C / C++ / MFC

Alison P wrote:

I knew you were going to say that!

int num1 = 0;
int num2 = 0;

<< (int)letter1 << endl;

<< (num1 + num2) << endl;
<< (num1 * num2) << endl;

cout << "The Value of num1 is : " << num1 << endl;

KERNEL FireLayer(CUDA_FLOATING_TYPE * inputs, CUDA_FLOATING_TYPE * weights, CUDA_FLOATING_TYPE * m, int mOffset, int totalNeuronsWithSelectiveActivation, CUDA_FLOATING_TYPE * outputs);

KERNEL FireOutputLayer(CUDA_FLOATING_TYPE * inputs, CUDA_FLOATING_TYPE * weights, CUDA_FLOATING_TYPE * m, int mOffset, int totalNeuronsWithSelectiveActivation, CUDA_FLOATING_TYPE * desiredOutputs, CUDA_FLOATING_TYPE * outputs, CUDA_FLOATING_TYPE * localGradient, CUDA_FLOATING_TYPE * rms, CUDA_FLOATING_TYPE * localGradientSpaceNet);

void KernelFireLayer(cudaStream_t stream, dim3 & gridSize, int blockSize, CUDA_FLOATING_TYPE * inputs, CUDA_FLOATING_TYPE * weights, CUDA_FLOATING_TYPE * m, int mOffset, int totalNeuronsWithSelectiveActivation, CUDA_FLOATING_TYPE * outputs, int numInputs);

void KernelFireOutputLayer(cudaStream_t stream, dim3 & gridSize, int blockSize, CUDA_FLOATING_TYPE * inputs, CUDA_FLOATING_TYPE * weights, CUDA_FLOATING_TYPE * m, int mOffset, int totalNeuronsWithSelectiveActivation, CUDA_FLOATING_TYPE * desiredOutputs, CUDA_FLOATING_TYPE * outputs, CUDA_FLOATING_TYPE * localGradient, CUDA_FLOATING_TYPE * rms, CUDA_FLOATING_TYPE * localGradientSpaceNet, int numInputs);

#include "../cuda.h"
#include "../MultipleBackPropagation.h"
#include "../../Common/CUDA/CudaDefinitions.h"
#include "../../Common/CUDA/Arrays/DeviceArray.h"
#include "../../Common/CUDA/Arrays/HostArray.h"

class CudaMultipleBackPropagation {
	private:
		class DeviceLayer {
			friend class CudaMultipleBackPropagation;

			private:
				static int neuronsWithSelectiveActivation;

				int patterns;
				int neurons;
				int inputs;
				int inputsWithoutBias;
				int connections;

				DeviceArray<CUDA_FLOATING_TYPE> weights;
				DeviceArray<CUDA_FLOATING_TYPE> bestWeights;
				DeviceArray<CUDA_FLOATING_TYPE> learnRate;
				DeviceArray<CUDA_FLOATING_TYPE> lastDelta;
				DeviceArray<CUDA_FLOATING_TYPE> lastDeltaWithoutLearningMomentum;
				DeviceArray<CUDA_FLOATING_TYPE> outputs;
				DeviceArray<CUDA_FLOATING_TYPE> localGradient;

				CUDA_FLOATING_TYPE * inputValues;
				CUDA_FLOATING_TYPE * desOutputs;
				CUDA_FLOATING_TYPE * m;
				int mOffset;				

				CUDA_FLOATING_TYPE * lgSpaceNet;
				CUDA_FLOATING_TYPE * rms;

				dim3 dimNeuronsPatterns;
				dim3 dimInputsNeurons;
				dim3 dimOutputsNeurons;

				int inputsBlockSize;
				int sharedMemFire;
				int sharedMemGradients;

				bool isOutputLayer;
			public:
				DeviceLayer(HostArray<CUDA_FLOATING_TYPE> & hweights, HostArray<CUDA_FLOATING_TYPE> & hlearnRate, HostArray<CUDA_FLOATING_TYPE> & hlastDelta, HostArray<CUDA_FLOATING_TYPE> & hlastDeltaWithoutLearningMomentum, DeviceArray<CUDA_FLOATING_TYPE> * layerInputs, int inputs, int neurons, int nextLayerNeurons, int patterns, CUDA_FLOATING_TYPE * m, int mOffset, CUDA_FLOATING_TYPE * lgSpaceNet) : weights(hweights), learnRate(hlearnRate), lastDelta(hlastDelta), lastDeltaWithoutLearningMomentum(hlastDeltaWithoutLearningMomentum), outputs(neurons * patterns), localGradient(neurons * patterns), dimNeuronsPatterns(neurons, patterns), dimInputsNeurons(inputs, neurons), bestWeights(hweights.Lenght()), dimOutputsNeurons(nextLayerNeurons, neurons) {
					connections = hweights.Lenght();

