TEST( testforward, softmax_byplane ) {
EasyCL *cl = EasyCL::createForFirstGpuOtherwiseCpu();
NeuralNet *net = NeuralNet::maker(cl)->imageSize(2)->planes(1)->instance();
net->addLayer( SoftMaxMaker::instance()->perPlane() );
net->setBatchSize( 1 );
int imageSizeSquared = net->getLayer(0)->getOutputSize() * net->getLayer(0)->getOutputSize();
float *input = new float[imageSizeSquared];
input[0] = 0;
input[1] = 1;
input[2] = 3;
input[3] = 2;
net->forward( input );
float const*output = net->getOutput();
float sum = 0;
for( int i = 0; i < imageSizeSquared; i++ ) {
cout << "output[" << i << "]=" << output[i] << endl;
sum += output[i];
EXPECT_LE( 0, output[i] );
EXPECT_GE( 1, output[i] );
}
EXPECT_FLOAT_NEAR( 1.0f, sum );
EXPECT_FLOAT_NEAR( (float)( exp(0.0f)/(exp(0.0f)+exp(1.0f)+exp(3.0f)+exp(2.0f)) ), output[0] );
EXPECT_FLOAT_NEAR( (float)( exp(1.0f)/(exp(0.0f)+exp(1.0f)+exp(3.0f)+exp(2.0f)) ), output[1] );
EXPECT_FLOAT_NEAR( (float)( exp(3.0f)/(exp(0.0f)+exp(1.0f)+exp(3.0f)+exp(2.0f)) ), output[2] );
EXPECT_FLOAT_NEAR( (float)( exp(2.0f)/(exp(0.0f)+exp(1.0f)+exp(3.0f)+exp(2.0f)) ), output[3] );
float *expected = new float[imageSizeSquared];
memset( expected, 0, sizeof(float) * imageSizeSquared );
expected[2] = 1;
float loss = net->calcLoss( expected );
cout << "loss " << loss << endl;
EXPECT_LT( 0, loss );
EXPECT_FLOAT_NEAR( - log(output[2]), loss );
memset( expected, 0, sizeof(float) * imageSizeSquared );
expected[0] = 1;
loss = net->calcLoss( expected );
cout << "loss " << loss << endl;
EXPECT_LT( 0, loss );
EXPECT_FLOAT_NEAR( - log(output[0]), loss );
memset( expected, 0, sizeof(float) * imageSizeSquared );
expected[1] = 1;
loss = net->calcLoss( expected );
cout << "loss " << loss << endl;
EXPECT_LT( 0, loss );
EXPECT_FLOAT_NEAR( - log(output[1]), loss );
memset( expected, 0, sizeof(float) * imageSizeSquared );
expected[3] = 1;
loss = net->calcLoss( expected );
cout << "loss " << loss << endl;
EXPECT_LT( 0, loss );
EXPECT_FLOAT_NEAR( - log(output[3]), loss );
delete[] input;
delete[] expected;
delete net;
delete cl;
}
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;
}
}
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;
}
