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Error forming mini-batch of targets for network output. Layer output format is not consistent with the input data.

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I want to classify images into one of 5 categories by trying to train a network using trainnet, and I have both numeric and image data. The image data is RGB images of type double and size 61x61x3. Every image is associated with a numeric vector 1x5 and a categorical label (5 classes in total). I have created three CombinedDatastores (for train, validation and testing) by combining imageDatastores and arrayDatastores. The command:
gives a 1x2 cell array of a 61x61x3 double (the image) and a 1x5 vector (the numeric data).
I have defined the options as follows:
options = trainingOptions("sgdm", ...
InitialLearnRate=0.01, ...
MaxEpochs=15, ...
Shuffle="every-epoch", ...
ValidationData=dsValidation, ...
ValidationFrequency=30, ...
Plots="training-progress", ...
Metrics="accuracy", ...
Verbose=true, ...
MiniBatchSize=64, ...
LearnRateSchedule="piecewise", ...
The network architecture looks like this (sorry for the bad quality):
When trying to run trainnet, I get the following error:
Error using trainnet
Error forming mini-batch of targets for network output "softmax". Layer output format "BC" is not consistent with the input data.
Caused by: Index exceeds the number of array elements. Index must not exceed 2.
Is it possible that I have done something wrong then combining the datastores, or have I constructed the network wrong? Using Matlab 2023b.
All help is deeply appreciated,

Answers (1)

Pratyush Swain
Pratyush Swain on 16 May 2024
Hi mad_de,
This error suggests there was a mismatch between the network's expected output format and the format of the training labels/targets being provided.CombinedDatastore in your case should consist of Datastores of images,numeric vectors (predictors) and also labels (responses)
The data loading and combining workflow in the above example can be analyzed as follows:
% Load the digits images, labels, and clockwise rotation angles.
load DigitsDataTrain
% To train a network with multiple inputs using the trainnet function,
% create a single datastore that contains the training predictors and responses.
dsX1Train = arrayDatastore(XTrain,IterationDimension=4);
dsX2Train = arrayDatastore(anglesTrain);
dsTTrain = arrayDatastore(labelsTrain);
% Combining images, clockwise rotation angles (numeric vector) &
% label(categorical)
dsTrain = combine(dsX1Train,dsX2Train,dsTTrain);
Hence by adding the labels as a arrayDatastore and combining it with the original CombinedDatastore, you effectively aligned the training targets with the network's expectations.
Hope this helps.

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