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媒体网站网页设计,广州自助建站,wordpress1003无标题,室内装饰装修施工图集#x1f368; 本文为#x1f517;365天深度学习训练营中的学习记录博客#x1f356; 原作者#xff1a;K同学啊 一、 前期准备

1. 设置GPU 如果设备上支持GPU就使用GPU,否则使用CPU import torch import torch.nn as nn import torchvision.transforms as transforms im… 本文为365天深度学习训练营中的学习记录博客 原作者K同学啊 一、 前期准备

2. 设置GPU 如果设备上支持GPU就使用GPU,否则使用CPU import torch import torch.nn as nn import torchvision.transforms as transforms import torchvision from torchvision import transforms, datasets import os,PIL,pathlib,warningswarnings.filterwarnings(ignore) #忽略警告信息device torch.device(cuda if torch.cuda.is_available() else cpu) device device(typecuda) 2. 导入数据 import pathlibdata_dir ./data/weather_photos/ data_dir pathlib.Path(data_dir)# 获取所有子目录路径 data_paths list(data_dir.glob(*))# 使用 path.parts 获取正确的目录名称 classeNames [path.parts[-1] for path in data_paths] print(classeNames)[cloudy, rain, shine, sunrise] # 关于transforms.Compose的更多介绍可以参考https://blog.csdn.net/qq_38251616/article/details/124878863 train_transforms transforms.Compose([transforms.Resize([224, 224]), # 将输入图片resize成统一尺寸# transforms.RandomHorizontalFlip(), # 随机水平翻转transforms.ToTensor(), # 将PIL Image或numpy.ndarray转换为tensor并归一化到[0,1]之间transforms.Normalize( # 标准化处理–转换为标准正太分布高斯分布使模型更容易收敛mean[0.485, 0.456, 0.406], std[0.229, 0.224, 0.225]) # 其中 mean[0.485,0.456,0.406]与std[0.229,0.224,0.225] 从数据集中随机抽样计算得到的。 ])test_transform transforms.Compose([transforms.Resize([224, 224]), # 将输入图片resize成统一尺寸transforms.ToTensor(), # 将PIL Image或numpy.ndarray转换为tensor并归一化到[0,1]之间transforms.Normalize( # 标准化处理–转换为标准正太分布高斯分布使模型更容易收敛mean[0.485, 0.456, 0.406], std[0.229, 0.224, 0.225]) # 其中 mean[0.485,0.456,0.406]与std[0.229,0.224,0.225] 从数据集中随机抽样计算得到的。 ])total_data datasets.ImageFolder(./data/weather_photos/,transformtrain_transforms) total_data Dataset ImageFolderNumber of datapoints: 1125Root location: ./data/weather_photos/StandardTransform Transform: Compose(Resize(size[224, 224], interpolationbilinear, max_sizeNone, antialiasTrue)ToTensor()Normalize(mean[0.485, 0.456, 0.406], std[0.229, 0.224, 0.225])) total_data.class_to_idx {cloudy: 0, rain: 1, shine: 2, sunrise: 3} 3. 划分数据集 train_size int(0.8 * len(total_data)) test_size len(total_data) - train_size train_dataset, test_dataset torch.utils.data.random_split(total_data, [train_size, test_size]) train_dataset, test_dataset (torch.utils.data.dataset.Subset at 0x19600429450,  torch.utils.data.dataset.Subset at 0x196004297e0) batch_size 4train_dl torch.utils.data.DataLoader(train_dataset,batch_sizebatch_size,shuffleTrue,num_workers1) test_dl torch.utils.data.DataLoader(test_dataset,batch_sizebatch_size,shuffleTrue,num_workers1) for X, y in test_dl:print(Shape of X [N, C, H, W]: , X.shape)print(Shape of y: , y.shape, y.dtype)break Shape of X [N, C, H, W]:  torch.Size([4, 3, 224, 224]) Shape of y:  torch.Size([4]) torch.int64 二、搭建包含C3模块的模型 K同学啊提示是否可以尝试通过增加/调整C3模块与Conv模块来提高准确率 1. 搭建模型 import torch.nn.functional as Fdef autopad(k, pNone): # kernel, padding# Pad to sameif p is None:p k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-padreturn pclass Conv(nn.Module):# Standard convolutiondef init(self, c1, c2, k1, s1, pNone, g1, actTrue): # ch_in, ch_out, kernel, stride, padding, groupssuper().init()self.conv nn.Conv2d(c1, c2, k, s, autopad(k, p), groupsg, biasFalse)self.bn nn.BatchNorm2d(c2)self.act nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())def forward(self, x):return self.act(self.bn(self.conv(x)))class Bottleneck(nn.Module):# Standard bottleneckdef init(self, c1, c2, shortcutTrue, g1, e0.5): # ch_in, chout, shortcut, groups, expansionsuper().init()c int(c2 * e) # hidden