private static NeuralNetwork InitializeDefaultNeuralNetwork(DataProvider dp)
{
using (CNNDataSet.TrainingRatesDataTable table = new CNNDataSet.TrainingRatesDataTable())
{
table.ReadXml(@"D:\prj\cnnwb\CNNWB.Data\TrainingSchemes\LeCun2.scheme-xml");
CNNDataSet.TrainingRatesRow row = table.Rows[0] as CNNDataSet.TrainingRatesRow;
_data = new TrainingRate(row.Rate, row.Epochs, row.MinimumRate, row.WeightDecayFactor, row.Momentum, row.BatchSize, row.InitialAvgLoss, row.DecayFactor, row.DecayAfterEpochs, row.WeightSaveTreshold, row.Distorted, row.DistortionPercentage, row.SeverityFactor, row.MaxScaling, row.MaxRotation, row.ElasticSigma, row.ElasticScaling);
}
//NeuralNetwork network = new NeuralNetwork(_dp, "LeNet-5", 10, 0.8D, LossFunctions.MeanSquareError,
// DataProviderSets.MNIST, TrainingStrategy.SGDLevenbergMarquardt, 0.02D);
//network.AddLayer(LayerTypes.Input, 1, 32, 32);
//network.AddLayer(LayerTypes.Convolutional, ActivationFunctions.Tanh, 6, 28, 28, 5, 5);
//network.AddLayer(LayerTypes.AvgPooling, ActivationFunctions.Tanh, 6, 14, 14, 2, 2);
//bool[] maps = new bool[6 * 16]
//{
// true, false,false,false,true, true, true, false,false,true, true, true, true, false,true, true,
// true, true, false,false,false,true, true, true, false,false,true, true, true, true, false,true,
// true, true, true, false,false,false,true, true, true, false,false,true, false,true, true, true,
// false,true, true, true, false,false,true, true, true, true, false,false,true, false,true, true,
// false,false,true, true, true, false,false,true, true, true, true, false,true, true, false,true,
// false,false,false,true, true, true, false,false,true, true, true, true, false,true, true, true
//};
//network.AddLayer(LayerTypes.Convolutional, ActivationFunctions.Tanh, 16, 10, 10, 5, 5, mappings: new Mappings(maps));
//network.AddLayer(LayerTypes.AvgPooling, ActivationFunctions.Tanh, 16, 5, 5, 2, 2);
//network.AddLayer(LayerTypes.Convolutional, ActivationFunctions.Tanh, 120, 1, 1, 5, 5);
//network.AddLayer(LayerTypes.FullyConnected, ActivationFunctions.Tanh, 10);
//network.InitializeWeights();
NeuralNetwork network = new NeuralNetwork(_dp, "Simard-6", 10, 0.8D, LossFunctions.MeanSquareError, DataProviderSets.MNIST, TrainingStrategy.SGDLevenbergMarquardt, 0.02D);
network.AddLayer(LayerTypes.Input, 1, 32, 32);
network.AddLayer(LayerTypes.ConvolutionalSubsampling, ActivationFunctions.Tanh, 6, 14, 14, 5, 5);
network.AddLayer(LayerTypes.ConvolutionalSubsampling, ActivationFunctions.Tanh, 50, 5, 5, 5, 5);
network.AddLayer(LayerTypes.FullyConnected, ActivationFunctions.Tanh, 100);
network.AddLayer(LayerTypes.FullyConnected, ActivationFunctions.Tanh, 10);
network.InitializeWeights();
//NeuralNetwork network = new NeuralNetwork(DataProvider, "Simard-16", 10, 0.8D, LossFunctions.MeanSquareError, DataProviderSets.MNIST, TrainingStrategy.SGDLevenbergMarquardt, 0.1D);
//network.AddLayer(LayerTypes.Input, 1, 32, 32);
//network.AddLayer(LayerTypes.ConvolutionalSubsampling, ActivationFunctions.Tanh, 16, 14, 14, 5, 5);
//network.AddLayer(LayerTypes.ConvolutionalSubsampling, ActivationFunctions.Tanh, 64, 5, 5, 5, 5);
//network.AddLayer(LayerTypes.FullyConnected, ActivationFunctions.Tanh, 196);
//network.AddLayer(LayerTypes.FullyConnected, ActivationFunctions.Tanh, 10);
//network.InitializeWeights();
//NeuralNetwork network = new NeuralNetwork(DataProvider, "LeNet-5", 10, 0.8D, LossFunctions.MeanSquareError, DataProviderSets.MNIST, TrainingStrategy.SGDLevenbergMarquardt, 0.02D);
