add doc of optimizer.set_lr, test=develop (#2251)

add doc of optimizer.set_lr

doc/fluid/api_cn/optimizer_cn/AdadeltaOptimizer_cn.rst

@@ -101,6 +101,49 @@ Adadelta优化器，具体细节可参考论文 `ADADELTA: AN ADAPTIVE LEARNING
         optimizer.minimize(out)
         optimizer.clear_gradients()
 
+.. py:method:: set_lr()
+
+**注意：**
+
+  **1. 该API只在** `Dygraph <../../user_guides/howto/dygraph/DyGraph.html>`_ **模式下生效**  
+
+手动设置当前 ``optimizer`` 的学习率。当使用LearningRateDecay时，无法使用该API手动设置学习率，因为这将导致冲突。
+
+参数：
+    value (float|Variable) - 需要设置的学习率的值。
+
+返回：无
+
+**代码示例**
+
+.. code-block:: python
+
+    import paddle.fluid as fluid
+            
+    with fluid.dygraph.guard():
+        linear = fluid.dygraph.nn.Linear(10, 10)
+        adam = fluid.optimizer.Adam(0.1, parameter_list=linear.parameters())
+        # 通过Python float数值手动设置学习率
+        lr_list = [0.2, 0.3, 0.4, 0.5, 0.6]
+        for i in range(5):
+            adam.set_lr(lr_list[i])
+            print("current lr is {}".format(adam.current_step_lr()))
+        # 打印结果:
+        #    current lr is 0.2
+        #    current lr is 0.3
+        #    current lr is 0.4
+        #    current lr is 0.5
+        #    current lr is 0.6
+
+
+        # 通过 框架的Variable 设置学习率
+        lr_var = fluid.layers.create_global_var(shape=[1], value=0.7, dtype='float32')
+        adam.set_lr(lr_var)
+        print("current lr is {}".format(adam.current_step_lr()))
+        # 打印结果:
+        #    current lr is 0.7
+
+
 
 .. py:method:: current_step_lr()
 

doc/fluid/api_cn/optimizer_cn/AdagradOptimizer_cn.rst

@@ -120,6 +120,49 @@ Adaptive Gradient 优化器(自适应梯度优化器，简称Adagrad)可以针
         optimizer.minimize(out)
         optimizer.clear_gradients()
 
+.. py:method:: set_lr()
+
+**注意：**
+
+  **1. 该API只在** `Dygraph <../../user_guides/howto/dygraph/DyGraph.html>`_ **模式下生效**  
+
+手动设置当前 ``optimizer`` 的学习率。当使用LearningRateDecay时，无法使用该API手动设置学习率，因为这将导致冲突。
+
+参数：
+    value (float|Variable) - 需要设置的学习率的值。
+
+返回：无
+
+**代码示例**
+
+.. code-block:: python
+
+    import paddle.fluid as fluid
+            
+    with fluid.dygraph.guard():
+        linear = fluid.dygraph.nn.Linear(10, 10)
+        adam = fluid.optimizer.Adam(0.1, parameter_list=linear.parameters())
+        # 通过Python float数值手动设置学习率
+        lr_list = [0.2, 0.3, 0.4, 0.5, 0.6]
+        for i in range(5):
+            adam.set_lr(lr_list[i])
+            print("current lr is {}".format(adam.current_step_lr()))
+        # 打印结果:
+        #    current lr is 0.2
+        #    current lr is 0.3
+        #    current lr is 0.4
+        #    current lr is 0.5
+        #    current lr is 0.6
+
+
+        # 通过 框架的Variable 设置学习率
+        lr_var = fluid.layers.create_global_var(shape=[1], value=0.7, dtype='float32')
+        adam.set_lr(lr_var)
+        print("current lr is {}".format(adam.current_step_lr()))
+        # 打印结果:
+        #    current lr is 0.7
+
+
 
 .. py:method:: current_step_lr()
 

doc/fluid/api_cn/optimizer_cn/AdamOptimizer_cn.rst

@@ -194,6 +194,49 @@ Adam优化器出自 `Adam论文 <https://arxiv.org/abs/1412.6980>`_ 的第二节
         optimizer.minimize(out)
         optimizer.clear_gradients()
 
