pytorch猫狗二分类模型Python代码

以下是一个基于PyTorch的猫狗二分类模型的Python代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
from torchvision import transforms
from torch.utils.data import DataLoader

# 定义数据变换
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
train_dataset = torchvision.datasets.ImageFolder(root='./train', transform=transform)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)

# 定义模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 16, kernel_size=3)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=3)
        self.conv3 = nn.Conv2d(32, 64, kernel_size=3)
        self.pool = nn.MaxPool2d(2, 2)
        self.fc1 = nn.Linear(64 * 26 * 26, 512)
        self.fc2 = nn.Linear(512, 2)

    def forward(self, x):
        x = self.pool(nn.functional.relu(self.conv1(x)))
        x = self.pool(nn.functional.relu(self.conv2(x)))
        x = self.pool(nn.functional.relu(self.conv3(x)))
        x = x.view(-1, 64 * 26 * 26)
        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.Adam(net.parameters(), lr=0.001)

# 训练模型
for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / len(train_loader)))

print('Finished Training')

# 保存模型
PATH = './cat_dog_net.pth'
torch.save(net.state_dict(), PATH)

这个示例代码中，我们使用了一个简单的卷积神经网络模型，该模型包含了三个卷积层和两个全连接层。我们使用了PyTorch的内置的损失函数`CrossEntropyLoss`和优化器`Adam`来训练模型。在训练完成后，我们将模型的状态字典保存到了一个.pth文件中，以便后续的推理和预测使用。