confuse about the define of placeholder

[meeio]

when I change the position of placeholder of x, it goes wrong
the original is:

self.z = tf.placeholder(tf.float32, shape=(params.batch_size, 1), name="z")

with tf.variable_scope('G'):
 ……

self.x = tf.placeholder(tf.float32, shape=(params.batch_size, 1), name="x")

with tf.variable_scope('D'):
 ……

I change to:

self.z = tf.placeholder(tf.float32, shape=(params.batch_size, 1), name="z")
self.x = tf.placeholder(tf.float32, shape=(params.batch_size, 1), name="x")

with tf.variable_scope('G'):
 ……

with tf.variable_scope('D'):
 ……

the training result is completely wrong. I am so confused, can anyone tell me why this change will effect the result.

[meeio]

the original code is :

class GAN(object):
    def __init__(self, params):
        # This defines the generator network - it takes samples from a noise
        # distribution as input, and passes them through an MLP.
        with tf.variable_scope('G'):
            self.z = tf.placeholder(tf.float32, shape=(params.batch_size, 1))
            self.G = generator(self.z, params.hidden_size)

        # The discriminator tries to tell the difference between samples from
        # the true data distribution (self.x) and the generated samples
        # (self.z).
        #
        # Here we create two copies of the discriminator network
        # that share parameters, as you cannot use the same network with
        # different inputs in TensorFlow.
        self.x = tf.placeholder(tf.float32, shape=(params.batch_size, 1))
        with tf.variable_scope('D'):
            self.D1 = discriminator(
                self.x,
                params.hidden_size,
                params.minibatch
            )
        with tf.variable_scope('D', reuse=True):
            self.D2 = discriminator(
                self.G,
                params.hidden_size,
                params.minibatch
            )

        # Define the loss for discriminator and generator networks
        # (see the original paper for details), and create optimizers for both
        self.loss_d = tf.reduce_mean(-log(self.D1) - log(1 - self.D2))
        self.loss_g = tf.reduce_mean(-log(self.D2))

        vars = tf.trainable_variables()
        self.d_params = [v for v in vars if v.name.startswith('D/')]
        self.g_params = [v for v in vars if v.name.startswith('G/')]

        self.opt_d = optimizer(self.loss_d, self.d_params)
        self.opt_g = optimizer(self.loss_g, self.g_params)

self.G is not relevent to self.x, I am not understand why define self.x earlier will affect the result.