Model_03-SGD-k=5.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 [==============================] - 16s 666ms/step - loss: 0.6329 - acc: 0.6302 - val_loss: 0.4643 - val_acc: 0.7547\n",
      "Epoch 2/50\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.5301 - acc: 0.7322 - val_loss: 0.4086 - val_acc: 0.7609\n",
      "Epoch 3/50\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.3922 - acc: 0.7897 - val_loss: 0.2666 - val_acc: 0.8665\n",
      "Epoch 4/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.3310 - acc: 0.8637 - val_loss: 0.2754 - val_acc: 0.8820\n",
      "Epoch 5/50\n",
      "24/24 [==============================] - 12s 494ms/step - loss: 0.7407 - acc: 0.6389 - val_loss: 0.6465 - val_acc: 0.5559\n",
      "Epoch 6/50\n",
      "24/24 [==============================] - 12s 493ms/step - loss: 0.6758 - acc: 0.5600 - val_loss: 0.7537 - val_acc: 0.5839\n",
      "Epoch 7/50\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.5969 - acc: 0.6387 - val_loss: 0.5812 - val_acc: 0.7143\n",
      "Epoch 8/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.5439 - acc: 0.7452 - val_loss: 0.7091 - val_acc: 0.4689\n",
      "Epoch 9/50\n",
      "24/24 [==============================] - 12s 495ms/step - loss: 0.6961 - acc: 0.5359 - val_loss: 0.6564 - val_acc: 0.5342\n",
      "Epoch 10/50\n",
      "24/24 [==============================] - 12s 496ms/step - loss: 0.6730 - acc: 0.5913 - val_loss: 0.6729 - val_acc: 0.5311\n",
      "Epoch 11/50\n",
      "24/24 [==============================] - 12s 497ms/step - loss: 0.6020 - acc: 0.6291 - val_loss: 0.4562 - val_acc: 0.7888\n",
      "Epoch 12/50\n",
      "24/24 [==============================] - 12s 499ms/step - loss: 0.5378 - acc: 0.7250 - val_loss: 0.4140 - val_acc: 0.8043\n",
      "Epoch 13/50\n",
      "24/24 [==============================] - 12s 500ms/step - loss: 0.4249 - acc: 0.8089 - val_loss: 0.3373 - val_acc: 0.8602\n",
      "Train loss: 0.279057286459\n",
      "Train accuracy: 0.875975039002\n",
      "Test loss: 0.26657407017\n",
      "Test accuracy: 0.866459627329\n",
      "\n",
      "===================FOLD= 1\n",
      "Epoch 1/50\n",
      "24/24 [==============================] - 14s 573ms/step - loss: 0.6786 - acc: 0.5983 - val_loss: 0.5424 - val_acc: 0.7383\n",
      "Epoch 2/50\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.5679 - acc: 0.6899 - val_loss: 0.4882 - val_acc: 0.7539\n",
      "Epoch 3/50\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.4078 - acc: 0.8083 - val_loss: 0.2963 - val_acc: 0.8598\n",
      "Epoch 4/50\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.3297 - acc: 0.8425 - val_loss: 0.2949 - val_acc: 0.8505\n",
      "Epoch 5/50\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.3061 - acc: 0.8578 - val_loss: 0.2368 - val_acc: 0.8723\n",
      "Epoch 6/50\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.2676 - acc: 0.8787 - val_loss: 0.2929 - val_acc: 0.8598\n",
      "Epoch 7/50\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.2625 - acc: 0.8863 - val_loss: 0.2535 - val_acc: 0.8847\n",
      "Epoch 8/50\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2526 - acc: 0.8885 - val_loss: 0.2082 - val_acc: 0.9097\n",
      "Epoch 9/50\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.2316 - acc: 0.9015 - val_loss: 0.2200 - val_acc: 0.9159\n",
      "Epoch 10/50\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.2630 - acc: 0.8816 - val_loss: 0.2176 - val_acc: 0.9097\n",
      "Epoch 11/50\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.2383 - acc: 0.8895 - val_loss: 0.2277 - val_acc: 0.8941\n",
      "Epoch 12/50\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.2183 - acc: 0.9041 - val_loss: 0.2249 - val_acc: 0.9128\n",
      "Epoch 13/50\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.2126 - acc: 0.9146 - val_loss: 0.2191 - val_acc: 0.9065\n",
      "Epoch 14/50\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.2718 - acc: 0.8743 - val_loss: 0.2271 - val_acc: 0.8972\n",
      "Epoch 15/50\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.2149 - acc: 0.9172 - val_loss: 0.2126 - val_acc: 0.9003\n",
