使用PyTorch借助VGG16模型进行猫狗识别可按以下步骤实现： ### 1. 导入必要的库 ```python import time import os import numpy as np import torch import torch.nn.functional as F import torch.nn as nn from torch.utils.data import Dataset from torch.utils.data import DataLoader from torchvision import transforms from PIL import Image import matplotlib.pyplot as plt DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") ``` ### 2. 获取数据 使用`torchvision.datasets.ImageFolder`函数读取数据，该函数要求路径下是文件夹，所以要将训练数据分为`dog`和`cat`两个子文件夹 [^1][^4]。 ### 3. 数据预处理 定义数据预处理的转换操作，例如调整图像大小、归一化等： ```python transform = transforms.Compose([ transforms.Resize((224, 224)), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) ``` ### 4. 加载数据集 使用`DataLoader`加载数据集： ```python from torchvision.datasets import ImageFolder train_dataset = ImageFolder(root='path/to/train', transform=transform) train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True) test_dataset = ImageFolder(root='path/to/test', transform=transform) test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False) ``` ### 5. 加载预训练的VGG16模型 ```python from torchvision.models import vgg16 model = vgg16(pretrained=True) # 修改最后一层全连接层以适应猫狗二分类任务 num_ftrs = model.classifier[6].in_features model.classifier[6] = nn.Linear(num_ftrs, 2) model = model.to(DEVICE) ``` ### 6. 定义损失函数和优化器 ```python criterion = nn.CrossEntropyLoss() optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9) ``` ### 7. 训练模型 ```python num_epochs = 10 for epoch in range(num_epochs): model.train() running_loss = 0.0 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() running_loss += loss.item() print(f'Epoch {epoch+1}/{num_epochs}, Loss: {running_loss/len(train_loader)}') ``` ### 8. 评估模型 ```python model.eval() correct = 0 total = 0 with torch.no_grad(): 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() print(f'Accuracy of the network on the test images: {100 * correct / total}%') ```