Floating-point tensor

View of Tensor:

This tensor is used to define the elements with float type. We can create a floating-point Tensor using an integer element by using the FloatTensor property.

Syntax:

torch.FloatTensor([element1,element 2,.,element n])

Example: Python program to create float tensor and get elements.

Python3

# importing torch module 
import torch 
  
# create one dimensional Float Tensor  with  
# integer type elements 
a = torch.FloatTensor([10, 20, 30, 40, 50]) 
  
# display data type 
print(a.dtype) 
  
# access elements from 0 to 3 
print(a[0:3]) 
  
# access from 4 
print(a[4:]) 

Output:

torch.float32
tensor([10., 20., 30.])
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.