Model_03-Adam-k=10-amsgrad-0001-15p-Copy2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Mandatory imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import log_loss\n",
    "from sklearn.model_selection import StratifiedKFold, StratifiedShuffleSplit\n",
    "from os.path import join as opj\n",
    "from matplotlib import pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import pylab\n",
    "plt.rcParams['figure.figsize'] = 10, 10\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_json(\"input/train.json\")\n",
    "target_train=train['is_iceberg']\n",
    "test = pd.read_json(\"input/test.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_train=train['is_iceberg']\n",
    "test['inc_angle']=pd.to_numeric(test['inc_angle'], errors='coerce')\n",
    "train['inc_angle']=pd.to_numeric(train['inc_angle'], errors='coerce')#We have only 133 NAs.\n",
    "train['inc_angle']=train['inc_angle'].fillna(method='pad')\n",
    "X_angle=train['inc_angle']\n",
    "test['inc_angle']=pd.to_numeric(test['inc_angle'], errors='coerce')\n",
    "X_test_angle=test['inc_angle']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Generate the training data\n",
    "X_band_1=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in train[\"band_1\"]])\n",
    "X_band_2=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in train[\"band_2\"]])\n",
    "X_band_3=(X_band_1+X_band_2)/2\n",
    "#X_band_3=np.array([np.full((75, 75), angel).astype(np.float32) for angel in train[\"inc_angle\"]])\n",
    "X_train = np.concatenate([X_band_1[:, :, :, np.newaxis]\n",
    "                          , X_band_2[:, :, :, np.newaxis]\n",
    "                         , X_band_3[:, :, :, np.newaxis]], axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_band_test_1=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in test[\"band_1\"]])\n",
    "X_band_test_2=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in test[\"band_2\"]])\n",
    "X_band_test_3=(X_band_test_1+X_band_test_2)/2\n",
    "#X_band_test_3=np.array([np.full((75, 75), angel).astype(np.float32) for angel in test[\"inc_angle\"]])\n",
    "X_test = np.concatenate([X_band_test_1[:, :, :, np.newaxis]\n",
    "                          , X_band_test_2[:, :, :, np.newaxis]\n",
    "                         , X_band_test_3[:, :, :, np.newaxis]], axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "#Import Keras.\n",
    "from matplotlib import pyplot\n",
    "from keras.optimizers import RMSprop\n",
    "# from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPooling2D, Dense, Dropout, Input, Flatten, Activation\n",
    "from keras.layers import GlobalMaxPooling2D\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.layers.merge import Concatenate\n",
    "from keras.models import Model\n",
    "from keras import initializers\n",
    "from keras.optimizers import Adam\n",
    "from keras.optimizers import rmsprop\n",
    "from keras.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": {},
   "outputs": [],
   "source": [
    "#Data Aug for multi-input\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "batch_size=64\n",
    "# Define the image transformations here\n",
    "gen = ImageDataGenerator(horizontal_flip = True,\n",
    "                         vertical_flip = True,\n",
    "                         width_shift_range = 0.,\n",
    "                         height_shift_range = 0.,\n",
    "                         channel_shift_range=0,\n",
    "                         zoom_range = 0.2,\n",
    "                         rotation_range = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here is the function that merges our two generators\n",
    "# We use the exact same generator with the same random seed for both the y and angle arrays\n",
    "def gen_flow_for_two_inputs(X1, X2, y):\n",
    "    genX1 = gen.flow(X1,y,  batch_size=batch_size,seed=55)\n",
    "    genX2 = gen.flow(X1,X2, batch_size=batch_size,seed=55)\n",
    "    while True:\n",
    "            X1i = genX1.next()\n",
    "            X2i = genX2.next()\n",
    "            #Assert arrays are equal - this was for peace of mind, but slows down training\n",
    "            #np.testing.assert_array_equal(X1i[0],X2i[0])\n",
    "            yield [X1i[0], X2i[1]], X1i[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Finally create generator\n",
    "def get_callbacks(filepath, patience=2):\n",
    "    es = EarlyStopping('val_loss', patience=15, mode=\"min\")\n",
    "    msave = ModelCheckpoint(filepath, save_best_only=True)\n",
    "    return [es, msave]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getVggAngleModel():\n",
    "    input_2 = Input(shape=[1], name=\"angle\")\n",
    "    angle_layer = Dense(1, )(input_2)\n",
    "    base_model = VGG16(weights='imagenet', include_top=False, \n",
    "                 input_shape=X_train.shape[1:], classes=1)\n",
    "    x = base_model.get_layer('block5_pool').output\n",
    "    \n",
    "\n",
    "    x = GlobalMaxPooling2D()(x)\n",
    "    merge_one = concatenate([x, angle_layer])\n",
    "    merge_one = Dense(512, activation='relu', name='fc2')(merge_one)\n",
    "    merge_one = Dropout(0.3)(merge_one)\n",
    "    merge_one = Dense(512, activation='relu', name='fc3')(merge_one)\n",
    "    merge_one = Dropout(0.3)(merge_one)\n",
    "    \n",
    "    predictions = Dense(1, activation='sigmoid')(merge_one)\n",
    "    \n",
    "    model = Model(inputs=[base_model.input, input_2], outputs=predictions)\n",
    "    \n",
    "    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 774ms/step - loss: 0.6669 - acc: 0.6256 - val_loss: 0.4895 - val_acc: 0.7531\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 586ms/step - loss: 0.4551 - acc: 0.7798 - val_loss: 0.3795 - val_acc: 0.8148\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 587ms/step - loss: 0.3688 - acc: 0.8366 - val_loss: 0.2798 - val_acc: 0.8519\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.3414 - acc: 0.8429 - val_loss: 0.2864 - val_acc: 0.8704\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.3190 - acc: 0.8525 - val_loss: 0.2238 - val_acc: 0.8889\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.2742 - acc: 0.8849 - val_loss: 0.3073 - val_acc: 0.9074\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 14s 594ms/step - loss: 0.2698 - acc: 0.8809 - val_loss: 0.2150 - val_acc: 0.9012\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.2680 - acc: 0.8846 - val_loss: 0.2573 - val_acc: 0.9012\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.2477 - acc: 0.8967 - val_loss: 0.2257 - val_acc: 0.9136\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.2221 - acc: 0.8942 - val_loss: 0.2168 - val_acc: 0.9136\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.2253 - acc: 0.9066 - val_loss: 0.2401 - val_acc: 0.9012\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 14s 596ms/step - loss: 0.2102 - acc: 0.9106 - val_loss: 0.2090 - val_acc: 0.9321\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.2748 - acc: 0.8738 - val_loss: 0.2492 - val_acc: 0.9074\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 14s 600ms/step - loss: 0.2144 - acc: 0.9095 - val_loss: 0.2079 - val_acc: 0.9136\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1923 - acc: 0.9272 - val_loss: 0.2438 - val_acc: 0.9074\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.2036 - acc: 0.9099 - val_loss: 0.2477 - val_acc: 0.9136\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2084 - acc: 0.9138 - val_loss: 0.2427 - val_acc: 0.8951\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2069 - acc: 0.9123 - val_loss: 0.2108 - val_acc: 0.9074\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1902 - acc: 0.9298 - val_loss: 0.2325 - val_acc: 0.9198\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.1969 - acc: 0.9226 - val_loss: 0.1968 - val_acc: 0.9136\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.1764 - acc: 0.9238 - val_loss: 0.1916 - val_acc: 0.9198\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1918 - acc: 0.9186 - val_loss: 0.2015 - val_acc: 0.9259\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 13s 543ms/step - loss: 0.1868 - acc: 0.9201 - val_loss: 0.2331 - val_acc: 0.9136\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1836 - acc: 0.9179 - val_loss: 0.1935 - val_acc: 0.9198\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1614 - acc: 0.9362 - val_loss: 0.1990 - val_acc: 0.9136\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1609 - acc: 0.9311 - val_loss: 0.2443 - val_acc: 0.9012\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.1735 - acc: 0.9258 - val_loss: 0.1909 - val_acc: 0.9198\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1778 - acc: 0.9228 - val_loss: 0.2396 - val_acc: 0.9136\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1603 - acc: 0.9408 - val_loss: 0.2032 - val_acc: 0.9259\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1499 - acc: 0.9377 - val_loss: 0.2590 - val_acc: 0.8889\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1461 - acc: 0.9391 - val_loss: 0.1930 - val_acc: 0.9383\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1684 - acc: 0.9356 - val_loss: 0.2464 - val_acc: 0.9259\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1421 - acc: 0.9369 - val_loss: 0.2051 - val_acc: 0.9321\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1457 - acc: 0.9374 - val_loss: 0.2227 - val_acc: 0.9259\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1458 - acc: 0.9429 - val_loss: 0.2074 - val_acc: 0.9136\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1135 - acc: 0.9570 - val_loss: 0.2539 - val_acc: 0.9198\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1219 - acc: 0.9499 - val_loss: 0.2483 - val_acc: 0.9321\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1461 - acc: 0.9393 - val_loss: 0.2309 - val_acc: 0.9198\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1373 - acc: 0.9409 - val_loss: 0.2257 - val_acc: 0.9136\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1041 - acc: 0.9604 - val_loss: 0.2406 - val_acc: 0.9321\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.1292 - acc: 0.9526 - val_loss: 0.1904 - val_acc: 0.9383\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1440 - acc: 0.9398 - val_loss: 0.2122 - val_acc: 0.9321\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 12s 521ms/step - loss: 0.1216 - acc: 0.9422 - val_loss: 0.2446 - val_acc: 0.9074\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1079 - acc: 0.9538 - val_loss: 0.2419 - val_acc: 0.9321\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1345 - acc: 0.9450 - val_loss: 0.2239 - val_acc: 0.9259\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.1122 - acc: 0.9505 - val_loss: 0.2267 - val_acc: 0.9074\n",
      "Epoch 47/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.0993 - acc: 0.9564 - val_loss: 0.2992 - val_acc: 0.9321\n",
      "Epoch 48/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.0993 - acc: 0.9578 - val_loss: 0.2423 - val_acc: 0.9074\n",
      "Epoch 49/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1221 - acc: 0.9532 - val_loss: 0.2409 - val_acc: 0.9074\n",
      "Epoch 50/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1036 - acc: 0.9630 - val_loss: 0.2068 - val_acc: 0.9074\n",
      "Epoch 51/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.0959 - acc: 0.9578 - val_loss: 0.2983 - val_acc: 0.9259\n",
      "Epoch 52/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1230 - acc: 0.9526 - val_loss: 0.2374 - val_acc: 0.9136\n",
      "Epoch 53/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.0975 - acc: 0.9663 - val_loss: 0.2100 - val_acc: 0.9321\n",
      "Epoch 54/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.0840 - acc: 0.9675 - val_loss: 0.2409 - val_acc: 0.9321\n",
      "Epoch 55/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.0975 - acc: 0.9616 - val_loss: 0.2499 - val_acc: 0.9321\n",
      "Epoch 56/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.0819 - acc: 0.9649 - val_loss: 0.2263 - val_acc: 0.9259\n",
      "Train loss: 0.0844974876608\n",
      "Train accuracy: 0.966019417476\n",
      "Test loss: 0.190390807374\n",
      "Test accuracy: 0.938271604938\n",
      "\n",
      "===================FOLD= 1\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 16s 680ms/step - loss: 1.0556 - acc: 0.5137 - val_loss: 0.6202 - val_acc: 0.6646\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.7781 - acc: 0.6182 - val_loss: 0.4156 - val_acc: 0.7826\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.5719 - acc: 0.7393 - val_loss: 0.4749 - val_acc: 0.8012\n",
      "Epoch 4/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24/24 [==============================] - 15s 605ms/step - loss: 0.4538 - acc: 0.8008 - val_loss: 0.2491 - val_acc: 0.8882\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.3543 - acc: 0.8486 - val_loss: 0.2315 - val_acc: 0.8820\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 15s 628ms/step - loss: 0.3483 - acc: 0.8492 - val_loss: 0.2007 - val_acc: 0.9068\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.3056 - acc: 0.8619 - val_loss: 0.2379 - val_acc: 0.9193\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.3277 - acc: 0.8642 - val_loss: 0.2318 - val_acc: 0.8882\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 14s 602ms/step - loss: 0.2884 - acc: 0.8812 - val_loss: 0.1885 - val_acc: 0.9379\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.2687 - acc: 0.8870 - val_loss: 0.1626 - val_acc: 0.9317\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2555 - acc: 0.8929 - val_loss: 0.2120 - val_acc: 0.9006\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2547 - acc: 0.9022 - val_loss: 0.1672 - val_acc: 0.9379\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2415 - acc: 0.8976 - val_loss: 0.2370 - val_acc: 0.8882\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2322 - acc: 0.8960 - val_loss: 0.1844 - val_acc: 0.9068\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2408 - acc: 0.8999 - val_loss: 0.1941 - val_acc: 0.9255\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2223 - acc: 0.9058 - val_loss: 0.1934 - val_acc: 0.9379\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2135 - acc: 0.9098 - val_loss: 0.1951 - val_acc: 0.9193\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2165 - acc: 0.9124 - val_loss: 0.1933 - val_acc: 0.9068\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.2145 - acc: 0.9169 - val_loss: 0.1986 - val_acc: 0.9130\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.1893 - acc: 0.9167 - val_loss: 0.2055 - val_acc: 0.9317\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1826 - acc: 0.9238 - val_loss: 0.1850 - val_acc: 0.9130\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2084 - acc: 0.9045 - val_loss: 0.2507 - val_acc: 0.8758\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 13s 539ms/step - loss: 0.2056 - acc: 0.9142 - val_loss: 0.1718 - val_acc: 0.9379\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1980 - acc: 0.9149 - val_loss: 0.1858 - val_acc: 0.9255\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 15s 604ms/step - loss: 0.1868 - acc: 0.9210 - val_loss: 