Скачать или смотреть Understanding Python's Callable Objects: The layers.Dense(128 * 16 * 16)(inputs) Pattern Explained

Learn how Python's callable objects and chaining constructor calls work, using the example of TensorFlow Keras Dense layers and arguments passed during instantiation.
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Introduction

If you've come across a Python expression like:

[[See Video to Reveal this Text or Code Snippet]]

where layers.Dense is a class from TensorFlow Keras, you might wonder: what's going on when parentheses appear twice consecutively? How is this different from just calling a constructor or a function?

This guide uncovers the Python programming construct behind this pattern and clarifies how arguments are interpreted when you chain calls like this.



The Core Concept: Callables in Python

The expression:

[[See Video to Reveal this Text or Code Snippet]]

is two calls in succession:

layers.Dense(128 * 16 * 16) creates an instance of the Dense class with an argument (here, the number of units).

The resulting object is then called with (inputs).

What does that mean?

In Python, objects can be "called" if they implement the special _call_ method.

This means that the instance returned by Dense(...) behaves like a function.

Behind the scenes, Dense constructs a callable layer object, and calling it with inputs applies the layer transformation to your data.



How Python Interprets layers.Dense(128 * 16 * 16)(inputs)

First, the constructor Dense(128 * 16 * 16) runs, returning a callable layer object.

Next, that object is immediately invoked with (inputs).

This chaining is equivalent to:

[[See Video to Reveal this Text or Code Snippet]]



Generalizing with an Example

Here's a simplified Python example illustrating this behavior:

[[See Video to Reveal this Text or Code Snippet]]

Breakdown:

adder_maker(2) returns a function add_n configured with n=2.

You immediately call add_n(1) returning 3.

This is the same principle applied in your layers example.



Callable Objects via _call_

Functions are callable by default. But Python lets you make any object callable by defining the _call_ method:

[[See Video to Reveal this Text or Code Snippet]]

layers.Dense(...) returns such an object with _call_ implemented, so you can apply it directly to inputs.



Summary

The expression layers.Dense(...)(inputs) is two calls chained together.

The first call constructs a callable layer object.

The second call applies the layer to the inputs.

Python enables this through the _call_ method, turning objects into callable entities.

Understanding this pattern unlocks powerful and expressive APIs in Python, particularly in libraries like TensorFlow Keras.