Why is Keras the perfect deep learning library?

Keras [Chollet, François. "Keras (2015)." (2017)] is a popular deep learning library with over 250,000 developers at the time of writing, a number that is more than doubling every year. Over 600 contributors actively maintain it. Some of the examples we'll use in this book have been contributed to the official Keras GitHub repository. Google's TensorFlow, a popular open source deep learning library, uses Keras as a high-level API to its library. In the industry, Keras is used by major technology companies like Google, Netflix, Uber, and NVIDIA. In this chapter, we introduce how to use Keras Sequential API.

We have chosen Keras as our tool of choice to work within this book because Keras is a library dedicated to accelerating the implementation of deep learning models. This makes Keras ideal for when we want to be practical and hands-on, such as when we're exploring the advanced deep learning concepts in this book. Because Keras is intertwined with deep learning, it is essential to learn the key concepts of deep learning before someone can maximize the use of Keras libraries.

Note

All examples in this book can be found on GitHub at the following link: https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras.

Keras is a deep learning library that enables us to build and train models efficiently. In the library, layers are connected to one another like pieces of Lego, resulting in a model that is clean and easy to understand. Model training is straightforward requiring only data, a number of epochs of training, and metrics to monitor. The end result is that most deep learning models can be implemented with a significantly smaller number of lines of code. By using Keras, we'll gain productivity by saving time in code implementation which can instead be spent on more critical tasks such as formulating better deep learning algorithms. We're combining Keras with deep learning, as it offers increased efficiency when introduced with the three deep learning networks that we will introduce in the following sections of this chapter.

Likewise, Keras is ideal for the rapid implementation of deep learning models, like the ones that we will be using in this book. Typical models can be built in few lines of code using the Sequential Model API. However, do not be misled by its simplicity. Keras can also build more advanced and complex models using its API and Model and Layer classes which can be customized to satisfy unique requirements. Functional API supports building graph-like models, layers reuse, and models that are behaving like Python functions. Meanwhile, Model and Layer classes provide a framework for implementing uncommon or experimental deep learning models and layers.

Installing Keras and TensorFlow

Keras is not an independent deep learning library. As shown in Figure 1.1.1, it is built on top of another deep learning library or backend. This could be Google's TensorFlow, MILA's Theano or Microsoft's CNTK. Support for Apache's MXNet is nearly completed. We'll be testing examples in this book on a TensorFlow backend using Python 3. This due to the popularity of TensorFlow, which makes it a common backend.

We can easily switch from one back-end to another by editing the Keras configuration file .keras/keras.json in Linux or macOS. Due to the differences in the way low-level algorithms are implemented, networks can often have different speeds on different backends.

On hardware, Keras runs on a CPU, GPU, and Google's TPU. In this book, we'll be testing on a CPU and NVIDIA GPUs (Specifically, the GTX 1060 and GTX 1080Ti models).

Figure 1.1.1: Keras is a high-level library that sits on top of other deep learning models. Keras is supported on CPU, GPU, and TPU.

Before proceeding with the rest of the book, we need to ensure that Keras and TensorFlow are correctly installed. There are multiple ways to perform the installation; one example is installing using pip3:

$ sudo pip3 install tensorflow

If we have a supported NVIDIA GPU, with properly installed drivers, and both NVIDIA's CUDA Toolkit and cuDNN Deep Neural Network library, it is recommended that we install the GPU-enabled version since it can accelerate both training and prediction:

$ sudo pip3 install tensorflow-gpu

The next step for us is to then install Keras:

$ sudo pip3 install keras

The examples presented in this book will require additional packages, such as pydot, pydot_ng, vizgraph, python3-tk and matplotlib. We'll need to install these packages before proceeding beyond this chapter.

The following should not generate any error if both TensorFlow and Keras are installed along with their dependencies:

$ python3
>>> import tensorflow as tf
>>> message = tf.constant('Hello world!')
>>> session = tf.Session()
>>> session.run(message)
b'Hello world!'
>>> import keras.backend as K
Using TensorFlow backend.
>>> print(K.epsilon())
1e-07

The warning message about SSE4.2 AVX AVX2 FMA, which is similar to the one below can be safely ignored. To remove the warning message, you'll need to recompile and install the TensorFlow source code from https://github.com/tensorflow/tensorflow.

tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.2 AVX AVX2 FMA

This book does not cover the complete Keras API. We'll only be covering the materials needed to explain the advanced deep learning topics in this book. For further information, we can consult the official Keras documentation, which can be found at https://keras.io.