      "Epoch 16/50\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.2272 - acc: 0.8947 - val_loss: 0.2246 - val_acc: 0.9097\n",
      "Epoch 17/50\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.2113 - acc: 0.9159 - val_loss: 0.1980 - val_acc: 0.9283\n",
      "Epoch 18/50\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.2529 - acc: 0.8921 - val_loss: 0.2101 - val_acc: 0.9065\n",
      "Epoch 19/50\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.2093 - acc: 0.9113 - val_loss: 0.2328 - val_acc: 0.9034\n",
      "Epoch 20/50\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1977 - acc: 0.9178 - val_loss: 0.1954 - val_acc: 0.9097\n",
      "Epoch 21/50\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.1853 - acc: 0.9262 - val_loss: 0.2439 - val_acc: 0.9034\n",
      "Epoch 22/50\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1972 - acc: 0.9204 - val_loss: 0.2100 - val_acc: 0.9252\n",
      "Epoch 23/50\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.2119 - acc: 0.9025 - val_loss: 0.2216 - val_acc: 0.8972\n",
      "Epoch 24/50\n",
      "24/24 [==============================] - 12s 518ms/step - loss: 0.2112 - acc: 0.9067 - val_loss: 0.1944 - val_acc: 0.9190\n",
      "Epoch 25/50\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1999 - acc: 0.9058 - val_loss: 0.2149 - val_acc: 0.9034\n",
      "Epoch 26/50\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.2043 - acc: 0.9211 - val_loss: 0.1967 - val_acc: 0.9190\n",
      "Epoch 27/50\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.2830 - acc: 0.8875 - val_loss: 0.2014 - val_acc: 0.9065\n",
      "Epoch 28/50\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.2154 - acc: 0.9118 - val_loss: 0.2172 - val_acc: 0.9190\n",
      "Epoch 29/50\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1841 - acc: 0.9296 - val_loss: 0.1806 - val_acc: 0.9159\n",
      "Epoch 30/50\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1762 - acc: 0.9302 - val_loss: 0.1924 - val_acc: 0.9221\n",
      "Epoch 31/50\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1597 - acc: 0.9413 - val_loss: 0.1962 - val_acc: 0.9252\n",
      "Epoch 32/50\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.2115 - acc: 0.8960 - val_loss: 0.2079 - val_acc: 0.8972\n",
      "Epoch 33/50\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1597 - acc: 0.9341 - val_loss: 0.1943 - val_acc: 0.9252\n",
      "Epoch 34/50\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1696 - acc: 0.9296 - val_loss: 0.1838 - val_acc: 0.9315\n",
      "Epoch 35/50\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.1503 - acc: 0.9353 - val_loss: 0.1910 - val_acc: 0.9128\n",
      "Epoch 36/50\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1785 - acc: 0.9354 - val_loss: 0.1903 - val_acc: 0.9190\n",
      "Epoch 37/50\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1571 - acc: 0.9309 - val_loss: 0.1746 - val_acc: 0.9315\n",
      "Epoch 38/50\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1336 - acc: 0.9407 - val_loss: 0.2197 - val_acc: 0.9190\n",
      "Epoch 39/50\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1593 - acc: 0.9374 - val_loss: 0.2063 - val_acc: 0.9128\n",
      "Epoch 40/50\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1307 - acc: 0.9485 - val_loss: 0.2145 - val_acc: 0.9315\n",
      "Epoch 41/50\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1614 - acc: 0.9341 - val_loss: 0.2095 - val_acc: 0.9221\n",
      "Epoch 42/50\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.1663 - acc: 0.9370 - val_loss: 0.3770 - val_acc: 0.8723\n",
      "Epoch 43/50\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1574 - acc: 0.9348 - val_loss: 0.2035 - val_acc: 0.9346\n",
      "Epoch 44/50\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1707 - acc: 0.9361 - val_loss: 0.1749 - val_acc: 0.9346\n",
      "Epoch 45/50\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1547 - acc: 0.9407 - val_loss: 0.2027 - val_acc: 0.9346\n",
      "Epoch 46/50\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1354 - acc: 0.9439 - val_loss: 0.2050 - val_acc: 0.9221\n",
      "Epoch 47/50\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1304 - acc: 0.9439 - val_loss: 0.2071 - val_acc: 0.9221\n",
