pytorch 图像分类代码
时间: 2024-05-31 21:06:44 浏览: 11
PyTorch 是一种基于 Python 的机器学习库,它可以帮助你建立和训练深度学习模型。下面是一个 PyTorch 图像分类代码的简单示例:
```
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
# 定义数据预处理
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))])
# 加载数据集
trainset = datasets.MNIST(root='./data', train=True,
download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64,
shuffle=True, num_workers=2)
testset = datasets.MNIST(root='./data', train=False,
download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=64,
shuffle=False, num_workers=2)
# 定义网络模型
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 4 * 4, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool(torch.relu(self.conv1(x)))
x = self.pool(torch.relu(self.conv2(x)))
x = x.view(-1, 16 * 4 * 4)
x = torch.relu(self.fc1(x))
x = torch.relu(self.fc2(x))
x = self.fc3(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:
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 100))
running_loss = 0.0
print('Finished Training')
# 测试网络
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 of the network on the 10000 test images: %d %%' % (
100 * correct / total))
```
该示例使用了 MNIST 数据集,通过定义数据预处理和加载数据集,然后构建一个卷积神经网络模型来对手写数字进行分类。其中 `Net` 类定义了卷积层、池化层、全连接层等结构,`criterion` 定义了损失函数,`optimizer` 定义了优化器。在训练过程中,通过循环遍历训练数据集,计算损失并反向传播更新权重,最终得到训练好的模型。在测试过程中,通过遍历测试数据集,计算模型的准确率。
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