Classify requires at least 3 arguments

Question

Raphael Ruschel 2018-11-1

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评论： Walter Roberson 2023-8-2

采纳的回答： Walter Roberson

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Hi,

I am having trouble when trying to use the "classify" function to evaluate the performance of my neural network.

I am using the following code:

net = load('mynet.mat'); %this returns my previsouly trained SeriesNetwork object
test_folder = './test_data';
test_images = imageDatastore(test_folder,'FileExtensions','.jpg');
[Y,scores] = classify(net,test_images);

But the classify function throws me an error that it requires at least 3 arguments, which means it is trying to use the classify function from the statistics package.

What can I make do force the use of the classify from the deep learning package?

Thanks,

Raphael

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Answer 1

Walter Roberson 2018-11-1

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编辑：Walter Roberson 2018-11-5

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net = load('mynet.mat'); %this returns my previsouly trained SeriesNetwork object

Not exactly. The output of load applied to a mat file, is a struct that has one field for each variable loaded. So you would need net.net where the first net is the struct returned from load and the second is the variable loaded.

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Moh. Saadat 2023-8-2

Hi Walter,

I was trying to figure out whether using the layerGraph() function on a DAGNetwork (trained model) removes the trained weights. So I loaded my trained model which is a DAGNetwork:

load(RESTORE_CKPT); % RESTORE_CKPT is the full path to the mat file containing my trained network

Then, I used layerGraph() function to convert the DAGNetwork to a layer graph:

net = layerGraph(net);

But then, I use classify to infer the results, it throws me this 'requires at least 3 arguments' error:

[pred,score] = classify(net,data); % data is a cell array

Does this mean the weights have been removed from net?

Walter Roberson 2023-8-2

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@Moh. Saadat

At the point of the error, what shows up for

class(net)

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Answer 2

Walter Roberson 2020-6-2

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https://ww2.mathworks.cn/matlabcentral/answers/427309-classify-requires-at-least-3-arguments#answer_445205

编辑：Walter Roberson 2020-12-6

There are multiple functions named classify()

Most of the classify() routines are methods of particular data type, and will only be considered to be invoked if the first input argument is an object of the proper data type.

The only classify() that accepts general input arguments (rather than objects) is classify() from the Statistics and Machine Learning Toolbox. That classify() happens to require at least 3 input arguments, so if you get an unexpected error message about classify requiring at least three input arguments, then you have accidentally invoked the classify() from the Statistics and Machine Learning Toolbox, and the cause of the problem is that your first parameter is not a proper member of the datatype that your desired function acts upon.

For example you might have accidentally provided the name of a dataset (as a character vector), or you might have accidentally provided a datastore, or you might have accidentally provided a description of a deep learning network instead of a trained instance of the network.

classify() can be used with:

SeriesNetwork CNN, Deep Learning Toolbox: https://www.mathworks.com/help/deeplearning/ref/seriesnetwork.html such as alexnet; https://www.mathworks.com/help/deeplearning/ref/classify.html
DAGNetwork CNN, Deep Learning Toolbox: https://www.mathworks.com/help/deeplearning/ref/dagnetwork.html such as googlenet or resnet50; https://www.mathworks.com/help/deeplearning/ref/classify.html
discrete-time markov chains, Econometics toolbox: https://www.mathworks.com/help/econ/dtmc.html ; https://www.mathworks.com/help/econ/dtmc.classify.html
Numeric arrays (for discriminent analysis) and character arrays, Statistics and Machine Learning Toolbox: https://www.mathworks.com/help/stats/classify.html . This is the function that will get invoked for inputs other than the ones mentioned above, so errors about number of arguments or about datatypes not being numeric, will typically be from this function having been accidentally invoked.

classify() is not the correct function for some other cases:

