tf.contrib.rnn.TimeReversedFusedRNN

Class `TimeReversedFusedRNN`

This is an adaptor to time-reverse a FusedRNNCell.

Inherits From: FusedRNNCell

For example,

cell = tf.compat.v1.nn.rnn_cell.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)

`init`

View source

__init__(cell)

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

Methods

`tf.contrib.rnn.TimeReversedFusedRNN.call`

View source

__call__(
    inputs,
    initial_state=None,
    dtype=None,
    sequence_length=None,
    scope=None
)

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

Args:

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.

Returns:

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.