LRScheduler¶

class paddle.callbacks. LRScheduler ( by_step=True, by_epoch=False ) [source]

Lr scheduler callback function

Parameters

• by_step (bool, optional) – whether to update learning rate scheduler by step. Default: True.

• by_epoch (bool, optional) – whether to update learning rate scheduler by epoch. Default: False.

Examples

>>> 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)

on_epoch_end ( epoch, logs=None )

on_epoch_end¶

Called at the end of each epoch.

Parameters

• epoch (int) – The index of epoch.

• logs (dict) – The logs is a dict or None. The logs passed by paddle.Model is a dict, contains ‘loss’, metrics and ‘batch_size’ of last batch.

on_train_batch_end ( step, logs=None )

on_train_batch_end¶

Called at the end of each batch in training.

Parameters

• step (int) – The index of step (or iteration).

• logs (dict) – The logs is a dict or None. The logs passed by paddle.Model is a dict, contains ‘loss’, metrics and ‘batch_size’ of current batch.