LeNet原理及代码实现

1.原理及介绍

2.代码实现

2.1model.py

2.2model_train.py

2.3model.test.py

1.原理及介绍

2.代码实现

2.1model.py

import torch
from torch import nn
from torchsummary import summaryclass LeNet(nn.Module):def __init__(self):super(LeNet, self).__init__()# 定义第一层卷积(in_channels；输入通道；out_channels：输出通道；kernel_size：卷积核大小；stride：步长，默认为1；padding：填充，默认为0)self.conv1 = nn.Conv2d(in_channels=1, out_channels=6, kernel_size=5, stride=1, padding=2)self.sig = nn.Sigmoid()  # 激活函数self.pool1 = nn.AvgPool2d(kernel_size=2, stride=2)  # 第一次池化self.conv2 = nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5)  # 定义第二层卷积self.pool2 = nn.AvgPool2d(kernel_size=2, stride=2)  # 第二次池化self.flatten = nn.Flatten()  # 平展操作self.fc1 = nn.Linear(5 * 5 * 16, 120)  # 第一个全连接层self.fc2 = nn.Linear(120, 84)  # 第二个全连接层self.fc3 = nn.Linear(84, 10)  # 第三个全连接层def forward(self, x):x = self.conv1(x)x = self.sig(x)x = self.pool1(x)x = self.conv2(x)x = self.sig(x)x = self.pool2(x)x = self.flatten(x)x = self.fc1(x)x = self.fc2(x)x = self.fc3(x)return xif __name__ == "__main__":device = torch.device("cuda" if torch.cuda.is_available() else "cpu")model = LeNet().to(device)print(summary(model, (1, 28, 28)))  # 打印模型信息

2.2model_train.py

import copy
import time
import torch
import torch.nn as nn
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import torch.utils.data as data
from torchvision.datasets import FashionMNIST
from torchvision import transforms
from model import LeNetdef train_val_data_process():train_dataset = FashionMNIST(root='./data',  # 指定下载文件夹train=True,  # 只下载训练集# 归一化处理数据集transform=transforms.Compose([transforms.Resize(size=28),transforms.ToTensor()]),  # 数据转换成张量形式，方便模型应用download=True  # 下载数据)# 划分训练集和验证集train_data, val_data = data.random_split(train_dataset,[round(0.8 * len(train_dataset)), round(0.2 * len(train_dataset))])# 训练集加载train_dataloader = data.DataLoader(dataset=train_data,batch_size=32,  # 一个批次数据的数量shuffle=True,  # 数据打乱num_workers=2)  # 分配的进程数目# 验证集加载val_dataloader = data.DataLoader(dataset=val_data,batch_size=32,shuffle=True,num_workers=2)return train_dataloader, val_dataloaderdef train_model_process(model, train_dataloader, val_dataloader, num_epochs):# 定义训练使用的设备，有GPU则用，没有则用CPUdevice = torch.device("cuda" if torch.cuda.is_available() else "cpu")# 使用Adam优化器进行模型参数更新，学习率为0.001optimizer = torch.optim.Adam(model.parameters(), lr=0.001)# 损失函数为交叉熵损失函数criterion = nn.CrossEntropyLoss()# 将模型放入训练设备内model = model.to(device)# 复制当前模型参数(w,b等)，以便将最好的模型参数权重保存下来best_model_wts = copy.deepcopy(model.state_dict())# 初始化参数# 最高准确度best_acc = 0.0# 训练集损失值列表train_loss_all = []# 验证集损失值列表val_loss_all = []# 训练集准确度列表train_acc_all = []# 验证集准确度列表val_acc_all = []# 当前时间since = time.time()for epoch in range(num_epochs):print("Epoch {}/{}".format(epoch, num_epochs - 1))print("-" * 10)# 初始化参数# 训练集损失值train_loss = 0.0# 训练集精确度train_corrects = 0# 验证集损失值val_loss = 0.0# 验证集精确度val_corrects = 0# 训练集样本数量train_num = 0# 验证集样本数量val_num = 0# 对每一个mini-batch训练和计算for step, (b_x, b_y) in enumerate(train_dataloader):# 将特征放入到训练设备中b_x = b_x.to(device)  # batch_size*28*28*1的tensor数据# 将标签放入到训练设备中b_y = b_y.to(device)  # batch_size大小的向量tensor数据# 设置模型为训练模式model.train()# 前向传播过程，输入为一个batch，输出为一个batch中对应的预测output = model(b_x)  # 输出为：batch_size大小的行和10列组成的矩阵# 查找每一行中最大值对应的行标pre_lab = torch.argmax(output, dim=1)  # batch_size大小的向量表示属于物品的标签# 计算每一个batch的损失函数，向量形式的交叉熵损失函数loss = criterion(output, b_y)# 将梯度初始化为0，防止梯度累积optimizer.zero_grad()# 反向传播计算loss.backward()# 根据网络反向传播的梯度信息来更新网络的参数，以起到降低loss函数计算值的作用optimizer.step()# 对损失函数进行累加，该批次的loss值乘于该批次数量得到批次总体loss值，在将其累加得到轮次总体loss值train_loss += loss.item() * b_x.size(0)# 如果预测正确，则准确度train_corrects加1train_corrects += torch.sum(pre_lab == b_y.data)# 当前用于训练的样本数量train_num += b_x.size(0)for step, (b_x, b_y) in enumerate(val_dataloader):# 将特征放入到验证设备中b_x = b_x.to(device)# 将标签放入到验证设备中b_y = b_y.to(device)# 设置模型为评估模式model.eval()# 前向传播过程，输入为一个batch，输出为一个batch中对应的预测output = model(b_x)# 查找每一行中最大值对应的行标pre_lab = torch.argmax(output, dim=1)# 计算每一个batch的损失函数loss = criterion(output, b_y)# 对损失函数进行累加val_loss += loss.item() * b_x.size(0)# 如果预测正确，则准确度val_corrects加1val_corrects += torch.sum(pre_lab == b_y.data)# 当前用于验证的样本数量val_num += b_x.size(0)# 计算并保存每一轮次迭代的loss值和准确率# 计算并保存训练集的loss值train_loss_all.append(train_loss / train_num)# 计算并保存训练集的准确率train_acc_all.append(train_corrects.double().item() / train_num)# 计算并保存验证集的loss值val_loss_all.append(val_loss / val_num)# 计算并保存验证集的准确率val_acc_all.append(val_corrects.double().item() / val_num)# 打印每一轮次的loss值和准确度print("{} train loss:{:.4f} train acc: {:.4f}".format(epoch, train_loss_all[-1], train_acc_all[-1]))print("{} val loss:{:.4f} val acc: {:.4f}".format(epoch, val_loss_all[-1], val_acc_all[-1]))if val_acc_all[-1] > best_acc:# 保存当前最高准确度best_acc = val_acc_all[-1]# 保存当前最高准确度的模型参数best_model_wts = copy.deepcopy(model.state_dict())# 计算训练和验证的耗时time_use = time.time() - sinceprint("训练和验证耗费的时间{:.0f}m{:.0f}s".format(time_use // 60, time_use % 60))# 选择最优参数，保存最优参数的模型torch.save(best_model_wts, "./model_save/LeNet_best_model.pth")# 将产生的数据保存成表格，方便查看train_process = pd.DataFrame(data={"epoch": range(num_epochs),"train_loss_all": train_loss_all,"val_loss_all": val_loss_all,"train_acc_all": train_acc_all,"val_acc_all": val_acc_all})return train_processdef matplot_acc_loss(train_process):# 显示每一次迭代后的训练集和验证集的损失函数和准确率plt.figure(figsize=(12, 4))plt.subplot(1, 2, 1)  # 表示一行两列的第一张图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")plt.subplot(1, 2, 2)  # 表示一行两列的第二张图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.xlabel("epoch")plt.ylabel("acc")plt.legend()plt.show()if __name__ == '__main__':# 加载需要的模型LeNet = LeNet()# 加载数据集train_data, val_data = train_val_data_process()# 利用现有的模型进行模型的训练train_process = train_model_process(LeNet, train_data, val_data, num_epochs=20)matplot_acc_loss(train_process)

