metadata
title: Convolution Layer
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
, whereh_o = (h_i + 2 * pad_h - kernel_h) / stride_h + 1
andw_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 filterskernel_size
(orkernel_h
andkernel_w
): specifies height and width of each filter
- Strongly Recommended
weight_filler
[defaulttype: 'constant' value: 0
]
- Optional
bias_term
[defaulttrue
]: specifies whether to learn and apply a set of additive biases to the filter outputspad
(orpad_h
andpad_w
) [default 0]: specifies the number of pixels to (implicitly) add to each side of the inputstride
(orstride_h
andstride_w
) [default 1]: specifies the intervals at which to apply the filters to the inputgroup
(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.
- Required
- From
./src/caffe/proto/caffe.proto
):
{% highlight Protobuf %} {% include proto/ConvolutionParameter.txt %} {% endhighlight %}