Fixed typo in batchnorm docstrings

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15975

Differential Revision: D13642271

Pulled By: soumith

fbshipit-source-id: 60ffa392bf1f916f2b93c943bb44a642a9815c42

diff --git a/torch/nn/modules/batchnorm.py b/torch/nn/modules/batchnorm.py
index fbd40be..b6a692d 100644 (file)
--- a/torch/nn/modules/batchnorm.py
+++ b/torch/nn/modules/batchnorm.py
@@ -123,7 +123,7 @@ class BatchNorm1d(_BatchNorm):
         This :attr:`momentum` argument is different from one used in optimizer
         classes and the conventional notion of momentum. Mathematically, the
         update rule for running statistics here is
-        :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momemtum} \times x_t`,
+        :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`,
         where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the
         new observed value.
 
@@ -197,7 +197,7 @@ class BatchNorm2d(_BatchNorm):
         This :attr:`momentum` argument is different from one used in optimizer
         classes and the conventional notion of momentum. Mathematically, the
         update rule for running statistics here is
-        :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momemtum} \times x_t`,
+        :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`,
         where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the
         new observed value.
 
@@ -271,7 +271,7 @@ class BatchNorm3d(_BatchNorm):
         This :attr:`momentum` argument is different from one used in optimizer
         classes and the conventional notion of momentum. Mathematically, the
         update rule for running statistics here is
-        :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momemtum} \times x_t`,
+        :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`,
         where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the
         new observed value.