static void Real()
{
var inputLayer = new InputLayer3D(1, 1, 1);
var outputLayer = new OutputLayer(1)
{
ActivationFunction = new ConstOutputArrayFunction()
};
var dataProvider = new FunctionProvider();
InitializeTrainingData(dataProvider.TrainData);
var perceptron1 = new PerceptronLayer(10, 2)
{
ActivationFunction = new SigmoidFunction()
};
var perceptron2 = new PerceptronLayer(10, 10)
{
ActivationFunction = new SigmoidFunction()
};
var perceptron3 = new PerceptronLayer(8, 10)
{
ActivationFunction = new SigmoidFunction()
};
var perceptron4 = new PerceptronLayer(6, 8)
{
ActivationFunction = new SigmoidFunction()
};
var perceptron5 = new PerceptronLayer(2, 6)
{
ActivationFunction = new SigmoidFunction()
};
MultiLayerPerceptron network = new MultiLayerPerceptron
{
InputLayer = inputLayer,
OutputLayer = outputLayer,
DataProvider = dataProvider
};
network.HiddenLayers.Add(perceptron1);
network.HiddenLayers.Add(perceptron2);
network.HiddenLayers.Add(perceptron3);
network.HiddenLayers.Add(perceptron4);
network.HiddenLayers.Add(perceptron5);
var trainer = new FCTrainer(network, 10, 1, dataProvider);
trainer.Train(1);
var error = network.Test(1);
}
static void OneTrainingData()
{
var inputLayer = new InputLayer3D(1, 1, 3);
var outputLayer = new OutputLayer(2)
{
ActivationFunction = new ConstOutputArrayFunction()
};
var weight1 = new List <Array3D> {
new Array3D(0.1, 0.3), new Array3D(0.3, 0.1)
};
var weight2 = new List <Array3D> {
new Array3D(0.4, 0.5), new Array3D(0.3, 0.5)
};
var perceptron1 = new PerceptronLayer(weight1)
{
ActivationFunction = new SigmoidFunction()
};
var perceptron2 = new PerceptronLayer(weight2)
{
ActivationFunction = new SigmoidFunction()
};
var dataProvider = new FunctionProvider
{
TrainData =
{
new TrainingData <Array3D, Array3D> {
Input = new Array3D(0.3, 0.4, 0.5), Expected = new Array3D(0.2, 0.6)
},
new TrainingData <Array3D, Array3D> {
Input = new Array3D(0.2, 0.4, 0.7), Expected = new Array3D(0.1, 0.8)
}
}
};
var network = new MultiLayerPerceptron
{
InputLayer = inputLayer,
OutputLayer = outputLayer,
DataProvider = dataProvider
};
network.HiddenLayers.Add(perceptron1);
network.HiddenLayers.Add(perceptron2);
var trainer = new FCTrainer(network, 2, 1, dataProvider);
trainer.Train(100);
}
public static Layer ToPercLayer(this PerceptronLayer layer)
{
var dbWeights = layer.Weights.Weights.Split(';');
var weights = new double[layer.Weights.Height][];
for (int i = 0; i < layer.Weights.Height; ++i)
{
weights[i] = new double[layer.Weights.Width];
for (int j = 0; j < layer.Weights.Width; ++j)
{
var w = dbWeights[i * layer.Weights.Width + j];
weights[i][j] = double.Parse(dbWeights[i * layer.Weights.Width + j]);
}
}
return(new Layer(layer.NeuronsCount, weights));
}
public void Initialize_Test(int neurals, int weights, double between)
{
var layer = new PerceptronLayer(neurals, weights);
var fit = 0;
foreach (var perceptron in layer.Neurals)
{
foreach (double weight in perceptron.Weights)
{
if (-between <= weight && weight < between)
{
fit++;
}
}
}
Assert.Greater(fit, neurals * weights * 0.9);
}
public void TestMethod1()
{
PerceptronLayer layer = new PerceptronLayer(4, 2);
var error = new Array(4, true)
{
[0] = 0.2,
[1] = 0.3,
[2] = 0.4,
[3] = 0.5
};
var input = new Array(2, true)
{
