diff --git a/python/paddle/nn/layer/container.py b/python/paddle/nn/layer/container.py
index 750e65e349d743..8fa65aa8f340eb 100644
--- a/python/paddle/nn/layer/container.py
+++ b/python/paddle/nn/layer/container.py
@@ -303,7 +303,7 @@ class ParameterList(Layer):
     This container acts like a Python list, but parameters it contains will be properly added.
 
     Parameters:
-        parameters (iterable, optional): Iterable Parameters to be added
+        parameters (iterable, optional): Iterable Parameters to be added.
 
     Examples:
         .. code-block:: python
diff --git a/python/paddle/nn/layer/norm.py b/python/paddle/nn/layer/norm.py
index e01e426a75ff2c..bea68c61d79a9e 100644
--- a/python/paddle/nn/layer/norm.py
+++ b/python/paddle/nn/layer/norm.py
@@ -894,8 +894,8 @@ class BatchNorm(Layer):
     - :math:`x` : mini-batch data
     - :math:`m` : the size of the mini-batch data
 
-    When use_global_stats = True, the :math:`\\mu_{\\beta}`
-    and :math:`\\sigma_{\\beta}^{2}` are not the statistics of one mini-batch.
+    When use_global_stats = True, the :math:`\mu_{\beta}`
+    and :math:`\sigma_{\beta}^{2}` are not the statistics of one mini-batch.
     They are global or running statistics (moving_mean and moving_variance). It usually got from the
     pre-trained model. Calculated as follows:
 
@@ -1181,6 +1181,9 @@ class BatchNorm1D(_BatchNormBase):
         \sigma_{\beta}^{2} &\gets \frac{1}{m} \sum_{i=1}^{m}(x_i - \
         \mu_{\beta})^2 \qquad &//\ mini-batch\ variance \\
 
+    - :math:`x` : mini-batch data
+    - :math:`m` : the size of the mini-batch data
+
     When use_global_stats = True, the :math:`\mu_{\beta}`
     and :math:`\sigma_{\beta}^{2}` are not the statistics of one mini-batch.
     They are global or running statistics (moving_mean and moving_variance). It usually got from the
diff --git a/python/paddle/tensor/creation.py b/python/paddle/tensor/creation.py
index 528a3c40b9a4b2..1ad7c82c39a37c 100644
--- a/python/paddle/tensor/creation.py
+++ b/python/paddle/tensor/creation.py
@@ -1065,7 +1065,7 @@ def zeros(shape, dtype=None, name=None):
             If ``shape`` is a list or tuple, each element of it should be integer or 0-D Tensor with shape [].
             If ``shape`` is an Tensor, it should be an 1-D Tensor which represents a list.
         dtype(np.dtype|str, optional): Data type of output Tensor, it supports
-            bool, float16, float32, float64, int32 and int64. Default: if None, the date type is float32.
+            bool, float16, float32, float64, int32 and int64. Default: if None, the data type is float32.
         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`.