Simple PyTorch implementation of Adversarially Learned Inference
$ ./train.py [-h] [-g GPU] [-b N] [-e E] [--lr-g LR] [--lr-d LR] [--decay D] [-z Z]
optional arguments:
-h, --help show this help message and exit
-g GPU, --gpu GPU set GPU id (default: -1)
-b N, --batch-size N input batch size for training (default: 32)
-e E, --epochs E how many epochs to train (default: 100)
--lr-g LR initial ADAM learning rate of G (default: 2e-4)
--lr-d LR initial ADAM learning rate of D (default: 1e-5)
--decay D weight decay or L2 penalty (default: 0)
-z Z, --zdim Z dimension of latent vector (default: 128)