Question

我正在尝试实现一个模型，该模型将图像作为输入并给出26个数字的向量。我目前正在通过以下Matlab代码使用VGG-16：

analyzeNetwork(net);
NUM_OUTPUT = 26;
layers = net.Layers;
%output = fullyConnectedLayer(NUM_OUTPUT, ...
%                             'Name','output_layer', ...
%                             'WeightLearnRateFactor',10, ...
%                             'BiasLearnRateFactor',10);
layers = [
    layers(1:38)
    fullyConnectedLayer(NUM_OUTPUT)
    regressionLayer];

%layers(1:67) = freezeWeights(layers(1:67));
miniBatchSize  = 5;
validationFrequency = floor(numel(YTrain)/miniBatchSize);
options = trainingOptions('sgdm',...
    'InitialLearnRate',0.001, ...
    'ValidationData',{XValidation,YValidation},...
    'Plots','training-progress',...
    'Verbose',false);

net = trainNetwork(XTrain,YTrain,layers,options);
YPred = predict(net,XValidation);
predictionError = YValidation - YPred;
thr = 10;
numCorrect = sum(abs(predictionError) < thr);
numImagesValidation = numel(YValidation);

accuracy = numCorrect/numImagesValidation;
rmse = sqrt(mean(predictionError.^2));

XTrain和YTrain的形状如下：
XTrain：：224 224 3 140
火车： 26140

通过运行上面的代码（它是代码的一部分，而不是全部），我得到以下错误：

使用trainNetwork时出错（第170行） X和Y的观察数不一致。

如果有人可以帮助我找出问题所在，我将不胜感激，因为据我所知，这两个样本的数量相等，而其余维度不必相等。

Answer 1

将YTrain设置为140x26。

命名您的新图层，并将其设为layerGraph

回归很容易变得不稳定，因此如果您遇到一些困难，则会降低学习率或增加批量大小。

net = vgg16 ; % analyzeNetwork(net);
LAYERS_FREEZE_UNTIL=35;
LAYERS_COPY_UNTIL=38;


NUM_TRAIN_SAMPLES = size(YTrain,1);
NUM_OUTPUT = size(YTrain,2);


my_layers =layerGraph([
    freezeWeights(net.Layers(1:LAYERS_FREEZE_UNTIL))
    net.Layers(LAYERS_FREEZE_UNTIL+1:LAYERS_COPY_UNTIL)
    fullyConnectedLayer(NUM_OUTPUT*2,'Name','my_fc1')
    fullyConnectedLayer(NUM_OUTPUT,'Name','my_fc2')
    regressionLayer('Name','my_regr')
    ]);
% figure; plot(my_layers), ylim([0.5,6.5])
% analyzeNetwork(my_layers);

MINI_BATCH_SIZE = 16;

options = trainingOptions('sgdm', ...
    'MiniBatchSize',MINI_BATCH_SIZE, ...
    'MaxEpochs',20, ...
    'InitialLearnRate',1e-4, ...
    'Shuffle','every-epoch', ...
    'ValidationData',{XValidation,YValidation}, ...
    'ValidationFrequency',floor(NUM_TRAIN_SAMPLES/MINI_BATCH_SIZE), ...
    'Verbose',true, ...
    'Plots','training-progress');

my_net = trainNetwork(XTrain,YTrain,my_layers,options);

在Matlab中进行回归学习

1 个答案: