piecewise_decay¶
对初始学习率进行分段衰减。
该算法可用如下代码描述。
boundaries = [10000, 20000]
values = [1.0, 0.5, 0.1]
if step < 10000:
learning_rate = 1.0
elif 10000 <= step < 20000:
learning_rate = 0.5
else:
learning_rate = 0.1
参数¶
boundaries(list) - 代表步数的数字
values(list) - 学习率的值,不同的步边界中的学习率值
返回¶
衰减的学习率
代码示例¶
import paddle.fluid as fluid
boundaries = [10000, 20000]
values = [1.0, 0.5, 0.1]
optimizer = fluid.optimizer.Momentum(
momentum=0.9,
learning_rate=fluid.layers.piecewise_decay(boundaries=boundaries, values=values),
regularization=fluid.regularizer.L2Decay(1e-4))