基于卷积神经网络实现手写数字识别-编程知识

基于卷积神经网络实现手写数字识别

基于卷积神经网络实现手写数字识别。具体过程如下：

（1）定义ConvNet结构类及其前向传播方式

（2）设置超参数以及导入相关的包。

（3）定义训练网络函数和绘图函数，并在main函数中完成调用过程

程序

import os 
import numpy as np 
#from sklearn.datasets import fetch_openml # 引入openml数据源
from matplotlib import pyplot as plt # 引入绘图工具
import torch
from torchvision.datasets import mnist
#from mnist_models import AlexNet, ConvNet
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from torch.autograd import VariableBASE_PATH = os.path.dirname(__file__)# 设置模型超参数
EPOCHS = 50
SAVE_PATH = './models''''
# 载入MNIST数据集并显示部分样本
def load_mnist():# 从openml源载入MNIST数据集mnist = fetch_openml('mnist_784', version=1, data_home=os.path.join(BASE_PATH, './dataset'))X, y = mnist['data'], mnist['target']#X = mnist['data']#.astype(np.float32)#y = mnist['target']#.astype(np.int32)print('MNIST数据集大小：{}'.format(X.shape))# 显示其中25张样本图片for i in range(25):#print(i)digit = X.iloc[i * 2500]# 将图片恢复到28*28大小digit_image = digit.values.reshape(28, 28)# 绘制图片plt.subplot(5, 5, i + 1)# 隐藏坐标轴plt.axis('off')# 按灰度图绘制图片plt.imshow(digit_image, cmap='gray')# 显示图片plt.show()return X, y
'''# 定义卷积网络结构
class ConvNet(torch.nn.Module):def __init__(self):super(ConvNet, self).__init__()self.conv1 = torch.nn.Sequential(torch.nn.Conv2d(1, 10, 5, 1, 1),torch.nn.MaxPool2d(2),torch.nn.ReLU(),torch.nn.BatchNorm2d(10))self.conv2 = torch.nn.Sequential(torch.nn.Conv2d(10, 20, 5, 1, 1),torch.nn.MaxPool2d(2),torch.nn.ReLU(),torch.nn.BatchNorm2d(20))self.fc1 = torch.nn.Sequential(torch.nn.Linear(500, 60),torch.nn.Dropout(0.5),torch.nn.ReLU())self.fc2 = torch.nn.Sequential(torch.nn.Linear(60, 20),torch.nn.Dropout(0.5),torch.nn.ReLU())self.fc3 = torch.nn.Linear(20, 10)# 定义网络前向传播方式def forward(self, x):x = self.conv1(x)x = self.conv2(x)x = x.view(-1, 500)x = self.fc1(x)x = self.fc2(x)x = self.fc3(x)return x# 定义AlexNet结构
class AlexNet(torch.nn.Module):def __init__(self, num_classes=10):super(AlexNet, self).__init__()self.features = torch.nn.Sequential(torch.nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2),torch.nn.ReLU(inplace=True),torch.nn.MaxPool2d(kernel_size=3, stride=1),torch.nn.Conv2d(64, 192, kernel_size=3, padding=2),torch.nn.ReLU(inplace=True),torch.nn.MaxPool2d(kernel_size=3, stride=2),torch.nn.Conv2d(192, 384, kernel_size=3, padding=1),torch.nn.ReLU(inplace=True),torch.nn.Conv2d(384, 256, kernel_size=3, padding=1),torch.nn.ReLU(inplace=True),torch.nn.Conv2d(256, 256, kernel_size=3, padding=1),torch.nn.ReLU(inplace=True),torch.nn.MaxPool2d(kernel_size=3, stride=2))self.classifier = torch.nn.Sequential(torch.nn.Dropout(),torch.nn.Linear(256 * 6 * 6, 4096),torch.nn.ReLU(inplace=True),torch.nn.Dropout(),torch.nn.Linear(4096, 4096),torch.nn.ReLU(inplace=True),torch.nn.Linear(4096, num_classes))# 定义AlexNet前向传播过程def forward(self, x):x = self.features(x)x = x.view(x.size(0), 256 * 6 * 6)x = self.classifier(x)return x    # 训练网络函数
