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Initial size handling

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IlyaShurupov 2024-10-10 11:17:03 +03:00 • committed by Ilya Shurupov
parent 9f9dcd5882
commit 0b73f8037b
28 changed files with 543 additions and 135 deletions
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26
DataAnalysis/private/FCNN.cpp
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@ -32,7 +32,7 @@ void FCNN::initializeRandom(const Buffer<halni>& description) {
mLayers.reserve(description.size());
for (auto i : Range<halni>(0, (halni) description.size())) {
for (auto i : IterRange<halni>(0, (halni) description.size())) {
mLayers[i].neurons.reserve(description[i]);
if (i == 0) {
continue;
@ -50,17 +50,17 @@ void FCNN::initializeRandom(const Buffer<halni>& description) {
void FCNN::evaluate(const Buffer<halnf>& input, Buffer<halnf>& output) {
ASSERT(output.size() == mLayers.last().neurons.size() && input.size() == mLayers.first().neurons.size())
for (auto idx : Range(input.size())) {
for (auto idx : IterRange(input.size())) {
mLayers.first().neurons[idx].activationValue = input[idx];
}
for (auto layerIdx : Range<halni>(1, (halni) mLayers.size())) {
for (auto layerIdx : IterRange<halni>(1, (halni) mLayers.size())) {
auto& layer = mLayers[layerIdx];
auto& layerPrev = mLayers[layerIdx - 1];
for (auto neuron : layer.neurons) {
neuron->activationValue = 0;
for (auto connectionIdx : Range(neuron->weights.size())) {
for (auto connectionIdx : IterRange(neuron->weights.size())) {
neuron->activationValue += neuron->weights[connectionIdx].val * layerPrev.neurons[connectionIdx].activationValue;
}
neuron->activationValue += neuron->bias.val;
@ -69,26 +69,26 @@ void FCNN::evaluate(const Buffer<halnf>& input, Buffer<halnf>& output) {
}
}
for (auto idx : Range(output.size())) {
for (auto idx : IterRange(output.size())) {
output[idx] = mLayers.last().neurons[idx].activationValue;
}
}
halnf FCNN::calcCost(const Buffer<halnf>& output) {
halnf out = 0;
for (auto neuronIdx : Range(mLayers.last().neurons.size())) {
for (auto neuronIdx : IterRange(mLayers.last().neurons.size())) {
out += pow(output[neuronIdx] - mLayers.last().neurons[neuronIdx].activationValue, 2);
}
return out;
}
void FCNN::clearGrad() {
for (auto layIdx : Range<halni>(1, (halni) mLayers.size())) {
for (auto layIdx : IterRange<halni>(1, (halni) mLayers.size())) {
auto& layer = mLayers[layIdx];
for (auto neuron : layer.neurons) {
neuron->bias.grad = 0;
for (auto weightIdx : Range(neuron->weights.size())) {
for (auto weightIdx : IterRange(neuron->weights.size())) {
neuron->weights[weightIdx].grad = 0;
}
}
@ -102,7 +102,7 @@ void FCNN::calcGrad(const Buffer<halnf>& output) {
auto& lastLayer = mLayers.last();
// calculate chaining cache value for each neuron in last layer
for (auto neuronIdx : Range(lastLayer.neurons.size())) {
for (auto neuronIdx : IterRange(lastLayer.neurons.size())) {
auto& neuron = lastLayer.neurons[neuronIdx];
neuron.cache = 2 * (neuron.activationValue - output[neuronIdx]);
}
@ -113,7 +113,7 @@ void FCNN::calcGrad(const Buffer<halnf>& output) {
auto& currentLayer = mLayers[layerIdx];
auto& inputLayer = mLayers[layerIdx - 1];
for (auto currentNeuronIdx : Range(currentLayer.neurons.size())) {
for (auto currentNeuronIdx : IterRange(currentLayer.neurons.size())) {
auto& currentNeuron = currentLayer.neurons[currentNeuronIdx];
// calculate cache value (chaining)
@ -131,7 +131,7 @@ void FCNN::calcGrad(const Buffer<halnf>& output) {
currentNeuron.bias.grad += currentNeuron.cache;
// calculate gradient for weights of current neuron
for (auto weightIdx : Range(currentNeuron.weights.size())) {
for (auto weightIdx : IterRange(currentNeuron.weights.size())) {
currentNeuron.weights[weightIdx].grad += inputLayer.neurons[weightIdx].activationValue * currentNeuron.cache;
}
}
@ -142,12 +142,12 @@ void FCNN::calcGrad(const Buffer<halnf>& output) {
void FCNN::applyGrad(halnf step) {
for (auto layIdx : Range<halni>(1, (halni) mLayers.size())) {
for (auto layIdx : IterRange<halni>(1, (halni) mLayers.size())) {
auto& layer = mLayers[layIdx];
for (auto neuron : layer.neurons) {
neuron->bias.val -= neuron->bias.grad / (halnf) mAvgCount * step;
for (auto weightIdx : Range(neuron->weights.size())) {
for (auto weightIdx : IterRange(neuron->weights.size())) {
neuron->weights[weightIdx].val -= (neuron->weights[weightIdx].grad / (halnf) mAvgCount) * step;
}
}