mindspore.nn.probability
Probability.
The high-level components used to construct the probabilistic network.
Bijectors
Bijecotr class. |
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Exponential Bijector. |
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GumbelCDF Bijector. |
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Invert Bijector. |
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Power Bijector. |
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Scalar Affine Bijector. |
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Softplus Bijector. |
Bayesian Layers
Convolutional variational layers with Reparameterization. |
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Dense variational layers with Local Reparameterization. |
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Dense variational layers with Reparameterization. |
Prior and Posterior Distributions
Build Normal distributions with trainable parameters. |
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To initialize a normal distribution of mean 0 and standard deviation 0.1. |
Bayesian Wrapper Functions
Generate a suitable WithLossCell for BNN to wrap the bayesian network with loss function. |
Distributions
Bernoulli Distribution. |
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Beta distribution. |
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Create a categorical distribution parameterized by event probabilities. |
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Cauchy distribution. |
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Base class for all mathematical distributions. |
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Example class: Exponential Distribution. |
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Gamma distribution. |
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Geometric Distribution. |
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Gumbel distribution. |
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Logistic distribution. |
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LogNormal distribution. |
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Normal distribution. |
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Poisson Distribution. |
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Transformed Distribution. |
Example class: Uniform Distribution. |
Deep Probability Networks
Conditional Variational Auto-Encoder (CVAE). |
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Variational Auto-Encoder (VAE). |
Infer
The Evidence Lower Bound (ELBO). |
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Stochastic Variational Inference(SVI). |
ToolBox
Toolbox for Uncertainty Evaluation. |
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Toolbox for anomaly detection by using VAE. |
Model Transformer
Transform Deep Neural Network (DNN) model to Bayesian Neural Network (BNN) model. |