# tf.contrib.rnn.FusedRNNCell

## Class FusedRNNCell

Abstract object representing a fused RNN cell.

A fused RNN cell represents the entire RNN expanded over the time dimension. In effect, this represents an entire recurrent network.

Unlike RNN cells which are subclasses of rnn_cell.RNNCell, a FusedRNNCell operates on the entire time sequence at once, by putting the loop over time inside the cell. This usually leads to much more efficient, but more complex and less flexible implementations.

Every FusedRNNCell must implement __call__ with the following signature.

## Methods

### __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.