Integriertes Training

Trainieren von Deep-Learning-Netzen für Sequenz- und Tabellendaten mit integrierten Trainingsfunktionen

Nachdem Sie die Netzarchitektur definiert haben, können Sie die Trainingsparameter mit der Funktion trainingOptions festlegen. Sie können das Netz dann mit der Funktion trainnet trainieren. Verwenden Sie das trainierte Netz zur Vorhersage von Klassenbezeichnungen oder numerischen Antworten oder zur Prognose zukünftiger Zeitschritte.

Sie können ein neuronales Netz auf einer CPU, einer Grafikkarte, mehreren CPUs oder Grafikkarten oder parallel auf einem Cluster oder in der Cloud trainieren. Das Training auf einer Grafikkarte und das parallele Training erfordern die Parallel Computing Toolbox™. Bei der Verwendung einer Grafikkarte ist eine unterstützte Grafikkarte erforderlich (Informationen zu unterstützten Geräten finden Sie unter GPU Computing Requirements (Parallel Computing Toolbox)). Geben Sie die Ausführungsumgebung mit der Funktion trainingOptions an.

Apps

Deep Network Designer

Entwurf und Visualisierung von Deep-Learning-Netzen

Funktionen

alle erweitern

Trainieren des Netzes

`trainingOptions`	Options for training deep learning neural network
`trainnet`	Train deep learning neural network (Seit R2023b)
`analyzeNetwork`	Analyze deep learning network architecture

Vorhersage

`predict`	Compute deep learning network output for inference
`minibatchpredict`	Mini-batched neural network prediction (Seit R2024a)
`scores2label`	Convert prediction scores to labels (Seit R2024a)
`confusionchart`	Create confusion matrix chart for classification problem
`sortClasses`	Sort classes of confusion matrix chart

Themen

Mehrschichtige Perzeptron-Netze

Train Network with Numeric Features
This example shows how to create and train a simple neural network for deep learning feature data classification.
Compare Deep Learning Networks for Credit Default Prediction
Create, train, and compare three deep learning networks for predicting credit default probability.
- SCHRITT 1: Predict Battery State of Charge Using Deep Learning
- SCHRITT 2: Evaluate Code Generation Inference Time of Compressed Deep Neural Network
- SCHRITT 3: Battery State of Charge Estimation in Simulink Using Feedforward Neural Network

Rekurrente Netze

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.
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.
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.

Convolutional Networks

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 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.
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 on Image and Feature Data
This example shows how to train a network that classifies handwritten digits using both image and feature input data.

Deep Learning mit 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.
Speed Up Deep Neural Network Training
Learn how to accelerate deep neural network training.
Data Sets for Deep Learning
Discover data sets for various deep learning tasks.

Enthaltene Beispiele

Time Series Forecasting Using Deep Learning

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

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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.

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Chemical Process Fault Detection Using Deep Learning

Use simulation data to train a neural network than can detect faults in a chemical process.

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