public void Train_RuningTraining_NetworkIsTrained()
{
var network = new SimpleNeuralNetwork(3);
var layerFactory = new NeuralLayerFactory();
network.AddLayer(layerFactory.CreateNeuralLayer(3, new RectifiedActivationFuncion(), new WeightedSumFunction()));
network.AddLayer(layerFactory.CreateNeuralLayer(1, new SigmoidActivationFunction(0.7), new WeightedSumFunction()));
network.PushExpectedValues(
new double[][] {
new double[] { 0 },
new double[] { 1 },
new double[] { 1 },
new double[] { 0 },
new double[] { 1 },
new double[] { 0 },
new double[] { 0 },
});
network.Train(
new double[][] {
new double[] { 150, 2, 0 },
new double[] { 1002, 56, 1 },
new double[] { 1060, 59, 1 },
new double[] { 200, 3, 0 },
new double[] { 300, 3, 1 },
new double[] { 120, 1, 0 },
new double[] { 80, 1, 0 },
}, 10000);
network.PushInputValues(new double[] { 1054, 54, 1 });
var outputs = network.GetOutput();
}
public void CreateNeuralLayer_NumberOfNeuronsPasses_CorrectLayerCreated()
{
var neuralLayerFactory = new NeuralLayerFactory();
var neuralLayer = neuralLayerFactory.CreateNeuralLayer(11, new StepActivationFunction(0.5), new WeightedSumFunction());
Assert.AreEqual(11, neuralLayer.Neurons.Count);
}
public SimpleNeuralNetwork(int inputNeurons)
{
_layers = new List <NeuralLayer>();
_layerFactory = new NeuralLayerFactory();
CreateInputLayer(inputNeurons);
_learningRate = 2.95;
}
public AgentSmith()
{
_network = new SimpleNeuralNetwork(11);
var layerFactory = new NeuralLayerFactory();
_network.AddLayer(layerFactory.CreateNeuralLayer(10, new RectifiedActivationFuncion(), new WeightedSumFunction()));
_network.AddLayer(layerFactory.CreateNeuralLayer(1, new SigmoidActivationFunction(1), new WeightedSumFunction()));
}
public void NeuralNetworkInitializer()
{
var layerFactory = new NeuralLayerFactory();
network.AddLayer(layerFactory.CreateNeuralLayer(5, weights.Take(10).ToArray(), new StepActivationFunction(0.5),
new WeightedSumFunction()));
network.AddLayer(layerFactory.CreateNeuralLayer(1, weights.Skip(10).Take(5).ToArray(), new StepActivationFunction(0.5), //
new WeightedSumFunction()));
}
public void AddLayer_NeuralAddingNewLayer_LayerAdded()
{
var network = new SimpleNeuralNetwork(3);
var layerFactory = new NeuralLayerFactory();
network.AddLayer(layerFactory.CreateNeuralLayer(3, new RectifiedActivationFuncion(), new WeightedSumFunction()));
Assert.AreEqual(2, network._layers.Count);
}
/// <summary>
/// Constructor of the Neural Network.
/// Note:
/// Initially input layer with defined number of inputs will be created.
/// </summary>
/// <param name="numberOfInputNeurons">
/// Number of neurons in input layer.
/// </param>
public SimpleNeuralNetwork(int numberOfInputNeurons)
{
_layers = new List <NeuralLayer>();
_layerFactory = new NeuralLayerFactory();
// Create input layer that will collect inputs.
