>- **🍨 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]**

任务：
●阅读ResNeXt论文，了解作者的构建思路
●对比我们之前介绍的ResNet50V2、DenseNet算法
●使用ResNeXt-50算法完成猴痘病识别

🏡 我的环境：

• 语言环境：Python3.8

• 编译器：Jupyter Notebook

• 深度学习环境：Pytorch

• • torch==2.3.1+cu118

• • torchvision==0.18.1+cu118

       本文完全根据 第J6周：ResNeXt-50实战解析（TensorFlow版）中的内容转换为pytorch版本，所以前述性的内容不在一一重复，仅就pytorch版本中的内容进行叙述。

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 warnings
warnings.filterwarnings("ignore")import torch
device=torch.device("cuda" if torch.cuda.is_available() else "cpu")
device

运行结果：

device(type='cuda')

2. 导入数据

同时查看数据集中图片的数量

import pathlibdata_dir=r'D:\THE MNIST DATABASE\P4-data'
data_dir=pathlib.Path(data_dir)image_count=len(list(data_dir.glob('*/*')))
print("图片总数为：",image_count)

运行结果：

图片总数为： 2142

3. 查看数据集分类

data_paths=list(data_dir.glob('*'))
classNames=[str(path).split("\\")[3] for path in data_paths]
classNames

运行结果：

['Monkeypox', 'Others']

4. 随机查看图片

随机抽取数据集中的20张图片进行查看

import PIL,random
import matplotlib.pyplot as plt
from PIL import Imageplt.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data_paths2=list(data_dir.glob('*/*'))
plt.figure(figsize=(10,4))
for i in range(10):plt.subplot(2,5,i+1)plt.axis("off")image=random.choice(data_paths2)  #随机选择一个图片plt.title(image.parts[-2])  #通过glob对象取出他的文件夹名称，即分类名plt.imshow(Image.open(str(image)))  #显示图片

运行结果：

5. 图片预处理 

import torchvision.transforms as transforms
from torchvision import transforms,datasetstrain_transforms=transforms.Compose([transforms.Resize([224,224]), #将图片统一尺寸transforms.RandomHorizontalFlip(), #将图片随机水平翻转transforms.RandomRotation(0.2), #将图片按照0.2的弧度值随机翻转transforms.ToTensor(), #将图片转换为tensortransforms.Normalize(  #标准化处理-->转换为正态分布，使模型更容易收敛mean=[0.485,0.456,0.406],std=[0.229,0.224,0.225])
])total_data=datasets.ImageFolder(r'D:\THE MNIST DATABASE\P4-data',transform=train_transforms
)
total_data

运行结果：

Dataset ImageFolderNumber of datapoints: 2142Root location: D:\THE MNIST DATABASE\P4-dataStandardTransform
Transform: Compose(Resize(size=[224, 224], interpolation=bilinear, max_size=None, antialias=True)RandomHorizontalFlip(p=0.5)RandomRotation(degrees=[-0.2, 0.2], interpolation=nearest, expand=False, fill=0)ToTensor()Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]))

将数据集分类情况进行映射输出：

total_data.class_to_idx

运行结果：

{'Monkeypox': 0, 'Others': 1}

6. 划分数据集

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

运行结果：

(<torch.utils.data.dataset.Subset at 0x207565a54d0>,<torch.utils.data.dataset.Subset at 0x2075514cf90>)

查看训练集和测试集的数据数量：

train_size,test_size

运行结果：

(1713, 429)

7. 加载数据集

batch_size=16
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
)

查看测试集的情况：

for x,y in train_dl:print("Shape of x [N,C,H,W]:",x.shape)print("Shape of y:",y.shape,y.dtype)break

运行结果：

Shape of x [N,C,H,W]: torch.Size([16, 3, 224, 224])
Shape of y: torch.Size([16]) torch.int64

二、搭建模型

1. 创建卷积块

import torch.nn as nn
import torch.nn.functional as F
class BN_Conv2d(nn.Module):"""BN_CONV_RELU"""def __init__(self,in_channels,out_channels,kernel_size,stride,padding,dilation=1,groups=1,bias=False):super(BN_Conv2d,self).__init__()self.seq=nn.Sequential(nn.Conv2d(in_channels,out_channels,kernel_size=kernel_size,stride=stride,padding=padding,dilation=dilation,groups=groups,bias=bias),nn.BatchNorm2d(out_channels))def forward(self,x):return F.relu(self.seq(x))

