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Reshape a tensor in Pytorch

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by aryaman@openweaver.com dot icon Updated: Apr 3, 2023

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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).

Code

In this solution, we use the unsqueeze function of the torch library

Pytorch reshape tensor dimension

PythondotLines of Code : 17dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
>>> import torch
>>> a = torch.Tensor([1,2,3,4,5])
>>> a

 1
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 5
[torch.FloatTensor of size 5]

>>> a = a.unsqueeze(0)
>>> a

 1  2  3  4  5
[torch.FloatTensor of size 1x5]
  1. Copy the code using the "Copy" button above, and paste it in a Python file in your IDE.
  2. Modify the values.
  3. 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.

Dependent Libraries

pytorchby pytorch

Python doticonstar image 67874 doticonVersion:v2.0.1doticon
License: Others (Non-SPDX)

Tensors and Dynamic neural networks in Python with strong GPU acceleration

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            pytorchby pytorch

            Python doticon star image 67874 doticonVersion:v2.0.1doticon License: Others (Non-SPDX)

            Tensors and Dynamic neural networks in Python with strong GPU acceleration
            Support
              Quality
                Security
                  License
                    Reuse

                      Environment Tested 

                      I tested this solution in the following versions. Be mindful of changes when working with other versions. 

                      1. The solution is created in Python3.11. 
                      2. The solution is tested on torch 2.0.0 version. 


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