//network.AddLayer(LayerTypes.Input, 1, 32, 32);
//network.AddLayer(LayerTypes.Convolutional, ActivationFunctions.Tanh, 6, 28, 28, 5, 5, 1, 1);
//network.AddLayer(LayerTypes.AvgPooling, ActivationFunctions.Tanh, 6, 14, 14, 2, 2, 2, 2);
//bool[] maps = new bool[6 * 16]
//{
// true, false, false, false, true, true, true, false, false, true, true, true, true, false, true, true,
// true, true, false, false, false, true, true, true, false, false, true, true, true, true, false, true,
// true, true, true, false, false, false, true, true, true, false, false, true, false, true, true, true,
// false, true, true, true, false, false, true, true, true, true, false, false, true, false, true, true,
// false, false, true, true, true, false, false, true, true, true, true, false, true, true, false, true,
// false, false, false, true, true, true, false, false, true, true, true, true, false, true, true, true
//};
//network.AddLayer(LayerTypes.Convolutional, ActivationFunctions.Tanh, 16, 10, 10, 5, 5, 1, 1, 0, 0, new Mappings(maps));
//network.AddLayer(LayerTypes.AvgPooling, ActivationFunctions.Tanh, 16, 5, 5, 2, 2, 2, 2);
//network.AddLayer(LayerTypes.Convolutional, ActivationFunctions.Tanh, 120, 1, 1, 5, 5, 1, 1);
//network.AddLayer(LayerTypes.FullyConnected, ActivationFunctions.Tanh, 10);
//network.InitializeWeights();
//NeuralNetwork network = new NeuralNetwork(DataProvider, "LeNet-5", 10, 1D, LossFunctions.CrossEntropy, DataProviderSets.MNIST, TrainingStrategy.SGDLevenbergMarquardt, 0.02D);
//network.AddLayer(LayerTypes.Input, 1, 32, 32);
//network.AddLayer(LayerTypes.Convolutional, ActivationFunctions.ReLU, 6, 28, 28, 5, 5);
//network.AddLayer(LayerTypes.AvgPooling, ActivationFunctions.Ident, 6, 14, 14, 2, 2, 2, 2);
//bool[] maps = new bool[6 * 16]
//{
// true, false, false, false, true, true, true, false, false, true, true, true, true, false, true, true,
// true, true, false, false, false, true, true, true, false, false, true, true, true, true, false, true,
// true, true, true, false, false, false, true, true, true, false, false, true, false, true, true, true,
// false, true, true, true, false, false, true, true, true, true, false, false, true, false, true, true,
// false, false, true, true, true, false, false, true, true, true, true, false, true, true, false, true,
// false, false, false, true, true, true, false, false, true, true, true, true, false, true, true, true
//};
//network.AddLayer(LayerTypes.Convolutional, ActivationFunctions.ReLU, 16, 10, 10, 5, 5, 1, 1, 0, 0, new Mappings(maps));
//network.AddLayer(LayerTypes.AvgPooling, ActivationFunctions.Ident, 16, 5, 5, 2, 2, 2, 2);
//network.AddLayer(LayerTypes.Local, ActivationFunctions.ReLU, 120, 1, 1, 5, 5);
//network.AddLayer(LayerTypes.FullyConnected, ActivationFunctions.SoftMax, 10);
//network.InitializeWeights();
//NeuralNetwork network = new NeuralNetwork(DataProvider, "MyNet-16", 10, 0.8D, LossFunctions.MeanSquareError, DataProviderSets.MNIST, TrainingStrategy.SGDLevenbergMarquardt, 0.02D);
//network.AddLayer(LayerTypes.Input, 1, 32, 32);
//network.AddLayer(LayerTypes.Convolutional, ActivationFunctions.Tanh, 16, 28, 28, 5, 5);
//network.AddLayer(LayerTypes.AvgPooling, ActivationFunctions.Tanh, 16, 14, 14, 2, 2);
//network.AddLayer(LayerTypes.Convolutional, ActivationFunctions.Tanh, 64, 10, 10, 5, 5, 1, 1, 0, 0, new Mappings(16, 64, 66, 1));
//network.AddLayer(LayerTypes.AvgPooling, ActivationFunctions.Tanh, 64, 5, 5, 2, 2);
