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NumRec Fixes

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This commit is contained in:
IlyaShurupov 2023-10-25 20:41:48 +03:00
parent 0542b7ba2e
commit 826713c212
5 changed files with 181 additions and 148 deletions
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40
DataAnalysis/private/FullyConnectedNN.cpp
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@ -1,6 +1,6 @@
// #include "NewPlacement.hpp"
#include "FullyConnectedNN.hpp"
#include "FCNN.hpp"
#include "Utils.hpp"
#include <cmath>
@ -22,11 +22,13 @@ static halnf relu(halnf val) { return val < 0 ? 0 : val; }
static halnf reluDerivative(halnf val) { return val < 0 ? 0 : 1; }
static halnf activationFunction(halnf val) { return sigmoid(val); }
static halnf activationFunction(halnf val) { return relu(val); }
static halnf activationFunctionDerivative(halnf val) { return sigmoidDerivative(val); }
static halnf activationFunctionDerivative(halnf val) { return reluDerivative(val); }
void FullyConnectedNN::initializeRandom(const Buffer<halni>& description) {
FCNN::FCNN(const Buffer<halni>& description) { initializeRandom(description); }
void FCNN::initializeRandom(const Buffer<halni>& description) {
ASSERT(description.size() > 1);
mLayers.reserve(description.size());
@ -39,14 +41,14 @@ void FullyConnectedNN::initializeRandom(const Buffer<halni>& description) {
for (auto neuron : mLayers[i].neurons) {
neuron->weights.reserve(description[i - 1]);
for (auto weight : neuron->weights) {
weight->val = (halnf) (randomFloat() - 0) * 2;
weight->val = (halnf) (randomFloat() - 0.5) * 2;
}
neuron->bias.val = (halnf) (randomFloat() - 0) * 2;
neuron->bias.val = (halnf) (randomFloat() - 0.5) * 2;
}
}
}
void FullyConnectedNN::evaluate(const Buffer<halnf>& input, Buffer<halnf>& output) {
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())) {
@ -73,7 +75,7 @@ void FullyConnectedNN::evaluate(const Buffer<halnf>& input, Buffer<halnf>& outpu
}
}
halnf FullyConnectedNN::calcCost(const Buffer<halnf>& output) {
halnf FCNN::calcCost(const Buffer<halnf>& output) {
halnf out = 0;
for (auto neuronIdx : Range(mLayers.last().neurons.size())) {
out += pow(output[neuronIdx] - mLayers.last().neurons[neuronIdx].activationValue, 2);
@ -81,7 +83,21 @@ halnf FullyConnectedNN::calcCost(const Buffer<halnf>& output) {
return out;
}
void FullyConnectedNN::calcGrad(const Buffer<halnf>& output) {
void FCNN::clearGrad() {
for (auto layIdx : Range<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())) {
neuron->weights[weightIdx].grad = 0;
}
}
}
mAvgCount = 0;
}
void FCNN::calcGrad(const Buffer<halnf>& output) {
ASSERT(mLayers.last().neurons.size() == output.size())
auto& lastLayer = mLayers.last();
@ -113,11 +129,11 @@ void FullyConnectedNN::calcGrad(const Buffer<halnf>& output) {
}
// gradient for current neuron bias
currentNeuron.bias.grad = currentNeuron.cache;
currentNeuron.bias.grad += currentNeuron.cache;
// calculate gradient for weights of current neuron
for (auto weightIdx : Range(currentNeuron.weights.size())) {
currentNeuron.weights[weightIdx].grad = inputLayer.neurons[weightIdx].activationValue * currentNeuron.cache;
currentNeuron.weights[weightIdx].grad += inputLayer.neurons[weightIdx].activationValue * currentNeuron.cache;
}
}
}
@ -125,7 +141,7 @@ void FullyConnectedNN::calcGrad(const Buffer<halnf>& output) {
mAvgCount++;
}
void FullyConnectedNN::applyGrad(halnf step) {
void FCNN::applyGrad(halnf step) {
for (auto layIdx : Range<halni>(1, (halni) mLayers.size())) {
auto& layer = mLayers[layIdx];