我们需要帮助来理解用于较小训练集（6000 jpgs）和val（170 jpgs）jpgs的参数。在Iteration 0中测试得分0/1之后，我们的执行被杀死并退出。
我们正尝试在
的caffe网站教程上运行imagenet示例http://caffe.berkeleyvision.org/gathered/examples/imagenet.html.
我们使用自己的训练集6000个jpeg和val集170个jpeg图像，而不是在包中使用全套ILSVRC2图像。它们分别是train和val目录中的256 x 256个jpeg文件。我们运行脚本来获取辅助数据：
./data/ilsvrc12/get_ilsvrc_aux.sh
设置train.txt和val.txt文件以描述每个jpeg文件的两个可能类别之一。 然后我们运行脚本来计算似乎正确运行的平均图像数据：
./examples/imagenet/make_imagenet_mean.sh
我们使用了教程中提供的imagenet_train.prototxt和imagenet_val.prototxt的模型定义。 由于我们正在训练更少的图像，因此我们修改了imagenet_solver.prototxt，如下所示：
train_net: "./imagenet_train.prototxt"
test_net: "./imagenet_val.prototxt"
test_iter: 3
test_interval: 10
base_lr: 0.01
lr_policy: "step"
gamma: 0.1
stepsize: 10
display: 20
max_iter: 45
momentum: 0.9
weight_decay: 0.0005
snapshot: 10
snapshot_prefix: "caffe_imagenet_train"
solver_mode: CPU
当我们使用：
运行它时./train_imagenet.sh
我们得到以下输出信息：
.......
.......
I0520 23:07:53.175761 4678 net.cpp:85] drop7 <- fc7
I0520 23:07:53.175791 4678 net.cpp:99] drop7 -> fc7 (in-place)
I0520 23:07:53.176246 4678 net.cpp:126] Top shape: 50 4096 1 1 (204800)
I0520 23:07:53.176275 4678 net.cpp:152] drop7 needs backward computation.
I0520 23:07:53.176296 4678 net.cpp:75] Creating Layer fc8
I0520 23:07:53.176306 4678 net.cpp:85] fc8 <- fc7
I0520 23:07:53.176314 4678 net.cpp:111] fc8 -> fc8
I0520 23:07:53.184213 4678 net.cpp:126] Top shape: 50 1000 1 1 (50000)
I0520 23:07:53.184908 4678 net.cpp:152] fc8 needs backward computation.
I0520 23:07:53.185607 4678 net.cpp:75] Creating Layer prob
I0520 23:07:53.186135 4678 net.cpp:85] prob <- fc8
I0520 23:07:53.186538 4678 net.cpp:111] prob -> prob
I0520 23:07:53.187166 4678 net.cpp:126] Top shape: 50 1000 1 1 (50000)
I0520 23:07:53.187696 4678 net.cpp:152] prob needs backward computation.
I0520 23:07:53.188244 4678 net.cpp:75] Creating Layer accuracy
I0520 23:07:53.188431 4678 net.cpp:85] accuracy <- prob
I0520 23:07:53.188540 4678 net.cpp:85] accuracy <- label
I0520 23:07:53.188870 4678 net.cpp:111] accuracy -> accuracy
I0520 23:07:53.188907 4678 net.cpp:126] Top shape: 1 2 1 1 (2)
I0520 23:07:53.188915 4678 net.cpp:152] accuracy needs backward computation.
I0520 23:07:53.188922 4678 net.cpp:163] This network produces output accuracy
I0520 23:07:53.188942 4678 net.cpp:181] Collecting Learning Rate and Weight Decay.
I0520 23:07:53.188954 4678 net.cpp:174] Network initialization done.
I0520 23:07:53.188961 4678 net.cpp:175] Memory required for Data 210114408
I0520 23:07:53.189008 4678 solver.cpp:49] Solver scaffolding done.
I0520 23:07:53.189018 4678 solver.cpp:61] Solving CaffeNet
I0520 23:07:53.189033 4678 solver.cpp:106] Iteration 0, Testing net
I0520 23:09:06.699695 4678 solver.cpp:142] Test score #0: 0
I0520 23:09:06.700203 4678 solver.cpp:142] Test score #1: 7.07406
Killed
Done.