0.1599 - val_acc: 0.9565\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1677 - acc: 0.9337 - val_loss: 0.1903 - val_acc: 0.9317\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1519 - acc: 0.9365 - val_loss: 0.3511 - val_acc: 0.9130\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1741 - acc: 0.9316 - val_loss: 0.2068 - val_acc: 0.9006\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1545 - acc: 0.9421 - val_loss: 0.2489 - val_acc: 0.9193\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1482 - acc: 0.9368 - val_loss: 0.2123 - val_acc: 0.9317\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1472 - acc: 0.9354 - val_loss: 0.1866 - val_acc: 0.9627\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1586 - acc: 0.9423 - val_loss: 0.2480 - val_acc: 0.9379\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1590 - acc: 0.9350 - val_loss: 0.2354 - val_acc: 0.9006\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1806 - acc: 0.9343 - val_loss: 0.2073 - val_acc: 0.9317\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1631 - acc: 0.9268 - val_loss: 0.1994 - val_acc: 0.9193\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1563 - acc: 0.9424 - val_loss: 0.1859 - val_acc: 0.9441\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1172 - acc: 0.9532 - val_loss: 0.2057 - val_acc: 0.9317\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 14s 601ms/step - loss: 0.1400 - acc: 0.9434 - val_loss: 0.1559 - val_acc: 0.9255\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.1346 - acc: 0.9480 - val_loss: 0.2197 - val_acc: 0.9379\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1212 - acc: 0.9541 - val_loss: 0.2500 - val_acc: 0.9441\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1125 - acc: 0.9573 - val_loss: 0.2021 - val_acc: 0.9379\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1466 - acc: 0.9364 - val_loss: 0.1923 - val_acc: 0.9565\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1423 - acc: 0.9483 - val_loss: 0.2287 - val_acc: 0.9317\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1457 - acc: 0.9448 - val_loss: 0.2574 - val_acc: 0.9193\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1426 - acc: 0.9392 - val_loss: 0.2119 - val_acc: 0.9379\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.1121 - acc: 0.9546 - val_loss: 0.2720 - val_acc: 0.9068\n",
      "Epoch 47/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.0935 - acc: 0.9639 - val_loss: 0.2362 - val_acc: 0.9317\n",
      "Epoch 48/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1019 - acc: 0.9625 - val_loss: 0.2466 - val_acc: 0.9379\n",
      "Epoch 49/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.0888 - acc: 0.9656 - val_loss: 0.3338 - val_acc: 0.9193\n",
      "Epoch 50/100\n",
      "24/24 [==============================] - 12s 521ms/step - loss: 0.1273 - acc: 0.9522 - val_loss: 0.2620 - val_acc: 0.9379\n",
      "Epoch 51/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.0880 - acc: 0.9664 - val_loss: 0.2391 - val_acc: 0.9379\n",
      "Epoch 52/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1347 - acc: 0.9450 - val_loss: 0.2364 - val_acc: 0.9379\n",
      "Epoch 53/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.0941 - acc: 0.9644 - val_loss: 0.2442 - val_acc: 0.9379\n",
      "Train loss: 0.0923410113888\n",
      "Train accuracy: 0.972972972973\n",
      "Test loss: 0.155892800193\n",
      "Test accuracy: 0.925465838509\n",
      "\n",
      "===================FOLD= 2\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 14s 594ms/step - loss: 0.9895 - acc: 0.5322 - val_loss: 0.4125 - val_acc: 0.7826\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 15s 604ms/step - loss: 0.6511 - acc: 0.6989 - val_loss: 0.2418 - val_acc: 0.8882\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.5023 - acc: 0.7747 - val_loss: 0.2176 - val_acc: 0.8882\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.4171 - acc: 0.8128 - val_loss: 0.3425 - val_acc: 0.7764\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.4100 - acc: 0.8119 - val_loss: 0.2868 - val_acc: 0.8323\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.3590 - acc: 0.8332 - val_loss: 0.2178 - val_acc: 0.8882\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.3371 - acc: 0.8545 - val_loss: 0.2427 - val_acc: 0.8944\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.3140 - acc: 0.8546 - val_loss: 0.2491 - val_acc: 0.8571\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 15s 609ms/step - loss: 0.3233 - acc: 0.8551 - val_loss: 0.2139 - val_acc: 0.9130\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.2999 - acc: 0.8681 - val_loss: 0.1889 - val_acc: 0.9317\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 11/100\n",
      "24/24 [==============================] - 15s 604ms/step - loss: 0.2813 - acc: 0.8719 - val_loss: 0.1767 - val_acc: 0.9317\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2461 - acc: 0.8928 - val_loss: 0.2078 - val_acc: 0.9130\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2431 - acc: 0.9012 - val_loss: 0.2244 - val_acc: 0.9255\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2395 - acc: 0.9042 - val_loss: 0.2011 - val_acc: 0.9130\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2397 - acc: 0.8980 - val_loss: 0.2435 - val_acc: 0.9130\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2349 - acc: 0.9131 - val_loss: 0.2171 - val_acc: 0.9130\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2549 - acc: 0.8859 - val_loss: 0.1768 - val_acc: 0.9193\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.2317 - acc: 0.8969 - val_loss: 0.1673 - val_acc: 0.9193\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1996 - acc: 0.9149 - val_loss: 0.2509 - val_acc: 0.9068\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2372 - acc: 0.8999 - val_loss: 0.2175 - val_acc: 0.9006\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1917 - acc: 0.9200 - val_loss: 0.2021 - val_acc: 0.9130\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2250 - acc: 0.9006 - val_loss: 0.1861 - val_acc: 0.9317\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1977 - acc: 0.9203 - val_loss: 0.2211 - val_acc: 0.9068\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.1816 - acc: 0.9265 - val_loss: 0.1655 - val_acc: 0.9317\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1835 - acc: 0.9204 - val_loss: 0.1782 - val_acc: 0.9130\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 15s 607ms/step - loss: 0.2167 - acc: 0.9074 - val_loss: 0.1572 - val_acc: 0.9317\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1762 - acc: 0.9329 - val_loss: 0.1606 - val_acc: 0.9255\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1873 - acc: 0.9333 - val_loss: 0.1761 - val_acc: 0.9193\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1681 - acc: 0.9346 - val_loss: 0.1917 - val_acc: 0.9317\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1587 - acc: 0.9389 - val_loss: 0.1792 - val_acc: 0.9255\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1739 - acc: 0.9248 - val_loss: 0.1591 - val_acc: 0.9317\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1671 - acc: 0.9326 - val_loss: 0.1782 - val_acc: 0.9255\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1789 - acc: 0.9295 - val_loss: 0.1898 - val_acc: 0.9130\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1769 - acc: 0.9285 - val_loss: 0.1865 - val_acc: 0.9255\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1584 - acc: 0.9379 - val_loss: 0.2150 - val_acc: 0.9006\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1436 - acc: 0.9433 - val_loss: 0.2036 - val_acc: 0.9193\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1845 - acc: 0.9325 - val_loss: 0.2185 - val_acc: 0.9130\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1518 - acc: 0.9321 - val_loss: 0.1819 - val_acc: 0.9193\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1590 - acc: 0.9337 - val_loss: 0.2232 - val_acc: 0.9255\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1479 - acc: 0.9403 - val_loss: 0.2600 - val_acc: 0.9068\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1611 - acc: 0.9306 - val_loss: 0.2679 - val_acc: 0.8882\n",
