An implementation of the VAE in pytorch with the fastai data api, applied on MNIST TINY (only contains 3 and 7). The notebook is the most comprehensive, but the script is runnable on its own as well. Results from sampling are saved in the results directory.
usage: vae.py [-h] [--batch-size N] [--epochs N] [--no-cuda]
[--emb-size EMB_SIZE]
VAE MNIST Example
optional arguments:
-h, --help show this help message and exit
--batch-size N input batch size for training (default: 128)
--epochs N number of epochs to train (default: 10)
--no-cuda enables CUDA training
--emb-size EMB_SIZE size of embedding (default 10)
