Book Image

Hands-On Neural Networks

By : Leonardo De Marchi, Laura Mitchell
Book Image

Hands-On Neural Networks

By: Leonardo De Marchi, Laura Mitchell

Overview of this book

Neural networks play a very important role in deep learning and artificial intelligence (AI), with applications in a wide variety of domains, right from medical diagnosis, to financial forecasting, and even machine diagnostics. Hands-On Neural Networks is designed to guide you through learning about neural networks in a practical way. The book will get you started by giving you a brief introduction to perceptron networks. You will then gain insights into machine learning and also understand what the future of AI could look like. Next, you will study how embeddings can be used to process textual data and the role of long short-term memory networks (LSTMs) in helping you solve common natural language processing (NLP) problems. The later chapters will demonstrate how you can implement advanced concepts including transfer learning, generative adversarial networks (GANs), autoencoders, and reinforcement learning. Finally, you can look forward to further content on the latest advancements in the field of neural networks. By the end of this book, you will have the skills you need to build, train, and optimize your own neural network model that can be used to provide predictable solutions.
Table of Contents (16 chapters)
Free Chapter
1
Section 1: Getting Started
4
Section 2: Deep Learning Applications
9
Section 3: Advanced Applications

Implementing TL in PyTorch

Now we will see how it's possible to implement TL in PyTorch by performing the following steps. We will use a standard training set, cats and dogs, and a pre-trained network:

  1. Import the necessary libraries as follows:
import torch
import torchvision
import torch.nn as nn
import numpy as np
import torch.optim as optim
from torchvision import models
from torchvision import transforms
import copy
import os
from os import listdir
import shutil
from torchvision import datasets
import random
from torch.optim import lr_scheduler
import matplotlib.pyplot as plt
  1. Now, we will use a handy PyTorch function:
# # Create train and test dataset

data_dir = os.path.join('kagglecatsanddogs_3367a','PetImages')

# # Create the train and test set folder
train_dir = os.path.join(data_dir, 'train')
validation_dir = os.path.join(data_dir, 'validation...