TEST(TestNeuralNetwork, getWeights)
{
NeuralNetwork nn {400, 25, 10};
EXPECT_EQ(401*25 + 26 * 10, nn.getWeights().size());
for (auto w : nn.getWeights()) {
EXPECT_FLOAT_EQ(0.0f, w);
}
}
TEST(TestNeuralNetwork, setWeights)
{
NeuralNetwork nn {400, 25, 10};
float curWeight = 0.0f;
nn.setWeights([&curWeight] {return curWeight++;});
for (int i = 0; i < nn.getWeights().size(); ++i) {
EXPECT_FLOAT_EQ((float)i, nn.getWeights()[i]);
}
}
static void testConvergence(NeuralNetwork &nn, void (NeuralNetwork::*algorithm)(const std::vector<float> &, std::vector<float> &), bool (*sampleGenerator)(int, std::vector<float>&, std::vector<float>&))
{
vector<float> input;
vector<float> expected;
vector<float> gradient;
const float alpha = 0.2f;
float cost = 0.0f;
float prevCost = 0.0f;
input.resize(2);
expected.resize(2);
int sampleId = 0;
for (int i = 0; i < 128; ++i) {
bool lastInBatch = sampleGenerator(sampleId++, input, expected);
nn.setInput(input);
nn.calc();
(nn.*algorithm)(expected, gradient);
ASSERT_EQ(nn.getWeights().size(), gradient.size());
cost += nn.calcCost(expected);
if (lastInBatch) {
nn.applyGradient(gradient, alpha);
for (int w = 0; w < gradient.size(); ++w)
gradient[w] = 0.0f;
if (prevCost != 0.0f)
ASSERT_LT(cost - prevCost, 0.0f);
prevCost = cost;
cost = 0.0f;
}
}
}
