testGAN function for generating new objects
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- Dominant language
- Python
- Stars
- 287
- Forks
- 78
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Description
I'm using the 3dgan_mit_biasfree model. I want to generate new instances.
first of all, I set changed one of the lines to this::
net_g_test = generator(z_vector, phase_train=False, reuse=False)
because it is testing so phase_train should not be true, right? Also, as another person said in another issue, there seems to be a problem with batchnorm when setting reuse=True.
Also, around line 250, there is next_sigma, which is the SD of the normal distributon from which elements of the latent vector z are drawn. My question is since I hardcoded it as .33 [what you found to be good during training], instead of raw_input(), is there a better value I should be using?
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Research direction
Start with the testGAN function and the code around line 250, then trace how phase_train, reuse, batch normalization, and next_sigma are passed into generation. Done means validating the inference settings and latent-vector standard deviation needed to generate new instances, with a reproducible test or example.
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Assessment
- Tech stack
- python, tensorflow
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 30/100