SNetwork class

A sequential model that stacks layers linearly.

SNetwork provides a high-level API for building, training, and evaluating neural networks, similar to Keras's Sequential model. It manages the network's layers, parameters, and the entire training lifecycle.

Example

// 1. Define the model
var network = SNetwork([
  DenseLayer(8),
  ReLULayer(),
  DenseLayer(1),
]);

// 2. Compile the model
network.compile(
  configuredOptimizer: Adam(network.parameters, learningRate: 0.01)
);

// 3. Train and evaluate
network.fit(inputs, targets, epochs: 100);
network.evaluate(inputs, targets);

Inheritance

• Object
• Layer
• SNetwork

Constructors

SNetwork(List<Layer> layers, {String name = 'snetwork'})

Properties

hashCode → int

The hash code for this object.

no setter, inherited

layers → List<Layer>

final

name → String

A user-friendly name for the layer (e.g., 'dense', 'lstm').

final

optimizer ↔ Optimizer

getter/setter pair

parameters → List<Tensor>

A list of all trainable tensors (weights and biases) in the layer.

no setter, override

runtimeType → Type

A representation of the runtime type of the object.

no setter, inherited

Methods

build(Tensor input) → void

Initializes the layer's parameters based on the shape of the first input.

inherited

call(Tensor input) → Tensor

The public, callable interface for the layer.

inherited

compile({required Optimizer configuredOptimizer}) → void

evaluate(List<List<double>> inputs, List<List<double>> targets) → void

fit(List<List<double>> inputs, List<List<double>> targets, {int epochs = 100, bool averageWeight = false, bool debug = true}) → void

forward(Tensor input) → Tensor

The core logic of the layer's transformation.

override

noSuchMethod(Invocation invocation) → dynamic

Invoked when a nonexistent method or property is accessed.

inherited

predict(Tensor input) → Tensor

toString() → String

A string representation of this object.

inherited

Operators

operator ==(Object other) → bool

The equality operator.

inherited