Simple mod of keras example cifar10_cnn.py

cifar10_opt_test.py

#!/usr/bin/python
'''Train a simple deep CNN on the CIFAR10 small images dataset and compare different optimizers
GPU run command: THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python cifar10_opt_test.py

For the original script and Keras see https://github.com/fchollet/keras
'''

from __future__ import print_function
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.optimizers import SGD, SMORMS3, Adam
from keras.utils import np_utils
import time

batch_size = 32
nb_classes = 10
nb_epoch = 10

# input image dimensions
img_rows, img_cols = 32, 32
# the CIFAR10 images are RGB
img_channels = 3

# the data, shuffled and split between train and test sets
(X_train, y_train), (X_test, y_test) = cifar10.load_data()
print('X_train shape:', X_train.shape)
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, border_mode='same',
                        input_shape=(img_channels, img_rows, img_cols)))
model.add(Activation('relu'))
model.add(Convolution2D(32, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
# model.add(Dropout(0.25))

model.add(Convolution2D(64, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
# model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
# model.add(Dropout(0.5))

model.add(Dense(nb_classes))
model.add(Activation('softmax'))


X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255

saved_weights = model.get_weights()

optimizers = ['adam', 'smorms3', 'rmsprop']
for opt in optimizers:
    print("Training with %s"%opt)
    model.set_weights(saved_weights)

    model.compile(loss='categorical_crossentropy',
                  optimizer=opt,
                  metrics=['accuracy'])

    t_start = time.time()
    model.fit(X_train, Y_train,
              batch_size=batch_size,
              nb_epoch=nb_epoch,
              validation_data=(X_test, Y_test),
              shuffle=True)
    t_stop = time.time()

    score = model.evaluate(X_test, Y_test, verbose=0)
    print('Test score:', score[0])
    print('Test accuracy:', score[1])
    print('Training time: %fs'%(t_stop-t_start))