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 NeuronLayer("Hidden");
hidden.AddNode(new Neuron("o1")
{
ActivationFunction = ActivationFunction.Tanh, OutputValue = 2.364, SumValue = 2.364
});
hidden.AddNode(new Neuron("o2")
{
ActivationFunction = ActivationFunction.LeakyRelu, OutputValue = -0.552, SumValue = 55.44
});
hidden.AddNode(new Neuron("o4")
{
ActivationFunction = ActivationFunction.Relu, OutputValue = 1.324, SumValue = 4.34
});
//hidden.AddNode(new Neuron("o3") { ActivationFunction = ActivationFunction.Linear, OutputValue = 0.0, SumValue = 19.22 });
//hidden.AddNode(new Neuron("o5") { ActivationFunction = ActivationFunction.Sigmoid, OutputValue = -0.12, SumValue = 25.224 });
//hidden.AddNode(new Neuron("o6") { ActivationFunction = ActivationFunction.Tanh, OutputValue = 10.3, SumValue = 1.222 });
_input.Connect(hidden);
var output = new NeuronLayer("Output");
output.AddNode(new Neuron("s1")
{
ActivationFunction = ActivationFunction.Softmax, OutputValue = 0.567656, SumValue = 0.454
});
output.AddNode(new Neuron("s2")
{
ActivationFunction = ActivationFunction.Softmax, OutputValue = 0.176545, SumValue = 0.54
});
//output.AddNode(new Neuron("s3") { ActivationFunction = ActivationFunction.Softmax, OutputValue = 0.9545, SumValue = 0.133 });
//output.AddNode(new Neuron("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;
}
