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3 changes: 0 additions & 3 deletions docs/Project.toml
Original file line number Diff line number Diff line change
Expand Up @@ -12,6 +12,3 @@ Literate = "98b081ad-f1c9-55d3-8b20-4c87d4299306"
MLDatasets = "eb30cadb-4394-5ae3-aed4-317e484a6458"
VisionHeatmaps = "27106da1-f8bc-4ca8-8c66-9b8289f1e035"
Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"

[compat]
VisionHeatmaps = "1.4"
1 change: 0 additions & 1 deletion docs/make.jl
Original file line number Diff line number Diff line change
Expand Up @@ -35,7 +35,6 @@ makedocs(;
"Home" => "index.md",
"Getting started" => "generated/example.md",
"General usage" => Any[
"Heatmapping" => "generated/heatmapping.md",
"Input augmentations" => "generated/augmentations.md",
],
"API Reference" => "api.md",
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1 change: 0 additions & 1 deletion docs/src/index.md
Original file line number Diff line number Diff line change
Expand Up @@ -21,7 +21,6 @@ julia> ]add ExplainableAI
```@contents
Pages = [
"generated/example.md",
"generated/heatmapping.md",
"generated/augmentations.md",
]
Depth = 3
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23 changes: 16 additions & 7 deletions docs/src/literate/example.jl
Original file line number Diff line number Diff line change
Expand Up @@ -81,11 +81,6 @@ heatmap(expl)
# into a single function call:
heatmap(input, analyzer)

# For a more detailed explanation of the `heatmap` function,
# refer to the [heatmapping section](@ref docs-heatmapping).

# ## [List of analyzers](@id docs-analyzers-list)

# ## Neuron selection
# By passing an additional index to our call to [`analyze`](@ref),
# we can compute an explanation with respect to a specific output neuron.
Expand Down Expand Up @@ -114,5 +109,19 @@ expl = analyze(batch, analyzer);
# Calling `heatmap` on `expl` will detect the batch dimension and return a vector of heatmaps.
heatmap(expl)

# For more information on heatmapping batches,
# refer to the [heatmapping documentation](@ref docs-heatmapping-batches).
## Custom heatmaps

# The function `heatmap` automatically applies common presets for each method.
#
# Since [`InputTimesGradient`](@ref) computes attributions,
# heatmaps are shown in a blue-white-red color scheme.
# Gradient methods however are typically shown in grayscale:
analyzer = Gradient(model)
heatmap(input, analyzer)
#-
analyzer = InputTimesGradient(model)
heatmap(input, analyzer)

# Using [VisionHeatmaps.jl](https://julia-xai.github.io/XAIDocs/VisionHeatmaps/stable/),
# heatmaps can be heavily customized.
# Check out the [heatmapping documentation](https://julia-xai.github.io/XAIDocs/VisionHeatmaps/stable/) for more information.
161 changes: 0 additions & 161 deletions docs/src/literate/heatmapping.jl

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