比较与torch.optim.lr_scheduler.ExponentialLR的差异
torch.optim.lr_scheduler.ExponentialLR
torch.optim.lr_scheduler.ExponentialLR(
optimizer,
gamma,
last_epoch=-1,
verbose=False
)
mindspore.nn.exponential_decay_lr
mindspore.nn.exponential_decay_lr(
learning_rate,
decay_rate,
total_step,
step_per_epoch,
decay_epoch,
is_stair=False
)
mindspore.nn.ExponentialDecayLR
mindspore.nn.ExponentialDecayLR(
learning_rate,
decay_rate,
decay_steps,
is_stair=False
)
差异对比
PyTorch(torch.optim.lr_scheduler.ExponentialLR):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。 verbose
为True时,每一次更新打印相关信息。
MindSpore(mindspore.nn.exponential_decay_lr):计算方式为 \(lr * decay\_rate^{p}\) , exponential_decay_lr
预生成学习率列表,将列表传入优化器。
分类 |
子类 |
PyTorch |
MindSpore |
差异 |
---|---|---|---|---|
参数 |
参数1 |
optimizer |
- |
PyTorch应用的优化器,MindSpore无此参数 |
参数2 |
gamma |
decay_rate |
衰减学习率的参数,功能一致,参数名不同 |
|
参数3 |
last_epoch |
- |
MindSpore无此参数 |
|
参数4 |
verbose |
- |
PyTorch |
|
参数5 |
learning_rate |
MindSpore设置学习率的初始值 |
||
参数6 |
total_step |
MindSpore的step总数 |
||
参数7 |
step_per_epoch |
MindSpore每个epoch的step数 |
||
参数8 |
- |
decay_steps |
MindSpore进行衰减的step数 |
|
参数9 |
- |
is_stair |
MindSpore |
MindSpore(mindspore.nn.ExponentialDecayLR):计算方式为 \(lr * decay\_rate^{p}\) , ExponentialDecayLR
是通过计算图的方式传入优化器中参与训练。
分类 |
子类 |
PyTorch |
MindSpore |
差异 |
---|---|---|---|---|
参数 |
参数1 |
optimizer |
- |
PyTorch应用的优化器,MindSpore无此参数 |
参数2 |
gamma |
decay_rate |
衰减学习率的参数,功能一致,参数名不同 |
|
参数3 |
last_epoch |
- |
MindSpore无此参数 |
|
参数4 |
verbose |
- |
PyTorch的 |
|
参数5 |
learning_rate |
MindSpore设置学习率的初始值 |
||
参数6 |
- |
decay_steps |
MindSpore进行衰减的step数 |
|
参数7 |
- |
is_stair |
MindSpore |
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
# 0.094868325
# 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]