Neural Network
==============

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](http://scikit-learn.org/stable/modules/neural_networks_supervised.html) that can learn non-linear models as well as linear.

![](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*.

Preprocessing
-------------

Neural Network uses default preprocessing when no other preprocessors are given. It executes them in the following order:

- removes instances with unknown target values
- continuizes categorical variables (with one-hot-encoding)
- removes empty columns
- imputes missing values with mean values
- normalizes the data by centering to mean and scaling to standard deviation of 1

To remove default preprocessing, connect an empty [Preprocess](../data/preprocess.md) widget to the learner.

Examples
--------

The first example is a classification task on *iris* dataset. We compare the results of **Neural Network** with the [Logistic Regression](../model/logisticregression.md).

![](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](../evaluate/predictions.md) and observe the predicted values.

![](images/NN-Example-Predict.png)