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TensorFlow 1.x and 2.x
Let's have a look into the non-linear network topology:
Figure 3: An example of a non-linear topology
We will see multiple examples of using the Functional API in subsequent chapters
of this book.
Model subclassing
Model subclassing offers the highest flexibility and it is generally used when
you need to define your own layer. In other words, it is useful when you are in
the business of building your own special lego brick instead of composing more
standard and well-known bricks. There is indeed a higher cost in terms of complexity
and therefore subclassing should be used only when really needed. In the majority
of situations, Sequential and Functional APIs are more appropriate, but you can still
use model subclassing if you prefer to think in an object-oriented manner, as the
typical Python/NumPy developer does.
So, in order to create a custom layer, we can subclass tf.keras.layers.Layer and
implement the following methods:
• __init__: Optionally used to define all the sublayers to be used by this layer.
This is the constructor where you can declare your model.
• build: Used to create the weights of the layer. You can add weights with
add_weight().
• call: Used to define the forward pass. This is where your layer is called and
chained in functional style.
• Optionally, a layer can be serialized by using get_config() and deserialized
using from_config().
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