Body: <p>I'm working with a DCGAN, a deep CNN for classifying images with a GAN that competes with the classifier to generate images of what we are classifying. </p>
  
  <p>The goal of the project at the moment is to produce AI generated memes in the form of pepe the frog, based on a dataset found on the internet of roughly 2000 images. I scaled them all maintaining aspect ratio as my only form of normalization. </p>
  
  <p>As I train my data (I've tried many combinations of hyperparameters) upwards of 100k epochs (batch 32) my classifying network gets around 1^-6 average while my GAN approaches nearly 16, yes 16, with a properly defined loss function.</p>
  
  <p>Now because my images are typically made with random features, some containing text, others containing full body renditions, turned away from viewport etc... I'm  assuming that this is because of the data I'm training with and it's diverse amount of features, is my reasoning correct? Also if allowed to continue is it possible that the GAN learns to properly generate the data?</p>
  
  <p>The main reason I have come to the above conclusion is that if I train on a few hand picked examples that have similar artistic styles/orientations and such (less than 100) and let it train my GAN will generate decent images, however they have low variability.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-06-13T20:28:31.280Z
Comments.Text: Noone seems to know?
Comments.UserDisplayName: John R.
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-07-05T19:27:24.290Z
Comments.Text: Still looking for input on this matter
Comments.UserDisplayName: John R.
Tags: <neural-networks><generative-adversarial-networks>
Title: DCGAN loss determining data normalization problems
UserDisplayName: John R.
ThreadText: # DCGAN loss determining data normalization problems
  
  (Asked by: username_0 on 2019-06-11)
  
  I'm working with a DCGAN, a deep CNN for classifying images with a GAN that competes with the classifier to generate images of what we are classifying.
  
  The goal of the project at the moment is to produce AI generated memes in the form of pepe the frog, based on a dataset found on the internet of roughly 2000 images. I scaled them all maintaining aspect ratio as my only form of normalization.
  
  As I train my data (I've tried many combinations of hyperparameters) upwards of 100k epochs (batch 32) my classifying network gets around 1^-6 average while my GAN approaches nearly 16, yes 16, with a properly defined loss function.
  
  Now because my images are typically made with random features, some containing text, others containing full body renditions, turned away from viewport etc... I'm assuming that this is because of the data I'm training with and it's diverse amount of features, is my reasoning correct? Also if allowed to continue is it possible that the GAN learns to properly generate the data?
  
  The main reason I have come to the above conclusion is that if I train on a few hand picked examples that have similar artistic styles/orientations and such (less than 100) and let it train my GAN will generate decent images, however they have low variability.
  
  **Tags:** neural-networks, generative-adversarial-networks
  > **Comments:**
  > **username_0**: Noone seems to know?  
  > **username_0**: Still looking for input on this matter  
  
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  ## 0 answers
  
  