def main():
"""build, train, and test an implementation of the alexnet CNN model in keras.
This model is trained and tested on the CIFAR-100 dataset
"""
# parse arguments
args = parse_arguments()
save_dir = os.path.join(os.getcwd(), args['d'])
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
model_path = os.path.join(save_dir, args['n'])
epochs = int(args['e'])
predict_flag = args['p']
show_flag = args['f']
cifar10_label = [
'airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog',
'horse', 'ship', 'truck'
]
# build and train the model
if predict_flag == True:
if show_flag == True:
print(filter)
alexnet = AlexNet(10)
model = alexnet.build_model(class_count=10)
model.load_weights(model_path)
print("load complete")
(x_train, y_train), (x_test, y_test) = load_dataset()
batch_generator = generator_ramdom_one(10, 112, 112, x_test,
y_test)
test_x, test_y = next(batch_generator)
layer = "conv2d_2"
show_filter(model, test_x, layer)
else:
alexnet = AlexNet(10)
model = alexnet.build_model(class_count=10)
model.load_weights(model_path)
print("load complete")
(x_train, y_train), (x_test, y_test) = load_dataset()
batch_generator = generator_ramdom_one(10, 112, 112, x_test,
y_test)
test_x, test_y = next(batch_generator)
print(cifar10_label[np.argmax(test_y)])
result = model.predict(test_x)
print(cifar10_label[np.argmax(result)])
test_img_x = test_x[0, ::-1].copy()
plt.imshow(test_img_x / 255, interpolation='nearest')
plt.title(cifar10_label[np.argmax(test_y)] + "/" +
cifar10_label[np.argmax(result)])
plt.show()
else:
alexnet = AlexNet(10)
model = alexnet.build_model(class_count=10)
print(model.summary())
train_model(model, class_count=10, epochs=epochs)
# test the model
evaluate(model, class_count=10)
# save the trained model
model.save(model_path)
print("Alexnet model saved to: %s" % model_path)
from alexnet import AlexNet alexnet = AlexNet() alexnet.build_model(alexnet.weights, alexnet.biases)
