AD.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Imports\n",
    "# Basics\n",
    "from __future__ import print_function, division\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import shutil\n",
    "import glob\n",
    "import os\n",
    "import cv2\n",
    "from scipy.misc import imread\n",
    "# sklearn\n",
    "from sklearn.metrics import f1_score, accuracy_score\n",
    "\n",
    "import heapq\n",
    "from pprint import pprint\n",
    "import coremltools\n",
    "from IPython.display import SVG\n",
    "\n",
    "# keras\n",
    "from keras.layers import Dense, Flatten, Embedding, Reshape, Activation, SimpleRNN, GRU, LSTM, GlobalAveragePooling1D,GlobalAveragePooling2D, Convolution1D,Convolution2D, MaxPooling1D,MaxPooling2D, Merge, Dropout\n",
    "from keras import backend as K\n",
    "K.set_image_dim_ordering('tf')\n",
    "from keras.models import Sequential, Model\n",
    "from keras.models import load_model\n",
    "from keras.layers import Dense\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.utils import np_utils\n",
    "from keras.utils.data_utils import get_file\n",
    "from keras.utils.visualize_util import model_to_dot, plot\n",
    "from keras.datasets import imdb, reuters\n",
    "from keras.preprocessing import sequence, image\n",
    "from keras.optimizers import SGD, Adam, RMSprop, Adagrad, Adadelta, Nadam\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.applications.inception_v3 import InceptionV3\n",
    "from keras.applications.imagenet_utils import decode_predictions, preprocess_input, _obtain_input_shape\n",
    "\n",
    "# logging (set to INFO)\n",
    "import logging\n",
    "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "def image_array(file_path):\n",
    "    '''Turn image into an array\n",
    "    INPUT: Filepath\n",
    "    OUTPUT: Numpy array'''\n",
    "    image = cv2.imread(file_path)\n",
    "    return np.array(image)\n",
    "\n",
    "def plot_image(file_path):\n",
    "    '''Plot an image\n",
    "    INPUT: Filepath\n",
    "    OUTPUT: Image plot'''\n",
    "    image = cv2.imread(file_path)\n",
    "    return plt.imshow(image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Open data frame only with car models appearing more than 10 times (> 30 pictures per class)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_10 = pd.read_csv('grouped10.csv')\n",
    "df_10.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Class labels\n",
    "df_10['merged'] = df_10['Make']+'_'+df_10['Model']\n",
    "models = df_10.merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "classes_countunder30 = df_10.sort_values('Count',ascending=True)[:200].merged"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Put file paths and corresponding labels in a data frame.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "car_models = {}\n",
    "for model in df_10.merged:\n",
    "    car_models[model] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Folder set up"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Car models called 'Other' were deleted  \n",
    "Delete all car models with less than 11 cars / 30 pictures  \n",
    "Input: 'AD/AutoDetect/'  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def delete_underpopulated_car(folder):\n",
    "    '''\n",
    "    INPUT: Folder path\n",
    "    Function to delete folders of car models with under 30 images. \n",
    "    '''\n",
    "    for filename in glob.iglob(folder+'*.jpg'):\n",
    "        car_model = '_'.join([filename.split('_')[1],filename.split('_')[2]])\n",
    "        if car_model not in car_models:\n",
    "            os.remove(filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create test set folders with 10  \n",
    "size of test/validation set: number_samples  \n",
    "Image folder: 'AD/AutoDetect/'  \n",
    "Test/validation folder: 'AD/validation/'  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def test_set_folders(image_folder,test_folder,number_samples):\n",
    "    '''Set up folders of validation set by moving images from training set\n",
    "    INPUT: Folder with images, validation set folder, Number of samples\n",
    "    '''\n",
    "    #number_samples = 2633\n",
    "    count = 0\n",
    "    for filename in glob.iglob(image_folder+'*.jpg'):\n",
    "        model = '_'.join([filename.split('_')[1],filename.split('_')[2]])\n",
    "        model2 = '_'.join([filename.split('/')[2]])\n",
    "\n",
    "        if os.path.exists(test_folder+str(model)):\n",
    "            shutil.move(filename, test_folder+str(model)+'/'+str(model2))\n",
    "            count += 1\n",
    "            #pass\n",
    "        else:\n",
    "            os.makedirs(test_folder+str(model))\n",
    "            shutil.move(filename, test_folder+str(model)+'/'+str(model2))\n",
    "\n",
    "        if count == number_samples:\n",
    "            break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create train set folders  \n",
    "Image folder: 'AD/AutoDetect/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def train_set_folders(folder):    \n",
    "    '''Set up folders of training set\n",
    "    INPUT: Folder path\n",
    "    OUTPUT: Folders with car model names 'Audi_S6'\n",
    "    '''    \n",
    "    for filename in glob.iglob(folder+'*.jpg'):\n",
    "        model = '_'.join([filename.split('_')[1],filename.split('_')[2]])\n",
    "        model2 = '_'.join([filename.split('/')[2]])\n",
    "\n",
    "        if os.path.exists(folder+str(model)):\n",
    "            shutil.move(filename, folder+str(model)+'/'+str(model2))\n",
    "        else:\n",
    "            os.makedirs(folder+str(model))\n",
    "            shutil.move(filename, folder+str(model)+'/'+str(model2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_class_labels():\n",
    "    '''Get all class labels / car models'''\n",
    "    labels = []\n",
    "    image_files = []\n",
    "    for filename in glob.iglob('AD/AutoDetect/*.jpg'):\n",
    "        model = '_'.join([filename.split('_')[1],filename.split('_')[2]])\n",
    "        if model in set(models):\n",
    "            image_files.append(filename)\n",
    "            labels.append(model)\n",
    "    return labels, image_files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "All folders / car models which had to be renamed to combine all of them.  \n",
    "Example: Audi TT, Audi TT 2.0, Audi TT TDI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cars = ['Audi_A3','Audi_A4','Audi_A6','Audi_A8','Audi_Q5','Audi_R8','Audi_RS6','Audi_S4','Audi_S6','Audi_TT','Bentley_Continental','Bentley_Flying','BMW_3','BMW_6','BMW_M3','BMW_M4',\\\n",
    "        'BMW_M5','BMW_X1','BMW_X3','BMW_X5','BMW_Z4','Buick_Electra','Buick_Regal','Buick_Lucerne','Buick_GS',\\\n",
    "        'Buick_Special','Buick_Super','Cadillac_60','Cadillac_CTS','Cadillac_Coupe','Cadillac_DeVille','Cadillac_Deville',\\\n",
    "        'Cadillac_Eldorado','Cadillac_DTS','Cadillac_Escalade','Cadillac_Fleetwood','Cadillac_SRX','Cadillac_XTS',\\\n",
    "        'Chevrolet_5','Chevrolet_210','Chevrolet_Corvette','Chevrolet_Deluxe','Chevrolet_Monte','Chevrolet_Nova',\\\n",
    "        'Chrysler_200','Chrysler_300','Chrysler_Pacifica','Chrysler_PT','Chrysler_Sebring','Chrysler_Town',\\\n",
    "         'Edsel_Ranger','Excalibur_Phaeton','Ferrari_250','Ferrari_355','Ferrari_360','Ferrari_458','Ferrari_488',\\\n",
    "        'Ferrari_550','Ferrari_599','Ferrari_612','Ferarri_California','Ferrari_F12','Fiat_124','Fiat_600','Fiat Spider',\\\n",
    "        'Ford_Custom','Ford_E-Series','Ford_Deluxe','Ford_Explorer','Ford_Roadster','Ford_Super','Ford_Transit',\\\n",
    "         'FREIGHTLINER_M2-112','Freightliner_Sprinter','Genesis_G90','GMC_C4500','GMC_1000','GMC_Savana','GMC_Sierra',\\\n",
    "         'GMC_Yukon','Honda_Accord','Honda_Odyssey','Honda_Pilot','Honda_Ridgeline','Hudson_Hornet','Hudson_Super',\\\n",
    "        'Hyundai_Elantra','Hummer_H1','Hummer_H2','Hummer_H3','Hyundai_Sonata','Hyundai_Veloster','Infiniti_G37',\\\n",
    "         'Infiniti_Q50','Infiniti_QX80','INTERNATIONAL_4300','Isuzu_NPR','Jaguar_XF','Jaguar_XJ','Jeep_Cherokee',\\\n",
    "         'Jeep_Compass','Jeep_J10','Jeep_Pickup','Jeep_Wrangler','Kia_Optima','Kia_Rio','Lamborghini_Aventador',\\\n",
    "        'Lamborghini_Countach','Lamborghini_Diablo','Lamborghini_Gallardo','Lamborghini_Hurracan','Lamborghini_Murcielago',\\\n",
    "         'Lamborghini_Urraco','Land\\ Rover_Range Rover','Lexus_GS','Lexus_ES','Lexus_GX','Lexus_HS','Lexus_IS','Lexus_LS',\\\n",
    "        'Lexus_LX','Lexus_RC','Lexus_RX','Lexus_SC','Lincoln_Continental','Lincoln_MKC','Lincoln_Town','Lincoln_Zephyr',\\\n",
    "        'Maserati_Ghibli','Maserati_Quattroporte','Maybaych_57','Mazda_CX-9','Mercedes-Benz_280','Mercedes-Benz_300',\\\n",
    "        'Mercedes-Benz_C','Mercedes-Benz_CLS63','Mercedes-Benz_E','Mercedes-Benz_GLA','Mercedes-Benz_GLE','Mercedes-Benz_GLK',\\\n",
    "        'Mercedes-Benz_Metris','Mercedes-Benz_Replica','Mercedes-Benz_SLS','Mercedes-Benz_Sprinter','Mercury_convertible',\\\n",
    "        'Mercury_Eight','Mercury_Grand Marquis','Mini_Cooper','Mitsubishi_Eclipse','Mitsubishi_Galant','Mitsubishi_Outlander',\\\n",
    "        'Morris_Minor','Nash_Metropolitan','Nissan_Skyline','Oldsmobile_Cutlass','Oldsmobile_Jetstar','Packard_Clipper',\\\n",
    "        'Packard_Series','Plymouth_Belvedere','Plymouth_GTX','Plymouth_Special','Pontiac_Bonneville','Pontiac_Firebird',\\\n",
    "        'Pontiac_Montana','Pontiac_Safari','Pontiac_Solstice','Pontiac_Ventura','Porsche_356','Porsche_550','Porsche_911 GT3',\\\n",
    "        'Porsche_911 Turbo','Porsche_911 Carrera','Porsche_944','Porsche_964','Porsche_Boxster','Porsche_Cayman','Porsche_Carrera',\\\n",
    "        'Porsche_Cayenne','Porsche_Panamera','Ram_1500','Ram_2500','Ram_3500','Ram_4500','Ram_Ram','Renault_4','Rolls-Royce_Corniche',\\\n",
    "        'Rolls-Royce_Phantom','Rolls-Royce_Silver','Rolls-Royce_SILVER','Rolls-Royce_Wraith','Saab_9-5','Shelby_AC','Shelby_Cobra',\\\n",
    "        'Shelby_CSX','Studebaker_Champion','Studebaker_Commander','Subaru_Impreza','Subaru_WRX','Sunbeam_Alpine',\\\n",
    "        'Toyota_4Runner','Toyota_Avalon','Toyota_Corolla','Toyota_FJ40','Toyota_Crown','Toyota_MR2','Toyota_Pickup',\\\n",
    "        'Toyota_Prius','Toyota_RAV4','Toyota_Tacoma','Toyota_Tundra','Toyota_Supra','Toyota_Sienna','Triumph_GT6',\\\n",
