Machine learning Caffe培训损失为0

我正在用faceScrub数据集训练一个alexnet.caffemodel，我正在关注

问题是，当我训练模型时，我得到以下输出：

I0302 10:59:50.184250 11346 solver.cpp:331] Iteration 0, Testing net (#0)
I0302 11:09:01.198473 11346 solver.cpp:398]     Test net output #0: accuracy = 0.96793
I0302 11:09:01.198635 11346 solver.cpp:398]     Test net output #1: loss = 0.354751 (* 1 = 0.354751 loss)
I0302 11:09:12.543730 11346 solver.cpp:219] Iteration 0 (0 iter/s, 562.435s/20 iters), loss = 0.465583
I0302 11:09:12.543861 11346 solver.cpp:238]     Train net output #0: loss = 0.465583 (* 1 = 0.465583 loss)
I0302 11:09:12.543902 11346 sgd_solver.cpp:105] Iteration 0, lr = 0.001
I0302 11:14:41.847237 11346 solver.cpp:219] Iteration 20 (0.0607343 iter/s, 329.303s/20 iters), loss = 4.65581e-09
I0302 11:14:41.847409 11346 solver.cpp:238]     Train net output #0: loss = 0 (* 1 = 0 loss)
I0302 11:14:41.847447 11346 sgd_solver.cpp:105] Iteration 20, lr = 0.001
I0302 11:18:25.848346 11346 solver.cpp:219] Iteration 40 (0.0892857 iter/s, 224s/20 iters), loss = 4.65581e-09
I0302 11:18:25.848526 11346 solver.cpp:238]     Train net output #0: loss = 0 (* 1 = 0 loss)
I0302 11:18:25.848565 11346 sgd_solver.cpp:105] Iteration 40, lr = 0.001

它继续着同样的趋势

我唯一怀疑的是，在人脸检测链接train_val.prototxt中，它在fc8_flickr层中使用num_output:2，因此我有一个.txt文件，其中包含以下格式的所有图像：

/media/jose/B430F55030F51A56/faceScrub/download/Steve_Carell/face/a3b1b70acd0fda72c98be121a2af3ea2f4209fe7.jpg 1
/media/jose/B430F55030F51A56/faceScrub/download/Matt_Czuchry/face/98882354bbf3a508b48c6f53a84a68ca6797e617.jpg 1
/media/jose/B430F55030F51A56/faceScrub/download/Linda_Gray/face/ca9356b2382d2595ba8a9ff399dc3efa80873d72.jpg 1
/media/jose/B430F55030F51A56/faceScrub/download/Veronica_Hamel/face/900da3a6a22b25b3974e1f7602686f460126d028.jpg 1

其中1是包含面的类。如果我删除1，它将陷入迭代0，即测试网络（#0）

---

对此有何见解？

为什么要删除

1

？你是否对你的培训示例进行了修改？。示例没有修改，我会尝试，谢谢@shaiplese阅读它确实有效，请随意将其作为答案发布，我会将其标记为答案@您为什么要删除

1

？你是否对你的培训示例进行了修改？。示例没有修改，我会尝试，谢谢@shaiplese阅读它确实有效，请随意将其作为答案发布，我会将其标记为答案@谢