VGGTRAINED.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cat vs. Dog Classification\n",
    "### Colin Santos and Cynthia Lai\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading in Train and Test Images\n",
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "from tqdm import tqdm \n",
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_train_label(file_name):\n",
    "    \"\"\"Gives binary based on image name\"\"\"\n",
    "    category = file_name.split('.')[0]\n",
    "    if   category == 'cat': return 0 # [1, 0]\n",
    "    elif category == 'dog': return 1 # [0, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "OSError",
     "evalue": "[Errno 2] No such file or directory: 'test1/'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-61fe21101d61>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# Form list of testing images\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mtest_dir_files\u001b[0m   \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_directory\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;31m# Remove hidden MAC files\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m# test_dir   = [i for i in test_dir if i!= '.DS_Store' ]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mOSError\u001b[0m: [Errno 2] No such file or directory: 'test1/'"
     ]
    }
   ],
   "source": [
    "test_directory = 'test1/' # may change on your laptop and mine\n",
    "\n",
    "# Form list of testing images\n",
    "test_dir_files   = os.listdir(test_directory)\n",
    "# Remove hidden MAC files\n",
    "# test_dir   = [i for i in test_dir if i!= '.DS_Store' ]\n",
    "# Convert to grayscale 224 x 224 images \n",
    "# !!! Modify image augmentation/normalization as necessary !!!\n",
    "test_im_list = [Image.open(test_directory + im).resize((224,224)) for im in test_dir_files]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "test_dir_files[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to display an image, you can just list it out *(i.e. test_im_list[30])*\n",
    "However, if you are working on terminal, you have to do *{img file}.show()*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "test_im_list[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_directory = 'train_small/'\n",
    "# Form list of training images names\n",
    "train_dir_files  = os.listdir(train_directory)\n",
    "# Remove hidden MAC files\n",
    "train_dir_files     = [i for i in train_dir_files if i!= '.DS_Store' ]\n",
    "# Convert to 224 x 224 images\n",
    "# !!! Modify image augmentation/normalization as necessary !!!\n",
    "train_im_list = [Image.open(train_directory + im).resize((224,224)) for im in train_dir_files]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "numPics = len(train_dir_files) # fc_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Get training labels \n",
    "train_labels = [get_train_label(file) for file in train_dir_files]\n",
    "\n",
    "# Get testing numbers (file names) \n",
    "#test_numbers = [file_name.split('.')[0] for file_name in test_dir_files]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0, 0, 0, 0, 0], [1, 1, 1, 1, 1])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(train_labels[0:5], train_labels[20000:20005])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import _pickle as cPickle\n",
    "f = open(\"im_list.pl\", 'wb') # .pl is a pickle file\n",
    "cPickle.dump(train_im_list, f)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Formatted data as (numpy arrays of) list of tuples of modified image and respective label or number\n",
    "train_data = [(np.array(train_im, dtype=np.float64), np.array(train_label)) for (train_im, train_label) in zip(train_im_list, train_labels)]\n",
    "#test_data  = [(np.array(test_im), np.array(test_number)) for (test_im, test_number) in zip(test_im_list, test_numbers)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# just the data itself in np.array format\n",
    "train_dat = [np.array(train_im, dtype=np.float64) for train_im in train_im_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(224, 224, 3)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## IGNORE THIS\n",
    "\n",
    "from keras.preprocessing import image as image_utils\n",
    "\n",
    "im = image_utils.load_img(train_directory + train_dir_files[0], target_size=(224, 224))\n",
    "test = image_utils.img_to_array(train_im_list[0])\n",
    "test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convolutional Neural Network\n",
    "----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "No module named imagenet_utils",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-132814cd17a8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapplications\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mimagenet_utils\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mimagenet_utils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdecode_predictions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mimagenet_utils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpreprocess_input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mvgg16\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mVGG16\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mImportError\u001b[0m: No module named imagenet_utils"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import keras\n",
