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BackProp Initial

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IlyaShurupov 2023-10-23 13:38:00 +03:00
parent 744c01c5d0
commit efbb09f8ba
6 changed files with 166 additions and 4 deletions
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90
DataAnalysis/private/FullyConnectedNN.cpp
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@ -8,6 +8,8 @@ static halnf sigmoid(halnf val) { return 0; }
static halnf relu(halnf val) { return val < 0 ? 0 : val; }
static halnf reluDerivative(halnf val) { return val < 0 ? 0 : 1; }
void FullyConnectedNN::initializeRandom(Buffer<halni> description) {
mLayers.reserve(description.size());
@ -19,9 +21,9 @@ void FullyConnectedNN::initializeRandom(Buffer<halni> description) {
for (auto neuron : mLayers[i].mNeurons) {
neuron->mWeights.reserve(description[i - 1]);
for (auto weight : neuron->mWeights) {
weight.data() = (halnf) randomFloat();
weight.data() = (halnf) (randomFloat() - 0.5) * 2;
}
neuron->mBias = (halnf) randomFloat();
neuron->mBias = (halnf) (randomFloat() - 0.5) * 2;
}
}
}
@ -43,6 +45,7 @@ void FullyConnectedNN::evaluate(const Buffer<halnf>& input, Buffer<halnf>& outpu
neuron->mActivationValue += neuron->mWeights[connectionIdx] * layerPrev.mNeurons[connectionIdx].mActivationValue;
}
neuron->mActivationValue += neuron->mBias;
neuron->mActivationValueLinear = neuron->mActivationValue;
neuron->mActivationValue = relu(neuron->mActivationValue);
}
}
@ -51,3 +54,86 @@ void FullyConnectedNN::evaluate(const Buffer<halnf>& input, Buffer<halnf>& outpu
output[idx] = mLayers.last().mNeurons[idx].mActivationValue;
}
}
halnf FullyConnectedNN::calcCost(const Buffer<halnf>& output) {
halnf out = 0;
for (auto neuronIdx : Range(mLayers.last().mNeurons.size())) {
out += pow(output[neuronIdx] - mLayers.last().mNeurons[neuronIdx].mActivationValue, 2);
}
return out;
}
void FullyConnectedNN::clearGrad() {
mLayersCache.reserve(mLayers.size());
for (auto layIdx : Range<halni>(0, (halni) mLayers.size())) {
mLayersCache[layIdx].mNeurons.reserve(mLayers[layIdx].mNeurons.size());
mLayersCache[layIdx].mCache = 1;
for (auto weightIdx : Range(mLayersCache[layIdx].mNeurons.size())) {
mLayersCache[layIdx].mNeurons[weightIdx].mBiasGrad = 0;
mLayersCache[layIdx].mNeurons[weightIdx].mWeightsGrad.reserve(mLayers[layIdx].mNeurons[weightIdx].mWeights.size());
for (auto weight : mLayersCache[layIdx].mNeurons[weightIdx].mWeightsGrad) {
weight.data() = 0;
}
}
}
mAvgCount = 0;
}
void FullyConnectedNN::calcGrad(const Buffer<halnf>& output) {
ASSERT(mLayers.last().mNeurons.size() == output.size())
auto& lastLayer = mLayers.last();
auto& lastCache = mLayersCache.last();
lastCache.mCache = 1;
for (auto neuronIdx : Range(lastLayer.mNeurons.size())) {
auto& neuronCache = lastCache.mNeurons[neuronIdx];
auto& neuron = lastLayer.mNeurons[neuronIdx];
lastCache.mCache += 2 * pow(output[neuronIdx] - neuron.mActivationValue, 2);
}
for (auto layerIdx = mLayers.size() - 1; layerIdx > 0; layerIdx--) {
auto& layer = mLayers[layerIdx];
auto& cache = mLayersCache[layerIdx];
auto& layerNext = mLayers[layerIdx - 1];
auto& cacheNext = mLayersCache[layerIdx - 1];
cacheNext.mCache = 1;
for (auto neuronIdx : Range(layer.mNeurons.size())) {
auto& neuron = layer.mNeurons[neuronIdx];
auto& neuronCache = cache.mNeurons[neuronIdx];
auto tmp = cache.mCache * reluDerivative(neuron.mActivationValueLinear);
neuronCache.mBiasGrad = tmp;
for (auto weightIdx : Range(neuron.mWeights.size())) {
neuronCache.mWeightsGrad[weightIdx] = tmp * layerNext.mNeurons[weightIdx].mActivationValue;
}
cacheNext.mCache += tmp;
}
}
}
void FullyConnectedNN::applyGrad() {
for (auto layIdx : Range<halni>(1, (halni) mLayers.size())) {
auto& layerCache = mLayersCache[layIdx];
auto& layer = mLayers[layIdx];
for (auto neuronIdx : Range(layer.mNeurons.size())) {
auto& neuron = layer.mNeurons[neuronIdx];
auto& neuronCache = layerCache.mNeurons[neuronIdx];
neuron.mBias += neuronCache.mBiasGrad / (halnf) mAvgCount;
for (auto weightIdx : Range(neuron.mWeights.size())) {
neuron.mWeights[weightIdx] += neuronCache.mWeightsGrad[weightIdx] / (halnf) mAvgCount;
}
}
}
}