From a933cc9bb2f40a313660ebefd37c6aa55d828c67 Mon Sep 17 00:00:00 2001
From: Pat Alt <55311242+pat-alt@users.noreply.github.com>
Date: Tue, 8 Aug 2023 14:10:45 +0200
Subject: [PATCH] LeNet implemented
---
artifacts/results/mnist_vae.jls | Bin 1003682 -> 1003682 bytes
artifacts/results/mnist_vae_weak.jls | Bin 105130 -> 105130 bytes
notebooks/mnist.qmd | 35 ++++++++++++---------------
src/model.jl | 32 +++++++++++++-----------
4 files changed, 34 insertions(+), 33 deletions(-)
diff --git a/artifacts/results/mnist_vae.jls b/artifacts/results/mnist_vae.jls
index 872e5a43473de969e478c182fe76d324da88d0dc..0eed13cca664e891d5f09c05ef2f341b4fe67efd 100644
GIT binary patch
delta 113
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diff --git a/artifacts/results/mnist_vae_weak.jls b/artifacts/results/mnist_vae_weak.jls
index 42d8d05d0113c303f09bad12ce13865f76cff0cb..d1c8752d6b813e87b8ae1e8c82bf6125a31a30d7 100644
GIT binary patch
delta 63
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delta 62
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diff --git a/notebooks/mnist.qmd b/notebooks/mnist.qmd
index 3fa961e0..b5b68d58 100644
--- a/notebooks/mnist.qmd
+++ b/notebooks/mnist.qmd
@@ -388,11 +388,6 @@ function _plot_eccco_mnist(
ces = Dict()
for (mod_name, mod) in model_dict
- if mod.model.model isa ProbabilisticPipeline
- x = mod.model.model.f(x)
- _data = counterfactual_data
- _data.X = mod.model.model.f(_data.X)
- end
ce = generate_counterfactual(
x, target, counterfactual_data, mod, eccco_generator;
decision_threshold=γ, max_iter=T,
@@ -455,7 +450,11 @@ mutable struct LeNetBuilder
channels2::Int
end
-function MLJFlux.build(b::LeNetBuilder, rng, n_in, n_out, n_channels)
+preproc(X) = reshape(X, (28, 28, 1, :))
+
+function MLJFlux.build(b::LeNetBuilder, rng, n_in, n_out)
+
+ _n_in = Int(sqrt(n_in))
k, c1, c2 = b.filter_size, b.channels1, b.channels2
@@ -464,42 +463,35 @@ function MLJFlux.build(b::LeNetBuilder, rng, n_in, n_out, n_channels)
# padding to preserve image size on convolution:
p = div(k - 1, 2)
+ preproc(x) = reshape(x, (_n_in, _n_in, 1, :))
+
front = Flux.Chain(
- Conv((k, k), n_channels => c1, pad=(p, p), relu),
+ Conv((k, k), 1 => c1, pad=(p, p), relu),
MaxPool((2, 2)),
Conv((k, k), c1 => c2, pad=(p, p), relu),
MaxPool((2, 2)),
Flux.flatten
)
- d = Flux.outputsize(front, (n_in..., n_channels, 1)) |> first
+ d = Flux.outputsize(front, (_n_in, _n_in, 1, 1)) |> first
back = Flux.Chain(
Dense(d, 120, relu),
Dense(120, 84, relu),
Dense(84, n_out),
)
- chain = Flux.Chain(front, back)
+ chain = Flux.Chain(preproc, front, back)
return chain
end
# Final model:
-lenet = ImageClassifier(
+lenet = NeuralNetworkClassifier(
builder=LeNetBuilder(5, 6, 16),
epochs=epochs,
batch_size=batch_size,
finaliser=_finaliser,
loss=_loss,
)
-
-# Convert to image:
-function toimg(X)
- X = matrix(X) |> x -> reshape(x, 28, 28, :)
- X = coerce(X, GrayImage)
- return X
-end
-
-lenet = (X -> toimg(X)) |> lenet
```
```{julia}
@@ -508,8 +500,12 @@ add_retrain = true
# Deep Ensemble:
mlp_large_ens = EnsembleModel(model=mlp, n=50)
+# LeNet-5 Ensemble:
+lenet_ens = EnsembleModel(model=lenet, n=5)
+
add_models = Dict(
"LeNet-5" => lenet,
+ "LeNet-5 Ensemble" => lenet_ens,
"Large Ensemble (n=50)" => mlp_large_ens,
)
@@ -528,6 +524,7 @@ _plt_order = [
"MLP Ensemble",
"Large Ensemble (n=50)",
"LeNet-5",
+ "LeNet-5 Ensemble",
"JEM",
"JEM Ensemble",
]
diff --git a/src/model.jl b/src/model.jl
index bf3dbe8a..a865d559 100644
--- a/src/model.jl
+++ b/src/model.jl
@@ -1,3 +1,4 @@
+using ChainRules: ignore_derivatives
using ConformalPrediction
using CounterfactualExplanations.Models
using Flux
@@ -38,13 +39,18 @@ end
Private function that extracts the chains from a fitted model.
"""
function _get_chains(fitresult)
- if fitresult isa MLJEnsembles.WrappedEnsemble
- chains = map(res -> res[1], fitresult.ensemble)
- elseif fitresult isa MLJBase.Signature
- chains = [fitted_params(fitresult)[:image_classifier][1]] # for piped image classifiers
- else
- chains = [fitresult[1]]
+
+ chains = []
+
+ ignore_derivatives() do
+ if fitresult isa MLJEnsembles.WrappedEnsemble
+ _chains = map(res -> res[1], fitresult.ensemble)
+ else
+ _chains = [fitresult[1]]
+ end
+ push!(chains, _chains...)
end
+
return chains
end
@@ -147,7 +153,9 @@ In the binary case logits are fed through the sigmoid function instead of softma
which follows from the derivation here: https://stats.stackexchange.com/questions/233658/softmax-vs-sigmoid-function-in-logistic-classifier
"""
function Models.logits(M::ConformalModel, X::AbstractArray)
+
fitresult = M.fitresult
+
function predict_logits(fitresult, x)
ŷ = MLUtils.stack(map(chain -> get_logits(chain,x),_get_chains(fitresult))) |>
y -> mean(y, dims=ndims(y)) |>
@@ -162,14 +170,9 @@ function Models.logits(M::ConformalModel, X::AbstractArray)
ŷ = ndims(ŷ) > 1 ? ŷ : permutedims([ŷ])
return ŷ
end
- if ndims(X) > 2
- yhat = map(eachslice(X, dims=ndims(X))) do x
- predict_logits(fitresult, x)
- end
- yhat = MLUtils.stack(yhat)
- else
- yhat = predict_logits(fitresult, X)
- end
+
+ yhat = predict_logits(fitresult, X)
+
return yhat
end
@@ -179,6 +182,7 @@ end
Returns the estimated probabilities for a Conformal Classifier.
"""
function Models.probs(M::ConformalModel, X::AbstractArray)
+
if M.likelihood == :classification_binary
output = σ.(Models.logits(M, X))
elseif M.likelihood == :classification_multi
--
GitLab