private void run()
{
FPA t1 = PA.Add(PA.InnerProduct(dinput, diwt), theta); // summation and theta
FPA ohidden = PA.Reciprocal(PA.Add(PA.Pow2(PA.Negate(t1)), 1.0f)); //applying sigmoid function
FPA t2 = PA.Add(PA.InnerProduct(ohidden, dowt), tau); // summation and tau
FPA ooutput = PA.Reciprocal(PA.Add(PA.Pow2(PA.Negate(t2)), 1.0f));
FPA oerror = PA.Subtract(doutput, ooutput); //numpat no
FPA herror = PA.Transpose(PA.InnerProduct(dowt, PA.Transpose(oerror, new int[] { 1, 0 })), new int[] { 1, 0 }); // doubtful transpose
herror = PA.Multiply(PA.Multiply(PA.Subtract(1.0f, t1), t1), herror);
FPA _owt = PA.Add(dowt, PA.Multiply(PA.InnerProduct(PA.Transpose(t1, new int[] { 1, 0 }), oerror), betao)); // orig no transpose
FPA _iwt = PA.Multiply(PA.InnerProduct(PA.Transpose(dinput, new int[] { 1, 0 }), herror), betah); //original dinput herror and no transpose
dtau = PA.Add(PA.Multiply(betao, oerror), dtau);
dtheta = PA.Add(PA.Multiply(betah, herror), dtheta); //orig oerror
PA.ToArray(_owt, out owt);
PA.ToArray(_iwt, out iwt);
cleanup();
diwt = new DFPA(iwt);
dowt = new DFPA(owt);
}
private void run()
{
FPA t1 = PA.Add(PA.InnerProduct(dinput, diwt), theta);
FPA ohidden = PA.Reciprocal(PA.Add(PA.Pow2(PA.Negate(t1)), 1.0f));
FPA t2 = PA.Add(PA.InnerProduct(ohidden, dowt), tau);
FPA ooutput = PA.Reciprocal(PA.Add(PA.Pow2(PA.Negate(t2)), 1.0f));
FPA oerror = PA.Subtract(doutput, ooutput);
FPA herror = PA.InnerProduct(dowt, PA.Transpose(oerror, new int[] { 1, 0 }));
herror = PA.InnerProduct(PA.Multiply(PA.Subtract(1.0f, t1), t1), herror);
FPA _owt = PA.Add(dowt, PA.Multiply(PA.InnerProduct(t1, oerror), betao));
FPA _iwt = PA.Multiply(PA.InnerProduct(herror, dinput), betah); //original dinput herror
dtau = PA.Add(PA.Multiply(betao, oerror), dtau);
dtheta = PA.Add(PA.Multiply(betah, herror), dtheta); //orig herror
PA.ToArray(_owt, out owt);
PA.ToArray(_iwt, out iwt);
diwt = new DFPA(owt);
dowt = new DFPA(iwt);
}
public float[] Test(float[] iinput)
{
float[,] tinput = new float[1, ni];
for (int i = 0; i < ni; i++)
{
tinput[0, i] = iinput[i];
}
dinput = new DFPA(tinput);
diwt = new DFPA(iwt);
dowt = new DFPA(owt);
dtheta = PA.Section(dtheta, new Slice(0, 1), new Slice(0, nh));
dtau = PA.Section(dtau, new Slice(0, 1), new Slice(0, no));
FPA t1 = PA.Add(PA.InnerProduct(dinput, diwt), dtheta);
FPA ohidden = PA.Reciprocal(PA.Add(PA.Pow2(PA.Negate(t1)), 1.0f));
FPA t2 = PA.Add(PA.InnerProduct(ohidden, dowt), dtau);
FPA ooutput = PA.Reciprocal(PA.Add(PA.Pow2(PA.Negate(t2)), 1.0f));
float[,] output;
float[] routput = new float[no];
PA.ToArray(ooutput, out output);
for (int i = 0; i < no; i++)
{
routput[i] = output[0, i];
}
/*Disposable Floating arrays need to be explicitly "disposed" */
dinput.Dispose();
diwt.Dispose();
dowt.Dispose();
doutput.Dispose();
/*Releasing all GPU Resources*/
PA.UnInit();
return(routput);
}
/*
* Function which performs all the GPU operations
*/
private void run()
{
/* Note : Inner product --- Matrix multiplication
* Multiply -- Element by element multiplication */
FPA t1 = PA.Add(PA.InnerProduct(dinput, diwt), dtheta);
/* ohidden is the output of hidden layer
* Only Sigmoid function is used for timebeing */
FPA ohidden = PA.Reciprocal(PA.Add(PA.Pow(new FPA(2.71828f, new int[] { numpat, nh }), PA.Negate(t1)), 1.0f));
FPA t2 = PA.Add(PA.InnerProduct(ohidden, dowt), dtau);
/* ooutput is the "actual" output of hidden layer
* Only Sigmoid function is used for timebeing */
FPA ooutput = PA.Reciprocal(PA.Add(PA.Pow(new FPA(2.71828f, new int[] { numpat, no }), PA.Negate(t2)), 1.0f));
/* Error between expected and actual */
FPA oerror = PA.Subtract(doutput, ooutput);
/* Checking if error has fallen below 1% if so terminatinf further cycles */
BoolParallelArray b = PA.All(PA.CompareGreater(derror, PA.Abs(oerror)), 1);
b = PA.All(b);
bool[] bt;
PA.ToArray(b, out bt);
if (bt[0] == true)
{
traincycles = 0;
}
/* herror is the error in the hidden layer */
FPA herror = PA.Transpose(PA.InnerProduct(dowt, PA.Transpose(oerror, new int[] { 1, 0 })), new int[] { 1, 0 });
herror = PA.Multiply(PA.Multiply(PA.Subtract(1.0f, ohidden), ohidden), herror);
/* Weights between hidden and output layer being updated */
FPA _owt = PA.Add(PA.Multiply(PA.InnerProduct(PA.Transpose(ohidden, new int[] { 1, 0 }), oerror), betao), dowt);
/* Weights between input and hidden layer being updated */
FPA _iwt = PA.Add(PA.Multiply(PA.InnerProduct(PA.Transpose(dinput, new int[] { 1, 0 }), herror), betah), diwt);
/*Updating threshold for output layer */
dtau = PA.Add(PA.Multiply(betao, oerror), dtau);
/*Updating threshold for hidden layer */
dtheta = PA.Add(PA.Multiply(betah, herror), dtheta);
/* Casting the Parallel arrays to normal arrays */
PA.ToArray(_owt, out owt);
PA.ToArray(_iwt, out iwt);
/* Rebuilding the disposable arrays from newly formed arrays */
diwt = new DFPA(iwt);
dowt = new DFPA(owt);
}
