Most Deep Learning frameworks either prioritize usability or performance. But, Pytorch demonstrates that these two objectives may coexist. Pytorch is a Python-based Machine Learning framework. It is designed to support imperative and Pythonic Programming Styles, supporting codes as models. It will make debugging easier, and it will remain efficient. It will support hardware accelerator tools like GPU (Graphic Processing Unit) and TPU (Turbo or Tensor Processing Unit).
In this solution, we use the unsqueeze function of the torch library
- Copy the code using the "Copy" button above, and paste it in a Python file in your IDE.
- Modify the values.
- Run the file and check the output.
I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.
I tested this solution in the following versions. Be mindful of changes when working with other versions.
- The solution is created in Python3.11.
- The solution is tested on torch 2.0.0 version.