//network.AddLayer(LayerTypes.Convolutional, ActivationFunctions.Tanh, 256, 1, 1, 5, 5, 1, 1, 0, 0, new Mappings(64, 256, 66, 2));
//network.AddLayer(LayerTypes.FullyConnected, ActivationFunctions.Tanh, 10);
//network.InitializeWeights();
//NeuralNetwork network = new NeuralNetwork(DataProvider, "CNN-CIFAR10-A", 10, 0.8D, LossFunctions.MeanSquareError, DataProviderSets.CIFAR10, TrainingStrategy.SGDLevenbergMarquardt, 0.02D);
//network.AddLayer(LayerTypes.Input, 3, 32, 32);
//bool[] maps = new bool[3 * 64]
//{
// true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true,
// false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true,
// false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true,false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true
//};
//network.AddLayer(LayerTypes.Convolutional, ActivationFunctions.ReLU, 64, 28, 28, 5, 5, 1, 1, 0, 0, new Mappings(maps));
//network.AddLayer(LayerTypes.StochasticPooling, ActivationFunctions.Ident, 64, 14, 14, 3, 3, 2, 2);
//network.AddLayer(LayerTypes.Convolutional, ActivationFunctions.ReLU, 64, 10, 10, 5, 5, 1, 1, 0, 0, new Mappings(64, 64, 66, 1));
//network.AddLayer(LayerTypes.StochasticPooling, ActivationFunctions.Ident, 64, 5, 5, 3, 3, 2, 2);
//network.AddLayer(LayerTypes.Local, ActivationFunctions.ReLU, 64, 3, 3, 3, 3, 1, 1, 0, 0, new Mappings(64, 64, 66, 2));
//network.AddLayer(LayerTypes.Local, ActivationFunctions.ReLU, 386, 1, 1, 3, 3, 1, 1, 0, 0, 50);
//network.AddLayer(LayerTypes.FullyConnected, ActivationFunctions.SoftSign, 10);
//network.InitializeWeights();
//NeuralNetwork network = new NeuralNetwork(DataProvider, "CNN-CIFAR10-Z2", 10, 1D, LossFunctions.CrossEntropy, DataProviderSets.CIFAR10, TrainingStrategy.SGDLevenbergMarquardt);
//network.AddLayer(LayerTypes.Input, 3, 32, 32);
//bool[] maps = new bool[3 * 64]
//{
// true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true,
// false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true,
// false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true,false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true
//};
//bool[] maps = new bool[3 * 96]
//{
// true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false,
// false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false,
// false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true, false, false, true
//};
//network.AddLayer(LayerTypes.Convolutional, ActivationFunctions.ReLU, 64, 28, 28, 5, 5, 1, 1, 0, 0, new Mappings(maps));
//network.AddLayer(LayerTypes.StochasticPooling, ActivationFunctions.Ident, 64, 14, 14, 3, 3, 2, 2);
////network.AddLayer(LayerTypes.LocalResponseNormalizationCM, ActivationFunctions.None, 64, 14, 14, 3, 3);
//network.AddLayer(LayerTypes.Convolutional, ActivationFunctions.ReLU, 64, 10, 10, 5, 5, 1, 1, 0, 0, new Mappings(64, 64, 66, 1));
////network.AddLayer(LayerTypes.LocalResponseNormalizationCM, ActivationFunctions.None, 64, 10, 10, 3, 3);
//network.AddLayer(LayerTypes.StochasticPooling, ActivationFunctions.Ident, 64, 5, 5, 3, 3, 2, 2);
////network.AddLayer(LayerTypes.Local, ActivationFunctions.ReLU, 64, 1, 1, 5, 5, 1, 1, 0, 0, new Mappings(64, 64, 66, 2));
//network.AddLayer(LayerTypes.Local, ActivationFunctions.Logistic, 384, 1, 1, 5, 5, 1, 1, 0, 0, 50);
//network.AddLayer(LayerTypes.FullyConnected, ActivationFunctions.SoftMax, 10);
//network.InitializeWeights();
//network.LoadWeights(StorageDirectory + @"\CNN-CIFAR10-Z2 (2259 errors).weights-bin");
