//layer2 plane0=0 "planes not both -1 and planes not both 1" // weights = plane0*(-1) + plane1*(-1) // plane1=1 "planes both -1 or planes both 1" // weights = plane0*(1) + plane1*(1) TEST( testlogicaloperators, Convolve_2layers_relu_Xor ) { cout << "Xor, convolve" << endl; // LogicalDataCreator ldc(new TanhActivation()); // ldc.applyXorGate(); // int imageSize = 1; // int inPlanes = 2; int numExamples = 4; // int filterSize = 1; float data[] = { -1, -1, -1, 1, 1, -1, 1, 1 }; float layer1weights[] = { // going to preset these, to near an optimal solution, // and at least show the network is stable, and gives the correct -0.4f,-0.55f, // result... 0.52f, 0.53f, }; float layer1bias[] = { 0.1f, -0.1f }; float layer2weights[] = { 1.1f, 0.9f, -0.8f, -1.2f }; float layer2bias[] = { 0.1f, 1.1 }; float expectedOutput[] = { 1, 0, 0, 1, 0, 1, 1, 0 }; int labels[] = { 0, 1, 1, 0 }; EasyCL *cl = EasyCL::createForFirstGpuOtherwiseCpu(); ClBlasInstance blasInstance; NeuralNet *net = NeuralNet::maker(cl)->planes(2)->imageSize(1)->instance(); net->addLayer( ConvolutionalMaker::instance()->numFilters(2)->filterSize(1)->biased(1) ); net->addLayer( ActivationMaker::instance()->relu() ); net->addLayer( ConvolutionalMaker::instance()->numFilters(2)->filterSize(1)->biased(1) ); net->addLayer( ActivationMaker::instance()->relu() ); net->addLayer( SquareLossMaker::instance() );; cout << "hand-setting weights..." << endl; net->initWeights( 1, layer1weights, layer1bias ); net->initWeights( 3, layer2weights, layer2bias ); // net->printWeights(); // net->setBatchSize(4); // net->forward( data ); // net->print(); SGD *sgd = SGD::instance( cl, 0.1f, 0 ); for( int epoch = 0; epoch < 200; epoch++ ) { net->epochMaker(sgd)->batchSize(numExamples)->numExamples(numExamples)->inputData(data) ->expectedOutputs(expectedOutput)->run( epoch ); if( epoch % 5 == 0 ) cout << "Loss L " << net->calcLoss(expectedOutput) << endl; } net->print(); AccuracyHelper::printAccuracy( numExamples, 2, labels, net->getOutput() ); float loss = net->calcLoss(expectedOutput); cout << "loss, E, " << loss << endl; EXPECT_GE( 0.0000001f, loss ); delete sgd; delete net; delete cl; }
TEST(testbackward, squareloss) { // here's the plan: // generate some input, randomly // generate some expected output, randomly // forward propagate // calculate loss // calculate gradInput // change some of the inputs, forward prop, recalculate loss, check corresponds // to the gradient EasyCL *cl = EasyCL::createForFirstGpuOtherwiseCpu(); NeuralNet *net = new NeuralNet(cl, 3, 5); net->addLayer(ForceBackpropLayerMaker::instance()); net->addLayer(SquareLossMaker::instance()); cout << net->asString() << endl; int batchSize = 32; net->setBatchSize(batchSize); int inputCubeSize = net->getInputCubeSize(); int outputCubeSize = net->getOutputCubeSize(); int inputTotalSize = inputCubeSize * batchSize; int outputTotalSize = outputCubeSize * batchSize; cout << "inputtotalsize=" << inputTotalSize << " outputTotalSize=" << outputTotalSize << endl; float *input = new float[inputTotalSize]; float *expectedOutput = new float[outputTotalSize]; WeightRandomizer::randomize(0, input, inputTotalSize, -2.0f, 2.0f); WeightRandomizer::randomize(1, expectedOutput, outputTotalSize, -2.0f, 2.0f); // now, forward prop // net->input(input); net->forward(input); net->print(); // net->printOutput(); // calculate loss float lossBefore = net->calcLoss(expectedOutput); // calculate gradInput net->backward(expectedOutput); // modify input slightly mt19937 random; const int numSamples = 10; for(int i = 0; i < numSamples; i++) { int inputIndex; WeightRandomizer::randomizeInts(i, &inputIndex, 1, 0, inputTotalSize); // cout << "i=" << i << " index " << inputIndex << endl; float oldValue = input[inputIndex]; // grad for this index is.... float grad = net->getLayer(2)->getGradInput()[inputIndex]; // cout << "grad=" << grad << endl; // tweak slightly float newValue = oldValue * 1.01f; float inputDelta = newValue - oldValue; float predictedLossChange = inputDelta * grad; input[inputIndex] = newValue; // cout << "oldvalue=" << oldValue << " newvalue=" << newValue << endl; // forwardProp net->forward(input); input[inputIndex] = oldValue; // net->printOutput(); float lossAfter = net->calcLoss(expectedOutput); float lossChange = lossAfter - lossBefore; cout << "idx=" << inputIndex << " predicted losschange=" << predictedLossChange << " actual=" << lossChange << endl; } delete[] expectedOutput; delete[] input; delete net; delete cl; }
