python main.py
optional arguments:
-h, --help show this help message and exit
--backend BACKEND theano or tensorflow
--generator GENERATOR
upsampling or deconv
--dset DSET mnist or celebA
--batch_size BATCH_SIZE
Batch size
--n_batch_per_epoch N_BATCH_PER_EPOCH
Number of training epochs
--nb_epoch NB_EPOCH Number of batches per epoch
--epoch EPOCH Epoch at which weights were saved for evaluation
--nb_classes NB_CLASSES
Number of classes
--do_plot DO_PLOT Debugging plot
--bn_mode BN_MODE Batch norm mode
--img_dim IMG_DIM Image width == height
--noise_scale NOISE_SCALE
variance of the normal from which we sample the noise
--label_smoothing smooth the positive labels when training D
--use_mbd use mini batch disc
--label_flipping LABEL_FLIPPING
Probability (0 to 1.) to flip the labels when training
D
Example:
python main.py
N.B. If using the CelebA dataset, make sure to specify the corresponding img_dim value. For instance, if you saved a 64x64 CelebA hdf5 dataset, call python main.py --img_dim 64
- Weights are saved in GAN/models
- Figures are saved in GAN/figures
- Save model weights every few epochs
You can choose the type of generator:
upsampling:generate the image with a series ofUpsampling2DandConvolution2Doperationsdeconv:use keras' transposed convolutionsDeconvolution2D. This is closer to the original DCGAN implementation.
At this stage, deconv only works with the tensorflow backend.