Addestramento integrato
Dopo aver definito l'architettura della rete, è possibile definire i parametri di addestramento utilizzando la funzione trainingOptions
. È quindi possibile addestrare la rete utilizzando la funzione trainnet
. Utilizzare la rete addestrata per prevedere le etichette delle classi o le risposte numeriche, nonché per prevedere i passi temporali futuri.
È possibile addestrare una rete neurale su una CPU, una GPU, più CPU o GPU, oppure in parallelo su un cluster o nel cloud. L'addestramento su GPU o in parallelo richiede Parallel Computing Toolbox™. L’utilizzo di una GPU richiede un dispositivo GPU supportato (per informazioni sui dispositivi supportati, vedere GPU Computing Requirements (Parallel Computing Toolbox)). Specificare l’ambiente di esecuzione utilizzando la funzione trainingOptions
.
App
Deep Network Designer | Progettare e visualizzare reti di Deep Learning |
Funzioni
Argomenti
Reti perceptron multilivello
- 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.
Reti ricorrenti
- 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. - 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.
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 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 con MATLAB
- Deep Learning in MATLAB
Scoprire le capacità del Deep Learning in MATLAB® utilizzando le reti neurali convoluzionali per la classificazione e la regressione, incluse le reti preaddestrate e il transfer learning, nonché l’addestramento su GPU, CPU, cluster e cloud. - 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.