void testNumerically(float learningRate, int batchSize, int imageSize, int filterSize, int numPlanes, ActivationFunction *fn, bool padZeros, int its = 20) { EasyCL *cl = EasyCL::createForFirstGpuOtherwiseCpu(); ClBlasInstance clblasInstance; NeuralNet *net = NeuralNet::maker(cl)->planes(numPlanes)->imageSize(imageSize)->instance(); net->addLayer(ConvolutionalMaker::instance()->numFilters(1)->filterSize(filterSize)->biased(0)->padZeros(padZeros)); net->addLayer(ActivationMaker::instance()->fn(fn)); net->addLayer(ConvolutionalMaker::instance()->numFilters(1)->filterSize(filterSize)->biased(0)->padZeros(padZeros)); net->addLayer(ActivationMaker::instance()->fn(fn)); net->addLayer(SquareLossMaker::instance()); net->setBatchSize(batchSize); int inputNumElements = net->getLayer(0)->getOutputNumElements(); int outputNumElements = net->getLastLayer()->getOutputNumElements(); int weightsSize1 = net->getLayer(1)->getWeightsSize(); int weightsSize2 = net->getLayer(3)->getWeightsSize(); float *inputData = new float[std::max<int>(10000, inputNumElements)]; float *expectedOutput = new float[std::max<int>(10000, outputNumElements)]; memset(inputData, 0, sizeof(float) * std::max<int>(10000, inputNumElements)); memset(expectedOutput, 0, sizeof(float) * std::max<int>(10000, outputNumElements)); // int seed = 0; std::mt19937 random = WeightRandomizer::randomize(inputData, std::max<int>(10000, inputNumElements), -2.0f, 2.0f); WeightRandomizer::randomize(random, expectedOutput, std::max<int>(10000, outputNumElements), -2.0f, 2.0f); WeightRandomizer::randomize(random, dynamic_cast<ConvolutionalLayer*>(net->getLayer(1))->weights, weightsSize1, -2.0f, 2.0f); dynamic_cast<ConvolutionalLayer*>(net->getLayer(1))->weightsWrapper->copyToDevice(); WeightRandomizer::randomize(random, dynamic_cast<ConvolutionalLayer*>(net->getLayer(3))->weights, weightsSize2, -2.0f, 2.0f); dynamic_cast<ConvolutionalLayer*>(net->getLayer(3))->weightsWrapper->copyToDevice(); SGD *sgd = SGD::instance(cl, learningRate, 0.0f); for(int it = 0; it < its; it++) { float *weightsBefore1 = new float[weightsSize1]; float *currentWeights = net->getLayer(1)->getWeights(); for(int i = 0; i < weightsSize1; i++) { weightsBefore1[i] = currentWeights[i]; } float *weightsBefore2 = new float[weightsSize2]; currentWeights = net->getLayer(3)->getWeights(); for(int i = 0; i < weightsSize2; i++) { weightsBefore2[i] = currentWeights[i]; } net->forward(inputData); // net->print(); float loss = net->calcLoss(expectedOutput); dynamic_cast<LossLayer*>(net->getLayer(5))->calcLoss(expectedOutput); // net->backward(expectedOutput); TrainingContext context(0, 0); sgd->train(net, &context, inputData, expectedOutput); dynamic_cast<ConvolutionalLayer*>(net->getLayer(1))->weightsWrapper->copyToHost(); // restore 2nd layer weights :-) for(int i = 0; i < weightsSize2; i++) { // dynamic_cast<ConvolutionalLayer*>(net->getLayer(2))->weights[i] = weightsBefore2[i]; } dynamic_cast<ConvolutionalLayer*>(net->getLayer(3))->weightsWrapper->copyToDevice(); net->forward(inputData); float loss2 = net->calcLoss(expectedOutput); float lossChange = loss - loss2; cout << " loss " << loss << " loss2 " << loss2 << " change: " << lossChange << endl; float *newWeights = net->getLayer(1)->getWeights(); float sumWeightDiff = 0; float sumWeightDiffSquared = 0; for(int i = 0; i < weightsSize1; i++) { float diff = newWeights[i] - weightsBefore1[i]; sumWeightDiff += diff; sumWeightDiffSquared += diff * diff; } newWeights = net->getLayer(3)->getWeights(); for(int i = 0; i < weightsSize2; i++) { float diff = newWeights[i] - weightsBefore2[i]; sumWeightDiff += diff; sumWeightDiffSquared += diff * diff; } cout << "sumweightsdiff " << sumWeightDiff << endl; // cout << "sumweightsdiff / learningrate " << (sumWeightDiff / learningRate) << endl; // cout << "sum weightsdiffsquared " << (sumWeightDiffSquared/ learningRate / learningRate * imageSize) << endl; float estimatedLossChangeFromW = sumWeightDiffSquared/ learningRate; // / filterSize; cout << " loss change " << lossChange << endl; cout << " estimatedLossChangeFromW " << estimatedLossChangeFromW << endl; // cout << abs(estimatedLossChangeFromW - lossChange) / lossChange << endl; // cout << abs(estimatedLossChangeFromW - lossChange) / estimatedLossChangeFromW << endl; EXPECT_GT(0.01f * imageSize * imageSize, abs(estimatedLossChangeFromW - lossChange) / lossChange); EXPECT_GT(0.01f * imageSize * imageSize, abs(estimatedLossChangeFromW - lossChange) / estimatedLossChangeFromW); delete[] weightsBefore1; delete[] weightsBefore2; } // delete[] weights1; // delete[] errors; // delete[] output; delete sgd; delete[] inputData; delete[] expectedOutput; delete net; delete cl; }
