deepof.model_utils.ClusterControl

class deepof.model_utils.ClusterControl(*args, **kwargs)

Identity layer.

Evaluates different clustering metrics between the components of the latent Gaussian Mixture using the entropy of the nearest neighbourhood. If self.loss_weight > 0, it also adds a regularization penalty to the loss function which attempts to maximize the number of populated clusters during training.

__init__(batch_size: int, n_components: int, encoding_dim: int, k: int = 15, *args, **kwargs)

Initialize the ClusterControl layer.

Parameters:

batch_size (int) – batch size of the model
n_components (int) – number of components in the latent Gaussian Mixture
encoding_dim (int) – dimension of the latent Gaussian Mixture
k (int) – number of nearest components of the latent Gaussian Mixture to consider
loss_weight (float) – weight of the regularization penalty applied to the local entropy of each training instance
*args – additional positional arguments
**kwargs – additional keyword arguments

Methods

`__init__`(batch_size, n_components, encoding_dim)	Initialize the ClusterControl layer.
`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)	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.
`build`(input_shape)	Creates the variables of the layer (for subclass implementers).
`build_from_config`(config)
`call`(inputs)	Update Layer's call method.
`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_build_config`()
`get_config`()	Update Constraint metadata.
`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_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_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`	Return Functional API nodes upstream of this layer.
`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`	Return Functional API nodes downstream of this layer.
`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.

__init__(batch_size: int, n_components: int, encoding_dim: int, k: int = 15, *args, **kwargs)

Initialize the ClusterControl layer.

Parameters:

batch_size (int) – batch size of the model
n_components (int) – number of components in the latent Gaussian Mixture
encoding_dim (int) – dimension of the latent Gaussian Mixture
k (int) – number of nearest components of the latent Gaussian Mixture to consider
loss_weight (float) – weight of the regularization penalty applied to the local entropy of each training instance
*args – additional positional arguments
**kwargs – additional keyword arguments