//NeuralNetwork network = new NeuralNetwork(DataProvider, "CNN-CIFAR10-B2", 10, 0.8D, LossFunctions.MeanSquareError, DataProviderSets.CIFAR10, TrainingStrategy.SGDLevenbergMarquardt, 0.02D);
//network.AddLayer(LayerTypes.Input, 3, 32, 32);
//bool[] maps = new bool[3 * 64]
//{
// true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true,
// false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true,
// false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true,false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true
//};
//network.AddLayer(LayerTypes.Convolutional, ActivationFunctions.ReLU, 64, 28, 28, 5, 5, 1, 1, 0, 0, new Mappings(maps));
//network.AddLayer(LayerTypes.StochasticPooling, ActivationFunctions.Ident, 64, 14, 14, 3, 3, 2, 2);
//network.AddLayer(LayerTypes.Convolutional, ActivationFunctions.ReLU, 64, 10, 10, 5, 5, 1, 1, 0, 0, new Mappings(64, 64, 66, 1));
//network.AddLayer(LayerTypes.StochasticPooling, ActivationFunctions.Ident, 64, 5, 5, 3, 3, 2, 2);
//network.AddLayer(LayerTypes.Local, ActivationFunctions.Tanh, 512, 1, 1, 5, 5, 1, 1, 0, 0, 50);
//network.AddLayer(LayerTypes.FullyConnected, ActivationFunctions.Tanh, 10);
//network.InitializeWeights();
//NeuralNetwork network = new NeuralNetwork(DataProvider, "CNN-CIFAR10-Z3", 10, 1D, LossFunctions.CrossEntropy, DataProviderSets.CIFAR10, TrainingStrategy.SGDLevenbergMarquardt, 0.02D);
//network.AddLayer(LayerTypes.Input, 3, 32, 32);
//bool[] maps = new bool[3 * 64]
//{
// true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true,
// false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true,
// false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true,false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true
//};
//network.AddLayer(LayerTypes.Convolutional, ActivationFunctions.ReLU, 64, 32, 32, 5, 5, 1, 1, 2, 2, new Mappings(maps));
//network.AddLayer(LayerTypes.StochasticPooling, ActivationFunctions.Ident, 64, 16, 16, 3, 3, 2, 2);
////network.AddLayer(LayerTypes.LocalResponseNormalizationCM, ActivationFunctions.None, 64, 16, 16, 5, 5);
//network.AddLayer(LayerTypes.Convolutional, ActivationFunctions.ReLU, 64, 16, 16, 5, 5, 1, 1, 2, 2, new Mappings(64, 64, 66, 1));
////network.AddLayer(LayerTypes.LocalResponseNormalizationCM, ActivationFunctions.None, 64, 16, 16, 5, 5);
//network.AddLayer(LayerTypes.StochasticPooling, ActivationFunctions.Ident, 64, 8, 8, 3, 3, 2, 2);
//network.AddLayer(LayerTypes.Local, ActivationFunctions.ReLU, 32, 8, 8, 3, 3, 1, 1, 1, 1, new Mappings(64, 32, 50, 2));
//network.AddLayer(LayerTypes.Local, ActivationFunctions.SoftSign, 32, 8, 8, 3, 3, 1, 1, 1, 1, 50);
//network.AddLayer(LayerTypes.FullyConnected, ActivationFunctions.SoftMax, 10);
//network.InitializeWeights();
//NeuralNetwork network = new NeuralNetwork(DataProvider, "CNN-CIFAR10-C", 10, 0.8D, LossFunctions.MeanSquareError, DataProviderSets.CIFAR10, TrainingStrategy.SGDLevenbergMarquardt, 0.02D);
//network.AddLayer(LayerTypes.Input, 3, 32, 32);
//bool[] maps = new bool[3 * 64]
//{
// true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true,
// false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true,
// false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true,false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true
//};
//network.AddLayer(LayerTypes.Convolutional, ActivationFunctions.ReLU, 64, 28, 28, 5, 5, 1, 1, 0, 0, new Mappings(maps));
