scene-recognition.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "from keras import models, layers\n",
    "import numpy as np\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "import os\n",
    "from scipy.ndimage import imread\n",
    "from scipy.misc import imresize\n",
    "from nbutil import imshow_multi\n",
    "from PIL import Image\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_pil(img):\n",
    "    if img.shape[-1] == 1:\n",
    "        img = img[:,:,0] # these are grayscale\n",
    "    return Image.fromarray(np.clip(img * 255, 0, 255).astype(np.uint8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = '../data/proj6/data/15SceneData'\n",
    "categories = [c for c in os.listdir(os.path.join(data_dir, 'train')) if c[0] != '.']\n",
    "\n",
    "SIZE = 64\n",
    "\n",
    "def load_dataset(name):\n",
    "    images = []\n",
    "    labels = []\n",
    "    for cat in categories:\n",
    "        for filename in os.listdir(os.path.join(data_dir, name, cat)):\n",
    "            if filename.endswith('.jpg'): \n",
    "                path = os.path.join(data_dir, name, cat, filename)\n",
    "                image = imread(path)\n",
    "                h,w = image.shape[:2]\n",
    "                # top-left crop\n",
    "                if h > w:\n",
    "                    image = image[:w, :]\n",
    "                else:\n",
    "                    image = image[:, :h]\n",
    "                image = imresize(image, size=[SIZE, SIZE], mode='L')\n",
    "                images.append(image)\n",
    "                labels.append(categories.index(cat))\n",
    "    images = np.expand_dims(np.stack(images) / 255.0, -1)\n",
    "    labels = np.expand_dims(np.stack(labels).astype(int), -1)\n",
    "    return images, labels\n",
    "\n",
    "trainx, trainy = load_dataset('train')\n",
    "testx, testy = load_dataset('test')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAEAAAABACAAAAACPAi4CAAAEH0lEQVR4nG1X0XbdMAiTfPzh+/FZ\nexAC0q5bT+9NYpCFEA7/kCBIgDwACeKAIECJAAACICT4R4DyT5deXXcoQpTXCLmRv7UcAuhfHQgy\ngIorSdBTJ2QSMP9XHBw42OdH8s1axh8f4BxOfQqsYWEtxGw6OYUHydeNUDgVmQbKJmnhlL8yyYMe\nAt7Jcz/wyxSqHqx1JmlWSLihhRAX6xSTRZ9aqL+KAHiULOKAX5BWBYsTU1iP6laNVBSoYmuoV9Ox\nqalkuuZH/4HX18Tw2PVKfvBWomhWC3LBnnzUXAiyCxk4fR/qezQ09R61BBbGdL0+yv4oNZR0MeQd\n9TYE4Ioq5Kp+QP3NclUnqlalaP5+jRlI3SnmitjqLvEyshAkgaotOAkN7keEYbLFOAqVcF+y8yMh\nnSkEQr53T2VLEHDV6Nl4KVCKDehFI3rMAwmgW5t6JQHRC+UoBf1D25AKSDclQrOvI4CPp2qjFwVK\n7AYPlbfszd2bevpKHoIMsT9rmdZdygJeaVIU8MCDOFCrsGqtWvMSwDiW8GoHEPBo0AOgEIIoHQz3\nNRNkTykSJv24GRNgusNSfu1Q7bYLgaJ4R6V03wwFigBFCuB5PXEk4aFxl92VL93xG1rroGN45CE0\n2mLMrfvS1b2CfIGQWMPKbVQRXu9dAJ5spHGW+5dphDBVoiTI6jRpWr0aqxV+pROyIkgK4Ds1s73Z\nknV1sRNBBO7jaxvchsiGsMas6eD0KHBrBCFqRrzpkcJqfoAPkXj0IN1Hoq/0eGxB4oEqjTz0Y20b\nunbSWK/ducWWCkdI+cBPAGuM04bdNRlWGsieWmnUBKjH5qQjvtOmvFhwTbvtBOGqJFwi3URWxwRW\n5tIeQcI12QCAR2+mtT3tV8TElbUCqGJ31qWGlFzjFjGSlkJZWt03v1IdE4NA+Ezo1oAoXHfCq37s\nKogkcGKVaSVo8QOCuI/VjQHd87GOGRqS5oey0NsPGtc8rKYaJcZ9WmuZ3dJxnv5MCKiddRpoEVhz\nQVziNlssobEsl32zXTnecN+Rjj4tlGd6BxM+twfLnQbwEyxhP0YflZATSQ3Ls3FCk9PcBfojvFIk\nM4kB1DlRtfxTNW7axL2HSWMp90GmpssqZryCBWiTQgG3OW4k8uxMITBOu/WWGt1COY7UpjgloO30\nV5N5C+zIGYffpJxj7KbWMsr5gDEw1IGrqcLnnazpVPh1GacJqHnc1kTFYducRmWykAJh9zwT4+Uw\nxBxMegwTgO7zgcJTZO2z5fStXPyg63BCxuwbM3uFeWtskn20iMHdFX7Oq4aQbHHa7StxWAdgPLdo\nL92TaXvkBPTlsEx1bH9Plo//Yix5zNFbPQWeh8dyz7loDXYeDgPoAwcB4B6/vrsR3Ul9RoJfbJHX\nyH4VqhOUJPwDJtD27rbnE1gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<PIL.Image.Image image mode=L size=64x64 at 0x7FD038745C88>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_gen = ImageDataGenerator(rotation_range=5, \n",
    "                               zoom_range=0.2, \n",
    "                               width_shift_range=0.1, \n",
    "                               height_shift_range=0.1, \n",
    "                               horizontal_flip=True,\n",
    "                               channel_shift_range=0.2)\n",
    "# samplewise centering is broken in keras (https://github.com/fchollet/keras/issues/4047)\n",
    "# so do it manually:\n",
    "mean_img = np.mean(trainx, axis=0)\n",
    "to_pil(mean_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for x in image_gen.flow(trainx):\n",
    "#     p = to_pil(x[0])\n",
    "#     break\n",
    "# p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(?, 32, 32, 64)\n",
      "(?, 16, 16, 64)\n",
      "(?, 8, 8, 64)\n",
      "(?, 4, 4, 64)\n",
      "(?, 1, 1, 64)\n",
      "(?, 15)\n",
      "____________________________________________________________________________________________________\n",
      "Layer (type)                     Output Shape          Param #     Connected to                     \n",
      "====================================================================================================\n",
