Array formation and parentheses-style indexing with objects of class 'matlab.io​.datastore​.ImageData​store' is not allowed.

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sun rise
sun rise 2021-12-11
评论： yanqi liu 2021-12-19
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.Files),'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: []
Array formation and parentheses-style indexing with objects of class 'matlab.io.datastore.ImageDatastore'
is not allowed. Use objects of class 'matlab.io.datastore.ImageDatastore' only as scalars or use a cell
array.
Error in cnnv3 (line 44)
annotationsTrain = imds(idxTrain);

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采纳的回答

yanqi liu
yanqi liu 2021-12-13
yes，sir，may be use
annotationsTrain = subset(imds,idxTrain);
  2 个评论
sun rise
sun rise 2021-12-18
ans =
ImageInputLayer with properties:
Name: 'input_1'
InputSize: [299 299 3]
Hyperparameters
DataAugmentation: 'none'
Normalization: 'none'
AverageImage: []
Unrecognized method, property, or field 'imds' for class 'matlab.io.datastore.ImageDatastore'.
Error in cnnv3 (line 49)
tblFilenames = table(cat(1,annotationsTrain.imds));
>>
yanqi liu
yanqi liu 2021-12-19
yes，sir，may be make imds first，such as
imds = imageDatastore('D:\Project\DB1\train','IncludeSubfolders',true,'LabelSource','foldernames');

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