tensorflow学习笔记(三十九):双向rnn

tensorflow 双向 rnn

如何在tensorflow中实现双向rnn

单层双向rnn

单层双向rnn (cs224d)

tensorflow中已经提供了双向rnn的接口,它就是tf.nn.bidirectional_dynamic_rnn(). 我们先来看一下这个接口怎么用.

 bidirectional_dynamic_rnn(
     cell_fw, #前向 rnn cell
     cell_bw, #反向 rnn cell
     inputs, #输入序列.
     sequence_length=None,# 序列长度
     initial_state_fw=None,#前向rnn_cell的初始状态
     initial_state_bw=None,#反向rnn_cell的初始状态
     dtype=None,#数据类型
     parallel_iterations=None,
     swap_memory=False,
     time_major=False,
     scope=None
 )

返回值:一个tuple(outputs, outputs_states), 其中,outputs是一个tuple(outputs_fw, outputs_bw). 关于outputs_fw和outputs_bw,如果time_major=True则它俩也是time_major的,vice versa. 如果想要concatenate的话,直接使用tf.concat(outputs, 2)即可.

如何使用: 
bidirectional_dynamic_rnn 在使用上和 dynamic_rn

n是非常相似的.

定义前向和反向rnn_cell
定义前向和反向rnn_cell的初始状态
准备好序列
调用bidirectional_dynamic_rnn
import tensorflow as tf
from tensorflow.contrib import rnn
cell_fw = rnn.LSTMCell(10)
cell_bw = rnn.LSTMCell(10)
initial_state_fw = cell_fw.zero_state(batch_size)
initial_state_bw = cell_bw.zero_state(batch_size)
seq = ...
seq_length = ...
(outputs, states)=tf.nn.bidirectional_dynamic_rnn(cell_fw, cell_bw, seq,
 seq_length, initial_state_fw,initial_state_bw)
out = tf.concat(outputs, 2)

# ....

多层双向rnn

多层双向rnn(cs224d)

单层双向rnn可以通过上述方法简单的实现,但是多层的双向rnn就不能使将MultiRNNCell传给bidirectional_dynamic_rnn了. 
想要知道为什么,我们需要看一下bidirectional_dynamic_rnn的源码片段.

 with vs.variable_scope(scope or "bidirectional_rnn"):
   # Forward direction
   with vs.variable_scope("fw") as fw_scope:
     output_fw, output_state_fw = dynamic_rnn(
         cell=cell_fw, inputs=inputs, sequence_length=sequence_length,
         initial_state=initial_state_fw, dtype=dtype,
         parallel_iterations=parallel_iterations, swap_memory=swap_memory,
         time_major=time_major, scope=fw_scope)

这只是一小部分代码,但足以看出,bi-rnn实际上是依靠dynamic-rnn实现的,如果我们使用MuitiRNNCell的话,那么每层之间不同方向之间交互就被忽略了.所以我们可以自己实现一个工具函数,通过多次调用bidirectional_dynamic_rnn来实现多层的双向RNN 这是我对多层双向RNN的一个精简版的实现,如有错误,欢迎指出

bidirectional_dynamic_rnn源码一探

上面我们已经看到了正向过程的代码实现,下面来看一下剩下的反向部分的实现. 
其实反向的过程就是做了两次reverse 
1. 第一次reverse:将输入序列进行reverse,然后送入dynamic_rnn做一次运算. 
2. 第二次reverse:将上面dynamic_rnn返回的outputs进行reverse,保证正向和反向输出的time是对上的.

 def _reverse(input_, seq_lengths, seq_dim, batch_dim):
   if seq_lengths is not None:
     return array_ops.reverse_sequence(
         input=input_, seq_lengths=seq_lengths,
         seq_dim=seq_dim, batch_dim=batch_dim)
   else:
     return array_ops.reverse(input_, axis=[seq_dim])

 with vs.variable_scope("bw") as bw_scope:
   inputs_reverse = _reverse(
       inputs, seq_lengths=sequence_length,
       seq_dim=time_dim, batch_dim=batch_dim)
   tmp, output_state_bw = dynamic_rnn(
       cell=cell_bw, inputs=inputs_reverse, sequence_length=sequence_length,
       initial_state=initial_state_bw, dtype=dtype,
       parallel_iterations=parallel_iterations, swap_memory=swap_memory,
       time_major=time_major, scope=bw_scope)

 output_bw = _reverse(
   tmp, seq_lengths=sequence_length,
   seq_dim=time_dim, batch_dim=batch_dim)

 outputs = (output_fw, output_bw)
 output_states = (output_state_fw, output_state_bw)

 return (outputs, output_states)

tf.reverse_sequence

对序列中某一部分进行反转

 reverse_sequence(
     input,#输入序列,将被reverse的序列
     seq_lengths,#1Dtensor,表示输入序列长度
     seq_axis=None,# 哪维代表序列
     batch_axis=None, #哪维代表 batch
     name=None,
     seq_dim=None,
     batch_dim=None
 )

官网上的例子给的非常好,这里就直接粘贴过来:

 # Given this:
 batch_dim = 0
 seq_dim = 1
 input.dims = (4, 8, ...)
 seq_lengths = [7, 2, 3, 5]

 # then slices of input are reversed on seq_dim, but only up to seq_lengths:
 output[0, 0:7, :, ...] = input[0, 7:0:-1, :, ...]
 output[1, 0:2, :, ...] = input[1, 2:0:-1, :, ...]
 output[2, 0:3, :, ...] = input[2, 3:0:-1, :, ...]
 output[3, 0:5, :, ...] = input[3, 5:0:-1, :, ...]

 # while entries past seq_lens are copied through:
 output[0, 7:, :, ...] = input[0, 7:, :, ...]
 output[1, 2:, :, ...] = input[1, 2:, :, ...]
 output[2, 3:, :, ...] = input[2, 3:, :, ...]
 output[3, 2:, :, ...] = input[3, 2:, :, ...]

例二:

 # Given this:
 batch_dim = 2
 seq_dim = 0
 input.dims = (8, ?, 4, ...)
 seq_lengths = [7, 2, 3, 5]

 # then slices of input are reversed on seq_dim, but only up to seq_lengths:
 output[0:7, :, 0, :, ...] = input[7:0:-1, :, 0, :, ...]
 output[0:2, :, 1, :, ...] = input[2:0:-1, :, 1, :, ...]
 output[0:3, :, 2, :, ...] = input[3:0:-1, :, 2, :, ...]
 output[0:5, :, 3, :, ...] = input[5:0:-1, :, 3, :, ...]

 # while entries past seq_lens are copied through:
 output[7:, :, 0, :, ...] = input[7:, :, 0, :, ...]
 output[2:, :, 1, :, ...] = input[2:, :, 1, :, ...]
 output[3:, :, 2, :, ...] = input[3:, :, 2, :, ...]
 output[2:, :, 3, :, ...] = input[2:, :, 3, :, ...]