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tf.contrib.recurrent.bidirectional_functional_rnn

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Creates a bidirectional recurrent neural network.

Performs fully dynamic unrolling of inputs in both directions. Built to be API compatible with tf.compat.v1.nn.bidirectional_dynamic_rnn, but implemented with functional control flow for TPU compatibility.

cell_fw An instance of tf.compat.v1.nn.rnn_cell.RNNCell.
cell_bw An instance of tf.compat.v1.nn.rnn_cell.RNNCell.
inputs The RNN inputs. If time_major == False (default), this must be a Tensor (or hierarchical structure of Tensors) of shape [batch_size, max_time, ...]. If time_major == True, this must be a Tensor (or hierarchical structure of Tensors) of shape: [max_time, batch_size, ...]. The first two dimensions must match across all the inputs, but otherwise the ranks and other shape components may differ.
initial_state_fw An optional initial state for cell_fw. Should match cell_fw.zero_state in structure and type.
initial_state_bw An optional initial state for cell_bw. Should match cell_bw.zero_state in structure and type.
dtype (optional) The data type for the initial state and expected output. Required if initial_states are not provided or RNN state has a heterogeneous dtype.
sequence_length An optional 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.
time_major Whether the inputs tensor is in "time major" format.
use_tpu Whether to enable TPU-compatible operation. If True, does not truly reverse inputs in the backwards RNN. Once b/69305369 is fixed, we can remove this flag.
fast_reverse Whether to use fast tf.reverse to replace tf.reverse_sequence. This is only possible when either all sequence lengths are the same inside the batch, or when the cell function does not change the state on padded input.
scope An optional scope name for the dynamic RNN.

outputs A tuple of (output_fw, output_bw). The output of the forward and backward RNN. If time_major == False (default), these will be Tensors shaped: [batch_size, max_time, cell.output_size]. If time_major == True, these will be Tensors shaped: [max_time, batch_size, cell.output_size]. Note, if cell.output_size is a (possibly nested) tuple of integers or TensorShape objects, then the output for that direction 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.
final_states A tuple of (final_state_fw, final_state_bw). A Tensor or hierarchical structure of Tensors indicating the final cell state in each direction. Must have the same structure and shape as cell.zero_state.

ValueError If initial_state_fw is None or initial_state_bw is None and dtype is not provided.