2. 创建block

class ResNeXt_Block(nn.Module):"""ResNeXt block with group convolutions"""def __init__(self,in_channnls,cardinality,group_depth,stride):super(ResNeXt_Block,self).__init__()self.group_channels=cardinality*group_depthself.conv1=BN_Conv2d(in_channnls,self.group_channels,1,stride=1,padding=0)self.conv2=BN_Conv2d(self.group_channels,self.group_channels,3,stride=stride,padding=1,groups=cardinality)self.conv3=nn.Conv2d(self.group_channels,self.group_channels*2,1,stride=1,padding=0)self.bn=nn.BatchNorm2d(self.group_channels*2)self.short_cut=nn.Sequential(nn.Conv2d(in_channnls,self.group_channels*2,1,stride,0,bias=False),nn.BatchNorm2d(self.group_channels*2))def forward(self,x):out=self.conv1(x)out=self.conv2(out)out=self.bn(self.conv3(out))out+=self.short_cut(x)return F.relu(out)

3. 搭建ResNeXt 模型

class ResNeXt(nn.Module):"""ResNeXt builder"""def __init__(self,layers:object,cardinality,group_depth,num_classes):super(ResNeXt,self).__init__()self.cardinality=cardinalityself.channels=64self.conv1=BN_Conv2d(3,self.channels,7,stride=2,padding=3)d1=group_depthself.conv2=self.__make_layers(d1,layers[0],stride=1)d2=d1*2self.conv3=self.__make_layers(d2,layers[1],stride=2)d3=d2*2self.conv4=self.__make_layers(d3,layers[2],stride=2)d4=d3*2self.conv5=self.__make_layers(d4,layers[3],stride=2)self.fc=nn.Linear(self.channels,num_classes)  #224*224  input sizedef __make_layers(self,d,blocks,stride):strides=[stride]+[1]*(blocks-1)layers=[]for stride in strides:layers.append(ResNeXt_Block(self.channels,self.cardinality,d,stride))self.channels=self.cardinality*d*2return nn.Sequential(*layers)def forward(self,x):out=self.conv1(x)out=F.max_pool2d(out,3,2,1)out=self.conv2(out)out=self.conv3(out)out=self.conv4(out)out=self.conv5(out)out=F.avg_pool2d(out,7)out=out.view(out.size(0),-1)out=F.softmax(self.fc(out),dim=1)return out

4. 查看 ResNeXt-50 模型的参数

model=ResNeXt([3,4,6,3],32,4,4)
model.to(device)#统计模型参数量以及其他指标
import torchsummary as summary
summary.summary(model,(3,224,224))

