My Kernel.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['train.csv', 'sample_submission.csv', 'protein-trainval-split', 'test', 'train']\n"
     ]
    }
   ],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "%matplotlib inline\n",
    "from PIL import Image\n",
    "from scipy.misc import imread\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "import os\n",
    "print(os.listdir(\"./dataset\"))\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
    "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
    "\n",
    "# Any results you write to the current directory are saved as output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_labels = pd.read_csv(\"./dataset/train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_names = {\n",
    "    0:  \"Nucleoplasm\",  \n",
    "    1:  \"Nuclear membrane\",   \n",
    "    2:  \"Nucleoli\",   \n",
    "    3:  \"Nucleoli fibrillar center\",   \n",
    "    4:  \"Nuclear speckles\",\n",
    "    5:  \"Nuclear bodies\",   \n",
    "    6:  \"Endoplasmic reticulum\",   \n",
    "    7:  \"Golgi apparatus\",   \n",
    "    8:  \"Peroxisomes\",   \n",
    "    9:  \"Endosomes\",   \n",
    "    10:  \"Lysosomes\",   \n",
    "    11:  \"Intermediate filaments\",   \n",
    "    12:  \"Actin filaments\",   \n",
    "    13:  \"Focal adhesion sites\",   \n",
    "    14:  \"Microtubules\",   \n",
    "    15:  \"Microtubule ends\",   \n",
    "    16:  \"Cytokinetic bridge\",   \n",
    "    17:  \"Mitotic spindle\",   \n",
    "    18:  \"Microtubule organizing center\",   \n",
    "    19:  \"Centrosome\",   \n",
    "    20:  \"Lipid droplets\",   \n",
    "    21:  \"Plasma membrane\",   \n",
    "    22:  \"Cell junctions\",   \n",
    "    23:  \"Mitochondria\",   \n",
    "    24:  \"Aggresome\",   \n",
    "    25:  \"Cytosol\",   \n",
    "    26:  \"Cytoplasmic bodies\",   \n",
    "    27:  \"Rods & rings\"\n",
    "}\n",
    "\n",
    "reverse_train_labels = dict((v,k) for k,v in label_names.items())\n",
    "\n",
    "def fill_targets(row):\n",
    "    row.Target = np.array(row.Target.split(\" \")).astype(np.int)\n",
    "    for num in row.Target:\n",
    "        name = label_names[int(num)]\n",
    "        row.loc[name] = 1\n",
    "    return row\n",
    "\n",
    "for key in label_names.keys():\n",
    "    train_labels[label_names[key]] = 0\n",
    "    \n",
    "train_labels = train_labels.apply(fill_targets, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                     Id        Target  Nucleoplasm  \\\n",
      "0  00070df0-bbc3-11e8-b2bc-ac1f6b6435d0       [16, 0]            1   \n",
      "1  000a6c98-bb9b-11e8-b2b9-ac1f6b6435d0  [7, 1, 2, 0]            1   \n",
      "2  000a9596-bbc4-11e8-b2bc-ac1f6b6435d0           [5]            0   \n",
      "3  000c99ba-bba4-11e8-b2b9-ac1f6b6435d0           [1]            0   \n",
      "4  001838f8-bbca-11e8-b2bc-ac1f6b6435d0          [18]            0   \n",
      "\n",
      "   Nuclear membrane  Nucleoli  Nucleoli fibrillar center  Nuclear speckles  \\\n",
      "0                 0         0                          0                 0   \n",
      "1                 1         1                          0                 0   \n",
      "2                 0         0                          0                 0   \n",
      "3                 1         0                          0                 0   \n",
      "4                 0         0                          0                 0   \n",
      "\n",
      "   Nuclear bodies  Endoplasmic reticulum  Golgi apparatus      ...       \\\n",
      "0               0                      0                0      ...        \n",
      "1               0                      0                1      ...        \n",
      "2               1                      0                0      ...        \n",
      "3               0                      0                0      ...        \n",
      "4               0                      0                0      ...        \n",
      "\n",
      "   Microtubule organizing center  Centrosome  Lipid droplets  Plasma membrane  \\\n",
      "0                              0           0               0                0   \n",
      "1                              0           0               0                0   \n",
      "2                              0           0               0                0   \n",
      "3                              0           0               0                0   \n",
      "4                              1           0               0                0   \n",
      "\n",
      "   Cell junctions  Mitochondria  Aggresome  Cytosol  Cytoplasmic bodies  \\\n",
      "0               0             0          0        0                   0   \n",
      "1               0             0          0        0                   0   \n",
      "2               0             0          0        0                   0   \n",
      "3               0             0          0        0                   0   \n",
      "4               0             0          0        0                   0   \n",
      "\n",
      "   Rods & rings  \n",
      "0             0  \n",
      "1             0  \n",
      "2             0  \n",
      "3             0  \n",
      "4             0  \n",
      "\n",
      "[5 rows x 30 columns]\n"
     ]
    }
   ],
   "source": [
    "print(train_labels.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_path = \"./dataset/train/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K fold cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The test set size turns out to be 38.0 % compared to the train set.\n"
     ]
    }
   ],
   "source": [
    "from os import listdir\n",
    "\n",
    "train_files = listdir(\"./dataset/train\")\n",
    "test_files = listdir(\"./dataset/test\")\n",
    "percentage = np.round(len(test_files) / len(train_files) * 100)\n",
    "\n",
    "print(\"The test set size turns out to be {} % compared to the train set.\".format(percentage))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RepeatedKFold\n",
    "\n",
    "splitter = RepeatedKFold(n_splits=3, n_repeats=2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: [    1     2     3 ... 31063 31064 31065] TEST: [    0     4     6 ... 31069 31070 31071]\n",
      "TRAIN: 20714 TEST: 10358\n",
      "TRAIN: [    0     4     6 ... 31069 31070 31071] TEST: [    1     2     3 ... 31060 31061 31065]\n",
      "TRAIN: 20715 TEST: 10357\n",
      "TRAIN: [    0     1     2 ... 31069 31070 31071] TEST: [   10    11    13 ... 31062 31063 31064]\n",
      "TRAIN: 20715 TEST: 10357\n",
      "TRAIN: [    0     1     3 ... 31068 31070 31071] TEST: [    2     8    10 ... 31064 31067 31069]\n",
      "TRAIN: 20714 TEST: 10358\n",
      "TRAIN: [    2     4     7 ... 31067 31069 31070] TEST: [    0     1     3 ... 31066 31068 31071]\n",
      "TRAIN: 20715 TEST: 10357\n",
