Neural Network Toolbox | ![]() ![]() |
Calculate weight and bias performance gradient as a single vector
Syntax
[gX,normgX] = calcgx(net,X,Pd,BZ,IWZ,LWZ,N,Ac,El,perf,Q,TS);
Description
This function calculates the gradient of a network's performance with respect to its vector of weight and bias values X
.
If the network has no layer delays with taps greater than 0 the result is the true gradient.
If the network as layer delays greater than 0, the result is the Elman gradient, an approximation of the true gradient.
[gX,normgX] = calcgx(net,X,Pd,BZ,IWZ,LWZ,N,Ac,El,perf,Q,TS)
takes,
net -
Neural network.
X -
Vector of weight and bias values.
Pd -
Delayed inputs.
BZ -
Concurrent biases.
IWZ -
Weighted inputs.
LWZ -
Weighted layer outputs.
N -
Net inputs.
Ac -
Combined layer outputs.
El -
Layer errors.
perf -
Network performance.
Q -
Concurrent size.
TS -
Time steps.
gX -
Gradient dPerf/dX.
normgX -
Norm of gradient.
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.
X = getx(net); [perf,El,Ac,N,BZ,IWZ,LWZ] = calcperf(net,X,Pd,Tl,Ai,1,5);
Finally we can use calcgz
to calculate the gradient of performance with respect to the weight and bias values X
.
[gX,normgX] = calcgx(net,X,Pd,BZ,IWZ,LWZ,N,Ac,El,perf,1,5);
See Also
![]() | calce1 | calcjejj | ![]() |