Class TimeReversedFusedRNN

Inherits From: FusedRNNCell

Defined in tensorflow/contrib/rnn/python/ops/

See the guide: RNN and Cells (contrib) > Core RNN Cell wrappers (RNNCells that wrap other RNNCells)

This is an adaptor to time-reverse a FusedRNNCell.

For example,

cell = tf.contrib.rnn.BasicRNNCell(10)
fw_lstm = tf.contrib.rnn.FusedRNNCellAdaptor(cell, use_dynamic_rnn=True)
bw_lstm = tf.contrib.rnn.TimeReversedFusedRNN(fw_lstm)
fw_out, fw_state = fw_lstm(inputs)
bw_out, bw_state = bw_lstm(inputs)




Initialize self. See help(type(self)) for accurate signature.



Run this fused RNN on inputs, starting from the given state.


  • inputs: 3-D tensor with shape [time_len x batch_size x input_size] or a list of time_len tensors of shape [batch_size x input_size].
  • initial_state: either a tensor with shape [batch_size x state_size] or a tuple with shapes [batch_size x s] for s in state_size, if the cell takes tuples. If this is not provided, the cell is expected to create a zero initial state of type dtype.
  • dtype: The data type for the initial state and expected output. Required if initial_state is not provided or RNN state has a heterogeneous dtype.
  • sequence_length: Specifies the length of each sequence in inputs. An int32 or int64 vector (tensor) size [batch_size], values in [0, time_len). Defaults to time_len for each element.
  • scope: VariableScope or string for the created subgraph; defaults to class name.


A pair containing:

  • Output: A 3-D tensor of shape [time_len x batch_size x output_size] or a list of time_len tensors of shape [batch_size x output_size], to match the type of the inputs.
  • Final state: Either a single 2-D tensor, or a tuple of tensors matching the arity and shapes of initial_state.