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Deep Learning con dati di serie temporali e dati di sequenza

Creare e addestrare reti per attività di classificazione, regressione e previsione delle serie temporali

Creare e addestrare reti per attività di classificazione, regressione e previsione delle serie temporali. Addestrare reti long short-term memory (LSTM) per problemi di classificazione e regressione da sequenza-a-uno o da sequenza-a-etichetta. È possibile addestrare le reti LSTM su dati di testo utilizzando livelli di incorporazione di parole (richiede Text Analytics Toolbox™) o reti neurali convoluzionali su dati audio utilizzando spettrogrammi (richiede Audio Toolbox™).

App

Deep Network Designer

Progetta, visualizza e addestra le reti di Deep Learning

Funzioni

espandi tutto

Addestramento della rete

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

Livelli

Livelli di input

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

Livelli ricorrenti

`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)

Livelli convoluzionali e livelli completamente connessi

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

Livelli di pooling

`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

Livelli di attivazione e abbandono

`reluLayer`	Livello dell’unità lineare rettificata (ReLU)
`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
`geluLayer`	Gaussian error linear unit (GELU) layer
`softmaxLayer`	Livello softmax
`dropoutLayer`	Dropout layer
`functionLayer`	Function layer

Manipolazione dei dati

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

Livelli di output

`classificationLayer`	Classification output layer
`regressionLayer`	Livello di output della regressione

Previsione

`classify`	Classify data using trained deep learning neural network
`predict`	Predict responses using 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 state parameters of neural network
`confusionchart`	Create confusion matrix chart for classification problem
`sortClasses`	Sort classes of confusion matrix chart

Personalizzazione dei loop di addestramento

padsequences Pad or truncate sequence data to same length

Blocchi

espandi tutto

Reti neurali profonde

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

Proprietà

ConfusionMatrixChart Properties

Confusion matrix chart appearance and behavior

Argomenti

Reti ricorrenti

Long Short-Term Memory Neural Networks
Learn about long short-term memory (LSTM) neural networks.
Previsione delle serie temporali tramite il Deep Learning
Questo esempio mostra come prevedere i dati delle serie temporali utilizzando una rete con memoria a breve e lungo termine (LSTM).
Classificazione di sequenze utilizzando il Deep Learning
Questo esempio mostra come classificare i dati sequenziali utilizzando una rete con memoria a breve e lungo termine (LSTM).
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.
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.
Creazione di una rete di classificazione di sequenze semplice con Deep Network Designer
Questo esempio mostra come creare una rete long short-term memory di classificazione semplice con l’uso di 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.
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.
Creazione di una rete di classificazione di sequenze semplice con Deep Network Designer
Questo esempio mostra come creare una rete long short-term memory di classificazione semplice con l’uso di 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.

Reti convoluzionali

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.

Esempi in primo piano

Classificazione di sequenze utilizzando il Deep Learning

Questo esempio mostra come classificare i dati sequenziali utilizzando una rete con memoria a breve e lungo termine (LSTM).

Apri live script

Previsione delle serie temporali tramite il Deep Learning

Questo esempio mostra come prevedere i dati delle serie temporali utilizzando una rete con memoria a breve e lungo termine (LSTM).

Apri live script

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.

Apri live script