model.py 文件源码

def _cls_mining(self, scores, status, hard_neg_ratio=3.0, scope=None):
    """
    Positive classification loss and hard negative classificatin loss
    ARGS
      scores: [n, n_classes]
      status: int [n] node or link matching status
    RETURNS
      pos_loss: []
      n_pos: int []
      hard_neg_loss: []
      n_hard_neg: []
    """
    with tf.variable_scope(scope or 'cls_mining'):
      # positive classification loss
      pos_mask = tf.equal(status, MATCH_STATUS_POS)
      pos_scores = tf.boolean_mask(scores, pos_mask)
      n_pos = tf.shape(pos_scores)[0]
      pos_labels = tf.fill([n_pos], POS_LABEL)
      pos_loss = tf.reduce_sum(tf.nn.sparse_softmax_cross_entropy_with_logits(
        logits=pos_scores, labels=pos_labels))

      # hard negative classification loss
      neg_mask = tf.equal(status, MATCH_STATUS_NEG)
      neg_scores = tf.boolean_mask(scores, neg_mask)
      n_neg = tf.shape(neg_scores)[0]
      n_hard_neg = tf.cast(n_pos, tf.float32) * hard_neg_ratio
      n_hard_neg = tf.minimum(n_hard_neg, tf.cast(n_neg, tf.float32))
      n_hard_neg = tf.cast(n_hard_neg, tf.int32)
      neg_prob = tf.nn.softmax(neg_scores)[:, NEG_LABEL]
      # find the k examples with the least negative probabilities
      _, hard_neg_indices = tf.nn.top_k(-neg_prob, k=n_hard_neg)
      hard_neg_scores = tf.gather(neg_scores, hard_neg_indices)
      hard_neg_labels = tf.fill([n_hard_neg], NEG_LABEL)
      hard_neg_loss = tf.reduce_sum(tf.nn.sparse_softmax_cross_entropy_with_logits(
        logits=hard_neg_scores, labels=hard_neg_labels))

      return pos_loss, n_pos, hard_neg_loss, n_hard_neg