Answers.Body: <p>Loss curves of generative adversarial networks (GANs) are very difficult to interpret. It depends on the type of GAN used, the type of data used, the size of the networks, the size of the dataset, etc. etc..</p>
  <p>I have trained a ton of GANs and have seen all types of loss curves work. I know this recommendation is not going to be a fun one but the best way to gauge whether your GAN is converging is just to look at the output it generates. If that is improving, then it likely is going well.</p>
  <p>There are metrics for assessing GAN quality which are more interpretable. If you are working with images, there are quite good metrics that depend on pretrained image classifiers (such as FID).</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-04-30T10:26:26.867Z
Answers.Text: thank you! when I check the output from epoch 2000 and compare it with the output of epoch 3000 I can't see any difference... maybe these metrics don't matter much then. Thanks!
Answers.UserDisplayName: user17952421
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-04-30T06:53:28.017Z
Answers.LastActivityDate: 2024-04-30T06:53:28.017Z
Answers.UserDisplayName: Robin van Hoorn
Body: <p>I'm still learning ml/ai and I'm running a training where the curves look like this.</p>
  <p>I was told that this looks good by some and that it doesn't look good by others... But none told me exactly why, I was told that I should look for both lines to indicate a reduction in loss, but does it look good or not?</p>
  <p>thanks!</p>
  <p><a href="https://i.sstatic.net/IY4tLrFW.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/IY4tLrFW.png" alt="enter image description here" /></a></p>
  <p>this is with smoothing set to 0.6:
  <a href="https://i.sstatic.net/V00vioMt.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/V00vioMt.png" alt="enter image description here" /></a></p>
  
Tags: <training><generative-adversarial-networks>
Title: How to interpret this training
UserDisplayName: user17952421
ThreadText: # How to interpret this training
  
  (Asked by: username_0 on 2024-04-29)
  
  I'm still learning ml/ai and I'm running a training where the curves look like this.
  
  I was told that this looks good by some and that it doesn't look good by others... But none told me exactly why, I was told that I should look for both lines to indicate a reduction in loss, but does it look good or not?
  
  thanks!
  
  [![enter image description here](https://i.sstatic.net/IY4tLrFW.png)](https://i.sstatic.net/IY4tLrFW.png)
  
  this is with smoothing set to 0.6:
  [![enter image description here](https://i.sstatic.net/V00vioMt.png)](https://i.sstatic.net/V00vioMt.png)
  
  **Tags:** training, generative-adversarial-networks
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2024-04-30. Score: 2 | Accepted answer
  
  Loss curves of generative adversarial networks (GANs) are very difficult to interpret. It depends on the type of GAN used, the type of data used, the size of the networks, the size of the dataset, etc. etc..
  
  I have trained a ton of GANs and have seen all types of loss curves work. I know this recommendation is not going to be a fun one but the best way to gauge whether your GAN is converging is just to look at the output it generates. If that is improving, then it likely is going well.
  
  There are metrics for assessing GAN quality which are more interpretable. If you are working with images, there are quite good metrics that depend on pretrained image classifiers (such as FID).
  
  > **Comments:**
  > **username_0**: thank you! when I check the output from epoch 2000 and compare it with the output of epoch 3000 I can't see any difference... maybe these metrics don't matter much then. Thanks!  
  
  ---
  
  