tf.compat.v1.nn.dynamic_rnn

Creates a recurrent neural network specified by RNNCell cell. (deprecated)

tf.compat.v1.nn.dynamic_rnn(
    cell,
    inputs,
    sequence_length=None,
    initial_state=None,
    dtype=None,
    parallel_iterations=None,
    swap_memory=False,
    time_major=False,
    scope=None
)

Migrate to TF2

tf.compat.v1.nn.dynamic_rnn is not compatible with eager execution and tf.function. Please use tf.keras.layers.RNN instead for TF2 migration. Take LSTM as an example, you can instantiate a tf.keras.layers.RNN layer with tf.keras.layers.LSTMCell, or directly via tf.keras.layers.LSTM. Once the keras layer is created, you can get the output and states by calling the layer with input and states. Please refer to this guide for more details about Keras RNN. You can also find more details about the difference and comparison between Keras RNN and TF compat v1 rnn in this document

Structural Mapping to Native TF2

Before:

# create 2 LSTMCells
rnn_layers = [tf.compat.v1.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.compat.v1.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.nn.rnn_cell.LSTMStateTuple for each cell
outputs, state = tf.compat.v1.nn.dynamic_rnn(cell=multi_rnn_cell,
                                             inputs=data,
                                             dtype=tf.float32)

After:

# RNN layer can take a list of cells, which will then stack them together.
# By default, keras RNN will only return the last timestep output and will not
# return states. If you need whole time sequence output as well as the states,
# you can set `return_sequences` and `return_state` to True.
rnn_layer = tf.keras.layers.RNN([tf.keras.layers.LSTMCell(128),
                                 tf.keras.layers.LSTMCell(256)],
                                return_sequences=True,
                                return_state=True)
outputs, output_states = rnn_layer(inputs, states)

How to Map Arguments

TF1 Arg Name TF2 Arg Name Note
cell cell In the RNN layer constructor
inputs inputs In the RNN layer __call__
sequence_length Not used Adding masking layer before RNN : to achieve the same result.
initial_state initial_state In the RNN layer __call__
dtype dtype In the RNN layer constructor
parallel_iterations Not supported
swap_memory Not supported
time_major time_major In the RNN layer constructor
scope Not supported

Description

Performs fully dynamic unrolling of inputs.

Example:

# create a BasicRNNCell
rnn_cell = tf.compat.v1.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.compat.v1.nn.dynamic_rnn(rnn_cell, input_data,
                                   initial_state=initial_state,
                                   dtype=tf.float32)

# create 2 LSTMCells
rnn_layers = [tf.compat.v1.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.compat.v1.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.nn.rnn_cell.LSTMStateTuple for each cell
outputs, state = tf.compat.v1.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 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]. Used to copy-through state and zero-out outputs when past a batch element's sequence length. This parameter enables users to extract the last valid state and properly padded outputs, so it is provided for correctness.
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".

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. If cells are LSTMCells state will be a tuple containing a LSTMStateTuple for each cell.

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