文档反馈

问题文档片段

问题文档片段包含公式时，显示为空格。

提交类型

issue

有点复杂...

找人问问吧。

请选择提交类型

问题类型

规范和低错类

- 规范和低错类：

- 错别字或拼写错误，标点符号使用错误、公式错误或显示异常。

- 链接错误、空单元格、格式错误。

- 英文中包含中文字符。

- 界面和描述不一致，但不影响操作。

- 表述不通顺，但不影响理解。

- 版本号不匹配：如软件包名称、界面版本号。

易用性

- 易用性：

- 关键步骤错误或缺失，无法指导用户完成任务。

- 缺少主要功能描述、关键词解释、必要前提条件、注意事项等。

- 描述内容存在歧义指代不明、上下文矛盾。

- 逻辑不清晰，该分类、分项、分步骤的没有给出。

正确性

- 正确性：

- 技术原理、功能、支持平台、参数类型、异常报错等描述和软件实现不一致。

- 原理图、架构图等存在错误。

- 命令、命令参数等错误。

- 代码片段错误。

- 命令无法完成对应功能。

- 界面错误，无法指导操作。

- 代码样例运行报错、运行结果不符。

风险提示

- 风险提示：

- 对重要数据或系统存在风险的操作，缺少安全提示。

内容合规

- 内容合规：

- 违反法律法规，涉及政治、领土主权等敏感词。

- 内容侵权。

请选择问题类型

问题描述

点击输入详细问题描述，以帮助我们快速定位问题。

提交

文档反馈

mindspore.ops.AdamWeightDecay

class mindspore.ops.AdamWeightDecay(use_locking=False)

Updates gradients by the Adaptive Moment Estimation (AdamWeightDecay) algorithm with weight decay.

The Adam algorithm is proposed in Adam: A Method for Stochastic Optimization. The AdamWeightDecay variant was proposed in Decoupled Weight Decay Regularization.

The updating formulas are as follows,

$$\begin{array}{r}\begin{array}{l}\\ m={\beta }_1\ast m+(1-{\beta }_1)\ast g\\ v={\beta }_2\ast v+(1-{\beta }_2)\ast g\ast g\\ update=\frac{m}{\sqrt{v}+eps}\\ update=\{\begin{array}{ll}update+weight\mathrm{\_}decay\ast w& \text{ if }weight\mathrm{\_}decay>0\\ update& \text{ otherwise }\end{array}\\ w=w-lr\ast update\end{array}\end{array}$$

$m$ represents the 1st moment vector, $v$ represents the 2nd moment vector, $g$ represents gradient, ${\beta }_1,{\beta }_2$ represent beta1 and beta2, $lr$ represents learning_rate, $w$ represents var, $decay$ represents weight_decay, $ϵ$ represents epsilon.

Parameters

use_locking (bool) – Whether to enable a lock to protect variable tensors from being updated. If true, updates of the var, m, and v tensors will be protected by a lock. If false, the result is unpredictable. Default: False.

Inputs:

• var (Tensor) - Weights to be updated. The shape is $(N,\ast )$ where $\ast$ means, any number of additional dimensions. The data type can be float16 or float32.

• m (Tensor) - The 1st moment vector in the updating formula, the shape and data type value should be the same as var.

• v (Tensor) - the 2nd moment vector in the updating formula, the shape and data type value should be the same as var. Mean square gradients with the same type as var.

• lr (float) - $l$ in the updating formula. The paper suggested value is ${10}^{-8}$, the data type value should be the same as var.

• beta1 (float) - The exponential decay rate for the 1st moment estimations, the data type value should be the same as var. The paper suggested value is $0.9$

• beta2 (float) - The exponential decay rate for the 2nd moment estimations, the data type value should be the same as var. The paper suggested value is $0.999$

• epsilon (float) - Term added to the denominator to improve numerical stability.

• decay (float) - The weight decay value, must be a scalar tensor with float data type. Default: 0.0.

• gradient (Tensor) - Gradient, has the same shape and data type as var.

Outputs:

Tuple of 3 Tensor, the updated parameters.

• var (Tensor) - The same shape and data type as var.

• m (Tensor) - The same shape and data type as m.

• v (Tensor) - The same shape and data type as v.

Supported Platforms:

GPU CPU

Examples

>>> import numpy as np
>>> import mindspore.nn as nn
>>> from mindspore import Tensor, Parameter, ops
>>> class Net(nn.Cell):
...     def __init__(self):
...         super(Net, self).__init__()
...         self.adam_weight_decay = ops.AdamWeightDecay()
...         self.var = Parameter(Tensor(np.ones([2, 2]).astype(np.float32)), name="var")
...         self.m = Parameter(Tensor(np.ones([2, 2]).astype(np.float32)), name="m")
...         self.v = Parameter(Tensor(np.ones([2, 2]).astype(np.float32)), name="v")
...     def construct(self, lr, beta1, beta2, epsilon, decay, grad):
...         out = self.adam_weight_decay(self.var, self.m, self.v, lr, beta1, beta2,
...                               epsilon, decay, grad)
...         return out
>>> net = Net()
>>> gradient = Tensor(np.ones([2, 2]).astype(np.float32))
>>> output = net(0.001, 0.9, 0.999, 1e-8, 0.0, gradient)
>>> print(net.var.asnumpy())