      "Train loss: 0.110759765607\n",
      "Train accuracy: 0.955572876072\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.174603711317\n",
      "Test accuracy: 0.931464174455\n",
      "\n",
      "===================FOLD= 2\n",
      "Epoch 1/50\n",
      "24/24 [==============================] - 13s 555ms/step - loss: 0.6602 - acc: 0.6289 - val_loss: 0.3859 - val_acc: 0.7726\n",
      "Epoch 2/50\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.4598 - acc: 0.7750 - val_loss: 0.4187 - val_acc: 0.8318\n",
      "Epoch 3/50\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.3980 - acc: 0.8226 - val_loss: 0.3007 - val_acc: 0.8972\n",
      "Epoch 4/50\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.3584 - acc: 0.8383 - val_loss: 0.2489 - val_acc: 0.9065\n",
      "Epoch 5/50\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.2856 - acc: 0.8728 - val_loss: 0.2046 - val_acc: 0.9315\n",
      "Epoch 6/50\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.2919 - acc: 0.8640 - val_loss: 0.2123 - val_acc: 0.9283\n",
      "Epoch 7/50\n",
      "24/24 [==============================] - 13s 534ms/step - loss: 0.2834 - acc: 0.8524 - val_loss: 0.1910 - val_acc: 0.9221\n",
      "Epoch 8/50\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2610 - acc: 0.8904 - val_loss: 0.1891 - val_acc: 0.9377\n",
      "Epoch 9/50\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.2561 - acc: 0.8823 - val_loss: 0.2174 - val_acc: 0.9097\n",
      "Epoch 10/50\n",
      "24/24 [==============================] - 12s 521ms/step - loss: 0.2472 - acc: 0.8976 - val_loss: 0.1750 - val_acc: 0.9377\n",
      "Epoch 11/50\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.2266 - acc: 0.8957 - val_loss: 0.1790 - val_acc: 0.9408\n",
      "Epoch 12/50\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.3023 - acc: 0.8663 - val_loss: 0.1847 - val_acc: 0.9346\n",
      "Epoch 13/50\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.2297 - acc: 0.9054 - val_loss: 0.1925 - val_acc: 0.9252\n",
      "Epoch 14/50\n",
      "24/24 [==============================] - 12s 516ms/step - loss: 0.2581 - acc: 0.8867 - val_loss: 0.4015 - val_acc: 0.8224\n",
      "Epoch 15/50\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.2511 - acc: 0.8878 - val_loss: 0.1870 - val_acc: 0.9346\n",
      "Epoch 16/50\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.2393 - acc: 0.8973 - val_loss: 0.1886 - val_acc: 0.9533\n",
      "Epoch 17/50\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.2605 - acc: 0.8986 - val_loss: 0.2074 - val_acc: 0.9377\n",
      "Epoch 18/50\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.2200 - acc: 0.9022 - val_loss: 0.1780 - val_acc: 0.9408\n",
      "Epoch 19/50\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.2284 - acc: 0.8963 - val_loss: 0.1679 - val_acc: 0.9439\n",
      "Epoch 20/50\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1923 - acc: 0.9211 - val_loss: 0.1854 - val_acc: 0.9346\n",
      "Epoch 21/50\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.1798 - acc: 0.9249 - val_loss: 0.1975 - val_acc: 0.9283\n",
      "Epoch 22/50\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1921 - acc: 0.9178 - val_loss: 0.1579 - val_acc: 0.9564\n",
      "Epoch 23/50\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1724 - acc: 0.9341 - val_loss: 0.1948 - val_acc: 0.9346\n",
      "Epoch 24/50\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1829 - acc: 0.9244 - val_loss: 0.1678 - val_acc: 0.9470\n",
      "Epoch 25/50\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.2269 - acc: 0.9250 - val_loss: 0.2347 - val_acc: 0.9128\n",
      "Epoch 26/50\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1707 - acc: 0.9250 - val_loss: 0.1997 - val_acc: 0.9377\n",
      "Epoch 27/50\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1600 - acc: 0.9354 - val_loss: 0.2143 - val_acc: 0.9346\n",
      "Epoch 28/50\n",
      "24/24 [==============================] - 12s 515ms/step - loss: 0.1585 - acc: 0.9314 - val_loss: 0.1839 - val_acc: 0.9470\n",
      "Epoch 29/50\n",
      "24/24 [==============================] - 12s 501ms/step - loss: 0.1545 - acc: 0.9367 - val_loss: 0.1858 - val_acc: 0.9439\n",
