MultiStepDecay

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

该接口提供 MultiStep 衰减学习率的功能。

算法可以描述为:

  1. learning_rate = 0.5
  2. milestones = [30, 50]
  3. decay_rate = 0.1
  4. if epoch < 30:
  5. learning_rate = 0.5
  6. elif epoch < 50:
  7. learning_rate = 0.05
  8. else:
  9. learning_rate = 0.005

参数:

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

  • milestones (tuple|list) - 列表或元组。必须是递增的。

  • 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。

返回: 无

代码示例

  1. import paddle.fluid as fluid
  2. import numpy as np
  3. with fluid.dygraph.guard():
  4. x = np.random.uniform(-1, 1, [10, 10]).astype("float32")
  5. linear = fluid.dygraph.Linear(10, 10)
  6. input = fluid.dygraph.to_variable(x)
  7. scheduler = fluid.dygraph.MultiStepDecay(0.5, milestones=[3, 5])
  8. adam = fluid.optimizer.Adam(learning_rate = scheduler, parameter_list = linear.parameters())
  9. for epoch in range(6):
  10. for batch_id in range(5):
  11. out = linear(input)
  12. loss = fluid.layers.reduce_mean(out)
  13. adam.minimize(loss)
  14. scheduler.epoch()
  15. print("epoch:{}, current lr is {}" .format(epoch, adam.current_step_lr()))
  16. # epoch:0, current lr is 0.5
  17. # epoch:1, current lr is 0.5
  18. # epoch:2, current lr is 0.5
  19. # epoch:3, current lr is 0.05
  20. # epoch:4, current lr is 0.05
  21. # epoch:5, current lr is 0.005

epoch ( epoch=None )

通过当前的 epoch 调整学习率,调整后的学习率将会在下一次调用 optimizer.minimize 时生效。

参数:

  • 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。

返回:

代码示例:

参照上述示例代码。