Update intermediate_source/char_rnn_classification_tutorial.py

mgs28 · svekars · web-flow · commit 3a7f9df691f3 · 2024-10-30T21:23:57.000-04:00
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
@@ -284,7 +284,7 @@ def label_from_output(output, output_labels):
 # Now all it takes to train this network is show it a bunch of examples,
 # have it make guesses, and tell it if it's wrong.
 #
-# We do this by defining a train() function which trains on a given dataset with minibatches. RNNs 
+# We do this by defining a ``train()`` function which trains the model on a given dataset using minibatches. RNNs 
 # train similar to other networks so for completeness we include a batched training method here.
 # The loop (``for i in batch``) computes the losses for each of the items in the batch before adjusting the 
 # weights. This operation is repeated until the number of epochs is reached. 

Original file line number	Diff line number	Diff line change
`@@ -284,7 +284,7 @@ def label_from_output(output, output_labels):`
`284`	`284`	`# Now all it takes to train this network is show it a bunch of examples,`
`285`	`285`	`# have it make guesses, and tell it if it's wrong.`
`286`	`286`	`#`
`287`		`-# We do this by defining a train() function which trains on a given dataset with minibatches. RNNs`
	`287`	+# We do this by defining a ``train()`` function which trains the model on a given dataset using minibatches. RNNs
`288`	`288`	`# train similar to other networks so for completeness we include a batched training method here.`
`289`	`289`	# The loop (``for i in batch``) computes the losses for each of the items in the batch before adjusting the
`290`	`290`	`# weights. This operation is repeated until the number of epochs is reached.`