// tests if the network successfully calculates a complex formula
public static void testCalculation()
{
SlimRnn rnn = new SlimRnn();
rnn.neurons.Add(SlimRnnNeuron.makeInputNeuron()); // [0] input
rnn.neurons.Add(SlimRnnNeuron.makeInputNeuron()); // [1] input
rnn.neurons.Add(SlimRnnNeuron.makeInputNeuron()); // [2] input
rnn.neurons.Add(new SlimRnnNeuron(SlimRnnNeuron.EnumType.ADDITIVE)); // [3]
rnn.neurons.Add(new SlimRnnNeuron(SlimRnnNeuron.EnumType.MULTIPLICATIVE)); // [4] output
rnn.neurons.Add(new SlimRnnNeuron(SlimRnnNeuron.EnumType.ADDITIVE)); // [5] termination
rnn.outputNeuronsStartIndex = 4;
rnn.numberOfOutputNeurons = 1;
rnn.terminatingNeuronIndex = 5;
rnn.terminatingNeuronThreshold = 0.5f;
rnn.numberOfInputNeurons = 3;
rnn.t_lim = 5; // are actually less steps but fine for testing
rnn.world = new TestWorld();
rnn.neurons[0].outNeuronsWithWeights.Add(new SlimRnnNeuronWithWeight(rnn.neurons[0], rnn.neurons[3], 0.5f, 0));
rnn.neurons[1].outNeuronsWithWeights.Add(new SlimRnnNeuronWithWeight(rnn.neurons[1], rnn.neurons[3], 0.81f, 0));
rnn.neurons[2].outNeuronsWithWeights.Add(new SlimRnnNeuronWithWeight(rnn.neurons[2], rnn.neurons[4], 1.5f, 0));
rnn.neurons[3].outNeuronsWithWeights.Add(new SlimRnnNeuronWithWeight(rnn.neurons[3], rnn.neurons[4], 0.5f, 0));
rnn.neurons[4].outNeuronsWithWeights.Add(new SlimRnnNeuronWithWeight(rnn.neurons[4], rnn.neurons[5], 5000.0f, 0)); // terminate if output is set
rnn.initializeNeurons();
IList <SlimRnnNeuronWithWeight> trace;
double time;
bool wasTerminated;
rnn.spread(out trace, out time, out wasTerminated);
Debug.Assert(wasTerminated); // must terminate
}
// used for interactivly checking if the learning algorithm works correctly
public static void interactiveCheckLearningAlgorithm()
{
SlimRnn rnn = new SlimRnn();
rnn.neurons.Add(SlimRnnNeuron.makeInputNeuron()); // [0] input
rnn.neurons.Add(SlimRnnNeuron.makeInputNeuron()); // [1] input
rnn.neurons.Add(SlimRnnNeuron.makeInputNeuron()); // [2] input
rnn.neurons.Add(new SlimRnnNeuron(SlimRnnNeuron.EnumType.ADDITIVE)); // [3] termination/output
rnn.neurons.Add(new SlimRnnNeuron(SlimRnnNeuron.EnumType.ADDITIVE)); // [4] output
rnn.outputNeuronsStartIndex = 3;
rnn.numberOfOutputNeurons = 2;
rnn.terminatingNeuronIndex = 3;
rnn.terminatingNeuronThreshold = 0.5f;
rnn.numberOfInputNeurons = 3;
rnn.t_lim = 5; // are actually less steps but fine for testing
rnn.world = new TestWorld();
// insert unused neurons as possible neurons for learning
rnn.neurons[0].outNeuronsWithWeights.Add(new SlimRnnNeuronWithWeight(rnn.neurons[0], rnn.neurons[3], 0.0f, 0, true));
rnn.neurons[0].outNeuronsWithWeights.Add(new SlimRnnNeuronWithWeight(rnn.neurons[0], rnn.neurons[4], 0.0f, 1, true));
rnn.neurons[1].outNeuronsWithWeights.Add(new SlimRnnNeuronWithWeight(rnn.neurons[1], rnn.neurons[3], 1.0f, 0));
rnn.neurons[1].outNeuronsWithWeights.Add(new SlimRnnNeuronWithWeight(rnn.neurons[1], rnn.neurons[4], 0.0f, 1, true));
rnn.neurons[2].outNeuronsWithWeights.Add(new SlimRnnNeuronWithWeight(rnn.neurons[2], rnn.neurons[3], 1.0f, 0));
rnn.initializeNeurons();
UniversalSlimRnnSearch learningAlgorithm = new UniversalSlimRnnSearch(rnn, new AlwaysSuccessfulTester());
learningAlgorithm.weightWithPropabilityTable = new List <UniversalSlimRnnSearch.WeightWithPropability> {
/*new UniversalSlimRnnSearch.WeightWithPropability(-50.0f, 0.01),
* new UniversalSlimRnnSearch.WeightWithPropability(-40.0f, 0.02),
* new UniversalSlimRnnSearch.WeightWithPropability(-30.0f, 0.02),
* new UniversalSlimRnnSearch.WeightWithPropability(-20.0f, 0.02),
* new UniversalSlimRnnSearch.WeightWithPropability(-10.0f, 0.02),
*
* new UniversalSlimRnnSearch.WeightWithPropability(-9.0f, 0.02),
* new UniversalSlimRnnSearch.WeightWithPropability(-8.0f, 0.02),
* new UniversalSlimRnnSearch.WeightWithPropability(-7.0f, 0.02),
* new UniversalSlimRnnSearch.WeightWithPropability(-6.0f, 0.02),
* new UniversalSlimRnnSearch.WeightWithPropability(-5.0f, 0.02),
* new UniversalSlimRnnSearch.WeightWithPropability(-4.0f, 0.02),
* new UniversalSlimRnnSearch.WeightWithPropability(-3.0f, 0.02),
* new UniversalSlimRnnSearch.WeightWithPropability(-2.0f, 0.02),