      "Epoch 30/50\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1589 - acc: 0.9322 - val_loss: 0.2223 - val_acc: 0.9346\n",
      "Epoch 31/50\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1784 - acc: 0.9224 - val_loss: 0.1876 - val_acc: 0.9377\n",
      "Epoch 32/50\n",
      "24/24 [==============================] - 12s 501ms/step - loss: 0.1837 - acc: 0.9289 - val_loss: 0.1783 - val_acc: 0.9439\n",
      "Train loss: 0.144797152522\n",
      "Train accuracy: 0.939984411535\n",
      "Test loss: 0.157898356239\n",
      "Test accuracy: 0.956386292835\n",
      "\n",
      "===================FOLD= 3\n",
      "Epoch 1/50\n",
      "24/24 [==============================] - 14s 573ms/step - loss: 0.7623 - acc: 0.6028 - val_loss: 0.4600 - val_acc: 0.7781\n",
      "Epoch 2/50\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.4959 - acc: 0.7602 - val_loss: 0.3868 - val_acc: 0.8031\n",
      "Epoch 3/50\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.4592 - acc: 0.7833 - val_loss: 0.3954 - val_acc: 0.7812\n",
      "Epoch 4/50\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.3302 - acc: 0.8354 - val_loss: 0.2751 - val_acc: 0.8719\n",
      "Epoch 5/50\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.2906 - acc: 0.8676 - val_loss: 0.3353 - val_acc: 0.8562\n",
      "Epoch 6/50\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.2648 - acc: 0.8817 - val_loss: 0.2599 - val_acc: 0.9000\n",
      "Epoch 7/50\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2851 - acc: 0.8608 - val_loss: 2.0321 - val_acc: 0.5750\n",
      "Epoch 8/50\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.4323 - acc: 0.8165 - val_loss: 0.3804 - val_acc: 0.8313\n",
      "Epoch 9/50\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.2540 - acc: 0.8931 - val_loss: 0.2674 - val_acc: 0.8844\n",
      "Epoch 10/50\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.2281 - acc: 0.9074 - val_loss: 0.3027 - val_acc: 0.8719\n",
      "Epoch 11/50\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.2284 - acc: 0.9041 - val_loss: 0.2576 - val_acc: 0.8938\n",
      "Epoch 12/50\n",
      "24/24 [==============================] - 12s 521ms/step - loss: 0.2199 - acc: 0.9035 - val_loss: 0.2551 - val_acc: 0.8906\n",
      "Epoch 13/50\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1938 - acc: 0.9133 - val_loss: 0.2555 - val_acc: 0.8844\n",
      "Epoch 14/50\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2240 - acc: 0.9116 - val_loss: 0.4858 - val_acc: 0.7781\n",
      "Epoch 15/50\n",
      "24/24 [==============================] - 12s 501ms/step - loss: 0.2243 - acc: 0.9039 - val_loss: 0.2670 - val_acc: 0.8781\n",
      "Epoch 16/50\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.2651 - acc: 0.8993 - val_loss: 0.3458 - val_acc: 0.8531\n",
      "Epoch 17/50\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.2211 - acc: 0.9041 - val_loss: 0.2604 - val_acc: 0.9000\n",
      "Epoch 18/50\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1736 - acc: 0.9367 - val_loss: 0.2447 - val_acc: 0.9094\n",
      "Epoch 19/50\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1888 - acc: 0.9270 - val_loss: 0.2456 - val_acc: 0.8969\n",
      "Epoch 20/50\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.2119 - acc: 0.9098 - val_loss: 0.2302 - val_acc: 0.9000\n",
      "Epoch 21/50\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2056 - acc: 0.9260 - val_loss: 0.2508 - val_acc: 0.8812\n",
      "Epoch 22/50\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1959 - acc: 0.9217 - val_loss: 0.3115 - val_acc: 0.8719\n",
      "Epoch 23/50\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1840 - acc: 0.9237 - val_loss: 0.2382 - val_acc: 0.8938\n",
      "Epoch 24/50\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1683 - acc: 0.9367 - val_loss: 0.2636 - val_acc: 0.8750\n",
      "Epoch 25/50\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.2045 - acc: 0.9287 - val_loss: 0.2328 - val_acc: 0.8969\n",
      "Epoch 26/50\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1648 - acc: 0.9309 - val_loss: 0.2210 - val_acc: 0.9031\n",
      "Epoch 27/50\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1899 - acc: 0.9202 - val_loss: 0.2341 - val_acc: 0.9000\n",