[0] = 0.5,
[1] = 2
};
var actual = layer.FormOutput(input);
var result = layer.Train(error, input, actual);
}
private InputLayer InicializarModeloVisualizer()
{
var inputLayer = new InputLayer("Input");
var sepalLengthInput = new Input("Sepal Length");
var SepalWidthInput = new Input("Sepal Width");
var PetalLengthInput = new Input("PetalLength");
var PetalWidthInput = new Input("Petal Width");
inputLayer.AddNode(sepalLengthInput);
inputLayer.AddNode(SepalWidthInput);
inputLayer.AddNode(PetalLengthInput);
inputLayer.AddNode(PetalWidthInput);
inputLayer.Bias = new Bias("Bias Input")
{
OutputValue = 1.0
};
var capaOculta = new PerceptronLayer("Oculta");
var cantNeuronasOcultas = (int)spinNeuronasOculta.Value;
var funcionActivacion = (cboFuncionActivacionOculta.SelectedItem as EnumInfo <ActivationType>).Valor.Map();
for (int i = 0; i < cantNeuronasOcultas; i++)
{
var neurona = new Perceptron("oculta" + i)
{
ActivationFunction = funcionActivacion
};
capaOculta.AddNode(neurona);
}
capaOculta.Bias = new Bias("Bias Oculta")
{
OutputValue = 1.0
};
inputLayer.Connect(capaOculta);
funcionActivacion = (cboFuncionActivacionSalida.SelectedItem as EnumInfo <ActivationType>).Valor.Map();
var setosaOutput = new Perceptron("Setosa")
{
ActivationFunction = funcionActivacion
};
var versicolorOutput = new Perceptron("Versicolor")
{
ActivationFunction = funcionActivacion
};
var virginicaOutput = new Perceptron("Virginica")
{
ActivationFunction = funcionActivacion
};
var capaSalida = new PerceptronLayer("Salida");
capaSalida.AddNode(setosaOutput);
capaSalida.AddNode(versicolorOutput);
capaSalida.AddNode(virginicaOutput);
capaOculta.Connect(capaSalida);
return(inputLayer);
}
private Guid ProcessSave(CNN network, string name, NetworkContext context)
{
var cnnModel = new CnnModel
{
CnnModelId = Guid.NewGuid()
};
var layers = new List <CnnLayer>();
var cnnWeights = new List <CnnWeights>();
for (int i = 0; i < network.LayersCount; i++)
{
if (network.Layers[i] is ConvolutionalLayer)
{
var cnnLayer = network.Layers[i] as ConvolutionalLayer;
var layer = new CnnLayer
{
CnnLayerId = Guid.NewGuid(),
PositionIn = i,
KernelHeight = cnnLayer.KernelSize,
KernelWidth = cnnLayer.KernelSize,
KernelsCount = cnnLayer.KernelsCount,
FeatureMapsCountIn = cnnLayer.KernelDepth,
LayerType = (byte)LayerType.CovolutionalLayer,
Model = cnnModel
};
var weights = new CnnWeights
{
CnnWeightsId = Guid.NewGuid(),
Layer = layer,
LayerId = layer.CnnLayerId
};
var w = string.Empty;
var builder = new StringBuilder(String.Empty);
for (int j = 0; j < cnnLayer.KernelsCount; ++j)
{
for (int k = 0; k < cnnLayer.KernelDepth; ++k)
{
for (int a = 0; a < cnnLayer.Kernels[j][k].Length; ++a)
{
for (int b = 0; b < cnnLayer.Kernels[j][k][a].Length; ++b)
{
builder.Append(cnnLayer.Kernels[j][k][a][b] + ";");
}
}
}
}
w = builder.ToString();
weights.Weights = w;
cnnWeights.Add(weights);
layer.Weights = weights;
layers.Add(layer);
}
else if (network.Layers[i] is PollingLayer)
{
var cnnLayer = network.Layers[i] as PollingLayer;
var layer = new CnnLayer
{
CnnLayerId = Guid.NewGuid(),
PositionIn = i,
KernelHeight = cnnLayer.KernelSize,
KernelWidth = cnnLayer.KernelSize,
LayerType = (byte)LayerType.PoolingLayer,
Model = cnnModel
};
layers.Add(layer);
}
else
{