    "        'Volkswagen_Golf','Volkswagen_Jetta','Volkswagen_Karmann','Volkswagen_Routan','Volvo_C70','Volvo_S60',\\\n",
    "        'Volvo_V60','Willys_CJ2A','Willys_Jeep']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building the model with only a few number of images before scaling up.\n",
    "#### Create two data sets - train and test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def train_test_dfs(num_images):\n",
    "    '''Create train and test data frames.\n",
    "    INPUT: Number of images\n",
    "    OUTPUT: Two data frames\n",
    "    '''\n",
    "    train_split = 0.75*num_images\n",
    "    test_split = 0.25*num_images\n",
    "    \n",
    "    train = pd.DataFrame([image_files[:train_split],labels[:train_split]]).transpose()\n",
    "    test = pd.DataFrame([image_files[train_split:test_split],labels[train_split:test_split]]).transpose()\n",
    "    \n",
    "    return train, test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_df, test_df = train_test_dfs(5000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create image arrays for both sets and resize images to the same size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def create_img_array(df):\n",
    "    '''\n",
    "    INPUT: Data frame\n",
    "    OUTPUT: image array with training data\n",
    "    '''\n",
    "    img_train = []\n",
    "    for img_name in df[0]:\n",
    "        img = image.load_img(img_name, target_size=(224,224))\n",
    "        '''image array -> (3,155,155)'''\n",
    "        img = image.img_to_array(img)\n",
    "        img_train.append(img)\n",
    "    #expand by 1 dimension to -> (1,3,155,155)\n",
    "    #np.expand_dims(img, axis=0)\n",
    "    img_train = np.array(img_train)\n",
    "    #preprocess image by subtracting the mean RGB pixel intensity from the ImageNet dataset'''\n",
    "    img_train = preprocess_input(img_train)\n",
    "    img_train = np.array(img_train)\n",
    "    \n",
    "    return img_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "img_train = create_img_array(train_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#train_x = np.stack(img_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#test_x = np.stack(temp2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Change class labels into numeric values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def numerize_labels(num_classes):\n",
    "    '''Create class labels'''\n",
    "    classes = [i for i in range(1,(num_classes+1))]\n",
    "    map_labels = dict(zip(train[1].unique(),classes))\n",
    "    train_y = []\n",
    "    for i in train[1].values:\n",
    "        train_y.append(map_labels[i])\n",
    "    return train_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_y = numerize_labels(468)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_y = np.array(train_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create a validation set out of the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "split_size = int(img_train.shape[0]*0.8)\n",
    "train_x, val_x = img_train[:split_size], img_train[split_size:]\n",
    "train_y, val_y = train_y[:split_size], train_y[split_size:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_x.shape,val_x.shape,val_x.shape,len(train_y),len(val_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.fit(train_x, train_y,batch_size=64,nb_epoch=10,shuffle=True,verbose=1,validation_data=(val_x, val_y))\n",
    "\n",
    "# Make predictions\n",
    "predictions_valid = model.predict(val_x, batch_size=64, verbose=1)\n",
    "predictions_valid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modelling with a generator to scale up and use all 170k images (468 classes/car models).\n",
    "### Best accuracy: 90% on training set and 80% on validation set.\n",
    "#### Usage of the pre-trained model InceptionV3 as a base model and then train it with our data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the pre-trained model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_model = InceptionV3(weights='imagenet', include_top=False)#, input_shape=(224,224,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Add additional layers and create generator that reads images directly from folders."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setup:\n",
    "- created a folder AD/\n",
    "- created AutoDetect/ (train) and validation/ subfolders inside AD/\n",
    "- created classes/car models subfolders inside AutoDetect/ and validation/\n",
    "- Split: 130000 in train/AutoDetect and 30000 in validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 127647 images belonging to 468 classes.\n",
      "Found 27864 images belonging to 468 classes.\n",
      "First fitting:\n",
      "Epoch 1/100\n",
      "2048/1994 [==============================] - 47s - loss: 6.5503 - acc: 0.0015 - val_loss: 6.4458 - val_acc: 0.0045\n",
      "Epoch 2/100\n",
      "2048/1994 [==============================] - 44s - loss: 6.4694 - acc: 0.0039 - val_loss: 6.3504 - val_acc: 0.0045\n",
      "Epoch 3/100\n",
      "2048/1994 [==============================] - 43s - loss: 6.3394 - acc: 0.0117 - val_loss: 6.2329 - val_acc: 0.0201\n",
      "Epoch 4/100\n",
      "2048/1994 [==============================] - 44s - loss: 6.2947 - acc: 0.0166 - val_loss: 6.2396 - val_acc: 0.0223\n",
      "Epoch 5/100\n",
      "2048/1994 [==============================] - 43s - loss: 6.2384 - acc: 0.0249 - val_loss: 6.1703 - val_acc: 0.0268\n",
      "Epoch 6/100\n",
      "2048/1994 [==============================] - 43s - loss: 6.1987 - acc: 0.0210 - val_loss: 6.0390 - val_acc: 0.0379\n",
      "Epoch 7/100\n",
      "2048/1994 [==============================] - 44s - loss: 6.0653 - acc: 0.0303 - val_loss: 5.9226 - val_acc: 0.0379\n",
      "Epoch 8/100\n",
      "2048/1994 [==============================] - 43s - loss: 6.0157 - acc: 0.0317 - val_loss: 6.0707 - val_acc: 0.0312\n",
      "Epoch 9/100\n",
      "2048/1994 [==============================] - 44s - loss: 5.9635 - acc: 0.0459 - val_loss: 5.9176 - val_acc: 0.0513\n",
      "Epoch 10/100\n",
      "2048/1994 [==============================] - 43s - loss: 5.9037 - acc: 0.0415 - val_loss: 5.8707 - val_acc: 0.0357\n",
      "Epoch 11/100\n",
      "2048/1994 [==============================] - 44s - loss: 5.7794 - acc: 0.0547 - val_loss: 5.9189 - val_acc: 0.0379\n",
      "Epoch 12/100\n",
      "2048/1994 [==============================] - 43s - loss: 5.7755 - acc: 0.0493 - val_loss: 5.8215 - val_acc: 0.0513\n",
      "Epoch 13/100\n",
      "2048/1994 [==============================] - 43s - loss: 5.7943 - acc: 0.0532 - val_loss: 5.8602 - val_acc: 0.0424\n",
      "Epoch 14/100\n",
      "2048/1994 [==============================] - 43s - loss: 5.7117 - acc: 0.0566 - val_loss: 5.6416 - val_acc: 0.0781\n",
      "Epoch 15/100\n",
      "2048/1994 [==============================] - 44s - loss: 5.6256 - acc: 0.0615 - val_loss: 5.5750 - val_acc: 0.0625\n",
      "Epoch 16/100\n",
      "2048/1994 [==============================] - 44s - loss: 5.5899 - acc: 0.0732 - val_loss: 5.5903 - val_acc: 0.0670\n",
      "Epoch 17/100\n",
      "1408/1994 [====================>.........] - ETA: 10s - loss: 5.5734 - acc: 0.0710"
     ]
    }
   ],
   "source": [
    "x = base_model.output\n",
    "\n",
    "# Global spatial average pooling layer\n",
    "x = GlobalAveragePooling2D()(x)\n",
    "\n",
    "# Fully-connected layer\n",
    "x = Dense(468, activation='relu')(x)\n",
    "\n",
    "# Removed Dropout layer and replaced it with a batch normalization layer since model was underfitting\n",
    "#x = Dropout(0.2)(x)\n",
    "x = BatchNormalization()(x)\n",
    "\n",
    "# Logistic layer\n",
    "predictions = Dense(468, activation='softmax')(x)\n",
    "\n",
    "model = Model(base_model.input,predictions)\n",
    "\n",
    "# Train only the top layers (randomly selected)\n",
    "# -> freeze all convolutional InceptionV3 layers\n",
    "for layer in base_model.layers:\n",
    "    layer.trainable = False\n",
    "\n",
    "# Compile the model after freezing layers\n",
    "adam = Adam(lr=0.0001, beta_1=0.9)\n",
    "rms = RMSprop(lr=0.0001, rho=0.9, epsilon=1e-08, decay=0.0)\n",
    "model.compile(optimizer=adam, loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# Train the model on the image data\n",
    "#model.fit(train_x, train_y, nb_epoch=10, batch_size=128, validation_data=(val_x, val_y))\n",
    "batch_size = 64\n",
    "epochs = 100\n",
    "img_height = 224\n",
    "img_width = 224\n",
    "train_samples = 127647\n",
    "val_samples = 27865\n",
    "\n",
    "# Image Augmentation: rescale, zoom, zca whitening, horizontal flip\n",
    "train_datagen = ImageDataGenerator(featurewise_center=True,\n",
    "        rescale=1./255,\n",
    "        #shear_range=0.2,\n",
    "        zoom_range=0.2,\n",
    "        zca_whitening=True,\n",
    "        #rotation_range=0.5,\n",
    "        horizontal_flip=True)\n",
    "\n",
    "test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "# Process images directly from the folder using generators\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "        'AD/AutoDetect/',\n",
    "        target_size=(img_height, img_width),\n",
    "        batch_size=batch_size,\n",
    "        class_mode='categorical')\n",
    "\n",
    "validation_generator = test_datagen.flow_from_directory(\n",
    "        'AD/validation/',\n",
    "        target_size=(img_height, img_width),\n",
    "        batch_size=batch_size,\n",
    "        class_mode='categorical')\n",
    "\n",
    "# Fine-tune the model\n",
    "print('First fitting:')\n",
    "model.fit_generator(\n",
    "        train_generator,\n",
    "        samples_per_epoch=train_samples // batch_size,\n",
    "        nb_epoch=epochs,\n",
    "        validation_data=validation_generator,\n",
    "        nb_val_samples=val_samples // batch_size)\n",
    "\n",
    "# Now, the top layers are well trained and we can start fine-tuning convolutional layers from inception V3.\n",
    "# -> freeze bottom layers and train the rest of the layers.\n",
    "\n",
    "# Look at all layers to determine how many layers we should freeze:\n",
    "for i, layer in enumerate(base_model.layers):\n",
    "    print(i, layer.name)\n",
    "\n",
    "# we will freeze the first 158 layers and unfreeze/train the rest:\n",
    "# 28,44,60,70,92,114,136,158,172,191\n",
