Gradient descent with momentum and adaptive learning rate backpropagation
net.trainFcn = 'traingdx' sets the network
trains the network with
tr] = train(
traingdx is a network training function that updates weight and bias
values according to gradient descent momentum and an adaptive learning rate.
Training occurs according to
traingdx training parameters, shown here
with their default values:
net.trainParam.epochs— Maximum number of epochs to train. The default value is 1000.
net.trainParam.goal— Performance goal. The default value is 0.
net.trainParam.lr— Learning rate. The default value is 0.01.
net.trainParam.lr_inc— Ratio to increase learning rate. The default value is 1.05.
net.trainParam.lr_dec— Ratio to decrease learning rate. The default value is 0.7.
net.trainParam.max_fail— Maximum validation failures. The default value is
net.trainParam.max_perf_inc— Maximum performance increase. The default value is
net.trainParam.mc— Momentum constant. The default value is
net.trainParam.min_grad— Minimum performance gradient. The default value is
net.trainParam.show— Epochs between displays (
NaNfor no displays). The default value is 25.
net.trainParam.showCommandLine— Generate command-line output. The default value is
net.trainParam.showWindow— Show training GUI. The default value is
net.trainParam.time— Maximum time to train in seconds. The default value is
net — Input network
Input network, specified as a network object. To create a network object, use for
trainedNet — Trained network
Trained network, returned as a
tr — Training record
Training record (
perf), returned as a
structure whose fields depend on the network training function
net.NET.trainFcn). It can include fields such as:
Training, data division, and performance functions and parameters
Data division indices for training, validation and test sets
Data division masks for training validation and test sets
Number of epochs (
num_epochs) and the best epoch (
A list of training state names (
Fields for each state name recording its value throughout training
Performances of the best network (
You can create a standard network that uses
cascadeforwardnet. To prepare a custom
network to be trained with
'traingdx'. This sets
traingdx’s default parameters.
net.trainParamproperties to desired values.
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
Performance is minimized to the
The performance gradient falls below
Validation performance (validation error) has increased more than
max_failtimes since the last time it decreased (when using validation).
Introduced before R2006a