Deep Learning with Time Series, Sequences, and Text

Create and train networks for time series classification, regression, and forecasting tasks

Create and train networks for time series classification, regression, and forecasting tasks. Train long short-term memory (LSTM) networks for sequence-to-one or sequence-to-label classification and regression problems. You can train LSTM networks on text data using word embedding layers (requires Text Analytics Toolbox™) or convolutional neural networks on audio data using spectrograms (requires Audio System Toolbox™).

Apps

Deep Network Designer

Edit and build deep learning networks

Functions

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Train Network

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

Layers

`sequenceInputLayer`	Sequence input layer
`lstmLayer`	Long short-term memory (LSTM) layer
`bilstmLayer`	Bidirectional long short-term memory (BiLSTM) layer
`fullyConnectedLayer`	Fully connected layer
`reluLayer`	Rectified Linear Unit (ReLU) layer
`leakyReluLayer`	Leaky Rectified Linear Unit (ReLU) layer
`clippedReluLayer`	Clipped Rectified Linear Unit (ReLU) layer
`dropoutLayer`	辍学层
`softmaxLayer`	Softmax layer
`classificationLayer`	Classification output layer
`regressionLayer`	Create a regression output layer

Prediction

`predict`	预测的反应using a trained deep learning neural network
`classify`	Classify data using a trained deep learning neural network
`predictAndUpdateState`	预测的反应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 the state of a recurrent neural network
`confusionchart`	Create confusion matrix chart for classification problem
ConfusionMatrixChart Properties	Confusion matrix chart appearance and behavior
`sortClasses`	Sort classes of confusion matrix chart

Examples and How To

Sequences and Time Series

Sequence Classification Using Deep Learning

This example shows how to classify sequence data using a long short-term memory (LSTM) network.

Sequence-to-Sequence Classification Using Deep Learning

This example shows how to classify each time step of sequence data using a long short-term memory (LSTM) network.

Sequence-to-Sequence Regression Using Deep Learning

This example shows how to predict the remaining useful life (RUL) of engines by using deep learning.

Time Series Forecasting Using Deep Learning

This example shows how to forecast time series data using a long short-term memory (LSTM) network.

Speech Command Recognition Using Deep Learning

This example shows how to train a simple deep learning model that detects the presence of speech commands in audio.

Train Network Using Out-of-Memory Sequence Data

This example shows how to train a deep learning network on out-of-memory sequence data using a custom mini-batch datastore.

Build Networks with Deep Network Designer

Interactively build and edit deep learning networks.

Text Data

Classify Text Data Using Deep Learning

This example shows how to classify text descriptions of weather reports using a deep learning long short-term memory (LSTM) network.

Generate Text Using Deep Learning

This example shows how to train a deep learning long short-term memory (LSTM) network to generate text.

Pride and Prejudice and MATLAB

This example shows how to train a deep learning LSTM network to generate text using character embeddings.

Word-By-Word Text Generation Using Deep Learning

This example shows how to train a deep learning LSTM network to generate text word-by-word.

Classify Out-of-Memory Text Data Using Custom Mini-Batch Datastore

这个例子展示了如何分类t内存不足ext data with a deep learning network using a custom mini-batch datastore.

Concepts

Long Short-Term Memory Networks

Learn about long short-term memory (LSTM) networks

List of Deep Learning Layers

Discover all the deep learning layers in MATLAB^®.

Deep Learning in MATLAB

Discover deep learning capabilities in MATLAB using convolutional neural networks for classification and regression, including pretrained networks and transfer learning, and training on GPUs, CPUs, clusters, and clouds.

Deep Learning Tips and Tricks

Learn how to improve the accuracy of deep learning networks.

Featured Examples

Sequence Classification Using Deep Learning

This example shows how to classify sequence data using a long short-term memory (LSTM) network.

Open Live Script

Time Series Forecasting Using Deep Learning