var cnnLayer = network.Layers[i] as ReLuLayer;
var layer = new CnnLayer
{
CnnLayerId = Guid.NewGuid(),
PositionIn = i,
KernelHeight = cnnLayer.KernelSize,
KernelWidth = cnnLayer.KernelSize,
LayerType = (byte)LayerType.ReluLayer,
Model = cnnModel
};
layers.Add(layer);
}
}
var l = new CnnLayer
{
CnnLayerId = Guid.NewGuid(),
KernelHeight = network.FlattenLayer.KernelSize,
KernelWidth = network.FlattenLayer.KernelSize,
LayerType = (byte)LayerType.FlattenLayer,
Model = cnnModel
};
layers.Add(l);
cnnModel.Layers = layers;
var perceptronModel = new PerceptronModel
{
PerceptronModelId = Guid.NewGuid()
};
var percLayers = new List <PerceptronLayer>();
var percWeights = new List <PerceptronWeights>();
for (var i = 0; i < network.Perceptron.LayersCount; ++i)
{
var layer = network.Perceptron.Layers[i];
var perLayer = new PerceptronLayer
{
PerceptronLayerId = Guid.NewGuid(),
NeuronsCount = layer.NeuronsCount,
PositionIn = i,
Perceptron = perceptronModel
};
var weights = new PerceptronWeights
{
PerceptronWeightsId = perLayer.PerceptronLayerId,
Height = layer.WeightRowsCount,
Width = layer.WeightColumnsCount
};
string w = String.Empty;
var builder = new StringBuilder(String.Empty);
for (int a = 0; a < layer.Weights.Length; ++a)
{
for (int b = 0; b < layer.Weights[a].Length; ++b)
{
builder.Append(layer.Weights[a][b] + ";");
}
}
w = builder.ToString();
weights.Weights = w;
percWeights.Add(weights);
percLayers.Add(perLayer);
}
perceptronModel.Layers = percLayers;
//save
var networkModel = new NetworkModel
{
NetworkModelId = Guid.NewGuid(),
Perceptron = perceptronModel,
PerceptronId = perceptronModel.PerceptronModelId,
Cnn = cnnModel,
CnnId = cnnModel.CnnModelId,
Name = name
};
cnnModel.NetworkModel = networkModel;
cnnModel.NetworkModelId = networkModel.NetworkModelId;
perceptronModel.NetworkModel = networkModel;
perceptronModel.NetworkModelId = networkModel.NetworkModelId;
context.NetworkModels.Add(networkModel);
context.CnnLayers.AddRange(layers);
context.CnnWeightsSet.AddRange(cnnWeights);
context.CnnModels.Add(cnnModel);
context.PerceptronLayers.AddRange(percLayers);
context.PerceptronWeights.AddRange(percWeights);
context.PerceptronModels.Add(perceptronModel);
context.SaveChanges();
return(networkModel.NetworkModelId);
}
static void Xor()
{
const int batchSize = 4;
const int epochSize = 16;
var inputLayer = new InputLayer3D(1, 1, 1);
var outputLayer = new OutputLayer(1)
{
ActivationFunction = new ConstOutputArrayFunction()
};
var dataProvider = new FunctionProvider
{
TrainData =
{
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(0, 0), Expected = new Array3D(0.0)
},
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(0, 1), Expected = new Array3D(1.0)
},
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(1, 0), Expected = new Array3D(1.0)
},
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(1, 1), Expected = new Array3D(0.0)
}
},
TestData = { new TrainingData <Array3D, Array3D>
{
Input = new Array3D(0, 0), Expected = new Array3D(0)
},
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(0, 1), Expected = new Array3D(1)
},
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(1, 0), Expected = new Array3D(1)
},
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(1, 1), Expected = new Array3D(0)
} },
IsQueue = false