TEST(testbackward, softmaxloss) { // here's the plan: // generate some input, randomly // generate some expected output, randomly // forward propagate // calculate loss // calculate gradInput // change some of the inputs, forward prop, recalculate loss, check corresponds // to the gradient EasyCL *cl = EasyCL::createForFirstGpuOtherwiseCpu(); NeuralNet *net = new NeuralNet(cl, 5, 1); net->addLayer(ForceBackpropLayerMaker::instance()); net->addLayer(SoftMaxMaker::instance()); cout << net->asString() << endl; const int batchSize = 2; net->setBatchSize(batchSize); const int outputPlanes = net->getOutputPlanes(); int inputCubeSize = net->getInputCubeSize(); int outputCubeSize = net->getOutputCubeSize(); int inputTotalSize = inputCubeSize * batchSize; int outputTotalSize = outputCubeSize * batchSize; cout << "inputtotalsize=" << inputTotalSize << " outputTotalSize=" << outputTotalSize << endl; float *input = new float[inputTotalSize]; float *expectedOutput = new float[outputTotalSize]; WeightRandomizer::randomize(0, input, inputTotalSize, 0.0f, 1.0f); WeightRandomizer::randomize(1, expectedOutput, outputTotalSize, 0.0f, 1.0f); // we should make the input and output a probability distribution I think // so: add up the input, and divide each by that. do same for expectedoutput (?) // normalizeAsProbabilityDistribution(input, inputTotalSize); normalizeAsProbabilityDistribution(outputPlanes, expectedOutput, outputTotalSize); // set all to zero, and one to 1, ie like labelled data // for(int i = 0; i < outputTotalSize; i++) { // expectedOutput[i] = 0; // } // for(int n = 0; n < batchSize; n++) { // int chosenLabel = 0; // WeightRandomizer::randomizeInts(n, &chosenLabel, 1, 0, net->getOutputPlanes()); // expectedOutput[ n * outputPlanes + chosenLabel ] = 1; // } // for(int i = 0; i < outputTotalSize; i++) { // cout << "expected[" << i << "]=" << expectedOutput[i] << endl; // } // // now, forward prop // net->input(input); net->forward(input); net->print(); // net->printOutput(); // calculate loss float lossBefore = net->calcLoss(expectedOutput); // calculate gradInput net->backward(expectedOutput); // modify input slightly mt19937 random; const int numSamples = 10; for(int i = 0; i < numSamples; i++) { int inputIndex; WeightRandomizer::randomizeInts(i, &inputIndex, 1, 0, inputTotalSize); // cout << "i=" << i << " index " << inputIndex << endl; float oldValue = input[inputIndex]; // grad for this index is.... float grad = net->getLayer(2)->getGradInput()[inputIndex]; // cout << "grad=" << grad << endl; // tweak slightly float newValue = oldValue * 1.001f; float inputDelta = newValue - oldValue; float predictedLossChange = inputDelta * grad; input[inputIndex] = newValue; // cout << "oldvalue=" << oldValue << " newvalue=" << newValue << endl; // forwardProp net->forward(input); input[inputIndex] = oldValue; // net->printOutput(); float lossAfter = net->calcLoss(expectedOutput); float lossChange = lossAfter - lossBefore; cout << "idx=" << inputIndex << " predicted losschange=" << predictedLossChange << " actual=" << lossChange << endl; } delete[] expectedOutput; delete[] input; delete net; delete cl; }
int main() { // init variables double error = 0.; int truecnt = 0; int times,timed; // print useful info for reference std::cout << "\n" << "hidden neurons: " << "\t \t" << HIDDEN << std::endl; // init random number generator srand((int)time(NULL)); // create network std::cout << "initializing network..." << "\t \t"; NeuralNet DigitNet; NeuralLayer * pHiddenLayer1 = new NeuralTanhLayer(INPUT,HIDDEN); DigitNet.addLayer( pHiddenLayer1 ); NeuralLayer * pOutputLayer = new NeuralSoftmaxLayer(HIDDEN,OUTPUT); DigitNet.addLayer( pOutputLayer ); // set output type: // SCALAR = tanh or sigmoid output layer (use one output neuron) // PROB = softmax output layer, 1-of-N output encoding (use two output neurons) const unsigned int outType = PROB; // set learning rate, momentum, decay rate const double learningRate = 0.15; const double momentum = 0.0; const double decayRate = 0.0; DigitNet.setParams(learningRate,momentum,decayRate,outType); std::cout << "done" << std::endl; // load training and test data std::cout << "loading data..." << "\t \t \t"; std::vector< std::vector<double> > bigData( DATA_SIZE,std::vector<double>(INPUT+1,0.0) ); loadFromFile(bigData,"train.txt"); std::vector< std::vector<double> > trainData( TRAIN_SIZE,std::vector<double>(INPUT+1,0.0) ); std::vector< std::vector<double> > testData( TEST_SIZE,std::vector<double>(INPUT+1,0.0) ); buildData(bigData,trainData,TRAIN_SIZE,testData,TEST_SIZE); std::cout << "done" << std::endl; // loop over training data points and train net // slice off first column of each row (example) times=(int)time(NULL); // init time counter std::cout << "\n" << "training examples: " << "\t \t" << TRAIN_SIZE << std::endl; std::cout << "learning rate: " << "\t \t \t" << learningRate << std::endl; std::cout << "momentum: " << "\t \t \t" << momentum << std::endl; std::cout << "weight decay: " << "\t \t \t" << decayRate << std::endl; std::cout << "training network..." << "\t \t"; for(int i=0;i<TRAIN_SIZE;++i) { std::vector<double> data = trainData[i]; // extract data point double label = data[0]; // extract point label data.erase(data.begin()); std::vector<double> nLabel = encode((int)label); // encode to 1-of-N std::vector<double> outputs = DigitNet.runNet(data); error = DigitNet.trainNet(data,nLabel,outType); // train net, return MSE // decode output and compare to correct output if( decode(outputs) == (int)label ) truecnt++; } // stop timer and print out useful info timed=(int)time(NULL); times=timed-times; std::cout << "done" << std::endl; std::cout << "training time: " << "\t \t \t" << times << " seconds " << std::endl; std::cout << "training accuracy: " << "\t \t" << truecnt*100./TRAIN_SIZE << "%" << std::endl; // test net on test data times=(int)time(NULL); // init time counter std::cout << "\n" << "test points: " << "\t \t \t" << TEST_SIZE << std::endl; std::cout << "testing network..." << "\t \t"; truecnt = 0; for(int i=0;i<TEST_SIZE;++i) { std::vector<double> data = testData[i]; // extract data point double label = data[0]; // extract label data.erase(data.begin()); std::vector<double> outputs = DigitNet.runNet(data); // run net // decode output and compare to correct output if( decode(outputs) == (int)label ) truecnt++; } // stop timer and print out useful info timed=(int)time(NULL); times=timed-times; std::cout << "done" << std::endl; std::cout << "testing time: " << "\t \t \t" << times << " seconds " << std::endl; std::cout << "test accuracy: " << "\t \t \t" << truecnt*100./TEST_SIZE << "% " << std::endl; // save weights to reuse net in the future DigitNet.saveNet(); }
void go(Config config) { Timer timer; int Ntrain; int Ntest; int numPlanes; int imageSize; unsigned char *trainData = 0; unsigned char *testData = 0; int *trainLabels = 0; int *testLabels = 0; int trainAllocateN = 0; int testAllocateN = 0; // int totalLinearSize; GenericLoader::getDimensions( config.dataDir + "/" + config.trainFile, &Ntrain, &numPlanes, &imageSize ); Ntrain = config.numTrain == -1 ? Ntrain : config.numTrain; // long allocateSize = (long)Ntrain * numPlanes * imageSize * imageSize; cout << "Ntrain " << Ntrain << " numPlanes " << numPlanes << " imageSize " << imageSize << endl; if( config.loadOnDemand ) { trainAllocateN = config.batchSize; // can improve this later } else { trainAllocateN = Ntrain; } trainData = new unsigned char[ (long)trainAllocateN * numPlanes * imageSize * imageSize ]; trainLabels = new int[trainAllocateN]; if( !config.loadOnDemand && Ntrain > 0 ) { GenericLoader::load( config.dataDir + "/" + config.trainFile, trainData, trainLabels, 0, Ntrain ); } GenericLoader::getDimensions( config.dataDir + "/" + config.validateFile, &Ntest, &numPlanes, &imageSize ); Ntest = config.numTest == -1 ? Ntest : config.numTest; if( config.loadOnDemand ) { testAllocateN = config.batchSize; // can improve this later } else { testAllocateN = Ntest; } testData = new unsigned char[ (long)testAllocateN * numPlanes * imageSize * imageSize ]; testLabels = new int[testAllocateN]; if( !config.loadOnDemand && Ntest > 0 ) { GenericLoader::load( config.dataDir + "/" + config.validateFile, testData, testLabels, 0, Ntest ); } cout << "Ntest " << Ntest << " Ntest" << endl; timer.timeCheck("after load images"); const int inputCubeSize = numPlanes * imageSize * imageSize; float translate; float scale; int normalizationExamples = config.normalizationExamples > Ntrain ? Ntrain : config.normalizationExamples; if( !config.loadOnDemand ) { if( config.normalization == "stddev" ) { float mean, stdDev; NormalizationHelper::getMeanAndStdDev( trainData, normalizationExamples * inputCubeSize, &mean, &stdDev ); cout << " image stats mean " << mean << " stdDev " << stdDev << endl; translate = - mean; scale = 1.0f / stdDev / config.normalizationNumStds; } else if( config.normalization == "maxmin" ) { float mean, stdDev; NormalizationHelper::getMinMax( trainData, normalizationExamples * inputCubeSize, &mean, &stdDev ); translate = - mean; scale = 1.0f / stdDev; } else { cout << "Error: Unknown