resize images for cnn

Hi, I use below code to run cnn for images but my size of images are 875*656*3 and I want to imput images for cnn in size 64*64*1 how can resize images ?

clear all
clc
% digitDatasetPath = fullfile(matlabroot,'toolbox','nnet','nndemos', ...
%     'nndatasets','DigitDataset');
subject2path='D:\data\';
imds = imageDatastore(subject2path, ...
    'IncludeSubfolders',true,'LabelSource','foldernames');
 figure;
 perm = randperm(45,20);
 for i = 1:20
     subplot(4,5,i);
     imshow(imds.Files{perm(i)});
 end
labelCount1 = countEachLabel(imds)
 img = readimage(imds,1);
 size(img)
numTrainFiles1 = 25;
[imdsTrain1,imdsValidation] = splitEachLabel(imds,numTrainFiles1,'randomize');
labelCount2 = countEachLabel(imdsTrain1)
numTrainFiles2= 25;
[imdsTrain2,imdsTest] = splitEachLabel(imdsTrain1,numTrainFiles2,'randomize');
labelCount3 = countEachLabel(imdsTrain2)
layers = [
    imageInputLayer([64 64 1])
    
    convolution2dLayer([64, 2],6) % 1, 328, 6 % spatial conv
    batchNormalizationLayer
    reluLayer
    maxPooling2dLayer([1, 2],'Stride',[2, 1]) % 1, 164, 6
    
    convolution2dLayer([1, 11], 12) % 1, 154, 12 % temporal conv
    batchNormalizationLayer
    reluLayer    
    maxPooling2dLayer([1,2],'Stride',[2,1]) % 1, 77, 12
%     
    convolution2dLayer([1, 10], 24) % 1, 68, 24 
    batchNormalizationLayer
    reluLayer    
    maxPooling2dLayer([1,2],'Stride',[2,1]) % 1, 34, 24
    dropoutLayer
    fullyConnectedLayer(15)
    reluLayer
    fullyConnectedLayer(2)
    softmaxLayer
    classificationLayer];
options = trainingOptions('sgdm', ...
    'Momentum',0.9,...
    'InitialLearnRate', 0.001, ...
    'LearnRateSchedule', 'piecewise', ...  
    'LearnRateDropFactor', 0.1, ... 
    'LearnRateDropPeriod', 10, ...
    'MiniBatchSize', 100, ...
    'MaxEpochs', 20 , ...
    'Verbose', false, ...
    'VerboseFrequency', 10 , ...   
    'ValidationData', imdsValidation, ...
    'ValidationFrequency',10, ...
    'ValidationPatience', 15, ...
    'Shuffle', 'once', ...
    'ExecutionEnvironment', 'auto', ...
    'Plots','training-progress', ...
    'SequenceLength', 'longest');
% 
% 'Epsilon',  1.0000e-08, ...,
%     'SquaredGradientDecayFactor',0.99, ...
net = trainNetwork(imdsTrain2,layers,options);
YPred_val = classify(net,imdsValidation);
YValidation = imdsValidation.Labels;
accuracy_Val = sum(YPred_val == YValidation)/numel(YValidation)
YPred_test = classify(net,imdsTest);
YTest = imdsTest.Labels;
accuracy_Test = sum(YPred_test == YTest)/numel(YTest)
 CMstoc=crosstab(YPred_test, YTest), save CMstoc CMstoc

Answer 1

[Image Analyst]

Use imresize():

img = imresize(img, [64, 64]);

[Hamid Ebrahimi]

Thank you for answered , where should I use this code ,I used it after img = readimage(imds,1); but gave below error:

Error using trainNetwork (line 165)

The training images are of size 656x875x3 but the input layer expects images of size 64x64x1.

Error in test1 (line 67)

net = trainNetwork(imdsTrain2,layers,options);