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modify docs of API of probability

This commit is contained in:
zhangxinfeng3 2021-06-07 14:23:21 +08:00
parent 878cb6ac3b
commit 1e5c247c57
2 changed files with 3 additions and 3 deletions
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4
mindspore/nn/probability/bnn_layers/dense_variational.py
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@ -131,11 +131,11 @@ class DenseReparam(_DenseVariational):
Args:
in_channels (int): The number of input channel.
out_channels (int): The number of output channel .
has_bias (bool): Specifies whether the layer uses a bias vector. Default: False.
activation (str, Cell): A regularization function applied to the output of the layer.
The type of `activation` can be a string (eg. 'relu') or a Cell (eg. nn.ReLU()).
Note that if the type of activation is Cell, it must be instantiated beforehand.
Default: None.
has_bias (bool): Specifies whether the layer uses a bias vector. Default: False.
weight_prior_fn: The prior distribution for weight.
It must return a mindspore distribution instance.
Default: NormalPrior. (which creates an instance of standard
@ -220,11 +220,11 @@ class DenseLocalReparam(_DenseVariational):
Args:
in_channels (int): The number of input channel.
out_channels (int): The number of output channel .
has_bias (bool): Specifies whether the layer uses a bias vector. Default: False.
activation (str, Cell): A regularization function applied to the output of the layer.
The type of `activation` can be a string (eg. 'relu') or a Cell (eg. nn.ReLU()).
Note that if the type of activation is Cell, it must be instantiated beforehand.
Default: None.
has_bias (bool): Specifies whether the layer uses a bias vector. Default: False.
weight_prior_fn: The prior distribution for weight.
It must return a mindspore distribution instance.
Default: NormalPrior. (which creates an instance of standard

2
mindspore/nn/probability/infer/variational/svi.py
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@ -54,8 +54,8 @@ class SVI:
Optimize the parameters by training the probability network, and return the trained network.
Args:
epochs (int): Total number of iterations on the data. Default: 10.
train_dataset (Dataset): A training dataset iterator.
epochs (int): Total number of iterations on the data. Default: 10.
Outputs:
Cell, the trained probability network.