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python>=2.7
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tensorflow>=1.9
Have tested with mnsit data set. It will be automatically downloaded to data folder
python train-WGAN-GP-VDB.py
- Generator and Discriminator Architectures are inside model.py script
- Normal wgan-gp loss and discriminator bottleneck layer loss are insode the vdb_losses.py script
- I_c - This is the information contrain. This is a hyper paramter
- Bottleneck Layer Dimentions
- Alpha - This paramters is to update the adaptive lagrange paramters (Documentation can be found inside the code)
- Trained paramters can be found inside train-WGAN-GP-VDB.py script
- There are pre-trained checkpoints in the checkpoints folder
- The images generated during the training progress are insode the sample_images_while_training folder
- Tensorbored Visualizations can be find inside summeries folder
- You can examine the learning progress by visualizing two loss functions of generator and discriminator
- Also its impotant to undertand the change in beta parameter(Eq(6) in the paper) with adaptive update method. Here we maximize the beta
- The modified descriminator can be easily use with GAIL.
- Cheking the effect of varios hyper paramters and how generator behaves acording to them