CreateInputLayer(numberOfInputNeurons);
_learningRate = 2.95;
}
public void AddLayer_NeuralAddingNewLayer_LayerAdded()
{
var network = new SimpleNeuralNetwork(6);
var layerFactory = new NeuralLayerFactory();
network.PushInputValues(new double[] { 23, 565, 789, 3, 90, 23 });
network.AddLayer(layerFactory.CreateNeuralLayer(3, new RectifiedActivationFuncion(), new WeightedSumFunction()));
Assert.AreEqual(2, network._layers.Count);
Console.WriteLine(JsonConvert.SerializeObject(network, Formatting.Indented));
}
private NeuralNet(Builder settings)
{
ActivationFunc = settings.ActivationFunc;
LearningOptimizing = settings.LearningOptimizing ?? default(LearningOptimizing);
LearningRate = settings.LearningRate ?? _defaultLearningRate;
MomentumRate = settings.MomentumRate ?? _defaultMomentumRate;
LossFunc = settings.LossFunc;
var layersFactory = new NeuralLayerFactory(
settings.InputNeurons,
settings.NeuronLayers.ToArray(),
settings.IsBiasNeurons,
settings.WeightsInitializer,
settings.BiasInitializer);
_NeuralLayers = layersFactory.CreateLayers();
}
static void Main(string[] args)
{
Console.WriteLine("START");
var network = new SimpleNeuralNetwork(3);
var layerFactory = new NeuralLayerFactory();
network.AddLayer(layerFactory.CreateNeuralLayer(3, new RectifiedActivationFunction(), new WeightedSumFunction()));
network.AddLayer(layerFactory.CreateNeuralLayer(1, new SigmoidActivationFunction(0.7), new WeightedSumFunction()));
network.PushExpectedValues(
new double[][]
{
new double[] { 0 },
new double[] { 1 },
new double[] { 1 },
new double[] { 0 },
new double[] { 1 },
new double[] { 0 },
new double[] { 0 },
});
Console.WriteLine("TRAINING NETWORK");
network.Train(
new double[][]
{
new double[] { 150, 2, 0 },
new double[] { 1002, 56, 1 },
new double[] { 1002, 59, 1 },
new double[] { 200, 3, 0 },
new double[] { 300, 2, 1 },
new double[] { 120, 1, 0 },
new double[] { 80, 1, 0 },
}, 10000);
network.PushInputValues(new double[] { 1054, 54, 1 });
var outputs = network.GetOutput();
Console.WriteLine("OUTPUT");
outputs.ForEach(x => Console.WriteLine(x));
Console.WriteLine("DONE");
Console.ReadLine();
}
public static void Main(string[] args)
{
var numberOfInputNeurons = 2;
var numberOfOutputNeurons = 1;
var network = new NNetwork(numberOfInputNeurons, numberOfOutputNeurons, 1);
var layerFactory = new NeuralLayerFactory();
//// TODO: Mix these 2 lines. eliminate the foreach
//var inputLayer = layerFactory.CreateNeuralLayer(numberOfInputNeurons, new RectifiedActivationFuncion());
//foreach (var x in inputLayer.Neurons)
//{
// x.AddInputSynapse(0);
//}
//network.AddLayer(inputLayer);
//network.AddLayer(layerFactory.CreateNeuralLayer(3, new SigmoidActivationFunction(0.7)));
//network.AddLayer(layerFactory.CreateNeuralLayer(1, new SigmoidActivationFunction(0.7)));
//network.PushExpectedValues(
// new double[][] {
// new double[] { 0 },
// new double[] { 1 },
// new double[] { 1 },
// new double[] { 1 },
// });
//network.Train(
// new double[][] {
// new double[] { 0, 0},
// new double[] { 0, 1},
// new double[] { 1, 0},
// new double[] { 1, 1},
// }, 10000);
//network.PushInputValues(new double[] { 1, 1 });
//var outputs = network.GetOutput();
//foreach (var output in outputs)
//{
// System.Console.WriteLine(output);
//}
}
static void Main(string[] args)
{
var network = new SimpleNeuralNetwork(1);
var layerFactory = new NeuralLayerFactory();
network.AddLayer(layerFactory.CreateNeuralLayer(2, new RectifiedActivationFuncion(), new WeightedSumFunction()));