* new UniversalSlimRnnSearch.WeightWithPropability(-1.5f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-1.0f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.99f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.98f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.95f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.92f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.9f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.85f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.8f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.75f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.7f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.65f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.6f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.55f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.5f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.45f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.4f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.35f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.3f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.25f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.2f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.15f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.1f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.05f, 0.01),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.02f, 0.01),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.01f, 0.01),
*
* // TODO< positive side >
*/
// just for testing
new UniversalSlimRnnSearch.WeightWithPropability(1.0f, 0.4),
new UniversalSlimRnnSearch.WeightWithPropability(-1.0f, 0.4),
new UniversalSlimRnnSearch.WeightWithPropability(0.5f, 0.1),
new UniversalSlimRnnSearch.WeightWithPropability(-0.5f, 0.1),
};
// invoke learning algorithm
bool wasSolved;
SlimRnn solutionRnn;
learningAlgorithm.search(1, true, out wasSolved, out solutionRnn);
}
public Solver returnInitialProgram()
{
Solver solver = new Solver();
solver.slimRnn = new SlimRnn();
SlimRnn rnn = solver.slimRnn;
// neurons for the retina input
for (int i = 0; i < 5 * 5; i++)
{
rnn.neurons.Add(SlimRnnNeuron.makeInputNeuron());
}
// neurons for periphal vision of the retina
for (int i = 0; i < 3 * 3 - 1; i++)
{
rnn.neurons.Add(SlimRnnNeuron.makeInputNeuron());
}
neuronIndexConstantOne = (uint)rnn.neurons.Count;
rnn.neurons.Add(SlimRnnNeuron.makeInputNeuron());
uint neuronTerminationIndex = (uint)rnn.neurons.Count;
// output neuron for termination
rnn.neurons.Add(new SlimRnnNeuron(SlimRnnNeuron.EnumType.ADDITIVE));
uint neuronOutputStartIndex = (uint)rnn.neurons.Count;
uint numberOfOutputNeurons = 8;
// output neurons for controlling the retina
for (int i = 0; i < numberOfOutputNeurons; i++)
{
rnn.neurons.Add(new SlimRnnNeuron(SlimRnnNeuron.EnumType.ADDITIVE));
}
rnn.outputNeuronsStartIndex = neuronOutputStartIndex;
rnn.numberOfOutputNeurons = numberOfOutputNeurons;
rnn.terminatingNeuronIndex = neuronTerminationIndex;
rnn.terminatingNeuronThreshold = 0.5f;
rnn.numberOfInputNeurons = (5 * 5) + (9 - 1) + 1 /* constant neuron */;
rnn.t_lim = double.MaxValue; // is set by the learning algorithm
// add and initialize "hidden" neurons
neuronIndexOfHiddenUnits = (uint)rnn.neurons.Count;
uint numberOfHiddenNeuronsWtaGroups = 50;
uint numberOfNeuronsInWtaGroup = 4; // 4 is a good number as chosen by Schmidhuber
for (uint groupI = 0; groupI < numberOfHiddenNeuronsWtaGroups; groupI++)
{
for (int neuronI = 0; neuronI < numberOfNeuronsInWtaGroup; neuronI++)
{
bool isNeuronIEvent = (neuronI % 2) == 0;
SlimRnnNeuron.EnumType neuronType = isNeuronIEvent ? SlimRnnNeuron.EnumType.ADDITIVE : SlimRnnNeuron.EnumType.MULTIPLICATIVE;
SlimRnnNeuron neuron = new SlimRnnNeuron(neuronType);
neuron.winnerTakesAllGroup = groupI;
rnn.neurons.Add(neuron);
}
}
rnn.initializeNeurons();
// set initial network
{ // wire the central input sensor to the termination
int retinaX = 0;
int retinaY = 0;
int absoluteRetinaIndexX = 2 + retinaX;
int absoluteRetinaIndexY = 2 + retinaY;
int retinaInputNeuronIndex = 0 + 5 * absoluteRetinaIndexY + absoluteRetinaIndexX;
rnn.neurons[retinaInputNeuronIndex].outNeuronsWithWeights.Add(new SlimRnnNeuronWithWeight(rnn.neurons[retinaInputNeuronIndex], rnn.neurons[(int)neuronTerminationIndex], 0.55f, 0));
}
return(solver);
}