					this->m = m;
					this->mOffset = mOffset;
					this->lgSpaceNet = lgSpaceNet;

					this->inputs = inputs;					
					this->neurons = neurons;
					this->patterns = patterns;					
					inputsWithoutBias = inputs - 1;

	    	        inputsBlockSize = 1;
	                while(inputsBlockSize < MAX_THREADS_PER_BLOCK && inputsBlockSize < inputs) inputsBlockSize <<= 1;

					sharedMemFire = weights.Lenght() * sizeof(CUDA_FLOATING_TYPE);
					sharedMemGradients = (nextLayerNeurons * (neurons + 1)) * sizeof(CUDA_FLOATING_TYPE);

					inputValues = layerInputs->Pointer();

					desOutputs = rms = NULL;
					isOutputLayer = false;
				}

				void DefineOutputLayer(CudaMultipleBackPropagation * cmbp) {
					isOutputLayer = true;
					desOutputs = cmbp->d_desOutputs->Pointer();
					rms = cmbp->d_rms->Pointer();
					sharedMemFire += neurons * sizeof(CUDA_FLOATING_TYPE);
				}

				void Fire(cudaStream_t stream);

				void CalculateLocalGradient(cudaStream_t stream, CUDA_FLOATING_TYPE * rms, CUDA_FLOATING_TYPE * bestRMS, CUDA_FLOATING_TYPE rmsGrowToApplyRobustLearning, DeviceLayer * nextLayer);

				void CorrectWeights(cudaStream_t stream, int patternsBlockSize, CUDA_FLOATING_TYPE * rms, CUDA_FLOATING_TYPE * bestRMS, CUDA_FLOATING_TYPE rmsGrowToApplyRobustLearning, CUDA_FLOATING_TYPE robustFactor, CUDA_FLOATING_TYPE momentum);
		};

		List<DeviceLayer> layersSpaceNetwork;
		List<DeviceLayer> layers;

		Pointer< DeviceArray<CUDA_FLOATING_TYPE> > d_inputs;
		Pointer< DeviceArray<CUDA_FLOATING_TYPE> > d_desOutputs;
		Pointer< DeviceArray<CUDA_FLOATING_TYPE> > d_rms;
		Pointer< DeviceArray<CUDA_FLOATING_TYPE> > d_bestRMS;

		DeviceArray<CUDA_FLOATING_TYPE> d_rmsOut;

		CUDA_FLOATING_TYPE * rms;

		// Robust learning
		Pointer< DeviceArray<int> > d_numberWeightsLayer;
		Pointer< DeviceArray<CUDA_FLOATING_TYPE *> > d_weightsLayers;
		Pointer< DeviceArray<CUDA_FLOATING_TYPE *> > d_bestWeightsLayers;
		Pointer< DeviceArray<CUDA_FLOATING_TYPE *> > d_learnRatesLayers; 
		Pointer< DeviceArray<CUDA_FLOATING_TYPE *> > d_lastDeltaLayers;
		Pointer< DeviceArray<CUDA_FLOATING_TYPE *> > d_lastDeltaWithoutLMlayers;

		cudaStream_t streamKernels;
		cudaStream_t streamRMS;

		int layersRobustTraining;
		int maxNumberWeigths;
		int patternsBlockSize;
		CUDA_FLOATING_TYPE numberPatternsNeurons;

		void CreateDeviceLayers(List<Layer> & hostLayers, List<DeviceLayer> & deviceLayers, int patterns, int * neuronsWithSelectiveActivation);
		void CopyLayersToHost(List<DeviceLayer> & deviceLayers, List<Layer> & hostLayers);

	public:
		CudaMultipleBackPropagation(Pointer <MultipleBackPropagation> & mbp, Matrix<double> & trainInputPatterns, Matrix<double> & trainDesiredOutputPatterns);

		~CudaMultipleBackPropagation();

		void Train(double momentum, double spaceMomentum, bool robustLearning, double rmsGrowToApplyRobustLearning, double robustFactor);

