How to add a regression layer at the end of an existing CNN?

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Tobias Pahlberg 2017-5-29

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https://ww2.mathworks.cn/matlabcentral/answers/342327-how-to-add-a-regression-layer-at-the-end-of-an-existing-cnn

回答： Tobias Pahlberg 2017-10-6

Hi

I have been trying to use transfer learning with a regression layer at the end. However, I end up with NaNs when I try to predict new images with the ConvNet. How should I put together the layers at the end? Like this?

% Load VGG network
net = vgg19; 
% Remove last 3 layers.
layersTransfer = net.Layers(1:end-3);
% Add a fully connected layer and a regression layer
layers = [layersTransfer
          fullyConnectedLayer(1)
          regressionLayer];
% Training options
transferLayerOptions = trainingOptions('sgdm','MiniBatchSize',25,...
                                       'InitialLearnRate',0.001, ...
                                       'MaxEpochs',7, 'Verbose', true);

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Birju Patel 2017-7-20

What happened during training? Did you see NaNs printed in the training log printed in the command window? If so, it's likely your learning rate is too high.

Tobias Pahlberg 2017-9-13

在 MATLAB Online 中打开

Yes! I tried lowering the initial learning rate first to 1e-4 but had the same problem, then I lowered to 1e-5 and I got numbers now. Could you explain why I need such a low rate here, is it because of the size of the network? Here is what the training log looks like now.

|=========================================================================================|
|     Epoch    |   Iteration  | Time Elapsed |  Mini-batch  |  Mini-batch  | Base Learning|
|              |              |  (seconds)   |     Loss     |     RMSE     |     Rate     |
|=========================================================================================|
|            1 |            1 |         7.98 |     366.5998 |        27.08 |     1.00e-05 |
|            1 |           50 |     30791.43 |     338.3933 |        26.02 |     1.00e-05 |
|            1 |          100 |     31443.52 |      73.1415 |        12.09 |     1.00e-05 |
|            1 |          150 |     32114.34 |      74.8708 |        12.24 |     1.00e-05 |
|            1 |          200 |     32688.59 |      26.7964 |         7.32 |     1.00e-05 |
|            2 |          250 |     33330.43 |      28.9653 |         7.61 |     1.00e-05 |
|            2 |          300 |     33941.49 |      29.8938 |         7.73 |     1.00e-05 |
|            2 |          350 |     34541.52 |      27.4190 |         7.41 |     1.00e-05 |
|            2 |          400 |     35179.22 |      28.7636 |         7.58 |     1.00e-05 |
|            3 |          450 |     35758.08 |      32.7189 |         8.09 |     1.00e-05 |
|            3 |          500 |     36335.59 |      18.0422 |         6.01 |     1.00e-05 |
|            3 |          550 |     37002.46 |      26.7821 |         7.32 |     1.00e-05 |
|            3 |          600 |     37619.53 |      17.1531 |         5.86 |     1.00e-05 |
|            4 |          650 |     38210.78 |      17.5978 |         5.93 |     1.00e-05 |
|            4 |          700 |     38796.26 |      13.0254 |         5.10 |     1.00e-05 |
|            4 |          750 |     39360.78 |      15.2743 |         5.53 |     1.00e-05 |
|            4 |          800 |     39947.61 |      21.7468 |         6.59 |     1.00e-05 |
|            5 |          850 |     40587.68 |      17.7648 |         5.96 |     1.00e-05 |
|            5 |          900 |     41240.12 |       9.3954 |         4.33 |     1.00e-05 |
|            5 |          950 |     41841.30 |      23.7572 |         6.89 |     1.00e-05 |
|            5 |         1000 |     42466.40 |       9.1774 |         4.28 |     1.00e-05 |
|            6 |         1050 |     43082.04 |      10.6128 |         4.61 |     1.00e-05 |
|            6 |         1100 |     43654.45 |       8.0469 |         4.01 |     1.00e-05 |
|            6 |         1150 |     44324.75 |      13.1615 |         5.13 |     1.00e-05 |
|            6 |         1200 |     44899.66 |       4.5212 |         3.01 |     1.00e-05 |
|            7 |         1250 |     45491.29 |      13.4922 |         5.19 |     1.00e-05 |
|            7 |         1300 |     46126.86 |       5.1565 |         3.21 |     1.00e-05 |
|            7 |         1350 |     46704.71 |      14.9740 |         5.47 |     1.00e-05 |
|            7 |         1400 |     47259.80 |       6.1847 |         3.52 |     1.00e-05 |
|=========================================================================================|