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traingd

Gradient descent backpropagation

Syntax

[net,TR,Ac,El] = traingd(net,Pd,Tl,Ai,Q,TS,VV,TV)

info = traingd(code)

Description

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

traingd(net,Pd,Tl,Ai,Q,TS,VV) takes these inputs,

and returns,

Training occurs according to the traingd's training parameters shown here with their default values:

Dimensions for these variables are

where

If VV or TV is not [], it must be a structure of validation vectors,

Validation vectors are used to stop training early if the network performance on the validation vectors fails to improve or remains the same for max_fail epochs in a row. Test vectors are used as a further check that the network is generalizing well, but do not have any effect on training.

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

Network Use

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

To prepare a custom network to be trained with traingd:

  1. Set net.trainFcn to 'traingd'. This will set net.trainParam to traingd's default parameters.
  2. Set net.trainParam properties to desired values.

In either case, calling train with the resulting network will train the network with traingd.

See newff, newcf, and newelm for examples.

Algorithm

traingd 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:

Training stops when any of these conditions occur:

See Also

newff, newcf, traingdm, traingda, traingdx, trainlm


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