      "input_5 (InputLayer)             (None, 64, 64, 1)     0                                            \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_144 (Conv2D)              (None, 64, 64, 16)    32                                           \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_142 (Conv2D)              (None, 64, 64, 16)    32                                           \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_145 (Conv2D)              (None, 64, 64, 16)    2320                                         \n",
      "____________________________________________________________________________________________________\n",
      "average_pooling2d_41 (AveragePoo (None, 64, 64, 1)     0                                            \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_141 (Conv2D)              (None, 64, 64, 16)    32                                           \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_143 (Conv2D)              (None, 64, 64, 16)    2320                                         \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_146 (Conv2D)              (None, 64, 64, 16)    2320                                         \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_147 (Conv2D)              (None, 64, 64, 16)    32                                           \n",
      "____________________________________________________________________________________________________\n",
      "concatenate_21 (Concatenate)     (None, 64, 64, 64)    0                                            \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_21 (BatchNor (None, 64, 64, 64)    256                                          \n",
      "____________________________________________________________________________________________________\n",
      "dropout_21 (Dropout)             (None, 64, 64, 64)    0                                            \n",
      "____________________________________________________________________________________________________\n",
      "average_pooling2d_42 (AveragePoo (None, 32, 32, 64)    0                                            \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_151 (Conv2D)              (None, 32, 32, 16)    1040                                         \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_149 (Conv2D)              (None, 32, 32, 16)    1040                                         \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_152 (Conv2D)              (None, 32, 32, 16)    2320                                         \n",
      "____________________________________________________________________________________________________\n",
      "average_pooling2d_43 (AveragePoo (None, 32, 32, 64)    0                                            \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_148 (Conv2D)              (None, 32, 32, 16)    1040                                         \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_150 (Conv2D)              (None, 32, 32, 16)    2320                                         \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_153 (Conv2D)              (None, 32, 32, 16)    2320                                         \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_154 (Conv2D)              (None, 32, 32, 16)    1040                                         \n",
      "____________________________________________________________________________________________________\n",
      "concatenate_22 (Concatenate)     (None, 32, 32, 64)    0                                            \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_22 (BatchNor (None, 32, 32, 64)    256                                          \n",
      "____________________________________________________________________________________________________\n",
      "dropout_22 (Dropout)             (None, 32, 32, 64)    0                                            \n",
      "____________________________________________________________________________________________________\n",
      "average_pooling2d_44 (AveragePoo (None, 16, 16, 64)    0                                            \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_158 (Conv2D)              (None, 16, 16, 16)    1040                                         \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_156 (Conv2D)              (None, 16, 16, 16)    1040                                         \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_159 (Conv2D)              (None, 16, 16, 16)    2320                                         \n",
      "____________________________________________________________________________________________________\n",
