Neural Network Toolbox

calcjx

Calculate weight and bias performance Jacobian as a single matrix

Syntax

jx = calcjx(net,PD,BZ,IWZ,LWZ,N,Ac,Q,TS)

Description

This function calculates the Jacobian of a network's errors with respect to its vector of weight and bias values X.

[jX] = calcjx(net,PD,BZ,IWZ,LWZ,N,Ac,Q,TS) takes,

• net -- Neural network

  PD -- Delayed inputs

  BZ -- Concurrent biases

  IWZ -- Weighted inputs

  LWZ -- Weighted layer outputs

  N -- Net inputs

  Ac -- Combined layer outputs

  Q -- Concurrent size

  TS -- Time steps

  and returns,

  jX -- Jacobian of network errors with respect to X

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, and the layer targets for the two neurons over five time steps are defined.

• Ai = {[0.5; 0.1] [0.6; 0.5]};
  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, and the network's performance and other signals are calculated.

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

Finally we can use calcjx to calculate the Jacobian.

• jX = calcjx(net,Pd,BZ,IWZ,LWZ,N,Ac,1,5);calcpd
  

See Also

calcgx, calcjejj

calcjejj

calcpd

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