wehub-resource-sync
70 lines
1.7 KiB
Markdown
70 lines
1.7 KiB
Markdown
## optim
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```python
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module optim
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```
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optim时优化器模块，提供了一个优化器基类`Optimizer`，并提供了`SGD`和`ADAM`优化器实现；主要用于训练阶段迭代优化
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---
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### `optim Types`
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- [Optimizer](Optimizer.md)
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---
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### `optim.Regularization_Method`
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优化器的正则化方法，提供了L1和L2正则化方法
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- 类型：`Enum`
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- 枚举值：
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- `L1`
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- `L2`
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- `L1L2`
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---
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### `SGD(module, lr, momentum, weight_decay, regularization_method)`
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创建一个SGD优化器
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参数：
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- `module:_Module` 模型实例
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- `lr:float` 学习率
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- `momentum:float` 动量，默认为0.9
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- `weight_decay:float` 权重衰减，默认为0.0
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- `regularization_method:RegularizationMethod` 正则化方法，默认为L2正则化
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返回：SGD优化器实例
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返回类型：`Optimizer`
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示例：
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```python
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model = Net()
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sgd = optim.SGD(model, 0.001, 0.9, 0.0005, optim.Regularization_Method.L2)
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# feed some date to the model, then get the loss
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loss = ...
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sgd.step(loss) # backward and update parameters in the model
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```
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---
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### `ADAM(module, lr, momentum, momentum2, weight_decay, eps, regularization_method)`
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创建一个ADAM优化器
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参数：
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- `module:_Module` 模型实例
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- `lr:float` 学习率
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- `momentum:float` 动量，默认为0.9
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- `momentum2:float` 动量2，默认为0.999
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- `weight_decay:float` 权重衰减，默认为0.0
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- `eps:float` 正则化阈值，默认为1e-8
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- `regularization_method:RegularizationMethod` 正则化方法，默认为L2正则化
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返回：ADAM优化器实例
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返回类型：`Optimizer`
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示例：
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```python
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model = Net()
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sgd = optim.ADAM(model, 0.001)
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# feed some date to the model, then get the loss
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loss = ...
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sgd.step(loss) # backward and update parameters in the model
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``` |