text: #!/usr/bin/env python3
  
  class config_train(object):
      mode = 'gan-train'
      num_epochs = 5
      batch_size = 1
      ema_decay = 0.999
      G_learning_rate = 2e-4
      D_learning_rate = 2e-4
      lr_decay_rate = 2e-5
      momentum = 0.9
      weight_decay = 5e-4
      noise_dim = 128
      optimizer = 'adam'
      kernel_size = 3
      diagnostic_steps = 256
  
      # WGAN
      gradient_penalty = True
      lambda_gp = 10
      weight_clipping = False
      max_c = 1e-2
      n_critic_iterations = 20
  
      # Compression
      lambda_X = 12
      channel_bottleneck = 8
      sample_noise = False
      use_vanilla_GAN = False
      use_feature_matching_loss = True
      upsample_dim = 256
      multiscale = True
      feature_matching_weight = 10
      use_conditional_GAN = False
  
  class config_test(object):
      mode = 'gan-test'
      num_epochs = 512
      batch_size = 1
      ema_decay = 0.999
      G_learning_rate = 2e-4
      D_learning_rate = 2e-4
      lr_decay_rate = 2e-5
      momentum = 0.9
      weight_decay = 5e-4
      noise_dim = 128
      optimizer = 'adam'
      kernel_size = 3
      diagnostic_steps = 256
  
      # WGAN
      gradient_penalty = True
      lambda_gp = 10
      weight_clipping = False
      max_c = 1e-2
      n_critic_iterations = 5
  
      # Compression
      lambda_X = 12
      channel_bottleneck = 8
      sample_noise = True
      use_vanilla_GAN = False
      use_feature_matching_loss = True
      upsample_dim = 256
      multiscale = True
      feature_matching_weight = 10
      use_conditional_GAN = False
  
  class directories(object):
      train = 'data/celeb_paths/validation_paths.h5'
      test = 'data/celeb_paths/test_paths.h5'
      # val = 'data/cityscapes_paths_val.h5'
      tensorboard = 'tensorboard'
      checkpoints = 'checkpoints'
      checkpoints_best = 'checkpoints/best'
      samples = 'samples/cityscapes'
  
  