API Reference¶

Generative Classification¶

class vbll.layers.classification.GenClassification(*args: Any, **kwargs: Any)[source]¶

Variational Bayesian Generative Classification

in_featuresint: Number of input features
out_featuresint: Number of output features
regularization_weightfloat: Weight on regularization term in ELBO
parameterizationstr: Parameterization of covariance matrix. Currently supports ‘dense’ and ‘diagonal’
softmax_boundstr: Bound to use for softmax. Currently supports ‘jensen’
return_oodbool: Whether to return OOD scores
prior_scalefloat: Scale of prior covariance matrix
wishart_scalefloat: Scale of Wishart prior on noise covariance
doffloat: Degrees of freedom of Wishart prior on noise covariance

class vbll.layers.classification.DiscClassification(*args: Any, **kwargs: Any)[source]¶

Variational Bayesian Disciminative Classification

in_featuresint: Number of input features
out_featuresint: Number of output features
regularization_weightfloat: Weight on regularization term in ELBO
parameterizationstr: Parameterization of covariance matrix. Currently supports ‘dense’ and ‘diagonal’
softmax_boundstr: Bound to use for softmax. Currently supports ‘jensen’
return_oodbool: Whether to return OOD scores
prior_scalefloat: Scale of prior covariance matrix
wishart_scalefloat: Scale of Wishart prior on noise covariance
doffloat: Degrees of freedom of Wishart prior on noise covariance

class vbll.layers.regression.Regression(*args: Any, **kwargs: Any)[source]¶

Variational Bayesian Linear Regression

in_featuresint: Number of input features
out_featuresint: Number of output features
regularization_weightfloat: Weight on regularization term in ELBO
parameterizationstr: Parameterization of covariance matrix. Currently supports ‘dense’ and ‘diagonal’
prior_scalefloat: Scale of prior covariance matrix
wishart_scalefloat: Scale of Wishart prior on noise covariance
doffloat: Degrees of freedom of Wishart prior on noise covariance