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Robert Coleman
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added batch normalization script & notebook
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‎cats_n_dogs_BN.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Image Classification of Dogs vs. Cats Using CNN Ensemble"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Imports & environment"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Using Theano backend.\n",
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"Using gpu device 0: GeForce GTX 980M (CNMeM is enabled with initial size: 90.0% of memory, cuDNN 5105)\n",
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"/home/robert/anaconda3/lib/python3.5/site-packages/theano/sandbox/cuda/__init__.py:600: UserWarning: Your cuDNN version is more recent than the one Theano officially supports. If you see any problems, try updating Theano or downgrading cuDNN to version 5.\n",
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" warnings.warn(warn)\n",
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"/home/robert/anaconda3/lib/python3.5/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
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" warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n",
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"/home/robert/anaconda3/lib/python3.5/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
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" warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
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]
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}
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],
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"source": [
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"import os\n",
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"import numpy as np\n",
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"\n",
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"from glob import glob\n",
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"from shutil import copyfile\n",
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"from vgg_bn import Vgg16BN\n",
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"from keras.callbacks import ModelCheckpoint\n",
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"\n",
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"ROOT_DIR = os.getcwd()\n",
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"DATA_HOME_DIR = ROOT_DIR + '/data'\n",
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Config & Hyperparameters"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# paths\n",
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"data_path = DATA_HOME_DIR + '/' \n",
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"train_path = data_path + '/train/'\n",
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"valid_path = data_path + '/valid/'\n",
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"test_path = DATA_HOME_DIR + '/test/'\n",
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"model_path = ROOT_DIR + '/models/'\n",
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"submission_path = ROOT_DIR + '/submissions/'\n",
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"\n",
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"# data\n",
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"img_width, img_height = 224, 224\n",
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"batch_size = 64\n",
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"nb_train_samples = 23000\n",
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"nb_valid_samples = 2000\n",
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"nb_test_samples = 12500\n",
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"classes = [\"cats\", \"dogs\"]\n",
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"n_classes = len(classes)\n",
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"\n",
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"# model\n",
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"nb_epoch = 10\n",
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"nb_aug = 5\n",
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"lr = 0.001"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Build the VGG model w/ Batch Normalization"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": false,
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"scrolled": true
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"____________________________________________________________________________________________________\n",
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"Layer (type) Output Shape Param # Connected to \n",
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"====================================================================================================\n",
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"lambda_1 (Lambda) (None, 3, 224, 224) 0 lambda_input_1[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"zeropadding2d_1 (ZeroPadding2D) (None, 3, 226, 226) 0 lambda_1[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"convolution2d_1 (Convolution2D) (None, 64, 224, 224) 0 zeropadding2d_1[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"zeropadding2d_2 (ZeroPadding2D) (None, 64, 226, 226) 0 convolution2d_1[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"convolution2d_2 (Convolution2D) (None, 64, 224, 224) 0 zeropadding2d_2[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"maxpooling2d_1 (MaxPooling2D) (None, 64, 112, 112) 0 convolution2d_2[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"zeropadding2d_3 (ZeroPadding2D) (None, 64, 114, 114) 0 maxpooling2d_1[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"convolution2d_3 (Convolution2D) (None, 128, 112, 112) 0 zeropadding2d_3[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"zeropadding2d_4 (ZeroPadding2D) (None, 128, 114, 114) 0 convolution2d_3[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"convolution2d_4 (Convolution2D) (None, 128, 112, 112) 0 zeropadding2d_4[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"maxpooling2d_2 (MaxPooling2D) (None, 128, 56, 56) 0 convolution2d_4[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"zeropadding2d_5 (ZeroPadding2D) (None, 128, 58, 58) 0 maxpooling2d_2[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"convolution2d_5 (Convolution2D) (None, 256, 56, 56) 0 zeropadding2d_5[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"zeropadding2d_6 (ZeroPadding2D) (None, 256, 58, 58) 0 convolution2d_5[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"convolution2d_6 (Convolution2D) (None, 256, 56, 56) 0 zeropadding2d_6[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"zeropadding2d_7 (ZeroPadding2D) (None, 256, 58, 58) 0 convolution2d_6[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"convolution2d_7 (Convolution2D) (None, 256, 56, 56) 0 zeropadding2d_7[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"maxpooling2d_3 (MaxPooling2D) (None, 256, 28, 28) 0 convolution2d_7[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"zeropadding2d_8 (ZeroPadding2D) (None, 256, 30, 30) 0 maxpooling2d_3[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"convolution2d_8 (Convolution2D) (None, 512, 28, 28) 0 zeropadding2d_8[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"zeropadding2d_9 (ZeroPadding2D) (None, 512, 30, 30) 0 convolution2d_8[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"convolution2d_9 (Convolution2D) (None, 512, 28, 28) 0 zeropadding2d_9[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"zeropadding2d_10 (ZeroPadding2D) (None, 512, 30, 30) 0 convolution2d_9[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"convolution2d_10 (Convolution2D) (None, 512, 28, 28) 0 zeropadding2d_10[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"maxpooling2d_4 (MaxPooling2D) (None, 512, 14, 14) 0 convolution2d_10[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"zeropadding2d_11 (ZeroPadding2D) (None, 512, 16, 16) 0 maxpooling2d_4[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"convolution2d_11 (Convolution2D) (None, 512, 14, 14) 0 zeropadding2d_11[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"zeropadding2d_12 (ZeroPadding2D) (None, 512, 16, 16) 0 convolution2d_11[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"convolution2d_12 (Convolution2D) (None, 512, 14, 14) 0 zeropadding2d_12[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"zeropadding2d_13 (ZeroPadding2D) (None, 512, 16, 16) 0 convolution2d_12[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"convolution2d_13 (Convolution2D) (None, 512, 14, 14) 0 zeropadding2d_13[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"maxpooling2d_5 (MaxPooling2D) (None, 512, 7, 7) 0 convolution2d_13[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"flatten_1 (Flatten) (None, 25088) 0 maxpooling2d_5[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"dense_1 (Dense) (None, 4096) 0 flatten_1[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"batchnormalization_1 (BatchNormal(None, 4096) 0 dense_1[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"dropout_1 (Dropout) (None, 4096) 0 batchnormalization_1[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"dense_2 (Dense) (None, 4096) 0 dropout_1[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"batchnormalization_2 (BatchNormal(None, 4096) 0 dense_2[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"dropout_2 (Dropout) (None, 4096) 0 batchnormalization_2[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"dense_4 (Dense) (None, 2) 8194 dropout_2[0][0] \n",