network.AddLayer(layerFactory.CreateNeuralLayer(1, new SigmoidActivationFunction(0.4), new WeightedSumFunction()));
double[][] expectedValues = new double[samples][];
double[][] trainingValues = new double[samples][];
for (int i = 0; i < samples; i++)
{
Random rng = new Random();
Random rng2 = new Random();
int val1 = rng.Next(rng.Next() % 1000);
int val2 = rng2.Next(i % 900);
expectedValues[i] = new double[] { (val1 + val2) % 2 };
trainingValues[i] = new double[] { val1, val2 };
Console.WriteLine($"val1: {val1} val2: {val2} sum: { (val1 + val2) % 2 }");
}
network.PushExpectedValues(expectedValues);
network.Train(trainingValues, 5000);
network.PushInputValues(new double[] { 1054, 54 });
var outputs = network.GetOutput();
Console.WriteLine($"network output: {string.Join(", ", outputs)}");
Console.ReadKey();
}
public void TrainNetwork_6Inputs_3HiddenLayer_2Outputs()
{
var network = new SimpleNeuralNetwork(6, 1.95); // six input nuerons
var layerFactory = new NeuralLayerFactory();
network.AddLayer(layerFactory.CreateNeuralLayer(3, new SigmoidActivationFunction(0.7), new WeightedSumFunction())); // three hidden layers
network.AddLayer(layerFactory.CreateNeuralLayer(2, new LazyOutputFunction(), new WeightedSumFunction())); // two output layers
network.PushExpectedValues(
new double[][] {
new double[] { 0.25, 0.20 },
new double[] { 0.10, 0.05 },
new double[] { 0.16, 0.30 },
new double[] { 0.30, 0.10 },
new double[] { 0.25, 0.20 },
new double[] { 0.10, 0.05 },
new double[] { 0.16, 0.30 },
new double[] { 0.30, 0.10 },
});
network.Train(
new double[][] {
new double[] { 150, 0, 0, 34, 35, 56 },
new double[] { 190, 23, 56, 0, 29, 529 },
new double[] { 290, 3, 108, 24, 189, 20 },
new double[] { 290, 67, 6, 0, 1, 0 },
new double[] { 150, 0, 0, 34, 35, 56 },
new double[] { 190, 23, 56, 0, 29, 529 },
new double[] { 290, 3, 108, 24, 189, 20 },
new double[] { 290, 67, 6, 0, 1, 0 },
}, 10000);
network.PushInputValues(new double[] { 150, 0, 0, 34, 35, 56 });
var outputs = network.GetOutput();
Console.WriteLine(outputs[0].ToString() + " " + outputs[1].ToString() + "\n\n" + JsonConvert.SerializeObject(network, Formatting.Indented));
}
static void Main(string[] args)
{
var network = new NeuralNetwork(3);
var layerFactory = new NeuralLayerFactory();
network.AddLayer(layerFactory.CreateNeuralLayer(3, new RectifiedActivationFuncion(), new WeightedSumFunction()));
//network.AddLayer(layerFactory.CreateNeuralLayer(3, new RectifiedActivationFuncion(), new WeightedSumFunction()));
network.AddLayer(layerFactory.CreateNeuralLayer(1, new SigmoidActivationFunction(0.7), new WeightedSumFunction()));
/* network.PushExpectedValues(
* new double[][] {
* new double[] { 0 },
* new double[] { 1 },
* new double[] { 1 },
* new double[] { 0 },
* new double[] { 1 },
* new double[] { 0 },
* new double[] { 0 },
* });
*
* network.Train(
* new double[][] {
* new double[] { 150, 2, 0 },
* new double[] { 1002, 56, 1 },
* new double[] { 1060, 59, 1 },
* new double[] { 200, 3, 0 },
* new double[] { 300, 3, 1 },
* new double[] { 120, 1, 0 },
* new double[] { 80, 1, 0 },
* }, 10000);
*
* network.PushInputValues(new double[] { 1054, 54, 1 });
* var outputs = network.GetOutput(); */
}
/// <summary>
/// Constructor of the Neural Network.
/// Note: Initially input layer with defined number of inputs will be created.
/// </summary>
/// <param name="numberOfInputNeurons">
/// Number of neurons in input layer.
/// </param>
public SimpleNeuralNetwork(int numberOfInputNeurons)
{
this.layers = new List <NeuralLayer>();
this.layerFactory = new NeuralLayerFactory();
this.learningRate = 2.95;
}
