92 lines
2.1 KiB
C++
92 lines
2.1 KiB
C++
#include <NeuralNetwork/FeedForward/Network.h>
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#pragma GCC diagnostic push
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#pragma GCC diagnostic ignored "-Weffc++"
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#include <gtest/gtest.h>
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#pragma GCC diagnostic pop
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TEST(FeedForward, XOR) {
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NeuralNetwork::FeedForward::Network n(2);
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NeuralNetwork::ActivationFunction::Sigmoid a(-1);
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NeuralNetwork::FeedForward::Layer &hidden=n.appendLayer(2,a);
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NeuralNetwork::FeedForward::Layer &out = n.appendLayer(1,a);
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hidden[1].weight(n[0][0])=7;
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hidden[1].weight(n[0][1])=-4.7;
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hidden[1].weight(n[0][2])=-4.7;
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hidden[2].weight(n[0][0])=2.6;
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hidden[2].weight(n[0][1])=-6.4;
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hidden[2].weight(n[0][2])=-6.4;
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out[1].weight(hidden[0])=-4.5;
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out[1].weight(hidden[1])=9.6;
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out[1].weight(hidden[2])=-6.8;
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{
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std::vector<float> ret =n.computeOutput({1,1});
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ASSERT_LT(ret[0], 0.5);
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}
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{
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std::vector<float> ret =n.computeOutput({0,1});
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ASSERT_GT(ret[0], 0.5);
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}
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{
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std::vector<float> ret =n.computeOutput({1,0});
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ASSERT_GT(ret[0], 0.5);
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}
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{
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std::vector<float> ret =n.computeOutput({0,0});
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ASSERT_LT(ret[0], 0.5);
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}
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}
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TEST(FeedForward, Serialization) {
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NeuralNetwork::FeedForward::Network n(2);
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NeuralNetwork::ActivationFunction::Sigmoid a(-1);
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NeuralNetwork::FeedForward::Layer &hidden=n.appendLayer(2,a);
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NeuralNetwork::FeedForward::Layer &out = n.appendLayer(1,a);
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hidden[1].weight(n[0][0])=7;
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hidden[1].weight(n[0][1])=-4.7;
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hidden[1].weight(n[0][2])=-4.7;
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hidden[2].weight(n[0][0])=2.6;
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hidden[2].weight(n[0][1])=-6.4;
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hidden[2].weight(n[0][2])=-6.4;
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out[1].weight(hidden[0])=-4.5;
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out[1].weight(hidden[1])=9.6;
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out[1].weight(hidden[2])=-6.8;
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std::string serialized = n.serialize().serialize();
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NeuralNetwork::FeedForward::Network *deserialized=NeuralNetwork::FeedForward::Network::Factory::deserialize(serialized).release();
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{
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std::vector<float> ret =deserialized->computeOutput({1,1});
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ASSERT_LT(ret[0], 0.5);
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}
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{
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std::vector<float> ret =deserialized->computeOutput({0,1});
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ASSERT_GT(ret[0], 0.5);
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}
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{
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std::vector<float> ret =deserialized->computeOutput({1,0});
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ASSERT_GT(ret[0], 0.5);
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}
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{
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std::vector<float> ret =deserialized->computeOutput({0,0});
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ASSERT_LT(ret[0], 0.5);
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}
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delete deserialized;
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} |