Two-Class Neural Network

Creates a binary classifier using a neural network algorithm

Category: Machine Learning / Initialize Model / Classification

Module overview

This article describes how to use the Two-Class Neural Network module in Machine Learning Studio (classic), to create a neural network model that can be used to predict a target that has only two values.

Classification using neural networks is a supervised learning method, and therefore requires a tagged dataset, which includes a label column. For example, you could use this neural network model to predict binary outcomes such as whether or not a patient has a certain disease, or whether a machine is likely to fail within a specified window of time.

After you define the model, train it by providing a tagged dataset and the model as an input to Train Model or to Tune Model Hyperparameters. The trained model can then be used to predict values for new inputs.

More about neural networks

A neural network is a set of interconnected layers. The inputs are the first layer, and are connected to an output layer by an acyclic graph comprised of weighted edges and nodes.

Between the input and output layers you can insert multiple hidden layers. Most predictive tasks can be accomplished easily with only one or a few hidden layers. However, recent research has shown that deep neural networks (DNN) with many layers can be very effective in complex tasks such as image or speech recognition. The successive layers are used to model increasing levels of semantic depth.

The relationship between inputs and outputs is learned from training the neural network on the input data. The direction of the graph proceeds from the inputs through the hidden layer and to the output layer. All nodes in a layer are connected by the weighted edges to nodes in the next layer.

To compute the output of the network for a particular input, a value is calculated at each node in the hidden layers and in the output layer. The value is set by calculating the weighted sum of the values of the nodes from the previous layer. An activation function is then applied to that weighted sum.

How to configure Two-Class Neural Network

1. Add the Two-Class Neural Network module to your experiment in Studio (classic). You can find this module under Machine Learning, Initialize, in the Classification category.

2. Specify how you want the model to be trained, by setting the Create trainer mode option.

   • Single Parameter: Choose this option if you already know how you want to configure the model.

   • Parameter Range: Choose this option if you are not sure of the best parameters. Then, specify a range of values and use the Tune Model Hyperparameters module to iterate over the combinations and find the optimal configuration.

3. For Hidden layer specification, select the type of network architecture to create.

   • Fully-connected case: Uses the default neural network architecture, defined for two-class neural networks as follows:

     • Has one hidden layer.

     • The output layer is fully connected to the hidden layer, and the hidden layer is fully connected to the input layer.

     • The number of nodes in the input layer equals the number of features in the training data.

     • The number of nodes in the hidden layer is set by the user. The default value is 100.

     • The number of nodes equals the number of classes. For a two-class neural network, this means that all inputs must map to one of two nodes in the output layer.

   • Custom definition script: Choose this option to create a custom neural network architecture, using the Net# language. With this option, you can define the number of hidden layers, their connections, and the mappings between layers.

   After selecting the custom script option, in the Neural network definition text box, type or paste Net# statements that define the network. For examples, see Guide to the Net# Neural Networks Specification Language.

4. If you are not using the script option, use Number of hidden nodes, and type the number of hidden nodes. The default is one hidden layer with 100 nodes.

5. For Learning rate, define the size of the step taken at each iteration, before correction. A larger value for learning rate can cause the model to converge faster, but it can overshoot local minima.

6. For Number of learning iterations, specify the maximum number of times the algorithm should process the training cases.

7. For The initial learning weights diameter, specify the node weights at the start of the learning process.

8. For The momentum, specify a weight to apply during learning to nodes from previous iterations

9. In The type of normalizer, select a method to use for feature normalization. The following normalization methods are supported:

   • Binning normalizer: The binning normalizer creates bins of equal size, and then normalizes every value in each bin, dividing by the total number of bins.

   • Gaussian normalizer: The Gaussian normalizer rescales the values of each feature to have mean 0 and variance 1. This is done by computing the mean and the variance of each feature. For each instance, the mean value is subtracted, and the result divided by the square root of the variance (the standard deviation).

   • Min-max normalizer: The min-max normalizer linearly rescales every feature to the [0,1] interval.

     Rescaling to the [0,1] interval is done by shifting the values of each feature so that the minimal value is 0, and then dividing by the new maximal value (which is the difference between the original maximal and minimal values).

   • Do not normalize: No normalization is performed.

10. Select the Shuffle examples option to shuffle cases between iterations. If you deselect this option, cases are processed in exactly the same order each time you run the experiment.

11. For Random number seed, type a value to use as the seed.

    Specifying a seed value is useful when you want to ensure repeatability across runs of the same experiment. Otherwise, a system clock value is used as the seed, which can cause slightly different results each time you run the experiment.

12. Select the Allow unknown categorical levels option to create a grouping for unknown values in the training and validation sets. The model might be less precise on known values but provide better predictions for new (unknown) values.

    If you deselect this option, the model can accept only the values contained in the training data.

13. Add a tagged dataset to the experiment, and connect one of the training modules.

    • If you set Create trainer mode to Single Parameter, use the Train Model module.

    • If you set Create trainer mode to Parameter Range, use the Tune Model Hyperparameters module.

    Note

    If you pass a parameter range to Train Model, it uses only the first value in the parameter range list.

    If you pass a single set of parameter values to the Tune Model Hyperparameters module, when it expects a range of settings for each parameter, it ignores the values and uses the default values for the learner.

    If you select the Parameter Range option and enter a single value for any parameter, that single value is used throughout the sweep, even if other parameters change across a range of values.

14. Run the experiment.

Results

After training is complete:

• To see a summary of the model's parameters, together with the feature weights learned from training, and other parameters of the neural network, right-click the output of Train Model or Tune Model Hyperparameters, and select Visualize.

• To save a snapshot of the trained model, right-click the Trained model output and select Save As Trained Model. This model is not updated on successive runs of the same experiment.

• To perform cross-validation against a labeled data set, connect the untrained model to Cross-Validate Model.

Examples

For examples of how this learning algorithm is used, see the Azure AI Gallery. These experiments are related and described in a single document that progresses from basic to advanced configurations:

• Deep Neural networks sample (part A)

• Deep Neural networks sample (part B)

• Deep Neural networks sample (part C)

• Deep Neural networks sample (part D)

Technical notes

This section contains implementation details, tips, and answers to frequently asked questions.

More about Net#

In Machine Learning Studio (classic), you can customize the architecture of a neural network model by using the Net# language. Customizations supported by the Net# language include:

• Specifying the number of hidden layers and the number of nodes in each layer
• Specifying mappings between layers
• Defining convolutions and weight-sharing bundles
• Choosing the activation function

A neural network model is defined by the structure of its graph, which includes these attributes:

• The number of hidden layers
• The number of nodes in each hidden layer
• How the layers are connected
• Which activation function is used
• Weights on the graph edges

In general, the network has these defaults:

• The first layer is always the input layer.
• The last layer is always the output layer.
• The number of nodes in the output layer should be equal to the number of classes.

You can define any number of intermediate layers (sometimes called hidden layers, because they are contained within the model, and they are not directly exposed as endpoints).

The Net# reference guide explains the syntax and provides sample network definitions. It explains how you can use Net# to add hidden layers and define the way that the different layers interact with each other.

For example, the following script uses the auto keyword, which sets the number of features automatically for input and output layers, and uses the default values for the hidden layer.

input Data auto;  
hidden Hidden auto from Data all;  
output Result auto from Hidden all;   

For additional script examples, see Guide to the Net# Neural Networks Specification Language.

Module parameters

Output

See also

Classification
Neural Network Regression
Multiclass Neural Network
A-Z Module List