tf.compat.v1.nn.bidirectional_dynamic_rnn

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

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.

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_f