doc fixes (#15990)

Summary: fixes  #15597 ,  #15283 and #10258

Differential Revision: D13649905

Pulled By: soumith

fbshipit-source-id: 753f46c2c96c61fba460019d9ed3e0d047d42ee7

diff --git a/torch/_torch_docs.py b/torch/_torch_docs.py
index aa4edc7..d85f2a1 100644 (file)
--- a/torch/_torch_docs.py
+++ b/torch/_torch_docs.py
@@ -2496,7 +2496,7 @@ the output tensor having 1 fewer dimension than :attr:`input`.
 
 Args:
     input (Tensor): the input tensor
-    dim (int or tuple of ints): the dimension or dimensions to reduce
+    dim (int): the dimension to reduce
     keepdim (bool): whether the output tensor has :attr:`dim` retained or not
     out (Tensor, optional): the output tensor
 

diff --git a/torch/functional.py b/torch/functional.py
index 54e87f2..197509e 100644 (file)
--- a/torch/functional.py
+++ b/torch/functional.py
@@ -244,8 +244,8 @@ def isinf(tensor):
 
 def meshgrid(*tensors, **kwargs):
     r"""Take :math:`N` tensors, each of which can be either scalar or 1-dimensional
-vector, and create :math:`N` N-dimensional grids, where the :math:`i`th grid is defined by
-expanding the :math:`i`th input over dimensions defined by other inputs.
+vector, and create :math:`N` N-dimensional grids, where the :math:`i` :sup:`th` grid is defined by
+expanding the :math:`i` :sup:`th` input over dimensions defined by other inputs.
 
 
     Args:

diff --git a/torch/nn/modules/loss.py b/torch/nn/modules/loss.py
index ff1c6a0..900756b 100644 (file)
--- a/torch/nn/modules/loss.py
+++ b/torch/nn/modules/loss.py
@@ -821,9 +821,9 @@ class CrossEntropyLoss(_WeightedLoss):
     assigning weight to each of the classes.
     This is particularly useful when you have an unbalanced training set.
 
-    The `input` is expected to contain scores for each class.
+    The `input` is expected to contain raw, unnormalized scores for each class.
 
-    `input` has to be a Tensor of size either :math:`(minibatch, C)` or
+     `input` has to be a Tensor of size either :math:`(minibatch, C)` or
     :math:`(minibatch, C, d_1, d_2, ..., d_K)`
     with :math:`K \geq 2` for the `K`-dimensional case (described later).