pairwise_losses.py 文件源码

def mce_loss(positive_scores, negative_scores):
    """
    Minimum Classification Error (MCE) loss [1]:
        loss(p, n) = \sum_i \sigma(- p_i + n_i)

    [1] http://yann.lecun.com/exdb/publis/pdf/lecun-06.pdf

    Args:
        positive_scores: (N,) Tensor containing scores of positive examples.
        negative_scores: (N,) Tensor containing scores of negative examples.
    Returns:
        Loss value.
    """
    mce_losses = tf.sigmoid(- positive_scores + negative_scores)
    loss = tf.reduce_sum(mce_losses)
    return loss