public void Train2(DataSet.DataSet dataSet, int epochs)
{
Console.WriteLine("Initial Loss:" + CalculateMeanErrorOverDataSet(dataSet));
for (int i = 0; i < epochs; i++)
{
dataSet.Shuffle();
List <DataRow> batch = dataSet.NextBatch(this.Batching);
int count = 0;
foreach (DataRow example in batch)
{
count++;
double[] result = this.FeedForward(example.GetFeatures());
double[] labels = example.GetLabels();
if (result.Length != labels.Length)
{
throw new Exception("Inconsistent array size, Incorrect implementation.");
}
else
{
double error = CalculateExampleLost(example);
for (int l = this.Layers.Count - 1; l > 0; l--)
{
if (l == this.Layers.Count - 1)
{
for (int j = 0; j < this.Layers[l].CostDerivatives.Length; j++)
{
this.Layers[l].CostDerivatives[j] = ErrorFunction.GetDerivativeValue(labels[j], this.Layers[l].Activations[j]);
}
}
else
{
for (int j = 0; j < this.Layers[l].CostDerivatives.Length; j++)
{
double acum = 0;
for (int j2 = 0; j2 < Layers[l + 1].Size; j2++)
{
acum += Layers[l + 1].WeightMatrix[j2, j] * this.Layers[l + 1].ActivationFunction.GetDerivativeValue(Layers[l + 1].WeightedSum[j2]) * Layers[l + 1].CostDerivatives[j2];
}
this.Layers[l].CostDerivatives[j] = acum;
}
}
for (int j = 0; j < this.Layers[l].Activations.Length; j++)
{
this.Layers[l].BiasVectorChangeRecord[j] += this.Layers[l].ActivationFunction.GetDerivativeValue(Layers[l].WeightedSum[j]) * Layers[l].CostDerivatives[j];
for (int k = 0; k < Layers[l].WeightMatrix.GetLength(1); k++)
{
this.Layers[l].WeightMatrixChangeRecord[j, k] += Layers[l - 1].Activations[k]
* this.Layers[l].ActivationFunction.GetDerivativeValue(Layers[l].WeightedSum[j])
* Layers[l].CostDerivatives[j];
}
}
}
}
}
TakeGradientDescentStep(batch.Count);
if ((i + 1) % (epochs / 10) == 0)
{
Console.WriteLine("Epoch " + (i + 1) + ", Avg.Loss:" + CalculateMeanErrorOverDataSet(dataSet));
}
}
}
private void Train(DataSet.DataSet dataSet, int epochs)
{
Console.WriteLine("MSE:" + CalculateMeanErrorOverDataSet(dataSet));
for (int i = 0; i < epochs; i++)
{
dataSet.Shuffle();
List <List <DataRow> > batch = dataSet.Batch(this.Batching);
int step = 0;
foreach (List <DataRow> row in batch)
{
foreach (DataRow example in row)
{
double[] result = this.FeedForward(example.GetFeatures());
double[] labels = example.GetLabels();
if (result.Length != labels.Length)
{
throw new Exception("Inconsistent array size, Incorrect implementation.");
}
else
{
double error = labels.Zip(result, (x, y) => Math.Pow(x - y, 2)).Sum();
for (int l = this.Layers.Count - 1; l > 0; l--)
{
if (l == this.Layers.Count - 1)
{
for (int j = 0; j < this.Layers[l].CostDerivatives.Length; j++)
{
this.Layers[l].CostDerivatives[j] = 2.0 * (this.Layers[l].Activations[j] - labels[j]);
}
}
else
{
for (int j = 0; j < this.Layers[l].CostDerivatives.Length; j++)
{
//this.Layers[l].CostDerivatives[j] = 2.0 * (this.Layers[l].Activations[j] - labels[j]);
double acum = 0;
for (int j2 = 0; j2 < Layers[l + 1].Size; j2++)
{
acum += Layers[l + 1].WeightMatrix[j2, j] * Layers[l + 1].ActivationFunction.GetDerivativeValue(Layers[l + 1].WeightedSum[j2]) * Layers[l + 1].CostDerivatives[j2];
}
this.Layers[l].CostDerivatives[j] = acum;
}
}
for (int j = 0; j < this.Layers[l].Activations.Length; j++)
{
this.Layers[l].BiasVectorChangeRecord[j] += Layers[l].ActivationFunction.GetDerivativeValue(Layers[l].WeightedSum[j]) * Layers[l].CostDerivatives[j];
for (int k = 0; k < Layers[l].WeightMatrix.GetLength(1); k++)
{
this.Layers[l].WeightMatrixChangeRecord[j, k] += Layers[l - 1].Activations[k]
* Layers[l].ActivationFunction.GetDerivativeValue(Layers[l].WeightedSum[j])
* Layers[l].CostDerivatives[j];
}
}
}
}
}
// Console.WriteLine("Step "+step);
step++;
TakeGradientDescentStep(row.Count);
//
}
Console.WriteLine(i + ":" + CalculateMeanErrorOverDataSet(dataSet));
}
}