};
var oneData = new FunctionProvider {
TrainData =
{
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(0, 0), Expected = new Array3D(0.0)
},
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(1, 1), Expected = new Array3D(0.0)
}
}, IsQueue = false
};
var function = new FunctionProvider(x => Math.Pow(x, 2));
var weight1 = new List <Array3D> {
new Array3D(0.1, 0.3), new Array3D(0.3, 0.1)
};
var weight2 = new List <Array3D> {
new Array3D(0.4, 0.5)
};
var perceptron1 = new PerceptronLayer(5, 2)
{
ActivationFunction = new TanhActivationFunction()
};
perceptron1.Trainer = new MiniBatchPerceptronTrainer(perceptron1.Neurals, false)
{
BatchSize = batchSize, ActivationFunction = new TanhActivationFunction(), LearningRate = 0.1, Momentum = 0.1
};
var perceptron2 = new PerceptronLayer(1, 5)
{
ActivationFunction = new TanhActivationFunction()
};
perceptron2.Trainer = new MiniBatchPerceptronTrainer(perceptron2.Neurals, true)
{
BatchSize = batchSize, ActivationFunction = new TanhActivationFunction(), LearningRate = 0.1, Momentum = 0.1
};
var network = new MultiLayerPerceptron
{
InputLayer = inputLayer,
OutputLayer = outputLayer,
DataProvider = dataProvider
};
network.HiddenLayers.Add(perceptron1);
network.HiddenLayers.Add(perceptron2);
var trainer = new FCTrainer(network, epochSize, batchSize, dataProvider);
trainer.Train(200);
}
private void AddPerceptronLayerText(PerceptronLayer layer, StringBuilder builder)
{
AddLayerText(layer, builder);
builder.AppendLine("Previous layer: " + layer.Previous.Id);
}
public void TestNetwork()
{
const int layer0 = 3, layer1 = 2, layer2 = 3;
#region 1 Iteration
double[] input = { 0.8, 0.76, 0.54 };
double[] weight1 = { 0.35, 0.46, 0.51 };
double[] weight2 = { 0.4, 0.87, 0.36 };
double[] sum1 = { 0, 0 };
for (var i = 0; i < input.Length; i++)
{
sum1[0] += input[i] * weight1[i];
sum1[1] += input[i] * weight2[i];
}
double[] output1 = { Expanent(sum1[0]), Expanent(sum1[1]) };
#endregion
#region 2 Iteration
double[] weight3 = { 0.5, 0.14 };
double[] weight4 = { 0.76, 0.86 };
double[] weight5 = { 0.95, 0.12 };
double[] sum2 = { 0, 0, 0 };
for (var i = 0; i < output1.Length; i++)
{
sum2[0] += output1[i] * weight3[i];
sum2[1] += output1[i] * weight4[i];
sum2[2] += output1[i] * weight5[i];
}
double[] output2 = { Expanent(sum2[0]), Expanent(sum2[1]), Expanent(sum2[2]) };
#endregion
#region Train
double[] expected = { 0.87, 1, 0.32 };
double[] error = new double[layer2];
for (var i = 0; i < error.Length; i++)
{
error[i] = output2[i] - expected[i];
}
double[] weightDelta = new double[layer2];
for (var i = 0; i < layer2; i++)
{
weightDelta[i] = error[i] * output2[i] * (1 - output2[i]);
}
var newWeight3 = new double[layer1];
var newWeight4 = new double[layer1];
var newWeight5 = new double[layer1];
const double learningRate = 0.1;
for (var i = 0; i < layer1; i++)
{
newWeight3[i] = weight3[i] - output1[i] * weightDelta[0] * learningRate;
newWeight4[i] = weight4[i] - output1[i] * weightDelta[1] * learningRate;
newWeight5[i] = weight5[i] - output1[i] * weightDelta[2] * learningRate;
}
#region 2 Iteration
double[] error2 = new double[layer1];
for (var i = 0; i < layer1; i++)
{
error2[i] += weight3[i] * weightDelta[0];
error2[i] += weight4[i] * weightDelta[1];
error2[i] += weight5[i] * weightDelta[2];
