这个是一个在matlab中实现卷积神经网络的代码，但是还有很多不足的地方

Question

%digitDatasetpath=fullfile('D:','cnn','DigitDataset');
%%digitDatasetPath = fullfile(matlabroot,'toolbox','nnet','nndemos', ...
'nndatasets','DigitDataset');

imds=imageDatastore(digitdatasetpath,"IncludeSubfolders",true,"LabelSource","foldernames")

numTrainFiles = 750;

[imdsTrain,imdsValidation] = splitEachLabel(imds,numTrainFiles,'randomize');

inputSize=[28 28 1];
numclass=10;

layers = [
imageInputLayer([28 28 1],"Name","imageinput")
convolution2dLayer(5,50,"Name","conv","Padding","same")
batchNormalizationLayer("Name","batchnorm")
reluLayer("Name","relu")
fullyConnectedLayer(10,"Name","fc")
softmaxLayer("Name","softmax")
classificationLayer("Name","classoutput")];

options = trainingOptions('sgdm', ...
'MaxEpochs',4, ...
'ValidationData',imdsValidation, ...
'ValidationFrequency',30, ...
'Verbose',false, ...
'Plots','training-progress');

net=trainNetwork(imds,layers,options)

YPred=classify(net,imdsValidation);
YValidation=imdsValidation.Labels;
accuracy=mean(YPred==YValidation)
有没有人能解决一下这个代码中的问题：
一.前两行数据引入中第一行为何不能引用，第二行的含义是什么？
二.在我选择了第二行作为引入数据的方法后为何要框架中的卷积核数量很大准确率才会降到百分之98以下
三.options括号里的含义问题，以及如何打出来