docs-site/docs_chs/optimizer.md

---
id: optimizer
title: Optimizer
---

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SINGA支持各种流行的优化器，包括动量随机梯度下降、Adam、RMSProp和AdaGrad等。对于每一种优化器，它都支持使用衰减调度器来安排不同时间段的学习率。优化器和衰减调度器包含在`singa/opt.py`中。

## 创建一个优化器

1. 带动量的SGD

```python
# define hyperparameter learning rate
lr = 0.001
# define hyperparameter momentum
momentum = 0.9
# define hyperparameter weight decay
weight_decay = 0.0001

from singa import opt
sgd = opt.SGD(lr=lr, momentum=momentum, weight_decay=weight_decay)
```

2. RMSProp

```python
# define hyperparameter learning rate
lr = 0.001
# define hyperparameter rho
rho = 0.9
# define hyperparameter epsilon
epsilon = 1e-8
# define hyperparameter weight decay
weight_decay = 0.0001

from singa import opt
sgd = opt.RMSProp(lr=lr, rho=rho, epsilon=epsilon, weight_decay=weight_decay)
```

3. AdaGrad

```python
# define hyperparameter learning rate
lr = 0.001
# define hyperparameter epsilon
epsilon = 1e-8
# define hyperparameter weight decay
weight_decay = 0.0001

from singa import opt
sgd = opt.AdaGrad(lr=lr, epsilon=epsilon, weight_decay=weight_decay)
```

4. Adam

```python
# define hyperparameter learning rate
lr = 0.001
# define hyperparameter beta 1
beta_1= 0.9
# define hyperparameter beta 2
beta_1= 0.999
# define hyperparameter epsilon
epsilon = 1e-8
# define hyperparameter weight decay
weight_decay = 0.0001

from singa import opt
sgd = opt.Adam(lr=lr, beta_1=beta_1, beta_2=beta_2, epsilon=epsilon, weight_decay=weight_decay)
```

## 创建一个衰减调度器

```python
from singa import opt

# define initial learning rate
lr_init = 0.001
# define the rate of decay in the decay scheduler
decay_rate = 0.95
# define whether the learning rate schedule is a staircase shape
staircase=True
# define the decay step of the decay scheduler (in this example the lr is decreased at every 2 steps)
decay_steps = 2

# create the decay scheduler, the schedule of lr becomes lr_init * (decay_rate ^ (step // decay_steps) )
lr = opt.ExponentialDecay(0.1, 2, 0.5, True)
# Use the lr to create an optimizer
sgd = opt.SGD(lr=lr, momentum=0.9, weight_decay=0.0001)
```

## 使用模型API中的优化器

当我们创建模型时，我们需要将优化器附加到模型上：

```python
# create a CNN using the Model API
model = CNN()

# initialize optimizer and attach it to the model
sgd = opt.SGD(lr=0.005, momentum=0.9, weight_decay=1e-5)
model.set_optimizer(sgd)
```

然后，当我们调用模型时，它会运行利用优化器的 `train_one_batch` 方法。

因此，一个迭代循环优化模型的例子是：

```python
for b in range(num_train_batch):
    # generate the next mini-batch
    x, y = ...

    # Copy the data into input tensors
    tx.copy_from_numpy(x)
    ty.copy_from_numpy(y)

    # Training with one batch
    out, loss = model(tx, ty)
```