Adaline#
Adaptive Linear Neuron is a single layer neural network with a linear output neuron. Training is equivalent to solving Ridge regression 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 | object | The gradient descent optimizer used to train the underlying network. |
| 3 | alpha | 1e-4 | float | The amount of L2 regularization to apply to the weights of the network. |
| 4 | epochs | 100 | int | The maximum number of training epochs to execute. |
| 5 | min change | 1e-4 | float | The minimum change in the cost function necessary to continue training. |
| 6 | cost fn | Least Squares | object | The function that computes the cost of an erroneous activation during training. |
| 7 | window | 5 | int | The number of epochs without improvement in the training loss to wait before considering an early stop. |
Additional Methods#
Return the average loss of a sample at each epoch of training:
public steps() : array
Return the underlying neural network instance or null if untrained:
public network() : Network|null
Example#
use Rubix\ML\Classifers\Adaline;
use Rubix\ML\NeuralNet\Optimizers\Adam;
use Rubix\ML\NeuralNet\CostFunctions\HuberLoss;
$estimator = new Adaline(10, new Adam(0.001), 500, 1e-6, new HuberLoss(2.5), 5);