Importing PyTorch

Let’s start by importing PyTorch and checking the version we’re using.

import torch
torch.__version__

2.5.1+cu121

It looks like we’ve got PyTorch 2.5.1+.

Introduction to Tensors

Tensors are multi-dimensional arrays with a uniform type. In machine learning, the term tensor informally refers to two different concepts :

1. a way of organizing data,
2. a multilinear (tensor) transformation. Data may be organized in a multidimensional array (M-way array).

For example, you could represent an image as a tensor with shape [3, 224, 224] which could mean [colour_channels, height, width]. Thus, the tensor would have dimensions.

For a detailed explanation of tensors, you can refer to a video by Dan Fleisch: What’s a Tensor?.

Creating Tensors

There is a whole documentation page for the torch.Tensor class.

Let’s see a few of the data types which can be defined as :

• torch.float32 or torch.float for 32-bit floating point,

• torch.float64 or torch.double for 64-bit floating point, and

• torch.complex64 or torch.cfloat for 64-bit complex point.

The first thing we’re going to create is a scalar.

A scalar is a single number and in tensor-speak it’s a zero dimension tensor.

# Scalar
scalar = torch.tensor(7)
scalar

tensor(7)

That means although scalar is a single number, it’s of type torch.Tensor.

(Point to note that you can write t in torch.tensor() as T too!)

We can check the dimensions of a tensor using the ndim attribute.

scalar.ndim

0

Now, what if we wanted to retrieve the number from the Tensor?

To do that, we can use the the item() attribute.

# Get the Python number within a tensor (only works with one-element tensors)
scalar.item()

7

Now, let’s see a vector.

A vector is a single dimension tensor.

vector = torch.tensor([3, 4])
vector

tensor([7, 7])

You could have a vector [5, 4] to describe [bedrooms, bathrooms] in your house.

Let’s check the number of dimensions our vector has :

# Check the number of dimensions of vector
vector.ndim

1

The number of dimensions of a tensor in PyTorch can be determined by counting the square brackets ([ or ]) on one side, as they correspond to the tensor’s nesting levels.

Let’s check that with another example :

twoDim = torch.tensor([[3, 4]])
twoDim.ndim

2

See, 2 dimensions!

Another important attribute is size(), which used to retrieve the dimensions (shape) of a tensor. It provides the size of each dimension in the tensor, similar to numpy.ndarray.shape. This method is particularly useful when you want to know how many elements exist in each dimension of the tensor without modifying it.

sizeDim0 = torch.tensor(3)
sizeDim1 = torch.tensor([3, 4])
sizeDim2_moreElements = torch.tensor([[3, 4], [4, 5]]) /* It is a matrix, focus on size() for now */
sizeDim2 = torch.tensor([[3, 4]])

sizeDim0.size(), sizeDim1.size(), sizeDim2_moreElements.size(), sizeDim2.size()

(torch.Size([]), torch.Size([2]), torch.Size([2, 2]), torch.Size([1, 2]))