屏东县网站建设_网站建设公司_SQL Server_seo优化

• 🍨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

目录

一、 前期准备

1. 设置GPU

2. 导入数据

3. 划分数据集

二、构建简单的CNN网络

三、 训练模型

1. 设置超参数

2. 编写训练函数

3. 编写测试函数

4. 正式训练

四、 结果可视化

1. Loss与Accuracy图

2. 指定图片进行预测

五、保存并加载模型

六、个人总结

一、 前期准备

1. 设置GPU

import torch import torch.nn as nn import torchvision.transforms as transforms import torchvision from torchvision import transforms, datasets import os,PIL,pathlib device = torch.device("cuda" if torch.cuda.is_available() else "cpu") device

2. 导入数据

import os,PIL,random,pathlib data_dir = './4-data/' data_dir = pathlib.Path(data_dir) data_paths = list(data_dir.glob('*')) classeNames = [str(path).split("\\")[1] for path in data_paths] classeNames

total_datadir = './4-data/' # 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.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(total_datadir,transform=train_transforms) total_data

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 train_size,test_size batch_size = 32 train_dl = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=1) test_dl = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=True, num_workers=1) 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

二、构建简单的CNN网络

import torch.nn.functional as F class Network_bn(nn.Module): def __init__(self): super(Network_bn, self).__init__() """ nn.Conv2d()函数： 第一个参数（in_channels）是输入的channel数量 第二个参数（out_channels）是输出的channel数量 第三个参数（kernel_size）是卷积核大小 第四个参数（stride）是步长，默认为1 第五个参数（padding）是填充大小，默认为0 """ self.conv1 = nn.Conv2d(in_channels=3, out_channels=12, kernel_size=5, stride=1, padding=0) self.bn1 = nn.BatchNorm2d(12) self.conv2 = nn.Conv2d(in_channels=12, out_channels=12, kernel_size=5, stride=1, padding=0) self.bn2 = nn.BatchNorm2d(12) self.pool = nn.MaxPool2d(2,2) self.conv4 = nn.Conv2d(in_channels=12, out_channels=24, kernel_size=5, stride=1, padding=0) self.bn4 = nn.BatchNorm2d(24) self.conv5 = nn.Conv2d(in_channels=24, out_channels=24, kernel_size=5, stride=1, padding=0) self.bn5 = nn.BatchNorm2d(24) self.fc1 = nn.Linear(24*50*50, len(classeNames)) def forward(self, x): x = F.relu(self.bn1(self.conv1(x))) x = F.relu(self.bn2(self.conv2(x))) x = self.pool(x) x = F.relu(self.bn4(self.conv4(x))) x = F.relu(self.bn5(self.conv5(x))) x = self.pool(x) x = x.view(-1, 24*50*50) x = self.fc1(x) return x device = "cuda" if torch.cuda.is_available() else "cpu" print("Using {} device".format(device)) model = Network_bn().to(device) model

三、 训练模型

1. 设置超参数

loss_fn = nn.CrossEntropyLoss() # 创建损失函数 learn_rate = 1e-4 # 学习率 opt = torch.optim.SGD(model.parameters(),lr=learn_rate)

2. 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，一共60000张图片 num_batches = len(dataloader) # 批次数目，1875（60000/32） 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loss train_acc += (pred.argmax(1) == y).type(torch.float).sum().item() train_loss += loss.item() train_acc /= size train_loss /= num_batches return train_acc, train_loss

3. 编写测试函数

def test (dataloader, model, loss_fn): size = len(dataloader.dataset) # 测试集的大小，一共10000张图片 num_batches = len(dataloader) # 批次数目，313（10000/32=312.5，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loss target_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 /= size test_loss /= num_batches return test_acc, test_loss

4. 正式训练

epochs = 20 train_loss = [] train_acc = [] test_loss = [] test_acc = [] for epoch in range(epochs): model.train() epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, opt) model.eval() epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn) train_acc.append(epoch_train_acc) train_loss.append(epoch_train_loss) test_acc.append(epoch_test_acc) test_loss.append(epoch_test_loss) template = ('Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%，Test_loss:{:.3f}') print(template.format(epoch+1, epoch_train_acc*100, epoch_train_loss, epoch_test_acc*100, epoch_test_loss)) 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 from datetime import datetime current_time = datetime.now() # 获取当前时间 epochs_range = range(epochs) plt.figure(figsize=(12, 3)) plt.subplot(1, 2, 1) plt.plot(epochs_range, train_acc, label='Training Accuracy') plt.plot(epochs_range, test_acc, label='Test Accuracy') plt.legend(loc='lower right') plt.title('Training and Validation Accuracy') plt.xlabel(current_time) # 打卡请带上时间戳，否则代码截图无效 plt.subplot(1, 2, 2) plt.plot(epochs_range, train_loss, label='Training Loss') plt.plot(epochs_range, test_loss, label='Test Loss') plt.legend(loc='upper right') plt.title('Training and Validation Loss') plt.show()

