(self, inputs, inference=False, beta=0.0)
return self.outputs
Normalize inputs (x) over batch mean and variance.
        xhat = (x - xmean) / xvar

        Scale and shift normalized inputs (xhat) by learned parameters gamma and beta.
        y = xhat * gamma + beta

        Accumulate partial results to global mean and variance buffers used for inference.

        Arguments:
            inputs:
            inference:  (Default value = False)
            beta:  (Default value = 0.0)

        Returns:
            Tensor: output data
Normalize inputs (x) over batch mean and variance.
        xhat = (x - xmean) / xvar
Normalize inputs ( x ) over batch mean and variance . xhat = ( x - xmean ) / xvar
def fprop(self, inputs, inference=False, beta=0.0):
        """
        Normalize inputs (x) over batch mean and variance.
        xhat = (x - xmean) / xvar

        Scale and shift normalized inputs (xhat) by learned parameters gamma and beta.
        y = xhat * gamma + beta

        Accumulate partial results to global mean and variance buffers used for inference.

        Arguments:
            inputs:
            inference:  (Default value = False)
            beta:  (Default value = 0.0)

        Returns:
            Tensor: output data
        """
        if self.inputs is None or self.inputs.base is not inputs:
            self.inputs = inputs.reshape((self.nfm, -1))

        self.be.compound_fprop_bn(
            self.inputs, self.xsum, self.xvar, self.gmean, self.gvar, self.gamma,
            self.beta, self.y, self.eps, self.rho, self.compute_batch_sum, beta, self.relu,
            binary=self.binary, inference=inference, outputs=self.outputs, layer=self.nglayer)

        return self.outputs
def fprop ( self , inputs , inference = False , beta = 0.0 ) : if self . inputs is None or self . inputs . base is not inputs : self . inputs = inputs . reshape ( ( self . nfm , - 1 ) ) self . be . compound_fprop_bn ( self . inputs , self . xsum , self . xvar , self . gmean , self . gvar , self . gamma , self . beta , self . y , self . eps , self . rho , self . compute_batch_sum , beta , self . relu , binary = self . binary , inference = inference , outputs = self . outputs , layer = self . nglayer ) return self . outputs