Comments (1)
NICE is simply Real NVP but without the scale in the affine map, so you can follow the Real NVP example and set the scale
argument in the AffineCouplingBlock
to False
. Note that this reduces the number of parameters by a factor of two, so in the example notebook you have to change the network outputting the parameters to
param_map = nf.nets.MLP([1, 64, 64, 1], init_zeros=True)
Best regards,
Vincent
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
- 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
- Generalization for higher-dimensional data
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from normalizing-flows.