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Add StandardAutoencoder, an autoencoder of Dense layers - #336
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The unstructured counterpart of SymplecticAutoencoder: an encoder and a decoder of Dense layers at a hidden width, each ending in an affine layer. It is there to compare the latent space of a symplectic autoencoder with that of an autoencoder whose decoder is not symplectic, at a similar number of parameters. Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
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Included review availability: This review used your included allowance. Your plan provides up to 10 included reviews per hour; 8 remain after this review. 📝 WalkthroughWalkthroughAdds and exports ChangesStandardAutoencoder
Priority: ➖ Normal Estimated code review effort: 3 (Moderate) | ~20 minutes Change: Feature Suggested reviewers: Merge Risk: ⚪ Minimal · up to The new autoencoder’s construction and persistence paths have corresponding tests, and equal-dimension iteration handling is addressed. No concrete merge-blocking behavior is established. Architecture SummaryArchitecture risk: 🔵 Low · up to The change affects 4 systems. Changed systems: Architecture concerns Review detailsSystems and components
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benedict-96
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Oct 6, 2026
AE_ARCH=standard makes train_sae.jl train a StandardAutoencoder of Dense layers at about the symplectic one's number of parameters (4408 against 4402), on the same data with the same loss, optimizer and checkpoint selection. Weights files record their architecture, and branch_report.jl and latent_plot.jl load whichever one a file holds. The scripts environment takes GeometricMachineLearning from the branch of JuliaGNI/GeometricMachineLearning.jl#336 until a release has it. Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
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Review comments at @src/architectures/standard_autoencoder.jl:
- Around line 58-68: Handle equal dimensions in StandardAutoencoder iteration
calculation so full_dim == reduced_dim does not pass an invalid zero-step range
to compute_iterations. Add an architecture-specific iteration result for the
equal-dimension case, and route both encoder and decoder iteration calculations
through it while preserving the existing compute_iterations behavior when
dimensions differ.
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CHANGELOG.mddocs/src/reduced_order_modeling/pod_autoencoders.mdsrc/GeometricMachineLearning.jlsrc/architectures/standard_autoencoder.jltest/architectures/standard_autoencoder_tests.jltest/integration/hdf5_support.jltest/runtests.jl
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`Dense` has no docstring in this manual, so [`Dense`](@ref) left two unresolved references; docs/make.jl stops on them, and so does test/quality/docs_make_checks_references.jl in every test job. It is now plain code. With full_dim == reduced_dim, which the constructor allows, the generic compute_iterations built a range of step zero and NeuralNetwork failed. The layers depend only on the two end dimensions, so StandardAutoencoder now returns just those (from a CodeRabbit review comment). Tested at (4, 4). The SymplecticAutoencoder docstring points to StandardAutoencoder. Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
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Adds
StandardAutoencoder(full_dim, reduced_dim; width, n_encoder_layers, n_decoder_layers, activation), anAutoEncoderwhose encoder and decoder are feedforward networks ofDenselayers:n_*_layerslayers with activation at the hidden width, then an affine output layer. The decoder is not symplectic, and neither dimension has to be even.It is the unstructured counterpart of
SymplecticAutoencoder, so that the latent space of a symplectic autoencoder can be compared with that of a standard one at a similar number of parameters (the pendulum separatrix study).encoder/decodersplit it through the genericAutoEncodermethods;saveandload(NeuralNetwork, path, arch)round-trip it.test/architectures/standard_autoencoder_tests.jl(core): the split composes to the network, the parameter count matches the closed formula, training reduces the loss, and the decoder is not symplectic at a random point.test/integration/hdf5_support.jl: the save/load round trip. All 63 assertions of the two files pass locally (Julia 1.13.0).pod_autoencoders.md; CHANGELOG entry.Reduced dynamics for a non-symplectic decoder (a manifold-Galerkin reduced system) are left for a later PR.
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Summary by CodeRabbit
StandardAutoencoder, a configurable feedforward autoencoder with separate encoder and decoder layer counts, hidden width, and activation function.