Below is an inception module in the form of a single net. In particular, this is my attempt at rendering inception (3a) as shown in Table 1 and Figure 3 of the paper. I've tested it in as far as I wrote some code that parses it and loads into a net, and then forward propagates a blob through it, and verified that its output blob has the correct dimensions ( 256 channels x 28 H x 28 W). The only trick was padding the convolutions so as to get the right dimensions of the resulting top blob. And some of the layer params are vestigial/extraneous (like the weight filler) from the lenet prototxt I copied from, not something specified by the paper.

One thing that might be wrong is I don't think the paper was explicit about whether there are relus inbetween the 3x3 max pool and the 1x1 pool proj. I didn't put a relu layer in there.

So if you wanted to implement the whole GoogLeNet, you have to make 9 copies of the layers in this hunk of prototxt, and adjust the layer and top and bottom names, and number of channels for the convolutions and max pools to match Table 1. Plus add all the non-inception layers. That seems... cumbersome... at best.

I expect repeated modules like inception are going to be a thing, and that what we need is a new layer type which is itself a network ("Network-in-network"?), so that a network could recursively include other networks. Such that you have a single layer standing in for the included network. It would need a param to identify the network that implements it, and specify the parameters that are distinctive in its instantiation (e.g. the # of output channels of the conv layers, which differ from inception module to inception module). Seems straightfoward in principle, but possibly tricky in making the implementation work for all scenarios.

[Edit: stuff deleted, now that I see they say they used DistBelief]

name: "Inception_3a"

input: "maxpool_2"
input_dim: 1
input_dim: 192
input_dim: 28
input_dim: 28

layers {
  name: "1x1_conv"
  type: CONVOLUTION
  bottom: "maxpool_2"
  top: "1x1_conv"
  blobs_lr: 1
  blobs_lr: 2
  convolution_param {
    num_output: 64
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}

layers {
  name: "1x1_relu"
  type: RELU
  bottom: "1x1_conv"
  top: "1x1_relu"
}

layers {
  name: "3x3reduce_conv"
  type: CONVOLUTION
  bottom: "maxpool_2"
  top: "3x3reduce_conv"
  blobs_lr: 1
  blobs_lr: 2
  convolution_param {
    num_output: 96
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}

layers {
  name: "3x3reduce_relu"
  type: RELU
  bottom: "3x3reduce_conv"
  top: "3x3reduce_relu"
}

layers {
  name: "3x3_conv"
  type: CONVOLUTION
  bottom: "3x3reduce_relu"
  top: "3x3_conv"
  blobs_lr: 1
  blobs_lr: 2
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}

layers {
  name: "3x3_relu"
  type: RELU
  bottom: "3x3_conv"
  top: "3x3_relu"
}

layers {
  name: "5x5reduce_conv"
  type: CONVOLUTION
  bottom: "maxpool_2"
  top: "5x5reduce_conv"
  blobs_lr: 1
  blobs_lr: 2
  convolution_param {
    num_output: 16
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}

layers {
  name: "5x5reduce_relu"
  type: RELU
  bottom: "5x5reduce_conv"
  top: "5x5reduce_relu"
}

layers {
  name: "5x5_conv"
  type: CONVOLUTION
  bottom: "5x5reduce_relu"
  top: "5x5_conv"
  blobs_lr: 1
  blobs_lr: 2
  convolution_param {
    num_output: 32
    pad: 2
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}

layers {
  name: "5x5_relu"
  type: RELU
  bottom: "5x5_conv"
  top: "5x5_relu"
}

layers {
  name: "maxpool_3x3"
  type: POOLING
  bottom: "maxpool_2"
  top: "maxpool_3x3"
  pooling_param {
    pool: MAX
    pad: 1
    kernel_size: 3
    stride: 1
  }
}

layers {
  name: "poolproj_1x1conv"
  type: CONVOLUTION
  bottom: "maxpool_3x3"
  top: "poolproj_1x1conv"
  blobs_lr: 1
  blobs_lr: 2
  convolution_param {
    num_output: 32
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}

layers {
  name: "poolproj_relu"
  type: RELU
  bottom: "poolproj_1x1conv"
  top: "poolproj_relu"
}

layers {
  name: "DepthConcatenation"
  type: CONCAT
  concat_param {
    concat_dim: 1
  }
  bottom: "1x1_relu"
  bottom: "3x3_relu"
  bottom: "5x5_relu"
  bottom: "poolproj_relu"
  top: "inception3a_Output"
}

How to implement the GoogleNet? #1106

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