tf.compat.v1.nn.rnn_cell.DeviceWrapper

Operator that ensures an RNNCell runs on a particular device.

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(
    *args, **kwargs
)

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

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(
    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(
    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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Licensed under the Creative Commons Attribution License 4.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/compat/v1/nn/rnn_cell/DeviceWrapper