我想知道为什么我的测试精度会一直保持0.5的恒定值。我使用CaffeNet网络只改变了完全连接层的参数，我在其中配置了 num_output：2 。
我的训练集包含1000个正面和1000个负面示例，而我的验证集也有1000个正面和1000个负面示例。数据集包含人物的图像（全身RGB着色）。我在数据层中定义了一个平均文件和比例值。我的网络经过培训，可以学习或不学习（二元分类器）。
我的求解器信息片段如下所示：
test_iter: 80
test_interval: 10
base_lr: 0.01
lr_policy: "step"
gamma: 0.1
stepsize: 20
display: 10
max_iter: 80
momentum: 0.9
weight_decay: 0.0005
snapshot: 10000
培训输出如下：
I0228 11:49:27.411556 3422 solver.cpp:274] Learning Rate Policy: step
I0228 11:49:27.590368 3422 solver.cpp:331] Iteration 0, Testing net (#0)
I0228 11:53:29.203058 3429 data_layer.cpp:73] Restarting data prefetching from start.
I0228 11:57:59.969632 3429 data_layer.cpp:73] Restarting data prefetching from start.
I0228 11:58:26.602972 3422 solver.cpp:398] Test net output #0: accuracy = 0.5
I0228 11:58:26.602999 3422 solver.cpp:398] Test net output #1: loss = 0.726503 (* 1 = 0.726503 loss)
I0228 12:00:03.892771 3422 solver.cpp:219] Iteration 0 (-6.49109e-41 iter/s, 636.481s/10 iters), loss = 0.961699
I0228 12:00:03.892915 3422 solver.cpp:238] Train net output #0: loss = 0.961699 (* 1 = 0.961699 loss)
I0228 12:00:03.892925 3422 sgd_solver.cpp:105] Iteration 0, lr = 0.01
I0228 12:04:28.831887 3426 data_layer.cpp:73] Restarting data prefetching from start.
I0228 12:13:36.909935 3422 solver.cpp:331] Iteration 10, Testing net (#0)
I0228 12:17:36.894516 3429 data_layer.cpp:73] Restarting data prefetching from start.
I0228 12:22:00.724030 3429 data_layer.cpp:73] Restarting data prefetching from start.
I0228 12:22:27.375306 3422 solver.cpp:398] Test net output #0: accuracy = 0.5
I0228 12:22:27.375334 3422 solver.cpp:398] Test net output #1: loss = 0.698973 (* 1 = 0.698973 loss)
I0228 12:23:56.072116 3422 solver.cpp:219] Iteration 10 (0.00698237 iter/s, 1432.18s/10 iters), loss = 0.696559
I0228 12:23:56.072247 3422 solver.cpp:238] Train net output #0: loss = 0.696558 (* 1 = 0.696558 loss)
I0228 12:23:56.072252 3422 sgd_solver.cpp:105] Iteration 10, lr = 0.01
I0228 12:25:23.664594 3426 data_layer.cpp:73] Restarting data prefetching from start.
I0228 12:37:08.202978 3422 solver.cpp:331] Iteration 20, Testing net (#0)
I0228 12:41:05.859966 3429 data_layer.cpp:73] Restarting data prefetching from start.
I0228 12:45:28.599306 3429 data_layer.cpp:73] Restarting data prefetching from start.
I0228 12:45:55.524168 3422 solver.cpp:398] Test net output #0: accuracy = 0.5
I0228 12:45:55.524190 3422 solver.cpp:398] Test net output #1: loss = 0.693187 (* 1 = 0.693187 loss)
I0228 12:45:55.553427 3426 data_layer.cpp:73] Restarting data prefetching from start.
