60 lines
2.1 KiB
Markdown
60 lines
2.1 KiB
Markdown
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---
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title: Inner Product / Fully Connected Layer
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---
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# Inner Product / Fully Connected Layer
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* Layer type: `InnerProduct`
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* [Doxygen Documentation](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1InnerProductLayer.html)
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* Header: [`./include/caffe/layers/inner_product_layer.hpp`](https://github.com/BVLC/caffe/blob/master/include/caffe/layers/inner_product_layer.hpp)
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* CPU implementation: [`./src/caffe/layers/inner_product_layer.cpp`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/inner_product_layer.cpp)
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* CUDA GPU implementation: [`./src/caffe/layers/inner_product_layer.cu`](https://github.com/BVLC/caffe/blob/master/src/caffe/layers/inner_product_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 * 1 * 1`
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* Sample
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layer {
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name: "fc8"
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type: "InnerProduct"
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# learning rate and decay multipliers for the weights
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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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inner_product_param {
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num_output: 1000
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weight_filler {
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type: "gaussian"
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std: 0.01
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}
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bias_filler {
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type: "constant"
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value: 0
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}
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}
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bottom: "fc7"
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top: "fc8"
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}
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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).
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## Parameters
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* Parameters (`InnerProductParameter inner_product_param`)
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- Required
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- `num_output` (`c_o`): the number of filters
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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_filler` [default `type: 'constant' value: 0`]
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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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* 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/InnerProductParameter.txt %}
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{% endhighlight %}
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