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

Fine-tuning with Xception

The network was made by the creator of Keras, François Chollet in Xception: Deep Learning with Depthwise Separable Convolutions ( Xception is an extension of the Inception architecture, where the Inception modules are replaced with depthwise separable convolutions. In the previous recipe, we focused on training the top layers only while keeping the original Inception weights frozen during training. However, we can also choose to train all weights with a smaller rate. This is called fine-tuning. This technique can give the model a small performance boost by removing some biased weights from the original network. 

How to do it...

  1. First, we with importing all the needed, as follows:
import numpy as np
from keras.models import Model
from keras.applications import Xception
from keras.layers import Dense, GlobalAveragePooling2D
from keras.optimizers import Adam

from keras.applications import imagenet_utils
from keras.utils import np_utils...