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tf.compat.v1.nn.rnn_cell.MultiRNNCell

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Class MultiRNNCell

RNN cell composed sequentially of multiple simple cells.

Inherits From: RNNCell

Example:

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

__init__

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__init__(
    cells,
    state_is_tuple=True
)

Create a RNN cell composed sequentially of a number of RNNCells. (deprecated)

Args:

  • 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.

Raises:

  • 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.

Properties

graph

DEPRECATED FUNCTION

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

get_initial_state

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

zero_state

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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.