PyTorch implementation of Auto-Encoding Variational Bayes, arxiv:1312.6114
$ git clone https://github.com/kuc2477/pytorch-vae && cd pytorch-vae
$ pip install -r requirements.txt
Implementation CLI is provided by main.py
$ ./main.py --help
$ usage: VAE PyTorch implementation [-h] [--dataset {mnist,cifar10,cifar100}]
[--kernel-num KERNEL_NUM] [--z-size Z_SIZE]
[--epochs EPOCHS] [--batch-size BATCH_SIZE]
[--sample-size SAMPLE_SIZE] [--lr LR]
[--weight-decay WEIGHT_DECAY]
[--loss-log-interval LOSS_LOG_INTERVAL]
[--image-log-interval IMAGE_LOG_INTERVAL]
[--resume] [--checkpoint-dir CHECKPOINT_DIR]
[--sample-dir SAMPLE_DIR] [--no-gpus]
(--test | --train)
optional arguments:
-h, --help show this help message and exit
--dataset {mnist,cifar10,cifar100}
--kernel-num KERNEL_NUM
--z-size Z_SIZE
--epochs EPOCHS
--batch-size BATCH_SIZE
--sample-size SAMPLE_SIZE
--lr LR
--weight-decay WEIGHT_DECAY
--loss-log-interval LOSS_LOG_INTERVAL
--image-log-interval IMAGE_LOG_INTERVAL
--resume
--checkpoint-dir CHECKPOINT_DIR
--sample-dir SAMPLE_DIR
--no-gpus
--test
--train
./main.py --train
./main.py --test
Ha Junsoo / @kuc2477 / MIT License