tf.contrib.recurrent.bidirectional_functional_rnn( cell_fw, cell_bw, inputs, initial_state_fw=None, initial_state_bw=None, dtype=None, sequence_length=None, time_major=False, use_tpu=False, scope=None )
Creates a bidirectional recurrent neural network.
Performs fully dynamic unrolling of inputs in both directions. Built to be API
tf.nn.bidirectional_dynamic_rnn, but implemented with
functional control flow for TPU compatibility.
cell_fw: An instance of
cell_bw: An instance of
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_statein structure and type.
initial_state_bw: An optional initial state for
cell_bw. Should match
cell_bw.zero_statein 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
inputstensor is in "time major" format.
use_tpu: Whether to enable TPU-compatible operation. If True, does not truly reverse
inputsin the backwards RNN. Once b/69305369 is fixed, we can remove this flag.
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.
initial_state_fwis None or
initial_state_bwis None and
dtypeis not provided.