|MSRAFiller (const FillerParameter ¶m)|
|virtual void||Fill (Blob< Dtype > *blob)|
|Public Member Functions inherited from caffe::Filler< Dtype >|
|Filler (const FillerParameter ¶m)|
|Protected Attributes inherited from caffe::Filler< Dtype >|
Fills a Blob with values where is set inversely proportional to number of incoming nodes, outgoing nodes, or their average.
A Filler based on the paper [He, Zhang, Ren and Sun 2015]: Specifically accounts for ReLU nonlinearities.
Aside: for another perspective on the scaling factor, see the derivation of [Saxe, McClelland, and Ganguli 2013 (v3)].
It fills the incoming matrix by randomly sampling Gaussian data with std = sqrt(2 / n) where n is the fan_in, fan_out, or their average, depending on the variance_norm option. You should make sure the input blob has shape (num, a, b, c) where a * b * c = fan_in and num * b * c = fan_out. Note that this is currently not the case for inner product layers.