//Sends the inputs once through the network
public void Train(params double[] inputs)
{
int i = 0;
InputLayer.ForEach(a => a.Value = inputs[i++]); //Assign input data to input-neurons
HiddenLayer.ForEach(a => a.Calc_Value()); //Hidden Calc
OutputLayer.ForEach(a => a.Calc_Value()); //Outuput Calc
}
//Obliczenie wartości wyjściowej sieci neuronowej dla pojedyńczego elementu
private void ForwardPropagate(double[] inputs)
{
var i = 0;
InputLayer.ForEach(a => a.Value = inputs[i++]);
HiddenLayers.ForEach(a => a.ForEach(b => b.CalculateValue()));
OutputLayer.ForEach(a => a.CalculateValue());
}
public void updateWeights(double[] weights)
{
// Set each weight in the neural net to its gene representation
int i = 0;
InputLayer.ForEach(neuron => neuron.OutputSynapses.ForEach(synapse => synapse.Weight = weights[i++]));
HiddenLayer.ForEach(neuron => neuron.OutputSynapses.ForEach(synapse => synapse.Weight = weights[i++]));
}
private void ForwardPropagate(params double[] inputs)
{
int i = 0;
InputLayer.ForEach(a => a.Value = inputs[i++]);
HiddenLayers.ForEach(a => a.AsParallel().ForAll(b => b.CalculateValue()));
OutputLayer.AsParallel().ForAll(a => a.CalculateValue());
}
public void ForwardPropagate(params double[] inputs)
{
var i = 0;
InputLayer.ForEach(a => a.Value = inputs[i++]);
HiddenLayer.ForEach(a => a.CalculateValue());
OutputLayer.ForEach(a => a.CalculateValue());
}
public void Train(params double[] inputs)
{
int i = 0;
InputLayer.ForEach(a => a.Value = inputs[i++]);
HiddenLayer.ForEach(a => a.CalculateValue());
OutputLayer.ForEach(a => a.CalculateValue());
}
/// <summary>
/// Feed the input to the network and propagate forwards
/// </summary>
/// <param name="inputs">the feeded input data</param>
private void ForwardPropagate(params double[] inputs)
{
int i = 0;
InputLayer.ForEach(p => p.Value = inputs[i++]);
HiddenLayer.ForEach(p => p.CalculateValue());
OutputLayer.ForEach(p => p.CalculateValue());
}
private void ForwardPropagate(params double[] inputs)
{
var i = 0;
InputLayer.ForEach(a => a.Value = inputs[i++]);
foreach (var layer in HiddenLayers)
{
layer.ForEach(a => a.CalculateValue());
}
OutputLayer.ForEach(a => a.CalculateValue());
}
private void ForwardPropagate(params double[] inputs)
{
int i = 0;
InputLayer.ForEach(a => a.Value = inputs[i++]);
foreach (List <Neuron> Layer in HiddenLayers)
{
Layer.ForEach(a => a.CalculateValue());
}
OutputLayer.ForEach(a => a.CalculateValue());
}
private void ForwardPropagate(params float[] inputs)
{
int i = 0;
InputLayer.ForEach(neuron => neuron.Value = inputs[i++]);
foreach (var layer in HiddenLayer)
{
layer.ForEach(neuron => neuron.CalculateValue());
}
OutputLayer.ForEach(neuron => neuron.CalculateValue());
}
//前向演进
public void ForwardPropagate(params double[] inputs)
{
var i = 0;
InputLayer.ForEach(a => a.OutputValue = inputs[i++]);
//HiddenLayers.ForEach(a => a.ForEach(b => b.CalculateValue()));
//OutputLayer.ForEach(a => a.CalculateValue());
foreach (List <Neuron> HiddenLayer in HiddenLayers)
{
Parallel.ForEach(HiddenLayer, a =>
{
a.CalculateValue();
});
}
Parallel.ForEach(OutputLayer, a =>
{
a.CalculateValue();
});
}
