Glorot Uniform Initialiser

glorot_uniform_init_type

glorot_uniform_init_type()

Also known as Xavier uniform initialisation. Draws samples from a uniform distribution within bounds that depend on the number of input and output units.

\[W \sim U\left[-\sqrt{\frac{6}{n_{in} + n_{out}}}, \sqrt{\frac{6}{n_{in} + n_{out}}}\right]\]

where \(n_{in}\) is the number of input units and \(n_{out}\) is the number of output units.

This initialisation helps maintain the variance of activations across layers and is well-suited for networks using sigmoid or tanh activations.

Shape:

Initialises weights based on the shape provided during layer setup.