给一个猫狗图像分类的代码
时间: 2024-03-05 21:50:59 浏览: 17
好的,以下是一个简单的猫狗图像分类示例代码,使用了卷积神经网络(CNN)模型和PyTorch框架:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
# 定义超参数
num_epochs = 5
batch_size = 4
learning_rate = 0.001
# 定义数据预处理
transform = transforms.Compose(
[transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
# 加载数据集
trainset = torchvision.datasets.ImageFolder(root='./data/train', transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=2)
testset = torchvision.datasets.ImageFolder(root='./data/test', transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=2)
# 定义模型
class ConvNet(nn.Module):
def __init__(self):
super(ConvNet, self).__init__()
self.conv1 = nn.Conv2d(3, 16, 3)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(16, 32, 3)
self.fc1 = nn.Linear(32 * 54 * 54, 120)
self.fc2 = nn.Linear(120, 2)
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, 32 * 54 * 54)
x = nn.functional.relu(self.fc1(x))
x = self.fc2(x)
return x
net = ConvNet()
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=learning_rate, momentum=0.9)
# 训练模型
for epoch in range(num_epochs):
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 % 2000 == 1999:
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 2000))
running_loss = 0.0
print('Finished Training')
# 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 of the network on the test images: %d %%' % (100 * correct / total))
```
这个代码假设你的数据集已经按照如下的目录结构组织:
```
data/
train/
cat/
cat.1.jpg
cat.2.jpg
...
dog/
dog.1.jpg
dog.2.jpg
...
test/
cat/
cat.1001.jpg
cat.1002.jpg
...
dog/
dog.1001.jpg
dog.1002.jpg
...
```
其中,data/train 目录下是用于训练的猫狗图片,data/test 目录下是用于测试的猫狗图片。每个子目录(cat/dog)下包含若干张图片,图片的命名格式为 "类别.序号.jpg"。