fix docs

python/paddle/fluid/layers/nn.py

@@ -3222,6 +3222,7 @@ def data_norm(input,
               summary_decay_rate=0.9999999,
               enable_scale_and_shift=False):
     """
+    :alias_main: paddle.static.nn.data_norm
     :api_attr: Static Graph
 
     **Data Normalization Layer**
@@ -3246,7 +3247,7 @@ def data_norm(input,
         y_i &\\gets \\gamma \\hat{x_i} + \\beta \\qquad &//\ scale\ and\ shift
 
     Args:
-        input(variable): The input variable which is a LoDTensor.
+        input(Tensor): The input Tensor.
         act(string, Default None): Activation type, linear|relu|prelu|...
         epsilon(float, Default 1e-05):
         param_attr(ParamAttr): The parameter attribute for Parameter `scale`.
@@ -3274,16 +3275,16 @@ def data_norm(input,
         enable_scale_and_shift(bool, Default False): do scale&shift after normalization.
 
     Returns:
-        Variable: A tensor variable which is the result after applying data normalization on the input.
+        Tensor: A tensor which is the result after applying data normalization on the input.
 
     Examples:
 
         .. code-block:: python
 
-            import paddle.fluid as fluid
+            import paddle
 
-            hidden1 = fluid.data(name="hidden1", shape=[64, 200])
-            hidden2 = fluid.layers.data_norm(name="hidden2", input=hidden1)
+            x = paddle.randn(shape=[32,100])
+            hidden2 = paddle.static.nn.data_norm(input=x)
     """
     helper = LayerHelper('data_norm', **locals())
     dtype = helper.input_dtype()

python/paddle/tensor/creation.py

@@ -807,34 +807,27 @@ def meshgrid(*args, **kwargs):
     vector, and creates N-dimensional grids.
     
     Args:
-        *args(Variable|list of Variable) : tensors (tuple(list) of tensor): the shapes of input k tensors are (N1,), 
+        *args(Tensor|list of Tensor) : tensors (tuple(list) of tensor): the shapes of input k tensors are (N1,), 
             (N2,),..., (Nk,). Support data types: ``float64``, ``float32``, ``int32``, ``int64``.
         **kwargs (optional): Currently, we only accept name in **kwargs 
             The default value is None. Normally there is no need for
             user to set this property. For more information, please refer to :ref:`api_guide_Name`.
  
     Returns:
-         Variable: k tensors. The shape of each tensor is (N1, N2, ..., Nk)
+         Tensor: k tensors. The shape of each tensor is (N1, N2, ..., Nk)
 
     Examples:
       .. code-block:: python
 
           import paddle
-          import paddle.fluid as fluid
-          import numpy as np
 
-          x = fluid.data(name='x', shape=[100], dtype='int32')
-          y = fluid.data(name='y', shape=[200], dtype='int32')
+          x = paddle.randint(low=0, high=100, shape=[100])
+          y = paddle.randint(low=0, high=100, shape=[200])
 
-          input_1 = np.random.randint(0, 100, [100, ]).astype('int32')
-          input_2 = np.random.randint(0, 100, [200, ]).astype('int32')
+          grid_x, grid_y = paddle.meshgrid(x, y)
 
-          exe = fluid.Executor(place=fluid.CPUPlace())
-          grid_x, grid_y = paddle.tensor.meshgrid(x, y)
-          res_1, res_2 = exe.run(fluid.default_main_program(),
-                                 feed={'x': input_1,
-                                       'y': input_2},
-                                 fetch_list=[grid_x, grid_y])
+          print(grid_x.shape)
+          print(grid_y.shape)
 
           #the shape of res_1 is (100, 200)
           #the shape of res_2 is (100, 200)

python/paddle/tensor/linalg.py

@@ -813,19 +813,17 @@ def bmm(x, y, name=None):
     if x is a (b, m, k) tensor, y is a (b, k, n) tensor, the output will be a (b, m, n) tensor.
 
     Args:
-        x (Variable): The input variable which is a Tensor or LoDTensor.
-        y (Variable): The input variable which is a Tensor or LoDTensor.
+        x (Tensor): The input Tensor.
+        y (Tensor): The input Tensor.
         name(str|None): A name for this layer(optional). If set None, the layer
             will be named automatically.
 
     Returns:
-        Variable: The product Tensor (or LoDTensor) variable.
+        Tensor: The product Tensor.
 
