NN documentation

doc/visual-programming/source/index.rst

@@ -80,19 +80,20 @@ Model
 .. toctree::
    :maxdepth: 1
 
-   widgets/model/naivebayes
-   widgets/model/logisticregression
-   widgets/model/tree
-   widgets/model/knn
-   widgets/model/loadmodel
    widgets/model/constant
+   widgets/model/cn2ruleinduction
+   widgets/model/knn
+   widgets/model/tree
    widgets/model/randomforest
-   widgets/model/savemodel
    widgets/model/svm
-   widgets/model/cn2ruleinduction
+   widgets/model/linearregression
+   widgets/model/logisticregression
+   widgets/model/naivebayes
    widgets/model/adaboost
+   widgets/model/neuralnetwork
    widgets/model/stochasticgradient
-   widgets/model/linearregression
+   widgets/model/loadmodel
+   widgets/model/savemodel
 
 
 Unsupervised

doc/visual-programming/source/widgets/model/neuralnetwork.rst

@@ -0,0 +1,69 @@
+Neural Network
+==============
+
+.. figure:: icons/nn.png
+
+A multi-layer perceptron (MLP) algorithm with backpropagation.
+
+Signals
+-------
+
+**Inputs**:
+
+-  **Data**
+
+   A data set
+
+-  **Preprocessor**
+
+   Preprocessing method(s)
+
+**Outputs**:
+
+-  **Learner**
+
+   A MLP learning algorithm with settings as specified in the dialog.
+
+-  **Model**
+
+   A trained model. Output signal sent only if input *Data* is present.
+
+Description
+-----------
+
+The **Neural Network** widget uses sklearn's `Multi\-layer Perceptron algorithm <http://scikit-learn.org/stable/modules/neural_networks_supervised.html>`_ that can learn non-linear models as well as linear.
+
+.. figure:: images/NeuralNetwork-stamped.png
+
+1. A name under which it will appear in other widgets. The default name is
+   "Neural Network".
+2. 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 <http://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html>`_.
+
+3. Produce a report. 
+4. 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* data set. We compare the results of **Neural Network** with the :doc:`Logistic Regression <../model/logisticregression>`.
+
+.. figure:: 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 :doc:`Predictions <../evaluation/predictions>` and observe the predicted values.
+
+.. figure:: images/NN-Example-Predict.png