cifar10_cnn_bn_100epochs.py

'''
This code was originally written by the Keras team. It has been modified by 
Sunita Nayak at BigVision LLC. to include Batch Normalization in the architecture.

Train a simple deep CNN on the CIFAR10 small images dataset using Batch Normalization. 
It gets to a maximum of 87% validation accuracy. It gets to 79% in only 7 epochs. Note 
that the keras team's maximum accuracy was 79% in 50 epochs. With Batch Normalization,
it exceeds 85% in just 21 epochs, and gets to 87% in 39 epochs.
'''

from __future__ import print_function
import keras
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten, BatchNormalization
from keras.layers import Conv2D, MaxPooling2D
import os
import pickle

from numpy.random import seed
seed(7)

batch_size = 32
num_classes = 10
epochs = 100
data_augmentation = True
num_predictions = 20
save_dir = os.path.join(os.getcwd(), 'saved_models_bn_100_s7')
model_name = 'keras_cifar10_trained_model.h5'

# The data, 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 = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

model = Sequential()
model.add(Conv2D(32, (3, 3), padding='same',
                 input_shape=x_train.shape[1:]))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Conv2D(32, (3, 3)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
#model.add(Dropout(0.25))

model.add(Conv2D(64, (3, 3), padding='same'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Conv2D(64, (3, 3)))
model.add(BatchNormalization())
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(BatchNormalization())
model.add(Activation('relu'))
#model.add(Dropout(0.5))
model.add(Dense(num_classes))
model.add(BatchNormalization())
model.add(Activation('softmax'))

# initiate RMSprop optimizer
opt = keras.optimizers.rmsprop(lr=0.001, decay=1e-6)

# Let's train the model using RMSprop
model.compile(loss='categorical_crossentropy',
              optimizer=opt,
              metrics=['accuracy'])

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255

if not data_augmentation:
    print('Not using data augmentation.')
    history = model.fit(x_train, y_train,
              batch_size=batch_size,
              epochs=epochs,
              validation_data=(x_test, y_test),
              shuffle=True)
else:
    print('Using real-time data augmentation.')
    # This will do preprocessing and realtime data augmentation:
    datagen = ImageDataGenerator(
                                 featurewise_center=False,  # set input mean to 0 over the dataset
                                 samplewise_center=False,  # set each sample mean to 0
                                 featurewise_std_normalization=False,  # divide inputs by std of the dataset
                                 samplewise_std_normalization=False,  # divide each input by its std
                                 zca_whitening=False,  # apply ZCA whitening
                                 zca_epsilon=1e-06,  # epsilon for ZCA whitening
                                 rotation_range=0,  # randomly rotate images in the range (degrees, 0 to 180)
                                 width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)
                                 height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)
                                 shear_range=0.,  # set range for random shear
                                 zoom_range=0.,  # set range for random zoom
                                 channel_shift_range=0.,  # set range for random channel shifts
                                 fill_mode='nearest',  # set mode for filling points outside the input boundaries
                                 cval=0.,  # value used for fill_mode = "constant"
                                 horizontal_flip=True,  # randomly flip images
                                 vertical_flip=False,  # randomly flip images
                                 rescale=None,  # set rescaling factor (applied before any other transformation)
                                 preprocessing_function=None,  # set function that will be applied on each input
                                 data_format=None,  # image data format, either "channels_first" or "channels_last"
                                 validation_split=0.0)  # fraction of images reserved for validation (strictly between 0 and 1)
        
    # Compute quantities required for feature-wise normalization
    # (std, mean, and principal components if ZCA whitening is applied).
    datagen.fit(x_train)
            
    # Fit the model on the batches generated by datagen.flow().
    history = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size), epochs=epochs, validation_data=(x_test, y_test), workers=4)

with open('./trainHistoryDictWithBn1', 'wb') as file_pi:
            pickle.dump(history.history, file_pi)

# Save model and weights
if not os.path.isdir(save_dir):
    os.makedirs(save_dir)
model_path = os.path.join(save_dir, model_name)
model.save(model_path)
print('Saved trained model at %s ' % model_path)

# Score trained model.
scores = model.evaluate(x_test, y_test, verbose=1)
print('Test loss:', scores[0])
print('Test accuracy:', scores[1])