keras_sample_cifar10.py

#!/usr/bin/env python3
import time
import numpy as np
import matplotlib.pyplot as plt
from keras.utils import np_utils
from keras.datasets import cifar10
from keras.models import Sequential
from keras.optimizers import RMSprop
from keras.preprocessing.image import ImageDataGenerator
from keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense


def summary(history):
	# plot loss
	plt.subplot(211)
	plt.title('Cross Entropy Loss')
	plt.plot(history.history['loss'], color='blue', label='train')
	plt.plot(history.history['val_loss'], color='orange', label='test')
	plt.legend(loc=0)
	# plot accuracy
	plt.subplot(212)
	plt.title('Classification Accuracy')
	plt.plot(history.history['accuracy'], color='blue', label='train')
	plt.plot(history.history['val_accuracy'], color='orange', label='test')
	plt.legend(loc=0)
	# save plot to file
	plt.subplots_adjust(wspace=None, hspace=1.0)
	filename = time.strftime("%Y-%m-%d_%H-%M-%S", time.localtime())
	plt.savefig(filename + ".png")
	plt.close()

def mdlTrain(train_feature, train_label, test_feature, test_label):
	# model definition
	model = Sequential()

	model.add(Conv2D(filters=32, kernel_size=(3, 3), padding='same', activation='relu', input_shape=(32, 32, 3)))
	model.add(Conv2D(filters=32, kernel_size=(3, 3), padding='same', activation='relu'))
	model.add(MaxPooling2D())
	model.add(Dropout(0.25))

	model.add(Conv2D(filters=64, kernel_size=(3, 3), padding='same', activation='relu'))
	model.add(Conv2D(filters=64, kernel_size=(3, 3), padding='same', activation='relu'))
	model.add(MaxPooling2D())
	model.add(Dropout(0.25))

	model.add(Flatten())
	model.add(Dense(units=512, activation='relu'))
	model.add(Dropout(0.5))
	model.add(Dense(units=10, activation='softmax'))

	print(model.summary())

	# training definition
	batch_num = 64
	epoch_num = 100
	opt = RMSprop(lr=0.0001, decay=1e-6)
	datagen = ImageDataGenerator(rotation_range=15, width_shift_range=0.1, height_shift_range=0.1, horizontal_flip=True)
	datagen.fit(train_feature)
	model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])
	history = model.fit_generator(datagen.flow(train_feature, train_label, batch_size=batch_num), \
		steps_per_epoch=int(len(train_feature) / batch_num), epochs=epoch_num, validation_data=(test_feature, test_label), verbose=2)
	summary(history)

	# accuracy evaluation
	accuracy = model.evaluate(test_feature, test_label)
	print('\n[Accuracy] = ', accuracy[1])

	return model

# ---------------------------------------------------------------------------

# load cifar10 data
(train_feature, train_label), (test_feature, test_label) = cifar10.load_data()

# data preprocessing
# reshape
train_feature_vector = train_feature.reshape(len(train_feature), 32, 32, 3).astype('float32')
test_feature_vector = test_feature.reshape(len(test_feature), 32, 32, 3).astype('float32')

# feature normalization
#z-score
mean = np.mean(train_feature_vector, axis=(0, 1, 2, 3))
std = np.std(train_feature_vector, axis=(0, 1, 2, 3))
train_feature_normal = (train_feature_vector - mean) / (std + 1e-7)
test_feature_normal = (test_feature_vector - mean) / (std + 1e-7)

# one-hot encoding
train_label_onehot = np_utils.to_categorical(train_label)
test_label_onehot = np_utils.to_categorical(test_label)

# train model
model = mdlTrain(train_feature_normal, train_label_onehot, test_feature_normal, test_label_onehot)
accuracy = model.evaluate(test_feature_normal, test_label_onehot)
print('\n[Accuracy] = ', accuracy)

# save model
filename = time.strftime("%Y-%m-%d_%H-%M-%S", time.localtime())
model.save(filename + ".h5")
del model