Model_03-SGD-k=7.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Mandatory imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import log_loss\n",
    "from sklearn.model_selection import StratifiedKFold, StratifiedShuffleSplit\n",
    "from os.path import join as opj\n",
    "from matplotlib import pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import pylab\n",
    "plt.rcParams['figure.figsize'] = 10, 10\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_json(\"input/train.json\")\n",
    "target_train=train['is_iceberg']\n",
    "test = pd.read_json(\"input/test.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_train=train['is_iceberg']\n",
    "test['inc_angle']=pd.to_numeric(test['inc_angle'], errors='coerce')\n",
    "train['inc_angle']=pd.to_numeric(train['inc_angle'], errors='coerce')#We have only 133 NAs.\n",
    "train['inc_angle']=train['inc_angle'].fillna(method='pad')\n",
    "X_angle=train['inc_angle']\n",
    "test['inc_angle']=pd.to_numeric(test['inc_angle'], errors='coerce')\n",
    "X_test_angle=test['inc_angle']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Generate the training data\n",
    "X_band_1=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in train[\"band_1\"]])\n",
    "X_band_2=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in train[\"band_2\"]])\n",
    "X_band_3=(X_band_1+X_band_2)/2\n",
    "#X_band_3=np.array([np.full((75, 75), angel).astype(np.float32) for angel in train[\"inc_angle\"]])\n",
    "X_train = np.concatenate([X_band_1[:, :, :, np.newaxis]\n",
    "                          , X_band_2[:, :, :, np.newaxis]\n",
    "                         , X_band_3[:, :, :, np.newaxis]], axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_band_test_1=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in test[\"band_1\"]])\n",
    "X_band_test_2=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in test[\"band_2\"]])\n",
    "X_band_test_3=(X_band_test_1+X_band_test_2)/2\n",
    "#X_band_test_3=np.array([np.full((75, 75), angel).astype(np.float32) for angel in test[\"inc_angle\"]])\n",
    "X_test = np.concatenate([X_band_test_1[:, :, :, np.newaxis]\n",
    "                          , X_band_test_2[:, :, :, np.newaxis]\n",
    "                         , X_band_test_3[:, :, :, np.newaxis]], axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "#Import Keras.\n",
    "from matplotlib import pyplot\n",
    "from keras.optimizers import RMSprop\n",
    "# from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPooling2D, Dense, Dropout, Input, Flatten, Activation\n",
    "from keras.layers import GlobalMaxPooling2D\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.layers.merge import Concatenate\n",
    "from keras.models import Model\n",
    "from keras import initializers\n",
    "from keras.optimizers import Adam\n",
    "from keras.optimizers import rmsprop\n",
    "from keras.optimizers import SGD\n",
    "from keras.optimizers import Nadam\n",
    "from keras.optimizers import Adadelta\n",
    "from keras.layers.advanced_activations import LeakyReLU, PReLU\n",
    "from keras.callbacks import ModelCheckpoint, Callback, EarlyStopping\n",
    "\n",
    "from keras.datasets import cifar10\n",
    "from keras.applications.inception_v3 import InceptionV3\n",
    "from keras.applications.vgg16 import VGG16\n",
    "from keras.applications.xception import Xception\n",
    "from keras.applications.mobilenet import MobileNet\n",
    "from keras.applications.vgg19 import VGG19\n",
    "from keras.layers import Concatenate, Dense, LSTM, Input, concatenate\n",
    "from keras.preprocessing import image\n",
    "from keras.applications.vgg16 import preprocess_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data Aug for multi-input\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "batch_size=64\n",
    "# Define the image transformations here\n",
    "gen = ImageDataGenerator(horizontal_flip = True,\n",
    "                         vertical_flip = True,\n",
    "                         width_shift_range = 0.,\n",
    "                         height_shift_range = 0.,\n",
    "                         channel_shift_range=0,\n",
    "                         zoom_range = 0.2,\n",
    "                         rotation_range = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here is the function that merges our two generators\n",
    "# We use the exact same generator with the same random seed for both the y and angle arrays\n",
    "def gen_flow_for_two_inputs(X1, X2, y):\n",
    "    genX1 = gen.flow(X1,y,  batch_size=batch_size,seed=55)\n",
    "    genX2 = gen.flow(X1,X2, batch_size=batch_size,seed=55)\n",
    "    while True:\n",
    "            X1i = genX1.next()\n",
    "            X2i = genX2.next()\n",
    "            #Assert arrays are equal - this was for peace of mind, but slows down training\n",
    "            #np.testing.assert_array_equal(X1i[0],X2i[0])\n",
    "            yield [X1i[0], X2i[1]], X1i[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Finally create generator\n",
    "def get_callbacks(filepath, patience=2):\n",
    "    es = EarlyStopping('val_loss', patience=10, mode=\"min\")\n",
    "    msave = ModelCheckpoint(filepath, save_best_only=True)\n",
    "    return [es, msave]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getVggAngleModel():\n",
    "    input_2 = Input(shape=[1], name=\"angle\")\n",
    "    angle_layer = Dense(1, )(input_2)\n",
    "    base_model = VGG16(weights='imagenet', include_top=False, \n",
    "                 input_shape=X_train.shape[1:], classes=1)\n",
    "    x = base_model.get_layer('block5_pool').output\n",
    "    \n",
    "\n",
    "    x = GlobalMaxPooling2D()(x)\n",
    "    merge_one = concatenate([x, angle_layer])\n",
    "    merge_one = Dense(512, activation='relu', name='fc2')(merge_one)\n",
    "    merge_one = Dropout(0.3)(merge_one)\n",
    "    merge_one = Dense(512, activation='relu', name='fc3')(merge_one)\n",
    "    merge_one = Dropout(0.3)(merge_one)\n",
    "    \n",
    "    predictions = Dense(1, activation='sigmoid')(merge_one)\n",
    "    \n",
    "    model = Model(inputs=[base_model.input, input_2], outputs=predictions)\n",
    "    \n",
    "#     adadelta= Adadelta(lr=1.0, rho=0.95, epsilon=None, decay=0.0)\n",
    "#     model.compile(loss='binary_crossentropy',\n",
    "#                   optimizer=adadelta,\n",
    "#                   metrics=['accuracy'])\n",
    "    \n",
    "    sgd = SGD(lr=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 1/50\n",
      "24/24 [==============================] - 17s 725ms/step - loss: 0.6835 - acc: 0.6235 - val_loss: 0.3858 - val_acc: 0.7826\n",
      "Epoch 2/50\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.4142 - acc: 0.7943 - val_loss: 0.3707 - val_acc: 0.8043\n",
      "Epoch 3/50\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.3654 - acc: 0.8323 - val_loss: 0.2853 - val_acc: 0.8652\n",
      "Epoch 4/50\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.3237 - acc: 0.8552 - val_loss: 0.2441 - val_acc: 0.8913\n",
      "Epoch 5/50\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.3039 - acc: 0.8656 - val_loss: 0.2227 - val_acc: 0.8913\n",
