NFM模型实现是不是少了pooling层

[wangych6]

def call(self, inputs):

Inputs layer

    sparse_inputs = inputs
    # Embedding layer
    sparse_embed = [self.embed_layers['embed_{}'.format(i)](sparse_inputs[:, i])
             for i in range(sparse_inputs.shape[1])]
    sparse_embed = tf.transpose(tf.convert_to_tensor(sparse_embed), [1, 0, 2])  # (None, filed_num, embed_dim)
    # Bi-Interaction Layer
    sparse_embed = 0.5 * (tf.pow(tf.reduce_sum(sparse_embed, axis=1), 2) -
                   tf.reduce_sum(tf.pow(sparse_embed, 2), axis=1))  # (None, embed_dim)
    # Concat
    # 这里是不是和原论文有出入，少了pooling层
    #x = tf.concat([dense_inputs, sparse_embed], axis=-1)
    # BatchNormalization
    x = sparse_embed
    x = self.bn(x, training=self.bn_use)
    # Hidden Layers
    x = self.dnn_network(x)
    outputs = tf.nn.sigmoid(self.dense(x))
    return outputs

[KeShengchuan]

没有少哇，pooling层转化后是这一行：

Bi-Interaction Layer

sparse_embed = 0.5 * (tf.pow(tf.reduce_sum(sparse_embed, axis=1), 2) - tf.reduce_sum(tf.pow(sparse_embed, 2), axis=1)) # (None, embed_dim)