		CUDA_FLOATING_TYPE GetRMS() {
			return *rms;
		}

		void CopyNetworkHost(Pointer <MultipleBackPropagation> & mbp);
};

#endif

#include "CudaMultipleBackPropagation.h"
#include "MBPkernels.h"

/*#define TRY_SOLVE_PROBLEM

#ifdef TRY_SOLVE_PROBLEM

#include <afxadv.h>
#include <afxole.h>

void ShowArrayInfo(HostArray<CUDA_FLOATING_TYPE> & ha, int startingElements, int finalElements) {
	CString s;

	for(int i = 0; i < startingElements; i++) {
		CString v;
		v.Format(L"%f\t", ha[i]);
		s += v;
	}

	s+= L"...\t";

	for(int i = 0; i < finalElements; i++) {
		CString v;
		v.Format(L"%f\t", ha[ha.Lenght() - finalElements + i]);
		s += v;
	}

	AfxMessageBox(s);
}

void ShowArrayInfo(DeviceArray<CUDA_FLOATING_TYPE> & da, int startingElements, int finalElements) {
	HostArray<CUDA_FLOATING_TYPE> ha(da);
	ShowArrayInfo(ha, startingElements, finalElements);
}

void ShowInfo(int v) {
	CString s;
	s.Format(L"%d", v);
	AfxMessageBox(s);
}

#endif*/

int CudaMultipleBackPropagation::DeviceLayer::neuronsWithSelectiveActivation = 0;

void CudaMultipleBackPropagation::DeviceLayer::Fire(cudaStream_t stream) {
	if (isOutputLayer) {
		if(connections > MAX_THREADS_PER_BLOCK) {
			KernelFireOutputLayer(stream, dimNeuronsPatterns, inputsBlockSize, inputValues, weights.Pointer(), m, mOffset, neuronsWithSelectiveActivation, desOutputs, outputs.Pointer(), localGradient.Pointer(), rms, lgSpaceNet, inputsWithoutBias);
		} else {
			FireOutputLayer<<<patterns, dimInputsNeurons, sharedMemFire, stream>>>(inputValues, weights.Pointer(), m, mOffset, neuronsWithSelectiveActivation, desOutputs, outputs.Pointer(), localGradient.Pointer(), rms, lgSpaceNet);
		}
	} else {
		if(connections > MAX_THREADS_PER_BLOCK) {
			KernelFireLayer(stream, dimNeuronsPatterns, inputsBlockSize, inputValues, weights.Pointer(), m, mOffset, neuronsWithSelectiveActivation, outputs.Pointer(), inputsWithoutBias);
		} else {
			FireLayer<<<patterns, dimInputsNeurons, sharedMemFire, stream>>>(inputValues, weights.Pointer(), m, mOffset, neuronsWithSelectiveActivation, outputs.Pointer());
		}
	}
}

void CudaMultipleBackPropagation::DeviceLayer::CalculateLocalGradient(cudaStream_t stream, CUDA_FLOATING_TYPE * rms, CUDA_FLOATING_TYPE * bestRMS, CUDA_FLOATING_TYPE rmsGrowToApplyRobustLearning, DeviceLayer * nextLayer) {
	::CalculateLocalGradient<<<patterns, dimOutputsNeurons, sharedMemGradients, stream>>>(rms, bestRMS, rmsGrowToApplyRobustLearning, outputs.Pointer(), nextLayer->weights.Pointer(), m, mOffset, neuronsWithSelectiveActivation, nextLayer->localGradient.Pointer(), localGradient.Pointer(), lgSpaceNet);
}

void CudaMultipleBackPropagation::DeviceLayer::CorrectWeights(cudaStream_t stream, int patternsBlockSize, CUDA_FLOATING_TYPE * rms, CUDA_FLOATING_TYPE * bestRMS, CUDA_FLOATING_TYPE rmsGrowToApplyRobustLearning, CUDA_FLOATING_TYPE robustFactor, CUDA_FLOATING_TYPE momentum) {
	KernelCorrectLayerWeights(stream, dimInputsNeurons, patternsBlockSize, rms, bestRMS, rmsGrowToApplyRobustLearning, inputValues, localGradient.Pointer(), weights.Pointer(), learnRate.Pointer(), lastDeltaWithoutLearningMomentum.Pointer(), lastDelta.Pointer(), (CUDA_FLOATING_TYPE) Connection::u, (CUDA_FLOATING_TYPE) Connection::d, robustFactor, momentum, patterns);	
}

void CudaMultipleBackPropagation::CreateDeviceLayers(List<Layer> & hostLayers, List<DeviceLayer> & deviceLayers, int patterns, int * neuronsWithSelectiveActivation) {
	Layer * l = hostLayers.First();
	int inputsWithoutBias = l->neurons.Lenght();

