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Xavier 1

The Xavier 1 initializer draws from a uniform distribution [-limit, limit] where limit is equal to sqrt(6 / (fanIn + fanOut)). This initializer is best suited for layers that feed into an activation layer that outputs a value between 0 and 1 such as Softmax or Sigmoid.

Parameters

This initializer does not have any parameters.

Example

use Rubix\ML\NeuralNet\Initializers\Xavier1;

$initializer = new Xavier1();

References

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1. X. Glorot et al. (2010). Understanding the Difficulty of Training Deep Feedforward Neural Networks. ↩

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Last update: 2021-03-03