Evaluating Multi-Class Image Classification

Here I written a code for evaluating multi class image classification. Kindly correct if I'm wrong

C=confusionmat(Yactual,YPred)

[row,col]= size(C);

n_class=row;

for i=1:n_class

TP(i)=C(i,i);

FN(i)=sum(C(i,:))-C(i,i);

FP(i)=sum(C(:,i))-C(i,i);

TN(i)=sum(C(:))-TP(i)-FP(i)-FN(i);

end

TP1=sum(TP)

FP1=sum(FP)

FN1=sum(FN)

TN1=sum(TN)

Accuracy=(TP1+TN1)/(TP1+TN1+FP1+FN1)

Error=1-Acc

Recall=TP1/(TP1+FN1)

Precision=TP1/(TP1+FP1)

Specificity = TN1/(TN1+FP1)

Sensitivity = TP1/(TP1+FN1)

FPR=1-Specificity

beta=1;

F1_score=( (1+(beta^2))*(Recall.*Precision)) ./ ( (beta^2)*(Precision+Recall))

I want to know above code is correct or not?

and also when I'm using accuracy = mean(YPred == Yactual) it gives precision value. why?

kindly help me in this regard. Thank you.