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Caffe:Api:ConvolutionLayer

컨볼루션(Convolution) 레이어는 출력 이미지에 하나의 피쳐 맵을 생성하는 일련의 학습 필터로 입력 이미지를 컨볼 루션합니다.

ConvolutionParameter

num_output (c_o)
the number of filters
kernel_size (or kernel_h and kernel_w)
specifies height and width of each filter
weight_filler [default type: 'constant' value: 0]
[Strongly Recommended]
bias_term [default true]
specifies whether to learn and apply a set of additive biases to the filter outputs
pad (or pad_h and pad_w) [default 0]
specifies the number of pixels to (implicitly) add to each side of the input
stride (or stride_h and stride_w) [default 1]
specifies the intervals at which to apply the filters to the input
group (g) [default 1]
If g > 1, we restrict the connectivity of each filter to a subset of the input. Specifically, the input and output channels are separated into g groups, and the iith output group channels will be only connected to the iith input group channels.

Protobuf example 

  layer {
    name: "conv1"
    type: "Convolution"
    bottom: "data"
    top: "conv1"
    # learning rate and decay multipliers for the filters
    param { lr_mult: 1 decay_mult: 1 }
    # learning rate and decay multipliers for the biases
    param { lr_mult: 2 decay_mult: 0 }
    convolution_param {
      num_output: 96     # learn 96 filters
      kernel_size: 11    # each filter is 11x11
      stride: 4          # step 4 pixels between each filter application
      weight_filler {
        type: "gaussian" # initialize the filters from a Gaussian
        std: 0.01        # distribution with stdev 0.01 (default mean: 0)
      }
      bias_filler {
        type: "constant" # initialize the biases to zero (0)
        value: 0
      }
    }
  }

I/O

Input
n * c_i * h_i * w_i
Output
n * c_o * h_o * w_o
  • h_o = (h_i + 2 * pad_h - kernel_h) / stride_h + 1
  • w_o = (w_i + 2 * pad_w - kernel_w) / stride_w + 1

See also

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