Exploring Tensor Representation in TensorFlow

How are Tensors Represented in TensorFlow?

In TensorFlow, tensors are used to encapsulate data for easy manipulation and computation. Every tensor is characterized by three main attributes: rank, shape, and data type.

Rank of a Tensor

The rank of a tensor is the number of dimensions it possesses, which corresponds to the levels of nested arrays. The rank provides a way to categorize tensors:

• Rank 0: Scalar (single number)
• Rank 1: Vector (array of numbers)
• Rank 2: Matrix (two-dimensional array)
• Rank 3 or higher: Tensors with 3 or more dimensions

Shape of a Tensor

The shape of a tensor refers to the tuple of integers that describes the tensor’s dimensionality. Each number in the shape represents the size of the tensor along a specific axis. For example, a matrix with 2 rows and 3 columns has a shape of (2, 3).

Data Type of a Tensor

Tensors in TensorFlow are created with a specific data type, such as tf.int32, tf.float64, or tf.string. This attribute defines the type of data elements that the tensor can store.

Practical Example

Here is an example demonstrating how to create a tensor in TensorFlow and identify its rank, shape, and data type:

import * as tf from '@tensorflow/tfjs';

// Create a 2D tensor (matrix)
const tensor = tf.tensor([[1, 2, 3], [4, 5, 6]], [2, 3], 'int32');
console.log(`Rank: ${tensor.rank}`);
console.log(`Shape: ${tensor.shape}`);
console.log(`Data Type: ${tensor.dtype}`);