recurrent_layers.py 文件源码

def map(self, is_train, x, mask=None):
        x = tf.transpose(x, [1, 0, 2])

        if self.bidirectional:
            with tf.variable_scope("forward"):
                fw = self._apply_transposed(is_train, x)[0]
            with tf.variable_scope("backward"):
                bw = self._apply_transposed(is_train, tf.reverse_sequence(x, mask, 0, 1))[0]
                bw = tf.reverse_sequence(bw, mask, 0, 1)
            out = tf.concat([fw, bw], axis=2)
        else:
            out = self._apply_transposed(is_train, x)[0]
        out = tf.transpose(out, [1, 0, 2])
        if mask is not None:
            out *= tf.expand_dims(tf.cast(tf.sequence_mask(mask, tf.shape(out)[1]), tf.float32), 2)
        return out