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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 data types that can be found in the TensorFlow library are described at the following link: https://www.tensorflow.org/api_docs/python/tf/DType.

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

Use TensorFlow data types for defining tensors i nstead of native data types from Python or data types from NumPy.

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.