Xavier¶
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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
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__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
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