深度学习基础案例4--构建CNN卷积神经网络实现对猴痘病的识别(测试集准确率86.5%)

🍨 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

前言

下一周会很忙，更新可能不及时，请大家见谅
这个项目我感觉是一个很好的入门案例，但是自己测试的时候测试集准确率只比较稳定的达到了86.5%附近，说明对神经网络结构的添加还不是很熟，后期还需要多看论文积累😢😢😢😢😢😢😢😢😢
图片看着有点渗人😨😨😨😨😨😨
最后说一句：看论文、复现好难啊！！！😢😢😢😢😢

目标

测试集准确率达到88%以上

结果

通过调整卷积核大小、通道大小，有一次准确率达到了88.1%，但是不太稳定，最后，经过反复调整，将测试集准确率稳定在86.5%附近，没有达到预期大小，主要原因是不会设置网络结构。

环境

语言环境：Python3.8.19
编译器：Jupyter、VsCode
深度学习环境：Pytorch

文章目录

1、前期准备
- 1、导入库
- 2、查看数据文件夹
- 3、显示部分图像
- 4、导入数据
- 5、划分数据集
- 6、动态加载数据
2、构建CNN神经网络
3、模型训练
- 1、设置超参数
- 2、编写训练函数
- 3、编写测试函数
- 4、开始训练
4、结果可视化
5、预测
6、模型保存
7、总结
- CNN神经网络总结
- 准确率和损失率总结

1、前期准备

1、导入库

import numpy as np 
import torch 
import torch.nn as nn 
import torchvision import PIL, os, pathlibdevice = ('cuda' if torch.cuda.is_available() else 'cpu')
device

结果：

'cuda'

2、查看数据文件夹

# 设置根目录
data_dir = './data/'
data_dir = pathlib.Path(data_dir)  # 转化为 pathlib 对象# 查看data下的文件
data_path = data_dir.glob('*')   # 获取data文件下的所有文件夹
classNames = [str(path).split('\\')[1] for path in data_path] 
classNames

结果：

['Monkeypox', 'Others']

3、显示部分图像

import matplotlib.pyplot as plt 
from PIL import Image # 设置目录
image_dir = './data/Monkeypox/'
image_paths = [f for f in os.listdir(image_dir) if f.endswith((".jpg", ".png", ".jpeg"))]# 创建画板
fig, axes = plt.subplots(3, 8, figsize=(16, 6))  # fig：画板，ases子图for ax, img_file in zip(axes.flat, image_paths):img_path = os.path.join(image_dir, img_file)   # 拼接完整路径img = Image.open(img_path)ax.imshow(img)ax.axis('off')plt.show()

在这里插入图片描述

4、导入数据

from torchvision import transforms, datasets# 加载所有图像
total_data = './data/'# 图像统一化
train_transforms = transforms.Compose([transforms.Resize([224, 224]),   # 统一大小transforms.ToTensor(),           # 统一类型transforms.Normalize(           # 数据标准化mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225] )
])total_data = datasets.ImageFolder(total_data, transform=train_transforms)   
total_data

结果：

Dataset ImageFolderNumber of datapoints: 2142Root location: ./data/StandardTransform
Transform: Compose(Resize(size=[224, 224], interpolation=bilinear, max_size=None, antialias=True)ToTensor()Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]))

5、划分数据集

训练集：0.8，测试集：0.2
划分顺序：随机划分

train_size = int(len(total_data) * 0.8)
test_size = len(total_data) - train_sizetrain_dataset, test_dataset =  torch.utils.data.random_split(total_data, [train_size, test_size])print(train_dataset)
print(train_dataset)

<torch.utils.data.dataset.Subset object at 0x000001B7222E6EE0>
<torch.utils.data.dataset.Subset object at 0x000001B7222E6EE0>

查看训练集和测试集大小：

train_size, test_size

结果：

(1713, 429)

6、动态加载数据

# 每一批次的大小
batch_size = 32train_dl = torch.utils.data.DataLoader(train_dataset,batch_size=batch_size, shuffle=True,    # 每一次训练重新打乱数据num_workers=1)test_dl = torch.utils.data.DataLoader(test_dataset,batch_size=batch_size,shuffle=True,num_workers=1)

# 随机选取一张，查看图片参数
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([32, 3, 224, 224])
Shape of y:  torch.Size([32]) torch.int64

