Loss Functions
- class softsensor.losses.BetaNLL(beta=0.15)[source]
Compute the beta negative log likelihood loss
“On the Pitfalls of Heteroscedastic Uncertainty Estimation with Probabilistic Neural Networks” [Seitzer et al. 2022 https://openreview.net/forum?id=aPOpXlnV1T]
- Parameters:
beta (float in range (0, 1)) – beta parameter that defines the degree to which the gradients are weighted by predicted variance
- Return type:
None
- class softsensor.losses.DistributionalMSELoss[source]
Wrapper that computes MSE loss on the mean of the distribution prediction
- Parameters:
None
- Return type:
None
- class softsensor.losses.GaussianNLLLoss[source]
Compute the Gaussian negative log likelihood loss
Heteroscedastic NLL loss for aleatoric uncertainty (equation 5) from the paper “What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?” [Kendall & Gal 2017 https://arxiv.org/pdf/1703.04977.pdf]
- Parameters:
None
- Return type:
None
- class softsensor.losses.PSDLoss(fs, freq_range=None, window=128, type='msle')[source]
Compute the PSD loss
- Parameters:
None
- Return type:
None
- class softsensor.losses.PinballLoss(quantiles)[source]
Compute the Pinball loss (quantile loss)
Based on the qr loss as defined in “Estimating conditional quantiles with the help of the pinball loss” [Steinwart & Christmann 2011] https://arxiv.org/pdf/1102.2101.pdf
- Parameters:
quantiles (list[x] with x float in range (0, 1)) – quantiles to compute the loss on
- Return type:
None