You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: cnn_train_dag.m
    path of file: ./repos/DeepCO3/Lib/matconvnet-1.0-beta24
    
    the code of the file until where you have to start completion: function [net,stats] = cnn_train_dag(net, imdb, getBatch, varargin)
%CNN_TRAIN_DAG Demonstrates training a CNN using the DagNN wrapper
%    CNN_TRAIN_DAG() is similar to CNN_TRAIN(), but works with
%    the DagNN wrapper instead of the SimpleNN wrapper.

% Copyright (C) 2014-16 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).
% addpath(fullfile(vl_rootnn, 'examples'));

opts.expDir = fullfile('data','exp') ;
opts.continue = true ;
opts.batchSize = 256 ;
opts.numSubBatches = 1 ;
opts.train = [] ;
opts.val = [] ;
opts.gpus = [] ;
opts.prefetch = false ;
opts.epochSize = inf;
opts.numEpochs = 300 ;
opts.learningRate = 0.001 ;
opts.weightDecay = 0.0005 ;
opts.SaveEachEpoch = false;
opts.solver = [] ;  % Empty array means use the default SGD solver
[opts, varargin] = vl_argparse(opts, varargin) ;
if ~isempty(opts.solver)
  assert(isa(opts.solver, 'function_handle') && nargout(opts.solver) == 2,...
    'Invalid solver; expected a function handle with two outputs.') ;
  % Call without input arguments, to get default options
 
    
function to update weights
          [net.params(p).value, state.solverState{p}] = ...
            params.solver(net.params(p).value, state.solverState{p}, ...
            parDer, params.solverOpts, thisLR) ;
        end
      end
    otherwise
      error('Unknown training method ''%s'' for parameter ''%s''.', ...
        net.params(p).trainMethod, ...
        net.params(p).name) ;
  end
end

% -------------------------------------------------------------------------
function stats = accumulateStats(stats_)
% -------------------------------------------------------------------------

for s = {'train', 'val'}
  s = char(s) ;
  total = 0 ;

  % initialize stats stucture with same fields and same order as
  % stats_{1}
  stats__ = stats_{1} ;
  names = fieldnames(stats__.(s))' ;
  values = zeros(1, numel(names)) ;
  fields = cat(1, names, num2cell(values)) ;
  stats.(s) = struct(fields{:}) ;

  for g = 1:numel(stats_)
    stats__ = stats_{g} ;
    num__ = stats__.(s).num ;
    total = total + num__ ;

    for f = setdiff(fieldnames(stats__.(s))', 'num')
      f = char(f) ;
      stats.(s).(f) = stats.(s).(f) + stats__.(s).(f) * num__ ;

      if g == numel(stats_)
        stats.(s).(f) = stats.(s).(f) / total ;
      end