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()