比较与torch.optim.lr_scheduler.ExponentialLR的功能差异

torch.optim.lr_scheduler.ExponentialLR

torch.optim.lr_scheduler.ExponentialLR(
    optimizer,
    gamma,
    last_epoch=-1
)

更多内容详见torch.optim.lr_scheduler.ExponentialLR。

mindspore.nn.exponential_decay_lr

mindspore.nn.exponential_decay_lr(
      learning_rate,
      decay_rate,
      total_step,
      step_per_epoch,
      decay_epoch,
      is_stair=False
)

更多内容详见mindspore.nn.exponential_decay_lr。

mindspore.nn.ExponentialDecayLR

mindspore.nn.ExponentialDecayLR(
  learning_rate,
  decay_rate,
  decay_steps,
  is_stair=False
)

更多内容详见mindspore.nn.ExponentialDecayLR。

使用方式

PyTorch：计算方式为lr*gamma^{epoch}。使用时，优化器作为输入，通过调用step方法进行学习率的更新。

MindSpore：计算方式为lr*decay_rate^{p}，这种动态学习率的调整方式在mindspore里有两种实现方式：exponential_decay_lr预生成学习率列表，将列表传入优化器；ExponentialDecayLR则是通过计算图的方式传入优化器中参与训练。

代码示例

# In MindSpore：
import mindspore as ms
from mindspore import nn

# In MindSpore：exponential_decay_lr
learning_rate = 0.1
decay_rate = 0.9
total_step = 6
step_per_epoch = 2
decay_epoch = 1
output = nn.exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch)
print(output)
# out
# [0.1, 0.1, 0.09000000000000001, 0.09000000000000001, 0.08100000000000002, 0.08100000000000002]

# In MindSpore：ExponentialDecayLR
learning_rate = 0.1
decay_rate = 0.9
decay_steps = 4
global_step = ms.Tensor(2, ms.int32)
exponential_decay_lr = nn.ExponentialDecayLR(learning_rate, decay_rate, decay_steps)
result = exponential_decay_lr(global_step)
print(result)
#  out
# 0.09486833

# In torch:
import torch
import numpy as np
from torch import optim

model = torch.nn.Sequential(torch.nn.Linear(20, 1))
optimizer = optim.SGD(model.parameters(), 0.1)
exponential_decay_lr = optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9)
myloss = torch.nn.MSELoss()
dataset = [(torch.tensor(np.random.rand(1, 20).astype(np.float32)), torch.tensor([1.]))]

for epoch in range(5):
    for input, target in dataset:
        optimizer.zero_grad()
        output = model(input)
        loss = myloss(output.view(-1), target)
        loss.backward()
        optimizer.step()
    exponential_decay_lr.step()
    print(exponential_decay_lr.get_last_lr())
#  out
# [0.09000000000000001]
# [0.08100000000000002]
# [0.07290000000000002]
# [0.06561000000000002]
# [0.05904900000000002]