Netlab: netderiv(w, net, x) - File Exchange

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Highlights from
Netlab

demnlab(action); DEMNLAB A front-end Graphical User Interface to the demos
demprgp(action); DEMPRGP Demonstrate sampling from a Gaussian Process prior.
demprior(action); DEMPRIOR Demonstrate sampling from a multi-parameter Gaussian prior.
demtrain(action); DEMTRAIN Demonstrate training of MLP network.
[C,rate]=confmat(Y,T) CONFMAT Compute a confusion matrix.
conjgrad(f, x, options, gradf... CONJGRAD Conjugate gradients optimization.
consist(model, type, inputs, ... CONSIST Check that arguments are consistent.
datread(filename) DATREAD Read data from an ascii file.
datwrite(filename, x, t) DATWRITE Write data to ascii file.
dem2ddat(ndata) DEM2DDAT Generates two dimensional data for demos.
demgpot(x, mix) DEMGPOT Computes the gradient of the negative log likelihood for a mixture model.
demhint(nin, nhidden, nout) DEMHINT Demonstration of Hinton diagram for 2-layer feed-forward network.
demmet1(plot_wait) DEMMET1 Demonstrate Markov Chain Monte Carlo sampling on a Gaussian.
demopt1(xinit) DEMOPT1 Demonstrate different optimisers on Rosenbrock's function.
dempot(x, mix) DEMPOT Computes the negative log likelihood for a mixture model.
dist2(x, c) DIST2 Calculates squared distance between two sets of points.
eigdec(x, N) EIGDEC Sorted eigendecomposition
errbayes(net, edata) ERRBAYES Evaluate Bayesian error function for network.
evidence(net, x, t, num) EVIDENCE Re-estimate hyperparameters using evidence approximation.
fevbayes(net, y, a, x, t, x_t... FEVBAYES Evaluate Bayesian regularisation for network forward propagation.
fh=conffig(y, t) CONFFIG Display a confusion matrix.
gauss(mu, covar, x) GAUSS Evaluate a Gaussian distribution.
gbayes(net, gdata) GBAYES Evaluate gradient of Bayesian error function for network.
glm(nin, nout, outfunc, prior... GLM Create a generalized linear model.
glmderiv(net, x) GLMDERIV Evaluate derivatives of GLM outputs with respect to weights.
glmerr(net, x, t) GLMERR Evaluate error function for generalized linear model.
glmevfwd(net, x, t, x_test, i... GLMEVFWD Forward propagation with evidence for GLM
glmfwd(net, x) GLMFWD Forward propagation through generalized linear model.
glmgrad(net, x, t) GLMGRAD Evaluate gradient of error function for generalized linear model.
glmhess(net, x, t, hdata) GLMHESS Evaluate the Hessian matrix for a generalised linear model.
glminit(net, prior) GLMINIT Initialise the weights in a generalized linear model.
glmpak(net) GLMPAK Combines weights and biases into one weights vector.
glmtrain(net, options, x, t) GLMTRAIN Specialised training of generalized linear model
glmunpak(net, w) GLMUNPAK Separates weights vector into weight and bias matrices.
gmm(dim, ncentres, covar_type... GMM Creates a Gaussian mixture model with specified architecture.
gmmactiv(mix, x) GMMACTIV Computes the activations of a Gaussian mixture model.
gmmem(mix, x, options) GMMEM EM algorithm for Gaussian mixture model.
gmminit(mix, x, options) GMMINIT Initialises Gaussian mixture model from data
gmmpak(mix) GMMPAK Combines all the parameters in a Gaussian mixture model into one vector.
gmmpost(mix, x) GMMPOST Computes the class posterior probabilities of a Gaussian mixture model.
gmmprob(mix, x) GMMPROB Computes the data probability for a Gaussian mixture model.
gmmsamp(mix, n) GMMSAMP Sample from a Gaussian mixture distribution.
gmmunpak(mix, p) GMMUNPAK Separates a vector of Gaussian mixture model parameters into its components.
gp(nin, covar_fn, prior) GP Create a Gaussian Process.
gpcovar(net, x) GPCOVAR Calculate the covariance for a Gaussian Process.
gpcovarf(net, x1, x2) GPCOVARF Calculate the covariance function for a Gaussian Process.
gpcovarp(net, x1, x2) GPCOVARP Calculate the prior covariance for a Gaussian Process.
gperr(net, x, t) GPERR Evaluate error function for Gaussian Process.
gpfwd(net, x, cninv) GPFWD Forward propagation through Gaussian Process.
gpgrad(net, x, t) GPGRAD Evaluate error gradient for Gaussian Process.
