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* initial commit * style fixes * update of ACKNOWLEDGMENTS * fixed comment * minor refactoring; removed unused imports * added cifar and cvae to top-level README.md * removed mention of cuda/mps in argparse * fixed training status output * load_weights() with strict=True * pretrained model update * fixed imports and style * requires mlx>=0.0.9 * updated with results using mlx 0.0.9 * removed mention of private repo * simplify and combine to one file, more consistency with other exmaples * few more nits * nits * spell * format --------- Co-authored-by: Awni Hannun <awni@apple.com>
69 lines
1.7 KiB
Markdown
69 lines
1.7 KiB
Markdown
# Convolutional Variational Autoencoder (CVAE) on MNIST
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Convolutional variational autoencoder (CVAE) implementation in MLX using
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MNIST.[^1]
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## Setup
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Install the requirements:
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```
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pip install -r requirements.txt
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```
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## Run
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To train a VAE run:
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```shell
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python main.py
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```
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To see the supported options, do `python main.py -h`.
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Training with the default options should give:
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```shell
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$ python train.py
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Options:
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Device: GPU
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Seed: 0
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Batch size: 128
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Max number of filters: 64
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Number of epochs: 50
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Learning rate: 0.001
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Number of latent dimensions: 8
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Number of trainable params: 0.1493 M
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Epoch 1 | Loss 14626.96 | Throughput 1803.44 im/s | Time 34.3 (s)
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Epoch 2 | Loss 10462.21 | Throughput 1802.20 im/s | Time 34.3 (s)
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...
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Epoch 50 | Loss 8293.13 | Throughput 1804.91 im/s | Time 34.2 (s)
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```
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The throughput was measured on a 32GB M1 Max.
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Reconstructed and generated images will be saved after each epoch in the
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`models/` path. Below are examples of reconstructed training set images and
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generated images.
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#### Reconstruction
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#### Generation
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## Limitations
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At the time of writing, MLX does not have transposed 2D convolutions. The
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example approximates them with a combination of nearest neighbor upsampling and
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regular convolutions, similar to the original U-Net. We intend to update this
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example once transposed 2D convolutions are available.
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[^1]: For a good overview of VAEs see the original paper [Auto-Encoding
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Variational Bayes](https://arxiv.org/abs/1312.6114) or [An Introduction to
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Variational Autoencoders](https://arxiv.org/abs/1906.02691).
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