(self, chars_logit)
return ids, log_prob, scores
Returns confidence scores (softmax values) for predicted characters.

    Args:
      chars_logit: chars logits, a tensor with shape
        [batch_size x seq_length x num_char_classes]

    Returns:
      A tuple (ids, log_prob, scores), where:
        ids - predicted characters, a int32 tensor with shape
          [batch_size x seq_length];
        log_prob - a log probability of all characters, a float tensor with
          shape [batch_size, seq_length, num_char_classes];
        scores - corresponding confidence scores for characters, a float
        tensor
          with shape [batch_size x seq_length].
Returns confidence scores (softmax values) for predicted characters.
Returns confidence scores ( softmax values ) for predicted characters .
def char_predictions(self, chars_logit):
    """Returns confidence scores (softmax values) for predicted characters.

    Args:
      chars_logit: chars logits, a tensor with shape
        [batch_size x seq_length x num_char_classes]

    Returns:
      A tuple (ids, log_prob, scores), where:
        ids - predicted characters, a int32 tensor with shape
          [batch_size x seq_length];
        log_prob - a log probability of all characters, a float tensor with
          shape [batch_size, seq_length, num_char_classes];
        scores - corresponding confidence scores for characters, a float
        tensor
          with shape [batch_size x seq_length].
    """
    log_prob = utils.logits_to_log_prob(chars_logit)
    ids = tf.to_int32(tf.argmax(log_prob, axis=2), name='predicted_chars')
    mask = tf.cast(
      slim.one_hot_encoding(ids, self._params.num_char_classes), tf.bool)
    all_scores = tf.nn.softmax(chars_logit)
    selected_scores = tf.boolean_mask(all_scores, mask, name='char_scores')
    scores = tf.reshape(selected_scores, shape=(-1, self._params.seq_length))
    return ids, log_prob, scores
def char_predictions ( self , chars_logit ) : log_prob = utils . logits_to_log_prob ( chars_logit ) ids = tf . to_int32 ( tf . argmax ( log_prob , axis = 2 ) , name = 'predicted_chars' ) mask = tf . cast ( slim . one_hot_encoding ( ids , self . _params . num_char_classes ) , tf . bool ) all_scores = tf . nn . softmax ( chars_logit ) selected_scores = tf . boolean_mask ( all_scores , mask , name = 'char_scores' ) scores = tf . reshape ( selected_scores , shape = ( - 1 , self . _params . seq_length ) ) return ids , log_prob , scores