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addapting more tests to gtest

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Shin
2016-03-31 16:48:02 +02:00
parent 5c1d1efcc0
commit e6a5882e58
4 changed files with 169 additions and 174 deletions
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206
tests/backpropagation.cpp
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@@ -1,116 +1,118 @@
#include <NeuralNetwork/FeedForward/Network.h>
#include <NeuralNetwork/Learning/BackPropagation.h>
#include <cassert>
#include <iostream>
#include "../include/NeuralNetwork/Learning/BackPropagation.h"
#include "gtest/gtest.h"
int main() {
{ // XOR problem
NeuralNetwork::FeedForward::Network n(2);
NeuralNetwork::ActivationFunction::Sigmoid a(-1);
n.appendLayer(2,a);
n.appendLayer(1,a);
TEST(BackProp,XOR) {
NeuralNetwork::FeedForward::Network n(2);
NeuralNetwork::ActivationFunction::Sigmoid a(-1);
n.appendLayer(2,a);
n.appendLayer(1,a);
n.randomizeWeights();
n.randomizeWeights();
NeuralNetwork::Learning::BackPropagation prop(n);
for(int i=0;i<10000;i++) {
prop.teach({1,0},{1});
prop.teach({1,1},{0});
prop.teach({0,0},{0});
prop.teach({0,1},{1});
}
NeuralNetwork::Learning::BackPropagation prop(n);
{
std::vector<float> ret =n.computeOutput({1,1});
assert(ret[0] < 0.1);
}
{
std::vector<float> ret =n.computeOutput({0,1});
assert(ret[0] > 0.9);
}
{
std::vector<float> ret =n.computeOutput({1,0});
assert(ret[0] > 0.9);
}
{
std::vector<float> ret =n.computeOutput({0,0});
assert(ret[0] < 0.1);
}
for(int i=0;i<10000;i++) {
prop.teach({1,0},{1});
prop.teach({1,1},{0});
prop.teach({0,0},{0});
prop.teach({0,1},{1});
}
{ // AND problem
NeuralNetwork::FeedForward::Network n(2);
NeuralNetwork::ActivationFunction::Sigmoid a(-1);
n.appendLayer(2,a);
n.appendLayer(1,a);
n.randomizeWeights();
NeuralNetwork::Learning::BackPropagation prop(n);
for(int i=0;i<10000;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(ret[0] > 0.9);
}
{
std::vector<float> ret =n.computeOutput({0,1});
assert(ret[0] < 0.1);
}
{
std::vector<float> ret =n.computeOutput({1,0});
assert(ret[0] < 0.1);
}
{
std::vector<float> ret =n.computeOutput({0,0});
assert(ret[0] < 0.1);
}
{
std::vector<float> ret =n.computeOutput({1,1});
ASSERT_LT(ret[0], 0.1);
}
{ // NOT AND problem
NeuralNetwork::FeedForward::Network n(2);
NeuralNetwork::ActivationFunction::Sigmoid a(-1);
n.appendLayer(2,a);
n.appendLayer(1,a);
n.randomizeWeights();
{
std::vector<float> ret =n.computeOutput({0,1});
ASSERT_GT(ret[0], 0.9);
}
NeuralNetwork::Learning::BackPropagation prop(n);
for(int i=0;i<10000;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({1,0});
ASSERT_GT(ret[0], 0.9);
}
{
std::vector<float> ret =n.computeOutput({1,1});
assert(ret[0] < 0.1);
}
{
std::vector<float> ret =n.computeOutput({0,1});
assert(ret[0] > 0.9);
}
{
std::vector<float> ret =n.computeOutput({1,0});
assert(ret[0] > 0.9);
}
{
std::vector<float> ret =n.computeOutput({0,0});
assert(ret[0] > 0.9);
}
{
std::vector<float> ret =n.computeOutput({0,0});
ASSERT_LT(ret[0], 0.1);
}
}
TEST(BackProp,AND) {
NeuralNetwork::FeedForward::Network n(2);
NeuralNetwork::ActivationFunction::Sigmoid a(-1);
n.appendLayer(2,a);
n.appendLayer(1,a);
n.randomizeWeights();
NeuralNetwork::Learning::BackPropagation prop(n);
for(int i=0;i<10000;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(BackProp,NOTAND) {
NeuralNetwork::FeedForward::Network n(2);
NeuralNetwork::ActivationFunction::Sigmoid a(-1);
n.appendLayer(2,a);
n.appendLayer(1,a);
n.randomizeWeights();
NeuralNetwork::Learning::BackPropagation prop(n);
for(int i=0;i<10000;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);
}
}