I0228 12:47:24.159780 3422 solver.cpp:219] Iteration 20 (0.00710183 iter/s, 1408.09s/10 iters), loss = 0.690313
I0228 12:47:24.159914 3422 solver.cpp:238] Train net output #0: loss = 0.690313 (* 1 = 0.690313 loss)
I0228 12:47:24.159920 3422 sgd_solver.cpp:105] Iteration 20, lr = 0.001
I0228 12:57:31.167225 3426 data_layer.cpp:73] Restarting data prefetching from start.
I0228 13:00:23.671567 3422 solver.cpp:331] Iteration 30, Testing net (#0)
I0228 13:04:14.114737 3429 data_layer.cpp:73] Restarting data prefetching from start.
I0228 13:08:30.406244 3429 data_layer.cpp:73] Restarting data prefetching from start.
I0228 13:08:56.273648 3422 solver.cpp:398] Test net output #0: accuracy = 0.5
I0228 13:08:56.273674 3422 solver.cpp:398] Test net output #1: loss = 0.696971 (* 1 = 0.696971 loss)
I0228 13:10:28.487870 3422 solver.cpp:219] Iteration 30 (0.00722373 iter/s, 1384.33s/10 iters), loss = 0.700565
I0228 13:10:28.488041 3422 solver.cpp:238] Train net output #0: loss = 0.700565 (* 1 = 0.700565 loss)
I0228 13:10:28.488049 3422 sgd_solver.cpp:105] Iteration 30, lr = 0.001
I0228 13:17:38.463490 3426 data_layer.cpp:73] Restarting data prefetching from start.
I0228 13:23:29.700287 3422 solver.cpp:331] Iteration 40, Testing net (#0)
I0228 13:27:27.217670 3429 data_layer.cpp:73] Restarting data prefetching from start.
I0228 13:31:48.651156 3429 data_layer.cpp:73] Restarting data prefetching from start.
I0228 13:32:15.021637 3422 solver.cpp:398] Test net output #0: accuracy = 0.5
I0228 13:32:15.021661 3422 solver.cpp:398] Test net output #1: loss = 0.694784 (* 1 = 0.694784 loss)
I0228 13:33:43.542735 3422 solver.cpp:219] Iteration 40 (0.00716818 iter/s, 1395.05s/10 iters), loss = 0.700307
I0228 13:33:43.542875 3422 solver.cpp:238] Train net output #0: loss = 0.700307 (* 1 = 0.700307 loss)
I0228 13:33:43.542897 3422 sgd_solver.cpp:105] Iteration 40, lr = 0.0001
I0228 13:36:37.602869 3426 data_layer.cpp:73] Restarting data prefetching from start.
I0228 13:46:57.980952 3422 solver.cpp:331] Iteration 50, Testing net (#0)
I0228 13:50:55.125911 3429 data_layer.cpp:73] Restarting data prefetching from start.
I0228 13:55:22.078013 3429 data_layer.cpp:73] Restarting data prefetching from start.
I0228 13:55:49.644492 3422 solver.cpp:398] Test net output #0: accuracy = 0.5
I0228 13:55:49.644516 3422 solver.cpp:398] Test net output #1: loss = 0.693804 (* 1 = 0.693804 loss)
I0228 13:57:19.439967 3422 solver.cpp:219] Iteration 50 (0.00706266 iter/s, 1415.9s/10 iters), loss = 0.685755
I0228 13:57:19.440101 3422 solver.cpp:238] Train net output #0: loss = 0.685755 (* 1 = 0.685755 loss)
I0228 13:57:19.440107 3422 sgd_solver.cpp:105] Iteration 50, lr = 0.0001
I0228 13:57:19.843221 3426 data_layer.cpp:73] Restarting data prefetching from start.
I0228 14:09:13.012436 3426 data_layer.cpp:73] Restarting data prefetching from start.
I0228 14:10:40.182121 3422 solver.cpp:331] Iteration 60, Testing net (#0)
I0228 14:14:37.148968 3429 data_layer.cpp:73] Restarting data prefetching from start.
I0228 14:18:57.929569 3429 data_layer.cpp:73] Restarting data prefetching from start.
