Built-In Training

Train deep learning networks using built-in training functions

After defining the network architecture, you can define training parameters using the trainingOptions function. You can then train the network using the trainnet function. Use the trained network to predict class labels or numeric responses.

App

Deep Network Designer

Progetta, visualizza e addestra le reti di Deep Learning

Funzioni

espandi tutto

Training

`dlnetwork`	Deep learning neural network (Da R2019b)
`trainingOptions`	Opzioni per l’addestramento della rete neurale di Deep Learning
`trainnet`	Train deep learning neural network (Da R2023b)
`TrainingInfo`	Neural network training information (Da R2023b)
`show`	Show training information plot (Da R2023b)
`close`	Close training information plot (Da R2023b)

Metrics

`accuracyMetric`	Deep learning accuracy metric (Da R2023b)
`aucMetric`	Deep learning area under ROC curve (AUC) metric (Da R2023b)
`fScoreMetric`	Deep learning F-score metric (Da R2023b)
`precisionMetric`	Deep learning precision metric (Da R2023b)
`recallMetric`	Deep learning recall metric (Da R2023b)
`rmseMetric`	Deep learning root mean squared error metric (Da R2023b)

Prediction

`predict`	Compute deep learning network output for inference (Da R2019b)
`minibatchpredict`	Mini-batched neural network prediction (Da R2024a)
`scores2label`	Convert prediction scores to labels (Da R2024a)
`confusionchart`	Create confusion matrix chart for classification problem
`sortClasses`	Sort classes of confusion matrix chart
`classifyAndUpdateState`	(Not recommended) Classify data using a trained recurrent neural network and update the network state

Argomenti

Creazione di una rete neurale semplice di Deep Learning per la classificazione
Questo esempio mostra come creare e addestrare una rete neurale convoluzionale semplice per la classificazione tramite Deep Learning.
Train Convolutional Neural Network for Regression
This example shows how to train a convolutional neural network to predict the angles of rotation of handwritten digits.
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).
Classificazione sequenza-sequenza utilizzando il Deep Learning
Questo esempio mostra come classificare ogni fase temporale dei dati sequenziali utilizzando una rete con memoria a breve e lungo termine (LSTM).
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.
Create Custom Deep Learning Training Plot
This example shows how to create a custom training plot that updates at each iteration during training of deep learning neural networks using trainnet. (Da R2023b)
Custom Stopping Criteria for Deep Learning Training
This example shows how to stop training of deep learning neural networks based on custom stopping criteria using trainnet. (Da R2023b)
Speed Up Deep Neural Network Training
Learn how to accelerate deep neural network training.

Esempi in primo piano

Transfer Learning con Deep Network Designer

Eseguire in modo interattivo il perfezionamento di una rete preaddestrata di Deep Learning per apprendere una nuova attività di classificazione delle immagini.

Apri live script

Train Network with Numeric Features

Create and train a simple neural network for deep learning feature data classification.

Apri live script

Build Time Series Forecasting Network Using Deep Network Designer

Create a simple long short-term memory (LSTM) network to forecast time series data using the Deep Network Designer app.

Apri live script

Train Residual Network for Image Classification

Create a deep learning neural network with residual connections and train it on CIFAR-10 data. Residual connections are a popular element in convolutional neural network architectures. Using residual connections improves gradient flow through the network and enables training of deeper networks.

Apri live script

Train Network on Image and Feature Data

Train a network that classifies handwritten digits using both image and feature input data.

Apri live script

Multilabel Image Classification Using Deep Learning

Use transfer learning to train a deep learning model for multilabel image classification.

Apri live script

Build Image-to-Image Regression Network Using Deep Network Designer

Use Deep Network Designer to construct an image-to-image regression network for super resolution.

Apri live script

Adapt Pretrained Audio Network for New Data Using Deep Network Designer

Interactively adapt a pretrained network to classify new audio signals using Deep Network Designer.

Apri live script

Classify Videos Using Deep Learning

Create a network for video classification by combining a pretrained image classification model and an LSTM network.

Apri live script