      "Epoch 28/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1913 - acc: 0.9223 - val_loss: 0.2336 - val_acc: 0.8938\n",
      "Epoch 29/50\n",
      "24/24 [==============================] - 12s 501ms/step - loss: 0.1824 - acc: 0.9244 - val_loss: 0.2302 - val_acc: 0.9031\n",
      "Epoch 30/50\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1689 - acc: 0.9248 - val_loss: 0.2257 - val_acc: 0.9062\n",
      "Epoch 31/50\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1458 - acc: 0.9420 - val_loss: 0.2374 - val_acc: 0.9062\n",
      "Epoch 32/50\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.1510 - acc: 0.9472 - val_loss: 0.2114 - val_acc: 0.9031\n",
      "Epoch 33/50\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1423 - acc: 0.9446 - val_loss: 0.2138 - val_acc: 0.9031\n",
      "Epoch 34/50\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1434 - acc: 0.9420 - val_loss: 0.2073 - val_acc: 0.9000\n",
      "Epoch 35/50\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1664 - acc: 0.9379 - val_loss: 0.2251 - val_acc: 0.9062\n",
      "Epoch 36/50\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1498 - acc: 0.9413 - val_loss: 0.3030 - val_acc: 0.8688\n",
      "Epoch 37/50\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1682 - acc: 0.9293 - val_loss: 0.2484 - val_acc: 0.8906\n",
      "Epoch 38/50\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1488 - acc: 0.9420 - val_loss: 0.2357 - val_acc: 0.9000\n",
      "Epoch 39/50\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1345 - acc: 0.9426 - val_loss: 0.2179 - val_acc: 0.9094\n",
      "Epoch 40/50\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1314 - acc: 0.9511 - val_loss: 0.2148 - val_acc: 0.9156\n",
      "Epoch 41/50\n",
      "24/24 [==============================] - 13s 521ms/step - loss: 0.1671 - acc: 0.9293 - val_loss: 0.2056 - val_acc: 0.9125\n",
      "Epoch 42/50\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1288 - acc: 0.9530 - val_loss: 0.2975 - val_acc: 0.8906\n",
      "Epoch 43/50\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1352 - acc: 0.9439 - val_loss: 0.2131 - val_acc: 0.9000\n",
      "Epoch 44/50\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.1148 - acc: 0.9570 - val_loss: 0.1963 - val_acc: 0.9031\n",
      "Epoch 45/50\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1246 - acc: 0.9485 - val_loss: 0.2251 - val_acc: 0.9094\n",
      "Epoch 46/50\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1151 - acc: 0.9524 - val_loss: 0.2264 - val_acc: 0.8844\n",
      "Epoch 47/50\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.0998 - acc: 0.9583 - val_loss: 0.2111 - val_acc: 0.9031\n",
      "Epoch 48/50\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1181 - acc: 0.9563 - val_loss: 0.2093 - val_acc: 0.9094\n",
      "Epoch 49/50\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.0987 - acc: 0.9602 - val_loss: 0.2031 - val_acc: 0.9000\n",
      "Epoch 50/50\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.1190 - acc: 0.9482 - val_loss: 0.2365 - val_acc: 0.8844\n",
      "Train loss: 0.0715398964736\n",
      "Train accuracy: 0.979750778816\n",
      "Test loss: 0.196345988661\n",
      "Test accuracy: 0.903125\n",
      "\n",
      "===================FOLD= 4\n",
      "Epoch 1/50\n",
      "24/24 [==============================] - 14s 567ms/step - loss: 0.7795 - acc: 0.6039 - val_loss: 0.4420 - val_acc: 0.7688\n",
      "Epoch 2/50\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.4869 - acc: 0.7744 - val_loss: 0.3817 - val_acc: 0.8000\n",
      "Epoch 3/50\n",
      "24/24 [==============================] - 13s 524ms/step - loss: 0.3293 - acc: 0.8468 - val_loss: 0.3314 - val_acc: 0.8375\n",
      "Epoch 4/50\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.3232 - acc: 0.8406 - val_loss: 0.2961 - val_acc: 0.8594\n",
      "Epoch 5/50\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.2890 - acc: 0.8807 - val_loss: 0.2990 - val_acc: 0.8656\n",
      "Epoch 6/50\n",
      "24/24 [==============================] - 13s 523ms/step - loss: 0.2799 - acc: 0.8759 - val_loss: 0.2909 - val_acc: 0.8719\n",