	DeviceArray<CUDA_FLOATING_TYPE> * layerInputs = d_inputs;

	DeviceLayer * outputLayerSpaceNetwork = layersSpaceNetwork.Last();

	CUDA_FLOATING_TYPE * m = (neuronsWithSelectiveActivation == NULL) ? NULL : outputLayerSpaceNetwork->outputs.Pointer();
	CUDA_FLOATING_TYPE * lgSpaceNet = (neuronsWithSelectiveActivation == NULL) ? NULL : outputLayerSpaceNetwork->localGradient.Pointer();
	int mOffset = 0;
	
	Layer * nextLayer = hostLayers.Next();
	for (int ln = 1; (l = nextLayer) != NULL; ln++) {
		int neurons = l->neurons.Lenght();
		int inputs = inputsWithoutBias + 1;
		int connections = inputs * neurons;

		if (connections > maxNumberWeigths) maxNumberWeigths = connections;

		HostArray<CUDA_FLOATING_TYPE> weights(connections);
		HostArray<CUDA_FLOATING_TYPE> learningRate(connections);
		HostArray<CUDA_FLOATING_TYPE> lDelta(connections);
		HostArray<CUDA_FLOATING_TYPE> lastDeltaWithoutLearningMomentum(connections);
		int w = 0;

		for(NeuronWithInputConnections * n = static_cast<NeuronWithInputConnections *> (l->neurons.First()); n != NULL; n = static_cast<NeuronWithInputConnections *> (l->neurons.Next())) {
			for(Connection * c = n->inputs.First(); c != NULL; c = n->inputs.Next()) {
				weights[w] = (CUDA_FLOATING_TYPE) c->weight;
				learningRate[w] = (CUDA_FLOATING_TYPE) c->learningRate;
				lDelta[w] = (CUDA_FLOATING_TYPE) c->delta;
				lastDeltaWithoutLearningMomentum[w] = (CUDA_FLOATING_TYPE) c->lastDeltaWithoutLearningMomentum;

				w++;
			}
		}

		int numberNeuronsWithSelectiveActivation =  (m == NULL) ? 0 : neuronsWithSelectiveActivation[ln];
		CUDA_FLOATING_TYPE * ml = (numberNeuronsWithSelectiveActivation) ? m : NULL;
		CUDA_FLOATING_TYPE * lgSpaceNetl = (numberNeuronsWithSelectiveActivation) ? lgSpaceNet : NULL;

		nextLayer = hostLayers.Next();
		int nextLayerNeurons = (nextLayer == NULL) ? 0 : nextLayer->neurons.Lenght();

		DeviceLayer * dl = new DeviceLayer(weights, learningRate, lDelta, lastDeltaWithoutLearningMomentum, layerInputs, inputs, neurons, nextLayerNeurons, patterns, ml, mOffset, lgSpaceNetl);
		deviceLayers.Add(dl);

		mOffset += numberNeuronsWithSelectiveActivation;

		layerInputs = &(dl->outputs);
		inputsWithoutBias = neurons;
	}
}

CudaMultipleBackPropagation::CudaMultipleBackPropagation(Pointer <MultipleBackPropagation> & mbp, Matrix<double> & trainInputPatterns, Matrix<double> & trainDesiredOutputPatterns) : d_rmsOut(1) {
    /*******
    Create the device vectors : d_inputs, d_desOutputs
    *******/
	int patterns = trainInputPatterns.Rows();
	int ninputs = mbp->Inputs();
	int noutputs = mbp->Outputs();

	HostArray<CUDA_FLOATING_TYPE> inputs(ninputs * patterns);
	HostArray<CUDA_FLOATING_TYPE> desiredOutputs(noutputs * patterns);

	for(int p = 0; p < patterns; p++) {
		for (int i = 0; i < ninputs; i++) inputs[p * ninputs + i] = (CUDA_FLOATING_TYPE) trainInputPatterns[p][i];
		for (int o = 0; o < noutputs; o++) desiredOutputs[p * noutputs + o] = (CUDA_FLOATING_TYPE) trainDesiredOutputPatterns[p][o];
	}

	d_inputs = new DeviceArray<CUDA_FLOATING_TYPE>(inputs);
	d_desOutputs = new DeviceArray<CUDA_FLOATING_TYPE>(desiredOutputs);

