| public SetWeight ( int fromLayer, int fromNeuron, int toNeuron, double v ) : void | ||
| fromLayer | int | The from layer. |
| fromNeuron | int | The from neuron. |
| toNeuron | int | The to neuron. |
| v | double | The to value. |
| return | void | |
/// <summary>
/// Randomize one level of a neural network.
/// </summary>
///
/// <param name="network">The network to randomize</param>
/// <param name="fromLayer">The from level to randomize.</param>
public override void Randomize(BasicNetwork network, int fromLayer)
{
int fromCount = network.GetLayerTotalNeuronCount(fromLayer);
int toCount = network.GetLayerNeuronCount(fromLayer + 1);
for (int fromNeuron = 0; fromNeuron < fromCount; fromNeuron++)
{
for (int toNeuron = 0; toNeuron < toCount; toNeuron++)
{
double v = CalculateValue(toCount);
network.SetWeight(fromLayer, fromNeuron, toNeuron, v);
}
}
}
/// <summary>
/// Randomize one level of a neural network.
/// </summary>
///
/// <param name="network">The network to randomize</param>
/// <param name="fromLayer">The from level to randomize.</param>
public virtual void Randomize(BasicNetwork network, int fromLayer)
{
int fromCount = network.GetLayerTotalNeuronCount(fromLayer);
int toCount = network.GetLayerNeuronCount(fromLayer + 1);
for (int fromNeuron = 0; fromNeuron < fromCount; fromNeuron++)
{
for (int toNeuron = 0; toNeuron < toCount; toNeuron++)
{
double v = network.GetWeight(fromLayer, fromNeuron, toNeuron);
v = Randomize(v);
network.SetWeight(fromLayer, fromNeuron, toNeuron, v);
}
}
}
/// <summary>
/// Randomize the connections between two layers.
/// </summary>
/// <param name="network">The network to randomize.</param>
/// <param name="fromLayer">The starting layer.</param>
private void RandomizeSynapse(BasicNetwork network, int fromLayer)
{
int toLayer = fromLayer + 1;
int toCount = network.GetLayerNeuronCount(toLayer);
int fromCount = network.GetLayerNeuronCount(fromLayer);
int fromCountTotalCount = network.GetLayerTotalNeuronCount(fromLayer);
IActivationFunction af = network.GetActivation(toLayer);
double low = CalculateRange(af, Double.NegativeInfinity);
double high = CalculateRange(af, Double.PositiveInfinity);
double b = 0.7d * Math.Pow(toCount, (1d / fromCount)) / (high - low);
for (int toNeuron = 0; toNeuron < toCount; toNeuron++)
{
if (fromCount != fromCountTotalCount)
{
double w = RangeRandomizer.Randomize(-b, b);
network.SetWeight(fromLayer, fromCount, toNeuron, w);
}
for (int fromNeuron = 0; fromNeuron < fromCount; fromNeuron++)
{
double w = RangeRandomizer.Randomize(0, b);
network.SetWeight(fromLayer, fromNeuron, toNeuron, w);
}
}
}
/// <summary>
/// Randomize one level of a neural network.
