print('Reading CIFAR-10...')
X_train, Y_train, X_test, Y_test = read_cifar_10(image_width=INPUT_WIDTH,
image_height=INPUT_HEIGHT)
alexnet = AlexNet(input_width=INPUT_WIDTH,
input_height=INPUT_HEIGHT,
input_channels=INPUT_CHANNELS,
num_classes=NUM_CLASSES,
learning_rate=LEARNING_RATE,
momentum=MOMENTUM,
keep_prob=KEEP_PROB)
with tf.Session() as sess:
print('Evaluating dataset...')
print()
sess.run(tf.global_variables_initializer())
print('Loading model...')
print()
alexnet.restore(sess, './model')
print('Evaluating...')
train_accuracy = alexnet.evaluate(sess, X_train, Y_train, BATCH_SIZE)
test_accuracy = alexnet.evaluate(sess, X_test, Y_test, BATCH_SIZE)
print('Train Accuracy = {:.3f}'.format(train_accuracy))
print('Test Accuracy = {:.3f}'.format(test_accuracy))
print()
LEARNING_RATE = 0.001 # Original value: 0.01
MOMENTUM = 0.9
KEEP_PROB = 0.5
EPOCHS = 100
BATCH_SIZE = 128
print('Reading CIFAR-10...')
X_train, Y_train, X_test, Y_test = read_cifar_10(image_width=INPUT_WIDTH, image_height=INPUT_HEIGHT)
alexnet = AlexNet(input_width=INPUT_WIDTH, input_height=INPUT_HEIGHT, input_channels=INPUT_CHANNELS,
num_classes=NUM_CLASSES, learning_rate=LEARNING_RATE, momentum=MOMENTUM, keep_prob=KEEP_PROB)
with tf.Session() as tf_session:
print('Evaluating dataset...')
print()
tf_session.run(tf.global_variables_initializer())
print('Loading model...')
print()
alexnet.restore(tf_session, './model')
print('Evaluating...')
train_accuracy = alexnet.evaluate(tf_session, X_train, Y_train, BATCH_SIZE)
test_accuracy = alexnet.evaluate(tf_session, X_test, Y_test, BATCH_SIZE)
print('Train Accuracy = {:.3f}'.format(train_accuracy))
print('Test Accuracy = {:.3f}'.format(test_accuracy))
print()