2. 指定图片进行预测

>>> x = torch.zeros(2, 1, 2, 1, 2) >>> x.size() torch.Size([2, 1, 2, 1, 2]) >>> y = torch.squeeze(x) >>> y.size() torch.Size([2, 2, 2]) >>> y = torch.squeeze(x, 0) >>> y.size() torch.Size([2, 1, 2, 1, 2]) >>> y = torch.squeeze(x, 1) >>> y.size() torch.Size([2, 2, 1, 2]) >>> x = torch.tensor([1, 2, 3, 4]) >>> torch.unsqueeze(x, 0) tensor([[ 1, 2, 3, 4]]) >>> torch.unsqueeze(x, 1) tensor([[ 1], [ 2], [ 3], [ 4]]) from PIL import Image classes = list(total_data.class_to_idx) def predict_one_image(image_path, model, transform, classes): test_img = Image.open(image_path).convert('RGB') # plt.imshow(test_img) # 展示预测的图片 test_img = transform(test_img) img = test_img.to(device).unsqueeze(0) model.eval() output = model(img) _,pred = torch.max(output,1) pred_class = classes[pred] print(f'预测结果是：{pred_class}') # 预测训练集中的某张照片 predict_one_image(image_path='./4-data/Monkeypox/M01_01_00.jpg', model=model, transform=train_transforms, classes=classes)

五、保存并加载模型

# 模型保存 PATH = './model.pth' # 保存的参数文件名 torch.save(model.state_dict(), PATH) # 将参数加载到model当中 model.load_state_dict(torch.load(PATH, map_location=device))

六、个人总结

上述模型存在两个主要问题：1.过拟合 ；2.训练集数量小

现对模型进行改进

total_datadir = './4-data/' train_transforms = transforms.Compose([ transforms.RandomResizedCrop(224), transforms.RandomHorizontalFlip(), transforms.RandomRotation(15), transforms.ColorJitter( brightness=0.3, contrast=0.3, saturation=0.3 ), transforms.ToTensor(), transforms.Normalize( mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] ) ]) test_transforms = transforms.Compose([ transforms.Resize((224, 224)), transforms.ToTensor(), transforms.Normalize( mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] ) ]) total_data = datasets.ImageFolder(total_datadir,transform=train_transforms) total_data batch_size = 32 train_dl = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True) test_dl = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=True) 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 num_classes = len(classeNames) model = models.resnet18(pretrained=True) # 冻结 backbone（小数据集关键） for param in model.parameters(): param.requires_grad = False # 替换分类头 model.fc = nn.Linear(model.fc.in_features, num_classes) model = model.to(device) print(model) def train(dataloader, model, loss_fn, optimizer): size = len(dataloader.dataset) num_batches = len(dataloader) train_loss, train_acc = 0, 0 for X, y in dataloader: X, y = X.to(device), y.to(device) pred = model(X) loss = loss_fn(pred, y) optimizer.zero_grad() loss.backward() optimizer.step() train_acc += (pred.argmax(1) == y).type(torch.float).sum().item() train_loss += loss.item() train_acc /= size train_loss /= num_batches return train_acc, train_loss loss_fn = nn.CrossEntropyLoss() learn_rate = 1e-4 opt = torch.optim.AdamW( model.fc.parameters(), lr=1e-4, ) epochs = 20 train_loss = [] train_acc = [] test_loss = [] test_acc = [] for epoch in range(epochs): model.train() epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, opt) model.eval() epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn) train_acc.append(epoch_train_acc) train_loss.append(epoch_train_loss) test_acc.append(epoch_test_acc) test_loss.append(epoch_test_loss) template = ('Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%，Test_loss:{:.3f}') print(template.format(epoch+1, epoch_train_acc*100, epoch_train_loss, epoch_test_acc*100, epoch_test_loss)) print('Done')

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 from datetime import datetime current_time = datetime.now() # 获取当前时间 epochs_range = range(epochs) plt.figure(figsize=(12, 3)) plt.subplot(1, 2, 1) plt.plot(epochs_range, train_acc, label='Training Accuracy') plt.plot(epochs_range, test_acc, label='Test Accuracy') plt.legend(loc='lower right') plt.title('Training and Validation Accuracy') plt.xlabel(current_time) # 打卡请带上时间戳，否则代码截图无效 plt.subplot(1, 2, 2) plt.plot(epochs_range, train_loss, label='Training Loss') plt.plot(epochs_range, test_loss, label='Test Loss') plt.legend(loc='upper right') plt.title('Training and Validation Loss') plt.show()

总结：正确率升高，误差减小。