nn.py 文件源码

def RNN(self, inputs):
    """"""

    input_size = inputs.get_shape().as_list()[-1]
    cell = self.recur_cell(self._config, input_size=input_size, moving_params=self.moving_params)
    lengths = tf.reshape(tf.to_int64(self.sequence_lengths), [-1])

    if self.moving_params is None:
      ff_keep_prob = self.ff_keep_prob
      recur_keep_prob = self.recur_keep_prob
    else:
      ff_keep_prob = 1
      recur_keep_prob = 1

    if self.recur_bidir:
      top_recur, fw_recur, bw_recur = rnn.dynamic_bidirectional_rnn(cell, cell, inputs,
                                                                    lengths,
                                                                    ff_keep_prob=ff_keep_prob,
                                                                    recur_keep_prob=recur_keep_prob,
                                                                    dtype=tf.float32)
      fw_cell, fw_out = tf.split(1, 2, fw_recur)
      bw_cell, bw_out = tf.split(1, 2, bw_recur)
      end_recur = tf.concat(1, [fw_out, bw_out])
      top_recur.set_shape([tf.Dimension(None), tf.Dimension(None), tf.Dimension(2*self.recur_size)])
    else:
      top_recur, end_recur = rnn.dynamic_rnn(cell, inputs,
                                             lengths,
                                             ff_keep_prob=ff_keep_prob,
                                             recur_keep_prob=recur_keep_prob,
                                             dtype=tf.float32)
      top_recur.set_shape([tf.Dimension(None), tf.Dimension(None), tf.Dimension(self.recur_size)])
    return top_recur, end_recur

  #=============================================================