      "TRAIN: [    0     1     2 ... 31068 31069 31071] TEST: [    4     7     9 ... 31061 31065 31070]\n",
      "TRAIN: 20715 TEST: 10357\n"
     ]
    }
   ],
   "source": [
    "partitions = []\n",
    "\n",
    "for train_idx, test_idx in splitter.split(train_labels.index.values):\n",
    "    partition = {}\n",
    "    partition[\"train\"] = train_labels.Id.values[train_idx]\n",
    "    partition[\"validation\"] = train_labels.Id.values[test_idx]\n",
    "    partitions.append(partition)\n",
    "    print(\"TRAIN:\", train_idx, \"TEST:\", test_idx)\n",
    "    print(\"TRAIN:\", len(train_idx), \"TEST:\", len(test_idx))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'train': array(['000a6c98-bb9b-11e8-b2b9-ac1f6b6435d0',\n",
      "       '000a9596-bbc4-11e8-b2bc-ac1f6b6435d0',\n",
      "       '000c99ba-bba4-11e8-b2b9-ac1f6b6435d0', ...,\n",
      "       'ffe55eba-bbba-11e8-b2ba-ac1f6b6435d0',\n",
      "       'ffe61798-bbc3-11e8-b2bc-ac1f6b6435d0',\n",
      "       'ffe8cf0c-bba9-11e8-b2ba-ac1f6b6435d0'], dtype=object), 'validation': array(['00070df0-bbc3-11e8-b2bc-ac1f6b6435d0',\n",
      "       '001838f8-bbca-11e8-b2bc-ac1f6b6435d0',\n",
      "       '0020af02-bbba-11e8-b2ba-ac1f6b6435d0', ...,\n",
      "       'fff189d8-bbab-11e8-b2ba-ac1f6b6435d0',\n",
      "       'fffdf7e0-bbc4-11e8-b2bc-ac1f6b6435d0',\n",
      "       'fffe0ffe-bbc0-11e8-b2bb-ac1f6b6435d0'], dtype=object)}, {'train': array(['00070df0-bbc3-11e8-b2bc-ac1f6b6435d0',\n",
      "       '001838f8-bbca-11e8-b2bc-ac1f6b6435d0',\n",
      "       '0020af02-bbba-11e8-b2ba-ac1f6b6435d0', ...,\n",
      "       'fff189d8-bbab-11e8-b2ba-ac1f6b6435d0',\n",
      "       'fffdf7e0-bbc4-11e8-b2bc-ac1f6b6435d0',\n",
      "       'fffe0ffe-bbc0-11e8-b2bb-ac1f6b6435d0'], dtype=object), 'validation': array(['000a6c98-bb9b-11e8-b2b9-ac1f6b6435d0',\n",
      "       '000a9596-bbc4-11e8-b2bc-ac1f6b6435d0',\n",
      "       '000c99ba-bba4-11e8-b2b9-ac1f6b6435d0', ...,\n",
      "       'ffdbe064-bbbb-11e8-b2ba-ac1f6b6435d0',\n",
      "       'ffdcc5e4-bbaa-11e8-b2ba-ac1f6b6435d0',\n",
      "       'ffe8cf0c-bba9-11e8-b2ba-ac1f6b6435d0'], dtype=object)}, {'train': array(['00070df0-bbc3-11e8-b2bc-ac1f6b6435d0',\n",
      "       '000a6c98-bb9b-11e8-b2b9-ac1f6b6435d0',\n",
      "       '000a9596-bbc4-11e8-b2bc-ac1f6b6435d0', ...,\n",
      "       'fff189d8-bbab-11e8-b2ba-ac1f6b6435d0',\n",
      "       'fffdf7e0-bbc4-11e8-b2bc-ac1f6b6435d0',\n",
      "       'fffe0ffe-bbc0-11e8-b2bb-ac1f6b6435d0'], dtype=object), 'validation': array(['002ff91e-bbb8-11e8-b2ba-ac1f6b6435d0',\n",
      "       '00301238-bbb2-11e8-b2ba-ac1f6b6435d0',\n",
      "       '00344514-bbc2-11e8-b2bb-ac1f6b6435d0', ...,\n",
      "       'ffe454e6-bb99-11e8-b2b9-ac1f6b6435d0',\n",
      "       'ffe55eba-bbba-11e8-b2ba-ac1f6b6435d0',\n",
      "       'ffe61798-bbc3-11e8-b2bc-ac1f6b6435d0'], dtype=object)}, {'train': array(['00070df0-bbc3-11e8-b2bc-ac1f6b6435d0',\n",
      "       '000a6c98-bb9b-11e8-b2b9-ac1f6b6435d0',\n",
      "       '000c99ba-bba4-11e8-b2b9-ac1f6b6435d0', ...,\n",
      "       'fff0a998-bbae-11e8-b2ba-ac1f6b6435d0',\n",
      "       'fffdf7e0-bbc4-11e8-b2bc-ac1f6b6435d0',\n",
      "       'fffe0ffe-bbc0-11e8-b2bb-ac1f6b6435d0'], dtype=object), 'validation': array(['000a9596-bbc4-11e8-b2bc-ac1f6b6435d0',\n",
      "       '00285ce4-bba0-11e8-b2b9-ac1f6b6435d0',\n",
      "       '002ff91e-bbb8-11e8-b2ba-ac1f6b6435d0', ...,\n",
      "       'ffe61798-bbc3-11e8-b2bc-ac1f6b6435d0',\n",
      "       'ffed4430-bbac-11e8-b2ba-ac1f6b6435d0',\n",
      "       'fff189d8-bbab-11e8-b2ba-ac1f6b6435d0'], dtype=object)}, {'train': array(['000a9596-bbc4-11e8-b2bc-ac1f6b6435d0',\n",
      "       '001838f8-bbca-11e8-b2bc-ac1f6b6435d0',\n",
      "       '002679c2-bbb6-11e8-b2ba-ac1f6b6435d0', ...,\n",
      "       'ffed4430-bbac-11e8-b2ba-ac1f6b6435d0',\n",
      "       'fff189d8-bbab-11e8-b2ba-ac1f6b6435d0',\n",
      "       'fffdf7e0-bbc4-11e8-b2bc-ac1f6b6435d0'], dtype=object), 'validation': array(['00070df0-bbc3-11e8-b2bc-ac1f6b6435d0',\n",
      "       '000a6c98-bb9b-11e8-b2b9-ac1f6b6435d0',\n",
      "       '000c99ba-bba4-11e8-b2b9-ac1f6b6435d0', ...,\n",
      "       'ffeae6f0-bbc9-11e8-b2bc-ac1f6b6435d0',\n",
      "       'fff0a998-bbae-11e8-b2ba-ac1f6b6435d0',\n",
      "       'fffe0ffe-bbc0-11e8-b2bb-ac1f6b6435d0'], dtype=object)}, {'train': array(['00070df0-bbc3-11e8-b2bc-ac1f6b6435d0',\n",
      "       '000a6c98-bb9b-11e8-b2b9-ac1f6b6435d0',\n",
      "       '000a9596-bbc4-11e8-b2bc-ac1f6b6435d0', ...,\n",
      "       'fff0a998-bbae-11e8-b2ba-ac1f6b6435d0',\n",
      "       'fff189d8-bbab-11e8-b2ba-ac1f6b6435d0',\n",
      "       'fffe0ffe-bbc0-11e8-b2bb-ac1f6b6435d0'], dtype=object), 'validation': array(['001838f8-bbca-11e8-b2bc-ac1f6b6435d0',\n",
      "       '002679c2-bbb6-11e8-b2ba-ac1f6b6435d0',\n",
      "       '002daad6-bbc9-11e8-b2bc-ac1f6b6435d0', ...,\n",
      "       'ffdcc5e4-bbaa-11e8-b2ba-ac1f6b6435d0',\n",
      "       'ffe8cf0c-bba9-11e8-b2ba-ac1f6b6435d0',\n",
      "       'fffdf7e0-bbc4-11e8-b2bc-ac1f6b6435d0'], dtype=object)}]\n"
     ]
    }
   ],
   "source": [
    "print(partitions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ImagePreprocessor:\n",
    "    \n",
    "    def __init__(self, modelparameter):\n",
    "        self.parameter = modelparameter\n",
    "        self.basepath = self.parameter.basepath\n",
    "        self.scaled_row_dim = self.parameter.scaled_row_dim\n",
    "        self.scaled_col_dim = self.parameter.scaled_col_dim\n",
    "        self.n_channels = self.parameter.n_channels\n",
    "    \n",
    "    def preprocess(self, image_id):\n",
    "        image = self.load_image(image_id)\n",
    "#         image = self.reshape(image)\n",
    "        image = self.resize(image)\n",
    "        image = self.normalize(image)\n",
    "        return image\n",
    "    \n",
    "    def resize(self, image):\n",
    "        image = resize(image, (self.scaled_row_dim, self.scaled_col_dim))\n",
    "        return image\n",
    "    \n",
    "    def reshape(self, image):\n",
    "        image = np.rollaxis(image, 0,3)\n",
    "        return image\n",
    "    \n",
    "    def normalize(self, image):\n",
    "        image /= 255 \n",
    "        return image\n",
    "    \n",
    "    def load_image(self, image_id):\n",
    "        image = np.zeros(shape=(512,512,4))\n",
    "        image[:,:,0] = imread(self.basepath + image_id + \"_green\" + \".png\")\n",
    "        image[:,:,1] = imread(self.basepath + image_id + \"_blue\" + \".png\")\n",
    "        image[:,:,2] = imread(self.basepath + image_id + \"_red\" + \".png\")\n",
    "        image[:,:,3] = imread(self.basepath + image_id + \"_yellow\" + \".png\")\n",
    "        return image[:,:,0:self.parameter.n_channels]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from skimage.transform import resize\n",
