old_contents: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import numpy as np
  import sklearn.preprocessing as prep
  import tensorflow as tf
  from tensorflow.examples.tutorials.mnist import input_data
  
  from autoencoder_models.DenoisingAutoencoder import MaskingNoiseAutoencoder
  
  mnist = input_data.read_data_sets('MNIST_data', one_hot = True)
  
  
  def standard_scale(X_train, X_test):
      preprocessor = prep.StandardScaler().fit(X_train)
      X_train = preprocessor.transform(X_train)
      X_test = preprocessor.transform(X_test)
      return X_train, X_test
  
  
  def get_random_block_from_data(data, batch_size):
      start_index = np.random.randint(0, len(data) - batch_size)
      return data[start_index:(start_index + batch_size)]
  
  
  X_train, X_test = standard_scale(mnist.train.images, mnist.test.images)
  
  n_samples = int(mnist.train.num_examples)
  training_epochs = 100
  batch_size = 128
  display_step = 1
  
  autoencoder = MaskingNoiseAutoencoder(
      n_input = 784,
      n_hidden = 200,
      transfer_function = tf.nn.softplus,
      optimizer = tf.train.AdamOptimizer(learning_rate = 0.001),
      dropout_probability = 0.95)
  
  for epoch in range(training_epochs):
      avg_cost = 0.
      total_batch = int(n_samples / batch_size)
      for i in range(total_batch):
          batch_xs = get_random_block_from_data(X_train, batch_size)
  
          cost = autoencoder.partial_fit(batch_xs)
  
          avg_cost += cost / n_samples * batch_size
  
      if epoch % display_step == 0:
          print("Epoch:", '%d,' % (epoch + 1),
                "Cost:", "{:.9f}".format(avg_cost))
  
  print("Total cost: " + str(autoencoder.calc_total_cost(X_test)))
  
new_contents: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import numpy as np
  import sklearn.preprocessing as prep
  import tensorflow as tf
  from tensorflow.examples.tutorials.mnist import input_data
  
  from autoencoder_models.DenoisingAutoencoder import MaskingNoiseAutoencoder
  
  mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
  
  
  def standard_scale(X_train, X_test):
      preprocessor = prep.StandardScaler().fit(X_train)
      X_train = preprocessor.transform(X_train)
      X_test = preprocessor.transform(X_test)
      return X_train, X_test
  
  
  def get_random_block_from_data(data, batch_size):
      start_index = np.random.randint(0, len(data) - batch_size)
      return data[start_index:(start_index + batch_size)]
  
  
  X_train, X_test = standard_scale(mnist.train.images, mnist.test.images)
  
  n_samples = int(mnist.train.num_examples)
  training_epochs = 100
  batch_size = 128
  display_step = 1
  
  autoencoder = MaskingNoiseAutoencoder(
      n_input=784,
      n_hidden=200,
      transfer_function=tf.nn.softplus,
      optimizer=tf.train.AdamOptimizer(learning_rate=0.001),
      dropout_probability=0.95)
  
  for epoch in range(training_epochs):
      avg_cost = 0.
      total_batch = int(n_samples / batch_size)
      for i in range(total_batch):
          batch_xs = get_random_block_from_data(X_train, batch_size)
  
          cost = autoencoder.partial_fit(batch_xs)
  
          avg_cost += cost / n_samples * batch_size
  
      if epoch % display_step == 0:
          print("Epoch:", '%d,' % (epoch + 1),
                "Cost:", "{:.9f}".format(avg_cost))
  
  print("Total cost: " + str(autoencoder.calc_total_cost(X_test)))
  
subject: Update MaskingNoiseAutoencoderRunner