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
Creation of One-Dimensional Tensors:
One dimensional vector is created using the torch.tensor() method.
Syntax:
torch.tensor([element1,element2,.,element n])
Where elements are input elements to a tensor
Example: Python program to create tensor elements
Python3
# importing torch module import torch # create one dimensional tensor with integer type elements a = torch.tensor([10, 20, 30, 40, 50]) print(a) # create one dimensional tensor with float type elements b = torch.tensor([10.12, 20.56, 30.00, 40.3, 50.4]) print(b) |
Output:
tensor([10, 20, 30, 40, 50]) tensor([10.1200, 20.5600, 30.0000, 40.3000, 50.4000])
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])
Tensor size:
This is used to get the length(number of elements) in a tensor using the size() method.
Syntax:
tensor.size()
Example: Python program to get the tensor size.
Python3
# importing torch module import torch # create one dimensional tensor integer type elements a = torch.FloatTensor([10, 20, 30, 40, 50]) # size of tensor print(a.size()) # create one dimensional tensor integer type elements b = torch.FloatTensor([10, 20, 30, 40, 50, 45, 67, 43]) # size of tensor print(b.size()) |
Output:
torch.Size([5]) torch.Size([8])
Data Types of Elements of Tensors:
We can get the data type of the tensor data elements. Then dtype() is used to get the data type of the tensor
Syntax:
tensor_vector.dtype
Where tensor_vector is the one-dimensional tensor vector.
Example:
Python3
# importing torch module import torch # create one dimensional tensor with integer type elements a = torch.tensor([10, 20, 30, 40, 50]) # get data type of vector a print(a.dtype) # create one dimensional tensor with float type elements b = torch.tensor([10.12, 20.56, 30.00, 40.3, 50.4]) # get data type of vector b print(b.dtype) |
Output:
torch.int64 torch.float32
View of Tensor:
The view() is used to view the tensor in two-dimensional format ie, rows and columns. We have to specify the number of rows and the number of columns to be viewed.
Syntax:
tensor.view(no_of_rows,no_of_columns)
Where,
- tensor is an input one-dimensional tensor
- no_of_rows is the total number of the rows that the tensor is viewed
- no_of_columns is the total number of the columns that the tensor is viewed
Example: Python program to create a tensor with 10 elements and view with 5 rows and 2 columns and vice versa.
Python3
# importing torch module import torch # create one dimensional tensor 10 elements a = torch.FloatTensor([10, 20, 30, 40, 50, 1, 2, 3, 4, 5]) # view tensor in 5 rows and 2 columns print(a.view(5, 2)) # view tensor in 2 rows and 5 columns print(a.view(2, 5)) |
Output:
tensor([[10., 20.],
[30., 40.],
[50., 1.],
[ 2., 3.],
[ 4., 5.]])
tensor([[10., 20., 30., 40., 50.],
[ 1., 2., 3., 4., 5.]])
Floating-point 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.])
