fix 2.0 api docs (#28445)

python/paddle/fluid/layers/nn.py

@@ -9730,15 +9730,13 @@ def swish(x, beta=1.0, name=None):
     return out
 
 
-@deprecated(since="2.0.0", update_to="paddle.nn.functional.prelu")
+@deprecated(since="2.0.0", update_to="paddle.static.nn.prelu")
 def prelu(x, mode, param_attr=None, name=None):
     """
-    :api_attr: Static Graph
-
-    Equation:
+    prelu activation.
 
     .. math::
-        y = \max(0, x) + \\alpha * \min(0, x)
+        prelu(x) = max(0, x) + \\alpha * min(0, x)
 
     There are three modes for the activation:
 
@@ -9748,34 +9746,28 @@ def prelu(x, mode, param_attr=None, name=None):
         channel: Elements in same channel share same alpha.
         element: All elements do not share alpha. Each element has its own alpha.
 
-    Args:
-        x (Variable): The input Tensor or LoDTensor with data type float32.
+    Parameters:
+        x (Tensor): The input Tensor or LoDTensor with data type float32.
         mode (str): The mode for weight sharing.
-        param_attr(ParamAttr|None): The parameter attribute for the learnable
+        param_attr (ParamAttr|None, optional): The parameter attribute for the learnable
             weight (alpha), it can be create by ParamAttr. None by default.
             For detailed information, please refer to :ref:`api_fluid_ParamAttr`.
-        name(str|None): For detailed information, please refer
-          to :ref:`api_guide_Name`. Usually name is no need to set and
-          None by default.
+        name (str, optional): Name for the operation (optional, default is None).
+            For more information, please refer to :ref:`api_guide_Name`.
 
     Returns:
-        Variable:
-
-        output(Variable): The tensor or LoDTensor with the same shape as input.
-        The data type is float32.
+        Tensor: A tensor with the same shape and data type as x.
 
     Examples:
 
         .. code-block:: python
 
-            import paddle.fluid as fluid
             import paddle
-            paddle.enable_static()
-            from paddle.fluid.param_attr import ParamAttr
-            x = fluid.data(name="x", shape=[None,5,10,10], dtype="float32")
-            mode = 'channel'
-            output = fluid.layers.prelu(
-                     x,mode,param_attr=ParamAttr(name='alpha'))
+
+            x = paddle.to_tensor([-1., 2., 3.])
+            param = paddle.ParamAttr(initializer=paddle.nn.initializer.Constant(0.2))
+            out = paddle.static.nn.prelu(x, 'all', param)
+            # [-0.2, 2., 3.]
 
     """
     check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'prelu')

python/paddle/nn/functional/activation.py

@@ -79,9 +79,8 @@ def elu(x, alpha=1.0, name=None):
 
             import paddle
             import paddle.nn.functional as F
-            import numpy as np
 
-            x = paddle.to_tensor(np.array([[-1,6],[1,15.6]]))
+            x = paddle.to_tensor([[-1., 6.], [1., 15.6]])
             out = F.elu(x, alpha=0.2)
             # [[-0.12642411  6.        ]
             #  [ 1.          15.6      ]]
@@ -131,11 +130,14 @@ def gelu(x, approximate=False, name=None):
 
             import paddle
             import paddle.nn.functional as F
-            import numpy as np
 
-            x = paddle.to_tensor(np.array([[-1, 0.5],[1, 1.5]]))
-            out1 = F.gelu(x) # [-0.158655 0.345731 0.841345 1.39979]
-            out2 = F.gelu(x, True) # [-0.158808 0.345714 0.841192 1.39957]
+            x = paddle.to_tensor([[-1, 0.5], [1, 1.5]])
+            out1 = F.gelu(x)
+            # [[-0.15865529,  0.34573123],
+            #  [ 0.84134471,  1.39978933]]
+            out2 = F.gelu(x, True)
+            # [[-0.15880799,  0.34571400],
+            #  [ 0.84119201,  1.39957154]]
     """
 
     if in_dygraph_mode():
@@ -181,11 +183,8 @@ def hardshrink(x, threshold=0.5, name=None):
 
             import paddle
             import paddle.nn.functional as F
-            import numpy as np
 
