ninesky110

> > concat之后再输出，在计算结果上，和论文中 sigmoid(y_FM+y_DNN) 单独计算再加和是一样的。 > > 我觉得first order乘以feature_bias是多余的。因为embedding的结果与deep、second order拼接最后接一个projection layer，只看feat_value-projection这一块就已经是等价LR 的形式（论文中的公式2），在前面乘以一个feature_bias又不加非线性激活函数完全没必要。 > > PS：在gayhub上讨论，是不是还是用英语更合适？ > > 我也有此疑问， > 按论文中的意思，y_FM= reduce_sum(first_order,1) + reduce_sum(second_order,1) > y_DNN = reduce_sum(y_deep,1)，这个和 > concat([first_order, second_order,...

同问 def call(self, inputs, mask=None, training=None, **kwargs): if self.supports_masking: if mask is None: raise ValueError( "When supports_masking=True,input must support masking") queries, keys = inputs key_masks = tf.expand_dims(mask[-1], axis=1) key_masks怎么得出来的？程序竟然还没报错

I have the same problem, have you solved it？


when I changed the "data_batch_size" or "max_data_per_step"，I got the error. 

me too !!! I used unsupervised method and it not work

Have you ever tried to adjust parameters or replace unsupervised tasks with supervised tasks?

I replace unsupervised tasks with supervised tasks and find that it worked

> The negative sampling is not used in the supervised case. @ninesky110 will you elaborate on how it did not work? The embeddings learned during unsupervised should be different as...