Convolution Layer

• Layer type: Convolution
• Doxygen Documentation
• Header: ./include/caffe/layers/conv_layer.hpp
• CPU implementation: ./src/caffe/layers/conv_layer.cpp
• CUDA GPU implementation: ./src/caffe/layers/conv_layer.cu
• Input
  • n * c_i * h_i * w_i
• Output
  • n * c_o * h_o * w_o, where h_o = (h_i + 2 * pad_h - kernel_h) / stride_h + 1 and w_o likewise.

The Convolution layer convolves the input image with a set of learnable filters, each producing one feature map in the output image.

Sample

Sample (as seen in ./models/bvlc_reference_caffenet/train_val.prototxt):

  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

• 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 $$i$$th output group channels will be only connected to the $$i$$th input group channels.
• From ./src/caffe/proto/caffe.proto):

{% highlight Protobuf %} {% include proto/ConvolutionParameter.txt %} {% endhighlight %}