我已经开始实施Nguyen Widrow算法(如下所示),它似乎运行正常,但我有一些后续问题:
这看起来是正确的实现吗?
Nguyen Widrow初始化是否适用于任何网络拓扑/ 大小? (即5层AutoEncoder)
Nguyen Widrow初始化对任何输入范围都有效吗? (0/1,-1 / + 1等)
Nguyen Widrow初始化对任何激活函数都有效吗? (即Logistic,Tanh,Linear)
以下代码假设网络已经被随机化为-1 / + 1:
' Calculate the number of hidden neurons
Dim HiddenNeuronsCount As Integer = Me.TotalNeuronsCount - (Me.InputsCount - Me.OutputsCount)
' Calculate the Beta value for all hidden layers
Dim Beta As Double = (0.7 * Math.Pow(HiddenNeuronsCount, (1.0 / Me.InputsCount)))
' Loop through each layer in neural network, skipping input layer
For i As Integer = 1 To Layers.GetUpperBound(0)
' Loop through each neuron in layer
For j As Integer = 0 To Layers(i).Neurons.GetUpperBound(0)
Dim InputsNorm As Double = 0
' Loop through each weight in neuron inputs, add weight value to InputsNorm
For k As Integer = 0 To Layers(i).Neurons(j).ConnectionWeights.GetUpperBound(0)
InputsNorm += Layers(i).Neurons(j).ConnectionWeights(k) * Layers(i).Neurons(j).ConnectionWeights(k)
Next
' Add bias value to InputsNorm
InputsNorm += Layers(i).Neurons(j).Bias * Layers(i).Neurons(j).Bias
' Finalize euclidean norm calculation
InputsNorm = Math.Sqrt(InputsNorm)
' Loop through each weight in neuron inputs, scale the weight based on euclidean norm and beta
For k As Integer = 0 To Layers(i).Neurons(j).ConnectionWeights.GetUpperBound(0)
Layers(i).Neurons(j).ConnectionWeights(k) = (Beta * Layers(i).Neurons(j).ConnectionWeights(k)) / InputsNorm
Next
' Scale the bias based on euclidean norm and beta
Layers(i).Neurons(j).Bias = (Beta * Layers(i).Neurons(j).Bias) / InputsNorm
Next
Next
答案 0 :(得分:1)
Nguyen& Widrow在他们的论文中假设输入介于-1和+1之间。 Nguyen Widrow初始化对任何长度有限的激活函数都有效。 在他们的论文中,他们只讨论了一个2层NN,不确定是否有5层NN。
取值