(self, shared_outputs=None, accumulate_updates=False)
Allocate output buffer to store activations from fprop.

        Arguments:
            shared_outputs (Tensor, optional): pre-allocated tensor for activations to be
                                               computed into
            accumulate_updates bool: allocate additional scratch accumulation
                                               buffers.
Allocate output buffer to store activations from fprop.
Allocate output buffer to store activations from fprop .
def allocate(self, shared_outputs=None, accumulate_updates=False):
        """
        Allocate output buffer to store activations from fprop.

        Arguments:
            shared_outputs (Tensor, optional): pre-allocated tensor for activations to be
                                               computed into
            accumulate_updates bool: allocate additional scratch accumulation
                                               buffers.
        """
        super(BatchNorm, self).allocate(shared_outputs)
        self.y = self.outputs.reshape((self.nfm, -1))
        self.xvar = self.be.zeros((self.nfm, 1), dtype=self.stats_dtype)
        self.accumulate_updates = accumulate_updates
        if self.allparams is None:
            self.init_params(self.nfm)
        if self.prev_layer in (None, True) or not getattr(self.prev_layer, 'batch_sum', None):
            self.xsum = self.be.zeros((self.nfm, 1),
                                      dtype=self.stats_dtype, **self.get_param_attrs())
            self.xsum.auto_reduce = False
            self.compute_batch_sum = True
        else:
            self.xsum = self.prev_layer.batch_sum
            self.compute_batch_sum = False
def allocate ( self , shared_outputs = None , accumulate_updates = False ) : super ( BatchNorm , self ) . allocate ( shared_outputs ) self . y = self . outputs . reshape ( ( self . nfm , - 1 ) ) self . xvar = self . be . zeros ( ( self . nfm , 1 ) , dtype = self . stats_dtype ) self . accumulate_updates = accumulate_updates if self . allparams is None : self . init_params ( self . nfm ) if self . prev_layer in ( None , True ) or not getattr ( self . prev_layer , 'batch_sum' , None ) : self . xsum = self . be . zeros ( ( self . nfm , 1 ) , dtype = self . stats_dtype , * * self . get_param_attrs ( ) ) self . xsum . auto_reduce = False self . compute_batch_sum = True else : self . xsum = self . prev_layer . batch_sum self . compute_batch_sum = False