Scale layer in Caffe

Question

I am looking through the Caffe prototxt for deep residual networks and have noticed the appearance of a \"Scale\" layer.

layer {
    bottom: \"r         


        

Answer 1

There's also some documentation on it in the caffe.proto file, you can search for 'ScaleParameter'.

Thanks a heap for your post :) Scale layer was exactly what I was looking for. In case anyone wants an example for a layer that scales by a scalar (0.5) and then "adds" -2 (and those values shouldn't change):

layer {
  name: "scaleAndAdd"
  type: "Scale"
  bottom: "bot"
  top: "scaled"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  scale_param {
    filler {
      value: 0.5    }
    bias_term: true
    bias_filler {
      value: -2
    }
  }
}

(Probably, the decay_mult's are unnecessary here though. But dunno. See comments.) Other than that:

• lr_mult: 0 - switches off learning for "that param" - I think the first "param {" always(?) refers to the weights, the second to bias (lr_mult is not ScaleLayer specific)
• filler: a "FillerParameter" [see caffe.proto] telling how to fill the ommited second blob. Default is one constant "value: ...".
• bias_filler: parameter telling how to fill an optional bias blob
• bias_term: whether there is a bias blob

All taken from caffe.proto. And: I only tested the layer above with both filler values = 1.2.

Answer 2

You can find a detailed documentation on caffe here.

Specifically, for "Scale" layer the doc reads:

Computes a product of two input Blobs, with the shape of the latter Blob "broadcast" to match the shape of the former. Equivalent to tiling the latter Blob, then computing the elementwise product.
The second input may be omitted, in which case it's learned as a parameter of the layer.

It seems like, in your case, (single "bottom"), this layer learns a scale factor to multiply "res2b_branch2b". Moreover, since scale_param { bias_term: true } means the layer learns not only a multiplicative scaling factor, but also a constant term. So, the forward pass computes:

res2b_branch2b <- res2b_branch2b * \alpha + \beta

During training the net tries to learn the values of \alpha and \beta.