我正在使用bvlc_reference_caffenet进行培训。我正在做培训和测试。以下是我训练有素的网络的示例日志：
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
令我困惑的是损失。当损失低于0.0001时，我将停止训练我的网络，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，但测试损失为0.388，远高于我设定的阈值。我用哪一个来阻止我的训练？
答案 0 :(得分：1)
测试和火车性能之间存在如此大的差距可能表明您over-fit您的数据 验证集的目的是确保您不会过度配戴。您应该使用验证集上的性能来决定是停止训练还是先行。
答案 1 :(得分：0)
通常，您希望在验证准确度达到稳定水平时停止培训。您上面的数据表明您确实过度训练了您的模型。
理想情况下，培训，测试和验证错误应大致相等。在实践中，这种情况很少发生。
请注意，损失不是一个好的指标，除非您的损失函数和权重对于所有评估阶段都相同。例如，GoogleNet对三层的训练损失函数进行加权，但验证测试仅担心最终的准确性。