tensorflow tf.nn.raw_rnn()使用总结

最新推荐文章于 2021-04-25 10:51:29 发布

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最新推荐文章于 2021-04-25 10:51:29 发布 · 1.7k 阅读

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#tensorflow #raw_rnn #rnn

本文详细介绍了TensorFlow中的tf.nn.raw_rnn()函数，它提供了一个更底层的接口来控制RNN的运行。通过示例，解释了如何使用loop_fn参数来控制输入输出，并讨论了在不同时间步的初始化和数据转换。强调了raw_rnn相比dynamic_rnn的灵活性，并给出了实际的使用场景。

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tensorflow tf.nn.raw_rnn()使用总结

tensorflow tf.nn.raw_rnn()使用总结
- api阅读
  - raw_rnn()概要
  - 使用demo
- 　参考阅读

api阅读

raw_rnn()概要

def raw_rnn(cell, loop_fn,
            parallel_iterations=None, swap_memory=False, scope=None):
  """Creates an `RNN` specified by RNNCell `cell` and loop function `loop_fn`.

  **NOTE: This method is still in testing, and the API may change.**

  This function is a more primitive version of `dynamic_rnn` that provides
  more direct access to the inputs each iteration.  It also provides more
  control over when to start and finish reading the sequence, and
  what to emit for the output.

  For example, it can be used to implement the dynamic decoder of a seq2seq
  model.

  Instead of working with `Tensor` objects, most operations work with
  `TensorArray` objects directly.

  The operation of `raw_rnn`, in pseudo-code, is basically the following:

  ```python
  time = tf.constant(0, dtype=tf.int32)
  (finished, next_input, initial_state, emit_structure, loop_state) = loop_fn(
      time=time, cell_output=None, cell_state=None, loop_state=None)
  emit_ta = TensorArray(dynamic_size=True, dtype=initial_state.dtype)
  state = initial_state
  while not all(finished):
    (output, cell_state) = cell(next_input, state)
    (next_finish