tf.contrib.rnn.FusedRNNCellAdaptor

Class FusedRNNCellAdaptor

Inherits From: FusedRNNCell

Defined in tensorflow/contrib/rnn/python/ops/fused_rnn_cell.py.

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

This is an adaptor for RNNCell classes to be used with FusedRNNCell.

Methods

__init__

__init__(
    cell,
    use_dynamic_rnn=False
)

Initialize the adaptor.

Args:

• cell: an instance of a subclass of a rnn_cell.RNNCell.
• use_dynamic_rnn: whether to use dynamic (or static) RNN.

__call__

__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.