| Neural Network Toolbox | |
Syntax
Description
This function calculates the errors of each layer in response to layer outputs and targets.
El = calce(net,Ac,Tl,TS) takes,
net - Neural network.
Ac - Combined layer outputs.
Tl - Layer targets.
Q - Concurrent size.
El - Layer errors.
Examples
Here we create a linear network with a single input element ranging from 0 to 1, two neurons, and a tap delay on the input with taps at 0, 2, and 4 time steps. The network is also given a recurrent connection from layer 1 to itself with tap delays of [1 2].
net = newlin([0 1],2);
net.layerConnect(1,1) = 1;
net.layerWeights{1,1}.delays = [1 2];
Here is a single (Q = 1) input sequence P with five time steps (TS = 5), and the four initial input delay conditions Pi, combined inputs Pc, and delayed inputs Pd.
P = {0 0.1 0.3 0.6 0.4};
Pi = {0.2 0.3 0.4 0.1};
Pc = [Pi P];
Pd = calcpd(net,5,1,Pc);
Here the two initial layer delay conditions for each of the two neurons are defined, and the networks combined outputs Ac and other signals are calculated.
Ai = {[0.5; 0.1] [0.6; 0.5]};
[Ac,N,LWZ,IWZ,BZ] = calca(net,Pd,Ai,1,5);
Here we define the layer targets for the two neurons for each of the five time steps, and calculate the layer errors.
Tl = {[0.1;0.2] [0.3;0.1], [0.5;0.6] [0.8;0.9], [0.5;0.1]};
El = calce(net,Ac,Tl,5)
Here we view the network's error for layer 1 at time step 2.
El{1,2}
| | calca1 | calce1 | |