[Docathon][Update Doc No.29] fix the LossFunction

docs/api_guides/low_level/layers/loss_function.rst

@@ -11,30 +11,30 @@ Paddle Fluid 中提供了面向多种任务的多种类型的损失函数，以
 ====
 
 平方误差损失（squared error loss）使用预测值和真实值之间误差的平方作为样本损失，是回归问题中最为基本的损失函数。
-API Reference 请参考 :ref:`cn_api_fluid_layers_square_error_cost`。
+API Reference 请参考 :ref:`cn_api_nn_functional_square_error_cost`。
 
 平滑 L1 损失（smooth_l1 loss）是一种分段的损失函数，较平方误差损失其对异常点相对不敏感，因而更为鲁棒。
-API Reference 请参考 :ref:`cn_api_fluid_layers_smooth_l1`。
+API Reference 请参考 :ref:`cn_api_nn_functional_smooth_l1_loss`。
 
 
 分类
 ====
 
 `交叉熵（cross entropy） <https://en.wikipedia.org/wiki/Cross_entropy>`_ 是分类问题中使用最为广泛的损失函数，Paddle Fluid 中提供了接受归一化概率值和非归一化分值输入的两种交叉熵损失函数的接口，并支持 soft label 和 hard label 两种样本类别标签。
-API Reference 请参考 :ref:`cn_api_fluid_layers_cross_entropy` 和 :ref:`cn_api_fluid_layers_softmax_with_cross_entropy`。
+API Reference 请参考 :ref:`cn_api_nn_functional_cross_entropy`。
 
 多标签分类
 ---------
 对于多标签分类问题，如一篇文章同属于政治、科技等多个类别的情况，需要将各类别作为独立的二分类问题计算损失，Paddle Fluid 中为此提供了 sigmoid_cross_entropy_with_logits 损失函数，
-API Reference 请参考 :ref:`cn_api_fluid_layers_sigmoid_cross_entropy_with_logits`。
+API Reference 请参考 :ref:`cn_api_nn_functional_binary_cross_entropy`。
 
 大规模分类
 ---------
 对于大规模分类问题，通常需要特殊的方法及相应的损失函数以加速训练，常用的方法有 `噪声对比估计（Noise-contrastive estimation，NCE） <http://proceedings.mlr.press/v9/gutmann10a/gutmann10a.pdf>`_ 和 `层级 sigmoid <http://www.iro.umontreal.ca/~lisa/pointeurs/hierarchical-nnlm-aistats05.pdf>`_ 。
 
 * 噪声对比估计通过将多分类问题转化为学习分类器来判别数据来自真实分布和噪声分布的二分类问题，基于二分类来进行极大似然估计，避免在全类别空间计算归一化因子从而降低了计算复杂度。
 * 层级 sigmoid 通过二叉树进行层级的二分类来实现多分类，每个样本的损失对应了编码路径上各节点二分类交叉熵的和，避免了归一化因子的计算从而降低了计算复杂度。
-这两种方法对应的损失函数在 Paddle Fluid 中均有提供，API Reference 请参考 :ref:`cn_api_fluid_layers_nce` 和 :ref:`cn_api_fluid_layers_hsigmoid`。
+这两种方法对应的损失函数在 Paddle Fluid 中均有提供，API Reference 请参考 :ref:`cn_api_static_nn_nce` 和 :ref:`cn_api_nn_functional_hsigmoid_loss`。
 
 序列分类
 -------
@@ -55,6 +55,6 @@ API Reference 请参考 :ref:`cn_api_fluid_layers_sigmoid_cross_entropy_with_log
 更多
 ====
 
-对于一些较为复杂的损失函数，可以尝试使用其他损失函数组合实现；Paddle Fluid 中提供的用于图像分割任务的 :ref:`cn_api_fluid_layers_dice_loss` 即是使用其他 OP 组合（计算各像素位置似然概率的均值）而成；多目标损失函数也可看作这样的情况，如 Faster RCNN 就使用 cross entropy 和 smooth_l1 loss 的加权和作为损失函数。
+对于一些较为复杂的损失函数，可以尝试使用其他损失函数组合实现；Paddle Fluid 中提供的用于图像分割任务的 :ref:`cn_api_nn_functional_dice_loss` 即是使用其他 OP 组合（计算各像素位置似然概率的均值）而成；多目标损失函数也可看作这样的情况，如 Faster RCNN 就使用 cross entropy 和 smooth_l1 loss 的加权和作为损失函数。
 
