HOG feature Extraction with CNN for Handwritten Recognition

Hi im trying to combine HOG feature extraction with CNN and below is the script that im working on right now. But the script gave me an error saying:

Error using trainNetwork (line 183)

Number of observations in X and Y disagree.

Error in HOGfeature (line 62)

net = trainNetwork(trainingfeatures,trainingLabels,layers,options); %Network Training

Can somone help me with this btw the dataset that im using for this project are MNIST.

close all
clear
clc

path1='D:\CNN test\Imagedb\HOGtrainset';
path2='D:\CNN test\Imagedb\HOGtestset';

traindb = imageDatastore(path1,'IncludeSubfolders' ,true,'LabelSource','foldernames');
testdb = imageDatastore(path2,'IncludeSubfolders' ,true,'LabelSource','foldernames');

%training
img = readimage(traindb,1);
CS=[8,8]; %cellsize
[hogfv,hogvis] = extractHOGFeatures(img,'CellSize',CS);
hogfeaturesize = length(hogfv);
totaltrainimages = numel(traindb.Files);
trainingfeatures = zeros(totaltrainimages, hogfeaturesize,'single');

for i = 1:totaltrainimages
    img = readimage(traindb,i);
    trainingfeatures(i, :) = extractHOGFeatures(img,'CellSize',CS);
end

trainingLabels = traindb.Labels;

%% Building CNN

layers=[
         imageInputLayer([28 28 1],'Name','Input')
         
         convolution2dLayer(3,8,'Padding','same','Name','Conv_1')
         batchNormalizationLayer('Name','BN_1')
         reluLayer('Name','Relu_1')
         maxPooling2dLayer(2,'Stride',2,'Name','MaxPool_1')
         
         convolution2dLayer(3,16,'Padding','same','Name','Conv_2')
         batchNormalizationLayer('Name','BN_2')
         reluLayer('Name','Relu_2')
         maxPooling2dLayer(2,'Stride',2,'Name','MaxPool_2')
         
         convolution2dLayer(3,32,'Padding','same','Name','Conv_3')
         batchNormalizationLayer('Name','BN_3')
         reluLayer('Name','Relu_3')
         maxPooling2dLayer(2,'Stride',2,'Name','Maxpool_3')
         
         convolution2dLayer(3,64,'Padding','same','Name','Conv_4')
         batchNormalizationLayer('Name','BN_4')
         reluLayer('Name','Relu_4')
         
         fullyConnectedLayer(10,'Name','FC')
         softmaxLayer('Name','Softmax');
         classificationLayer('Name','Output Classification');
         
       ];

   %Igraph = layerGraph(layers);
   %plot(Igraph); %Plotting Network Structure
   
   %-----------------------------------Training Options-----------------
   options = trainingOptions('sgdm','InitialLearnRate',0.01,'MaxEpochs',4,'Shuffle','every-epoch','ValidationData',testdb,'ValidationFrequency',30,'Verbose',false,'Plots','training-progress');
   
   net = trainNetwork(trainingfeatures,trainingLabels,layers,options); %Network Training
   
   Ypred = classify(net,testdb); %Recognizing Digits
   YValidation = testdb.Labels; %Getting Labels
   
   accuracy = sum(Ypred == YValidation)/numel(YValidation); %Finding %age accuracy