Implementing a Siamese Network

TODAY’S DAILY DOSE OF DATA SCIENCE

Implementing a Siamese Network

In yesterday’s issue, we learned how a Siamese network trained using contrastive loss can help us build a face unlock system.

Today, let’s understand the implementation.

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Quick recap

If you haven’t read yesterday’s issue, I highly recommend it before reading ahead. It will help you understand the true motivation behind WHY we are using it:

Contrastive Learning Using Siamese Networks

Building a face unlock system.

Daily Dose of Data ScienceAvi Chawla

Here’s a quick recap of the overall idea:

• If a pair belongs to different entities, the true label will be 1.
• If a pair belongs to the same entity, the true label will be 0.
• Pass both inputs through the same network to generate two embeddings.
• If the true label is 0 (same entity) → minimize the distance between the two embeddings.
• If the true label is 1 (different entities) → maximize the distance between the two embeddings.

Next, define a network like this:

Create a dataset of face pairs:

Contrastive loss (defined below) helps us train such a model:

where:

• y is the true label.
• D is the distance between two embeddings.
• margin is a hyperparameter, typically greater than 1.

If you haven’t read yesterday’s issue, I highly recommend it before reading ahead. It will help you understand the true motivation behind why we are using it:Contrastive Learning Using Siamese NetworksAvi Chawla·28 SeptRead full story

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Implementation

Next, let’s look at the implementation of this model.

For simplicity, we shall begin with a simple implementation utilizing the MNIST dataset. In a future issue, we shall explore the face unlock model.

Let’s implement it.

As always, we start with some standard imports:

Next, we download/load the MNIST dataset:

Now, recall to build a Siamese network, we have to create image pairs:

• In some pairs, the two images will have the same true label.
• In other pairs, the two images will have a different true label.

To do this, we define a SiameseDataset class that inherits from the Dataset class of PyTorch:

This class will have three methods:

• The __init__ method:

• The __len__ method:

• The __getitem__ method, which is used to return an instance of the train data. In our case, we shall pair the current instance from the training dataset with:
  • Either another image from the same class as the current instance.
  • Or another image from a different class.
  • Which class to pair with will be decided randomly.

The __getitem__ method is implemented below:

• Line 5: We obtain the current instance.
• Line 7: We randomly decide whether this instance should be paired with the same class or not.
• Lines 9-12: If flag=1, continue to find an instance until we get an instance of the same class.
• Lines 14-17: If flag=0, continue to find an instance until we get an instance of a different class.
• Line 19-21: Apply transform if needed.
• Line 23:
  • If the two labels are different, the true label for the pair will be 1.
  • If the two labels are the same, the true label for the pair will be 0.

After defining the class, we create the dataset objects below:

Next, we define the neural network:

• The two input images are fed through the same network to generate an embedding (outputA and outputB).

Moving on, we define the contrastive loss:

Almost done!

Next, we define the dataloader, the model, the optimizer, and the loss function:

Finally, we train it:

And with that, we have successfully implemented a Siamese Network using PyTorch.

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Results

Let’s look at some results using images in the test dataset:

We can generate a similarity score as follows:

• Image pair #1: Similarity is high since both images depict the same digit:

• Image pair #2: Similarity is low since both images depict different digits:

• Image pair #3: Similarity is high since both images depict the same digit:

• Image pair #4: Similarity is low since both images depict different digits:

Great, it works as expected!

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And with that, we have successfully implemented and verified the predictions of a Siamese Network.

That said, there’s one thing to note.

Since during the data preparation step, we paired the instance either with the same or a different class…

…this inherently meant that this approach demands labeled data.

There are several techniques to get around this, which we shall discuss soon.

👉 In the meantime, it’s over to you: Can you tell how you would handle unlabeled data in this case?

The code for today’s issue is available here: Siamese Network Implementation.

ROADMAP

From local ML to production ML​​​

Once a model has been trained, we move to productionizing and deploying it.

If ideas related to production and deployment intimidate you, here’s a quick roadmap for you to upskill (assuming you know how to train a model):

• First, you would have to compress the model and productionize it. Read these guides:
  • Reduce their size with ​Model Compression techniques​.
  • ​Supercharge ​​PyTorch Models​​ With TorchScript.​
  • If you use sklearn, learn how to ​optimize them with tensor operations​.
• Next, you move to deployment. ​Here’s a beginner-friendly hands-on guide​ that teaches you how to deploy a model, manage dependencies, set up model registry, etc.
• Although you would have tested the model locally, it is still wise to test it in production. There are risk-free (or low-risk) methods to do that. ​Learn what they are and how to implement them here​.

This roadmap should set you up pretty well, even if you have NEVER deployed a single model before since everything is practical and implementation-driven.

No-Fluff Industry ML resources to

Succeed in DS/ML roles

At the end of the day, all businesses care about impact. That’s it!

• Can you reduce costs?
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• Can you scale ML models?
• Predict trends before they happen?

We have discussed several other topics (with implementations) in the past that align with such topics.

Here are some of them:

• Learn sophisticated graph architectures and how to train them on graph data in this crash course.
• So many real-world NLP systems rely on pairwise context scoring. Learn scalable approaches here.
• Run large models on small devices using Quantization techniques.
• Learn how to generate prediction intervals or sets with strong statistical guarantees for increasing trust using Conformal Predictions.
• Learn how to identify causal relationships and answer business questions using causal inference in this crash course.
• Learn how to scale and implement ML model training in this practical guide.
• Learn 5 techniques with implementation to reliably test ML models in production.
• Learn how to build and implement privacy-first ML systems using Federated Learning.
• Learn 6 techniques with implementation to compress ML models.

All these resources will help you cultivate key skills that businesses and companies care about the most.

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