-            paddle.disable_static()
-
-            x = paddle.to_tensor(np.array([-1, 0.3, 2.5]))
+            x = paddle.to_tensor([-1, 0.3, 2.5])
             out = F.hardshrink(x) # [-1., 0., 2.5]
 
     """
@@ -385,11 +384,8 @@ def leaky_relu(x, negative_slope=0.01, name=None):
 
             import paddle
             import paddle.nn.functional as F
-            import numpy as np
-
-            paddle.disable_static()
 
-            x = paddle.to_tensor(np.array([-2, 0, 1], 'float32'))
+            x = paddle.to_tensor([-2., 0., 1.])
             out = F.leaky_relu(x) # [-0.02, 0., 1.]
 
     """
@@ -1147,8 +1143,10 @@ def log_softmax(x, axis=-1, dtype=None, name=None):
 
     .. math::
 
-        log\\_softmax[i, j] = log(softmax(x))
-                            = log(\\frac{\exp(X[i, j])}{\\sum_j(exp(X[i, j])})
+        \\begin{aligned} 
+        log\\_softmax[i, j] &= log(softmax(x)) \\\\
+        &= log(\\frac{\\exp(X[i, j])}{\\sum_j(\\exp(X[i, j])})
+        \\end{aligned}
 
     Parameters:
         x (Tensor): The input Tensor with data type float32, float64.
@@ -1174,16 +1172,13 @@ def log_softmax(x, axis=-1, dtype=None, name=None):
 
             import paddle
             import paddle.nn.functional as F
-            import numpy as np
 
-            paddle.disable_static()
-
-            x = np.array([[[-2.0, 3.0, -4.0, 5.0],
+            x = [[[-2.0, 3.0, -4.0, 5.0],
                   [3.0, -4.0, 5.0, -6.0],
                   [-7.0, -8.0, 8.0, 9.0]],
                  [[1.0, -2.0, -3.0, 4.0],
                   [-5.0, 6.0, 7.0, -8.0],
-                            [6.0, 7.0, 8.0, 9.0]]], 'float32')
+                  [6.0, 7.0, 8.0, 9.0]]]
             x = paddle.to_tensor(x)
             out1 = F.log_softmax(x)
             out2 = F.log_softmax(x, dtype='float64')

python/paddle/nn/layer/activation.py

@@ -70,9 +70,8 @@ class ELU(layers.Layer):
         .. code-block:: python
 
             import paddle
-            import numpy as np
 
-            x = paddle.to_tensor(np.array([[-1,6],[1,15.6]]))
+            x = paddle.to_tensor([[-1. ,6.], [1., 15.6]])
             m = paddle.nn.ELU(0.2)
             out = m(x)
             # [[-0.12642411  6.        ]
@@ -166,11 +165,8 @@ class Hardshrink(layers.Layer):
         .. code-block:: python
 
             import paddle
-            import numpy as np
-
-            paddle.disable_static()
 
-            x = paddle.to_tensor(np.array([-1, 0.3, 2.5]))
+            x = paddle.to_tensor([-1, 0.3, 2.5])
             m = paddle.nn.Hardshrink()
             out = m(x) # [-1., 0., 2.5]
     """
@@ -293,11 +289,10 @@ class Hardtanh(layers.Layer):
         .. code-block:: python
 
             import paddle
-            import numpy as np
 
-            x = paddle.to_tensor(np.array([-1.5, 0.3, 2.5]))
+            x = paddle.to_tensor([-1.5, 0.3, 2.5])
             m = paddle.nn.Hardtanh()
-            out = m(x) # # [-1., 0.3, 1.]
+            out = m(x) # [-1., 0.3, 1.]
     """
 
     def __init__(self, min=-1.0, max=1.0, name=None):
@@ -397,9 +392,8 @@ class ReLU(layers.Layer):
         .. code-block:: python
 
             import paddle
-            import numpy as np
 
-            x = paddle.to_tensor(np.array([-2, 0, 1]).astype('float32'))
+            x = paddle.to_tensor([-2., 0., 1.])
             m = paddle.nn.ReLU()
             out = m(x) # [0., 0., 1.]
     """
@@ -613,7 +607,7 @@ class Hardsigmoid(layers.Layer):
 
           import paddle
 
-          m = paddle.nn.Sigmoid()
+          m = paddle.nn.Hardsigmoid()
           x = paddle.to_tensor([-4., 5., 1.])
           out = m(x) # [0., 1, 0.666667]
     """
@@ -1016,8 +1010,10 @@ class LogSoftmax(layers.Layer):
 
