public static NeuralNetwork<Matrix> Create(IDataSet<Matrix, Vector> dataSet)
{
var count = 5;
var a = new ISingleLayer<Matrix, Matrix>[count];
for (var i = 0; i < count; ++i)
a[i] = new MatrixConvolutor(28, 28, 24, 24, new Tanh());
var b = new ISingleLayer<Matrix, Matrix>[count];
for (var i = 0; i < count; ++i)
b[i] = new MatrixSubsampler(24, 24, 12, 12, new Tanh());
var c = new ISingleLayer<Matrix, Matrix>[count];
for (var i = 0; i < count; ++i)
c[i] = new MatrixConvolutor(12, 12, 8, 8, new Tanh());
var d = new ISingleLayer<Matrix, Matrix>[count];
for (var i = 0; i < count; ++i)
d[i] = new MatrixSubsampler(8, 8, 4, 4, new Tanh());
var splitter = new Splitter<Matrix, Matrix>(a);
var applicator1 = new Applicator<Matrix, Matrix>(b);
var applicator2 = new Applicator<Matrix, Matrix>(c);
var merger = new MatrixMerger<Matrix>(d);
var classif = new FullyConnectedLayer(16 * count, 10, new Tanh());
var comp = CompositeLayer<Vector, Vector[], Vector>.Compose(splitter,
applicator1,
applicator2,
merger,
classif);
return new NeuralNetwork<Matrix>(comp);
}
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 static NeuralNetwork<Vector> Create(IDataSet<Vector, Vector> dataSet, IActivator activator, List<int> hiddenSizes)
{
if (hiddenSizes.Count == 0)
return Create(dataSet, activator);
var inputSize = dataSet.FirstInput.Size;
var outputSize = dataSet.FirstOutput.Size;
var sizes = new List<int>{inputSize};
sizes.AddRange(hiddenSizes);
sizes.Add(outputSize);
var layerCount = sizes.Count - 1;
var layers = new ISingleLayer<Vector, Vector>[layerCount];
for (var i = 0; i < layerCount; ++i)
layers[i] = new FullyConnectedLayer(sizes[i], sizes[i + 1], activator);
var compositeLayer = LayerCompositor.ComposeGeteroneneous(layers);
return new NeuralNetwork<Vector>(compositeLayer);
}
public static NeuralNetwork<Matrix> CreateSemi(IDataSet<Matrix, Vector> dataSet)
{
var count = 5;
var a = new ISingleLayer<Matrix, Matrix>[count];
for (var i = 0; i < count; ++i)
a[i] = new MatrixConvolutor(28, 28, 24, 24, new Tanh());
var b = new ISingleLayer<Matrix, Matrix>[count];
for (var i = 0; i < count; ++i)
b[i] = new MatrixSubsampler(24, 24, 12, 12, new Tanh());
var splitter = new Splitter<Matrix, Matrix>(a);
var merger = new MatrixMerger<Matrix>(b);
var classif = new FullyConnectedLayer(144 * count, 50, new Tanh());
var classif2 = new FullyConnectedLayer(100, 10, new Tanh());
var comp = CompositeLayer<Vector, Vector[], Vector>.Compose(splitter,
merger,
classif,
classif2);
return new NeuralNetwork<Matrix>(comp);
}
public static NeuralNetwork<Matrix> CreateNorb(IDataSet<Matrix, Vector> dataSet)
{
var count = 12;
var branch = 5;
var a = new ISingleLayer<Matrix, Matrix>[count];
for (var i = 0; i < count; ++i)
a[i] = new MatrixConvolutor(96, 96, 92, 92, new Tanh());
var b = new ISingleLayer<Matrix, Matrix>[count];
for (var i = 0; i < count; ++i)
b[i] = new MatrixSubsampler(92, 92, 46, 46, new Tanh());
var c = new ISingleLayer<Matrix, Matrix>[count];
for (var i = 0; i < count; ++i)
c[i] = new MatrixSubsampler(46, 46, 23, 23, new Tanh());
var splitter = new Splitter<Matrix, Matrix>(a);
var applicator1 = new Applicator<Matrix, Matrix>(b);
var merger = new MatrixMerger<Matrix>(c);
var classif = new FullyConnectedLayer(23 * 23 * count, 5, new Tanh());
var comp = CompositeLayer<Vector, Vector[], Vector>.Compose(splitter,
applicator1,
merger,
classif
);
return new NeuralNetwork<Matrix>(comp);
}
