person-re-ranking/caffe/docs/tutorial/layers/innerproduct.md

title
Inner Product / Fully Connected Layer

Inner Product / Fully Connected Layer

• Layer type: InnerProduct

• Doxygen Documentation

• Header: ./include/caffe/layers/inner_product_layer.hpp

• CPU implementation: ./src/caffe/layers/inner_product_layer.cpp

• CUDA GPU implementation: ./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:

{% highlight Protobuf %} {% include proto/InnerProductParameter.txt %} {% endhighlight %}