      "Epoch 7/50\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2681 - acc: 0.8909 - val_loss: 0.3404 - val_acc: 0.8438\n",
      "Epoch 8/50\n",
      "24/24 [==============================] - 13s 522ms/step - loss: 0.2418 - acc: 0.9054 - val_loss: 0.2710 - val_acc: 0.8688\n",
      "Epoch 9/50\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.2124 - acc: 0.9211 - val_loss: 0.3117 - val_acc: 0.8750\n",
      "Epoch 10/50\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1924 - acc: 0.9244 - val_loss: 0.3143 - val_acc: 0.8750\n",
      "Epoch 11/50\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.2108 - acc: 0.9165 - val_loss: 0.2750 - val_acc: 0.8844\n",
      "Epoch 12/50\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.2059 - acc: 0.9237 - val_loss: 0.2777 - val_acc: 0.8688\n",
      "Epoch 13/50\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.2519 - acc: 0.8935 - val_loss: 0.2889 - val_acc: 0.8688\n",
      "Epoch 14/50\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2773 - acc: 0.8713 - val_loss: 0.3560 - val_acc: 0.8438\n",
      "Epoch 15/50\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.2011 - acc: 0.9244 - val_loss: 0.2951 - val_acc: 0.8625\n",
      "Epoch 16/50\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.1856 - acc: 0.9302 - val_loss: 0.2751 - val_acc: 0.8719\n",
      "Epoch 17/50\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.1960 - acc: 0.9195 - val_loss: 0.2849 - val_acc: 0.8906\n",
      "Epoch 18/50\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1876 - acc: 0.9195 - val_loss: 0.2813 - val_acc: 0.8812\n",
      "Train loss: 0.181291673631\n",
      "Train accuracy: 0.931464174455\n",
      "Test loss: 0.270958095789\n",
      "Test accuracy: 0.86875\n",
      "\n",
      " Train Log Loss Validation=  0.147759799214\n",
      " Test Log Loss Validation=  0.213283862784\n"
     ]
    }
   ],
   "source": [
    "#def myAngleCV(X_train, X_angle, X_test):\n",
    "K=5\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_5folds.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['val_loss', 'val_acc', 'acc', 'loss'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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6EorAWQ87iePzR51pn698wxkc5q3y/fDRz52LxuDrYNLTvq36MMfnlKucSRhn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\nJQ1jjDFes6RhjDHGa5Y0jDHGeO3/Ay3y0PMBPzxKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5f51e2bef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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TaYzZB+zEShQBl54Sx7acYlwuE+hQlFLKr3yZGNYCfUSkh4hEANOABbXKfIh1tYCIJGFV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\nStWgiUEppVQN/x9GJkDENTPGNQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5ed8ec5f28>"
      ]
     },
     "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": [
       "(320, 75, 75, 3)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_holdout2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13      1\n",
       "14      0\n",
       "26      1\n",
       "38      1\n",
       "42      0\n",
       "43      0\n",
       "44      0\n",
       "52      0\n",
       "73      1\n",
       "80      0\n",
       "86      1\n",
       "96      1\n",
       "101     0\n",
       "104     1\n",
       "120     0\n",
       "127     0\n",
       "129     0\n",
       "137     0\n",
       "138     1\n",
       "141     1\n",
       "151     1\n",
       "166     0\n",
       "167     1\n",
       "168     0\n",
       "171     0\n",
       "180     0\n",
       "186     0\n",
       "189     0\n",
       "195     1\n",
       "199     0\n",
       "       ..\n",
       "1470    0\n",
       "1477    1\n",
       "1487    1\n",
       "1488    0\n",
       "1489    1\n",
       "1495    0\n",
       "1504    1\n",
       "1506    1\n",
       "1516    0\n",
       "1525    0\n",
       "1530    0\n",
       "1533    0\n",
       "1537    0\n",
       "1539    0\n",
       "1541    0\n",
       "1542    0\n",
       "1543    0\n",
       "1549    0\n",
       "1552    0\n",
       "1559    0\n",
       "1566    0\n",
       "1567    0\n",
       "1577    0\n",
       "1585    0\n",
       "1588    0\n",