      "average_pooling2d_45 (AveragePoo (None, 16, 16, 64)    0                                            \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_155 (Conv2D)              (None, 16, 16, 16)    1040                                         \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_157 (Conv2D)              (None, 16, 16, 16)    2320                                         \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_160 (Conv2D)              (None, 16, 16, 16)    2320                                         \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_161 (Conv2D)              (None, 16, 16, 16)    1040                                         \n",
      "____________________________________________________________________________________________________\n",
      "concatenate_23 (Concatenate)     (None, 16, 16, 64)    0                                            \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_23 (BatchNor (None, 16, 16, 64)    256                                          \n",
      "____________________________________________________________________________________________________\n",
      "dropout_23 (Dropout)             (None, 16, 16, 64)    0                                            \n",
      "____________________________________________________________________________________________________\n",
      "average_pooling2d_46 (AveragePoo (None, 8, 8, 64)      0                                            \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_165 (Conv2D)              (None, 8, 8, 16)      1040                                         \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_163 (Conv2D)              (None, 8, 8, 16)      1040                                         \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_166 (Conv2D)              (None, 8, 8, 16)      2320                                         \n",
      "____________________________________________________________________________________________________\n",
      "average_pooling2d_47 (AveragePoo (None, 8, 8, 64)      0                                            \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_162 (Conv2D)              (None, 8, 8, 16)      1040                                         \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_164 (Conv2D)              (None, 8, 8, 16)      2320                                         \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_167 (Conv2D)              (None, 8, 8, 16)      2320                                         \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_168 (Conv2D)              (None, 8, 8, 16)      1040                                         \n",
      "____________________________________________________________________________________________________\n",
      "concatenate_24 (Concatenate)     (None, 8, 8, 64)      0                                            \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_24 (BatchNor (None, 8, 8, 64)      256                                          \n",
      "____________________________________________________________________________________________________\n",
      "dropout_24 (Dropout)             (None, 8, 8, 64)      0                                            \n",
      "____________________________________________________________________________________________________\n",
      "average_pooling2d_48 (AveragePoo (None, 4, 4, 64)      0                                            \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_172 (Conv2D)              (None, 4, 4, 16)      1040                                         \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_170 (Conv2D)              (None, 4, 4, 16)      1040                                         \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_173 (Conv2D)              (None, 4, 4, 16)      2320                                         \n",
      "____________________________________________________________________________________________________\n",
      "average_pooling2d_49 (AveragePoo (None, 4, 4, 64)      0                                            \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_169 (Conv2D)              (None, 4, 4, 16)      1040                                         \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_171 (Conv2D)              (None, 4, 4, 16)      2320                                         \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_174 (Conv2D)              (None, 4, 4, 16)      2320                                         \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_175 (Conv2D)              (None, 4, 4, 16)      1040                                         \n",
      "____________________________________________________________________________________________________\n",
      "concatenate_25 (Concatenate)     (None, 4, 4, 64)      0                                            \n",
      "____________________________________________________________________________________________________\n",
      "batch_normalization_25 (BatchNor (None, 4, 4, 64)      256                                          \n",
