if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Train dcnn for cifar10')
parser.add_argument('model',
choices=['vgg', 'alexnet', 'resnet', 'caffe'],
default='alexnet')
parser.add_argument('data', default='cifar-10-batches-py')
parser.add_argument('--use_cpu', action='store_true')
args = parser.parse_args()
assert os.path.exists(args.data), \
'Pls download the cifar10 dataset via "download_data.py py"'
print('Loading data ..................')
train_x, train_y = load_train_data(args.data)
test_x, test_y = load_test_data(args.data)
if args.model == 'caffe':
train_x, test_x = normalize_for_alexnet(train_x, test_x)
net = caffe_net.create_net(args.use_cpu)
# for cifar10_full_train_test.prototxt
train((train_x, train_y, test_x, test_y),
net,
160,
alexnet_lr,
0.004,
use_cpu=args.use_cpu)
# for cifar10_quick_train_test.prototxt
# train((train_x, train_y, test_x, test_y), net, 18, caffe_lr, 0.004,
# use_cpu=args.use_cpu)
elif args.model == 'alexnet':
train_x, test_x = normalize_for_alexnet(train_x, test_x)
net = alexnet.create_net(args.use_cpu)
train((train_x, train_y, test_x, test_y),
net,
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Train dcnn for cifar10')
parser.add_argument('model', choices=['vgg', 'cnn', 'resnet', 'caffe'],
default='vgg')
parser.add_argument('data', default='cifar-10-batches-py')
parser.add_argument('--use_cpu', action='store_true')
args = parser.parse_args()
assert os.path.exists(args.data), \
'Pls download the cifar10 dataset via "download_data.py py"'
print('Loading data ..................')
train_x, train_y = load_train_data(args.data)
test_x, test_y = load_test_data(args.data)
if args.model == 'caffe':
train_x, test_x = normalize_for_alexnet(train_x, test_x)
net = caffe_net.create_net(args.use_cpu)
# for cifar10_full_train_test.prototxt
train((train_x, train_y, test_x, test_y), net, 160, alexnet_lr, 0.004,
use_cpu=args.use_cpu)
# for cifar10_quick_train_test.prototxt
# train((train_x, train_y, test_x, test_y), net, 18, caffe_lr, 0.004,
# use_cpu=args.use_cpu)
elif args.model == 'cnn':
train_x, test_x = normalize_for_alexnet(train_x, test_x)
net = cnn.create_net(args.use_cpu)
train((train_x, train_y, test_x, test_y), net, 2, alexnet_lr, 0.004,
use_cpu=args.use_cpu)
elif args.model == 'vgg':
train_x, test_x = normalize_for_vgg(train_x, test_x)
net = vgg.create_net(args.use_cpu)
train((train_x, train_y, test_x, test_y), net, 250, vgg_lr, 0.0005,