Neural Networks are great and very popular in AI/ML spaces, but they require too much data to train. For tasks like object detection, signature verification, voice verification, and prescription pills recognition regular neural network techniques would be much more time-consuming and expensive because of this excessive data requirement. In these types of work, a Siamese network can be very powerful because it requires a lot less data than a regular neural network. In addition, an imbalanced dataset can also perform well.
This tutorial will give you a high-level overview of a Siamese Network and a complete example of working with it. I worked with the fashion-mnist dataset here but this similar structure is good for a lot of other use cases.
What is a Siamese Network?
Siamese networks contain one or more identical networks, and those identical networks have the same parameters and weights. If the weights of one network update, the weights of the other network also update. They have to be identical. The final layer is usually an embedding layer that calculates the distance between the outputs.
You feed them a pair of inputs. Each network will compute the features of inputs and find the similarity between two inputs using the distance between the two images. So, there are only two classes. Either the images are similar or dissimilar.
The concept will be much clearer when you will work on an example. Learning by doing is always the best idea.
Necessary Imports and Functions Definition
Let’s start with the necessary imports. We will import more if necessary.
import tensorflow.keras.backend as K
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import…