StepDecay¶

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

该接口提供 step_size 衰减学习率的功能，每经过 step_size 个 epoch 时会通过 decay_rate 衰减一次学习率。

算法可以描述为：

         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。

返回¶

无

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)
    adam = fluid.optimizer.Adam(learning_rate = scheduler, parameter_list = linear.parameters())
    for epoch in range(9):
        for batch_id in range(5):
            out = linear(input)
            loss = fluid.layers.reduce_mean(out)
            adam.minimize(loss)
        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)¶

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

无

代码示例

参照上述示例代码。