网站 系统概述微信网站开发新开页面

网站 系统概述,微信网站开发新开页面,wordpress获取当前用户id,六安住房和城乡建设部网站# DAY 40 简单 CNN 知识回顾: 1. 数据增强 2. 卷积神经网络定义的写法 3. batch 归一化#xff1a;调整一个批次的分布#xff0c;常用与图像数据 4. 特征图#xff1a;只有卷积操作输出的才叫特征图 5. 调度器#xff1a;直接修改基础学习率 卷积操作常见流程如下调整一个批次的分布常用与图像数据4. 特征图只有卷积操作输出的才叫特征图5. 调度器直接修改基础学习率卷积操作常见流程如下- 输入 → 卷积层 → Batch 归一化层可选→ 池化层 → 激活函数 → 下一层- Flatten → Dense (with Dropout, 可选) → Dense (Output)作业尝试手动修改不同的调度器和 CNN 的结构观察训练的差异。import torch import torch.nn as nn import torch.optim as optim import matplotlib.pyplot as plt from torch.utils.data import DataLoader from torchvision import datasets, transforms # ---------------------- 1. 数据准备 ---------------------- # 数据预处理CIFAR-10 transform transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)) # CIFAR-10官方均值/标准差 ]) # 加载数据集 train_dataset datasets.CIFAR10(root./data, trainTrue, downloadTrue, transformtransform) test_dataset datasets.CIFAR10(root./data, trainFalse, downloadTrue, transformtransform) train_loader DataLoader(train_dataset, batch_size64, shuffleTrue) test_loader DataLoader(test_dataset, batch_size64, shuffleFalse) # 设备选择 device torch.device(cuda if torch.cuda.is_available() else cpu) epochs 20 # 统一训练轮次保证对比公平 # ---------------------- 2. 定义不同的CNN结构 ---------------------- # 原CNN对照组 class CNN_Base(nn.Module): def __init__(self): super(CNN_Base, self).__init__() # 卷积块1 self.conv1 nn.Conv2d(3, 32, 3, padding1) self.bn1 nn.BatchNorm2d(32) self.relu1 nn.ReLU() self.pool1 nn.MaxPool2d(2) # 卷积块2 self.conv2 nn.Conv2d(32, 64, 3, padding1) self.bn2 nn.BatchNorm2d(64) self.relu2 nn.ReLU() self.pool2 nn.MaxPool2d(2) # 卷积块3 self.conv3 nn.Conv2d(64, 128, 3, padding1) self.bn3 nn.BatchNorm2d(128) self.relu3 nn.ReLU() self.pool3 nn.MaxPool2d(2) # 全连接层 self.fc1 nn.Linear(128 * 4 * 4, 512) self.dropout nn.Dropout(0.5) self.fc2 nn.Linear(512, 10) def forward(self, x): x self.pool1(self.relu1(self.bn1(self.conv1(x)))) x self.pool2(self.relu2(self.bn2(self.conv2(x)))) x self.pool3(self.relu3(self.bn3(self.conv3(x)))) x x.view(-1, 128 * 4 * 4) x self.dropout(self.relu3(self.fc1(x))) x self.fc2(x) return x # 变体1轻量化CNN减少通道数、简化结构 class CNN_Light(nn.Module): def __init__(self): super(CNN_Light, self).__init__() # 卷积块1通道数减半 self.conv1 nn.Conv2d(3, 16, 3, padding1) self.bn1 nn.BatchNorm2d(16) self.relu1 nn.ReLU() self.pool1 nn.MaxPool2d(2) # 卷积块2减少1个卷积块 self.conv2 nn.Conv2d(16, 32, 3, padding1) self.bn2 nn.BatchNorm2d(32) self.relu2 nn.ReLU() self.pool2 nn.MaxPool2d(2) # 全连接层神经元数减半 self.fc1 nn.Linear(32 * 8 * 8, 256) # 尺寸32×8×8因少了1次池化 self.dropout nn.Dropout(0.3) # 降低Dropout率 self.fc2 nn.Linear(256, 10) def forward(self, x): x self.pool1(self.relu1(self.bn1(self.conv1(x)))) # 尺寸16×16×16 x self.pool2(self.relu2(self.bn2(self.conv2(x)))) # 尺寸32×8×8 x x.view(-1, 32 * 8 * 8) x self.dropout(self.relu1(self.fc1(x))) x self.fc2(x) return x # 变体2加深CNN增加卷积块、提升通道数 class CNN_Deep(nn.Module): def __init__(self): super(CNN_Deep, self).__init__() # 卷积块1 self.conv1 nn.Conv2d(3, 32, 3, padding1) self.bn1 nn.BatchNorm2d(32) self.relu1 nn.ReLU() self.pool1 nn.MaxPool2d(2) # 卷积块2 self.conv2 nn.Conv2d(32, 64, 3, padding1) self.bn2 nn.BatchNorm2d(64) self.relu2 nn.ReLU() self.pool2 nn.MaxPool2d(2) # 卷积块3 self.conv3 nn.Conv2d(64, 128, 3, padding1) self.bn3 nn.BatchNorm2d(128) self.relu3 nn.ReLU() self.pool3 nn.MaxPool2d(2) # 新增卷积块4 self.conv4 nn.Conv2d(128, 256, 3, padding1) self.bn4 nn.BatchNorm2d(256) self.relu4 nn.ReLU() self.pool4 nn.MaxPool2d(2) # 新增池化 # 全连接层适配新尺寸 self.fc1 nn.Linear(256 * 2 * 2, 512) # 尺寸256×2×2多1次池化 self.dropout nn.Dropout(0.5) self.fc2 nn.Linear(512, 10) def forward(self, x): x self.pool1(self.relu1(self.bn1(self.conv1(x)))) x self.pool2(self.relu2(self.bn2(self.conv2(x)))) x self.pool3(self.relu3(self.bn3(self.conv3(x)))) x self.pool4(self.relu4(self.bn4(self.conv4(x)))) # 新增卷积块处理 x x.view(-1, 256 * 2 * 2) x self.dropout(self.relu3(self.fc1(x))) x self.fc2(x) return x # ---------------------- 3. 