calcperf (Neural Network Toolbox)

Calculate network outputs, signals, and performance

Syntax

[perf,El,Ac,N,BZ,IWZ,LWZ]=calcperf(net,X,Pd,Tl,Ai,Q,TS)

Description

This function calculates the outputs of each layer in response to a networks delayed inputs and initial layer delay conditions.

[perf,El,Ac,N,LWZ,IWZ,BZ] = calcperf(net,X,Pd,Tl,Ai,Q,TS) takes,

net -- Neural network

X -- Network weight and bias values in a single vector

Pd -- Delayed inputs

Tl -- Layer targets

Ai -- Initial layer delay conditions

Q -- Concurrent size

TS -- Time steps

and returns,

perf -- Network performance

El -- Layer errors

Ac -- Combined layer outputs = [Ai, calculated layer outputs]

N -- Net inputs

LWZ -- Weighted layer outputs

IWZ -- Weighted inputs

BZ -- Concurrent biases

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.

```
Ai = {[0.5; 0.1] [0.6; 0.5]};
```

Here we define the layer targets for the two neurons for each of the five time steps.

Tl = {[0.1;0.2] [0.3;0.1], [0.5;0.6] [0.8;0.9], [0.5;0.1]};

Here the network's weight and bias values are extracted.

```
X = getx(net);
```

Here we calculate the network's combined outputs Ac, and other signals described above.

[perf,El,Ac,N,BZ,IWZ,LWZ] = calcperf(net,X,Pd,Tl,Ai,1,5)

calcpd combvec