# 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()