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Boucles d’apprentissage personnalisées
Si la fonction trainingOptions ne propose pas les options d’apprentissage dont vous avez besoin pour votre tâche ou que les couches de sortie personnalisées ne supportent pas les fonctions de perte dont vous avez besoin, vous pouvez définir une boucle d’apprentissage personnalisée. Si un modèle ne peut pas être spécifié comme un réseau de couches, vous pouvez le définir comme une fonction. Pour en savoir plus, veuillez consulter Define Custom Training Loops, Loss Functions, and Networks.
Fonctions
Rubriques
Boucles d’apprentissage personnalisées
- Train Deep Learning Model in MATLAB
Learn how to training deep learning models in MATLAB®. - Define Custom Training Loops, Loss Functions, and Networks
Learn how to define and customize deep learning training loops, loss functions, and models. - Train Network Using Custom Training Loop
This example shows how to train a network that classifies handwritten digits with a custom learning rate schedule. - Train Sequence Classification Network Using Custom Training Loop
This example shows how to train a network that classifies sequences with a custom learning rate schedule. - Specify Training Options in Custom Training Loop
Learn how to specify common training options in a custom training loop. - Define Model Loss Function for Custom Training Loop
Learn how to define a model loss function for a custom training loop. - Update Batch Normalization Statistics in Custom Training Loop
This example shows how to update the network state in a custom training loop. - Make Predictions Using dlnetwork Object
This example shows how to make predictions using adlnetworkobject by looping over mini-batches. - Monitor Custom Training Loop Progress
Track and plot custom training loop progress. - Multiple-Input and Multiple-Output Networks
Learn how to define and train deep learning networks with multiple inputs or multiple outputs. - Train Network with Multiple Outputs
This example shows how to train a deep learning network with multiple outputs that predict both labels and angles of rotations of handwritten digits. - Train Image Classification Network Robust to Adversarial Examples
This example shows how to train a neural network that is robust to adversarial examples using fast gradient sign method (FGSM) adversarial training. - Train Robust Deep Learning Network with Jacobian Regularization
Train a neural network that is robust to adversarial examples using a Jacobian regularization scheme. - Solve ODE Using Physics-Informed Neural Network
This example shows how to train a physics-informed neural network (PINN) to predict the solutions of an ordinary differential equation (ODE). - Train Network in Parallel with Custom Training Loop
This example shows how to set up a custom training loop to train a network in parallel. - Run Custom Training Loops on a GPU and in Parallel
Speed up custom training loops by running on a GPU, in parallel using multiple GPUs, or on a cluster. - Speed Up Deep Neural Network Training
Learn how to accelerate deep neural network training.
Différenciation automatique
- Deep Learning Data Formats
Learn about deep learning data formats. - List of Functions with dlarray Support
View the list of functions that supportdlarrayobjects. - Automatic Differentiation Background
Learn how automatic differentiation works. - Use Automatic Differentiation In Deep Learning Toolbox
How to use automatic differentiation in deep learning.
Accélération des fonctions de Deep Learning
- Deep Learning Function Acceleration for Custom Training Loops
Accelerate model functions and model loss functions for custom training loops by caching and reusing traces. - Accelerate Custom Training Loop Functions
This example shows how to accelerate deep learning custom training loop and prediction functions. - Check Accelerated Deep Learning Function Outputs
This example shows how to check that the outputs of accelerated functions match the outputs of the underlying function. - Evaluate Performance of Accelerated Deep Learning Function
This example shows how to evaluate the performance gains of using an accelerated function.
Informations connexes
Sélection d՚exemples
Train Generative Adversarial Network (GAN)
Train a generative adversarial network to generate images.
Train Conditional Generative Adversarial Network (CGAN)
Train a conditional generative adversarial network to generate images.
Train Wasserstein GAN with Gradient Penalty (WGAN-GP)
Train a Wasserstein generative adversarial network with a gradient penalty (WGAN-GP) to generate images.
Generate Images Using Diffusion
Generate new images using a diffusion model.
Train Fast Style Transfer Network
Train a network to transfer the style of an image to a second image. It is based on the architecture defined in [1].
Train a Twin Network for Dimensionality Reduction
Train a twin neural network with shared weights to compare handwritten digits using dimensionality reduction.
Train a Twin Neural Network to Compare Images
Train a twin neural network with shared weights to identify similar images of handwritten characters.
Image Captioning Using Attention
Train a deep learning model for image captioning using attention.
Sequence-to-Sequence Translation Using Attention
Convert decimal strings to Roman numerals using a recurrent sequence-to-sequence encoder-decoder model with attention.
Train Network in Parallel with Custom Training Loop
Set up a custom training loop to train a network in parallel.
Train Variational Autoencoder (VAE) to Generate Images
Train a deep learning variational autoencoder (VAE) to generate images.
Train Network Using Cyclical Learning Rate for Snapshot Ensembling
Train a network to classify images of objects using a cyclical learning rate schedule and snapshot ensembling for better test accuracy. In the example, you learn how to use a cosine function for the learning rate schedule, take snapshots of the network during training to create a model ensemble, and add L2-norm regularization (weight decay) to the training loss.
Solve PDE Using Physics-Informed Neural Network
Train a physics-informed neural network (PINN) to predict the solutions of an partial differential equation (PDE).
Dynamical System Modeling Using Neural ODE
Train a neural network with neural ordinary differential equations (ODEs) to learn the dynamics of a physical system.
Reduced Order Modeling Using Continuous-Time Echo State Network
Train a continuous-time echo state network (CTESN) model to solve Robertson's equation.
Node Classification Using Graph Convolutional Network
Classify nodes in a graph using a graph convolutional network (GCN).
Multilabel Graph Classification Using Graph Attention Networks
Classify graphs that have multiple independent labels using graph attention networks (GATs).
Train Network Using Federated Learning
Train a network using federated learning. Federated learning is a technique that enables you to train a network in a distributed, decentralized way [1].
