Day40

单通道图片的规范写法

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader , Dataset 
from torchvision import datasets, transforms 
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings("ignore")
torch.manual_seed(42)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"使用设备: {device}")transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))  
])train_dataset = datasets.MNIST(root='./data',train=True,download=True,transform=transform
)test_dataset = datasets.MNIST(root='./data',train=False,transform=transform
)batch_size = 64  
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)class MLP(nn.Module):def __init__(self):super(MLP, self).__init__()self.flatten = nn.Flatten()  self.layer1 = nn.Linear(784, 128)  self.relu = nn.ReLU()  # 激活函数self.layer2 = nn.Linear(128, 10) def forward(self, x):x = self.flatten(x) x = self.layer1(x)  x = self.relu(x)     x = self.layer2(x)   return xmodel = MLP()
model = model.to(device)  criterion = nn.CrossEntropyLoss()  
optimizer = optim.Adam(model.parameters(), lr=0.001)  def train(model, train_loader, test_loader, criterion, optimizer, device, epochs):model.train()all_iter_losses = []  iter_indices = []    for epoch in range(epochs):running_loss = 0.0correct = 0total = 0for 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()  output = model(data) loss = criterion(output, target)  loss.backward()  optimizer.step()  iter_loss = loss.item()all_iter_losses.append(iter_loss)iter_indices.append(epoch * len(train_loader) + batch_idx + 1)running_loss += loss.item() _, predicted = output.max(1) total += target.size(0) correct += predicted.eq(target).sum().item() 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}')epoch_train_loss = running_loss / len(train_loader)epoch_train_acc = 100. * correct / totalepoch_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}%')plot_iter_losses(all_iter_losses, iter_indices)return epoch_test_acc  

测试函数和绘图函数均被封装在了train函数中，但是test和绘图函数在定义train函数之后，这是因为在 Python 中，函数定义的顺序不影响调用，只要在调用前已经完成定义即可。

#测试模型（不变）
def test(model, test_loader, criterion, device):model.eval()  # 设置为评估模式test_loss = 0correct = 0total = 0with 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 / totalreturn avg_loss, accuracy  # 返回损失和准确率# 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()# 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