-**注意**，在定义损失函数之后为能够使用 :ref:`api_guide_optimizer` 进行优化，通常需要使用 :ref:`cn_api_fluid_layers_mean` 或其他操作将损失函数返回的高维 Tensor 转换为 Scalar 值。
+**注意**，在定义损失函数之后为能够使用 :ref:`api_guide_optimizer` 进行优化，通常需要使用 :ref:`cn_api_mean` 或其他操作将损失函数返回的高维 Tensor 转换为 Scalar 值。

docs/api_guides/low_level/layers/loss_function_en.rst

@@ -11,22 +11,22 @@ Regression
 ===========
 
 The squared error loss uses the square of the error between the predicted value and the ground-truth value as the sample loss, which is the most basic loss function in the regression problems.
-For API Reference,  please refer to :ref:`api_fluid_layers_square_error_cost`.
+For API Reference,  please refer to :ref:`api_nn_functional_square_error_cost`.
 
 Smooth L1 loss (smooth_l1 loss) is a piecewise loss function that is relatively insensitive to outliers and therefore more robust.
-For API Reference,  please refer to :ref:`api_fluid_layers_smooth_l1`.
+For API Reference,  please refer to :ref:`api_nn_functional_smooth_l1_loss`.
 
 
 Classification
 ================
 
 `cross entropy <https://en.wikipedia.org/wiki/Cross_entropy>`_ is the most widely used loss function in classification problems.  The interfaces in Paddle Fluid for the cross entropy loss functions are divided into the one accepting fractional input of normalized probability values ​​and another for non-normalized input. And Fluid supports two types labels, namely soft label and hard label.
-For API Reference,  please refer to :ref:`api_fluid_layers_cross_entropy` and :ref:`api_fluid_layers_softmax_with_cross_entropy`.
+For API Reference,  please refer to :ref:`api_nn_functional_cross_entropy`.
 
 Multi-label classification
 ----------------------------
 For the multi-label classification, such as the occasion that an article belongs to multiple categories like politics, technology, it is necessary to calculate the loss by treating each category as an independent binary-classification problem. We provide the sigmoid_cross_entropy_with_logits loss function for this purpose.
-For API Reference,  please refer to :ref:`api_fluid_layers_sigmoid_cross_entropy_with_logits`.
+For API Reference,  please refer to :ref:`api_nn_functional_binary_cross_entropy`.
 
 Large-scale classification
 -----------------------------
@@ -35,7 +35,7 @@ For large-scale classification problems, special methods and corresponding loss
 
 * NCE solves the binary-classification problem of discriminating the true distribution and the noise distribution by converting the multi-classification problem into a classifier. The maximum likelihood estimation is performed based on the binary-classification to avoid calculating the normalization factor in the full-class space to reduce computational complexity.
 * Hierarchical sigmoid realizes multi-classification by hierarchical classification of binary trees. The loss of each sample corresponds to the sum of the cross-entropy of the binary-classification for each node on the coding path, which avoids the calculation of the normalization factor and reduces the computational complexity.
-The loss functions for both methods are available in Paddle Fluid. For API Reference please refer to :ref:`api_fluid_layers_nce` and :ref:`api_fluid_layers_hsigmoid`.
+The loss functions for both methods are available in Paddle Fluid. For API Reference please refer to :ref:`api_static_nn_nce` and :ref:`api_nn_functional_hsigmoid_loss`.
 
 Sequence classification
 -------------------------
@@ -56,6 +56,6 @@ Rank
 More
 ====
 
-For more complex loss functions, try to use combinations of other loss functions; the :ref:`api_fluid_layers_dice_loss` provided in Paddle Fluid for image segmentation tasks is an example of using combinations of other operators  (calculate the average likelihood probability of each pixel position). The multi-objective loss function can also be considered similarly, such as Faster RCNN that uses the weighted sum of cross entropy and smooth_l1 loss as a loss function.
+For more complex loss functions, try to use combinations of other loss functions; the :ref:`api_nn_functional_dice_loss` provided in Paddle Fluid for image segmentation tasks is an example of using combinations of other operators  (calculate the average likelihood probability of each pixel position). The multi-objective loss function can also be considered similarly, such as Faster RCNN that uses the weighted sum of cross entropy and smooth_l1 loss as a loss function.
 
-**Note**, after defining the loss function, in order to optimize with :ref:`api_guide_optimizer_en`, you usually need to use :ref:`api_fluid_layers_mean` or other operations to convert the high-dimensional Tensor returned by the loss function to a Scalar value.
+**Note**, after defining the loss function, in order to optimize with :ref:`api_guide_optimizer_en`, you usually need to use :ref:`api_mean` or other operations to convert the high-dimensional Tensor returned by the loss function to a Scalar value.