Python深度学习项目：CNN手写数字识别测试准确率

import matplotlib.pyplot as plt import seaborn as sns import torch import torch.nn as nn import torch.optim as optim from sklearn.metrics import confusion_matrix from torch.utils.data import DataLoader from torchvision import datasets, transforms # 设置matplotlib支持中文的字体 plt.rcParams['font.sans-serif'] = ['Microsoft YaHei'] # 例如使用微软雅黑 plt.rcParams['axes.unicode_minus'] = False # 解决负号无法显示的问题 # 定义 LeNet 模型 class LeNet(nn.Module): def __init__(self): super(LeNet, self).__init__() self.conv1 = nn.Conv2d(1, 6, 5) self.pool = nn.MaxPool2d(2, 2) self.conv2 = nn.Conv2d(6, 16, 5) self.fc1 = nn.Linear(16 * 5 * 5, 120) self.fc2 = nn.Linear(120, 84) self.fc3 = nn.Linear(84, 10) 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, 16 * 5 * 5) x = nn.functional.relu(self.fc1(x)) x = nn.functional.relu(self.fc2(x)) x = self.fc3(x) return x # 数据预处理 transform = transforms.Compose([transforms.Resize((32, 32)), transforms.ToTensor()]) # 加载数据集 train_set = datasets.MNIST(root='./data', train=True, download=True, transform=transform) test_set = datasets.MNIST(root='./data', train=False, download=True, transform=transform) train_loader = DataLoader(train_set, batch_size=256, shuffle=True) test_loader = DataLoader(test_set, batch_size=256, shuffle=False) # 实例化模型、损失函数和优化器 model = LeNet() criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=0.001) # 初始化列表以跟踪每个周期后的损失和准确率 train_losses = [] test_accuracy = [] # 训练模型 for epoch in range(5): # 训练 5 个周期 model.train() running_loss = 0.0 for i, (images, labels) in enumerate(train_loader): optimizer.zero_grad() outputs = model(images) loss = criterion(outputs, labels) loss.backward() optimizer.step() running_loss += loss.item() avg_train_loss = running_loss / len(train_loader) train_losses.append(avg_train_loss) # 测试模型准确率 model.eval() correct = 0 total = 0 all_labels = [] all_predicted = [] with torch.no_grad(): for images, labels in test_loader: outputs = model(images) _, predicted = torch.max(outputs.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() all_labels.extend(labels.cpu().numpy()) all_predicted.extend(predicted.cpu().numpy()) epoch_accuracy = 100 * correct / total test_accuracy.append(epoch_accuracy) # 输出训练结果 print(f'Epoch [{epoch+1}/5], Loss: {avg_train_loss:.4f}, 准确率: {epoch_accuracy:.2f}%') # 绘制训练损失图 plt.figure(figsize=(10, 5)) plt.plot(train_losses, label='训练损失') plt.title('训练损失') plt.xlabel('周期数') plt.ylabel('损失') plt.legend() plt.show() # 绘制测试准确率图 plt.figure(figsize=(10, 5)) plt.plot(test_accuracy, label='测试准确率') plt.title('测试准确率') plt.xlabel('周期数') plt.ylabel('准确率 (%)') plt.legend() plt.show() # 绘制混淆矩阵 conf_mat = confusion_matrix(all_labels, all_predicted) plt.figure(figsize=(10, 10)) sns.heatmap(conf_mat, annot=True, fmt='d', cmap='Blues') plt.xlabel('预测') plt.ylabel('实际') plt.title('混淆矩阵') plt.show()