bayesian-neural-network-mnist

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Critical Error with Updated Dependencies

4

Using updated versions of torch, torchvision, and pyro dependencies, an error (below) occurs in the instance of SVI where the event_dims between the model and guide disagree at site 'module$$$out.weight':...

jbhatch

Possible minor fix needed in test_batch(...)

5

When working with random data, ```python test_batch(images_random, labels_random) ``` multiple runs interrupted with this small snafu: ```python-traceback Summary Total images: 100 Predicted for: 0 --------------------------------------------------------------------------- ZeroDivisionError Traceback (most recent call...

NirantK

Hi, In model(x_data, y_data), lhat = log_softmax(lifted_reg_model(x_data)). In give_uncertainities(x), yhats = [F.log_softmax(model(x.view(-1,28*28)).data, 1).detach().numpy() for model in sampled_models] Does it mean you use log_softmax twice?

RaptorMai

Hi! I really like your work and I'm trying to do something similar. I didn't catch how do you 'predict' uncertainty. For sure I'm missing something but for me it's...

antoalli