Initializers¶

Initializers provide init values for network parameter blobs. In Caffe, they are called Fillers.

class NullInitializer

An initializer that does nothing. To initialize with zeros, use a ConstantInitializer.

class ConstantInitializer

Set everything to a constant.

value

The value used to initialize a parameter blob. Typically this is set to 0.

class XavierInitializer

An initializer based on [BengioGlorot2010], but does not use the fan-out value. It fills the parameter blob by randomly sampling uniform data from $$[-S,S]$$ where the scale $$S=\sqrt{3 / F_{\text{in}}}$$. Here $$F_{\text{in}}$$ is the fan-in: the number of input nodes.

Heuristics are used to determine the fan-in: For a ND tensor parameter blob, the product of all the 1 to N-1 dimensions are considered as fan-in, while the last dimension is considered as fan-out.

 [BengioGlorot2010] Y. Bengio and X. Glorot, Understanding the difficulty of training deep feedforward neural networks, in Proceedings of AISTATS 2010, pp. 249-256.
class GaussianInitializer

Initialize each element in the parameter blob as independent and identically distributed Gaussian random variables.

mean

Default 0.

std

Default 1.

class OrthogonalInitializer

Initialize the parameter blob to be a random orthogonal matrix (i.e. $$W^TW=I$$), times a scalar gain factor. Based on [Saxe2013].

 [Saxe2013] Andrew M. Saxe, James L. McClelland, Surya Ganguli, Exact solutions to the nonlinear dynamics of learning in deep linear neural networks, http://arxiv.org/abs/1312.6120 with a presentation https://www.youtube.com/watch?v=Ap7atx-Ki3Q
gain

Default 1. Use $$\sqrt{2}$$ for layers with ReLU activations.