rnnsearch.py 文件源码

def _encoder(cell_fw, cell_bw, inputs, sequence_length, dtype=None,
             scope=None):
    with tf.variable_scope(scope or "encoder",
                           values=[inputs, sequence_length]):
        inputs_fw = inputs
        inputs_bw = tf.reverse_sequence(inputs, sequence_length,
                                        batch_axis=0, seq_axis=1)

        with tf.variable_scope("forward"):
            output_fw, state_fw = _gru_encoder(cell_fw, inputs_fw,
                                               sequence_length, None,
                                               dtype=dtype)

        with tf.variable_scope("backward"):
            output_bw, state_bw = _gru_encoder(cell_bw, inputs_bw,
                                               sequence_length, None,
                                               dtype=dtype)
            output_bw = tf.reverse_sequence(output_bw, sequence_length,
                                            batch_axis=0, seq_axis=1)

        results = {
            "annotation": tf.concat([output_fw, output_bw], axis=2),
            "outputs": {
                "forward": output_fw,
                "backward": output_bw
            },
            "final_states": {
                "forward": state_fw,
                "backward": state_bw
            }
        }

        return results