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Softmax Classifier#

A multiclass generalization of Logistic Regression using a single layer neural network with a Softmax output layer.

Interfaces: Estimator, Learner, Online, Probabilistic, Verbose, Persistable

Data Type Compatibility: Continuous


# Name Default Type Description
1 batchSize 256 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 CrossEntropy ClassificationLoss The function that computes the loss associated with an erroneous activation during training.


use Rubix\ML\Classifiers\SoftmaxClassifier;
use Rubix\ML\NeuralNet\Optimizers\Momentum;
use Rubix\ML\NeuralNet\CostFunctions\CrossEntropy;

$estimator = new SoftmaxClassifier(256, new Momentum(0.001), 1e-4, 300, 1e-4, 10, new CrossEntropy());

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);


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