Adaline#
Adaptive Linear Neuron is a single layer neural network with a continuous linear output neuron. Training is equivalent to solving L2 regularized linear regression (Ridge) iteratively using Mini Batch Gradient Descent.
Interfaces: Estimator, Learner, Online, Verbose, Persistable
Data Type Compatibility: Continuous
Parameters#
| # | Param | Default | Type | Description |
|---|---|---|---|---|
| 1 | batch size | 100 | 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 | alpha | 1e-4 | float | The amount of L2 regularization to apply to the parameters of the network. |
| 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 | min change | 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 | cost fn | LeastSquares | RegressionLoss | The function that computes the loss associated with an erroneous activation during training. |
Additional Methods#
Return the training loss at each epoch:
public steps() : array
Return the underlying neural network instance or null if untrained:
public network() : Network|null
Example#
use Rubix\ML\Regressors\Adaline;
use Rubix\ML\NeuralNet\Optimizers\Adam;
use Rubix\ML\NeuralNet\CostFunctions\HuberLoss;
$estimator = new Adaline(10, new Adam(0.001), 500, 1e-6, 5, new HuberLoss(2.5));
References#
- B. Widrow. (1960). An Adaptive "Adaline" Neuron Using Chemical "Memistors".