public void PredictDiabetes()
{
/*
* Create and train a neural network to predict if an individual has diabetes
* among the pima indians.
* The method print out the prediction error to the console for the training and
* validation set to demonstrate how the network improve
*/
FeedForwardNeuralNetwork network = new FeedForwardNeuralNetwork(NUMBER_OF_INPUTS, NUMBER_OF_OUTPUTS, NEURONS_IN_LAYER);
for (int i = 0; i < 80000; i++)
{
//Train network on trainingset
network.Train(permuteData('t'));
//Print error of trainingset
network.PrintAccuracy(permuteData('t')); //Root-mean-square-error
//Measure error of validationset
}
} //improve method! for example: visualize the learning, quit training before overfitting etc.
public void PredictDiabetes()
{
/*
* Create and train a neural network to predict if an individual has diabetes
* among the pima indians.
* The method print out the prediction error to the console for the training and
* validation set to demonstrate how the network improve
*/
FeedForwardNeuralNetwork network = new FeedForwardNeuralNetwork(NUMBER_OF_INPUTS, NUMBER_OF_OUTPUTS, NEURONS_IN_LAYER);
for (int i = 0; i < 8000; i++)
{
//Train network on trainingset
network.Train(getStochasticData(this.trainingset.Count, 't'));
//Print error of trainingset
Console.WriteLine("Total error (trainingset): ");
network.PrintTotalError(getStochasticData(this.trainingset.Count, 't'));
//Measure error of validationset
}
} //improve method! for example: visualize the learning, quit training before overfitting etc.
