(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.
        """
        input_ms = self.be.empty_like(inputs)
        input_ms[:] = inputs - self.gmean
        inv_v = self.be.empty_like(self.gvar)
        inv_v[:] = 1.0 / self.be.sqrt(self.gvar + self.eps)
        xhat = self.be.shift(input_ms, inv_v)
        self.y[:] = self.y * beta + self.be.shift(xhat, self.gamma) + self.beta
        return self.outputs
def _fprop_inference ( self , inputs , beta = 0.0 ) : input_ms = self . be . empty_like ( inputs ) input_ms [ : ] = inputs - self . gmean inv_v = self . be . empty_like ( self . gvar ) inv_v [ : ] = 1.0 / self . be . sqrt ( self . gvar + self . eps ) xhat = self . be . shift ( input_ms , inv_v ) self . y [ : ] = self . y * beta + self . be . shift ( xhat , self . gamma ) + self . beta return self . outputs