+.. py:method:: set_lr()
+
+**注意：**
+
+  **1. 该API只在** `Dygraph <../../user_guides/howto/dygraph/DyGraph.html>`_ **模式下生效**  
+
+手动设置当前 ``optimizer`` 的学习率。当使用LearningRateDecay时，无法使用该API手动设置学习率，因为这将导致冲突。
+
+参数：
+    value (float|Variable) - 需要设置的学习率的值。
+
+返回：无
+
+**代码示例**
+
+.. code-block:: python
+
+    import paddle.fluid as fluid
+            
+    with fluid.dygraph.guard():
+        linear = fluid.dygraph.nn.Linear(10, 10)
+        adam = fluid.optimizer.Adam(0.1, parameter_list=linear.parameters())
+        # 通过Python float数值手动设置学习率
+        lr_list = [0.2, 0.3, 0.4, 0.5, 0.6]
+        for i in range(5):
+            adam.set_lr(lr_list[i])
+            print("current lr is {}".format(adam.current_step_lr()))
+        # 打印结果:
+        #    current lr is 0.2
+        #    current lr is 0.3
+        #    current lr is 0.4
+        #    current lr is 0.5
+        #    current lr is 0.6
+
+
+        # 通过 框架的Variable 设置学习率
+        lr_var = fluid.layers.create_global_var(shape=[1], value=0.7, dtype='float32')
+        adam.set_lr(lr_var)
+        print("current lr is {}".format(adam.current_step_lr()))
+        # 打印结果:
+        #    current lr is 0.7
+
+
 
 .. py:method:: current_step_lr()
 

doc/fluid/api_cn/optimizer_cn/AdamaxOptimizer_cn.rst

@@ -134,6 +134,49 @@ Adamax优化器是参考 `Adam论文 <https://arxiv.org/abs/1412.6980>`_ 第7节
         optimizer.minimize(out)
         optimizer.clear_gradients()
 
+.. py:method:: set_lr()
+
+**注意：**
+
+  **1. 该API只在** `Dygraph <../../user_guides/howto/dygraph/DyGraph.html>`_ **模式下生效**  
+
+手动设置当前 ``optimizer`` 的学习率。当使用LearningRateDecay时，无法使用该API手动设置学习率，因为这将导致冲突。
+
+参数：
+    value (float|Variable) - 需要设置的学习率的值。
+
+返回：无
+
+**代码示例**
+
+.. code-block:: python
+
+    import paddle.fluid as fluid
+            
+    with fluid.dygraph.guard():
+        linear = fluid.dygraph.nn.Linear(10, 10)
+        adam = fluid.optimizer.Adam(0.1, parameter_list=linear.parameters())
+        # 通过Python float数值手动设置学习率
+        lr_list = [0.2, 0.3, 0.4, 0.5, 0.6]
+        for i in range(5):
+            adam.set_lr(lr_list[i])
+            print("current lr is {}".format(adam.current_step_lr()))
+        # 打印结果:
+        #    current lr is 0.2
+        #    current lr is 0.3
+        #    current lr is 0.4
+        #    current lr is 0.5
+        #    current lr is 0.6
+
+
+        # 通过 框架的Variable 设置学习率
+        lr_var = fluid.layers.create_global_var(shape=[1], value=0.7, dtype='float32')
+        adam.set_lr(lr_var)
+        print("current lr is {}".format(adam.current_step_lr()))
+        # 打印结果:
+        #    current lr is 0.7
+
+
 
 .. py:method:: current_step_lr()
 

doc/fluid/api_cn/optimizer_cn/DecayedAdagradOptimizer_cn.rst

@@ -114,6 +114,49 @@ Decayed Adagrad优化器，可以看做是引入了衰减率的 `Adagrad <http:/
         optimizer.minimize(out)
         optimizer.clear_gradients()
 
