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.load("SoftmaxTrainingData.grt") ){
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.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();
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;
}
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;
}