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BackProp Fixes

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IlyaShurupov 2023-10-25 11:03:15 +03:00 • committed by Ilya Shurupov
parent 7e17b32a81
commit 90ce453b60
11 changed files with 189 additions and 106 deletions
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33
DataAnalysis/tests/Tests.cpp
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@ -1,14 +1,45 @@
#include "NewPlacement.hpp"
#include "FullyConnectedNN.hpp"
#include "Testing.hpp"
#include "Utils.hpp"
#include <stdio.h>
static bool init(const tp::ModuleManifest* self) {
tp::gTesting.setRootName(self->getName());
return true;
}
void test() {}
void test() {
using namespace tp;
Buffer<halni> layers = { 4, 4, 3, 2 };
Buffer<halnf> input = { (halnf) randomFloat() * 100, (halnf) randomFloat() * 100, (halnf) randomFloat() * 100, (halnf) randomFloat() * 100 };
Buffer<halnf> outputExpected = { (halnf) randomFloat(), (halnf) randomFloat() };
FullyConnectedNN nn;
Buffer<halnf> output(2);
nn.initializeRandom(layers);
// nn.mLayers.last().neurons.first().weights = { 0.35, 0.35, 0.35, 0.35 };
// nn.mLayers.last().neurons.first().bias = 0.9;
// nn.mLayers.last().neurons.last().weights = { -0.35, -0.35, -0.35, -0.35 };
// nn.mLayers.last().neurons.last().bias = -3.9;
for (auto i : Range(50)) {
nn.evaluate(input, output);
auto lossBefore = nn.calcCost(outputExpected);
nn.calcGrad(outputExpected);
nn.applyGrad(0.1);
printf("Loss %f \n", lossBefore);
}
}
int main() {
tp::ModuleManifest* deps[] = { &tp::gModuleDataAnalysis, &tp::gModuleUtils, nullptr };