Initialize neural network
net = init(net)
Type help network/init.
net = init(net) returns neural network net with
weight and bias values updated according to the network initialization function, indicated by
net.initFcn, and the parameter values, indicated by
net.initParam.
Here a perceptron is created, and then configured so that its input, output, weight, and bias dimensions match the input and target data.
x = [0 1 0 1; 0 0 1 1];
t = [0 0 0 1];
net = perceptron;
net = configure(net,x,t);
net.iw{1,1}
net.b{1}Training the perceptron alters its weight and bias values.
net = train(net,x,t);
net.iw{1,1}
net.b{1}init reinitializes those weight and bias values.
net = init(net);
net.iw{1,1}
net.b{1}The weights and biases are zeros again, which are the initial values used by perceptron networks.
init calls net.initFcn to initialize the weight and
bias values according to the parameter values net.initParam.
Typically, net.initFcn is set to 'initlay', which
initializes each layer’s weights and biases according to its
net.layers{i}.initFcn.
Backpropagation networks have net.layers{i}.initFcn set to
'initnw', which calculates the weight and bias values for layer
i using the Nguyen-Widrow initialization method.
Other networks have net.layers{i}.initFcn set to
'initwb', which initializes each weight and bias with its own initialization
function. The most common weight and bias initialization function is rands,
which generates random values between –1 and 1.