/// </summary>
///
/// <param name="network">The network to randomize</param>
/// <param name="fromLayer">The from level to randomize.</param>
public override void Randomize(BasicNetwork network, int fromLayer)
{
int fromCount = network.GetLayerTotalNeuronCount(fromLayer);
int toCount = network.GetLayerNeuronCount(fromLayer + 1);
for (int toNeuron = 0; toNeuron < toCount; toNeuron++)
{
double n = 0.0;
for (int fromNeuron = 0; fromNeuron < fromCount; fromNeuron++)
{
double w = network.GetWeight(fromLayer, fromNeuron, toNeuron);
n += w * w;
}
n = Math.Sqrt(n);
for (int fromNeuron = 0; fromNeuron < fromCount; fromNeuron++)
{
double w = network.GetWeight(fromLayer, fromNeuron, toNeuron);
w = _beta * w / n;
network.SetWeight(fromLayer, fromNeuron, toNeuron, w);
}
}
}
public override void Randomize(BasicNetwork network, int fromLayer)
{
int num2;
int num3;
double num4;
int num5;
double num6;
int num7;
double num8;
int layerTotalNeuronCount = network.GetLayerTotalNeuronCount(fromLayer);
goto Label_00DF;
Label_0011:
if (num3 < num2)
{
num4 = 0.0;
num5 = 0;
}
else if ((((uint) num8) - ((uint) layerTotalNeuronCount)) >= 0)
{
return;
}
while (true)
{
if (num5 >= layerTotalNeuronCount)
{
num4 = Math.Sqrt(num4);
num7 = 0;
if ((((uint) num4) + ((uint) num2)) < 0)
{
break;
}
goto Label_0065;
}
num6 = network.GetWeight(fromLayer, num5, num3);
num4 += num6 * num6;
num5++;
}
Label_0044:
if ((((uint) fromLayer) + ((uint) num6)) > uint.MaxValue)
{
goto Label_00DF;
}
num7++;
Label_0065:
if (num7 < layerTotalNeuronCount)
{
num8 = network.GetWeight(fromLayer, num7, num3);
}
else
{
num3++;
goto Label_0011;
}
Label_009C:
num8 = (this._xd7d571ecee49d1e4 * num8) / num4;
network.SetWeight(fromLayer, num7, num3, num8);
goto Label_0044;
Label_00DF:
num2 = network.GetLayerNeuronCount(fromLayer + 1);
if (((uint) num8) > uint.MaxValue)
{
goto Label_009C;
}
num3 = 0;
goto Label_0011;
}
public override void Randomize(BasicNetwork network, int fromLayer)
{
int num4;
double num5;
int layerTotalNeuronCount = network.GetLayerTotalNeuronCount(fromLayer);
int layerNeuronCount = network.GetLayerNeuronCount(fromLayer + 1);
int fromNeuron = 0;
if (((uint) fromLayer) < 0)
{
goto Label_0012;
}
Label_000E:
if (fromNeuron < layerTotalNeuronCount)
{
num4 = 0;
goto Label_0054;
}
Label_0012:
if (((uint) num5) > uint.MaxValue)
{
goto Label_0054;
}
if ((((uint) fromNeuron) + ((uint) fromNeuron)) >= 0)
{
return;
}
Label_003C:
num5 = this.x7417261f548b2c9b(layerNeuronCount);
network.SetWeight(fromLayer, fromNeuron, num4, num5);
num4++;
Label_0054:
if (num4 < layerNeuronCount)
{
goto Label_003C;
}
fromNeuron++;
goto Label_000E;
}
public virtual void Randomize(BasicNetwork network, int fromLayer)
{
int num4;
double num5;
int layerTotalNeuronCount = network.GetLayerTotalNeuronCount(fromLayer);
int layerNeuronCount = network.GetLayerNeuronCount(fromLayer + 1);
int fromNeuron = 0;
goto Label_002C;
Label_000D:
fromNeuron++;
if ((((uint) fromNeuron) + ((uint) fromNeuron)) > uint.MaxValue)
{
goto Label_004B;
}
if (0 != 0)
{
goto Label_003C;
}
Label_002C:
if (fromNeuron < layerTotalNeuronCount)
{
goto Label_0067;
}
return;
Label_003C:
network.SetWeight(fromLayer, fromNeuron, num4, num5);
num4++;
Label_004B:
if (num4 < layerNeuronCount)
{
num5 = network.GetWeight(fromLayer, fromNeuron, num4);
if (((uint) num5) >= 0)
{
num5 = this.Randomize(num5);
goto Label_003C;
}
goto Label_000D;
}
if ((((uint) fromLayer) + ((uint) fromLayer)) >= 0)
{
goto Label_000D;
}
Label_0067:
num4 = 0;
goto Label_004B;
}
public List<double[]> Learn(double[][] data, double[][] ideal)
{
double[][] origData = (double[][])data.Clone();
