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# One-Dimensional Tensor in Pytorch

• Last Updated : 29 Jun, 2021

In this article, we are going to discuss a one-dimensional tensor in Python. We will look into the following concepts:

1. Creation of One-Dimensional Tensors
2. Accessing Elements of Tensor
3. Size of Tensor
4. Data Types of Elements of Tensors
5. View of Tensor
6. 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()
tensor()```

## 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()
torch.Size()```

## 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.])```

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