Runnable Effnet training script (trains on CIFAR-10 dataset)

train.py

'''Train a EffNet CNN on the CIFAR10 small images dataset.

https://towardsdatascience.com/3-small-but-powerful-convolutional-networks-27ef86faa42d
'''

from __future__ import print_function
import keras
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Model
from keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D, Input, LeakyReLU, BatchNormalization, DepthwiseConv2D
from keras.activations import *
from keras.callbacks import *
import os

def get_post(x_in):
    x = LeakyReLU()(x_in)
    x = BatchNormalization()(x)
    return x

def get_block(x_in, ch_in, ch_out):
    x = Conv2D(ch_in,
               kernel_size=(1, 1),
               padding='same',
               use_bias=False)(x_in)
    x = get_post(x)

    x = DepthwiseConv2D(kernel_size=(1, 3), padding='same', use_bias=False)(x)
    x = get_post(x)
    x = MaxPooling2D(pool_size=(2, 1),
                     strides=(2, 1))(x) # Separable pooling

    x = DepthwiseConv2D(kernel_size=(3, 1),
                        padding='same',
                        use_bias=False)(x)
    x = get_post(x)

    x = Conv2D(ch_out,
               kernel_size=(2, 1),
               strides=(1, 2),
               padding='same',
               use_bias=False)(x)
    x = get_post(x)

    return x

def EffNet(input_shape, num_classes, include_top=True, weights=None):
    x_in = Input(shape=input_shape)

    x = get_block(x_in, 32, 64)
    x = get_block(x, 64, 128)
    x = get_block(x, 128, 256)

    if include_top:
        x = Flatten()(x)
        x = Dense(num_classes, activation='softmax')(x)

    model = Model(inputs=x_in, outputs=x)

    if weights is not None:
        model.load_weights(weights, by_name=True)

    return model

batch_size = 32
num_classes = 10
data_augmentation = False

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 = EffNet(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
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))