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
This function is functionally identical to the function
rnn above, but
performs fully dynamic unrolling of
rnn, the input
inputs is not a Python list of
Tensors, one for
each frame. Instead,
inputs may be a single
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
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().
cell: An instance of RNNCell.
inputs: The RNN inputs.
time_major == False(default), this must be a
[batch_size, max_time, ...], or a nested tuple of such elements.
time_major == True, this must be a
[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
cellat each time-step will replicate the structure of these tuples, except for the time dimension (from which the time is taken).
The input to
cellat each time step will be a
Tensoror (possibly nested) tuple of Tensors each with dimensions
sequence_length: (optional) An int32/int64 vector sized
initial_state: (optional) An initial state for the RNN. If
cell.state_sizeis an integer, this must be a
Tensorof appropriate type and shape
[batch_size, cell.state_size]. If
cell.state_sizeis 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
outputsTensors. If true, these
Tensorsmust be shaped
[max_time, batch_size, depth]. If false, these
Tensorsmust be shaped
[batch_size, max_time, depth]. Using
time_major = Trueis 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".
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.
cellis not an instance of RNNCell.
ValueError: If inputs is None or an empty list.