“忽略源代码层Python*”以及

Question

我曾经训练过一段时间的神经网络，但直到最近才开始使用Caffe。对于我当前的任务，我使用来自here的引导策略搜索（GPS）算法的原始代码和Caffe训练的策略网络。全球定位系统工作并或多或少地了解了预期的政策，但最初几次迭代的结果在我看来非常可疑和不合逻辑。你知道吗

简短的问题是，以下两条在培训期间经常出现的线路是否会成为问题？你知道吗

I0123 09:09:12.799830  6960 net.cpp:676] Ignoring source layer Python1
I0123 09:09:21.119698  6960 net.cpp:676] Ignoring source layer Python4

更长远的问题是网络架构是正确的还是有问题。这可能与上述问题以及网络图与网络描述不对应的事实有关（参见下面的附加信息）。你知道吗

===附加信息===

创建的培训网络具有以下描述：

state {
  phase: TRAIN
}
layer {
  name: "Python1"
  type: "Python"
  top: "Python1"
  top: "Python2"
  top: "Python3"
  python_param {
    module: "policy_layers"
    layer: "PolicyDataLayer"
    param_str: "{\"shape\": [{\"dim\": [25, 30]}, {\"dim\": [25, 6]}, {\"dim\": [25, 6, 6]}]}"
  }
}
layer {
  name: "InnerProduct1"
  type: "InnerProduct"
  bottom: "Python1"
  top: "InnerProduct1"
  inner_product_param {
    num_output: 42
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "ReLU1"
  type: "ReLU"
  bottom: "InnerProduct1"
  top: "InnerProduct1"
}
layer {
  name: "InnerProduct2"
  type: "InnerProduct"
  bottom: "InnerProduct1"
  top: "InnerProduct2"
  inner_product_param {
    num_output: 42
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "ReLU2"
  type: "ReLU"
  bottom: "InnerProduct2"
  top: "InnerProduct2"
}
layer {
  name: "InnerProduct3"
  type: "InnerProduct"
  bottom: "InnerProduct2"
  top: "InnerProduct3"
  inner_product_param {
    num_output: 6
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "Python4"
  type: "Python"
  bottom: "InnerProduct3"
  bottom: "Python2"
  bottom: "Python3"
  top: "Python4"
  loss_weight: 1
  python_param {
    module: "policy_layers"
    layer: "WeightedEuclideanLoss"
  }
}

我通过Netscope将网络形象化。 图像可用here。 请注意，这里没有描述blob Python2和Python3，就好像它们没有被使用一样。你知道吗

但是，创建网络的Caffe的调试输出似乎使用了所有层。你知道吗

I0123 09:08:17.156579  6960 layer_factory.hpp:77] Creating layer Python1
I0123 09:08:17.157122  6960 net.cpp:84] Creating Layer Python1
I0123 09:08:17.157131  6960 net.cpp:380] Python1 -> Python1
I0123 09:08:17.157140  6960 net.cpp:380] Python1 -> Python2
I0123 09:08:17.157145  6960 net.cpp:380] Python1 -> Python3
I0123 09:08:17.157940  6960 net.cpp:122] Setting up Python1
I0123 09:08:17.157953  6960 net.cpp:129] Top shape: 25 30 (750)
I0123 09:08:17.157956  6960 net.cpp:129] Top shape: 25 6 (150)
I0123 09:08:17.157959  6960 net.cpp:129] Top shape: 25 6 6 (900)
I0123 09:08:17.157961  6960 net.cpp:137] Memory required for data: 7200
I0123 09:08:17.157964  6960 layer_factory.hpp:77] Creating layer InnerProduct1
I0123 09:08:17.157971  6960 net.cpp:84] Creating Layer InnerProduct1
I0123 09:08:17.157974  6960 net.cpp:406] InnerProduct1 <- Python1
I0123 09:08:17.157979  6960 net.cpp:380] InnerProduct1 -> InnerProduct1
I0123 09:08:17.158669  6960 net.cpp:122] Setting up InnerProduct1
I0123 09:08:17.158679  6960 net.cpp:129] Top shape: 25 42 (1050)
I0123 09:08:17.158682  6960 net.cpp:137] Memory required for data: 11400
I0123 09:08:17.158690  6960 layer_factory.hpp:77] Creating layer ReLU1
I0123 09:08:17.158696  6960 net.cpp:84] Creating Layer ReLU1
I0123 09:08:17.158699  6960 net.cpp:406] ReLU1 <- InnerProduct1
I0123 09:08:17.158704  6960 net.cpp:367] ReLU1 -> InnerProduct1 (in-place)
I0123 09:08:17.158708  6960 net.cpp:122] Setting up ReLU1
I0123 09:08:17.158712  6960 net.cpp:129] Top shape: 25 42 (1050)
I0123 09:08:17.158715  6960 net.cpp:137] Memory required for data: 15600
I0123 09:08:17.158718  6960 layer_factory.hpp:77] Creating layer InnerProduct2
I0123 09:08:17.158722  6960 net.cpp:84] Creating Layer InnerProduct2
I0123 09:08:17.158725  6960 net.cpp:406] InnerProduct2 <- InnerProduct1
I0123 09:08:17.158730  6960 net.cpp:380] InnerProduct2 -> InnerProduct2
I0123 09:08:17.158814  6960 net.cpp:122] Setting up InnerProduct2
I0123 09:08:17.158820  6960 net.cpp:129] Top shape: 25 42 (1050)
I0123 09:08:17.158823  6960 net.cpp:137] Memory required for data: 19800
I0123 09:08:17.158828  6960 layer_factory.hpp:77] Creating layer ReLU2
I0123 09:08:17.158833  6960 net.cpp:84] Creating Layer ReLU2
I0123 09:08:17.158836  6960 net.cpp:406] ReLU2 <- InnerProduct2
I0123 09:08:17.158840  6960 net.cpp:367] ReLU2 -> InnerProduct2 (in-place)
I0123 09:08:17.158843  6960 net.cpp:122] Setting up ReLU2
I0123 09:08:17.158848  6960 net.cpp:129] Top shape: 25 42 (1050)
I0123 09:08:17.158849  6960 net.cpp:137] Memory required for data: 24000
I0123 09:08:17.158851  6960 layer_factory.hpp:77] Creating layer InnerProduct3
I0123 09:08:17.158855  6960 net.cpp:84] Creating Layer InnerProduct3
I0123 09:08:17.158859  6960 net.cpp:406] InnerProduct3 <- InnerProduct2
I0123 09:08:17.158864  6960 net.cpp:380] InnerProduct3 -> InnerProduct3
I0123 09:08:17.158922  6960 net.cpp:122] Setting up InnerProduct3
I0123 09:08:17.158927  6960 net.cpp:129] Top shape: 25 6 (150)
I0123 09:08:17.158929  6960 net.cpp:137] Memory required for data: 24600
I0123 09:08:17.158934  6960 layer_factory.hpp:77] Creating layer Python4
I0123 09:08:17.158954  6960 net.cpp:84] Creating Layer Python4
I0123 09:08:17.158958  6960 net.cpp:406] Python4 <- InnerProduct3
I0123 09:08:17.158962  6960 net.cpp:406] Python4 <- Python2
I0123 09:08:17.158964  6960 net.cpp:406] Python4 <- Python3
I0123 09:08:17.158968  6960 net.cpp:380] Python4 -> Python4
I0123 09:08:17.158999  6960 net.cpp:122] Setting up Python4
I0123 09:08:17.159006  6960 net.cpp:129] Top shape: 1 (1)
I0123 09:08:17.159009  6960 net.cpp:132]     with loss weight 1
I0123 09:08:17.159016  6960 net.cpp:137] Memory required for data: 24604
I0123 09:08:17.159020  6960 net.cpp:198] Python4 needs backward computation.
I0123 09:08:17.159024  6960 net.cpp:198] InnerProduct3 needs backward computation.
I0123 09:08:17.159027  6960 net.cpp:198] ReLU2 needs backward computation.
I0123 09:08:17.159030  6960 net.cpp:198] InnerProduct2 needs backward computation.
I0123 09:08:17.159034  6960 net.cpp:198] ReLU1 needs backward computation.
I0123 09:08:17.159036  6960 net.cpp:198] InnerProduct1 needs backward computation.
I0123 09:08:17.159040  6960 net.cpp:200] Python1 does not need backward computation.
I0123 09:08:17.159044  6960 net.cpp:242] This network produces output Python4
I0123 09:08:17.159049  6960 net.cpp:255] Network initialization done.

最后，政策_图层.py模块已定义here