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
[net,tr] = train(net,...) trains the network with
Training occurs according to
traingdx training parameters, shown here
with their default values:
Maximum number of epochs to train
Ratio to increase learning rate
Ratio to decrease learning rate
Maximum validation failures
Maximum performance increase
Minimum performance gradient
Epochs between displays (
Generate command-line output
Show training GUI
Maximum time to train in seconds
You can create a standard network that uses
cascadeforwardnet. To prepare a custom
network to be trained with
net.trainParam properties to desired
In either case, calling
train with the resulting network trains the
help feedforwardnet and
traingdx combines adaptive learning rate with momentum
training. It is invoked in the same way as
traingda, except that it has the
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
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
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
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
The performance gradient falls below
Validation performance has increased more than
max_fail times since
the last time it decreased (when using validation).