def train_net(net, train_data, test_data):losses = []acces = []# 测试集上Loss变化情况eval_losses = []eval_acces = []# 损失函数设置为交叉熵函数criterion = torch.nn.CrossEntropyLoss()# 优化方法选用SGD，初始学习率为1e-2optimizer = torch.optim.SGD(net.parameters(), 1e-2)for e in range(EPOCHS):train_loss = 0train_acc = 0# 将网络设置为训练模型net.train()for image, label in train_data:image = Variable(image)label = Variable(label)# 前向传播out = net(image)loss = criterion(out, label)# 反向传播optimizer.zero_grad()loss.backward()optimizer.step()# 记录误差train_loss += loss.data# 计算分类的准确率_, pred = out.max(1)num_correct = (np.array(pred, dtype=np.int32) == np.array(label, dtype=np.int32)).sum()acc = num_correct / image.shape[0]train_acc += acctrain_loss_rate = train_loss / len(train_data)train_acc_rate = train_acc / len(train_data)losses.append(train_loss_rate)acces.append(train_acc_rate)# 在测试集上检验效果eval_loss = 0eval_acc = 0net.eval() # 将模型改为预测模式for image, label in test_data:image = Variable(image)label = Variable(label)out = net(image)loss = criterion(out, label)# 记录误差eval_loss += loss.data# 记录准确率_, pred = out.max(1)num_correct = (np.array(pred, dtype=np.int32) == np.array(label, dtype=np.int32)).sum()acc = num_correct / image.shape[0]eval_acc += acceval_loss_rate = eval_loss / len(test_data)eval_acc_rate = eval_acc / len(test_data)eval_losses.append(eval_loss_rate)eval_acces.append(eval_acc_rate)print('epoch:{}, Train Loss: {:.6f}, Train Acc:{:.6f}, Eval Loss:{:.6f}, Eval Acc:{:.6f}'.format(e, train_loss_rate, train_acc_rate, eval_loss_rate, eval_acc_rate))torch.save(net.state_dict(), os.path.join(BASE_PATH, SAVE_PATH, 'Alex_model_epoch' + str(e) + '.pkl'))return eval_losses, eval_accesdef draw_result(eval_losses, eval_acces):x = range(1, EPOCHS + 1)fig, left_axis = plt.subplots()p1, = left_axis.plot(x, eval_losses, 'ro-')right_axis = left_axis.twinx()p2, = right_axis.plot(x, eval_acces, 'bo-')plt.xticks(x, rotation=0)# 设置左坐标轴以及右坐标轴的范围、精度left_axis.set_ylim(0, 0.5)left_axis.set_yticks(np.arange(0, 0.5, 0.1))right_axis.set_ylim(0.9, 1.01)right_axis.set_yticks(np.arange(0.9, 1.01, 0.02))# 设置坐标及标题的大小、颜色left_axis.set_xlabel('Labels')left_axis.set_ylabel('Loss', color='r')left_axis.tick_params(axis='y', colors='r')right_axis.set_ylabel('Accuracy', color='b')right_axis.tick_params(axis='y', colors='b')plt.show()if __name__ == '__main__':#x, y = load_mnist()print("基于卷积神经网络实现手写数字识别")train_set = mnist.MNIST('./data', train=True, download=True, transform=transforms.ToTensor())//需要转化成tensor数据格式test_set = mnist.MNIST('./data', train=False, download=True, transform=transforms.ToTensor())train_data = DataLoader(train_set, batch_size=64, shuffle=True)test_data = DataLoader(test_set, batch_size=64, shuffle=False)a, a_label = next(iter(train_data))#net = AlexNet()net = ConvNet()eval_losses, eval_acces = train_net(net, train_data, test_data)draw_result(eval_losses, eval_acces)