how to make the same image size ?

clear all;
clc;
close all;
imds = imageDatastore('D:\matlab aml\dataset1','FileExtensions',{'.jpg'},'IncludeSubfolders',true,'LabelSource','foldernames');
imgs = readall(imds);
figure;
perm = randperm(200,20);%Display some of the images in the datastore.
for i = 1:20
    subplot(4,5,i);
    imshow(imds.Files{perm(i)});
end

img = readimage(imds,1); %Check the size of the first image in digitData. Each image is 201-by-173-by-3 pixels.
size(img)
%Specify Training and Validation Sets
labelCount = countEachLabel(imds) %Calculate the number of images in each category
[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7);%Divide the data into training and validation data sets. Use 70% of the images for training and 30% for validation.
%Define the convolutional neural network architecture.
%%%%%%%code for resizing
inputSize=[227 227 1];
imdsTrain=augmentedImageDatastore(inputSize, imdsTrain,'ColorPreprocessing','RGB');

imdsValidation=augmentedImageDatastore(inputSize, imdsValidation,'ColorPreprocessing','RGB');

layers = [
    imageInputLayer([227 227 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
    
    fullyConnectedLayer(2)
    softmaxLayer
    classificationLayer];
%Specify Training Options
options = trainingOptions('sgdm', ...
    'InitialLearnRate',0.01, ...
    'MaxEpochs',4, ...
    'Shuffle','every-epoch', ...
    'ValidationData',imdsValidation, ...
    'ValidationFrequency',30, ...
    'Verbose',false, ...
    'Plots','training-progress');
%Train Network Using Training Data
net = trainNetwork(imdsTrain,layers,options);
%Classify Validation Images and Compute Accuracy
YPred = classify(net,imdsValidation);
YValidation = imdsValidation.Labels;
accuracy = sum(YPred == YValidation)/numel(YValidation)

this is my code and error is

ans =

201 173 3

labelCount =

2×2 table

Label Count

_____ _____

no 91

yes 154

Unrecognized method, property, or field 'Labels' for class 'augmentedImageDatastore'.

Error in ccn (line 59)

YValidation = imdsValidation.Labels;

Answer 1

[Sanyam]

Hey @Mohamed Nasr,

The imdsValidation in your code is an augmentedImageDatastore, it is used to apply transformations to imageDatastore and does not have a 'labels' field.

Instead, you can proceed in this manner.

//refer to code

Here we have seperated the imageDatastore and augmentedImageDatastore and we can access the labels by

YValidation = imdsValidation.Labels; i.e referring the labels field of imageDataStore.

Also, when training the model and making predictions, please use the newly created augmentedImageDatastores (augdsTrain, augdsValidation).

Hope that helps! Thanks!!

[imdsTrain imdsValidation] = splitEachLabel(imds,0.7);

augdsTrain=augmentedImageDatastore(inputSize, imdsTrain,'ColorPreprocessing','RGB');
augdsValidation=augmentedImageDatastore(inputSize, imdsValidation,'ColorPreprocessing','RGB');