帮我写一段pytorch alnext代码
时间: 2023-05-29 10:03:15 浏览: 114
alexnet pytorch代码
以下是一段简单的 PyTorch ALNExt 代码,用于训练一个简单的卷积神经网络模型来分类 CIFAR-10 数据集中的图像。
```
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
# 定义数据预处理步骤
transform = transforms.Compose(
[transforms.RandomHorizontalFlip(),
transforms.RandomCrop(32, padding=4),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
# 加载 CIFAR-10 数据集
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=128,
shuffle=True, num_workers=2)
testset = torchvision.datasets.CIFAR10(root='./data', train=False,
download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=128,
shuffle=False, num_workers=2)
classes = ('plane', 'car', 'bird', 'cat',
'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
# 定义卷积神经网络模型
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 64, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(64, 128, 5)
self.fc1 = nn.Linear(128 * 5 * 5, 512)
self.fc2 = nn.Linear(512, 10)
def forward(self, x):
x = self.pool(nn.functional.relu(self.conv1(x)))
x = self.pool(nn.functional.relu(self.conv2(x)))
x = x.view(-1, 128 * 5 * 5)
x = nn.functional.relu(self.fc1(x))
x = self.fc2(x)
return x
net = Net()
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
# 训练网络
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: # 每 100 个 mini-batch 打印一次损失值
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 100))
running_loss = 0.0
print('Finished Training')
# 在测试集上测试网络
correct = 0
total = 0
with torch.no_grad():
for data in testloader:
inputs, labels = data
outputs = net(inputs)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the network on the 10000 test images: %d %%' % (
100 * correct / total))
```
这段代码使用 PyTorch 中的 `nn.Module` 和 `nn.Sequential` 来定义卷积神经网络模型,并使用 SGD 优化器和交叉熵损失函数来训练模型。在训练过程中,每个 mini-batch 的输出和标签都会被传入损失函数中计算损失,并通过反向传播更新模型参数。在训练结束后,该代码会在测试集上测试网络,并输出网络的分类准确率。
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