piecewise_decay

• paddle.fluid.layers.piecewise_decay(boundaries, values)[源代码]

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))