Comments (6)
有,是因为tf和caffe 对于padding采用的是不同方式,tf里面的pad分为valid和same两种模式,默认为valid(原ssrnet),但是caffe 采用的是same方式,所以需要重新指定训练,可以采用我提供的SSR-Net目录下的脚本自己重新训练一下,再转化
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非常感谢回复,以为是dense层的问题,想起来tf和caffe的处理机制不同,caffe中的pooling层有个向上取整的操作,刚好feature map大小两边对应不上,最后我改了caffe的源码,就当是ssrnet的定制版了,目前转换已完成。https://blog.csdn.net/lwplwf/article/details/82418110
from ssrnet-caffe.
I1112 03:44:32.535120 6105 solver.cpp:414] Test net output #0: accuracy = 0.0179104
I1112 03:44:32.535167 6105 solver.cpp:414] Test net output #1: loss = 38.5024
I1112 03:44:34.301174 6105 solver.cpp:239] Iteration 2100 (49.6661 iter/s, 7.04706s/350 iters), loss = 5.30641
I1112 03:44:34.301216 6105 solver.cpp:258] Train net output #0: accuracy_train = 0.6
I1112 03:44:34.301228 6105 solver.cpp:258] Train net output #1: loss = 5.30641 (* 1 = 5.30641 loss)
大佬请问我的trainloss可以下降,为什么test的loss一直很高反而增加
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可能是过拟合了吧
from ssrnet-caffe.
非常感谢回复,以为是dense层的问题,想起来tf和caffe的处理机制不同,caffe中的pooling层有个向上取整的操作,刚好feature map大小两边对应不上,最后我改了caffe的源码,就当是ssrnet的定制版了,目前转换已完成。https://blog.csdn.net/lwplwf/article/details/82418110
May I ask you for your caffe model?Thanks very much.
from ssrnet-caffe.
非常感谢回复,以为是dense层的问题,想起来tf和caffe的处理机制不同,caffe中的pooling层有个向上取整的操作,刚好feature map大小两边对应不上,最后我改了caffe的源码,就当是ssrnet的定制版了,目前转换已完成。https://blog.csdn.net/lwplwf/article/details/82418110
@lwplw 你好 怎么修改caffe源码 pooling的w、h不要向上取整?可以提供下你修改的截图吗 多谢了
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