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Adaline

Adaptive Linear Neuron is a single layer feed-forward neural network with a continuous linear output neuron suitable for regression tasks. Training is equivalent to solving L2 regularized linear regression (Ridge) online using Mini Batch Gradient Descent.

Interfaces: Estimator, Learner, Online, Ranks Features, Verbose, Persistable

Data Type Compatibility: Continuous

Parameters

#	Name	Default	Type	Description
1	batchSize	128	int	The number of training samples to process at a time.
2	optimizer	Adam	Optimizer	The gradient descent optimizer used to update the network parameters.
3	l2Penalty	1e-4	float	The amount of L2 regularization applied to the weights of the output layer.
4	epochs	1000	int	The maximum number of training epochs. i.e. the number of times to iterate over the entire training set before terminating.
5	minChange	1e-4	float	The minimum change in the training loss necessary to continue training.
6	window	5	int	The number of epochs without improvement in the training loss to wait before considering an early stop.
7	costFn	LeastSquares	RegressionLoss	The function that computes the loss associated with an erroneous activation during training.

Example

use Rubix\ML\Regressors\Adaline;
use Rubix\ML\NeuralNet\Optimizers\Adam;
use Rubix\ML\NeuralNet\CostFunctions\HuberLoss;

$estimator = new Adaline(256, new Adam(0.001), 1e-4, 500, 1e-6, 5, new HuberLoss(2.5));

Additional Methods

Return an iterable progress table with the steps from the last training session:

public steps() : iterable

use Rubix\ML\Extractors\CSV;

$extractor = new CSV('progress.csv', true);

$extractor->export($estimator->steps());

Return the loss for each epoch from the last training session:

public losses() : float[]|null

Return the underlying neural network instance or null if untrained:

public network() : Network|null

References

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1. B. Widrow. (1960). An Adaptive "Adaline" Neuron Using Chemical "Memistors". ↩