Tensors

Tensors are the basic components in TensorFlow. A tensor is a multidimensional collection of data elements. It is generally identified by shape, type, and rank. Rank refers to the number of dimensions of a tensor, while shape refers to the size of each dimension. You may have seen several examples of tensors before, such as in a zero-dimensional collection (also known as a scalar), a one-dimensional collection (also known as a vector), and a two-dimensional collection (also known as a matrix).

A scalar value is a tensor of rank 0 and shape []. A vector, or a one-dimensional array, is a tensor of rank 1 and shape [number_of_columns] or [number_of_rows]. A matrix, or a two-dimensional array, is a tensor of rank 2 and shape [number_of_rows, number_of_columns]. A three-dimensional array is a tensor of rank 3. In the same way, an n-dimensional array is a tensor of rank n.

A tensor can store data of one type in all of its dimensions, and the data type of a tensor is the same as the data type of its elements.

The following are the most commonly used data types in TensorFlow:

Tensors can be created in the following ways:

• By defining constants, operations, and variables, and passing the values to their constructor
• By defining placeholders and passing the values to session.run()
• By converting Python objects, such as scalar values, lists, NumPy arrays, and pandas DataFrames, with the tf.convert_to_tensor() function

Let's explore different ways of creating Tensors.