       "1590    0\n",
       "1597    0\n",
       "1601    0\n",
       "1602    0\n",
       "1603    0\n",
       "Name: is_iceberg, Length: 320, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_holdout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f5ed8d52f98>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kKKKlrS2nuIqC4Bn6sVOhf4zsVowEg0ri4zGU5AXaGS+QE3zKZTpuQIAtLAE0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\nQSsbayExqC5/sUs5TRwdOE0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kKvZfwnC4Be3rKgbgGhO/P0XH788c2tjF3AMkW0jFC0sc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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5f51e2b9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[0], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[13])+ \",\"+\" iceberg_probability : \"+str(pred_valid[0])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f5ed8cbf710>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nuw+38eqaJwwsYO82hO0boxVW6Md5DHxnkTHmSR/VsIhDHnjWbDd9TiPFhISE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\nvAdtPTmq0UbMWwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5ed8d8b160>"
      ]
     },
     "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": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(320, 1)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_valid.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(320,)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_holdout.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f5ed8ca31d0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CwG9pAbusirRyCBoGkbuAik31stjkofuKhQYYn441oKEKd3MfF1Z3rlBrm2px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\nBIEQQjEIgkD4HY59/Eab2FaHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5ed8c7c438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[7], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[52])+ \",\"+\" iceberg_probability : \"+str(pred_valid[7])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f5ed8c068d0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kJERsLGVWHZQdtbSh3kJQM+jDHDT0MooCWcybhQG0urseaEynZboUXxuB7rEt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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5ed8c5bef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[319], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[1602])+ \",\"+\" iceberg_probability : \"+str(pred_valid[319])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f5ed8b4eba8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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X9WgXUFwXMcdz5MzogsrcQv5sDto5rZwOMTB4lPu+Q+Sq0n7LQOUU1fEFcoMX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5ed8b2bb38>"
      ]
     },
     "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": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f5ed8ab5438>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yTqECPWv7HbUJYo+jo/SgiedA6yasstMCkm6wpj69cOf1bs8dvEvEmEPdTG04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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5ed8b0b668>"
      ]
     },
     "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": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f5ed8a95cf8>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tnKZ4XlpICIgO6jkVAWh3f+B966DIWxbF+NtKn9NMMSEhIQFIL8WEhIQEYGvp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\nOIJPio7jOIJPio7jOIJPio7jOMJ/AABiWlB/Ej8lAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5ed8a6fe48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[8], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[73])+ \",\"+\" iceberg_probability : \"+str(pred_valid[8])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f5ed89fc6a0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4w0DZIz9Pcxs5pnPkhB6CdxPxosodbzcDmcumU8154W1cLGHzVHpfTHBjmlR+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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5ed8a52390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[10], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[86])+ \",\"+\" iceberg_probability : \"+str(pred_valid[10])\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
}