| --- |
| title: Convolution Layer |
| --- |
| |
| # Convolution Layer |
|
|
| * Layer type: `Convolution` |
| * [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1ConvolutionLayer.html) |
| * Header: [`./include/caffe/layers/conv_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/conv_layer.hpp) |
| * CPU implementation: [`./src/caffe/layers/conv_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/conv_layer.cpp) |
| * CUDA GPU implementation: [`./src/caffe/layers/conv_layer.cu`](https://github.com/BVLC/caffe/blob/master/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`](https://github.com/BVLC/caffe/blob/master/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`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto)): |
|
|
| {% highlight Protobuf %} |
| {% include proto/ConvolutionParameter.txt %} |
| {% endhighlight %} |
|
|