//network.AddLayer(LayerTypes.StochasticPooling, ActivationFunctions.Ident, 64, 14, 14, 3, 3, 2, 2);
//network.AddLayer(LayerTypes.Convolutional, ActivationFunctions.ReLU, 64, 10, 10, 5, 5, 1, 1, 0, 0, new Mappings(64, 64, 66, 1));
//network.AddLayer(LayerTypes.StochasticPooling, ActivationFunctions.Ident, 64, 5, 5, 3, 3, 2, 2);
//network.AddLayer(LayerTypes.Local, ActivationFunctions.ReLU, 32, 5, 5, 3, 3, 1, 1, 1, 1, new Mappings(64, 32, 50, 2));
//network.AddLayer(LayerTypes.Local, ActivationFunctions.ReLU, 32, 5, 5, 3, 3, 1, 1, 1, 1, 50);
//network.AddLayer(LayerTypes.FullyConnected, ActivationFunctions.Tanh, 10);
//network.InitializeWeights();
//NeuralNetwork network = new NeuralNetwork(DataProvider, "CNN-CIFAR10-Z4", 10, 0.8D, LossFunctions.MeanSquareError, DataProviderSets.CIFAR10, TrainingStrategy.SGDLevenbergMarquardt, 0.02D, 2000, false);
//network.AddLayer(LayerTypes.Input, 3, 32, 32);
////bool[] maps = new bool[3 * 64]
////{
//// true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, false, true, false, true, false, true, false, true,
//// false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, true, false, true, false, true, false,
//// false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, true, false, true, false, true, false, true
////};
//bool[] maps = new bool[3 * 64]
//{
// true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true,
// false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true,
// false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true,false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true
//};
////bool[] maps = new bool[3 * 48] //Z3
////{
//// true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false,
//// false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false,
//// false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true
////};
////bool[] maps = new bool[3 * 48] //Z3
////{
//// true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false,
//// false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true,
//// false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true
////};
//network.AddLayer(LayerTypes.Convolutional, ActivationFunctions.ReLU, 64, 32, 32, 5, 5, 1, 1, 2, 2, new Mappings(maps));
//network.AddLayer(LayerTypes.StochasticPooling, ActivationFunctions.Ident, 64, 16, 16, 3, 3, 2, 2);
////network.AddLayer(LayerTypes.LocalContrastNormalization, ActivationFunctions.None, 64, 16, 16, 5, 5);
//network.AddLayer(LayerTypes.Convolutional, ActivationFunctions.ReLU, 64, 16, 16, 5, 5, 1, 1, 2, 2, new Mappings(64, 64, 66, 1));
////network.AddLayer(LayerTypes.LocalContrastNormalization, ActivationFunctions.None, 64, 16, 16, 5, 5);
//network.AddLayer(LayerTypes.StochasticPooling, ActivationFunctions.Ident, 64, 8, 8, 3, 3, 2, 2);
//network.AddLayer(LayerTypes.Local, ActivationFunctions.ReLU, 32, 8, 8, 3, 3, 1, 1, 1, 1, new Mappings(64, 32, 50, 2));
//network.AddLayer(LayerTypes.Local, ActivationFunctions.ReLU, 32, 8, 8, 3, 3, 1, 1, 1, 1, 50);
//network.AddLayer(LayerTypes.FullyConnected, ActivationFunctions.SoftSign, 10);
//network.InitializeWeights();
//NeuralNetwork network = new NeuralNetwork(DataProvider, "CNN-CIFAR10-D", 10, 0.8D, LossFunctions.MeanSquareError, DataProviderSets.CIFAR10, TrainingStrategy.SGDLevenbergMarquardt, 0.02D);
//network.AddLayer(LayerTypes.Input, 3, 32, 32);
//bool[] maps = new bool[3 * 64]
//{
// true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true,
// false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true,
