From ddcdd4a70991a7651116d0ec602b96b6f5331978 Mon Sep 17 00:00:00 2001
From: Yang Zhang <yangzhang@live.com>
Date: Fri, 24 Apr 2020 09:30:59 +0800
Subject: [PATCH] Minor fix to `MSELoss` docstring (#24077)

* Indent MSELoss example docs

* Point out input tensors should be of same shape

test=develop

* Document `MSELoss` input and return parameters

test=develop,test=document_fix
---
 python/paddle/nn/layer/loss.py | 80 +++++++++++++++++++---------------
 1 file changed, 45 insertions(+), 35 deletions(-)

diff --git a/python/paddle/nn/layer/loss.py b/python/paddle/nn/layer/loss.py
index e9c1485bff5..05fc3e26292 100644
--- a/python/paddle/nn/layer/loss.py
+++ b/python/paddle/nn/layer/loss.py
@@ -155,48 +155,58 @@ class MSELoss(fluid.dygraph.layers.Layer):
     .. math::
         Out = \operatorname{sum}((input - label)^2)
 
-    where `input` and `label` are `float32` tensors of arbitrary shapes.
+    where `input` and `label` are `float32` tensors of same shape.
 
     Parameters:
+        input (Variable): Input tensor, the data type is float32,
+        label (Variable): Label tensor, the data type is float32,
         reduction (string, optional): The reduction method for the output,
             could be 'none' | 'mean' | 'sum'.
-            'none': no reduction will be applied
-            'mean': the output will be averaged
-            'sum': the output will be summed
+            If :attr:`reduction` is ``'mean'``, the reduced mean loss is returned. 
+            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'``.
+
+    Returns:
+        The tensor variable storing the MSE loss of input and label.
+
+    Return type:
+        Variable.
 
     Examples:
         .. code-block:: python
-        import numpy as np
-        import paddle
-        from paddle import fluid
-        import paddle.fluid.dygraph as dg
-
-        mse_loss = paddle.nn.loss.MSELoss()
-        input = fluid.data(name="input", shape=[1])
-        label = fluid.data(name="label", shape=[1])
-        place = fluid.CPUPlace()
-        input_data = np.array([1.5]).astype("float32")
-        label_data = np.array([1.7]).astype("float32")
-
-        # declarative mode
-        output = mse_loss(input,label)
-        exe = fluid.Executor(place)
-        exe.run(fluid.default_startup_program())
-        output_data = exe.run(
-            fluid.default_main_program(),
-            feed={"input":input_data, "label":label_data},
-            fetch_list=[output],
-            return_numpy=True)
-        print(output_data)
-        # [array([0.04000002], dtype=float32)]
-
-        # imperative mode
-        with dg.guard(place) as g:
-            input = dg.to_variable(input_data)
-            label = dg.to_variable(label_data)
-            output = mse_loss(input, label)
-            print(output.numpy())
-            # [0.04000002]
+
+            import numpy as np
+            import paddle
+            from paddle import fluid
+            import paddle.fluid.dygraph as dg
+
+            mse_loss = paddle.nn.loss.MSELoss()
+            input = fluid.data(name="input", shape=[1])
+            label = fluid.data(name="label", shape=[1])
+            place = fluid.CPUPlace()
+            input_data = np.array([1.5]).astype("float32")
+            label_data = np.array([1.7]).astype("float32")
+
+            # declarative mode
+            output = mse_loss(input,label)
+            exe = fluid.Executor(place)
+            exe.run(fluid.default_startup_program())
+            output_data = exe.run(
+                fluid.default_main_program(),
+                feed={"input":input_data, "label":label_data},
+                fetch_list=[output],
+                return_numpy=True)
+            print(output_data)
+            # [array([0.04000002], dtype=float32)]
+
+            # imperative mode
+            with dg.guard(place) as g:
+                input = dg.to_variable(input_data)
+                label = dg.to_variable(label_data)
+                output = mse_loss(input, label)
+                print(output.numpy())
+                # [0.04000002]
     """
 
     def __init__(self, reduction='mean'):
-- 
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