rnn.py 文件源码

def birnn(cell, inputs, sequence_length, initial_state_fw=None, initial_state_bw=None, ff_keep_prob=1., recur_keep_prob=1., dtype=tf.float32, scope=None):
  """"""

  # Forward direction
  with tf.variable_scope(scope or 'BiRNN_FW') as fw_scope:
    output_fw, output_state_fw = rnn(cell, inputs, sequence_length, initial_state_fw, ff_keep_prob, recur_keep_prob, dtype, scope=fw_scope)

  # Backward direction
  rev_inputs = tf.reverse_sequence(inputs, sequence_length, 1, 0)
  with tf.variable_scope(scope or 'BiRNN_BW') as bw_scope:
    output_bw, output_state_bw = rnn(cell, rev_inputs, sequence_length, initial_state_bw, ff_keep_prob, recur_keep_prob, dtype, scope=bw_scope)
  output_bw = tf.reverse_sequence(output_bw, sequence_length, 1, 0)
  # Concat each of the forward/backward outputs
  outputs = tf.concat([output_fw, output_bw], 2)

  return outputs, tf.tuple([output_state_fw, output_state_bw])

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