Comments (2)
nice to hear that you enjoy the package.
Instead of modifying the method, you could also just compute log_q
using this method and then applying the weights when computing your loss in an extra step. This might be more efficient since in an importance sampling context the weights might depend on log_q
as well, so you can directly reuse the results and do not have to iterate through the flow twice by calling the forward_kld
and the log_prob
method.
What do you think?
Best regards,
Vincent
from normalizing-flows.
Ah, didn't realize the log_prob
method was almost identical to forward_kld
. This is a much better solution than modifying forward_kld
. I just tried it and it works well. Thanks so much for getting back to me!
from normalizing-flows.
Related Issues (20)
- Putting examples in the documentation HOT 5
- Forward and Inverse with log det function for `MultiscaleFlow`
- multi-gpu implementation HOT 1
- How the inverse was calculated HOT 1
- Conditional Flows implementation / documentation HOT 2
- Remove Lambda's HOT 6
- Negative KL divergence HOT 3
- issue about ConditionalNormalizingFlow HOT 2
- The original glow seems to use `ConditionalDiagGaussian` HOT 1
- exp and sigmoid may cause inf. HOT 3
- Could you give an example for NICE? HOT 1
- NICE demo? HOT 1
- What dou you mean by "Augmented Normalizing Flow based on Real NVP"? HOT 1
- one-dimensional coupling flows do not work HOT 3
- Seeking Advice on Designing an Invertible Neural Network for Fission HOT 2
- Calculating forward KL divergence (probability density maximization), I get negative loss results on my dataset, is this reasonable? HOT 1
- Cannot have an odd latent_size (working with 2, 4, etc. , but not 3 or 5), shape problem HOT 2
- Conditional Coupling Layers
- Documentation clarification of forward_kld HOT 1
- Generalization for higher-dimensional data HOT 1
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from normalizing-flows.