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modify docs

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changzherui 2023-02-13 23:52:44 +08:00
parent d283a1ef26
commit e2fc4d1656
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4
docs/api/api_python/nn/mindspore.nn.Cell.rst
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@ -512,6 +512,10 @@
设置当前Cell和所有子Cell的训练模式。对于训练和预测具有不同结构的网络层(如 `BatchNorm`)，将通过这个属性区分分支。如果设置为True，则执行训练分支，否则执行另一个分支。
.. note::
当执行Model.train()的时候，框架会默认调用Cell.set_train(True)。
当执行Model.eval()的时候，框架会默认调用Cell.set_train(False)。
参数：
- **mode** (bool) - 指定模型是否为训练模式。默认值：True。

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docs/api/api_python/ops/mindspore.ops.ApplyCenteredRMSProp.rst
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@ -35,7 +35,7 @@
- **use_locking** (bool) - 是否对参数更新增加锁保护。默认值：False。
输入：
- **var** (Tensor) - 要更新的权重。
- **var** (Parameter) - 要更新的权重。
- **mean_gradient** (Tensor) - 均值梯度，数据类型必须与 `var` 相同。
- **mean_square** (Tensor) - 均方梯度，数据类型必须与 `var` 相同。
- **moment** (Tensor) - `var` 的增量，数据类型必须与 `var` 相同。

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docs/api/api_python/ops/mindspore.ops.ApplyRMSProp.rst
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@ -28,7 +28,7 @@ mindspore.ops.ApplyRMSProp
- **use_locking** (bool) - 是否对参数更新加锁保护。默认值: False。
输入：
- **var** (Tensor) - 待更新的网络参数。
- **var** (Parameter) - 待更新的网络参数。
- **mean_square** (Tensor) - 均方梯度，数据类型需与 `var` 相同。
- **moment** (Tensor) - 一阶矩，数据类型需与 `var` 相同。
- **learning_rate** (Union[Number, Tensor]) - 学习率。需为浮点数或者数据类型为float16或float32的标量矩阵。

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mindspore/python/mindspore/nn/cell.py
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@ -1604,6 +1604,10 @@ class Cell(Cell_):
for training and predicting, such as `BatchNorm`, will distinguish between the branches by this attribute. If
set to true, the training branch will be executed, otherwise another branch.
Note:
When execute function Model.train(), framework will call Cell.set_train(True).
When execute function Model.eval(), framework will call Cell.set_train(False).
Args:
mode (bool): Specifies whether the model is training. Default: True.

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mindspore/python/mindspore/ops/operations/nn_ops.py
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@ -3313,7 +3313,7 @@ class ApplyRMSProp(PrimitiveWithInfer):
from being updated. Default: False.
Inputs:
- **var** (Tensor) - Weights to be updated.
- **var** (Parameter) - Weights to be updated.
- **mean_square** (Tensor) - Mean square gradients, must be the same type as `var`.
- **moment** (Tensor) - Delta of `var`, must be the same type as `var`.
- **learning_rate** (Union[Number, Tensor]) - Learning rate. Must be a float number or
@ -3407,7 +3407,7 @@ class ApplyCenteredRMSProp(Primitive):
from being updated. Default: False.
Inputs:
- **var** (Tensor) - Weights to be updated.
- **var** (Parameter) - Weights to be updated.
- **mean_gradient** (Tensor) - Mean gradients, must be the same type as `var`.
- **mean_square** (Tensor) - Mean square gradients, must be the same type as `var`.
- **moment** (Tensor) - Delta of `var`, must be the same type as `var`.