    "\n",
    "for layer in model.layers[:158]:\n",
    "    layer.trainable = False\n",
    "for layer in model.layers[158:]:\n",
    "    layer.trainable = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('Second fitting:')\n",
    "\n",
    "# Recompile the model before training it again.\n",
    "model.compile(optimizer=adam, loss='categorical_crossentropy',metrics=['accuracy'])\n",
    "\n",
    "# fine-tune the model\n",
    "final_model = model.fit_generator(\n",
    "        train_generator,\n",
    "        samples_per_epoch=train_samples // batch_size,\n",
    "        nb_epoch=epochs,\n",
    "        validation_data=validation_generator,\n",
    "        nb_val_samples=val_samples // batch_size)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scores_report(final_model):\n",
    "    '''Function to return model accuracy & loss\n",
    "    INPUT: model\n",
    "    OUTPUT: epoch history'''\n",
    "    \n",
    "    history = final_model.history\n",
    "    print('Highest accuracy{:.2%}'.format(max(history['acc'].values())*100))\n",
    "    print('Lowest loss'+min(history['loss'].values()))\n",
    "    print('Highest validation accuracy{:.2%}'.format(max(history['val_acc'].values())*100))\n",
    "    print('Lowest validation loss'+min(history['val_loss'].values()))\n",
    "    \n",
    "    return history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scores_report(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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f6OhoIOC7cOHC7MyN3r7uPXv3cl5+9+6d5crmwEDm0sVTpcLy5QsnTp94i2dJ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VvOJ09SRrhltMVN9BFCgLgQjJyvQkAQQqALQlhWFtpYSjnlHtYIAMAYay0aU8sQIU5KWGPGXCgH\naynzJwaqlBJKE4MwxggjrTVGmFKOMS5FIaWklHY6ncOHDxNC8kI1G1Oj4SiuVEpppDbcj4zFCFNL\nmB9UXn717B/+yZ++fun0/v373/Hud7342kvVZnxt43pUifNS+r7vBbxMi6OHb1tqTsuyjLxwNBpt\nra2/9NKpTqfjeQGjfhiG1tLFhX15Xiaj7nCY1au17e1tTgKtkChNXuog9kejkc+9KIrSLAGAUiov\nCnNRCq2UNcaYqdmZarUK2GKui1wDgB/6fsAY4NUbN4qi8HHwj//xP56fm7EGLFit0Ve//NWvfuNZ\nn9IsS0WWirKkhHOKH3jLQ3v3Le/bv1drY60tlfI0xUhhAgiQAWGMRYRKrXd2dj74wQ/+/h/+nrVI\nCNEfjjzPW1zYMzc3V6nVFxcXMcadTmf1xvlHH330zhO3LSwscO5jRJUySZJ0d3r1Ro1zHkWRYwxK\nKTHCrn41xggh3GiAtaC1zvM8yzIX0DHG7g4uB3XHrYWqV+siy+vNRmj1hz7y0U/9/m9Liy0gazGh\nDFzgvWWgCsHYGqkrRwxY0NqBf+5L4xweIkwZk5eFxmOlasY8o0EIpbWux1G3P6jXmgbjwWDUXt/c\nuLHhkg+XgLrv3FZvx62Pdu/bhfUyE8YYpzs1NTXFOQdlXNLmXv3IkSNLS0tAcyREnuc8qPZHmQaS\npTqKoiAM00JmQiOMn3v+6WEv2bt/X2N67otf+spXn36mNTWz3lkxYF9++WXik631rWa1vtPbkbmO\noigbZbKU7fbGF079mRRCWxUEAcHM8/xWcy6Oq1kqrAFCCA/YcJhRSmdn5tLhaH52HhGMEKpWq2mW\n8ZA0imJ5cQkje/nipTiO2bA/SqVSJef+7GyjUqnkeY4QHibDWq1lbOF7IaEIYbPT7QVhhAD/1E/9\nwwfuuw+BRQCEoEvnrhiDV66tFFnGsCF+4EaphJQ//Dd+RIPeGXQxA4RQFHqFkD4noK21GlOMsdXW\nIoSFUHedOPnrv/kbC/OLM/tnarXGwvxSo9HQ2mZ5MT01MzM7ffDgwYWFBcYYodo1upqNKa0tIWR6\nepoy4tykm3kyxkgpxsAkpS7DllIWpfA8bxd44VtbW3Ece4wIUTjc1+WHYE0cx+v9QT2IKIF8VDAv\nRMxLRqMgoMNROs7xGJ1EToBxTkkJYdZavDsT2GpVfd932kCU0jzPESK+z1jMikL4vt/rDijl3PcI\npsaY1vTcdqf7+3/4eJYWwzTdaW9PvkkAMCYsMupwV611vV4fDAYYYy3B5TRuLtGBrE7jOE1TrfXR\no0f379/f7bWllFLKc+fOHjhw4E1vetN8vereWFIMXj59+tSpU71e9757Hzxx172/+G9+eac/aLVa\n7e1NTPDGxsZzzz338Hc8zJinpbEaNSt1i6C0qBHXPv8nnxVJ4TE+PddqNccVWCFFkZVCaPddimPP\n9/04jqM4BG0456UUnHOltUEgtRJKrm1uDHv9I4cPBkHQ2wmjTLTbbd/3gzDMi0JpnScJCzlhXJlC\nWSOFxsT4XlgUxTAZPfzww57nITBGKktImuZxVM2S1GNUyBxbJI32vSAvimarlee5tkZrLZRikiKE\nBUEUgbWIUwxgwSKMsFOLXtqzfOjQoSiqHD58OEuLre02Y2zv8oETJ07sWV6amZkhhLTbm4PhjjGm\n1Wq1mlPDYTIRuho7P62dgbqCxs1XuplSay2hRBs1HA0455igMAo8nzOsB4OB53muzHW9RKmEtdZi\nFERRd3vTICykDsLYGGMBO33CoihdKui+Bu6dUISp0doCdqBAfzDCo5RzDgAEMGWeMUYqJTLBGBsl\nRVypa61dB/n6avvZZ59DhEWV2a3uSn8kc8MtYgabscQhAAAQTPyIMcZGo9Eg0YhWMCGEE/ddxF7F\nTYZ0B4OhGFFKrQWM2ekLV185f+Xwof2dTgfAVKcWXnz17JNPfeMtDz88Pz9/3333VShd31jdd/To\nJ9785v/wy7/6249/anp62gv8re2NqampxlTr2Wef2dzcJISUUgBDlUrFys7a2togSWUh33zvW1dX\nVmtxpZfulKU2mjpeJQIWhuOtQk63ol6v9vt9jJHUknI6ytMKr7LQwxTtX9rTajSrcZiOkixL5pYW\nrl5aDYIojuPRKHVQRqVSE6AKUXLfU0ZXKlEch93NTrvd/vEf/zvz8/MITDoc1Zr1UZ5Ryra3O0Ya\ng6RPqURgAaTR+w4dvrq6Mje/aDkvlSaFQkRoyy1gRQgCYAwDjKflMKZBEL3vvR9yUavXHRw4cOC7\nvuu7Gs1aJW5orUejwcrKNaVFHMeLi4sAIITa3t6u15tZljnTdNYphHD2ighO8yzPc1fvMsYsWKML\npRRlkKT9bDv7gR/4a1/72l+sXl+dmZnp9Xq+7wOyCAMg5FTHLOCV1XUt1fL+w0Jpa5XW2vN89xIU\n+xMwJ8tLV93TshSEECGktfbcuXMrKyv1er3aqA8Gg2q1Oj8/H4YhQggh3O0PjTFSdjY3273uoN1u\nX7+2Wa01rEWvvH6WEh5VqtIIhBBgMNZKYcCJXhtUlAIhyXkgpSSYSGUsQkIbKEvGmBJWa2Gxlwtl\nS+F5nhYaQGOM/+K5lwlBCCE/4HedvPell1565vkXKpXKHXedbE3PsCjgnG/1epeuXUeYNlpNzunS\n8ny73b5y8dL999x/9uzZL37+y3fdc/e169cxxv12t95oJElarzdf+otTPg+yfqloXpYDzrnn+WCx\nKIuw4gshtNYYQxAE7fZmtVpllI6SRFntR6yQGTJY5Hp7p3P44KEz53a0FNOtlusjOBkzFz3Gp8uI\ntiiIQmuh3mwWZXJjbfWf/MzPfvd3fphSSghWWpx//fXUqvn5AytXV5QyjCJAGhCNa/U0y37wR36Y\nh5GluD8aeoRaLBDBRmMATCkmgEALxgmmhFAIg2jQz4Syd9x54siRI0tLC4QgIQQgtL6+aq2tVKPW\nVIOQMSTkzLFarUZRlCQJRi40W5f9CyGKosjzfFK/TsK6hQIAiiJXSjCGP/rRj21tbX3HI2+ZmZlx\nPhh21Vtd7LbWMo/XopmV61c+/4Uv3XXXXVEUEea5AQFp5TjEY+QFvqMmos888btZln31qa+vrq7V\narWdTg8hxJgXhuHi4vyN9bWNzbW9e/dqik+dOiWlRojswkygFVdKOblDjLE7VJd6uj7QG4oyl5O5\nX2VWccp0IXzO3Y4VHPp5nvu+P+FS5HluRbJn7zLGeKO9OTc/Pzs7+8CbH4jjWGSCc88ajaw0Rq2v\nrp0+fXpzc1MonaapH4ZgIoQQ9+i5c+cYI1mWSVlSRK21YRhTymvV+mAwyPOSUIUxrtVqrnQIw3B+\nfn44dN9GGcexS1polSGEoigihAwGAyklr1Wd2L4QRb1ej6Ko3+9GhHS73anWQr83CoIo8OPRKC2J\nwBaa9VatVrfKnjt74cd+6Ht+5Ef/X4wRz/OKUmsFQhjf5wjBxz/+fWdee11KiRDC9cZwOPT98Ff+\n/X/Y2tqu1WpRVPE8FoZ+JQopRp7PPEYppZwSSgkhMBrlr732WpIk9VpreXlZKbW+vm6tjeM4CAKF\n6S4ZaoxVa4vd/7oF3Ts7O2VZVrygLMs0Td0sitZayNTloO7Orloqy7xSqSRJcvLkyZ/92Z+9dOnS\nyZMn3/HOd3meJ4Rw8BMAhJwBwaUsEAZdFCDU9YsXf++JJyjFR44c2rtv+dChA73ezpHlpTzPNzY2\nut0uIaRSqXieh97+locajUaSpNZa3wtXV9fjOA6CiBCyurqCCGaMCCEKo8YNJINgXGEhSsJmswkA\no9GoLMvJhKHbZUap0wkCjHEYhkeOHCnL0n35iqI4d/2KEtInTJbl/ffeRwg5deZ1lwZMPFAYhh63\nnPPFxcWHHn7IC4M4jrWRUkoKtNvt+R7HoLSWCEyz2QREwjBW0gDBly6s7+x0Xn/99VEy2Nxc932O\nMUbWlmVpDZqfXxRCFIUQQgAoIUSn03Fsmrm5uU6n02w2r1+/7jqWWuvhcEgixhhzLBuXAllGdzMB\ngzF2WzGLdMCZz3mglfX9uL214/t+qpIyF7cfuW17u2O1+Wf/7J8dO7A0PdPyfS6VKUtpNJLSCiFq\ntcr3f/8PvvrKaacrOLAwPT398Y9/olFvhmHsjCyKAp8zj3Hf5xhZAggAMBnHZcbY+fPnr1y5cvvR\n45TSWq3m+/5EtVQT5g5ogtVoi90txpgoivr9/mAwUFkxcYHOxUqVubrCzfU7SgelOEkSAPjc5z43\nMzNz6NChV1999e3veIwx5qSp3EOsFISzUhYzs9M7m5tI6vbq6n/65Cc5p3meZnnabNazLPERcTsa\nMcZRFC0tLa2srNCt9vZwlAihBoMRox7nfKt9rV5vGgRxXBVCxLXmYDCgsnCYBSHUuU+3pHA4HCKE\nnFbWBKtyCegEt3I42UsvvcQ5d32IMAwPHzxUFMX25pYx5i+ee9YYU6nVsiShGBullBCMsVqlct+b\n777//vulVoNBH3PaT4YeJWEYVsNqo9FcX1s12jQaDUyskppzppTywkrgh8eONYQQDzzwACBjrczy\nFMCM+oO1tbVr11bSNFtb25FCe563tr7e6XRmZ2eFLLIs6/V3GPWstTMzM+7AhsPhI4888trZMwih\nLMkwxlmeUUo10QCltdaYsQ/ube/4nPfyEUa0LDVGxPfjMhcKSmvtlcsXv/8T3/fY299Rr9fn5uas\ntWUptQGltDVIGciKwu3gckZgrQ24N+oPTt55YjgccuZZbdZvrCajrjFGlKUsSq3EGPox0tlTFEVu\nb+y733vA6XK59apKaco9KRXsNggdwAfWAEJGSinlencny7Isy3zGJyCgQ7W0lsaMPSjabdvkeemM\n6cSJE5zzQ4cOLS4uDpMR4xVMACEgBAEAJX4hBWNsbW3Nwxgj5Hkepdjps5aiAIBmcyobZTwIpMXp\nMN1/+Oh6u5OWiuZCGoQxosv7Dqyvrwtt6q0phBAjBGHcaLbCOMqKIsmyMXZqMYD7DwgheZ7DLjI/\naXLCLZ0Ad3HO3S2c8zHemySuGaiUwpTWKhW3ptd93MaYe+65553vfCev8PWtzUql4gV+mqZ+5FfC\n2PM8QgghKIoiUVjG2Cjpu7kWqZGFklF/ZmZuOOyXZdHr72hdaiMZY1E1Olo/etsdt6dpvrm5eeXy\n1aIoZhdnvv71r3cHvbIsOeeNRgMhstXZjqKIDvpLS0sWo/ZOp1FtWGs54XmeCyWwxZhwY4xUEiwi\nhKajxBhTWiGF9jweRX6Rl3mel2VpkAhD/0d/5Mfe9tZHtFStZt0YwzkpCm2sMcZoi6xFlNL1zU3f\n9y0C13i21k4GdCil7Xbn2rVrohgopYo0M8ZopayyxhhFxuFrMBgNh8OHH37YWuvgdDeA71ImINTu\nXs5SGeVKqSRJhBBus5nv+1bpW92ntdZajRC2Fqw1CGEA6044DEO3ErssS9/3L126FFcjhCyAwZi4\nwT2tjLtnKQqMEAGw1nLOpSxHozQMw3q9GYZ+kUtEKGBiET5/8VKWZWma0rvvvnt7e3tjfWtra4sx\nD2N8+PDh06++yn0v5tWdbv/62jrGVBZuoIUrZWD8+WEA7TqqjUZDStnv9x3DwNF5JiEeAPI8d3bp\nQgkAXL9+3XVs4yAMw9AZqzEmjuMPf/jDR44cCYJACDHQSVyrKy0JxtNz06urqx4i7Xb7wPKBPC8c\nYuxOsRCyyEvPo/1yRKhv9A6ASdO0KDLPY4RyazVl46+KMWr//r1LS0uVSiXLsk984hOuF7C1tXXj\nxo3hIO10OsPh0E1oOKutRw0AqMa16daMC5c7g4FxKAoiWmjGeSnzQspqpZ6meaHLarW6Z2nv4cOH\n3/+Bd339619/5E0PHj10ECGgmFCKkyTnvi9yIbUFQMYq3/dX19ed2DkgAAv1WmVmeq7b2ebcN1Rv\nbqy1N7cIke7UtVRo1x0obaw11loxSu+7777v+djHh8OEcw4IUeY5WQNKmeM2OF4wQqgsy95O1wF5\nAGCNoYzleeGGLAghSkkXzREeEy0mjAuXkRZF4bSV4zgeDAazs7NSC855WZaTKKqMdOh9tVqVWUaQ\nq0nM9PT0/Pz8lauX3XgWYWFajKLIEMKur254nocQpVcuXvA8b9/yUpJk1trFhT31eu32Ow5vbW0D\nWO7R0Pi94agWV4qiUEphTBFghIgxhhBwX+48z40xTq3AlTiuBnRu1XUdyrJ0JAPHmpuaailrBoMB\n9lh3NCiK4r677vnu7/5ul74IpVSaIoSY59fr1SJPNzc3hCwqlWg0GlUqlY2NjTCMMMZO8oV5fpoX\nzAsY56ChOxhiPQiCgHEyPz+vlBglfUKYQYYyhjFGpNDaIoyzPGecK61LITzfX9679+ChQwjRSdfE\nIbVFUWxcW+v1em7RqDHG97mQqizLze6OS0ArUeAxsrBnYe/e/QcPHqzX63FUnZqaarVaC4szC/PT\nx24/6jHs7MAgoJyLUhVSGQNGG4Sh3W73+/37Hnjg05/+9NzcXLVaLQx84hPfW6vVCGbt9jYYm4wG\nYRxSShf3LM1Ot+Zn56IoQgjxOKaU+r4/NTUlpRwOE2vBRWeMMULYwe3a6jGOk2WujqmGcTfLGaEO\n/MaAMCBl9CTddIUOJjdjo2vEu4k/Z3mEEIeMuiPe7c7jwWBgjIn4eJuAUirLsqlqnTG2tGdxa2vr\n3PmzUqhBfxgEgVC5CxS1Wo0xliSJ1ppSZI4ePmANIvMUY3zxwuWrF89tdjf9MCLYC4KKVqoeR0rL\nIPAwpllWYIzBamMMwtTFfZfoAEBZluiWeW3397hXcs7VWut5XhiGFqEiL6RWZVl+9/d8z8Ejh5FC\nmZBIqlar5aRdACBX6bWrVwPO9iwu5mnS2WnXaw0nJKG1DoKAYmOtzguhNBKyGCal74WeF8zNTDnS\nxvZ2ryxLz+dh6GuMcyGFUEWpOeeEYKWUUcqdIuN8V9gIF7tFaF6WzPPiarXVaLo/zS1QlFIqZay1\nV69e1VoKIaIoCkO/VqkwxmZmZnzfn56e3rdvX60WPffiiyeOHadkd3rdWqm0lFIrhIAgQAgZQBBV\n4jzPH3vssfsffDBP0yRJZhf3VKtVx0HpdLYJIZ1Ox/T5xz72sXe/8x1R6JdlJspSaw1k3PJJspRz\nThjVyggpnbN3WRMAaKPc0I+LyKdPn97e3PjABz4gpaQMM+5JKUbJyPd9QIAxJsQB5BoMGDOG7ido\nJd51q2PivLWMMa0txlRrK2XRbE5RSlVeFlJYa43RDivAGGst3/KWhzc2tq5cvsq5n2VZq14DgM3N\n9X5vx7FtOOf0F/7lzwGgGytrp0+fXl1dn27V77/3ZDftvnr6dQCysLiolV1ZWWMhT5JUKUUJRwgB\nsgjAhQmyS4yeNPjd5fyo1trzPDem7GJ6tVqdnZ1db29NT0//4Pt/aN/+/e2dDmG0Wmm63/q+L4RI\ns0xrrW0WMg/AdDbbSovYC8YbMEBbW2IEnAIhSCPKaCC08jlFGEsttjubxphGvSkEAwAEKM/EqBww\n6gFAEIRaWSkNALFgAJA21holSuVmtTzPcy5kUv8ibJyBZjJzud2g19da15sVIUqMK1NTU3Ec751f\nrDeqMzMzlUqFcyqEyLOUezgImJIKEYIBGw3GGKNBg5VSWWsNGGRtq9Xivnf27NlHHnnkyKFDe/bs\nIZ736unXa3FUqzWsslmax0F45L6Hvvnsi2979FFTlkZryigLWJopi7AyuigKrjQhxFpkLBhjlQUL\naJikeZ4bVbrYHMfx6upqHMf3v/tdtx06+PLLL58+dergwYOU0la9lpWFyyOlVOPZIEQmAD7GmFKO\nELJWO7jX5W8Ow/f80BpUFtI1qyZsNYQAAXIB1rU/nnnmmcXFPTMzM2trG0eP3vHcc9/knHuMMoIr\nzUZRFFJKeurFlzjnly9f2dnpVePwzuN3tVqtla2VwwcONlqz1uDLl6+vr29qgLm52V6vr6RBCMaE\npl2Z8AlO4YKC86DOGzlnk6apq98dQxQhVKlUPvDhD1Vq1bIsW9NT1Wo16RZua7ebaHH4RZJ05uZm\nPI5H/T4jVsnSWMwYwwZbC2ANITgM/fbOCDNKyJisTinFRoVhqLR0xGHPCwhBnPluGQoCgjEQoifv\nf1Kccs4doOvOY5fvQgxShFKEkNQizRN3zL7vMcYqlbhSjdys7fzs7NTUVByHToKeEEQIr1SiJE1C\nP3Jf3Ul4AYsdEQJbwBiEELVa7erVq4899li1WhVFcX1tzSVFV69evXr1uihlkiRbm9tBFD37wouP\nPvoWWWohc6ssxmOlu6mpKVfxAIzrdKe35RBou/sXOYeysLCAELp69epTTz11/vz5I0eOuNvtLfTc\n3Xc7jocuN53c7u7jPij3zO6xYRg6AJVSWhS5MYZSgtGYHcg5v3DhQq83YMxjlDvB23q1ghCSlGit\nyzwDa33O6MEDywih/fuWut3u4uKiEw/K0cyNG6tPPPHE2urm4uKe2emZO+65M03zdrv9yiunERCM\nLWNeqZUQgmCQUmIwxhiKGcYEE2KMFbpw0zYME2slxkaILM/10aOH3vrWt9anpgtZRDHz/JBg3+cV\nNBtbY/r9QfvGDUboVL2GAGYPLHW73dFQxHEM4Kd5FyNeCoWxMcZwjybCFEbwICiKIop8pRRnZDgc\nhEEs0gIhRDw/8gMhhLFmutLK85xYxBHRoKUx1lpBkAXADFFGGGNZlnLM4ig0xhRFQQiSUuqy0ISV\nhXJkGqfxW62QKIoajUaz2ZydnZ2fn0cIccAAYDVgIEaZPC/CMAy5V4lCYyyAVUYpMB73RVkSZMNK\n6OKMlJJh+5EPvs9VmYQQkUspkJbGStHb3gQAiRCEFRxXkee9fP76sZP3LTSinc6OT1HJkMu1hsOh\nCwLcI0mSjEYjV4C6rjpDXCsjtKzXWlpBZ7v34tULTz31VKvVOnH3PUFc0VqnWTZJQJ3NTTAZZ5GT\ngO5+hTGWUgYBt1ZTyvMsmV9cIIRoC1KbQkigTpdORqEXeIRQU2lFe/fe7vnrw1HmeTKqBBZJI6Es\nC+5Rn/tTU1Pt9lYQBBRTBgDaWqlNu7Ozubm5s7PTSdIgiFqtlpKWEHbw4MH21saFC5eGw+Hdd91V\nFGWapkqZzc72iTuPBUHw6isvnzhxYjgcrt1Yd/1fQojjYhFCAJOiKIIgePOb3/zII48EQeAQJWpp\nlmWjJKvEze2dgUCIYPC5t7g0n49GQuYEYZdBWmudA+acS2Exxm4HNaDxhmMppeOGOUVt19yaHLy1\nNggCN3PtIEA3wuaqOiWF1lJLg8AYU2ptsiQtisz3fc/zyjIfBylMRJEHvh94vNFoTE1NHdgz02g0\nqtUqZ9xO5hF20xxMkDEQRdHGxsbc0mIpDSFECMGZzz1kNESRl+fStRDHCRzlvV4PYzwcDufm5ur1\nutbro2RgpMAYC6WyrLCIeH6IMRZS/9b/+bs/9N3vn67XrSzKopwA0o5Ms9XenGAmEwaPKxLCMIyi\nCGP8qU99an316uHDhw8dOsQ5d8muq1PfkFxOOrcTJHFirxMKvXOZlVrV9/00ybUoPC+oVCrdQd9a\ng5FGxhayQNZijO88cezUb78Uhr7WwhgzNz9T8UIAOHfuXBD4jUa9LEshC7q1veMGC69fv766unro\n0CFCiNJ2lGRFKSwC7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eV5asYVBSEUCSF44FtrLUaYUa01JpSmacr5zdTHWjsajUohs6IU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WEioe92aIQAhFNhZCmKCvpKRIu0J847nn+93eYDCYmZmZnZ1dXV1tt9vGmHK5fP7SvJSlkTFV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\nr9frNRcw9QgFJRS3BNn9H+SFTJXAjbiqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=224x224 