tf.compat.v1.nn.rnn_cell.MultiRNNCell

RNN cell composed sequentially of multiple simple cells.

Inherits From: RNNCell, Layer, Layer, Module

tf.compat.v1.nn.rnn_cell.MultiRNNCell(
    cells, state_is_tuple=True
)

Example:

num_units = [128, 64]
cells = [BasicLSTMCell(num_units=n) for n in num_units]
stacked_rnn_cell = MultiRNNCell(cells)

cells list of RNNCells that will be composed in this order.
state_is_tuple If True, accepted and returned states are n-tuples, where n = len(cells). If False, the states are all concatenated along the column axis. This latter behavior will soon be deprecated.

ValueError if cells is empty (not allowed), or at least one of the cells returns a state tuple but the flag state_is_tuple is False.

graph

output_size Integer or TensorShape: size of outputs produced by this cell.
scope_name

state_size size(s) of state(s) used by this cell.

It can be represented by an Integer, a TensorShape or a tuple of Integers or TensorShapes.

Methods

apply

View source

apply(
    *args, **kwargs
)

get_initial_state

View source

get_initial_state(
    inputs=None, batch_size=None, dtype=None
)

get_losses_for

View source

get_losses_for(
    inputs
)

Retrieves losses relevant to a specific set of inputs.

Args
inputs Input tensor or list/tuple of input tensors.

Returns
List of loss tensors of the layer that depend on inputs.

get_updates_for

View source

get_updates_for(
    inputs
)

Retrieves updates relevant to a specific set of inputs.

Args
inputs Input tensor or list/tuple of input tensors.

Returns
List of update ops of the layer that depend on inputs.

zero_state

View source

zero_state(
    batch_size, dtype
)

Return zero-filled state tensor(s).

Args
batch_size int, float, or unit Tensor representing the batch size.
dtype the data type to use for the state.

Returns
If state_size is an int or TensorShape, then the return value is a N-D tensor of shape [batch_size, state_size] filled with zeros.

If state_size is a nested list or tuple, then the return value is a nested list or tuple (of the same structure) of 2-D tensors with the shapes [batch_size, s] for each s in state_size.