fix 2.0 api docs (#28445)

Author: zhupengyang

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
-          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.
+        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, 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],
-                            [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')
+            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]]]
             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],
-                           [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]]])
+            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]]]
             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``.
 
@@ -323,18 +320,16 @@ def ones_like(x, dtype=None, name=None):
 
     Raise:
         TypeError: If ``dtype`` is not None and is not bool, float16, float32,
-            float64, int32 or int64.
+        float64, int32 or int64.
 
     Examples:
         .. code-block:: python
 
             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``.
 
@@ -403,16 +395,14 @@ def zeros_like(x, dtype=None, name=None):
 
     Raise:
         TypeError: If ``dtype`` is not None and is not bool, float16, float32,
-            float64, int32 or int64.
+        float64, int32 or int64.
 
     Examples:
         .. code-block:: python
 
             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
@@ -552,33 +539,30 @@ def arange(start=0, end=None, step=1, dtype=None, name=None):
 
     Returns: 
         Tensor: A 1-D Tensor with values from the interval [``start``, ``end``)
-            taken with common difference ``step`` beginning from ``start``. Its
-            data type is set by ``dtype``.
+        taken with common difference ``step`` beginning from ``start``. Its
+        data type is set by ``dtype``.
 
     Raises:
         TypeError: If ``dtype`` is not int32, int64, float32, float64.
 
-    examples:
-
+    Examples:
         .. code-block:: python
 
-        import paddle
-
-        paddle.disable_static()
+            import paddle
 
-        out1 = paddle.arange(5)
-        # [0, 1, 2, 3, 4]
+            out1 = paddle.arange(5)
+            # [0, 1, 2, 3, 4]
 
-        out2 = paddle.arange(3, 9, 2.0)
-        # [3, 5, 7]
+            out2 = paddle.arange(3, 9, 2.0)
+            # [3, 5, 7]
 
-        # use 4.999 instead of 5.0 to avoid floating point rounding errors
-        out3 = paddle.arange(4.999, dtype='float32')
-        # [0., 1., 2., 3., 4.]
+            # use 4.999 instead of 5.0 to avoid floating point rounding errors
+            out3 = paddle.arange(4.999, dtype='float32')
+            # [0., 1., 2., 3., 4.]
 
-        start_var = paddle.to_tensor([3])
-        out4 = paddle.arange(start_var, 7)
-        # [3, 4, 5, 6]
+            start_var = paddle.to_tensor([3])
+            out4 = paddle.arange(start_var, 7)
+            # [3, 4, 5, 6]
              
     """
     if dtype is None: