VGG16_work_in_progress.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import glob\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "from sklearn.model_selection import train_test_split\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def generator(data, im_height = 224, im_width = 224):\n",
    "    while 1:\n",
    "        for i in xrange(0, len(data)):\n",
    "            labels = []\n",
    "            images = np.zeros((5,224,224,3))\n",
    "            for idx, image_path in enumerate(train_data[5*i:5*(i+1)]):\n",
    "                img = cv2.imread(image_path)\n",
    "                img = cv2.resize(img, (im_width, im_height))\n",
    "                images[idx,:,:,:] = img\n",
    "                if 'cat' in image_path:\n",
    "                    labels.append(0)\n",
    "                else:\n",
    "                    labels.append(1)\n",
    "\n",
    "            labels = np.array(labels)\n",
    "            images = np.swapaxes(images, 1, 3)\n",
    "            images = np.swapaxes(images, 2, 3)\n",
    "            yield images, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data = [image for image in glob.glob('../Data/train/*jpg')]\n",
    "test_data = [image for image in glob.glob('../Data/train/*jpg')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data, cv_data = train_test_split(train_data, test_size = 0.2, random_state = 5)\n",
    "train_data, hold_data = train_test_split(train_data, test_size = 0.25, random_state = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using Theano backend.\n",
      "Using gpu device 0: GeForce GTX 980M (CNMeM is enabled with initial size: 80.0% of memory, cuDNN 5103)\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Dropout, Activation, Flatten\n",
    "from keras.layers.convolutional import Convolution2D, MaxPooling2D\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.optimizers import SGD\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Convolution2D(64, 3, 3, activation='relu',input_shape = (3,224,224), border_mode='same', name='block1_conv1'))\n",
    "model.add(Convolution2D(64, 3, 3, activation='relu', border_mode='same', name='block1_conv2'))\n",
    "model.add(MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool'))\n",
    "\n",
    "# Block 2\n",
    "model.add(Convolution2D(128, 3, 3, activation='relu', border_mode='same', name='block2_conv1'))\n",
    "model.add(Convolution2D(128, 3, 3, activation='relu', border_mode='same', name='block2_conv2'))\n",
    "model.add(MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool'))\n",
    "\n",
    "# Block 3\n",
    "model.add(Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='block3_conv1'))\n",
    "model.add(Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='block3_conv2'))\n",
    "model.add(Convolution2D(256, 3, 3, activation='relu', border_mode='same', name='block3_conv3'))\n",
    "model.add(MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool'))\n",
    "\n",
    "# Block 4\n",
    "model.add(Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block4_conv1'))\n",
    "model.add(Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block4_conv2'))\n",
    "model.add( Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block4_conv3'))\n",
    "model.add(MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool'))\n",
    "\n",
    "# Block 5\n",
    "model.add(Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv1'))\n",
    "model.add(Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv2'))\n",
    "model.add(Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block5_conv3'))\n",
    "model.add(MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model.load_weights('../Data/vgg16_weights_th_dim_ordering_th_kernels_notop.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model.add(Flatten())\n",
    "model.add(Dense(4096, activation='relu'))\n",
    "model.add(Dense(4096, activation='relu'))\n",
    "model.add(Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer=SGD(lr = 0.1, nesterov=True, momentum = 0.9, decay = 1e10-6), loss = 'binary_crossentropy', metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "5000/5000 [==============================] - 358s - loss: 8.0008 - acc: 0.4994   \n",
      "Epoch 2/10\n",
      "5000/5000 [==============================] - 359s - loss: 7.9673 - acc: 0.5012   \n",
      "Epoch 3/10\n",
      "5000/5000 [==============================] - 361s - loss: 7.9331 - acc: 0.5038   \n",
      "Epoch 4/10\n"
     ]
    },
    {
     "ename": "Exception",
     "evalue": "Input arrays should have the same number of samples as target arrays. Found 5 input samples and 0 target samples.