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

Intuitively building networks with Keras 

Keras is a deep learning framework that is known and adopted by deep learning engineers. It provides a wrapper around the TensorFlow, CNTK, and the Theano frameworks. This wrapper you gives the ability to easily create deep learning models by stacking different types of layers. The power of Keras lies in its simplicity and readability of the code. If you want to use multiple GPUs during training, you need to set the devices in the same way as with TensorFlow.

How to do it...

  1. We start by installing Keras on our local Anaconda environment as follows:
conda install -c conda-forge keras

Make sure your deep learning environment is activated before executing this command.

  1. Next, we import keras library into our Python environment:
from keras.models import Sequential
from keras.layers import Dense

This command outputs the used by Keras. By default, the TensorFlow framework is used:

Figure 1.3: Keras prints the backend used

  1. To provide a dummy dataset, we will use numpy and the following code:
import numpy as np
x_input = np.array([[1,2,3,4,5]])
y_input = np.array([[10]])
  1. When using sequential mode, it's straightforward to stack multiple layers in Keras. In this example, we use one hidden layer with 32 units and an output layer with one unit:
model = Sequential()
model.add(Dense(units=32, input_dim=x_input.shape[1]))
  1. Next, we need to compile our model. While compiling, we can set different settings such as loss function, optimizer, and metrics:
  1. In Keras, you can easily print a summary of your model. It will also show the number of parameters within the defined model:

In the following figure, you can see the summary of our build model:

Figure 1.4: Example of a Keras model summary

  1. Training the model is straightforward with one command, while simultaneously saving the results to a variable called history:
history =, y_input, epochs=10, batch_size=32)
  1. For testing, the prediction function can be used after training:
pred = model.predict(x_input, batch_size=128)


In this short introduction to Keras, we have demonstrated how easy it is to implement a neural network in just a couple of lines of code. However, don't confuse simplicity with power. The Keras framework provides much more than we've just demonstrated here and one can adjust their model up to a granular level if needed.