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

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mad_de
mad_de 2024-5-8
回答： Pratyush Swain 2024-5-16
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:
read(dsTrain)
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", ...
L2Regularization=1e-2);
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,

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回答（1 个）

Pratyush Swain
Pratyush Swain 2024-5-16
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)
You can refer to the example here:https://www.mathworks.com/help/deeplearning/ug/train-network-on-image-and-feature-data.html
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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