}
double[] weightDelta2 = new double[layer1];
for (var i = 0; i < layer1; i++)
{
weightDelta2[i] = error2[i] * output1[i] * (1 - output1[i]);
}
var newWeight1 = new double[layer0];
var newWeight2 = new double[layer0];
for (var i = 0; i < layer0; i++)
{
newWeight1[i] = weight1[i] - input[i] * weightDelta2[0] * learningRate;
newWeight2[i] = weight2[i] - input[i] * weightDelta2[1] * learningRate;
}
#endregion
#endregion
#region Compare
var inputLayer = new InputLayer3D(1, 1, 3);
var outputLayer = new OutputLayer(3)
{
ActivationFunction = new ConstArrayFunction()
};
var weightList1 = new List <List <double> > {
new List <double>(weight1), new List <double>(weight2)
};
var hiddenLayer1 = new PerceptronLayer(weightList1);
var weightList2 = new List <List <double> >
{
new List <double>(weight3),
new List <double>(weight4),
new List <double>(weight5)
};
var hiddenLayer2 = new PerceptronLayer(weightList2);
var trainingData = new TrainingData <Array3D, Array3D>
{
Input = new Array3D(input[0], input[1], input[2]),
Expected = new Array3D(expected[0], expected[1], expected[2])
};
var trainingData2 = new TrainingData <Array3D, Array3D>
{
Input = new Array3D(0.15, 0.44, 0.83),
Expected = new Array3D(0.1, 0.24, 0.18)
};
var network = new MultiLayerPerceptron
{
InputLayer = inputLayer,
OutputLayer = outputLayer,
HiddenLayers = { hiddenLayer1, hiddenLayer2 },
DataProvider = new FunctionProvider {
TrainData = { trainingData, trainingData2 }, IsQueue = true
}
};
for (var i = 0; i < 3; i++)
{
Assert.AreEqual(network.HiddenLayers[0].Neurals[0].Weights[i], newWeight1[i], 0.001);
Assert.AreEqual(network.HiddenLayers[0].Neurals[1].Weights[i], newWeight2[i], 0.001);
}
#endregion
}
private void InitializeNetwork()
{
var inputLayer = new InputLayer3D(1, 1, 1);
var outputLayer = new OutputLayer(1)
{
ActivationFunction = new ConstOutputArrayFunction()
};
var dataProvider = new FunctionProvider
{
TrainData =
{
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(0, 0), Expected = new Array3D(0.0)
},
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(0, 1), Expected = new Array3D(1.0)
},
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(1, 0), Expected = new Array3D(1.0)
},
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(1, 1), Expected = new Array3D(0.0)
}
},
TestData = { new TrainingData <Array3D, Array3D>
{
Input = new Array3D(0, 0), Expected = new Array3D(0)
},
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(0, 1), Expected = new Array3D(1)
},
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(1, 0), Expected = new Array3D(1)
},
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(1, 1), Expected = new Array3D(0)
} },
IsQueue = false
};
var oneData = new FunctionProvider
{
TrainData =
{
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(0, 0), Expected = new Array3D(0.0)
},
new TrainingData <Array3D, Array3D>
{
Input = new Array3D(1, 1), Expected = new Array3D(0.0)
}
},
IsQueue = false
};
var function = new FunctionProvider(x => Math.Pow(x, 2));
var perceptron1 = new PerceptronLayer(10, 1)
{
ActivationFunction = new TanhActivationFunction()
};
perceptron1.Trainer = new MiniBatchPerceptronTrainer(perceptron1.Neurals, false)
{
