MultiStepDecay¶

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

Api_attr

imperative

Decays the learning rate of optimizer by decay_rate once epoch reaches one of the milestones.

The algorithm can be described as the code below.

learning_rate = 0.5
milestones = [30, 50]
decay_rate = 0.1
if epoch < 30:
    learning_rate = 0.5
elif epoch < 50:
    learning_rate = 0.05
else:
    learning_rate = 0.005

Parameters

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

• milestones (tuple|list) – List or tuple of each boundaries. Must be increasing.

• 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.MultiStepDecay(0.5, milestones=[3, 5])
    adam = fluid.optimizer.Adam(learning_rate = scheduler, parameter_list = linear.parameters())

    for epoch in range(6):
        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 0.005