5a9e9622a7309794cc0cf511080fc2bd6b73a20a,deepctr/layers/sequence.py,SequenceMultiplyLayer,call,#SequenceMultiplyLayer#Any#Any#,756

Before Change

            mask = tf.transpose(mask, (0, 2, 1))

        embedding_size = key_input.shape[-1]
        mask = tf.tile(mask, [1, 1, embedding_size])
        key_input *= mask
        if len(tf.shape(value_input)) == 2:
            value_input = tf.expand_dims(value_input, axis=2)
            value_input = tf.tile(value_input, [1, 1, embedding_size])
        return tf.multiply(key_input,value_input)

After Change

            paddings = tf.ones_like(value_input) * (-2 ** 32 + 1)
        else:
            paddings = tf.zeros_like(value_input)
        value_input = tf.where(mask, value_input, paddings)

        if self.weight_normalization:
           value_input = softmax(value_input,dim=1)


        if len(value_input.shape) == 2:
            value_input = tf.expand_dims(value_input, axis=2)

Instances