基于pytorch编写狗图片的识别

以下是一个基于PyTorch的狗图片识别的代码示例：

首先，需要导入必要的库和模块：

import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision.datasets as datasets

然后，定义一个卷积神经网络模型：

class DogNet(nn.Module):
    def __init__(self):
        super(DogNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
        self.conv3 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(64 * 28 * 28, 500)
        self.fc2 = nn.Linear(500, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = nn.functional.relu(x)
        x = self.pool(x)
        x = self.conv2(x)
        x = nn.functional.relu(x)
        x = self.pool(x)
        x = self.conv3(x)
        x = nn.functional.relu(x)
        x = self.pool(x)
        x = x.view(-1, 64 * 28 * 28)
        x = self.fc1(x)
        x = nn.functional.relu(x)
        x = self.fc2(x)
        return x

接着，定义训练和测试函数：

def train(model, train_loader, optimizer, criterion):
    model.train()
    train_loss = 0.0
    for data, target in train_loader:
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()
        train_loss += loss.item() * data.size(0)
    train_loss = train_loss / len(train_loader.dataset)
    return train_loss

def test(model, test_loader, criterion):
    model.eval()
    test_loss = 0.0
    correct = 0.0
    with torch.no_grad():
        for data, target in test_loader:
            output = model(data)
            loss = criterion(output, target)
            test_loss += loss.item() * data.size(0)
            pred = output.argmax(dim=1, keepdim=True)
            correct += pred.eq(target.view_as(pred)).sum().item()
    test_loss = test_loss / len(test_loader.dataset)
    accuracy = 100.0 * correct / len(test_loader.dataset)
    return test_loss, accuracy

最后，读取训练集和测试集数据，并进行训练和测试：

train_set = datasets.ImageFolder('train', transform=transforms.ToTensor())
train_loader = torch.utils.data.DataLoader(train_set, batch_size=64, shuffle=True)
test_set = datasets.ImageFolder('test', transform=transforms.ToTensor())
test_loader = torch.utils.data.DataLoader(test_set, batch_size=64, shuffle=True)

model = DogNet()
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

for epoch in range(10):
    train_loss = train(model, train_loader, optimizer, criterion)
    test_loss, accuracy = test(model, test_loader, criterion)
    print("Epoch: {}, Train Loss: {:.4f}, Test Loss: {:.4f}, Accuracy: {:.2f}%".format(epoch+1, train_loss, test_loss, accuracy))

这段代码将训练10个epoch，并输出每个epoch的训练损失、测试损失和准确率。在训练结束后，我们可以使用模型来预测新的狗图片。