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This repo contains loss functions for dropout / Gaussian ("fast") dropout and the delta method dropout. These methods are described in our papers

Sida Wang and Chris Manning. Fast dropout training. ICML2013,

Stefan Wager, Sida Wang and Percy Liang. Dropout Training as Adaptive Regularization. NIPS2013

If you have minFunc, you can execute the script run_experiment.m The example_data loaded are vectors from the atheism vs. christian newsgroup task. With the fixed random seed, I got:

DetDropout: 0.882188
Dropout: 0.882188
LR: 0.827489

Loss functions

Loss for logistic regression

These loss functions take weight w, data X, labels y and return the negative loglikihood nll and the gradient g.

These are all binary loss functions and they can be found in ./binaryLRloss Similar multiclass loss functions are in ./softmaxLoss

% w: model weights
% X: data of size numdata by dimdata in the design matrix convention.
% y: binary label vector
% ps: dropout rate

function [nll,g,H,T] = LogisticLoss(w,X,y)
% Baseline loss function from PMTK, with some optimizations

function [nll,g,H] = LogisticLossMCDropout(w,X,y,ps)
% Monte Carlo real dropout, single sample

function [nll,g,H] = LogisticLossMCDropoutSample(w,X,y,ps, miniter, numiter)
% Monte Carlo real dropout, numiter samples

function [nll,g] = LogisticLossDetObjDropout(w,X,y,ps)
% This is eq 8) of the ICML paper, intergrating the Gaussian

function [nll,g] = LogisticLossDetObjDropoutDelta(w,X,y,ps)
% Dropout loss via the Delta method, somewhat less accurate. 
% Note this is the loss function used in our later NIPS paper "Dropout as adaptive regularization"

function [nll,g] = LogisticLossDetObjDropoutDeltaMoreData(w,X,y,ps,Xu,a)
% The semisupervised version used in  "Dropout as adaptive regularization", where Xu is the unlabelled data, and a is the discounting coefficient.

function [nll,g] = LogisticLossMCDropoutSampleGauss(w,X,y,ps, numsample)
% Sample from the Gaussian

function [nll,g] = LogisticLossMCDropoutSampleGaussIntegrate(w,X,y,ps, numsample)
% Differenitate inside the expectation and then intergrate

function [nll,g] = LogisticLossMCDropoutSampleGaussNumDiff(w,X,y,ps, numsample)
% Use numerical diff to compute derivative

Loss for 2 layer neural networks

% W: weights, vectorized, @params decode contains how to intepret them
% X: data of size numdata by dimdata in the design matrix convention
% k: number of classes
% decode: information on how to decode w
% params: things like
%    params.numsamples = 50; 
%    params.p = 0.5; % dropout rate
%    params.p1 = 1; %dropout rate for bias
% y1ofn: labels in 1 out of n encoding, so numclass by numdata

function [nll,g, expHwdZ, labels] = Softmax2NN(W,X,k,decode,params,y1ofn)
% Baseline softmax classification with 2 layer NN

function [nll,g, expHwdZ, labels] = Softmax2NN_MCDropoutMulti(W,X,k,decode,params,y1ofn)
% Real dropout 2NN take many samples

function [se, g, yhat] = Linear2NNDet(W,X,k,decode,params,y)
% 2 layer NN for regression with fast dropout

function [nll,g, Mu3, labels] = LogisticOneVsAllDetLoss(W,X,k,decode, params,y1ofn)
% 2 layer NN for 1 vs. all classification with dropout

function [nll,g, EexpdZ, labels] = Softmax2NNLossFastSample(W,X,k,decode,params,y1ofn)
% Sample from the Gaussian

function [nll,g, weightedsum, labels] = Softmax2NNLossFastSigmaPoints(W,X,k,decode,params,y1ofn)
% Use Sigma point

minFunc can be found here:

License: MIT


code for fast dropout







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