tf.compat.v1.nn.bidirectional_dynamic_rnn

Creates a dynamic version of bidirectional recurrent neural network. (deprecated)

tf.compat.v1.nn.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
)

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.
scope	VariableScope for the created subgraph; defaults to "bidirectional_rnn"

Returns

A tuple (outputs, output_states) where: outputs: A tuple (output_fw, output_bw) containing the forward and the backward rnn output Tensor. If time_major == False (default), output_fw will be a Tensor shaped: [batch_size, max_time, cell_fw.output_size] and output_bw will be a Tensor shaped: [batch_size, max_time, cell_bw.output_size]. If time_major == True, output_fw will be a Tensor shaped: [max_time, batch_size, cell_fw.output_size] and output_bw will be a Tensor shaped: [max_time, batch_size, cell_bw.output_size]. It returns a tuple instead of a single concatenated Tensor, unlike in the bidirectional_rnn. If the concatenated one is preferred, the forward and backward outputs can be concatenated as tf.concat(outputs, 2). output_states: A tuple (output_state_fw, output_state_bw) containing the forward and the backward final states of bidirectional rnn.

Raises
TypeError	If cell_fw or cell_bw is not an instance of RNNCell.