DNN 的奇怪分类输出
Strange classification output with DNN
我尝试使用微调网络 CaffeNet class 化图像。我遵循了 Caffe 的教程并将训练文件中的输出数量更改为 3，同时我关闭了前两个卷积层的学习。出于某种原因，当我将 classifier 与经过训练的模型一起使用时，我从测试集中的每个图像的所有 classes 中得到 0.3。
number of classes: 3
train set size: 6570 images (80%)
test set size: 1645 images (20%)
求解器：
net: "train.prototxt"
test_iter: 100
test_interval: 1000
base_lr: 0.0001
lr_policy: "step"
gamma: 0.1
stepsize: 20000
display: 200
max_iter: 60000
momentum: 0.9
weight_decay: 0.0005
snapshot: 10000
snapshot_prefix: "snapshot"
solver_mode: GPU
我如何运行训练：
caffe train -solver solver.prototxt -weights bvlc_reference_caffenet.caffemodel
一些输出：
I0531 00:35:52.622647 2944 data_layer.cpp:73] Restarting data prefetching from start.
I0531 00:36:02.699782 2944 data_layer.cpp:73] Restarting data prefetching from start.
I0531 00:36:03.900009 5528 solver.cpp:218] Iteration 3600 (10.1266 iter/s, 19.7499s/200 iters), loss = 0.679402
I0531 00:36:03.900009 5528 solver.cpp:237] Train net output #0: loss = 0.679402 (* 1 = 0.679402 loss)
I0531 00:36:03.900009 5528 sgd_solver.cpp:105] Iteration 3600, lr = 0.0001
I0531 00:41:20.139937 2944 data_layer.cpp:73] Restarting data prefetching from start.
I0531 00:41:30.934025 2944 data_layer.cpp:73] Restarting data prefetching from start.
I0531 00:41:34.199774 5528 solver.cpp:218] Iteration 6800 (9.66881 iter/s, 20.6851s/200 iters), loss = 0.451174
I0531 00:41:34.199774 5528 solver.cpp:237] Train net output #0: loss = 0.451174 (* 1 = 0.451174 loss)
I0531 00:41:34.199774 5528 sgd_solver.cpp:105] Iteration 6800, lr = 0.0001
I0531 00:41:41.794001 2944 data_layer.cpp:73] Restarting data prefetching from start.
I0531 00:41:52.743448 2944 data_layer.cpp:73] Restarting data prefetching from start.
I0531 00:41:55.126147 5528 solver.cpp:330] Iteration 7000, Testing net (#0)
I0531 00:41:55.891929 3704 data_layer.cpp:73] Restarting data prefetching from start.
I0531 00:41:58.393698 3704 data_layer.cpp:73] Restarting data prefetching from start.
I0531 00:41:58.862452 5528 solver.cpp:397] Test net output #0: accuracy = 0.6952
I0531 00:41:58.862452 5528 solver.cpp:397] Test net output #1: loss = 0.873388 (* 1 = 0.873388 loss)
I0531 00:43:08.320360 2944 data_layer.cpp:73] Restarting data prefetching from start.
I0531 00:43:18.514559 2944 data_layer.cpp:73] Restarting data prefetching from start.
I0531 00:43:18.920881 5528 solver.cpp:218] Iteration 7800 (10.0073 iter/s, 19.9854s/200 iters), loss = 0.196175
I0531 00:43:18.920881 5528 solver.cpp:237] Train net output #0: loss = 0.196175 (* 1 = 0.196175 loss)
I0531 00:43:18.920881 5528 sgd_solver.cpp:105] Iteration 7800, lr = 0.0001
I0531 00:43:28.660408 2944 data_layer.cpp:73] Restarting data prefetching from start.
I0531 00:43:38.561293 5528 solver.cpp:330] Iteration 8000, Testing net (#0)
I0531 00:43:40.405230 3704 data_layer.cpp:73] Restarting data prefetching from start.
I0531 00:43:42.077230 5528 solver.cpp:397] Test net output #0: accuracy = 0.7004
I0531 00:43:42.077230 5528 solver.cpp:397] Test net output #1: loss = 0.991567 (* 1 = 0.991567 loss)
I0531 00:45:22.426592 3704 data_layer.cpp:73] Restarting data prefetching from start.
I0531 00:45:24.761165 3704 data_layer.cpp:73] Restarting data prefetching from start.
I0531 00:45:25.329238 5528 solver.cpp:397] Test net output #0: accuracy = 0.6856
I0531 00:45:25.329238 5528 solver.cpp:397] Test net output #1: loss = 1.08582 (* 1 = 1.08582 loss)
I0531 00:45:25.394567 5528 solver.cpp:218] Iteration 9000 (8.39955 iter/s, 23.8108s/200 iters), loss = 0.107816
I0531 00:45:25.394567 5528 solver.cpp:237] Train net output #0: loss = 0.107816 (* 1 = 0.107816 loss)
I0531 00:46:49.099460 2944 data_layer.cpp:73] Restarting data prefetching from start.
