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 Toolbox™).

Apps

Deep Network Designer

Design, visualize, and train deep learning networks

Functions

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Train Network

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

Layers

`sequenceInputLayer`	Sequence input layer
`featureInputLayer`	Feature input layer
`lstmLayer`	Long short-term memory (LSTM) layer
`bilstmLayer`	Bidirectional long short-term memory (BiLSTM) layer
`gruLayer`	Gated recurrent unit (GRU) layer
`sequenceFoldingLayer`	Sequence folding layer
`sequenceUnfoldingLayer`	Sequence unfolding layer
`flattenLayer`	Flatten 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
`eluLayer`	Exponential linear unit (ELU) layer
`tanhLayer`	Hyperbolic tangent (tanh) layer
`swishLayer`	Swish layer
`dropoutLayer`	Dropout layer
`softmaxLayer`	Softmax layer
`classificationLayer`	Classification output layer
`regressionLayer`	Create a regression output layer

Prediction

`classify`	Classify data using a trained deep learning neural network
`predict`	Predict responses using a trained deep learning neural network
`activations`	Compute deep learning network layer activations
`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 the state of a recurrent neural network
`confusionchart`	Create confusion matrix chart for classification problem
`sortClasses`	Sort classes of confusion matrix chart

Custom Training Loops

padsequences Pad or truncate sequence data to same length

Blocks

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Deep Neural Networks

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

Properties

ConfusionMatrixChart Properties

Confusion matrix chart appearance and behavior

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.

Classify Videos Using Deep Learning

This example shows how to create a network for video classification by combining a pretrained image classification model and an LSTM network.

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.

Speech Command Recognition Using Deep Learning

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

Image Captioning Using Attention

This example shows how to train a deep learning model for image captioning using attention.

Train Network Using Custom Mini-Batch Datastore for Sequence Data

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

Visualize Activations of LSTM Network

This example shows how to investigate and visualize the features learned by LSTM networks by extracting the activations.

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).

Chemical Process Fault Detection Using Deep Learning

This example shows how to use simulation data to train a neural network that can detect faults in a chemical process.

Build Networks with Deep Network Designer

Interactively build and edit deep learning networks.

Create Simple Sequence Classification Network Using Deep Network Designer

This example shows how to create a simple long short-term memory (LSTM) classification network using Deep Network Designer.

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.

Text Data

Classify Text Data Using Deep Learning

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

Classify Text Data Using Convolutional Neural Network

This example shows how to classify text data using a convolutional neural network.

Multilabel Text Classification Using Deep Learning

This example shows how to classify text data that has multiple independent labels.

Classify Out-of-Memory Text Data Using Deep Learning

This example shows how to classify out-of-memory text data with a deep learning network using a transformed datastore.

Sequence-to-Sequence Translation Using Attention

This example shows how to convert decimal strings to Roman numerals using a recurrent sequence-to-sequence encoder-decoder model with attention.

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.

Generate Text Using Autoencoders

This example shows how to generate text data using autoencoders.

Define Text Encoder Model Function

This example shows how to define a text encoder model function.

Define Text Decoder Model Function

This example shows how to define a text decoder model function.

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^®.

Datastores for Deep Learning

Learn how to use datastores in deep learning applications.

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.

Data Sets for Deep Learning

Discover data sets for various deep learning tasks.

Featured Examples

Sequence Classification Using Deep Learning

Classify sequence data using a long short-term memory (LSTM) network.

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Time Series Forecasting Using Deep Learning

Forecast time series data using a long short-term memory (LSTM) network.

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Speech Command Recognition Using Deep Learning

Train a deep learning model that detects the presence of speech commands in audio. The example uses the Speech Commands Dataset [1] to train a convolutional neural network to recognize a given set of commands.

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