# 优化器

## 定义模型

```python
import torch
from torch.nn import Linear

model=Linear(1, 1)
```

## 1\_ torch.optim.SGD

随机梯度下降算法，带有动量（momentum）的算法作为一个可选参数可以进行设置，样例如下：

```python
#lr参数为学习率，对于SGD来说一般选择0.1 0.01.0.001
#如果设置了momentum，就是带有动量的SGD
optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9)
```

## 2\_ torch.optim.RMSprop

加快梯度下降的算法，利用RMSprop算法，可以减小某些维度梯度更新波动较大的情况，使其梯度下降的速度变得更快

```python
optimizer = torch.optim.RMSprop(model.parameters(), lr=0.01, alpha=0.99)
```

## 3\_ torch.optim.Adam

```python
# 这里的lr，betas，还有eps都是用默认值即可，所以Adam是一个使用起来最简单的优化方法
optimizer = torch.optim.Adam(model.parameters(), lr=0.001, betas=(0.9, 0.999), eps=1e-08)
```


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