The value of 'ValidationData' is invalid. Validation data must be a table, a datastore, or a cell array with input data and responses.

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));
a = imresize(a,[299 299]);

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

end
a2=a;
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, ...
    '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 trainingOptions (line 269)

The value of 'ValidationData' is invalid. Validation data must be a table, a datastore, or a cell array

with input data and responses.

Error in cnn (line 32)

options = trainingOptions('adam', ...

>>

[源]

Dear sun rise,

Would you solve this problem yet? If possible, could you tell me how to solve it.I have the same problem as you.

Thanks for your help

Answer 1

[Walter Roberson]

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

In those two loops, you process each of a number of files, but you throw away the result of the processing as soon as you start the next loop iteration.

You should consider either using a custom read function for your datastore, or else use a transform store; https://www.mathworks.com/help/matlab/ref/matlab.io.datastore.transform.html

    'ValidationData',a2, ...

Your a2 is the result of transforming the very last of your test files only

Use a transform store.

[sun rise]

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));
targetSize = [299,299];
imdsReSz = transform(imdsTrain,@(x) imresize(x,targetSize));
%imgReSz1 = read(imdsReSz);

%a = imread(char(a));
%a = imresize(a,[299 299]);

end

for i=1:numel(imdsTest.Files)
a=(imdsTest.Files(i));
targetSize1 = [299,299];
imdsReSz1 = transform(imdsTest,@(x) imresize(x,targetSize1));
%a = imread(char(a));
%a = imresize(a,[299 299]);

end

load('HW');
%%

%Select the Test images and save in Y_test
Y_test = imdsReSz1.UnderlyingDatastore.Labels;
%%

% optimzation techniques selection and hyperparamter selection
options = trainingOptions('adam', ...
    'MiniBatchSize',16, ...
    'MaxEpochs',20, ...
    'InitialLearnRate',1e-4, ...
    'Shuffle','every-epoch', ...
    'ValidationData',imdsReSz1, ...
    'ValidationFrequency',3, ...
    'Verbose',false, ...
    'Plots','training-progress');
%%
%CNN model training
netTransfer = trainNetwork(imdsReSz,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,imdsReSz1);

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

Error using trainingOptions (line 269) The value of 'ValidationData' is invalid. The datastore used for 'ValidationData' must return a 2-column table or an M-by-2 cell array.

[sun rise]

Dear Walter Roberson,

Thanks for your advice

Although I tried to use this method, it still throws following error..

Error using trainingOptions (line 269) The value of 'ValidationData' is invalid. The datastore used for 'ValidationData' must return a 2-column table or an M-by-2 cell array.

In addition, my images are grayscale image. In this case, how can i modify my code to apply into inception_v3?

However, I'm very new to programming and not sure how to apply a method to resizing and RGB to grayscale into my code...

Thanks for you in advance!