Update intermediate_source/char_rnn_classification_tutorial.py

mgs28 · svekars · web-flow · commit 0702a6495ead · 2024-10-30T21:22:54.000-04:00
Co-authored-by: Svetlana Karslioglu &lt;svekars@meta.com&gt;
diff --git a/intermediate_source/char_rnn_classification_tutorial.py b/intermediate_source/char_rnn_classification_tutorial.py
@@ -228,7 +228,7 @@ def __getitem__(self, idx):
 # First, we use the `nn.RNN implementation <https://pytorch.org/docs/stable/generated/torch.nn.RNN.html>`__.
 # Next, we define a layer that maps the RNN hidden layers to our output. And finally, we apply a ``softmax`` function. Using ``nn.RNN``
 # leads to a significant improvement in performance, such as cuDNN-accelerated kernels, versus implementing 
-# each layer as a nn.Linear. It also simplifies the implementation in forward().
+# each layer as a ``nn.Linear``. It also simplifies the implementation in ``forward()``.
 #
 
 import torch.nn as nn

Original file line number	Diff line number	Diff line change
`@@ -228,7 +228,7 @@ def __getitem__(self, idx):`
`228`	`228`	# First, we use the `nn.RNN implementation <https://pytorch.org/docs/stable/generated/torch.nn.RNN.html>`__.
`229`	`229`	# Next, we define a layer that maps the RNN hidden layers to our output. And finally, we apply a ``softmax`` function. Using ``nn.RNN``
`230`	`230`	`# leads to a significant improvement in performance, such as cuDNN-accelerated kernels, versus implementing`
`231`		`-# each layer as a nn.Linear. It also simplifies the implementation in forward().`
	`231`	+# each layer as a ``nn.Linear``. It also simplifies the implementation in ``forward()``.
`232`	`232`	`#`
`233`	`233`
`234`	`234`	`import torch.nn as nn`