## Building relation networks using TensorFlow

The relation function is pretty simple, right? We will understand relation networks better by implementing one in TensorFlow.

You can also check the code available as a Jupyter Notebook with an explanation here: https://github.com/sudharsan13296/Hands-On-Meta-Learning-With-Python/blob/master/04.%20Relation%20and%20Matching%20Networks%20Using%20Tensorflow/4.5%20Building%20Relation%20Network%20Using%20Tensorflow.ipynb.

First, we import all of the required libraries:

import tensorflow as tf import numpy as np

We will randomly generate our data points. Let's say we have two classes in our dataset; we will randomly generate some 1,000 data points for each of these classes:

classA = np.random.rand(1000,18) ClassB = np.random.rand(1000,18)

We create our dataset by combining both of these classes:

data = np.vstack([classA, ClassB])

Now, we set the labels; we assign the `1`

label for `classA`

and the `0`

label for `classB`

:

label = np.vstack([np.ones((len(classA),1)),np.zeros...