      "Epoch 6/50\n",
      "24/24 [==============================] - 12s 492ms/step - loss: 0.2789 - acc: 0.8774 - val_loss: 0.2307 - val_acc: 0.8957\n",
      "Epoch 7/50\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.2590 - acc: 0.8870 - val_loss: 0.2461 - val_acc: 0.8913\n",
      "Epoch 8/50\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.2644 - acc: 0.8865 - val_loss: 0.2128 - val_acc: 0.8913\n",
      "Epoch 9/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2434 - acc: 0.8936 - val_loss: 0.2223 - val_acc: 0.8913\n",
      "Epoch 10/50\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.2203 - acc: 0.9091 - val_loss: 0.1817 - val_acc: 0.9174\n",
      "Epoch 11/50\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2362 - acc: 0.8973 - val_loss: 0.2043 - val_acc: 0.9130\n",
      "Epoch 12/50\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2324 - acc: 0.9078 - val_loss: 0.1943 - val_acc: 0.9217\n",
      "Epoch 13/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2234 - acc: 0.9072 - val_loss: 0.1874 - val_acc: 0.9348\n",
      "Epoch 14/50\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2118 - acc: 0.9100 - val_loss: 0.1819 - val_acc: 0.9261\n",
      "Epoch 15/50\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.1944 - acc: 0.9191 - val_loss: 0.1644 - val_acc: 0.9217\n",
      "Epoch 16/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1997 - acc: 0.9204 - val_loss: 0.1708 - val_acc: 0.9217\n",
      "Epoch 17/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1763 - acc: 0.9264 - val_loss: 0.1875 - val_acc: 0.9174\n",
      "Epoch 18/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2015 - acc: 0.9235 - val_loss: 0.1792 - val_acc: 0.9217\n",
      "Epoch 19/50\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.1934 - acc: 0.9249 - val_loss: 0.1604 - val_acc: 0.9261\n",
      "Epoch 20/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1656 - acc: 0.9327 - val_loss: 0.1647 - val_acc: 0.9348\n",
      "Epoch 21/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1968 - acc: 0.9236 - val_loss: 0.1651 - val_acc: 0.9217\n",
      "Epoch 22/50\n",
      "24/24 [==============================] - 12s 490ms/step - loss: 0.1734 - acc: 0.9339 - val_loss: 0.1614 - val_acc: 0.9217\n",
      "Epoch 23/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1732 - acc: 0.9302 - val_loss: 0.1633 - val_acc: 0.9217\n",
      "Epoch 24/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1789 - acc: 0.9328 - val_loss: 0.1822 - val_acc: 0.9217\n",
      "Epoch 25/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1632 - acc: 0.9302 - val_loss: 0.1615 - val_acc: 0.9348\n",
      "Epoch 26/50\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1621 - acc: 0.9359 - val_loss: 0.1708 - val_acc: 0.9391\n",
      "Epoch 27/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1545 - acc: 0.9335 - val_loss: 0.1951 - val_acc: 0.9087\n",
      "Epoch 28/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1578 - acc: 0.9346 - val_loss: 0.1804 - val_acc: 0.9130\n",
      "Epoch 29/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1643 - acc: 0.9341 - val_loss: 0.1700 - val_acc: 0.9261\n",
      "Train loss: 0.14104952494\n",
      "Train accuracy: 0.949053858045\n",
      "Test loss: 0.16044411439\n",
      "Test accuracy: 0.926086956522\n",
      "\n",
      "===================FOLD= 1\n",
      "Epoch 1/50\n",
      "24/24 [==============================] - 13s 543ms/step - loss: 0.6701 - acc: 0.6249 - val_loss: 0.3969 - val_acc: 0.8087\n",
      "Epoch 2/50\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.4166 - acc: 0.7963 - val_loss: 0.2401 - val_acc: 0.8870\n",
      "Epoch 3/50\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.3407 - acc: 0.8421 - val_loss: 0.2066 - val_acc: 0.9130\n",
      "Epoch 4/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.3471 - acc: 0.8425 - val_loss: 0.2451 - val_acc: 0.8783\n",
      "Epoch 5/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2669 - acc: 0.8779 - val_loss: 0.2068 - val_acc: 0.9217\n",
      "Epoch 6/50\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.2626 - acc: 0.8820 - val_loss: 0.1945 - val_acc: 0.9174\n",
      "Epoch 7/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2453 - acc: 0.8962 - val_loss: 0.2162 - val_acc: 0.8957\n",
      "Epoch 8/50\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.2548 - acc: 0.8955 - val_loss: 0.1784 - val_acc: 0.9348\n",
      "Epoch 9/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2292 - acc: 0.9021 - val_loss: 0.1888 - val_acc: 0.9261\n",
      "Epoch 10/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2265 - acc: 0.9026 - val_loss: 0.2124 - val_acc: 0.9087\n",
      "Epoch 11/50\n",
      "24/24 [==============================] - 12s 489ms/step - loss: 0.2284 - acc: 0.9104 - val_loss: 0.1914 - val_acc: 0.9304\n",
      "Epoch 12/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2165 - acc: 0.9145 - val_loss: 0.1975 - val_acc: 0.9261\n",
      "Epoch 13/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2138 - acc: 0.9164 - val_loss: 0.1842 - val_acc: 0.9348\n",
      "Epoch 14/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1945 - acc: 0.9197 - val_loss: 0.2506 - val_acc: 0.8870\n",
      "Epoch 15/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2008 - acc: 0.9105 - val_loss: 0.1840 - val_acc: 0.9348\n",
      "Epoch 16/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1809 - acc: 0.9327 - val_loss: 0.1905 - val_acc: 0.9130\n",
      "Epoch 17/50\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1975 - acc: 0.9171 - val_loss: 0.1807 - val_acc: 0.9217\n",
      "Epoch 18/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1742 - acc: 0.9264 - val_loss: 0.1902 - val_acc: 0.9217\n",
      "Train loss: 0.185507226415\n",
      "Train accuracy: 0.925036392184\n",
      "Test loss: 0.178434981471\n",
      "Test accuracy: 0.934782608955\n",
      "\n",
      "===================FOLD= 2\n",
      "Epoch 1/50\n",
      "24/24 [==============================] - 13s 546ms/step - loss: 0.6877 - acc: 0.6163 - val_loss: 0.3743 - val_acc: 0.7957\n",
      "Epoch 2/50\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.5029 - acc: 0.7435 - val_loss: 0.3149 - val_acc: 0.8565\n",
      "Epoch 3/50\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.3569 - acc: 0.8408 - val_loss: 0.2990 - val_acc: 0.8609\n",
      "Epoch 4/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.3032 - acc: 0.8578 - val_loss: 0.3154 - val_acc: 0.8565\n",
      "Epoch 5/50\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.2776 - acc: 0.8805 - val_loss: 0.2789 - val_acc: 0.8609\n",
      "Epoch 6/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.3070 - acc: 0.8656 - val_loss: 0.3136 - val_acc: 0.8522\n",
      "Epoch 7/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2444 - acc: 0.8937 - val_loss: 0.2942 - val_acc: 0.8609\n",
      "Epoch 8/50\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.2548 - acc: 0.8859 - val_loss: 0.2770 - val_acc: 0.8826\n",
      "Epoch 9/50\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.2658 - acc: 0.8809 - val_loss: 0.2299 - val_acc: 0.9130\n",
