private double[] GetResultVector(NeuralNetworkObj neuralNetworkObj)
{
double[] result = new double[neuralNetworkObj.GetLastLayer().GetCountNeurons()];
List <NeuronObj> neurons = neuralNetworkObj.GetLastLayer().GetListNeurons();
for (int i = 0; i < neurons.Count; i++)
{
result[i] = neurons[i].GetOutputData();
}
return(result);
}
public int GetAnswer(double[] inputVector)
{
if (inputVector.Length != INPUT_VECTOR_LENGTH)
{
throw new Exception("Invalid input vector length.");
}
NeuralNetworkObj neuralNetwork = this.GetNeuralNetwork();
neuralNetwork.SetInputVector(inputVector);
neuralNetwork.Process();
if (neuralNetwork.GetLastLayer().GetNeuronById(1).GetOutputData() >= MIN_CORRECT_ANSWER)
{
return(1);
}
return(0);
}
private void educateNetworkButton_Click(object sender, EventArgs e)
{
UncertaintyPropagationMethod uncertaintyPropagationMethod = new UncertaintyPropagationMethod(LEARNING_RATE);
NumberRecognizerNeuralNetwork numberRecognizerNeuralNetwork = new NumberRecognizerNeuralNetwork(this.neuralNetwork);
MachineLearning.NeuralNetwork.Report.Manager reportManager = new MachineLearning.NeuralNetwork.Report.Manager();
NeuralNetworkObj neuralNetworkObj = numberRecognizerNeuralNetwork.GetNeuralNetwork();
var prepareData = this.GetPrepareData(neuralNetworkObj.GetLastLayer().GetCountNeurons());
int countEpoch = Convert.ToInt16(educateNetworkNumericUpDown.Value);
for (int epoch = 0; epoch < countEpoch; epoch++)
{
reportManager.AddDataBeforeEducate(neuralNetworkObj, prepareData);
for (int iteration = 0; iteration < COUNT_ITERATION; iteration++)
{
foreach (var entity in prepareData)
{
double[] inputVector = entity.Key;
double[] expectedResultVector = entity.Value;
neuralNetworkObj = uncertaintyPropagationMethod.GetTaughtNeuralNetwork(
numberRecognizerNeuralNetwork.GetNeuralNetwork(),
inputVector,
expectedResultVector
);
}
}
educateNetworkProgressBar.Value = Convert.ToInt16((epoch + 1.0) / countEpoch * 100);
reportManager.AddDataAfterEducate(neuralNetworkObj, prepareData);
reportManager.SaveReport(neuralNetworkObj);
neuralNetworkObj.EpochPassed();
}
numberRecognizerNeuralNetwork.UpdateNeuralNetwork(neuralNetworkObj);
}
public int GetNumberFromImgVector(double[] inputVector)
{
if (inputVector.Length != this.countNeuronsInFirstLayer)
{
throw new Exception("Invalid input vector length.");
}
NeuralNetworkObj neuralNetworkObj = this.GetNeuralNetwork();
neuralNetworkObj.Clear();
neuralNetworkObj.SetInputVector(inputVector);
neuralNetworkObj.Process();
foreach (NeuronObj neuron in neuralNetworkObj.GetLastLayer().GetListNeurons())
{
if (neuron.GetOutputData() > MINIMUM_OUTPUT_DATA_FOR_SUCCESS_RESULT)
{
return(neuron.Id);
}
}
return(-1);
}