losses.py 文件源码

def absolute_difference(weights=1.0, name='AbsoluteDifference', scope=None, collect=True):
    """Adds an Absolute Difference loss to the training procedure.

    `weights` acts as a coefficient for the loss. If a scalar is provided, then
    the loss is simply scaled by the given value. If `weights` is a `Tensor` of
    shape `[batch_size]`, then the total loss for each sample of the batch is
    rescaled by the corresponding element in the `weights` vector. If the shape of
    `weights` matches the shape of `predictions`, then the loss of each
    measurable element of `predictions` is scaled by the corresponding value of
    `weights`.

    Args:
        weights: Optional `Tensor` whose rank is either 0, or the same rank as
            `labels`, and must be broadcastable to `labels` (i.e., all dimensions must
            be either `1`, or the same as the corresponding `losses` dimension).
        name: operation name.
        scope: operation scope.
        collect: whether to collect this metric under the metric collection.

    Returns:
        A scalar `Tensor` representing the loss value.
    """
    def inner_loss(y_true, y_pred):
        losses = math_ops.abs(math_ops.subtract(y_pred, y_true))
        return losses

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