def training_pipeline():
model_num = 1
best_model_num = 0
best_model = CNNModel(best_model_num)
best_model.save_weights()
for _ in range(NUM_TRAINS):
states, valids, improved_policy, win_loss = self_play(best_model_num)
contender = CNNModel(model_num)
contender.train_model(np.array(states, np.uint32),
np.array(valids, np.float32), np.array(win_loss),
np.array(improved_policy))
contender_wins = bot_fight(best_model.model_num, contender.model_num)
if contender_wins >= np.ceil(BOT_GAMES * 0.55):
best_model = contender
best_model_num = contender.model_num
logging.info(
f'best model: {best_model_num}, new model won {contender_wins}')
best_model.save_weights(best=True)
model_num += 1
def train_model(work_dir,
pkl_data_dir,
initial_epoch=0,
batch_size=16,
initial_lr=0.001,
model_name='cnn',
num_gpus=1):
model_path = None
if initial_epoch > 0:
model_paths = glob(work_dir + '%s_e%02d*.hd5' %
(model_name, initial_epoch))
if len(model_paths) != 1:
print('cannot found model save file!!!!')
assert (False)
else:
model_path = model_paths[0]
initial_lr = initial_lr * (config['train']['lr_decay']
**(initial_epoch - 1))
else:
initial_epoch = 0
dataset = DataSet(data_dir=config['data']['data_dir'],
train=True,
pkl_file_dir=pkl_data_dir)
cnn_model = CNNModel(model_path, True, initial_lr, num_gpus)
train_data_gen = dataset.get_train_data_gen(batch_size=batch_size)
train_data_num = dataset.get_train_data_num()
eval_data_gen = dataset.get_eval_data_gen(batch_size=batch_size)
eval_data_num = dataset.get_eval_data_num()
cnn_model.train_model(train_data_gen,
train_data_num,
eval_data_gen,
eval_data_num,
batch_size,
work_dir,
model_name=model_name,
initial_epoch=0)
