如何在张量流中使用变量批量大小的双向RNN

Question

似乎tensorflow不支持双向RNN的可变批量大小.在这个例子中,sequence_length它绑定到batch_size,这是一个Python整数:

  _seq_len = tf.fill([batch_size], tf.constant(n_steps, dtype=tf.int64))
  outputs, state1,state2 = rnn.bidirectional_rnn(rnn_fw_cell, rnn_bw_cell, input,
                                    dtype="float",
                                    sequence_length=_seq_len)

如何使用不同的批量大小进行培训和测试？

Answer 1

双向代码适用于可变批量大小.例如,看一下这个测试代码,它会创建一个tf.placeholder(..., shape=(None, input_size))(这None意味着批量大小可以变化).

您可以将代码段转换为使用可变批量大小并进行少量修改:

# Compute the batch size based on the shape of the (presumably fed-in) `input`
# tensor. (Assumes that `input = tf.placeholder(..., shape=[None, input_size])`.)
batch_size = tf.shape(input)[0]

_seq_len = tf.fill(tf.expand_dims(batch_size, 0),
                   tf.constant(n_steps, dtype=tf.int64))
outputs, state1, state2 = rnn.bidirectional_rnn(rnn_fw_cell, rnn_bw_cell, input,
                                                dtype=tf.float32,
                                                sequence_length=_seq_len)