BatchSize = BatchSize, ActivationFunction = new TanhActivationFunction(), LearningRate = 0.1, Momentum = 0.1
};
var perceptron2 = new PerceptronLayer(1, 10)
{
ActivationFunction = new TanhActivationFunction()
};
perceptron2.Trainer = new MiniBatchPerceptronTrainer(perceptron2.Neurals, true)
{
BatchSize = BatchSize, ActivationFunction = new TanhActivationFunction(), LearningRate = 0.1, Momentum = 0.1
};
Network = new MultiLayerPerceptron
{
InputLayer = inputLayer,
OutputLayer = outputLayer,
DataProvider = function
};
Network.HiddenLayers.Add(perceptron1);
Network.HiddenLayers.Add(perceptron2);
Trainer = new FCTrainer(Network, EpochSize, BatchSize, function);
}
public void Calculate()
{
const int layer0 = 3, layer1 = 2, layer2 = 3;
#region 1 Iteration
double[] input = { 0.8, 0.76, 0.54 };
double[] weight1 = { 0.35, 0.46, 0.51 };
double[] weight2 = { 0.4, 0.87, 0.36 };
double[] sum1 = { 0, 0 };
for (var i = 0; i < input.Length; i++)
{
sum1[0] += input[i] * weight1[i];
sum1[1] += input[i] * weight2[i];
}
double[] output1 = { Expanent(sum1[0]), Expanent(sum1[1]) };
#endregion
#region 2 Iteration
double[] weight3 = { 0.5, 0.14 };
double[] weight4 = { 0.76, 0.86 };
double[] weight5 = { 0.95, 0.12 };
double[] sum2 = { 0, 0, 0 };
for (var i = 0; i < output1.Length; i++)
{
sum2[0] += output1[i] * weight3[i];
sum2[1] += output1[i] * weight4[i];
sum2[2] += output1[i] * weight5[i];
}
double[] output2 = { Expanent(sum2[0]), Expanent(sum2[1]), Expanent(sum2[2]) };
#endregion
#region Train
double[] expected = { 0.87, 1, 0.32 };
double[] error = new double[layer2];
for (var i = 0; i < error.Length; i++)
{
error[i] = output2[i] - expected[i];
}
double[] weightDelta = new double[layer2];
for (var i = 0; i < layer2; i++)
{
weightDelta[i] = error[i] * output2[i] * (1 - output2[i]);
}
var newWeight3 = new double[layer1];
var newWeight4 = new double[layer1];
var newWeight5 = new double[layer1];
const double learningRate = 0.1;
for (var i = 0; i < layer1; i++)
{
newWeight3[i] = weight3[i] - output1[i] * weightDelta[0] * learningRate;
newWeight4[i] = weight4[i] - output1[i] * weightDelta[1] * learningRate;
newWeight5[i] = weight5[i] - output1[i] * weightDelta[2] * learningRate;
}
#region 2 Iteration
double[] error2 = new double[layer1];
for (var i = 0; i < layer1; i++)
{
error2[i] += weight3[i] * weightDelta[0];
error2[i] += weight4[i] * weightDelta[1];
error2[i] += weight5[i] * weightDelta[2];
}
double[] weightDelta2 = new double[layer1];
for (var i = 0; i < layer1; i++)
{
weightDelta2[i] = error2[i] * output1[i] * (1 - output1[i]);
}
var newWeight1 = new double[layer0];
var newWeight2 = new double[layer0];
for (var i = 0; i < layer0; i++)
{
newWeight1[i] = weight1[i] - input[i] * weightDelta2[0] * learningRate;
newWeight2[i] = weight2[i] - input[i] * weightDelta2[1] * learningRate;
}
#endregion
#endregion
#region Compare
var weightList1 = new List <List <double> > {
new List <double>(weight1), new List <double>(weight2)
};
var perceptronLayer1 = new PerceptronLayer(weightList1);
var weightList2 = new List <List <double> >
{
new List <double>(weight3),
new List <double>(weight4),
new List <double>(weight5)
};
var perceptronLayer2 = new PerceptronLayer(weightList2);
var inputArray = new Array(input);
var result1 = (Array)perceptronLayer1.FormOutput(inputArray);