      "Train loss: 0.138653764472\n",
      "Train accuracy: 0.945252945253\n",
      "Test loss: 0.15717990863\n",
      "Test accuracy: 0.931677018634\n",
      "\n",
      "===================FOLD= 3\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 16s 680ms/step - loss: 0.8798 - acc: 0.5371 - val_loss: 0.5319 - val_acc: 0.7250\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.5821 - acc: 0.6805 - val_loss: 0.2690 - val_acc: 0.8688\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.4267 - acc: 0.8107 - val_loss: 0.2822 - val_acc: 0.8562\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.4251 - acc: 0.8218 - val_loss: 0.2409 - val_acc: 0.8875\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.3112 - acc: 0.8580 - val_loss: 0.2781 - val_acc: 0.8812\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 15s 610ms/step - loss: 0.2780 - acc: 0.8782 - val_loss: 0.2255 - val_acc: 0.9000\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.3245 - acc: 0.8546 - val_loss: 0.2451 - val_acc: 0.9125\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 15s 609ms/step - loss: 0.2845 - acc: 0.8687 - val_loss: 0.2033 - val_acc: 0.9187\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.2483 - acc: 0.8948 - val_loss: 0.3520 - val_acc: 0.8625\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.2463 - acc: 0.8992 - val_loss: 0.2267 - val_acc: 0.9062\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.2339 - acc: 0.8967 - val_loss: 0.2120 - val_acc: 0.9125\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.2019 - acc: 0.9091 - val_loss: 0.2780 - val_acc: 0.8938\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.2490 - acc: 0.8888 - val_loss: 0.2643 - val_acc: 0.9062\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2257 - acc: 0.9016 - val_loss: 0.2223 - val_acc: 0.9125\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2388 - acc: 0.8933 - val_loss: 0.2103 - val_acc: 0.9062\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.2139 - acc: 0.9147 - val_loss: 0.2486 - val_acc: 0.9000\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2135 - acc: 0.9189 - val_loss: 0.2473 - val_acc: 0.9062\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.1989 - acc: 0.9124 - val_loss: 0.2228 - val_acc: 0.8938\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2150 - acc: 0.9140 - val_loss: 0.2767 - val_acc: 0.9125\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1935 - acc: 0.9192 - val_loss: 0.2224 - val_acc: 0.9062\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1706 - acc: 0.9298 - val_loss: 0.2151 - val_acc: 0.9000\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.1770 - acc: 0.9287 - val_loss: 0.2004 - val_acc: 0.9062\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 13s 533ms/step - loss: 0.1803 - acc: 0.9268 - val_loss: 0.2217 - val_acc: 0.9188\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1672 - acc: 0.9329 - val_loss: 0.2682 - val_acc: 0.9062\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 531ms/step - loss: 0.1703 - acc: 0.9262 - val_loss: 0.2380 - val_acc: 0.9000\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.1700 - acc: 0.9345 - val_loss: 0.3009 - val_acc: 0.8875\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1819 - acc: 0.9285 - val_loss: 0.3226 - val_acc: 0.9125\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1475 - acc: 0.9363 - val_loss: 0.2161 - val_acc: 0.9125\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1508 - acc: 0.9365 - val_loss: 0.2287 - val_acc: 0.9062\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1681 - acc: 0.9218 - val_loss: 0.2511 - val_acc: 0.9062\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1395 - acc: 0.9410 - val_loss: 0.3174 - val_acc: 0.9125\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1715 - acc: 0.9298 - val_loss: 0.3509 - val_acc: 0.9187\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.1836 - acc: 0.9191 - val_loss: 0.1694 - val_acc: 0.9187\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1773 - acc: 0.9238 - val_loss: 0.2006 - val_acc: 0.9125\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1595 - acc: 0.9332 - val_loss: 0.1958 - val_acc: 0.9187\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1557 - acc: 0.9401 - val_loss: 0.2585 - val_acc: 0.9062\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1229 - acc: 0.9539 - val_loss: 0.2250 - val_acc: 0.9313\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1508 - acc: 0.9342 - val_loss: 0.2148 - val_acc: 0.9250\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1548 - acc: 0.9415 - val_loss: 0.1882 - val_acc: 0.9187\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1418 - acc: 0.9370 - val_loss: 0.2757 - val_acc: 0.9250\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1474 - acc: 0.9401 - val_loss: 0.2935 - val_acc: 0.9062\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1306 - acc: 0.9552 - val_loss: 0.1985 - val_acc: 0.9125\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1157 - acc: 0.9609 - val_loss: 0.2285 - val_acc: 0.9125\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1221 - acc: 0.9524 - val_loss: 0.2462 - val_acc: 0.9062\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.0980 - acc: 0.9611 - val_loss: 0.3520 - val_acc: 0.8875\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.1231 - acc: 0.9487 - val_loss: 0.4375 - val_acc: 0.8875\n",
      "Epoch 47/100\n",
      "24/24 [==============================] - 12s 519ms/step - loss: 0.1055 - acc: 0.9511 - val_loss: 0.4442 - val_acc: 0.9062\n",
      "Epoch 48/100\n",
      "24/24 [==============================] - 12s 521ms/step - loss: 0.1203 - acc: 0.9520 - val_loss: 0.2846 - val_acc: 0.8938\n",
      "Train loss: 0.0995594597631\n",
      "Train accuracy: 0.961218836565\n",
      "Test loss: 0.169442845136\n",
      "Test accuracy: 0.91875\n",
      "\n",
      "===================FOLD= 4\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 15s 610ms/step - loss: 0.7380 - acc: 0.5772 - val_loss: 0.4911 - val_acc: 0.7812\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.5559 - acc: 0.7024 - val_loss: 0.3405 - val_acc: 0.8938\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.3993 - acc: 0.8050 - val_loss: 0.3566 - val_acc: 0.8125\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.3314 - acc: 0.8445 - val_loss: 0.2126 - val_acc: 0.9313\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.2969 - acc: 0.8580 - val_loss: 0.1849 - val_acc: 0.9500\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.2715 - acc: 0.8762 - val_loss: 0.1419 - val_acc: 0.9500\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.3125 - acc: 0.8489 - val_loss: 0.2178 - val_acc: 0.8938\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2686 - acc: 0.8697 - val_loss: 0.1623 - val_acc: 0.9313\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2382 - acc: 0.8974 - val_loss: 0.1509 - val_acc: 0.9375\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2418 - acc: 0.8953 - val_loss: 0.2006 - val_acc: 0.9313\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2258 - acc: 0.9000 - val_loss: 0.1911 - val_acc: 0.9313\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2292 - acc: 0.9042 - val_loss: 0.1588 - val_acc: 0.9500\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2185 - acc: 0.9010 - val_loss: 0.1885 - val_acc: 0.9375\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.2055 - acc: 0.9148 - val_loss: 0.1520 - val_acc: 0.9500\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.2134 - acc: 0.9129 - val_loss: 0.1911 - val_acc: 0.9187\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 530ms/step - loss: 0.2227 - acc: 0.9067 - val_loss: 0.1710 - val_acc: 0.9500\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 530ms/step - loss: 0.1890 - acc: 0.9241 - val_loss: 0.1978 - val_acc: 0.9250\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.1858 - acc: 0.9152 - val_loss: 0.2220 - val_acc: 0.9313\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.1977 - acc: 0.9086 - val_loss: 0.1767 - val_acc: 0.9500\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.2264 - acc: 0.9070 - val_loss: 0.1562 - val_acc: 0.9500\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.1957 - acc: 0.9164 - val_loss: 0.1832 - val_acc: 0.9375\n",
      "Train loss: 0.230850750555\n",
      "Train accuracy: 0.902354570637\n",
      "Test loss: 0.141949307919\n",
      "Test accuracy: 0.95\n",
      "\n",
      "===================FOLD= 5\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 15s 632ms/step - loss: 0.6781 - acc: 0.6186 - val_loss: 0.4010 - val_acc: 0.7688\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.4623 - acc: 0.7464 - val_loss: 0.2857 - val_acc: 0.8625\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.3745 - acc: 0.8247 - val_loss: 0.3988 - val_acc: 0.8313\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.3409 - acc: 0.8352 - val_loss: 0.2100 - val_acc: 0.9187\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.3057 - acc: 0.8624 - val_loss: 0.2394 - val_acc: 0.9125\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.3020 - acc: 0.8585 - val_loss: 0.2322 - val_acc: 0.9125\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.2480 - acc: 0.8845 - val_loss: 0.1987 - val_acc: 0.9250\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2639 - acc: 0.8922 - val_loss: 0.3552 - val_acc: 0.8438\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 15s 604ms/step - loss: 0.2479 - acc: 0.8928 - val_loss: 0.1912 - val_acc: 0.9250\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2314 - acc: 0.8963 - val_loss: 0.2478 - val_acc: 0.9500\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 14s 602ms/step - loss: 0.2292 - acc: 0.8993 - val_loss: 0.1737 - val_acc: 0.9500\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2339 - acc: 0.9059 - val_loss: 0.1850 - val_acc: 0.9375\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.2019 - acc: 0.9228 - val_loss: 0.2075 - val_acc: 0.9250\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2281 - acc: 0.9015 - val_loss: 0.2742 - val_acc: 0.9187\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.1927 - acc: 0.9197 - val_loss: 0.1704 - val_acc: 0.9437\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2020 - acc: 0.9067 - val_loss: 0.1868 - val_acc: 0.9437\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2318 - acc: 0.9029 - val_loss: 0.2801 - val_acc: 0.9062\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.2180 - acc: 0.9096 - val_loss: 0.1987 - val_acc: 0.9313\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.2189 - acc: 0.9086 - val_loss: 0.2061 - val_acc: 0.9250\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 531ms/step - loss: 0.1655 - acc: 0.9261 - val_loss: 0.1829 - val_acc: 0.9688\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1730 - acc: 0.9231 - val_loss: 0.3194 - val_acc: 0.9125\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 15s 607ms/step - loss: 0.1937 - acc: 0.9243 - val_loss: 0.1698 - val_acc: 0.9500\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.1792 - acc: 0.9293 - val_loss: 0.1765 - val_acc: 0.9438\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2159 - acc: 0.9098 - val_loss: 0.1856 - val_acc: 0.9375\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1599 - acc: 0.9305 - val_loss: 0.2357 - val_acc: 0.9437\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1699 - acc: 0.9259 - val_loss: 0.1953 - val_acc: 0.9500\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1647 - acc: 0.9316 - val_loss: 0.1959 - val_acc: 0.9563\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.1394 - acc: 0.9492 - val_loss: 0.2332 - val_acc: 0.9500\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1533 - acc: 0.9334 - val_loss: 0.1873 - val_acc: 0.9625\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1550 - acc: 0.9293 - val_loss: 0.2202 - val_acc: 0.9500\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1508 - acc: 0.9319 - val_loss: 0.2294 - val_acc: 0.9062\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1542 - acc: 0.9404 - val_loss: 0.1979 - val_acc: 0.9625\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1840 - acc: 0.9287 - val_loss: 0.1803 - val_acc: 0.9563\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1412 - acc: 0.9461 - val_loss: 0.2384 - val_acc: 0.9375\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1191 - acc: 0.9563 - val_loss: 0.2243 - val_acc: 0.9375\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1516 - acc: 0.9414 - val_loss: 0.2285 - val_acc: 0.9250\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1239 - acc: 0.9523 - val_loss: 0.3146 - val_acc: 0.9187\n",
      "Train loss: 0.133270528054\n",
      "Train accuracy: 0.940443213296\n",
      "Test loss: 0.169821105897\n",
      "Test accuracy: 0.95\n",
      "\n",
      "===================FOLD= 6\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 15s 635ms/step - loss: 0.7798 - acc: 0.6081 - val_loss: 0.4393 - val_acc: 0.7438\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.5127 - acc: 0.7681 - val_loss: 0.4905 - val_acc: 0.7625\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 15s 607ms/step - loss: 0.4197 - acc: 0.7984 - val_loss: 0.3695 - val_acc: 0.8375\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.3126 - acc: 0.8603 - val_loss: 0.3460 - val_acc: 0.8562\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2601 - acc: 0.8847 - val_loss: 0.3690 - val_acc: 0.8438\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.3395 - acc: 0.8529 - val_loss: 0.3634 - val_acc: 0.8250\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.3351 - acc: 0.8630 - val_loss: 0.3968 - val_acc: 0.8375\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 15s 607ms/step - loss: 0.2878 - acc: 0.8766 - val_loss: 0.3142 - val_acc: 0.8812\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2687 - acc: 0.8767 - val_loss: 0.3143 - val_acc: 0.8938\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2227 - acc: 0.9041 - val_loss: 0.3227 - val_acc: 0.9000\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2435 - acc: 0.8969 - val_loss: 0.3850 - val_acc: 0.9000\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2190 - acc: 0.9064 - val_loss: 0.3652 - val_acc: 0.8750\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.2195 - acc: 0.9075 - val_loss: 0.2488 - val_acc: 0.9062\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2199 - acc: 0.9104 - val_loss: 0.2603 - val_acc: 0.8938\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2100 - acc: 0.9114 - val_loss: 0.2870 - val_acc: 0.9000\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2299 - acc: 0.9062 - val_loss: 0.3013 - val_acc: 0.9125\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1897 - acc: 0.9212 - val_loss: 0.3168 - val_acc: 0.8688\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1774 - acc: 0.9251 - val_loss: 0.2508 - val_acc: 0.9125\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1960 - acc: 0.9267 - val_loss: 0.2668 - val_acc: 0.8750\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1808 - acc: 0.9239 - val_loss: 0.3043 - val_acc: 0.9125\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1679 - acc: 0.9319 - val_loss: 0.2593 - val_acc: 0.8938\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1625 - acc: 0.9277 - val_loss: 0.2886 - val_acc: 0.8812\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 14s 598ms/step - loss: 0.1820 - acc: 0.9255 - val_loss: 0.2460 - val_acc: 0.9125\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1610 - acc: 0.9342 - val_loss: 0.2926 - val_acc: 0.8812\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1516 - acc: 0.9393 - val_loss: 0.2649 - val_acc: 0.8812\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1638 - acc: 0.9349 - val_loss: 0.2748 - val_acc: 0.8812\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.1657 - acc: 0.9340 - val_loss: 0.2803 - val_acc: 0.9000\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 15s 608ms/step - loss: 0.1735 - acc: 0.9316 - val_loss: 0.2308 - val_acc: 0.9000\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1509 - acc: 0.9368 - val_loss: 0.2633 - val_acc: 0.9250\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1371 - acc: 0.9466 - val_loss: 0.2804 - val_acc: 0.8750\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1330 - acc: 0.9467 - val_loss: 0.2934 - val_acc: 0.8938\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1430 - acc: 0.9384 - val_loss: 0.2927 - val_acc: 0.8875\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1292 - acc: 0.9510 - val_loss: 0.3053 - val_acc: 0.8812\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 14s 602ms/step - loss: 0.1237 - acc: 0.9477 - val_loss: 0.2130 - val_acc: 0.9250\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.1476 - acc: 0.9428 - val_loss: 0.2831 - val_acc: 0.9000\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1236 - acc: 0.9515 - val_loss: 0.3064 - val_acc: 0.8812\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1315 - acc: 0.9476 - val_loss: 0.2701 - val_acc: 0.8875\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1420 - acc: 0.9453 - val_loss: 0.3225 - val_acc: 0.8750\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1338 - acc: 0.9472 - val_loss: 0.2832 - val_acc: 0.8625\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1182 - acc: 0.9580 - val_loss: 0.2711 - val_acc: 0.9062\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1123 - acc: 0.9524 - val_loss: 0.3511 - val_acc: 0.8875\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1150 - acc: 0.9526 - val_loss: 0.3934 - val_acc: 0.8812\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 43/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1195 - acc: 0.9588 - val_loss: 0.3248 - val_acc: 0.9125\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1068 - acc: 0.9642 - val_loss: 0.3345 - val_acc: 0.8750\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1175 - acc: 0.9531 - val_loss: 0.4012 - val_acc: 0.8812\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.1042 - acc: 0.9583 - val_loss: 0.2503 - val_acc: 0.8875\n",
      "Epoch 47/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.0914 - acc: 0.9681 - val_loss: 0.3020 - val_acc: 0.8688\n",
      "Epoch 48/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.0916 - acc: 0.9647 - val_loss: 0.3481 - val_acc: 0.8812\n",
      "Epoch 49/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1362 - acc: 0.9419 - val_loss: 0.3102 - val_acc: 0.9062\n",
      "Train loss: 0.0857308234534\n",
      "Train accuracy: 0.963988919668\n",
      "Test loss: 0.213032518327\n",
      "Test accuracy: 0.925\n",
      "\n",
      "===================FOLD= 7\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 16s 660ms/step - loss: 0.8236 - acc: 0.5966 - val_loss: 0.4214 - val_acc: 0.7562\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.6530 - acc: 0.6912 - val_loss: 0.4048 - val_acc: 0.8250\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.3857 - acc: 0.8246 - val_loss: 0.3524 - val_acc: 0.8688\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.3992 - acc: 0.8306 - val_loss: 0.2827 - val_acc: 0.8688\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.3327 - acc: 0.8466 - val_loss: 0.2754 - val_acc: 0.8875\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.3657 - acc: 0.8425 - val_loss: 0.2519 - val_acc: 0.9000\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2744 - acc: 0.8761 - val_loss: 0.3364 - val_acc: 0.8438\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.2745 - acc: 0.8811 - val_loss: 0.2244 - val_acc: 0.8875\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2505 - acc: 0.8977 - val_loss: 0.2262 - val_acc: 0.8875\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2269 - acc: 0.9013 - val_loss: 0.2366 - val_acc: 0.9000\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.2438 - acc: 0.9002 - val_loss: 0.2076 - val_acc: 0.9250\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2563 - acc: 0.8884 - val_loss: 0.2252 - val_acc: 0.9062\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 14s 604ms/step - loss: 0.2489 - acc: 0.8847 - val_loss: 0.1813 - val_acc: 0.9250\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.2198 - acc: 0.9181 - val_loss: 0.2038 - val_acc: 0.9125\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1979 - acc: 0.9132 - val_loss: 0.1905 - val_acc: 0.9250\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2068 - acc: 0.9057 - val_loss: 0.1859 - val_acc: 0.9187\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1654 - acc: 0.9269 - val_loss: 0.2114 - val_acc: 0.9375\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.2062 - acc: 0.9184 - val_loss: 0.1853 - val_acc: 0.9375\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1899 - acc: 0.9288 - val_loss: 0.2073 - val_acc: 0.9062\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 15s 605ms/step - loss: 0.2042 - acc: 0.9163 - val_loss: 0.1595 - val_acc: 0.9375\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1732 - acc: 0.9233 - val_loss: 0.1774 - val_acc: 0.9187\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1944 - acc: 0.9212 - val_loss: 0.2220 - val_acc: 0.8875\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 14s 601ms/step - loss: 0.1827 - acc: 0.9245 - val_loss: 0.1528 - val_acc: 0.9375\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1916 - acc: 0.9239 - val_loss: 0.2845 - val_acc: 0.9000\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2251 - acc: 0.9098 - val_loss: 0.1776 - val_acc: 0.9062\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1756 - acc: 0.9415 - val_loss: 0.2251 - val_acc: 0.9000\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1718 - acc: 0.9267 - val_loss: 0.2799 - val_acc: 0.8812\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1877 - acc: 0.9264 - val_loss: 0.1948 - val_acc: 0.9313\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1801 - acc: 0.9267 - val_loss: 0.1948 - val_acc: 0.9313\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1800 - acc: 0.9256 - val_loss: 0.2319 - val_acc: 0.9125\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1542 - acc: 0.9365 - val_loss: 0.1821 - val_acc: 0.9187\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1400 - acc: 0.9451 - val_loss: 0.1672 - val_acc: 0.9187\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1300 - acc: 0.9466 - val_loss: 0.1541 - val_acc: 0.9313\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1460 - acc: 0.9469 - val_loss: 0.1579 - val_acc: 0.9187\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1357 - acc: 0.9463 - val_loss: 0.1609 - val_acc: 0.9250\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.1226 - acc: 0.9430 - val_loss: 0.1917 - val_acc: 0.9000\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1289 - acc: 0.9508 - val_loss: 0.2880 - val_acc: 0.8875\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1725 - acc: 0.9236 - val_loss: 0.1935 - val_acc: 0.9187\n",
