loss.py 文件源码

def logistic_loss_cond(scores, labels):
    # Classification loss as the average of weighed per-score loss
    cond = tf.select(tf.equal(labels, tf.zeros(tf.shape(labels))), 
        tf.zeros(tf.shape(labels)),
        tf.nn.sigmoid_cross_entropy_with_logits(logits = scores, labels = labels)
        )
    cls_loss = tf.reduce_mean(tf.reduce_sum(cond, [1, 2, 3]))

    return cls_loss