    /*******
    Create the device layers
    *******/
	maxNumberWeigths = 0;

	int * neuronsWithSelectiveActivation = NULL;

	if (!mbp->spaceNetwork.IsNull()) {
		CreateDeviceLayers(mbp->spaceNetwork->layers, layersSpaceNetwork, patterns, NULL);
		neuronsWithSelectiveActivation = mbp->neuronsWithSelectiveActivation.Pointer();
		DeviceLayer::neuronsWithSelectiveActivation = layersSpaceNetwork.Last()->neurons;
	}

	CreateDeviceLayers(mbp->layers, layers, patterns, neuronsWithSelectiveActivation);

	DeviceLayer * dlOut = layers.Last();

    /*******
    Robust Learning
    *******/
	layersRobustTraining = layersSpaceNetwork.Lenght() + layers.Lenght();

	HostArray<int> numberWeightsLayer(layersRobustTraining);	
	HostArray<CUDA_FLOATING_TYPE *> weightsLayers(layersRobustTraining);
	HostArray<CUDA_FLOATING_TYPE *> bestWeightsLayers(layersRobustTraining);
	HostArray<CUDA_FLOATING_TYPE *> learnRatesLayers(layersRobustTraining);
	HostArray<CUDA_FLOATING_TYPE *> lastDeltaLayers(layersRobustTraining);
	HostArray<CUDA_FLOATING_TYPE *> lastDeltaWithoutLMlayers(layersRobustTraining);

	int ll = 0;
	for(DeviceLayer * l = layersSpaceNetwork.First(); l != NULL; l = layersSpaceNetwork.Next()) {		
		numberWeightsLayer[ll] = l->connections;
		weightsLayers[ll] = l->weights.Pointer();
		bestWeightsLayers[ll] = l->bestWeights.Pointer();
		learnRatesLayers[ll] = l->learnRate.Pointer();
		lastDeltaLayers[ll] = l->lastDelta.Pointer();
		lastDeltaWithoutLMlayers[ll] = l->lastDeltaWithoutLearningMomentum.Pointer();

		ll++;
	}
	for(DeviceLayer * l = layers.First(); l != NULL; l = layers.Next()) {
		numberWeightsLayer[ll] = l->connections;
		weightsLayers[ll] = l->weights.Pointer();
		bestWeightsLayers[ll] = l->bestWeights.Pointer();
		learnRatesLayers[ll] = l->learnRate.Pointer();
		lastDeltaLayers[ll] = l->lastDelta.Pointer();
		lastDeltaWithoutLMlayers[ll] = l->lastDeltaWithoutLearningMomentum.Pointer();

		ll++;
	}

	d_numberWeightsLayer = new DeviceArray<int>(numberWeightsLayer);
	d_weightsLayers = new DeviceArray<CUDA_FLOATING_TYPE *>(weightsLayers);
	d_bestWeightsLayers = new DeviceArray<CUDA_FLOATING_TYPE *>(bestWeightsLayers);
	d_learnRatesLayers = new DeviceArray<CUDA_FLOATING_TYPE *>(learnRatesLayers);
	d_lastDeltaLayers = new DeviceArray<CUDA_FLOATING_TYPE *>(lastDeltaLayers);
	d_lastDeltaWithoutLMlayers = new DeviceArray<CUDA_FLOATING_TYPE *>(lastDeltaWithoutLMlayers);

    /*******
    Create the RMS vectors
    *******/
	int sizeRMSvector = (dlOut->connections > MAX_THREADS_PER_BLOCK) ? patterns * dlOut->neurons : patterns;
	d_rms = new DeviceArray<CUDA_FLOATING_TYPE>(sizeRMSvector);

	dlOut->DefineOutputLayer(this);

	HostArray<CUDA_FLOATING_TYPE> h_bestRMS(1);
	h_bestRMS[0] = (patterns * CUDA_VALUE(3.0));
	d_bestRMS = new DeviceArray<CUDA_FLOATING_TYPE>(h_bestRMS);

	cudaMallocHost((void**) &rms, sizeof(CUDA_FLOATING_TYPE));
	*rms = CUDA_VALUE(1.0);

