From 9c59d42b8b742cab6aaad5b81c816dde06b2b4c2 Mon Sep 17 00:00:00 2001
From: whisky-12 <79358023+whisky-12@users.noreply.github.com>
Date: Fri, 17 Feb 2023 14:22:52 +0800
Subject: [PATCH] =?UTF-8?q?[note]-MSELoss-=20API=E8=8B=B1=E6=96=87?=
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---
 python/paddle/nn/layer/loss.py | 13 +++++++++++++
 1 file changed, 13 insertions(+)

diff --git a/python/paddle/nn/layer/loss.py b/python/paddle/nn/layer/loss.py
index c91a04f8d17..b2cec383eb9 100644
--- a/python/paddle/nn/layer/loss.py
+++ b/python/paddle/nn/layer/loss.py
@@ -525,16 +525,24 @@ class MSELoss(Layer):
     r"""
     **Mean Square Error Loss**
     Computes the mean square error (squared L2 norm) of given input and label.
+
     If :attr:`reduction` is set to ``'none'``, loss is calculated as:
+
     .. math::
         Out = (input - label)^2
+
     If :attr:`reduction` is set to ``'mean'``, loss is calculated as:
+
     .. math::
         Out = \operatorname{mean}((input - label)^2)
+
     If :attr:`reduction` is set to ``'sum'``, loss is calculated as:
+
     .. math::
         Out = \operatorname{sum}((input - label)^2)
+
     where `input` and `label` are `float32` tensors of same shape.
+
     Parameters:
         reduction (str, optional): The reduction method for the output,
             could be 'none' | 'mean' | 'sum'.
@@ -542,12 +550,16 @@ class MSELoss(Layer):
             If :attr:`size_average` is ``'sum'``, the reduced sum loss is returned.
             If :attr:`reduction` is ``'none'``, the unreduced loss is returned.
             Default is ``'mean'``.
+
     Shape:
         input (Tensor): Input tensor, the data type is float32 or float64
         label (Tensor): Label tensor, the data type is float32 or float64
         output (Tensor): output tensor storing the MSE loss of input and label, the data type is same as input.
+
     Examples:
+
         .. code-block:: python
+
             import paddle
             mse_loss = paddle.nn.loss.MSELoss()
             input = paddle.to_tensor([1.5])
@@ -555,6 +567,7 @@ class MSELoss(Layer):
             output = mse_loss(input, label)
             print(output)
             # [0.04000002]
+
     """
 
     def __init__(self, reduction='mean'):
-- 
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