Скачать или смотреть Understanding torch.sub: Resolving the Issue of Unexpected Output Shapes in PyTorch

Discover the solution to unexpected tensor shapes when using `torch.sub` in PyTorch. Learn how to correct the output of your tensor operations effectively!
---
This video is based on the question https://stackoverflow.com/q/70633857/ asked by the user 'Jonathan Couture' ( https://stackoverflow.com/u/16568244/ ) and on the answer https://stackoverflow.com/a/70634217/ provided by the user 'Ivan' ( https://stackoverflow.com/u/6331369/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Wrong outputs from torch.sub?

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
The Challenge of Tensor Shapes in PyTorch

If you've been working with PyTorch, you might have encountered frustrating issues surrounding tensor operations, particularly when performing mathematical operations like subtraction. A common question among developers is: Why am I getting unexpected output shapes when using torch.sub?

This question arises often, especially for those calculating performance metrics like the Mean Absolute Percentage Error (MAPE) for neural networks. Let's look into an example where a user faced this issue while implementing torch.sub alongside torch.div to compute MAPE between predicted labels and true values.

The Example Explained

Consider the following tensors which represent true labels (y_true) and predicted labels (y_pred):

[[See Video to Reveal this Text or Code Snippet]]

When the user attempts to subtract these two tensors:

[[See Video to Reveal this Text or Code Snippet]]

They unexpectedly receive a tensor of shape 4x4 instead of the anticipated 4x1. This discrepancy prompts confusion and often leads to more questions than answers.

Understanding the Issue

The problem lies in the dimensions of the tensors involved in the subtraction operation. The tensor y_pred has an extra dimension that affects how PyTorch performs the subtraction. In simpler terms, y_true is getting broadcasted to match the shape of y_pred, which causes the unexpected 4x4 output.

What is Broadcasting?

Broadcasting is a powerful feature in PyTorch (and many other numerical libraries), which allows operations on tensors of different shapes. When shapes do not match, PyTorch automatically expands the smaller tensor to match the larger tensor’s shape.

Identifying the Extra Dimension

In our case, y_pred has an extra dimension due to its structure:

y_pred tensor shape: 4x1x1 (four elements in a dimension that results from the additional brackets).

y_true tensor shape: 4x1 (four elements).

The Solution

To resolve the unexpected output shape and achieve the desired 4x1 tensor, we can eliminate the extra dimension from y_pred. Here’s how to do it effectively:

Slicing the Last Dimension: You can slice the tensor y_pred to retain only the first dimension.

[[See Video to Reveal this Text or Code Snippet]]

Checking the Result: The output shape should now be as expected:

[[See Video to Reveal this Text or Code Snippet]]

This simple adjustment allows you to conduct the subtraction correctly and get your expected results without the confusion of unexpected tensor shapes.

Conclusion

Understanding tensor dimensions and the issue of broadcasting is crucial when working with operations like torch.sub in PyTorch. By ensuring that tensors are of compatible shapes before performing arithmetic, you can avoid unexpected outputs and streamline your workflow in deep learning projects.

Now that you are aware of how to resolve this common issue, happy coding! If you have any further questions, feel free to ask or share experiences you’ve encountered while working with tensor operations.