Complex Batch Normalization¶

class
ComplexBatchNormalization
¶ Complex BatchNormalization as defined in section 3.5 of [TRABELESI2017]

__init__
(axis=1, momentum=0.99, center=True, scale=True, epsilon=0.001, beta_initializer=Zeros(), gamma_initializer=Ones(), dtype=DEFAULT_COMPLEX_TYPE, moving_mean_initializer=Zeros(), moving_variance_initializer=Ones(), cov_method: int = 2, **kwargs)¶ Parameters:  axis – Integer, the axis that should be normalized (typically the features axis).
 momentum – Float. Momentum for the moving average.
 center – If True, add offset of beta to normalized tensor. If False, beta is ignored.
 scale – If True, multiply by gamma. If False, gamma is not used. When the next layer is linear (also e.g. nn.relu), this can be disabled since the scaling will be done by the next layer.
 epsilon – Small float added to variance to avoid dividing by zero.
 beta_initializer – Initializer for the beta weight.
 gamma_initializer – Initializer for the gamma weight.
 dtype – tf.complex32
 moving_mean_initializer – Initializer for the moving mean.
 moving_variance_initializer – Initializer for the moving variance.
 cov_method – Either 1 or 2. Algorithm to be applie. It should be the same results but calculated in a different manner. Used for debugging.
[TRABELESI2017]  Trabelsi, Chiheb et al. “Deep Complex Networks” arXiv:1705.09792 [cs]. 2017. 