import torch import torch.nn as nn import torch.optim as optim from torchvision import datasets, transforms from torch.utils.data import DataLoader import matplotlib.pyplot as plt import numpy as np # 设置中文字体支持 plt.rcParams["font.family"] = ["SimHei"] plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题 # 1. 数据预处理 transform = transforms.Compose([ transforms.ToTensor(), # 转换为张量并归一化到[0,1] transforms.Normalize((0.1307,), (0.3081,)) # MNIST数据集的均值和标准差 ]) # 2. 加载MNIST数据集 train_dataset = datasets.MNIST( root='./data', train=True, download=True, transform=transform ) test_dataset = datasets.MNIST( root='./data', train=False, transform=transform ) # 3. 创建数据加载器 batch_size = 64 # 每批处理64个样本 train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True) test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False) # 4. 定义模型、损失函数和优化器 class MLP(nn.Module): def __init__(self): super(MLP, self).__init__() self.flatten = nn.Flatten() # 将28x28的图像展平为784维向量 self.layer1 = nn.Linear(784, 128) # 第一层：784个输入，128个神经元 self.relu = nn.ReLU() # 激活函数 self.layer2 = nn.Linear(128, 10) # 第二层：128个输入，10个输出（对应10个数字类别） def forward(self, x): x = self.flatten(x) # 展平图像 x = self.layer1(x) # 第一层线性变换 x = self.relu(x) # 应用ReLU激活函数 x = self.layer2(x) # 第二层线性变换，输出logits return x # 检查GPU是否可用 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 初始化模型 model = MLP() model = model.to(device) # 将模型移至GPU（如果可用） criterion = nn.CrossEntropyLoss() # 交叉熵损失函数，适用于多分类问题 optimizer = optim.Adam(model.parameters(), lr=0.001) # Adam优化器 # 5. 训练模型（记录每个 iteration 的损失） def train(model, train_loader, test_loader, criterion, optimizer, device, epochs): model.train() # 设置为训练模式 # 新增：记录每个 iteration 的损失 all_iter_losses = [] # 存储所有 batch 的损失 iter_indices = [] # 存储 iteration 序号（从1开始） for epoch in range(epochs): running_loss = 0.0 correct = 0 total = 0 for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device), target.to(device) # 移至GPU(如果可用) optimizer.zero_grad() # 梯度清零 output = model(data) # 前向传播 loss = criterion(output, target) # 计算损失 loss.backward() # 反向传播 optimizer.step() # 更新参数 # 记录当前 iteration 的损失（注意：这里直接使用单 batch 损失，而非累加平均） iter_loss = loss.item() all_iter_losses.append(iter_loss) iter_indices.append(epoch * len(train_loader) + batch_idx + 1) # iteration 序号从1开始 # 统计准确率和损失（原逻辑保留，用于 epoch 级统计） running_loss += iter_loss _, predicted = output.max(1) total += target.size(0) correct += predicted.eq(target).sum().item() # 每100个批次打印一次训练信息（可选：同时打印单 batch 损失） if (batch_idx + 1) % 100 == 0: print(f'Epoch: {epoch+1}/{epochs} | Batch: {batch_idx+1}/{len(train_loader)} ' f'| 单Batch损失: {iter_loss:.4f} | 累计平均损失: {running_loss/(batch_idx+1):.4f}') # 原 epoch 级逻辑（测试、打印 epoch 结果）不变 epoch_train_loss = running_loss / len(train_loader) epoch_train_acc = 100. * correct / total epoch_test_loss, epoch_test_acc = test(model, test_loader, criterion, device) print(f'Epoch {epoch+1}/{epochs} 完成 | 训练准确率: {epoch_train_acc:.2f}% | 测试准确率: {epoch_test_acc:.2f}%') # 绘制所有 iteration 的损失曲线 plot_iter_losses(all_iter_losses, iter_indices) # 保留原 epoch 级曲线（可选） # plot_metrics(train_losses, test_losses, train_accuracies, test_accuracies, epochs) return epoch_test_acc # 返回最终测试准确率 # 6. 测试模型 def test(model, test_loader, criterion, device): model.eval() # 设置为评估模式 test_loss = 0 correct = 0 total = 0 with torch.no_grad(): # 不计算梯度，节省内存和计算资源 for data, target in test_loader: data, target = data.to(device), target.to(device) output = model(data) test_loss += criterion(output, target).item() _, predicted = output.max(1) total += target.size(0) correct += predicted.eq(target).sum().item() avg_loss = test_loss / len(test_loader) accuracy = 100. * correct / total return avg_loss, accuracy # 返回损失和准确率 # 7.绘制每个 iteration 的损失曲线 def plot_iter_losses(losses, indices): plt.figure(figsize=(10, 4)) plt.plot(indices, losses, 'b-', alpha=0.7, label='Iteration Loss') plt.xlabel('Iteration（Batch序号）') plt.ylabel('损失值') plt.title('每个 Iteration 的训练损失') plt.legend() plt.grid(True) plt.tight_layout() plt.show() # 8. 执行训练和测试（设置 epochs=2 验证效果） epochs = 2 print("开始训练模型...") final_accuracy = train(model, train_loader, test_loader, criterion, optimizer, device, epochs) print(f"训练完成！最终测试准确率: {final_accuracy:.2f}%")@浙大疏锦行
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