fix the api example for create_global_var, create_parameter, SGDOptim… (#17371)

* fix the api example for create_global_var, create_parameter, SGDOptimizer, RMSPropOptimizer, MomentumOptimizer, LarsMomentumOptimizer, FtrlOptimizer
test=develop

* add example for adamoptimizer
fix API.spec
test=develop

* test=develop

* test=develop

paddle/fluid/API.spec

@@ -252,8 +252,8 @@ paddle.fluid.layers.Preprocessor.inputs (ArgSpec(args=['self'], varargs=None, ke
 paddle.fluid.layers.Preprocessor.outputs (ArgSpec(args=['self'], varargs='outs', keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
 paddle.fluid.layers.load (ArgSpec(args=['out', 'file_path', 'load_as_fp16'], varargs=None, keywords=None, defaults=(None,)), ('document', '9d1a4bc97bbce9fa1d4f7a4200a771ff'))
 paddle.fluid.layers.create_tensor (ArgSpec(args=['dtype', 'name', 'persistable'], varargs=None, keywords=None, defaults=(None, False)), ('document', 'c0c3d0194f83fff8ea99ce0820657dae'))
-paddle.fluid.layers.create_parameter (ArgSpec(args=['shape', 'dtype', 'name', 'attr', 'is_bias', 'default_initializer'], varargs=None, keywords=None, defaults=(None, None, False, None)), ('document', 'd62b866c899bc1fedb5385f95b88e1f8'))
-paddle.fluid.layers.create_global_var (ArgSpec(args=['shape', 'value', 'dtype', 'persistable', 'force_cpu', 'name'], varargs=None, keywords=None, defaults=(False, False, None)), ('document', 'ab914fac893607e29ac6e52bbdbea1a4'))
+paddle.fluid.layers.create_parameter (ArgSpec(args=['shape', 'dtype', 'name', 'attr', 'is_bias', 'default_initializer'], varargs=None, keywords=None, defaults=(None, None, False, None)), ('document', 'b6fe28cffff32d15e45c411bcf815cb7'))
+paddle.fluid.layers.create_global_var (ArgSpec(args=['shape', 'value', 'dtype', 'persistable', 'force_cpu', 'name'], varargs=None, keywords=None, defaults=(False, False, None)), ('document', '90eb79e0d1261ec2bac7c775ee4f459b'))
 paddle.fluid.layers.cast (ArgSpec(args=['x', 'dtype'], varargs=None, keywords=None, defaults=None), ('document', '992eb42590fc1c380841a6db72ce78b3'))
 paddle.fluid.layers.tensor_array_to_tensor (ArgSpec(args=['input', 'axis', 'name'], varargs=None, keywords=None, defaults=(1, None)), ('document', 'b12717d3d4567e6119589f7f655b0cbb'))
 paddle.fluid.layers.concat (ArgSpec(args=['input', 'axis', 'name'], varargs=None, keywords=None, defaults=(0, None)), ('document', 'f9e905b48123914c78055a45fe23106a'))

python/paddle/fluid/layers/tensor.py

@@ -83,9 +83,10 @@ def create_parameter(shape,
         the created parameter.
 
     Examples:
-        >>> W = fluid.layers.create_parameter(shape=[784, 200], dtype='float32')
-        >>> data = fluid.layers.data(name="img", shape=[64, 784], append_batch_size=False)
-        >>> hidden = fluid.layers.matmul(x=data, y=W)
+        .. code-block:: python
+
+            import paddle.fluid.layers as layers
+            W = layers.create_parameter(shape=[784, 200], dtype='float32')
     """
     helper = LayerHelper("create_parameter", **locals())
     if attr is None:
@@ -122,7 +123,8 @@ def create_global_var(shape,
     Examples:
         .. code-block:: python
 
-            var = fluid.create_global_var(shape=[2,3], value=1.0, dtype='float32',
+            import paddle.fluid.layers as layers
+            var = layers.create_global_var(shape=[2,3], value=1.0, dtype='float32',
                                           persistable=True, force_cpu=True, name='new_var')
     """
     helper = LayerHelper("global_var", **locals())

python/paddle/fluid/optimizer.py

@@ -524,8 +524,31 @@ class SGDOptimizer(Optimizer):
     Examples:
         .. code-block:: python
 
-            sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.2)
-            sgd_optimizer.minimize(cost)
+            import paddle
+            import paddle.fluid as fluid
+            import numpy as np
+
+            place = fluid.CPUPlace()
+            main = fluid.Program()
+            with fluid.program_guard(main):
+                x = fluid.layers.data(name='x', shape=[13], dtype='float32')
+                y = fluid.layers.data(name='y', shape=[1], dtype='float32')
+                y_predict = fluid.layers.fc(input=x, size=1, act=None)
+                cost = fluid.layers.square_error_cost(input=y_predict, label=y)
+                avg_cost = fluid.layers.mean(cost)
+
+                sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
+                sgd_optimizer.minimize(avg_cost)
+
+                fetch_list = [avg_cost]
+                train_reader = paddle.batch(
+                    paddle.dataset.uci_housing.train(), batch_size=1)
+                feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
+                exe = fluid.Executor(place)
+                exe.run(fluid.default_startup_program())
+                for data in train_reader():
+                    exe.run(main, feed=feeder.feed(data), fetch_list=fetch_list)
+
     """
 
     def __init__(self, learning_rate, regularization=None, name=None):
@@ -586,8 +609,31 @@ class MomentumOptimizer(Optimizer):
     Examples:
         .. code-block:: python
 