at 0x7F51ED5544D0>"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image.load_img('test3.jpg', target_size=(224,224))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def predict_img(filepath):\n",
    "    '''Function to predict car model for single image.\n",
    "    INPUT: File path\n",
    "    OUTPUT: Five car models with highest probabilities\n",
    "    '''\n",
    "    # preprocess image\n",
    "    img = image.load_img(filepath, target_size=(224,224))\n",
    "    \n",
    "    # image array -> (3,155,155)\n",
    "    img = image.img_to_array(img)/255\n",
    "    \n",
    "    # expand by 1 dimension to -> (1,3,155,155)\n",
    "    img = np.expand_dims(img, axis=0)\n",
    "    \n",
    "    x = model.predict(img)[0]\n",
    "    \n",
    "    return predict_top5(x,all_classes)\n",
    "\n",
    "def predict_top5(pred_array, all_classes):\n",
    "    '''Print out the five car models with highest probabilities\n",
    "    INPUT: Array with predicted probabilities / output from model.predict()\n",
    "    OUTPUT: Zipped list with car model name and probability\n",
    "    '''\n",
    "    # Take index of five highest values\n",
    "    top5 = heapq.nlargest(5, enumerate(pred_array), key=lambda x: x[1])\n",
    "    \n",
    "    names = [all_classes[model[0]] for model in top5]\n",
    "    zipped = list(zip(names, top5))\n",
    "\n",
    "    return zipped\n",
    "\n",
    "\n",
    "#folder: 'AD/AutoDetect/*'\n",
    "def get_all_classes(folder_path):\n",
    "    '''Get all car model names in a folder.'''\n",
    "    all_models = []\n",
    "    \n",
    "    for filename in glob.iglob(folder_path):\n",
    "        all_models.append(filename.split('/')[2])\n",
    "        \n",
    "    return dict(zip(list(range(468)),sorted(all_models)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Predict with a generator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_classes = get_all_classes('AD/AutoDetect/*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gen = ImageDataGenerator(rescale=1./255).flow_from_directory('test_images/',\n",
    "        target_size=(img_height, img_width),\n",
    "        #batch_size=batch_size,\n",
    "        class_mode='categorical',shuffle=False)\n",
    "preds = model.predict_generator(gen, len(gen.filenames))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "for i in range(len(preds)):\n",
    "    pprint(predict_top5(preds[i],all_classes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Jaguar_XJ', (232, 0.64862746)),\n",
       " ('Jaguar_XJR', (234, 0.059021827)),\n",
       " ('BMW_6-Series', (24, 0.044623341)),\n",
       " ('Infiniti_Q60', (221, 0.033171169)),\n",
       " ('Infiniti_G37', (217, 0.025852574))]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_img('jag_xj.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test predictions on random car images from Google images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_set = ['test_images/Chevrolet_Spark/chev_spark.jpeg','test_images/BMW_5-Series/bmw_5.jpg',\\\n",
    "           'test_images/Audi_S6/Audis6.jpg','test_images/Ferrari_California/test6.jpg',\\\n",
    "            'test_images/Ford_Flex/fordcoupe.jpg','test_images/Jeep_Wrangler/Jeep_wrangler1.jpg',\\\n",
    "            'test_images/test5.jpg', 'test_images/test1.jpg','test_images/test4.jpg']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chevrolet_Spark\n",
      "[('Chevrolet_Spark', (102, 0.98404789)),\n",
      " ('Ford_Fiesta', (169, 0.0045500197)),\n",
      " ('Hyundai_Accent', (207, 0.0033944729)),\n",
      " ('Honda_Fit', (198, 0.0028861202)),\n",
      " ('Toyota_Matrix', (433, 0.0013247475))]\n",
      "BMW_5-Series\n",
      "[('BMW_5-Series', (23, 0.63634896)),\n",
      " ('BMW_7-Series', (25, 0.35353664)),\n",
      " ('Rolls-Royce_Ghost', (404, 0.0016020428)),\n",
      " ('BMW_X5', (34, 0.00150186)),\n",
      " ('BMW_2-Series', (19, 0.0011010148))]\n",
      "Audi_S6\n",
      "[('Audi_A6', (3, 0.499273)),\n",
      " ('Audi_S3', (12, 0.31383553)),\n",
      " ('Audi_S4', (13, 0.085752383)),\n",
      " ('Audi_RS4', (10, 0.028720986)),\n",
      " ('Audi_S6', (15, 0.027203791))]\n",
      "Ferrari_California\n",
      "[('Ferrari_California', (146, 0.92036408)),\n",
      " ('Fisker_Karma', (150, 0.042004593)),\n",
      " ('Maserati_Gran Turismo', (294, 0.01026198)),\n",
      " ('Lotus_Evora', (289, 0.0042934767)),\n",
      " ('Jaguar_F-Type', (228, 0.0037164725))]\n",
      "Ford_Flex\n",
      "[('Chrysler_PT Cruiser', (114, 0.35224703)),\n",
      " ('Chevrolet_HHR', (89, 0.2901603)),\n",
      " ('Chevrolet_Spark', (102, 0.05344915)),\n",
      " ('Volkswagen_Beetle - Classic', (447, 0.050430838)),\n",
      " ('Nissan_Frontier', (350, 0.041510683))]\n",
      "Jeep_Wrangler\n",
      "[('Jeep_Wrangler', (248, 0.99882466)),\n",
      " ('Jeep_Liberty', (244, 0.00054385711)),\n",
      " ('Jeep_Patriot', (245, 7.5061333e-05)),\n",
      " ('Jeep_Compass', (242, 6.0779221e-05)),\n",
      " ('Jeep_Commander', (241, 2.0783757e-05))]\n",
      "test5.jpg\n",
      "[('Lamborghini_Gallardo', (258, 0.78225267)),\n",
      " ('Lamborghini_Huracan', (259, 0.059856743)),\n",
      " ('Lamborghini_Aventador', (257, 0.055952523)),\n",
      " ('Scion_FR-S', (410, 0.05334894)),\n",
      " ('McLaren_MP4-12C', (305, 0.014776988))]\n",
      "test1.jpg\n",
      "[('Audi_RS7', (11, 0.92427021)),\n",
      " ('Audi_A7', (4, 0.02230834)),\n",
      " ('Audi_TT', (17, 0.011447801)),\n",
      " ('Ford_Mustang', (177, 0.0095759118)),\n",
      " ('Audi_RS4', (10, 0.0081396531))]\n",
      "test4.jpg\n",
      "[('Mercedes-Benz_E-Class', (316, 0.93849951)),\n",
      " ('Mercedes-Benz_C-Class', (311, 0.046198744)),\n",
      " ('Mercedes-Benz_SL-Class', (323, 0.0035601009)),\n",
      " ('Mercedes-Benz_GLK-Class', (319, 0.0027705769)),\n",
      " ('Mercedes-Benz_S-Class', (322, 0.0013207986))]\n"
     ]
    }
   ],
   "source": [
    "for i in test_set:\n",
    "    print(i.split('/')[1])\n",
    "    pprint(predict_img(i))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize neural net architecture."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "</svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.utils.visualize_util import plot, model_to_dot\n",
    "SVG(model_to_dot(model).create(prog='dot', format='svg'))#,'model.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### CoreML\n",
    "#### Convert the keras into a CoreML model to use it in Xcode for iOS development."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scale = 1./255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 : input_1, <keras.engine.topology.InputLayer object at 0x7f0f15e44110>\n",
      "1 : convolution2d_1, <keras.layers.convolutional.Convolution2D object at 0x7f0f15e44690>\n",
      "2 : convolution2d_1__activation__, <keras.layers.core.Activation object at 0x7f0df4a77f50>\n",
      "3 : batchnormalization_1, <keras.layers.normalization.BatchNormalization object at 0x7f0f16698750>\n",
      "4 : convolution2d_2, <keras.layers.convolutional.Convolution2D object at 0x7f0eb0004f50>\n",
      "5 : convolution2d_2__activation__, <keras.layers.core.Activation object at 0x7f0dedead150>\n",
      "6 : batchnormalization_2, <keras.layers.normalization.BatchNormalization object at 0x7f0eaff02b50>\n",
      "7 : convolution2d_3, <keras.layers.convolutional.Convolution2D object at 0x7f0eafeda050>\n",
      "8 : convolution2d_3__activation__, <keras.layers.core.Activation object at 0x7f0df4a24b50>\n",
      "9 : batchnormalization_3, <keras.layers.normalization.BatchNormalization object at 0x7f0eafe17e50>\n",
      "10 : maxpooling2d_1, <keras.layers.pooling.MaxPooling2D object at 0x7f0eafd3de90>\n",
      "11 : convolution2d_4, <keras.layers.convolutional.Convolution2D object at 0x7f0eafd4fb50>\n",
      "12 : convolution2d_4__activation__, <keras.layers.core.Activation object at 0x7f0defb266d0>\n",
      "13 : batchnormalization_4, <keras.layers.normalization.BatchNormalization object at 0x7f0eafcb7c10>\n",
      "14 : convolution2d_5, <keras.layers.convolutional.Convolution2D object at 0x7f0eafc4fd90>\n",
      "15 : convolution2d_5__activation__, <keras.layers.core.Activation object at 0x7f0dea543a50>\n",
      "16 : batchnormalization_5, <keras.layers.normalization.BatchNormalization object at 0x7f0eafc03fd0>\n",
      "17 : maxpooling2d_2, <keras.layers.pooling.MaxPooling2D object at 0x7f0eafb324d0>\n",
      "18 : convolution2d_9, <keras.layers.convolutional.Convolution2D object at 0x7f0eaf7e3cd0>\n",
      "19 : convolution2d_9__activation__, <keras.layers.core.Activation object at 0x7f0df4a65f90>\n",
      "20 : batchnormalization_9, <keras.layers.normalization.BatchNormalization object at 0x7f0eaf74fe50>\n",
      "21 : convolution2d_7, <keras.layers.convolutional.Convolution2D object at 0x7f0eaf9b7b90>\n",
