forked from mindspore-Ecosystem/mindspore
!5186 Add note of limitation for prarmeters of uniform
Merge pull request !5186 from peixu_ren/custom_pp_ops
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@ -32,7 +32,7 @@ The objective of MDP is to integrate deep learning with Bayesian learning. On th
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- Context: context managers for models and layers.
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**Layer 3: Toolbox provides a set of BNN tools for some specific applications**
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- Uncertainty Estimation([mindspore.nn.probability.toolbox.uncertainty_evaluate](https://gitee.com/mindspore/mindspore/tree/master/mindspore/nn/probability/toolbox/uncertainty_evaluate.py)): Interfaces to estimate epistemic uncertainty and aleatoric uncertainty.
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- 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.
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- OoD detection: Interfaces to detect out of distribution samples.
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![Structure of MDP](https://images.gitee.com/uploads/images/2020/0820/115117_2a20da64_7825995.png "MDP.png")
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@ -96,6 +96,9 @@ def uniform(shape, a, b, seed=0, dtype=mstype.float32):
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"""
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Generates random numbers according to the Uniform random number distribution.
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Note:
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The number in tensor a should be strictly less than b at any position after broadcasting.
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Args:
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shape (tuple): The shape of random tensor to be generated.
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a (Tensor): The a distribution parameter.
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