def generation_start(fileWriter, action_index): sess = tf.InteractiveSession() if MODEL_TYPE == "v3": nn = td_prediction_lstm_V3(FEATURE_NUMBER, H_SIZE, MAX_TRACE_LENGTH, learning_rate) elif MODEL_TYPE == "v4": nn = td_prediction_lstm_V4(FEATURE_NUMBER, H_SIZE, MAX_TRACE_LENGTH, learning_rate) else: raise ValueError("MODEL_TYPE error") generate(sess, nn, fileWriter, action_index)
def train_start(): sess = tf.InteractiveSession() if MODEL_TYPE == "v3": nn = td_prediction_lstm_V3(FEATURE_NUMBER, H_SIZE, MAX_TRACE_LENGTH, learning_rate) elif MODEL_TYPE == "v4": nn = td_prediction_lstm_V4(FEATURE_NUMBER, H_SIZE, MAX_TRACE_LENGTH, learning_rate) else: raise ValueError("MODEL_TYPE error") dict_object = eval_teams(sess, nn) save_obj(dict_object, "team_eval_dict")
def train_start(): if not os.path.isdir(LOG_DIR): os.mkdir(LOG_DIR) if not os.path.isdir(SAVED_NETWORK): os.mkdir(SAVED_NETWORK) sess = tf.InteractiveSession() if MODEL_TYPE == "v3": nn = td_prediction_lstm_V3(FEATURE_NUMBER, H_SIZE, MAX_TRACE_LENGTH, learning_rate) elif MODEL_TYPE == "v4": nn = td_prediction_lstm_V4(FEATURE_NUMBER, H_SIZE, MAX_TRACE_LENGTH, learning_rate) else: raise ValueError("MODEL_TYPE error") train_network(sess, nn)