Difference between revisions of "Orange: Neural Network"

From OnnoWiki
Jump to navigation Jump to search
(Created page with "center|400px|thumb")
 
Line 1: Line 1:
 +
Sumber:https://docs.biolab.si//3/visual-programming/widgets/model/neuralnetwork.html
 +
 +
A multi-layer perceptron (MLP) algorithm with backpropagation.
 +
 +
Inputs
 +
 +
    Data: input dataset
 +
 +
    Preprocessor: preprocessing method(s)
 +
 +
Outputs
 +
 +
    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.
 +
 +
../../_images/NeuralNetwork-stamped.png
 +
 +
    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.
 +
 +
Examples
 +
 +
The first example is a classification task on iris dataset. We compare the results of Neural Network with the Logistic Regression.
 +
 +
../../_images/NN-Example-Test.png
 +
 +
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.
 +
 +
../../_images/NN-Example-Predict.png
 +
 +
 +
 +
 
[[File:Orange-NN.png|center|400px|thumb]]
 
[[File:Orange-NN.png|center|400px|thumb]]
 +
 +
 +
==Referensi==
 +
 +
* https://docs.biolab.si//3/visual-programming/widgets/model/neuralnetwork.html
 +
 +
==Pranala Menarik==
 +
 +
* [[Orange]]

Revision as of 14:09, 16 January 2020

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

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

Inputs 

   Data: input dataset

   Preprocessor: preprocessing method(s)

Outputs 

   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.

../../_images/NeuralNetwork-stamped.png 

   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.

Examples

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

../../_images/NN-Example-Test.png

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.

../../_images/NN-Example-Predict.png



Orange-NN.png


Referensi

Pranala Menarik

Retrieved from "https://onnocenter.or.id/wiki/index.php?title=Orange:_Neural_Network&oldid=58565"