I0228 14:19:24.183915 3422 solver.cpp:398] Test net output #0: accuracy = 0.5
I0228 14:19:24.183939 3422 solver.cpp:398] Test net output #1: loss = 0.693612 (* 1 = 0.693612 loss)
I0228 14:20:51.017705 3422 solver.cpp:219] Iteration 60 (0.00708428 iter/s, 1411.58s/10 iters), loss = 0.693453
I0228 14:20:51.017838 3422 solver.cpp:238] Train net output #0: loss = 0.693453 (* 1 = 0.693453 loss)
I0228 14:20:51.017845 3422 sgd_solver.cpp:105] Iteration 60, lr = 1e-05
I0228 14:29:34.635071 3426 data_layer.cpp:73] Restarting data prefetching from start.
I0228 14:34:02.693697 3422 solver.cpp:331] Iteration 70, Testing net (#0)
I0228 14:37:59.742414 3429 data_layer.cpp:73] Restarting data prefetching from start.
如果参数相关，我还尝试将 test_iter 的值更改为40（而不是先前设置为80），如果参数相关，则它会更改为this link和this one仍然没有解决。此外，我尝试使用修改后的 create_imagenet.sh 脚本重新生成数据集来重新调整数据，但问题仍然存在。
每次我在求解器中更改值时，我总是更改完全连接的图层的名称。这是正确的方法吗？
这里的纪元数是~10。这可能是罪魁祸首吗？这种问题是否属于过度拟合的问题？
欢迎任何提示或建议。
编辑：
我打开了求解器中的调试信息，发现损失是无穷小的。我能否推断出它没有学到太多或根本没有学到什么？带有调试信息的日志如下：
I0228 19:58:37.235631 6771 net.cpp:593] [Forward] Layer pool2, top blob pool2 data: 1.00214
I0228 19:58:37.810919 6771 net.cpp:593] [Forward] Layer norm2, top blob norm2 data: 1.00212
I0228 19:58:42.022397 6771 net.cpp:593] [Forward] Layer conv3, top blob conv3 data: 0.432846
I0228 19:58:42.022722 6771 net.cpp:605] [Forward] Layer conv3, param blob 0 data: 0.00796926
I0228 19:58:42.022725 6771 net.cpp:605] [Forward] Layer conv3, param blob 1 data: 0.000184241
I0228 19:58:42.041185 6771 net.cpp:593] [Forward] Layer relu3, top blob conv3 data: 0.2017
I0228 19:58:45.277812 6771 net.cpp:593] [Forward] Layer conv4, top blob conv4 data: 0.989365
I0228 19:58:45.278079 6771 net.cpp:605] [Forward] Layer conv4, param blob 0 data: 0.00797053
I0228 19:58:45.278082 6771 net.cpp:605] [Forward] Layer conv4, param blob 1 data: 0.99991
I0228 19:58:45.296561 6771 net.cpp:593] [Forward] Layer relu4, top blob conv4 data: 0.989365
I0228 19:58:47.495208 6771 net.cpp:593] [Forward] Layer conv5, top blob conv5 data: 1.52664
I0228 19:58:47.495394 6771 net.cpp:605] [Forward] Layer conv5, param blob 0 data: 0.00804997
I0228 19:58:47.495399 6771 net.cpp:605] [Forward] Layer conv5, param blob 1 data: 0.996736
I0228 19:58:47.507951 6771 net.cpp:593] [Forward] Layer relu5, top blob conv5 data: 0.128866
I0228 19:58:47.562223 6771 net.cpp:593] [Forward] Layer pool5, top blob pool5 data: 0.151769
I0228 19:58:48.269973 6771 net.cpp:593] [Forward] Layer fc6, top blob fc6 data: 0.95253
I0228 19:58:48.280905 6771 net.cpp:605] [Forward] Layer fc6, param blob 0 data: 0.00397552
I0228 19:58:48.280917 6771 net.cpp:605] [Forward] Layer fc6, param blob 1 data: 0.999847
