Configure network inputs and outputs to best match input and target data
net = configure(net,x,t)
net = configure(net,x)
net = configure(net,'inputs',x,i)
net = configure(net,'outputs',t,i)
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.
net = configure(net,x,t) takes input data x and
target data t, and configures the network’s inputs and outputs to match.
net = configure(net,x) configures only inputs.
net = configure(net,'inputs',x,i) configures the inputs specified with
the index vector i. If i is not specified all inputs are
configured.
net = configure(net,'outputs',t,i) configures the outputs specified
with the index vector i. If i is not specified all targets
are configured.
Here a feedforward network is created and manually configured for a simple fitting problem
(as opposed to allowing train to configure it).
[x,t] = simplefit_dataset; net = feedforwardnet(20); view(net) net = configure(net,x,t); view(net)
init | isconfigured | train | unconfigure