      "22 : convolution2d_7__activation__, <keras.layers.core.Activation object at 0x7f0df4a65fd0>\n",
      "23 : convolution2d_10, <keras.layers.convolutional.Convolution2D object at 0x7f0eaf677e90>\n",
      "24 : convolution2d_10__activation__, <keras.layers.core.Activation object at 0x7f0df4a65550>\n",
      "25 : batchnormalization_7, <keras.layers.normalization.BatchNormalization object at 0x7f0eaf995f10>\n",
      "26 : batchnormalization_10, <keras.layers.normalization.BatchNormalization object at 0x7f0eaf659990>\n",
      "27 : averagepooling2d_1, <keras.layers.pooling.AveragePooling2D object at 0x7f0eaf4d39d0>\n",
      "28 : convolution2d_6, <keras.layers.convolutional.Convolution2D object at 0x7f0eafb92dd0>\n",
      "29 : convolution2d_6__activation__, <keras.layers.core.Activation object at 0x7f0df4a358d0>\n",
      "30 : convolution2d_8, <keras.layers.convolutional.Convolution2D object at 0x7f0eaf8bde90>\n",
      "31 : convolution2d_8__activation__, <keras.layers.core.Activation object at 0x7f0df4a35d10>\n",
      "32 : convolution2d_11, <keras.layers.convolutional.Convolution2D object at 0x7f0eaf5bb8d0>\n",
      "33 : convolution2d_11__activation__, <keras.layers.core.Activation object at 0x7f0df4a35950>\n",
      "34 : convolution2d_12, <keras.layers.convolutional.Convolution2D object at 0x7f0eaf4c4f10>\n",
      "35 : convolution2d_12__activation__, <keras.layers.core.Activation object at 0x7f0df4a35590>\n",
      "36 : batchnormalization_6, <keras.layers.normalization.BatchNormalization object at 0x7f0eafa7fb10>\n",
      "37 : batchnormalization_8, <keras.layers.normalization.BatchNormalization object at 0x7f0eaf89e9d0>\n",
      "38 : batchnormalization_11, <keras.layers.normalization.BatchNormalization object at 0x7f0eaf50ae10>\n",
      "39 : batchnormalization_12, <keras.layers.normalization.BatchNormalization object at 0x7f0eaf455f10>\n",
      "40 : mixed0, <keras.engine.topology.Merge object at 0x7f0eaf348650>\n",
      "41 : convolution2d_16, <keras.layers.convolutional.Convolution2D object at 0x7f0eaefef990>\n",
      "42 : convolution2d_16__activation__, <keras.layers.core.Activation object at 0x7f0df4a35f50>\n",
      "43 : batchnormalization_16, <keras.layers.normalization.BatchNormalization object at 0x7f0eaf000a90>\n",
      "44 : convolution2d_14, <keras.layers.convolutional.Convolution2D object at 0x7f0eaf1f1b10>\n",
      "45 : convolution2d_14__activation__, <keras.layers.core.Activation object at 0x7f0df4a357d0>\n",
      "46 : convolution2d_17, <keras.layers.convolutional.Convolution2D object at 0x7f0eaeec4610>\n",
      "47 : convolution2d_17__activation__, <keras.layers.core.Activation object at 0x7f0df4a35e10>\n",
      "48 : batchnormalization_14, <keras.layers.normalization.BatchNormalization object at 0x7f0eaf16fdd0>\n",
      "49 : batchnormalization_17, <keras.layers.normalization.BatchNormalization object at 0x7f0eaee42e50>\n",
      "50 : averagepooling2d_2, <keras.layers.pooling.AveragePooling2D object at 0x7f0eaed24610>\n",
      "51 : convolution2d_13, <keras.layers.convolutional.Convolution2D object at 0x7f0eaf2fcc50>\n",
      "52 : convolution2d_13__activation__, <keras.layers.core.Activation object at 0x7f0df4a35710>\n",
      "53 : convolution2d_15, <keras.layers.convolutional.Convolution2D object at 0x7f0eaf1449d0>\n",
      "54 : convolution2d_15__activation__, <keras.layers.core.Activation object at 0x7f0df4a35110>\n",
      "55 : convolution2d_18, <keras.layers.convolutional.Convolution2D object at 0x7f0eaee18fd0>\n",
      "56 : convolution2d_18__activation__, <keras.layers.core.Activation object at 0x7f0df4a35350>\n",
      "57 : convolution2d_19, <keras.layers.convolutional.Convolution2D object at 0x7f0eaecb1850>\n",
      "58 : convolution2d_19__activation__, <keras.layers.core.Activation object at 0x7f0df4a35f10>\n",
      "59 : batchnormalization_13, <keras.layers.normalization.BatchNormalization object at 0x7f0eaf2d6f10>\n",
      "60 : batchnormalization_15, <keras.layers.normalization.BatchNormalization object at 0x7f0eaf0bcfd0>\n",
      "61 : batchnormalization_18, <keras.layers.normalization.BatchNormalization object at 0x7f0eaed4ca10>\n",
      "62 : batchnormalization_19, <keras.layers.normalization.BatchNormalization object at 0x7f0eaec02e50>\n",
      "63 : mixed1, <keras.engine.topology.Merge object at 0x7f0eaeb98d50>\n",
      "64 : convolution2d_23, <keras.layers.convolutional.Convolution2D object at 0x7f0eae83b890>\n",
      "65 : convolution2d_23__activation__, <keras.layers.core.Activation object at 0x7f0df4a35690>\n",
      "66 : batchnormalization_23, <keras.layers.normalization.BatchNormalization object at 0x7f0eae79bf90>\n",
      "67 : convolution2d_21, <keras.layers.convolutional.Convolution2D object at 0x7f0eaea71c90>\n",
      "68 : convolution2d_21__activation__, <keras.layers.core.Activation object at 0x7f0df4a35810>\n",
      "69 : convolution2d_24, <keras.layers.convolutional.Convolution2D object at 0x7f0eae674390>\n",
      "70 : convolution2d_24__activation__, <keras.layers.core.Activation object at 0x7f0df4a35a50>\n",
      "71 : batchnormalization_21, <keras.layers.normalization.BatchNormalization object at 0x7f0eae9b5950>\n",
      "72 : batchnormalization_24, <keras.layers.normalization.BatchNormalization object at 0x7f0eae754fd0>\n",
      "73 : averagepooling2d_3, <keras.layers.pooling.AveragePooling2D object at 0x7f0eae5139d0>\n",
      "74 : convolution2d_20, <keras.layers.convolutional.Convolution2D object at 0x7f0eaeb5d450>\n",
      "75 : convolution2d_20__activation__, <keras.layers.core.Activation object at 0x7f0df4a35c10>\n",
      "76 : convolution2d_22, <keras.layers.convolutional.Convolution2D object at 0x7f0eae979d50>\n",
      "77 : convolution2d_22__activation__, <keras.layers.core.Activation object at 0x7f0df4a35c90>\n",
      "78 : convolution2d_25, <keras.layers.convolutional.Convolution2D object at 0x7f0eae5c0b10>\n",
      "79 : convolution2d_25__activation__, <keras.layers.core.Activation object at 0x7f0df4a359d0>\n",
      "80 : convolution2d_26, <keras.layers.convolutional.Convolution2D object at 0x7f0eae4f53d0>\n",
      "81 : convolution2d_26__activation__, <keras.layers.core.Activation object at 0x7f0df4a355d0>\n",
      "82 : batchnormalization_20, <keras.layers.normalization.BatchNormalization object at 0x7f0eaeab7e90>\n",
      "83 : batchnormalization_22, <keras.layers.normalization.BatchNormalization object at 0x7f0eae8e59d0>\n",
      "84 : batchnormalization_25, <keras.layers.normalization.BatchNormalization object at 0x7f0eae53ddd0>\n",
      "85 : batchnormalization_26, <keras.layers.normalization.BatchNormalization object at 0x7f0eae461790>\n",
      "86 : mixed2, <keras.engine.topology.Merge object at 0x7f0eae37c5d0>\n",
      "87 : convolution2d_28, <keras.layers.convolutional.Convolution2D object at 0x7f0eae2c7690>\n",
      "88 : convolution2d_28__activation__, <keras.layers.core.Activation object at 0x7f0df4a35b50>\n",
      "89 : batchnormalization_28, <keras.layers.normalization.BatchNormalization object at 0x7f0eae201e10>\n",
      "90 : convolution2d_29, <keras.layers.convolutional.Convolution2D object at 0x7f0eae1759d0>\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "92 : batchnormalization_29, <keras.layers.normalization.BatchNormalization object at 0x7f0eae0c6f90>\n",
      "93 : convolution2d_27, <keras.layers.convolutional.Convolution2D object at 0x7f0eafb41350>\n",
      "94 : convolution2d_27__activation__, <keras.layers.core.Activation object at 0x7f0df4a227d0>\n",
      "95 : convolution2d_30, <keras.layers.convolutional.Convolution2D object at 0x7f0eae05fb10>\n",
      "96 : convolution2d_30__activation__, <keras.layers.core.Activation object at 0x7f0df4c20c50>\n",
      "97 : batchnormalization_27, <keras.layers.normalization.BatchNormalization object at 0x7f0eae30ad50>\n",
      "98 : batchnormalization_30, <keras.layers.normalization.BatchNormalization object at 0x7f0eae011410>\n",
      "99 : maxpooling2d_3, <keras.layers.pooling.MaxPooling2D object at 0x7f0eadf35510>\n",
      "100 : mixed3, <keras.engine.topology.Merge object at 0x7f0eadee8310>\n",
      "101 : convolution2d_35, <keras.layers.convolutional.Convolution2D object at 0x7f0eada83e10>\n",
      "102 : convolution2d_35__activation__, <keras.layers.core.Activation object at 0x7f0de9e4d390>\n",
      "103 : batchnormalization_35, <keras.layers.normalization.BatchNormalization object at 0x7f0eadaa4e50>\n",
      "104 : convolution2d_36, <keras.layers.convolutional.Convolution2D object at 0x7f0ead9eab10>\n",
      "105 : convolution2d_36__activation__, <keras.layers.core.Activation object at 0x7f0de9e4d750>\n",
      "106 : batchnormalization_36, <keras.layers.normalization.BatchNormalization object at 0x7f0ead8fad50>\n",
      "107 : convolution2d_32, <keras.layers.convolutional.Convolution2D object at 0x7f0eade59910>\n",
      "108 : convolution2d_32__activation__, <keras.layers.core.Activation object at 0x7f0df4a2ac50>\n",
      "109 : convolution2d_37, <keras.layers.convolutional.Convolution2D object at 0x7f0ead86e790>\n",
      "110 : convolution2d_37__activation__, <keras.layers.core.Activation object at 0x7f0df4a2afd0>\n",
      "111 : batchnormalization_32, <keras.layers.normalization.BatchNormalization object at 0x7f0eadd9db90>\n",
      "112 : batchnormalization_37, <keras.layers.normalization.BatchNormalization object at 0x7f0ead7bbd10>\n",
      "113 : convolution2d_33, <keras.layers.convolutional.Convolution2D object at 0x7f0eadcb2d50>\n",
      "114 : convolution2d_33__activation__, <keras.layers.core.Activation object at 0x7f0de9e45e50>\n",
      "115 : convolution2d_38, <keras.layers.convolutional.Convolution2D object at 0x7f0ead7a53d0>\n",
      "116 : convolution2d_38__activation__, <keras.layers.core.Activation object at 0x7f0de9e45fd0>\n",
      "117 : batchnormalization_33, <keras.layers.normalization.BatchNormalization object at 0x7f0eadc75450>\n",
      "118 : batchnormalization_38, <keras.layers.normalization.BatchNormalization object at 0x7f0ead6b9a90>\n",
      "119 : averagepooling2d_4, <keras.layers.pooling.AveragePooling2D object at 0x7f0ead5469d0>\n",
      "120 : convolution2d_31, <keras.layers.convolutional.Convolution2D object at 0x7f0eaded8f10>\n",
      "121 : convolution2d_31__activation__, <keras.layers.core.Activation object at 0x7f0deedab950>\n",
      "122 : convolution2d_34, <keras.layers.convolutional.Convolution2D object at 0x7f0eadbcc810>\n",
