mlp.py 文件源码

def train_model_for_id(self, product_id, data):
        product_model = MLPRegressor(hidden_layer_sizes=(5,),
                                     activation='relu',
                                     solver='adam',
                                     learning_rate='adaptive',
                                     max_iter=1000,
                                     learning_rate_init=0.01,
                                     alpha=0.01)
        product_model.fit(data[0], data[1])
        self.set_product_model_thread_safe(product_id, product_model)