Thank you for sharing your code. It actually helped me a lot!
def discriminator(x):
with tf.variable_scope('discriminator'):
nn_x = tf.reshape(x, [tf.shape(x)[0], 28, 28, 1])
conv1 = tf.layers.conv2d(nn_x, filters=64, kernel_size=4, strides=2, activation=leaky_relu)
conv2 = tf.layers.conv2d(conv1, filters=128, kernel_size=4, strides=2, activation=leaky_relu)
bn = tf.layers.batch_normalization(conv2, training=True)
flt = tf.contrib.layers.flatten(bn)
dense = tf.layers.dense(flt, 1024, activation=leaky_relu)
logits= tf.layers.dense(dense, 1)
return logits