# StepDecay¶

class paddle.fluid.dygraph. StepDecay ( learning_rate, step_size, decay_rate=0.1 ) [源代码]

```learning_rate = 0.5
step_size = 30
decay_rate = 0.1
learning_rate = 0.5     if epoch < 30
learning_rate = 0.05    if 30 <= epoch < 60
learning_rate = 0.005   if 60 <= epoch < 90
...
```

## 参数¶

• learning_rate (float|int) - 初始化的学习率。可以是Python的float或int。

• step_size (int) - 学习率每衰减一次的间隔。

• decay_rate (float, optional) - 学习率的衰减率。`new_lr = origin_lr * decay_rate`。其值应该小于1.0。默认：0.1。

## 代码示例¶

```import paddle.fluid as fluid
import numpy as np
with fluid.dygraph.guard():
x = np.random.uniform(-1, 1, [10, 10]).astype("float32")
linear = fluid.dygraph.Linear(10, 10)
input = fluid.dygraph.to_variable(x)
scheduler = fluid.dygraph.StepDecay(0.5, step_size=3)
for epoch in range(9):
for batch_id in range(5):
out = linear(input)
loss = fluid.layers.reduce_mean(out)
scheduler.epoch()
print("epoch:{}, current lr is {}" .format(epoch, adam.current_step_lr()))
# epoch:0, current lr is 0.5
# epoch:1, current lr is 0.5
# epoch:2, current lr is 0.5
# epoch:3, current lr is 0.05
# epoch:4, current lr is 0.05
# epoch:5, current lr is 0.05
# epoch:6, current lr is 0.005
# epoch:7, current lr is 0.005
# epoch:8, current lr is 0.005
```

## 方法¶

### epoch(epoch=None)¶

• epoch (int|float，可选) - 类型：int或float。指定当前的epoch数。默认：无，此时将会自动累计epoch数。