| Neural Network Toolbox | |
Calculate layer errors for one time step
Syntax
Description
This function calculates the errors of each layer in response to layer outputs and targets, for a single time step. Calculating errors for a single time step is useful for sequential iterative algorithms such as trains which need to calculate the network response for each time step individually.
net - Neural network.
A - Layer outputs, for a single time step.
Tl - Layer targets, for a single time step.
El - Layer errors, for a single time step.
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 zero, two, and four 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 error using the first time step layer output Ac(:,5) (The five is found by adding the number of layer delays, 2, to the time step 1.), and the first time step targets Tl(:,1).
Tl = {[0.1;0.2] [0.3;0.1], [0.5;0.6] [0.8;0.9], [0.5;0.1]};
El = calce1(net,Ac(:,3),Tl(:,1))
Here we view the network's error for layer 1.
El{1}
| | calce | calcgx | |