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); }
public FPA LearningRate(FPA t) //Epsilon(t) { FPA exponent = -t / m_Theta; FPA Epsilont = m_EpsilonInitial * PA.Pow2(PA.Log2(LogBase) * exponent); return(Epsilont); }
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 FPA NeighborhoodRatio(FPA t) //Sigma(t) { FPA exponent = -t / m_Theta; FPA Sigmat = m_SigmaInitial * PA.Pow2(PA.Log2(LogBase) * exponent); return(Sigmat); }
public void Terminate() { dinput.Dispose(); diwt.Dispose(); dowt.Dispose(); doutput.Dispose(); PA.UnInit(); }
public KohonenMap(int width, int height, int pattern_length, GPUK_COMPUTATION_TYPE CompType) { m_Width = width; m_Height = height; m_PatternLength = pattern_length; m_CompType = CompType; PA.InitGPU(); InitMap(); }
/* * 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); }
}//Init public void FindBMU() { //Useful locals int alen = m_Height * m_Width; //Compute the distances from pattern to code vectors FPA a = PA.AddDimension(m_CurrentPatternGPU, 1); FPA x = PA.Stretch(a, 1, alen); FPA pol = PA.Subtract(m_Weights, x); FPA pol2 = PA.Multiply(pol, pol); FPA pol3 = PA.Sum(pol2, 0); m_Distances = PA.Sqrt(pol3); //Find the minimal distance FPA dist2 = PA.AddDimension(m_Distances, 1); FPA minval = PA.MinVal(dist2, 0); FPA xxx = PA.Stretch(minval, alen); //Prepare trigger array BPA trigger = PA.CompareEqual(xxx, m_Distances); //BMU Coord ZEROS BPA trigger2 = PA.AddDimension(trigger, 0); BPA trigger3 = PA.Stretch(trigger2, 2, 1); //Extract BMU Coord FPA lol = PA.Cond(trigger3, m_Shape, zeros); m_BMUCoord = PA.Sum(lol, 1); //m_BMUCoord = PA.Evaluate(m_BMUCoord); //BMU Code Vector ZEROS BPA triggercv2 = PA.AddDimension(trigger, 0); BPA triggercv3 = PA.Stretch(triggercv2, m_PatternLength, 1); //Extract BMU code vector FPA mdr = PA.Cond(triggercv3, m_Weights, zeroscv); m_BMUCodeVector = PA.Sum(mdr, 1); //m_BMUCodeVector = PA.Evaluate(m_BMUCodeVector); //Begin computation ! ^^ AND OUTPUT PA.Evaluate(m_BMUCodeVector); PA.Evaluate(m_BMUCoord); }
public Bitmap GetBitmap() { Bitmap bm = new Bitmap(m_Height, m_Width); float[,] polbak = new float[80, 80]; PA.ToArray(m_Weights, out polbak); for (int i = 0; i < m_Height; ++i) { for (int j = 0; j < m_Width; ++j) { bm.SetPixel(j, i, Color.FromArgb((int)Math.Floor(Math.Min(Math.Max(polbak[0, m_Width * i + j], 0), 255)), (int)Math.Floor(Math.Min(Math.Max(polbak[1, m_Width * i + j], 0), 255)), (int)Math.Floor(Math.Min(Math.Max(polbak[2, m_Width * i + j], 0), 255)) )); } } return(bm); }
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); }
public void start() { init(); PA.InitGPU(); dinput = new DFPA(input); doutput = new DFPA(output); diwt = new DFPA(iwt); dowt = new DFPA(owt); while (traincycles-- > 0) { run(); } cleanup(); PA.UnInit(); }
public void DoEpoch(int nb) { int alen = m_Width * m_Height; for (int i = 0; i < nb; ++i) { //Neighborhood Function FPA sbmuc = PA.AddDimension(m_BMUCoord, 1); sbmuc = PA.Stretch(sbmuc, 1, alen); sbmuc = sbmuc - m_Shape; sbmuc = PA.Pow2(sbmuc); FPA sqdist = PA.Sum(sbmuc, 0); sqdist = PA.AddDimension(sqdist, 0); sqdist = PA.Stretch(sqdist, m_PatternLength, 1); //PA.Evaluate(sqdist); // //Learning Rate FPA lrate = new FPA((float)_CPU_LearningRate(m_time_val), m_PatternLength, alen); /*FPA sLearningRate = PA.AddDimension(LearningRate(m_Time), 1); * sLearningRate = PA.Stretch(sLearningRate, m_PatternLength, alen);*/ //Difference between units and current pattern FPA a = PA.AddDimension(m_CurrentPatternGPU, 1); FPA x = PA.Stretch(a, 1, alen); FPA pol = x - m_Weights; //Calcul des deltas FPA deltaW = lrate * pol; //Mise à jour des poids m_Weights = m_Weights + deltaW; //Incrémente le compteur de temps //m_Time = PA.Add(m_Time, 1.0f); m_time_val += 1; } }
/* * Entry Function */ public void start() { /* Initialisation of all layers*/ init(); /*Normalisation of weights */ normali(); normalo(); /*Initialisation of GPU*/ PA.InitGPU(); /*Measurement starts*/ QueryPerformanceCounter(ref timbeg); diwt = new DFPA(iwt); dowt = new DFPA(owt); dinput = new DFPA(input); doutput = new DFPA(output); /* Minimum permissible error */ derror = PA.Abs(PA.Multiply(doutput, 0.01f)); while (traincycles > 0) { traincycles--; numcycles++; run(); } long freq = 0; /*Measurement ends */ QueryPerformanceCounter(ref timend); QueryPerformanceFrequency(ref freq); _timtaken = (timend - timbeg) * 1.0 / freq; }
public float[] GetBMUCodeVector() { PA.ToArray(m_BMUCodeVector, out m_bmucodevector_vals); return(m_bmucodevector_vals); }