Body: <p>I am running a code on generative adversarial networks. The code is designed in such a way that it outputs a fake image after every 5 epochs. The total number of epochs is 800 in number.</p>
  <p>After the completion of the program, when I check the images generated by the generator of the generative adversarial network while training, I am so confused about the results.</p>
  <p>The phenomenon is as follows:</p>
  <blockquote>
  <p>Image after epoch n is very realistic, while the images n -5 and n + 5 are
  not highly realistic. I can see many such n's. And vice-versa sometimes.</p>
  </blockquote>
  <p>Although I am interested to know why it happens. My question here is not directly about why it happens. My doubt is about the decision regarding the generator on which I need to evaluate some metrics.</p>
  <p>It is a general practice to evaluate the metric on the generator after the last epoch, that is, the 800th generator. But if the phenomena I told holds, then it may be true that the last generator is not capable of generating realistic images, and the generator 795 or the generator 805 may be good.</p>
  <p>So, if I am correct, I need to check the fake sample generated by the generator and then apply my metric to the generator which generated high quality and realistic image. Am I correct?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-09-30T08:40:42.277Z
Comments.Text: You are generating *random* images, right? Perhaps the images it generates are sometimes realistic and sometimes unrealistic.
Comments.UserDisplayName: Criticize SE actions means ban
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-09-30T08:42:50.677Z
Comments.Text: @user253751 So, you are saying that it is a normal phenomenon?
Comments.UserDisplayName: hanugm
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-09-30T08:46:46.363Z
Comments.Text: Well, that depends on what you count as normal. Have you tried generating *several* images at the same time, to see whether all of them are unrealistic, or just some of them?
Comments.UserDisplayName: Criticize SE actions means ban
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-10-07T07:45:52.167Z
Comments.Text: @user253751 Generating several images. Every of the following happens: All are good, All are bad, Some are good.
Comments.UserDisplayName: hanugm
Tags: <generative-adversarial-networks><generator>
Title: How to understand the results of a generator that switches, for metric evaluation?
UserDisplayName: hanugm
ThreadText: # How to understand the results of a generator that switches, for metric evaluation?
  
  (Asked by: username_0 on 2021-09-29)
  
  I am running a code on generative adversarial networks. The code is designed in such a way that it outputs a fake image after every 5 epochs. The total number of epochs is 800 in number.
  
  After the completion of the program, when I check the images generated by the generator of the generative adversarial network while training, I am so confused about the results.
  
  The phenomenon is as follows:
  
  > Image after epoch n is very realistic, while the images n -5 and n + 5 are
  > not highly realistic. I can see many such n's. And vice-versa sometimes.
  
  Although I am interested to know why it happens. My question here is not directly about why it happens. My doubt is about the decision regarding the generator on which I need to evaluate some metrics.
  
  It is a general practice to evaluate the metric on the generator after the last epoch, that is, the 800th generator. But if the phenomena I told holds, then it may be true that the last generator is not capable of generating realistic images, and the generator 795 or the generator 805 may be good.
  
  So, if I am correct, I need to check the fake sample generated by the generator and then apply my metric to the generator which generated high quality and realistic image. Am I correct?
  
  **Tags:** generative-adversarial-networks, generator
  
  > **Comments:**
  > **username_1**: You are generating *random* images, right? Perhaps the images it generates are sometimes realistic and sometimes unrealistic.  
  > **username_0**: @user253751 So, you are saying that it is a normal phenomenon?  
  > **username_1**: Well, that depends on what you count as normal. Have you tried generating *several* images at the same time, to see whether all of them are unrealistic, or just some of them?  
  > **username_0**: @user253751 Generating several images. Every of the following happens: All are good, All are bad, Some are good.  
  
  ---
  
  ## 0 answers
  
  