コード例 #1
0
ファイル: SoftmaxExample.cpp プロジェクト: GaoXiaojian/grt
int main (int argc, const char * argv[])
{
    
    //Create a new Softmax instance
    Softmax softmax;
    
    //Load some training data to train the classifier
    ClassificationData trainingData;
    
    if( !trainingData.loadDatasetFromFile("SoftmaxTrainingData.txt") ){
        cout << "Failed to load training data!\n";
        return EXIT_FAILURE;
    }
    
    //Use 20% of the training dataset to create a test dataset
    ClassificationData testData = trainingData.partition( 80 );
    
    //Train the classifier
    if( !softmax.train( trainingData ) ){
        cout << "Failed to train classifier!\n";
        return EXIT_FAILURE;
    }
    
    //Save the Softmax model to a file
    if( !softmax.saveModelToFile("SoftmaxModel.txt") ){
        cout << "Failed to save the classifier model!\n";
        return EXIT_FAILURE;
    }
    
    //Load the Softmax model from a file
    if( !softmax.loadModelFromFile("SoftmaxModel.txt") ){
        cout << "Failed to load the classifier model!\n";
        return EXIT_FAILURE;
    }
    
    //Use the test dataset to test the softmax model
    double accuracy = 0;
    for(UINT i=0; i<testData.getNumSamples(); i++){
        //Get the i'th test sample
        UINT classLabel = testData[i].getClassLabel();
        vector< double > inputVector = testData[i].getSample();
        
        //Perform a prediction using the classifier
        if( !softmax.predict( inputVector ) ){
            cout << "Failed to perform prediction for test sample: " << i <<"\n";
            return EXIT_FAILURE;
        }
        
        //Get the predicted class label
        UINT predictedClassLabel = softmax.getPredictedClassLabel();
        vector< double > classLikelihoods = softmax.getClassLikelihoods();
        vector< double > classDistances = softmax.getClassDistances();
        
        //Update the accuracy
        if( classLabel == predictedClassLabel ) accuracy++;
        
        cout << "TestSample: " << i <<  " ClassLabel: " << classLabel << " PredictedClassLabel: " << predictedClassLabel << endl;
    }
    
    cout << "Test Accuracy: " << accuracy/double(testData.getNumSamples())*100.0 << "%" << endl;
    
    return EXIT_SUCCESS;
}
コード例 #2
0
ファイル: SoftmaxExample.cpp プロジェクト: sgrignard/grt
int main (int argc, const char * argv[])
{
    //Parse the data filename from the argument list
    if( argc != 2 ){
        cout << "Error: failed to parse data filename from command line. You should run this example with one argument pointing to the data filename!\n";
        return EXIT_FAILURE;
    }
    const string filename = argv[1];
    
    //Create a new Softmax instance
    Softmax softmax;
    
    //Load some training data to train the classifier
    ClassificationData trainingData;
    
    if( !trainingData.load( filename ) ){
        cout << "Failed to load training data: " << filename << endl;
        return EXIT_FAILURE;
    }
    
    //Use 20% of the training dataset to create a test dataset
    ClassificationData testData = trainingData.split( 80 );
    
    //Train the classifier
    if( !softmax.train( trainingData ) ){
        cout << "Failed to train classifier!\n";
        return EXIT_FAILURE;
    }
    
    //Save the Softmax model to a file
    if( !softmax.save("SoftmaxModel.grt") ){
        cout << "Failed to save the classifier model!\n";
        return EXIT_FAILURE;
    }
    
    //Load the Softmax model from a file
    if( !softmax.load("SoftmaxModel.grt") ){
        cout << "Failed to load the classifier model!\n";
        return EXIT_FAILURE;
    }
    
    //Use the test dataset to test the softmax model
    double accuracy = 0;
    for(UINT i=0; i<testData.getNumSamples(); i++){
        //Get the i'th test sample
        UINT classLabel = testData[i].getClassLabel();
        VectorFloat inputVector = testData[i].getSample();
        
        //Perform a prediction using the classifier
        if( !softmax.predict( inputVector ) ){
            cout << "Failed to perform prediction for test sample: " << i <<"\n";
            return EXIT_FAILURE;
        }
        
        //Get the predicted class label
        UINT predictedClassLabel = softmax.getPredictedClassLabel();
        VectorFloat classLikelihoods = softmax.getClassLikelihoods();
        VectorFloat classDistances = softmax.getClassDistances();
        
        //Update the accuracy
        if( classLabel == predictedClassLabel ) accuracy++;
        
        cout << "TestSample: " << i <<  " ClassLabel: " << classLabel << " PredictedClassLabel: " << predictedClassLabel << endl;
    }
    
    cout << "Test Accuracy: " << accuracy/double(testData.getNumSamples())*100.0 << "%" << endl;
    
    return EXIT_SUCCESS;
}