     Examples:
         import paddle
 
-        paddle.disable_static()
-
         # In imperative mode:
         # size x: (2, 2, 3) and y: (2, 3, 2)
         x = paddle.to_tensor([[[1.0, 1.0, 1.0],

python/paddle/tensor/manipulation.py

@@ -213,7 +213,7 @@ def flatten(x, start_axis=0, stop_axis=-1, name=None):
             Out.shape = (3 * 100 * 100 * 4)
 
     Args:
-        x (Variable): A tensor of number of dimentions >= axis. A tensor with data type float32,
+        x (Tensor): A tensor of number of dimentions >= axis. A tensor with data type float32,
                       float64, int8, int32, int64.
         start_axis (int): the start axis to flatten
         stop_axis (int): the stop axis to flatten
@@ -221,12 +221,12 @@ def flatten(x, start_axis=0, stop_axis=-1, name=None):
                         Generally, no setting is required. Default: None.
 
     Returns:
-        Variable: A tensor with the contents of the input tensor, with input \
+        Tensor: A tensor with the contents of the input tensor, with input \
                   axes flattened by indicated start axis and end axis. \
                   A Tensor with data type same as input x.
 
     Raises:
-        ValueError: If x is not a Variable.
+        ValueError: If x is not a Tensor.
         ValueError: If start_axis or stop_axis is illegal.
 
     Examples:
@@ -234,20 +234,16 @@ def flatten(x, start_axis=0, stop_axis=-1, name=None):
         .. code-block:: python
 
             import paddle
-            import numpy as np
-
-            paddle.disable_static()
 
             image_shape=(2, 3, 4, 4)
-            x = np.arange(image_shape[0] * image_shape[1] * image_shape[2] * image_shape[3]).reshape(image_shape) / 100.
-            x = x.astype('float32')
+            x = paddle.arange(end=image_shape[0] * image_shape[1] * image_shape[2] * image_shape[3])
+            img = paddle.reshape(x, image_shape) / 100
             
-            img = paddle.to_tensor(x)
             out = paddle.flatten(img, start_axis=1, stop_axis=2)
             # out shape is [2, 12, 4]
     """
     if not (isinstance(x, Variable)):
-        raise ValueError("The input x should be a Variable")
+        raise ValueError("The input x should be a Tensor")
 
     check_variable_and_dtype(
         x, 'x', ['float32', 'float64', 'int8', 'int32', 'int64'], 'flatten')
@@ -297,20 +293,18 @@ def roll(x, shifts, axis=None, name=None):
     the tensor will be flattened before rolling and then restored to the original shape.
 
     Args:
-        x (Variable): The x tensor variable as input.
+        x (Tensor): The x tensor variable as input.
         shifts (int|list|tuple): The number of places by which the elements
                            of the `x` tensor are shifted.
         axis (int|list|tuple|None): axis(axes) along which to roll.
 
     Returns:
-        Variable: A Tensor with same data type as `x`.
+        Tensor: A Tensor with same data type as `x`.
 
     Examples:
         .. code-block:: python
             import paddle
-            import paddle.fluid as fluid
 
-            paddle.disable_static()
             x = paddle.to_tensor([[1.0, 2.0, 3.0],
                                   [4.0, 5.0, 6.0],
                                   [7.0, 8.0, 9.0]])

python/paddle/tensor/math.py

@@ -931,31 +931,24 @@ def addmm(input, x, y, beta=1.0, alpha=1.0, name=None):
     $Input$, $x$ and $y$ can carry the LoD (Level of Details) information, or not. But the output only shares the LoD information with input $input$.
 
     Args:
-        input (Variable): The input Tensor/LoDTensor to be added to the final result.
-        x (Variable): The first input Tensor/LoDTensor for matrix multiplication.
-        y (Variable): The second input Tensor/LoDTensor for matrix multiplication.
+        input (Tensor): The input Tensor to be added to the final result.
+        x (Tensor): The first input Tensor for matrix multiplication.
+        y (Tensor): The second input Tensor for matrix multiplication.
         beta (float): Coefficient of $input$.
         alpha (float): Coefficient of $x*y$.
         name (str, optional): Name of the output. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Default is None.
 
     Returns:
-        Variable(Tensor/LoDTensor): The output Tensor/LoDTensor of addmm op.
+        Tensor: The output Tensor of addmm op.
 
     Examples:
         ..  code-block:: python
             
-            import numpy as np
             import paddle
 
-            data_x = np.ones((2, 2)).astype(np.float32)
-            data_y = np.ones((2, 2)).astype(np.float32)
-            data_input = np.ones((2, 2)).astype(np.float32)
-
-            paddle.disable_static()
-
-            x = paddle.to_tensor(data_x)
-            y = paddle.to_tensor(data_y)
-            input = paddle.to_tensor(data_input)
+            x = paddle.ones([2,2])
+            y = paddle.ones([2,2])
+            input = paddle.ones([2,2])
 
             out = paddle.tensor.addmm( input=input, x=x, y=y, beta=0.5, alpha=5.0 )