int n = data.Length;
int m = data[0].Length;
double[][] output = new double[n][];
double[][] sgmNeighbours = new double[n][];
for (var i = 0; i < n; i++)
{
double[] sgmN = new double[SegmentationData.SEGMENT_NEIGHBOURS];
Array.Copy(data[i], m - SegmentationData.SEGMENT_NEIGHBOURS, sgmN, 0, SegmentationData.SEGMENT_NEIGHBOURS);
sgmNeighbours[i] = sgmN;
data[i] = data[i].Take(m - SegmentationData.SEGMENT_NEIGHBOURS).ToArray();
output[i] = new double[m - SegmentationData.SEGMENT_NEIGHBOURS];
data[i].CopyTo(output[i], 0);
}
IMLDataSet trainingSet = new BasicMLDataSet(data, output);
int inputLayerSize = layersConfiguration[0] - SegmentationData.SEGMENT_NEIGHBOURS;
int trainingLayerSize = layersConfiguration[1];
BasicNetwork oneLayerAutoencoder = new BasicNetwork();
oneLayerAutoencoder.AddLayer(new BasicLayer(null, BIAS, inputLayerSize));
oneLayerAutoencoder.AddLayer(new BasicLayer(CurrentActivationFunction(), BIAS, trainingLayerSize));
oneLayerAutoencoder.AddLayer(new BasicLayer(CurrentActivationFunction(), false, inputLayerSize));
oneLayerAutoencoder.Structure.FinalizeStructure();
oneLayerAutoencoder.Reset();
IMLTrain train = new ResilientPropagation(oneLayerAutoencoder, trainingSet);
//IMLTrain train = new Backpropagation(oneLayerAutoencoder, trainingSet, LEARNING_RATE, MOMENTUM);
int epoch = 1;
List<double[]> errors = new List<double[]>();
double[] trainError = new double[AUTOENCODER_MAX_ITER];
do
{
train.Iteration();
ActiveForm.Text = @"Epoch #" + epoch + @" Error:" + train.Error;
Console.WriteLine(@"Epoch #" + epoch + @" Error:" + train.Error);
trainError[epoch - 1] = train.Error;
epoch++;
//errors.Add(train.Error);
} while (train.Error > EPS && epoch < AUTOENCODER_MAX_ITER);
errors.Add(trainError);
train.FinishTraining();
BasicNetwork encoder = new BasicNetwork();
encoder.AddLayer(new BasicLayer(null, BIAS, oneLayerAutoencoder.GetLayerNeuronCount(0)));
encoder.AddLayer(new BasicLayer(CurrentActivationFunction(), false, oneLayerAutoencoder.GetLayerNeuronCount(1)));
encoder.Structure.FinalizeStructure();
encoder.Reset();
//przypisanie wag do encodera
for (int i = 0; i < encoder.LayerCount - 1; i++)
for (int f = 0; f < encoder.GetLayerNeuronCount(i); f++)
for (int t = 0; t < encoder.GetLayerNeuronCount(i + 1); t++)
encoder.SetWeight(i, f, t, oneLayerAutoencoder.GetWeight(i, f, t));
//Compare2Networks(oneLayerAutoencoder, encoder);
for(int l=1; l<layersConfiguration.Count -2; l++)
{
inputLayerSize = layersConfiguration[l];
trainingLayerSize = layersConfiguration[l+1];
oneLayerAutoencoder = new BasicNetwork();
oneLayerAutoencoder.AddLayer(new BasicLayer(null, BIAS, inputLayerSize));
oneLayerAutoencoder.AddLayer(new BasicLayer(CurrentActivationFunction(), BIAS, trainingLayerSize));
oneLayerAutoencoder.AddLayer(new BasicLayer(CurrentActivationFunction(), false, inputLayerSize));
oneLayerAutoencoder.Structure.FinalizeStructure();
oneLayerAutoencoder.Reset();
//liczenie outputu z dotychczasowego encodera
double[][] input = new double[n][];
double[][] newOutput = new double[n][];
for(int ni = 0; ni <n; ni++)
{
IMLData res = encoder.Compute(new BasicMLData(data[ni]));
double[] resD = new double[res.Count];
for(int i=0; i<res.Count; i++)
resD[i] = res[i];
input[ni] = resD;
newOutput[ni] = new double[res.Count];
input[ni].CopyTo(newOutput[ni], 0);
}
BasicMLDataSet newTrainingSet = new BasicMLDataSet(input, newOutput);
train = new ResilientPropagation(oneLayerAutoencoder, newTrainingSet);
//train = new Backpropagation(oneLayerAutoencoder, newTrainingSet, LEARNING_RATE, MOMENTUM);
epoch = 1;
trainError = new double[AUTOENCODER_MAX_ITER];
do
{
train.Iteration();
