[xdoctest] reformat example code with google style No.102-104 (#56124)

* fix: updated code examples.

* fix: updated blank lines.

* fix: updated code style

* fix: removed extra changes

* fix: refine detail

python/paddle/optimizer/adadelta.py

@@ -70,39 +70,39 @@ class Adadelta(Optimizer):
     Examples:
         .. code-block:: python
 
-            import paddle
-
-            inp = paddle.uniform([10, 10], dtype="float32", min=-0.1, max=0.1)
-            linear = paddle.nn.Linear(10, 10)
-            out = linear(inp)
-            loss = paddle.mean(out)
-            beta1 = paddle.to_tensor([0.9], dtype="float32")
-            beta2 = paddle.to_tensor([0.99], dtype="float32")
-            adadelta = paddle.optimizer.Adadelta(learning_rate=0.1, parameters=linear.parameters(), weight_decay=0.01)
-            back = out.backward()
-            adadelta.step()
-            adadelta.clear_grad()
-
-            #Note that the learning_rate of linear_2 is 0.01.
-            linear_1 = paddle.nn.Linear(10, 10)
-            linear_2 = paddle.nn.Linear(10, 10)
-            inp = paddle.uniform(shape=[10, 10], min=-0.1, max=0.1)
-            out = linear_1(inp)
-            out = linear_2(out)
-            loss = paddle.mean(out)
-            adadelta = paddle.optimizer.Adadelta(
-                learning_rate=0.1,
-                parameters=[{
-                    'params': linear_1.parameters()
-                }, {
-                    'params': linear_2.parameters(),
-                    'weight_decay': 0.001,
-                    'learning_rate': 0.1,
-                }],
-                weight_decay=0.01)
-            out.backward()
-            adadelta.step()
-            adadelta.clear_grad()
+            >>> import paddle
+
+            >>> inp = paddle.uniform([10, 10], dtype="float32", min=-0.1, max=0.1)
+            >>> linear = paddle.nn.Linear(10, 10)
+            >>> out = linear(inp)
+            >>> loss = paddle.mean(out)
+            >>> beta1 = paddle.to_tensor([0.9], dtype="float32")
+            >>> beta2 = paddle.to_tensor([0.99], dtype="float32")
+            >>> adadelta = paddle.optimizer.Adadelta(learning_rate=0.1, parameters=linear.parameters(), weight_decay=0.01)
+            >>> back = out.backward()
+            >>> adadelta.step()
+            >>> adadelta.clear_grad()
+
+            >>> # Note that the learning_rate of linear_2 is 0.01.
+            >>> linear_1 = paddle.nn.Linear(10, 10)
+            >>> linear_2 = paddle.nn.Linear(10, 10)
+            >>> inp = paddle.uniform(shape=[10, 10], min=-0.1, max=0.1)
+            >>> out = linear_1(inp)
+            >>> out = linear_2(out)
+            >>> loss = paddle.mean(out)
+            >>> adadelta = paddle.optimizer.Adadelta(
+            ...     learning_rate=0.1,
+            ...     parameters=[{
+            ...         'params': linear_1.parameters()
+            ...     }, {
+            ...         'params': linear_2.parameters(),
+            ...         'weight_decay': 0.001,
+            ...         'learning_rate': 0.1,
+            ...     }],
+            ...     weight_decay=0.01)
+            >>> out.backward()
+            >>> adadelta.step()
+            >>> adadelta.clear_grad()
 
     """
 

python/paddle/optimizer/adagrad.py

@@ -70,38 +70,38 @@ class Adagrad(Optimizer):
     Examples:
         .. code-block:: python
 