    "\n",
    "def load_image(basepath, image_id, modelparameters):\n",
    "    images = np.zeros(shape=(4,512,512))\n",
    "    images[0,:,:] = imread(basepath + image_id + \"_green\" + \".png\")\n",
    "    images[1,:,:] = imread(basepath + image_id + \"_red\" + \".png\")\n",
    "    images[2,:,:] = imread(basepath + image_id + \"_blue\" + \".png\")\n",
    "    images[3,:,:] = imread(basepath + image_id + \"_yellow\" + \".png\")\n",
    "    \n",
    "    images = np.rollaxis(images, 0,3)\n",
    "    \n",
    "    images = resize(images,(modelparameters.scaled_row_dim, modelparameters.scaled_col_dim))\n",
    "    \n",
    "    return images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ModelParameter:\n",
    "    \n",
    "    def __init__(self, basepath,\n",
    "                 num_classes=28,\n",
    "                 image_rows=512,\n",
    "                 image_cols=512,\n",
    "                 batch_size=200,\n",
    "                 n_channels=1,\n",
    "                 row_scale_factor=1,\n",
    "                 col_scale_factor=1,\n",
    "                 shuffle=False,\n",
    "                 n_epochs=1):\n",
    "        self.basepath = basepath\n",
    "        self.num_classes = num_classes\n",
    "        self.image_rows = image_rows\n",
    "        self.image_cols = image_cols\n",
    "        self.batch_size = batch_size\n",
    "        self.n_channels = n_channels\n",
    "        self.shuffle = shuffle\n",
    "        self.row_scale_factor = row_scale_factor\n",
    "        self.col_scale_factor = col_scale_factor\n",
    "        self.scaled_row_dim = np.int(self.image_rows / self.row_scale_factor)\n",
    "        self.scaled_col_dim = np.int(self.image_cols / self.col_scale_factor)\n",
    "        self.n_epochs = n_epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameter = ModelParameter(train_path, batch_size=256, n_channels=3, n_epochs=5)\n",
    "preprocessor = ImagePreprocessor(parameter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(512, 512, 3)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = preprocessor.preprocess(partitions[0][\"train\"][0])\n",
    "print(image.shape)\n",
    "plt.imshow(image)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras\n",
    "\n",
    "class DataGenerator(keras.utils.Sequence):\n",
    "    \n",
    "    def __init__(self, list_IDs, labels, modelparameter, imagepreprocessor):\n",
    "        self.current_epoch = 0\n",
    "        self.params = modelparameter\n",
    "        self.labels = labels\n",
    "        self.list_IDs = list_IDs\n",
    "        self.dim = (self.params.scaled_row_dim, self.params.scaled_col_dim)\n",
    "        self.batch_size = self.params.batch_size\n",
    "        self.n_channels = self.params.n_channels\n",
    "        self.num_classes = self.params.num_classes\n",
    "        self.shuffle = self.params.shuffle\n",
    "        self.preprocessor = imagepreprocessor\n",
    "        self.on_epoch_end()\n",
    "    \n",
    "    def on_epoch_end(self):\n",
    "        'Updates indexes after each epoch'\n",
    "        self.indexes = np.arange(len(self.list_IDs))\n",
    "        if self.shuffle == True:\n",
    "            np.random.shuffle(self.indexes)\n",
    "            \n",
    "    def get_targets_per_image(self, identifier):\n",
    "        return self.labels.loc[self.labels.Id==identifier].drop([\"Id\", \"Target\"], axis=1).values\n",
    "    \n",
    "    def __data_generation(self, list_IDs_temp):\n",
    "        'Generates data containing batch_size samples' # X : (n_samples, *dim, n_channels)\n",
    "        # Initialization\n",
    "        X = np.empty((self.batch_size, *self.dim, self.n_channels))\n",
    "        Y = np.empty((self.batch_size, self.num_classes), dtype=int)\n",
    "        # Generate data\n",
    "        for i, ID in enumerate(list_IDs_temp):\n",
    "            # Store sample\n",
    "            X[i,] = self.preprocessor.preprocess(ID)\n",
    "            # Store class\n",
    "            Y[i] = self.get_targets_per_image(ID)            \n",
    "        return X, Y\n",
    "\n",
    "    def __len__(self):\n",
    "        'Denotes the number of batches per epoch'\n",
    "        return int(np.floor(len(self.list_IDs) / self.batch_size))\n",
    "    \n",
    "    def __getitem__(self, index):\n",
    "        'Generate one batch of data'\n",
    "        # Generate indexes of the batch\n",
    "        indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size]\n",
    "        \n",
    "        # Find list of IDs\n",
    "        list_IDs_temp = [self.list_IDs[k] for k in indexes]\n",
    "        \n",
    "        # Generate data\n",
    "        X, y = self.__data_generation(list_IDs_temp)\n",
    "        return X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PredictGenerator:\n",
    "    \n",
    "    def __init__(self, predict_Ids, imagepreprocessor, predict_path):\n",
    "        self.preprocessor = imagepreprocessor\n",
    "        self.preprocessor.basepath = predict_path\n",
    "        self.identifiers = predict_Ids\n",
    "    \n",
    "    def predict(self, model):\n",
    "        y = np.empty(shape=(len(self.identifiers), self.preprocessor.parameter.num_classes))\n",
    "        for n in range(len(self.identifiers)):\n",
    "            image = self.preprocessor.preprocess(self.identifiers[n])\n",
    "            image = image.reshape((1, *image.shape))\n",
    "            y[n] = model.predict(image)\n",
    "        return y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CNN Baseline Model using Keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Flatten\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras.losses import binary_crossentropy\n",
    "from keras.optimizers import Adadelta\n",
    "from keras.initializers import VarianceScaling\n",
    "from keras.utils import multi_gpu_model\n",
    "\n",
    "class BaseLineModel:\n",
    "    \n",
    "    def __init__(self, modelparameter):\n",
    "        self.params = modelparameter\n",
    "        self.num_classes = self.params.num_classes\n",
    "        self.img_rows = self.params.scaled_row_dim\n",
    "        self.img_cols = self.params.scaled_col_dim\n",
    "        self.n_channels = self.params.n_channels\n",
    "        self.input_shape = (self.img_rows, self.img_cols, self.n_channels)\n",
    "        self.my_metrics = ['accuracy']\n",
    "    \n",
    "    def build_model(self):\n",
    "        self.model = Sequential()\n",
    "        self.model.add(Conv2D(16, kernel_size=(3, 3), activation='relu', input_shape=self.input_shape,\n",
    "                             kernel_initializer=VarianceScaling(seed=0)))\n",
    "        self.model.add(Conv2D(32, (3, 3), activation='relu',\n",
    "                             kernel_initializer=VarianceScaling(seed=0)))\n",
    "        self.model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "        self.model.add(Dropout(0.25))\n",
    "        self.model.add(Flatten())\n",
    "        self.model.add(Dense(64, activation='relu',\n",
    "                            kernel_initializer=VarianceScaling(seed=0),))\n",
    "        self.model.add(Dropout(0.5))\n",
    "        self.model.add(Dense(self.num_classes, activation='sigmoid'))\n",
    "#         self.model = multi_gpu_model(self.model, gpus=2)\n",
    "    \n",
    "    def compile_model(self):\n",
    "        self.model.compile(loss=keras.losses.categorical_crossentropy,\n",
    "              optimizer=keras.optimizers.Adadelta(),\n",
    "              metrics=self.my_metrics)\n",
    "    \n",
    "    def set_generators(self, train_generator, validation_generator):\n",
    "        self.training_generator = train_generator\n",
    "        self.validation_generator = validation_generator\n",
    "    \n",
    "    def learn(self):\n",
    "        return self.model.fit_generator(generator=self.training_generator,\n",
    "                    validation_data=self.validation_generator,\n",
    "                    epochs=self.params.n_epochs, \n",
    "                    use_multiprocessing=True,\n",
    "                    workers=8)\n",
    "    \n",
    "    def score(self):\n",
    "        return self.model.evaluate_generator(generator=self.validation_generator,\n",
    "                                      use_multiprocessing=True, \n",
    "                                      workers=8)\n",
    "    \n",
    "    def predict(self, predict_generator):\n",
    "        y = predict_generator.predict(self.model)\n",
    "        return y\n",
    "    \n",
    "    def save(self, modeloutputpath):\n",
    "        self.model.save(modeloutputpath)\n",
    "    \n",
    "    def load(self, modelinputpath):\n",
    "        self.model = load_model(modelinputpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "class TrainModel:\n",
    "    def __init__(self, parameter, partitions, train_labels, train_path, preprocessor):\n",
    "        self.parameter = parameter\n",
    "        self.partitions = partitions\n",
    "        self.train_path = train_path\n",
    "        self.labels = train_labels\n",
    "        self.preprocessor = preprocessor\n",
    "        self.target_names = train_labels.drop([\"Target\", \"Id\"], axis=1).columns\n",
    "        self.model = BaseLineModel(parameter)\n",
    "        self.model.build_model()\n",
    "        self.model.compile_model()\n",
    "        \n",
    "    def train(self):\n",
    "        self.predictions = []\n",
    "        for i, partition in enumerate(partitions):\n",
    "            print(\"training in partition \",i+1)\n",
    "            training_generator = DataGenerator(partition['train'], self.labels, self.parameter, self.preprocessor)\n",
    "            validation_generator = DataGenerator(partition['validation'], self.labels, self.parameter, self.preprocessor)\n",
    "            predict_generator = PredictGenerator(partition['validation'], self.preprocessor, self.train_path)\n",
    "            self.model.set_generators(training_generator, validation_generator)\n",
    "            history = self.model.learn()\n",
    "\n",
    "            proba_predictions = self.model.predict(predict_generator)\n",
    "            baseline_proba_predictions = pd.DataFrame(index = partition['validation'],\n",
    "                                                      data=proba_predictions,\n",
    "                                                      columns=self.target_names)\n",
    "            self.predictions.append(baseline_proba_predictions)\n",
    "            baseline_losses = pd.DataFrame(history.history[\"loss\"], columns=[\"train_loss\"])\n",
    "            baseline_losses[\"val_loss\"] = history.history[\"val_loss\"]\n",
    "            baseline_losses.to_csv(\"baseline_losses\"+str(i)+\".csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainmodel = TrainModel(parameter, partitions, train_labels, train_path, preprocessor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_3 (Conv2D)            (None, 510, 510, 16)      448       \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 508, 508, 32)      4640      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 254, 254, 32)      0         \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 254, 254, 32)      0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 2064512)           0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 64)                132128832 \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 28)                1820      \n",
      "=================================================================\n",
      "Total params: 132,135,740\n",
      "Trainable params: 132,135,740\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(trainmodel.model.model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = tf.ConfigProto(\n",
    "        device_count = {'GPU': 0}\n",
    "    )\n",
    "sess = tf.Session(config=config)\n",
    "from keras import backend as K\n",
    "K.set_session(sess)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training in partition  1\n",
      "Epoch 1/5\n",
      "18/80 [=====>........................] - ETA: 12:35 - loss: 5.2295 - acc: 0.1417"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Process ForkPoolWorker-7:\n",
      "Process ForkPoolWorker-14:\n",
      "Process ForkPoolWorker-15:\n",
      "Process ForkPoolWorker-10:\n",
      "Process ForkPoolWorker-8:\n",
      "Process ForkPoolWorker-16:\n",
      "Process ForkPoolWorker-1:\n",
      "Process ForkPoolWorker-2:\n",
      "Process ForkPoolWorker-6:\n",
      "Process ForkPoolWorker-11:\n",
      "Process ForkPoolWorker-13:\n",
      "Process ForkPoolWorker-3:\n",
      "Process ForkPoolWorker-12:\n",
      "Process ForkPoolWorker-9:\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/pool.py\", line 119, in worker\n",
      "    result = (True, func(*args, **kwds))\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/pool.py\", line 119, in worker\n",
      "    result = (True, func(*args, **kwds))\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/queues.py\", line 335, in get\n",
      "    res = self._reader.recv_bytes()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/site-packages/keras/utils/data_utils.py\", line 401, in get_index\n",
      "    return _SHARED_SEQUENCES[uid][i]\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/site-packages/keras/utils/data_utils.py\", line 401, in get_index\n",
      "    return _SHARED_SEQUENCES[uid][i]\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/queues.py\", line 334, in get\n",
      "    with self._rlock:\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/queues.py\", line 334, in get\n",