normalization: " << config.normalization << endl; return; } } else { if( config.normalization == "stddev" ) { float mean, stdDev; NormalizeGetStdDev<unsigned char> normalizeGetStdDev( trainData, trainLabels ); BatchProcess::run<unsigned char>( config.dataDir + "/" + config.trainFile, 0, config.batchSize, normalizationExamples, inputCubeSize, &normalizeGetStdDev ); normalizeGetStdDev.calcMeanStdDev( &mean, &stdDev ); cout << " image stats mean " << mean << " stdDev " << stdDev << endl; translate = - mean; scale = 1.0f / stdDev / config.normalizationNumStds; } else if( config.normalization == "maxmin" ) { NormalizeGetMinMax<unsigned char> normalizeGetMinMax( trainData, trainLabels ); BatchProcess::run( config.dataDir + "/" + config.trainFile, 0, config.batchSize, normalizationExamples, inputCubeSize, &normalizeGetMinMax ); normalizeGetMinMax.calcMinMaxTransform( &translate, &scale ); } else { cout << "Error: Unknown normalization: " << config.normalization << endl; return; } } cout << " image norm translate " << translate << " scale " << scale << endl; timer.timeCheck("after getting stats"); // const int numToTrain = Ntrain; // const int batchSize = config.batchSize; NeuralNet *net = new NeuralNet(); // net->inputMaker<unsigned char>()->numPlanes(numPlanes)->imageSize(imageSize)->insert(); net->addLayer( InputLayerMaker<unsigned char>::instance()->numPlanes(numPlanes)->imageSize(imageSize) ); net->addLayer( NormalizationLayerMaker::instance()->translate(translate)->scale(scale) ); if( !NetdefToNet::createNetFromNetdef( net, config.netDef ) ) { return; } net->print(); bool afterRestart = false; int restartEpoch = 0; int restartBatch = 0; float restartAnnealedLearningRate = 0; int restartNumRight = 0; float restartLoss = 0; if( config.loadWeights && config.weightsFile != "" ) { afterRestart = WeightsPersister::loadWeights( config.weightsFile, config.getTrainingString(), net, &restartEpoch, &restartBatch, &restartAnnealedLearningRate, &restartNumRight, &restartLoss ); if( !afterRestart && FileHelper::exists( config.weightsFile ) ) { cout << "Weights file " << config.weightsFile << " exists, but doesnt match training options provided => aborting" << endl; cout << "Please either check the training options, or choose a weights file that doesnt exist yet" << endl; return; } } timer.timeCheck("before learning start"); if( config.dumpTimings ) { StatefulTimer::dump( true ); } StatefulTimer::timeCheck("START"); Trainable *trainable = net; MultiNet *multiNet = 0; if( config.multiNet > 1 ) { multiNet = new MultiNet( config.multiNet, net ); trainable = multiNet; } if( config.loadOnDemand ) { NetLearnerOnDemand<unsigned char> netLearner( trainable ); netLearner.setTrainingData( config.dataDir + "/" + config.trainFile, Ntrain ); netLearner.setTestingData( config.dataDir + "/" + config.validateFile, Ntest ); netLearner.setSchedule( config.numEpochs, afterRestart ? restartEpoch : 1 ); netLearner.setBatchSize( config.fileReadBatches, config.batchSize ); netLearner.setDumpTimings( config.dumpTimings ); WeightsWriter weightsWriter( net, &config ); if( config.weightsFile != "" ) { netLearner.addPostEpochAction( &weightsWriter ); } netLearner.learn( config.learningRate, config.annealLearningRate ); } else { NetLearner<unsigned char> netLearner( trainable ); netLearner.setTrainingData( Ntrain, trainData, trainLabels ); netLearner.setTestingData( Ntest, testData, testLabels ); netLearner.setSchedule( config.numEpochs, afterRestart ? restartEpoch : 1 ); netLearner.setBatchSize( config.batchSize ); netLearner.setDumpTimings( config.dumpTimings ); WeightsWriter weightsWriter( net, &config ); if( config.weightsFile != "" ) { netLearner.addPostEpochAction( &weightsWriter ); } netLearner.learn( config.learningRate, config.annealLearningRate ); } if( multiNet != 0 ) { delete multiNet; } delete net; if( trainData != 0 ) { delete[] trainData; } if( testData != 0 ) { delete[] testData; } if( testLabels != 0 ) { delete[] testLabels; } if( trainLabels != 0 ) { delete[] trainLabels; } }