-            optimizer = fluid.optimizer.Momentum(learning_rate=0.2, momentum=0.1)
-            optimizer.minimize(cost)
+            import paddle
+            import paddle.fluid as fluid
+            import numpy as np
+
+            place = fluid.CPUPlace()
+            main = fluid.Program()
+            with fluid.program_guard(main):
+                x = fluid.layers.data(name='x', shape=[13], dtype='float32')
+                y = fluid.layers.data(name='y', shape=[1], dtype='float32')
+                y_predict = fluid.layers.fc(input=x, size=1, act=None)
+                cost = fluid.layers.square_error_cost(input=y_predict, label=y)
+                avg_cost = fluid.layers.mean(cost)
+
+                moment_optimizer = fluid.optimizer.MomentumOptimizer(learning_rate=0.001, momentum=0.9)
+                moment_optimizer.minimize(avg_cost)
+
+                fetch_list = [avg_cost]
+                train_reader = paddle.batch(
+                    paddle.dataset.uci_housing.train(), batch_size=1)
+                feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
+                exe = fluid.Executor(place)
+                exe.run(fluid.default_startup_program())
+                for data in train_reader():
+                    exe.run(main, feed=feeder.feed(data), fetch_list=fetch_list)
+
     """
     _velocity_acc_str = "velocity"
 
@@ -1125,8 +1171,29 @@ class AdamOptimizer(Optimizer):
     Examples:
         .. code-block:: python
 
-            optimizer = fluid.optimizer.Adam(learning_rate=0.2)
-            optimizer.minimize(cost)
+            import paddle
+            import paddle.fluid as fluid
+
+            place = fluid.CPUPlace()
+            main = fluid.Program()
+            with fluid.program_guard(main):
+                x = fluid.layers.data(name='x', shape=[13], dtype='float32')
+                y = fluid.layers.data(name='y', shape=[1], dtype='float32')
+                y_predict = fluid.layers.fc(input=x, size=1, act=None)
+                cost = fluid.layers.square_error_cost(input=y_predict, label=y)
+                avg_cost = fluid.layers.mean(cost)
+
+                adam_optimizer = fluid.optimizer.AdamOptimizer(0.01)
+                adam_optimizer.minimize(avg_cost)
+
+                fetch_list = [avg_cost]
+                train_reader = paddle.batch(
+                    paddle.dataset.uci_housing.train(), batch_size=1)
+                feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
+                exe = fluid.Executor(place)
+                exe.run(fluid.default_startup_program())
+                for data in train_reader():
+                    exe.run(main, feed=feeder.feed(data), fetch_list=fetch_list)
 
     """
     _moment1_acc_str = "moment1"
@@ -1657,8 +1724,31 @@ class RMSPropOptimizer(Optimizer):
     Examples:
           .. code-block:: python
 
-              optimizer = fluid.optimizer.RMSProp(0.0001)
-              _, params_grads = optimizer.minimize(cost)
+            import paddle
+            import paddle.fluid as fluid
+            import numpy as np
+
+            place = fluid.CPUPlace()
+            main = fluid.Program()
+            with fluid.program_guard(main):
+                x = fluid.layers.data(name='x', shape=[13], dtype='float32')
+                y = fluid.layers.data(name='y', shape=[1], dtype='float32')
+                y_predict = fluid.layers.fc(input=x, size=1, act=None)
+                cost = fluid.layers.square_error_cost(input=y_predict, label=y)
+                avg_cost = fluid.layers.mean(cost)
+
+                rms_optimizer = fluid.optimizer.RMSProp(learning_rate=0.1)
+                rms_optimizer.minimize(avg_cost)
+
+                fetch_list = [avg_cost]
+                train_reader = paddle.batch(
+                    paddle.dataset.uci_housing.train(), batch_size=1)
+                feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
+                exe = fluid.Executor(place)
+                exe.run(fluid.default_startup_program())
+                for data in train_reader():
+                    exe.run(main, feed=feeder.feed(data), fetch_list=fetch_list)
+
     """
 
     _momentum_acc_str = "momentum"
@@ -1793,8 +1883,30 @@ class FtrlOptimizer(Optimizer):
     Examples:
           .. code-block:: python
 
-              optimizer = fluid.optimizer.Ftrl(0.0001)
-              _, params_grads = optimizer.minimize(cost)
+            import paddle
+            import paddle.fluid as fluid
+            import numpy as np
+
+            place = fluid.CPUPlace()
+            main = fluid.Program()
+            with fluid.program_guard(main):
+                x = fluid.layers.data(name='x', shape=[13], dtype='float32')
+                y = fluid.layers.data(name='y', shape=[1], dtype='float32')
+                y_predict = fluid.layers.fc(input=x, size=1, act=None)
+                cost = fluid.layers.square_error_cost(input=y_predict, label=y)
+                avg_cost = fluid.layers.mean(cost)
+
+                ftrl_optimizer = fluid.optimizer.Ftrl(learning_rate=0.1)
+                ftrl_optimizer.minimize(avg_cost)
+
+                fetch_list = [avg_cost]
+                train_reader = paddle.batch(
+                    paddle.dataset.uci_housing.train(), batch_size=1)
+                feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
+                exe = fluid.Executor(place)
+                exe.run(fluid.default_startup_program())
+                for data in train_reader():
+                    exe.run(main, feed=feeder.feed(data), fetch_list=fetch_list)
 
     Notes:
        Currently, FtrlOptimizer doesn't support sparse parameter optimization.