How to predict using trained neural network obtained from Classification Learner app

My output tained neural network model name is trainedModel. I want to perform prediction by using weights and biases which i acquired using following lines of code

wi=trainedModel.ClassificationNeuralNetwork.LayerWeights{1,1};

wo=trainedModel.ClassificationNeuralNetwork.LayerWeights{1,2};

bi=trainedModel.ClassificationNeuralNetwork.LayerBiases{1,1};

bo=trainedModel.ClassificationNeuralNetwork.LayerBiases{1,2};

Then I perform the prediction task on the input features using the network predictFcn

indice_net=100;

a1=trainedModel.predictFcn(trainedModel.ClassificationNeuralNetwork.X{indice_net,:});

But when I perform the prediction using the following code the result is some times different from the output I get from predictFcn in the trained model. Can you please guide me how can I replace the functionality of predictFcn in simple MATLAB code as below? Or if I am doing some mistake?

x=trainedModel.ClassificationNeuralNetwork.X{indice_net,:};

lay1=wi*x';

lay1_b=lay1+bi;

y = sigmoid(lay1_b);

lay2=wo*y;

lay2_b=lay2+bo;

predict_class=softmax(lay2_b);

Answer 1

[Drew]

It looks like you are trying to re-implement the "predict" method for a neural network model obtained from Classification Learner.

If this answer helps you, please remember to "Accept" this answer.

Some pointers toward a solution:

(1) As you point out, a predictFcn method is already provided. It might help to know why you want to re-implement the predict method. This may suggest another solution.

(2) You say that the output from your code and the predictFcn are "some times different". Can you elaborate on when the results are the same, and when they are different? When there are differences, what is the numerical size of the differences? Are they just small differences? In general, small differences could arise due to differences in floating point computations between your code and the built-in predict method. If you can provide your exported model (with training data included in the X property), and which inputs you are seeing differences, more specific feedback could be provided.

(3) Did you use feature selection or PCA in Classification Learner? If so, you will need to account for those steps when testing the model. "trainedModel.predictFcn" is a wrapper around the model-specific predict function "trainedModel.ClassificationNeuralNetwork.predict". The predictFcn wrapper is there to handle feature selection and PCA prior to calling the "trainedModel.ClassificationNeuralNetwork.predict" method. For documentation of the ClassificationNeuralNetwork model-specific predict function, see: https://www.mathworks.com/help/stats/classificationneuralnetwork.predict.html

(4) Remember to examine the model, including its methods and properties, to ensure you are correctly taking into account all aspects of the model. Did you check which type of "Activations" your model is using? By default, the "ReLU" activation is used in Classification Learner, but you can select to change the activation type to "Sigmoid" or other. For example for a bilayered Neural Network model exported from Classification Learner based on fisheriris data, where "Sigmoid" activation was chosen in Classification Learner before the model was trained, we see the following when examining the model at the command line:

>> trainedModel.ClassificationNeuralNetwork

ans = 

  ClassificationNeuralNetwork
           PredictorNames: {'SepalLength'  'SepalWidth'  'PetalLength'  'PetalWidth'}
             ResponseName: 'Y'
    CategoricalPredictors: []
               ClassNames: {'setosa'  'versicolor'  'virginica'}
           ScoreTransform: 'none'
          NumObservations: 135
               LayerSizes: [10 10]
              Activations: 'sigmoid'
    OutputLayerActivation: 'softmax'
                   Solver: 'LBFGS'
          ConvergenceInfo: [1×1 struct]
          TrainingHistory: [64×7 table]
          
>> properties(trainedModel.ClassificationNeuralNetwork)

Properties for class ClassificationNeuralNetwork:

    ConvergenceInfo
    Solver
    TrainingHistory
    Y
    X
    RowsUsed
    W
    ModelParameters
    NumObservations
    BinEdges
    HyperparameterOptimizationResults
    PredictorNames
    CategoricalPredictors
    ResponseName
    ExpandedPredictorNames
    ClassNames
    Prior
    Cost
    ScoreTransform
    OutputLayerActivation
    LayerSizes
    Activations
    LayerWeights
    LayerBiases
 
>> methods(trainedModel.ClassificationNeuralNetwork)

Methods for class ClassificationNeuralNetwork:

compact                edge                   partialDependence      resubEdge              resubPredict           
compareHoldout         loss                   plotPartialDependence  resubLoss              
crossval               margin                 predict                resubMargin