message: Updating loss calculation to use one_hot vectors
  The in-depth description for the code uses one_hot labels as input to the softmax_cross_entropy() function, therefore the code should use one hot labels as well.
diff: @@ -169,7 +169,8 @@ def cnn_model_fn(features, labels, mode):
  return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
  # Calculate Loss (for both TRAIN and EVAL modes)
  - loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
  + onehot_labels = tf.one_hot(indices=tf.cast(labels, tf.int32), depth=10)
  + loss = tf.losses.sparse_softmax_cross_entropy(labels=onehot_labels, logits=logits)
  # Configure the Training Op (for TRAIN mode)
  if mode == tf.estimator.ModeKeys.TRAIN:
  