（原）torch中显示nn.Sequential()网络的详细情况

转载请注明出处：

http://www.cnblogs.com/darkknightzh/p/6065526.html

本部分多试几次就可以弄得清每一层具体怎么访问了。

step1. 网络定义如下：

require "dpnn"
local net = nn.Sequential()
net:add(nn.SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3))
net:add(nn.SpatialBatchNormalization(64))
net:add(nn.ReLU())
net:add(nn.SpatialMaxPooling(3, 3, 2, 2, 1, 1))
net:add(nn.Inception{
     inputSize = 64,
     kernelSize = {3, 5},
     kernelStride = {1, 1},
     outputSize = {128, 32},
     reduceSize = {96, 16, 32, 64},
     pool = nn.SpatialMaxPooling(3, 3, 1, 1, 1, 1),
     batchNorm = true
   })
net:evaluate()

上面的网络，包含conv+BatchNorm+ReLU+Maxpool+Inception层。

step2. 直接通过print(net)便可得到其网络结构：

nn.Sequential {
  [input -> (1) -> (2) -> (3) -> (4) -> (5) -> output]
  (1): nn.SpatialConvolution(3 -> 64, 7x7, 2,2, 3,3)
  (2): nn.SpatialBatchNormalization
  (3): nn.ReLU
  (4): nn.SpatialMaxPooling(3x3, 2,2, 1,1)
  (5): nn.Inception @ nn.DepthConcat {
    input
      |`-> (1): nn.Sequential {
      |      [input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> output]
      |      (1): nn.SpatialConvolution(64 -> 96, 1x1)
      |      (2): nn.SpatialBatchNormalization
      |      (3): nn.ReLU
      |      (4): nn.SpatialConvolution(96 -> 128, 3x3, 1,1, 1,1)
      |      (5): nn.SpatialBatchNormalization
      |      (6): nn.ReLU
      |    }
      |`-> (2): nn.Sequential {
      |      [input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> output]
      |      (1): nn.SpatialConvolution(64 -> 16, 1x1)
      |      (2): nn.SpatialBatchNormalization
      |      (3): nn.ReLU
      |      (4): nn.SpatialConvolution(16 -> 32, 5x5, 1,1, 2,2)
      |      (5): nn.SpatialBatchNormalization
      |      (6): nn.ReLU
      |    }
      |`-> (3): nn.Sequential {
      |      [input -> (1) -> (2) -> (3) -> (4) -> output]
      |      (1): nn.SpatialMaxPooling(3x3, 1,1, 1,1)
      |      (2): nn.SpatialConvolution(64 -> 32, 1x1)
      |      (3): nn.SpatialBatchNormalization
      |      (4): nn.ReLU
      |    }
      |`-> (4): nn.Sequential {
             [input -> (1) -> (2) -> (3) -> output]
             (1): nn.SpatialConvolution(64 -> 64, 1x1)
             (2): nn.SpatialBatchNormalization
             (3): nn.ReLU
           }
       ... -> output
  }
}

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