      "____________________________________________________________________________________________________\n",
      "dropout_25 (Dropout)             (None, 4, 4, 64)      0                                            \n",
      "____________________________________________________________________________________________________\n",
      "average_pooling2d_50 (AveragePoo (None, 1, 1, 64)      0                                            \n",
      "____________________________________________________________________________________________________\n",
      "flatten_5 (Flatten)              (None, 64)            0                                            \n",
      "____________________________________________________________________________________________________\n",
      "dense_8 (Dense)                  (None, 15)            975                                          \n",
      "====================================================================================================\n",
      "Total params: 53,823\n",
      "Trainable params: 53,183\n",
      "Non-trainable params: 640\n",
      "____________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "input = layers.Input(shape=(SIZE, SIZE, 1))\n",
    "x = input\n",
    "y_dim = len(categories)\n",
    "\n",
    "l2 = keras.regularizers.l2(0.001)\n",
    "\n",
    "def conv(x, chans, ksize=3):\n",
    "    return layers.Conv2D(chans, (ksize, ksize), activation='relu', padding='SAME', kernel_regularizer=l2)(x)\n",
    "\n",
    "def conv_chain(x, chans, ksizes, average_pool_before=1):\n",
    "    if average_pool_before > 1:\n",
    "        x = layers.AveragePooling2D((average_pool_before, average_pool_before), strides=(1,1), padding='SAME')(x)\n",
    "    for ksize in ksizes:\n",
    "        x = conv(x, chans, ksize)\n",
    "    return x\n",
    "\n",
    "def layer(x, channels):\n",
    "    c = int(channels/4)\n",
    "    branch1 = conv_chain(x, c, [1])\n",
    "    branch2 = conv_chain(x, c, [1, 3])\n",
    "    branch3 = conv_chain(x, c, [1, 3, 3])\n",
    "    branch4 = conv_chain(x, c, [1], average_pool_before=3)\n",
    "    \n",
    "    x = layers.Concatenate()([branch1, branch2, branch3, branch4])\n",
    "    x = layers.BatchNormalization()(x)\n",
    "    x = layers.Dropout(0.5)(x)\n",
    "    return x\n",
    "\n",
    "for chans, pooling in [(64, 2), (64, 2), (64, 2), (64, 2), (64, 4)]:\n",
    "    x = layer(x, chans)\n",
    "    x = layers.AveragePooling2D((pooling, pooling), padding='SAME')(x)\n",
    "    print(x.shape)\n",
    "\n",
    "x = layers.Flatten()(x)\n",
    "# x = layers.Dense(y_dim * 4, activation='relu', kernel_regularizer=l2)(x)\n",
    "x = layers.Dense(y_dim, activation='softmax', kernel_regularizer=l2)(x)\n",
    "print(x.shape)\n",
    "model = models.Model(inputs=[input], outputs=[x])\n",
    "\n",
    "model.compile(loss=keras.losses.sparse_categorical_crossentropy,\n",
    "              optimizer=keras.optimizers.Adam(lr=1e-3),\n",
    "              metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1, loss: 1.4811153629916398, accuracy: 0.6422110553362861\n",
      "epoch: 2, loss: 1.6769794817146544, accuracy: 0.5742043551687819\n",
      "epoch: 3, loss: 1.748390801428151, accuracy: 0.5510887772993027\n",
      "epoch: 4, loss: 1.4742309900584134, accuracy: 0.6291457287230883\n",
      "epoch: 5, loss: 1.558857083759915, accuracy: 0.5996649916846948\n",
      "epoch: 6, loss: 1.4289289396212528, accuracy: 0.6512562814869074\n",
      "epoch: 7, loss: 1.4578919251759848, accuracy: 0.6378559464385961\n",
      "epoch: 8, loss: 1.3749191202310662, accuracy: 0.6683417086225858\n",
      "epoch: 9, loss: 1.4720640347031893, accuracy: 0.6321608040999728\n",
      "epoch: 10, loss: 1.5814037129707272, accuracy: 0.6067001675640917\n",
      "epoch: 11, loss: 1.7732292723216403, accuracy: 0.5400335008974251\n",
      "epoch: 12, loss: 1.3942365555307974, accuracy: 0.649246231175747\n",
      "epoch: 13, loss: 1.3118601918819561, accuracy: 0.6830820771118304\n",
      "epoch: 14, loss: 1.6403914937222224, accuracy: 0.5886097153027853\n",
      "epoch: 15, loss: 1.3551533885137919, accuracy: 0.6753768844620467\n",
      "epoch: 16, loss: 1.8024728265439645, accuracy: 0.5614740368534172\n",
      "epoch: 17, loss: 1.4637751175530591, accuracy: 0.6331658291557126\n",
      "epoch: 18, loss: 1.3762896210304656, accuracy: 0.6579564489511589\n",
      "epoch: 19, loss: 1.3121783416674564, accuracy: 0.6750418760868373\n",
      "epoch: 20, loss: 1.268197040901312, accuracy: 0.6857621440935374\n",
      "epoch: 21, loss: 1.5924614121566465, accuracy: 0.5909547739092829\n",
