本页对应的英文页面已更新，但尚未翻译。 若要查看最新内容，请点击此处访问英文页面。

模式识别

训练神经网络以从示例输入及其分类进行归纳，并训练自编码器

App

神经网络模式识别Classify data by training a two-layer feed-forward network

类

自编码器Autoencoder class

函数

全部展开

nnstartNeural network getting started GUI
viewView shallow neural network
trainAutoencoderTrain an autoencoder
trainSoftmaxLayerTrain a softmax layer for classification
decodeDecode encoded data
encodeEncode input data
predictReconstruct the inputs using trained autoencoder
stackStack encoders from several autoencoders together
networkConvert Autoencoder object into network object
patternnetPattern recognition network
lvqnetLearning vector quantization neural network
trainTrain shallow neural network
trainlmLevenberg-Marquardt backpropagation
trainbrBayesian regularization backpropagation
trainscgScaled conjugate gradient backpropagation
trainrpResilient backpropagation
mseMean squared normalized error performance function
regressionLinear regression
rocReceiver operating characteristic
plotconfusionPlot classification confusion matrix
ploterrhistPlot error histogram
plotperformPlot network performance
plotregressionPlot linear regression
plotrocPlot receiver operating characteristic
plottrainstatePlot training state values
crossentropyNeural network performance
genFunctionGenerate MATLAB function for simulating shallow neural network

示例和操作指南

基本设计

使用浅层神经网络对模式进行分类

使用神经网络进行分类。

Deploy Shallow Neural Network Functions

Simulate and deploy trained shallow neural networks using MATLAB® tools.

Deploy Training of Shallow Neural Networks

Learn how to deploy training of shallow neural networks.

训练可扩展性和效率

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.

最优解

Choose Neural Network Input-Output Processing Functions

Preprocess inputs and targets for more efficient training.

Configure Shallow 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 Shallow 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.

分类

螃蟹分类

此示例说明如何使用神经网络作为分类器来根据螃蟹的物理尺寸识别螃蟹的性别。

葡萄酒分类

此示例说明模式识别神经网络如何根据葡萄酒的化学特性按酒庄对葡萄酒进行分类。

癌症检测

此示例说明如何训练一个神经网络来使用蛋白质表达谱上的质谱数据检测癌症。

字符识别

此示例说明如何训练神经网络以执行简单的字符识别。

自编码器

训练堆叠自编码器进行图像分类

此示例说明如何训练堆叠自编码器以对数字图像进行分类。

概念

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 functionality.

Multilayer Shallow Neural Networks and Backpropagation Training

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

Multilayer Shallow Neural Network Architecture

Learn the architecture of a multilayer shallow neural network.

Understanding Shallow Network Data Structures

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

浅层神经网络的样本数据集

试验浅层神经网络时要使用的样本数据集列表。

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.


Deep Learning Toolbox 文档
支持
Introducing Deep Learning with MATLAB

Introducing Deep Learning with MATLAB

Download ebook