int main(int argc, char** argv){
NNet network;
network.setSeed(0);
NNet::Layer* inputLayer = new NNet::Layer(2);
network.addLayer(inputLayer);
network.addLayer(NNet::sigmoidFunc, 2000, -IW, IW);
network.addLayer(NNet::sigmoidFunc, 2000, -IW, IW);
network.addLayer(NNet::sigmoidFunc, 2000, -IW, IW);
network.addLayer(NNet::sigmoidFunc, 2, -IW, IW);
NNet::Layer* outputLayer = network.backLayer(0);
// normally we would train the network before executing it, for the
// sake of demonstration, this step is omitted. See examples/nnet_train.
// test the performance by executing it on the C++ side
double t1 = NSys::now();
inputLayer->neuron(0)->input(0.25);
inputLayer->neuron(1)->input(0.70);
double dt = NSys::now() - t1;
cout << "c++ time is: " << dt << endl;
cout << "output[0]= " << outputLayer->neuron(0)->output() << endl;
cout << "output[1]= " << outputLayer->neuron(1)->output() << endl;
// an NNModule holds compiled neural networks
NNModule module;
t1 = NSys::now();
// compile the neural network to run with 8 threads, giving it the
// name "test"
module.compile("test", network, 8);
dt = NSys::now() - t1;
cout << "compile time is: " << dt << endl;
// the input and output layers must be a NPLVector of double's and
// of the length which matches the C++ side network as constructed
// above
typedef NPLVector<double, 2> Vec2;
Vec2 inputs;
inputs[0] = 0.25;
inputs[1] = 0.70;
Vec2 outputs;
t1 = NSys::now();
// execute the network
module.run("test", &inputs, &outputs);
dt = NSys::now() - t1;
cout << "run time is: " << dt << endl;
// verify that these outputs match our first outputs
cout << "outputs: " << outputs << endl;
// delete the resources used by the compiled neural network named "test"
module.remove("test");
return 0;
}
bool compile(const nstr& name,
NNet& network,
size_t threads){
RunNetwork* runNetwork = new RunNetwork;
NNet::Layer* inputLayer = network.layer(0);
size_t numLayers = network.numLayers();
RunLayer* lastRunLayer = 0;
for(size_t l = 1; l < numLayers; ++l){
RunLayer* runLayer = new RunLayer;
runLayer->queue = new Queue(threads);
size_t inputLayerSize = inputLayer->size();
NNet::Layer* layer = network.layer(l);
size_t layerSize = layer->size();
if(l > 1){
runLayer->inputVecStart = lastRunLayer->outputVecStart;
runLayer->inputVec = lastRunLayer->outputVec;
}
if(l < numLayers - 1){
double* outputVecPtrStart;
double* outputVecPtr;
allocVector(layerSize, &outputVecPtrStart, &outputVecPtr);
runLayer->outputVecStart = outputVecPtrStart;
runLayer->outputVec = outputVecPtr;
}
TypeVec args;
args.push_back(getPointer(doubleVecType(inputLayerSize)));
args.push_back(getPointer(doubleVecType(inputLayerSize)));
args.push_back(getPointer(doubleType()));
args.push_back(int32Type());
FunctionType* ft = FunctionType::get(voidType(), args, false);
Function* f =
Function::Create(ft, Function::ExternalLinkage,
name.c_str(), &module_);
BasicBlock* entry = BasicBlock::Create(context_, "entry", f);
builder_.SetInsertPoint(entry);
auto aitr = f->arg_begin();
Value* inputVecPtr = aitr;
inputVecPtr->setName("input_vec_ptr");
++aitr;
Value* weightVecPtr = aitr;
weightVecPtr->setName("weight_vec_ptr");
++aitr;
Value* outputVecPtr = aitr;
outputVecPtr->setName("output_vec_ptr");
++aitr;
Value* outputIndex = aitr;
outputIndex->setName("output_index");
Value* inputVec =
builder_.CreateLoad(inputVecPtr, "input_vec");
Value* weightVec =
builder_.CreateLoad(weightVecPtr, "weight_vec");
Value* mulVec =
builder_.CreateFMul(inputVec, weightVec, "mul_vec");
Value* sumActivation =
builder_.CreateExtractElement(mulVec, getInt32(0), "sum_elem");
for(size_t i = 1; i < inputLayerSize; ++i){
Value* elem =
builder_.CreateExtractElement(mulVec, getInt32(i), "sum_elem");
sumActivation =
builder_.CreateFAdd(sumActivation, elem, "sum_activation");
}
Value* output =
getActivationOutput(layer->neuron(0), sumActivation);
Value* outputElement =
builder_.CreateGEP(outputVecPtr, outputIndex, "out_elem");
builder_.CreateStore(output, outputElement);
builder_.CreateRetVoid();
runLayer->f = f;
runLayer->fp = (void (*)(void*, void*, void*, int))
engine_->getPointerToFunction(f);
for(size_t j = 0; j < layerSize; ++j){
NNet::Neuron* nj = layer->neuron(j);
RunNeuron* runNeuron = new RunNeuron;
runNeuron->layer = runLayer;
runNeuron->outputIndex = j;
double* weightVecPtrStart;
double* weightVecPtr;
allocVector(inputLayerSize, &weightVecPtrStart, &weightVecPtr);
runNeuron->weightVecStart = weightVecPtrStart;
runNeuron->weightVec = weightVecPtr;
for(size_t i = 0; i < inputLayerSize; ++i){
NNet::Neuron* ni = inputLayer->neuron(i);
weightVecPtr[i] = nj->weight(ni);
}
runLayer->queue->add(runNeuron);
}
runNetwork->layerVec.push_back(runLayer);
inputLayer = layer;
lastRunLayer = runLayer;
}
networkMap_.insert(make_pair(name, runNetwork));
return true;
}