var result2 = (Array)perceptronLayer2.FormOutput(result1);
var errorResult1 = new Array(error);
var errorResult2 = (Array)perceptronLayer2.Train(errorResult1, result1, result2);
var errorResult3 = (Array)perceptronLayer1.Train(errorResult2, inputArray, result1);
for (var i = 0; i < 3; i++)
{
Assert.AreEqual(perceptronLayer1.Neurals[0].Weights[i], newWeight1[i], 0.001);
Assert.AreEqual(perceptronLayer1.Neurals[1].Weights[i], newWeight2[i], 0.001);
}
#endregion
}
private void btnStart_Click(object sender, EventArgs e)
{
_input = new InputLayer("Input")
{
Bias = new Bias("bias")
{
OutputValue = 1.234
}
};
_input.AddNode(new Input("e1")
{
OutputValue = 0.255
});
_input.AddNode(new Input("e2")
{
OutputValue = 0.455
});
_input.AddNode(new Input("e3")
{
OutputValue = -0.78967656
});
_input.AddNode(new Input("e4")
{
OutputValue = 0.0
});
//_input.AddNode(new Input("e5") { OutputValue = 0.255 });
//_input.AddNode(new Input("e6") { OutputValue = 0.455 });
//_input.AddNode(new Input("e7") { OutputValue = -0.78967656 });
//_input.AddNode(new Input("e8") { OutputValue = 0.011 });
//_input.AddNode(new Input("e9") { OutputValue = 0.2255 });
//_input.AddNode(new Input("e10") { OutputValue = 43.455 });
//_input.AddNode(new Input("e11") { OutputValue = -11.67656 });
//_input.AddNode(new Input("e12") { OutputValue = -1.001 });
var hidden = new PerceptronLayer("Hidden");
hidden.AddNode(new Perceptron("o1")
{
ActivationFunction = ActivationFunction.BinaryStep, OutputValue = 2.364, SumValue = 2.364
});
hidden.AddNode(new Perceptron("o2")
{
ActivationFunction = ActivationFunction.LeakyRelu, OutputValue = -0.552, SumValue = 55.44
});
hidden.AddNode(new Perceptron("o4")
{
ActivationFunction = ActivationFunction.Relu, OutputValue = 1.324, SumValue = 4.34
});
//hidden.AddNode(new Perceptron("o3") { ActivationFunction = ActivationFunction.Linear, OutputValue = 0.0, SumValue = 19.22 });
//hidden.AddNode(new Perceptron("o5") { ActivationFunction = ActivationFunction.Sigmoid, OutputValue = -0.12, SumValue = 25.224 });
//hidden.AddNode(new Perceptron("o6") { ActivationFunction = ActivationFunction.Tanh, OutputValue = 10.3, SumValue = 1.222 });
_input.Connect(hidden);
var output = new PerceptronLayer("Output");
output.AddNode(new Perceptron("s1")
{
ActivationFunction = ActivationFunction.Softmax, OutputValue = 0.567656, SumValue = 0.454
});
output.AddNode(new Perceptron("s2")
{
ActivationFunction = ActivationFunction.Sigmoid, OutputValue = 0.176545, SumValue = 0.54
});
//output.AddNode(new Perceptron("s3") { ActivationFunction = ActivationFunction.Softmax, OutputValue = 0.9545, SumValue = 0.133 });
//output.AddNode(new Perceptron("s4") { ActivationFunction = ActivationFunction.Softmax, OutputValue = 0.145, SumValue = 0.88 });
hidden.Connect(output);
var aleatorio = new Random(2);
foreach (var p in hidden.Nodes)
{
foreach (var edge in p.Edges)
{
int sign = aleatorio.Next(-1, 2);
edge.Weight = aleatorio.NextDouble() * sign;
}
}
foreach (var p in output.Nodes)
{
foreach (var edge in p.Edges)
{
int sign = aleatorio.Next(-1, 1);
edge.Weight = aleatorio.NextDouble() * sign;
}
}
NeuralNetworkVisualizerControl1.InputLayer = _input;
btnChangeValue.Enabled = btnAddBias.Enabled = btnClear.Enabled = trackZoom.Enabled = cboQuality.Enabled = true;
}
