tf.compat.v1.nn.static_bidirectional_rnn

Creates a bidirectional recurrent neural network. (deprecated)

tf.compat.v1.nn.static_bidirectional_rnn(
    cell_fw, cell_bw, inputs, initial_state_fw=None, initial_state_bw=None,
    dtype=None, sequence_length=None, scope=None
)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use keras.layers.Bidirectional(keras.layers.RNN(cell, unroll=True)), which is equivalent to this API

Similar to the unidirectional case above (rnn) but takes input and builds independent forward and backward RNNs with the final forward and backward outputs depth-concatenated, such that the output will have the format [time][batch][cell_fw.output_size + cell_bw.output_size]. 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: A length T list of inputs, each a tensor of shape [batch_size, input_size], or a nested tuple of such elements.

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 state. Required if either of the initial states are not provided.

sequence_length: (optional) An int32/int64 vector, size [batch_size], containing the actual lengths for each of the sequences.

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

Returns:

A tuple (outputs, output_state_fw, output_state_bw) where: outputs is a length T list of outputs (one for each input), which are depth-concatenated forward and backward outputs. output_state_fw is the final state of the forward rnn. output_state_bw is the final state of the backward rnn.

Raises:

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

ValueError: If inputs is None or an empty list.