This is an improved implementation of the paper Auto-Encoding Variational Bayes by Kingma and Welling. It uses ReLUs and the adam optimizer, instead of sigmoids and adagrad. These changes make the network converge much faster.
pip install -r requirements.txt
python main.pyThe main.py script accepts the following arguments:
optional arguments:
--batch-size input batch size for training (default: 128)
--epochs number of epochs to train (default: 10)
--no-cuda enables CUDA training
--seed random seed (default: 1)
--log-interval how many batches to wait before logging training status