A comprehensive tutorial on how to implement various autoencoder models using PyTorch
- TrainSimpleFCAutoencoder notebook demonstrates how to implement and train very simple a fully-connected autoencoder with a single-layer encoder and a single-layer decoder.
- TrainDeepSimpleFCAutoencoder and TrainDeeperSimpleFCAutoencoder notebooks demonstrate how to implement and train a fully-connected autoencoder with a multi-layer encoder and a multi-layer decoder. Please first train single-layer autoencoder using the TrainSimpleFCAutoencoder notebook as the very initial pretrain model for the deeper autoencoder training notebooks. Then, gradually increase depth of the autoencoder and use previously trained (shallower) autoencoder as the pretrained model.
- TrainSimpleConvAutoencoder notebook demonstrates how to implement and train an autoencoder with a convolutional encoder and a fully-connected decoder.
- TrainSimpleSparseFCAutoencoder notebook demonstrates how to implement and train an autoencoder with hard (feature) sparsity and lifetime (winner takes all) sparsity.
- TrainSimpleFCAutoencoderWithSparseLoss notebook demonstrates how to implement and train autoencoder with soft sparsity loss.
- TrainSimpleDenoiseFCAutoencoder notebook demonstrates how to implement and train an autoencoder for denoise applications.
- Notebooks with name starting with "Plot*" are used to plot the training results.
A step by step tutorial on how to build and train VGG using PyTorch can be found in my blog post (URL: https://jianzhongdev.github.io/VisionTechInsights/posts/autoencoders_with_pytorch_full_code_guide/ )
This repo has been implemented and tested on the following dependencies:
- Python 3.10.13
- matplotlib 3.8.2
- numpy 1.26.2
- torch 2.1.1+cu118
- torchvision 0.16.1+cu118
- notebook 7.0.6
This repo has been tested on a laptop computer with the following specs:
- CPU: Intel(R) Core(TM) i7-9750H CPU
- Memory: 32GB
- GPU: NVIDIA GeForce RTX 2060
[1] Hinton, G. E. & Salakhutdinov, R. R. Reducing the Dimensionality of Data with Neural Networks. Science 313, 504–507 (2006).
[2] Kramer, M. A. Nonlinear principal component analysis using autoassociative neural networks. AIChE Journal 37, 233–243 (1991).
[3] Masci, J., Meier, U., Cireşan, D. & Schmidhuber, J. Stacked Convolutional Auto-Encoders for hierarchical feature extraction. in Lecture notes in computer science 52–59 (2011). doi:10.1007/978-3-642-21735-7_7.
[4] Makhzani, A. & Frey, B. J. A Winner-Take-All method for training sparse convolutional autoencoders. arXiv (Cornell University) (2014).
[5] A. Ng, “Sparse autoencoder,” CS294A Lecture notes, vol. 72, 2011.
If you found this article helpful, please cite it as:
Zhong, Jian (June 2024). Autoencoders with PyTorch: Full Code Guide. Vision Tech Insights. https://jianzhongdev.github.io/VisionTechInsights/posts/autoencoders_with_pytorch_full_code_guide/.
Or
@article{zhong2024buildtrainAutoencoderPyTorch,
title = "Autoencoders with PyTorch: Full Code Guide",
author = "Zhong, Jian",
journal = "jianzhongdev.github.io",
year = "2024",
month = "June",
url = "https://jianzhongdev.github.io/VisionTechInsights/posts/autoencoders_with_pytorch_full_code_guide/"
}