mlp.py 文件源码

def train_universal_model(self, features: dict):
        logging.debug('Start training universal model')
        universal_model = MLPRegressor(hidden_layer_sizes=(5,),
                                       activation='relu',
                                       solver='adam',
                                       learning_rate='adaptive',
                                       max_iter=1000,
                                       learning_rate_init=0.01,
                                       alpha=0.01)
        start_time = int(time() * 1000)
        f_vector = []
        s_vector = []
        for product_id, vector_tuple in features.items():
            f_vector.extend(vector_tuple[0])
            s_vector.extend(vector_tuple[1])
        universal_model.fit(f_vector, s_vector)
        end_time = int(time() * 1000)
        logging.debug('Finished training universal model')
        logging.debug('Training took {} ms'.format(end_time - start_time))
        self.set_universal_model_thread_safe(universal_model)