     .. math::
 
-        Out[i, j] = log(softmax(x))
-                  = log(\\frac{\exp(X[i, j])}{\\sum_j(exp(X[i, j])})
+        \\begin{aligned} 
+        Out[i, j] &= log(softmax(x)) \\\\
+        &= log(\\frac{\\exp(X[i, j])}{\\sum_j(\\exp(X[i, j])})
+        \\end{aligned}
 
     Parameters:
         axis (int, optional): The axis along which to perform log_softmax
@@ -1035,16 +1031,13 @@ class LogSoftmax(layers.Layer):
         .. code-block:: python
 
             import paddle
-            import numpy as np
 
-            paddle.disable_static()
-
-            x = np.array([[[-2.0, 3.0, -4.0, 5.0],
+            x = [[[-2.0, 3.0, -4.0, 5.0],
                   [3.0, -4.0, 5.0, -6.0],
                   [-7.0, -8.0, 8.0, 9.0]],
                  [[1.0, -2.0, -3.0, 4.0],
                   [-5.0, 6.0, 7.0, -8.0],
-                           [6.0, 7.0, 8.0, 9.0]]])
+                  [6.0, 7.0, 8.0, 9.0]]]
             m = paddle.nn.LogSoftmax()
             x = paddle.to_tensor(x)
             out = m(x)

python/paddle/tensor/creation.py

@@ -300,9 +300,6 @@ def ones(shape, dtype=None, name=None):
 
 def ones_like(x, dtype=None, name=None):
     """
-	:alias_main: paddle.ones_like
-	:alias: paddle.tensor.ones_like, paddle.tensor.creation.ones_like
-
     This OP returns a Tensor filled with the value 1, with the same shape and
     data type (use ``dtype`` if ``dtype`` is not None) as ``x``.
 
@@ -330,11 +327,9 @@ def ones_like(x, dtype=None, name=None):
 
             import paddle
 
-            paddle.disable_static()
-
             x = paddle.to_tensor([1,2,3])
-            out1 = paddle.zeros_like(x) # [1., 1., 1.]
-            out2 = paddle.zeros_like(x, dtype='int32') # [1, 1, 1]
+            out1 = paddle.ones_like(x) # [1., 1., 1.]
+            out2 = paddle.ones_like(x, dtype='int32') # [1, 1, 1]
 
     """
     return full_like(x=x, fill_value=1, dtype=dtype, name=name)
@@ -380,9 +375,6 @@ def zeros(shape, dtype=None, name=None):
 
 def zeros_like(x, dtype=None, name=None):
     """
-	:alias_main: paddle.zeros_like
-	:alias: paddle.tensor.zeros_like, paddle.tensor.creation.zeros_like
-
     This OP returns a Tensor filled with the value 0, with the same shape and
     data type (use ``dtype`` if ``dtype`` is not None) as ``x``.
 
@@ -410,9 +402,7 @@ def zeros_like(x, dtype=None, name=None):
 
             import paddle
 
-            paddle.disable_static()
-
-            x = paddle.to_tensor([1,2,3])
+            x = paddle.to_tensor([1, 2, 3])
             out1 = paddle.zeros_like(x) # [0., 0., 0.]
             out2 = paddle.zeros_like(x, dtype='int32') # [0, 0, 0]
 
@@ -519,9 +509,6 @@ def full(shape, fill_value, dtype=None, name=None):
 
 def arange(start=0, end=None, step=1, dtype=None, name=None):
     """
-	:alias_main: paddle.arange
-	:alias: paddle.tensor.arange, paddle.tensor.creation.arange
-
     This OP returns a 1-D Tensor with spaced values within a given interval.
 
     Values are generated into the half-open interval [``start``, ``end``) with
@@ -558,14 +545,11 @@ def arange(start=0, end=None, step=1, dtype=None, name=None):
     Raises:
         TypeError: If ``dtype`` is not int32, int64, float32, float64.
 
