tf.get_variablesが定義されないためtf.train.AdamOptimizerが使えない原因

下記のようにGeneratorとDiscriminatorを定義したあとにtf.train.AdamOptimizerを使おうとすると下記エラーが出て使用できないのは何故なのでしょうか？

ValueError: Variable discriminator/d_h0_conv/w/Adam/ does not exist, or was not created with tf.get_variable(). Did you mean to set reuse=None in VarScope?

tf.train.GradientDescentOptimizer(1e-3)は使えるようですが、tf.train.AdamOptimizerを使わないと上手くいかないのです。どうしたらこのoptimizerが使えるようになるのでしょうか？
ご教授お願いいたします。

追記
早急に解決の必要があるためマルチポストをしてしましました。複数の人に見てもらった方が早く解決できると思ったからです。
リンク

def discriminator(image, reuse=False):
    batch_size=64
    with tf.variable_scope("discriminator") as scope:
        if reuse:
            scope.reuse_variales()

        h0 = lrelu(conv2d(image, 64, name='d_h0_conv'))
        h1 = lrelu(batch_norm(conv2d(h0, 128, name='d_h1_conv'),'d_bn1'))
        h2 = lrelu(batch_norm(conv2d(h1, 256, name='d_h2_conv'),'d_bn2'))
        h3 = lrelu(batch_norm(conv2d(h2, 512, name='d_h3_conv'),'d_bn3'))  # shape=(batch_size, 64, 64, 3) 
        h4 = linear_d(tf.reshape(h3, [batch_size, -1]),2,'d_h4_lin')
        return h4



    # shape=(batch_size, 64, 64, 3)
def generator(z_):
    batch_size=64
    with tf.variable_scope("generator") as scope:
        # project `z` and reshape
        z, h0_w, h0_b = linear(z_, 64*8*4*4, 'g_h0_lin',with_w=True)
        h0 = tf.nn.relu(batch_norm(tf.reshape(z, [-1, 4, 4, 64*8]), 'g_bn0'))
        h1, h1_w, h1_b = deconv2d(h0, [batch_size, 8, 8, 64*4], name='g_h1', with_w=True)
        h1 = tf.nn.relu(batch_norm(h1, 'g_bn1'))
        h2, h2_w, h2_b = deconv2d(h1, [batch_size, 16, 16, 64*2], name='g_h2', with_w=True)
        h2 = tf.nn.relu(batch_norm(h2, 'g_bn2'))
        h3, h3_w, h3_b = deconv2d(h2, [batch_size, 32, 32, 64*1], name='g_h3', with_w=True)
        h3 = tf.nn.relu(batch_norm(h3, 'g_bn3'))
        h4, h4_w, h4_b = deconv2d(h3, [batch_size, 64, 64, 3], name='g_h4', with_w=True)
        return tf.nn.tanh(h4)  #shape=(batch_size, 64, 64, 3)


def sampler(z_):# shape=(batch_size, 64, 64, 3) 
    batch_size=64
    with tf.variable_scope("generator") as scope:
        # project `z` and reshape
        z= linear(z_, 64*8*4*4,'g_h0_lin')
        h0 = tf.nn.relu(batch_norm(tf.reshape(z, [-1, 4, 4, 64*8]),'g_bn0',train=False))
        h1 = deconv2d(h0, [batch_size, 8, 8, 64*4], name='g_h1')
        h1 = tf.nn.relu(batch_norm(h1,'g_bn1',train=False))
        h2 = deconv2d(h1, [batch_size, 16, 16, 64*2], name='g_h2')
        h2 = tf.nn.relu(batch_norm(h2,'g_bn2',train=False))
        h3 = deconv2d(h2, [batch_size, 32, 32, 64*1], name='g_h3')
        h3 = tf.nn.relu(batch_norm(h3,'g_bn3',train=False))
        h4 = deconv2d(h3, [batch_size, 64, 64, 3], name='g_h4')
        return tf.nn.tanh(h4)  #shape=(batch_size, 64, 64, 3)

G=generator(z)  #G(z)
D_logits = discriminator(image) #D(x)

tf.get_variable_scope().reuse_variables()
sampler = sampler(z)
D_logits_ = discriminator(G)   #D(G(z))

batch_label=64

d_loss_real = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=D_logits, labels=tf.ones([batch_label], dtype=tf.int64)))
d_loss_fake = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=D_logits_, labels=tf.zeros([batch_label], dtype=tf.int64)))

g_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=D_logits_, labels=tf.ones([batch_label], dtype=tf.int64)))
d_loss = d_loss_real + d_loss_fake

d_vars = [var for var in tf.trainable_variables() if 'd_' in var.name]
g_vars = [var for var in tf.trainable_variables() if 'g_' in var.name]

saver=tf.train.Saver()


g_optim = tf.train.AdamOptimizer(learning_rate=0.0002, beta1=0.5).minimize(g_loss, var_list=g_vars)
d_optim = tf.train.AdamOptimizer(learning_rate=0.0002, beta1=0.5).minimize(d_loss, var_list=d_vars)

ValueError: Variable discriminator/d_h0_conv/w/Adam/ does not exist, or was not created with tf.get_variable(). Did you mean to set reuse=None in VarScope?