learngdm (Neural Network Toolbox)

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learngdm

Examples See Also

Gradient descent w/ momentum weight/bias learning function

Syntax

[dW,LS] = learngdm(W,P,Z,N,A,T,E,gW,gA,D,LP,LS)

[db,LS] = learngdm(b,ones(1,Q),Z,N,A,T,E,gW,gA,D,LP,LS)

info = learngdm(code)

Description

learngdm is the gradient descent with momentum weight/bias learning function.

learngdm(W,P,Z,N,A,T,E,gW,gA,D,LP,LS) takes several inputs,

W - S x R weight matrix (or S x 1 bias vector).

P - R x Q input vectors (or ones(1,Q)).

Z - S x Q weighted input vectors.

N - S x Q net input vectors.

A - S x Q output vectors.

T - S x Q layer target vectors.

E - S x Q layer error vectors.

gW - S x R gradient with respect to performance.

gA - S x Q output gradient with respect to performance.

D - S x S neuron distances.

LP - Learning parameters, none, LP = [].

LS - Learning state, initially should be = [].

and returns,

dW - S x R weight (or bias) change matrix.

LS - New learning state.

Learning occurs according to learngdm's learning parameters, shown here with their default values.

LP.lr - 0.01 - Learning rate.

LP.mc - 0.9 - Momentum constant.

learngdm(code) returns useful information for each code string:

'pnames' - Names of learning parameters.

'pdefaults' - Default learning parameters.

'needg' - Returns 1 if this function uses gW or gA.

Examples

Here we define a random gradient G for a weight going to a layer with 3 neurons, from an input with 2 elements. We also define a learning rate of 0.5 and momentum constant of 0.8;

gW = rand(3,2);
lp.lr = 0.5;
lp.mc = 0.8;

Since learngdm only needs these values to calculate a weight change (see algorithim below), we will use them to do so. We will use the default initial learning state.

ls = [];
[dW,ls] = learngdm([],[],[],[],[],[],[],gW,[],[],lp,ls)

learngdm returns the weight change and a new learning state.

Network Use

You can create a standard network that uses learngdm with newff, newcf, or newelm.

To prepare the weights and the bias of layer i of a custom network to adapt with learngdm:

1.: Set net.adaptFcn to 'adaptwb'. net.adaptParam will automatically become trainwb's default parameters.

2.: Set each net.inputWeights{i,j}.learnFcn to 'learngdm'. Set each net.layerWeights{i,j}.learnFcn to 'learngdm'. Set net.biases{i}.learnFcn to 'learngdm'. Each weight and bias learning parameter property will automatically be set to learngdm's default parameters.

To allow the network to adapt:

1.: Set net.adaptParam properties to desired values.

2.: Call adapt with the network.

See newff or newcf for examples.

Algorithm

learngdm calculates the weight change dW for a given neuron from the neuron's input P and error E, the weight (or bias) W, learning rate LR, and momentum constant MC, according to gradient descent with momentum:

dW = mc*dWprev + (1-mc)*lr*gW

The previous weight change dWprev is stored and read from the learning state LS.