cnn_train_dag.rar_CNN_load_cnn_dag_thrownaqr_train_神经网络

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.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 opts.solverOpts = opts.solver() ; end opts.momentum = 0.9 ; opts.saveSolverState = true ; opts.nesterovUpdate = false ; opts.randomSeed = 0 ; opts.profile = false ; opts.parameterServer.method = 'mmap' ; opts.parameterServer.prefix = 'mcn' ; opts.derOutputs = {'objective', 1} ; opts.extractStatsFn = @extractStats ; opts.plotStatistics = true; opts.postEpochFn = [] ; % postEpochFn(net,params,state) called after each epoch; can return a new learning rate, 0 to stop, [] for no change opts = vl_argparse(opts, varargin) ; if ~exist(opts.expDir, 'dir'), mkdir(opts.expDir) ; end if isempty(opts.train), opts.train = find(imdb.images.set==1) ; end if isempty(opts.val), opts.val = find(imdb.images.set==2) ; end if isscalar(opts.train) && isnumeric(opts.train) && isnan(opts.train) opts.train = [] ; end if isscalar(opts.val) && isnumeric(opts.val) && isnan(opts.val) opts.val = [] ; end % ------------------------------------------------------------------------- % Initialization % ------------------------------------------------------------------------- evaluateMode = isempty(opts.train) ; if ~evaluateMode if isempty(opts.derOutputs) error('DEROUTPUTS must be specified when training.\n') ; end end % ------------------------------------------------------------------------- % Train and validate % ------------------------------------------------------------------------- modelPath = @(ep) fullfile(opts.expDir, sprintf('net-epoch-%d.mat', ep)); modelFigPath = fullfile(opts.expDir, 'net-train.pdf') ; start = opts.continue * findLastCheckpoint(opts.expDir) ; if start >= 1 fprintf('%s: resuming by loading epoch %d\n', mfilename, start) ; [net, state, stats] = loadState(modelPath(start)) ; else state = [] ; end for epoch=start+1:opts.numEpochs % Set the random seed based on the epoch and opts.randomSeed. % This is important for reproducibility, including when training % is restarted from a checkpoint. rng(epoch + opts.randomSeed) ; prepareGPUs(opts, epoch == start+1) ; % Train for one epoch. params = opts ; params.epoch = epoch ; params.learningRate = opts.learningRate(min(epoch, numel(opts.learningRate))) ; params.train = opts.train(randperm(numel(opts.train))) ; % shuffle params.train = params.train(1:min(opts.epochSize, numel(opts.train))); params.val = opts.val(randperm(numel(opts.val))) ; params.imdb = imdb ; params.getBatch = getBatch ; if numel(opts.gpus) <= 1 [net, state] = processEpoch(net, state, params, 'train') ; [net, state] = processEpoch(net, state, params, 'val') ; if ~evaluateMode saveState(modelPath(epoch), net, state) ; end lastStats = state.stats ; else spmd [net, state] = processEpoch(net, state, params, 'train') ; [net, state] = processEpoch(net, state, params, 'val') ; if labindex == 1 && ~evaluateMode saveState(modelPath(epoch), net, state) ; end lastStats = state.stats ; end lastStats = accumulateStats(lastStats) ; end stats.train(epoch) = lastStats.train ; stats.val(epoch) = lastStats.val ; clear lastStats ; saveStats(modelPath(epoch), stats) ; if opts.plotStatistics switchFigure(1) ; clf ; plots = setdiff(... cat(2,... fieldnames(stats.train)', ... fieldnames(stats.val)'), {'num', 'time'}) ; for p = plots p = char(p) ; values = zeros(0, epoch) ; leg = {} ; for f = {'train', 'val'} f = char(f) ; if isfield(stats.(f), p) tmp = [stats.(f).(p)] ; values(end+1,:) = tmp(1,:)' ; leg{end+1} = f ; end end subplot(1,numel(plots),find(strcmp(p,plots))) ; plot(1:epoch, values','o-') ; xlabel('epoch') ; title(p) ; legend(leg{:}) ; grid on ; end drawnow ; print(1, modelFigPath, '-dpdf') ; end if ~isempty(opts.postEpochFn) if nargout(opts.postEpochFn) == 0 opts.postEpochFn(net, params, state) ; else lr = opts.postEpochFn(net, params, state) ; if ~isempty(lr), opts.learningRate = lr; end if opts.learningRate == 0, break; end end end end % With multiple GPUs, return one copy if isa(net, 'Composite'), net = net{1} ; end % ------------------------------------------------------------------------- function [net, state] = processEpoch(net, state, params, mode) % ------------------------------------------------------------------------- % Note that net is not strictly needed as an output argument as net % is a handle class. However, this fixes some aliasing issue in the % spmd caller. % initialize with momentum 0 if isempty(state) || isempty(state.solverState) state.solverState = cell(1, numel(net.params)) ; state.solverState(:) = {0} ; end % move CNN to GPU as needed numGpus = numel(params.gpus) ; if numGpus >= 1 net.move('gpu') ; for i = 1:numel(state.solverState) s = state.solverState{i} ; if isnumeric(s) state.solverState{i} = gpuArray(s) ; elseif isstruct(s) state.solverState{i} = structfun(@gpuArray, s, 'UniformOutput', false) ; end end end if numGpus > 1 parserv = ParameterServer(params.parameterServer) ; net.setParameterServer(parserv) ; else parserv = [] ; end % profile if params.profile if numGpus <= 1 profile clear ; profile on ; else mpiprofile reset ; mpiprofile on ; end end num = 0 ; epoch = params.epoch ; subset = params.(mode) ; adjustTime = 0 ; stats.num = 0 ; % return something even if subset = [] stats.time = 0 ; start = tic ; for t=1:params.batchSize:numel(subset) fprintf('%s: epoch %02d: %3d/%3d:', mode, epoch, ... fix((t-1)/params.batchSize)+1, ceil(numel(subset)/params.batchSize)) ; batchSize = min(params.batchSize, numel(subset) - t + 1) ; for s=1:params.numSubBatches % get this image batch and prefetch the next batchStart = t + (labindex-1) + (s-1) * numlabs ; batchEnd = min(t+params.batchSize-1, numel(subset)) ; batch = subset(batchStart : params.numSubBatches * numlabs : batchEnd) ; num = num + numel(batch) ; if numel(batch) == 0, continue ; end inputs = params.getBatch(params.imdb, batch) ; if params.prefetch if s == params.numSubBatches batchStart = t + (labindex-1) + params.batchSize ; batchEnd = min(t+2*params.batchSize-1, numel(subset)) ; else batchStart = batchStart + numlabs ; end nextBatch = subset(batchStart : params.numSubBatches * numlabs : batchEnd) ; params.getBatch(params.imdb, nextBatch) ; end if strcmp(mode, 'train') net.mode = 'normal' ; net.accumulateParamDers = (s ~= 1) ; net.eval(inputs, params.derOutputs, 'holdOn', s < params.numSubBatches) ; else