“忽略源层Python *”及其可能的原因

时间：2019-01-23 06:55:23

标签： python neural-network caffe

我曾经训练神经网络一段时间，但直到最近才使用Caffe。对于我当前的任务，我将here中的引导策略搜索（GPS）算法的原始代码与经过Caffe训练的策略网络一起使用。 GPS可以工作或大致了解预期的策略，但是最初几次迭代的结果对我来说似乎非常可疑和不合逻辑。

简短的问题是，在训练过程中以下两条频繁出现的线是否会成为问题？

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

更长的问题是网络体系结构正确还是有问题。这可能与前面提到的问题以及网络的图形不符合其描述这一事实有关（请参见下面的其他信息）。

===其他信息===

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可视化网络。 该图像可用here。 请注意，此处未显示blob Python2和Python3，就好像未使用它们一样。

但是，创建网络的Caffe的调试输出似乎使用了所有层。

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，定义了policy_layers.py模块here

0 个答案:

没有答案
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