Stuck on an issue?

Lightrun Answers was designed to reduce the constant googling that comes with debugging 3rd party libraries. It collects links to all the places you might be looking at while hunting down a tough bug.

And, if you’re still stuck at the end, we’re happy to hop on a call to see how we can help out.

Unable to reproduce results: Generator output has only two face types

See original GitHub issue

Anyone else having trouble training and reproducing example results for CelebA dataset?

After training on the CelebA dataset for 500,000 iterations, or about 33 hours, with the default settings, I had a look at the output and noticed something odd. The generated images, *_G.png, and fake images, fake.png, have only two face types in each file. Between files they were different, but in each file there are always just two distinct faces.

The same problem occurs in the interpolation tests. The output from setting --is_train=False has the same problem same problem and tries to interpolate between two distinct faces: python --dataset=CelebA --load_path=CelebA_0625_080047 --use_gpu=True --is_train=False --split valid

  1. Is this an example of mode collapse?
  2. Is this a bug or incorrect parameter defaults?
  3. Has anyone been able to reproduce the results on the README?

Issue Analytics

  • State:open
  • Created 6 years ago
  • Comments:6

github_iconTop GitHub Comments

luban-emc2commented, Jul 21, 2017

I recommend you to adopt fwiffo’s advice: change --d_lr to 0.00004, change --g_lr to 0.00004, reduce --lr_update_step, (you can also set --batch_size to 4 for faster training.)


zacharynevincommented, Apr 4, 2018

@duduheihei The z input does change. tf.random_normal changes every time is called (

Read more comments on GitHub >

github_iconTop Results From Across the Web

python - Not able to reproduce results with Tensorflow even ...
Here is the code I am using to construct and train the model: def construct_autoencoder(input_dim, encoded_dim): # Add input input = Input(shape ......
Read more >
can't reproduce results even set all random seeds #7068
I set all random seeds but I still can't reproduce results. ... here only imply consistency across e.g. two consecutive runs that both...
Read more >
How to Solve Reproducibility in ML -
Complex challenges make it seem almost impossible to replicate ML results from papers, and we're going to explore these challenges in a second....
Read more >
Measuring Mode Collapse in GANs Using Weights & Biases
The two most common types of failures are convergence failure (failing ... when the generator can only produce a single type of output...
Read more >
Re-run, Repeat, Reproduce, Reuse, Replicate - Frontiers
Scientific code, by producing results that are then analyzed and ... With only the article, the researcher will often be unable to ...
Read more >

github_iconTop Related Medium Post

No results found

github_iconTop Related StackOverflow Question

No results found

github_iconTroubleshoot Live Code

Lightrun enables developers to add logs, metrics and snapshots to live code - no restarts or redeploys required.
Start Free

github_iconTop Related Reddit Thread

No results found

github_iconTop Related Hackernoon Post

No results found

github_iconTop Related Tweet

No results found

github_iconTop Related Post

No results found

github_iconTop Related Hashnode Post

No results found