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

Storing the network topology and trained weights

In most deep learning frameworks, it is to store the architecture and the trained weights. However, because this can be extremely important, we will demonstrate how to store your model with TensorFlow in the following recipe.

How to do it... 

  1. We start by importing the libraries:
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
  1. Next, we load the MNIST data:
mnist = input_data.read_data_sets('Data/mnist', one_hot=True)
  1. We define the placeholders as follows:
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. For convenience, we create functions to build our deep learning network:
def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)

def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable...