The number of observations must be a positive integer greater than one.

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sun rise
sun rise on 28 Nov 2021
Commented: Walter Roberson on 2 Dec 2021
clear;clc;close all
% Load the Image Dataset of Normal and Malignant WBC
%imds = imageDatastore('D:\Project\DB1\train','IncludeSubfolders',true,'LabelSource','foldernames');
%img = readimage(imds,1);
%size(img)
%%labelCount = countEachLabel(imds);
%Perform Cross-Validation using Hold-out method with a percentage split of 70% training and 30% testing
%[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7,'randomized');
%%
net = inceptionv3;
inputSizeNet = net.Layers(1).InputSize;
%Convert the network to a dlnetwork object for feature extraction and remove the last four layers, leaving the "mixed10" layer as the last layer.
lgraph = layerGraph(net);
lgraph = removeLayers(lgraph,["avg_pool" "predictions" "predictions_softmax" "ClassificationLayer_predictions"]);
%View the input layer of the network. The Inception-v3 network uses symmetric-rescale normalization with a minimum value of 0 and a maximum value of 255.
lgraph.Layers(1)
%net1 = net;
%%
%Custom training does not support this normalization, so you must disable normalization in the network and perform the normalization in the custom training loop instead. Save the minimum and maximum values as doubles in variables named inputMin and inputMax, respectively, and replace the input layer with an image input layer without normalization.
%inputMin = double(lgraph.Layers(1).Min);
%inputMax = double(lgraph.Layers(1).Max);
layer = imageInputLayer(inputSizeNet,'Normalization','none','Name','input');
lgraph = replaceLayer(lgraph,'input_1',layer);
%Determine the output size of the network. Use the analyzeNetwork function to see the activation sizes of the last layer. To analyze the network for custom training loop workflows, set the TargetUsage option to 'dlnetwork'.
%analyzeNetwork(lgraph,'TargetUsage','dlnetwork')
%Create a variable named outputSizeNet containing the network output size.
outputSizeNet = [8 8 2048];
%Convert the layer graph to a dlnetwork object and view the output layer. The output layer is the "mixed10" layer of the Inception-v3 network.
%dlnet = dlnetwork(lgraph);
%Load the Image Dataset of Normal and Malignant WBC
imds = imageDatastore('D:\Project\DB1\train','IncludeSubfolders',true,'LabelSource','foldernames');
labelCount = countEachLabel (imds);
%Partition the data into training and validation sets. Hold out 5% of the observations for testing.
cvp = cvpartition(numel(imds),'HoldOut',0.05);
idxTrain = training(cvp);
idxTest = test(cvp);
annotationsTrain = imds(idxTrain);
annotationsTest = imds(idxTest);
%Create an augmented image datastore containing the images corresponding to the captions. Set the output size to match the input size of the convolutional network. To keep the images synchronized with the captions, specify a table of file names for the datastore by reconstructing the file names using the image ID. To return grayscale images as 3-channel RGB images, set the 'ColorPreprocessing' option to 'gray2rgb'.
tblFilenames = table(cat(1,annotationsTrain.Filename));
augimdsTrain = augmentedImageDatastore(inputSizeNet,tblFilenames,'ColorPreprocessing','gray2rgb')
%%Perform Cross-Validation using Hold-out method with a percentage split of 70% training and 30% testing
%%[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7,'randomized');
%Select the Test images and save in Y_test
ans =
ImageInputLayer with properties:
Name: 'input_1'
InputSize: [299 299 3]
Hyperparameters
DataAugmentation: 'none'
Normalization: 'none'
AverageImage: []
Error using internal.stats.cvpartitionInMemoryImpl (line 129)
The number of observations must be a positive integer greater than one.
Error in cvpartition (line 175)
cv.Impl = internal.stats.cvpartitionInMemoryImpl(varargin{:});
Error in cnnv3 (line 40)
cvp = cvpartition(numel(imds),'HoldOut',0.05);

Accepted Answer

yanqi liu
yanqi liu on 29 Nov 2021
yes,sir,may be numel(imds) is smaller than 20,so please use
cvp = cvpartition(numel(imds),'HoldOut',1/numel(imds));

More Answers (1)

Walter Roberson
Walter Roberson on 29 Nov 2021
Your imageStore is empty, or at least does not have any files of the default file extensions. For example perhaps your data is all .nii files, which will not be detected by default.
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