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This commit is contained in:
IlyaShurupov 2023-10-22 17:07:28 +03:00
parent 43e374f269
commit 744c01c5d0
928 changed files with 14515 additions and 21480 deletions
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5
DataAnalysis/private/DataAnalysisCommon.cpp
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@ -1,8 +1,9 @@
#include "DataAnalysisCommon.hpp"
#include "Allocators.hpp"
#include "ContainersCommon.hpp"
#include "MathCommon.hpp"
namespace tp {
static ModuleManifest* deps[] = {&gModuleMath, &gModuleContainers, nullptr};
ModuleManifest gModuleDataAnalysis = ModuleManifest("DataAnalysis", nullptr, nullptr, deps);
static ModuleManifest* deps[] = { &gModuleMath, &gModuleContainers, &gModuleAllocators, nullptr };
ModuleManifest gModuleDataAnalysis = ModuleManifest("DataAnalysis", nullptr, nullptr, deps);
}

53
DataAnalysis/private/FullyConnectedNN.cpp Normal file
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@ -0,0 +1,53 @@
#include "FullyConnectedNN.hpp"
#include "NewPlacement.hpp"
#include "Utils.hpp"
using namespace tp;
static halnf sigmoid(halnf val) { return 0; }
static halnf relu(halnf val) { return val < 0 ? 0 : val; }
void FullyConnectedNN::initializeRandom(Buffer<halni> description) {
mLayers.reserve(description.size());
for (auto i : Range<halni>(0, (halni) description.size())) {
mLayers[i].mNeurons.reserve(description[i]);
if (i == 0) {
continue;
}
for (auto neuron : mLayers[i].mNeurons) {
neuron->mWeights.reserve(description[i - 1]);
for (auto weight : neuron->mWeights) {
weight.data() = (halnf) randomFloat();
}
neuron->mBias = (halnf) randomFloat();
}
}
}
void FullyConnectedNN::evaluate(const Buffer<halnf>& input, Buffer<halnf>& output) {
ASSERT(output.size() == mLayers.last().mNeurons.size() && input.size() == mLayers.first().mNeurons.size())
for (auto idx : Range(input.size())) {
mLayers.first().mNeurons[idx].mActivationValue = input[idx];
}
for (auto layerIdx : Range<halni>(1, (halni) mLayers.size())) {
auto& layer = mLayers[layerIdx];
auto& layerPrev = mLayers[layerIdx - 1];
for (auto neuron : layer.mNeurons) {
neuron->mActivationValue = 0;
for (auto connectionIdx : Range(neuron->mWeights.size())) {
neuron->mActivationValue += neuron->mWeights[connectionIdx] * layerPrev.mNeurons[connectionIdx].mActivationValue;
}
neuron->mActivationValue += neuron->mBias;
neuron->mActivationValue = relu(neuron->mActivationValue);
}
}
for (auto idx : Range(output.size())) {
output[idx] = mLayers.last().mNeurons[idx].mActivationValue;
}
}