      "123 : convolution2d_34__activation__, <keras.layers.core.Activation object at 0x7f0deedab2d0>\n",
      "124 : convolution2d_39, <keras.layers.convolutional.Convolution2D object at 0x7f0ead692690>\n",
      "125 : convolution2d_39__activation__, <keras.layers.core.Activation object at 0x7f0deedab390>\n",
      "126 : convolution2d_40, <keras.layers.convolutional.Convolution2D object at 0x7f0ead505f10>\n",
      "127 : convolution2d_40__activation__, <keras.layers.core.Activation object at 0x7f0deedab710>\n",
      "128 : batchnormalization_31, <keras.layers.normalization.BatchNormalization object at 0x7f0eade75490>\n",
      "129 : batchnormalization_34, <keras.layers.normalization.BatchNormalization object at 0x7f0eadb5ae50>\n",
      "130 : batchnormalization_39, <keras.layers.normalization.BatchNormalization object at 0x7f0ead5cde10>\n",
      "131 : batchnormalization_40, <keras.layers.normalization.BatchNormalization object at 0x7f0ead491d90>\n",
      "132 : mixed4, <keras.engine.topology.Merge object at 0x7f0ead4694d0>\n",
      "133 : convolution2d_45, <keras.layers.convolutional.Convolution2D object at 0x7f0eacf489d0>\n",
      "134 : convolution2d_45__activation__, <keras.layers.core.Activation object at 0x7f0deedabdd0>\n",
      "135 : batchnormalization_45, <keras.layers.normalization.BatchNormalization object at 0x7f0eacf2af10>\n",
      "136 : convolution2d_46, <keras.layers.convolutional.Convolution2D object at 0x7f0eace03490>\n",
      "137 : convolution2d_46__activation__, <keras.layers.core.Activation object at 0x7f0deedabed0>\n",
      "138 : batchnormalization_46, <keras.layers.normalization.BatchNormalization object at 0x7f0eacdf0e50>\n",
      "139 : convolution2d_42, <keras.layers.convolutional.Convolution2D object at 0x7f0ead2f7610>\n",
      "140 : convolution2d_42__activation__, <keras.layers.core.Activation object at 0x7f0deedaba10>\n",
      "141 : convolution2d_47, <keras.layers.convolutional.Convolution2D object at 0x7f0eacd53d90>\n",
      "142 : convolution2d_47__activation__, <keras.layers.core.Activation object at 0x7f0deedab090>\n",
      "143 : batchnormalization_42, <keras.layers.normalization.BatchNormalization object at 0x7f0ead244c50>\n",
      "144 : batchnormalization_47, <keras.layers.normalization.BatchNormalization object at 0x7f0eacd05610>\n",
      "145 : convolution2d_43, <keras.layers.convolutional.Convolution2D object at 0x7f0ead227950>\n",
      "146 : convolution2d_43__activation__, <keras.layers.core.Activation object at 0x7f0deedab790>\n",
      "147 : convolution2d_48, <keras.layers.convolutional.Convolution2D object at 0x7f0eacc397d0>\n",
      "148 : convolution2d_48__activation__, <keras.layers.core.Activation object at 0x7f0deedabc90>\n",
      "149 : batchnormalization_43, <keras.layers.normalization.BatchNormalization object at 0x7f0ead16a990>\n",
      "150 : batchnormalization_48, <keras.layers.normalization.BatchNormalization object at 0x7f0eacb8ad50>\n",
      "151 : averagepooling2d_5, <keras.layers.pooling.AveragePooling2D object at 0x7f0eaca5e6d0>\n",
      "152 : convolution2d_41, <keras.layers.convolutional.Convolution2D object at 0x7f0ead3dead0>\n",
      "153 : convolution2d_41__activation__, <keras.layers.core.Activation object at 0x7f0deedab210>\n",
      "154 : convolution2d_44, <keras.layers.convolutional.Convolution2D object at 0x7f0ead07fd90>\n",
      "155 : convolution2d_44__activation__, <keras.layers.core.Activation object at 0x7f0deedabf90>\n",
      "156 : convolution2d_49, <keras.layers.convolutional.Convolution2D object at 0x7f0eacb434d0>\n",
      "157 : convolution2d_49__activation__, <keras.layers.core.Activation object at 0x7f0deedab490>\n",
      "158 : convolution2d_50, <keras.layers.convolutional.Convolution2D object at 0x7f0eac9eb910>\n",
      "159 : convolution2d_50__activation__, <keras.layers.core.Activation object at 0x7f0deedabad0>\n",
      "160 : batchnormalization_41, <keras.layers.normalization.BatchNormalization object at 0x7f0ead33a410>\n",
      "161 : batchnormalization_44, <keras.layers.normalization.BatchNormalization object at 0x7f0ead045490>\n",
      "162 : batchnormalization_49, <keras.layers.normalization.BatchNormalization object at 0x7f0eacac4e90>\n",
      "163 : batchnormalization_50, <keras.layers.normalization.BatchNormalization object at 0x7f0eac956bd0>\n",
      "164 : mixed5, <keras.engine.topology.Merge object at 0x7f0eac8e1b10>\n",
      "165 : convolution2d_55, <keras.layers.convolutional.Convolution2D object at 0x7f0eac3f1390>\n",
      "166 : convolution2d_55__activation__, <keras.layers.core.Activation object at 0x7f0deedab0d0>\n",
      "167 : batchnormalization_55, <keras.layers.normalization.BatchNormalization object at 0x7f0eac47f090>\n",
      "168 : convolution2d_56, <keras.layers.convolutional.Convolution2D object at 0x7f0eac307850>\n",
      "169 : convolution2d_56__activation__, <keras.layers.core.Activation object at 0x7f0deedaba90>\n",
      "170 : batchnormalization_56, <keras.layers.normalization.BatchNormalization object at 0x7f0eac2e9090>\n",
      "171 : convolution2d_52, <keras.layers.convolutional.Convolution2D object at 0x7f0eac737d10>\n",
      "172 : convolution2d_52__activation__, <keras.layers.core.Activation object at 0x7f0deedabe50>\n",
      "173 : convolution2d_57, <keras.layers.convolutional.Convolution2D object at 0x7f0eac1aefd0>\n",
      "174 : convolution2d_57__activation__, <keras.layers.core.Activation object at 0x7f0deedabf50>\n",
      "175 : batchnormalization_52, <keras.layers.normalization.BatchNormalization object at 0x7f0eac76aa10>\n",
      "176 : batchnormalization_57, <keras.layers.normalization.BatchNormalization object at 0x7f0eac1be9d0>\n",
      "177 : convolution2d_53, <keras.layers.convolutional.Convolution2D object at 0x7f0eac6a0910>\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "179 : convolution2d_58, <keras.layers.convolutional.Convolution2D object at 0x7f0eac163990>\n",
      "180 : convolution2d_58__activation__, <keras.layers.core.Activation object at 0x7f0deedabd10>\n",
      "181 : batchnormalization_53, <keras.layers.normalization.BatchNormalization object at 0x7f0eac602e90>\n",
      "182 : batchnormalization_58, <keras.layers.normalization.BatchNormalization object at 0x7f0eac0d5710>\n",
      "183 : averagepooling2d_6, <keras.layers.pooling.AveragePooling2D object at 0x7f0eabf21e50>\n",
      "184 : convolution2d_51, <keras.layers.convolutional.Convolution2D object at 0x7f0eac893990>\n",
      "185 : convolution2d_51__activation__, <keras.layers.core.Activation object at 0x7f0deedab650>\n",
      "186 : convolution2d_54, <keras.layers.convolutional.Convolution2D object at 0x7f0eac558dd0>\n",
      "187 : convolution2d_54__activation__, <keras.layers.core.Activation object at 0x7f0deedabb50>\n",
      "188 : convolution2d_59, <keras.layers.convolutional.Convolution2D object at 0x7f0eabfcde10>\n",
      "189 : convolution2d_59__activation__, <keras.layers.core.Activation object at 0x7f0deedab6d0>\n",
      "190 : convolution2d_60, <keras.layers.convolutional.Convolution2D object at 0x7f0eabf124d0>\n",
      "191 : convolution2d_60__activation__, <keras.layers.core.Activation object at 0x7f0deedabc10>\n",
      "192 : batchnormalization_51, <keras.layers.normalization.BatchNormalization object at 0x7f0eac8736d0>\n",
      "193 : batchnormalization_54, <keras.layers.normalization.BatchNormalization object at 0x7f0eac52a4d0>\n",
      "194 : batchnormalization_59, <keras.layers.normalization.BatchNormalization object at 0x7f0eabf56d50>\n",
      "195 : batchnormalization_60, <keras.layers.normalization.BatchNormalization object at 0x7f0eabdee910>\n",
      "196 : mixed6, <keras.engine.topology.Merge object at 0x7f0eabdd6310>\n",
      "197 : convolution2d_65, <keras.layers.convolutional.Convolution2D object at 0x7f0eab927d90>\n",
      "198 : convolution2d_65__activation__, <keras.layers.core.Activation object at 0x7f0deedab250>\n",
      "199 : batchnormalization_65, <keras.layers.normalization.BatchNormalization object at 0x7f0eab875490>\n",
      "200 : convolution2d_66, <keras.layers.convolutional.Convolution2D object at 0x7f0eab7abdd0>\n",
      "201 : convolution2d_66__activation__, <keras.layers.core.Activation object at 0x7f0deedab850>\n",
      "202 : batchnormalization_66, <keras.layers.normalization.BatchNormalization object at 0x7f0eab7cad50>\n",
      "203 : convolution2d_62, <keras.layers.convolutional.Convolution2D object at 0x7f0eabbffc90>\n",
      "204 : convolution2d_62__activation__, <keras.layers.core.Activation object at 0x7f0deedab7d0>\n",
      "205 : convolution2d_67, <keras.layers.convolutional.Convolution2D object at 0x7f0eab6e3dd0>\n",
      "206 : convolution2d_67__activation__, <keras.layers.core.Activation object at 0x7f0deedab750>\n",
      "207 : batchnormalization_62, <keras.layers.normalization.BatchNormalization object at 0x7f0eabbade10>\n",
      "208 : batchnormalization_67, <keras.layers.normalization.BatchNormalization object at 0x7f0eab63fe50>\n",
      "209 : convolution2d_63, <keras.layers.convolutional.Convolution2D object at 0x7f0eabb93b50>\n",
      "210 : convolution2d_63__activation__, <keras.layers.core.Activation object at 0x7f0deedab590>\n",
      "211 : convolution2d_68, <keras.layers.convolutional.Convolution2D object at 0x7f0eab5ead50>\n",
      "212 : convolution2d_68__activation__, <keras.layers.core.Activation object at 0x7f0deedaba50>\n",
      "213 : batchnormalization_63, <keras.layers.normalization.BatchNormalization object at 0x7f0eabab79d0>\n",
      "214 : batchnormalization_68, <keras.layers.normalization.BatchNormalization object at 0x7f0eab589f90>\n",
      "215 : averagepooling2d_7, <keras.layers.pooling.AveragePooling2D object at 0x7f0eab398dd0>\n",
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      "218 : convolution2d_64, <keras.layers.convolutional.Convolution2D object at 0x7f0eab9ee8d0>\n",
      "219 : convolution2d_64__activation__, <keras.layers.core.Activation object at 0x7f0deedab050>\n",
      "220 : convolution2d_69, <keras.layers.convolutional.Convolution2D object at 0x7f0eab4e9f90>\n",
      "221 : convolution2d_69__activation__, <keras.layers.core.Activation object at 0x7f0deedab510>\n",
      "222 : convolution2d_70, <keras.layers.convolutional.Convolution2D object at 0x7f0eab338fd0>\n",
      "223 : convolution2d_70__activation__, <keras.layers.core.Activation object at 0x7f0deedab290>\n",
