Documentation Center

  • Trial Software
  • Product Updates

Contents

initnw

Nguyen-Widrow layer initialization function

Syntax

net = initnw(net,i)

Description

initnw is a layer initialization function that initializes a layer's weights and biases according to the Nguyen-Widrow initialization algorithm. This algorithm chooses values in order to distribute the active region of each neuron in the layer approximately evenly across the layer's input space. The values contain a degree of randomness, so they are not the same each time this function is called.

initnw requires that the layer it initializes have a transfer function with a finite active input range. This includes transfer functions such as tansig and satlin, but not purelin, whose active input range is the infinite interval [-inf, inf]. Transfer functions, such as tansig, will return their active input range as follows:

activeInputRange = tansig('active')
activeInputRange =
    -2     2

net = initnw(net,i) takes two arguments,

net

Neural network

i

Index of a layer

and returns the network with layer i's weights and biases updated.

There is a random element to Nguyen-Widrow initialization. Unless the default random generator is set to the same seed before each call to initnw, it will generate different weight and bias values each time.

Network Use

You can create a standard network that uses initnw by calling feedforwardnet or cascadeforwardnet.

To prepare a custom network to be initialized with initnw,

  1. Set net.initFcn to 'initlay'. This sets net.initParam to the empty matrix [], because initlay has no initialization parameters.

  2. Set net.layers{i}.initFcn to 'initnw'.

To initialize the network, call init.

More About

expand all

Algorithms

The Nguyen-Widrow method generates initial weight and bias values for a layer so that the active regions of the layer's neurons are distributed approximately evenly over the input space.

Advantages over purely random weights and biases are

  • Few neurons are wasted (because all the neurons are in the input space).

  • Training works faster (because each area of the input space has neurons). The Nguyen-Widrow method can only be applied to layers

    • With a bias

    • With weights whose weightFcn is dotprod

    • With netInputFcn set to netsum

    • With transferFcn whose active region is finite

If these conditions are not met, then initnw uses rands to initialize the layer's weights and biases.

See Also

| | | |

Was this topic helpful?