title: Two questions on DCGAN: data normalization and fake/real batch
body: <p>I am analyzing a meta-learning <a href="https://github.com/LuEE-C/FIGR/blob/master/train.py" rel="nofollow noreferrer">class</a> that uses DCGAN + Reptile within the image generation.</p>
  
  <p>I have two questions about this code. </p>
  
  <p>First question: why during DCGAN training (line 74)</p>
  
  <pre><code>training_batch = torch.cat ([real_batch, fake_batch])
  </code></pre>
  
  <p>is a training_batch made up of real examples (real_batch) and fake examples (fake_batch) created? Why is training done by mixing real and false images? I have seen many DCGANs, but never with training done in this way.</p>
  
  <p>The second question: why is the normalize_data function (line 49) and the unnormalize_data function (line 55) used during training?</p>
  
  <pre><code>def normalize_data(data):
      data *= 2
      data -= 1
      return data
  
  
  def unnormalize_data(data):
      data += 1
      data /= 2
      return data
  </code></pre>
  
  <p>The project uses the Mnist dataset, if I wanted to use a color dataset like CIFAR10, do I have to modify those normalizations?</p>