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
Performs fully dynamic unrolling of
# create a BasicRNNCell rnn_cell = tf.nn.rnn_cell.BasicRNNCell(hidden_size) # 'outputs' is a tensor of shape [batch_size, max_time, cell_state_size] # defining initial state initial_state = rnn_cell.zero_state(batch_size, dtype=tf.float32) # 'state' is a tensor of shape [batch_size, cell_state_size] outputs, state = tf.nn.dynamic_rnn(rnn_cell, input_data, initial_state=initial_state, dtype=tf.float32)
# create 2 LSTMCells rnn_layers = [tf.nn.rnn_cell.LSTMCell(size) for size in [128, 256]] # create a RNN cell composed sequentially of a number of RNNCells multi_rnn_cell = tf.nn.rnn_cell.MultiRNNCell(rnn_layers) # 'outputs' is a tensor of shape [batch_size, max_time, 256] # 'state' is a N-tuple where N is the number of LSTMCells containing a # tf.contrib.rnn.LSTMStateTuple for each cell outputs, state = tf.nn.dynamic_rnn(cell=multi_rnn_cell, inputs=data, dtype=tf.float32)
cell: An instance of RNNCell.
inputs: The RNN inputs. If
time_major == False(default), this must be a
[batch_size, max_time, ...], or a nested tuple of such elements. If
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
[batch_size]. 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.
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
If time_major == False (default), this will be a
[batch_size, max_time, cell.output_size].
If time_major == True, this will be a
[max_time, batch_size, cell.output_size].
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
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. If cells are
state will be a tuple containing a
LSTMStateTuple for each cell.
cellis not an instance of RNNCell.
ValueError: If inputs is None or an empty list.
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.