2、构建CNN神经网络

import torch.nn.functional as F class NetWork_bn(nn.Module):def __init__(self):super(NetWork_bn, self).__init__()# 构建CNN神经网络self.conv1 = nn.Conv2d(in_channels=3, out_channels=24, kernel_size=3, stride=1, padding=0)self.bn1 = nn.BatchNorm2d(24)     # 对输出通道数据进行** 归一化 **self.conv2 = nn.Conv2d(in_channels=24, out_channels=24, kernel_size=3, stride=1, padding=0)self.bn2 = nn.BatchNorm2d(24)self.pool1 = nn.MaxPool2d(2, 2)self.conv3 = nn.Conv2d(in_channels=24, out_channels=48, kernel_size=3, stride=1, padding=0)self.bn3 = nn.BatchNorm2d(48)self.conv4 = nn.Conv2d(in_channels=48, out_channels=48, kernel_size=3, stride=1, padding=0)self.bn4 = nn.BatchNorm2d(48)self.pool2 = nn.MaxPool2d(2, 2)self.fc1 = nn.Linear(48 * 53 * 53, len(classNames))def forward(self, x):x = F.relu(self.bn1(self.conv1(x)))x = F.relu(self.bn2(self.conv2(x)))x = self.pool1(x)   # 卷积层降维，不用激活函数x = F.relu(self.bn3(self.conv3(x)))x = F.relu(self.bn4(self.conv4(x)))x = self.pool2(x)x = x.view(-1, 48 * 53 * 53)   # 展开x = self.fc1(x)return x

# 将模型导入cuda中
model = NetWork_bn().to(device)   
model

结果：

NetWork_bn((conv1): Conv2d(3, 24, kernel_size=(3, 3), stride=(1, 1))(bn1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv2): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1))(bn2): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)(conv3): Conv2d(24, 48, kernel_size=(3, 3), stride=(1, 1))(bn3): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(conv4): Conv2d(48, 48, kernel_size=(3, 3), stride=(1, 1))(bn4): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)(pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)(fc1): Linear(in_features=134832, out_features=2, bias=True)
)

3、模型训练

1、设置超参数

loss_fn = nn.CrossEntropyLoss()   # 创建损失函数
lr = 1e-4   # 设置学习率
opt = torch.optim.SGD(model.parameters(), lr=lr)   # 梯度下降方法

2、编写训练函数

def train(dataloader, model, loss_fn, optimizer):# 获取数据大小size = len(dataloader.dataset)# 获取批次大小batch_size = len(dataloader)  # 总数 / 32# 准确率和损失率train_acc, train_loss = 0, 0for X, y in dataloader:  # 每一批次的规格请看上面：动态加载数据哪里X, y = X.to(device), y.to(device)# 预测pred = model(X)# 计算损失loss = loss_fn(pred, y)# 梯度清零optimizer.zero_grad()# 求导loss.backward()# 梯度下降法更新optimizer.step()# 记录准确率和误差train_acc += (pred.argmax(1) == y).type(torch.float64).sum().item()  # 数据怎么存在的，要做到心中有数train_loss += loss.item()   # .item 获取数据项# 计算损失函数和梯度，注意：数据做到心中有数train_acc /= size train_loss /= batch_sizereturn train_acc, train_loss

3、编写测试函数

def test(dataloader, model, loss_fn):# 获取数据大小和批次大小size = len(dataloader.dataset)batch_size = len(dataloader)# 准确率和损失率test_acc, test_loss = 0, 0with torch.no_grad():for X, y in dataloader:X, y = X.to(device), y.to(device)# 预测和计算损失pred = model(X)loss = loss_fn(pred, y)test_acc += (pred.argmax(1) == y).type(torch.float64).sum().item()test_loss += loss.item()# 计算损失率和准确率       test_acc /= sizetest_loss /= batch_sizereturn test_acc, test_loss

4、开始训练

train_acc = []
train_loss = []
test_acc = []
test_loss = []epochs = 30for 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))

