# 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,
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,
use_cpu=args.use_cpu)
else:
train_x, test_x = normalize_for_alexnet(train_x, test_x)
net = resnet.create_net(args.use_cpu)
train((train_x, train_y, test_x, test_y),
net,
200,
resnet_lr,
1e-4,
use_cpu=args.use_cpu)
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,
use_cpu=args.use_cpu)
else:
train_x, test_x = normalize_for_alexnet(train_x, test_x)
net = resnet.create_net(args.use_cpu)
train((train_x, train_y, test_x, test_y), net, 200, resnet_lr, 1e-4,
use_cpu=args.use_cpu)
