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View Code? Open in Web Editor NEWPyTorch implementation of "Weight Uncertainty in Neural Networks"
Home Page: https://www.nitarshan.com/bayes-by-backprop/
PyTorch implementation of "Weight Uncertainty in Neural Networks"
Home Page: https://www.nitarshan.com/bayes-by-backprop/
Hi, in Class BayesianLinear, the weight is sampled from a unimodal Gaussian distribution
self.weight = Gaussian(self.weight_mu, self.weight_rho)
, and the prior of weight is sampled from a mixture Gaussian
self.weight_prior = ScaleMixtureGaussian(PI, SIGMA_1, SIGMA_2)
But is it possible to use a single Gaussian distribution to approximate mixture Gaussian distribution theoretically? It seems meaningless...
Hi, shouldn't pi be a parameter rather than a fixed value during training? Because you don't know what the real distribution of weight is. Thanks.
Is .backward() doing the rep trick automatically?
The return statement should be:
return self.l1.log_variational_posterior
+ self.l2.log_variational_posterior
+ self.l3.log_variational_posterior
instead of the current :
return self.l1.log_variational_posterior
+ self.l2.log_variational_posterior
+ self.l2.log_variational_posterior
Currently, the log posterior of l2 is being counted twice and the log posterior of l3 is not being taken into account at all.
Hi, I can figure out the role of self.training, please help me
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