public void ResetSpeedTest()
{
int iter = 1000;
mlpMN = new MultiLayerMathsNet(seed, null, shapes, 1, initialValueWeights);
mlp = new MultiLayer(shapes, seed, 1, null);
var watch = System.Diagnostics.Stopwatch.StartNew();
for (int i = 0; i < iter; i++)
{
mlpMN.Reset(true);
}
watch.Stop();
var elapsedMs = watch.ElapsedMilliseconds;
Debug.Log("Reset Time MathNet: " + elapsedMs);
watch = System.Diagnostics.Stopwatch.StartNew();
for (int i = 0; i < iter; i++)
{
mlp.Reset(1.0f, true);
}
watch.Stop();
elapsedMs = watch.ElapsedMilliseconds;
Debug.Log("Reset Time Array: " + elapsedMs);
}
public void TestMultipleLayersFromAbstractClass()
{
ICsConfigurationBuilder cb = tang.NewConfigurationBuilder();
cb.BindImplementation(GenericType <MultiLayer> .Class, GenericType <LowerLayer> .Class);
MultiLayer o = tang.NewInjector(cb.Build()).GetInstance <MultiLayer>();
Assert.IsNotNull(o);
}
public NeuralGenTrainTask()
{
ISingleLayer <double>[] layers = new ISingleLayer <double> [2];
layers[0] = new SingleLayer(6, 6, new Neuro.MLP.ActivateFunction.BipolarTreshhold(), new Random());
layers[1] = new SingleLayer(6, 1, new Neuro.MLP.ActivateFunction.BipolarTreshhold(), new Random());
MultiLayer mLayer = new MultiLayer(layers);
DifferintiableLearningConfig config = new DifferintiableLearningConfig(new Neuro.MLP.ErrorFunction.HalfEuclid());
config.Step = 0.1;
config.OneImageMinError = 0.01;
config.MinError = 0.5;
config.MinChangeError = 0.0000001;
config.UseRandomShuffle = true;
config.MaxEpoch = 10000;
SimpleBackPropogation learn = new SimpleBackPropogation(config);
network = new MultiLayerNeuralNetwork(mLayer, learn);
}
public void SetUp()
{
mlpMN = new MultiLayerMathsNet(seed, null, shapes, 1, initialValueWeights);
mlp = new MultiLayer(shapes, seed, 1, null);
}