      "224 : batchnormalization_61, <keras.layers.normalization.BatchNormalization object at 0x7f0eabd58f90>\n",
      "225 : batchnormalization_64, <keras.layers.normalization.BatchNormalization object at 0x7f0eab9cef50>\n",
      "226 : batchnormalization_69, <keras.layers.normalization.BatchNormalization object at 0x7f0eab464d50>\n",
      "227 : batchnormalization_70, <keras.layers.normalization.BatchNormalization object at 0x7f0eab2b6210>\n",
      "228 : mixed7, <keras.engine.topology.Merge object at 0x7f0eab23c950>\n",
      "229 : convolution2d_73, <keras.layers.convolutional.Convolution2D object at 0x7f0eaafcdc90>\n",
      "230 : convolution2d_73__activation__, <keras.layers.core.Activation object at 0x7f0deedab4d0>\n",
      "231 : batchnormalization_73, <keras.layers.normalization.BatchNormalization object at 0x7f0eaaf7de10>\n",
      "232 : convolution2d_74, <keras.layers.convolutional.Convolution2D object at 0x7f0eaaf63b50>\n",
      "233 : convolution2d_74__activation__, <keras.layers.core.Activation object at 0x7f0deedab450>\n",
      "234 : batchnormalization_74, <keras.layers.normalization.BatchNormalization object at 0x7f0eaae869d0>\n",
      "235 : convolution2d_71, <keras.layers.convolutional.Convolution2D object at 0x7f0eab26d890>\n",
      "236 : convolution2d_71__activation__, <keras.layers.core.Activation object at 0x7f0deedabd50>\n",
      "237 : convolution2d_75, <keras.layers.convolutional.Convolution2D object at 0x7f0eaadb98d0>\n",
      "238 : convolution2d_75__activation__, <keras.layers.core.Activation object at 0x7f0deedabbd0>\n",
      "239 : batchnormalization_71, <keras.layers.normalization.BatchNormalization object at 0x7f0eab1af650>\n",
      "240 : batchnormalization_75, <keras.layers.normalization.BatchNormalization object at 0x7f0eaad9cf50>\n",
      "241 : convolution2d_72, <keras.layers.convolutional.Convolution2D object at 0x7f0eab179e50>\n",
      "242 : convolution2d_72__activation__, <keras.layers.core.Activation object at 0x7f0deedabf10>\n",
      "243 : convolution2d_76, <keras.layers.convolutional.Convolution2D object at 0x7f0eaac72d90>\n",
      "244 : convolution2d_76__activation__, <keras.layers.core.Activation object at 0x7f0deedab3d0>\n",
      "245 : batchnormalization_72, <keras.layers.normalization.BatchNormalization object at 0x7f0eab106610>\n",
      "246 : batchnormalization_76, <keras.layers.normalization.BatchNormalization object at 0x7f0eaac43490>\n",
      "247 : averagepooling2d_8, <keras.layers.pooling.AveragePooling2D object at 0x7f0eaab73dd0>\n",
      "248 : mixed8, <keras.engine.topology.Merge object at 0x7f0eaac19610>\n",
      "249 : convolution2d_81, <keras.layers.convolutional.Convolution2D object at 0x7f0eaa6d4e50>\n",
      "250 : convolution2d_81__activation__, <keras.layers.core.Activation object at 0x7f0deedabcd0>\n",
      "251 : batchnormalization_81, <keras.layers.normalization.BatchNormalization object at 0x7f0eaa681c90>\n",
      "252 : convolution2d_78, <keras.layers.convolutional.Convolution2D object at 0x7f0eaaa71c10>\n",
      "253 : convolution2d_78__activation__, <keras.layers.core.Activation object at 0x7f0deedab1d0>\n",
      "254 : convolution2d_82, <keras.layers.convolutional.Convolution2D object at 0x7f0eaa609cd0>\n",
      "255 : convolution2d_82__activation__, <keras.layers.core.Activation object at 0x7f0deedab150>\n",
      "256 : batchnormalization_78, <keras.layers.normalization.BatchNormalization object at 0x7f0eaaad16d0>\n",
      "257 : batchnormalization_82, <keras.layers.normalization.BatchNormalization object at 0x7f0eaa5bba90>\n",
      "258 : convolution2d_79, <keras.layers.convolutional.Convolution2D object at 0x7f0eaa957d10>\n",
      "259 : convolution2d_79__activation__, <keras.layers.core.Activation object at 0x7f0deedab910>\n",
      "260 : convolution2d_80, <keras.layers.convolutional.Convolution2D object at 0x7f0eaa88d690>\n",
      "261 : convolution2d_80__activation__, <keras.layers.core.Activation object at 0x7f0deedab310>\n",
      "262 : convolution2d_83, <keras.layers.convolutional.Convolution2D object at 0x7f0eaa4b0e50>\n",
      "263 : convolution2d_83__activation__, <keras.layers.core.Activation object at 0x7f0deedabe10>\n",
      "264 : convolution2d_84, <keras.layers.convolutional.Convolution2D object at 0x7f0eaa419c90>\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "265 : convolution2d_84__activation__, <keras.layers.core.Activation object at 0x7f0deedabc50>\n",
      "266 : averagepooling2d_9, <keras.layers.pooling.AveragePooling2D object at 0x7f0eaa311b10>\n",
      "267 : convolution2d_77, <keras.layers.convolutional.Convolution2D object at 0x7f0eaac271d0>\n",
      "268 : convolution2d_77__activation__, <keras.layers.core.Activation object at 0x7f0deedab890>\n",
      "269 : batchnormalization_79, <keras.layers.normalization.BatchNormalization object at 0x7f0eaa8d4e50>\n",
      "270 : batchnormalization_80, <keras.layers.normalization.BatchNormalization object at 0x7f0eaa7fde90>\n",
      "271 : batchnormalization_83, <keras.layers.normalization.BatchNormalization object at 0x7f0eaa4419d0>\n",
      "272 : batchnormalization_84, <keras.layers.normalization.BatchNormalization object at 0x7f0eaa366790>\n",
      "273 : convolution2d_85, <keras.layers.convolutional.Convolution2D object at 0x7f0eaa260710>\n",
      "274 : convolution2d_85__activation__, <keras.layers.core.Activation object at 0x7f0deedab110>\n",
      "275 : batchnormalization_77, <keras.layers.normalization.BatchNormalization object at 0x7f0eaab18450>\n",
      "276 : mixed9_0, <keras.engine.topology.Merge object at 0x7f0eaa713a10>\n",
      "277 : merge_1, <keras.engine.topology.Merge object at 0x7f0eaa31c950>\n",
      "278 : batchnormalization_85, <keras.layers.normalization.BatchNormalization object at 0x7f0eaa2adcd0>\n",
      "279 : mixed9, <keras.engine.topology.Merge object at 0x7f0eaa193e10>\n",
      "280 : convolution2d_90, <keras.layers.convolutional.Convolution2D object at 0x7f0ea9d32990>\n",
      "281 : convolution2d_90__activation__, <keras.layers.core.Activation object at 0x7f0deedab990>\n",
      "282 : batchnormalization_90, <keras.layers.normalization.BatchNormalization object at 0x7f0ea9c91f50>\n",
      "283 : convolution2d_87, <keras.layers.convolutional.Convolution2D object at 0x7f0eaa04f850>\n",
      "284 : convolution2d_87__activation__, <keras.layers.core.Activation object at 0x7f0deedab690>\n",
      "285 : convolution2d_91, <keras.layers.convolutional.Convolution2D object at 0x7f0ea9bc8850>\n",
      "286 : convolution2d_91__activation__, <keras.layers.core.Activation object at 0x7f0deedab410>\n",
      "287 : batchnormalization_87, <keras.layers.normalization.BatchNormalization object at 0x7f0ea9faf050>\n",
      "288 : batchnormalization_91, <keras.layers.normalization.BatchNormalization object at 0x7f0ea9b2c050>\n",
      "289 : convolution2d_88, <keras.layers.convolutional.Convolution2D object at 0x7f0ea9ef6fd0>\n",
      "290 : convolution2d_88__activation__, <keras.layers.core.Activation object at 0x7f0deedabb10>\n",
      "291 : convolution2d_89, <keras.layers.convolutional.Convolution2D object at 0x7f0ea9e2c990>\n",
      "292 : convolution2d_89__activation__, <keras.layers.core.Activation object at 0x7f0deedabd90>\n",
      "293 : convolution2d_92, <keras.layers.convolutional.Convolution2D object at 0x7f0ea9a73fd0>\n",
      "294 : convolution2d_92__activation__, <keras.layers.core.Activation object at 0x7f0deedab550>\n",
      "295 : convolution2d_93, <keras.layers.convolutional.Convolution2D object at 0x7f0ea9a28990>\n",
      "296 : convolution2d_93__activation__, <keras.layers.core.Activation object at 0x7f0deedab5d0>\n",
      "297 : averagepooling2d_10, <keras.layers.pooling.AveragePooling2D object at 0x7f0ea98ae990>\n",
      "298 : convolution2d_86, <keras.layers.convolutional.Convolution2D object at 0x7f0eaa1b7990>\n",
      "299 : convolution2d_86__activation__, <keras.layers.core.Activation object at 0x7f0deedab190>\n",
      "300 : batchnormalization_88, <keras.layers.normalization.BatchNormalization object at 0x7f0ea9f069d0>\n",
      "301 : batchnormalization_89, <keras.layers.normalization.BatchNormalization object at 0x7f0ea9e19710>\n",
      "302 : batchnormalization_92, <keras.layers.normalization.BatchNormalization object at 0x7f0ea9a819d0>\n",
      "303 : batchnormalization_93, <keras.layers.normalization.BatchNormalization object at 0x7f0ea9998710>\n",
      "304 : convolution2d_94, <keras.layers.convolutional.Convolution2D object at 0x7f0ea98bbcd0>\n",
      "305 : convolution2d_94__activation__, <keras.layers.core.Activation object at 0x7f0deedab610>\n",
      "306 : batchnormalization_86, <keras.layers.normalization.BatchNormalization object at 0x7f0eaa117f50>\n",
      "307 : mixed9_1, <keras.engine.topology.Merge object at 0x7f0ea9d10e10>\n",
      "308 : merge_2, <keras.engine.topology.Merge object at 0x7f0ea988ee10>\n",
      "309 : batchnormalization_94, <keras.layers.normalization.BatchNormalization object at 0x7f0ea981ac90>\n",
      "310 : mixed10, <keras.engine.topology.Merge object at 0x7f0ea9744d50>\n",
      "311 : globalaveragepooling2d_6, <keras.layers.pooling.GlobalAveragePooling2D object at 0x7f0decd00990>\n",
      "312 : dense_11, <keras.layers.core.Dense object at 0x7f0decd009d0>\n",
      "313 : dense_11__activation__, <keras.layers.core.Activation object at 0x7f0deedab810>\n",
      "314 : batchnormalization_100, <keras.layers.normalization.BatchNormalization object at 0x7f0dece24390>\n",
      "315 : dense_12, <keras.layers.core.Dense object at 0x7f0decd093d0>\n",
      "316 : dense_12__activation__, <keras.layers.core.Activation object at 0x7f0deedab8d0>\n"
     ]
    }
   ],
   "source": [
    "coreml_model = coremltools.converters.keras.convert(model,\n",
    "                                                    input_names='image',\n",
    "                                                    #output_names=['probs'],\n",
    "                                                    image_input_names='image',\n",
    "                                                    class_labels='classes.txt',\n",
    "                                                    predicted_feature_name='class',\n",
    "                                                    image_scale=scale)\n",
    "\n",
    "# Save Core ML Model\n",
    "coreml_model.author = 'FP'\n",
