tf.compat.v1.nn.rnn_cell.DeviceWrapper

Operator that ensures an RNNCell runs on a particular device.

Inherits From: RNNCell, Layer, Layer, Module

tf.compat.v1.nn.rnn_cell.DeviceWrapper(
    cell, device, **kwargs
)

Args
cell	An instance of RNNCell.
device	A device string or function, for passing to tf.device.
**kwargs	dict of keyword arguments for base layer.

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

apply

View source

apply(
    *args, **kwargs
)

get_initial_state

View source

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

get_losses_for

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get_losses_for(
    inputs
)

Retrieves losses relevant to a specific set of inputs.

Args
inputs	Input tensor or list/tuple of input tensors.

Returns
List of loss tensors of the layer that depend on inputs.

get_updates_for

View source

get_updates_for(
    inputs
)

Retrieves updates relevant to a specific set of inputs.

Args
inputs	Input tensor or list/tuple of input tensors.

Returns
List of update ops of the layer that depend on inputs.

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.