Comments (2)
I believe the initializations made it faster to train. logits
are mixture parameters and initialized to be uniform. means
are initialized to be uniformly distributed across the entire range of possible values (e.g. evenly spaced bins from 0-255 if we're doing rgb values). log_scales
are just initialized randomly, but could also be initialized to unit variance. Overall, I think the means
initialization helped the most and the logits
and log_scales
initializations could be zeros or randn.
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Thank you very much !
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Related Issues (13)
- Bug in MADE sampling implementation in 'hw1_solutions.ipynb' HOT 1
- `optimizer.zero_grad` called after calculating loss instead of before in `lecture3_flow_models_demos.ipynb`.
- HW2 autoregressive flow for images solution issue HOT 3
- Is log_det in preprocess function useful? HW2 HOT 1
- Where is HW 4? HOT 1
- Question regarding solutions availability
- clarification question
- HW1 MADE | possible flaws in solution implementaiotn. HOT 2
- Flows demo fails because of missing `.to(ptu.device)` HOT 2
- ActNorm implementation missing division by `std` on the shift parameter
- Reporting bugs/errors in `lecture3_flow_models_demos.ipynb` HOT 4
- What is `self.loc` in `MixtureCDFFlow` class? HOT 1
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