return a (b, 1) logits

def discriminator(self, imgs, y):
        """ return a (b, 1) logits"""
        yv = y
        y = tf.reshape(y, [-1, 1, 1, 10])
        with argscope(Conv2D, kernel_size=5, strides=1):
            l = (LinearWrap(imgs)
                 .ConcatWith(tf.tile(y, [1, 28, 28, 1]), 3)
                 .Conv2D('conv0', 11)
                 .tf.nn.leaky_relu()

                 .ConcatWith(tf.tile(y, [1, 14, 14, 1]), 3)
                 .Conv2D('conv1', 74)
                 .BatchNorm('bn1')
                 .tf.nn.leaky_relu()

                 .apply(batch_flatten)
                 .ConcatWith(yv, 1)
                 .FullyConnected('fc1', 1024, activation=tf.identity)
                 .BatchNorm('bn2')
                 .tf.nn.leaky_relu()

                 .ConcatWith(yv, 1)
                 .FullyConnected('fct', 1, activation=tf.identity)())
        return l