训练函数 ---------------------- def train(model, train_loader, test_loader, criterion, optimizer, scheduler, device, epochs): model.train() all_iter_losses [] iter_indices [] train_acc_history [] test_acc_history [] train_loss_history [] test_loss_history [] 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) optimizer.zero_grad() output model(data) loss criterion(output, target) loss.backward() optimizer.step() # 记录损失 iter_loss loss.item() all_iter_losses.append(iter_loss) iter_indices.append(epoch * len(train_loader) batch_idx 1) # 统计训练指标 running_loss iter_loss _, predicted output.max(1) total target.size(0) correct predicted.eq(target).sum().item() if (batch_idx 1) % 100 0: print(fEpoch: {epoch1}/{epochs} | Batch: {batch_idx1}/{len(train_loader)} f| 单Batch损失: {iter_loss:.4f} | 累计平均损失: {running_loss/(batch_idx1):.4f}) # 训练指标统计 epoch_train_loss running_loss / len(train_loader) epoch_train_acc 100. * correct / total train_acc_history.append(epoch_train_acc) train_loss_history.append(epoch_train_loss) # 测试阶段 model.eval() test_loss 0 correct_test 0 total_test 0 with torch.no_grad(): 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_test target.size(0) correct_test predicted.eq(target).sum().item() epoch_test_loss test_loss / len(test_loader) epoch_test_acc 100. * correct_test / total_test test_acc_history.append(epoch_test_acc) test_loss_history.append(epoch_test_loss) # 更新学习率不同调度器的step方式一致 scheduler.step(epoch_test_loss) print(fEpoch {epoch1}/{epochs} 完成 | 训练准确率: {epoch_train_acc:.2f}% | 测试准确率: {epoch_test_acc:.2f}%) # 绘图 plot_iter_losses(all_iter_losses, iter_indices) plot_epoch_metrics(train_acc_history, test_acc_history, train_loss_history, test_loss_history) return epoch_test_acc # ---------------------- 4. 绘图函数 ---------------------- def plot_iter_losses(losses, indices): plt.figure(figsize(10, 4)) plt.plot(indices, losses, b-, alpha0.7, labelIteration Loss) plt.xlabel(IterationBatch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() def plot_epoch_metrics(train_acc, test_acc, train_loss, test_loss): epochs range(1, len(train_acc) 1) plt.figure(figsize(12, 4)) # 准确率 plt.subplot(1, 2, 1) plt.plot(epochs, train_acc, b-, label训练准确率) plt.plot(epochs, test_acc, r-, label测试准确率) plt.xlabel(Epoch) plt.ylabel(准确率 (%)) plt.title(训练和测试准确率) plt.legend() plt.grid(True) # 损失 plt.subplot(1, 2, 2) plt.plot(epochs, train_loss, b-, label训练损失) plt.plot(epochs, test_loss, r-, label测试损失) plt.xlabel(Epoch) plt.ylabel(损失值) plt.title(训练和测试损失) plt.legend() plt.grid(True) plt.tight_layout() plt.show() # ---------------------- 5. 多个对比实验 ---------------------- criterion nn.CrossEntropyLoss() # 统一损失函数 # 实验1原CNN ReduceLROnPlateau对照组 print(\n 实验1原CNN ReduceLROnPlateau ) model1 CNN_Base().to(device) optimizer1 optim.Adam(model1.parameters(), lr0.001) scheduler1 optim.lr_scheduler.ReduceLROnPlateau(optimizer1, modemin, patience3, factor0.5) acc1 train(model1, train_loader, test_loader, criterion, optimizer1, scheduler1, device, epochs) # 实验2原CNN StepLR仅改调度器 print(\n 实验2原CNN StepLR ) model2 CNN_Base().to(device) optimizer2 optim.Adam(model2.parameters(), lr0.001) scheduler2 optim.lr_scheduler.StepLR(optimizer2, step_size5, gamma0.5) # 每5个epoch降50% acc2 train(model2, train_loader, test_loader, criterion, optimizer2, scheduler2, device, epochs) # 实验3轻量化CNN ReduceLROnPlateau仅改结构 print(\n 实验3轻量化CNN ReduceLROnPlateau ) model3 CNN_Light().to(device) optimizer3 optim.Adam(model3.parameters(), lr0.001) scheduler3 optim.lr_scheduler.ReduceLROnPlateau(optimizer3, modemin, patience3, factor0.5) acc3 train(model3, train_loader, test_loader, criterion, optimizer3, scheduler3, device, epochs) # 实验4加深CNN CosineAnnealingLR改结构调度器 print(\n 实验4加深CNN CosineAnnealingLR ) model4 CNN_Deep().to(device) optimizer4 optim.Adam(model4.parameters(), lr0.001) scheduler4 optim.lr_scheduler.CosineAnnealingLR(optimizer4, T_max10) # 周期10个epoch acc4 train(model4, train_loader, test_loader, criterion, optimizer4, scheduler4, device, epochs) # 打印最终结果对比 print(\n 所有实验最终测试准确率 ) print(f实验1原CNNReduceLROnPlateau{acc1:.2f}%) print(f实验2原CNNStepLR{acc2:.2f}%) print(f实验3轻量化CNNReduceLROnPlateau{acc3:.2f}%) print(f实验4加深CNNCosineAnnealingLR{acc4:.2f}%)浙大疏锦行