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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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18
tests/CMakeLists.txt
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@@ -5,17 +5,18 @@ project(NeuralNetworkTests CXX)
set(CMAKE_CXX_FLAGS " --std=c++14")
add_executable(activation activation.cpp)
target_link_libraries(activation NeuralNetwork)
target_link_libraries(activation gtest gtest_main)
target_link_libraries(activation NeuralNetwork gtest gtest_main)
add_executable(basis basis.cpp)
target_link_libraries(basis NeuralNetwork)
target_link_libraries(basis gtest gtest_main)
#[[
target_link_libraries(basis NeuralNetwork gtest gtest_main)
add_executable(backpropagation backpropagation.cpp)
target_link_libraries(backpropagation NeuralNetwork)
target_link_libraries(backpropagation NeuralNetwork gtest gtest_main)
add_executable(feedforward feedforward.cpp)
target_link_libraries(feedforward NeuralNetwork gtest gtest_main)
#[[
add_executable(backpropagation_function_cmp backpropagation_function_cmp.cpp)
target_link_libraries(backpropagation_function_cmp NeuralNetwork)
@@ -23,9 +24,6 @@ target_link_libraries(backpropagation_function_cmp NeuralNetwork)
add_executable(backpropagation_perf backpropagation_perf.cpp)
target_link_libraries(backpropagation_perf NeuralNetwork)
add_executable(feedforward feedforward.cpp)
target_link_libraries(feedforward NeuralNetwork)
add_executable(feedforward_perf feedforward_perf.cpp)
target_link_libraries(feedforward_perf NeuralNetwork)

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);
}
}

70
tests/feedforward.cpp
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@@ -1,47 +1,43 @@
#include <NeuralNetwork/FeedForward/Network.h>
#include <cassert>
#include <iostream>
#include "gtest/gtest.h"
int main() {
{ // XOR problem
NeuralNetwork::FeedForward::Network n(2);
NeuralNetwork::ActivationFunction::Sigmoid a(-1);
NeuralNetwork::FeedForward::Layer &hidden=n.appendLayer(2,a);
NeuralNetwork::FeedForward::Layer &out = n.appendLayer(1,a);
TEST(FeedForward, XOR) {
NeuralNetwork::FeedForward::Network n(2);
NeuralNetwork::ActivationFunction::Sigmoid a(-1);
NeuralNetwork::FeedForward::Layer &hidden=n.appendLayer(2,a);
NeuralNetwork::FeedForward::Layer &out = n.appendLayer(1,a);
hidden[1].weight(n[0][0])=7;
hidden[1].weight(n[0][1])=-4.7;
hidden[1].weight(n[0][2])=-4.7;
hidden[1].weight(n[0][0])=7;
hidden[1].weight(n[0][1])=-4.7;
hidden[1].weight(n[0][2])=-4.7;
hidden[2].weight(n[0][0])=2.6;
hidden[2].weight(n[0][1])=-6.4;
hidden[2].weight(n[0][2])=-6.4;
hidden[2].weight(n[0][0])=2.6;
hidden[2].weight(n[0][1])=-6.4;
hidden[2].weight(n[0][2])=-6.4;
out[1].weight(hidden[0])=-4.5;
out[1].weight(hidden[1])=9.6;
out[1].weight(hidden[2])=-6.8;
out[1].weight(hidden[0])=-4.5;
out[1].weight(hidden[1])=9.6;
out[1].weight(hidden[2])=-6.8;
{
std::vector<float> ret =n.computeOutput({1,1});
assert(ret[0] < 0.5);
}
{
std::vector<float> ret =n.computeOutput({0,1});
assert(ret[0] > 0.5);
}
{
std::vector<float> ret =n.computeOutput({1,0});
assert(ret[0] > 0.5);
}
{
std::vector<float> ret =n.computeOutput({0,0});
assert(ret[0] < 0.5);
}
{
std::vector<float> ret =n.computeOutput({1,1});
ASSERT_LT(ret[0], 0.5);
}
}
{
std::vector<float> ret =n.computeOutput({0,1});
ASSERT_GT(ret[0], 0.5);
}
{
std::vector<float> ret =n.computeOutput({1,0});
ASSERT_GT(ret[0], 0.5);
}
{
std::vector<float> ret =n.computeOutput({0,0});
ASSERT_LT(ret[0], 0.5);
}
}