运行结果：

----------------------------------------------------------------Layer (type)               Output Shape         Param #
================================================================Conv2d-1         [-1, 64, 112, 112]           9,408BatchNorm2d-2         [-1, 64, 112, 112]             128BN_Conv2d-3         [-1, 64, 112, 112]               0Conv2d-4          [-1, 128, 56, 56]           8,192BatchNorm2d-5          [-1, 128, 56, 56]             256BN_Conv2d-6          [-1, 128, 56, 56]               0Conv2d-7          [-1, 128, 56, 56]           4,608BatchNorm2d-8          [-1, 128, 56, 56]             256BN_Conv2d-9          [-1, 128, 56, 56]               0Conv2d-10          [-1, 256, 56, 56]          33,024BatchNorm2d-11          [-1, 256, 56, 56]             512Conv2d-12          [-1, 256, 56, 56]          16,384BatchNorm2d-13          [-1, 256, 56, 56]             512ResNeXt_Block-14          [-1, 256, 56, 56]               0Conv2d-15          [-1, 128, 56, 56]          32,768BatchNorm2d-16          [-1, 128, 56, 56]             256BN_Conv2d-17          [-1, 128, 56, 56]               0Conv2d-18          [-1, 128, 56, 56]           4,608BatchNorm2d-19          [-1, 128, 56, 56]             256BN_Conv2d-20          [-1, 128, 56, 56]               0Conv2d-21          [-1, 256, 56, 56]          33,024BatchNorm2d-22          [-1, 256, 56, 56]             512Conv2d-23          [-1, 256, 56, 56]          65,536BatchNorm2d-24          [-1, 256, 56, 56]             512ResNeXt_Block-25          [-1, 256, 56, 56]               0Conv2d-26          [-1, 128, 56, 56]          32,768BatchNorm2d-27          [-1, 128, 56, 56]             256BN_Conv2d-28          [-1, 128, 56, 56]               0Conv2d-29          [-1, 128, 56, 56]           4,608BatchNorm2d-30          [-1, 128, 56, 56]             256BN_Conv2d-31          [-1, 128, 56, 56]               0Conv2d-32          [-1, 256, 56, 56]          33,024BatchNorm2d-33          [-1, 256, 56, 56]             512Conv2d-34          [-1, 256, 56, 56]          65,536BatchNorm2d-35          [-1, 256, 56, 56]             512ResNeXt_Block-36          [-1, 256, 56, 56]               0Conv2d-37          [-1, 256, 56, 56]          65,536BatchNorm2d-38          [-1, 256, 56, 56]             512BN_Conv2d-39          [-1, 256, 56, 56]               0Conv2d-40          [-1, 256, 28, 28]          18,432BatchNorm2d-41          [-1, 256, 28, 28]             512BN_Conv2d-42          [-1, 256, 28, 28]               0Conv2d-43          [-1, 512, 28, 28]         131,584BatchNorm2d-44          [-1, 512, 28, 28]           1,024Conv2d-45          [-1, 512, 28, 28]         131,072BatchNorm2d-46          [-1, 512, 28, 28]           1,024ResNeXt_Block-47          [-1, 512, 28, 28]               0Conv2d-48          [-1, 256, 28, 28]         131,072BatchNorm2d-49          [-1, 256, 28, 28]             512BN_Conv2d-50          [-1, 256, 28, 28]               0Conv2d-51          [-1, 256, 28, 28]          18,432BatchNorm2d-52          [-1, 256, 28, 28]             512BN_Conv2d-53          [-1, 256, 28, 28]               0Conv2d-54          [-1, 512, 28, 28]         131,584BatchNorm2d-55          [-1, 512, 28, 28]           1,024Conv2d-56          [-1, 512, 28, 28]         262,144BatchNorm2d-57          [-1, 512, 28, 28]           1,024ResNeXt_Block-58          [-1, 512, 28, 28]               0Conv2d-59          [-1, 256, 28, 28]         131,072BatchNorm2d-60          [-1, 256, 28, 28]             512BN_Conv2d-61          [-1, 256, 28, 28]               0Conv2d-62          [-1, 256, 28, 28]          18,432BatchNorm2d-63          [-1, 256, 28, 28]             512BN_Conv2d-64          [-1, 256, 28, 28]               0Conv2d-65          [-1, 512, 28, 28]         131,584BatchNorm2d-66          [-1, 512, 28, 28]           1,024Conv2d-67          [-1, 512, 28, 28]         262,144BatchNorm2d-68          [-1, 512, 28, 28]           1,024ResNeXt_Block-69          [-1, 512, 28, 28]               0Conv2d-70          [-1, 256, 28, 28]         131,072BatchNorm2d-71          [-1, 256, 28, 28]             512BN_Conv2d-72          [-1, 256, 28, 28]               0Conv2d-73          [-1, 256, 28, 28]          18,432BatchNorm2d-74          [-1, 256, 28, 28]             512BN_Conv2d-75          [-1, 256, 28, 28]               0Conv2d-76          [-1, 512, 28, 28]         131,584BatchNorm2d-77          [-1, 512, 28, 28]           1,024Conv2d-78          [-1, 512, 28, 28]         262,144BatchNorm2d-79          [-1, 512, 28, 28]           1,024ResNeXt_Block-80          [-1, 512, 28, 28]               0Conv2d-81          [-1, 512, 28, 28]         262,144BatchNorm2d-82          [-1, 512, 28, 28]           1,024BN_Conv2d-83          [-1, 512, 28, 28]               0Conv2d-84          [-1, 512, 14, 14]          73,728BatchNorm2d-85          [-1, 512, 14, 14]           1,024BN_Conv2d-86          [-1, 512, 14, 14]               0Conv2d-87         [-1, 1024, 14, 14]         525,312BatchNorm2d-88         [-1, 1024, 14, 14]           2,048Conv2d-89         [-1, 1024, 14, 14]         524,288BatchNorm2d-90         [-1, 1024, 14, 14]           2,048ResNeXt_Block-91         [-1, 1024, 14, 14]               0Conv2d-92          [-1, 512, 14, 14]         524,288BatchNorm2d-93          [-1, 512, 14, 14]           1,024BN_Conv2d-94          [-1, 512, 14, 14]               0Conv2d-95          [-1, 512, 14, 14]          73,728BatchNorm2d-96          [-1, 512, 14, 14]           1,024BN_Conv2d-97          [-1, 512, 14, 14]               0Conv2d-98         [-1, 1024, 14, 14]         525,312BatchNorm2d-99         [-1, 1024, 14, 14]           2,048Conv2d-100         [-1, 1024, 14, 14]       1,048,576BatchNorm2d-101         [-1, 1024, 14, 14]           2,048ResNeXt_Block-102         [-1, 1024, 14, 14]               0Conv2d-103          [-1, 512, 14, 14]         524,288BatchNorm2d-104          [-1, 512, 14, 14]           1,024BN_Conv2d-105          [-1, 512, 14, 14]               0Conv2d-106          [-1, 512, 14, 14]          73,728BatchNorm2d-107          [-1, 512, 14, 14]           1,024BN_Conv2d-108          [-1, 512, 14, 14]               0Conv2d-109         [-1, 1024, 14, 14]         525,312BatchNorm2d-110         [-1, 1024, 14, 14]           2,048Conv2d-111         [-1, 1024, 14, 14]       1,048,576BatchNorm2d-112         [-1, 1024, 14, 14]           2,048ResNeXt_Block-113         [-1, 1024, 14, 14]               0Conv2d-114          [-1, 512, 14, 14]         524,288BatchNorm2d-115          [-1, 512, 14, 14]           1,024BN_Conv2d-116          [-1, 512, 14, 14]               0Conv2d-117          [-1, 512, 14, 14]          73,728BatchNorm2d-118          [-1, 512, 14, 14]           1,024BN_Conv2d-119          [-1, 512, 14, 14]               0Conv2d-120         [-1, 1024, 14, 14]         525,312BatchNorm2d-121         [-1, 1024, 14, 14]           2,048Conv2d-122         [-1, 1024, 14, 14]       1,048,576BatchNorm2d-123         [-1, 1024, 14, 14]           2,048ResNeXt_Block-124         [-1, 1024, 14, 14]               0Conv2d-125          [-1, 512, 14, 14]         524,288BatchNorm2d-126          [-1, 512, 14, 14]           1,024BN_Conv2d-127          [-1, 512, 14, 14]               0Conv2d-128          [-1, 512, 14, 14]          73,728BatchNorm2d-129          [-1, 512, 14, 14]           1,024BN_Conv2d-130          [-1, 512, 14, 14]               0Conv2d-131         [-1, 1024, 14, 14]         525,312BatchNorm2d-132         [-1, 1024, 14, 14]           2,048Conv2d-133         [-1, 1024, 14, 14]       1,048,576BatchNorm2d-134         [-1, 1024, 14, 14]           2,048ResNeXt_Block-135         [-1, 1024, 14, 14]               0Conv2d-136          [-1, 512, 14, 14]         524,288BatchNorm2d-137          [-1, 512, 14, 14]           1,024BN_Conv2d-138          [-1, 512, 14, 14]               0Conv2d-139          [-1, 512, 14, 14]          73,728BatchNorm2d-140          [-1, 512, 14, 14]           1,024BN_Conv2d-141          [-1, 512, 14, 14]               0Conv2d-142         [-1, 1024, 14, 14]         525,312BatchNorm2d-143         [-1, 1024, 14, 14]           2,048Conv2d-144         [-1, 1024, 14, 14]       1,048,576BatchNorm2d-145         [-1, 1024, 14, 14]           2,048ResNeXt_Block-146         [-1, 1024, 14, 14]               0Conv2d-147         [-1, 1024, 14, 14]       1,048,576BatchNorm2d-148         [-1, 1024, 14, 14]           2,048BN_Conv2d-149         [-1, 1024, 14, 14]               0Conv2d-150           [-1, 1024, 7, 7]         294,912BatchNorm2d-151           [-1, 1024, 7, 7]           2,048BN_Conv2d-152           [-1, 1024, 7, 7]               0Conv2d-153           [-1, 2048, 7, 7]       2,099,200BatchNorm2d-154           [-1, 2048, 7, 7]           4,096Conv2d-155           [-1, 2048, 7, 7]       2,097,152BatchNorm2d-156           [-1, 2048, 7, 7]           4,096ResNeXt_Block-157           [-1, 2048, 7, 7]               0Conv2d-158           [-1, 1024, 7, 7]       2,097,152BatchNorm2d-159           [-1, 1024, 7, 7]           2,048BN_Conv2d-160           [-1, 1024, 7, 7]               0Conv2d-161           [-1, 1024, 7, 7]         294,912BatchNorm2d-162           [-1, 1024, 7, 7]           2,048BN_Conv2d-163           [-1, 1024, 7, 7]               0Conv2d-164           [-1, 2048, 7, 7]       2,099,200BatchNorm2d-165           [-1, 2048, 7, 7]           4,096Conv2d-166           [-1, 2048, 7, 7]       4,194,304BatchNorm2d-167           [-1, 2048, 7, 7]           4,096ResNeXt_Block-168           [-1, 2048, 7, 7]               0Conv2d-169           [-1, 1024, 7, 7]       2,097,152BatchNorm2d-170           [-1, 1024, 7, 7]           2,048BN_Conv2d-171           [-1, 1024, 7, 7]               0Conv2d-172           [-1, 1024, 7, 7]         294,912BatchNorm2d-173           [-1, 1024, 7, 7]           2,048BN_Conv2d-174           [-1, 1024, 7, 7]               0Conv2d-175           [-1, 2048, 7, 7]       2,099,200BatchNorm2d-176           [-1, 2048, 7, 7]           4,096Conv2d-177           [-1, 2048, 7, 7]       4,194,304BatchNorm2d-178           [-1, 2048, 7, 7]           4,096ResNeXt_Block-179           [-1, 2048, 7, 7]               0Linear-180                    [-1, 4]           8,196
================================================================
Total params: 37,574,724
Trainable params: 37,574,724
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 379.37
Params size (MB): 143.34
Estimated Total Size (MB): 523.28
----------------------------------------------------------------