-    examples:
-
+    Examples:
         .. code-block:: python
 
             import paddle
 
-        paddle.disable_static()
-
             out1 = paddle.arange(5)
             # [0, 1, 2, 3, 4]
 

python/paddle/tensor/random.py

@@ -252,16 +252,14 @@ def standard_normal(shape, dtype=None, name=None):
 
             import paddle
 
-            paddle.disable_static()
-
             # example 1: attr shape is a list which doesn't contain Tensor.
             out1 = paddle.standard_normal(shape=[2, 3])
             # [[-2.923464  ,  0.11934398, -0.51249987],  # random
             #  [ 0.39632758,  0.08177969,  0.2692008 ]]  # random
 
             # example 2: attr shape is a list which contains Tensor.
-            dim1 = paddle.full([1], 2, "int64")
-            dim2 = paddle.full([1], 3, "int32")
+            dim1 = paddle.to_tensor([2], 'int64')
+            dim2 = paddle.to_tensor([3], 'int32')
             out2 = paddle.standard_normal(shape=[dim1, dim2, 2])
             # [[[-2.8852394 , -0.25898588],  # random
             #   [-0.47420555,  0.17683524],  # random
@@ -272,8 +270,7 @@ def standard_normal(shape, dtype=None, name=None):
 
             # example 3: attr shape is a Tensor, the data type must be int64 or int32.
             shape_tensor = paddle.to_tensor([2, 3])
-            result_3 = paddle.standard_normal(shape_tensor)
-
+            out3 = paddle.standard_normal(shape_tensor)
             # [[-2.878077 ,  0.17099959,  0.05111201]  # random
             #  [-0.3761474, -1.044801  ,  1.1870178 ]]  # random
 
@@ -281,7 +278,58 @@ def standard_normal(shape, dtype=None, name=None):
     return gaussian(shape=shape, mean=0.0, std=1.0, dtype=dtype, name=name)
 
 
-randn = standard_normal
+def randn(shape, dtype=None, name=None):
+    """
+    This OP returns a Tensor filled with random values sampled from a standard
+    normal distribution with mean 0 and standard deviation 1, with ``shape``
+    and ``dtype``.
+
+    Args:
+        shape (list|tuple|Tensor): The shape of the output Tensor. If ``shape``
+            is a list or tuple, the elements of it should be integers or Tensors
+            (with the shape [1], and the data type int32 or int64). If ``shape``
+            is a Tensor, it should be a 1-D Tensor(with the data type int32 or
+            int64).
+        dtype (str|np.dtype, optional): The data type of the output Tensor.
+            Supported data types: float32, float64.
+            Default is None, use global default dtype (see ``get_default_dtype``
+            for details).
+        name (str, optional): Name for the operation (optional, default is None).
+            For more information, please refer to :ref:`api_guide_Name`.
+
+    Returns:
+        Tensor: A Tensor filled with random values sampled from a standard
+        normal distribution with mean 0 and standard deviation 1, with
+        ``shape`` and ``dtype``.
+
+    Examples:
+        .. code-block:: python
+
+            import paddle
+
+            # example 1: attr shape is a list which doesn't contain Tensor.
+            out1 = paddle.randn(shape=[2, 3])
+            # [[-2.923464  ,  0.11934398, -0.51249987],  # random
+            #  [ 0.39632758,  0.08177969,  0.2692008 ]]  # random
+
+            # example 2: attr shape is a list which contains Tensor.
+            dim1 = paddle.to_tensor([2], 'int64')
+            dim2 = paddle.to_tensor([3], 'int32')
+            out2 = paddle.randn(shape=[dim1, dim2, 2])
+            # [[[-2.8852394 , -0.25898588],  # random
+            #   [-0.47420555,  0.17683524],  # random
+            #   [-0.7989969 ,  0.00754541]],  # random
+            #  [[ 0.85201347,  0.32320443],  # random
+            #   [ 1.1399018 ,  0.48336947],  # random
+            #   [ 0.8086993 ,  0.6868893 ]]]  # random
+
+            # example 3: attr shape is a Tensor, the data type must be int64 or int32.
+            shape_tensor = paddle.to_tensor([2, 3])
+            out3 = paddle.randn(shape_tensor)
+            # [[-2.878077 ,  0.17099959,  0.05111201]  # random
+            #  [-0.3761474, -1.044801  ,  1.1870178 ]]  # random
+    """
+    return standard_normal(shape, dtype, name)
 
