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--- |
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title: Convolution Layer |
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--- |
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# Convolution Layer |
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* Layer type: `Convolution` |
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* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1ConvolutionLayer.html) |
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* Header: [`./include/caffe/layers/conv_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/conv_layer.hpp) |
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* CPU implementation: [`./src/caffe/layers/conv_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/conv_layer.cpp) |
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* CUDA GPU implementation: [`./src/caffe/layers/conv_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/conv_layer.cu) |
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* Input |
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- `n * c_i * h_i * w_i` |
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* Output |
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- `n * c_o * h_o * w_o`, where `h_o = (h_i + 2 * pad_h - kernel_h) / stride_h + 1` and `w_o` likewise. |
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The `Convolution` layer convolves the input image with a set of learnable filters, each producing one feature map in the output image. |
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## Sample |
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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)): |
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layer { |
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name: "conv1" |
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type: "Convolution" |
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bottom: "data" |
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top: "conv1" |
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# learning rate and decay multipliers for the filters |
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param { lr_mult: 1 decay_mult: 1 } |
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# learning rate and decay multipliers for the biases |
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param { lr_mult: 2 decay_mult: 0 } |
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convolution_param { |
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num_output: 96 # learn 96 filters |
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kernel_size: 11 # each filter is 11x11 |
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stride: 4 # step 4 pixels between each filter application |
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weight_filler { |
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type: "gaussian" # initialize the filters from a Gaussian |
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std: 0.01 # distribution with stdev 0.01 (default mean: 0) |
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} |
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bias_filler { |
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type: "constant" # initialize the biases to zero (0) |
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value: 0 |
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} |
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} |
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} |
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## Parameters |
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* Parameters (`ConvolutionParameter convolution_param`) |
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- Required |
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- `num_output` (`c_o`): the number of filters |
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- `kernel_size` (or `kernel_h` and `kernel_w`): specifies height and width of each filter |
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- Strongly Recommended |
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- `weight_filler` [default `type: 'constant' value: 0`] |
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- Optional |
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- `bias_term` [default `true`]: specifies whether to learn and apply a set of additive biases to the filter outputs |
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- `pad` (or `pad_h` and `pad_w`) [default 0]: specifies the number of pixels to (implicitly) add to each side of the input |
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- `stride` (or `stride_h` and `stride_w`) [default 1]: specifies the intervals at which to apply the filters to the input |
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- `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. |
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* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto)): |
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{% highlight Protobuf %} |
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{% include proto/ConvolutionParameter.txt %} |
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{% endhighlight %} |
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