      "Train loss: 0.134156083095\n",
      "Train accuracy: 0.941828254848\n",
      "Test loss: 0.152781239152\n",
      "Test accuracy: 0.9375\n",
      "\n",
      "===================FOLD= 8\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 16s 661ms/step - loss: 0.6085 - acc: 0.6635 - val_loss: 0.3892 - val_acc: 0.7875\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.3709 - acc: 0.8181 - val_loss: 0.4735 - val_acc: 0.7562\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 15s 608ms/step - loss: 0.3204 - acc: 0.8489 - val_loss: 0.3368 - val_acc: 0.8125\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2485 - acc: 0.8974 - val_loss: 0.3837 - val_acc: 0.8500\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 15s 607ms/step - loss: 0.2911 - acc: 0.8718 - val_loss: 0.3335 - val_acc: 0.8313\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.2475 - acc: 0.9015 - val_loss: 0.3879 - val_acc: 0.8063\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.2515 - acc: 0.8992 - val_loss: 0.3958 - val_acc: 0.8187\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.2371 - acc: 0.8954 - val_loss: 0.4503 - val_acc: 0.8500\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.2349 - acc: 0.9049 - val_loss: 0.3775 - val_acc: 0.8438\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2349 - acc: 0.9062 - val_loss: 0.4238 - val_acc: 0.8125\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 529ms/step - loss: 0.2257 - acc: 0.9072 - val_loss: 0.3535 - val_acc: 0.8500\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1938 - acc: 0.9173 - val_loss: 0.3570 - val_acc: 0.8375\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1842 - acc: 0.9231 - val_loss: 0.3378 - val_acc: 0.8562\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1827 - acc: 0.9215 - val_loss: 0.4206 - val_acc: 0.8313\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.2013 - acc: 0.9148 - val_loss: 0.3881 - val_acc: 0.8250\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1883 - acc: 0.9244 - val_loss: 0.4172 - val_acc: 0.8375\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 528ms/step - loss: 0.2021 - acc: 0.9153 - val_loss: 0.3954 - val_acc: 0.8313\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 15s 606ms/step - loss: 0.1852 - acc: 0.9261 - val_loss: 0.3145 - val_acc: 0.8375\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1869 - acc: 0.9261 - val_loss: 0.3595 - val_acc: 0.8500\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1676 - acc: 0.9308 - val_loss: 0.4107 - val_acc: 0.8562\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1632 - acc: 0.9348 - val_loss: 0.3452 - val_acc: 0.8562\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1792 - acc: 0.9241 - val_loss: 0.3501 - val_acc: 0.8375\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1524 - acc: 0.9360 - val_loss: 0.3784 - val_acc: 0.8687\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1559 - acc: 0.9339 - val_loss: 0.4143 - val_acc: 0.8375\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 527ms/step - loss: 0.1555 - acc: 0.9370 - val_loss: 0.3517 - val_acc: 0.8562\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1368 - acc: 0.9479 - val_loss: 0.4091 - val_acc: 0.8562\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1368 - acc: 0.9451 - val_loss: 0.4893 - val_acc: 0.8187\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.1453 - acc: 0.9373 - val_loss: 0.3651 - val_acc: 0.8812\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1434 - acc: 0.9412 - val_loss: 0.3864 - val_acc: 0.8625\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1302 - acc: 0.9502 - val_loss: 0.5000 - val_acc: 0.8063\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1437 - acc: 0.9467 - val_loss: 0.3324 - val_acc: 0.8562\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1228 - acc: 0.9489 - val_loss: 0.5858 - val_acc: 0.8625\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.1422 - acc: 0.9487 - val_loss: 0.3780 - val_acc: 0.8812\n",
      "Train loss: 0.128323483149\n",
      "Train accuracy: 0.957756232687\n",
      "Test loss: 0.314504086971\n",
      "Test accuracy: 0.8375\n",
      "\n",
      "===================FOLD= 9\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 17s 688ms/step - loss: 0.7792 - acc: 0.5951 - val_loss: 0.5545 - val_acc: 0.7188\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 601ms/step - loss: 0.5098 - acc: 0.7621 - val_loss: 0.4599 - val_acc: 0.7625\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 602ms/step - loss: 0.3845 - acc: 0.8350 - val_loss: 0.3117 - val_acc: 0.8438\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.3914 - acc: 0.8148 - val_loss: 0.3184 - val_acc: 0.8812\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 14s 601ms/step - loss: 0.3488 - acc: 0.8396 - val_loss: 0.3097 - val_acc: 0.8250\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 14s 602ms/step - loss: 0.3144 - acc: 0.8562 - val_loss: 0.2441 - val_acc: 0.8625\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 14s 599ms/step - loss: 0.3010 - acc: 0.8653 - val_loss: 0.2340 - val_acc: 0.8875\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.2367 - acc: 0.8989 - val_loss: 0.2711 - val_acc: 0.8562\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 14s 601ms/step - loss: 0.2487 - acc: 0.8974 - val_loss: 0.1970 - val_acc: 0.9062\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2565 - acc: 0.8878 - val_loss: 0.1999 - val_acc: 0.9000\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2091 - acc: 0.9163 - val_loss: 0.2746 - val_acc: 0.8562\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 14s 603ms/step - loss: 0.2548 - acc: 0.8897 - val_loss: 0.1805 - val_acc: 0.9062\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2460 - acc: 0.9011 - val_loss: 0.2127 - val_acc: 0.8938\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.2266 - acc: 0.9018 - val_loss: 0.2377 - val_acc: 0.9062\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2368 - acc: 0.9094 - val_loss: 0.2718 - val_acc: 0.8688\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.2033 - acc: 0.9150 - val_loss: 0.2020 - val_acc: 0.9125\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 13s 525ms/step - loss: 0.2209 - acc: 0.9106 - val_loss: 0.2229 - val_acc: 0.9062\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1836 - acc: 0.9215 - val_loss: 0.2792 - val_acc: 0.9062\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2025 - acc: 0.9145 - val_loss: 0.2210 - val_acc: 0.9125\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2014 - acc: 0.9205 - val_loss: 0.2083 - val_acc: 0.9000\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.2047 - acc: 0.9156 - val_loss: 0.2764 - val_acc: 0.8688\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.2128 - acc: 0.9039 - val_loss: 0.2147 - val_acc: 0.8938\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.1777 - acc: 0.9229 - val_loss: 0.1813 - val_acc: 0.9000\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1800 - acc: 0.9256 - val_loss: 0.2885 - val_acc: 0.8688\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1591 - acc: 0.9358 - val_loss: 0.2326 - val_acc: 0.9125\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1562 - acc: 0.9409 - val_loss: 0.2031 - val_acc: 0.9125\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1472 - acc: 0.9386 - val_loss: 0.2551 - val_acc: 0.8938\n",
