def class_balanced_sigmoid_cross_entropy(logits, label, name='cross_entropy_loss'):
    """
    The class-balanced cross entropy loss,
    as in `Holistically-Nested Edge Detection
    <http://arxiv.org/abs/1504.06375>`_.

    Args:
        logits: of shape (b, ...).
        label: of the same shape. the ground truth in {0,1}.
    Returns:
        class-balanced cross entropy loss.
    """
    with tf.name_scope('class_balanced_sigmoid_cross_entropy'):
        y = tf.cast(label, tf.float32)

        count_neg = tf.reduce_sum(1. - y)
        count_pos = tf.reduce_sum(y)
        beta = count_neg / (count_neg + count_pos)

        pos_weight = beta / (1 - beta)
        cost = tf.nn.weighted_cross_entropy_with_logits(logits=logits, targets=y, pos_weight=pos_weight)
        cost = tf.reduce_mean(cost * (1 - beta))
        zero = tf.equal(count_pos, 0.0)
    return tf.where(zero, 0.0, cost, name=name)

    The class-balanced cross entropy loss,
    as in `Holistically-Nested Edge Detection
    <http://arxiv.org/abs/1504.06375>`_.

    Args:
        logits: of shape (b, ...).
        label: of the same shape. the ground truth in {0,1}.
    Returns:
        class-balanced cross entropy loss.
    
examples/HED/hed.py