I'm trying to show the progress of my deep learning network training via plots. When adding 'Plots','training-progress',… to my trainingOptions I get following error: * Error using nnet.cnn.TrainingOptionsSGDM.parseInputArguments (line 176) 'Plots' is not a recognized parameter. For a list of valid name-value pair arguments, see the documentation for this function.* Does anybody have an idea why this happens? Here are all my trainingOptions: options = trainingOptions('sgdm',… 'MiniBatchSize',12,… 'MaxEpochs',3, … 'ExecutionEnvironment','gpu',… 'Plots','training-progress',… 'Verbose',false);
MATLAB: ‘Plots’ not a recognized parameter in deep learning network training
deep learningnetwork training
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digitDatasetPath = fullfile(matlabroot,'toolbox','nnet','nndemos','nndatasets','DigitDataset');imds = imageDatastore(digitDatasetPath, 'IncludeSubfolders',true,'LabelSource','foldernames');numTrainFiles = 750;[imdsTrain,imdsValidation] = splitEachLabel(imds,numTrainFiles,'randomize');inputSize = [28 28 1];numClasses = 10;layers = [ imageInputLayer(inputSize) convolution2dLayer(5,20) batchNormalizationLayer reluLayer fullyConnectedLayer(numClasses) softmaxLayer classificationLayer];options = trainingOptions('sgdm', ... 'MaxEpochs',4, ... 'ValidationData',imdsValidation, ... 'ValidationFrequency',30, ... 'Verbose',false, ... 'Plots','training-progress');net = trainNetwork(imdsTrain,layers,options);I was able to view the training progress
Please crosscheck the spellings of Name Value pairs in trainingOptions
One way to do this is to make use of the "OutputFcn" field in the training options structure. You can customize a function, and assign it as the value of this field when calling "trainingOptions". See the 'OutputFcn' section in the below MATLAB documentation.
This function will execute once before the training, once after the training, and once after every iteration. It takes as input an information structure with information about the progress of the training. This is useful since you could, for instance, check the "State" field of the info structure to see if the training is over, and then save the final result figure using "savefig".
As a caveat, the figures produced during the training are hidden by default, so the function "findall" may be needed to find the handles that are invisible.
>> findall(groot, 'Type', 'Figure')
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