      "Train loss: 0.166974221859\n",
      "Train accuracy: 0.932825484765\n",
      "Test loss: 0.180545258522\n",
      "Test accuracy: 0.90625\n",
      "\n",
      " Train Log Loss Validation=  0.117666415278\n",
      " Test Log Loss Validation=  0.18452631478\n"
     ]
    }
   ],
   "source": [
    "#def myAngleCV(X_train, X_angle, X_test):\n",
    "K=10\n",
    "folds = list(StratifiedKFold(n_splits=K, shuffle=True, random_state=16).split(X_train, target_train))\n",
    "y_test_pred_log = 0\n",
    "y_train_pred_log=0\n",
    "y_valid_pred_log = 0.0*target_train\n",
    "for j, (train_idx, test_idx) in enumerate(folds):\n",
    "    print('\\n===================FOLD=',j)\n",
    "    X_train_cv = X_train[train_idx]\n",
    "    y_train_cv = target_train[train_idx]\n",
    "    X_holdout = X_train[test_idx]\n",
    "    Y_holdout= target_train[test_idx]\n",
    "    \n",
    "    #Angle\n",
    "    X_angle_cv=X_angle[train_idx]\n",
    "    X_angle_hold=X_angle[test_idx]\n",
    "    #define file path and get callbacks\n",
    "    file_path = \"%s_aug_model_weights.hdf5\"%j\n",
    "    callbacks = get_callbacks(filepath=file_path, patience=5)\n",
    "    gen_flow = gen_flow_for_two_inputs(X_train_cv, X_angle_cv, y_train_cv)\n",
    "    galaxyModel= getVggAngleModel()\n",
    "    histr = galaxyModel.fit_generator(\n",
    "            gen_flow,\n",
    "            steps_per_epoch=24,\n",
    "            epochs=100,\n",
    "            shuffle=True,\n",
    "            verbose=1,\n",
    "            validation_data=([X_holdout,X_angle_hold], Y_holdout),\n",
    "            callbacks=callbacks)\n",
    "     #Getting the Best Model\n",
    "    galaxyModel.load_weights(filepath=file_path)\n",
    "    #Getting Training Score\n",
    "    score = galaxyModel.evaluate([X_train_cv,X_angle_cv], y_train_cv, verbose=0)\n",
    "    print('Train loss:', score[0])\n",
    "    print('Train accuracy:', score[1])\n",
    "    #Getting Test Score\n",
    "    score = galaxyModel.evaluate([X_holdout,X_angle_hold], Y_holdout, verbose=0)\n",
    "    print('Test loss:', score[0])\n",
    "    print('Test accuracy:', score[1])\n",
    "\n",
    "    #Getting validation Score.\n",
    "    pred_valid=galaxyModel.predict([X_holdout,X_angle_hold])\n",
    "    y_valid_pred_log[test_idx] = pred_valid.reshape(pred_valid.shape[0])\n",
    "\n",
    "    #Getting Test Scores\n",
    "    temp_test=galaxyModel.predict([X_test, X_test_angle])\n",
    "    y_test_pred_log+=temp_test.reshape(temp_test.shape[0])\n",
    "\n",
    "    #Getting Train Scores\n",
    "    temp_train=galaxyModel.predict([X_train, X_angle])\n",
    "    y_train_pred_log+=temp_train.reshape(temp_train.shape[0])\n",
    "\n",
    "y_test_pred_log=y_test_pred_log/K\n",
    "y_train_pred_log=y_train_pred_log/K\n",
    "\n",
    "print('\\n Train Log Loss Validation= ',log_loss(target_train, y_train_pred_log))\n",
    "print(' Test Log Loss Validation= ',log_loss(target_train, y_valid_pred_log))\n",
    "#return y_test_pred_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#preds=myAngleCV(X_train, X_angle, X_test)\n",
    "preds = y_test_pred_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Submission to kaggle\n",
    "submission = pd.DataFrame()\n",
    "submission['id']=test['id']\n",
    "submission['is_iceberg']=preds\n",
    "submission.to_csv('sub_Adam_10folds_amsgrad_0001_15p_2nd.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['loss', 'acc', 'val_loss', 'val_acc'])\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEWCAYAAACXGLsWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6f52bde10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe695803be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# list all data in history\n",
    "print(histr.history.keys())\n",
    "# summarize history for accuracy\n",
    "plt.plot(histr.history['acc'])\n",
    "plt.plot(histr.history['val_acc'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()\n",
    "# summarize history for loss\n",
    "plt.plot(histr.history['loss'])\n",
    "plt.plot(histr.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.image as mpimg\n",
    "\n",
    "min1 = np.min(X_holdout)\n",
    "max1 = np.max(X_holdout)\n",
    "diff1 = max1 - min1\n",
    "my_sum = lambda x: (x - min1)/diff1\n",
    "\n",
    "X_holdout2 = my_sum(X_holdout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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 0x7fe69414b9e8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUUAAAEICAYAAADIsubvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6941c54e0>"
      ]
     },
     "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 0x7fe694129ba8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUQAAAEICAYAAAAncI3RAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6941084a8>"
      ]
     },
     "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 0x7fe69408c198>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUUAAAEICAYAAADIsubvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ntos8zwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe69411d390>"
      ]
     },
     "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 0x7fe69406b5c0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUUAAAEICAYAAADIsubvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEAgxKQZBEAgxKQZBEAj/BTLXYA+k0iV+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6940875f8>"
      ]
     },
     "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 0x7fe68df910f0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUUAAAEICAYAAADIsubvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe68dfe4da0>"
      ]
     },
     "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 0x7fe68df749b0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQsAAAEICAYAAABIwdH8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nV/OhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe68df50c50>"
      ]
     },
     "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 0x7fe68dedb2b0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQsAAAEICAYAAABIwdH8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe68df30748>"
      ]
     },
     "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 0x7fe68debbc18>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUUAAAEICAYAAADIsubvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n6DiOI/wPv2fwErUZ8LMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe68de9bd68>"
      ]
     },
     "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 0x7fe68de22470>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUUAAAEICAYAAADIsubvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe68de77a20>"
      ]
     },
     "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 0x7fe68dd87d30>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUUAAAEICAYAAADIsubvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ncdqeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe68ddddcf8>"
      ]
     },
     "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": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}