Configure network inputs and outputs to best match input and target data
takes input data
net = configure(
x and target data
t, and configures
the network’s inputs and outputs to match.
Configuration is the process of setting network input and output sizes and ranges, input preprocessing settings and output postprocessing settings, and weight initialization settings to match input and target data.
Configuration must happen before a network’s weights and biases can be initialized.
Unconfigured networks are automatically configured and initialized the first time
train is called. Alternately, a network can be configured manually either by
calling this function or by setting a network’s input and output sizes, ranges, processing
settings, and initialization settings properties manually.
Configure Network with
This example shows how to manually configure a network for a simple fitting problem instead of using the train function.
[x,t] = simplefit_dataset; net = feedforwardnet(20); view(net)
net = configure(net,x,t); view(net)
net — Network to configure
Input network, specified as a network object. To create a network object, use for
x — Input data
Network inputs, specified as a matrix.
t — Target data
Network targets, specified as a matrix.
i — Index vector
Indexes of the inputs or outputs you want to configure, specified as a vector.
net — Configured network
Configured network, returned as a network object.
Introduced in R2010b