Introduction

In this course, we're using TensorFlow — one of the most powerful frameworks for building neural networks. But instead of working with it directly, we'll use Keras, which provides a simpler and more intuitive interface.

We'll delve into the core of Keras: its layers, which are the fundamental building blocks of neural networks.

Keras Layers

To recap, in the context of neural networks, a layer is a structured collection of neurons (also called nodes). Each neuron in a layer is typically connected to neurons in the previous and next layers. These connections are defined by weights, which are adjusted during training to help the network learn patterns in the data.

For instance, a standard multi-layer perceptron may look as follows:

Keras, on the other hand, adopts a more modular and flexible approach. In Keras:

• Layers are more granular and specialized: each layer typically performs a specific type of transformation or computation;
• Separation of concerns: instead of a single layer handling both weighted sums and activations, Keras often separates these into different layer types. For instance, a Dense layer handles weighted sums, and an Activation layer handles the activation functions.

All layer types in Keras are implemented as classes within the tensorflow.keras.layers module. Creating a specific layer involves simply instantiating the corresponding class:

from tensorflow.keras.layers import Layer

# Creating a layer
layer = Layer()

Here, Layer should be replaced with an actual class of a particular layer (e.g, Dense or Input).