probly.layers.torch

torch layer implementations.

Classes

BatchEnsembleConv2d	BatchEnsemble convolutional layer based on [WTB20].
BatchEnsembleLinear	BatchEnsemble linear layer based on [WTB20].
BayesConv2d	Implementation of a Bayesian convolutional layer based on [BCKW15].
BayesLinear	Implement a Bayesian linear layer based on [BCKW15].
DropConnectLinear	Custom Linear layer with DropConnect applied to weights during training based on [MYM+21].
HeteroscedasticLayer	A unified PyTorch implementation of the Heteroscedastic layer based on [CMK+21].
IRDHead	Head that converts encoded features into Dirichlet concentration parameters (alpha).
IntBatchNorm1d	Interval-valued batch normalization for 1D features based on [WCM+24].
IntBatchNorm2d	Interval-valued batch normalization for 2D feature maps based on [WCM+24].
IntConv2d	Interval-arithmetic 2D convolution based on [WCM+24].
IntLinear	Interval-arithmetic linear layer based on [WCM+24].
IntSoftmax	Interval SoftMax head based on [WCM+24].
NatPNClassHead	Dirichlet posterior head for evidential classification.
NatPNRegHead	Gaussian posterior head for evidential regression.
NormalInverseGammaLinear	Custom Linear layer for a normal-inverse-gamma-distribution based on [ASSR20].
RadialNormalizingFlow	Radial normalizing flow based on [RM15].
RadialNormalizingFlowStack	Stack of radial normalizing flows based on [RM15].
RegressionHead	Head that converts encoded features into evidential Normal-Gamma parameters.
SNGPLayer	Spectral-normalized Neural Gaussian Process (SNGP) layer based on [LLP+20].
SpectralNormWithMultiplier	Applies spectral normalization with a bounded multiplier to a module's weight suggested by [LLP+20].

Functions

pack_interval	Promote x to a packed interval tensor with lo == hi == x.
unpack_interval	Split a packed interval tensor on channel_dim into its (lo, hi) halves.