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cs501r_f2016:lab7 [2016/11/01 14:44]
wingated
cs501r_f2016:lab7 [2017/10/17 14:55] (current)
wingated
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 +====WARNING THIS LAB SPEC IS UNDER DEVELOPMENT:​====
 +
 +
 ====Objective:​==== ====Objective:​====
  
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 For this lab, you will need to implement a generative adversarial For this lab, you will need to implement a generative adversarial
-network (GAN).  ​You will generate images that look like MNIST digits.+network (GAN).  ​ 
 +Specifically,​ we will be using the technique outlined in the paper [[https://​arxiv.org/​pdf/​1704.00028|Improved Training of Wasserstein GANs]].
  
-You should turn in an iPython notebook that shows a single ​plot, which +You should turn in an iPython notebook that shows a two plots. ​ The first plot should ​be random ​samples from the final generator. ​ The second should show interpolation between two faces by interpolating in ''​z''​ space.
-will be samples from the final GAN.+
  
-An example of my final samples is shown at the right.+You must also turn in your code, but your code does not need to be in a notebook, if it's easier to turn it in separately (but please zip your code and notebook together in a single zip file).
  
-You are welcome to turn in your image and your code separately.+An example of my final samples is shown at the right.
  
 **NOTE:** this lab is complex. ​ Please read through **the entire **NOTE:** this lab is complex. ​ Please read through **the entire
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 discriminator and generator separately, we'll need to be able to train discriminator and generator separately, we'll need to be able to train
 on subsets of variables. on subsets of variables.
- 
-This lab is a bit more complex than some of the others, so we are 
-providing [[http://​liftothers.org/​byu/​lab7_scaffold.py|some scaffold code]] 
  
 In the scaffold code, you will find the following: In the scaffold code, you will find the following:
cs501r_f2016/lab7.txt · Last modified: 2017/10/17 14:55 by wingated