This is documentation for Orange 2.7. For the latest documentation, see Orange 3.
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: Return type: - __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:
- 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