public void run()
{
var dictionaryInput = new LetterDataTextFileReader();
var floatLetterInput = dictionaryInput.ConvertTextForFloatArray(@"C:\Users\BartlomiejLeja\source\repos\PerceptronLerning\PerceptronLerning\letterLearningData.txt");
var net = new BackpropagationNeuralNetwork(new int[] { 35, 35, 35, 20 }); //intiilize network 3 input (0 0 0) 2 hidden layers with 25 neurons 1 output (1)
// Itterate 5000 times and train each possible output
// 5000 * 8 = 40000 traning operations
for (int i = 0; i < 5000; i++)
{
for (int j = 0; j < floatLetterInput.Count; j++)
{
net.FeedForward(floatLetterInput[j].LetterPattern);
net.BackProp(floatLetterInput[j].LetterResult);
}
}
net.TestMethod(new float[] { 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1 }, 'A');
net.TestMethod(new float[] { 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0 }, 'B');
}
public void run()
{
var dictionaryInput = new LetterDataTextFileReader();
var letterInput = dictionaryInput.ConvertTextForArray(@"C:\Users\BartlomiejLeja\source\repos\PerceptronLerning\PerceptronLerning\letterLearningData.txt");
var oneLayerNetworkTest = new OneLayerNetwork(letterInput, 0.5);
}