gpinit(net, tr_in, tr_targets... GPINIT Initialise Gaussian Process model.
gppak(net) GPPAK Combines GP hyperparameters into one vector.
gpunpak(net, hp) GPUNPAK Separates hyperparameter vector into components.
gradchek(w, func, grad, varar... GRADCHEK Checks a user-defined gradient function using finite differences.
graddesc(f, x, options, gradf... GRADDESC Gradient descent optimization.
gsamp(mu, covar, nsamp) GSAMP Sample from a Gaussian distribution.
gtm(dim_latent, nlatent, dim_... GTM Create a Generative Topographic Map.
gtmem(net, t, options) GTMEM EM algorithm for Generative Topographic Mapping.
gtmfwd(net) GTMFWD Forward propagation through GTM.
gtminit(net, options, data, s... GTMINIT Initialise the weights and latent sample in a GTM.
gtmlmean(net, data) GTMLMEAN Mean responsibility for data in a GTM.
gtmlmode(net, data) GTMLMODE Mode responsibility for data in a GTM.
gtmmag(net, latent_data) GTMMAG Magnification factors for a GTM
gtmpost(net, data) GTMPOST Latent space responsibility for data in a GTM.
gtmprob(net, data) GTMPROB Probability for data under a GTM.
hbayes(net, hdata) HBAYES Evaluate Hessian of Bayesian error function for network.
hesschek(net, x, t) HESSCHEK Use central differences to confirm correct evaluation of Hessian matrix.
hintmat(w); HINTMAT Evaluates the coordinates of the patches for a Hinton diagram.
hinton(w); HINTON Plot Hinton diagram for a weight matrix.
histp(x, xmin, xmax, nbins) HISTP Histogram estimate of 1-dimensional probability distribution.
hmc(f, x, options, gradf, var... HMC Hybrid Monte Carlo sampling.
kmeans(centres, data, options) KMEANS Trains a k means cluster model.
knn(nin, nout, k, tr_in, tr_t... KNN Creates a K-nearest-neighbour classifier.
knnfwd(net, x) KNNFWD Forward propagation through a K-nearest-neighbour classifier.
linef(lambda, fn, x, d, varar... LINEF Calculate function value along a line.
linemin(f, pt, dir, fpt, opti... LINEMIN One dimensional minimization.
mdn(nin, nhidden, ncentres, d... MDN Creates a Mixture Density Network with specified architecture.
mdn2gmm(mdnmixes) MDN2GMM Converts an MDN mixture data structure to array of GMMs.
mdndist2(mixparams, t) MDNDIST2 Calculates squared distance between centres of Gaussian kernels and data
mdnerr(net, x, t) MDNERR Evaluate error function for Mixture Density Network.
mdnfwd(net, x) MDNFWD Forward propagation through Mixture Density Network.
mdngrad(net, x, t) MDNGRAD Evaluate gradient of error function for Mixture Density Network.
mdninit(net, prior, t, option... MDNINIT Initialise the weights in a Mixture Density Network.
mdnpak(net) MDNPAK Combines weights and biases into one weights vector.
mdnpost(mixparams, t) MDNPOST Computes the posterior probability for each MDN mixture component.
mdnprob(mixparams, t) MDNPROB Computes the data probability likelihood for an MDN mixture structure.
mdnunpak(net, w) MDNUNPAK Separates weights vector into weight and bias matrices.
metrop(f, x, options, gradf, ... METROP Markov Chain Monte Carlo sampling with Metropolis algorithm.
minbrack(f, a, b, fa, ... MINBRACK Bracket a minimum of a function of one variable.
mlp(nin, nhidden, nout, outfu... MLP Create a 2-layer feedforward network.
mlpbkp(net, x, z, deltas) MLPBKP Backpropagate gradient of error function for 2-layer network.
mlpderiv(net, x) MLPDERIV Evaluate derivatives of network outputs with respect to weights.
mlperr(net, x, t) MLPERR Evaluate error function for 2-layer network.
mlpevfwd(net, x, t, x_test, i... MLPEVFWD Forward propagation with evidence for MLP
mlpfwd(net, x) MLPFWD Forward propagation through 2-layer network.
mlpgrad(net, x, t) MLPGRAD Evaluate gradient of error function for 2-layer network.
mlphdotv(net, x, t, v) MLPHDOTV Evaluate the product of the data Hessian with a vector.
mlphess(net, x, t, hdata) MLPHESS Evaluate the Hessian matrix for a multi-layer perceptron network.
mlphint(net); MLPHINT Plot Hinton diagram for 2-layer feed-forward network.
mlpinit(net, prior) MLPINIT Initialise the weights in a 2-layer feedforward network.
