基于机器学习算法的数字识别app开发

参考文献刘辰雨.基于卷积神经网络的手写数字识别研究与设计[D].成都理工大学,2018.陈鸿宇.基于KNN算法手写数字识别技术的研究与实现[J].信息通信,2020(12):28-32.杨栩.基于卷积神经网络的手写数字图像识别方法[J].绵阳师范学院学报,2020,39(02):35-39.DOI:10.16276/51-1670/g.2020.02.008.汪愿.基于神经网络的手写数字图像识别研究设计[J].电工材料,2021(06):46-48.DOI:10.16786/ki.1671-8887.eem.2021.06.012.HongmeiWang,PengzhongLiu.Imagerecognitionbasedonimprovedconvolutionaldeepbeliefnetworkmodel[J].MultimediaToolsandApplications,2020,80(2):SavitaAhlawat,AmitChoudhary,AnandNayyar,SaurabhSingh,ByungunYoon.ImprovedHandwrittenDigitRecognitionUsingConvolutionalNeuralNetworks(CNN)[J].Sensors,2020,20(12):AliSaqib,LiJianqiang,PeiYan,AslamMuhammadSaqlain,ShaukatZeeshan,AzeemMuhammad.AnEffectiveandImprovedCNN-ELMClassifierforHandwrittenDigitsRecognitionandClassification[J].Symmetry,2020,12(10):翟高粤.基于卷积神经网络的手写数字识别应用[J].甘肃科技纵横,2021,50(01):1-3.安丽娜,蒋锐鹏.基于卷积神经网络的手写数字识别研究[J].无线互联科技,2019,16(20):31-32.余圣新.基于卷积神经网络的手写体实验数字识别[D].贵州大学,2020.DOI:10.27047/ki.ggudu.2020.000967.李晶晶.基于神经网络的图像识别方法研究[D].华北电力大学,2018.附录：相关代码代码SEQ代码\*ARABIC1加载数据类exportclassMnistData{

constructor(){

this.shuffledTrainIndex=0;

this.shuffledTestIndex=0;

}

//*对Minst数据的异步加载

asyncload(){

constimg=newImage();

constcanvas=document.createElement('canvas');

constctx=canvas.getContext('2d');

constimgRequest=newPromise((resolve,reject)=>{

img.crossOrigin='';

img.onload=()=>{

img.width=img.naturalWidth;

img.height=img.naturalHeight;

constdatasetBytesBuffer=

newArrayBuffer(NUM_DATASET_ELEMENTS*IMAGE_SIZE*4);

constchunkSize=5000;

canvas.width=img.width;

canvas.height=chunkSize;

for(leti=0;i<NUM_DATASET_ELEMENTS/chunkSize;i++){

constdatasetBytesView=newFloat32Array(

datasetBytesBuffer,i*IMAGE_SIZE*chunkSize*4,

IMAGE_SIZE*chunkSize);

ctx.drawImage(

img,0,i*chunkSize,img.width,chunkSize,0,0,img.width,

chunkSize);

constimageData=ctx.getImageData(0,0,canvas.width,canvas.height);

for(letj=0;j<imageData.data.length/4;j++){

datasetBytesView[j]=imageData.data[j*4]/255;

}

}

this.datasetImages=newFloat32Array(datasetBytesBuffer);

resolve();

};

img.src=MNIST_IMAGES_SPRITE_PATH;

});

constlabelsRequest=fetch(MNIST_LABELS_PATH)

const[imgResponse,labelsResponse]=

awaitPromise.all([imgRequest,labelsRequest]);

this.datasetLabels=newUint8Array(awaitlabelsResponse.arrayBuffer());

//当我们选择一个随机数据集元素进行训练/验证时，在训练/测试集中创建随机索引。

this.trainIndices=tf.util.createShuffledIndices(NUM_TRAIN_ELEMENTS);

this.testIndices=tf.util.createShuffledIndices(NUM_TEST_ELEMENTS);

//将图像和标签切片为训练集和测试集。

this.trainImages=

this.datasetImages.slice(0,IMAGE_SIZE*NUM_TRAIN_ELEMENTS);

this.testImages=this.datasetImages.slice(IMAGE_SIZE*NUM_TRAIN_ELEMENTS);

this.trainLabels=

this.datasetLabels.slice(0,NUM_CLASSES*NUM_TRAIN_ELEMENTS);

this.testLabels=

this.datasetLabels.slice(NUM_CLASSES*NUM_TRAIN_ELEMENTS);

}

//*从训练集返回随机批次的图片及其标签。

nextTrainBatch(batchSize){

returnthis.nextBatch(

batchSize,[this.trainImages,this.trainLabels],()=>{

this.shuffledTrainIndex=

(this.shuffledTrainIndex+1)%this.trainIndices.length;

returnthis.trainIndices[this.shuffledTrainIndex];

});

}

//*从测试集中返回一批图片及其标签

nextTestBatch(batchSize){

returnthis.nextBatch(batchSize,[this.testImages,this.testLabels],()=>{

this.shuffledTestIndex=

(this.shuffledTestIndex+1)%this.testIndices.length;

returnthis.testIndices[this.shuffledTestIndex];

});

}

nextBatch(batchSize,data,index){

constbatchImagesArray=newFloat32Array(batchSize*IMAGE_SIZE);

constbatchLabelsArray=newUint8Array(batchSize*NUM_CLASSES);

for(leti=0;i<batchSize;i++){

constidx=index();

constimage=

data[0].slice(idx*IMAGE_SIZE,idx*IMAGE_SIZE+IMAGE_SIZE);

batchImagesArray.set(image,i*IMAGE_SIZE);

constlabel=

data[1].slice(idx*NUM_CLASSES,idx*NUM_CLASSES+NUM_CLASSES);

batchLabelsArray.set(label,i*NUM_CLASSES);

}

constxs=tf.tensor2d(batchImagesArray,[batchSize,IMAGE_SIZE]);

constlabels=tf.tensor2d(batchLabelsArray,[batchSize,NUM_CLASSES]);

return{xs,labels};

}}代码2定义网络模型函数exportfunctiongetModel(){

constmodel=tf.sequential();

constIMAGE_WIDTH=28;

constIMAGE_HEIGHT=28;

constIMAGE_CHANNELS=1;

model.add(tf.layers.conv2d({

inputShape:[IMAGE_WIDTH,IMAGE_HEIGHT,IMAGE_CHANNELS],//28*28*1的图片

kernelSize:5,//卷积核大小5*5

filters:12,//卷积核数量

strides:1,//步长

padding:'valid',//边缘填充

activation:'relu',//激活函数

kernelInitializer:'heNormal'//随机初始化模型权重

}));

model.add(tf.layers.maxPooling2d({poolSize:[2,2],strides:[2,2]}));

model.add(tf.layers.conv2d({

kernelSize:5,

filters:32,

strides:1,

padding:'valid',//边缘填充

activation:'relu',

kernelInitializer:'heNormal'

}));

model.add(tf.layers.maxPooling2d({poolSize:[2,2],strides:[2,2]}));

model.add(tf.layers.flatten());

model.add(tf.layers.dropout(0.25))

model.add(tf.layers.dense({

units:256,//输出空间的维数

kernelInitializer:'heNormal',//权重矩阵的初始值设定项

activation:'relu'//激活函数

}));

model.add(tf.layers.dropout(0.25))

model.add(tf.layers.dense({

units:128,//输出空间的维数

kernelInitializer:'heNormal',//权重矩阵的初始值设定项

activation:'relu'//激活函数

}));

model.add(tf.layers.dropout(0.25))

constNUM_OUTPUT_CLASSES=10;

//全连接层

model.add(tf.layers.dense({

units:NUM_OUTPUT_CLASSES,//输出空间的维数

kernelInitializer:'varianceScaling',//权重矩阵的初始值设定项

activation:'softmax'//激活函数

}));

constoptimizer=tf.train.adam();

//constoptimizer=tf.train.sgd(0.01);

pile({

optimizer:optimizer,//Adam方式更新参数，优化器

loss:'categoricalCrossentropy',//交叉熵损失函数

metrics:['accuracy'],

});

console.log('getmodel完毕');

returnmodel;}代码3对模型训练函数exportasyncfunctiontrain(model,data){

constBATCH_SIZE=512;

constTRAIN_DATA_SIZE=5500;

constTEST_DATA_SIZE=1000;

const[trainXs,trainYs]=tf.tidy(()=>{

constd=data.nextTrainBatch(TRAIN_DATA_SIZE);

return[

d.xs.reshape([TRAIN_DATA_SIZE,28,28,1]),

d.labels

];

});

const[testXs,testYs]=tf.tidy(()=>{

constd=data.nextTestBatch(TEST_DATA_SIZE);

return[

d.xs.reshape([TEST_DATA_SIZE,28,28,1]),

d.labels

];

});

consth=awaitmodel.fit(trai