rnn.py 文件源码

def fused_birnn(fused_rnn, inputs, sequence_length, initial_state=(None, None), dtype=None, scope=None,
                time_major=False, backward_device=None):
    with tf.variable_scope(scope or "BiRNN"):
        sequence_length = tf.cast(sequence_length, tf.int32)
        if not time_major:
            inputs = tf.transpose(inputs, [1, 0, 2])
        outputs_fw, state_fw = fused_rnn(inputs, sequence_length=sequence_length, initial_state=initial_state[0],
                                         dtype=dtype, scope="FW")

        if backward_device is not None:
            with tf.device(backward_device):
                outputs_bw, state_bw = fused_rnn_backward(fused_rnn, inputs, sequence_length, initial_state[1], dtype,
                                                          scope="BW")
        else:
            outputs_bw, state_bw = fused_rnn_backward(fused_rnn, inputs, sequence_length, initial_state[1], dtype,
                                                      scope="BW")

        if not time_major:
            outputs_fw = tf.transpose(outputs_fw, [1, 0, 2])
            outputs_bw = tf.transpose(outputs_bw, [1, 0, 2])
    return (outputs_fw, outputs_bw), (state_fw, state_bw)