def add_evaluation_step(result_tensor, ground_truth_tensor):

  with tf.name_scope('accuracy'):
    with tf.name_scope('correct_prediction'):
      prediction = tf.argmax(result_tensor, 1)
      correct_prediction = tf.equal(
          prediction, tf.argmax(ground_truth_tensor, 1))
    with tf.name_scope('accuracy'):
      evaluation_step = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
  tf.summary.scalar('accuracy', evaluation_step)
  print('prediction shape :: {}'.format(ground_truth_tensor))

  #Add confusion matrix
  batch_confusion = tf.confusion_matrix(tf.argmax(ground_truth_tensor, 1), prediction,
                                             num_classes=7,
                                             name='batch_confusion')
  # Create an accumulator variable to hold the counts
  confusion = tf.Variable( tf.zeros([7,7],
                                          dtype=tf.int32 ),
                                 name='confusion' )
  # Create the update op for doing a "+=" accumulation on the batch
  confusion_update = confusion.assign( confusion + batch_confusion )
  # Cast counts to float so tf.summary.image renormalizes to [0,255]
  confusion_image = tf.reshape( tf.cast( confusion_update, tf.float32),
                                 [1, 7, 7, 1])

  tf.summary.image('confusion',confusion_image)

  return evaluation_step, prediction