mindspore.nn.probability

Probability.

The high-level components used to construct the probabilistic network.

Bijectors

Bayesian Layers

mindspore.nn.probability.bnn_layers.ConvReparam

Convolutional variational layers with Reparameterization.

mindspore.nn.probability.bnn_layers.DenseLocalReparam

Dense variational layers with Local Reparameterization.

mindspore.nn.probability.bnn_layers.DenseReparam

Dense variational layers with Reparameterization.

Prior and Posterior Distributions

mindspore.nn.probability.bnn_layers.NormalPosterior

Build Normal distributions with trainable parameters.

mindspore.nn.probability.bnn_layers.NormalPrior

To initialize a normal distribution of mean 0 and standard deviation 0.1.

Bayesian Wrapper Functions

mindspore.nn.probability.bnn_layers.WithBNNLossCell

Generate a suitable WithLossCell for BNN to wrap the bayesian network with loss function.

Distributions

Deep Probability Networks

mindspore.nn.probability.dpn.ConditionalVAE

Conditional Variational Auto-Encoder (CVAE).

mindspore.nn.probability.dpn.VAE

Variational Auto-Encoder (VAE).

Infer

mindspore.nn.probability.infer.ELBO

The Evidence Lower Bound (ELBO).

mindspore.nn.probability.infer.SVI

Stochastic Variational Inference(SVI).

ToolBox

mindspore.nn.probability.toolbox.UncertaintyEvaluation

Toolbox for Uncertainty Evaluation.

mindspore.nn.probability.toolbox.VAEAnomalyDetection

Toolbox for anomaly detection by using VAE.

Model Transformer

mindspore.nn.probability.transforms.TransformToBNN

Transform Deep Neural Network (DNN) model to Bayesian Neural Network (BNN) model.