candle.keras_utils.PermanentDropout

class candle.keras_utils.PermanentDropout(*args, **kwargs)

__init__(rate, **kwargs)

Initialize the BaseRandomLayer.

Note that the constructor is annotated with @no_automatic_dependency_tracking. This is to skip the auto tracking of self._random_generator instance, which is an AutoTrackable. The backend.RandomGenerator could contain a tf.random.Generator instance which will have tf.Variable as the internal state. We want to avoid saving that state into model.weights and checkpoints for backward compatibility reason. In the meantime, we still need to make them visible to SavedModel when it is tracing the tf.function for the call(). See _list_extra_dependencies_for_serialization below for more details.

Parameters:

• seed – optional integer, used to create RandomGenerator.

• force_generator – boolean, default to False, whether to force the RandomGenerator to use the code branch of tf.random.Generator.

• **kwargs – other keyword arguments that will be passed to the parent class

Methods

__init__(rate, **kwargs)	Initialize the BaseRandomLayer.
add_loss(losses, **kwargs)	Add loss tensor(s), potentially dependent on layer inputs.
add_metric(value[, name])	Adds metric tensor to the layer.
add_update(updates[, inputs])	Add update op(s), potentially dependent on layer inputs.
add_variable(*args, **kwargs)	Deprecated, do NOT use! Alias for add_weight.
add_weight([name, shape, dtype, ...])	Adds a new variable to the layer.
apply(inputs, *args, **kwargs)	Deprecated, do NOT use!
build(input_shape)	Creates the variables of the layer (optional, for subclass implementers).
call(x[, mask])	This is where the layer's logic lives.
compute_mask(inputs[, mask])	Computes an output mask tensor.
compute_output_shape(input_shape)	Computes the output shape of the layer.
compute_output_signature(input_signature)	Compute the output tensor signature of the layer based on the inputs.
count_params()	Count the total number of scalars composing the weights.
finalize_state()	Finalizes the layers state after updating layer weights.
from_config(config)	Creates a layer from its config.
get_config()	Returns the config of the layer.
get_input_at(node_index)	Retrieves the input tensor(s) of a layer at a given node.
get_input_mask_at(node_index)	Retrieves the input mask tensor(s) of a layer at a given node.
get_input_shape_at(node_index)	Retrieves the input shape(s) of a layer at a given node.
get_losses_for(inputs)	Deprecated, do NOT use!
get_output_at(node_index)	Retrieves the output tensor(s) of a layer at a given node.
get_output_mask_at(node_index)	Retrieves the output mask tensor(s) of a layer at a given node.
get_output_shape_at(node_index)	Retrieves the output shape(s) of a layer at a given node.
get_updates_for(inputs)	Deprecated, do NOT use!
get_weights()	Returns the current weights of the layer, as NumPy arrays.
set_weights(weights)	Sets the weights of the layer, from NumPy arrays.
with_name_scope(method)	Decorator to automatically enter the module name scope.

Attributes

activity_regularizer	Optional regularizer function for the output of this layer.
compute_dtype	The dtype of the layer's computations.
dtype	The dtype of the layer weights.
dtype_policy	The dtype policy associated with this layer.
dynamic	Whether the layer is dynamic (eager-only); set in the constructor.
inbound_nodes	Deprecated, do NOT use! Only for compatibility with external Keras.
input	Retrieves the input tensor(s) of a layer.
input_mask	Retrieves the input mask tensor(s) of a layer.
input_shape	Retrieves the input shape(s) of a layer.
input_spec	InputSpec instance(s) describing the input format for this layer.
losses	List of losses added using the add_loss() API.
metrics	List of metrics added using the add_metric() API.
name	Name of the layer (string), set in the constructor.
name_scope	Returns a tf.name_scope instance for this class.
non_trainable_variables	Sequence of non-trainable variables owned by this module and its submodules.
non_trainable_weights	List of all non-trainable weights tracked by this layer.
outbound_nodes	Deprecated, do NOT use! Only for compatibility with external Keras.
output	Retrieves the output tensor(s) of a layer.
output_mask	Retrieves the output mask tensor(s) of a layer.
output_shape	Retrieves the output shape(s) of a layer.
stateful
submodules	Sequence of all sub-modules.
supports_masking	Whether this layer supports computing a mask using compute_mask.
trainable
trainable_variables	Sequence of trainable variables owned by this module and its submodules.
trainable_weights	List of all trainable weights tracked by this layer.
updates
variable_dtype	Alias of Layer.dtype, the dtype of the weights.
variables	Returns the list of all layer variables/weights.
weights	Returns the list of all layer variables/weights.