Neural Network Learner (neural)

class Orange.classification.neural.NeuralNetworkLearner(name=NeuralNetwork, n_mid=10, reg_fact=1, max_iter=300, normalize=True, rand=None)

Bases: Orange.classification.Learner

NeuralNetworkLearner implements a multilayer perceptron. Learning is performed by minimizing an L2-regularized cost function with scipy’s implementation of L-BFGS. The current implementations is limited to a single hidden layer.

Regression is currently not supported.

Parameters:

• name (string) – learner name.
• n_mid (int) – Number of nodes in the hidden layer
• reg_fact (float) – Regularization factor.
• max_iter (int) – Maximum number of iterations.
• normalize (bool) – Normalize the data prior to learning (subtract each column by the mean and divide by the standard deviation)

Return type:

NeuralNetworkLearner or NeuralNetworkClassifier

__call__(data, weight=0)

Learn from the given table of data instances.

Parameters:

• instances (Orange.data.Table) – data for learning.
• weight (int) – weight.

Return type:

NeuralNetworkClassifier

class Orange.classification.neural.NeuralNetworkClassifier(domain, nn, normalize, mean, std, **kwargs)

Bases: Orange.classification.Classifier

Classifier induced by the NeuralNetworkLearner.

__call__(example, result_type=0)

Parameters:

• example (Orange.data.Instance) – instance to be classified.
• result_type – Orange.classification.Classifier.GetValue or Orange.classification.Classifier.GetProbabilities or Orange.classification.Classifier.GetBoth

Return type:

Orange.data.Value, Orange.statistics.Distribution or a tuple with both