内置训练

使用内置训练函数为序列和表格数据训练深度学习网络

定义网络架构后，您可以使用 trainingOptions 函数定义训练参数。然后，您可以使用 trainnet 函数训练网络。使用经过训练的网络预测类标签或数值响应，或预测将来的时间步。

您可以在一个 CPU、一个 GPU、多个 CPU 或 GPU 上训练神经网络，或者在集群中并行训练或在云中训练。在 GPU 上训练或并行训练需要 Parallel Computing Toolbox™。使用 GPU 需要支持的 GPU 设备（有关受支持设备的信息，请参阅GPU Computing Requirements (Parallel Computing Toolbox)）。使用 trainingOptions 函数指定执行环境。

App

深度网络设计器

设计和可视化深度学习网络

函数

全部展开

训练网络

`trainingOptions`	Options for training deep learning neural network
`trainnet`	Train deep learning neural network (自 R2023b 起)
`analyzeNetwork`	Analyze deep learning network architecture

预测

`predict`	Compute deep learning network output for inference (自 R2019b 起)
`minibatchpredict`	Mini-batched neural network prediction (自 R2024a 起)
`scores2label`	Convert prediction scores to labels (自 R2024a 起)
`confusionchart`	Create confusion matrix chart for classification problem
`sortClasses`	Sort classes of confusion matrix chart

主题

多层感知器网络

Train Network with Numeric Features
This example shows how to create and train a simple neural network for deep learning feature data classification.
Compare Deep Learning Networks for Credit Default Prediction
Create, train, and compare three deep learning networks for predicting credit default probability.
Battery State of Charge Workflow

An example workflow for training, compressing, and using a deep learning network in Simulink^®.
- 步骤 1: Predict Battery State of Charge Using Deep Learning
- 步骤 2: Compress Network for Estimating Battery State of Charge
- 步骤 3: Battery State of Charge Estimation in Simulink Using Deep Learning Network

循环网络

使用深度学习进行“序列到序列”分类
此示例说明如何使用长短期记忆 (LSTM) 网络对序列数据的每个时间步进行分类。
使用深度学习进行“序列到序列”回归
此示例说明如何使用深度学习预测发动机的剩余使用寿命 (RUL)。
使用深度学习进行“序列到单个”回归
此示例说明如何使用长短期记忆 (LSTM) 神经网络来预测波形的频率。
Train Network with LSTM Projected Layer
Train a deep learning network with an LSTM projected layer for sequence-to-label classification.
使用深度学习对视频进行分类
此示例说明如何通过将预训练图像分类模型和 LSTM 网络相结合来创建视频分类网络。
使用序列数据的自定义小批量数据存储来训练网络
此示例说明如何使用自定义小批量数据存储基于无法放入内存的序列数据来训练深度学习网络。

卷积网络

使用一维卷积进行序列分类
此示例说明如何使用一维卷积神经网络对序列数据进行分类。
Time Series Anomaly Detection Using Deep Learning
This example shows how to detect anomalies in sequence or time series data.
Train Sequence Classification Network Using Data With Imbalanced Classes
This example shows how to classify sequences with a 1-D convolutional neural network using class weights to modify the training to account for imbalanced classes.
Sequence-to-Sequence Classification Using 1-D Convolutions
This example shows how to classify each time step of sequence data using a generic temporal convolutional network (TCN).
Train Network with Complex-Valued Data
This example shows how to predict the frequency of a complex-valued waveform using a 1-D convolutional neural network.
Sequence Classification Using CNN-LSTM Network
This example shows how to create a 2-D CNN-LSTM network for speech classification tasks by combining a 2-D convolutional neural network (CNN) with a long short-term memory (LSTM) layer.
Train Network on Image and Feature Data
This example shows how to train a network that classifies handwritten digits using both image and feature input data.

使用 MATLAB 进行深度学习

在 MATLAB 中进行深度学习
通过使用卷积神经网络进行分类和回归来探索 MATLAB^® 的深度学习能力，包括预训练网络和迁移学习，以及在 GPU、CPU、集群和云上进行训练。
Deep Learning Tips and Tricks
Learn how to improve the accuracy of deep learning networks.
Speed Up Deep Neural Network Training
Learn how to accelerate deep neural network training.
Data Sets for Deep Learning
Discover data sets for various deep learning tasks.