Problem about ComplexBN

[jackie840129]

Hi,
I have some question about this ComplexBN class at line 409,410,

if training in {0, False}:
            return input_bn

why we don't need moving_mean and moving_variance when testing?

I also see another function at line 421,

def normalize_inference():
                if self.center:
                    inference_centred = inputs - K.reshape(self.moving_mean, broadcast_mu_shape)
                else:
                    inference_centred = inputs
                return ComplexBN(
                    inference_centred, self.moving_Vrr, self.moving_Vii,
                    self.moving_Vri, self.beta, self.gamma_rr, self.gamma_ri,
                    self.gamma_ii, self.scale, self.center, axis=self.axis
                )

When testing, this function doesn't be called. Is there any mistake?

The code below are the source code in Keras, they return "normalize_inference()" when testing.

 if training in {0, False}:
           return normalize_inference()

Thanks!

[DevKiHyun]

I think so. Why use not normalize_inference when test?