|XavierFiller (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 [Bengio and Glorot 2010]: Understanding the difficulty of training deep feedforward neuralnetworks.
It fills the incoming matrix by randomly sampling uniform data from [-scale, scale] where scale = sqrt(3 / 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.
TODO(dox): make notation in above comment consistent with rest & use LaTeX.