convolution layer

  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
      }
    }
  }

Parameters (ConvolutionParameter convolution_param)

• Required
  • num_output (c_o): the number of filters
  • kernel_size (or kernel_h and kernel_w): specifies height and width of each filter
• Strongly Recommended
  • weight_filler [default type: 'constant' value: 0]
• Optional
  • 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 i$i$th output group channels will be only connected to the i$i$th input group channels.