Accessing Elements of Tensor
We can access the elements in the tensor vector using the index of elements.
Syntax:
tensor_name([index])
Where the index is the position of the element in the tensor:
- Indexing starts from 0 from first
- Indexing starts from -1 from last
Example: Python program to access elements using the index.
Python3
# importing torch module import torch # create one dimensional tensor with integer type elements a = torch.tensor([10, 20, 30, 40, 50]) # get 0 and 1 index elements print(a[0], a[1]) # get 4 th index element print(a[4]) # get 4 index element from last print(a[-4]) # get 2 index element from last print(a[-2]) |
Output:
tensor(10) tensor(20) tensor(50) tensor(20) tensor(40)
We can access n elements at a time using the”:” operator, This is known as slicing.
Syntax:
tensor([start_index:end_index])
Where start_index is the starting index and end_index is the ending index.
Example: Python program to access multiple elements.
Python3
# importing torch module import torch # create one dimensional tensor with integer type elements a = torch.tensor([10, 20, 30, 40, 50]) # access elements from 1 to 4 print(a[1:4]) # access from 4 print(a[4:]) # access from last print(a[-1:]) |
Output:
tensor([20, 30, 40]) tensor([50]) tensor([50])
One-Dimensional Tensor in Pytorch
In this article, we are going to discuss a one-dimensional tensor in Python. We will look into the following concepts:
- Creation of One-Dimensional Tensors
- Accessing Elements of Tensor
- Size of Tensor
- Data Types of Elements of Tensors
- View of Tensor
- Floating Point Tensor
Introduction
The Pytorch is used to process the tensors. Tensors are multidimensional arrays. PyTorch accelerates the scientific computation of tensors as it has various inbuilt functions.
Vector:
A vector is a one-dimensional tensor that holds elements of multiple data types. We can create vectors using PyTorch. Pytorch is available in the Python torch module. So we need to import it.
Syntax:
import pytorch