ActiveForm.Text = @"Epoch #" + epoch + @" Error:" + train.Error;
Console.WriteLine(@"Epoch #" + epoch + @" Error:" + train.Error);
trainError[epoch - 1] = train.Error;
epoch++;
} while (train.Error > EPS && epoch < AUTOENCODER_MAX_ITER);
errors.Add(trainError);
train.FinishTraining();
BasicNetwork extendedEncoder = new BasicNetwork();
extendedEncoder.AddLayer(new BasicLayer(null, BIAS, encoder.GetLayerNeuronCount(0)));
for (int el = 1; el < encoder.LayerCount; el++ )
extendedEncoder.AddLayer(new BasicLayer(CurrentActivationFunction(), BIAS, encoder.GetLayerNeuronCount(el)));
extendedEncoder.AddLayer(new BasicLayer(CurrentActivationFunction(), false, oneLayerAutoencoder.GetLayerNeuronCount(1)));
extendedEncoder.Structure.FinalizeStructure();
//przypisanie wag do extendedencodera
for (int i = 0; i < extendedEncoder.LayerCount - 1; i++)
{
if (i < encoder.LayerCount-1)
{
for (int f = 0; f < extendedEncoder.GetLayerNeuronCount(i); f++)
for (int t = 0; t < extendedEncoder.GetLayerNeuronCount(i + 1); t++)
extendedEncoder.SetWeight(i, f, t, encoder.GetWeight(i, f, t));
}
else
{
for (int f = 0; f < extendedEncoder.GetLayerNeuronCount(i); f++)
for (int t = 0; t < extendedEncoder.GetLayerNeuronCount(i + 1); t++)
extendedEncoder.SetWeight(i, f, t, oneLayerAutoencoder.GetWeight(0, f, t));
}
}
encoder = extendedEncoder;
}
//tworzenie struktury ostatecznej sieci
network = new BasicNetwork();
network.AddLayer(new BasicLayer(null, BIAS, encoder.GetLayerNeuronCount(0) + SegmentationData.SEGMENT_NEIGHBOURS));
for (int el = 1; el < encoder.LayerCount; el++)
network.AddLayer(new BasicLayer(CurrentActivationFunction(), BIAS, encoder.GetLayerNeuronCount(el) + SegmentationData.SEGMENT_NEIGHBOURS));
network.AddLayer(new BasicLayer(CurrentActivationFunction(), false, layersConfiguration[layersConfiguration.Count - 1]));
network.Structure.FinalizeStructure();
network.Reset();
/*
for (int i = 0; i < encoder.LayerCount - 1; i++)
for (int f = 0; f < encoder.GetLayerNeuronCount(i); f++)
for (int t = 0; t < encoder.GetLayerNeuronCount(i + 1); t++)
network.SetWeight(i, f, t, encoder.GetWeight(i, f, t));
*/
//dla innych ustawic wagi 0, dla samych sobie 1
for (int i = 0; i < encoder.LayerCount - 1; i++)
for (int f = 0; f < network.GetLayerNeuronCount(i); f++)
for (int t = 0; t < network.GetLayerNeuronCount(i + 1); t++)
{
if (f < encoder.GetLayerNeuronCount(i) && t >= encoder.GetLayerNeuronCount(i + 1))
network.SetWeight(i, f, t, 0);
else if (f >= encoder.GetLayerNeuronCount(i) && t < encoder.GetLayerNeuronCount(i + 1))
network.SetWeight(i, f, t, 0);
else if (f >= encoder.GetLayerNeuronCount(i) && t >= encoder.GetLayerNeuronCount(i + 1))
network.SetWeight(i, f, t, 1);
else
network.SetWeight(i, f, t, encoder.GetWeight(i, f, t));
}
//uczenie koncowej sieci
trainingSet = new BasicMLDataSet(origData, ideal);
train = new ResilientPropagation(network, trainingSet);
//train = new Backpropagation(network, trainingSet, LEARNING_RATE, MOMENTUM);
epoch = 1;
trainError = new double[FINAL_NETWORK_MAX_ITER];
do
{
train.Iteration();
ActiveForm.Text = @"Epoch #" + epoch + @" Error:" + train.Error;
Console.WriteLine(@"Epoch #" + epoch + @" Error:" + train.Error);
trainError[epoch - 1] = train.Error;
epoch++;
} while (train.Error > EPS && epoch < FINAL_NETWORK_MAX_ITER);
errors.Add(trainError);
train.FinishTraining();
try
{
string networkFileName = "autoencoder wo cmp 300 125 50 3";
EncogDirectoryPersistence.SaveObject(new FileInfo(networkFileName), network);
MessageBox.Show("NETWORK SAVED TO FILE " + networkFileName);
}
catch (Exception ex)
{
MessageBox.Show(ex.Message);
}
return errors;
}
