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

Large-scale visual recognition with GoogLeNet/Inception

 In 2014, the paper Going Deeper with Convolutions ( was published by Google, introducing the architecture. Subsequently, newer versions ( in 2015) were published under the name Inception. In these GoogLeNet/Inception models, multiple convolutional layers are applied in parallel before being stacked and fed to the layer. A great benefit of the network architecture is that the computational cost is lower and the file size of the trained weights is much smaller. In this recipe, we will demonstrate how to load the InceptionV3 weights in Keras and apply the model to classify images.

How to do it...

  1. Keras has some great tools for using pretrained models. We start with importing the libraries and tools, as follows:
import numpy as np

from keras.applications.inception_v3 import InceptionV3
from keras.applications import imagenet_utils
from keras.preprocessing.image import load_img...