      "Epoch 10/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2199 - acc: 0.9133 - val_loss: 0.2694 - val_acc: 0.8652\n",
      "Epoch 11/50\n",
      "24/24 [==============================] - 12s 489ms/step - loss: 0.2153 - acc: 0.9150 - val_loss: 0.2384 - val_acc: 0.8783\n",
      "Epoch 12/50\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.2305 - acc: 0.9038 - val_loss: 0.2218 - val_acc: 0.8913\n",
      "Epoch 13/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2367 - acc: 0.8933 - val_loss: 0.2363 - val_acc: 0.8957\n",
      "Epoch 14/50\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.2111 - acc: 0.9139 - val_loss: 0.2067 - val_acc: 0.9217\n",
      "Epoch 15/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1997 - acc: 0.9159 - val_loss: 0.2155 - val_acc: 0.9130\n",
      "Epoch 16/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1977 - acc: 0.9249 - val_loss: 0.3294 - val_acc: 0.8565\n",
      "Epoch 17/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1857 - acc: 0.9211 - val_loss: 0.2210 - val_acc: 0.9348\n",
      "Epoch 18/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1630 - acc: 0.9309 - val_loss: 0.2862 - val_acc: 0.8826\n",
      "Epoch 19/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1840 - acc: 0.9275 - val_loss: 0.2401 - val_acc: 0.8957\n",
      "Epoch 20/50\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.2093 - acc: 0.9150 - val_loss: 0.1829 - val_acc: 0.9217\n",
      "Epoch 21/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2005 - acc: 0.9170 - val_loss: 0.2320 - val_acc: 0.8913\n",
      "Epoch 22/50\n",
      "24/24 [==============================] - 12s 489ms/step - loss: 0.1816 - acc: 0.9254 - val_loss: 0.2034 - val_acc: 0.9261\n",
      "Epoch 23/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1790 - acc: 0.9190 - val_loss: 0.2428 - val_acc: 0.8870\n",
      "Epoch 24/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1685 - acc: 0.9308 - val_loss: 0.2070 - val_acc: 0.9261\n",
      "Epoch 25/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1820 - acc: 0.9263 - val_loss: 0.1914 - val_acc: 0.9174\n",
      "Epoch 26/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1596 - acc: 0.9373 - val_loss: 0.2045 - val_acc: 0.9217\n",
      "Epoch 27/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1645 - acc: 0.9322 - val_loss: 0.2534 - val_acc: 0.8957\n",
      "Epoch 28/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1506 - acc: 0.9426 - val_loss: 0.2227 - val_acc: 0.9261\n",
      "Epoch 29/50\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1389 - acc: 0.9439 - val_loss: 0.2179 - val_acc: 0.9087\n",
      "Epoch 30/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1589 - acc: 0.9406 - val_loss: 0.2724 - val_acc: 0.8870\n",
      "Train loss: 0.128733034738\n",
      "Train accuracy: 0.953420670272\n",
      "Test loss: 0.182901575643\n",
      "Test accuracy: 0.921739130435\n",
      "\n",
      "===================FOLD= 3\n",
      "Epoch 1/50\n",
      "24/24 [==============================] - 13s 559ms/step - loss: 0.6150 - acc: 0.6424 - val_loss: 0.3566 - val_acc: 0.8348\n",
      "Epoch 2/50\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.3801 - acc: 0.8107 - val_loss: 0.2003 - val_acc: 0.9348\n",
      "Epoch 3/50\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.3199 - acc: 0.8529 - val_loss: 0.1702 - val_acc: 0.9435\n",
      "Epoch 4/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.3065 - acc: 0.8624 - val_loss: 0.1986 - val_acc: 0.9435\n",
      "Epoch 5/50\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2627 - acc: 0.8821 - val_loss: 0.1848 - val_acc: 0.9174\n",
      "Epoch 6/50\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.2885 - acc: 0.8741 - val_loss: 0.1538 - val_acc: 0.9435\n",
      "Epoch 7/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2645 - acc: 0.8773 - val_loss: 0.1756 - val_acc: 0.9304\n",
      "Epoch 8/50\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.2653 - acc: 0.8857 - val_loss: 0.1298 - val_acc: 0.9565\n",
      "Epoch 9/50\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.2333 - acc: 0.9093 - val_loss: 0.1204 - val_acc: 0.9609\n",
      "Epoch 10/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2310 - acc: 0.8995 - val_loss: 0.1316 - val_acc: 0.9565\n",
      "Epoch 11/50\n",
      "24/24 [==============================] - 12s 490ms/step - loss: 0.2286 - acc: 0.9084 - val_loss: 0.1348 - val_acc: 0.9478\n",
      "Epoch 12/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2204 - acc: 0.9060 - val_loss: 0.1919 - val_acc: 0.9000\n",
      "Epoch 13/50\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2163 - acc: 0.9091 - val_loss: 0.1482 - val_acc: 0.9522\n",
      "Epoch 14/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2150 - acc: 0.9091 - val_loss: 0.1271 - val_acc: 0.9435\n",
      "Epoch 15/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2076 - acc: 0.9217 - val_loss: 0.1278 - val_acc: 0.9522\n",
      "Epoch 16/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1842 - acc: 0.9131 - val_loss: 0.1379 - val_acc: 0.9522\n",
      "Epoch 17/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1962 - acc: 0.9204 - val_loss: 0.1265 - val_acc: 0.9652\n",
      "Epoch 18/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1865 - acc: 0.9230 - val_loss: 0.1365 - val_acc: 0.9435\n",
      "Epoch 19/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1691 - acc: 0.9348 - val_loss: 0.1343 - val_acc: 0.9391\n",
      "Train loss: 0.189650263502\n",
      "Train accuracy: 0.924308589452\n",
      "Test loss: 0.120357580289\n",
      "Test accuracy: 0.960869565736\n",
      "\n",
      "===================FOLD= 4\n",
      "Epoch 1/50\n",
      "24/24 [==============================] - 15s 630ms/step - loss: 0.8029 - acc: 0.6320 - val_loss: 0.4668 - val_acc: 0.8026\n",
      "Epoch 2/50\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.4364 - acc: 0.7904 - val_loss: 0.3635 - val_acc: 0.8421\n",
      "Epoch 3/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.3429 - acc: 0.8547 - val_loss: 0.3875 - val_acc: 0.8114\n",
      "Epoch 4/50\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.3115 - acc: 0.8555 - val_loss: 0.3504 - val_acc: 0.8377\n",
      "Epoch 5/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2738 - acc: 0.8803 - val_loss: 0.3643 - val_acc: 0.8640\n",
      "Epoch 6/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2877 - acc: 0.8853 - val_loss: 0.3603 - val_acc: 0.8202\n",
      "Epoch 7/50\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.2513 - acc: 0.8803 - val_loss: 0.3384 - val_acc: 0.8465\n",
      "Epoch 8/50\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.2445 - acc: 0.8985 - val_loss: 0.3072 - val_acc: 0.8904\n",
      "Epoch 9/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2280 - acc: 0.8984 - val_loss: 0.4198 - val_acc: 0.8289\n",
      "Epoch 10/50\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.2406 - acc: 0.8887 - val_loss: 0.2880 - val_acc: 0.8772\n",
      "Epoch 11/50\n",
      "24/24 [==============================] - 12s 490ms/step - loss: 0.2295 - acc: 0.8991 - val_loss: 0.3046 - val_acc: 0.8640\n",
