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

Freezing layers

Sometimes (for example, when using pretrained networks), it is desirable to freeze some of the layers. We can do this when we're sure that some of the layers most of the time the first couple of layers, also known as the bottom of the network have proven to be of value as feature extractors. In the following recipe, we will demonstrate how to freeze a part of the network after and only train the remaining subset of the network.

How to do it...

  1. First, we load all libraries as follows:
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
  1. In TensorFlow, it's straightforward to load the MNIST dataset:
mnist = input_data.read_data_sets('Data/mnist', one_hot=True)
  1. Next, we define the placeholders:
n_classes = 10
input_size = 784

x = tf.placeholder(tf.float32, shape=[None, input_size])
y = tf.placeholder(tf.float32, shape=[None, n_classes])
keep_prob = tf.placeholder(tf.float32)
  1. We define some functions we want to use repeatedly in our network architecture...