bad case generate by fusion net!

[ZHANG-SHI-CHANG]

good job for me!
follow is result generate by fusion net

some bad case in result, plus result also have bad case, but id and exp is good, do you have any idea about it?
plus:

id:

exp:

and what is the meaning of
bias = 0.9 * (self.m_list + self.M_list) / (self.M_list - self.m_list)
i find 0.9-->1.0 can improve expression perform

def test_generate_one(self, id_net, exp_net):
        def I(z):
            z_mean, z_log_var, z = id_net.encoder(z)
            dr_id = id_net.decoder(z)
            return dr_id
        def E(z):
            z_mean, z_log_var, z = exp_net.encoder(z)
            dr_exp = exp_net.decoder(z)
            return dr_exp
        def F(y, x):
            return self.gcn_comp([y, x])
        def load_model():
            our_model.load_weights(('../model/our_model/our_model{}{}.h5').format(self.prefix, self.suffix))

        z = self.real

        our_model = Model(z, [I(z), E(z), F(I(z), E(z))])

        if self.load:
            load_model()
            print('!!!!!!!!!!!load fusion model success!!!!!!!!!!!')

        test_dr_feature = np.fromfile(os.path.join('dr_feature', 'Tester_106', 'dr_1.dat'))
        test_dr_feature = test_dr_feature[np.newaxis, :]
        
        test_dr_feature = normalize_fromfile(test_dr_feature, self.M_list, self.m_list)

        id_code = id_net.encoder.predict(test_dr_feature, batch_size=self.batch_size)[0]
        exp_code = exp_net.encoder.predict(test_dr_feature, batch_size=self.batch_size)[0]

        print('\n')
        print(id_code)
        print(exp_code)
        print('\n')

        bias = 0.9 * (self.m_list + self.M_list) / (self.M_list - self.m_list)

        norm_id = id_net.decoder.predict(id_code, batch_size=self.batch_size)
        norm_exp = exp_net.decoder.predict(exp_code, batch_size=self.batch_size)

        plus = norm_id+norm_exp+bias
        recon = self.gcn_comp.predict([norm_id, norm_exp], batch_size=self.batch_size) + bias

        id_feature = denormalize_fromfile(norm_id, self.M_list, self.m_list)
        exp_feature = denormalize_fromfile(norm_exp, self.M_list, self.m_list)
        plus_feature = denormalize_fromfile(plus, self.M_list, self.m_list)
        recon_feature = denormalize_fromfile(recon, self.M_list, self.m_list)