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 | alpha | 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#
-
B. Widrow. (1960). An Adaptive "Adaline" Neuron Using Chemical "Memistors". ↩
Last update:
2021-05-08