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---
title: Inner Product / Fully Connected Layer
---
# Inner Product / Fully Connected Layer
* Layer type: `InnerProduct`
* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1InnerProductLayer.html)
* Header: [`./include/caffe/layers/inner_product_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/inner_product_layer.hpp)
* CPU implementation: [`./src/caffe/layers/inner_product_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/inner_product_layer.cpp)
* CUDA GPU implementation: [`./src/caffe/layers/inner_product_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/inner_product_layer.cu)
* Input
- `n * c_i * h_i * w_i`
* Output
- `n * c_o * 1 * 1`
* Sample
layer {
name: "fc8"
type: "InnerProduct"
# learning rate and decay multipliers for the weights
param { lr_mult: 1 decay_mult: 1 }
# learning rate and decay multipliers for the biases
param { lr_mult: 2 decay_mult: 0 }
inner_product_param {
num_output: 1000
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
bottom: "fc7"
top: "fc8"
}
The `InnerProduct` layer (also usually referred to as the fully connected layer) treats the input as a simple vector and produces an output in the form of a single vector (with the blob's height and width set to 1).
## Parameters
* Parameters (`InnerProductParameter inner_product_param`)
- Required
- `num_output` (`c_o`): the number of filters
- Strongly recommended
- `weight_filler` [default `type: 'constant' value: 0`]
- Optional
- `bias_filler` [default `type: 'constant' value: 0`]
- `bias_term` [default `true`]: specifies whether to learn and apply a set of additive biases to the filter outputs
* From [`./src/caffe/proto/caffe.proto`](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto):
{% highlight Protobuf %}
{% include proto/InnerProductParameter.txt %}
{% endhighlight %}
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