vl_nndropsum.m

function [y, mask] = vl_nndropsum(x,varargin)
%VL_NNDROPOUT CNN dropout.
%   [Y,MASK] = VL_NNDROPSUM(X) applies dropout to the data X. MASK
%   is the randomly sampled dropout mask. Both Y and MASK have the
%   same size as X.
%
%   VL_NNDROPOUT(X, 'rate', R) sets the dropout rate to R.
%
%   [DZDX] = VL_NNDROPOUT(X, DZDY, 'mask', MASK) computes the
%   derivatives of the blocks projected onto DZDY. Note that MASK must
%   be specified in order to compute the derivative consistently with
%   the MASK randomly sampled in the forward pass. DZDX and DZDY have
%   the same dimesnions as X and Y respectivey.
%
%   Note that in the original paper on dropout, at test time the
%   network weights for the dropout layers are scaled down to
%   compensate for having all the neurons active. In this
%   implementation the dropout function itself already does this
%   compensation during training. So at test time no alterations are
%   required.

% Copyright (C) 2014-15 Andrea Vedaldi.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).

opts.rate = 0.5 ;
opts.mask = [] ;

backMode = numel(varargin) > 0 && ~isstr(varargin{1}) ;
if backMode
  dzdy = varargin{1} ;
  opts = vl_argparse(opts, varargin(2:end)) ;
else
  opts = vl_argparse(opts, varargin) ;
end

% determine mask
mask = opts.mask ;
if backMode && isempty(mask)
  warning('vl_nndropsum: when using in backward mode, the mask should be specified') ;
end

numInputs = numel(x) ;
if isempty(mask)
  if isa(x{1},'gpuArray')
    mask = gpuArray(rand(numInputs, 1) >= opts.rate) ;
    mask(1) = gpuArray(1);
  else
    mask = single(rand(numInputs, 1) >= opts.rate) ;
    mask(1) = 1;
  end
end

% do job
if ~backMode
    
    y = mask(1) * x{1} ;
    for k = 2:numInputs
        y = y + mask(k) * x{k} ;
    end
else
  for k = 1:numInputs
      y{k} = dzdy{1} * mask(k);
  end
end