什么时候停止训练咖啡? [英] When to stop training in caffe?
问题描述
我正在使用bvlc_reference_caffenet进行培训.我正在训练和测试.下面是我训练有素的网络的示例日志:
I am using bvlc_reference_caffenet for training. I am doing both training and testing. Below is an example log of my trained network:
I0430 11:49:08.408740 23343 data_layer.cpp:73] Restarting data prefetching from start.
I0430 11:49:21.221074 23343 data_layer.cpp:73] Restarting data prefetching from start.
I0430 11:49:34.038710 23343 data_layer.cpp:73] Restarting data prefetching from start.
I0430 11:49:46.816813 23343 data_layer.cpp:73] Restarting data prefetching from start.
I0430 11:49:56.630870 23334 solver.cpp:397] Test net output #0: accuracy = 0.932502
I0430 11:49:56.630940 23334 solver.cpp:397] Test net output #1: loss = 0.388662 (* 1 = 0.388662 loss)
I0430 11:49:57.218236 23334 solver.cpp:218] Iteration 71000 (0.319361 iter/s, 62.625s/20 iters), loss = 0.00146191
I0430 11:49:57.218300 23334 solver.cpp:237] Train net output #0: loss = 0.00146191 (* 1 = 0.00146191 loss)
I0430 11:49:57.218308 23334 sgd_solver.cpp:105] Iteration 71000, lr = 0.001
I0430 11:50:09.168726 23334 solver.cpp:218] Iteration 71020 (1.67357 iter/s, 11.9505s/20 iters), loss = 0.000806865
I0430 11:50:09.168778 23334 solver.cpp:237] Train net output #0: loss = 0.000806868 (* 1 = 0.000806868 loss)
I0430 11:50:09.168787 23334 sgd_solver.cpp:105] Iteration 71020, lr = 0.001
I0430 11:50:21.127496 23334 solver.cpp:218] Iteration 71040 (1.67241 iter/s, 11.9588s/20 iters), loss = 0.000182312
I0430 11:50:21.127539 23334 solver.cpp:237] Train net output #0: loss = 0.000182314 (* 1 = 0.000182314 loss)
I0430 11:50:21.127562 23334 sgd_solver.cpp:105] Iteration 71040, lr = 0.001
I0430 11:50:33.248086 23334 solver.cpp:218] Iteration 71060 (1.65009 iter/s, 12.1206s/20 iters), loss = 0.000428604
I0430 11:50:33.248260 23334 solver.cpp:237] Train net output #0: loss = 0.000428607 (* 1 = 0.000428607 loss)
I0430 11:50:33.248272 23334 sgd_solver.cpp:105] Iteration 71060, lr = 0.001
I0430 11:50:45.518955 23334 solver.cpp:218] Iteration 71080 (1.62989 iter/s, 12.2707s/20 iters), loss = 0.00108446
I0430 11:50:45.519006 23334 solver.cpp:237] Train net output #0: loss = 0.00108447 (* 1 = 0.00108447 loss)
I0430 11:50:45.519011 23334 sgd_solver.cpp:105] Iteration 71080, lr = 0.001
I0430 11:50:51.287315 23341 data_layer.cpp:73] Restarting data prefetching from start.
I0430 11:50:57.851781 23334 solver.cpp:218] Iteration 71100 (1.62169 iter/s, 12.3328s/20 iters), loss = 0.00150949
I0430 11:50:57.851828 23334 solver.cpp:237] Train net output #0: loss = 0.0015095 (* 1 = 0.0015095 loss)
I0430 11:50:57.851837 23334 sgd_solver.cpp:105] Iteration 71100, lr = 0.001
I0430 11:51:09.912184 23334 solver.cpp:218] Iteration 71120 (1.65832 iter/s, 12.0604s/20 iters), loss = 0.00239335
I0430 11:51:09.912330 23334 solver.cpp:237] Train net output #0: loss = 0.00239335 (* 1 = 0.00239335 loss)
I0430 11:51:09.912340 23334 sgd_solver.cpp:105] Iteration 71120, lr = 0.001
I0430 11:51:21.968586 23334 solver.cpp:218] Iteration 71140 (1.65888 iter/s, 12.0563s/20 iters), loss = 0.00161807
I0430 11:51:21.968646 23334 solver.cpp:237] Train net output #0: loss = 0.00161808 (* 1 = 0.00161808 loss)
I0430 11:51:21.968654 23334 sgd_solver.cpp:105] Iteration 71140, lr = 0.001
让我感到困惑的是损失.当损失低于0.0001时，我将停止训练我的网络，但是有两个损失:训练损失和测试损失.训练损失似乎保持在0.0001左右，但测试损失为0.388，远高于我设定的阈值.我该使用哪一个停止训练?
What confuses me is the loss. I was going to stop training my network when loss goes below 0.0001 but there are two losses: training loss and test loss. Training loss seems to stay around 0.0001 but test loss is at 0.388 which is way above the threshold I set. Which one do I use to stop my training?
推荐答案
测试和训练效果之间的差距如此之大，可能表明您过度拟合您的数据.
验证集的目的是确保您不会过拟合.您应该使用验证集上的效果来决定是停止训练还是继续训练.
Having such a large gap between test and train performance might indicate that you over-fit your data.
The purpose of validation set is to make sure you do not overfit. You should use the performance on the validation set to decide whether to stop training or precede.
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