caffe::XavierFiller< Dtype > Class Template Reference

Fills a Blob with values where is set inversely proportional to number of incoming nodes, outgoing nodes, or their average. More...

`#include <filler.hpp>`

Inheritance diagram for caffe::XavierFiller< Dtype >:

## Public Member Functions

| XavierFiller (const FillerParameter &param)
virtual void | Fill (Blob< Dtype > *blob)
Public Member Functions inherited from caffe::Filler< Dtype >
| Filler (const FillerParameter &param)

## Additional Inherited Members

Protected Attributes inherited from caffe::Filler< Dtype >
FillerParameter | filler_param_

## Detailed Description

### template<typename Dtype>
class caffe::XavierFiller< 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.

The documentation for this class was generated from the following file:

- include/caffe/filler.hpp