Simplifying implementations of common architectures via the Keras API

You have already seen some examples of building a feedforward NN model (for instance, a multilayer perceptron) and defining a sequence of layers using Keras' Sequential class. Before we look at different approaches for configuring those layers, let's briefly recap the basic steps by building a model with two densely (fully) connected layers:

>>> model = tf.keras.Sequential()
>>> model.add(tf.keras.layers.Dense(units=16, activation='relu'))
>>> model.add(tf.keras.layers.Dense(units=32, activation='relu'))
>>> ## late variable creation
>>> model.build(input_shape=(None, 4))
>>> model.summary()
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense (Dense...