Invalid training data. X must be a 4-D array of images, an ImageDatastore, or a table.

clear;clc;close all
% Load the Image Dataset of Normal and Malignant WBC
imdsTrain = imageDatastore('D:\Project\DB1\train','IncludeSubfolders',true,'LabelSource','foldernames');
imdsTest = imageDatastore('D:\Project\DB1\test','IncludeSubfolders',true,'LabelSource','foldernames');
%Perform Cross-Validation using Hold-out method with a percentage split of 70% training and 30% testing
%[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7,'randomized');

%% 
%%
for i=1:numel(imdsTrain.Files)
a=[imdsTrain.Files(i)];
a = imread(char(a));
a1 = imresize(a,[299 299]);

end
for i=1:numel(imdsTest.Files)
a=[imdsTest.Files(i)];
a = imread(char(a));
a2 = imresize(a,[299 299]);

end
load('HW');
%%

%Select the Test images and save in Y_test
Y_test = imdsTest.Labels;
%%

% optimzation techniques selection and hyperparamter selection
options = trainingOptions('adam', ...
    'MiniBatchSize',16, ...
    'MaxEpochs',20, ...
    'InitialLearnRate',1e-4, ...
    'Shuffle','every-epoch', ...
    'ValidationData',{a2,Y_test}, ...
    'ValidationFrequency',3, ...
    'Verbose',false, ...
    'Plots','training-progress');
%%
%CNN model training
netTransfer = trainNetwork(a1,HW,options);

%%

% for i=1:numel(imdsValidation.Files)
%      a=[imdsValidation.Files(i)];
%      a = imread(char(a));
% %     featuresTest22 = activations(net,a,layer,'OutputAs','rows');
%     YPred(i) = classify(netTransfer,a);
%     imshow(a),title(char(YPred));
% i
% end
%%
% CNN Model validation
YPred = classify(netTransfer,a2);

%Performance evaluation of Deep Learning Trained Model
plotconfusion(Y_test,YPred)

Error using trainNetwork (line 165)

Invalid training data. X must be a 4-D array of images, an ImageDatastore, or a table.

Error in cnn (line 41)

netTransfer = trainNetwork(a1,HW,options);

[yanqi liu]

yes，sir，may be upload your mat file to debug

[sun rise]

My file

[sun rise]

A sample of the data set