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.