Fixed incorrect description of what each dimension represents

vaharoni · vaharoni · commit dda235279ab8 · 2025-10-27T21:47:11.000+01:00
diff --git a/beginner_source/basics/buildmodel_tutorial.py b/beginner_source/basics/buildmodel_tutorial.py
@@ -75,7 +75,7 @@ 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 the batch dimension (one entry per example in the input) and dim=1 corresponding to the 10 raw predicted class scores (logits) for each example.
 # 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
`@@ -75,7 +75,7 @@ def forward(self, x):`
`75`	`75`	# along with some `background operations <https://github.com/pytorch/pytorch/blob/270111b7b611d174967ed204776985cefca9c144/torch/nn/modules/module.py#L866>`_.
`76`	`76`	# Do not call ``model.forward()`` directly!
`77`	`77`	`#`
`78`		`-# 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.`
	`78`	`+# Calling the model on the input returns a 2-dimensional tensor with dim=0 corresponding to the batch dimension (one entry per example in the input) and dim=1 corresponding to the 10 raw predicted class scores (logits) for each example.`
`79`	`79`	# We get the prediction probabilities by passing it through an instance of the ``nn.Softmax`` module.
`80`	`80`
`81`	`81`	`X = torch.rand(1, 28, 28, device=device)`