What is a Tensor?

A tensor is a multi-dimensional array of data in TensorFlow. They are the fundamental building blocks of the library and are used to perform mathematical operations on the data. Tensors can be thought of as n-dimensional arrays of numbers, where n can be any positive integer. Tensors are typically used to represent inputs, outputs, and weights in a TensorFlow model.

Tensors can be created in a variety of ways in TensorFlow. One way to create a tensor is to use the tf.constant() function, which creates a tensor with a constant value. For example, the following code creates a tensor with the shape (2,3) and filled with the values 1:

Another way to create a tensor is to use the tf.Variable() function, which creates a tensor that can be modified later. For example, the following code creates a tensor with the shape (2,3) filled with random values:

You can also create tensors from existing data, such as NumPy arrays or Python lists, using the tf.converttotensor() function. For example, the following code creates a tensor from a NumPy array:

Once you have created a tensor, you can perform various mathematical operations on it using the TensorFlow library. For example, you can add, subtract, multiply, and divide tensors using the +, -, , and / operators, respectively. For example, the following code creates two tensors and adds them together:

TensorFlow also provides a number of other functions for performing mathematical operations on tensors, such as matrix multiplication, transpose, and dot product. These functions can be found in the tf.math module.

Tensors are the basic building blocks of TensorFlow and are used to represent inputs, outputs, and weights in a TensorFlow model. Tensors can be created in a variety of ways and mathematical operations can be performed on them using the TensorFlow library.

(Bruce Haydon — 2023)