Runnable Convolutional NN training script (trains on CIFAR-10 dataset)

train.py

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

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, Conv2D, MaxPooling2D
import os

def SimonNet(input_shape, num_classes):
    model = Sequential()
    model.add(Conv2D(32, (3, 3), padding='same', input_shape=input_shape))
    model.add(Activation('relu'))
    model.add(Conv2D(32, (3, 3)))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2),strides=1))
    model.add(Dropout(0.25))

    model.add(Conv2D(64, (3, 3), padding='same'))
    model.add(Activation('relu'))
    model.add(Conv2D(64, (3, 3)))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2),strides=1))
    model.add(Dropout(0.25))

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

    return model

batch_size = 32
num_classes = 10
data_augmentation = True

save_dir = os.path.join(os.getcwd(), 'saved_models')
if not os.path.isdir(save_dir):
    os.makedirs(save_dir)

# 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 = SimonNet(input_shape=x_train.shape[1:], num_classes=num_classes)

# initiate RMSprop optimizer
opt = keras.optimizers.rmsprop(lr=0.0001, 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.')

    iter = 1
    min_val_loss = 999
    max_val_acc = -1
    while True:
        print("iter = {}".format(iter))
        history = model.fit(x_train, y_train,
                            batch_size=batch_size,
                            epochs=1,
                            validation_data=(x_test, y_test),
                            shuffle=True)
        iter = iter + 1

        val_loss = history.history['val_loss'][0]
        val_acc = history.history['val_acc'][0]

        if val_loss < min_val_loss:
            min_val_loss = val_loss
            model_path = os.path.join(save_dir, "keras_cifar10_mvl{}_trained_model.h5".format(min_val_loss))
            model.save(model_path)

        if val_acc > max_val_acc:
            max_val_acc = val_acc
            model_path = os.path.join(save_dir, "keras_cifar10_mva{}_trained_model.h5".format(max_val_acc))
            model.save(model_path)

        print("val_loss = {} min_val_loss = {}".format(val_loss, min_val_loss))
        print("val_acc  = {} max_val_acc  = {}".format(val_acc, max_val_acc))

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=5,  # randomly rotate images in the range (degrees, 0 to 180)
        # randomly shift images horizontally (fraction of total width)
        width_shift_range=0.1,
        # randomly shift images vertically (fraction of total height)
        height_shift_range=0.1,
        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
        # set mode for filling points outside the input boundaries
        fill_mode='nearest',
        cval=0.,  # value used for fill_mode = "constant"
        horizontal_flip=True,  # randomly flip images
        vertical_flip=False,  # randomly flip images
        # set rescaling factor (applied before any other transformation)
        rescale=None,
        # set function that will be applied on each input
        preprocessing_function=None,
        # image data format, either "channels_first" or "channels_last"
        data_format=None,
        # fraction of images reserved for validation (strictly between 0 and 1)
        validation_split=0.0)

    # Compute quantities required for feature-wise normalization
    # (std, mean, and principal components if ZCA whitening is applied).
    datagen.fit(x_train)

    iter = 1
    min_val_loss = 999
    max_val_acc = -1
    while True:
        print("iter = {}".format(iter))
        history = model.fit_generator(datagen.flow(x_train, y_train,
                                                   batch_size=batch_size),
                                      epochs=1,
                                      validation_data=(x_test, y_test),
                                      workers=8,
                                      use_multiprocessing=True)
        iter = iter + 1

        val_loss = history.history['val_loss'][0]
        val_acc = history.history['val_acc'][0]

        if val_loss < min_val_loss:
            min_val_loss = val_loss
            model_path = os.path.join(save_dir, "keras_cifar10_mvl{}_trained_model.h5".format(min_val_loss))
            model.save(model_path)

        if val_acc > max_val_acc:
            max_val_acc = val_acc
            model_path = os.path.join(save_dir, "keras_cifar10_mva{}_trained_model.h5".format(max_val_acc))
            model.save(model_path)

        print("val_loss = {} min_val_loss = {}".format(val_loss, min_val_loss))
        print("val_acc  = {} max_val_acc  = {}".format(val_acc, max_val_acc))