Hi all,
I wrote this GAN (in the example it uses fake input):
  
      gist.github.com
  
  
    https://gist.github.com/pzaffino/7c3714ffe8eb867eb45b721ac4d2d808 5
pytorch_error.py
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.models
import torch.nn.functional as F
 
class Generator(nn.Module):
    def __init__(self, initial_features, num_channels=1, dropout=0.1):
        super(Generator, self).__init__()
 
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Basically, the generative accuracy is quantified in terms of image difference.
The generator net and the discriminator net get trained (individually), but it looks like they don’t “communicate”. I mean, the discriminator judgment doesn’t affect the generative training…the training goes parallel, without affecting each other.
Any idea? Am I missing something?
I multiply the discriminator score by a float factor in order to make it comparable to the image difference.
Thank you in advance.
Best,
Paolo