深度学习实战 | 卷积神经网络LeNet手写数字识别（带手写板GUI界面）

引言

在深度学习领域，卷积神经网络（Convolutional Neural Network, CNN）是一种广泛应用于图像识别任务的神经网络结构。LeNet是一种经典的CNN结构，被广泛应用于基础的图像分类任务。本文将介绍如何使用LeNet卷积神经网络实现手写数字识别，并使用Pytorch实现LeNet手写数字识别，使用PyQt5实现手写板GUI界面，使用户能够通过手写板输入数字并进行识别。

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完整代码下载：Python手写数字识别带手写板GUI界面 Pytorch代码含训练模型（付费资源，如果你觉得这篇博客对你有帮助，欢迎购买支持~）

1. LeNet卷积神经网络

LeNet是由Yann LeCun等人于1998年提出的卷积神经网络结构，主要用于手写字符识别。在本文中，我们将使用LeNet结构构建一个用于手写数字识别的神经网络模型。以下是LeNet的基本结构：

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Layer 1: Convolutional Layer- Input: 28x28x1 (灰度图像)- Filter: 5x5, Stride: 1, Depth: 6- Activation: Sigmoid- Output: 28x28x6Layer 2: Average Pooling Layer- Input: 28x28x6- Pooling: 2x2, Stride: 2- Output: 14x14x6Layer 3: Convolutional Layer- Input: 14x14x6- Filter: 5x5, Stride: 1, Depth: 16- Activation: Sigmoid- Output: 10x10x16Layer 4: Average Pooling Layer- Input: 10x10x16- Pooling: 2x2, Stride: 2- Output: 5x5x16Layer 5: Fully Connected Layer- Input: 5x5x16- Output: 120- Activation: SigmoidLayer 6: Fully Connected Layer- Input: 120- Output: 84- Activation: SigmoidLayer 7: Output Layer- Input: 84- Output: 10 (对应0-9的数字)- Activation: Softmax

2. 手写数字识别实现

使用深度学习框架（例如Pytorch）构建LeNet模型：

import torch
import torch.nn as nn
import torch.nn.functional as Fclass LeNet(nn.Module):def __init__(self):super(LeNet, self).__init__()self.conv1 = nn.Conv2d(1, 6, kernel_size=5)self.pool1 = nn.AvgPool2d(kernel_size=2, stride=2)self.conv2 = nn.Conv2d(6, 16, kernel_size=5)self.pool2 = nn.AvgPool2d(kernel_size=2, stride=2)self.fc1 = nn.Linear(16 * 5 * 5, 120)self.fc2 = nn.Linear(120, 84)self.fc3 = nn.Linear(84, 10)def forward(self, x):x = F.sigmoid(self.conv1(x))x = self.pool1(x)x = F.sigmoid(self.conv2(x))x = self.pool2(x)x = x.view(-1, 16 * 5 * 5)x = F.sigmoid(self.fc1(x))x = F.sigmoid(self.fc2(x))x = self.fc3(x)return F.log_softmax(x, dim=1)

并使用手写数字数据集MNIST进行训练。确保正确实现数据预处理和模型训练过程：

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
from net import Netif __name__ == "__main__":# 设置训练参数batch_size = 64epochs = 140device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")# 数据集transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.5,), (0.5,))])trainset = datasets.MNIST('~/.pytorch/MNIST_data/', download=True, train=True, transform=transform)trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True)# 输出提示信息print("batch_size:", batch_size)print("data_batches:", len(trainloader))print("epochs:", epochs)# 神经网络net = Net().to(device)net.load_state_dict(torch.load('model.pth'))# 损失函数和优化器criterion = nn.NLLLoss()optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)# 训练网络for epoch in range(epochs):running_loss = 0.0for i, data in enumerate(trainloader, 0):inputs, labels = datainputs, labels = Variable(inputs).to(device), Variable(labels).to(device)# 反向传播优化参数optimizer.zero_grad()outputs = net(inputs)loss = criterion(outputs, labels)loss.backward()optimizer.step()running_loss += loss.item()if i % 938 == 937:    # 每轮输出损失值print('[epoch: %d, batches: %d] loss: %.5f' %(epoch + 1, i + 1, running_loss / 2000))running_loss = 0.0torch.save(net.state_dict(), './model.pth')  # 每轮保存模型参数print('Finished Training')

3. 手写板GUI界面开发

模型训练完成后，为了让用户通过手写板输入数字，我们将开发一个简单直观的GUI界面。使用GUI库（例如PyQt5），创建一个窗口，包含一个手写板区域，用户可以在上面写数字。添加一个识别按钮，点击后将手写板上的数字送入LeNet模型进行识别，并在界面上显示识别结果。

以下是PyQt5代码示例：

from PyQt5.QtWidgets import *
from PyQt5.QtGui import *
from PyQt5.QtCore import *
import sysimport torch
from utils import *
from net import Netclass MainWindow(QMainWindow):def __init__(self):super().__init__()self.title = '手写数字识别'self.initUI()def initUI(self):self.setWindowTitle(self.title)self.setMinimumSize(500, 400)self.main_widget = QWidget()self.main_layout = QGridLayout()self.main_widget.setLayout(self.main_layout)self.setCentralWidget(self.main_widget)self.canvas = Canvas()self.canvas.setFixedSize(300,300)self.label = QLabel()self.label.setFixedSize(100,100)self.label.setText('识别结果')self.label.setStyleSheet("font-size:15px;color:red") self.clear_button = QPushButton('清除')self.clear_button.setFixedSize(100,50)self.clear_button.clicked.connect(self.canvas.clear)self.recognize_button = QPushButton('识别')self.recognize_button.setFixedSize(100,50)self.recognize_button.clicked.connect(self.recognize)self.main_layout.addWidget(self.canvas,0,0,3,1)self.main_layout.addWidget(self.label,0,1)self.main_layout.addWidget(self.clear_button,1,1)self.main_layout.addWidget(self.recognize_button,2,1)def recognize(self):self.canvas.recognize()self.label.setText('识别结果: ' + str(self.canvas.recognize()))class Canvas(QLabel):x0=-10; y0=-10; x1=-10; y1=-10def __init__(self):super(Canvas,self).__init__()self.pixmap = QPixmap(300, 300)self.pixmap.fill(Qt.white)self.Color=Qt.blueself.penwidth=10def paintEvent(self,event):painter=QPainter(self.pixmap)painter.setPen(QPen(self.Color,self.penwidth,Qt.SolidLine))painter.drawLine(self.x0,self.y0,self.x1,self.y1)Label_painter=QPainter(self)Label_painter.drawPixmap(2,2,self.pixmap)def mousePressEvent(self, event):self.x1=event.x()self.y1=event.y()def mouseMoveEvent(self, event):self.x0 = self.x1self.y0 = self.y1self.x1 = event.x()self.y1 = event.y()self.update()def clear(self):self.x0=-10; self.y0=-10; self.x1=-10; self.y1=-10self.pixmap.fill(Qt.white)self.update()def recognize(self):arr = pixmap2np(self.pixmap)arr = 255 - arr[:,:,2]arr = clip_image(arr)arr = resize_image(arr)arr = np.expand_dims(arr, axis=0)arr_batch = np.expand_dims(arr, axis=0)tensor = torch.FloatTensor(arr_batch)tensor = (tensor/255 - 0.5) * 2possibles = net(tensor).detach().numpy()result = np.argmax(possibles)return resultif __name__ == '__main__':net = Net()net.load_state_dict(torch.load('model.pth'))app = QApplication(sys.argv)win = MainWindow()win.show()sys.exit(app.exec_())