LRScheduler¶

import paddle
import paddle.vision.transforms as T
from paddle.static import InputSpec

inputs = [InputSpec([-1, 1, 28, 28], 'float32', 'image')]
labels = [InputSpec([None, 1], 'int64', 'label')]

transform = T.Compose([
    T.Transpose(),
    T.Normalize([127.5], [127.5])
])
train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=transform)

lenet = paddle.vision.models.LeNet()
model = paddle.Model(lenet,
    inputs, labels)

base_lr = 1e-3
boundaries = [5, 8]
wamup_steps = 4

def make_optimizer(parameters=None):
    momentum = 0.9
    weight_decay = 5e-4
    values = [base_lr * (0.1**i) for i in range(len(boundaries) + 1)]
    learning_rate = paddle.optimizer.lr.PiecewiseDecay(
        boundaries=boundaries, values=values)
    learning_rate = paddle.optimizer.lr.LinearWarmup(
        learning_rate=learning_rate,
        warmup_steps=wamup_steps,
        start_lr=base_lr / 5.,
        end_lr=base_lr,
        verbose=True)
    optimizer = paddle.optimizer.Momentum(
        learning_rate=learning_rate,
        weight_decay=weight_decay,
        momentum=momentum,
        parameters=parameters)
    return optimizer

optim = make_optimizer(parameters=lenet.parameters())
model.prepare(optimizer=optim,
            loss=paddle.nn.CrossEntropyLoss(),
            metrics=paddle.metric.Accuracy())

# if LRScheduler callback not set, an instance LRScheduler update by step
# will be created auto.
model.fit(train_dataset, batch_size=64)

# create a learning rate scheduler update by epoch
callback = paddle.callbacks.LRScheduler(by_step=False, by_epoch=True)
model.fit(train_dataset, batch_size=64, callbacks=callback)