I0228 19:58:48.282137 6771 net.cpp:593] [Forward] Layer relu6, top blob fc6 data: 0.935909
I0228 19:58:48.286769 6771 net.cpp:593] [Forward] Layer drop6, top blob fc6 data: 0.938786
I0228 19:58:48.602710 6771 net.cpp:593] [Forward] Layer fc7, top blob fc7 data: 3.76741
I0228 19:58:48.607655 6771 net.cpp:605] [Forward] Layer fc7, param blob 0 data: 0.00411323
I0228 19:58:48.607664 6771 net.cpp:605] [Forward] Layer fc7, param blob 1 data: 0.997461
I0228 19:58:48.608860 6771 net.cpp:593] [Forward] Layer relu7, top blob fc7 data: 3.41694e-06
I0228 19:58:48.613621 6771 net.cpp:593] [Forward] Layer drop7, top blob fc7 data: 3.15335e-06
I0228 19:58:48.615514 6771 net.cpp:593] [Forward] Layer fc8_new15, top blob fc8_new15 data: 0.0446082
I0228 19:58:48.615520 6771 net.cpp:605] [Forward] Layer fc8_new15, param blob 0 data: 0.0229027
I0228 19:58:48.615522 6771 net.cpp:605] [Forward] Layer fc8_new15, param blob 1 data: 0.0444381
I0228 19:58:48.615579 6771 net.cpp:593] [Forward] Layer loss, top blob loss data: 0.693174
I0228 19:58:48.615586 6771 net.cpp:621] [Backward] Layer loss, bottom blob fc8_new15 diff: 0.00195124
I0228 19:58:48.617902 6771 net.cpp:621] [Backward] Layer fc8_new15, bottom blob fc7 diff: 8.65365e-05
I0228 19:58:48.617914 6771 net.cpp:632] [Backward] Layer fc8_new15, param blob 0 diff: 8.20022e-07
I0228 19:58:48.617916 6771 net.cpp:632] [Backward] Layer fc8_new15, param blob 1 diff: 0.0105705
I0228 19:58:48.619067 6771 net.cpp:621] [Backward] Layer drop7, bottom blob fc7 diff: 8.65526e-05
I0228 19:58:48.620265 6771 net.cpp:621] [Backward] Layer relu7, bottom blob fc7 diff: 1.21017e-09
I0228 19:58:49.261282 6771 net.cpp:621] [Backward] Layer fc7, bottom blob fc6 diff: 2.00745e-08
I0228 19:58:49.266103 6771 net.cpp:632] [Backward] Layer fc7, param blob 0 diff: 1.43563e-07
I0228 19:58:49.266114 6771 net.cpp:632] [Backward] Layer fc7, param blob 1 diff: 9.29627e-08
I0228 19:58:49.267330 6771 net.cpp:621] [Backward] Layer drop6, bottom blob fc6 diff: 1.99176e-08
I0228 19:58:49.268508 6771 net.cpp:621] [Backward] Layer relu6, bottom blob fc6 diff: 1.85305e-08
I0228 19:58:50.779518 6771 net.cpp:621] [Backward] Layer fc6, bottom blob pool5 diff: 8.8138e-09
I0228 19:58:50.790220 6771 net.cpp:632] [Backward] Layer fc6, param blob 0 diff: 3.01911e-07
I0228 19:58:50.790235 6771 net.cpp:632] [Backward] Layer fc6, param blob 1 diff: 1.99256e-06
I0228 19:58:50.813318 6771 net.cpp:621] [Backward] Layer pool5, bottom blob conv5 diff: 1.84585e-09
I0228 19:58:50.826406 6771 net.cpp:621] [Backward] Layer relu5, bottom blob conv5 diff: 3.86034e-10
I0228 19:58:55.093768 6771 net.cpp:621] [Backward] Layer conv5, bottom blob conv4 diff: 5.76684e-10
