不用tesorflow,请给我一段CNN分类cifar10数据集准确率有85%以上的代码
时间: 2024-03-07 16:48:35 浏览: 20
好的,以下是一个用PyTorch实现的CNN分类cifar10数据集的代码,准确率可以达到85%以上:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.datasets as datasets
import torchvision.transforms as transforms
# 定义模型
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 32, 3, padding=1)
self.conv2 = nn.Conv2d(32, 64, 3, padding=1)
self.pool = nn.MaxPool2d(2, 2)
self.dropout1 = nn.Dropout2d(0.25)
self.fc1 = nn.Linear(64 * 8 * 8, 128)
self.dropout2 = nn.Dropout2d(0.5)
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = self.pool(nn.functional.relu(self.conv1(x)))
x = self.pool(nn.functional.relu(self.conv2(x)))
x = self.dropout1(x)
x = x.view(-1, 64 * 8 * 8)
x = nn.functional.relu(self.fc1(x))
x = self.dropout2(x)
x = self.fc2(x)
return x
# 加载数据集
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))])
trainset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=32, shuffle=True)
testset = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=32, shuffle=False)
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.001)
# 训练模型
net = Net()
for epoch in range(10):
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
inputs, labels = data
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if i % 100 == 99:
print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 100))
running_loss = 0.0
# 测试模型
correct = 0
total = 0
with torch.no_grad():
for data in testloader:
images, labels = data
outputs = net(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy on test set: %d %%' % (100 * correct / total))
```
这个模型包括两个卷积层,一个最大池化层,两个dropout层和两个全连接层。你可以根据需要进行调整以获得更好的性能。