比较与torch.optim.AdamW的差异

torch.optim.AdamW

class torch.optim.AdamW(
    params,
    lr=0.001,
    betas=(0.9, 0.999),
    eps=1e-08,
    weight_decay=0.01,
    amsgrad=False
)

更多内容详见torch.optim.AdamW。

mindspore.nn.AdamWeightDecay

class mindspore.nn.AdamWeightDecay(
    params,
    learning_rate=1e-3,
    beta1=0.9,
    beta2=0.999,
    eps=1e-6,
    weight_decay=0.0
)

更多内容详见mindspore.nn.AdamWeightDecay。

使用方式

PyTorch和MindSpore此优化器实现算法不同，详情请参考官网公式。

分类	子类	PyTorch	MindSpore	差异
参数	参数1	params	params	功能一致
	参数2	lr	learning_rate	功能一致，参数名及默认值不同
	参数3	betas	beta1, beta2	功能一致，参数名不同
	参数4	eps	eps	功能一致，默认值不同
	参数5	weight_decay	weight_decay	功能一致
	参数6	amsgrad	-	PyTorch的 amsgrad 表示是否应用amsgrad算法，MindSpore无此参数

代码示例

# MindSpore
import mindspore
from mindspore import nn

net = nn.Dense(2, 3)
optimizer = nn.AdamWeightDecay(net.trainable_params())
criterion = nn.MAELoss(reduction="mean")

def forward_fn(data, label):
    logits = net(data)
    loss = criterion(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

# PyTorch
import torch

model = torch.nn.Linear(2, 3)
criterion = torch.nn.L1Loss(reduction='mean')
optimizer = torch.optim.AdamW(model.parameters())
def train_step(data, label):
    optimizer.zero_grad()
    output = model(data)
    loss = criterion(output, label)
    loss.backward()
    optimizer.step()