      "epoch: 22, loss: 1.297071736262272, accuracy: 0.6877721943447938\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-34-01b609fd9735>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m                        \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m                        \u001b[0mworkers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m                        epochs=1)\n\u001b[0m\u001b[1;32m     10\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0mscore\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtestx\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mmean_img\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtesty\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/keras/legacy/interfaces.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     86\u001b[0m                 warnings.warn('Update your `' + object_name +\n\u001b[1;32m     87\u001b[0m                               '` call to the Keras 2 API: ' + signature, stacklevel=2)\n\u001b[0;32m---> 88\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     89\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_legacy_support_signature\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minspect\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetargspec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     90\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_q_size, workers, pickle_safe, initial_epoch)\u001b[0m\n\u001b[1;32m   1875\u001b[0m                     outs = self.train_on_batch(x, y,\n\u001b[1;32m   1876\u001b[0m                                                \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1877\u001b[0;31m                                                class_weight=class_weight)\n\u001b[0m\u001b[1;32m   1878\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1879\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[0;34m(self, x, y, sample_weight, class_weight)\u001b[0m\n\u001b[1;32m   1619\u001b[0m             \u001b[0mins\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0msample_weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1620\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_train_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1621\u001b[0;31m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1622\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1623\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2101\u001b[0m         \u001b[0msession\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2102\u001b[0m         updated = session.run(self.outputs + [self.updates_op],\n\u001b[0;32m-> 2103\u001b[0;31m                               feed_dict=feed_dict)\n\u001b[0m\u001b[1;32m   2104\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2105\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    776\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    777\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 778\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    779\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    780\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    980\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    981\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 982\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    983\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    984\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1030\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1031\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1032\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m   1033\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1034\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1037\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1038\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1039\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1040\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1041\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1019\u001b[0m         return tf_session.TF_Run(session, options,\n\u001b[1;32m   1020\u001b[0m                                  \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1021\u001b[0;31m                                  status, run_metadata)\n\u001b[0m\u001b[1;32m   1022\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1023\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# at epoch 53: loss: 1.3118601918819561, accuracy: 0.6830820771118304\n",
    "\n",
    "batch_size = 32\n",
    "\n",
    "for i in range(40):\n",
    "    model.optimizer.lr.assign(1e-4)\n",
    "    model.fit_generator(image_gen.flow(trainx - mean_img, trainy, batch_size=batch_size),\n",
    "                       steps_per_epoch=len(trainx)/batch_size,\n",
    "                       verbose=0,\n",
    "                       workers=2,\n",
    "                       epochs=1)\n",
    "    if i % 1 == 0:\n",
    "        score = model.evaluate(testx - mean_img, testy, verbose=0)\n",
    "        print('epoch: {}, loss: {}, accuracy: {}'.format(i+1, score[0], score[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "score = model.evaluate(testx, testy, verbose=0)\n",
    "print 'accuracy: {}'.format(score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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