traingdm

(To be removed) Gradient descent with momentum backpropagation

traingdm 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

net.trainFcn = 'traingdm'
[net,tr] = train(net,...)


Description

traingdm is a network training function that updates weight and bias values according to gradient descent with momentum.

net.trainFcn = 'traingdm' sets the network trainFcn property.

[net,tr] = train(net,...) trains the network with traingdm.

Training occurs according to traingdm training parameters, shown here with their default values:

net.trainParam.epochs	1000	Maximum number of epochs to train
net.trainParam.goal	0	Performance goal
net.trainParam.lr	0.01	Learning rate
net.trainParam.max_fail	6	Maximum validation failures
net.trainParam.mc	0.9	Momentum constant
net.trainParam.min_grad	1e-5	Minimum performance gradient
net.trainParam.show	25	Epochs between showing progress
net.trainParam.showCommandLine	false	Generate command-line output
net.trainParam.showWindow	true	Show training GUI
net.trainParam.time	inf	Maximum time to train in seconds

Network Use

You can create a standard network that uses traingdm with feedforwardnet or cascadeforwardnet. To prepare a custom network to be trained with traingdm,

1. Set net.trainFcn to 'traingdm'. This sets net.trainParam to traingdm’s default parameters.

2. Set net.trainParam properties to desired values.

In either case, calling train with the resulting network trains the network with traingdm.

See help feedforwardnet and help cascadeforwardnet for examples.

More About

Gradient Descent with Momentum

In addition to traingd, there are three other variations of gradient descent.

Gradient descent with momentum, implemented by traingdm, allows a network to respond not only to the local gradient, but also to recent trends in the error surface. Acting like a lowpass filter, momentum allows the network to ignore small features in the error surface. Without momentum a network can get stuck in a shallow local minimum. With momentum a network can slide through such a minimum. See page 12–9 of [HDB96] for a discussion of momentum.

Gradient descent with momentum depends on two training parameters. The parameter lr indicates the learning rate, similar to the simple gradient descent. The parameter mc is the momentum constant that defines the amount of momentum. mc is set between 0 (no momentum) and values close to 1 (lots of momentum). A momentum constant of 1 results in a network that is completely insensitive to the local gradient and, therefore, does not learn properly.

p = [-1 -1 2 2; 0 5 0 5];
t = [-1 -1 1 1];
net = feedforwardnet(3,'traingdm');
net.trainParam.lr = 0.05;
net.trainParam.mc = 0.9;
net = train(net,p,t);
y = net(p)

Algorithms

traingdm can train any network as long as its weight, net input, and transfer functions have derivative functions.

Backpropagation is used to calculate derivatives of performance perf with respect to the weight and bias variables X. Each variable is adjusted according to gradient descent with momentum,

dX = mc*dXprev + lr*(1-mc)*dperf/dX

where dXprev is the previous change to the weight or bias.

Training stops when any of these conditions occurs:

• The maximum number of epochs (repetitions) is reached.

• The maximum amount of time is exceeded.

• Performance is minimized to the goal.

• The performance gradient falls below min_grad.

• Validation performance (validation error) has increased more than max_fail times since the last time it decreased (when using validation).

Version History

Introduced before R2006a

R2026a: traingdm will be removed

traingdm 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.