tf.nn.dynamic_rnn(cell, inputs, sequence_length=None, initial_state=None, dtype=None, parallel_iterations=None, swap_memory=False, time_major=False, scope=None)

tf.nn.dynamic_rnn(cell, inputs, sequence_length=None, initial_state=None, dtype=None, parallel_iterations=None, swap_memory=False, time_major=False, scope=None)

See the guide: Neural Network > Recurrent Neural Networks

Creates a recurrent neural network specified by RNNCell cell.

This function is functionally identical to the function rnn above, but performs fully dynamic unrolling of inputs.

Unlike rnn, the input inputs is not a Python list of Tensors, one for each frame. Instead, inputs may be a single Tensor where the maximum time is either the first or second dimension (see the parameter time_major). Alternatively, it may be a (possibly nested) tuple of Tensors, each of them having matching batch and time dimensions. The corresponding output is either a single Tensor having the same number of time steps and batch size, or a (possibly nested) tuple of such tensors, matching the nested structure of cell.output_size.

The parameter sequence_length is optional and is used to copy-through state and zero-out outputs when past a batch element's sequence length. So it's more for correctness than performance, unlike in rnn().

Args:

  • cell: An instance of RNNCell.
  • 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.

    This may also be a (possibly nested) tuple of Tensors satisfying this property. The first two dimensions must match across all the inputs, but otherwise the ranks and other shape components may differ. In this case, input to cell at each time-step will replicate the structure of these tuples, except for the time dimension (from which the time is taken).

    The input to cell at each time step will be a Tensor or (possibly nested) tuple of Tensors each with dimensions [batch_size, ...].

  • sequence_length: (optional) An int32/int64 vector sized [batch_size].

  • initial_state: (optional) An initial state for the RNN. If cell.state_size is an integer, this must be a Tensor of appropriate type and shape [batch_size, cell.state_size]. If cell.state_size is a tuple, this should be a tuple of tensors having shapes [batch_size, s] for s in cell.state_size.

  • dtype: (optional) The data type for the initial state and expected output. Required if initial_state is not provided or RNN state has 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.

  • scope: VariableScope for the created subgraph; defaults to "rnn".

Returns:

A pair (outputs, state) where:

outputs: The RNN output `Tensor`.

  If time_major == False (default), this will be a `Tensor` shaped:
    `[batch_size, max_time, cell.output_size]`.

  If time_major == True, this will be a `Tensor` shaped:
    `[max_time, batch_size, cell.output_size]`.

  Note, if `cell.output_size` is a (possibly nested) tuple of integers
  or `TensorShape` objects, then `outputs` will be a tuple having the
  same structure as `cell.output_size`, containing Tensors having shapes
  corresponding to the shape data in `cell.output_size`.

state: The final state.  If `cell.state_size` is an int, this
  will be shaped `[batch_size, cell.state_size]`.  If it is a
  `TensorShape`, this will be shaped `[batch_size] + cell.state_size`.
  If it is a (possibly nested) tuple of ints or `TensorShape`, this will
  be a tuple having the corresponding shapes.

Raises:

  • TypeError: If cell is not an instance of RNNCell.
  • ValueError: If inputs is None or an empty list.

Defined in tensorflow/python/ops/rnn.py.