I0531 00:46:59.269830 2944 data_layer.cpp:73] Restarting data prefetching from start.
I0531 00:47:03.997443 5528 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10000.caffemodel
I0531 00:47:05.185039 5528 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10000.solverstate
I0531 00:47:05.403774 5528 solver.cpp:330] Iteration 10000, Testing net (#0)
I0531 00:47:07.122831 3704 data_layer.cpp:73] Restarting data prefetching from start.
I0531 00:47:08.870923 5528 solver.cpp:397] Test net output #0: accuracy = 0.7012
I0531 00:47:08.870923 5528 solver.cpp:397] Test net output #1: loss = 1.18649 (* 1 = 1.18649 loss)
I0531 00:47:08.964664 5528 solver.cpp:218] Iteration 10000 (8.12416 iter/s, 24.6179s/200 iters), loss = 0.0347012
I0531 00:47:08.964664 5528 solver.cpp:237] Train net output #0: loss = 0.0347012 (* 1 = 0.0347012 loss)
I0531 00:47:08.964664 5528 sgd_solver.cpp:105] Iteration 10000, lr = 0.0001
我如何运行class化：
classification deploy.prototxt snapshot_iter_10000.caffemodel labels.txt ..\test
一些输出：
"0.jpg",0.333333,0.333333,0.333333
"1.jpg",0.333333,0.333333,0.333333
"10.jpg",0.333333,0.333333,0.333333
"100.jpg",0.333333,0.333333,0.333333
"101.jpg",0.333333,0.333333,0.333333
"102.jpg",0.333333,0.333333,0.333333,
"103.jpg",0.333333,0.333333,0.333333
出于某种原因，70% 的准确率与 50% 的准确率相同 - 每个 class 都有 0.3。
你的分类输出没有什么奇怪的，只是你需要正确地解释它。 0.333for 3 类 的准确度仅仅意味着你的网络没有学习任何东西——它是随机猜测。对于 N 类，随机猜测会给您 1/N 的准确度。所以在你的情况下它是 1/3，即 0.333.
现在，没有这样的标准规则来设置超参数，但考虑到损失的巨大变化，我建议您将基础学习率降低到 0.00001。
我尝试使用微调网络 CaffeNet class 化图像。我遵循了 Caffe 的教程并将训练文件中的输出数量更改为 3，同时我关闭了前两个卷积层的学习。出于某种原因，当我将 classifier 与经过训练的模型一起使用时，我从测试集中的每个图像的所有 classes 中得到 0.3。
number of classes: 3
train set size: 6570 images (80%)
test set size: 1645 images (20%)
求解器：
net: "train.prototxt"
test_iter: 100
test_interval: 1000
base_lr: 0.0001
lr_policy: "step"
gamma: 0.1
stepsize: 20000
display: 200
max_iter: 60000
momentum: 0.9
weight_decay: 0.0005
snapshot: 10000
snapshot_prefix: "snapshot"
solver_mode: GPU
我如何运行训练：
caffe train -solver solver.prototxt -weights bvlc_reference_caffenet.caffemodel
一些输出：
I0531 00:35:52.622647 2944 data_layer.cpp:73] Restarting data prefetching from start.
I0531 00:36:02.699782 2944 data_layer.cpp:73] Restarting data prefetching from start.
I0531 00:36:03.900009 5528 solver.cpp:218] Iteration 3600 (10.1266 iter/s, 19.7499s/200 iters), loss = 0.679402
I0531 00:36:03.900009 5528 solver.cpp:237] Train net output #0: loss = 0.679402 (* 1 = 0.679402 loss)
I0531 00:36:03.900009 5528 sgd_solver.cpp:105] Iteration 3600, lr = 0.0001
I0531 00:41:20.139937 2944 data_layer.cpp:73] Restarting data prefetching from start.
I0531 00:41:30.934025 2944 data_layer.cpp:73] Restarting data prefetching from start.
I0531 00:41:34.199774 5528 solver.cpp:218] Iteration 6800 (9.66881 iter/s, 20.6851s/200 iters), loss = 0.451174
I0531 00:41:34.199774 5528 solver.cpp:237] Train net output #0: loss = 0.451174 (* 1 = 0.451174 loss)
I0531 00:41:34.199774 5528 sgd_solver.cpp:105] Iteration 6800, lr = 0.0001
I0531 00:41:41.794001 2944 data_layer.cpp:73] Restarting data prefetching from start.
I0531 00:41:52.743448 2944 data_layer.cpp:73] Restarting data prefetching from start.