      "    with self._rlock:\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/queues.py\", line 334, in get\n",
      "    with self._rlock:\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/queues.py\", line 335, in get\n",
      "    res = self._reader.recv_bytes()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/queues.py\", line 334, in get\n",
      "    with self._rlock:\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/queues.py\", line 334, in get\n",
      "    with self._rlock:\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"<ipython-input-13-7192ddf23a00>\", line 53, in __getitem__\n",
      "    X, y = self.__data_generation(list_IDs_temp)\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/pool.py\", line 119, in worker\n",
      "    result = (True, func(*args, **kwds))\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/queues.py\", line 334, in get\n",
      "    with self._rlock:\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/connection.py\", line 216, in recv_bytes\n",
      "    buf = self._recv_bytes(maxlength)\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/pool.py\", line 119, in worker\n",
      "    result = (True, func(*args, **kwds))\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/connection.py\", line 216, in recv_bytes\n",
      "    buf = self._recv_bytes(maxlength)\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"<ipython-input-13-7192ddf23a00>\", line 53, in __getitem__\n",
      "    X, y = self.__data_generation(list_IDs_temp)\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/queues.py\", line 334, in get\n",
      "    with self._rlock:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/site-packages/keras/utils/data_utils.py\", line 401, in get_index\n",
      "    return _SHARED_SEQUENCES[uid][i]\n",
      "KeyboardInterrupt\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/connection.py\", line 407, in _recv_bytes\n",
      "    buf = self._recv(4)\n",
      "  File \"<ipython-input-13-7192ddf23a00>\", line 35, in __data_generation\n",
      "    X[i,] = self.preprocessor.preprocess(ID)\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/connection.py\", line 407, in _recv_bytes\n",
      "    buf = self._recv(4)\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/queues.py\", line 334, in get\n",
      "    with self._rlock:\n",
      "  File \"<ipython-input-13-7192ddf23a00>\", line 37, in __data_generation\n",
      "    Y[i] = self.get_targets_per_image(ID)\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"<ipython-input-13-7192ddf23a00>\", line 25, in get_targets_per_image\n",
      "    return self.labels.loc[self.labels.Id==identifier].drop([\"Id\", \"Target\"], axis=1).values\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/site-packages/keras/utils/data_utils.py\", line 401, in get_index\n",
      "    return _SHARED_SEQUENCES[uid][i]\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "KeyboardInterrupt\n",
      "  File \"<ipython-input-13-7192ddf23a00>\", line 53, in __getitem__\n",
      "    X, y = self.__data_generation(list_IDs_temp)\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/connection.py\", line 379, in _recv\n",
      "    chunk = read(handle, remaining)\n",
      "KeyboardInterrupt\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/connection.py\", line 379, in _recv\n",
      "    chunk = read(handle, remaining)\n",
      "  File \"<ipython-input-8-de5ba417c027>\", line 11, in preprocess\n",
      "    image = self.load_image(image_id)\n",
      "KeyboardInterrupt\n",
      "  File \"<ipython-input-8-de5ba417c027>\", line 34, in load_image\n",
      "    image[:,:,3] = imread(self.basepath + image_id + \"_yellow\" + \".png\")\n",
      "KeyboardInterrupt\n",
      "  File \"<ipython-input-13-7192ddf23a00>\", line 35, in __data_generation\n",
      "    X[i,] = self.preprocessor.preprocess(ID)\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "  File \"<ipython-input-13-7192ddf23a00>\", line 53, in __getitem__\n",
      "    X, y = self.__data_generation(list_IDs_temp)\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/site-packages/pandas/core/ops.py\", line 1283, in wrapper\n",
      "    res = na_op(values, other)\n",
      "  File \"<ipython-input-8-de5ba417c027>\", line 13, in preprocess\n",
      "    image = self.resize(image)\n",
      "  File \"<ipython-input-13-7192ddf23a00>\", line 35, in __data_generation\n",
      "    X[i,] = self.preprocessor.preprocess(ID)\n",
      "KeyboardInterrupt\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/site-packages/pandas/core/ops.py\", line 1143, in na_op\n",
      "    result = _comp_method_OBJECT_ARRAY(op, x, y)\n",
      "  File \"<ipython-input-8-de5ba417c027>\", line 18, in resize\n",
      "    image = resize(image, (self.scaled_row_dim, self.scaled_col_dim))\n",
      "  File \"<ipython-input-8-de5ba417c027>\", line 13, in preprocess\n",
      "    image = self.resize(image)\n",
      "KeyboardInterrupt\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/site-packages/pandas/core/ops.py\", line 1122, in _comp_method_OBJECT_ARRAY\n",
      "    result = libops.scalar_compare(x, y, op)\n",
      "  File \"<ipython-input-8-de5ba417c027>\", line 18, in resize\n",
      "    image = resize(image, (self.scaled_row_dim, self.scaled_col_dim))\n",
      "KeyboardInterrupt\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/site-packages/skimage/transform/_warps.py\", line 169, in resize\n",
      "    preserve_range=preserve_range)\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/site-packages/skimage/transform/_warps.py\", line 852, in warp\n",
      "    order=order, mode=mode, cval=cval))\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/site-packages/skimage/transform/_warps.py\", line 169, in resize\n",
      "    preserve_range=preserve_range)\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/site-packages/skimage/transform/_warps.py\", line 852, in warp\n",
      "    order=order, mode=mode, cval=cval))\n",
      "  File \"skimage/transform/_warps_cy.pyx\", line 90, in skimage.transform._warps_cy._warp_fast\n",
      "  File \"skimage/transform/_warps_cy.pyx\", line 131, in skimage.transform._warps_cy._warp_fast\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/site-packages/numpy/core/numeric.py\", line 590, in ascontiguousarray\n",
      "    return array(a, dtype, copy=False, order='C', ndmin=1)\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/site-packages/numpy/core/numeric.py\", line 433, in asarray\n",
      "    def asarray(a, dtype=None, order=None):\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "Process ForkPoolWorker-18:\n",
      "Process ForkPoolWorker-28:\n",
      "Process ForkPoolWorker-21:\n",
      "Process ForkPoolWorker-23:\n",
      "Process ForkPoolWorker-30:\n",
      "Process ForkPoolWorker-24:\n",
      "Process ForkPoolWorker-26:\n",
