Torch.nn module uses Tensors and Automatic differentiation modules to train and build layers such as input, hidden, and output. We do not always get data in its final processed form that is required for training machine learning algorithms. We use transforms to manipulate the data and make it suitable for training.
The function torch.arrange() returns a 1-D tensor of size with values. It will be taken from the interval with a common difference step beginning from the start.
Please check the below code to know how to transform tensor in PyTorch.
Fig: Preview of the output that you will get on running this code from your IDE
In this solution we're using pytorch libraries
import torch import torch.nn.functional as F size = 7 loopback = 3 data = torch.arange(size, dtype=torch.float) # pad front of data with 2 values # replicate padding requires 3d, 4d, or 5d tensor, hence the creation of two unitary dimensions before padding data_padded = F.pad(data[None, None, ...], (loopback - 1, 0), 'replicate')[0, 0, ...] # unfold with window size of 3 with step size of 1 y = data_padded.unfold(dimension=0, size=loopback, step=1) tensor([[0., 0., 0.], [0., 0., 1.], [0., 1., 2.], [1., 2., 3.], [2., 3., 4.], [3., 4., 5.], [4., 5., 6.]])
Follow the steps carefully to get the output easily.
- Install pytorch on your IDE(Any of your favorite IDE).
- Copy the snippet using the 'copy' and paste it in your IDE.
- Add print statement in the end of the code
- Save and Run the file to generate the output.
I hope you found this useful. I have added the link to dependent library, version information in the following sections.
I found this code snippet by searching for 'Tensor transformation in pytorch' in kandi. You can try any such use case!
I tested this solution in the following versions. Be mindful of changes when working with other versions.
- The solution is created in Pycharm 2022.3.3(Community edition).
- The solution is tested on Python 3.8.10.
- torch version 2.0.0.
Using this solution, we are able to understand how to know how to transform tensor in pytorch with simple steps. This process also facilities an easy way to use, hassle-free method to create a hands-on working version of code which would help us how to know how to transform tensor in pytorch
Python 458 Version:Current License: Permissive (MIT)