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网站后台怎么上传表格,淘宝提货网站怎么做的,兰州优秀网站推广,违章建设举报网站# DAY 43 随机函数与广播机制 知识点回顾: 1. 随机张量的生成: torch.randn函数 2. 卷积和池化的计算公式 (可以不掌握, 会自动计算的) 3. pytorch的广播机制: 加法和乘法的广播机制 ps: numpy运算也有类似的广播机制, 基本一致 作业: 自己多举几个例子帮助自己理解即可…# DAY 43 随机函数与广播机制知识点回顾:1. 随机张量的生成: torch.randn函数2. 卷积和池化的计算公式 (可以不掌握, 会自动计算的)3. pytorch的广播机制: 加法和乘法的广播机制ps: numpy运算也有类似的广播机制, 基本一致作业: 自己多举几个例子帮助自己理解即可# CBAM 注意力知识点回顾:1. 通道注意力模块复习2. 空间注意力模块3. CBAM的定义作业尝试对今天的模型检查参数数目并用 tensorboard 查看训练过程浙大疏锦行# 导入依赖库 import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader from torch.utils.tensorboard import SummaryWriter import torchvision import torchvision.transforms as transforms import matplotlib.pyplot as plt # 1. 模型参数统计工具统计模型总参数、可训练/不可训练参数数量 def count_model_parameters(model): total_params 0 trainable_params 0 non_trainable_params 0 for param in model.parameters(): param_num param.numel() total_params param_num if param.requires_grad: trainable_params param_num else: non_trainable_params param_num def format_params(num): if num 1e6: return f{num/1e6:.2f}M elif num 1e3: return f{num/1e3:.2f}K else: return f{num} print(模型参数统计) print(f总参数: {format_params(total_params)} ({total_params:,})) print(f可训练参数: {format_params(trainable_params)} ({trainable_params:,})) print(f不可训练参数: {format_params(non_trainable_params)} ({non_trainable_params:,})) return total_params, trainable_params, non_trainable_params # 2. CBAM注意力模块定义 class CBAMBlock(nn.Module): def __init__(self, channels, reduction16): super().__init__() # 通道注意力 self.avg_pool nn.AdaptiveAvgPool2d(1) self.max_pool nn.AdaptiveMaxPool2d(1) self.fc nn.Sequential( nn.Linear(channels, channels//reduction), nn.ReLU(inplaceTrue), nn.Linear(channels//reduction, channels) ) # 空间注意力 self.spatial nn.Sequential( nn.Conv2d(2, 1, kernel_size7, padding3, biasFalse), nn.Sigmoid() ) self.sigmoid nn.Sigmoid() def forward(self, x): # 通道注意力计算 avg_out self.fc(self.avg_pool(x).view(x.size(0), -1)).view(x.size(0), x.size(1), 1, 1) max_out self.fc(self.max_pool(x).view(x.size(0), -1)).view(x.size(0), x.size(1), 1, 1) channel_att self.sigmoid(avg_out max_out) x x * channel_att # 空间注意力计算 avg_out torch.mean(x, dim1, keepdimTrue) max_out, _ torch.max(x, dim1, keepdimTrue) spatial_att self.spatial(torch.cat([avg_out, max_out], dim1)) x x * spatial_att return x # 3. 基于CBAM的CNN模型定义 class CBAM_CNN(nn.Module): def __init__(self, num_classes10): super().__init__() self.features nn.Sequential( nn.Conv2d(3, 64, kernel_size3, padding1), nn.ReLU(inplaceTrue), CBAMBlock(64), nn.MaxPool2d(2, 2), nn.Conv2d(64, 128, kernel_size3, padding1), nn.ReLU(inplaceTrue), CBAMBlock(128), nn.MaxPool2d(2, 2), ) self.classifier nn.Linear(128 * 8 * 8, num_classes) def forward(self, x): x self.features(x) x x.view(x.size(0), -1) x self.classifier(x) return x # 4. 训练函数集成TensorBoard可视化 def train(model, train_loader, test_loader, criterion, optimizer, scheduler, device, epochs, writer): model.train() all_iter_losses [] iter_indices [] 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() iter_loss loss.item() global_step epoch * len(train_loader) batch_idx 1 all_iter_losses.append(iter_loss) iter_indices.append(global_step) # TensorBoard记录batch级损失 writer.add_scalar(Train/Batch_Loss, iter_loss, global_step) 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)} | 单Batch损失: {iter_loss:.4f}) # 记录epoch级训练指标 epoch_train_loss running_loss / len(train_loader) epoch_train_acc 100. * correct / total writer.add_scalar(Train/Epoch_Loss, epoch_train_loss, epoch1) writer.add_scalar(Train/Epoch_Accuracy, epoch_train_acc, epoch1) # 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() # 记录epoch级测试指标 epoch_test_loss test_loss / len(test_loader) epoch_test_acc 100. * correct_test / total_test writer.add_scalar(Test/Epoch_Loss, epoch_test_loss, epoch1) writer.add_scalar(Test/Epoch_Accuracy, epoch_test_acc, epoch1) scheduler.step(epoch_test_loss) print(fEpoch {epoch1} 完成 | 训练准确率: {epoch_train_acc:.2f}% | 测试准确率: {epoch_test_acc:.2f}%) writer.close() return epoch_test_acc # 5. 绘图辅助函数 def plot_iter_losses(losses, indices): plt.figure(figsize(10, 4)) plt.plot(indices, losses, b-, alpha0.7) plt.xlabel(Iteration) plt.ylabel(Loss) plt.title(Training Loss per Iteration) plt.grid(True) plt.show() # 6. 主执行逻辑 if __name__ __main__: # 设备配置 device torch.device(cuda if torch.cuda.is_available() else cpu) # 数据加载CIFAR10 transform transforms.Compose([ transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ]) train_dataset torchvision.datasets.CIFAR10(root./data, trainTrue, downloadTrue, transformtransform) test_dataset torchvision.datasets.CIFAR10(root./data, trainFalse, downloadTrue, transformtransform) train_loader DataLoader(train_dataset, batch_size64, shuffleTrue, num_workers2) test_loader DataLoader(test_dataset, batch_size64, shuffleFalse, num_workers2) # 模型、损失函数、优化器初始化 model CBAM_CNN().to(device) criterion nn.CrossEntropyLoss() optimizer optim.Adam(model.parameters(), lr0.001) scheduler optim.lr_scheduler.ReduceLROnPlateau(optimizer, modemin, patience3, factor0.5) # 统计模型参数 count_model_parameters(model) # 初始化TensorBoard writer SummaryWriter(log_dir./cbam_logs) # 启动训练 epochs 50 print(开始模型训练...) final_accuracy train(model, train_loader, test_loader, criterion, optimizer, scheduler, device, epochs, writer) print(f训练完成 | 最终测试准确率: {final_accuracy:.2f}%)