analyze(input, method)
analyze(input, method, output_selection)

Apply the analyzer method for the given input, returning an Explanation. If output_selection is specified, the explanation will be calculated for that output. Otherwise, the output with the highest activation is automatically chosen.

See also Explanation.

Explanation(val, output, output_selection, analyzer, heatmap, extras)

Return type of analyzers when calling analyze.

Fields

• val: numerical output of the analyzer, e.g. an attribution or gradient
• output: model output for the given analyzer input
• output_selection: index of the output used for the explanation
• analyzer: symbol corresponding the used analyzer, e.g. :Gradient or :LRP
• heatmap: symbol indicating a preset heatmapping style, e.g. :attribution, :sensitivity or :cam
• extras: optional named tuple that can be used by analyzers to return additional information.

Gradient(model)

Analyze model by calculating the gradient of a neuron activation with respect to the input.

InputTimesGradient(model)

Analyze model by calculating the gradient of a neuron activation with respect to the input. This gradient is then multiplied element-wise with the input.

SmoothGrad(analyzer)
SmoothGrad(analyzer, [n, std, rng]])
SmoothGrad(analyzer, [n, distribution, rng])

Analyze model by calculating a smoothed sensitivity map. This is done by averaging sensitivity maps of a Gradient analyzer over random samples in a neighborhood of the input. Defaults to 50 samples from the normal distribution with zero mean and std=1.0f0.

For optimal results, Smilkov et al., SmoothGrad: removing noise by adding noise recommends setting std between 10% and 20% of the input range of each sample, e.g. std = 0.1 * (maximum(input) - minimum(input)).

References

• Smilkov et al., SmoothGrad: removing noise by adding noise

IntegratedGradients(analyzer, [n=50])
IntegratedGradients(analyzer, [n=50])

Analyze model by using the Integrated Gradients method.

References

• Sundararajan et al., Axiomatic Attribution for Deep Networks

GradCAM(feature_layers, adaptation_layers)

Calculates the Gradient-weighted Class Activation Map (GradCAM). GradCAM provides a visual explanation of the regions with significant neuron importance for the model's classification decision.

Parameters

• feature_layers: The layers of a convolutional neural network (CNN) responsible for extracting feature maps.
• adaptation_layers: The layers of the CNN used for adaptation and classification.

Note

Flux is not required for GradCAM. GradCAM is compatible with a wide variety of CNN model-families.

References

• Selvaraju et al., Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

NoiseAugmentation(analyzer, n, [std::Real, rng])
NoiseAugmentation(analyzer, n, [distribution::Sampleable, rng])

A wrapper around analyzers that augments the input with n samples of additive noise sampled from a scalar distribution. This input augmentation is then averaged to return an Explanation. Defaults to the normal distribution with zero mean and std=1.0f0.

For optimal results, Smilkov et al., SmoothGrad: removing noise by adding noise recommends setting std between 10% and 20% of the input range of each sample, e.g. std = 0.1 * (maximum(input) - minimum(input)).

Keyword arguments

• rng::AbstractRNG: Specify the random number generator that is used to sample noise from the distribution. Defaults to GLOBAL_RNG.
• show_progress:Bool: Show progress meter while sampling augmentations. Defaults to true.

InterpolationAugmentation(model, [n=50])

A wrapper around analyzers that augments the input with n steps of linear interpolation between the input and a reference input (typically zero(input)). The gradients w.r.t. this augmented input are then averaged and multiplied with the difference between the input and the reference input.