Hi Robinson,
I understand that you are trying to access the normalization limits that are automatically generated while using the “fitrnet” function so that you can manually get the response of the trained network on normalized test data inputs.
The normalization limits used by “mapminmax” are not directly accessible from the “RegressionNeuralNetwork” object created by the “fitrnet” function. However, you can manually calculate the normalization limits based on the input data following the below steps.
- Before training the “RegressionNeuralNetwork” object, you can use the “mapminmax” function to normalize the input data. This function returns the normalization limits used for each input dimension.
[normalizedData, normalizationSettings] = mapminmax(trainingPredictors);
- You can access the normalization limits for each input dimension using the “normalizationSettings” structure.
minLimits = normalizationSettings.xmin;
maxLimits = normalizationSettings.xmax;
- If you want to normalize new, unseen data, you should use the same normalization limits obtained using “mapminmax”, that are specific to the training data, to ensure consistency.
Please refer to the workflow mentioned in the below documentation to learn how to apply normalization manually on the test inputs using the “mapminmax” function:
Hope it helps.
Regards,
Sai Pavan
