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)

Gradient-based methods:

Vanilla gradients including 'Gradients x Input' (Gradient)

Smoothed gradients including 'SmoothGrad x Input' (SmoothGrad)

`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.

`new()`

Create a new instance of this super class.

```
InterpretingMethod$new(
converter,
data,
channels_first = TRUE,
output_idx = NULL,
ignore_last_act = TRUE,
dtype = "float"
)
```

`converter`

An instance of the R6 class

`Converter`

.`data`

The data for which this method is to be applied. It has to be an array or array-like format of size

*(batch_size, dim_in)*.`channels_first`

The format of the given data, i.e. channels on last dimension (

`FALSE`

) or after the batch dimension (`TRUE`

). If the data has no channels, use the default value`TRUE`

.`output_idx`

This vector determines for which output indices the method will be applied. By default (

`NULL`

), all outputs (but limited to the first 10) are considered.`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.`dtype`

dtype The data type for the calculations. Use either

`'float'`

for torch::torch_float or`'double'`

for torch::torch_double.

`get_result()`

This function returns the result of this method for the given data
either as an array (`'array'`

), a torch tensor (`'torch.tensor'`

,
or `'torch_tensor'`

) of size *(batch_size, dim_in, dim_out)* or as a
data.frame (`'data.frame'`

).