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Subclass of RNNCells that act like proper
For backwards compatibility purposes, most
RNNCell instances allow their
call methods to instantiate variables via
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
__init__( trainable=True, name=None, dtype=None, **kwargs )
Integer or TensorShape: size of outputs produced by this cell.
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.
get_initial_state( inputs=None, batch_size=None, dtype=None )
zero_state( batch_size, dtype )
Return zero-filled state tensor(s).
batch_size: int, float, or unit Tensor representing the batch size.
dtype: the data type to use for the state.
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.
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
[batch_size, s] for each s in