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

Extracting bottleneck features with ResNet

The ResNet architecture was in 2015 in the paper Deep Residual Learning for Image Recognition ( ResNet has a different network than VGG. It consists of micro-architectures that are stacked on top of each other. ResNet won the ILSVRC competition in 2015 and surpassed human performance on the ImageNet dataset. In this recipe, we will demonstrate how to leverage ResNet50 weights to extract bottleneck features. 

How to do it...

  1. We start by implementing all Keras tools:
from keras.models import Model
from keras.applications.resnet50 import ResNet50

from keras.applications.resnet50 import preprocess_input
from keras.preprocessing.image import load_img
from keras.preprocessing.image import img_to_array
from keras.applications import imagenet_utils
  1. Next, we load the ResNet50 model with the imagenet weights:
resnet_model = ResNet50(weights='imagenet')
  1. For this example, we will extract the final average pooling layer in the...