- Ensure the network is set up for classification: Use a softmax layer and a classification layer.
- Specify accuracy as a metric: Use the trainingOptions function to specify accuracy as a metric.
training network plot accuracy intead of rmse
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hello everyone
I am using LSTM for data prediction and I use trainNetwork for it but When I run my cde the training plot only plots rmse and I want to plot accuracy ?
Here is my layers and Option what sholud I do ![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/1268420/image.png)
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/1268420/image.png)
numResponses = 1 ;
featureDimension =1;
numHiddenUnits =200;
layers = [ ...
sequenceInputLayer(featureDimension)
lstmLayer(numHiddenUnits)
fullyConnectedLayer(numResponses)
regressionLayer
];
maxepochs = 500;
miniBatchSize = 45 ;
options = trainingOptions('adam', ... %%adam
'MaxEpochs',maxepochs, ...
'GradientThreshold',1, ...
'Shuffle','every-epoch', ...
'ValidationData',{XVal_ZaMir,YVal_ZaMir}, ...
'ValidationFrequency',25,...
'InitialLearnRate',0.005, ...
'MiniBatchSize',miniBatchSize, ...
'LearnRateSchedule','piecewise', ...
'LearnRateDropPeriod',50, ...
'LearnRateDropFactor',0.1, ...
'Verbose',1, ...
'Plots','training-progress');
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Answers (1)
Yash Sharma
on 24 Jul 2024 at 4:57
To plot accuracy instead of RMSE in the training progress graph when using trainNetwork with LSTM for a classification task, you need to ensure that your network and training options are set up for classification. This involves using a classification layer and specifying accuracy as a metric in the training options.
Here’s how you can do it:
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