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