text: #%%
  # Copyright 2015 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  import numpy as np
  import sklearn.preprocessing as prep
  import tensorflow as tf
  from tensorflow.examples.tutorials.mnist import input_data
  
  def xavier_init(fan_in, fan_out, constant = 1):
      low = -constant * np.sqrt(6.0 / (fan_in + fan_out))
      high = constant * np.sqrt(6.0 / (fan_in + fan_out))
      return tf.random_uniform((fan_in, fan_out),
                               minval = low, maxval = high,
                               dtype = tf.float32)
  
  class AdditiveGaussianNoiseAutoencoder(object):
      def __init__(self, n_input, n_hidden, transfer_function = tf.nn.softplus, optimizer = tf.train.AdamOptimizer(),
                   scale = 0.1):
          self.n_input = n_input
          self.n_hidden = n_hidden
          self.transfer = transfer_function
          self.scale = tf.placeholder(tf.float32)
          self.training_scale = scale
          network_weights = self._initialize_weights()
          self.weights = network_weights
  
          # model
          self.x = tf.placeholder(tf.float32, [None, self.n_input])
          self.hidden = self.transfer(tf.add(tf.matmul(self.x + scale * tf.random_normal((n_input,)),
                  self.weights['w1']),
                  self.weights['b1']))
          self.reconstruction = tf.add(tf.matmul(self.hidden, self.weights['w2']), self.weights['b2'])
  
          # cost
          self.cost = 0.5 * tf.reduce_sum(tf.pow(tf.subtract(self.reconstruction, self.x), 2.0))
          self.optimizer = optimizer.minimize(self.cost)
  
          init = tf.global_variables_initializer()
          self.sess = tf.Session()
          self.sess.run(init)
  
      def _initialize_weights(self):
          all_weights = dict()
          all_weights['w1'] = tf.Variable(xavier_init(self.n_input, self.n_hidden))
          all_weights['b1'] = tf.Variable(tf.zeros([self.n_hidden], dtype = tf.float32))
          all_weights['w2'] = tf.Variable(tf.zeros([self.n_hidden, self.n_input], dtype = tf.float32))
          all_weights['b2'] = tf.Variable(tf.zeros([self.n_input], dtype = tf.float32))
          return all_weights
  
      def partial_fit(self, X):
          cost, opt = self.sess.run((self.cost, self.optimizer), feed_dict = {self.x: X,
                                                                              self.scale: self.training_scale
                                                                              })
          return cost
  
      def calc_total_cost(self, X):
          return self.sess.run(self.cost, feed_dict = {self.x: X,
                                                       self.scale: self.training_scale
                                                       })
  
      def transform(self, X):
          return self.sess.run(self.hidden, feed_dict = {self.x: X,
                                                         self.scale: self.training_scale
                                                         })
  
      def generate(self, hidden = None):
          if hidden is None:
              hidden = np.random.normal(size = self.weights["b1"])
          return self.sess.run(self.reconstruction, feed_dict = {self.hidden: hidden})
  
      def reconstruct(self, X):
          return self.sess.run(self.reconstruction, feed_dict = {self.x: X,
                                                                 self.scale: self.training_scale
                                                                 })
  
      def getWeights(self):
          return self.sess.run(self.weights['w1'])
  
      def getBiases(self):
          return self.sess.run(self.weights['b1'])
          
          
          
          
  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 = 20
  batch_size = 128
  display_step = 1
  
  autoencoder = AdditiveGaussianNoiseAutoencoder(n_input = 784,
                                                 n_hidden = 200,
                                                 transfer_function = tf.nn.softplus,
                                                 optimizer = tf.train.AdamOptimizer(learning_rate = 0.001),
                                                 scale = 0.01)
  
  for epoch in range(training_epochs):
      avg_cost = 0.
      total_batch = int(n_samples / batch_size)
      # Loop over all batches
      for i in range(total_batch):
          batch_xs = get_random_block_from_data(X_train, batch_size)
  
          # Fit training using batch data
          cost = autoencoder.partial_fit(batch_xs)
          # Compute average loss
          avg_cost += cost / n_samples * batch_size
  
      # Display logs per epoch step
      if epoch % display_step == 0:
          print("Epoch:", '%04d' % (epoch + 1), "cost=", "{:.9f}".format(avg_cost))
  
  print("Total cost: " + str(autoencoder.calc_total_cost(X_test)))
  