fix english doc of some API (#38468)

python/paddle/fluid/initializer.py

@@ -1148,13 +1148,14 @@ def _global_bias_initializer():
 
 def calculate_gain(nonlinearity, param=None):
     """
-    Get the recommended gain value of some nonlinearity function.
+    Get the recommended ``gain`` value of some nonlinearity function. ``gain`` value can be used in some 
+    ``paddle.nn.initializer`` api to adjust the initialization value.
 
     Args:
-        nonlinearity(str): name of nonlinearity activation function. If it is a linear function, which is one of 
-        "linear/conv1d/conv2d/conv3d/conv1d_transpose/conv2d_transpose/conv3d_transpose" , 1.0 will be returned.
+        nonlinearity(str): name of nonlinearity activation function. If it is a linear function, such as: 
+            `linear/conv1d/conv2d/conv3d/conv1d_transpose/conv2d_transpose/conv3d_transpose` , 1.0 will be returned.
         param(bool|int|float, optional): optional parameter for somme nonlinearity function. Now, it only applies to 
-        'leaky_relu'. Default: None, it will be calculated as 0.01 in the formula.
+            'leaky_relu'. Default: None, it will be calculated as 0.01 in the formula.
 
     Returns:
         A float value, which is the recommended gain for this nonlinearity function.

python/paddle/nn/initializer/dirac.py

@@ -31,12 +31,13 @@ class Dirac(Initializer):
 
     .. math::
 
-        Assuming:  N=min(in\_channels, out\_channels)
-
         X[d, d, shape[2]//2, shape[3]//2, ...]=1,  \   d=0,1...N
+    
+    where, ``N`` is the minimum value of ``in_channels`` and ``out_channels``
 
     Args:
-        groups(int): 0-dimension of the Tensor will be divided by groups, each group has the same value.
+        groups(int, optional): 0-dimension of the Tensor will be divided by groups, 
+            each group has the same value. Default: 1.
         name(str, optional): 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`.
 

python/paddle/nn/initializer/orthogonal.py

@@ -24,18 +24,24 @@ __all__ = []
 class Orthogonal(Initializer):
     """The orthogonal initializer. The initialized tensor is (semi) orthogonal.
 
-    Assuming that 'weight' will be initialized, its shape is [M, N].
+    It's only applied to Tensor whose dimension is greater than or equal to 2. 
+    
+    For the Tensor whose dimension is greater than 2, the 0 dimension is seen as ``rows`` , 
+    and the >=1 dimension are flattened as ``cols`` .
+
+    Which can be describe as:
 
     .. code-block:: text
 
-        if M < N:
+        rows = shape[0]
+        cols = shape[1]·shape[2]···shape[N]
+        
+        if rows < cols:
             The rows are orthogonal vectors
-        elif M > N:
+        elif rows > cols:
             The columns are orthogonal vectors
-        else M = N:
+        else rows = cols:
             Both rows and columns are orthogonal vectors
-    
-    Only Tensor with 2 or more dimensions can initialized by Orthogonal.
 
     Args:
         gain(float, optional): The multiplication coefficient for initialized tensor. Default: 1.0.

python/paddle/nn/utils/transform_parameters.py

@@ -76,10 +76,10 @@ def parameters_to_vector(parameters, name=None):
 @dygraph_only
 def vector_to_parameters(vec, parameters, name=None):
     """
-    Transform a Tensor with 1-D shape to the parameters.
+    Transform a 1-D Tensor to the input ``parameters`` .
 
     Args:
-        vec (Tensor): A Tensor with 1-D shape, which represents the parameters of a Layer.
+        vec (Tensor): A 1-D Tensor, which will be sliced and copied to the input ``parameters`` .
         parameters (Iterable[Tensor]): Iterable Tensors that are trainable parameters of a Layer.
         name(str, optional): 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`.

python/paddle/tensor/random.py

@@ -85,7 +85,7 @@ def poisson(x, name=None):
 
     .. math::
 
-        out_i ~ Poisson (x_i)
+        out_i \sim Poisson (lambda = x_i)
 
     Args:
         x(Tensor):  A tensor with rate parameter of poisson Distribution. The data type 
@@ -101,12 +101,12 @@ def poisson(x, name=None):
 
             import paddle
             paddle.set_device('cpu')
-            paddle.seed(2021)
+            paddle.seed(100)
 
             x = paddle.uniform([2,3], min=1.0, max=5.0)
             out = paddle.poisson(x)
-            # [[2., 1., 4.],
-            #  [4., 5., 1.]]
+            #[[2., 5., 0.],
+            # [5., 1., 3.]]
 
     """
 
@@ -994,7 +994,7 @@ def exponential_(x, lam=1.0, name=None):
 
     Args:
         x(Tensor):  Input tensor. The data type should be float32, float64.
-        lam(float): :math:`\lambda` parameter of Exponential Distribution.
+        lam(float, optional): :math:`\lambda` parameter of Exponential Distribution. Default, 1.0.
         name(str, optional): 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`.