    /*******
    Other stuff and streams
    *******/
	patternsBlockSize = 1;
	while(patternsBlockSize < MAX_THREADS_PER_BLOCK && patternsBlockSize < patterns) patternsBlockSize <<= 1;

	numberPatternsNeurons = (CUDA_FLOATING_TYPE) patterns * (CUDA_FLOATING_TYPE) dlOut->neurons;

	cudaStreamCreate(&streamKernels);
	cudaStreamCreate(&streamRMS);
}

CudaMultipleBackPropagation::~CudaMultipleBackPropagation() {
	cudaStreamDestroy(streamKernels);
	cudaStreamDestroy(streamRMS);

	*rms = CUDA_VALUE(1.0);
	cudaFreeHost(rms);
}

void CudaMultipleBackPropagation::Train(double momentum, double spaceMomentum, bool robustLearning, double rmsGrowToApplyRobustLearning, double robustFactor) {
	/*******
    Determine the network outputs
    *******/
	for(DeviceLayer * l = layersSpaceNetwork.First(); l != NULL; l = layersSpaceNetwork.Next()) l->Fire(streamKernels);
	for(DeviceLayer * l = layers.First(); l != NULL; l = layers.Next()) l->Fire(streamKernels);

    /*******
    Calculate the RMS / Robust training
    *******/
	if (robustLearning) {
		KernelCalculateRMS(streamKernels, patternsBlockSize, d_rms->Pointer(), d_rmsOut.Pointer(), d_rms->Lenght(), numberPatternsNeurons);
	    if (cudaStreamQuery(streamRMS) == cudaSuccess) cudaMemcpyAsync(rms, d_rmsOut.Pointer(), sizeof(CUDA_FLOATING_TYPE), cudaMemcpyDeviceToHost, streamRMS);
		
		RobustLearning<<<1, maxNumberWeigths, 0, streamKernels>>>(d_rmsOut.Pointer(), d_bestRMS->Pointer(), (CUDA_FLOATING_TYPE) rmsGrowToApplyRobustLearning, layersRobustTraining, d_numberWeightsLayer->Pointer(), d_weightsLayers->Pointer(), d_bestWeightsLayers->Pointer(), d_learnRatesLayers->Pointer(), robustFactor, d_lastDeltaWithoutLMlayers->Pointer(), d_lastDeltaLayers->Pointer());
	} else {
		if (cudaStreamQuery(streamRMS) == cudaSuccess) {
			KernelCalculateRMS(streamRMS, patternsBlockSize, d_rms->Pointer(), d_rmsOut.Pointer(), d_rms->Lenght(), numberPatternsNeurons);
			cudaMemcpyAsync(rms, d_rmsOut.Pointer(), sizeof(CUDA_FLOATING_TYPE), cudaMemcpyDeviceToHost, streamRMS);
		}
	}

	/*******
	Calculate local gradients. The local gradient for the output layer was already calculated.
	*******/
	CUDA_FLOATING_TYPE * rms = (robustLearning) ? d_rmsOut.Pointer() : NULL;
	CUDA_FLOATING_TYPE * bestRMS = (robustLearning) ? d_bestRMS->Pointer() : NULL;

	DeviceLayer * nextLayer = layers.Last();
	for(DeviceLayer * l = layers.Previous(); l != NULL; l = layers.Previous()) {
		l->CalculateLocalGradient(streamKernels, rms, bestRMS, (CUDA_FLOATING_TYPE) rmsGrowToApplyRobustLearning, nextLayer);
		nextLayer = l;
	}

	nextLayer = layersSpaceNetwork.Last();
	for(DeviceLayer * l = layersSpaceNetwork.Previous(); l != NULL; l = layersSpaceNetwork.Previous()) {
		l->CalculateLocalGradient(streamKernels, rms, bestRMS, (CUDA_FLOATING_TYPE) rmsGrowToApplyRobustLearning, nextLayer);
		nextLayer = l;
	}

	/*******
	Train the network
	*******/
	for(DeviceLayer * l = layers.Last(); l != NULL; l = layers.Previous()) l->CorrectWeights(streamKernels, patternsBlockSize, rms, bestRMS, rmsGrowToApplyRobustLearning, robustFactor, momentum);
	for(DeviceLayer * l = layersSpaceNetwork.Last(); l != NULL; l = layersSpaceNetwork.Previous()) l->CorrectWeights(streamKernels, patternsBlockSize, rms, bestRMS, rmsGrowToApplyRobustLearning, robustFactor, spaceMomentum);
}

void CudaMultipleBackPropagation::CopyLayersToHost(List<DeviceLayer> & deviceLayers, List<Layer> & hostLayers) {
	hostLayers.First();

	for(DeviceLayer * l = deviceLayers.First(); l != NULL; l = layers.Next()) {
		Layer * hl = hostLayers.Next();

