pytorch第66页

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import torch
from torch import optim, nn
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
import torch.nn.functional as F
import numpy as np
from sklearn.metrics import classification_report
from PIL import Image
import time
from matplotlib import pyplot as pltdevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')#数据加载
transform_train = transforms.Compose([transforms.RandomCrop(32, padding=4),transforms.RandomHorizontalFlip(),transforms.ToTensor(),transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])transform_test = transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])# 加载CIFAR-10数据集
def load_data():train_dataset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train)test_dataset = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)train_loader = DataLoader(train_dataset, batch_size=128, shuffle=True, num_workers=2)test_loader = DataLoader(test_dataset, batch_size=128, shuffle=False, num_workers=2)return train_loader, test_loader, test_dataset#定义MYVGG模型
class MYVGG(nn.Module):def __init__(self, num_classes=10):super(MYVGG, self).__init__()self.features = nn.Sequential(# Block 1nn.Conv2d(3, 64, kernel_size=3, padding=1),nn.ReLU(inplace=True),nn.Conv2d(64, 64, kernel_size=3, padding=1),nn.ReLU(inplace=True),nn.MaxPool2d(2, 2),# Block 2nn.Conv2d(64, 128, kernel_size=3, padding=1),nn.ReLU(inplace=True),nn.Conv2d(128, 128, kernel_size=3, padding=1),nn.ReLU(inplace=True),nn.MaxPool2d(2, 2),# Block 3nn.Conv2d(128, 256, kernel_size=3, padding=1),nn.ReLU(inplace=True),nn.Conv2d(256, 256, kernel_size=3, padding=1),nn.ReLU(inplace=True),nn.MaxPool2d(2, 2),# Block 4nn.Conv2d(256, 512, kernel_size=3, padding=1),nn.ReLU(inplace=True),nn.Conv2d(512, 512, kernel_size=3, padding=1),nn.ReLU(inplace=True),nn.MaxPool2d(2, 2),# Block 5nn.Conv2d(512, 512, kernel_size=3, padding=1),nn.ReLU(inplace=True),nn.Conv2d(512, 512, kernel_size=3, padding=1),nn.ReLU(inplace=True),nn.MaxPool2d(2, 2),)self.classifier = nn.Sequential(nn.Dropout(0.5),nn.Linear(512, num_classes))def forward(self, x):x = self.features(x)x = x.view(x.size(0), -1)x = self.classifier(x)return x#训练函数
model = MYVGG().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)def train(model, train_loader, criterion, optimizer, epoch_num=50):model.train()train_loss = []train_acc = []for epoch in range(epoch_num):start_time = time.time()running_loss = 0.0current = 0total = 0for batch_idx, (data, target) in enumerate(train_loader):data, target = data.to(device), target.to(device)output = model(data)loss = criterion(output, target)optimizer.zero_grad()loss.backward()optimizer.step()running_loss += loss.item()_, predicted = torch.max(output.data, 1)total += target.size(0)current += (predicted == target).sum().item()epoch_loss = running_loss / len(train_loader)epoch_acc = 100.0 * current / totaltrain_loss.append(epoch_loss)train_acc.append(epoch_acc)end_time = time.time()print(f"Epoch [{epoch+1}/{epoch_num}], Loss: {epoch_loss:.4f}, Acc: {epoch_acc:.2f}%, Time: {end_time-start_time:.2f}s")return train_loss, train_acc#测试函数
def test(model, test_loader):model.eval()all_pred = []all_label = []with torch.no_grad():for data, target in test_loader:data, target = data.to(device), target.to(device)outputs = model(data)_, predicted = torch.max(outputs.data, 1)all_pred.extend(predicted.cpu().numpy())all_label.extend(target.cpu().numpy())all_pred = np.array(all_pred)all_label = np.array(all_label)accuracy = (all_pred == all_label).mean()accuracy = 100.0 * accuracyprint(f'测试准确率: {accuracy:.4f}%')print("分类效果评估：")target_names = [str(i) for i in range(10)]report = classification_report(all_label, all_pred, target_names=target_names)print(report)if __name__ == '__main__':print(f"24信计2班 佘婷婷 2024310143102")print(f"device:{device}")epoch_num = 20train_loader, test_loader, test_dataset = load_data()train_loss, train_acc = train(model, train_loader, criterion, optimizer, epoch_num)test(model, test_loader)#绘制结果plt.figure(figsize=(10, 5))plt.subplot(1, 2, 1)plt.plot(range(1, epoch_num+1), train_loss)plt.title("Training Loss")plt.xlabel("Epoch")plt.ylabel("Loss")plt.subplot(1, 2, 2)plt.plot(range(1, epoch_num+1), train_acc)plt.title("Training Accuracy")plt.xlabel("Epoch")plt.ylabel("Accuracy (%)")plt.tight_layout()plt.show()