text: from .model.gan_model import GAN
  from tensorflow.examples.tutorials.mnist import input_data
  import os
  
  os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'  # suppress tensorflow warnings
  
  # load data
  mnist = input_data.read_data_sets("./data/MNIST_data", one_hot=True)
  
  # create model
  input_size = 784  # 28*28 pixels
  gen_hidden_size = 256
  disc_hidden_size = 256
  noise_size = 100  # Noise data points
  gan = GAN(input_size, gen_hidden_size, disc_hidden_size, noise_size)
  
  # training
  # sess = gan.train(mnist, num_steps=70000, batch_size=128, sess=None)
  
  # restore model
  sess = gan.restore_last_session()
  
  # generate
  # gan.generate(sess=sess)
  gan.generate2(sess=sess)
  