n2j.losses.gaussian_nll
#
Gaussian mixture negative log likelihoods that can be evaluated, for use as loss functions, but also generate samples when parameters are given
Module Contents#
Classes#
The negative log likelihood (NLL) for a single Gaussian with diagonal |
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The negative log likelihood (NLL) for a single Gaussian with a full-rank |
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The negative log likelihood (NLL) for a mixture of two Gaussians, each |
- class n2j.losses.gaussian_nll.DiagonalGaussianNLL(Y_dim)[source]#
Bases:
BaseGaussianNLL
The negative log likelihood (NLL) for a single Gaussian with diagonal covariance matrix
BaseGaussianNLL.__init__ docstring for the parameter description.
- __call__(pred, target)[source]#
Evaluate the NLL. Must be overridden by subclasses.
Parameters#
- predtorch.Tensor
raw network output for the predictions
- targettorch.Tensor
Y labels
- class n2j.losses.gaussian_nll.FullRankGaussianNLL(Y_dim)[source]#
Bases:
BaseGaussianNLL
The negative log likelihood (NLL) for a single Gaussian with a full-rank covariance matrix
See BaseGaussianNLL.__init__ docstring for the parameter description.
- __call__(pred, target)[source]#
Evaluate the NLL. Must be overridden by subclasses.
Parameters#
- predtorch.Tensor
raw network output for the predictions
- targettorch.Tensor
Y labels
- class n2j.losses.gaussian_nll.DoubleGaussianNLL(Y_dim)[source]#
Bases:
BaseGaussianNLL
The negative log likelihood (NLL) for a mixture of two Gaussians, each with a full but constrained as low-rank plus diagonal covariance
Only rank 2 is currently supported. BaseGaussianNLL.__init__ docstring for the parameter description.
- __call__(pred, target)[source]#
Evaluate the NLL. Must be overridden by subclasses.
Parameters#
- predtorch.Tensor
raw network output for the predictions
- targettorch.Tensor
Y labels