channelsself.cv1 Conv(c1, c, 1, 1)self.cv2 Conv(c, c2, 3, 1, gg)self.add shortcut and c1 c2def forward(self, x):return x self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))class C3(nn.Module):# CSP Bottleneck with 3 convolutionsdef init(self, c1, c2, n1, shortcutTrue, g1, e0.5): # ch_in, chout, number, shortcut, groups, expansionsuper().init()c int(c2 * e) # hidden channelsself.cv1 Conv(c1, c, 1, 1)self.cv2 Conv(c1, c, 1, 1)self.cv3 Conv(2 * c, c2, 1) # actFReLU(c2)self.m nn.Sequential(*(Bottleneck(c, c_, shortcut, g, e1.0) for _ in range(n)))def forward(self, x):return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim1))class model_K(nn.Module):def init(self):super(model_K, self).init()# 卷积模块self.Conv Conv(3, 32, 3, 2) # C3模块1self.C3_1 C3(32, 64, 3, 2)# 全连接网络层用于分类self.classifier nn.Sequential(nn.Linear(in_features802816, out_features100),nn.ReLU(),nn.Linear(in_features100, out_features4))def forward(self, x):x self.Conv(x)x self.C3_1(x)x torch.flatten(x, start_dim1)x self.classifier(x)return xdevice cuda if torch.cuda.is_available() else cpu print(Using {} device.format(device))model model_K().to(device) model Using cuda device model_K(   (Conv): Conv(     (conv): Conv2d(3, 32, kernel_size(3, 3), stride(2, 2), padding(1, 1), biasFalse)     (bn): BatchNorm2d(32, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)     (act): SiLU()   )   (C3_1): C3(     (cv1): Conv(       (conv): Conv2d(32, 32, kernel_size(1, 1), stride(1, 1), biasFalse)       (bn): BatchNorm2d(32, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)       (act): SiLU()     )     (cv2): Conv(       (conv): Conv2d(32, 32, kernel_size(1, 1), stride(1, 1), biasFalse)       (bn): BatchNorm2d(32, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)       (act): SiLU()     )     (cv3): Conv(       (conv): Conv2d(64, 64, kernel_size(1, 1), stride(1, 1), biasFalse)       (bn): BatchNorm2d(64, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)       (act): SiLU()     )     (m): Sequential(       (0): Bottleneck(         (cv1): Conv(           (conv): Conv2d(32, 32, kernel_size(1, 1), stride(1, 1), biasFalse)           (bn): BatchNorm2d(32, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)           (act): SiLU()         )         (cv2): Conv(           (conv): Conv2d(32, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse)           (bn): BatchNorm2d(32, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)           (act): SiLU()         )       )       (1): Bottleneck(         (cv1): Conv(           (conv): Conv2d(32, 32, kernel_size(1, 1), stride(1, 1), biasFalse)           (bn): BatchNorm2d(32, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)           (act): SiLU()         )         (cv2): Conv(           (conv): Conv2d(32, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse)           (bn): BatchNorm2d(32, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)           (act): SiLU()         )       )       (2): Bottleneck(         (cv1): Conv(           (conv): Conv2d(32, 32, kernel_size(1, 1), stride(1, 1), biasFalse)           (bn): BatchNorm2d(32, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)           (act): SiLU()         )         (cv2): Conv(           (conv): Conv2d(32, 32, kernel_size(3, 3), stride(1, 1), padding(1, 1), biasFalse)           (bn): BatchNorm2d(32, eps1e-05, momentum0.1, affineTrue, track_running_statsTrue)           (act): SiLU()         )       )     )   )   (classifier): Sequential(     (0): Linear(in_features802816, out_features100, biasTrue)     (1): ReLU()     (2): Linear(in_features100, out_features4, biasTrue)   ) ) 2. 查看模型详情

