tensorflow - tensorflow dynamic_rnn和rnn有什么区别？

Question

tf.nn中有几个与RNN相关的类。在我在网上找到的示例中，tf.nn.dynamic_rnn和tf.nn.rnn似乎可以互换使用，或者至少我无法弄清楚为什么使用其中一个代替另一个。有什么不同？

Answer 1

摘自Denny Britz的RNNs in Tensorflow, a Practical Guide and Undocumented Features，于2016年8月21日发布。

tf.nn.rnn creates an unrolled graph for a fixed RNN length. That means, if you call tf.nn.rnn with inputs having 200 time steps you are creating a static graph with 200 RNN steps. First, graph creation is slow. Second, you’re unable to pass in longer sequences (> 200) than you’ve originally specified.

tf.nn.dynamic_rnn solves this. It uses a tf.While loop to dynamically construct the graph when it is executed. That means graph creation is faster and you can feed batches of variable size.