def __init__(self, zip_file, ratio=4):
data_path = zip_file.split(".zip")[0]
self.train_path = os.path.join(data_path, "train")
self.test_path = os.path.join(data_path, "test")
if not os.path.exists(data_path):
f = zipfile.ZipFile(zip_file, "r")
f.extractall(data_path)
all_image = self.get_all_images(
os.path.join(data_path,
data_path.split("/")[-1]))
self.get_data_result(all_image, ratio,
Tools.new_dir(self.train_path),
Tools.new_dir(self.test_path))
else:
Tools.print_info("data is exists")
pass
output_param.format(args.name, args.epochs, args.batch_size,
args.type_number, args.image_size,
args.image_channel, args.zip_file, args.keep_prob))
# now_train_path, now_test_path = PreData.main(zip_file=args.zip_file)
# now_data = Data(batch_size=args.batch_size, type_number=args.type_number, image_size=args.image_size,
# image_channel=args.image_channel, train_path=now_train_path, test_path=now_test_path)
now_data = Cifar10Data(batch_size=args.batch_size,
type_number=args.type_number,
image_size=args.image_size,
image_channel=args.image_channel)
now_net = VGGNet(now_data.type_number, now_data.image_size,
now_data.image_channel, now_data.batch_size)
runner = Runner(data=now_data,
classifies=now_net,
learning_rate=args.learning_rate,
decay_steps=args.decay_steps)
runner.train(epochs=args.epochs,
save_model=Tools.new_dir("model/") + args.name + args.name +
".ckpt",
min_loss=1e-10,
print_loss=100,
test=1000,
save=1000,
keep_prob=args.keep_prob)
pass
