165 lines
3.1 KiB
C++
165 lines
3.1 KiB
C++
#include <NeuralNetwork/FeedForward/Network.h>
|
|
#include <NeuralNetwork/Learning/RProp.h>
|
|
#include <NeuralNetwork/ActivationFunction/HyperbolicTangent.h>
|
|
|
|
#pragma GCC diagnostic push
|
|
#pragma GCC diagnostic ignored "-Weffc++"
|
|
|
|
#include <gtest/gtest.h>
|
|
|
|
#pragma GCC diagnostic pop
|
|
TEST(RProp,XOR) {
|
|
NeuralNetwork::FeedForward::Network n(2);
|
|
NeuralNetwork::ActivationFunction::Sigmoid a(-1);
|
|
n.appendLayer(3,a);
|
|
n.appendLayer(1,a);
|
|
|
|
n.randomizeWeights();
|
|
|
|
NeuralNetwork::Learning::RProp prop(n);
|
|
prop.setBatchSize(4);
|
|
|
|
for(int i=0;i<100;i++) {
|
|
prop.teach({1,0},{1});
|
|
prop.teach({1,1},{0});
|
|
prop.teach({0,0},{0});
|
|
prop.teach({0,1},{1});
|
|
}
|
|
|
|
{
|
|
std::vector<float> ret =n.computeOutput({1,1});
|
|
ASSERT_LT(ret[0], 0.1);
|
|
}
|
|
|
|
{
|
|
std::vector<float> ret =n.computeOutput({0,1});
|
|
ASSERT_GT(ret[0], 0.9);
|
|
}
|
|
|
|
{
|
|
std::vector<float> ret =n.computeOutput({1,0});
|
|
ASSERT_GT(ret[0], 0.9);
|
|
}
|
|
|
|
{
|
|
std::vector<float> ret =n.computeOutput({0,0});
|
|
ASSERT_LT(ret[0], 0.1);
|
|
}
|
|
}
|
|
|
|
TEST(RProp,XORHyperbolicTangent) {
|
|
srand(time(NULL));
|
|
NeuralNetwork::FeedForward::Network n(2);
|
|
NeuralNetwork::ActivationFunction::HyperbolicTangent a(-1);
|
|
n.appendLayer(2,a);
|
|
n.appendLayer(1,a);
|
|
|
|
n.randomizeWeights();
|
|
|
|
NeuralNetwork::Learning::RProp prop(n);
|
|
prop.setBatchSize(4);
|
|
|
|
for(int i=0;i<15000;i++) {
|
|
prop.teach({1,0},{1});
|
|
prop.teach({1,1},{0});
|
|
prop.teach({0,0},{0});
|
|
prop.teach({0,1},{1});
|
|
}
|
|
|
|
{
|
|
std::vector<float> ret =n.computeOutput({1,1});
|
|
ASSERT_LT(ret[0], 0.1);
|
|
}
|
|
|
|
{
|
|
std::vector<float> ret =n.computeOutput({0,1});
|
|
ASSERT_GT(ret[0], 0.9);
|
|
}
|
|
|
|
{
|
|
std::vector<float> ret =n.computeOutput({1,0});
|
|
ASSERT_GT(ret[0], 0.9);
|
|
}
|
|
|
|
{
|
|
std::vector<float> ret =n.computeOutput({0,0});
|
|
ASSERT_LT(ret[0], 0.1);
|
|
}
|
|
}
|
|
|
|
TEST(RProp,AND) {
|
|
NeuralNetwork::FeedForward::Network n(2);
|
|
NeuralNetwork::ActivationFunction::Sigmoid a(-1);
|
|
n.appendLayer(1,a);
|
|
|
|
n.randomizeWeights();
|
|
|
|
NeuralNetwork::Learning::RProp prop(n);
|
|
prop.setBatchSize(4);
|
|
|
|
for(int i=0;i<100000;i++) {
|
|
prop.teach({1,1},{1});
|
|
prop.teach({0,0},{0});
|
|
prop.teach({0,1},{0});
|
|
prop.teach({1,0},{0});
|
|
}
|
|
|
|
{
|
|
std::vector<float> ret =n.computeOutput({1,1});
|
|
ASSERT_GT(ret[0], 0.9);
|
|
}
|
|
|
|
{
|
|
std::vector<float> ret =n.computeOutput({0,1});
|
|
ASSERT_LT(ret[0], 0.1);
|
|
}
|
|
|
|
{
|
|
std::vector<float> ret =n.computeOutput({1,0});
|
|
ASSERT_LT(ret[0], 0.1);
|
|
}
|
|
|
|
{
|
|
std::vector<float> ret =n.computeOutput({0,0});
|
|
ASSERT_LT(ret[0], 0.1);
|
|
}
|
|
}
|
|
|
|
TEST(RProp,NOTAND) {
|
|
NeuralNetwork::FeedForward::Network n(2);
|
|
NeuralNetwork::ActivationFunction::Sigmoid a(-1);
|
|
n.appendLayer(2,a);
|
|
n.appendLayer(1,a);
|
|
|
|
n.randomizeWeights();
|
|
|
|
NeuralNetwork::Learning::RProp prop(n);
|
|
prop.setBatchSize(4);
|
|
|
|
for(int i=0;i<100000;i++) {
|
|
prop.teach({1,1},{0});
|
|
prop.teach({0,0},{1});
|
|
prop.teach({0,1},{1});
|
|
prop.teach({1,0},{1});
|
|
}
|
|
|
|
{
|
|
std::vector<float> ret =n.computeOutput({1,1});
|
|
ASSERT_LT(ret[0], 0.1);
|
|
}
|
|
|
|
{
|
|
std::vector<float> ret =n.computeOutput({0,1});
|
|
ASSERT_GT(ret[0], 0.9);
|
|
}
|
|
|
|
{
|
|
std::vector<float> ret =n.computeOutput({1,0});
|
|
ASSERT_GT(ret[0], 0.9);
|
|
}
|
|
|
|
{
|
|
std::vector<float> ret =n.computeOutput({0,0});
|
|
ASSERT_GT(ret[0], 0.9);
|
|
}
|
|
} |