    "coreml_model.save(\"autodetect_v3.mlmodel\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "input {\n",
       "  name: \"image\"\n",
       "  type {\n",
       "    imageType {\n",
       "      width: 224\n",
       "      height: 224\n",
       "      colorSpace: RGB\n",
       "    }\n",
       "  }\n",
       "}\n",
       "output {\n",
       "  name: \"output1\"\n",
       "  type {\n",
       "    dictionaryType {\n",
       "      stringKeyType {\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "}\n",
       "output {\n",
       "  name: \"class\"\n",
       "  type {\n",
       "    stringType {\n",
       "    }\n",
       "  }\n",
       "}\n",
       "predictedFeatureName: \"class\"\n",
       "predictedProbabilitiesName: \"output1\"\n",
       "metadata {\n",
       "  author: \"FP\"\n",
       "}"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coreml_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#m.predict(predict_img('test_images/test4.jpg'))\n",
    "def get_predictions_one_img_per_class():\n",
    "    '''OUTPUT: predictions for one 3 images per car model'''\n",
    "    predictions = []\n",
    "    f = []\n",
    "    for i in glob.iglob('AD/train_bottleneck/*'):\n",
    "        f.append(i)\n",
    "    for j in f:\n",
    "        #img = image.load_img(list(glob.iglob(j+'/*'))[0], target_size=(224,224))\n",
    "        img1 = image.load_img(list(glob.iglob(j+'/*'))[0], target_size=(224,224))\n",
    "        img2 = image.load_img(list(glob.iglob(j+'/*'))[0], target_size=(224,224))\n",
    "        img3 = image.load_img(list(glob.iglob(j+'/*'))[0], target_size=(224,224))\n",
    "\n",
    "        # image array -> (3,155,155)\n",
    "        #img = image.img_to_array(img)/255\n",
    "        img1 = image.img_to_array(img1)/255\n",
    "        img2 = image.img_to_array(img2)/255\n",
    "        img3 = image.img_to_array(img3)/255\n",
    "\n",
    "        # expand by 1 dimension to -> (1,3,155,155)\n",
    "        #img = np.expand_dims(img, axis=0)\n",
    "        img1 = np.expand_dims(img1, axis=0)\n",
    "        img2 = np.expand_dims(img2, axis=0)\n",
    "        img3 = np.expand_dims(img3, axis=0)\n",
    "\n",
    "        #x = m.predict(img)\n",
    "        x1 = m.predict(img1)[0]\n",
    "        x2 = m.predict(img2)[0]\n",
    "        x3 = m.predict(img3)[0]\n",
    "        #predictions.append(x)\n",
    "        predictions.append(x1)\n",
    "        predictions.append(x2)\n",
    "        predictions.append(x3)\n",
    "    return predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.99999982,  0.10085613, -0.06210266, ..., -0.07814679,\n",
       "        -0.06297778,  0.01459757],\n",
       "       [ 0.10085613,  0.99999988,  0.07933356, ...,  0.00840297,\n",
       "         0.3043901 ,  0.07832944],\n",
       "       [-0.06210266,  0.07933356,  1.        , ..., -0.12419236,\n",
       "         0.01293819,  0.11807769],\n",
       "       ..., \n",
       "       [-0.07814679,  0.00840297, -0.12419236, ...,  1.00000012,\n",
       "        -0.03347645, -0.04648396],\n",
       "       [-0.06297778,  0.3043901 ,  0.01293819, ..., -0.03347645,\n",
       "         1.00000036,  0.10327784],\n",
       "       [ 0.01459757,  0.07832944,  0.11807769, ..., -0.04648396,\n",
       "         0.10327784,  1.00000012]], dtype=float32)"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "\n",
    "def cosine_similarities(preds):\n",
    "    '''\n",
    "    INPUT: Predictions\n",
    "    OUTPUT: Cosine similarities\n",
    "    '''\n",
    "    preds = [i[0] for i in preds]\n",
    "    \n",
    "    return cosine_similarity(preds)\n",
    "\n",
    "cs = cosine_similarities(predictions)\n",
    "cs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### T-SNE visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RSq4AR8Z5sQwPBk3H63FSe2xtTqafxI+fLk3h6XttNEqqxbzJ4QiLQpxiKzcz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\n7+FMmz74C23A59M4c0ZoW3A9u5zVeYRXMaNCpmkWeLlbGSbeOl6Jg5U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jRrT/n7qT8Pfo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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff011bb4550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "from matplotlib.ticker import NullFormatter\n",
    "\n",
    "tsne = TSNE(n_components=3,init='pca')\n",
    "np.set_printoptions(suppress=True)\n",
    "Y = tsne.fit_transform(cs)\n",
    "fig = plt.figure(figsize=(30, 10))\n",
    "ax = fig.add_subplot(2, 5, 10)\n",
    "plt.scatter(Y[:, 0], Y[:, 1], c=['r','g','b'])#, cmap=plt.cm.Spectral)\n",
    "#plt.title(\"t-SNE (%.2g sec)\" % (t1 - t0))\n",
    "ax.xaxis.set_major_formatter(NullFormatter())\n",
    "ax.yaxis.set_major_formatter(NullFormatter())\n",
    "plt.axis('tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qr17cOnzYoQSO1seHD2/fli2rnh1qvfUWoVOmuAwN03p50c6JJm8ahrg4FjRt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\nC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1ccKa3XBeraSiql+dpuJmraQaFQKAKACr4KhUIRAEpz8P080B24SZTW6wJ1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\nk3ENgPMsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff010592850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from tsne import bh_sne\n",
    "\n",
    "# load up data\n",
    "x_data = predictions\n",
    "y_data = all_classes.keys()\n",
    "\n",
    "# convert image data to float64 matrix. float64 is need for bh_sne\n",
    "x_data = np.asarray(x_data).astype('float64')\n",
    "x_data = x_data.reshape((x_data.shape[0], -1))\n",
    "\n",
    "# For speed of computation, only run on a subset\n",
    "n = 1406\n",
    "x_data = x_data[:n]\n",
    "y_data = y_data[:n]\n",
    "\n",
    "# perform t-SNE embedding\n",
    "vis_data = bh_sne(x_data)\n",
    "\n",
    "# plot the result\n",
    "vis_x = vis_data[:, 0]\n",
    "vis_y = vis_data[:, 1]\n",
    "\n",
    "plt.scatter(vis_x, vis_y, c=y_data, cmap=plt.cm.get_cmap(\"jet\", 10))\n",
    "plt.colorbar(ticks=range(10))\n",
    "plt.clim(-0.5, 9.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create new model and train it without last layer to calculate distances between images.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def new_model(original_model):\n",
    "    inp = original_model.input\n",
    "    output = original_model.layers[-2].output\n",
    "    model = Model(inp,output)\n",
    "    batch_size = 20\n",
    "    datagen = ImageDataGenerator(rescale=1. / 255)\n",
    "    generator = datagen.flow_from_directory(\n",
    "            'AD/train_bottleneck',\n",
    "            target_size=(224, 224),\n",
    "            batch_size=batch_size,\n",
    "            class_mode=None,\n",
    "            shuffle=False)\n",
    "\n",
    "    # a generator that yields batches of numpy data\n",
    "    bottleneck_features_train = model.predict_generator(generator, 1404)\n",
    "    # save the output as a Numpy array\n",
    "    np.save('bottleneck_features_train.npy', bottleneck_features_train)\n",
    "\n",
    "    generator = datagen.flow_from_directory(\n",
    "            'AD/val_bottleneck',\n",
    "            target_size=(224, 224),\n",
    "            batch_size=batch_size,\n",
    "            class_mode=None,\n",
    "            shuffle=False)\n",
    "    \n",
    "    bottleneck_features_validation = model.predict_generator(generator, 918)\n",
    "    np.save('bottleneck_features_validation.npy', bottleneck_features_validation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1404 images belonging to 468 classes.\n",
      "Found 918 images belonging to 307 classes.\n"
     ]
    }
   ],
   "source": [
    "new_model(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t = np.load('/home/ubuntu/bottleneck_features_validation.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def folder_structure_train_bottleneck():\n",
    "    for foldername in glob.iglob('AD/AutoDetect/*'):\n",
    "        folder = 'AD/train_bottleneck/'+foldername.split('/')[2]\n",
    "        os.mkdir(folder)\n",
    "        img1 = list(glob.iglob(foldername+'/*'))[0]\n",
    "        img2 = list(glob.iglob(foldername+'/*'))[1]\n",
    "        img3 = list(glob.iglob(foldername+'/*'))[2]\n",
    "        shutil.copy2(img1, folder)\n",
    "        shutil.copy2(img2, folder)\n",
    "        shutil.copy2(img3, folder)\n",
    "        print(foldername)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def folder_structure_val_bottleneck():\n",
    "    for foldername in glob.iglob('AD/validation/*'):\n",
    "        folder = 'AD/val_bottleneck/'+foldername.split('/')[2]\n",
    "        os.mkdir(folder)\n",
    "        img1 = list(glob.iglob(foldername+'/*'))[0]\n",
    "        img2 = list(glob.iglob(foldername+'/*'))[1]\n",
    "        img3 = list(glob.iglob(foldername+'/*'))[2]\n",
    "        shutil.copy2(img1, folder)\n",
    "        shutil.copy2(img2, folder)\n",
    "        shutil.copy2(img3, folder)\n",
    "        print(foldername)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#folder_structure_train_bottleneck()\n",
    "#folder_structure_val_bottleneck()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def model_bottleneck():\n",
    "    batch_size = 20\n",
    "    train_data = np.load(open('bottleneck_features_train.npy'))\n",
    "    train_labels = sorted([i.split('/')[2] for i in glob.iglob('AD/train_bottleneck/*')])\n",
    "\n",
    "    validation_data = np.load(open('bottleneck_features_validation.npy'))\n",
    "    validation_labels = sorted([i.split('/')[2] for i in glob.iglob('AD/val_bottleneck//*')])\n",
    "\n",
    "    model = Sequential()\n",
    "    #model.add(output)\n",
    "    #model.add(GlobalAveragePooling2D())\n",
    "    model.add(Flatten(input_shape=train_data.shape[1:]))\n",
    "    model.add(Dense(256, activation='relu'))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(468, activation='softmax'))\n",
    "\n",
    "\n",
    "    model.compile(optimizer='adam',\n",
    "                  loss='categorical_crossentropy',\n",
    "                  metrics=['accuracy'])\n",
    "\n",
    "    model.fit(train_data, train_labels,\n",
    "              nb_epoch=10,\n",
    "              batch_size=batch_size,\n",
    "              validation_data=(validation_data, validation_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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