Multiclass Neural Network module
This article describes a module in Azure Machine Learning designer.
Use this module to create a neural network model that can be used to predict a target that has multiple values.
For example, neural networks of this kind might be used in complex computer vision tasks, such as digit or letter recognition, document classification, and pattern recognition.
Classification using neural networks is a supervised learning method, and therefore requires a tagged dataset that includes a label column.
You can train the model by providing the model and the tagged dataset as an input to Train Model. The trained model can then be used to predict values for the new input examples.
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 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.
Configure Multiclass Neural Network
Add the MultiClass Neural Network module to your pipeline in the designer. You can find this module under Machine Learning, Initialize, in the Classification category.
Create trainer mode: Use this option to specify how you want the model to be trained:
Single Parameter: Choose this option if you already know how you want to configure the model.
Parameter Range: Select this option if you are not sure of the best parameters, and want to run a parameter sweep. Select a range of values to iterate over, and the Tune Model Hyperparameters iterates over all possible combinations of the settings you provided to determine the hyperparameters that produce the optimal results.
Hidden layer specification: Select the type of network architecture to create.
Fully connected case: Select this option to create a model using the default neural network architecture. For multiclass neural network models, the defaults are as follows:
- One hidden layer
- The output layer is fully connected to the hidden layer.
- The hidden layer is fully connected to the input layer.
- The number of nodes in the input layer is determined by the number of features in the training data.
- The number of nodes in the hidden layer can be set by the user. The default is 100.
- The number of nodes in the output layer depends on the number of classes.
Number of hidden nodes: This option lets you customize the number of hidden nodes in the default architecture. Type the number of hidden nodes. The default is one hidden layer with 100 nodes.
The 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.
Number of learning iterations: Specify the maximum number of times the algorithm should process the training cases.
The initial learning weights diameter: Specify the node weights at the start of the learning process.
The momentum: Specify a weight to apply during learning to nodes from previous iterations.
Shuffle examples: Select this option to shuffle cases between iterations.
If you deselect this option, cases are processed in exactly the same order each time you run the pipeline.
Random number seed: Type a value to use as the seed, if you want to ensure repeatability across runs of the same pipeline.
Train the model:
If you set Create trainer mode to Single Parameter, connect a tagged dataset and the Train Model module.
If you set Create trainer mode to Parameter Range, connect a tagged dataset and train the model by using Tune Model Hyperparameters.
If you pass a parameter range to Train Model, it uses only the default value in the single parameter 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 you specified is used throughout the sweep, even if other parameters change across a range of values.
After training is complete:
- To save a snapshot of the trained model, select the Outputs tab in the right panel of the Train model module. Select the Register dataset icon to save the model as a reusable module.
See the set of modules available to Azure Machine Learning.