keras/examples/cifar10_cnn.py

import sys
sys.path.insert(0, '../')
sys.path.insert(0, '../../')

from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.optimizers import SGD, Adadelta, Adagrad
from keras.utils import np_utils, generic_utils

from theano import tensor as T

'''
    Train a (fairly simple) deep CNN on the CIFAR10 small images dataset.

    GPU run command:
        THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python examples/cifar10_cnn.py

    It gets to 0.65 test logloss in 25 epochs (still way underfitting at that point, though)
'''

batch_size = 32
nb_classes = 10
nb_epoch = 200
data_augmentation = True

# the data, shuffled and split between tran and test sets
(X_train, y_train), (X_test, y_test) = cifar10.load_data(test_split=0.1)
print X_train.shape[0], 'train samples'
print X_test.shape[0], 'test samples'

# convert class vectors to binary class matrices
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)

model = Sequential()

model.add(Convolution2D(32, 3, 3, 3, border_mode='full')) 
model.add(Activation('relu'))
model.add(Convolution2D(32, 32, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(poolsize=(2, 2)))
model.add(Dropout(0.25))

model.add(Convolution2D(64, 32, 3, 3, border_mode='full')) 
model.add(Activation('relu'))
model.add(Convolution2D(64, 64, 3, 3)) 
model.add(Activation('relu'))
model.add(MaxPooling2D(poolsize=(2, 2)))
model.add(Dropout(0.25))

model.add(Flatten(64*8*8))
model.add(Dense(64*8*8, 512, init='normal'))
model.add(Activation('relu'))
model.add(Dropout(0.5))

model.add(Dense(512, nb_classes, init='normal'))
model.add(Activation('softmax'))

# let's train the model using SGD + momentum (how original).
sgd = SGD(lr=0.01, decay=1e-7, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd)

if not data_augmentation:
    print "Not using data augmentation or normalization"

    X_train = X_train.astype("float32")
    X_test = X_train.astype("float32")
    X_train /= 255
    X_test /= 255
    print X_train[0]
    model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=10)
    score = model.evaluate(X_test, Y_test, batch_size=batch_size)
    print 'Test score:', score

else:
    print "Using real time data augmentation"

    # this will do preprocessing and realtime data augmentation
    datagen = ImageDataGenerator(
        featurewise_center=True, # set input mean to 0 over the dataset
        samplewise_center=False, # set each sample mean to 0
        featurewise_std_normalization=True, # divide inputs by std of the dataset
        samplewise_std_normalization=False, # divide each input by its std
        zca_whitening=False, # apply ZCA whitening
        rotation_range=20, # randomly rotate images in the range (degrees, 0 to 180)
        width_shift_range=0.2, # randomly shift images horizontally (fraction of total width)
        height_shift_range=0.2, # randomly shift images vertically (fraction of total height)
        horizontal_flip=True, # randomly flip images
        vertical_flip=False) # randomly flip images

    # compute quantities required for featurewise normalization 
    # (std, mean, and principal components if ZCA whitening is applied)
    datagen.fit(X_train)

    for e in range(nb_epoch):
        print '-'*40
        print 'Epoch', e
        print '-'*40
        print "Training..."
        # batch train with realtime data augmentation
        progbar = generic_utils.Progbar(X_train.shape[0])
        for X_batch, Y_batch in datagen.flow(X_train, Y_train):
            loss = model.train(X_batch, Y_batch)
            progbar.add(X_batch.shape[0], values=[("train loss", loss)])

        print "Testing..."
        # test time!
        progbar = generic_utils.Progbar(X_test.shape[0])
        for X_batch, Y_batch in datagen.flow(X_test, Y_test):
            score = model.test(X_batch, Y_batch)
            progbar.add(X_batch.shape[0], values=[("test loss", score)])