PolynomialDecay

class paddle.optimizer.lr. PolynomialDecay ( learning_rate, decay_steps, end_lr=0.0001, power=1.0, cycle=False, last_epoch=- 1, verbose=False ) [source]

Applies polynomial decay to the initial learning rate.

The algorithm can be described as following.

If cycle is set to True, then:

\[ \begin{align}\begin{aligned}\begin{split}decay\_steps & = decay\_steps * math.ceil(\\frac{epoch}{decay\_steps})\end{split}\\\begin{split}new\_learning\_rate & = (learning\_rate-end\_lr)*(1-\\frac{epoch}{decay\_steps})^{power}+end\_lr\end{split}\end{aligned}\end{align} \]

If cycle is set to False, then:

\[ \begin{align}\begin{aligned}epoch & = min(epoch, decay\_steps)\\\begin{split}new\_learning\_rate & = (learning\_rate-end\_lr)*(1-\\frac{epoch}{decay\_steps})^{power}+end\_lr\end{split}\end{aligned}\end{align} \]
Parameters
  • learning_rate (float) – The initial learning rate. It is a python float number.

  • decay_steps (int) – The decay step size. It determines the decay cycle.

  • end_lr (float, optional) – The minimum final learning rate. Default: 0.0001.

  • power (float, optional) – Power of polynomial. Default: 1.0.

  • cycle (bool, optional) – Whether the learning rate rises again. If True, then the learning rate will rise when it decrease to end_lr . If False, the learning rate is monotone decreasing. Default: False.

  • last_epoch (int, optional) – The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate.

  • verbose (bool, optional) – If True, prints a message to stdout for each update. Default: False .

Returns

PolynomialDecay instance to schedule learning rate.

Examples

import paddle
import numpy as np

# train on default dynamic graph mode
linear = paddle.nn.Linear(10, 10)
scheduler = paddle.optimizer.lr.PolynomialDecay(learning_rate=0.5, decay_steps=20, verbose=True)
sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameters=linear.parameters())
for epoch in range(20):
    for batch_id in range(5):
        x = paddle.uniform([10, 10])
        out = linear(x)
        loss = paddle.mean(out)
        loss.backward()
        sgd.step()
        sgd.clear_gradients()
        scheduler.step()    # If you update learning rate each step
  # scheduler.step()        # If you update learning rate each epoch

# train on static graph mode
paddle.enable_static()
main_prog = paddle.static.Program()
start_prog = paddle.static.Program()
with paddle.static.program_guard(main_prog, start_prog):
    x = paddle.static.data(name='x', shape=[None, 4, 5])
    y = paddle.static.data(name='y', shape=[None, 4, 5])
    z = paddle.static.nn.fc(x, 100)
    loss = paddle.mean(z)
    scheduler = paddle.optimizer.lr.PolynomialDecay(learning_rate=0.5, decay_steps=20, verbose=True)
    sgd = paddle.optimizer.SGD(learning_rate=scheduler)
    sgd.minimize(loss)

exe = paddle.static.Executor()
exe.run(start_prog)
for epoch in range(20):
    for batch_id in range(5):
        out = exe.run(
            main_prog,
            feed={
                'x': np.random.randn(3, 4, 5).astype('float32'),
                'y': np.random.randn(3, 4, 5).astype('float32')
            },
            fetch_list=loss.name)
        scheduler.step()    # If you update learning rate each step
  # scheduler.step()        # If you update learning rate each epoch
get_lr ( )

get_lr

For those subclass who overload LRScheduler (Base Class), User should have a custom implementation of get_lr() .

Otherwise, an NotImplementedError exception will be thrown.

set_dict ( state_dict )

set_dict

Loads the schedulers state.

set_state_dict ( state_dict )

set_state_dict

Loads the schedulers state.

state_dict ( )

state_dict

Returns the state of the scheduler as a dict.

It is a subset of self.__dict__ .

state_keys ( )

state_keys

For those subclass who overload LRScheduler (Base Class). Acquiescently, “last_epoch, last_lr” will be saved by self.keys = ['last_epoch', 'last_lr'] .

last_epoch is the current epoch num, and last_lr is the current learning rate.

If you want to change the default behavior, you should have a custom implementation of _state_keys() to redefine self.keys .

step ( epoch=None )

step

step should be called after optimizer.step . It will update the learning rate in optimizer according to current epoch . The new learning rate will take effect on next optimizer.step .

Parameters

epoch (int, None) – specify current epoch. Default: None. Auto-increment from last_epoch=-1.

Returns

None