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神经网络

用于回归的神经网络

神经网络模型由一系列反映大脑处理信息方式的层构成。Statistics and Machine Learning Toolbox™ 中提供的回归神经网络模型是完全连接的前馈神经网络,您可以调整全连接层的大小并更改层的激活函数。

要训练回归神经网络模型,可以使用回归学习器。为了获得更大的灵活性,请在命令行界面中使用 fitrnet 来训练回归神经网络模型。经过训练后,您可以通过将模型和新预测数据传递给 predict 来预测变量新数据的响应。

如果您要创建更复杂的深度学习网络并拥有 Deep Learning Toolbox™,您可以尝试深度网络设计器 (Deep Learning Toolbox)

App

回归学习器Train regression models to predict data using supervised machine learning

函数

全部展开

fitrnetTrain neural network regression model
compactReduce size of machine learning model
crossvalCross-validate machine learning model
kfoldLossLoss for cross-validated partitioned regression model
kfoldPredictPredict responses for observations in cross-validated regression model
kfoldfunCross-validate function for regression
lossLoss for regression neural network
resubLossResubstitution regression loss
predictPredict responses using regression neural network
resubPredictPredict responses for training data using trained regression model

对象

RegressionNeuralNetworkNeural network model for regression
CompactRegressionNeuralNetworkCompact neural network model for regression
RegressionPartitionedModelCross-validated regression model

主题

Assess Regression Neural Network Performance

Use fitrnet to create a feedforward regression neural network model with fully connected layers, and assess the performance of the model on test data.

Train Regression Neural Networks Using Regression Learner App

Create and compare regression neural networks, and export trained models to make predictions for new data.