      "Epoch 12/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2008 - acc: 0.9160 - val_loss: 0.3135 - val_acc: 0.8816\n",
      "Epoch 13/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1946 - acc: 0.9160 - val_loss: 0.3278 - val_acc: 0.8728\n",
      "Epoch 14/50\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.2195 - acc: 0.9102 - val_loss: 0.2843 - val_acc: 0.8947\n",
      "Epoch 15/50\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.1939 - acc: 0.9076 - val_loss: 0.2808 - val_acc: 0.8728\n",
      "Epoch 16/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1745 - acc: 0.9238 - val_loss: 0.3259 - val_acc: 0.8860\n",
      "Epoch 17/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1668 - acc: 0.9290 - val_loss: 0.3170 - val_acc: 0.8904\n",
      "Epoch 18/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1794 - acc: 0.9342 - val_loss: 0.3230 - val_acc: 0.8640\n",
      "Epoch 19/50\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.1804 - acc: 0.9303 - val_loss: 0.2791 - val_acc: 0.8816\n",
      "Epoch 20/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1852 - acc: 0.9161 - val_loss: 0.3839 - val_acc: 0.8509\n",
      "Epoch 21/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1645 - acc: 0.9343 - val_loss: 0.2875 - val_acc: 0.8947\n",
      "Epoch 22/50\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1667 - acc: 0.9357 - val_loss: 0.2667 - val_acc: 0.8991\n",
      "Epoch 23/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1573 - acc: 0.9362 - val_loss: 0.3279 - val_acc: 0.8816\n",
      "Epoch 24/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1592 - acc: 0.9316 - val_loss: 0.3386 - val_acc: 0.8640\n",
      "Epoch 25/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1420 - acc: 0.9349 - val_loss: 0.3102 - val_acc: 0.8684\n",
      "Epoch 26/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1542 - acc: 0.9433 - val_loss: 0.2971 - val_acc: 0.9035\n",
      "Epoch 27/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1630 - acc: 0.9303 - val_loss: 0.2765 - val_acc: 0.8991\n",
      "Epoch 28/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1412 - acc: 0.9421 - val_loss: 0.3055 - val_acc: 0.8816\n",
      "Epoch 29/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1445 - acc: 0.9407 - val_loss: 0.3396 - val_acc: 0.8772\n",
      "Epoch 30/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1351 - acc: 0.9433 - val_loss: 0.3012 - val_acc: 0.8860\n",
      "Epoch 31/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1401 - acc: 0.9427 - val_loss: 0.2926 - val_acc: 0.8728\n",
      "Epoch 32/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1353 - acc: 0.9440 - val_loss: 0.3146 - val_acc: 0.8684\n",
      "Train loss: 0.117846985196\n",
      "Train accuracy: 0.953488372093\n",
      "Test loss: 0.26669372717\n",
      "Test accuracy: 0.899122807018\n",
      "\n",
      "===================FOLD= 5\n",
      "Epoch 1/50\n",
      "24/24 [==============================] - 14s 573ms/step - loss: 0.8331 - acc: 0.5977 - val_loss: 0.4411 - val_acc: 0.7895\n",
      "Epoch 2/50\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.4752 - acc: 0.7792 - val_loss: 0.3289 - val_acc: 0.8377\n",
      "Epoch 3/50\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.3563 - acc: 0.8360 - val_loss: 0.3157 - val_acc: 0.8377\n",
      "Epoch 4/50\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.3113 - acc: 0.8619 - val_loss: 0.3080 - val_acc: 0.8377\n",
      "Epoch 5/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2855 - acc: 0.8757 - val_loss: 0.3633 - val_acc: 0.8070\n",
      "Epoch 6/50\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.2585 - acc: 0.8789 - val_loss: 0.2684 - val_acc: 0.8772\n",
      "Epoch 7/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2555 - acc: 0.8925 - val_loss: 0.2754 - val_acc: 0.8640\n",
      "Epoch 8/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2461 - acc: 0.8991 - val_loss: 0.2688 - val_acc: 0.8728\n",
      "Epoch 9/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2425 - acc: 0.8964 - val_loss: 0.2725 - val_acc: 0.8684\n",
      "Epoch 10/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2414 - acc: 0.9010 - val_loss: 0.2749 - val_acc: 0.8640\n",
      "Epoch 11/50\n",
      "24/24 [==============================] - 12s 490ms/step - loss: 0.2166 - acc: 0.9120 - val_loss: 0.2842 - val_acc: 0.8684\n",
      "Epoch 12/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2080 - acc: 0.9147 - val_loss: 0.2823 - val_acc: 0.8509\n",
      "Epoch 13/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1993 - acc: 0.9187 - val_loss: 0.3152 - val_acc: 0.8728\n",
      "Epoch 14/50\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.2088 - acc: 0.9154 - val_loss: 0.2522 - val_acc: 0.8772\n",
      "Epoch 15/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2004 - acc: 0.9147 - val_loss: 0.2854 - val_acc: 0.8509\n",
      "Epoch 16/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1630 - acc: 0.9297 - val_loss: 0.3098 - val_acc: 0.8728\n",
      "Epoch 17/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2085 - acc: 0.9166 - val_loss: 0.2707 - val_acc: 0.8640\n",
      "Epoch 18/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1867 - acc: 0.9188 - val_loss: 0.2580 - val_acc: 0.8860\n",
      "Epoch 19/50\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.1800 - acc: 0.9297 - val_loss: 0.2496 - val_acc: 0.8728\n",
      "Epoch 20/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.2004 - acc: 0.9226 - val_loss: 0.2644 - val_acc: 0.8728\n",
      "Epoch 21/50\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1811 - acc: 0.9290 - val_loss: 0.2537 - val_acc: 0.8816\n",
      "Epoch 22/50\n",
      "24/24 [==============================] - 12s 491ms/step - loss: 0.1700 - acc: 0.9277 - val_loss: 0.2597 - val_acc: 0.8904\n",
      "Epoch 23/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1410 - acc: 0.9420 - val_loss: 0.2698 - val_acc: 0.8860\n",
      "Epoch 24/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1646 - acc: 0.9342 - val_loss: 0.2563 - val_acc: 0.8947\n",
      "Epoch 25/50\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.1565 - acc: 0.9388 - val_loss: 0.2295 - val_acc: 0.9035\n",
      "Epoch 26/50\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1738 - acc: 0.9297 - val_loss: 0.2334 - val_acc: 0.9079\n",
      "Epoch 27/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1549 - acc: 0.9381 - val_loss: 0.2543 - val_acc: 0.8947\n",
      "Epoch 28/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1627 - acc: 0.9401 - val_loss: 0.2413 - val_acc: 0.8904\n",
      "Epoch 29/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1613 - acc: 0.9355 - val_loss: 0.2528 - val_acc: 0.8860\n",
      "Epoch 30/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1594 - acc: 0.9402 - val_loss: 0.2323 - val_acc: 0.8947\n",
      "Epoch 31/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1334 - acc: 0.9499 - val_loss: 0.2634 - val_acc: 0.8816\n",
      "Epoch 32/50\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.1558 - acc: 0.9368 - val_loss: 0.2282 - val_acc: 0.8991\n",