      "Process ForkPoolWorker-29:\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/queues.py\", line 334, in get\n",
      "    with self._rlock:\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/queues.py\", line 334, in get\n",
      "    with self._rlock:\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/queues.py\", line 334, in get\n",
      "    with self._rlock:\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/queues.py\", line 334, in get\n",
      "    with self._rlock:\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/queues.py\", line 334, in get\n",
      "    with self._rlock:\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/queues.py\", line 334, in get\n",
      "    with self._rlock:\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/queues.py\", line 334, in get\n",
      "    with self._rlock:\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/queues.py\", line 334, in get\n",
      "    with self._rlock:\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "KeyboardInterrupt\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "  File \"/opt/anaconda3/envs/py36/lib/python3.6/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/opt/anaconda3/envs/py36/lib/python3.6/site-packages/keras/engine/training_generator.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m    216\u001b[0m                                             \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 217\u001b[0;31m                                             class_weight=class_weight)\n\u001b[0m\u001b[1;32m    218\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/py36/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[0;34m(self, x, y, sample_weight, class_weight)\u001b[0m\n\u001b[1;32m   1216\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_train_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1217\u001b[0;31m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1218\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0munpack_singleton\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2714\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2715\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2716\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2674\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2675\u001b[0;31m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2676\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfetched\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m   1450\u001b[0m           return tf_session.TF_SessionRunCallable(\n\u001b[0;32m-> 1451\u001b[0;31m               self._session._session, self._handle, args, status, None)\n\u001b[0m\u001b[1;32m   1452\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: ",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-19-857a75a659c0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrainmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-16-25a02dab5ade>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     19\u001b[0m             \u001b[0mpredict_generator\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPredictGenerator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpartition\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'validation'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpreprocessor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_generators\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtraining_generator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_generator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m             \u001b[0mhistory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m             \u001b[0mproba_predictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredict_generator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-15-ae48b42a9fac>\u001b[0m in \u001b[0;36mlearn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     47\u001b[0m                     \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_epochs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m                     \u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m                     workers=8)\n\u001b[0m\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/py36/lib/python3.6/site-packages/keras/legacy/interfaces.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     89\u001b[0m                 warnings.warn('Update your `' + object_name + '` call to the ' +\n\u001b[1;32m     90\u001b[0m                               'Keras 2 API: ' + signature, stacklevel=2)\n\u001b[0;32m---> 91\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     92\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/py36/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m   1416\u001b[0m             \u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1417\u001b[0m             \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1418\u001b[0;31m             initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m   1419\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1420\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0minterfaces\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegacy_generator_methods_support\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/py36/lib/python3.6/site-packages/keras/engine/training_generator.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m    260\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    261\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mval_enqueuer\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 262\u001b[0;31m                 \u001b[0mval_enqueuer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    263\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    264\u001b[0m     \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/py36/lib/python3.6/site-packages/keras/utils/data_utils.py\u001b[0m in \u001b[0;36mstop\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    494\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqueue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munfinished_tasks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    495\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqueue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnot_full\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnotify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 496\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_thread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    497\u001b[0m         \u001b[0m_SHARED_SEQUENCES\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muid\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    498\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/py36/lib/python3.6/threading.py\u001b[0m in \u001b[0;36mjoin\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m   