/* * 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); }
public override void FindBMU() { //Normalize the weight vector FloatParallelArray transpose = ParallelArrays.Transpose(m_GPUWeight, 1, 0); FloatParallelArray weightsq = ParallelArrays.InnerProduct(m_GPUWeight, ParallelArrays.Transpose(m_GPUWeight, 1, 0)); FloatParallelArray weightsum = ParallelArrays.Sum(weightsq, 0); FloatParallelArray weightlength = ParallelArrays.Sqrt(weightsum); weightlength = ParallelArrays.Stretch(ParallelArrays.AddDimension(weightlength, 1), 1, m_Parent.DataSource.PatternLength); FloatParallelArray weightnorm = ParallelArrays.Divide(m_GPUWeight, weightlength); weightnorm = ParallelArrays.Transpose(weightnorm, 1, 0); //Normalize the input vector FloatParallelArray inputsq = ParallelArrays.InnerProduct(m_GPUInput, ParallelArrays.Transpose(m_GPUInput, 1, 0)); FloatParallelArray inputsum = ParallelArrays.Sum(inputsq, 0); FloatParallelArray inputlength = ParallelArrays.Sqrt(inputsum); inputlength = ParallelArrays.Stretch(ParallelArrays.AddDimension(inputlength, 1), 1, m_Parent.DataSource.PatternLength); FloatParallelArray inputnorm = ParallelArrays.Divide(m_GPUInput, inputlength); FloatParallelArray pacc = ParallelArrays.InnerProduct(inputnorm, weightnorm); //Replication bug here... FloatParallelArray bmxval = ParallelArrays.MaxVal(pacc, 1); //MSR Vivian Swelson workaround DisposableFloatParallelArray bmxvalEvaluated = ParallelArrays.Evaluate(bmxval); bmxval = ParallelArrays.AddDimension(bmxvalEvaluated, 1); bmxval = ParallelArrays.Stretch(bmxval, 1, m_Parent.NeuronMap.GetLength(0)); //Winner matrix (0 = winner) FloatParallelArray pwinner = ParallelArrays.Subtract(pacc, bmxval); //Convert to 1 = winner, 0 otherwise FloatParallelArray zero = new FloatParallelArray(0.0f, pwinner.Shape); FloatParallelArray one = new FloatParallelArray(1.0f, pwinner.Shape); BoolParallelArray bmask = ParallelArrays.CompareEqual(pwinner, zero); m_PWinner = ParallelArrays.Cond(bmask, one, zero); }