calce1 (Neural Network Toolbox)

Calculate layer errors for one time step

Syntax

El = calce1(net,A,Tl)

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.

El = calce1(net,A,Tl) takes,

net -- Neural network

A -- Layer outputs, for a single time step

Tl -- Layer targets, for a single time step

and returns,

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