Gradient descent with momentum and adaptive learning rate backpropagation
net.trainFcn = 'traingdx'
[net,tr] = train(net,...)
traingdx is a network training function that updates weight and bias
values according to gradient descent momentum and an adaptive learning rate.
net.trainFcn = 'traingdx' sets the network trainFcn
property.
[net,tr] = train(net,...) trains the network with
traingdx.
Training occurs according to traingdx 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.lr_inc | 1.05 | Ratio to increase learning rate |
net.trainParam.lr_dec | 0.7 | Ratio to decrease learning rate |
net.trainParam.max_fail | 6 | Maximum validation failures |
net.trainParam.max_perf_inc | 1.04 | Maximum performance increase |
net.trainParam.mc | 0.9 | Momentum constant |
net.trainParam.min_grad | 1e-5 | Minimum performance gradient |
net.trainParam.show | 25 | Epochs between displays ( |
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 |
You can create a standard network that uses traingdx with
feedforwardnet or cascadeforwardnet. To prepare a custom
network to be trained with traingdx,
Set net.trainFcn to 'traingdx'.
This sets net.trainParam to traingdx’s default
parameters.
Set net.trainParam properties to desired
values.
In either case, calling train with the resulting network trains the
network with traingdx.
See help feedforwardnet and help cascadeforwardnet
for examples.
The function traingdx combines adaptive learning rate with momentum
training. It is invoked in the same way as traingda, except that it has the
momentum coefficient mc as an additional training parameter.
traingdx 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*mc*dperf/dX
where dXprev is the previous change to the weight or bias.
For each epoch, if performance decreases toward the goal, then the learning rate is
increased by the factor lr_inc. If performance increases by more than the
factor max_perf_inc, the learning rate is adjusted by the factor
lr_dec and the change that increased the performance is not made.
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 has increased more than max_fail times since
the last time it decreased (when using validation).