code: #!/usr/bin/python
  '''
  A nearest neighbor learning algorithm example using TensorFlow library.
  This example is using the MNIST database of handwritten digits
  (http://yann.lecun.com/exdb/mnist/)
  
  Author: Aymeric Damien
  Project: https://github.com/aymericdamien/TensorFlow-Examples/
  '''
  
  from __future__ import print_function
  
  import numpy as np
  import tensorflow as tf
  import TensorFI as ti
  
  # Import MNIST data
  from tensorflow.examples.tutorials.mnist import input_data
  mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
  
  # In this example, we limit mnist data
  Xtr, Ytr = mnist.train.next_batch(5000)  #5000 for training (nn candidates)
  Xte, Yte = mnist.test.next_batch(200)  #200 for testing
  
  # tf Graph Input
  xtr = tf.placeholder("float", [None, 784])
  xte = tf.placeholder("float", [784])
  
  # Nearest Neighbor calculation using L1 Distance
  # Calculate L1 Distance
  distance = tf.reduce_sum(tf.abs(tf.add(xtr, tf.negative(xte))),
                           reduction_indices=1)
  # Prediction: Get min distance index (Nearest neighbor)
  pred = tf.arg_min(distance, 0)
  
  accuracy = 0.
  
  # Initialize the variables (i.e. assign their default value)
  init = tf.global_variables_initializer()
  
  # Start training
  with tf.compat.v1.Session() as sess:
  
      # Run the initializer
      sess.run(init)
  
      # Add the fault injection code here to instrument the graph
      # We start injecting the fault right away here unlike earlier
      fi = ti.TensorFI(sess, name="NearestNeighbor", logLevel=50)
  
      # loop over test data
      for i in range(len(Xte)):
          # Get nearest neighbor
          nn_index = sess.run(pred, feed_dict={xtr: Xtr, xte: Xte[i, :]})
          # Get nearest neighbor class label and compare it to its true label
          print("Test", i, "Prediction:", np.argmax(Ytr[nn_index]), \
              "True Class:", np.argmax(Yte[i]))
          # Calculate accuracy
          if np.argmax(Ytr[nn_index]) == np.argmax(Yte[i]):
              accuracy += 1. / len(Xte)
      print("Accuracy:", accuracy)
  
      # Make the log files in TensorBoard
      logs_path = "./logs"
      logWriter = tf.summary.FileWriter(logs_path, sess.graph)
  
apis: tensorflow.placeholder tensorflow.compat.v1.Session tensorflow.negative tensorflow.global_variables_initializer numpy.argmax tensorflow.examples.tutorials.mnist.input_data.read_data_sets tensorflow.arg_min tensorflow.summary.FileWriter