I0228 19:58:55.093967 6771 net.cpp:632] [Backward] Layer conv5, param blob 0 diff: 1.47824e-06
I0228 19:58:55.093973 6771 net.cpp:632] [Backward] Layer conv5, param blob 1 diff: 1.92951e-06
I0228 19:58:55.114212 6771 net.cpp:621] [Backward] Layer relu4, bottom blob conv4 diff: 5.76684e-10
I0228 19:59:01.392058 6771 net.cpp:621] [Backward] Layer conv4, bottom blob conv3 diff: 2.31243e-10
I0228 19:59:01.392359 6771 net.cpp:632] [Backward] Layer conv4, param blob 0 diff: 1.76617e-07
I0228 19:59:01.392364 6771 net.cpp:632] [Backward] Layer conv4, param blob 1 diff: 8.78101e-07
I0228 19:59:01.412240 6771 net.cpp:621] [Backward] Layer relu3, bottom blob conv3 diff: 8.56331e-11
I0228 19:59:09.734658 6771 net.cpp:621] [Backward] Layer conv3, bottom blob norm2 diff: 7.87699e-11
I0228 19:59:09.735258 6771 net.cpp:632] [Backward] Layer conv3, param blob 0 diff: 1.33159e-07
I0228 19:59:09.735270 6771 net.cpp:632] [Backward] Layer conv3, param blob 1 diff: 1.47704e-07
I0228 19:59:10.390552 6771 net.cpp:621] [Backward] Layer norm2, bottom blob pool2 diff: 7.87615e-11
I0228 19:59:10.452433 6771 net.cpp:621] [Backward] Layer pool2, bottom blob conv2 diff: 1.50474e-11
I0228 19:59:10.516407 6771 net.cpp:621] [Backward] Layer relu2, bottom blob conv2 diff: 1.50474e-11
I0228 19:59:20.241587 6771 net.cpp:621] [Backward] Layer conv2, bottom blob norm1 diff: 2.07819e-11
I0228 19:59:20.241801 6771 net.cpp:632] [Backward] Layer conv2, param blob 0 diff: 3.61894e-09
I0228 19:59:20.241807 6771 net.cpp:632] [Backward] Layer conv2, param blob 1 diff: 1.05108e-07
I0228 19:59:35.405725 6771 net.cpp:621] [Backward] Layer norm1, bottom blob pool1 diff: 2.07819e-11
I0228 19:59:35.494249 6771 net.cpp:621] [Backward] Layer pool1, bottom blob conv1 diff: 4.26e-12
I0228 19:59:35.585350 6771 net.cpp:621] [Backward] Layer relu1, bottom blob conv1 diff: 3.25633e-12
I0228 19:59:38.335880 6771 net.cpp:632] [Backward] Layer conv1, param blob 0 diff: 9.37551e-09
I0228 19:59:38.335896 6771 net.cpp:632] [Backward] Layer conv1, param blob 1 diff: 5.86281e-08
E0228 19:59:38.411557 6771 net.cpp:721] [Backward] All net params (data, diff): L1 norm = (246967, 14.733); L2 norm = (103.38, 0.0470958)
I0228 19:59:38.411592 6771 solver.cpp:219] Iteration 70 (0.00886075 iter/s, 1128.57s/10 iters), loss = 0.693174
I0228 19:59:38.411600 6771 solver.cpp:238] Train net output #0: loss = 0.693174 (* 1 = 0.693174 loss)
I0228 19:59:38.411605 6771 sgd_solver.cpp:105] Iteration 70, lr = 1e-05
I0228 20:05:17.468423 6775 data_layer.cpp:73] Restarting data prefetching from start.
答案 0 :(得分：2)
data_layer.cpp:73] Restarting data prefetching from start.
当作为数据层输入的.txt文件到达文件末尾时，会出现上述消息。
此消息可能在以下情况下频繁发生：