-            import paddle
-
-            inp = paddle.rand(shape=[10, 10])
-            linear = paddle.nn.Linear(10, 10)
-            out = linear(inp)
-            loss = paddle.mean(out)
-            adagrad = paddle.optimizer.Adagrad(learning_rate=0.1,
-                    parameters=linear.parameters())
-            out.backward()
-            adagrad.step()
-            adagrad.clear_grad()
-
-            #Note that the learning_rate of linear_2 is 0.01.
-            linear_1 = paddle.nn.Linear(10, 10)
-            linear_2 = paddle.nn.Linear(10, 10)
-            inp = paddle.uniform(shape=[10, 10], min=-0.1, max=0.1)
-            out = linear_1(inp)
-            out = linear_2(out)
-            loss = paddle.mean(out)
-            adagrad = paddle.optimizer.Adagrad(
-                learning_rate=0.1,
-                parameters=[{
-                    'params': linear_1.parameters()
-                }, {
-                    'params': linear_2.parameters(),
-                    'weight_decay': 0.001,
-                    'learning_rate': 0.1,
-                }],
-                weight_decay=0.01)
-            out.backward()
-            adagrad.step()
-            adagrad.clear_grad()
+            >>> import paddle
+
+            >>> inp = paddle.rand(shape=[10, 10])
+            >>> linear = paddle.nn.Linear(10, 10)
+            >>> out = linear(inp)
+            >>> loss = paddle.mean(out)
+            >>> adagrad = paddle.optimizer.Adagrad(learning_rate=0.1,
+            ...         parameters=linear.parameters())
+            >>> out.backward()
+            >>> adagrad.step()
+            >>> adagrad.clear_grad()
+
+            >>> # Note that the learning_rate of linear_2 is 0.01.
+            >>> linear_1 = paddle.nn.Linear(10, 10)
+            >>> linear_2 = paddle.nn.Linear(10, 10)
+            >>> inp = paddle.uniform(shape=[10, 10], min=-0.1, max=0.1)
+            >>> out = linear_1(inp)
+            >>> out = linear_2(out)
+            >>> loss = paddle.mean(out)
+            >>> adagrad = paddle.optimizer.Adagrad(
+            ...     learning_rate=0.1,
+            ...     parameters=[{
+            ...         'params': linear_1.parameters()
+            ...     }, {
+            ...         'params': linear_2.parameters(),
+            ...         'weight_decay': 0.001,
+            ...         'learning_rate': 0.1,
+            ...     }],
+            ...     weight_decay=0.01)
+            >>> out.backward()
+            >>> adagrad.step()
+            >>> adagrad.clear_grad()
 
     """
     _moment_acc_str = "moment"

python/paddle/optimizer/adam.py

@@ -98,63 +98,61 @@ class Adam(Optimizer):
         .. code-block:: python
             :name: code-example1
 
-            import paddle
-
-            linear = paddle.nn.Linear(10, 10)
-            inp = paddle.rand([10,10], dtype="float32")
-            out = linear(inp)
-            loss = paddle.mean(out)
-            adam = paddle.optimizer.Adam(learning_rate=0.1,
-                    parameters=linear.parameters())
-            loss.backward()
-            adam.step()
-            adam.clear_grad()
+            >>> import paddle
+
+            >>> linear = paddle.nn.Linear(10, 10)
+            >>> inp = paddle.rand([10,10], dtype="float32")
+            >>> out = linear(inp)
+            >>> loss = paddle.mean(out)
+            >>> adam = paddle.optimizer.Adam(learning_rate=0.1,
+            ...         parameters=linear.parameters())
+            >>> loss.backward()
+            >>> adam.step()
+            >>> adam.clear_grad()
 
