神经网络中的常量错误，MatConvNet

Question

解决：以前我的数据集有大约1000张图片。我将它增加到50 000，现在神经网络学习和工作。

我创建了一个卷积神经网络，用于识别面部表情（正面，中性，负面情绪）中的三种情绪。不知何故，我的错误函数没有变得更好（error image）。训练和验证错误在100个时期内是不变的。可能是什么原因？

为什么错误是不变的？

这是我的代码：

function training(varargin)

setup ;

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
rngNum = 1; % rng number for random weight initialization, e.g., 1,2,3
num_fcHiddenNeuron =1024; % # neurons in the fully-connected hidden layer
prob_fcDropout = 0.5; % dropout probability in the fully-connected hidden layer,


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% input data for training deep CNNs
imdb1 = load(['trainingdata']) ; 
imdb2 = load(['testdata']) ;

imdb.images.data = cat(4, imdb1.images.data, imdb2.images.data);
imdb.images.labels = cat(2, imdb1.images.labels, imdb2.images.labels);
imdb.images.set = cat(2, imdb1.images.set, imdb2.images.set);
imdb.meta = imdb1.meta;

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
trainOpts.batchSize = 200 ;
trainOpts.numEpochs = 100 ;
trainOpts.gpus = [] ;
trainOpts.continue = true ;
trainOpts.learningRate = [0.004*ones(1,25), 0.002*ones(1,25), 0.001*ones(1,25), 0.0005*ones(1,25)]; 
trainOpts = vl_argparse(trainOpts, varargin);   

%% Training Deep CNNs
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% CNN configuration
  net.layers = {} ;
% % 
% % %% Conv1 - MaxPool1
 rng(rngNum)    %control random number generation
 net.layers{end+1} = struct('type', 'conv', ...
                           'weights', {{0.01*randn(3,3,1,32, 'single'), 0.1*ones(1, 32, 'single')}}, ...
                           'stride', 1, ...
                           'pad', 1, ...
                           'filtersLearningRate', 1, ...
                           'biasesLearningRate', 1, ...
                           'filtersWeightDecay', 1/5, ...
                           'biasesWeightDecay', 0) ;                        
 net.layers{end+1} = struct('type', 'relu') ;
 net.layers{end+1} = struct('type', 'pool', ...
                        'method', 'max', ...
                        'pool', [2 2], ...
                        'stride', 2, ...
                        'pad', 0) ;

% %%% Conv2 - MaxPool2
 rng(rngNum)
 net.layers{end+1} = struct('type', 'conv', ...
                           'weights', {{0.01*randn(3,3,32,32, 'single'), 0.1*ones(1, 32, 'single')}}, ...
                           'stride', 1, ...
                           'pad', 0, ...
                           'filtersLearningRate', 1, ...
                           'biasesLearningRate', 1, ...
                           'filtersWeightDecay', 1/5, ...
                           'biasesWeightDecay', 0) ;     
 net.layers{end+1} = struct('type', 'relu') ;
 net.layers{end+1} = struct('type', 'pool', ...
                        'method', 'max', ...
                        'pool', [2 2], ...
                        'stride', 2, ...
                        'pad', [1, 0, 1, 0]) ;                   

% %%% Conv3 - MaxPool3
 rng(rngNum)
 net.layers{end+1} = struct('type', 'conv', ...
                           'weights', {{0.01*randn(3,3,32,64, 'single'), 0.1*ones(1, 64, 'single')}}, ...
                           'stride', 1, ...
                           'pad', 1, ...
                           'filtersLearningRate', 1, ...
                           'biasesLearningRate', 1, ...
                           'filtersWeightDecay', 1/5, ...
                           'biasesWeightDecay', 0) ;     
 net.layers{end+1} = struct('type', 'relu') ;       
 net.layers{end+1} = struct('type', 'pool', ...
                       'method', 'max', ...
                       'pool', [2 2], ...
                       'stride', 2, ...
                       'pad', 0) ;    
% %%% Fc Hidden
 rng(rngNum)
 net.layers{end+1} = struct('type', 'conv', ...
                           'weights', {{0.001*randn(5,5,64,num_fcHiddenNeuron, 'single'), 0.01*ones(1, num_fcHiddenNeuron, 'single')}}, ...
                           'stride', 1, ...
                           'pad', 0, ...
                           'filtersLearningRate', 1, ...
                           'biasesLearningRate', 1, ...
                           'filtersWeightDecay', 1/5, ...
                           'biasesWeightDecay', 0) ;  
 net.layers{end+1} = struct('type', 'relu') ;
 net.layers{end+1} = struct('type', 'dropout', ...
                            'rate', prob_fcDropout) ;
% 
% %%% Fc Output
 rng(rngNum)
 net.layers{end+1} = struct('type', 'conv', ...
                           'weights', {{zeros(1,1,num_fcHiddenNeuron, 3, 'single'), zeros(1, 3, 'single')}}, ...
                           'stride', 1, ...
                           'pad', 0, ...
                           'filtersLearningRate', 1, ...
                           'biasesLearningRate', 1, ...
                           'filtersWeightDecay', 4, ...
                           'biasesWeightDecay', 0) ;   
net.layers{end+1} = struct('type', 'softmaxloss') ;

% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% starting to train deep CNN
[net,info] = cnn_train(net, imdb, getBatch(opts), trainOpts, 'val', find(imdb.images.set == 2)) ;
net.layers(end) = [] ;




function fn = getBatch(opts)
% -------------------------------------------------------------------------
    fn = @(x,y) getSimpleNNBatch(x,y) ;
end

% -------------------------------------------------------------------------
function [images, labels] = getSimpleNNBatch(imdb, batch)
% -------------------------------------------------------------------------
images = imdb.images.data(:,:,:,batch) ;
labels = imdb.images.labels(1,batch) ;
end