		HostArray<CUDA_FLOATING_TYPE> dweights(l->weights);
		HostArray<CUDA_FLOATING_TYPE> dlearnRate(l->learnRate);
		HostArray<CUDA_FLOATING_TYPE> dlastDelta(l->lastDelta);
		HostArray<CUDA_FLOATING_TYPE> dlastDeltaWithoutLearningMomentum(l->lastDeltaWithoutLearningMomentum);

		int w = 0;
		for(NeuronWithInputConnections * n = static_cast<NeuronWithInputConnections *> (hl->neurons.First()); n != NULL; n = static_cast<NeuronWithInputConnections *> (hl->neurons.Next())) {
			for(Connection * c = n->inputs.First(); c != NULL; c = n->inputs.Next()) {
				c->weight = dweights[w];
				c->learningRate = dlearnRate[w];
				c->delta = dlastDelta[w];
				c->lastDeltaWithoutLearningMomentum = dlastDeltaWithoutLearningMomentum[w];

				w++;
			}
		}
	}
}

void CudaMultipleBackPropagation::CopyNetworkHost(Pointer <MultipleBackPropagation> & mbp) {
	if (!mbp->spaceNetwork.IsNull()) CopyLayersToHost(layersSpaceNetwork, mbp->spaceNetwork->layers);
	CopyLayersToHost(layers, mbp->layers);

void CudaMultipleBackPropagation::DeviceLayer::Fire(cudaStream_t stream) {
	if (isOutputLayer) {
		if(connections > MAX_THREADS_PER_BLOCK) {
			KernelFireOutputLayer(stream, dimNeuronsPatterns, inputsBlockSize, inputValues, weights.Pointer(), m, mOffset, neuronsWithSelectiveActivation, desOutputs, outputs.Pointer(), localGradient.Pointer(), rms, lgSpaceNet, inputsWithoutBias);
		} else {
			FireOutputLayer<<<patterns, dimInputsNeurons, sharedMemFire, stream>>>(inputValues, weights.Pointer(), m, mOffset, neuronsWithSelectiveActivation, desOutputs, outputs.Pointer(), localGradient.Pointer(), rms, lgSpaceNet);
		}
	} else {
		if(connections > MAX_THREADS_PER_BLOCK) {
			KernelFireLayer(stream, dimNeuronsPatterns, inputsBlockSize, inputValues, weights.Pointer(), m, mOffset, neuronsWithSelectiveActivation, outputs.Pointer(), inputsWithoutBias);
		} else {
			FireLayer<<<patterns, dimInputsNeurons, sharedMemFire, stream>>>(inputValues, weights.Pointer(), m, mOffset, neuronsWithSelectiveActivation, outputs.Pointer());
		}
	}
}



<pre>KERNEL FireLayer(CUDA_FLOATING_TYPE * inputs, CUDA_FLOATING_TYPE * weights, CUDA_FLOATING_TYPE * m, int mOffset, int totalNeuronsWithSelectiveActivation, CUDA_FLOATING_TYPE * outputs) {
    extern __shared__ CUDA_FLOATING_TYPE iw[];
    
	int connection = NEURON * NUM_INPUTS_INCLUDING_BIAS + INPUT;

	SumInputWeight(connection, inputs, weights);
    
    /*******
     For each layer neuron, calculate its output. Results for neuron n will be held on outputs[].
     Note that outputs[] will contain the layer neuron outputs for all the patterns.
    *******/
	if (INPUT == 0) {
		int n = PATTERN * NUM_NEURONS + NEURON;

		CUDA_FLOATING_TYPE output = CUDA_SIGMOID(iw[THREAD_ID]);
		if (m != NULL) output *= m[PATTERN * totalNeuronsWithSelectiveActivation + NEURON + mOffset];
		outputs[n] = output;
	}
}

</pre>
<div class="signature"><div class="modified">modified on Sunday, March 28, 2010 7:27 PM</div></div>

Krzysiaczek99 wrote:

:Fire(cudaStream_t stream) has just 2 parameters

Krzysiaczek99 wrote:

But i dont know what this __entry means.