(self, chars_labels, weight=0.1)
return one_hot_labels * pos_weight + neg_weight
Applies a label smoothing regularization.

    Uses the same method as in https://arxiv.org/abs/1512.00567.

    Args:
      chars_labels: ground truth ids of charactes,
        shape=[batch_size, seq_length];
      weight: label-smoothing regularization weight.

    Returns:
      A sensor with the same shape as the input.
Applies a label smoothing regularization.
Applies a label smoothing regularization .
def label_smoothing_regularization(self, chars_labels, weight=0.1):
    """Applies a label smoothing regularization.

    Uses the same method as in https://arxiv.org/abs/1512.00567.

    Args:
      chars_labels: ground truth ids of charactes,
        shape=[batch_size, seq_length];
      weight: label-smoothing regularization weight.

    Returns:
      A sensor with the same shape as the input.
    """
    one_hot_labels = tf.one_hot(
      chars_labels, depth=self._params.num_char_classes, axis=-1)
    pos_weight = 1.0 - weight
    neg_weight = weight / self._params.num_char_classes
    return one_hot_labels * pos_weight + neg_weight
def label_smoothing_regularization ( self , chars_labels , weight = 0.1 ) : one_hot_labels = tf . one_hot ( chars_labels , depth = self . _params . num_char_classes , axis = - 1 ) pos_weight = 1.0 - weight neg_weight = weight / self . _params . num_char_classes return one_hot_labels * pos_weight + neg_weight