public override void Load(BinaryReader r)
{
base.Load(r);
charsPerImage = r.ReadInt32();
charsSet = r.ReadString().ToCharArray().ToList().Select(c => c.ToString()).ToList();
learnRate = r.ReadDouble();
sann = SimpleNeuralNetwork.Load(r);
sann.OnTrainingProgressChange += new SimpleNeuralNetwork.TrainingProgressHandler(sann_OnTrainingProgressChange);
}
public override void Load(BinaryReader r)
{
base.Load(r);
charsPerImage = r.ReadInt32();
charsSet = r.ReadString().ToCharArray().ToList().Select(c => c.ToString()).ToList();
learnRate = r.ReadDouble();
int count = r.ReadInt32();
sann = new List <SpecialNeuralNet>();
for (int i = 0; i < count; i++)
{
sann.Add(new SpecialNeuralNet());
sann[i].NeuralNet = SimpleNeuralNetwork.Load(r);
sann[i].NeuralNet.OnTrainingProgressChange += new SimpleNeuralNetwork.TrainingProgressHandler(sann_OnTrainingProgressChange);
sann[i].Solution = r.ReadString();
sann[i].LastOutput = r.ReadDouble();
}
}
static void Load_and_predict(string filepath)
{
// train & testing data
var training_data = new Dictionary <int, Tuple <float[], float[]> >
{ // ys xs
[0] = new Tuple <float[], float[]>(new float[] { 0, 1 }, new float[] { 1 }),
[1] = new Tuple <float[], float[]>(new float[] { 1, 0 }, new float[] { 1 }),
[2] = new Tuple <float[], float[]>(new float[] { 0, 0 }, new float[] { 0 }),
[3] = new Tuple <float[], float[]>(new float[] { 1, 1 }, new float[] { 0 })
};
var snn = SimpleNeuralNetwork.Load(filepath);
Console.WriteLine("Red Neuronal cargada !!.\n");
// predict
Console.WriteLine("\nPredicciones:\n");
for (int i = 0; i < 4; i++)
{
var res = snn.Predict(training_data[i].Item1);
Console.WriteLine(string.Format("xs [ {0}, {1} ] = {2}", training_data[i].Item1[0], training_data[i].Item1[1], res[0]));
}
}