A PyTorch Implementation of Goodfellow et al.'s Paper on Generative Adversarial Networks. Find the paper at: https://arxiv.org/pdf/1406.2661.pdf
Currently has MNIST experiment implemented. Built with torch 1.1.0 and python3.6.
pip install -r requirements.txt
python train.py --epochs 300 --lr 1e-4 --batch-size 32
Once train.py is running one can open a new shell and running tensboard in order to track various metrics and current generated images during training.
tensorboard --logdir=runs/<CURRENT_RUN_DIRECTORY>
One can use different arguments defined in train.py to adjust various hyperparameters
--epochs EPOCHS number of epochs to train for (default: 300)
--lr LR learning rate for optimizer (default: 1e-4)
--batch-size BATCH_SIZE
number of examples in a batch (default: 32)
--device DEVICE device to train on (default: cuda:0 if cuda is
available otherwise cpu)
--latent-size LATENT_SIZE
size of latent space vectors (default: 64)
--g-hidden-size G_HIDDEN_SIZE
number of hidden units per layer in G (default: 256)
--d-hidden-size D_HIDDEN_SIZE
number of hidden units per layer in D (default: 256)
