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Subclass of RNNCells that act like proper tf.Layer
objects.
Inherits From: RNNCell
tf.contrib.rnn.LayerRNNCell(
trainable=True, name=None, dtype=None, **kwargs
)
For backwards compatibility purposes, most RNNCell
instances allow their
call
methods to instantiate variables via tf.compat.v1.get_variable
. The
underlying
variable scope thus keeps track of any variables, and returning cached
versions. This is atypical of tf.layer
objects, which separate this
part of layer building into a build
method that is only called once.
Here we provide a subclass for RNNCell
objects that act exactly as
Layer
objects do. They must provide a build
method and their
call
methods do not access Variables tf.compat.v1.get_variable
.
Attributes | |
---|---|
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
get_initial_state(
inputs=None, batch_size=None, dtype=None
)
zero_state
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 |