learngdm

(To be removed) Gradient descent with momentum weight and bias learning function

learngdm will be removed in a future release. For more information, see Transition Legacy Neural Network Code to dlnetwork Workflows.

For advice on updating your code, see Version History.

Syntax

[dW,LS] = learngdm(W,P,Z,N,A,T,E,gW,gA,D,LP,LS)
info = learngdm('code')


Description

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

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

W	S-by-R weight matrix (or S-by-1 bias vector)
P	R-by-Q input vectors (or ones(1,Q))
Z	S-by-Q weighted input vectors
N	S-by-Q net input vectors
A	S-by-Q output vectors
T	S-by-Q layer target vectors
E	S-by-Q layer error vectors
gW	S-by-R gradient with respect to performance
gA	S-by-Q output gradient with respect to performance
D	S-by-S neuron distances
LP	Learning parameters, none, LP = []
LS	Learning state, initially should be = []

and returns

dW	S-by-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

info = learngdm('code') returns useful information for each code character vector:

'pnames'	Names of learning parameters
'pdefaults'	Default learning parameters
'needg'	Returns 1 if this function uses gW or gA

Examples

Here you define a random gradient G for a weight going to a layer with three neurons from an input with two elements. 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;

Because learngdm only needs these values to calculate a weight change (see “Algorithm” below), use them to do so. Use the default initial learning state.

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

learngdm returns the weight change and a new learning state.

Algorithms

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.

Version History

Introduced before R2006a

R2026a: learngdm will be removed

learngdm will be removed in a future release.

To learn more about customizing training algorithms, see these examples and topics:

• Custom Training Loops

• Define Custom Learning Rate Schedule

• Train Network Using Custom Training Loop

• Compare Custom Solvers Using Custom Training Loop

• Speed Up Deep Neural Network Training

• Accelerate Custom Training Loop Functions

• Evaluate Deep Learning Experiments by Using Metric Functions

For more information, see Transition Legacy Neural Network Code to dlnetwork Workflows.