| 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 | |