   统计模型参数量以及其他指标

   import torchsummary as summary summary.summary(model, (3, 224, 224)) —————————————————————-Layer (type) Output Shape Param # Conv2d-1 [-1, 32, 112, 112] 864BatchNorm2d-2 [-1, 32, 112, 112] 64SiLU-3 [-1, 32, 112, 112] 0Conv-4 [-1, 32, 112, 112] 0Conv2d-5 [-1, 32, 112, 112] 1,024BatchNorm2d-6 [-1, 32, 112, 112] 64SiLU-7 [-1, 32, 112, 112] 0Conv-8 [-1, 32, 112, 112] 0Conv2d-9 [-1, 32, 112, 112] 1,024BatchNorm2d-10 [-1, 32, 112, 112] 64SiLU-11 [-1, 32, 112, 112] 0Conv-12 [-1, 32, 112, 112] 0Conv2d-13 [-1, 32, 112, 112] 9,216BatchNorm2d-14 [-1, 32, 112, 112] 64SiLU-15 [-1, 32, 112, 112] 0Conv-16 [-1, 32, 112, 112] 0Bottleneck-17 [-1, 32, 112, 112] 0Conv2d-18 [-1, 32, 112, 112] 1,024BatchNorm2d-19 [-1, 32, 112, 112] 64SiLU-20 [-1, 32, 112, 112] 0Conv-21 [-1, 32, 112, 112] 0Conv2d-22 [-1, 32, 112, 112] 9,216BatchNorm2d-23 [-1, 32, 112, 112] 64SiLU-24 [-1, 32, 112, 112] 0Conv-25 [-1, 32, 112, 112] 0Bottleneck-26 [-1, 32, 112, 112] 0Conv2d-27 [-1, 32, 112, 112] 1,024BatchNorm2d-28 [-1, 32, 112, 112] 64SiLU-29 [-1, 32, 112, 112] 0Conv-30 [-1, 32, 112, 112] 0Conv2d-31 [-1, 32, 112, 112] 9,216BatchNorm2d-32 [-1, 32, 112, 112] 64SiLU-33 [-1, 32, 112, 112] 0Conv-34 [-1, 32, 112, 112] 0Bottleneck-35 [-1, 32, 112, 112] 0Conv2d-36 [-1, 32, 112, 112] 1,024BatchNorm2d-37 [-1, 32, 112, 112] 64SiLU-38 [-1, 32, 112, 112] 0Conv-39 [-1, 32, 112, 112] 0Conv2d-40 [-1, 64, 112, 112] 4,096BatchNorm2d-41 [-1, 64, 112, 112] 128SiLU-42 [-1, 64, 112, 112] 0Conv-43 [-1, 64, 112, 112] 0C3-44 [-1, 64, 112, 112] 0Linear-45 [-1, 100] 80,281,700ReLU-46 [-1, 100] 0Linear-47 [-1, 4] 404Total params: 80,320,536 Trainable params: 80,320,536

   Non-trainable params: 0

   Input size (MB): 0.57 Forward/backward pass size (MB): 150.06 Params size (MB): 306.40

   Estimated Total Size (MB): 457.04

   三、 训练模型 1. 编写训练函数

   训练循环

   def train(dataloader, model, loss_fn, optimizer):size len(dataloader.dataset) # 训练集的大小num_batches len(dataloader) # 批次数目, (size/batch_size向上取整)train_loss, train_acc 0, 0 # 初始化训练损失和正确率for X, y in dataloader: # 获取图片及其标签X, y X.to(device), y.to(device)# 计算预测误差pred model(X) # 网络输出loss loss_fn(pred, y) # 计算网络输出和真实值之间的差距targets为真实值计算二者差值即为损失# 反向传播optimizer.zero_grad() # grad属性归零loss.backward() # 反向传播optimizer.step() # 每一步自动更新# 记录acc与losstrain_acc (pred.argmax(1) y).type(torch.float).sum().item()train_loss loss.item()train_acc / sizetrain_loss / num_batchesreturn train_acc, train_loss

