lib/model/siamese_net/weight_cropping_layer.py
def normal_init(m, mean, stddev, truncated=False):
            """
            weight initalizer: truncated normal and random normal.
            """
            # x is a parameter
            if truncated:
                m.weight.data.normal_().fmod_(2).mul_(stddev).add_(mean)  # not a perfect approximation
            else:
                m.weight.data.normal_(mean, stddev)
                if m.bias is not None:
                    m.bias.data.zero_()

            weight initalizer: truncated normal and random normal.
            
weight initalizer: truncated normal and random normal.
weight initalizer : truncated normal and random normal .
def normal_init(m, mean, stddev, truncated=False):
            if truncated:
                m.weight.data.normal_().fmod_(2).mul_(stddev).add_(mean)  
            else:
                m.weight.data.normal_(mean, stddev)
                if m.bias is not None:
                    m.bias.data.zero_()
def normal_init ( m , mean , stddev , truncated = False ) : if truncated : m . weight . data . normal_ ( ) . fmod_ ( 2 ) . mul_ ( stddev ) . add_ ( mean ) else : m . weight . data . normal_ ( mean , stddev ) if m . bias is not None : m . bias . data . zero_ ( )
weight initalizer: truncated normal and random normal.
weight initalizer : truncated normal and random normal .
"""
            weight initalizer: truncated normal and random normal.
            """ # x is a parameter
 # not a perfect approximation

m
mean
stddev
truncated