Gradient

pnpxai.explainers.gradient

Gradient

Bases: ZennitExplainer

Gradient explainer.

Supported Modules: Linear, Convolution, LSTM, RNN, Attention

Parameters:

Name	Type	Description	Default
model	Module	The PyTorch model for which attribution is to be computed. layer (Optional[Union[Union[str, Module], Sequence[Union[str, Module]]]]): The target module to be explained. n_classes (int): The number of classes.	required
forward_arg_extractor	Optional[Callable[[Tuple[Tensor]], Union[Tensor, Tuple[Tensor]]]]	A function that extracts forward arguments from the input batch(s) where the attribution scores are assigned.	None
additional_forward_arg_extractor	Optional[Callable[[Tuple[Tensor]], Union[Tensor, Tuple[Tensor]]]]	A secondary function that extract additional forward arguments from the input batch(s).	None
**kwargs		Keyword arguments that are forwarded to the base implementation of the Explainer	required

Reference

Gabriel Erion, Joseph D. Janizek, Pascal Sturmfels, Scott Lundberg, Su-In Lee. Improving performance of deep learning models with axiomatic attribution priors and expected gradients.

SUPPORTED_MODULES = [Linear, Convolution, LSTM, RNN, Attention] class-attribute instance-attribute

layer = layer instance-attribute

attributor: Union[GradientAttributor, LayerGradientAttributor] property

EXPLANATION_TYPE: ExplanationType = 'attribution' class-attribute instance-attribute

TUNABLES = {} class-attribute instance-attribute

model = model.eval() instance-attribute

forward_arg_extractor = forward_arg_extractor instance-attribute

additional_forward_arg_extractor = additional_forward_arg_extractor instance-attribute

device property

n_classes = n_classes instance-attribute

__init__(model: Module, forward_arg_extractor: Optional[Callable[[Tuple[Tensor]], Union[Tensor, Tuple[Tensor]]]] = None, additional_forward_arg_extractor: Optional[Callable[[Tuple[Tensor]], Union[Tensor, Tuple[Tensor]]]] = None, layer: Optional[Union[Union[str, Module], Sequence[Union[str, Module]]]] = None, n_classes: Optional[int] = None) -> None

attribute(inputs: Union[Tensor, Tuple[Tensor]], targets: Tensor) -> Union[Tensor, Tuple[Tensor]]

Computes attributions for the given inputs and targets.

Parameters:

Name	Type	Description	Default
inputs	Tensor	The input data.	required
targets	Tensor	The target labels for the inputs.	required

Returns:

Type	Description
Union[Tensor, Tuple[Tensor]]	Union[torch.Tensor, Tuple[torch.Tensor]]: The result of the explanation.

__repr__()

copy()

set_kwargs(**kwargs)

get_tunables() -> Dict[str, Tuple[type, dict]]

Returns a dictionary of tunable parameters for the explainer.

Returns:

Type	Description
Dict[str, Tuple[type, dict]]	Dict[str, Tuple[type, dict]]: Dictionary of tunable parameters.

__init_subclass__() -> None