# Define and use Tensors using Simple Tensorflow Examples

Hello coders, in this post we will discuss Tensor and we will also see how to use them.

**What is TensorFlow?**

In Python for mathematical problems we used the TensorFlow library, it is a platform for machine learning.

Tensorflow is used for:

- Mathematical problems.
- GPU support
- Machine learning technique

Before we will proceed we will discuss some basics

We can define one/two/three-dimension tensors.

**How to define one-dimensional Tensor?**

We define tensor in Python using the list and then we convert it into a tensor.

We will use NumPy to create an array

import numpy as np arr = np.array([1, 56, 31, 15, 20])

From the output, you can see the dimension and shape of the array

import numpy as np arr = np.array([1, 56, 31, 15, 20]) print(arr) print (arr.ndim) print (arr.shape) print (arr.dtype)

**Mathematical operations using tensors**

Suppose that we have 2 arrays like this:

arr1 = np.array([(1,2,3),(4,5,6)]) arr2 = np.array([(7,8,9),(10,11,12)])

You can use the add function like this:

arr3 = tf.add(arr1,arr2)

So the whole code will be like this:

import numpy as np import tensorflow as tf arr1 = np.array([(1,2,3),(4,5,6)]) arr2 = np.array([(7,8,9),(10,11,12)]) arr3 = tf.add(arr1,arr2) sess = tf.Session() tensor = sess.run(arr3) print(tensor)

You can multiply arrays like this:

import numpy as np import tensorflow as tf arr1 = np.array([(1,2,3),(4,5,6)]) arr2 = np.array([(7,8,9),(10,11,12)]) arr3 = tf.multiply(arr1,arr2) sess = tf.Session() tensor = sess.run(arr3) print(tensor)

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