import torch
import torch.nn.functional as F
from torch import nn
device=torch.device("cuda:0")
MLP = nn.Sequential(nn.Linear(128,64),
nn.ReLU(inplace=True),
nn.Linear(64,32),
nn.ReLU(inplace=True),
nn.Linear(32,10)
)
MLP.to(device)
loss_classify = nn.CrossEntropyLoss().to(device)
# L1范数
l1_loss = 0
for param in MLP.parameters():
l1_loss += torch.sum(torch.abs(param))
loss = loss_classify+l1_loss
import torch
import torch.nn.functional as F
from torch import nn
device=torch.device("cuda:0")
MLP = nn.Sequential(nn.Linear(128,64),
nn.ReLU(inplace=True),
nn.Linear(64,32),
nn.ReLU(inplace=True),
nn.Linear(32,10)
)
MLP.to(device)
# L2范数
opt = torch.optim.SGD(MLP.parameters(),lr=0.001,weight_decay=0.1) # 通过weight_decay实现L2
loss = nn.CrossEntropyLoss().to(device)
opt = torch.optim.SGD(model.parameters(),lr=0.001,momentum=0.78,weight_decay=0.1)
opt = torch.optim.SGD(net.parameters(),lr=1)
lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer=opt,mode="min",factor=0.1,patience=10)
for epoch in torch.arange(1000):
loss_val = train(...)
lr_scheduler.step(loss_val) # 监听loss
opt = torch.optim.SGD(net.parameters(),lr=1)
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer=opt,step_size=30,gamma=0.1)
for epoch in torch.arange(1000):
lr_scheduler.step() # 监听loss
train(...)
点击这里
model = nn.Sequential(
nn.Linear(256,128),
nn.Dropout(p=0.5),
nn.ReLu(),
)
by CyrusMay 2022 07 03