Note: This page has been translated by MathWorks. Please click here

To view all translated materals including this page, select Japan from the country navigator on the bottom of this page.

To view all translated materals including this page, select Japan from the country navigator on the bottom of this page.

Train a neural network to generalize from example
inputs and their classes, construct a deep network using autoencoders

Neural Net Pattern Recognition | Classify data by training a two-layer feed-forward network |

`Autoencoder` |
Autoencoder class |

`trainAutoencoder` |
Train an autoencoder |

`trainSoftmaxLayer` |
Train a softmax layer for classification |

`decode` |
Decode encoded data |

`encode` |
Encode input data |

`predict` |
Reconstruct the inputs using trained autoencoder |

`stack` |
Stack encoders from several autoencoders together |

`network` |
Convert Autoencoder object into network object |

`patternnet` |
Pattern recognition network |

`lvqnet` |
Learning vector quantization neural network |

`train` |
Train neural network |

`trainlm` |
Levenberg-Marquardt backpropagation |

`trainbr` |
Bayesian regularization backpropagation |

`trainscg` |
Scaled conjugate gradient backpropagation |

`trainrp` |
Resilient backpropagation |

`mse` |
Mean squared normalized error performance function |

`regression` |
Linear regression |

`roc` |
Receiver operating characteristic |

`plotconfusion` |
Plot classification confusion matrix |

`ploterrhist` |
Plot error histogram |

`plotperform` |
Plot network performance |

`plotregression` |
Plot linear regression |

`plotroc` |
Plot receiver operating characteristic |

`plottrainstate` |
Plot training state values |

`crossentropy` |
Neural network performance |

`genFunction` |
Generate MATLAB function for simulating neural network |

**Classify Patterns with a Neural Network**

Use a neural network for classification.

**Deploy Trained Neural Network Functions**

Simulate and deploy trained neural networks using MATLAB^{®} tools.

**Deploy Training of Neural Networks**

Learn how to deploy training of a network.

**Neural Networks with Parallel and GPU Computing**

Use parallel and distributed computing to speed up neural network training and simulation and handle large data.

**Automatically Save Checkpoints During Neural Network Training**

Save intermediate results to protect the value of long training runs.

**Representing Unknown or Don't-Care Targets**

Prevent unknown target values from impacting training.

**Choose Neural Network Input-Output Processing Functions**

Preprocess inputs and targets for more efficient training.

**Configure Neural Network Inputs and Outputs**

Learn how to manually configure the network before
training using the `configure`

function.

**Divide Data for Optimal Neural Network Training**

Use functions to divide the data into training, validation, and test sets.

**Choose a Multilayer Neural Network Training Function**

Comparison of training algorithms on different problem types.

**Improve Neural Network Generalization and Avoid Overfitting**

Learn methods to improve generalization and prevent overfitting.

**Train Neural Networks with Error Weights**

Learn how to use error weighting when training neural networks.

**Normalize Errors of Multiple Outputs**

Learn how to fit output elements with different ranges of values.

**Construct Deep Network Using Autoencoders**

Illustration of using autoencoders to construct and train a deep network for image classification

**Workflow for Neural Network Design**

Learn the primary steps in a neural network design process.

**Four Levels of Neural Network Design**

Learn the different levels of using Neural Network Toolbox functionality.

**Multilayer Neural Networks and Backpropagation Training**

Workflow for designing a multilayer feedforward neural network for function fitting and pattern recognition.

**Multilayer Neural Network Architecture**

Learn the architecture of a multilayer neural network.

**Understanding Neural Network Toolbox Data Structures**

Learn how the format of input data structures affects the simulation of networks.

**Neural Network Object Properties**

Learn properties that define the basic features of a network.

**Neural Network Subobject Properties**

Learn properties that define network details such as inputs, layers, outputs, targets, biases, and weights.

Was this topic helpful?