Python Class for Configuring GAN Training Parameters

class training_config(object): 'Wrapper class for parameters for training'

def __init__(self):
    self.gen_learning_rate = 0.01 #learning rate of generator
    self.gen_update_time = 1 #update times of generator in adversarial training
    self.dis_update_time_adv = 5 #update times of discriminator in adversarial training
    self.dis_update_epoch_adv = 3 #update epoch / times of discriminator
    self.dis_update_time_pre = 50 #pretraining times of discriminator
    self.dis_update_epoch_pre = 3 #number of epoch / time in pretraining
    self.pretrained_dis_path = 'pretrained_discriminator.pth' #path of pretrained discriminator model
    self.batch_size = 64 #batch size for training
    self.num_epochs = 100 #number of epochs for training
    self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') #device for training, use GPU if available
    self.log_step = 100 #log training progress every log_step iterations
    self.sample_step = 500 #generate samples every sample_step iterations
    self.save_step = 1000 #save model every save_step iterations
    self.save_path = 'saved_models/' #path to save trained models
    self.log_path = 'logs/' #path to save training logs