# StepDecay¶

class paddle.fluid.dygraph.learning_rate_scheduler. StepDecay ( learning_rate, step_size, decay_rate=0.1 ) [source]
Api_attr

imperative

Decays the learning rate of `optimizer` by `decay_rate` every `step_size` number of epoch.

The algorithm can be described as the code below.

```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
...
```
Parameters
• learning_rate (float|int) – The initial learning rate. It can be set to python float or int number.

• step_size (int) – Period of learning rate decay.

• decay_rate (float, optional) – The Ratio that the learning rate will be reduced. `new_lr = origin_lr * decay_rate` . It should be less than 1.0. Default: 0.1.

Returns

None.

Examples

```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
```
create_lr_var ( lr )

convert lr from float to variable

Parameters

lr – learning rate

Returns

learning rate variable

epoch ( epoch=None )

compueted learning_rate and update it when invoked.

set_dict ( state_dict )

Returns the state of the scheduler as a `dict`.