Orange: Neural Network

Sumber:https://docs.biolab.si//3/visual-programming/widgets/model/neuralnetwork.html

Sebuah multi-layer perceptron (MLP) algorithm dengan backpropagation.

Input

Data: input dataset
Preprocessor: preprocessing method(s)

Output

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

Widget Neural Network menggunakan sklearn Multi-layer Perceptron algorithm yang dapat learn non-linear maupun linear model.

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.

Contoh

Contoh pertama adalah task classification pada iris dataset. Kita membandingkan hasil dari Neural Network dengan Logistic Regression.

Contoh ke dua adalah task prediction, menggunakan iris data. Workflow ini memperlihatkan bagaimana cara menggunakan keluaran Learner. Kita memasukan Neural Network prediction model ke widget Predictions dan mengamati nilai hasil prediksi.