void go(Config config) { Timer timer; int Ntrain; int Ntest; int numPlanes; int imageSize; float *trainData = 0; float *testData = 0; int *trainLabels = 0; int *testLabels = 0; int trainAllocateN = 0; int testAllocateN = 0; // int totalLinearSize; GenericLoaderv2 trainLoader( config.dataDir + "/" + config.trainFile ); Ntrain = trainLoader.getN(); numPlanes = trainLoader.getPlanes(); imageSize = trainLoader.getImageSize(); // GenericLoader::getDimensions( , &Ntrain, &numPlanes, &imageSize ); Ntrain = config.numTrain == -1 ? Ntrain : config.numTrain; // long allocateSize = (long)Ntrain * numPlanes * imageSize * imageSize; cout << "Ntrain " << Ntrain << " numPlanes " << numPlanes << " imageSize " << imageSize << endl; if( config.loadOnDemand ) { trainAllocateN = config.batchSize; // can improve this later } else { trainAllocateN = Ntrain; } trainData = new float[ (long)trainAllocateN * numPlanes * imageSize * imageSize ]; trainLabels = new int[trainAllocateN]; if( !config.loadOnDemand && Ntrain > 0 ) { trainLoader.load( trainData, trainLabels, 0, Ntrain ); } GenericLoaderv2 testLoader( config.dataDir + "/" + config.validateFile ); Ntest = testLoader.getN(); numPlanes = testLoader.getPlanes(); imageSize = testLoader.getImageSize(); Ntest = config.numTest == -1 ? Ntest : config.numTest; if( config.loadOnDemand ) { testAllocateN = config.batchSize; // can improve this later } else { testAllocateN = Ntest; } testData = new float[ (long)testAllocateN * numPlanes * imageSize * imageSize ]; testLabels = new int[testAllocateN]; if( !config.loadOnDemand && Ntest > 0 ) { testLoader.load( testData, testLabels, 0, Ntest ); } cout << "Ntest " << Ntest << " Ntest" << endl; timer.timeCheck("after load images"); const int inputCubeSize = numPlanes * imageSize * imageSize; float translate; float scale; int normalizationExamples = config.normalizationExamples > Ntrain ? Ntrain : config.normalizationExamples; if( !config.loadOnDemand ) { if( config.normalization == "stddev" ) { float mean, stdDev; NormalizationHelper::getMeanAndStdDev( trainData, normalizationExamples * inputCubeSize, &mean, &stdDev ); cout << " image stats mean " << mean << " stdDev " << stdDev << endl; translate = - mean; scale = 1.0f / stdDev / config.normalizationNumStds; } else if( config.normalization == "maxmin" ) { float mean, stdDev; NormalizationHelper::getMinMax( trainData, normalizationExamples * inputCubeSize, &mean, &stdDev ); translate = - mean; scale = 1.0f / stdDev; } else { cout << "Error: Unknown normalization: " << config.normalization << endl; return; } } else { if( config.normalization == "stddev" ) { float mean, stdDev; NormalizeGetStdDev normalizeGetStdDev( trainData, trainLabels ); BatchProcessv2::run( &trainLoader, 0, config.batchSize, normalizationExamples, inputCubeSize, &normalizeGetStdDev ); normalizeGetStdDev.calcMeanStdDev( &mean, &stdDev ); cout << " image stats mean " << mean << " stdDev " << stdDev << endl; translate = - mean; scale = 1.0f / stdDev / config.normalizationNumStds; } else if( config.normalization == "maxmin" ) { NormalizeGetMinMax normalizeGetMinMax( trainData, trainLabels ); BatchProcessv2::run( &trainLoader, 0, config.batchSize, normalizationExamples, inputCubeSize, &normalizeGetMinMax ); normalizeGetMinMax.calcMinMaxTransform( &translate, &scale ); } else { cout << "Error: Unknown normalization: " << config.normalization << endl; return; } } cout << " image norm translate " << translate << " scale " << scale << endl; timer.timeCheck("after getting stats"); // const int numToTrain = Ntrain; // const int batchSize = config.batchSize; EasyCL *cl = 0; if( config.gpuIndex >= 0 ) { cl = EasyCL::createForIndexedGpu( config.gpuIndex ); } else { cl = EasyCL::createForFirstGpuOtherwiseCpu(); } NeuralNet *net; net = new NeuralNet(cl); WeightsInitializer *weightsInitializer = 0; if( toLower( config.weightsInitializer ) == "original" ) { weightsInitializer = new OriginalInitializer(); } else if( toLower( config.weightsInitializer ) == "uniform" ) { weightsInitializer = new UniformInitializer( config.initialWeights ); } else { cout << "Unknown weights initializer " << config.weightsInitializer << endl; return; } // net->inputMaker<unsigned char>()->numPlanes(numPlanes)->imageSize(imageSize)->insert(); net->addLayer( InputLayerMaker::instance()->numPlanes(numPlanes)->imageSize(imageSize) ); net->addLayer( NormalizationLayerMaker::instance()->translate(translate)->scale(scale) ); if( !NetdefToNet::createNetFromNetdef( net, config.netDef, weightsInitializer ) ) { return; } // apply the trainer Trainer *trainer = 0; if( toLower( config.trainer ) == "sgd" ) { SGD *sgd = new SGD( cl ); sgd->setLearningRate( config.learningRate ); sgd->setMomentum( config.momentum ); sgd->setWeightDecay( config.weightDecay ); trainer = sgd; } else if( toLower( config.trainer ) == "anneal" ) { Annealer *annealer = new Annealer( cl ); annealer->setLearningRate( config.learningRate ); annealer->setAnneal( config.anneal ); trainer = annealer; } else if( toLower( config.trainer ) == "nesterov" ) { Nesterov *nesterov = new Nesterov( cl ); nesterov->setLearningRate( config.learningRate ); nesterov->setMomentum( config.momentum ); trainer = nesterov; } else if( toLower( config.trainer ) == "adagrad" ) { Adagrad *adagrad = new Adagrad( cl ); adagrad->setLearningRate( config.learningRate ); trainer = adagrad; } else if( toLower( config.trainer ) == "rmsprop" ) { Rmsprop *rmsprop = new Rmsprop( cl ); rmsprop->setLearningRate( config.learningRate ); trainer = rmsprop; } else if( toLower( config.trainer ) == "adadelta" ) { Adadelta *adadelta = new Adadelta( cl, config.rho ); trainer = adadelta; } else { cout << "trainer " << config.trainer << " unknown." << endl; return; } cout << "Using trainer " << trainer->asString() << endl; // trainer->bindTo( net ); // net->setTrainer( trainer ); net->setBatchSize( config.batchSize ); net->print(); bool afterRestart = false; int restartEpoch = 0; int restartBatch = 0; float restartAnnealedLearningRate = 0; int restartNumRight = 0; float restartLoss = 0; if( config.loadWeights && config.weightsFile != "" ) { cout << "loadingweights" << endl; afterRestart = WeightsPersister::loadWeights( config.weightsFile, config.getTrainingString(), net, &restartEpoch, &restartBatch, &restartAnnealedLearningRate, &restartNumRight, &restartLoss ); if( !afterRestart && FileHelper::exists( config.weightsFile ) ) { // try old trainingstring afterRestart = WeightsPersister::loadWeights( config.weightsFile, config.getOldTrainingString(), net, &restartEpoch, &restartBatch, &restartAnnealedLearningRate, &restartNumRight, &restartLoss ); } if( !afterRestart && FileHelper::exists( config.weightsFile ) ) { cout << "Weights file " << config.weightsFile << " exists, but doesnt match training options provided." << endl; cout << "Continue loading anyway (might crash, or weights might be completely inappropriate)? (y/n)" << endl; string response; cin >> response; if( response != "y" ) { cout << "Please either check the training options, or choose a weights file that doesnt exist yet" << endl; return; } }
void go(Config config) { Timer timer; int Ntrain; int Ntest; int numPlanes; int imageSize; float *trainData = 0; float *testData = 0; int *trainLabels = 0; int *testLabels = 0; int trainAllocateN = 0; int testAllocateN = 0; // int totalLinearSize; GenericLoader::getDimensions((config.dataDir + "/" + config.trainFile).c_str(), &Ntrain, &numPlanes, &imageSize ); Ntrain = config.numTrain == -1 ? Ntrain : config.numTrain; // long allocateSize = (long)Ntrain * numPlanes * imageSize * imageSize; cout << "Ntrain " << Ntrain << " numPlanes " << numPlanes << " imageSize " << imageSize << endl; trainAllocateN = Ntrain; trainData = new float[ (long)trainAllocateN * numPlanes * imageSize * imageSize ]; trainLabels = new int[trainAllocateN]; if( Ntrain > 0 ) { GenericLoader::load((config.dataDir + "/" + config.trainFile).c_str(), trainData, trainLabels, 0, Ntrain ); } GenericLoader::getDimensions((config.dataDir + "/" + config.validateFile).c_str(), &Ntest, &numPlanes, &imageSize ); Ntest = config.numTest == -1 ? Ntest : config.numTest; testAllocateN = Ntest; testData = new float[ (long)testAllocateN * numPlanes * imageSize * imageSize ]; testLabels = new int[testAllocateN]; if( Ntest > 0 ) { GenericLoader::load((config.dataDir + "/" + config.validateFile).c_str(), testData, testLabels, 0, Ntest ); } timer.timeCheck("after load images"); const int inputCubeSize = numPlanes * imageSize * imageSize; float translate; float scale; int normalizationExamples = config.normalizationExamples > Ntrain ? Ntrain : config.normalizationExamples; if( config.normalization == "stddev" ) { float mean, stdDev; NormalizationHelper::getMeanAndStdDev( trainData, normalizationExamples * inputCubeSize, &mean, &stdDev ); cout << " image stats mean " << mean << " stdDev " << stdDev << endl; translate = - mean; scale = 1.0f / stdDev / config.normalizationNumStds; } else if( config.normalization == "maxmin" ) { float mean, stdDev; NormalizationHelper::getMinMax( trainData, normalizationExamples * inputCubeSize, &mean, &stdDev ); translate = - mean; scale = 1.0f / stdDev; } else { cout << "Error: Unknown normalization: " << config.normalization << endl; return; } cout << " image norm translate " << translate << " scale " << scale << endl; timer.timeCheck("after getting stats"); // const int numToTrain = Ntrain; // const int batchSize = config.batchSize; EasyCL *cl = new EasyCL(); NeuralNet *net = new NeuralNet( cl ); // net->inputMaker<unsigned char>()->numPlanes(numPlanes)->imageSize(imageSize)->insert(); net->addLayer( InputLayerMaker::instance()->numPlanes(numPlanes)->imageSize(imageSize) ); net->addLayer( NormalizationLayerMaker::instance()->translate(translate)->scale(scale) ); if( !NetdefToNet::createNetFromNetdef( net, config.netDef ) ) { return; } net->print(); for( int i = 1; i < net->getNumLayers() - 1; i++ ) { Layer *layer = net->getLayer(i); FullyConnectedLayer *fc = dynamic_cast< FullyConnectedLayer * >(layer); ConvolutionalLayer *conv = dynamic_cast< ConvolutionalLayer * >(layer); if( fc != 0 ) { conv = fc->convolutionalLayer; } if( conv == 0 ) { continue; } initrand.seed(0); int weightsSize = conv->getWeightsSize(); //int weightsSize = layer->getPersistSize(); if( weightsSize > 0 ) { cout << "weightsSize " << weightsSize << endl; float *weights = new float[weightsSize]; for( int j = 0; j < weightsSize; j++ ) { int thisrand = (int)initrand(); float thisweight = ( thisrand % 100000 ) / 1000000.0f; weights[j] = thisweight; } conv->initWeights( weights ); } if( conv->dim.biased ) { initrand.seed(0); int biasedSize = conv->getBiasSize(); float *biasWeights = new float[biasedSize]; for( int j = 0; j < biasedSize; j++ ) { int thisrand = (int)initrand(); float thisweight = ( thisrand % 100000 ) / 1000000.0f; biasWeights[j] = thisweight; //biasWeights[j] = 0; } conv->initBias( biasWeights ); } } cout << "weight samples before learning:" << endl; sampleWeights(net); bool afterRestart = false; int restartEpoch = 0; // int restartBatch = 0; // float restartAnnealedLearningRate = 0; // int restartNumRight = 0; // float restartLoss = 0; timer.timeCheck("before learning start"); if( config.dumpTimings ) { StatefulTimer::dump( true ); } StatefulTimer::timeCheck("START"); SGD *sgd = SGD::instance( cl, config.learningRate, 0.0f ); Trainable *trainable = net; NetLearner netLearner( sgd, trainable, Ntrain, trainData, trainLabels, Ntest, testData, testLabels, config.batchSize ); netLearner.setSchedule( config.numEpochs, afterRestart ? restartEpoch : 1 ); // netLearner.setBatchSize( config.batchSize ); netLearner.setDumpTimings( config.dumpTimings ); // netLearner.learn( config.learningRate, 1.0f ); cout << "forward output" << endl; for( int layerId = 0; layerId < net->getNumLayers(); layerId++ ) { Layer *layer = net->getLayer(layerId); FullyConnectedLayer *fc = dynamic_cast< FullyConnectedLayer * >( layer ); ConvolutionalLayer *conv = dynamic_cast< ConvolutionalLayer * >( layer ); PoolingLayer *pool = dynamic_cast< PoolingLayer * >( layer ); SoftMaxLayer *softMax = dynamic_cast< SoftMaxLayer * >( layer ); if( fc != 0 ) { conv = fc->convolutionalLayer; } int planes = 0; int imageSize = 0; if( conv != 0 ) { cout << "convolutional (or conv based, ie fc)" << endl; planes = conv->dim.numFilters; imageSize = conv->dim.outputSize; // continue; } else if( pool != 0 ) { cout << "pooling" << endl; planes = pool->numPlanes; imageSize = pool->outputSize; } else if( softMax != 0 ) { cout << "softmax" << endl; planes = softMax->numPlanes; imageSize = softMax->imageSize; } else { continue; } cout << "layer " << layerId << endl; // conv->getOutput(); float const*output = layer->getOutput(); // for( int i = 0; i < 3; i++ ) { // cout << conv->getOutput()[i] << endl; // } initrand.seed(0); // LayerDimensions &dim = conv->dim; for( int i = 0; i < 10; i++ ) { int thisrand = abs( (int)initrand() ); int seq = thisrand % ( planes * imageSize * imageSize ); int outPlane = seq / ( imageSize * imageSize ); int rowcol = seq % ( imageSize * imageSize ); int row = rowcol / imageSize; int col = rowcol % imageSize; cout << "out[" << outPlane << "," << row << "," << col << "]=" << output[ seq ] << endl; } } cout << "weight samples after learning:" << endl; sampleWeights(net); cout << "backprop output" << endl; for( int layerId = net->getNumLayers() - 1; layerId >= 0; layerId-- ) { Layer *layer = net->getLayer(layerId); FullyConnectedLayer *fc = dynamic_cast< FullyConnectedLayer * >( layer ); ConvolutionalLayer *conv = dynamic_cast< ConvolutionalLayer * >( layer ); if( fc != 0 ) { conv = fc->convolutionalLayer; } if( conv == 0 ) { continue; } cout << "layer " << layerId << endl; float const*weights = conv->getWeights(); float const*biases = conv->getBias(); int weightsSize = conv->getWeightsSize() / conv->dim.numFilters; for( int i = 0; i < weightsSize; i++ ) { cout << " weight " << i << " " << weights[i] << endl; } for( int i = 0; i < 3; i++ ) { cout << " bias " << i << " " << biases[i] << endl; } } cout << "done" << endl; delete sgd; delete net; delete cl; if( trainData != 0 ) { delete[] trainData; } if( testData != 0 ) { delete[] testData; } if( testLabels != 0 ) { delete[] testLabels; } if( trainLabels != 0 ) { delete[] trainLabels; } }