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