1054\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1055\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1056\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_wait_for_tstate_lock\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1057\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1058\u001b[0m             \u001b[0;31m# the behavior of a negative timeout isn't documented, but\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/py36/lib/python3.6/threading.py\u001b[0m in \u001b[0;36m_wait_for_tstate_lock\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m   1070\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlock\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# already determined that the C code is done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1071\u001b[0m             \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_stopped\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1072\u001b[0;31m         \u001b[0;32melif\u001b[0m \u001b[0mlock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1073\u001b[0m             \u001b[0mlock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelease\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1074\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "trainmodel.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = trainmodel.predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainmodel.model.save(\"baselinemodel1.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras.backend as K\n",
    "\n",
    "def base_f1(y_true, y_pred):\n",
    "    y_pred = K.round(y_pred)\n",
    "    tp = K.sum(K.cast(y_true*y_pred, 'float'), axis=0)\n",
    "    tn = K.sum(K.cast((1-y_true)*(1-y_pred), 'float'), axis=0)\n",
    "    fp = K.sum(K.cast((1-y_true)*y_pred, 'float'), axis=0)\n",
    "    fn = K.sum(K.cast(y_true*(1-y_pred), 'float'), axis=0)\n",
    "\n",
    "    p = tp / (tp + fp + K.epsilon())\n",
    "    r = tp / (tp + fn + K.epsilon())\n",
    "\n",
    "    f1 = 2*p*r / (p+r+K.epsilon())\n",
    "    f1 = tf.where(tf.is_nan(f1), tf.zeros_like(f1), f1)\n",
    "    return f1\n",
    "\n",
    "def f1_min(y_true, y_pred):\n",
    "    f1 = base_f1(y_true, y_pred)\n",
    "    return K.min(f1)\n",
    "\n",
    "def f1_max(y_true, y_pred):\n",
    "    f1 = base_f1(y_true, y_pred)\n",
    "    return K.max(f1)\n",
    "\n",
    "def f1_mean(y_true, y_pred):\n",
    "    f1 = base_f1(y_true, y_pred)\n",
    "    return K.mean(f1)\n",
    "\n",
    "def f1_std(y_true, y_pred):\n",
    "    f1 = base_f1(y_true, y_pred)\n",
    "    return K.std(f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.35719544\n",
      "0.41940627\n",
      "0.47700006\n",
      "0.5152696\n",
      "0.612444\n",
      "0.65134656\n"
     ]
    }
   ],
   "source": [
    "for partition,prediction in zip(partitions,predictions):\n",
    "    validation_labels = train_labels.loc[train_labels.Id.isin(partition[\"validation\"])].copy()\n",
    "    y_true = validation_labels.drop([\"Id\", \"Target\"], axis=1).values\n",
    "    y_pred = np.where(prediction.values > 0.5, 1, 0)\n",
    "    \n",
    "    with tf.Session() as sess:\n",
    "        print(sess.run(base_f1(y_true.flatten(), y_pred.flatten())))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Model\n",
    "from keras.applications.inception_v3 import InceptionV3\n",
    "from keras.layers import Dense, Dropout, Flatten\n",
    "from keras.layers import MaxPooling2D\n",
    "from keras.losses import binary_crossentropy\n",
    "from keras.optimizers import Adadelta\n",
    "from keras.initializers import VarianceScaling\n",
    "\n",
    "\n",
    "class InceModel:\n",
    "    \n",
    "    def __init__(self, modelparameter):\n",
    "        self.params = modelparameter\n",
    "        self.num_classes = self.params.num_classes\n",
    "        self.img_rows = self.params.scaled_row_dim\n",
    "        self.img_cols = self.params.scaled_col_dim\n",
    "        self.n_channels = self.params.n_channels\n",
    "        self.input_shape = (self.img_rows, self.img_cols, self.n_channels)\n",
    "        self.my_metrics = ['accuracy']\n",
    "    \n",
    "    def build_model(self):\n",
    "        base_model = InceptionV3(weights='imagenet', include_top=False, input_shape=(self.params.scaled_row_dim, self.params.scaled_col_dim, self.params.n_channels))\n",
    "        x = base_model.output\n",
    "        x = MaxPooling2D()(x)\n",
    "        x = Dropout(0.25)(x)\n",
    "        x = Flatten()(x)\n",
    "        x = Dense(64, activation='relu', kernel_initializer=VarianceScaling(seed=0),)(x)\n",
    "        x = Dropout(0.5)(x)\n",
    "        predictions = Dense(self.num_classes, activation='sigmoid')(x)\n",
    "        \n",
    "        self.model = Model(inputs=base_model.input, outputs=predictions)\n",
    "        \n",
    "        for layer in base_model.layers:\n",
    "            layer.trainable = False\n",
    "    \n",
    "    def compile_model(self):\n",
    "        self.model.compile(loss=keras.losses.categorical_crossentropy,\n",
    "              optimizer=keras.optimizers.Adadelta(),\n",
    "              metrics=self.my_metrics)\n",
    "    \n",
    "    def set_generators(self, train_generator, validation_generator):\n",
    "        self.training_generator = train_generator\n",
    "        self.validation_generator = validation_generator\n",
    "    \n",
    "    def learn(self):\n",
    "        return self.model.fit_generator(generator=self.training_generator,\n",
    "                    validation_data=self.validation_generator,\n",
    "                    epochs=self.params.n_epochs, \n",
    "                    use_multiprocessing=True,\n",
    "                    workers=8)\n",
    "    \n",
    "    def score(self):\n",
    "        return self.model.evaluate_generator(generator=self.validation_generator,\n",
    "                                      use_multiprocessing=True, \n",
    "                                      workers=8)\n",
    "    \n",
    "    def predict(self, predict_generator):\n",
    "        y = predict_generator.predict(self.model)\n",
    "        return y\n",
    "    \n",
    "    def save(self, modeloutputpath):\n",
    "        self.model.save(modeloutputpath)\n",
    "    \n",
    "    def load(self, modelinputpath):\n",
    "        self.model = load_model(modelinputpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ModelWrapper:\n",
    "    def __init__(self, modelObj, partitions, train_labels, preprocessor):\n",
    "        self.model = modelObj\n",
    "        self.parameter = modelObj.params\n",
    "        self.partitions = partitions\n",
    "        self.train_path = self.parameter.basepath\n",
    "        self.labels = train_labels\n",
    "        self.preprocessor = preprocessor\n",
    "        self.target_names = train_labels.drop([\"Target\", \"Id\"], axis=1).columns\n",
    "        \n",
    "    def train(self):\n",
    "        self.predictions = []\n",
