Configure network inputs and outputs to best match input and target data
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)
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.