Developer Guide for Intel® Data Analytics Acceleration Library 2019 Update 4

Xavier Initializer

A Xavier initializer is an initializer algorithm to initialize a p-dimensional tensor WR n 1 x ... x n p that represents weights and biases of the appropriate layer. The algorithm initializes this tensor with random numbers uniformly distributed on the interval [-α,α). The value of α is defined using the sizes of the r-dimensional input tensor X R n x m 2 x... x m r and q-dimensional value tensor YR n x k 2 x... x k q for the layer:



where:

For more details, see [Glorot2010].

Algorithm Parameters

In addition to common parameters of the initializer interface, a Xavier initializer has the following parameters:

Parameter

Default Value

Description

algorithmFPType

float

The floating-point type that the algorithm uses for intermediate computations. Can be float or double.

method

defaultDense

Performance-oriented computation method, the only method supported by the algorithm.

See Also