mlppak(net) MLPPAK Combines weights and biases into one weights vector.
mlpprior(nin, nhidden, nout, ... MLPPRIOR Create Gaussian prior for mlp.
mlptrain(net, x, t, its); MLPTRAIN Utility to train an MLP network for demtrain
mlpunpak(net, w) MLPUNPAK Separates weights vector into weight and bias matrices.
netderiv(w, net, x) NETDERIV Evaluate derivatives of network outputs by weights generically.
neterr(w, net, x, t) NETERR Evaluate network error function for generic optimizers
netevfwd(w, net, x, t, x_test... NETEVFWD Generic forward propagation with evidence for network
netgrad(w, net, x, t) NETGRAD Evaluate network error gradient for generic optimizers
nethess(w, net, x, t, varargi... NETHESS Evaluate network Hessian
netinit(net, prior) NETINIT Initialise the weights in a network.
netopt(net, options, x, t, al... NETOPT Optimize the weights in a network model.
netpak(net) NETPAK Combines weights and biases into one weights vector.
netunpak(net, w) NETUNPAK Separates weights vector into weight and bias matrices.
olgd(net, options, x, t) OLGD On-line gradient descent optimization.
pca(data, N) PCA Principal Components Analysis
plotmat(matrix, textcolour, g... PLOTMAT Display a matrix.
ppca(x, ppca_dim) PPCA Probabilistic Principal Components Analysis
quasinew(f, x, options, gradf... QUASINEW Quasi-Newton optimization.
rbf(nin, nhidden, nout, rbfun... RBF Creates an RBF network with specified architecture
rbfbkp(net, x, z, n2, deltas) RBFBKP Backpropagate gradient of error function for RBF network.
rbfderiv(net, x) RBFDERIV Evaluate derivatives of RBF network outputs with respect to weights.
rbferr(net, x, t) RBFERR Evaluate error function for RBF network.
rbfevfwd(net, x, t, x_test, i... RBFEVFWD Forward propagation with evidence for RBF
rbffwd(net, x) RBFFWD Forward propagation through RBF network with linear outputs.
rbfgrad(net, x, t) RBFGRAD Evaluate gradient of error function for RBF network.
rbfhess(net, x, t, hdata) RBFHESS Evaluate the Hessian matrix for RBF network.
rbfjacob(net, x) RBFJACOB Evaluate derivatives of RBF network outputs with respect to inputs.
rbfpak(net) RBFPAK Combines all the parameters in an RBF network into one weights vector.
rbfprior(rbfunc, nin, nhidden... RBFPRIOR Create Gaussian prior and output layer mask for RBF.
rbfsetbf(net, options, x) RBFSETBF Set basis functions of RBF from data.
rbfsetfw(net, scale) RBFSETFW Set basis function widths of RBF.
rbftrain(net, options, x, t) RBFTRAIN Two stage training of RBF network.
rbfunpak(net, w) RBFUNPAK Separates a vector of RBF weights into its components.
rosegrad(x) ROSEGRAD Calculate gradient of Rosenbrock's function.
rosen(x) ROSEN Calculate Rosenbrock's function.
scg(f, x, options, gradf, var... SCG Scaled conjugate gradient optimization.
som(nin, map_size) SOM Creates a Self-Organising Map.
somfwd(net, x) SOMFWD Forward propagation through a Self-Organising Map.
sompak(net) SOMPAK Combines node weights into one weights matrix.
somtrain(net, options, x) SOMTRAIN Kohonen training algorithm for SOM.
somunpak(net, w) SOMUNPAK Replaces node weights in SOM.
Contents.m
demard.m
demev1.m
demev2.m
demev3.m
demgauss.m
demglm1.m
demglm2.m
demgmm1.m
demgmm2.m
demgmm3.m
demgmm4.m
demgmm5.m
demgp.m
demgpard.m
demgtm1.m
demgtm2.m
demhmc1.m
demhmc2.m
demhmc3.m
demkmn1.m
demknn1.m
demmdn1.m
demmlp1.m
demmlp2.m
demns1.m
demolgd1.m
demrbf1.m
demsom1.m
View all files

from Netlab by Ian Nabney
Pattern analysis toolbox.

netderiv(w, net, x)

function g = netderiv(w, net, x)
%NETDERIV Evaluate derivatives of network outputs by weights generically.
%
%	Description
%
%	G = NETDERIV(W, NET, X) takes a weight vector W and a network data
%	structure NET, together with the matrix X of input vectors, and
%	returns the gradient of the outputs with respect to the weights
%	evaluated at W.
%
%	See also
%	NETEVFWD, NETOPT
%

%	Copyright (c) Ian T Nabney (1996-2001)

fstr = [net.type, 'deriv'];
net = netunpak(net, w);
g = feval(fstr, net, x);

Highlights from Netlab

Highlights from
Netlab