      "Epoch 33/50\n",
      "24/24 [==============================] - 12s 490ms/step - loss: 0.1509 - acc: 0.9382 - val_loss: 0.2927 - val_acc: 0.8772\n",
      "Epoch 34/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.1423 - acc: 0.9362 - val_loss: 0.2512 - val_acc: 0.9035\n",
      "Epoch 35/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1458 - acc: 0.9420 - val_loss: 0.2629 - val_acc: 0.8947\n",
      "Epoch 36/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1411 - acc: 0.9447 - val_loss: 0.2443 - val_acc: 0.8991\n",
      "Epoch 37/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1248 - acc: 0.9479 - val_loss: 0.2630 - val_acc: 0.8947\n",
      "Epoch 38/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1261 - acc: 0.9518 - val_loss: 0.2347 - val_acc: 0.8991\n",
      "Epoch 39/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1468 - acc: 0.9402 - val_loss: 0.2324 - val_acc: 0.8904\n",
      "Epoch 40/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1325 - acc: 0.9460 - val_loss: 0.2604 - val_acc: 0.8684\n",
      "Epoch 41/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1331 - acc: 0.9473 - val_loss: 0.2345 - val_acc: 0.9079\n",
      "Epoch 42/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1215 - acc: 0.9544 - val_loss: 0.2746 - val_acc: 0.8860\n",
      "Train loss: 0.105908668752\n",
      "Train accuracy: 0.964389534884\n",
      "Test loss: 0.228241795511\n",
      "Test accuracy: 0.899122807018\n",
      "\n",
      "===================FOLD= 6\n",
      "Epoch 1/50\n",
      "24/24 [==============================] - 14s 583ms/step - loss: 0.6849 - acc: 0.6451 - val_loss: 0.6145 - val_acc: 0.7061\n",
      "Epoch 2/50\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.4326 - acc: 0.7943 - val_loss: 0.3756 - val_acc: 0.8026\n",
      "Epoch 3/50\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.3429 - acc: 0.8380 - val_loss: 0.3016 - val_acc: 0.8465\n",
      "Epoch 4/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.3092 - acc: 0.8510 - val_loss: 0.3561 - val_acc: 0.8421\n",
      "Epoch 5/50\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.2703 - acc: 0.8809 - val_loss: 0.2598 - val_acc: 0.8947\n",
      "Epoch 6/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2354 - acc: 0.8906 - val_loss: 0.2712 - val_acc: 0.8860\n",
      "Epoch 7/50\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.2789 - acc: 0.8757 - val_loss: 0.2558 - val_acc: 0.8728\n",
      "Epoch 8/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2292 - acc: 0.9036 - val_loss: 0.2770 - val_acc: 0.8816\n",
      "Epoch 9/50\n",
      "24/24 [==============================] - 12s 517ms/step - loss: 0.2369 - acc: 0.8979 - val_loss: 0.2433 - val_acc: 0.8947\n",
      "Epoch 10/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2181 - acc: 0.9187 - val_loss: 0.2596 - val_acc: 0.8904\n",
      "Epoch 11/50\n",
      "24/24 [==============================] - 12s 491ms/step - loss: 0.2007 - acc: 0.9121 - val_loss: 0.2554 - val_acc: 0.8947\n",
      "Epoch 12/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.2074 - acc: 0.9108 - val_loss: 0.2846 - val_acc: 0.8816\n",
      "Epoch 13/50\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.2018 - acc: 0.9062 - val_loss: 0.2461 - val_acc: 0.9079\n",
      "Epoch 14/50\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1934 - acc: 0.9245 - val_loss: 0.2568 - val_acc: 0.8904\n",
      "Epoch 15/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1777 - acc: 0.9238 - val_loss: 0.3010 - val_acc: 0.8772\n",
      "Epoch 16/50\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.1828 - acc: 0.9193 - val_loss: 0.2387 - val_acc: 0.9079\n",
      "Epoch 17/50\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1886 - acc: 0.9173 - val_loss: 0.2645 - val_acc: 0.8947\n",
      "Epoch 18/50\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1839 - acc: 0.9212 - val_loss: 0.2498 - val_acc: 0.9035\n",
      "Epoch 19/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1593 - acc: 0.9375 - val_loss: 0.2580 - val_acc: 0.8860\n",
      "Epoch 20/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1785 - acc: 0.9310 - val_loss: 0.2599 - val_acc: 0.8904\n",
      "Epoch 21/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1820 - acc: 0.9264 - val_loss: 0.2729 - val_acc: 0.8947\n",
      "Epoch 22/50\n",
      "24/24 [==============================] - 12s 491ms/step - loss: 0.1796 - acc: 0.9199 - val_loss: 0.2960 - val_acc: 0.8904\n",
      "Epoch 23/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1536 - acc: 0.9356 - val_loss: 0.2720 - val_acc: 0.8596\n",
      "Epoch 24/50\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.1667 - acc: 0.9342 - val_loss: 0.2246 - val_acc: 0.9123\n",
      "Epoch 25/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.1522 - acc: 0.9395 - val_loss: 0.2504 - val_acc: 0.8904\n",
      "Epoch 26/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1730 - acc: 0.9290 - val_loss: 0.2541 - val_acc: 0.8991\n",
      "Epoch 27/50\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1441 - acc: 0.9420 - val_loss: 0.2719 - val_acc: 0.8728\n",
      "Epoch 28/50\n",
      "24/24 [==============================] - 12s 498ms/step - loss: 0.1676 - acc: 0.9304 - val_loss: 0.2392 - val_acc: 0.8991\n",
      "Epoch 29/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1364 - acc: 0.9492 - val_loss: 0.2516 - val_acc: 0.9035\n",
      "Epoch 30/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1578 - acc: 0.9362 - val_loss: 0.2726 - val_acc: 0.8991\n",
      "Epoch 31/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1261 - acc: 0.9499 - val_loss: 0.2518 - val_acc: 0.9079\n",
      "Epoch 32/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1279 - acc: 0.9538 - val_loss: 0.2484 - val_acc: 0.8991\n",
      "Epoch 33/50\n",
      "24/24 [==============================] - 12s 490ms/step - loss: 0.1251 - acc: 0.9532 - val_loss: 0.2515 - val_acc: 0.9035\n",
      "Epoch 34/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.1259 - acc: 0.9499 - val_loss: 0.2799 - val_acc: 0.8947\n",
      "Train loss: 0.111470835457\n",
      "Train accuracy: 0.953488372093\n",
      "Test loss: 0.224583443581\n",
      "Test accuracy: 0.912280701754\n",
      "\n",
      " Train Log Loss Validation=  0.135395049862\n",
      " Test Log Loss Validation=  0.194352943866\n"
     ]
    }
   ],
   "source": [
    "#def myAngleCV(X_train, X_angle, X_test):\n",
    "K=7\n",
    "folds = list(StratifiedKFold(n_splits=K, shuffle=True, random_state=16).split(X_train, target_train))\n",
    "y_test_pred_log = 0\n",
    "y_train_pred_log=0\n",
    "y_valid_pred_log = 0.0*target_train\n",
    "for j, (train_idx, test_idx) in enumerate(folds):\n",
    "    print('\\n===================FOLD=',j)\n",
    "    X_train_cv = X_train[train_idx]\n",
    "    y_train_cv = target_train[train_idx]\n",
    "    X_holdout = X_train[test_idx]\n",
    "    Y_holdout= target_train[test_idx]\n",
    "    \n",
    "    #Angle\n",
    "    X_angle_cv=X_angle[train_idx]\n",
    "    X_angle_hold=X_angle[test_idx]\n",
    "    #define file path and get callbacks\n",
    "    file_path = \"%s_aug_model_weights.hdf5\"%j\n",
