old_contents: #!/usr/bin/env python
  
  __author__ = "Meet Shah"
  __license__ = "MIT"
  
  import os
  
  import numpy as np
  import tensorflow as tf
  
  
  from utils import *
  
  '''
  Global Parameters
  '''
  n_epochs   = 20000
  batch_size = 64 
  
  
  def generator(z, batch_size=batch_size, phase_train=True, reuse=False):
   
      g_1 = tf.add(tf.matmul(z, weights['wd1']), biases['bd1'])
      g_1 = tf.reshape(g_1, [-1,4,4,4,512])
      g_1 = batchNorm(g_1, 512, phase_train)
  
      ## TODO : Add layers
  
      return g_1
  
  def discriminator(inputs, batch_size=batch_size, is_train=True, reuse=False):
      ## TODO : Add layers
      pass
  
  def trainGAN():
      ## TODO
      pass
  
  def testGAN():
      ## TODO
      pass
  
  def visualize():
      ## TODO
      pass
  
  def saveModel():
      ## TODO
      pass
  
new_contents: #!/usr/bin/env python
  
  __author__ = "Meet Shah"
  __license__ = "MIT"
  
  import os
  
  import numpy as np
  import tensorflow as tf
  
  
  from utils import *
  
  '''
  Global Parameters
  '''
  n_epochs   = 20000
  batch_size = 100
  g_lr       = 0.0025
  d_lr       = 0.00001
  beta       = 0.5
  alpha_1    = 5
  alpha_2    = 0.0001
  d_thresh   = 0.8    
  
  def generator(z, batch_size=batch_size, phase_train=True, reuse=False):
   
      g_1 = tf.add(tf.matmul(z, weights['wd1']), biases['bd1'])
      g_1 = tf.reshape(g_1, [-1,4,4,4,512])
      g_1 = batchNorm(g_1, 512, phase_train)
  
      ## TODO : Add layers
  
      return g_1
  
  def discriminator(inputs, batch_size=batch_size, is_train=True, reuse=False):
      ## TODO : Add layers
      pass
  
  def trainGAN():
      ## TODO
      pass
  
  def testGAN():
      ## TODO
      pass
  
  def visualize():
      ## TODO
      pass
  
  def saveModel():
      ## TODO
      pass
  
subject: Add parameters from paper