for patternnet(), Deep Learning Toolbox, use sim(); https://www.mathworks.com/help/deeplearning/ref/sim.html
For KNN Classification, use knnclassify() from Bioinformationcs Toolbox for old releases, https://www.mathworks.com/help/releases/R2015a/bioinfo/ref/knnclassify.html
For KNN Classifications, create a ClassificationKNN object, Statistics and Machine Learning Toolbox for newer releases, and use predict() https://www.mathworks.com/help/stats/classificationknn.predict.html
For SVM classificaiton, use svmclassify() from Statistics and Machine Learning Toolbox for old releases, https://www.mathworks.com/help/releases/R2015a/stats/svmclassify.html
For SVM classification, create an SVM Classifier object, Statistics and Machine Learning Toolbox for newer releases, https://www.mathworks.com/help/stats/fitcsvm.html and use predict() https://www.mathworks.com/help/stats/classreg.learning.classif.compactclassificationsvm.predict.html
Microarray analysis, Bioinformatics Toolbox: use KNN classifiers predict(), and classify() from the Statistics and Machine Learning Toolbox; https://www.mathworks.com/help/releases/R2015a/bioinfo/ref/classperf.html
for cross-validated classification trees, create a classification partitioned object, https://www.mathworks.com/help/stats/classreg.learning.partition.classificationpartitionedmodel-class.html using fitctree https://www.mathworks.com/help/stats/fitctree.html#bt6cr7t-tree and use kfoldPredict https://www.mathworks.com/help/stats/classificationpartitionedmodel.kfoldpredict.html
for other kinds of classification trees, create a ClassificationTree or CompactClassificationTree https://www.mathworks.com/help/stats/classificationtree-class.html using fitctree https://www.mathworks.com/help/stats/fitctree.html#bt6cr7t-tree and use predict https://www.mathworks.com/help/stats/compactclassificationtree.predict.html

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Yushan Zhang 2022-11-17

Hi Walter,

Thanks for your quick support.

trained_net is a DAGNetwork.

I am trying to combine my Matlab codes with Labview. At first, I tired to use NET.dll. However, compiler sdk needs to be paid extra. Then, I am trying standalone application and now I have this problem. I have codes for object detection using yolov4 and image classification, they all have similar probelms when compiling the application.

Walter Roberson 2022-11-17

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Try

%#function DAGNetwork

as a comment in your code. The compiler will recognize it and know to include the DAGNetwork class.

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Answer 3

Alaa ElDin ElHilaly 2019-1-22

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I face the same problem. would you please elaborate more about your suggested solution. I have trained the network and keeps giving me (required at least 3 arguments)

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Walter Roberson 2019-1-22

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When you load() a .mat file and you assign to output, the output is not directly any of the variables saved in the .mat file. Instead the output is a struct with one field for each variable loaded from the .mat file. For example if the .mat file contained the variables 'puppy' and 'butterfly', then

net = load('mynet.mat');

is not going to directly store puppy or butterfly in net, and it is not going to store into variables named puppy and butterfly in the environment of the function. Instead net would become a struct with fields named puppy and butterfly and net.puppy would hold whatever was in the puppy variable in mynet.mat

You are probably accidentally invoking https://www.mathworks.com/help/stats/classify.html instead of https://www.mathworks.com/help/deeplearning/ref/classify.html

Muhammad Suri 2020-9-28

yes I am accidentally invoking stats/classify vs ref/classify. How can I change that. Thanks

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Answer 4

hana razak 2019-2-19

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Hi,

Me too. I've got same ERROR when using webcam to classify the fast RCNN

camera = videoinput('winvideo', 2, 'MJPG_1024x576');
net = load('detector200.mat');
while true   
        picture = getsnapshot(camera);
        picture = imresize(picture,[150,150]);  
        label = classify(net, picture);  
 
         image(picture);  
         title(char(label)); 
    drawnow;   
end

Here are the errors,

Error using classify (line 123)
Requires at least three arguments.
Error in webcam_object_classification (line 7)
    label = classify(net, picture);    

I've tried net.net as suggested in command window and it showed this,

>> net.detector200
ans = 
  fasterRCNNObjectDetector with properties:
                ModelName: 'normal'
                  Network: [1×1 vision.cnn.FastRCNN]
    RegionProposalNetwork: [1×1 vision.cnn.RegionProposalNetwork]
              MinBoxSizes: [39 30]
          BoxPyramidScale: 1.2000
      NumBoxPyramidLevels: 14
               ClassNames: {'normal'  'abnormal'  'Background'}
            MinObjectSize: [18 18]

BUT I don't know how to use it in the code.

Any help would be greatly appreciated.

Thank you so much

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Walter Roberson 2019-2-19

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label = classify(net.detector200, picture);

hana razak 2019-2-20

It didn't work. I got the same error.

Is there any other solution?

Thank you

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Answer 5

Kinjal Joshi 2019-12-19

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net=patternnet(10);
[net,tr]=train(net,trainoflow,op);
[ypred,scores]=classify(net,testoflow);

The above code give me error at classify function that Requires atleast three arguments. trainoflow is training features,testoflow is testdata features and op is train data labels.