Advanced Deep Learning with Keras

Introducing Advanced Deep Learning with Keras
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.
Implementing the core deep learning
models - MLPs, CNNs, and RNNs
We've already mentioned that we'll be using three advanced deep learning models,
they are:
• MLPs: Multilayer perceptrons
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