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)
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.
|
Attributes |
|---|
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
get_initial_state
View source
get_initial_state(
inputs=None, batch_size=None, dtype=None
)
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.
|
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