# Two-Dimensional Tensors in Pytorch

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68 Final Up to date on January 14, 2022

Two-dimensional tensors are analogous to two-dimensional metrics. Like a two-dimensional metric, a two-dimensional tensor additionally has \$n\$ variety of rows and columns.

Let’s take a gray-scale picture for instance, which is a two-dimensional matrix of numeric values, generally referred to as pixels. Starting from ‘0’ to ‘255’, every quantity represents a pixel depth worth. Right here, the bottom depth quantity (which is ‘0’) represents black areas within the picture whereas the best depth quantity (which is ‘255’) represents white areas within the picture. Utilizing the PyTorch framework, this two-dimensional picture or matrix might be transformed to a two-dimensional tensor.

Within the earlier publish, we discovered about one-dimensional tensors in PyTorch and utilized some helpful tensor operations. On this tutorial, we’ll apply these operations to two-dimensional tensors utilizing the PyTorch library. Particularly, we’ll study:

• Methods to create two-dimensional tensors in PyTorch and discover their sorts and shapes.
• About slicing and indexing operations on two-dimensional tensors intimately.
• To use quite a few strategies to tensors comparable to, tensor addition, multiplication, and extra.

Let’s get began. Two-Dimensional Tensors in Pytorch
Image by dylan dolte. Some rights reserved.

## Tutorial Overview

This tutorial is split into components; they’re:

• Varieties and shapes of two-dimensional tensors
• Changing two-dimensional tensors into NumPy arrays
• Changing pandas collection to two-dimensional tensors
• Indexing and slicing operations on two-dimensional tensors
• Operations on two-dimensional tensors

## Varieties and Shapes of Two-Dimensional Tensors

Let’s first import just a few vital libraries we’ll use on this tutorial.

To test the categories and shapes of the two-dimensional tensors, we’ll use the identical strategies from PyTorch, launched beforehand for one-dimensional tensors. However, ought to it work the identical means it did for the one-dimensional tensors?

Let’s reveal by changing a 2D listing of integers to a 2D tensor object. For instance, we’ll create a 2D listing and apply `torch.tensor()` for conversion.

As you may see, the `torch.tensor()` methodology additionally works nicely for the two-dimensional tensors. Now, let’s use `form()`, `measurement()`, and `ndimension()` strategies to return the form, measurement, and dimensions of a tensor object.

## Changing Two-Dimensional Tensors to NumPy Arrays

PyTorch permits us to transform a two-dimensional tensor to a NumPy array after which again to a tensor. Let’s learn how.

## Changing Pandas Collection to Two-Dimensional Tensors

Equally, we will additionally convert a pandas DataFrame to a tensor. As with the one-dimensional tensors, we’ll use the identical steps for the conversion. Utilizing values attribute we’ll get the NumPy array after which use `torch.from_numpy` that permits you to convert a pandas DataFrame to a tensor.

Right here is how we’ll do it.

## Indexing and Slicing Operations on Two-Dimensional Tensors

For indexing operations, totally different components in a tensor object might be accessed utilizing sq. brackets. You’ll be able to merely put corresponding indices in sq. brackets to entry the specified components in a tensor.

Within the under instance, we’ll create a tensor and entry sure components utilizing two totally different strategies. Notice that the index worth ought to at all times be one lower than the place the aspect is positioned in a two-dimensional tensor.

What if we have to entry two or extra components on the similar time? That’s the place tensor slicing comes into play. Let’s use the earlier instance to entry first two components of the second row and first three components of the third row.

## Operations on Two-Dimensional Tensors

Whereas there are quite a lot of operations you may apply on two-dimensional tensors utilizing the PyTorch framework, right here, we’ll introduce you to tensor addition, and scalar and matrix multiplication.

### Including Two-Dimensional Tensors

Including two tensors is much like matrix addition. It’s fairly a straight ahead course of as you merely want an addition (+) operator to carry out the operation. Let’s add two tensors within the under instance.

### Scalar and Matrix Multiplication of Two-Dimensional Tensors

Scalar multiplication in two-dimensional tensors can be an identical to scalar multiplication in matrices. For example, by multiplying a tensor with a scalar, say a scalar 4, you’ll be multiplying each aspect in a tensor by 4.

Coming to the multiplication of the two-dimensional tensors, `torch.mm()` in PyTorch makes issues simpler for us. Just like the matrix multiplication in linear algebra, variety of columns in tensor object A (i.e. 2×3) have to be equal to the variety of rows in tensor object B (i.e. 3×2).

Developed similtaneously TensorFlow, PyTorch used to have an easier syntax till TensorFlow adopted Keras in its 2.x model. To study the fundamentals of PyTorch, it’s possible you’ll wish to learn the PyTorch tutorials:

Particularly the fundamentals of PyTorch tensor might be discovered within the Tensor tutorial web page:

There are additionally fairly just a few books on PyTorch which might be appropriate for newbies. A extra not too long ago revealed ebook ought to be really helpful because the instruments and syntax are actively evolving. One instance is

## Abstract

On this tutorial, you discovered about two-dimensional tensors in PyTorch.

Particularly, you discovered:

• Methods to create two-dimensional tensors in PyTorch and discover their sorts and shapes.
• About slicing and indexing operations on two-dimensional tensors intimately.
• To use quite a few strategies to tensors comparable to, tensor addition, multiplication, and extra.