    "        for i, partition in enumerate(partitions):\n",
    "            print(\"training in partition \",i+1)\n",
    "            training_generator = DataGenerator(partition['train'], self.labels, self.parameter, self.preprocessor)\n",
    "            validation_generator = DataGenerator(partition['validation'], self.labels, self.parameter, self.preprocessor)\n",
    "            predict_generator = PredictGenerator(partition['validation'], self.preprocessor, self.train_path)\n",
    "            self.model.set_generators(training_generator, validation_generator)\n",
    "            history = self.model.learn()\n",
    "\n",
    "            proba_predictions = self.model.predict(predict_generator)\n",
    "            baseline_proba_predictions = pd.DataFrame(index = partition['validation'],\n",
    "                                                      data=proba_predictions,\n",
    "                                                      columns=self.target_names)\n",
    "            self.predictions.append(baseline_proba_predictions)\n",
    "            baseline_losses = pd.DataFrame(history.history[\"loss\"], columns=[\"train_loss\"])\n",
    "            baseline_losses[\"val_loss\"] = history.history[\"val_loss\"]\n",
    "            baseline_losses.to_csv(\"baseline_losses\"+str(i)+\".csv\")\n",
    "            \n",
    "    def scores(self):\n",
    "        self.f1_scores = []\n",
    "        with tf.Session() as sess:\n",
    "            for partition,prediction in zip(self.partitions,self.predictions):\n",
    "                validation_labels = train_labels.loc[train_labels.Id.isin(partition[\"validation\"])].copy()\n",
    "                y_true = validation_labels.drop([\"Id\", \"Target\"], axis=1).values\n",
    "                y_pred = np.where(prediction.values > 0.5, 1, 0)\n",
    "                self.f1_scores.append(sess.run(base_f1(y_true.flatten(), y_pred.flatten())))\n",
    "        return self.f1_scores\n",
    "    \n",
    "    def save_model(self,path):\n",
    "        self.model.save(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ModelParameter' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-c2f94e153b46>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mincParameter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mModelParameter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m200\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_channels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_epochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mincPreprocessor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImagePreprocessor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mincParameter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'ModelParameter' is not defined"
     ]
    }
   ],
   "source": [
    "incParameter = ModelParameter(train_path, batch_size=200, n_channels=3, n_epochs=2)\n",
    "incPreprocessor = ImagePreprocessor(incParameter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "inceptionModel = InceModel(incParameter)\n",
    "inceptionModel.build_model()\n",
    "inceptionModel.compile_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "modWrap = ModelWrapper(inceptionModel, partitions, train_labels, incPreprocessor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(128, 128, 3)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = incPreprocessor.preprocess(partitions[0][\"train\"][0])\n",
    "print(image.shape)\n",
    "plt.imshow(image)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training in partition  1\n",
      "Epoch 1/2\n",
      "102/103 [============================>.] - ETA: 1s - loss: 4.7095 - acc: 0.3157Epoch 1/2\n",
      "Epoch 1/2\n",
      "103/103 [==============================] - 314s 3s/step - loss: 4.7061 - acc: 0.3165 - val_loss: 4.9083 - val_acc: 0.4185\n",
      "Epoch 2/2\n",
      "103/103 [==============================] - 280s 3s/step - loss: 4.3976 - acc: 0.3715 - val_loss: 4.7321 - val_acc: 0.4185\n",
      "training in partition  2\n",
      "Epoch 1/2\n",
      "103/103 [==============================] - 277s 3s/step - loss: 4.3362 - acc: 0.4044 - val_loss: 4.7150 - val_acc: 0.4130\n",
      "Epoch 2/2\n",
      "103/103 [==============================] - 279s 3s/step - loss: 4.2929 - acc: 0.4127 - val_loss: 4.7726 - val_acc: 0.4130\n",
      "training in partition  3\n",
      "Epoch 1/2\n",
      "103/103 [==============================] - 282s 3s/step - loss: 4.2748 - acc: 0.4150 - val_loss: 5.0756 - val_acc: 0.4123\n",
      "Epoch 2/2\n",
      "103/103 [==============================] - 257s 2s/step - loss: 4.2417 - acc: 0.4157 - val_loss: 4.7973 - val_acc: 0.4123\n",
      "training in partition  4\n",
      "Epoch 1/2\n",
      "102/103 [============================>.] - ETA: 1s - loss: 4.2124 - acc: 0.4114Epoch 1/2\n",
      "Epoch 1/2\n",
      "103/103 [==============================] - 275s 3s/step - loss: 4.2119 - acc: 0.4113 - val_loss: 5.4709 - val_acc: 0.4216\n",
      "Epoch 2/2\n",
      "103/103 [==============================] - 262s 3s/step - loss: 4.1839 - acc: 0.4112 - val_loss: 5.0361 - val_acc: 0.4216\n",
      "training in partition  5\n",
      "Epoch 1/2\n",
      "102/103 [============================>.] - ETA: 1s - loss: 4.1938 - acc: 0.4161Epoch 1/2\n",
      "Epoch 1/2\n",
      "103/103 [==============================] - 290s 3s/step - loss: 4.1920 - acc: 0.4161 - val_loss: 5.0098 - val_acc: 0.4112\n",
      "Epoch 2/2\n",
      "103/103 [==============================] - 263s 3s/step - loss: 4.1707 - acc: 0.4161 - val_loss: 5.1173 - val_acc: 0.4112\n",
      "training in partition  6\n",
      "Epoch 1/2\n",
      "102/103 [============================>.] - ETA: 1s - loss: 4.1971 - acc: 0.4155Epoch 1/2\n",
      "Epoch 1/2\n",
      "103/103 [==============================] - 274s 3s/step - loss: 4.1972 - acc: 0.4159 - val_loss: 4.8152 - val_acc: 0.4114\n",
      "Epoch 2/2\n",
      "103/103 [==============================] - 266s 3s/step - loss: 4.1844 - acc: 0.4160 - val_loss: 4.8291 - val_acc: 0.4114\n"
     ]
    }
   ],
   "source": [
    "modWrap.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.35984024, 0.37395075, 0.3569803, 0.3527103, 0.33049205, 0.3392444]\n"
     ]
    }
   ],
   "source": [
    "print(modWrap.scores())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "modWrap.save_model(\"ince.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameter = ModelParameter(train_path, batch_size=200, n_channels=4, n_epochs=5, row_scale_factor=1, col_scale_factor=1)\n",
    "preprocessor = ImagePreprocessor(parameter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(512, 512, 4)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = preprocessor.preprocess(partitions[0][\"train\"][0])\n",
    "print(image.shape)\n",
    "plt.imshow(image)\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}