+.. py:method:: set_lr()
+
+**注意：**
+
+  **1. 该API只在** `Dygraph <../../user_guides/howto/dygraph/DyGraph.html>`_ **模式下生效**  
+
+手动设置当前 ``optimizer`` 的学习率。当使用LearningRateDecay时，无法使用该API手动设置学习率，因为这将导致冲突。
+
+参数：
+    value (float|Variable) - 需要设置的学习率的值。
+
+返回：无
+
+**代码示例**
+
+.. code-block:: python
+
+    import paddle.fluid as fluid
+            
+    with fluid.dygraph.guard():
+        linear = fluid.dygraph.nn.Linear(10, 10)
+        adam = fluid.optimizer.Adam(0.1, parameter_list=linear.parameters())
+        # 通过Python float数值手动设置学习率
+        lr_list = [0.2, 0.3, 0.4, 0.5, 0.6]
+        for i in range(5):
+            adam.set_lr(lr_list[i])
+            print("current lr is {}".format(adam.current_step_lr()))
+        # 打印结果:
+        #    current lr is 0.2
+        #    current lr is 0.3
+        #    current lr is 0.4
+        #    current lr is 0.5
+        #    current lr is 0.6
+
+
+        # 通过 框架的Variable 设置学习率
+        lr_var = fluid.layers.create_global_var(shape=[1], value=0.7, dtype='float32')
+        adam.set_lr(lr_var)
+        print("current lr is {}".format(adam.current_step_lr()))
+        # 打印结果:
+        #    current lr is 0.7
+
+
 
 .. py:method:: current_step_lr()
 

doc/fluid/api_cn/optimizer_cn/FtrlOptimizer_cn.rst

@@ -125,6 +125,48 @@ FTRL 原始论文: ( `https://www.eecs.tufts.edu/~dsculley/papers/ad-click-predi
         optimizer.minimize(out)
         optimizer.clear_gradients()
 
+.. py:method:: set_lr()
+
+**注意：**
+
+  **1. 该API只在** `Dygraph <../../user_guides/howto/dygraph/DyGraph.html>`_ **模式下生效**  
+
+手动设置当前 ``optimizer`` 的学习率。当使用LearningRateDecay时，无法使用该API手动设置学习率，因为这将导致冲突。
+
+参数：
+    value (float|Variable) - 需要设置的学习率的值。
+
+返回：无
+
+**代码示例**
+
+.. code-block:: python
+
+    import paddle.fluid as fluid
+            
+    with fluid.dygraph.guard():
+        linear = fluid.dygraph.nn.Linear(10, 10)
+        adam = fluid.optimizer.Adam(0.1, parameter_list=linear.parameters())
+        # 通过Python float数值手动设置学习率
+        lr_list = [0.2, 0.3, 0.4, 0.5, 0.6]
+        for i in range(5):
+            adam.set_lr(lr_list[i])
+            print("current lr is {}".format(adam.current_step_lr()))
+        # 打印结果:
+        #    current lr is 0.2
+        #    current lr is 0.3
+        #    current lr is 0.4
+        #    current lr is 0.5
+        #    current lr is 0.6
+
+
+        # 通过 框架的Variable 设置学习率
+        lr_var = fluid.layers.create_global_var(shape=[1], value=0.7, dtype='float32')
+        adam.set_lr(lr_var)
+        print("current lr is {}".format(adam.current_step_lr()))
+        # 打印结果:
+        #    current lr is 0.7
+
 
 .. py:method:: current_step_lr()
 

doc/fluid/api_cn/optimizer_cn/LambOptimizer_cn.rst

@@ -131,6 +131,49 @@ Deep Learning: Training BERT in 76 minutes <https://arxiv.org/pdf/1904.00962.pdf
         optimizer.minimize(out)
         optimizer.clear_gradients()
 
