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yingchen 2021-12-17 14:30:25 +08:00
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docs/OWNERS
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@ -3,3 +3,5 @@ approvers:
- Hanshize
- rudy_tan
- jonyguo
- lvyufenghw
- zh_qh

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docs/api/api_python/mindspore/mindspore.dtype.rst
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============================ =================
类型 描述
============================ =================
``Tensor`` MindSpore中的张量类型。数据格式采用NCHW。详情请参考 `tensor <https://www.gitee.com/mindspore/mindspore/blob/master/mindspore/common/tensor.py>_`.
``Tensor`` MindSpore中的张量类型。数据格式采用NCHW。详情请参考 `tensor <https://www.gitee.com/mindspore/mindspore/blob/master/mindspore/python/mindspore/common/tensor.py>_`.
``bool_`` 布尔型，值为 ``True`` 或者 ``False`` 。
``int_`` 整数标量。
``uint`` 无符号整数标量。

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mindspore/python/mindspore/nn/probability/README.md
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@ -15,24 +15,24 @@ The objective of MDP is to integrate deep learning with Bayesian learning. On th
### Layer 1-1: Statistical distributions classes used to generate stochastic tensors
- Distributions ([mindspore.nn.probability.distribution](https://gitee.com/mindspore/mindspore/tree/master/mindspore/nn/probability/distribution)): A large collection of probability distributions.
- Bijectors([mindspore.nn.probability.bijectors](https://gitee.com/mindspore/mindspore/tree/master/mindspore/nn/probability/bijector)): Reversible and composable transformations of random variables.
- Distributions ([mindspore.nn.probability.distribution](https://gitee.com/mindspore/mindspore/tree/master/mindspore/python/mindspore/nn/probability/distribution)): A large collection of probability distributions.
- Bijectors([mindspore.nn.probability.bijectors](https://gitee.com/mindspore/mindspore/tree/master/mindspore/python/mindspore/nn/probability/bijector)): Reversible and composable transformations of random variables.
### Layer 1-2: Probabilistic inference algorithms
- SVI([mindspore.nn.probability.infer.variational](https://gitee.com/mindspore/mindspore/tree/master/mindspore/nn/probability/infer/variational)): A unified interface for stochastic variational inference.
- SVI([mindspore.nn.probability.infer.variational](https://gitee.com/mindspore/mindspore/tree/master/mindspore/python/mindspore/nn/probability/infer/variational)): A unified interface for stochastic variational inference.
- MC: Algorithms for approximating integrals via sampling.
## Layer 2: Deep Probabilistic Programming (DPP) aims to provide composable BNN modules
- Layers([mindspore.nn.probability.bnn_layers](https://gitee.com/mindspore/mindspore/tree/master/mindspore/nn/probability/bnn_layers)): BNN layers, which are used to construct BNN.
- Dpn([mindspore.nn.probability.dpn](https://gitee.com/mindspore/mindspore/tree/master/mindspore/nn/probability/dpn)): A bunch of BNN models that allow to be integrated into DNN;
- Transform([mindspore.nn.probability.transforms](https://gitee.com/mindspore/mindspore/tree/master/mindspore/nn/probability/transforms)): Interfaces for the transformation between BNN and DNN;
- Layers([mindspore.nn.probability.bnn_layers](https://gitee.com/mindspore/mindspore/tree/master/mindspore/python/mindspore/nn/probability/bnn_layers)): BNN layers, which are used to construct BNN.
- Dpn([mindspore.nn.probability.dpn](https://gitee.com/mindspore/mindspore/tree/master/mindspore/python/mindspore/nn/probability/dpn)): A bunch of BNN models that allow to be integrated into DNN;
- Transform([mindspore.nn.probability.transforms](https://gitee.com/mindspore/mindspore/tree/master/mindspore/python/mindspore/nn/probability/transforms)): Interfaces for the transformation between BNN and DNN;
- Context: context managers for models and layers.
## Layer 3: Toolbox provides a set of BNN tools for some specific applications
- Uncertainty Estimation([mindspore.nn.probability.toolbox.uncertainty_evaluation](https://gitee.com/mindspore/mindspore/tree/master/mindspore/nn/probability/toolbox/uncertainty_evaluation.py)): Interfaces to estimate epistemic uncertainty and aleatoric uncertainty.
- Uncertainty Estimation([mindspore.nn.probability.toolbox.uncertainty_evaluation](https://gitee.com/mindspore/mindspore/tree/master/mindspore/python/mindspore/nn/probability/toolbox/uncertainty_evaluation.py)): Interfaces to estimate epistemic uncertainty and aleatoric uncertainty.
- OoD detection: Interfaces to detect out of distribution samples.
![Structure of MDP](https://images.gitee.com/uploads/images/2020/0820/115117_2a20da64_7825995.png "MDP.png")