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一、整体结构

二、model.py

import torch
from torch import nn
from torchsummary import summaryclass LeNet(nn.Module):def __init__(self):super(LeNet,self).__init__()self.c1 = nn.Conv2d(in_channels=1,out_channels=6,kernel_size=5,padding=2)self.sig = nn.Sigmoid()self.s2 = nn.AvgPool2d(kernel_size=2,stride=2)self.c3 = nn.Conv2d(in_channels=6,out_channels=16,kernel_size=5)self.s4 = nn.AvgPool2d(kernel_size=2,stride=2)self.flatten = nn.Flatten()self.f5 = nn.Linear(in_features=5*5*16,out_features=120)self.f6 = nn.Linear(in_features=120,out_features=84)self.f7 = nn.Linear(in_features=84,out_features=10)def forward(self,x):x = self.sig(self.c1(x))x = self.s2(x)x = self.sig(self.c3(x))x = self.s4(x)x = self.flatten(x)x = self.f5(x)x = self.f6(x)x = self.f7(x)return x# if __name__ =="__main__":
#     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
#
#     model = LeNet().to(device)
#
#     print(summary(model,input_size=(1,28,28)))

三、model_train.py

# 导入所需的Python库
from torchvision.datasets import FashionMNIST
from torchvision import transforms
import torch.utils.data as Data
import torch
from torch import nn
import time
import copy
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from model import LeNet  # model.py中定义了LeNet模型
from tqdm import tqdm  # 导入tqdm库，用于显示进度条# 定义数据加载和处理函数
def train_val_data_process():# 加载FashionMNIST数据集，Resize到28x28尺寸，并转换为Tensortrain_data = FashionMNIST(root="./data",train=True,transform=transforms.Compose([transforms.Resize(size=28), transforms.ToTensor()]),download=True)# 将加载的数据集分为80%的训练数据和20%的验证数据train_data, val_data = Data.random_split(train_data, lengths=[round(0.8 * len(train_data)), round(0.2 * len(train_data))])# 为训练数据和验证数据创建DataLoader，设置批量大小为32，洗牌，2个进程加载数据train_dataloader = Data.DataLoader(dataset=train_data,batch_size=32,shuffle=True,num_workers=2)val_dataloader = Data.DataLoader(dataset=val_data,batch_size=32,shuffle=True,num_workers=2)# 返回训练和验证的DataLoaderreturn train_dataloader, val_dataloader# 定义模型训练和验证过程的函数
def train_model_process(model, train_dataloader, val_dataloader, num_epochs):# 设置使用CUDA如果可用device = torch.device("cuda" if torch.cuda.is_available() else "cpu")# 打印使用的设备dev = "cuda" if torch.cuda.is_available() else "cpu"print(f'当前模型训练设备为: {dev}')# 初始化Adam优化器和交叉熵损失函数optimizer = torch.optim.Adam(model.parameters(), lr=0.001)criterion = nn.CrossEntropyLoss()# 将模型移动到选定的设备上model = model.to(device)# 复制模型权重用于后续更新最佳模型best_model_wts = copy.deepcopy(model.state_dict())best_acc = 0.0  # 初始化最佳准确度# 初始化用于记录训练和验证过程中损失和准确度的列表train_loss_all = []val_loss_all = []train_acc_all = []val_acc_all = []# 记录训练开始时间start_time = time.time()# 迭代指定的训练轮数for epoch in range(1, num_epochs + 1):# 记录每个epoch开始的时间since = time.time()# 打印分隔符和当前epoch信息print("-" * 10)print(f"Epoch: {epoch}/{num_epochs}")# 初始化训练和验证过程中的损失和正确预测数量train_loss = 0.0train_corrects = 0val_loss = 0.0val_corrects = 0# 初始化批次计数器train_num = 0val_num = 0# 创建训练进度条progress_train_bar = tqdm(total=len(train_dataloader), desc=f'Training {epoch}', unit='batch')# 训练数据集的遍历for step, (b_x, b_y) in enumerate(train_dataloader):# 将数据移动到相应的设备上b_x = b_x.to(device)b_y = b_y.to(device)# 训练模型model.train()# 前向传播output = model(b_x)# 计算预测标签pre_label = torch.argmax(output, dim=1)# 计算损失loss = criterion(output, b_y)# 清空梯度optimizer.zero_grad()# 反向传播loss.backward()# 更新权重optimizer.step()# 累加损失和正确预测数量train_loss += loss.item() * b_x.size(0)train_corrects += torch.sum(pre_label == b_y.data)# 更新批次计数器train_num += b_x.size(0)# 更新训练进度条progress_train_bar.update(1)# 关闭训练进度条progress_train_bar.close()# 创建验证进度条progress_val_bar = tqdm(total=len(val_dataloader), desc=f'Validation {epoch}', unit='batch')# 验证数据集的遍历for step, (b_x, b_y) in enumerate(val_dataloader):# 将数据移动到相应的设备上b_x = b_x.to(device)b_y = b_y.to(device)# 评估模型model.eval()# 前向传播output = model(b_x)# 计算预测标签pre_label = torch.argmax(output, dim=1)# 计算损失loss = criterion(output, b_y)# 累加损失和正确预测数量val_loss += loss.item() * b_x.size(0)val_corrects += torch.sum(pre_label == b_y.data)# 更新批次计数器val_num += b_x.size(0)# 更新验证进度条progress_val_bar.update(1)# 关闭验证进度条progress_val_bar.close()# 计算并记录epoch的平均损失和准确度train_loss_all.append(train_loss / train_num)train_acc_all.append(train_corrects.double().item() / train_num)val_loss_all.append(val_loss / val_num)val_acc_all.append(val_corrects.double().item() / val_num)# 打印训练和验证的损失与准确度print(f'{epoch} Train Loss: {train_loss_all[-1]:.4f} Train Acc: {train_acc_all[-1]:.4f}')print(f'{epoch} Val Loss: {val_loss_all[-1]:.4f} Val Acc: {val_acc_all[-1]:.4f}')# 计算并打印epoch训练耗费的时间time_use = time.time() - sinceprint(f'第 {epoch} 个 epoch 训练耗费时间: {time_use // 60:.0f}m {time_use % 60:.0f}s')# 若当前epoch的验证准确度为最佳，则更新最佳模型权重if val_acc_all[-1] > best_acc:best_acc = val_acc_all[-1]best_model_wts = copy.deepcopy(model.state_dict())# 训练结束，保存最佳模型权重torch.save(best_model_wts, 'D:/Pycharm/deepl/LeNet/weight/best_model.pth')# 如果当前epoch为总epoch数，则保存最终模型权重if epoch == num_epochs:torch.save(model.state_dict(), f'D:/Pycharm/deepl/LeNet/weight/{num_epochs}_model.pth')# 将训练过程中的统计数据整理成DataFrametrain_process = pd.DataFrame(data={"epoch": range(1, num_epochs + 1),"train_loss_all": train_loss_all,"val_loss_all": val_loss_all,"train_acc_all": train_acc_all,"val_acc_all": val_acc_all})# 打印总训练时间consume_time = time.time() - start_timeprint(f'总耗时：{consume_time // 60:.0f}m {consume_time % 60:.0f}s')# 返回包含训练过程统计数据的DataFramereturn train_process# 定义绘制训练和验证过程中损失与准确度的函数
def matplot_acc_loss(train_process):# 创建图形和子图plt.figure(figsize=(12, 4))# 绘制训练和验证损失plt.subplot(1, 2, 1)plt.plot(train_process["epoch"], train_process["train_loss_all"], 'ro-', label="train_loss")plt.plot(train_process["epoch"], train_process["val_loss_all"], 'bs-', label="val_loss")plt.legend()plt.xlabel("epoch")plt.ylabel("loss")# 保存损失图像plt.savefig('./result_picture/training_loss_accuracy.png', bbox_inches='tight')# 绘制训练和验证准确度plt.subplot(1, 2, 2)plt.plot(train_process["epoch"], train_process["train_acc_all"], 'ro-', label="train_acc")plt.plot(train_process["epoch"], train_process["val_acc_all"], 'bs-', label="val_acc")plt.legend()plt.xlabel("epoch")plt.ylabel("accuracy")# 保存准确率曲线图plt.savefig('./result_picture/training_accuracy.png', bbox_inches='tight')plt.show()if __name__ == "__main__":model = LeNet()train_dataloader, val_dataloader = train_val_data_process()train_process = train_model_process(model, train_dataloader, val_dataloader, num_epochs=20)matplot_acc_loss(train_process)