+.. py:method:: set_lr()
+
+**注意：**
+
+  **1. 该API只在** `Dygraph <../../user_guides/howto/dygraph/DyGraph.html>`_ **模式下生效**  
+
+手动设置当前 ``optimizer`` 的学习率。当使用LearningRateDecay时，无法使用该API手动设置学习率，因为这将导致冲突。
+
+参数：
+    value (float|Variable) - 需要设置的学习率的值。
+
+返回：无
+
+**代码示例**
+
+.. code-block:: python
+
+    import paddle.fluid as fluid
+            
+    with fluid.dygraph.guard():
+        linear = fluid.dygraph.nn.Linear(10, 10)
+        adam = fluid.optimizer.Adam(0.1, parameter_list=linear.parameters())
+        # 通过Python float数值手动设置学习率
+        lr_list = [0.2, 0.3, 0.4, 0.5, 0.6]
+        for i in range(5):
+            adam.set_lr(lr_list[i])
+            print("current lr is {}".format(adam.current_step_lr()))
+        # 打印结果:
+        #    current lr is 0.2
+        #    current lr is 0.3
+        #    current lr is 0.4
+        #    current lr is 0.5
+        #    current lr is 0.6
+
+
+        # 通过 框架的Variable 设置学习率
+        lr_var = fluid.layers.create_global_var(shape=[1], value=0.7, dtype='float32')
+        adam.set_lr(lr_var)
+        print("current lr is {}".format(adam.current_step_lr()))
+        # 打印结果:
+        #    current lr is 0.7
+
+
 
 .. py:method:: current_step_lr()
 

doc/fluid/api_cn/optimizer_cn/LarsMomentumOptimizer_cn.rst

@@ -101,6 +101,49 @@ LarsMomentumOptimizer
         optimizer.minimize(out)
         optimizer.clear_gradients()
 
+.. py:method:: set_lr()
+
+**注意：**
+
+  **1. 该API只在** `Dygraph <../../user_guides/howto/dygraph/DyGraph.html>`_ **模式下生效**  
+
+手动设置当前 ``optimizer`` 的学习率。当使用LearningRateDecay时，无法使用该API手动设置学习率，因为这将导致冲突。
+
+参数：
+    value (float|Variable) - 需要设置的学习率的值。
+
+返回：无
+
+**代码示例**
+
+.. code-block:: python
+
+    import paddle.fluid as fluid
+            
+    with fluid.dygraph.guard():
+        linear = fluid.dygraph.nn.Linear(10, 10)
+        adam = fluid.optimizer.Adam(0.1, parameter_list=linear.parameters())
+        # 通过Python float数值手动设置学习率
+        lr_list = [0.2, 0.3, 0.4, 0.5, 0.6]
+        for i in range(5):
+            adam.set_lr(lr_list[i])
+            print("current lr is {}".format(adam.current_step_lr()))
+        # 打印结果:
+        #    current lr is 0.2
+        #    current lr is 0.3
+        #    current lr is 0.4
+        #    current lr is 0.5
+        #    current lr is 0.6
+
+
+        # 通过 框架的Variable 设置学习率
+        lr_var = fluid.layers.create_global_var(shape=[1], value=0.7, dtype='float32')
+        adam.set_lr(lr_var)
+        print("current lr is {}".format(adam.current_step_lr()))
+        # 打印结果:
+        #    current lr is 0.7
+
+
 
 .. py:method:: current_step_lr()
 

doc/fluid/api_cn/optimizer_cn/MomentumOptimizer_cn.rst

@@ -134,6 +134,50 @@ MomentumOptimizer
         optimizer.clear_gradients()
 
 
+.. py:method:: set_lr()
+
+**注意：**
+
+  **1. 该API只在** `Dygraph <../../user_guides/howto/dygraph/DyGraph.html>`_ **模式下生效**  
+
+手动设置当前 ``optimizer`` 的学习率。当使用LearningRateDecay时，无法使用该API手动设置学习率，因为这将导致冲突。
+
+参数：
+    value (float|Variable) - 需要设置的学习率的值。
+
+返回：无
+
+**代码示例**
+
+.. code-block:: python
+
+    import paddle.fluid as fluid
+            
+    with fluid.dygraph.guard():
+        linear = fluid.dygraph.nn.Linear(10, 10)
+        adam = fluid.optimizer.Adam(0.1, parameter_list=linear.parameters())
+        # 通过Python float数值手动设置学习率
+        lr_list = [0.2, 0.3, 0.4, 0.5, 0.6]
+        for i in range(5):
+            adam.set_lr(lr_list[i])
+            print("current lr is {}".format(adam.current_step_lr()))
+        # 打印结果:
+        #    current lr is 0.2
+        #    current lr is 0.3
+        #    current lr is 0.4
+        #    current lr is 0.5
+        #    current lr is 0.6
+
+
+        # 通过 框架的Variable 设置学习率
+        lr_var = fluid.layers.create_global_var(shape=[1], value=0.7, dtype='float32')
+        adam.set_lr(lr_var)
+        print("current lr is {}".format(adam.current_step_lr()))
+        # 打印结果:
+        #    current lr is 0.7
+
+
+
 .. py:method:: current_step_lr()
 