3. 编写测试函数 测试函数和训练函数大致相同但是由于不进行梯度下降对网络权重进行更新所以不需要传入优化器 def test (dataloader, model, loss_fn):size len(dataloader.dataset) # 测试集的大小num_batches len(dataloader) # 批次数目, (size/batch_size向上取整)test_loss, test_acc 0, 0# 当不进行训练时停止梯度更新节省计算内存消耗with torch.no_grad():for imgs, target in dataloader:imgs, target imgs.to(device), target.to(device)# 计算losstarget_pred model(imgs)loss loss_fn(target_pred, target)test_loss loss.item()test_acc (target_pred.argmax(1) target).type(torch.float).sum().item()test_acc / sizetest_loss / num_batchesreturn test_acc, test_loss 3. 正式训练 model.train()、model.eval()训练营往期文章中有详细的介绍。 如果将优化器换成 SGD 会发生什么呢请自行探索接下来发生的诡异事件的原因 import copyoptimizer torch.optim.Adam(model.parameters(), lr 1e-4) loss_fn nn.CrossEntropyLoss() # 创建损失函数epochs 20train_loss [] train_acc [] test_loss [] test_acc []best_acc 0 # 设置一个最佳准确率作为最佳模型的判别指标for epoch in range(epochs):model.train()epoch_train_acc, epoch_train_loss train(train_dl, model, loss_fn, optimizer)model.eval()epoch_test_acc, epoch_test_loss test(test_dl, model, loss_fn)# 保存最佳模型到 best_modelif epoch_test_acc best_acc:best_acc epoch_test_accbest_model copy.deepcopy(model)train_acc.append(epoch_train_acc)train_loss.append(epoch_train_loss)test_acc.append(epoch_test_acc)test_loss.append(epoch_test_loss)# 获取当前的学习率lr optimizer.state_dict()[param_groups][0][lr]template (Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%, Test_loss:{:.3f}, Lr:{:.2E})print(template.format(epoch1, epoch_train_acc*100, epoch_train_loss, epoch_test_acc*100, epoch_test_loss, lr))# 保存最佳模型到文件中 PATH ./best_model.pth # 保存的参数文件名 torch.save(model.state_dict(), PATH)print(Done) 四、 结果可视化 1. Loss与Accuracy图 import matplotlib.pyplot as plt #隐藏警告 import warnings warnings.filterwarnings(ignore) #忽略警告信息 plt.rcParams[font.sans-serif] [SimHei] # 用来正常显示中文标签 plt.rcParams[axes.unicode_minus] False # 用来正常显示负号 plt.rcParams[figure.dpi] 100 #分辨率epochs_range range(epochs)plt.figure(figsize(12, 3)) plt.subplot(1, 2, 1)plt.plot(epochs_range, train_acc, labelTraining Accuracy) plt.plot(epochs_range, test_acc, labelTest Accuracy) plt.legend(loclower right) plt.title(Training and Validation Accuracy)plt.subplot(1, 2, 2) plt.plot(epochs_range, train_loss, labelTraining Loss) plt.plot(epochs_range, test_loss, labelTest Loss) plt.legend(locupper right) plt.title(Training and Validation Loss) plt.show() best_model.eval() epoch_test_acc, epoch_test_loss test(test_dl, best_model, loss_fn) epoch_test_acc, epoch_test_loss (0.9333333333333333, 0.31915266352798577) 前期准备首先设置 GPU如果设备支持则使用 GPU否则使用 CPU。然后导入数据对数据进行预处理包括数据增强和归一化等操作。最后划分数据集将数据集分为训练集和测试集并创建数据加载器。搭建模型搭建了一个包含 C3 模块的自定义模型模型由卷积层、C3 模块和全连接层组成。其中 C3 模块由多个瓶颈层组成可以提高模型的准确率。训练模型编写了训练函数和测试函数分别用于训练和测试模型。在训练过程中使用 Adam 优化器和交叉熵损失函数对模型进行了 20 个 epoch 的训练并保存了最佳模型。结果可视化对训练和测试结果进行了可视化包括准确率和损失函数的变化曲线。最后使用最佳模型对测试集进行测试得到了最终的准确率和损失函数值