public double GetOuputValue()
{
double sum = 0;
for (int i = 0; i < InputSynapses.Count; i++)
{
sum += InputSynapses[i].Weight * InputSynapses[i].InputNeuron.Output;
}
return(Output = Sigmoid.Output(InputSynapses.Sum(a => a.Weight * a.InputNeuron.Output) + BiasWeight));
}
public float CalculateValue()
{
/*for (int i = 0; i < InputSynapses.Count; i++)
* {
* xSum += InputSynapses[i].Weight * InputSynapses[i].InputNeuron.Value;
* }
****** é o mesmo que fazer o Sum do LINQ
*/
float xSum = InputSynapses.Sum(x => x.Weight * x.InputNeuron.Value);
float u = xSum + Bias;
Value = Sigmoid.Output(u);
return(Value);
}
public virtual double CalculateValue()
{
return(Value = Sigmoid.Output(InputSynapses.Sum(a => a.Weight * a.InputNeuron.Value) + Bias));
}
public virtual double CalculateValue() //This function is used in forward propogation process.
{
return(Value = Sigmoid.Output(InputSynapses.Sum(a => a.Weight * a.InputNeuron.Value) + Bias)); //Calculate by ably activation function to (weight*input)+bias
}
public virtual void CalculateValue() => Value = Activation(InputSynapses.Sum(synapse => synapse.Weight * synapse.InputNeuron.Value));
public virtual double CalculateValue()
{
var d = InputSynapses.Sum(a => a.Weight * a.InputNeuron.Value); // + Bias;
return(Value = IsHidden ? NeuralNetwork.SigmoidFunction(d) : NeuralNetwork.IdentityFunction(d));
}
public void CalculateValueRecurrent()
{
OutputValue = Sigmoid.Output(InputSynapses.Sum(syn => syn.Weight * syn.InputNeuron.LastValue) + Bias);
}
public float CalculateValue()
{
return(Value = Sigmoid.Output(InputSynapses.Sum(x => x.Weight * x.InputNeuron.Value) + Bias));
}
public virtual double CalculateHiddenValue()
{
return(Value = Sigmoid.Relu(InputSynapses.Sum(a => a.Weight * a.InputNeuron.Value)));
}
public virtual double CalculateValue()
{
var inputSignals = InputSynapses.Sum(a => a.Weight * a.InputNeuron.Value);
return(Value = ActivationFunction.Output(inputSignals + Bias));
}
/// <summary>
/// Calculate the value of the neuron based on the weights and values of the Previous Layer
/// VALUE = SIGMOIDSQUISH(WEIGHT * VALUE) of every previous neuron + bias
/// </summary>
/// <returns></returns>
public float CalculateValue()
{
return(Value = NeuralMath.SigmoidSquish(InputSynapses.Sum(s => s.Weight * s.InputNeuron.Value) + Bias));
}
//计算输出值 ，输入值加上偏置，然后应用sigmoid函数
public virtual double CalculateValue()
{
this.InputValue = InputSynapses.Sum(a => a.Weight * a.InputNeuron.OutputValue);
return(OutputValue = Sigmoid.Output(InputValue + Bias));
}
public virtual double CalculateValue()
{
return(Value = NeuralNetwork.SigmoidFunction(InputSynapses.Sum(a => a.Weight * a.InputNeuron.Value) + Bias));
}