Deep learning con series de tiempo y datos secuenciales

Cree y entrene redes para tareas de clasificación, regresión y predicción de series de tiempo

Cree y entrene redes para tareas de clasificación, regresión y predicción de series de tiempo. Entrene redes de memoria de corto-largo plazo (LSTM) para problemas de clasificación y regresión de secuencia a uno o secuencia a etiqueta. Puede entrenar redes LSTM sobre datos de texto usando capas de incrustación de palabras (requiere Text Analytics Toolbox™) o redes neuronales convolucionales sobre datos de audio mediante espectrogramas (requiere Audio Toolbox™).

Apps

Deep Network Designer

Diseñar, visualizar y entrenar redes de deep learning

Funciones

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Entrenar la red

`trainingOptions`	Opciones para entrenar una red neuronal de deep learning
`trainNetwork`	Entrenar redes neuronales de deep learning
`analyzeNetwork`	Analyze deep learning network architecture

Capas

Capas de entrada

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

Capas recurrentes

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

Convolución y capas totalmente conectadas

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

Agrupar capas

`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

Capas de activación y abandono

`reluLayer`	Capa de unidad lineal rectificada (ReLU)
`leakyReluLayer`	Leaky Rectified Linear Unit (ReLU) layer
`clippedReluLayer`	Clipped Rectified Linear Unit (ReLU) layer
`eluLayer`	Exponential linear unit (ELU) layer
`tanhLayer`	Capa de tangente hiperbólica (tanh)
`swishLayer`	Swish layer
`geluLayer`	Gaussian error linear unit (GELU) layer
`softmaxLayer`	Capa softmax
`dropoutLayer`	Dropout layer
`functionLayer`	Function layer

Manipulación de datos

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

Capas de salida

`classificationLayer`	Capa de clasificación de salida
`regressionLayer`	Capa de salida de regresión

Predicción

`classify`	Classify data using trained deep learning neural network
`predict`	Predict responses using trained deep learning neural network
`activations`	Calcular las activaciones de las capas de una red de deep learning
`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

Bucles de entrenamiento personalizados

padsequences Pad or truncate sequence data to same length

Bloques

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Redes neuronales profundas

Predict	Predecir respuestas usando una red neuronal de deep learning entrenada
Stateful Predict	Predict responses using a trained recurrent neural network
Stateful Classify	Classify data using a trained deep learning recurrent neural network

Propiedades

ConfusionMatrixChart Properties

Confusion matrix chart appearance and behavior

Temas

Redes recurrentes

Redes de memoria de corto-largo plazo
Obtenga información sobre redes de memoria de corto-largo plazo (LSTM).
Pronóstico de series de tiempo mediante deep learning
Este ejemplo muestra cómo pronosticar datos de series de tiempo mediante una red de memoria de corto-largo plazo (LSTM).
Clasificación de secuencias mediante deep learning
Este ejemplo muestra cómo clasificar datos secuenciales mediante una red de memoria de corto-largo plazo (LSTM).
Clasificación secuencia a secuencia mediante deep learning
Este ejemplo muestra cómo clasificar cada unidad de tiempo de datos secuenciales mediante una red de memoria de corto-largo plazo (LSTM).
Regresión de secuencia a secuencia mediante deep learning
Este ejemplo muestra cómo predecir la vida útil restante (RUL) de motores mediante deep learning.
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.
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.
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.
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.
Visualizar activaciones de redes de LSTM
Este ejemplo muestra cómo investigar y visualizar las características aprendidas por las redes de LSTM extrayendo las activaciones.
Chemical Process Fault Detection Using Deep Learning
Use simulation data to train a neural network than can detect faults in a chemical process.
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.
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.

Redes convolucionales

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.
Train Speech Command Recognition Model Using Deep Learning
This example shows how to train a deep learning model that detects the presence of speech commands in audio.
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.

Deep learning con Simulink

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.

Deep learning con MATLAB

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.

Ejemplos destacados

Clasificación de secuencias mediante deep learning

Este ejemplo muestra cómo clasificar datos secuenciales mediante una red de memoria de corto-largo plazo (LSTM).

Abrir script en vivo

Pronóstico de series de tiempo mediante deep learning

Este ejemplo muestra cómo pronosticar datos de series de tiempo mediante una red de memoria de corto-largo plazo (LSTM).

Abrir script en vivo

Train Speech Command Recognition Model 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 set of commands.

Abrir script en vivo