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Abstract object representing an RNN cell.
Inherits From: Layer
tf.compat.v1.nn.rnn_cell.RNNCell(
trainable=True, name=None, dtype=None, **kwargs
)
Every RNNCell must have the properties below and implement call with
the signature (output, next_state) = call(input, state). The optional
third input argument, scope, is allowed for backwards compatibility
purposes; but should be left off for new subclasses.
This definition of cell differs from the definition used in the literature. In the literature, 'cell' refers to an object with a single scalar output. This definition refers to a horizontal array of such units.
An RNN cell, in the most abstract setting, is anything that has
a state and performs some operation that takes a matrix of inputs.
This operation results in an output matrix with self.output_size columns.
If self.state_size is an integer, this operation also results in a new
state matrix with self.state_size columns. If self.state_size is a
(possibly nested tuple of) TensorShape object(s), then it should return a
matching structure of Tensors having shape [batch_size].concatenate(s)
for each s in self.batch_size.
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 |