Tensorflow Expert In 5 Minutes

In this post, we will dive into the details of TensorFlow Tensors.

Tensors are TensorFlow’s multi-dimensional arrays with uniform types. They are very similar to NumPy arrays, and they are immutable, which means that they cannot be altered once created. You can only create a new copy with the edits.

Let’s see how Tensors work with a code example. But first, to work with TensorFlow objects, we need to import the TensorFlow library. We often use NumPy with TensorFlow, so let’s also import NumPy with the following lines:

import tensorflow as tf
import numpy as np

Creation of Tensors: Creating Tensor Objects

There are several ways to create a tf.Tensor object. Let's start with a few examples. You can create Tensor objects with several TensorFlow functions, as shown in the below examples:

# You can create Tensor objects with the `tf.constant` function:
x = tf.constant([[1, 2, 3, 4 ,5]])
# You can create Tensor objects only consisting of 1s with the `tf.ones` function:
y = tf.ones((1,5))
# You can create Tensor objects only consisting of 0s with the `tf.zeros` function:
z = tf.zeros((1,5))
# You can use the `tf.range()` function to create Tensor objects:
q = tf.range(start=1, limit=6, delta=1)

print(x)
print(y)
print(z)
print(q)