# Xavier¶

class ampligraph.latent_features.Xavier(initializer_params={}, verbose=True, seed=0)

Follows the xavier strategy for initialization of layers [GB10].

If uniform is set to True, then it initializes the layer from the following uniform distribution:

$\mathcal{U} ( - \sqrt{ \frac{6}{ fan_{in} + fan_{out} } }, \sqrt{ \frac{6}{ fan_{in} + fan_{out} } } )$

If uniform is False, then it initializes the layer from the following normal distribution:

$\mathcal{N} ( 0, \sqrt{ \frac{2}{ fan_{in} + fan_{out} } } )$

where $$fan_{in}$$ and $$fan_{out}$$ are number of input units and output units of the layer respectively.

Methods

 __init__([initializer_params, verbose, seed]) Initialize the Xavier strategy
__init__(initializer_params={}, verbose=True, seed=0)

Initialize the Xavier strategy

Parameters: initializer_params (dict) – Consists of key-value pairs. The initializer will check the keys to get the corresponding params: uniform: (bool). indicates whether to use Xavier Uniform or Xavier Normal initializer. Example: initializer_params={'uniform': False} verbose (bool) – Enable/disable verbose mode seed (int/np.random.RandomState) – random state for random number generator