Epoch: 1, Train_acc:61.0%, Train_loss:0.775, Test_acc:55.0%, Test_loss:0.784
Epoch: 2, Train_acc:68.6%, Train_loss:0.639, Test_acc:61.5%, Test_loss:0.918
Epoch: 3, Train_acc:74.0%, Train_loss:0.540, Test_acc:64.1%, Test_loss:0.703
Epoch: 4, Train_acc:78.9%, Train_loss:0.465, Test_acc:79.7%, Test_loss:0.472
Epoch: 5, Train_acc:82.4%, Train_loss:0.406, Test_acc:76.9%, Test_loss:0.515
Epoch: 6, Train_acc:83.2%, Train_loss:0.387, Test_acc:76.2%, Test_loss:0.495
Epoch: 7, Train_acc:86.3%, Train_loss:0.346, Test_acc:73.9%, Test_loss:0.532
Epoch: 8, Train_acc:87.6%, Train_loss:0.326, Test_acc:74.1%, Test_loss:0.583
Epoch: 9, Train_acc:88.9%, Train_loss:0.308, Test_acc:81.8%, Test_loss:0.421
Epoch:10, Train_acc:90.7%, Train_loss:0.282, Test_acc:81.8%, Test_loss:0.427
Epoch:11, Train_acc:91.9%, Train_loss:0.264, Test_acc:82.5%, Test_loss:0.413
Epoch:12, Train_acc:92.2%, Train_loss:0.260, Test_acc:84.1%, Test_loss:0.385
Epoch:13, Train_acc:91.9%, Train_loss:0.253, Test_acc:76.7%, Test_loss:0.551
Epoch:14, Train_acc:92.8%, Train_loss:0.241, Test_acc:83.4%, Test_loss:0.393
Epoch:15, Train_acc:92.6%, Train_loss:0.232, Test_acc:85.5%, Test_loss:0.372
Epoch:16, Train_acc:92.9%, Train_loss:0.226, Test_acc:83.2%, Test_loss:0.392
Epoch:17, Train_acc:93.6%, Train_loss:0.217, Test_acc:84.4%, Test_loss:0.376
Epoch:18, Train_acc:94.4%, Train_loss:0.207, Test_acc:83.9%, Test_loss:0.374
Epoch:19, Train_acc:94.8%, Train_loss:0.204, Test_acc:85.1%, Test_loss:0.381
Epoch:20, Train_acc:94.7%, Train_loss:0.192, Test_acc:84.4%, Test_loss:0.361
Epoch:21, Train_acc:95.2%, Train_loss:0.189, Test_acc:86.2%, Test_loss:0.358
Epoch:22, Train_acc:95.0%, Train_loss:0.184, Test_acc:85.5%, Test_loss:0.355
Epoch:23, Train_acc:95.2%, Train_loss:0.174, Test_acc:84.8%, Test_loss:0.378
Epoch:24, Train_acc:96.0%, Train_loss:0.169, Test_acc:86.0%, Test_loss:0.349
Epoch:25, Train_acc:96.4%, Train_loss:0.159, Test_acc:86.7%, Test_loss:0.355
Epoch:26, Train_acc:95.9%, Train_loss:0.166, Test_acc:86.9%, Test_loss:0.340
Epoch:27, Train_acc:96.6%, Train_loss:0.153, Test_acc:86.5%, Test_loss:0.334
Epoch:28, Train_acc:96.0%, Train_loss:0.154, Test_acc:86.5%, Test_loss:0.340
Epoch:29, Train_acc:97.3%, Train_loss:0.144, Test_acc:86.0%, Test_loss:0.335
Epoch:30, Train_acc:96.7%, Train_loss:0.150, Test_acc:86.5%, Test_loss:0.343

4、结果可视化

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epoch_range = range(epochs)# 创建画布
plt.figure(figsize=(12 ,3))# 第一个子图
plt.subplot(1, 2, 1)
# 画图
plt.plot(epoch_range, train_acc, label='Training Accurary')
plt.plot(epoch_range, test_acc, label='Test Accurary')
plt.legend(loc='lower right')
plt.title('Accurary')# 第二个子图
plt.subplot(1, 2, 2)
# 画图
plt.plot(epoch_range, train_loss, label='Training Loss')
plt.plot(epoch_range, test_loss, label='Test Loss')
plt.legend(loc='upper right')
plt.title('Losss')plt.show()

在这里插入图片描述

5、预测

from PIL import Image classes = list(total_data.class_to_idx)    # ./data下的文件类型def predict_one_image(image_path, model, transform, classes):test_img = Image.open(image_path).convert('RGB')   # 打开文件# 展示打开的文件plt.imshow(test_img) test_img = transform(test_img)  # 统一化数据标准# 压缩，去除 1img = test_img.to(device).unsqueeze(0)model.eval()   # 开启训练模型output = model(img) _, pred = torch.max(output, 1)   # 返回预测结果pred_class = classes[pred]print(f'预测结果是: {pred_class}')

# 指定图片进行预测
predict_one_image(image_path='./data/Monkeypox/M01_01_00.jpg',model=model,transform=train_transforms,classes=classes)

预测结果是: Monkeypox

在这里插入图片描述

6、模型保存

# 模型保存
path = './model.pth'
torch.save(model.state_dict(), path)# 将参数加载到model中
model.load_state_dict(torch.load(path, map_location=device))

结果：

<All keys matched successfully>

7、总结

CNN神经网络总结

卷积层：对特征进行提取，影响因素主要有：通道大小的变化，卷积核大小、卷积步伐大小，填充值等，通道大小和卷积核大小对特征提取上影响很大，卷积核小，通道变大，计算就会变得复杂，当然对特征的提取也更多
池化层：用于降维，卷积层数据提取完毕后，减少维度，可以更好的挖掘数据关系
全连接：将维度全部进行展开，然后进行降维
需要积累不同卷积模型，才能很好的提升精度

准确率和损失率总结

理想状态下：
- 准确率：训练集准确率逐步提高，测试集也是，并且训练集准确率和测试集的相近
- 损失率：训练集损失率降低，测试集也是，并且训练集损失率和测试集的相近
实际(本案例)：
- 开始测试集的准确率和损失率都不稳定，后面慢慢趋向稳定
- 准确率：后期相差百分之十
- 损失率：后期相差0.2附近
- 总结：如果训练后面，轮数很大，可能出现过拟合的现象