Built-In Training
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.
You can train a neural network on a CPU, a GPU, multiple CPUs or GPUs, or in parallel on a cluster or in the cloud. Training on a GPU or in parallel requires Parallel Computing Toolbox™. Using a GPU requires a supported GPU device (for information on supported devices, see GPU Computing Requirements (Parallel Computing Toolbox)). Specify the execution environment using the trainingOptions
function.
App
Deep Network Designer | Progetta, visualizza e addestra le reti di Deep Learning |
Funzioni
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.
- 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.