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Neural Net Pattern Recognition

Classify data by training a two-layer feed-forward network

Description

The Neural Net Pattern Recognition app leads you through solving a data classification problem using a two-layer feed-forward network. It helps you select data, divide it into training, validation, and testing sets, define the network architecture, and train the network. You can select your own data from the MATLAB® workspace or use one of the example datasets. After training the network, evaluate its performance using cross-entropy and percent misclassification error. Further analyze the results using visualization tools such as confusion matrices and receiver operating characteristic curves. You can then evaluate the performance of the network on a test set. If you are not satisfied with the results, you can retrain the network with modified settings or on a larger data set.

You can generate MATLAB scripts to reproduce results or customize the training process. You can also save the trained network to test on new data or use for solving similar classification problems. The app also provides the option to generate various deployable versions of your trained network. For example, you can deploy the trained network using MATLAB Compiler™, MATLAB Coder™, or Simulink® Coder tools.

Required Products

  • MATLAB

  • Deep Learning Toolbox™

Open the Neural Net Pattern Recognition App

  • MATLAB Toolstrip: On the Apps tab, under Machine Learning, click the app icon.

  • MATLAB command prompt: Enter nprtool.