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mException\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-d09df8cc997a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit_generator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgenerator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msamples_per_epoch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m5000\u001b[0m\u001b[1;33m,\u001b[0m  \u001b[0mnb_epoch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32mc:\\Users\\AeroDeath\\Miniconda2\\lib\\site-packages\\keras-1.1.0-py2.7.egg\\keras\\models.pyc\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, samples_per_epoch, nb_epoch, verbose, callbacks, validation_data, nb_val_samples, class_weight, max_q_size, nb_worker, pickle_safe, **kwargs)\u001b[0m\n\u001b[1;32m    872\u001b[0m                                         \u001b[0mmax_q_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmax_q_size\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    873\u001b[0m                                         \u001b[0mnb_worker\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnb_worker\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 874\u001b[0;31m                                         pickle_safe=pickle_safe)\n\u001b[0m\u001b[1;32m    875\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    876\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mevaluate_generator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgenerator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mval_samples\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmax_q_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnb_worker\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpickle_safe\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mc:\\Users\\AeroDeath\\Miniconda2\\lib\\site-packages\\keras-1.1.0-py2.7.egg\\keras\\engine\\training.pyc\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, samples_per_epoch, nb_epoch, verbose, callbacks, validation_data, nb_val_samples, class_weight, max_q_size, nb_worker, pickle_safe)\u001b[0m\n\u001b[1;32m   1441\u001b[0m                     outs = self.train_on_batch(x, y,\n\u001b[1;32m   1442\u001b[0m                                                \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1443\u001b[0;31m                                                class_weight=class_weight)\n\u001b[0m\u001b[1;32m   1444\u001b[0m                 \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1445\u001b[0m                     \u001b[0m_stop\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mc:\\Users\\AeroDeath\\Miniconda2\\lib\\site-packages\\keras-1.1.0-py2.7.egg\\keras\\engine\\training.pyc\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[0;34m(self, x, y, sample_weight, class_weight)\u001b[0m\n\u001b[1;32m   1213\u001b[0m                                                            \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1214\u001b[0m                                                            \u001b[0mclass_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1215\u001b[0;31m                                                            check_batch_dim=True)\n\u001b[0m\u001b[1;32m   1216\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muses_learning_phase\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mK\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlearning_phase\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1217\u001b[0m             \u001b[0mins\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0msample_weights\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m1.\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mc:\\Users\\AeroDeath\\Miniconda2\\lib\\site-packages\\keras-1.1.0-py2.7.egg\\keras\\engine\\training.pyc\u001b[0m in \u001b[0;36m_standardize_user_data\u001b[0;34m(self, x, y, sample_weight, class_weight, check_batch_dim, batch_size)\u001b[0m\n\u001b[1;32m    971\u001b[0m                           \u001b[1;32mfor\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mref\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    972\u001b[0m                           in zip(y, sample_weights, class_weights, self.sample_weight_modes)]\n\u001b[0;32m--> 973\u001b[0;31m         \u001b[0mcheck_array_lengths\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weights\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    974\u001b[0m         \u001b[0mcheck_loss_and_target_compatibility\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloss_functions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minternal_output_shapes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    975\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstateful\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mc:\\Users\\AeroDeath\\Miniconda2\\lib\\site-packages\\keras-1.1.0-py2.7.egg\\keras\\engine\\training.pyc\u001b[0m in \u001b[0;36mcheck_array_lengths\u001b[0;34m(X, Y, W)\u001b[0m\n\u001b[1;32m    175\u001b[0m                         \u001b[1;34m'the same number of samples as target arrays. Found '\u001b[0m \u001b[1;33m+\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    176\u001b[0m                         \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mset_x\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m' input samples and '\u001b[0m \u001b[1;33m+\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 177\u001b[0;31m                         str(list(set_y)[0]) + ' target samples.')\n\u001b[0m\u001b[1;32m    178\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mset_x\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mset_w\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m         raise Exception('Sample_weight arrays should have '\n",
      "\u001b[0;31mException\u001b[0m: Input arrays should have the same number of samples as target arrays. Found 5 input samples and 0 target samples."
     ]
    }
   ],
   "source": [
    "model.fit_generator(generator(train_data), samples_per_epoch = 5000,  nb_epoch = 10)"
   ]
  }
 ],
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