(self, inputs, beta=0.0)
return self.outputs
Apply one linear transformation that captures normalization, gamma scaling and beta shift.
Apply one linear transformation that captures normalization, gamma scaling and beta shift.
Apply one linear transformation that captures normalization gamma scaling and beta shift .
def _fprop_inference(self, inputs, beta=0.0):
        """
        Apply one linear transformation that captures normalization, gamma scaling and beta shift.
        """
        xhat = (inputs - self.gmean) / self.be.sqrt(self.gvar + self.eps)  # Op-tree only
        self.y[:] = self.y * beta + xhat * self.gamma + self.beta
        return self.outputs
def _fprop_inference ( self , inputs , beta = 0.0 ) : xhat = ( inputs - self . gmean ) / self . be . sqrt ( self . gvar + self . eps ) # Op-tree only self . y [ : ] = self . y * beta + xhat * self . gamma + self . beta return self . outputs