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

def test(dataloader,model,loss_fn):size=len(dataloader.dataset) #测试集的大小num_batches=len(dataloader)  #批次数目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

import copy
optimizer=torch.optim.Adam(model.parameters(),lr=1e-4)  #创建优化器，并设置学习率
loss_fn=nn.CrossEntropyLoss()  #创建损失函数 epochs=100train_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J6_modelif epoch_test_acc>best_acc:best_acc=epoch_test_accJ6_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(epoch+1,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=r'D:\THE MNIST DATABASE\J-series\J6_model.pth'
torch.save(model.state_dict(),PATH)print('Done')

Epoch: 1,Train_acc:57.0%,Train_loss:1.159,Test_acc:59.0%,Test_loss:1.152,Lr:1.00E-04
Epoch: 2,Train_acc:59.7%,Train_loss:1.133,Test_acc:64.6%,Test_loss:1.089,Lr:1.00E-04
Epoch: 3,Train_acc:64.0%,Train_loss:1.097,Test_acc:62.0%,Test_loss:1.117,Lr:1.00E-04
Epoch: 4,Train_acc:63.9%,Train_loss:1.095,Test_acc:63.9%,Test_loss:1.096,Lr:1.00E-04
Epoch: 5,Train_acc:64.2%,Train_loss:1.100,Test_acc:68.1%,Test_loss:1.067,Lr:1.00E-04
Epoch: 6,Train_acc:64.6%,Train_loss:1.094,Test_acc:61.5%,Test_loss:1.132,Lr:1.00E-04
Epoch: 7,Train_acc:65.5%,Train_loss:1.077,Test_acc:70.4%,Test_loss:1.032,Lr:1.00E-04
Epoch: 8,Train_acc:65.2%,Train_loss:1.088,Test_acc:66.4%,Test_loss:1.072,Lr:1.00E-04
Epoch: 9,Train_acc:67.2%,Train_loss:1.064,Test_acc:74.4%,Test_loss:1.008,Lr:1.00E-04
Epoch:10,Train_acc:66.1%,Train_loss:1.080,Test_acc:68.5%,Test_loss:1.052,Lr:1.00E-04
Epoch:11,Train_acc:65.6%,Train_loss:1.078,Test_acc:69.9%,Test_loss:1.040,Lr:1.00E-04
Epoch:12,Train_acc:66.9%,Train_loss:1.062,Test_acc:76.2%,Test_loss:0.982,Lr:1.00E-04
Epoch:13,Train_acc:65.9%,Train_loss:1.077,Test_acc:74.1%,Test_loss:1.002,Lr:1.00E-04
Epoch:14,Train_acc:65.5%,Train_loss:1.084,Test_acc:59.4%,Test_loss:1.144,Lr:1.00E-04
Epoch:15,Train_acc:62.5%,Train_loss:1.113,Test_acc:56.9%,Test_loss:1.171,Lr:1.00E-04
Epoch:16,Train_acc:66.5%,Train_loss:1.069,Test_acc:67.4%,Test_loss:1.065,Lr:1.00E-04
Epoch:17,Train_acc:68.0%,Train_loss:1.054,Test_acc:73.9%,Test_loss:1.005,Lr:1.00E-04
Epoch:18,Train_acc:67.5%,Train_loss:1.052,Test_acc:73.9%,Test_loss:0.989,Lr:1.00E-04
Epoch:19,Train_acc:68.6%,Train_loss:1.048,Test_acc:67.8%,Test_loss:1.049,Lr:1.00E-04
Epoch:20,Train_acc:70.0%,Train_loss:1.035,Test_acc:70.2%,Test_loss:1.033,Lr:1.00E-04
Epoch:21,Train_acc:70.6%,Train_loss:1.040,Test_acc:62.9%,Test_loss:1.107,Lr:1.00E-04
Epoch:22,Train_acc:71.0%,Train_loss:1.023,Test_acc:71.3%,Test_loss:1.036,Lr:1.00E-04
Epoch:23,Train_acc:72.5%,Train_loss:1.014,Test_acc:76.0%,Test_loss:0.981,Lr:1.00E-04
Epoch:24,Train_acc:70.9%,Train_loss:1.035,Test_acc:75.3%,Test_loss:0.993,Lr:1.00E-04
Epoch:25,Train_acc:72.5%,Train_loss:1.012,Test_acc:76.7%,Test_loss:0.974,Lr:1.00E-04
Epoch:26,Train_acc:70.8%,Train_loss:1.028,Test_acc:72.7%,Test_loss:1.004,Lr:1.00E-04
Epoch:27,Train_acc:72.7%,Train_loss:1.009,Test_acc:73.2%,Test_loss:1.011,Lr:1.00E-04
Epoch:28,Train_acc:73.8%,Train_loss:1.006,Test_acc:75.3%,Test_loss:0.991,Lr:1.00E-04
Epoch:29,Train_acc:74.5%,Train_loss:0.992,Test_acc:74.6%,Test_loss:0.986,Lr:1.00E-04
Epoch:30,Train_acc:73.3%,Train_loss:1.005,Test_acc:73.2%,Test_loss:1.004,Lr:1.00E-04
