# 超参数 IMG_SIZE = (224, 224) N_CLASSES = [105, 14, 14] BATCH_SIZE = 64 EPOCHS = 20 train_file = "./data.train" dev_file = "./data.dev" test_file = "./data.test" # 数据准备 train_lst = utils.reading(train_file) dev_lst = utils.reading(dev_file) test_lst = utils.reading(test_file) n_batchs = len(train_lst) // BATCH_SIZE dev_data, dev_labels = utils.loading(dev_lst, n_classes=N_CLASSES) test_data, test_labels = utils.loading(test_lst, n_classes=N_CLASSES) train_generator = utils.data_generator(train_lst, batch_size=BATCH_SIZE, n_classes=N_CLASSES) # 模型 输入 inputs = Input(name='the_inputs', shape=(IMG_SIZE[0], IMG_SIZE[1], 3), dtype='float32') # 定义模型 model = Model(inputs=inputs, outputs=vgg_16(inputs, n_classes=N_CLASSES, scale=0.25)) model.summary() # opt = Adam(lr=0.01, beta_1=0.9, beta_2=0.999, decay=0.001, epsilon=10e-8)
parser.add_argument('--algo', action="store", dest="algo", default='PPO', type=str) parser.add_argument('--checkpoint-path', action="store", dest="checkpoint_path", type=str) parser.add_argument('--version', action='version', version='1.0') args = parser.parse_args() if not args.checkpoint_path and not args.assets: raise ValueError('-a cannot be null') from utils import loading loading() from t_1000 import T1000 env = T1000(algo=args.algo, assets=args.assets, currency=args.currency, granularity=args.granularity, datapoints=args.datapoints, checkpoint_path=args.checkpoint_path, initial_account_balance=args.initial_account_balance, exchange_commission=args.exchange_commission, exchange=args.exchange) if not args.checkpoint_path: # train env.train(timesteps=int(float(args.timesteps)), checkpoint_freq=args.checkpoint_freq,
def progressApk(self, func): self.thread = loading() self.thread.change_value.connect(func) self.thread.start()