I0531 00:41:55.126147 5528 solver.cpp:330] Iteration 7000, Testing net (#0)
I0531 00:41:55.891929 3704 data_layer.cpp:73] Restarting data prefetching from start.
I0531 00:41:58.393698 3704 data_layer.cpp:73] Restarting data prefetching from start.
I0531 00:41:58.862452 5528 solver.cpp:397] Test net output #0: accuracy = 0.6952
I0531 00:41:58.862452 5528 solver.cpp:397] Test net output #1: loss = 0.873388 (* 1 = 0.873388 loss)
I0531 00:43:08.320360 2944 data_layer.cpp:73] Restarting data prefetching from start.
I0531 00:43:18.514559 2944 data_layer.cpp:73] Restarting data prefetching from start.
I0531 00:43:18.920881 5528 solver.cpp:218] Iteration 7800 (10.0073 iter/s, 19.9854s/200 iters), loss = 0.196175
I0531 00:43:18.920881 5528 solver.cpp:237] Train net output #0: loss = 0.196175 (* 1 = 0.196175 loss)
I0531 00:43:18.920881 5528 sgd_solver.cpp:105] Iteration 7800, lr = 0.0001
I0531 00:43:28.660408 2944 data_layer.cpp:73] Restarting data prefetching from start.
I0531 00:43:38.561293 5528 solver.cpp:330] Iteration 8000, Testing net (#0)
I0531 00:43:40.405230 3704 data_layer.cpp:73] Restarting data prefetching from start.
I0531 00:43:42.077230 5528 solver.cpp:397] Test net output #0: accuracy = 0.7004
I0531 00:43:42.077230 5528 solver.cpp:397] Test net output #1: loss = 0.991567 (* 1 = 0.991567 loss)
I0531 00:45:22.426592 3704 data_layer.cpp:73] Restarting data prefetching from start.
I0531 00:45:24.761165 3704 data_layer.cpp:73] Restarting data prefetching from start.
I0531 00:45:25.329238 5528 solver.cpp:397] Test net output #0: accuracy = 0.6856
I0531 00:45:25.329238 5528 solver.cpp:397] Test net output #1: loss = 1.08582 (* 1 = 1.08582 loss)
I0531 00:45:25.394567 5528 solver.cpp:218] Iteration 9000 (8.39955 iter/s, 23.8108s/200 iters), loss = 0.107816
I0531 00:45:25.394567 5528 solver.cpp:237] Train net output #0: loss = 0.107816 (* 1 = 0.107816 loss)
I0531 00:46:49.099460 2944 data_layer.cpp:73] Restarting data prefetching from start.
I0531 00:46:59.269830 2944 data_layer.cpp:73] Restarting data prefetching from start.
I0531 00:47:03.997443 5528 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10000.caffemodel
I0531 00:47:05.185039 5528 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10000.solverstate
I0531 00:47:05.403774 5528 solver.cpp:330] Iteration 10000, Testing net (#0)
I0531 00:47:07.122831 3704 data_layer.cpp:73] Restarting data prefetching from start.
I0531 00:47:08.870923 5528 solver.cpp:397] Test net output #0: accuracy = 0.7012
I0531 00:47:08.870923 5528 solver.cpp:397] Test net output #1: loss = 1.18649 (* 1 = 1.18649 loss)
I0531 00:47:08.964664 5528 solver.cpp:218] Iteration 10000 (8.12416 iter/s, 24.6179s/200 iters), loss = 0.0347012
I0531 00:47:08.964664 5528 solver.cpp:237] Train net output #0: loss = 0.0347012 (* 1 = 0.0347012 loss)
I0531 00:47:08.964664 5528 sgd_solver.cpp:105] Iteration 10000, lr = 0.0001
我如何运行class化：
classification deploy.prototxt snapshot_iter_10000.caffemodel labels.txt ..\test
一些输出：
"0.jpg",0.333333,0.333333,0.333333
"1.jpg",0.333333,0.333333,0.333333
"10.jpg",0.333333,0.333333,0.333333
"100.jpg",0.333333,0.333333,0.333333
"101.jpg",0.333333,0.333333,0.333333
"102.jpg",0.333333,0.333333,0.333333,
"103.jpg",0.333333,0.333333,0.333333
出于某种原因，70% 的准确率与 50% 的准确率相同 - 每个 class 都有 0.3。
你的分类输出没有什么奇怪的，只是你需要正确地解释它。 0.333for 3 类 的准确度仅仅意味着你的网络没有学习任何东西——它是随机猜测。对于 N 类，随机猜测会给您 1/N 的准确度。所以在你的情况下它是 1/3，即 0.333.
现在，没有这样的标准规则来设置超参数，但考虑到损失的巨大变化，我建议您将基础学习率降低到 0.00001。