Orange: Neural Network

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A multi-layer perceptron (MLP) algorithm with backpropagation.


   Data: input dataset
   Preprocessor: preprocessing method(s)


   Learner: multi-layer perceptron learning algorithm
   Model: trained model

The Neural Network widget uses sklearn’s Multi-layer Perceptron algorithm that can learn non-linear models as well as linear.


   A name under which it will appear in other widgets. The default name is “Neural Network”.
   Set model parameters:
       Neurons per hidden layer: defined as the ith element represents the number of neurons in the ith hidden layer. E.g. a neural network with 3 layers can be defined as 2, 3, 2.
       Activation function for the hidden layer:
           Identity: no-op activation, useful to implement linear bottleneck
           Logistic: the logistic sigmoid function
           tanh: the hyperbolic tan function
           ReLu: the rectified linear unit function
       Solver for weight optimization:
           L-BFGS-B: an optimizer in the family of quasi-Newton methods
           SGD: stochastic gradient descent
           Adam: stochastic gradient-based optimizer
       Alpha: L2 penalty (regularization term) parameter
       Max iterations: maximum number of iterations
   Other parameters are set to sklearn’s defaults.
   Produce a report.
   When the box is ticked (Apply Automatically), the widget will communicate changes automatically. Alternatively, click Apply.


The first example is a classification task on iris dataset. We compare the results of Neural Network with the Logistic Regression.


The second example is a prediction task, still using the iris data. This workflow shows how to use the Learner output. We input the Neural Network prediction model into Predictions and observe the predicted values.




Pranala Menarik