public Matrix Nonlin(INonlinearity neuron, Matrix m)
{
Matrix returnObj = new Matrix(m.Rows, m.Cols);
int n = m.W.Length;
for (int i = 0; i < n; i++)
{
if (!m.Dropped[i])
{
returnObj.W[i] = neuron.Forward(m.W[i]);
}
}
if (this.ApplyBackprop)
{
Runnable bp = new Runnable();
bp.Run = delegate()
{
for (int i = 0; i < n; i++)
{
if (!m.Dropped[i])
{
m.Dw[i] += neuron.Backward(m.W[i]) * returnObj.Dw[i];
}
}
};
Backprop.Add(bp);
}
return(returnObj);
}
public NNValue Nonlin(INonlinearity neuron, NNValue m)
{
NNValue returnObj = new NNValue(m.H, m.W);
int n = m.DataInTensor.Length;
returnObj = neuron.Forward(m);
if (this.ApplyBackprop)
{
Runnable bp = new Runnable();
bp.Run = delegate()
{
var data = neuron.Backward(m);
for (int i = 0; i < n; i++)
{
m.DifData[i] += data.DataInTensor[i] * returnObj.DifData[i];
}
};
Backprop.Add(bp);
}
return(returnObj);
}
public Matrix Nonlin(INonlinearity neuron, Matrix m)
{
Matrix returnObj = new Matrix(m.Rows, m.Cols);
int n = m.W.Length;
for (int i = 0; i < n; i++)
{
returnObj.W[i] = neuron.Forward(m.W[i]);
}
if (this.ApplyBackprop)
{
Runnable bp = new Runnable();
bp.Run = delegate()
{
for (int i = 0; i < n; i++)
{
m.Dw[i] += neuron.Backward(m.W[i]) * returnObj.Dw[i];
}
};
Backprop.Add(bp);
}
return returnObj;
}