 
 def normal(mean=0.0, std=1.0, shape=None, name=None):
@@ -322,8 +370,6 @@ def normal(mean=0.0, std=1.0, shape=None, name=None):
 
             import paddle
 
-            paddle.disable_static()
-
             out1 = paddle.normal(shape=[2, 3])
             # [[ 0.17501129  0.32364586  1.561118  ]  # random
             #  [-1.7232178   1.1545963  -0.76156676]]  # random
@@ -381,7 +427,7 @@ def uniform(shape, dtype=None, min=-1.0, max=1.0, seed=0, name=None):
 
     Examples:
 
-    ::
+    .. code-block:: text
 
         Input:
           shape = [1, 2]
@@ -423,33 +469,27 @@ def uniform(shape, dtype=None, min=-1.0, max=1.0, seed=0, name=None):
             
             import paddle
 
-            paddle.disable_static()
-
             # example 1:
             # attr shape is a list which doesn't contain Tensor.
-            result_1 = paddle.tensor.random.uniform(shape=[3, 4])
-            # [[ 0.84524226,  0.6921872,   0.56528175,  0.71690357],
-            #  [-0.34646994, -0.45116323, -0.09902662, -0.11397249],
-            #  [ 0.433519,    0.39483607, -0.8660099,   0.83664286]]
+            out1 = paddle.uniform(shape=[3, 4])
+            # [[ 0.84524226,  0.6921872,   0.56528175,  0.71690357], # random
+            #  [-0.34646994, -0.45116323, -0.09902662, -0.11397249], # random
+            #  [ 0.433519,    0.39483607, -0.8660099,   0.83664286]] # random
 
             # example 2:
             # attr shape is a list which contains Tensor.
-            dim_1 = paddle.full([1], 2, "int64")
-            dim_2 = paddle.full([1], 3, "int32")
-            result_2 = paddle.tensor.random.uniform(shape=[dim_1, dim_2])
-            # [[-0.9951253,   0.30757582, 0.9899647 ],
-            #  [ 0.5864527,   0.6607096,  -0.8886161 ]]
+            dim1 = paddle.to_tensor([2], 'int64')
+            dim2 = paddle.to_tensor([3], 'int32')
+            out2 = paddle.uniform(shape=[dim1, dim2])
+            # [[-0.9951253,   0.30757582, 0.9899647 ], # random
+            #  [ 0.5864527,   0.6607096,  -0.8886161]] # random
 
             # example 3:
             # attr shape is a Tensor, the data type must be int64 or int32.
             shape_tensor = paddle.to_tensor([2, 3])
-            result_3 = paddle.tensor.random.uniform(shape_tensor)
-            # if shape_tensor's value is [2, 3]
-            # result_3 is:
-            # [[-0.8517412,  -0.4006908,   0.2551912 ],
-            #  [ 0.3364414,   0.36278176, -0.16085452]]
-
-
+            out3 = paddle.uniform(shape_tensor)
+            # [[-0.8517412,  -0.4006908,   0.2551912 ], # random
+            #  [ 0.3364414,   0.36278176, -0.16085452]] # random
     """
     if dtype is None:
         dtype = paddle.framework.get_default_dtype()
@@ -517,8 +557,6 @@ def randint(low=0, high=None, shape=[1], dtype=None, name=None):
 
             import paddle
 
-            paddle.disable_static()
-
             # example 1:
             # attr shape is a list which doesn't contain Tensor.
             out1 = paddle.randint(low=-5, high=5, shape=[3])
@@ -526,18 +564,16 @@ def randint(low=0, high=None, shape=[1], dtype=None, name=None):
 
             # example 2:
             # attr shape is a list which contains Tensor.
-            dim1 = paddle.full([1], 2, "int64")
-            dim2 = paddle.full([1], 3, "int32")
-            out2 = paddle.randint(low=-5, high=5, shape=[dim1, dim2], dtype="int32")
+            dim1 = paddle.to_tensor([2], 'int64')
+            dim2 = paddle.to_tensor([3], 'int32')
+            out2 = paddle.randint(low=-5, high=5, shape=[dim1, dim2])
             # [[0, -1, -3],  # random
             #  [4, -2,  0]]  # random
 
             # example 3:
             # attr shape is a Tensor
-
             shape_tensor = paddle.to_tensor(3)
-            result_3 = paddle.randint(low=-5, high=5, shape=shape_tensor)
-
+            out3 = paddle.randint(low=-5, high=5, shape=shape_tensor)
             # [-2, 2, 3]  # random
 
             # example 4:
@@ -611,8 +647,6 @@ def randperm(n, dtype="int64", name=None):
 
             import paddle
 
-            paddle.disable_static()
-
             out1 = paddle.randperm(5)
             # [4, 1, 2, 3, 0]  # random
 
@@ -668,15 +702,14 @@ def rand(shape, dtype=None, name=None):
 
             import paddle
 
-            paddle.disable_static()
             # example 1: attr shape is a list which doesn't contain Tensor.
             out1 = paddle.rand(shape=[2, 3])
             # [[0.451152  , 0.55825245, 0.403311  ],  # random
             #  [0.22550228, 0.22106001, 0.7877319 ]]  # random
 
             # example 2: attr shape is a list which contains Tensor.
-            dim1 = paddle.full([1], 2, "int64")
-            dim2 = paddle.full([1], 3, "int32")
+            dim1 = paddle.to_tensor([2], 'int64')
+            dim2 = paddle.to_tensor([3], 'int32')
             out2 = paddle.rand(shape=[dim1, dim2, 2])
             # [[[0.8879919 , 0.25788337],  # random
             #   [0.28826773, 0.9712097 ],  # random
@@ -687,8 +720,7 @@ def rand(shape, dtype=None, name=None):
 
             # example 3: attr shape is a Tensor, the data type must be int64 or int32.
             shape_tensor = paddle.to_tensor([2, 3])
-            result_3 = paddle.rand(shape_tensor)
-
+            out3 = paddle.rand(shape_tensor)
             # [[0.22920267, 0.841956  , 0.05981819],  # random
             #  [0.4836288 , 0.24573246, 0.7516129 ]]  # random
 

python/paddle/tensor/stat.py

@@ -56,17 +56,13 @@ def mean(x, axis=None, keepdim=False, name=None):
         .. code-block:: python
 
             import paddle
-            import numpy as np
 
-            paddle.disable_static()
-
-            x = np.array([[[1, 2, 3, 4],
-                           [5, 6, 7, 8],
-                           [9, 10, 11, 12]],
-                          [[13, 14, 15, 16],
-                           [17, 18, 19, 20],
-                           [21, 22, 23, 24]]], 'float32')
-            x = paddle.to_tensor(x)
+            x = paddle.to_tensor([[[1., 2., 3., 4.],
+                                   [5., 6., 7., 8.],
+                                   [9., 10., 11., 12.]],
+                                  [[13., 14., 15., 16.],
+                                   [17., 18., 19., 20.],
+                                   [21., 22., 23., 24.]]])
             out1 = paddle.mean(x)
             # [12.5]
             out2 = paddle.mean(x, axis=-1)
@@ -145,12 +141,8 @@ def var(x, axis=None, unbiased=True, keepdim=False, name=None):
         .. code-block:: python
 
             import paddle
-            import numpy as np
-            
-            paddle.disable_static()
 
-            x = np.array([[1.0, 2.0, 3.0], [1.0, 4.0, 5.0]])
-            x = paddle.to_tensor(x)
+            x = paddle.to_tensor([[1.0, 2.0, 3.0], [1.0, 4.0, 5.0]])
             out1 = paddle.var(x)
             # [2.66666667]
             out2 = paddle.var(x, axis=1)
@@ -208,12 +200,8 @@ def std(x, axis=None, unbiased=True, keepdim=False, name=None):
         .. code-block:: python
 
             import paddle
-            import numpy as np
-            
-            paddle.disable_static()
 
-            x = np.array([[1.0, 2.0, 3.0], [1.0, 4.0, 5.0]])
-            x = paddle.to_tensor(x)
+            x = paddle.to_tensor([[1.0, 2.0, 3.0], [1.0, 4.0, 5.0]])
             out1 = paddle.std(x)
             # [1.63299316]
             out2 = paddle.std(x, axis=1)