什么时候停止训练？

Question

我正在使用bvlc_reference_caffenet进行培训。我正在做训练和测试。下面是我受过训练的网络的一个示例日志：

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

令我困惑的是失去。我打算停止训练我的网络时，损失低于0.0001，但有两个损失:训练损失和测试损失。训练损失似乎保持在0.0001左右，但测试损失在0.388，这远远高于我设定的门槛。我该用哪一种来停止训练？

Answer 1

在测试和培训性能之间存在如此大的差距可能意味着您需要超适合您的数据。

验证集的目的是确保您不过分适合。您应该使用验证集上的性能来决定是停止培训还是提前停止培训。

Answer 2

通常，当验证的准确性达到一个平台时，您希望停止训练。你上面的数据表明你确实对你的模型进行了过度训练.

理想情况下，培训、测试和验证错误应该大致相等。在实践中，这种情况很少发生。

注意，损失不是一个很好的度量，除非您的损失函数和权重对于评估的所有阶段都是相同的。例如，GoogleNet对三个层的训练损失函数进行加权，但是验证测试只担心最终的准确性。