[DOC] activation api doc, test=document_fix (#28405)

python/paddle/nn/__init__.py

@@ -61,7 +61,7 @@ from .layer.activation import SELU  #DEFINE_ALIAS
 from .layer.activation import LeakyReLU  #DEFINE_ALIAS
 from .layer.activation import Sigmoid  #DEFINE_ALIAS
 from .layer.activation import Hardsigmoid  #DEFINE_ALIAS
-from .layer.activation import LogSigmoid
+from .layer.activation import LogSigmoid  #DEFINE_ALIAS
 from .layer.activation import Softmax  #DEFINE_ALIAS
 from .layer.activation import Softplus  #DEFINE_ALIAS
 from .layer.activation import Softshrink  #DEFINE_ALIAS

python/paddle/nn/functional/activation.py

@@ -81,8 +81,6 @@ def elu(x, alpha=1.0, name=None):
             import paddle.nn.functional as F
             import numpy as np
 
-            paddle.disable_static()
-
             x = paddle.to_tensor(np.array([[-1,6],[1,15.6]]))
             out = F.elu(x, alpha=0.2)
             # [[-0.12642411  6.        ]
@@ -135,8 +133,6 @@ def gelu(x, approximate=False, name=None):
             import paddle.nn.functional as F
             import numpy as np
 
-            paddle.disable_static()
-
             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]
@@ -237,8 +233,6 @@ def hardtanh(x, min=-1.0, max=1.0, name=None):
             import paddle.nn.functional as F
             import numpy as np
 
-            paddle.disable_static()
-
             x = paddle.to_tensor(np.array([-1.5, 0.3, 2.5]))
             out = F.hardtanh(x) # [-1., 0.3, 1.]
     """
@@ -439,8 +433,6 @@ def prelu(x, weight, name=None):
             import paddle.nn.functional as F
             import numpy as np
 
-            paddle.disable_static()
-
             data = 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]],
@@ -512,8 +504,6 @@ def relu(x, name=None):
             import paddle.nn.functional as F
             import numpy as np
 
-            paddle.disable_static()
-
             x = paddle.to_tensor(np.array([-2, 0, 1]).astype('float32'))
             out = F.relu(x) # [0., 0., 1.]
     """
@@ -550,8 +540,6 @@ def log_sigmoid(x, name=None):
             import paddle
             import paddle.nn.functional as F
 
-            paddle.disable_static()
-
             x = paddle.to_tensor([1.0, 2.0, 3.0, 4.0])
             out = F.log_sigmoid(x) # [-0.313262 -0.126928 -0.0485874 -0.0181499]
     """
@@ -823,12 +811,7 @@ def softmax(x, axis=-1, dtype=None, name=None):
             calculations. It should be in range [-D, D), where D is the
             dimensions of ``x`` . If ``axis`` < 0, it works the same way as
             :math:`axis + D` . Default is -1.
-        dtype (str|np.dtype|core.VarDesc.VarType, optional): The desired data
-            type of the output tensor. If dtype is specified, ``x`` is casted
-            to ``dtype`` before the operation is performed. This is useful for
-            preventing data type overflows. Supported dtype: float32, float64.
-            If ``dtype`` is None, the output Tensor has the same dtype as x.
-            Default is None.
+        dtype (str, optional): The data type of the output tensor, can be float32, float64.
         name (str, optional): Name for the operation (optional, default is None).
             For more information, please refer to :ref:`api_guide_Name`.
 
@@ -843,8 +826,6 @@ def softmax(x, axis=-1, dtype=None, name=None):
             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]],

python/paddle/nn/layer/__init__.py

@@ -35,11 +35,11 @@ from .rnn import *
 from .vision import *
 
 from .transformer import *
-# from .activation import PReLU        #DEFINE_ALIAS
+from .activation import PReLU  #DEFINE_ALIAS
 from .activation import ReLU  #DEFINE_ALIAS
 from .activation import LeakyReLU  #DEFINE_ALIAS
 from .activation import Sigmoid  #DEFINE_ALIAS
-# from .activation import Softmax        #DEFINE_ALIAS
+from .activation import Softmax  #DEFINE_ALIAS
 from .activation import LogSoftmax  #DEFINE_ALIAS
 from .common import BilinearTensorProduct  #DEFINE_ALIAS
 from .common import Bilinear  #DEFINE_ALIAS

python/paddle/nn/layer/activation.py

@@ -72,8 +72,6 @@ class ELU(layers.Layer):
             import paddle
             import numpy as np
 
-            paddle.disable_static()
-
             x = paddle.to_tensor(np.array([[-1,6],[1,15.6]]))
             m = paddle.nn.ELU(0.2)
             out = m(x)
@@ -121,8 +119,6 @@ class GELU(layers.Layer):
             import paddle
             import numpy as np
 
-            paddle.disable_static()
-
             x = paddle.to_tensor(np.array([[-1, 0.5],[1, 1.5]]))
 
             m = paddle.nn.GELU()
@@ -301,8 +297,6 @@ class Hardtanh(layers.Layer):
             import paddle
             import numpy as np
 
-            paddle.disable_static()
-
             x = paddle.to_tensor(np.array([-1.5, 0.3, 2.5]))
             m = paddle.nn.Hardtanh()
             out = m(x) # # [-1., 0.3, 1.]
@@ -333,7 +327,7 @@ class PReLU(layers.Layer):
             Default is 1.
         init (float, optional): Init value of learnable `weight`. Default is 0.25.
         weight_attr(ParamAttr, optional): The parameter attribute for the learnable `weight`.
-            Default is None. For more information, please refer to :ref:`api_fluid_ParamAttr`.
+            Default is None. For more information, please refer to :ref:`api_paddle_ParamAttr`.
         name (str, optional): Name for the operation (optional, default is None).
             For more information, please refer to :ref:`api_guide_Name`.
 
@@ -347,7 +341,6 @@ class PReLU(layers.Layer):
             import paddle
             import numpy as np
 
-            paddle.disable_static()
             paddle.set_default_dtype("float64")
 
             data = np.array([[[[-2.0,  3.0, -4.0,  5.0],
@@ -408,8 +401,6 @@ class ReLU(layers.Layer):
             import paddle
             import numpy as np
 
-            paddle.disable_static()
-
             x = paddle.to_tensor(np.array([-2, 0, 1]).astype('float32'))
             m = paddle.nn.ReLU()
             out = m(x) # [0., 0., 1.]
@@ -885,8 +876,6 @@ class LogSigmoid(layers.Layer):
 
             import paddle
 
-            paddle.disable_static()
-
             x = paddle.to_tensor([1.0, 2.0, 3.0, 4.0])
             m = paddle.nn.LogSigmoid()
             out = m(x) # [-0.313262 -0.126928 -0.0485874 -0.0181499]
@@ -983,12 +972,6 @@ class Softmax(layers.Layer):
             calculations. It should be in range [-D, D), where D is the
             dimensions of ``x`` . If ``axis`` < 0, it works the same way as
             :math:`axis + D` . Default is -1.
-        dtype (str|np.dtype|core.VarDesc.VarType, optional): The desired data
-            type of the output tensor. If dtype is specified, ``x`` is casted
-            to ``dtype`` before the operation is performed. This is useful for
-            preventing data type overflows. Supported dtype: float32, float64.
-            If ``dtype`` is None, the output Tensor has the same dtype as x.
-            Default is None.
         name (str, optional): Name for the operation (optional, default is None).
             For more information, please refer to :ref:`api_guide_Name`.
 
@@ -1002,8 +985,6 @@ class Softmax(layers.Layer):
             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]],