    "from keras.applications import imagenet_utils\n",
    "from imagenet_utils import decode_predictions\n",
    "from imagenet_utils import preprocess_input\n",
    "from vgg16 import VGG16"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### First, work with VGG16 model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fc_size = numPics\n",
    "nb_classes = 2 # cat or dog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# train_data and test_data are 2 lists of np.array images\n",
    "# need to preprocess images a bit more first\n",
    "\n",
    "# make img file (1, 224, 224, 3)\n",
    "temp = [np.expand_dims(image, axis = 0) for image in train_dat]\n",
    "train_data2 = [preprocess_input(image, dim_ordering =  'tf') for image in temp]\n",
    "#test_data = [preprocess_input(np.expand_dims(image, axis = 0)) for image in test_data]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# load model\n",
    "model = VGG16(weights=\"imagenet\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "too many values to unpack (expected 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-17-071b72a48049>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mpreds\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mimage\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtrain_data2\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m251\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m750\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[1;33m(\u001b[0m\u001b[0minIDs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mdecode_predictions\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpred\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mpred\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mpreds\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m: too many values to unpack (expected 2)"
     ]
    }
   ],
   "source": [
    "# classify the image\n",
    "preds = [model.predict(image) for image in tqdm(train_data2[251:750])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### To speed up time, don't run the line above.\n",
    "Instead, load the pickle file **preds.pl** and run the code 2 lines below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import _pickle\n",
    "f = open(\"preds.pl\", 'wb') # .pl is a pickle file\n",
    "_pickle.dump(preds, f)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f = open(\"preds.pl\", \"rb\")\n",
    "preds = cPickle.load(f) # load PPMI matrix "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[('n02110185', 'Siberian_husky', 0.29487318),\n",
       "  ('n02123045', 'tabby', 0.14214493),\n",
       "  ('n02102040', 'English_springer', 0.079373576),\n",
       "  ('n02123159', 'tiger_cat', 0.078676835),\n",
       "  ('n02124075', 'Egyptian_cat', 0.05087084)],\n",
       " [('n04192698', 'shield', 0.2028589),\n",
       "  ('n03447721', 'gong', 0.16397992),\n",
       "  ('n04447861', 'toilet_seat', 0.099700734),\n",
       "  ('n03271574', 'electric_fan', 0.078470185),\n",
       "  ('n04548280', 'wall_clock', 0.070489772)],\n",
       " [('n02120079', 'Arctic_fox', 0.28693759),\n",
       "  ('n02441942', 'weasel', 0.18113261),\n",
       "  ('n02124075', 'Egyptian_cat', 0.085749686),\n",
       "  ('n02127052', 'lynx', 0.030233484),\n",
       "  ('n03887697', 'paper_towel', 0.023381973)],\n",
       " [('n03958227', 'plastic_bag', 0.29939675),\n",
       "  ('n02123159', 'tiger_cat', 0.077491708),\n",
       "  ('n07693725', 'bagel', 0.05460228),\n",
       "  ('n03127925', 'crate', 0.047913302),\n",
       "  ('n04399382', 'teddy', 0.047511745)],\n",
       " [('n02124075', 'Egyptian_cat', 0.59580404),\n",
       "  ('n02123597', 'Siamese_cat', 0.083924249),\n",
       "  ('n02127052', 'lynx', 0.074538566),\n",
       "  ('n02808304', 'bath_towel', 0.062744483),\n",
       "  ('n02123045', 'tabby', 0.012812228)]]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = [decode_predictions(pred)[0] for pred in preds]\n",
    "results[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "top_results = [decode_predictions(pred)[0][0] for pred in preds]\n",
    "labs = [res[1] for res in top_results]\n",
    "confs = [conf[2] for conf in top_results]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# display the predictions to our screen\n",
    "print(\"ImageNet ID: {}, Label: {}\".format(inID, label))\n",
    "cv2.putText(orig, \"Label: {}\".format(label), (10, 30),\n",
    "\tcv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)\n",
    "cv2.imshow(\"Classification\", orig)\n",
    "cv2.waitKey(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Next Steps:\n",
    "1. Take **labels** and figure out if cat or dog based on classification output list. {:|:}\n",
    "2. Calculate accuracy. {:|:}\n",
    "3. Modify model if needed.\n",
    "4. Try on other CNNs.\n",
    "5. profit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Load in Imagenet classications groupings for cats and dogs\n",
    "with open('cats.txt') as f:\n",
    "    cats = [line.rstrip() for line in f]\n",
    "with open('dogs.txt') as f:\n",
    "    dogs = [line.rstrip() for line in f]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def cat_or_dog(full_class_string):\n",
    "    \"\"\"Returns generalized classification of cat or dog\"\"\"\n",
    "    if full_class_string in cats: return 'cat'\n",
    "    if full_class_string in dogs: return 'dog'\n",
    "    \n",
    "def one_or_zero(full_class_string):\n",
    "    \"\"\"Returns generalize classification of cat or dog as bool\"\"\"\n",
    "    if full_class_string in cats: return 0\n",
    "    if full_class_string in dogs: return 1\n",
    "    \n",
    "from operator import eq\n",
    "def calculate_accuracy(pred_labels, true_labels):\n",
    "    return sum(map(eq, pred_labels, true_labels)) / float(len(true_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "simple_labels = [one_or_zero(label) for label in labels]\n",
    "\n",
    "accuracy = calculate_accuracy(simple_labels, train_labels) #Change 'train_labels' to test_labels\n",
    "error = 1 - accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Transfer Learning\n",
    "Change the last layer of the model to output only 2 categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras.layers import Activation, Dropout, Flatten, Dense\n",
    "from keras import backend as K\n",
    "from keras.applications import imagenet_utils\n",
    "from imagenet_utils import preprocess_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Borrowed from: https://github.com/DeepLearningSandbox/DeepLearningSandbox/blob/master/transfer_learning/fine-tune.py\n",
    "\n",
    "from keras.applications  import ResNet50\n",
    "from keras.models import Model\n",
    "from keras.layers import Dense, GlobalAveragePooling2D\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.optimizers import SGD\n",
    "\n",
    "\n",
    "NB_EPOCHS = 1\n",
    "SAMPLE_EPOCH = 200\n",
    "BAT_SIZE = 16\n",
    "NB_CLASSES = 2\n",
    "IM_WIDTH = 224\n",
    "IM_HEIGHT = 224\n",
    "FC_SIZE = 200 # number of outputs\n",
    "NUM_TEST = 100\n",
    "\n",
    "def add_new_last_layer(base_model, nb_classes):\n",
    "    \"\"\"Add last layer to the convnet\n",
    "    Args:\n",
    "    base_model: keras model excluding top\n",
    "    nb_classes: # of classes\n",
    "    Returns:\n",
    "    new keras model with last layer\n",
    "    \"\"\"\n",
    "    x = base_model.output\n",
    "    x = GlobalAveragePooling2D()(x)\n",
    "    x = Dense(FC_SIZE, activation='relu')(x) #new FC layer, random init\n",
    "    predictions = Dense(nb_classes, activation='softmax')(x) #new softmax layer\n",
    "    model = Model(inputs = base_model.input, outputs = predictions)\n",
    "    return model\n",
    "\n",
    "def setup_to_transfer_learn(model, base_model):\n",
    "    \"\"\"Freeze all layers and compile the model\"\"\"\n",
    "    for layer in base_model.layers:\n",
    "        layer.trainable = False\n",
    "    model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "  \n",
    "def vgg16_train(train_dir, val_dir):\n",
    "    \n",
    "    train_datagen =  ImageDataGenerator(shear_range=0.2, zoom_range=0.2, horizontal_flip=True, rescale=1. / 255)\n",
    "\n",
    "    test_datagen = ImageDataGenerator(rescale=1. / 255)\n",
    "\n",
    "    train_generator = train_datagen.flow_from_directory(train_dir, target_size=(IM_WIDTH, IM_HEIGHT), batch_size=BAT_SIZE)\n",
    "\n",
    "    validation_generator = test_datagen.flow_from_directory(val_dir, target_size=(IM_WIDTH, IM_HEIGHT), batch_size=BAT_SIZE)\n",
    "\n",
    "    base_model = VGG16(weights='imagenet', include_top=False) #include_top=False excludes final FC layer\n",
    "    model = add_new_last_layer(base_model, NB_CLASSES)\n",
    "    setup_to_transfer_learn(model, base_model)\n",
    "\n",
    "    model.fit_generator(train_generator, epochs =NB_EPOCHS, steps_per_epoch = SAMPLE_EPOCH, \\\n",
    "                                     validation_data=validation_generator, validation_steps = NUM_TEST)\n",
    "    model.save(\"vgg16_train.model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 200 images belonging to 2 classes.\n",
      "Found 100 images belonging to 2 classes.\n",
      "Epoch 1/1\n",
      "200/200 [==============================] - 4127s - loss: 0.4021 - acc: 0.8172 - val_loss: 0.2962 - val_acc: 0.8876\n"
     ]
    }
   ],
   "source": [
    "vgg16_train(\"train_small\", \"test_small\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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