         .. code-block:: python
             :name: code-example2
 
-            # Adam with beta1/beta2 as Tensor and weight_decay as float
-            import paddle
-
-            linear = paddle.nn.Linear(10, 10)
-            inp = paddle.rand([10,10], dtype="float32")
-            out = linear(inp)
-            loss = paddle.mean(out)
-
-            beta1 = paddle.to_tensor([0.9], dtype="float32")
-            beta2 = paddle.to_tensor([0.99], dtype="float32")
-
-            adam = paddle.optimizer.Adam(learning_rate=0.1,
-                    parameters=linear.parameters(),
-                    beta1=beta1,
-                    beta2=beta2,
-                    weight_decay=0.01)
-            loss.backward()
-            adam.step()
-            adam.clear_grad()
-
-            #Note that the learning_rate of linear_2 is 0.01.
-            linear_1 = paddle.nn.Linear(10, 10)
-            linear_2 = paddle.nn.Linear(10, 10)
-            inp = paddle.uniform(shape=[10, 10], min=-0.1, max=0.1)
-            out = linear_1(inp)
-            out = linear_2(out)
-            loss = paddle.mean(out)
-            adam = paddle.optimizer.Adam(
-                learning_rate=0.1,
-                parameters=[{
-                    'params': linear_1.parameters()
-                }, {
-                    'params': linear_2.parameters(),
-                    'weight_decay': 0.001,
-                    'learning_rate': 0.1,
-                    'beta1': 0.8
-                }],
-                weight_decay=0.01,
-                beta1=0.9)
-            loss.backward()
-            adam.step()
-            adam.clear_grad()
+            >>> # Adam with beta1/beta2 as Tensor and weight_decay as float
+            >>> import paddle
+
+            >>> linear = paddle.nn.Linear(10, 10)
+            >>> inp = paddle.rand([10,10], dtype="float32")
+            >>> out = linear(inp)
+            >>> loss = paddle.mean(out)
+            >>> beta1 = paddle.to_tensor([0.9], dtype="float32")
+            >>> beta2 = paddle.to_tensor([0.99], dtype="float32")
+            >>> adam = paddle.optimizer.Adam(learning_rate=0.1,
+            ...         parameters=linear.parameters(),
+            ...         beta1=beta1,
+            ...         beta2=beta2,
+            ...         weight_decay=0.01)
+            >>> loss.backward()
+            >>> adam.step()
+            >>> adam.clear_grad()
+
+            >>> # Note that the learning_rate of linear_2 is 0.01.
+            >>> linear_1 = paddle.nn.Linear(10, 10)
+            >>> linear_2 = paddle.nn.Linear(10, 10)
+            >>> inp = paddle.uniform(shape=[10, 10], min=-0.1, max=0.1)
+            >>> out = linear_1(inp)
+            >>> out = linear_2(out)
+            >>> loss = paddle.mean(out)
+            >>> adam = paddle.optimizer.Adam(
+            ...     learning_rate=0.1,
+            ...     parameters=[{
+            ...         'params': linear_1.parameters()
+            ...     }, {
+            ...         'params': linear_2.parameters(),
+            ...         'weight_decay': 0.001,
+            ...         'learning_rate': 0.1,
+            ...         'beta1': 0.8
+            ...     }],
+            ...     weight_decay=0.01,
+            ...     beta1=0.9)
+            >>> loss.backward()
+            >>> adam.step()
+            >>> adam.clear_grad()
 
     """
     _moment1_acc_str = "moment1"
@@ -409,17 +407,17 @@ class Adam(Optimizer):
         Examples:
             .. code-block:: python
 
-                import paddle
-
-                a = paddle.rand([2,13], dtype="float32")
-                linear = paddle.nn.Linear(13, 5)
-                # This can be any optimizer supported by dygraph.
-                adam = paddle.optimizer.Adam(learning_rate = 0.01,
-                                            parameters = linear.parameters())
-                out = linear(a)
-                out.backward()
-                adam.step()
-                adam.clear_grad()
+                >>> import paddle
+
+                >>> a = paddle.rand([2,13], dtype="float32")
+                >>> linear = paddle.nn.Linear(13, 5)
+                >>> # This can be any optimizer supported by dygraph.
+                >>> adam = paddle.optimizer.Adam(learning_rate = 0.01,
+                ...                             parameters = linear.parameters())
+                >>> out = linear(a)
+                >>> out.backward()
+                >>> adam.step()
+                >>> adam.clear_grad()
         """
         if paddle.fluid.dygraph.base.in_declarative_mode():
             self._declarative_step()