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"====================================================================================================\n",
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"Total params: 8194\n",
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"____________________________________________________________________________________________________\n"
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]
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}
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],
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"source": [
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"vgg = Vgg16BN(size=(img_width, img_height), n_classes=n_classes, batch_size=batch_size, lr=lr)\n",
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"model = vgg.model\n",
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"\n",
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"model.summary()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"info_string = \"{0}x{1}_{2}epoch_{3}aug_{4}lr_vgg16-bn\".format(img_width, img_height, nb_epoch, nb_aug, lr)\n",
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"ckpt_fn = model_path + '{val_loss:.2f}-loss_' + info_string + '.h5'\n",
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"\n",
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"ckpt = ModelCheckpoint(filepath=ckpt_fn,\n",
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" monitor='val_loss',\n",
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" save_best_only=True,\n",
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" save_weights_only=True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Train the Model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"vgg.fit(train_path, valid_path,\n",
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" nb_trn_samples=nb_train_samples,\n",
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" nb_val_samples=nb_valid_samples,\n",
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" nb_epoch=nb_epoch,\n",
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" callbacks=[ckpt],\n",
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" aug=nb_aug)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Predict on Test Data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Generating predictions for Augmentation... 0\n",
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"Found 12500 images belonging to 1 classes.\n",
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"Generating predictions for Augmentation... 1\n",
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"Found 12500 images belonging to 1 classes.\n",
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"Generating predictions for Augmentation... 2\n",
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"Found 12500 images belonging to 1 classes.\n",
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"Generating predictions for Augmentation... 3\n",
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"Found 12500 images belonging to 1 classes.\n",
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"Generating predictions for Augmentation... 4\n",
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"Found 12500 images belonging to 1 classes.\n",
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"Averaging Predictions Across Augmentations...\n"
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]
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}
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],
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"source": [
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"# generate predictions\n",
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"for aug in range(nb_aug):\n",
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" print(\"Generating predictions for Augmentation {0}...\",format(aug+1))\n",
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" if aug == 0:\n",
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" predictions, filenames = vgg.test(test_path, nb_test_samples, aug=nb_aug)\n",
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" else:\n",
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" aug_pred, filenames = vgg.test(test_path, nb_test_samples, aug=nb_aug)\n",
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" predictions += aug_pred\n",
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"\n",
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"print(\"Averaging Predictions Across Augmentations...\")\n",
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"predictions /= nb_aug"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# clip predictions\n",
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"c = 0.01\n",
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"preds = np.clip(predictions, c, 1-c)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Writing Predictions to CSV...\n",
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"0 / 12500\n",
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"2500 / 12500\n",
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"5000 / 12500\n",
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"7500 / 12500\n",
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"10000 / 12500\n",
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"Done.\n"
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]
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}
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],
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"source": [
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"sub_file = submission_path + info_string + '.csv'\n",
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"\n",
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"with open(sub_file, 'w') as f:\n",
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" print(\"Writing Predictions to CSV...\")\n",
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" f.write('id,label\\n')\n",
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" for i, image_name in enumerate(filenames):\n",
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" pred = ['%.6f' % p for p in preds[i, :]]\n",
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" if i % 2500 == 0:\n",
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" print(i, '/', nb_test_samples)\n",
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" f.write('%s,%s\\n' % (os.path.basename(image_name).replace('.jpg', ''), (pred[1])))\n",
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" print(\"Done.\")"
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]
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}
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],
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"metadata": {
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"anaconda-cloud": {},
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"kernelspec": {
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"display_name": "Python [conda root]",
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"language": "python",
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"name": "conda-root-py"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.5.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}

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