mxnet.initializer.Xavier¶
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class
mxnet.initializer.Xavier(rnd_type='uniform', factor_type='avg', magnitude=3)[source]¶ Returns an initializer performing “Xavier” initialization for weights.
This initializer is designed to keep the scale of gradients roughly the same in all layers.
By default, rnd_type is
'uniform'and factor_type is'avg', the initializer fills the weights with random numbers in the range of \([-c, c]\), where \(c = \sqrt{\frac{3.}{0.5 * (n_{in} + n_{out})}}\). \(n_{in}\) is the number of neurons feeding into weights, and \(n_{out}\) is the number of neurons the result is fed to.If rnd_type is
'uniform'and factor_type is'in', the \(c = \sqrt{\frac{3.}{n_{in}}}\). Similarly when factor_type is'out', the \(c = \sqrt{\frac{3.}{n_{out}}}\).If rnd_type is
'gaussian'and factor_type is'avg', the initializer fills the weights with numbers from normal distribution with a standard deviation of \(\sqrt{\frac{3.}{0.5 * (n_{in} + n_{out})}}\).- Parameters
rnd_type (str, optional) – Random generator type, can be
'gaussian'or'uniform'.factor_type (str, optional) – Can be
'avg','in', or'out'.magnitude (float, optional) – Scale of random number.
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__init__(rnd_type='uniform', factor_type='avg', magnitude=3)[source]¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__([rnd_type, factor_type, magnitude])Initialize self.
dumps()Saves the initializer to string
set_verbosity([verbose, print_func])Switch on/off verbose mode