqtFwbMWudT5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0db08ce320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[11], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[167])+ \",\"+\" iceberg_probability : \"+str(pred_valid[11])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f0db0864c88>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QXOSYjtlEnLmPYgjhT56y6RXPa0MSEhJelHih0OeEhISE5wVnbqT4ocIilLo6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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0db0842550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[12], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[195])+ \",\"+\" iceberg_probability : \"+str(pred_valid[12])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f0db07cd550>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fFKS1eaQQyHslTWFdZLpRN7DCWjL3FOrjAF1eHDr1alGIqYp2YYjzVg5Jb3i/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\nAEmwdV2aUCYSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0db081a400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[13], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[199])+ \",\"+\" iceberg_probability : \"+str(pred_valid[13])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}