使用时间序列和序列数据进行深度学习

创建和训练用于时间序列分类、回归和预测任务的网络

创建和训练用于时间序列分类、回归和预测任务的网络。训练用于“序列到单个”或“序列到标签”的分类和回归问题的长短期记忆 (LSTM) 网络。您可以使用单词嵌入层对文本数据训练 LSTM 网络（需要 Text Analytics Toolbox™），或使用频谱图对音频数据训练卷积神经网络（需要 Audio Toolbox™）。

App

深度网络设计器

设计、可视化和训练深度学习网络

函数

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训练网络

`trainingOptions`	Options for training deep learning neural network
`trainNetwork`	Train deep learning neural network
`analyzeNetwork`	Analyze deep learning network architecture

网络层

输入层

`sequenceInputLayer`	Sequence input layer
`featureInputLayer`	Feature input layer

循环层

`lstmLayer`	Long short-term memory (LSTM) layer for recurrent neural network (RNN)
`bilstmLayer`	Bidirectional long short-term memory (BiLSTM) layer for recurrent neural network (RNN)
`gruLayer`	Gated recurrent unit (GRU) layer for recurrent neural network (RNN)
`lstmProjectedLayer`	Long short-term memory (LSTM) projected layer for recurrent neural network (RNN)

卷积和全连接层

`convolution1dLayer`	1-D convolutional layer
`transposedConv1dLayer`	Transposed 1-D convolution layer
`fullyConnectedLayer`	Fully connected layer

池化层

`maxPooling1dLayer`	1-D max pooling layer
`averagePooling1dLayer`	1-D average pooling layer
`globalMaxPooling1dLayer`	1-D global max pooling layer
`globalAveragePooling1dLayer`	1-D global average pooling layer

激活层和丢弃层

`reluLayer`	修正线性单元 (ReLU) 层
`leakyReluLayer`	Leaky Rectified Linear Unit (ReLU) layer
`clippedReluLayer`	Clipped Rectified Linear Unit (ReLU) layer
`eluLayer`	Exponential linear unit (ELU) layer
`tanhLayer`	双曲正切 (tanh) 层
`swishLayer`	Swish layer
`geluLayer`	Gaussian error linear unit (GELU) layer
`softmaxLayer`	Softmax 层
`dropoutLayer`	Dropout layer
`functionLayer`	Function layer

数据操作

`sequenceFoldingLayer`	Sequence folding layer
`sequenceUnfoldingLayer`	Sequence unfolding layer
`flattenLayer`	Flatten layer

输出层

`classificationLayer`	分类输出层
`regressionLayer`	回归输出层

预测

`classify`	Classify data using trained deep learning neural network
`predict`	Predict responses using trained deep learning neural network
`activations`	计算深度学习网络层激活
`predictAndUpdateState`	Predict responses using a trained recurrent neural network and update the network state
`classifyAndUpdateState`	Classify data using a trained recurrent neural network and update the network state
`resetState`	Reset state parameters of neural network
`confusionchart`	Create confusion matrix chart for classification problem
`sortClasses`	Sort classes of confusion matrix chart

自定义训练循环

padsequences Pad or truncate sequence data to same length

模块

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深度神经网络

Predict	Predict responses using a trained deep learning neural network
Stateful Predict	Predict responses using a trained recurrent neural network
Stateful Classify	Classify data using a trained deep learning recurrent neural network

属性

ConfusionMatrixChart Properties

Confusion matrix chart appearance and behavior

主题

循环网络

长短期记忆网络
了解长短期记忆 (LSTM) 网络。
使用深度学习进行时间序列预测
此示例说明如何使用长期短期记忆 (LSTM) 网络预测时间序列数据。
使用深度学习进行序列分类
此示例说明如何使用长短期记忆 (LSTM) 网络对序列数据进行分类。
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)。
Sequence-to-One Regression Using Deep Learning
This example shows how to predict the frequency of a waveform using a long short-term memory (LSTM) neural network.
Train Network with LSTM Projected Layer
Train a deep learning network with an LSTM projected layer for sequence-to-label classification.
使用深度网络设计器创建简单的序列分类网络
此示例说明如何使用深度网络设计器创建简单的长短期记忆 (LSTM) 分类网络。
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。
Classify Videos Using Deep Learning with Custom Training Loop
This example shows how to create a network for video classification by combining a pretrained image classification model and a sequence classification network.
Image Captioning Using Attention
This example shows how to train a deep learning model for image captioning using attention.
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。
Visualize Activations of LSTM Network
This example shows how to investigate and visualize the features learned by LSTM networks by extracting the activations.
Chemical Process Fault Detection Using Deep Learning
Use simulation data to train a neural network than can detect faults in a chemical process.
使用深度网络设计器创建简单的序列分类网络
此示例说明如何使用深度网络设计器创建简单的长短期记忆 (LSTM) 分类网络。
Train Latent ODE Network with Irregularly Sampled Time-Series Data
This example shows how to train a latent ordinary differential equation (ODE) autoencoder with time-series data that is sampled at irregular time intervals.

卷积网络

Sequence Classification Using 1-D Convolutions
This example shows how to classify sequence data using a 1-D convolutional neural network.
Time Series Anomaly Detection Using Deep Learning
This example shows how to detect anomalies in sequence or time series data.
使用深度学习训练语音命令识别模型
此示例说明如何训练一个深度学习模型来检测音频中是否存在语音命令。此示例使用语音命令数据集 [1] 来训练卷积神经网络，以识别一组命令。
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.
Interpret Deep Learning Time-Series Classifications Using Grad-CAM
This example shows how to use the gradient-weighted class activation mapping (Grad-CAM) technique to understand the classification decisions of a 1-D convolutional neural network trained on time-series data.
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 with Numeric Features
This example shows how to create and train a simple neural network for deep learning feature data classification.

使用 Simulink 进行深度学习

Predict and Update Network State in Simulink
This example shows how to predict responses for a trained recurrent neural network in Simulink® by using the Stateful Predict block.
Classify and Update Network State in Simulink
This example shows how to classify data for a trained recurrent neural network in Simulink® by using the Stateful Classify block.
Predict Battery State of Charge Using Deep Learning
This example shows how to train a neural network to predict the state of charge of a battery by using deep learning.
Improve Performance of Deep Learning Simulations in Simulink
This example shows how to use code generation to improve the performance of deep learning simulations in Simulink®.
Physical System Modeling Using LSTM Network in Simulink
This example shows how to create a reduced order model (ROM) to replace a Simscape component in a Simulink® model by training a long short-term memory (LSTM) neural network.

使用 MATLAB 进行深度学习

深度学习层列表
探索 MATLAB^® 中的所有深度学习层。
Datastores for Deep Learning
Learn how to use datastores in deep learning applications.
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.
Data Sets for Deep Learning
Discover data sets for various deep learning tasks.