Epoch:31,Train_acc:75.7%,Train_loss:0.993,Test_acc:77.4%,Test_loss:0.968,Lr:1.00E-04
Epoch:32,Train_acc:74.6%,Train_loss:0.989,Test_acc:72.3%,Test_loss:1.016,Lr:1.00E-04
Epoch:33,Train_acc:76.6%,Train_loss:0.973,Test_acc:70.2%,Test_loss:1.042,Lr:1.00E-04
Epoch:34,Train_acc:75.2%,Train_loss:0.982,Test_acc:74.6%,Test_loss:0.992,Lr:1.00E-04
Epoch:35,Train_acc:71.5%,Train_loss:1.018,Test_acc:77.6%,Test_loss:0.977,Lr:1.00E-04
Epoch:36,Train_acc:74.4%,Train_loss:1.006,Test_acc:76.7%,Test_loss:0.973,Lr:1.00E-04
Epoch:37,Train_acc:72.0%,Train_loss:1.012,Test_acc:76.9%,Test_loss:0.978,Lr:1.00E-04
Epoch:38,Train_acc:71.5%,Train_loss:1.030,Test_acc:72.7%,Test_loss:1.017,Lr:1.00E-04
Epoch:39,Train_acc:75.1%,Train_loss:0.987,Test_acc:76.5%,Test_loss:0.979,Lr:1.00E-04
Epoch:40,Train_acc:75.4%,Train_loss:0.989,Test_acc:75.8%,Test_loss:0.979,Lr:1.00E-04
Epoch:41,Train_acc:78.1%,Train_loss:0.968,Test_acc:77.9%,Test_loss:0.963,Lr:1.00E-04
Epoch:42,Train_acc:77.2%,Train_loss:0.977,Test_acc:74.4%,Test_loss:0.987,Lr:1.00E-04
Epoch:43,Train_acc:77.9%,Train_loss:0.968,Test_acc:73.7%,Test_loss:0.994,Lr:1.00E-04
Epoch:44,Train_acc:79.1%,Train_loss:0.954,Test_acc:78.8%,Test_loss:0.953,Lr:1.00E-04
Epoch:45,Train_acc:79.6%,Train_loss:0.950,Test_acc:79.3%,Test_loss:0.949,Lr:1.00E-04
Epoch:46,Train_acc:80.2%,Train_loss:0.938,Test_acc:79.0%,Test_loss:0.948,Lr:1.00E-04
Epoch:47,Train_acc:80.6%,Train_loss:0.943,Test_acc:78.3%,Test_loss:0.962,Lr:1.00E-04
Epoch:48,Train_acc:75.9%,Train_loss:0.982,Test_acc:73.0%,Test_loss:1.013,Lr:1.00E-04
Epoch:49,Train_acc:77.3%,Train_loss:0.966,Test_acc:76.2%,Test_loss:0.977,Lr:1.00E-04
Epoch:50,Train_acc:79.9%,Train_loss:0.947,Test_acc:74.4%,Test_loss:0.991,Lr:1.00E-04
Epoch:51,Train_acc:80.4%,Train_loss:0.944,Test_acc:75.1%,Test_loss:0.986,Lr:1.00E-04
Epoch:52,Train_acc:79.2%,Train_loss:0.953,Test_acc:77.2%,Test_loss:0.970,Lr:1.00E-04
Epoch:53,Train_acc:80.0%,Train_loss:0.939,Test_acc:78.8%,Test_loss:0.951,Lr:1.00E-04
Epoch:54,Train_acc:79.0%,Train_loss:0.954,Test_acc:80.2%,Test_loss:0.944,Lr:1.00E-04
Epoch:55,Train_acc:82.7%,Train_loss:0.923,Test_acc:79.0%,Test_loss:0.945,Lr:1.00E-04
Epoch:56,Train_acc:81.9%,Train_loss:0.926,Test_acc:80.0%,Test_loss:0.939,Lr:1.00E-04
Epoch:57,Train_acc:82.8%,Train_loss:0.915,Test_acc:76.2%,Test_loss:0.973,Lr:1.00E-04
Epoch:58,Train_acc:81.7%,Train_loss:0.926,Test_acc:82.8%,Test_loss:0.918,Lr:1.00E-04
Epoch:59,Train_acc:83.2%,Train_loss:0.918,Test_acc:81.4%,Test_loss:0.931,Lr:1.00E-04
Epoch:60,Train_acc:82.5%,Train_loss:0.916,Test_acc:81.4%,Test_loss:0.926,Lr:1.00E-04
Epoch:61,Train_acc:79.6%,Train_loss:0.950,Test_acc:78.8%,Test_loss:0.946,Lr:1.00E-04
Epoch:62,Train_acc:83.4%,Train_loss:0.914,Test_acc:80.2%,Test_loss:0.940,Lr:1.00E-04
Epoch:63,Train_acc:86.0%,Train_loss:0.893,Test_acc:80.2%,Test_loss:0.940,Lr:1.00E-04
Epoch:64,Train_acc:84.1%,Train_loss:0.899,Test_acc:80.9%,Test_loss:0.921,Lr:1.00E-04
Epoch:65,Train_acc:84.2%,Train_loss:0.905,Test_acc:82.1%,Test_loss:0.917,Lr:1.00E-04
Epoch:66,Train_acc:85.5%,Train_loss:0.894,Test_acc:80.9%,Test_loss:0.934,Lr:1.00E-04
Epoch:67,Train_acc:83.7%,Train_loss:0.913,Test_acc:80.0%,Test_loss:0.942,Lr:1.00E-04
Epoch:68,Train_acc:83.4%,Train_loss:0.907,Test_acc:81.8%,Test_loss:0.913,Lr:1.00E-04
Epoch:69,Train_acc:85.2%,Train_loss:0.892,Test_acc:81.8%,Test_loss:0.926,Lr:1.00E-04
