Define a Cross Entropy loss using softmax_cross_entropy_with_logits.

  It can scale the loss by weight factor, and smooth the labels.

  Args:
    logits: [batch_size, num_classes] logits outputs of the network .
    one_hot_labels: [batch_size, num_classes] target one_hot_encoded labels.
    label_smoothing: if greater than 0 then smooth the labels.
    weight: scale the loss by this factor.
    scope: Optional scope for name_scope.

  Returns:
    A tensor with the softmax_cross_entropy loss.

def cross_entropy_loss(logits, one_hot_labels, label_smoothing=0,
                       weight=1.0, scope=None):
  """Define a Cross Entropy loss using softmax_cross_entropy_with_logits.

  It can scale the loss by weight factor, and smooth the labels.

  Args:
    logits: [batch_size, num_classes] logits outputs of the network .
    one_hot_labels: [batch_size, num_classes] target one_hot_encoded labels.
    label_smoothing: if greater than 0 then smooth the labels.
    weight: scale the loss by this factor.
    scope: Optional scope for name_scope.

  Returns:
    A tensor with the softmax_cross_entropy loss.
  """
  logits.get_shape().assert_is_compatible_with(one_hot_labels.get_shape())
  with tf.name_scope(scope, 'CrossEntropyLoss', [logits, one_hot_labels]):
    num_classes = one_hot_labels.get_shape()[-1].value
    one_hot_labels = tf.cast(one_hot_labels, logits.dtype)
    if label_smoothing > 0:
      smooth_positives = 1.0 - label_smoothing
      smooth_negatives = label_smoothing / num_classes
      one_hot_labels = one_hot_labels * smooth_positives + smooth_negatives
    cross_entropy = tf.contrib.nn.deprecated_flipped_softmax_cross_entropy_with_logits(
        logits, one_hot_labels, name='xentropy')

    weight = tf.convert_to_tensor(weight,
                                  dtype=logits.dtype.base_dtype,
                                  name='loss_weight')
    loss = tf.multiply(weight, tf.reduce_mean(cross_entropy), name='value')
    tf.add_to_collection(LOSSES_COLLECTION, loss)
    return loss