From a125d6331fba3634ceebab54c6f44ea9410ee449 Mon Sep 17 00:00:00 2001
From: ceci3 <ceci3@users.noreply.github.com>
Date: Wed, 6 Jan 2021 10:56:57 +0800
Subject: [PATCH] fix bn docs (#30096)

---
 python/paddle/nn/layer/norm.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/python/paddle/nn/layer/norm.py b/python/paddle/nn/layer/norm.py
index 05af0b178a..d8a4066cf0 100644
--- a/python/paddle/nn/layer/norm.py
+++ b/python/paddle/nn/layer/norm.py
@@ -652,7 +652,7 @@ class BatchNorm1D(_BatchNormBase):
     r"""
     Applies Batch Normalization over a 2D or 3D input (a mini-batch of 1D inputswith additional channel dimension) as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift .
 
-    When track_running_stats = False, the :math:`\\mu_{\\beta}`
+    When use_global_stats = False, the :math:`\\mu_{\\beta}`
     and :math:`\\sigma_{\\beta}^{2}` are the statistics of one mini-batch.
     Calculated as follows:
 
@@ -663,7 +663,7 @@ class BatchNorm1D(_BatchNormBase):
         \\sigma_{\\beta}^{2} &\\gets \\frac{1}{m} \\sum_{i=1}^{m}(x_i - \\
         \\mu_{\\beta})^2 \\qquad &//\ mini-batch\ variance \\\\
 
-    When track_running_stats = True, the :math:`\\mu_{\\beta}`
+    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:
@@ -743,7 +743,7 @@ class BatchNorm2D(_BatchNormBase):
     r"""
     Applies Batch Normalization over a 4D input (a mini-batch of 2D inputswith additional channel dimension) as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift .
 
-    When track_running_stats = False, the :math:`\\mu_{\\beta}`
+    When use_global_stats = False, the :math:`\\mu_{\\beta}`
     and :math:`\\sigma_{\\beta}^{2}` are the statistics of one mini-batch.
     Calculated as follows:
 
@@ -754,7 +754,7 @@ class BatchNorm2D(_BatchNormBase):
         \\sigma_{\\beta}^{2} &\\gets \\frac{1}{m} \\sum_{i=1}^{m}(x_i - \\
         \\mu_{\\beta})^2 \\qquad &//\ mini-batch\ variance \\\\
 
-    When track_running_stats = True, the :math:`\\mu_{\\beta}`
+    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:
@@ -832,7 +832,7 @@ class BatchNorm3D(_BatchNormBase):
     r"""
     Applies Batch Normalization over a 5D input (a mini-batch of 3D inputswith additional channel dimension) as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift .
 
-    When track_running_stats = False, the :math:`\\mu_{\\beta}`
+    When use_global_stats = False, the :math:`\\mu_{\\beta}`
     and :math:`\\sigma_{\\beta}^{2}` are the statistics of one mini-batch.
     Calculated as follows:
 
@@ -843,7 +843,7 @@ class BatchNorm3D(_BatchNormBase):
         \\sigma_{\\beta}^{2} &\\gets \\frac{1}{m} \\sum_{i=1}^{m}(x_i - \\
         \\mu_{\\beta})^2 \\qquad &//\ mini-batch\ variance \\\\
 
-    When track_running_stats = True, the :math:`\\mu_{\\beta}`
+    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:
-- 
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