fpderiv

(To be removed) Forward propagation derivative function

fpderiv will be removed in a future release. For more information, see Transition Legacy Neural Network Code to dlnetwork Workflows.

For advice on updating your code, see Version History.

Syntax

fpderiv('dperf_dwb',net,X,T,Xi,Ai,EW)
fpderiv('de_dwb',net,X,T,Xi,Ai,EW)

Description

This function calculates derivatives using the chain rule from inputs to outputs, and in the case of dynamic networks, forward through time.

fpderiv('dperf_dwb',net,X,T,Xi,Ai,EW) takes these arguments,

net

Neural network

X

Inputs, an R-by-Q matrix (or N-by-TS cell array of Ri-by-Q matrices)

T

Targets, an S-by-Q matrix (or M-by-TS cell array of Si-by-Q matrices)

Xi

Initial input delay states (optional)

Ai

Initial layer delay states (optional)

EW

Error weights (optional)

and returns the gradient of performance with respect to the network’s weights and biases, where R and S are the number of input and output elements and Q is the number of samples (or N and M are the number of input and output signals, Ri and Si are the number of each input and outputs elements, and TS is the number of timesteps).

fpderiv('de_dwb',net,X,T,Xi,Ai,EW) returns the Jacobian of errors with respect to the network’s weights and biases.

Examples

Here a feedforward network is trained and both the gradient and Jacobian are calculated.

[x,t] = simplefit_dataset;
net = feedforwardnet(20);
net = train(net,x,t);
y = net(x);
perf = perform(net,t,y);
gwb = fpderiv('dperf_dwb',net,x,t)
jwb = fpderiv('de_dwb',net,x,t)

Version History

Introduced in R2010b

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See Also

| (Statistics and Machine Learning Toolbox) | (Statistics and Machine Learning Toolbox) | | |

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