Book Image

Python Deep Learning Cookbook

By : Indra den Bakker
Book Image

Python Deep Learning Cookbook

By: Indra den Bakker

Overview of this book

Deep Learning is revolutionizing a wide range of industries. For many applications, deep learning has proven to outperform humans by making faster and more accurate predictions. This book provides a top-down and bottom-up approach to demonstrate deep learning solutions to real-world problems in different areas. These applications include Computer Vision, Natural Language Processing, Time Series, and Robotics. The Python Deep Learning Cookbook presents technical solutions to the issues presented, along with a detailed explanation of the solutions. Furthermore, a discussion on corresponding pros and cons of implementing the proposed solution using one of the popular frameworks like TensorFlow, PyTorch, Keras and CNTK is provided. The book includes recipes that are related to the basic concepts of neural networks. All techniques s, as well as classical networks topologies. The main purpose of this book is to provide Python programmers a detailed list of recipes to apply deep learning to common and not-so-common scenarios.
Table of Contents (21 chapters)
Title Page
About the Author
About the Reviewer
Customer Feedback

Upscaling the resolution of images with Super-Resolution GANs (SRGANs)

InChapter 3, Convolutional Neural Networks, we demonstrated how a CNN can be used to autoencode an image to obtain a compression of the image. In the digital age, it's even more important to be able to scale up the resolution of an image to high quality. For example, a version of an image can easily be shared via the internet. When the image arrives at the receiver, its quality will need to be increased, also known as Super-Resolution imaging (SR). In the following recipe, we will show you how to increase the resolution of image by training deep learning with the PyTorch framework.

How to do it...

  1. First, we need to import all the necessary libraries:
import os
from os import listdir
from os.path import join
import numpy as np
import random
import matplotlib.pyplot as plt

import torchvision
from torchvision import transforms
import torchvision.datasets as datasets

import torch
import torch.nn as nn
import torch.nn.functional...