Fix norm cn (#2532)

* fix AdamOptimizer, test=develop, test=document_fix

* fix AdamOptimizer, test=develop, test=document_fix

* fix AdamOptimizer, test=develop, test=document_fix

* fix AdamOptimizer, test=develop, test=document_fix

* fix python code

* fix norm bug cn doc, test=develop

* fix norm bug cn doc, test=develop

doc/fluid/api_cn/nn_cn/BatchNorm1d_cn.rst

@@ -70,5 +70,5 @@ BatchNorm1d
     batch_norm = paddle.nn.BatchNorm1d(1)
     batch_norm_out = batch_norm(x)
 
-    print(batch_norm_out.numpy)
+    print(batch_norm_out.numpy())
 

doc/fluid/api_cn/nn_cn/BatchNorm2d_cn.rst

@@ -70,5 +70,5 @@ BatchNorm2d
     batch_norm = paddle.nn.BatchNorm2d(1)
     batch_norm_out = batch_norm(x)
 
-    print(batch_norm_out.numpy)
+    print(batch_norm_out.numpy())
 

doc/fluid/api_cn/nn_cn/BatchNorm3d_cn.rst

@@ -70,5 +70,5 @@ BatchNorm3d
     batch_norm = paddle.nn.BatchNorm3d(1)
     batch_norm_out = batch_norm(x)
 
-    print(batch_norm_out.numpy)
+    print(batch_norm_out.numpy())
 

doc/fluid/api_cn/nn_cn/GroupNorm_cn.rst

@@ -3,15 +3,15 @@
 GroupNorm
 -------------------------------
 
-.. py:class:: paddle.nn.GroupNorm(num_channels, num_groups, epsilon=1e-05, weight_attr=None, bias_attr=None, data_layout='NCHW, 'name=None)
+.. py:class:: paddle.nn.GroupNorm(num_groups, num_channels, epsilon=1e-05, weight_attr=None, bias_attr=None, data_layout='NCHW, 'name=None)
 
 **Group Normalization层**
 
 该接口用于构建 ``GroupNorm`` 类的一个可调用对象，具体用法参照 ``代码示例`` 。其中实现了组归一化层的功能。更多详情请参考： `Group Normalization <https://arxiv.org/abs/1803.08494>`_ 。
 
 参数：
-    - **num_channels** (int) - 输入的通道数。
     - **num_groups** (int) - 从通道中分离出来的 ``group`` 的数目。
+    - **num_channels** (int) - 输入的通道数。
     - **epsilon** (float, 可选) - 为防止方差除零，增加一个很小的值。默认值：1e-05。
     - **weight_attr** (ParamAttr|bool, 可选) - 指定权重参数属性的对象。如果为False, 表示参数不学习。默认值为None，表示使用默认的权重参数属性。具体用法请参见 :ref:`cn_api_fluid_ParamAttr` 。
     - **bias_attr** (ParamAttr|bool, 可选) - 指定偏置参数属性的对象。如果为False, 表示参数不学习。默认值为None，表示使用默认的偏置参数属性。具体用法请参见 :ref:`cn_api_fluid_ParamAttr` 。
@@ -35,7 +35,7 @@ GroupNorm
    np.random.seed(123)
    x_data = np.random.random(size=(2, 6, 2, 2)).astype('float32')
    x = paddle.to_tensor(x_data) 
-   group_norm = paddle.nn.GroupNorm(num_channels=3, num_groups=6)
+   group_norm = paddle.nn.GroupNorm(num_channels=6, num_groups=6)
    group_norm_out = group_norm(x)
 
-   print(group_norm_out.numpy)
+   print(group_norm_out.numpy())

doc/fluid/api_cn/nn_cn/InstanceNorm1d_cn.rst

@@ -56,5 +56,5 @@ Note:
     instance_norm = paddle.nn.InstanceNorm1d(2)
     instance_norm_out = instance_norm(x)
 
-    print(instance_norm_out.numpy)
+    print(instance_norm_out.numpy())
 

doc/fluid/api_cn/nn_cn/InstanceNorm2d_cn.rst

@@ -55,6 +55,6 @@ Note:
     instance_norm = paddle.nn.InstanceNorm2d(2)
     instance_norm_out = instance_norm(x)
 
-    print(instance_norm_out.numpy)
+    print(instance_norm_out.numpy())
     
 

doc/fluid/api_cn/nn_cn/InstanceNorm3d_cn.rst

@@ -54,5 +54,5 @@ Note:
     instance_norm = paddle.nn.InstanceNorm3d(2)
     instance_norm_out = instance_norm(x)
 
-    print(instance_norm_out.numpy)
+    print(instance_norm_out.numpy())
 

doc/fluid/api_cn/nn_cn/LayerNorm_cn.rst

@@ -50,5 +50,5 @@ LayerNorm
     layer_norm = paddle.nn.LayerNorm(x_data.shape[1:])
     layer_norm_out = layer_norm(x)
 
-    print(layer_norm_out.numpy)
+    print(layer_norm_out.numpy())
 

doc/fluid/api_cn/nn_cn/functional_cn/batch_norm_cn.rst

@@ -44,4 +44,4 @@ batch_norm
     w = paddle.to_tensor(weight_data)
     b = paddle.to_tensor(bias_data)
     batch_norm_out = paddle.nn.functional.batch_norm(x, rm, rv, w, b)
-    print(batch_norm_out)
+    print(batch_norm_out.numpy())

doc/fluid/api_cn/nn_cn/functional_cn/instance_norm_cn.rst

@@ -44,4 +44,4 @@ instance_norm
     w = paddle.to_tensor(weight_data)
     b = paddle.to_tensor(bias_data)
     instance_norm_out = paddle.nn.functional.instance_norm(x, rm, rv, w, b)
-    print(instance_norm_out)
+    print(instance_norm_out.numpy())

doc/fluid/api_cn/nn_cn/functional_cn/layer_norm_cn.rst

@@ -33,5 +33,5 @@ layer_norm
     x = paddle.to_tensor(x_data) 
     layer_norm_out = paddle.nn.functional.layer_norm(x, x.shape[1:])
 
-    print(layer_norm_out.numpy)
+    print(layer_norm_out.numpy())