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    Metadata details:
    filename: vl_simplenn_diagnose.m
    path of file: ./repos/Image-Text-Embedding/matlab/simplenn
    
    the code of the file until where you have to start completion: function vl_simplenn_diagnose(net, res)
%VL_SIMPLENN_DIAGNOSE  Plot diagnostic information on a SimpleNN network.
%   VL_SIMPLENN_DIAGNOSE(NET, RES) plots in the current window
%   the average, maximum, and miminum element for all the filters
%   and biases in the network NET. If RES is also provided, it will
%   plot the average, minimum, and maximum element for all the
%   intermediate responses and deriviatives stored in RES as well.
%
%   This function can be used to rapidly glance at the evolution
%   of the paramters during training.

% 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).

n = numel(net.layers) ;
fmu = NaN + zeros(1, n) ;
fmi = fmu ;
fmx = fmu ;
bmu = fmu ;
bmi = fmu ;
bmx = fmu ;
xmu = fmu ;
xmi = fmi ;
xmx = fmx ;
dxmu = fmu ;
dxmi = fmi ;

    
function vl_simplenn_diagnose(net, res)
%VL_SIMPLENN_DIAGNOSE  Plot diagnostic information on a SimpleNN network.
%   VL_SIMPLENN_DIAGNOSE(NET, RES) plots in the current window
%   the average, maximum, and miminum element for all the filters
%   and biases in the network NET. If RES is also provided, it will
%   plot the average, minimum, and maximum element for all the
%   intermediate responses and deriviatives stored in RES as well.
%
%   This function can be used to rapidly glance at the evolution
%   of the paramters during training.

% 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).

n = numel(net.layers) ;
fmu = NaN + zeros(1, n) ;
fmi = fmu ;
fmx = fmu ;
bmu = fmu ;
bmi = fmu ;
bmx = fmu ;
xmu = fmu ;
xmi = fmi ;
xmx = fmx ;
dxmu = fmu ;
dxmi = fmi ;
dxmx = fmx ;
dfmu = fmu ;
dfmi = fmu ;
dfmx = fmu ;
dbmu = fmu ;
dbmi = fmu ;
dbmx = fmu ;

for i=1:numel(net.layers)
  ly = net.layers{i} ;
  if ismember(ly.type, {'conv', 'bnorm'}) && numel(ly.weights{1}) > 0
    x = gather(ly.weights{1}) ;
    fmu(i) = mean(x(:)) ;
    fmi(i) = min(x(:)) ;
    fmx(i) = max(x(:)) ;
  end