下面的代码是cnn识别cifar10,如果是cifar100,将数据集的改成cifar100,然后模型的输出神经元10改为100即可。
import torch,torchvision
import torch.nn as nn
import torchvision.transforms as transforms
#定义模型
class CNNCifar(nn.Module):
def __init__(self):
super(CNNCifar,self).__init__()
self.feature = nn.Sequential(
nn.Conv2d(3,64,3,padding=2), nn.BatchNorm2d(64), nn.ReLU(), nn.MaxPool2d(2,2),
nn.Conv2d(64,128,3,padding=2), nn.BatchNorm2d(128), nn.ReLU(), nn.MaxPool2d(2,2),
nn.Conv2d(128,256,3,padding=1),nn.BatchNorm2d(256), nn.ReLU(), nn.MaxPool2d(2,2),
nn.Conv2d(256,512,3,padding=1),nn.BatchNorm2d(512), nn.ReLU(), nn.MaxPool2d(2,2)
)
self.classifier=nn.Sequential(
nn.Flatten(),
nn.Linear(2048, 4096),nn.ReLU(),nn.Dropout(0.5),
nn.Linear(4096,4096), nn.ReLU(),nn.Dropout(0.5),
nn.Linear(4096,10)
)
def forward(self, x):
x = self.feature(x)
output = self.classifier(x)
return output
net = CNNCifar()
print(net)
#加载数据集
apply_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
train_dataset = torchvision.datasets.CIFAR10(root='./data/cifar10', train=True, download=True,transform=apply_transform)
test_dataset = torchvision.datasets.CIFAR10(root='./data/cifar10', train=False, download=False,transform=apply_transform)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=128, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=256, shuffle=False)
#定义损失函数和优化器
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(net.parameters(), lr=0.001,weight_decay=5e-4)
#如果有gpu就使用gpu,否则使用cpu
device = torch.device('cuda'if torch.cuda.is_available() else 'cpu')
net = net.to(device)
#训练模型
print('training on: ',device)
def test():
net.eval()
acc = 0.0
sum = 0.0
loss_sum = 0
for batch, (data, target) in enumerate(test_loader):
data, target = data.to(device), target.to(device)
output = net(data)
loss = criterion(output, target)
acc+=torch.sum(torch.argmax(output,dim=1)==target).item()
sum+=len(target)
loss_sum+=loss.item()
print('test acc: %.2f%%, loss: %.4f'%(100*acc/sum, loss_sum/(batch+1)))
def train():
net.train()
acc = 0.0
sum = 0.0
loss_sum = 0
for batch, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = net(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
acc +=torch.sum(torch.argmax(output,dim=1)==target).item()
sum+=len(target)
loss_sum+=loss.item()
if batch%200==0:
print('\tbatch: %d, loss: %.4f'%(batch, loss.item()))
print('train acc: %.2f%%, loss: %.4f'%(100*acc/sum, loss_sum/(batch+1)))
for epoch in range(20):
print('epoch: %d'%epoch)
train()
test()
实验结果: