The training images are of size 224×224×3 but the input layer expects images of size 224×224×1.

Question

Muhammad 2023-3-29

0
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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1937379-the-training-images-are-of-size-224x224x3-but-the-input-layer-expects-images-of-size-224x224x1

评论： Matt J 2023-4-4

I want to train a D.L network (googlenet) with images with "224×224×1" of size (they are panchromatic). I follow the steps described in the "mathworks" as described bellow but I get this error: " The training images are of size 224×224×3 but the input layer expects images of size 224×224×1." So what I should do in order to train my network ? Many thanks.

Below is the code.

clear all
close all
%faceDatasetPath = fullfile('c:','FaceDataset');
imds = imageDatastore("D:\test", ...
    'IncludeSubfolders',true,'LabelSource','foldernames');
%display some samples from  the dataset
figure;
perm = randperm(213,20);
for i = 1:20
    subplot(4,5,i);
    imshow(imds.Files{perm(i)});
    title(imds.Labels(perm(i)));
end
numTrainFiles = 6;
[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7,'randomize');
[no_of_TrainImages ~]=size(imdsTrain.Files);
[no_of_TestImages ~]=size(imdsValidation.Files);
layers = [
    
imageInputLayer([224 224 1])
    
    convolution2dLayer(3,8,'Padding','same')
    batchNormalizationLayer
    reluLayer
    maxPooling2dLayer(2,'Stride',2)
    
    convolution2dLayer(3,16,'Padding','same')
    batchNormalizationLayer
    reluLayer
    maxPooling2dLayer(2,'Stride',2)
    
    convolution2dLayer(3,32,'Padding','same')
    batchNormalizationLayer
    reluLayer
    maxPooling2dLayer(2,'Stride',2)
    
    convolution2dLayer(3,64,'Padding','same')
    batchNormalizationLayer
    reluLayer
    maxPooling2dLayer(2,'Stride',2)
    
    fullyConnectedLayer(7)
    softmaxLayer
    classificationLayer];
options = trainingOptions('sgdm', ...
    'InitialLearnRate',0.01, ...
    'MaxEpochs',10, ...
    'Shuffle','every-epoch', ...
    'ValidationData',imdsValidation, ...
    'ValidationFrequency',10, ...
    'Verbose',false, ...
    'Plots','training-progress');
net = trainNetwork(imdsTrain,layers,options);
YPred = classify(net,imdsValidation);
YValidation = imdsValidation.Labels;
accuracy = sum(YPred == YValidation)/numel(YValidation)

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Answer 1

Matt J 2023-3-29

1
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1937379-the-training-images-are-of-size-224x224x3-but-the-input-layer-expects-images-of-size-224x224x1#answer_1203819

在 MATLAB Online 中打开

imageInputLayer([224 224 3])

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Muhammad 2023-3-30

编辑：Walter Roberson 2023-3-30

在 MATLAB Online 中打开

Can you please modify my code? I spent lots of time but couldn't do that. Please modify the below code.

clear all
close all
%faceDatasetPath = fullfile('c:','FaceDataset');
imds = imageDatastore("D:\test", ...
    'IncludeSubfolders',true,'LabelSource','foldernames');
%display some samples from  the dataset
figure;
perm = randperm(213,20);
for i = 1:20
    subplot(4,5,i);
    imshow(imds.Files{perm(i)});
    title(imds.Labels(perm(i)));
end
numTrainFiles = 6;
[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7,'randomize');
[no_of_TrainImages ~]=size(imdsTrain.Files);
[no_of_TestImages ~]=size(imdsValidation.Files);
layers = [
    
imageInputLayer([224 224 1])
convolution2dLayer(3,8,'Padding','same')
batchNormalizationLayer
reluLayer
maxPooling2dLayer(2,'Stride',2)
convolution2dLayer(3,16,'Padding','same')
batchNormalizationLayer
reluLayer
maxPooling2dLayer(2,'Stride',2)
convolution2dLayer(3,32,'Padding','same')
batchNormalizationLayer
reluLayer
maxPooling2dLayer(2,'Stride',2)
convolution2dLayer(3,64,'Padding','same')
batchNormalizationLayer
reluLayer
maxPooling2dLayer(2,'Stride',2)
fullyConnectedLayer(7)
softmaxLayer
classificationLayer];
options = trainingOptions('sgdm', ...
    'InitialLearnRate',0.01, ...
    'MaxEpochs',10, ...
    'Shuffle','every-epoch', ...
    'ValidationData',imdsValidation, ...
    'ValidationFrequency',10, ...
    'Verbose',false, ...
    'Plots','training-progress');
net = trainNetwork(imdsTrain,layers,options);
YPred = classify(net,imdsValidation);
YValidation = imdsValidation.Labels;
accuracy = sum(YPred == YValidation)/numel(YValidation)

Muhammad 2023-4-4

在 MATLAB Online 中打开

I run this code now but getting this error

Error: File: testfetalMri.m Line: 16 Column: 1

Function definitions in a script must appear at the end of the file.

Move all statements after the "customreader" function definition to before the first local function definition.

clear all
close all
%faceDatasetPath = fullfile('c:','FaceDataset');
%imds = imageDatastore("D:\test", ...
   % 'IncludeSubfolders',true,'LabelSource','foldernames');
imds = imageDatastore("D:\test", ...
    'IncludeSubfolders',true,'LabelSource','foldernames','ReadFcn',@customreader);
function img=customreader(filename)
 img=imread(filename);
 img=img(:,:,1);
 
end
%display some samples from  the dataset
figure;
perm = randperm(213,20);
for i = 1:20
    subplot(4,5,i);
    imshow(imds.Files{perm(i)});
    title(imds.Labels(perm(i)));
end
numTrainFiles = 6;
[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7,'randomize');
[no_of_TrainImages ~]=size(imdsTrain.Files);
[no_of_TestImages ~]=size(imdsValidation.Files);
layers = [
    
imageInputLayer([224 224 1])
    
    convolution2dLayer(3,8,'Padding','same')
    batchNormalizationLayer
    reluLayer
    maxPooling2dLayer(2,'Stride',2)
    
    convolution2dLayer(3,16,'Padding','same')
    batchNormalizationLayer
    reluLayer
    maxPooling2dLayer(2,'Stride',2)
    
    convolution2dLayer(3,32,'Padding','same')
    batchNormalizationLayer
    reluLayer
    maxPooling2dLayer(2,'Stride',2)
    
    convolution2dLayer(3,64,'Padding','same')
    batchNormalizationLayer
    reluLayer
    maxPooling2dLayer(2,'Stride',2)
    
    fullyConnectedLayer(7)
    softmaxLayer
    classificationLayer];
options = trainingOptions('sgdm', ...
    'InitialLearnRate',0.01, ...
    'MaxEpochs',10, ...
    'Shuffle','every-epoch', ...
    'ValidationData',imdsValidation, ...
    'ValidationFrequency',10, ...
    'Verbose',false, ...
    'Plots','training-progress');
net = trainNetwork(imdsTrain,layers,options);
YPred = classify(net,imdsValidation);
YValidation = imdsValidation.Labels;
accuracy = sum(YPred == YValidation)/numel(YValidation)

Matt J 2023-4-4

Move customreader to the end of the file as the error message says.

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Answer 2

Image Analyst 2023-3-29

1
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1937379-the-training-images-are-of-size-224x224x3-but-the-input-layer-expects-images-of-size-224x224x1#answer_1203884

You should write a little script to convert all of your images to gray scale and save them in a different folder so you don't overwrite your originals. Or else change your input layer to accept color images, as Matt showed you.

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Answer 3

Walter Roberson 2023-3-30

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1937379-the-training-images-are-of-size-224x224x3-but-the-input-layer-expects-images-of-size-224x224x1#answer_1205174

https://www.mathworks.com/help/deeplearning/ref/augmentedimagedatastore.html

Use an augmentedImageDataStore with size 224 224 1 and 'ColorPreprocessing', 'rgb2gray' . This will automatically resize any image to the right size and will convert to grayscale if needed.