2.3model.test.py

import torch
import torch.utils.data as data
from torchvision import transforms
from torchvision.datasets import FashionMNIST
from model import LeNetdef test_data_process():test_data = FashionMNIST(root='./data',train=False,  # 用测试集进行测试transform=transforms.Compose([transforms.Resize(size=28), transforms.ToTensor()]),download=True)test_dataloader = data.DataLoader(dataset=test_data,batch_size=1,  # 该批次设为1shuffle=True,num_workers=0)return test_dataloaderdef test_model_process(model, test_dataloader):# 设定测试所用到的设备，有GPU用GPU没有GPU用CPUdevice = "cuda" if torch.cuda.is_available() else 'cpu'# 讲模型放入到训练设备中model = model.to(device)# 初始化参数test_corrects = 0.0test_num = 0# 只进行前向传播计算，不计算梯度，从而节省内存，加快运行速度with torch.no_grad():for test_data_x, test_data_y in test_dataloader:# 将特征放入到测试设备中test_data_x = test_data_x.to(device)# 将标签放入到测试设备中test_data_y = test_data_y.to(device)# 设置模型为评估模式model.eval()# 前向传播过程，输入为测试数据集，输出为对每个样本的预测值output = model(test_data_x)# 查找每一行中最大值对应的行标pre_lab = torch.argmax(output, dim=1)# 如果预测正确，则准确度test_corrects加1test_corrects += torch.sum(pre_lab == test_data_y.data)# 将所有的测试样本进行累加test_num += test_data_x.size(0)# 计算测试准确率test_acc = test_corrects.double().item() / test_numprint("测试的准确率为：", test_acc)if __name__ == "__main__":# 加载模型model = LeNet()model.load_state_dict(torch.load('./model_save/LeNet_best_model.pth'))  # 调用训练好的参数权重# 加载测试数据test_dataloader = test_data_process()# 加载模型测试的函数test_model_process(model, test_dataloader)

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