 **注意：**

doc/fluid/api_cn/optimizer_cn/RMSPropOptimizer_cn.rst

@@ -151,6 +151,48 @@ RMSPropOptimizer
         optimizer.minimize(out)
         optimizer.clear_gradients()
 
+.. py:method:: set_lr()
+
+**注意：**
+
+  **1. 该API只在** `Dygraph <../../user_guides/howto/dygraph/DyGraph.html>`_ **模式下生效**  
+
+手动设置当前 ``optimizer`` 的学习率。当使用LearningRateDecay时，无法使用该API手动设置学习率，因为这将导致冲突。
+
+参数：
+    value (float|Variable) - 需要设置的学习率的值。
+
+返回：无
+
+**代码示例**
+
+.. code-block:: python
+
+    import paddle.fluid as fluid
+            
+    with fluid.dygraph.guard():
+        linear = fluid.dygraph.nn.Linear(10, 10)
+        adam = fluid.optimizer.Adam(0.1, parameter_list=linear.parameters())
+        # 通过Python float数值手动设置学习率
+        lr_list = [0.2, 0.3, 0.4, 0.5, 0.6]
+        for i in range(5):
+            adam.set_lr(lr_list[i])
+            print("current lr is {}".format(adam.current_step_lr()))
+        # 打印结果:
+        #    current lr is 0.2
+        #    current lr is 0.3
+        #    current lr is 0.4
+        #    current lr is 0.5
+        #    current lr is 0.6
+
+
+        # 通过 框架的Variable 设置学习率
+        lr_var = fluid.layers.create_global_var(shape=[1], value=0.7, dtype='float32')
+        adam.set_lr(lr_var)
+        print("current lr is {}".format(adam.current_step_lr()))
+        # 打印结果:
+        #    current lr is 0.7
+
 
 .. py:method:: current_step_lr()
 

doc/fluid/api_cn/optimizer_cn/SGDOptimizer_cn.rst

@@ -127,6 +127,48 @@ SGDOptimizer
         optimizer.minimize(out)
         optimizer.clear_gradients()
 
+.. py:method:: set_lr()
+
+**注意：**
+
+  **1. 该API只在** `Dygraph <../../user_guides/howto/dygraph/DyGraph.html>`_ **模式下生效**  
+
+手动设置当前 ``optimizer`` 的学习率。当使用LearningRateDecay时，无法使用该API手动设置学习率，因为这将导致冲突。
+
+参数：
+    value (float|Variable) - 需要设置的学习率的值。
+
+返回：无
+
+**代码示例**
+
+.. code-block:: python
+
+    import paddle.fluid as fluid
+            
+    with fluid.dygraph.guard():
+        linear = fluid.dygraph.nn.Linear(10, 10)
+        adam = fluid.optimizer.Adam(0.1, parameter_list=linear.parameters())
+        # 通过Python float数值手动设置学习率
+        lr_list = [0.2, 0.3, 0.4, 0.5, 0.6]
+        for i in range(5):
+            adam.set_lr(lr_list[i])
+            print("current lr is {}".format(adam.current_step_lr()))
+        # 打印结果:
+        #    current lr is 0.2
+        #    current lr is 0.3
+        #    current lr is 0.4
+        #    current lr is 0.5
+        #    current lr is 0.6
+
+
+        # 通过 框架的Variable 设置学习率
+        lr_var = fluid.layers.create_global_var(shape=[1], value=0.7, dtype='float32')
+        adam.set_lr(lr_var)
+        print("current lr is {}".format(adam.current_step_lr()))
+        # 打印结果:
+        #    current lr is 0.7
+
 
 .. py:method:: current_step_lr()