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Hands-On One-shot Learning with Python

You're reading from   Hands-On One-shot Learning with Python Learn to implement fast and accurate deep learning models with fewer training samples using PyTorch

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Product type Paperback
Published in Apr 2020
Publisher Packt
ISBN-13 9781838825461
Length 156 pages
Edition 1st Edition
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Authors (2):
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Ankush Garg Ankush Garg
Author Profile Icon Ankush Garg
Ankush Garg
Shruti Jadon Shruti Jadon
Author Profile Icon Shruti Jadon
Shruti Jadon
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Toc

Table of Contents (11) Chapters Close

Preface 1. Section 1: One-shot Learning Introduction
2. Introduction to One-shot Learning FREE CHAPTER 3. Section 2: Deep Learning Architectures
4. Metrics-Based Methods 5. Model-Based Methods 6. Optimization-Based Methods 7. Section 3: Other Methods and Conclusion
8. Generative Modeling-Based Methods 9. Conclusions and Other Approaches 10. Other Books You May Enjoy

Coding exercise

In this section, we will learn about the implementation of Siamese networks and matching networks.

Let's begin with Siamese networks.

Siamese networks – the MNIST dataset

In this tutorial, we will do the following things in the order listed here:

  1. Data preprocessing: Creating pairs
  2. Creating a Siamese network architecture
  3. Training it using the small MNIST dataset
  4. Visualizing the embeddings

Perform the following steps to carry out the exercise:

  1. First, import all the libraries needed using the following code:
# -*- encoding: utf-8 -*-
import argparse
import torch
import torchvision.datasets as dsets
import random
import numpy as np
import time
import matplotlib.pyplot as plt
from torch.autograd import Variable...
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