Advanced Deep Learning with Keras

Chapter 1
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
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