四、model_test.py

import torch
import torch.utils.data as Data
from torchvision import transforms
from torchvision.datasets import FashionMNIST
from model import LeNet
from sklearn.metrics import confusion_matrix
import seaborn as sns
import matplotlib.pyplot as plt
# t代表testdef t_data_process():test_data = FashionMNIST(root="./data",train=False,transform=transforms.Compose([transforms.Resize(size=28), transforms.ToTensor()]),download=True)test_dataloader = Data.DataLoader(dataset=test_data,batch_size=1,shuffle=True,num_workers=0)return test_dataloaderdef t_model_process(model, test_dataloader):if model is not None:print('Successfully loaded the model.')device = "cuda" if torch.cuda.is_available() else "cpu"model = model.to(device)# 初始化参数test_corrects = 0.0test_num = 0all_preds = []  # 存储所有预测标签all_labels = []  # 存储所有实际标签# 只进行前向传播，不计算梯度with torch.no_grad():for test_x, test_y in test_dataloader:test_x = test_x.to(device)test_y = test_y.to(device)# 设置模型为验证模式model.eval()# 前向传播得到一个batch的结果output = model(test_x)# 查找最大值对应的行标pre_lab = torch.argmax(output, dim=1)# 收集预测和实际标签all_preds.extend(pre_lab.tolist())all_labels.extend(test_y.tolist())# 计算准确率test_corrects += torch.sum(pre_lab == test_y.data)# 将所有的测试样本进行累加test_num += test_x.size(0)# 计算准确率test_acc = test_corrects.double().item() / test_numprint(f'测试的准确率：{test_acc}')# 绘制混淆矩阵conf_matrix = confusion_matrix(all_labels, all_preds)sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues')plt.xlabel('Predicted labels')plt.ylabel('True labels')plt.title('Confusion Matrix')plt.show()plt.savefig('./result_picture/Confusion_Matrix.png', bbox_inches='tight')if __name__=="__main__":# 加载模型model = LeNet()print('loading model')# 加载权重model.load_state_dict(torch.load('D:/Pycharm/deepl/LeNet/weight/best_model.pth'))# 加载测试数据test_dataloader = t_data_process()# 加载模型测试的函数t_model_process(model,test_dataloader)device = "cuda" if torch.cuda.is_available() else "cpu"model = model.to(device)classes = ['T-shirt/top','Trouser','Pullover','Dress','coat','Sandal','Shirt','Sneaker','Bag','Ankle boot']with torch.no_grad():for b_x,b_y in test_dataloader:b_x = b_x.to(device)b_y = b_y.to(device)model.eval()output = model(b_x)pre_lab = torch.argmax(output,dim=1)result = pre_lab.item()label = b_y.item()print(f'预测值：{classes[result]}',"-----------",f'真实值：{classes[label]}')