    "    callbacks = get_callbacks(filepath=file_path, patience=5)\n",
    "    gen_flow = gen_flow_for_two_inputs(X_train_cv, X_angle_cv, y_train_cv)\n",
    "    galaxyModel= getVggAngleModel()\n",
    "    histr = galaxyModel.fit_generator(\n",
    "            gen_flow,\n",
    "            steps_per_epoch=24,\n",
    "            epochs=50,\n",
    "            shuffle=True,\n",
    "            verbose=1,\n",
    "            validation_data=([X_holdout,X_angle_hold], Y_holdout),\n",
    "            callbacks=callbacks)\n",
    "     #Getting the Best Model\n",
    "    galaxyModel.load_weights(filepath=file_path)\n",
    "    #Getting Training Score\n",
    "    score = galaxyModel.evaluate([X_train_cv,X_angle_cv], y_train_cv, verbose=0)\n",
    "    print('Train loss:', score[0])\n",
    "    print('Train accuracy:', score[1])\n",
    "    #Getting Test Score\n",
    "    score = galaxyModel.evaluate([X_holdout,X_angle_hold], Y_holdout, verbose=0)\n",
    "    print('Test loss:', score[0])\n",
    "    print('Test accuracy:', score[1])\n",
    "\n",
    "    #Getting validation Score.\n",
    "    pred_valid=galaxyModel.predict([X_holdout,X_angle_hold])\n",
    "    y_valid_pred_log[test_idx] = pred_valid.reshape(pred_valid.shape[0])\n",
    "\n",
    "    #Getting Test Scores\n",
    "    temp_test=galaxyModel.predict([X_test, X_test_angle])\n",
    "    y_test_pred_log+=temp_test.reshape(temp_test.shape[0])\n",
    "\n",
    "    #Getting Train Scores\n",
    "    temp_train=galaxyModel.predict([X_train, X_angle])\n",
    "    y_train_pred_log+=temp_train.reshape(temp_train.shape[0])\n",
    "\n",
    "y_test_pred_log=y_test_pred_log/K\n",
    "y_train_pred_log=y_train_pred_log/K\n",
    "\n",
    "print('\\n Train Log Loss Validation= ',log_loss(target_train, y_train_pred_log))\n",
    "print(' Test Log Loss Validation= ',log_loss(target_train, y_valid_pred_log))\n",
    "#return y_test_pred_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#preds=myAngleCV(X_train, X_angle, X_test)\n",
    "preds = y_test_pred_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Submission to kaggle\n",
    "submission = pd.DataFrame()\n",
    "submission['id']=test['id']\n",
    "submission['is_iceberg']=preds\n",
    "submission.to_csv('sub_SGD_7folds.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['acc', 'val_acc', 'loss', 'val_loss'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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rHsxwN8lGnU88vi3zYPkH7ma8fxsg0OJ0lwQ6jYDGXQ43RHvfvNd+Camr3fa4\nzq6xfl8yrPjQ3exH/BM6DfMthoI8+OVFmPMP0EJX8shKhfVfueo6CYbWA91rdBp++ItJzn6Y83f4\n5WWIiIbzHoFe10BQ5VQMVYdkcQUuEdzoeX4t0E9Vb/c65l3gF1V9VkQuAz4E4oDrgXBV/ZvnuAnA\nQVV9qsRrjAfGA7Rq1arPli1b/PJeaqqDeYWc+a/vaBNbl6k3DzhqnEROfiHLt+0jcbObiTVl70H+\ne3XvSl3nucKm3wbLp7pv+SW/yWXuckV2cKWOyCYn/no//Au+/Rvc/CM0PfXo/Vm7YfLl7tvgxf+D\nnlcd3pedBrMfgqWToUFz923Su3fMtsXw+gho1ht+M8PV85+s1n4JU0a799HurKP3z34Ifn4Gxn4E\nHYaUfa2iIld1NftBVyK5dT7EncDULdsWw6vnuh5s7c91N+OOw3xP0Hs2wbqvYO0XsOVnd1MfdDcM\nuuv4esXtS4Gv/uKqTsOjoMNQF1OHIWV3BNi5wn3pSJ4PLfq6Xnml/U1WkK/JAlX1yw9wBTDR6/m1\nwH9LHNMM+AhYAjyLq66KBu4FHvA6bgJwb1mv16dPHzUV88J367X1nz/ThZvSAx2Kb3YsV30oSnXm\nX459zPalqn+LV311iGrewRN7vdxs1X+0VX378rKPO7hP9Y1Rqg81UP35OdXCAtUFr6r+vaXqIw1V\nZ01Qzcks/dzl09x5029VLSo6sXgD6bN73Oeen1P6/ryDqs8nqD7dTTVn/7Gvs+NX1YlD3Wcycajq\n481Up1x9/HEVFam+PlL1H+1UD+w9/usUO5ihml1J/18yklUL8ip2TmGh6pJ33Pt5OFr18z+6mE4A\nkKg+3NP92cC9DfDuy9jCs+0QVd2uqpepam/gr55tGb6cW6MVFbpGt4xkv73EvgP5vDRnA+d1aUxC\nGx/qQA/sgQWvuuJ80bEbwMu1ZxMsfO34GuxmP+i+iZ1577GPadoTLnkRUhbCZ3edWE+SJW+73j2D\n/1D2ceENXKN310tg1gPwbC/3DTD+VNfgOfRRCKtf+rndL4cz/wRLJsP8F48/1mM5sAeWvQe/vALp\nGyr/+sU2fAttBkHIMaZZCQ13vaP2pbh/x5Jy9rmlj18+08V58f/g+plwxl2w5jPYMvf44lo3Ezb/\nCGff56pwTlR4VOW1GUS1gOAKrvsSFAS9rnYN5wm/hQWvwPMJ7t/Yz72m/DkobyHQUUTa4m70o4Gr\nvQ8QkTjaMBUlAAAgAElEQVRgj6oWAfcDkzy7vgKeEJHiMtn5nv21w/rZ7kYXWhfO/CMMuL3Sqyj+\n930SmbkF3DvMh/rgjK3w9mWQvt49j2jo6mk7DXdF57AyqqWOVf8bXAcufdkN2vLFhm9hwzdw/uPl\n99nvdgmk/gXmPOHqos+407fX8FaY7xq1W/Z39cjlCQmDKybBV03dgKzLX3OJwJcBWWff76qxZv0V\nGnVy4xRORNp6V2WydqarslCvLsVxnV09fecLXJ19UCW0Pe3dDHs2QN/xZR/Xsq9r05j3X5dY253l\nbnDLP3BJNmu3uwEOmXD433jAbZD4GsyaADd+XbEBboUFLjHFdnCNwzVJRIyrhuo91n0xWTIZTr2q\n/PNOgN+ShaoWiMjtuBt/MDBJVVeKyKO4Ys8M4Gzg7yKiwA/AbZ5z94jIY7iEA/Coqu7xV6zVzoZv\nIcRTv/rNI7D0XRj5FLQ7+4jDcvILCQsJqvC8Vzv35fDGz5u5tFfzI0ZMl2rXKlcnn5cN13wIeZmH\ne20sm+Ju+m0GuZtPp+EQ3dIdu+E7d1zJxrvTnnDHf3kfTPut++be96ayYygqhFkPuh4r5R1b7Kw/\nuRvw7Ieg0Sm+N0IWWz7NNWSO/Lfv5wQFu+V7K7qEb1CQS5yThsMHv3U3xUYVWIejsMAlheKEvMdT\ngmjSw40i7jQC6sZ46t2/hHkvuFHGdWNd3X3n4e5vraykX5ZDXWbPLf/Ycx9wMcy4Ha54w93Mt/wE\nzU6DMe+53m3e6tR153xyG6z82PcvFwBL3nK9la6aXPFv8CeLZr3hhq8hJ8PvI8VtBHd19Hwf19Ni\n7IewbhZ8+Sc3KrTbZTDscTSyKe97Vjc7q3Mjnhvdm5Bg32sU7/9oOdMWJfPtPWfTsmHdYx+4ZR5M\nucolrms/giZe3WULCyBlweFvsMWljtgOrvqsMBfColwvotIa7/IPuv7saz93padz/nrsP/al77qu\nsZe/Bj2u8Pl9kncAXh8O6Rth3KfuP5YviorcQKrgUNcNtKqma8jY6npIhTeAG78pu7ojZx8kfe0+\n+/Wz3M0iKBTanunpTTMMolv5fm5wHXfepa+4KqOKeO8a2LEM7lru22e1ZZ5r2EchPBrOexhOu+7Y\nvXuKCuGlwZCfDbctOHZVl7fcTNdrLrY9XP9ljZlywx9s8aOT1d4tkJ4ECTe4553OdzeAn5+BH5+m\naN1XTK1/LQ/sGEi7JtF8sXwn4SG/8tT/9fRpUaGNqVlMTUzm2v6ty04Ua76Aade7etWxHx3d8yg4\nxJUUWg+E8/8GaUmu3/rG76Hj+e7G02rAsb/RhUbAlW/B53e7HkdZu2Dkf9x1veUdcL2Rmp3mqnUq\nok5dGD3FfWN/Y5T7hlk8YKwsa79w/eEvf61qbzLRrWD0Oy7WD8a5Lwven9+eTa5Et/ZL1yunqMBV\nCXYe4X58LR2ER7nPsvvlh0slKz+GhROh7Vlw+g2+x1xY4MYQdLvE98+q9QD3N5OxBc66D+rFln18\nUDCc/6gr4S58DQbcWv5rzH3eTWY4ZoolikpiyaK6KS7Se3cvDA2Hs+/juzrnEDLrz4ze+xLD4r4m\n6pJn+e/GTjw9ex11w4J57OLu5VZJ/Xv2OsJCgrj93DJmZ138Fnx6JzTt5Qac1fNhyu+4DhB3Bwy8\nw4c36REcAhc+B/WbuIRxYA9cPvHI7ojz/+f6xV/26vH9p49q7ga+Tb4c3vk/uOzlspOOKvz0tCvZ\nda3kwX2+aNUfLnwWPrkVZt4Pp15Zen//AbdVTrtDcIirFmx9hhug9vOz7lt+yaR9LNsSIXe/Gz9S\nEQNvL/8Ybx3Og3bnuJHQva4uu7F6/w6XLLpdWv5gSeMzmxuqutnwreuTH3e4znrfgXzuem8J189I\n41+xj7FjxCRigvMIenMkdxS8yfjBbZg8fytPzlxT5iJIy1P28fmvO7hxcDviSpnqG1X48d8w4w73\nH/O6T31LFCdCxNVJj/gnrPncNaQXT7OQlQo/PeNuiieykE6DpnD9F+7GOu0GN7jpWDb94Brkz7jT\n9xtmZet9jevUsPBVeG2ou4HXi3OjhO9YDLcvcD2sWvWvnAZqcP8Og/7gvu2v/Nj385K+AQkqfWxF\nZTv/Mfe38WM57UhznnAdFIY85P+YahErWVQnhQWuGqfr4YFbP65P5Y8f/EpaVi53n9eJW89pT2hw\nEPQeAbP+isx7nvt77CLn9N/x8vcbiQwL4fZzSx/A9M+v1hBTN5SbBpcybXtREXx1P/zyEvS40nVz\nrMpBYv1udjfEj26G1y9wVTA//hvyD7gRqycqItq1u3x4o2sDytrtklTJ0spPT7uSTs+rS79OVRn6\nKNRv7L44lDdYq7J0vsB1BvjpP65tyJeS3IZvoXmfqokvvoebn+uXl+D0G0ufXmPXKtczqN8t0NCW\nJ6hMVrKoTrYvdnPrtD+XA3kFPPjJCq59bQH1w0P46NaB3HleR5cowNXHj3wazp2ALJ/KIwf+xlU9\nY3hq1jom/bTpqEv/nJTGj+vTuO2cDkSGl2hHKMiDj250/wn73+Z65gRiNHH3y121V8YWNz3Eoteh\nz3UV6xlUltAIN3fUade5uaVm3HHkLKPbFsPGOa6Kp6KNvJUtKNiVbnpcUXXTewcFuXENu1e6nlPl\nObDH/c1WtArqRJz7gCvJfPtY6ftnPwh1Il2nCVOpLFlUJ0nfAMKeJgMZ9dxPvDVvCzcMastndwzi\n1Bal1NGKuAFqFz6HbPyOJzMf4LJTwnn0s1VMXXh4QJ+q8s+Za2gWFc7Y/iW+jW2ZC6+c7ea6Oe8R\nGPZ4pc05c1zanwPjPnOT7YWEuzEIlSk4xLUJnPknN+hu6m8OTx3909Ou8Tfht5X7mieTHldAVCv3\nWZTXU3LT924Mhy9dZitLVHPof6sbm7Ft8ZH7Ns6BpNlw5j22nKsfWLKoTjZ8C81P47/z97A5PZvJ\nN/Rjwqiu5U/a1+c6uGoysnslT2X+mUvaFvLnj37l02VuvYWvVu5kWco+7h7a6fC1snbDx7e4Loy5\n+10f90F3VY+eI816w+9+9kyw17jyry8C5/4VLnjK9Xx6+1JIXuimDO87/vjHG9QEwaGuk0LyL+WP\nmk76xnWPbt6namIrNuguN0Zk9oOHE1pRkRu4F9UK+t5ctfHUEpYsqouDe2FbIpktzmTyL1u4/LQW\nDOpYgcblU0bCtR8TlL2b/2T+icuaZXD3+0v5auVO/vXVWjo2rs9lp7VwfdYXvOqmCFg+zQ3auu2X\n6rf0ZWR85cw2Wpa+N7mZZLctgknDXEmm3y3+fc2TQe+xUDfOlS6ORdUNvGx3ZtV3BAiPciXOzT8e\nri5bPtVNPT7kwcBXIdZQliyqi00/gBbxTloHVJXfDzmOWTZbD4Trv0QEnsq6j8vjtnLz24vYkJrN\nvcM6E7x9kZuV9Yt7oXlvuHWe+89Vp17lv5+TRbdL3bxOdepBv/H+7/11MqhT102fnfS1G2xXmrR1\nsD+latsrvPUZ5waAzn4QcrPcGidNe1V8LI7xmSWL6iLpG4rqRPLM6gaM6duq7AFzZWnSDW6YhdRv\nwpMHHuL62FWc1yaE85Meh4lDXPXTFa/DtdNPbNrnmqTdWXDvusrpdVVTnH6jayj+6T+l76/IFB/+\nEBzqRn6nrYW3LnKJ6/y/Bba9rYazrrPVgadIv6JOTwgO5fZzyhgw54voVvDbr5B3/48Htz8BhQ2Q\nXZmu7/7Z99XuOvljOZ51CWqyiGg3knvuc24W2OKFeIolfeO+2Ze1Opy/nTLKTfSYPN/NS9Z2cOBi\nqQUsDVcH6Umwbyvv7+3EdQPa0LhBJdS51ouF6z5FThmJNOvl5jga9rglCuO7/re6+aZ+fubI7fk5\nsPmnwJUqionAiH9A8wQYeoyutKbSWMmiOvAU6ReF9GbKWe3LObgC6tRz8yEZczwim7jG7sVvuQbl\nBs3c9uT5UHAwcO0V3pr1gpu+CXQUtYKVLKqB/StmsqmoCcMG9Sem3km8tKapec74vRtLMe+Fw9uS\nvnEljjaDAheXqXKWLAKtIJewlLksCOrFjaVNw2FMIMW0cT2MEl93I7bBdZlt1f/Yq/+ZGsmSRYCt\nWfg1YZpDgx7Dj56Gw5jqYNDdbi2JBa9C5i7YtTzw7RWmylmbRQCpKqt/mk4Hgjn7/EsDHY4xpWvS\n1a2298tLh8ehWLKodaxkEUA/JaXRMXMBaTG9iIisosnijDkeg/8AB/fA1w+70d3xpwY6IlPFLFkE\niKry6pfz6R60mbie1WyqDWNKatkXWg/yLHR0jg1+q4XsXzxAZq/aRcxON1FbSMdq0AXRmPIM/oP7\n3XFYYOMwAWFtFgFQVKQ8PXsdf6i7Cg1tiDTtFeiQjClfhyFw84/QpHugIzEBYCWLAPj01+2s2bmf\nM4NXIFakNyeTpqfa32stZf/qVaygsIhnvl7PiEbphOemVY9RsMYYUw6/JgsRGS4ia0UkSUTuK2V/\nKxH5TkSWiMivInKBZ3sbETkoIks9Py/5M86qoqr8a9ZaNqVlc3fbFLex/TmBDcoYY3zgtzYLEQkG\nXgCGAinAQhGZoaqrvA57AJiqqi+KSFfgC6CNZ98GVa0xlfmFRcoD01cwZcFWxvRtRcfMl6Fx18Pz\n7RhjTDXmz5JFXyBJVTeqah7wHnBxiWMUaOB5HAVs92M8AZNbUMjt7y5myoKt3HZOe54Y1Q7ZOs8G\nNhljThr+TBbNgWSv5ymebd4eBsaKSAquVHGH1762nuqp70Wk1InqRWS8iCSKSGJqamolhl55snIL\nuP71hXy5YicTRnXlj8NOQbbMhcI8SxbGmJNGoBu4xwBvqGoL4ALgbREJAnYArVS1N/AH4F0RaVDy\nZFV9RVUTVDWhUaNGVRp4qRa9AW9eBD8/B2nrScvKZcwr81mwaQ//uaonNwzyTBS44Vu33nPrgQEN\n1xhjfOXPcRbbgJZez1t4tnm7ARgOoKrzRCQciFPV3UCuZ/siEdkAdAIS/RjviVGFH/8NWamw6XuY\nPYGD0oxLC3vz6PAx9D41/vCxSd+4RGGrsxljThL+TBYLgY4i0haXJEYDV5c4ZiswBHhDRLoA4UCq\niDQC9qhqoYi0AzoCG/0Y64nbvgQytsLFL7Axsg8fTpnIwMKFjAuZRdC3n8PcaOh4PrQe4NYNPu3a\nQEdsjDE+81uyUNUCEbkd+AoIBiap6koReRRIVNUZwD3AqyJyN66xe5yqqoicCTwqIvlAEXCLqu7x\nV6yVYtV0CAphab0zuO6d9YSFjODCmx4iKBpX7bRuJqz7CpZPdcfb+ApjzElEVDXQMVSKhIQETUwM\nUC2VKjzXi70RLRmQfBvxDcJ5+4Z+tGxY98jjigoheQFk7YJulwQmVmOM8SIii1Q1obzjbG6oyrBj\nGezdzDv5F9OkQTjTfjeQuPphRx8XFOyqoYwx5iQT6N5QNcOq6agE81paF65MaFl6ojDGmJOYJYsT\npQqrPmFbzOnspQEX9Gga6IiMMabSWbI4UTuXw56NzMjvS5emDWgbVy/QERljTKWzZHGiPFVQE1O7\nMrJHfPnHG2PMSciSxYlQhZXT2RGTwB6rgjLG1GA+JQsR+UhERnqm4jDFdq2EPRv4tKAvp8RH0q5R\n/UBHZIwxfuHrzf9/uNHX60XkSRHp7MeYTh6rpqMSxKupXa1UYYyp0XxKFqr6tapeA5wGbAa+FpG5\nInK9iIT6M8Bqy1MFtTO6D2kaZcnCGFOj+VytJCKxwDjgRmAJ8Cwuecz2S2TV3e7VkL6ezwr70alJ\nfTo0tiooY0zN5dMIbhH5GOgMvA1cqKo7PLveF5HqOxOsP62ajiK8ktqVa4ZYqcIYU7P5Ot3Hc6r6\nXWk7fJlTpEZa9Qm7YvqQuiPaqqCMMTWer9VQXUUkuviJiMSIyK1+iqn6270GUtfwRVFfOjSuT6cm\nkYGOyBhj/MrXZHGTqmYUP1HVvcBN/gnpJOCpgnp5dzcrVRhjagVfk0WwiEjxExEJBur4J6STwKpP\n2B3Tm10awwU2atsYUwv4mixm4hqzh4jIEGCKZ1vtk7oOdq/iy6L+tGtUj85WBWWMqQV8beD+M3Az\n8DvP89nARL9EVN2tmg7Ay7u7csU5TfEqcBljTI3lU7JQ1SLgRc9P7eapgtqxoyEjult7hTGmdvB1\nbqiOIjJNRFaJyMbiH38HV+2kJcGuFXxV1J+2cfXo0tSqoIwxtYOvbRav40oVBcA5wFvAZH8FVW2t\n+hiAl1K7MaJ7vFVBGWNqDV+TRYSqfgOIqm5R1YeBkf4Lq5pa9Qlp0T3ZVtTQuswaY2oVX5NFrmd6\n8vUicruIXArUrsmQ0jfAzuV8xQBax9alW7MGgY7IGGOqjK/J4k6gLvB7oA8wFriuvJNEZLiIrBWR\nJBG5r5T9rUTkOxFZIiK/isgFXvvu95y3VkSG+Rin/3h6Qb20uxsjulsvKGNM7VJubyjPALyrVPVe\nIAu43pcLe857ARgKpAALRWSGqq7yOuwBYKqqvigiXYEvgDaex6OBbkAz3JTonVS1sALvrXKt/oz0\n6B4k74xlpFVBGWNqmXJLFp4b9KDjuHZfIElVN6pqHvAecHHJywPF9TlRwHbP44uB91Q1V1U3AUme\n6wVGUSHsWsmCos60bBhB9+ZWBWWMqV18HZS3RERmAB8A2cUbVfWjMs5pDiR7PU8B+pU45mFglojc\nAdQDzvM6d36Jc5uXfAERGQ+MB2jVqpUv7+P47EuBwlx+2hPNBQOtCsoYU/v42mYRDqQD5wIXen5G\nVcLrjwHeUNUWwAXA2xVZ51tVX1HVBFVNaNSoUSWEcwzp6wFIKmxqvaCMMbWSryO4fWqnKGEb0NLr\neQvPNm83AMM9rzFPRMKBOB/PrTrpGwA40KAtp7aIClgYxhgTKL6ulPc6rn3hCKr62zJOWwh0FJG2\nuBv9aODqEsdsBYYAb4hIF1wJJhWYAbwrIk/jGrg7Agt8idUv0taTRQRdOrS3KihjTK3ka5vFZ16P\nw4FLOdwYXSpVLRCR24GvgGBgkqquFJFHgURVnQHcA7wqInfjktE4VVVgpYhMBVbhRo3fFsieUJqe\nxMaipsRHRQQqBGOMCShfq6E+9H4uIlOAn3w47wtcd1jvbQ96PV4FnHGMcx8HHvclPn8rSl3PRm1F\no8iwQIdijDEB4XNjcgkdgcaVGUi1lX+Q4MwUNhY1s2RhjKm1fG2zyOTINouduDUuaj5P4/Ymjees\nyPAAB2OMMYHhazVU7Z2LOz0JgI3ajMZWsjDG1FK+rmdxqYhEeT2PFpFL/BdWNeIZY7FJ460ayhhT\na/naZvGQqu4rfqKqGcBD/gmpmknfwP7QRgSH1yc8NDjQ0RhjTED42nW2tKTi67knt7T17AhpQaNw\nK1UYY2ovX0sWiSLytIi09/w8DSzyZ2DVgiqkr2cLTa29whhTq/maLO4A8oD3cbPH5gC3+SuoauPA\nHsjZx7rCeBpZTyhjTC3ma2+obOCoxYtqPE/j9oqcxjS3koUxphbztTfUbBGJ9noeIyJf+S+sasLT\nbXZ1fmPrCWWMqdV8rYaK8/SAAkBV91IbRnCnrUeDQknRRjSqb8nCGFN7+ZosikTk0OpCItKGUmah\nrXHSk8iJbEUhwTRuYMnCGFN7+dr99a/ATyLyPSDAYDwr1NVo6Unsq9sGwKqhjDG1mk8lC1WdCSQA\na4EpuKnFD/oxrsArKoQ9G0mt0wLAqqGMMbWarxMJ3gjciVuxbinQH5iHW2a1ZsrYCoV5pAS3ICRI\niKlbJ9ARGWNMwPjaZnEncDqwRVXPAXoDGWWfcpIrnm22KJ64+mEEBdkKecaY2svXZJGjqjkAIhKm\nqmuAzv4LqxrwjLFYld/EGreNMbWerw3cKZ5xFtOB2SKyF9jiv7CqgfQkCItiY3YETaMtWRhjajdf\nR3Bf6nn4sIh8B0QBM/0WVXWQth7iOpC6O49TW0aXf7wxxtRgFZ45VlW/90cg1U76BopaDyR9Y65N\nImiMqfWOdw3umi0vG/ancKBBW4rUxlgYY4wli9Ls2QjA3nA3aN1mnDXG1HaWLEqT5npC7QhtCVjJ\nwhhj/JosRGS4iKwVkSQROWqKcxH5j4gs9fysE5EMr32FXvtm+DPOo3jGWCQTD2BtFsaYWs9vS6OK\nSDDwAjAUSAEWisgMVV1VfIyq3u11/B24wX7FDqpqL3/FV6b0JGjQgp0H3ZrbVrIwxtR2/ixZ9AWS\nVHWjqubhVti7uIzjx+DmnQq89PUQ257UzFwiw0MIDw0OdETGGBNQ/kwWzYFkr+cpnm1HEZHWQFvg\nW6/N4SKSKCLzReSSY5w33nNMYmpqauVErepKFnEdSc3MtVKFMcZQfRq4RwPTVLXQa1trVU0Argae\nEZH2JU9S1VdUNUFVExo1alQ5kWSnQc4+iO3A7swca68wxhj8myy2AS29nrfwbCvNaEpUQanqNs/v\njcAcjmzP8B/PUqrEFpcsrNusMcb4M1ksBDqKSFsRqYNLCEf1ahKRU4AY3JTnxdtiRCTM8zgOOANY\nVfJcv/BMIFjcZmElC2OM8WOyUNUC4HbgK2A1MFVVV4rIoyJykdeho4H3VNV7mdYuQKKILAO+A570\n7kXlV+lJEFyH7IhmZOcVWpuFMcbgx66zAKr6BfBFiW0Plnj+cCnnzQV6+DO2Y0pLgobtSM0uAGyF\nPGOMgerTwF19pCd5GrdzAWwtC2OMwZLFkQoL3LxQsR1I9SQLq4YyxhhLFkfatxWK8j1jLHIAaGy9\noYwxxpLFEdKKu826aqiQICE6IjSwMRljTDVgycJbiTEWcfXDCAqSwMZkjDHVgCULb+nrITwa6jZk\nd2auNW4bY4yHJQtvnjmhEHGjt63brDHGAJYsjpTmus0CpGbZJILGGFPMkkWx3CzI3A6xHSgsUtKz\nbKoPY4wpZsmimGfdbWI7kJ6dS5HaGAtjjClmyaJY8QSCnnUsAJtx1hhjPCxZFEvfAAg0bHdoqg8r\nWRhjjGPJoljaeohqCaERh0oW1mZhjDGOJYti6UkQ6xbjs3mhjDHmSJYs4Ih1t8Eli8jwEMJDgwMc\nmDHGVA+WLACyUyF3/+ExFrZCnjHGHMGSBbj2CjiULHZn5lgVlDHGeLFkAV4TCB4uWVi3WWOMOcyS\nBbgxFsFhrjcUuEkErWRhjDGHWLIAN8Yitj0EBZGdW8CBvEKrhjLGGC+WLMC1WXhVQQE246wxxnix\nZFFYAHs3eTVuewbk2VoWxhhziCWL7FSo1/iIMRZgA/KMMcabX5OFiAwXkbUikiQi95Wy/z8istTz\ns05EMrz2XSci6z0/1/ktyAZN4Z7V0HMMAKmZOQA0tt5QxhhzSIi/LiwiwcALwFAgBVgoIjNUdVXx\nMap6t9fxdwC9PY8bAg8BCYACizzn7vVXvIhba3t3Zi4hQUJ0RKjfXsoYY042/ixZ9AWSVHWjquYB\n7wEXl3H8GGCK5/EwYLaq7vEkiNnAcD/GekhqZi5x9cMICpKqeDljjDkp+DNZNAeSvZ6neLYdRURa\nA22BbytyroiMF5FEEUlMTU2tlKB3Z+Za47YxxpRQXRq4RwPTVLWwIiep6iuqmqCqCY0aNaqUQFIz\nc63brDHGlODPZLENaOn1vIVnW2lGc7gKqqLnVqrULCtZGGNMSf5MFguBjiLSVkTq4BLCjJIHicgp\nQAwwz2vzV8D5IhIjIjHA+Z5tflVYpKRnWcnCGGNK8ltvKFUtEJHbcTf5YGCSqq4UkUeBRFUtThyj\ngfdUVb3O3SMij+ESDsCjqrrHX7EWS8/OpUhtjIUxxpTkt2QBoKpfAF+U2PZgiecPH+PcScAkvwVX\nit37iwfk2RgLY4zxVl0auKuF1CwbvW2MMaWxZOGleKoPm57cGGOOZMnCi80LZYwxpbNk4SU1M5fI\n8BDCQ4MDHYoxxlQrliy8pNoKecYYUypLFl52Z+ZYFZQxxpTCkoWX1Mxc6zZrjDGlsGThZbdVQxlj\nTKksWXhk5xZwIK/QqqGMMaYUliw8bIyFMcYcmyULj902xsIYY47JkoWHDcgzxphjs2ThsTszB4DG\n1hvKGGOOYsnCIzUzl5AgIToiNNChGGNMtWPJwiM1M5e4+mEEBUmgQzHGmGrHkoXH7kxbTtUYY47F\nkoVHaqYtp2qMMcdiycIjNctKFsYYcyyWLIDCIiU9y0oWxhhzLJYsgPTsXIrUxlgYY8yxWLIAdu8v\nHpBnYyyMMaY0lixw7RVgJQtjjDkWSxbYJILGGFMevyYLERkuImtFJElE7jvGMVeKyCoRWSki73pt\nLxSRpZ6fGf6M0+aFMsaYsoX468IiEgy8AAwFUoCFIjJDVVd5HdMRuB84Q1X3ikhjr0scVNVe/orP\nW2pmLpHhIYSHBlfFyxljzEnHnyWLvkCSqm5U1TzgPeDiEsfcBLygqnsBVHW3H+M5plRbIc8YY8rk\nz2TRHEj2ep7i2eatE9BJRH4WkfkiMtxrX7iIJHq2X1LaC4jIeM8xiampqccd6O7MHKuCMsaYMgS6\ngTsE6AicDYwBXhWRaM++1qqaAFwNPCMi7UuerKqvqGqCqiY0atTouINIzcy1brPGGFMGfyaLbUBL\nr+ctPNu8pQAzVDVfVTcB63DJA1Xd5vm9EZgD9PZXoLutGsoYY8rkz2SxEOgoIm1FpA4wGijZq2k6\nrlSBiMThqqU2ikiMiIR5bT8DWIUfZOcWcCCv0KqhjDGmDH7rDaWqBSJyO/AVEAxMUtWVIvIokKiq\nMzz7zheRVUAh8EdVTReRgcDLIlKES2hPeveiqkx5BUVc2LMZXZs28MfljTGmRhBVDXQMlSIhIUET\nExMDHYYxxpxURGSRp324TIFu4DbGGHMSsGRhjDGmXJYsjDHGlMuShTHGmHJZsjDGGFMuSxbGGGPK\nZcnCGGNMuSxZGGOMKVeNGZQnIqnAlhO4RByQVknhVCWLu2pZ3FXL4va/1qpa7kysNSZZnCgRSfRl\nFKnvhO4AAAV2SURBVGN1Y3FXLYu7alnc1YdVQxljjCmXJQtjjDHlsmRx2CuBDuA4WdxVy+KuWhZ3\nNWFtFsYYY8plJQtjjDHlsmRhjDGmXLU+WYjIcBFZKyJJInJfoOPxlYhsFpHlIrJURKr1qk8iMklE\ndovICq9tDUVktois9/yOCWSMpTlG3A+LyDbP575URC4IZIylEZGWIvKdiKwSkZUicqdne7X+zMuI\nu1p/5iISLiILRGSZJ+5HPNvbisgvnnvL+57lpU9atbrNQkSCgXXAUCAFt274GH8t4VqZRGQzkKCq\n1X7gj4icCWQBb6lqd8+2fwJ7VPVJT5KOUdU/BzLOko4R98NAlqo+FcjYyiIiTYGmqrpYRCKBRcAl\nwDiq8WdeRtxXUo0/cxERoJ6qZolIKPATcCfwB+AjVX1P5P/bu5sQq8o4juPfX06FjZAE5kIr0YJe\nwKxAKA2kIGhlgb0r0qYWtnAXRREILXvZhEoUTGSZ+VLSqpKwWlSWWUm2KAkaMWdRVga96a/FeSam\nYWbOnTE953Z/HxjmnOecOfzvn7nnf89zzn0ebQA+s72+yVhPRq9fWSwGvrZ90PYfwGZgecMx/e/Y\nfhf4YVTzcmCgLA9QnRRaZZy4W8/2Ydt7y/IvwAFgDi3P+QRxt5orx8rqmeXHwA3A1tLeunxPVq8X\niznAdyPWB+mCf87CwJuSPpF0X9PBTMFs24fL8vfA7CaDmaQHJH1euqla1ZUzmqR5wFXAh3RRzkfF\nDS3PuaRpkvYBQ8BbwDfAUdt/lV266dwypl4vFt1sqe2rgZuBNaXLpCu56gvtlv7Q9cACYBFwGHii\n2XDGJ2kGsA1Ya/vnkdvanPMx4m59zm0ft70ImEvVY3FpwyH953q9WBwCLhixPre0tZ7tQ+X3ELCD\n6h+0mxwpfdTDfdVDDcfTEdtHyonhBPAsLc176TvfBmyyvb00tz7nY8XdLTkHsH0UeAe4Fpgpqa9s\n6ppzy3h6vVjsAS4pTy2cBdwJ7Gw4plqS+ssNQCT1AzcB+yf+q9bZCawuy6uB1xuMpWPDJ9viVlqY\n93LD9TnggO0nR2xqdc7Hi7vtOZc0S9LMsjyd6oGZA1RFY0XZrXX5nqyefhoKoDyG9zQwDXje9uMN\nh1RL0nyqqwmAPuClNsct6WVgGdWwzUeAx4DXgC3AhVRDy99uu1U3k8eJexlVd4iBb4H7R9wHaAVJ\nS4H3gC+AE6X5Yar+/9bmfIK476LFOZe0kOoG9jSqD+BbbK8r79PNwHnAp8BK2783F+nJ6fliERER\n9Xq9GyoiIjqQYhEREbVSLCIiolaKRURE1EqxiIiIWikWES0gaZmkN5qOI2I8KRYREVErxSJiEiSt\nLHMX7JO0sQwgd0zSU2Uug12SZpV9F0n6oAyAt2N4ADxJF0t6u8x/sFfSgnL4GZK2SvpK0qbyjeaI\nVkixiOiQpMuAO4AlZdC448A9QD/wse0rgN1U3/QGeAF40PZCqm8lD7dvAp6xfSVwHdXgeFCNsroW\nuByYDyw55S8qokN99btERHEjcA2wp3zon041GN8J4JWyz4vAdknnAjNt7y7tA8CrZUyvObZ3ANj+\nDaAc7yPbg2V9HzCPaiKdiMalWER0TsCA7Yf+1Sg9Omq/qY6hM3LcoOPk/Rktkm6oiM7tAlZIOh/+\nmdP6Iqr30fDooncD79v+CfhR0vWlfRWwu8wANyjplnKMsyWdc1pfRcQU5JNLRIdsfynpEaoZCs8A\n/gTWAL8Ci8u2Iar7GlANS72hFIODwL2lfRWwUdK6cozbTuPLiJiSjDobcZIkHbM9o+k4Ik6ldENF\nREStXFlEREStXFlEREStFIuIiKiVYhEREbVSLCIiolaKRURE1Pob0sDV1UkNcfoAAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbbf011fdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WFJRSCtcmhShgf6mf453bjnepiKwXkdki0qm8E4nINBGJFZHY5OTk+okuuB2IN6TpADal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\nSqkSmhSUUkqV0KSglFKqxP8DfkgYdOkkdLwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbb78648b38>"
      ]
     },
     "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": [
       "(228, 75, 75, 3)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_holdout2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fbb743c6dd8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6biAAWzABNJAzvEAwdAn6PC2dJl8Are3J52WVQ2oL17Q5q887z0BT6c0FZVyC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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbb78658c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[0], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[13])+ \",\"+\" iceberg_probability : \"+str(pred_valid[0])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13      1\n",
       "14      0\n",
       "26      1\n",
       "38      1\n",
       "43      0\n",
       "44      0\n",
       "52      0\n",
       "80      0\n",
       "104     1\n",
       "127     0\n",
       "137     0\n",
       "138     1\n",
       "141     1\n",
       "167     1\n",
       "180     0\n",
       "186     0\n",
       "195     1\n",
       "199     0\n",
       "206     1\n",
       "207     1\n",
       "212     0\n",
       "213     1\n",
       "217     0\n",
       "221     0\n",
       "234     1\n",
       "240     0\n",
       "256     0\n",
       "267     1\n",
       "274     1\n",
       "276     1\n",
       "       ..\n",
       "1417    0\n",
       "1435    0\n",
       "1440    1\n",
       "1445    1\n",
       "1447    1\n",
       "1460    1\n",
       "1465    0\n",
       "1466    0\n",
       "1468    0\n",
       "1470    0\n",
       "1488    0\n",
       "1489    1\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",
       "1597    0\n",
       "1602    0\n",
       "Name: is_iceberg, Length: 228, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_holdout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fbb743b1208>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cE50dOIMzygAK9ryDQlS4SVhAq0JgRk739BhNwegIPhGKwfqogfaKatvmUeY0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\nHQhahb/TgddLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbb744289b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[1], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[14])+ \",\"+\" iceberg_probability : \"+str(pred_valid[1])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(228, 1)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_valid.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(228,)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_holdout.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fbb74312c50>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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c4glydnGfrxiKeeE6JSzc6MU+geUZbcmWoK9lDKpGyvop1XyGfPQVcrD7cAmv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\nm52gsaBxdDjtIlZtllMaQxpbz+lJNyq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Sjfu/7OFvhe1en9C2jzOfqxdiCOH9kj5tw+dX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\nHX1R1FidyMoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbb74370390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[2], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[26])+ \",\"+\" iceberg_probability : \"+str(pred_valid[2])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fbb742f8780>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CPc/9eNXCr6e/9PWnXppAhWvmiyp06ns0MT9nN/NrsLYfe5rhmZj7cj7352zsj4gm8nPy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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbb742c9f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[227], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[1602])+ \",\"+\" iceberg_probability : \"+str(pred_valid[227])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fbb7425d0f0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5nsiu+nNI5afz6lkF6PUcquLbdR9/IU48h5K5zvk1nRNyZY7ylBjlZQh1LUfQ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\nouM4DuGDouM4DvFf3AndnxiHg2kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbb742b4b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[3], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[38])+ \",\"+\" iceberg_probability : \"+str(pred_valid[3])\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
}