猫狗识别pytorch

对于猫狗识别的任务，你可以使用PyTorch来构建一个深度学习模型。下面是一个简单的示例代码：

首先，你需要导入所需的库：

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

然后，定义一个自定义的卷积神经网络模型：

class CatDogClassifier(nn.Module):
    def __init__(self):
        super(CatDogClassifier, 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.fc1 = nn.Linear(64 * 56 * 56, 128)
        self.fc2 = nn.Linear(128, 2)

    def forward(self, x):
        x = nn.functional.relu(self.conv1(x))
        x = nn.functional.max_pool2d(x, 2)
        x = nn.functional.relu(self.conv2(x))
        x = nn.functional.max_pool2d(x, 2)
        x = nn.functional.relu(self.conv3(x))
        x = nn.functional.max_pool2d(x, 2)
        x = x.view(x.size(0), -1)
        x = nn.functional.relu(self.fc1(x))
        x = self.fc2(x)
        return x

接下来，加载数据集并进行数据预处理：

train_dataset = ImageFolder('path/to/train/dataset', transform=ToTensor())
test_dataset = ImageFolder('path/to/test/dataset', transform=ToTensor())

train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=16, shuffle=False)

然后，定义模型、损失函数和优化器：

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

接下来，进行模型训练：

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)

num_epochs = 10
for epoch in range(num_epochs):
    model.train()
    for images, labels in train_loader:
        images, labels = images.to(device), labels.to(device)
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
    
    model.eval()
    with torch.no_grad():
        correct = 0
        total = 0
        for images, labels in test_loader:
            images, labels = images.to(device), labels.to(device)
            outputs = model(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
        
        accuracy = 100 * correct / total
        print(f'Epoch {epoch+1}, Test Accuracy: {accuracy:.2f}%')

最后，你可以使用训练好的模型进行预测：

def predict(image_path):
    model.eval()
    with torch.no_grad():
        image = Image.open(image_path)
        image = ToTensor()(image).unsqueeze(0)
        image = image.to(device)
        output = model(image)
        _, predicted = torch.max(output.data, 1)
        return predicted.item()

这是一个简单的猫狗识别任务的PyTorch示例代码，你可以根据实际需求进行调整和扩展。