public RMSPropWeightUpdater Clone()
{
RMSPropWeightUpdater copy = new RMSPropWeightUpdater(_n.Length, EwmaDecayRate, Momentum, LearningRate, RMSRegularizer);
Array.Copy(_n, copy._n, _n.Length);
Array.Copy(_g, copy._g, _g.Length);
Array.Copy(_delta, copy._delta, _delta.Length);
return(copy);
}
static void Main()
{
DataStream reportStream = null;
try
{
YoVisionClientHelper yoVisionClientHelper = new YoVisionClientHelper();
yoVisionClientHelper.Connect(EndpointType.NetTcp, 8081, "localhost", "YoVisionServer");
reportStream = yoVisionClientHelper.RegisterDataStream("Copy task training",
new Int32DataType("Iteration"),
new DoubleDataType("Average data loss"),
new Int32DataType("Training time"),
new Int32DataType("Sequence length"));
}
catch (Exception ex)
{
Console.WriteLine(ex.Message);
}
double[] errors = new double[100];
long[] times = new long[100];
for (int i = 0; i < 100; i++)
{
errors[i] = 1;
}
const int seed = 32702;
Console.WriteLine(seed);
//TODO args parsing shit
Random rand = new Random(seed);
const int vectorSize = 8;
const int controllerSize = 100;
const int headsCount = 1;
const int memoryN = 128;
const int memoryM = 20;
const int inputSize = vectorSize + 2;
const int outputSize = vectorSize;
//TODO remove rand
NeuralTuringMachine machine = new NeuralTuringMachine(vectorSize + 2, vectorSize, controllerSize, headsCount, memoryN, memoryM, new RandomWeightInitializer(rand));
//TODO extract weight count calculation
int headUnitSize = Head.GetUnitSize(memoryM);
var weightsCount =
(headsCount * memoryN) +
(memoryN * memoryM) +
(controllerSize * headsCount * memoryM) +
(controllerSize * inputSize) +
(controllerSize) +
(outputSize * (controllerSize + 1)) +
(headsCount * headUnitSize * (controllerSize + 1));
Console.WriteLine(weightsCount);
RMSPropWeightUpdater rmsPropWeightUpdater = new RMSPropWeightUpdater(weightsCount, 0.95, 0.5, 0.001, 0.001);
//NeuralTuringMachine machine2 = NeuralTuringMachine.Load(@"NTM2015-03-22T210312");
BPTTTeacher teacher = new BPTTTeacher(machine, rmsPropWeightUpdater);
for (int i = 1; i < 10000; i++)
{
Tuple<double[][], double[][]> sequence = SequenceGenerator.GenerateSequence(rand.Next(20) + 1, vectorSize);
Stopwatch stopwatch = new Stopwatch();
stopwatch.Start();
double[][] machinesOutput = teacher.Train(sequence.Item1, sequence.Item2);
stopwatch.Stop();
times[i%100] = stopwatch.ElapsedMilliseconds;
double error = CalculateLogLoss(sequence.Item2, machinesOutput);
double averageError = error / (sequence.Item2.Length * sequence.Item2[0].Length);
errors[i % 100] = averageError;
if (reportStream != null)
{
reportStream.Set("Iteration", i);
reportStream.Set("Average data loss", averageError);
reportStream.Set("Training time", stopwatch.ElapsedMilliseconds);
reportStream.Set("Sequence length", (sequence.Item1.Length - 2)/2);
reportStream.SendData();
}
if (i % 100 == 0)
{
Console.WriteLine("Iteration: {0}, average error: {1}, iterations per second: {2:0.0}", i, errors.Average(), 1000/times.Average());
}
}
machine.Save("NTM"+DateTime.Now.ToString("s").Replace(":",""));
}
