static Matrix optimizeWithDerivative(const NeuralNetwork* network,
const Image& image, unsigned int neuron)
{
unsigned int iterations = util::KnobDatabase::getKnobValue(
"NeuronVisualizer::SolverIterations", 1);
float range = util::KnobDatabase::getKnobValue("NeuronVisualizer::InputRange", 0.01f);
float bestCost = std::numeric_limits<float>::max();
Matrix bestInputs;
util::log("NeuronVisualizer") << "Searching for lowest cost inputs...\n";
for(unsigned int iteration = 0; iteration < iterations; ++iteration)
{
util::log("NeuronVisualizer") << " Iteration " << iteration << "\n";
auto randomInputs = generateRandomImage(network, image, iteration, range);
float newCost = std::numeric_limits<float>::max();
auto newInputs = optimizeWithDerivative(newCost, network, randomInputs, neuron);
if(newCost < bestCost)
{
bestInputs = newInputs;
bestCost = newCost;
util::log("NeuronVisualizer") << " updated best cost: " << bestCost << "\n";
}
range /= 2.0f;
}
return bestInputs;
}
static ImageVector generateBatch(const Image& image, float noiseMagnitude,
size_t batchSize, std::default_random_engine& engine)
{
ImageVector images;
std::bernoulli_distribution distribution(0.5f);
for(size_t i = 0; i != batchSize; ++i)
{
bool generateRandom = distribution(engine);
if(generateRandom)
{
images.push_back(generateRandomImage(
image.y(), image.x(), image.colorComponents(), engine));
}
else
{
images.push_back(addRandomNoiseToImage(image,
noiseMagnitude, engine));
}
}
return images;
}
static Matrix optimizeWithoutDerivative(const NeuralNetwork* network,
const Image& image, unsigned int neuron)
{
Matrix bestSoFar = generateRandomImage(network, image, 0, 0.00f);
float bestCost = computeCost(network, neuron, bestSoFar);
std::string solverType = util::KnobDatabase::getKnobValue(
"NeuronVisualizer::SolverType", "SimulatedAnnealingSolver");
auto solver =
optimizer::GeneralNondifferentiableSolverFactory::create(solverType);
assert(solver != nullptr);
CostFunction costFunction(network, neuron, bestCost);
bestCost = solver->solve(bestSoFar, costFunction);
delete solver;
return bestSoFar;
}