#StackBounty: #python #neural-network #pytorch #generative-adversarial-network DCGAN debugging. Getting just garbage

Bounty: 50

Introduction:

I am trying to get a CDCGAN (Conditional Deep Convolutional Generative Adversarial Network) to work on the MNIST dataset which should be fairly easy considering that the library (PyTorch) I am using has a tutorial on its website.
But I can’t seem to get It working it just produces garbage or the model collapses or both.

What I tried:

  • making the model Conditional semi-supervised learning
  • using batch norm
  • using dropout on each layer besides the input/output layer on the generator and discriminator
  • label smoothing to combat overconfidence
  • Adding noise to the images (I guess you call this instance noise) to get a better data distribution
  • Use leaky relu to avoid vanishing gradients
  • Using a replay buffer to combat forgetting of learned stuff and overfitting
  • playing with hyperparameters
  • comparing it to the model from PyTorch tutorial
  • Basicaly what I did besides some things like Embedding layer ect.

Images my Model generated:

Hyperparameters:

batch_size=50, learning_rate_discrimiantor=0.0001, learning_rate_generator=0.0003, shuffle=True, ndf=64, ngf=64, droupout=0.5
enter image description here
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batch_size=50, learning_rate_discriminator=0.0003, learning_rate_generator=0.0003, shuffle=True, ndf=64, ngf=64, dropout=0
enter image description here
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enter image description here
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Images Pytorch tutorial Model generated:

Code for the pytorch tutorial dcgan model
As comparison here are the images from the DCGAN from the pytorch turoial:
enter image description here
enter image description here
enter image description here

My Code:

Placeholder code. I couldn't get the code formatting to work with my code. 
I got the whole time complains: 
"Your post appears to contain code that is not properly formatted as code".

First link to my Code (Pastebin)
Second link to my Code (0bin)

Conclusion:

Since I implemented all these things (e.g. label smoothing) which are considered beneficial to a GAN/DCGAN.
And my Model still performs worse than the Tutorial DCGAN from PyTorch I think I might have a bug in my code but I can’t seem to find it.

Reproducibility:

You should be able to just copy the code and run it if you have the libraries that I imported installed to look for yourself if you can find anything.

I appreciate any feedback.


Get this bounty!!!

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