修改COPY-FROM No.14 incubate

Signed-off-by: jjyaoao <[email protected]>

python/paddle/incubate/optimizer/functional/bfgs.py

@@ -79,20 +79,48 @@ def minimize_bfgs(
 
     Examples:
         .. code-block:: python
+            :name: code-example1
 
+            # Example1: 1D Grid Parameters
             import paddle
+            # Randomly simulate a batch of input data
+            inputs = paddle. normal(shape=(100, 1))
+            labels = inputs * 2.0
+            # define the loss function
+            def loss(w):
+                y = w * inputs
+                return paddle.nn.functional.square_error_cost(y, labels).mean()
+            # Initialize weight parameters
+            w = paddle.normal(shape=(1,))
+            # Call the bfgs method to solve the weight that makes the loss the smallest, and update the parameters
+            for epoch in range(0, 10):
+                # Call the bfgs method to optimize the loss, note that the third parameter returned represents the weight
+                w_update = paddle.incubate.optimizer.functional.minimize_bfgs(loss, w)[2]
+                # Use paddle.assign to update parameters in place
+                paddle. assign(w_update, w)
 
-            def func(x):
-                return paddle.dot(x, x)
-
-            x0 = paddle.to_tensor([1.3, 2.7])
-            results = paddle.incubate.optimizer.functional.minimize_bfgs(func, x0)
-            print("is_converge: ", results[0])
-            print("the minimum of func is: ", results[2])
-            # is_converge:  is_converge:  Tensor(shape=[1], dtype=bool, place=Place(gpu:0), stop_gradient=True,
-            #        [True])
-            # the minimum of func is:  Tensor(shape=[2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
-            #        [0., 0.])
+        .. code-block:: python
+            :name: code-example2
+
+            # Example2: Multidimensional Grid Parameters
+            import paddle
+            def flatten(x):
+                return x. flatten()
+            def unflatten(x):
+                return x.reshape((2,2))
+            # Assume the network parameters are more than one dimension
+            def net(x):
+                assert len(x.shape) > 1
+                return x.square().mean()
+            # function to be optimized
+            def bfgs_f(flatten_x):
+                return net(unflatten(flatten_x))
+            x = paddle.rand([2,2])
+            for i in range(0, 10):
+                # Flatten x before using minimize_bfgs
+                x_update = paddle.incubate.optimizer.functional.minimize_bfgs(bfgs_f, flatten(x))[2]
+                # unflatten x_update, then update parameters
+                paddle. assign(unflatten(x_update), x)
     """
 
     if dtype not in ['float32', 'float64']:

python/paddle/incubate/optimizer/functional/lbfgs.py

@@ -80,20 +80,49 @@ def minimize_lbfgs(
 
     Examples:
         .. code-block:: python
+            :name: code-example1
 
+            # Example1: 1D Grid Parameters
             import paddle
+            # Randomly simulate a batch of input data
+            inputs = paddle. normal(shape=(100, 1))
+            labels = inputs * 2.0
+            # define the loss function
+            def loss(w):
+                y = w * inputs
+                return paddle.nn.functional.square_error_cost(y, labels).mean()
+            # Initialize weight parameters
+            w = paddle.normal(shape=(1,))
+            # Call the bfgs method to solve the weight that makes the loss the smallest, and update the parameters
+            for epoch in range(0, 10):
+                # Call the bfgs method to optimize the loss, note that the third parameter returned represents the weight
+                w_update = paddle.incubate.optimizer.functional.minimize_bfgs(loss, w)[2]
+                # Use paddle.assign to update parameters in place
+                paddle. assign(w_update, w)
+
+        .. code-block:: python
+            :name: code-example2
+
+            # Example2: Multidimensional Grid Parameters
+            import paddle
+            def flatten(x):
+                return x. flatten()
+            def unflatten(x):
+                return x.reshape((2,2))
+            # Assume the network parameters are more than one dimension
+            def net(x):
+                assert len(x.shape) > 1
+                return x.square().mean()
+            # function to be optimized
+            def bfgs_f(flatten_x):
+                return net(unflatten(flatten_x))
+            x = paddle.rand([2,2])
+            for i in range(0, 10):
+                # Flatten x before using minimize_bfgs
+                x_update = paddle.incubate.optimizer.functional.minimize_bfgs(bfgs_f, flatten(x))[2]
+                # unflatten x_update, then update parameters
+                paddle. assign(unflatten(x_update), x)
 
-            def func(x):
-                return paddle.dot(x, x)
-
-            x0 = paddle.to_tensor([1.3, 2.7])
-            results = paddle.incubate.optimizer.functional.minimize_lbfgs(func, x0)
-            print("is_converge: ", results[0])
-            print("the minimum of func is: ", results[2])
-            # is_converge:  is_converge:  Tensor(shape=[1], dtype=bool, place=Place(gpu:0), stop_gradient=True,
-            #        [True])
-            # the minimum of func is:  Tensor(shape=[2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
-            #        [0., 0.])
     """
     if dtype not in ['float32', 'float64']:
         raise ValueError(

python/paddle/optimizer/lr.py

@@ -125,6 +125,19 @@ def step(self, epoch=None):
 
         Returns:
             None
+        Examples:
+            .. code-block:: python
+
+                import paddle
+                value = paddle.arange(26, dtype='float32')
+                a = paddle.reshape(value, [2, 13])
+                linear = paddle.nn.Linear(13, 5)
+                adadelta = paddle.optimizer.Adadelta(learning_rate=0.0003, epsilon=1e-06, rho=0.95,
+                                            parameters = linear.parameters())
+                out = linear(a)
+                out.backward()
+                adadelta.step()
+                adadelta.clear_grad()
 
         """
         if epoch is None: