losses.py 文件源码

def kullback_leibler_divergence(weights=1.0, name='KullbackLeiberDivergence', scope=None,
                                collect=False):
    """Adds a Kullback leiber diverenge loss to the training procedure.

     Args:
        name: name of the op.
        scope: The scope for the operations performed in computing the loss.
        collect: add to losses collection.

    Returns:
        A scalar `Tensor` representing the loss value.

    Raises:
        ValueError: If `predictions` shape doesn't match `labels` shape, or `weights` is `None`.
    """

    def inner_loss(y_true, y_pred):
        y_true = clip(y_true, EPSILON, 1)
        y_pred = clip(y_pred, EPSILON, 1)
        losses = tf.reduce_sum(input_tensor=y_true * tf.log(x=y_true / y_pred), axis=-1)
        return losses

    return built_loss(inner_loss, weights, name, scope, collect)