// false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true,false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true
//};
//network.AddLayer(LayerTypes.Convolutional, ActivationFunctions.ReLU, 64, 32, 32, 5, 5, 1, 1, 2, 2, new Mappings(maps));
//network.AddLayer(LayerTypes.MaxPoolingWeightless, ActivationFunctions.Ident, 64, 16, 16, 3, 3, 2, 2);
//network.AddLayer(LayerTypes.Convolutional, ActivationFunctions.ReLU, 64, 16, 16, 3, 3, 1, 1, 1, 1, new Mappings(64, 64, 50, 1));
//network.AddLayer(LayerTypes.AvgPoolingWeightless, ActivationFunctions.Ident, 64, 8, 8, 3, 3, 2, 2);
//network.AddLayer(LayerTypes.Local, ActivationFunctions.BReLU, 32, 8, 8, 3, 3, 1, 1, 1, 1, new Mappings(64, 32, 50, 2));
//network.AddLayer(LayerTypes.Local, ActivationFunctions.Tanh, 32, 8, 8, 3, 3, 1, 1, 1, 1, 50);
//network.AddLayer(LayerTypes.FullyConnected, ActivationFunctions.Tanh, 10);
////network.InitializeWeights();
//NeuralNetwork network = new NeuralNetwork(DataProvider, "CNN-CIFAR10-G", 10, 0.8D, LossFunctions.MeanSquareError, DataProviderSets.CIFAR10, TrainingStrategy.SGD, 0.02D);
//network.AddLayer(LayerTypes.Input, 3, 32, 32);
//bool[] maps = new bool[3 * 48]
//{
// true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false,
// false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true,
// false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, false, false, false, false, false, false, false, false, true, true, true, true, true, true, true, true
//};
//network.AddLayer(LayerTypes.Convolutional, ActivationFunctions.ReLU, 48, 32, 32, 5, 5, 1, 1, 2, 2, new Mappings(maps));
//network.AddLayer(LayerTypes.StochasticPooling, ActivationFunctions.Ident, 48, 16, 16, 3, 3, 2, 2);
//network.AddLayer(LayerTypes.Convolutional, ActivationFunctions.ReLU, 64, 16, 16, 5, 5, 1, 1, 2, 2, new Mappings(48, 64, 66, 1));
//network.AddLayer(LayerTypes.StochasticPooling, ActivationFunctions.Ident, 64, 8, 8, 3, 3, 2, 2);
//network.AddLayer(LayerTypes.Local, ActivationFunctions.ReLU, 24, 8, 8, 3, 3, 1, 1, 1, 1, new Mappings(64, 24, 50, 2));
//network.AddLayer(LayerTypes.Local, ActivationFunctions.Tanh, 24, 8, 8, 3, 3, 1, 1, 1, 1, 50);
//network.AddLayer(LayerTypes.FullyConnected, ActivationFunctions.Tanh, 10);
//network.InitializeWeights();
//NeuralNetwork network = new NeuralNetwork(DataProvider, "CNN-MNIST-A", 10, 1D, LossFunctions.CrossEntropy, DataProviderSets.MNIST, TrainingStrategy.SGD, 0.02D);
//network.AddLayer(LayerTypes.Input, 1, 32, 32);
//network.AddLayer(LayerTypes.Convolutional, ActivationFunctions.ReLU, 32, 24, 24, 9, 9, 1, 1, 0, 0);
//network.AddLayer(LayerTypes.StochasticPooling, ActivationFunctions.Ident, 32, 12, 12, 3, 3, 2, 2);
//network.AddLayer(LayerTypes.Convolutional, ActivationFunctions.ReLU, 32, 8, 8, 5, 5, 1, 1, 0, 0, new Mappings(32, 32, 66));
//network.AddLayer(LayerTypes.StochasticPooling, ActivationFunctions.Ident, 32, 4, 4, 3, 3, 2, 2);
//network.AddLayer(LayerTypes.Local, ActivationFunctions.ReLU, 256, 1, 1, 4, 4, 1, 1, 0, 0, 50);
//network.AddLayer(LayerTypes.FullyConnected, ActivationFunctions.SoftMax, 10);
//network.InitializeWeights();
//network.RaiseNetworkProgressEvent += new EventHandler<EventArgs>(NetworkProgressEvent);
//network.RaiseAddUnrecognizedTestSampleEvent += new EventHandler<AddUnrecognizedTestSampleEventArgs>(AddUnrecognizedTestSampleEvent);
network.MaxDegreeOfParallelism = 8;
network.AddGlobalTrainingRate(_data, true);
return(network);
}