Epoch:70,Train_acc:86.1%,Train_loss:0.884,Test_acc:82.1%,Test_loss:0.928,Lr:1.00E-04
Epoch:71,Train_acc:82.5%,Train_loss:0.918,Test_acc:81.4%,Test_loss:0.929,Lr:1.00E-04
Epoch:72,Train_acc:85.9%,Train_loss:0.892,Test_acc:81.6%,Test_loss:0.920,Lr:1.00E-04
Epoch:73,Train_acc:85.2%,Train_loss:0.893,Test_acc:79.3%,Test_loss:0.944,Lr:1.00E-04
Epoch:74,Train_acc:87.2%,Train_loss:0.875,Test_acc:85.8%,Test_loss:0.884,Lr:1.00E-04
Epoch:75,Train_acc:86.7%,Train_loss:0.876,Test_acc:84.8%,Test_loss:0.893,Lr:1.00E-04
Epoch:76,Train_acc:86.5%,Train_loss:0.875,Test_acc:83.4%,Test_loss:0.903,Lr:1.00E-04
Epoch:77,Train_acc:87.0%,Train_loss:0.878,Test_acc:85.8%,Test_loss:0.884,Lr:1.00E-04
Epoch:78,Train_acc:88.3%,Train_loss:0.861,Test_acc:86.0%,Test_loss:0.888,Lr:1.00E-04
Epoch:79,Train_acc:87.2%,Train_loss:0.869,Test_acc:86.0%,Test_loss:0.883,Lr:1.00E-04
Epoch:80,Train_acc:87.1%,Train_loss:0.877,Test_acc:85.8%,Test_loss:0.886,Lr:1.00E-04
Epoch:81,Train_acc:88.4%,Train_loss:0.859,Test_acc:82.8%,Test_loss:0.913,Lr:1.00E-04
Epoch:82,Train_acc:88.9%,Train_loss:0.851,Test_acc:85.8%,Test_loss:0.878,Lr:1.00E-04
Epoch:83,Train_acc:88.4%,Train_loss:0.859,Test_acc:84.8%,Test_loss:0.893,Lr:1.00E-04
Epoch:84,Train_acc:89.0%,Train_loss:0.860,Test_acc:84.1%,Test_loss:0.900,Lr:1.00E-04
Epoch:85,Train_acc:89.9%,Train_loss:0.850,Test_acc:84.1%,Test_loss:0.899,Lr:1.00E-04
Epoch:86,Train_acc:89.5%,Train_loss:0.850,Test_acc:83.0%,Test_loss:0.913,Lr:1.00E-04
Epoch:87,Train_acc:88.7%,Train_loss:0.854,Test_acc:86.0%,Test_loss:0.885,Lr:1.00E-04
Epoch:88,Train_acc:91.2%,Train_loss:0.837,Test_acc:80.9%,Test_loss:0.928,Lr:1.00E-04
Epoch:89,Train_acc:91.7%,Train_loss:0.831,Test_acc:86.0%,Test_loss:0.883,Lr:1.00E-04
Epoch:90,Train_acc:87.4%,Train_loss:0.863,Test_acc:84.1%,Test_loss:0.900,Lr:1.00E-04
Epoch:91,Train_acc:90.1%,Train_loss:0.851,Test_acc:86.2%,Test_loss:0.878,Lr:1.00E-04
Epoch:92,Train_acc:88.3%,Train_loss:0.855,Test_acc:86.7%,Test_loss:0.871,Lr:1.00E-04
Epoch:93,Train_acc:90.5%,Train_loss:0.844,Test_acc:85.8%,Test_loss:0.884,Lr:1.00E-04
Epoch:94,Train_acc:92.4%,Train_loss:0.821,Test_acc:85.3%,Test_loss:0.881,Lr:1.00E-04
Epoch:95,Train_acc:91.4%,Train_loss:0.835,Test_acc:86.2%,Test_loss:0.878,Lr:1.00E-04
Epoch:96,Train_acc:92.2%,Train_loss:0.829,Test_acc:82.3%,Test_loss:0.917,Lr:1.00E-04
Epoch:97,Train_acc:90.0%,Train_loss:0.848,Test_acc:83.2%,Test_loss:0.913,Lr:1.00E-04
Epoch:98,Train_acc:90.8%,Train_loss:0.836,Test_acc:87.9%,Test_loss:0.868,Lr:1.00E-04
Epoch:99,Train_acc:89.6%,Train_loss:0.848,Test_acc:83.7%,Test_loss:0.908,Lr:1.00E-04
Epoch:100,Train_acc:91.0%,Train_loss:0.832,Test_acc:86.2%,Test_loss:0.881,Lr:1.00E-04
Done

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']   #正常显示中文标签
plt.rcParams['axes.unicode_minus']=False   #正常显示负号
plt.rcParams['figure.dpi']=300   #分辨率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.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()

from PIL import Imageclasses=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=r'D:\THE MNIST DATABASE\P4-data\Others\NM01_01_00.jpg',model=model,transform=train_transforms,classes=classes)

预测结果是：Others

J6_model.eval()
epoch_test_acc,epoch_test_loss=test(test_dl,J6_model,loss_fn)
epoch_test_acc,epoch_test_loss