MATLAB构建神经网络识别Mnist手写数字数据集（附代码）

目录

Mnist数据集介绍

构建多层感知机模型

代码：

模型结构：

运行结果：

构建卷积神经网络：

代码：

模型结构：

运行结果：

希望能够对你有所帮助！如有错误之处，敬请指正！

Mnist数据集介绍

Mnist数据集中包含60000张共10类手写数字的图片，每张图片的大小为28*28。部分手写数字图片如下所示：

选用其中50000张图片作为训练集，10000张图片作为测试集。

数据集链接：https://pan.quark.cn/s/03a502977f21

构建多层感知机模型

代码：

clear
clc
%导入数据集
matlabroot = './mnist_train_jpg_60000';
digitDatasetPath = fullfile(matlabroot);
imds = imageDatastore(digitDatasetPath,'IncludeSubfolders',true,'LabelSource','foldernames');
%分割数据集
numTrainFiles = 5000;
[imdsTrain,imdsValidation] = splitEachLabel(imds,numTrainFiles,'randomize');
%创建模型
layers = [
    imageInputLayer([28 28 1],"Name","imageinput")
    fullyConnectedLayer(128,'Name',"fc")
    batchNormalizationLayer("Name","batchnorm")
    reluLayer("Name","relu")
    fullyConnectedLayer(64,'Name',"fc_1")
    batchNormalizationLayer("Name","batchnorm_1")
    reluLayer("Name","relu_1")
    fullyConnectedLayer(32,'Name',"fc_2")
    batchNormalizationLayer("Name","batchnorm_2")
    reluLayer("Name","relu_2")
    fullyConnectedLayer(10,"Name","fc_3")
    softmaxLayer("Name","softmax")
    classificationLayer("Name","classoutput")];
%训练参数
  options = trainingOptions('sgdm', ...
    'InitialLearnRate',0.01, ...
    'MaxEpochs',4, ...
    'Shuffle','every-epoch', ...
    'ValidationData',imdsValidation, ...
    'ValidationFrequency',30, ...
    'Verbose',false, ...
    'Plots','training-progress'); 
%进行训练，并输出最终结果
   net = trainNetwork(imdsTrain,layers,options); 
   YPred = classify(net,imdsValidation);
   YValidation = imdsValidation.Labels;
   accuracy = sum(YPred == YValidation)/numel(YValidation);
   disp(accuracy);

模型结构：

生成的模型输入为28*28*1大小的图片，有十个输出（与十种数字类型相对应）。模型包含输入层（imageInputLayer）、全连接层（fullyConnectedLayer）、批量归一化层（batchNormalizationLayer ）、激活函数（reluLayer），softmaxLayer层及分类层（classificationLayer）。各层的详细信息与连接顺序图如下所示：

运行结果：

构建卷积神经网络：

代码：

clear
clc
matlabroot = './mnist_train_jpg_60000';
digitDatasetPath = fullfile(matlabroot);
imds = imageDatastore(digitDatasetPath,'IncludeSubfolders',true,'LabelSource','foldernames');
numTrainFiles = 5000;
[imdsTrain,imdsValidation] = splitEachLabel(imds,numTrainFiles,'randomize');
layers = [
    imageInputLayer([28 28 1],"Name","imageinput")
    convolution2dLayer([3 3],8,"Name","conv","Padding","same")
    batchNormalizationLayer("Name","batchnorm")
    reluLayer("Name","relu")
    maxPooling2dLayer([5 5],"Name","maxpool","Padding","same")
    convolution2dLayer([3 3],16,"Name","conv_1","Padding","same")
    batchNormalizationLayer("Name","batchnorm_1")
    reluLayer("Name","relu_1")
    fullyConnectedLayer(10,"Name","fc_3")
    softmaxLayer("Name","softmax")
    classificationLayer("Name","classoutput")];
  options = trainingOptions('sgdm', ...
    'InitialLearnRate',0.01, ...
    'MaxEpochs',4, ...
    'Shuffle','every-epoch', ...
    'ValidationData',imdsValidation, ...
    'ValidationFrequency',30, ...
    'Verbose',false, ...
    'Plots','training-progress'); 
   net = trainNetwork(imdsTrain,layers,options); 
   YPred = classify(net,imdsValidation);
   YValidation = imdsValidation.Labels;
   accuracy = sum(YPred == YValidation)/numel(YValidation);
   disp(accuracy);

模型结构：

运行结果：

希望能够对你有所帮助！如有错误之处，敬请指正！