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---
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 %}
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