mindspore.experimental.optim.lr_scheduler.StepLR

class mindspore.experimental.optim.lr_scheduler.StepLR(optimizer, step_size, gamma=0.1, last_epoch=- 1)[源代码]

每 step_size 个epoch按 gamma 衰减每个参数组的学习率。StepLR 对于学习率的衰减可能与外部对于学习率的改变同时发生。

警告

这是一个实验性的动态学习率接口，需要和 mindspore.experimental.optim 下的接口配合使用。

参数：

optimizer (mindspore.experimental.optim.Optimizer) - 优化器实例。
step_size (int) - 学习率衰减的周期。
gamma (float，可选) - 学习率衰减的乘法因子。默认值: 0.1。
last_epoch (int，可选) - 当前scheduler的 step() 方法的执行次数。默认值： -1。

支持平台：

Ascend GPU CPU

样例：

>>> import mindspore
>>> from mindspore import nn
>>> from mindspore.experimental import optim
>>> # Define the network structure of LeNet5. Refer to
>>> # https://gitee.com/mindspore/docs/blob/r2.3.0rc2/docs/mindspore/code/lenet.py
>>> net = LeNet5()
>>> loss_fn = nn.SoftmaxCrossEntropyWithLogits(sparse=True)
>>> optimizer = optim.Adam(net.trainable_params(), lr=0.05)
>>> # Assuming optimizer uses lr = 0.05 for all groups
>>> # lr = 0.05     if epoch < 2
>>> # lr = 0.005    if 2 <= epoch < 4
>>> # lr = 0.0005   if 4 <= epoch < 6
>>> scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=2, gamma=0.1)
>>> def forward_fn(data, label):
...     logits = net(data)
...     loss = loss_fn(logits, label)
...     return loss, logits
>>> grad_fn = mindspore.value_and_grad(forward_fn, None, optimizer.parameters, has_aux=True)
>>> def train_step(data, label):
...     (loss, _), grads = grad_fn(data, label)
...     optimizer(grads)
...     return loss
>>> for epoch in range(6):
...     # Create the dataset taking MNIST as an example. Refer to
...     # https://gitee.com/mindspore/docs/blob/r2.3.0rc2/docs/mindspore/code/mnist.py
...     for data, label in create_dataset():
...         train_step(data, label)
...     scheduler.step()
...     current_lr = scheduler.get_last_lr()