Super class for gradient-based interpretation methods. This
class inherits from InterpretingMethod. It summarizes all implemented
gradient-based methods and provides a private function to calculate the
gradients w.r.t. to the input for given data. Implemented are:
Vanilla Gradients and Gradient\(\times\)Input (Gradient)
Integrated Gradients (IntegratedGradient)
SmoothGrad and SmoothGrad\(\times\)Input (SmoothGrad)
ExpectedGradients (ExpectedGradient)
innsight::InterpretingMethod -> GradientBased
times_input(logical(1))
This logical value indicates whether the results
were multiplied by the provided input data or not.
new()Create a new instance of this class. When initialized,
the method is applied to the given data and the results are stored in
the field result.
GradientBased$new(
converter,
data,
channels_first = TRUE,
output_idx = NULL,
output_label = NULL,
ignore_last_act = TRUE,
times_input = TRUE,
verbose = interactive(),
dtype = "float"
)converter(Converter)
An instance of the Converter class that includes the
torch-converted model and some other model-specific attributes. See
Converter for details.
data(array, data.frame, torch_tensor or list)
The data to which the method is to be applied. These must
have the same format as the input data of the passed model to the
converter object. This means either
an array, data.frame, torch_tensor or array-like format of
size (batch_size, dim_in), if e.g., the model has only one input layer, or
a list with the corresponding input data (according to the
upper point) for each of the input layers.
channels_first(logical(1))
The channel position of the given data (argument
data). If TRUE, the channel axis is placed at the second position
between the batch size and the rest of the input axes, e.g.,
c(10,3,32,32) for a batch of ten images with three channels and a
height and width of 32 pixels. Otherwise (FALSE), the channel axis
is at the last position, i.e., c(10,32,32,3). If the data
has no channel axis, use the default value TRUE.
output_idx(integer, list or NULL)
These indices specify the output nodes for which
the method is to be applied. In order to allow models with multiple
output layers, there are the following possibilities to select
the indices of the output nodes in the individual output layers:
An integer vector of indices: If the model has only one output
layer, the values correspond to the indices of the output nodes, e.g.,
c(1,3,4) for the first, third and fourth output node. If there are
multiple output layers, the indices of the output nodes from the first
output layer are considered.
A list of integer vectors of indices: If the method is to be
applied to output nodes from different layers, a list can be passed
that specifies the desired indices of the output nodes for each
output layer. Unwanted output layers have the entry NULL instead of
a vector of indices, e.g., list(NULL, c(1,3)) for the first and
third output node in the second output layer.
NULL (default): The method is applied to all output nodes in
the first output layer but is limited to the first ten as the
calculations become more computationally expensive for more output
nodes.
output_label(character, factor, list or NULL)
These values specify the output nodes for which
the method is to be applied. Only values that were previously passed with
the argument output_names in the converter can be used. In order to
allow models with multiple
output layers, there are the following possibilities to select
the names of the output nodes in the individual output layers:
A character vector or factor of labels: If the model has only one output
layer, the values correspond to the labels of the output nodes named in the
passed Converter object, e.g.,
c("a", "c", "d") for the first, third and fourth output node if the
output names are c("a", "b", "c", "d"). If there are
multiple output layers, the names of the output nodes from the first
output layer are considered.
A list of charactor/factor vectors of labels: If the method is to be
applied to output nodes from different layers, a list can be passed
that specifies the desired labels of the output nodes for each
output layer. Unwanted output layers have the entry NULL instead of
a vector of labels, e.g., list(NULL, c("a", "c")) for the first and
third output node in the second output layer.
NULL (default): The method is applied to all output nodes in
the first output layer but is limited to the first ten as the
calculations become more computationally expensive for more output
nodes.
ignore_last_act(logical(1))
Set this logical value to include the last
activation functions for each output layer, or not (default: TRUE).
In practice, the last activation (especially for softmax activation) is
often omitted.
times_input(logical(1)
Multiplies the gradients with the input features.
This method is called Gradient\(\times\)Input.
verbose(logical(1))
This logical argument determines whether a progress bar is
displayed for the calculation of the method or not. The default value is
the output of the primitive R function interactive().
dtype(character(1))
The data type for the calculations. Use
either 'float' for torch_float or 'double' for
torch_double.