Update buildmodel_tutorial.py (#2131)

fzyzcjy · Svetlana Karslioglu · web-flow · commit d549c383afe3 · 2022-12-02T15:56:06.000-08:00
Co-authored-by: Svetlana Karslioglu &lt;svekars@fb.com&gt;
diff --git a/beginner_source/basics/buildmodel_tutorial.py b/beginner_source/basics/buildmodel_tutorial.py
@@ -77,7 +77,7 @@ def forward(self, x):
 # along with some `background operations <https://github.com/pytorch/pytorch/blob/270111b7b611d174967ed204776985cefca9c144/torch/nn/modules/module.py#L866>`_.
 # Do not call ``model.forward()`` directly!
 #
-# Calling the model on the input returns a 2-dimensional tensor with dim=0 corresponding to each output of 10 raw predicted values for each class, and dim=1 corresponding to the individual values of each output.  .
+# Calling the model on the input returns a 2-dimensional tensor with dim=0 corresponding to each output of 10 raw predicted values for each class, and dim=1 corresponding to the individual values of each output.
 # We get the prediction probabilities by passing it through an instance of the ``nn.Softmax`` module.
 
 X = torch.rand(1, 28, 28, device=device)

Original file line number	Diff line number	Diff line change
`@@ -77,7 +77,7 @@ def forward(self, x):`
`77`	`77`	# along with some `background operations <https://github.com/pytorch/pytorch/blob/270111b7b611d174967ed204776985cefca9c144/torch/nn/modules/module.py#L866>`_.
`78`	`78`	# Do not call ``model.forward()`` directly!
`79`	`79`	`#`
`80`		`-# Calling the model on the input returns a 2-dimensional tensor with dim=0 corresponding to each output of 10 raw predicted values for each class, and dim=1 corresponding to the individual values of each output. .`
	`80`	`+# Calling the model on the input returns a 2-dimensional tensor with dim=0 corresponding to each output of 10 raw predicted values for each class, and dim=1 corresponding to the individual values of each output.`
`81`	`81`	# We get the prediction probabilities by passing it through an instance of the ``nn.Softmax`` module.
`82`	`82`
`83`	`83`	`X = torch.rand(1, 28, 28, device=device)`