Layer class

The Layer class represents a layer in a neural network, containing a list of neurons and references to the previous and next layers. This class provides factory constructors for creating input, hidden, and output layers, facilitating the construction of different types of layers in a neural network.

The Layer class is essential for organizing neurons into structured layers, enabling the definition of the network's architecture and the flow of data through the network.

Properties:

LayerType layerType: The type of the layer (input, hidden, or output).
List
Layer? previousLayer: A reference to the previous layer in the network.
Layer? nextLayer: A reference to the next layer in the network.

Example usage:

final inputNeurons = [Neuron(name: 'Input1'), Neuron(name: 'Input2')];
final inputLayer = Layer.input(neurons: inputNeurons);

final hiddenNeurons = [Neuron(name: 'Hidden1'), Neuron(name: 'Hidden2')];
final hiddenLayer = Layer.hidden(neurons: hiddenNeurons, previousLayer: inputLayer);

final outputNeurons = [Neuron(name: 'Output1')];
final outputLayer = Layer.output(neurons: outputNeurons);

inputLayer.nextLayer = hiddenLayer;
hiddenLayer.nextLayer = outputLayer;

The Layer class enables the construction of a neural network by linking layers of neurons together. Each layer can reference the previous and next layers, allowing for the creation of complex network architectures and the propagation of data through the network.

Constructors

Properties

Methods

Operators

layer library