This is a super class for all data-based interpreting methods. Implemented are the following methods:

• Deep Learning Important Features (DeepLift)

• Layer-wise Relevance Propagation (LRP)

## Public fields

data

The passed data as a torch tensor in the given data type (dtype) to be interpreted with the selected method.

converter

An instance of the R6 class Converter.

dtype

The data type for the calculations. Either 'float' for torch::torch_float or 'double' for torch::torch_double.

channels_first

The format of the given date, i.e. channels on last dimension (FALSE) or after the batch dimension (TRUE). If the data has no channels, the default value TRUE is used.

ignore_last_act

A boolean value to include the last activation into all the calculations, or not (default: TRUE). In some cases, the last activation leads to a saturation problem.

result

The methods result of the given data as a torch tensor of size (batch_size, dim_in, dim_out) in the given data type (dtype).

output_idx

This vector determines for which outputs the method will be applied. By default (NULL), all outputs (but limited to the first 10) are considered.

## Methods

### Method new()

Create a new instance of this super class.

#### Arguments

type

The data type of the result. Use one of 'array', 'torch.tensor', 'torch_tensor' or 'data.frame' (default: 'array').

#### Returns

The result of this method for the given data in the chosen type.

### Method clone()

The objects of this class are cloneable with this method.

#### Usage

InterpretingMethod\$clone(deep = FALSE)

#### Arguments

deep

Whether to make a deep clone.