Function bidirectional_dynamic_rnn

tensorflow/python/ops/rnn.py:364–514

Creates a dynamic version of bidirectional recurrent neural network. Takes input and builds independent forward and backward RNNs. The input_size of forward and backward cell must match. The initial state for both directions is zero by default (but can be set optionally) and no intermediate s

(cell_fw,
                              cell_bw,
                              inputs,
                              sequence_length=None,
                              initial_state_fw=None,
                              initial_state_bw=None,
                              dtype=None,
                              parallel_iterations=None,
                              swap_memory=False,
                              time_major=False,
                              scope=None)

                        "this API")
@tf_export(v1=["nn.bidirectional_dynamic_rnn"])
def bidirectional_dynamic_rnn(cell_fw,
                              cell_bw,
                              inputs,
                              sequence_length=None,
                              initial_state_fw=None,
                              initial_state_bw=None,
                              dtype=None,
                              parallel_iterations=None,
                              swap_memory=False,
                              time_major=False,
                              scope=None):
  """Creates a dynamic version of bidirectional recurrent neural network.

  Takes input and builds independent forward and backward RNNs. The input_size
  of forward and backward cell must match. The initial state for both directions
  is zero by default (but can be set optionally) and no intermediate states are
  ever returned -- the network is fully unrolled for the given (passed in)
  length(s) of the sequence(s) or completely unrolled if length(s) is not
  given.

  Args:
    cell_fw: An instance of RNNCell, to be used for forward direction.
    cell_bw: An instance of RNNCell, to be used for backward direction.
    inputs: The RNN inputs.
      If time_major == False (default), this must be a tensor of shape:
        `[batch_size, max_time, ...]`, or a nested tuple of such elements.
      If time_major == True, this must be a tensor of shape: `[max_time,
        batch_size, ...]`, or a nested tuple of such elements.
    sequence_length: (optional) An int32/int64 vector, size `[batch_size]`,
      containing the actual lengths for each of the sequences in the batch. If
      not provided, all batch entries are assumed to be full sequences; and time
      reversal is applied from time `0` to `max_time` for each sequence.
    initial_state_fw: (optional) An initial state for the forward RNN. This must
      be a tensor of appropriate type and shape `[batch_size,
      cell_fw.state_size]`. If `cell_fw.state_size` is a tuple, this should be a
      tuple of tensors having shapes `[batch_size, s] for s in
      cell_fw.state_size`.
    initial_state_bw: (optional) Same as for `initial_state_fw`, but using the
      corresponding properties of `cell_bw`.
    dtype: (optional) The data type for the initial states and expected output.
      Required if initial_states are not provided or RNN states have a
      heterogeneous dtype.
    parallel_iterations: (Default: 32).  The number of iterations to run in
      parallel.  Those operations which do not have any temporal dependency and
      can be run in parallel, will be.  This parameter trades off time for
      space.  Values >> 1 use more memory but take less time, while smaller
      values use less memory but computations take longer.
    swap_memory: Transparently swap the tensors produced in forward inference
      but needed for back prop from GPU to CPU.  This allows training RNNs which
      would typically not fit on a single GPU, with very minimal (or no)
      performance penalty.
    time_major: The shape format of the `inputs` and `outputs` Tensors. If true,
      these `Tensors` must be shaped `[max_time, batch_size, depth]`. If false,
      these `Tensors` must be shaped `[batch_size, max_time, depth]`. Using
      `time_major = True` is a bit more efficient because it avoids transposes
      at the beginning and end of the RNN calculation.  However, most TensorFlow
      data is batch-major, so by default this function accepts input and emits
      output in batch-major form.