pkulwj1994/mcvae

The repository compliments code of 'Monte Carlo VAE' paper.

All the experiments which appear in the paper can be run via the exps.sh script. For example:

python main.py --model L-MCVAE --dataset mnist --act_func gelu --binarize True --hidden_dim 64 --batch_size 100 --net_type conv --num_samples 1 --max_epochs 50 --step_size 0.01 --K 1 --use_transforms True --learnable_transitions False --use_cloned_decoder True

For each experiment we can set

• model -- which model we want to train: 'VAE', 'IWAE', 'L-MCVAE' or 'A-MCVAE'

• dataset -- which dataset to use (now available MNIST, CIFAR-10, OMNIGLOT, Fashion-MNIST, CelebA[should be downloaded separately])

• act_func -- activation function

  More arguments with the description are available in main.py

Visualization of toy example's posterior is located in 'toy_example.ipynb' notebook.

Parameter estimates for the toy example can be obtained via in 'run_toy.py'.