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Boucles d’apprentissage personnalisées

Personnaliser les boucles d’apprentissage du Deep Learning et les fonctions de perte

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

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Création de réseau

`dlnetwork`	Deep learning neural network (depuis R2019b)
`imagePretrainedNetwork`	Pretrained neural network for images (depuis R2024a)
`resnetNetwork`	2-D residual neural network (depuis R2024a)
`resnet3dNetwork`	3-D residual neural network (depuis R2024a)
`addLayers`	Add layers to neural network
`removeLayers`	Remove layers from neural network
`replaceLayer`	Replace layer in neural network
`connectLayers`	Connect layers in neural network
`disconnectLayers`	Disconnect layers in neural network
`addInputLayer`	Add input layer to network (depuis R2022b)
`initialize`	Initialize learnable and state parameters of a `dlnetwork` (depuis R2021a)
`networkDataLayout`	Deep learning network data layout for learnable parameter initialization (depuis R2022b)
`setL2Factor`	Set L₂ regularization factor of layer learnable parameter
`getL2Factor`	Get L₂ regularization factor of layer learnable parameter
`setLearnRateFactor`	Set learn rate factor of layer learnable parameter
`getLearnRateFactor`	Get learn rate factor of layer learnable parameter

Informations sur le réseau

`plot`	Plot neural network architecture
`summary`	Imprimer la synthèse d'un réseau (depuis R2022b)
`analyzeNetwork`	Analyze deep learning network architecture
`checkLayer`	Check validity of custom or function layer
`isequal`	Check equality of neural networks (depuis R2021a)
`isequaln`	Check equality of neural networks ignoring `NaN` values (depuis R2021a)

Boucles d’apprentissage personnalisées

`forward`	Compute deep learning network output for training (depuis R2019b)
`predict`	Compute deep learning network output for inference (depuis R2019b)
`adamupdate`	Update parameters using adaptive moment estimation (Adam) (depuis R2019b)
`rmspropupdate`	Update parameters using root mean squared propagation (RMSProp) (depuis R2019b)
`sgdmupdate`	Update parameters using stochastic gradient descent with momentum (SGDM) (depuis R2019b)
`lbfgsupdate`	Update parameters using limited-memory BFGS (L-BFGS) (depuis R2023a)
`lbfgsState`	State of limited-memory BFGS (L-BFGS) solver (depuis R2023a)
`dlupdate`	Update parameters using custom function (depuis R2019b)
`trainingProgressMonitor`	Monitor and plot training progress for deep learning custom training loops (depuis R2022b)
`updateInfo`	Update information values for custom training loops (depuis R2022b)
`recordMetrics`	Record metric values for custom training loops (depuis R2022b)
`groupSubPlot`	Group metrics in training plot (depuis R2022b)

Traitement de données

`padsequences`	Pad or truncate sequence data to same length (depuis R2021a)
`minibatchqueue`	Create mini-batches for deep learning (depuis R2020b)
`onehotencode`	Encode data labels into one-hot vectors (depuis R2020b)
`onehotdecode`	Decode probability vectors into class labels (depuis R2020b)
`next`	Obtain next mini-batch of data from minibatchqueue (depuis R2020b)
`reset`	Reset minibatchqueue to start of data (depuis R2020b)
`shuffle`	Shuffle data in minibatchqueue (depuis R2020b)
`hasdata`	Determine if minibatchqueue can return mini-batch (depuis R2020b)
`partition`	Partition minibatchqueue (depuis R2020b)

Différenciation automatique

`dlarray`	Deep learning array for customization (depuis R2019b)
`dlgradient`	Compute gradients for custom training loops using automatic differentiation (depuis R2019b)
`dlfeval`	Evaluate deep learning model for custom training loops (depuis R2019b)
`dims`	Étiquettes des dimensions de `dlarray` (depuis R2019b)
`finddim`	Find dimensions with specified label (depuis R2019b)
`stripdims`	Remove `dlarray` data format (depuis R2019b)
`extractdata`	Extract data from `dlarray` (depuis R2019b)
`isdlarray`	Check if object is `dlarray` (depuis R2020b)

Opérations de perte

`crossentropy`	Cross-entropy loss for classification tasks (depuis R2019b)
`l1loss`	L₁ loss for regression tasks (depuis R2021b)
`l2loss`	L₂ loss for regression tasks (depuis R2021b)
`huber`	Huber loss for regression tasks (depuis R2021a)
`mse`	Half mean squared error (depuis R2019b)
`ctc`	Connectionist temporal classification (CTC) loss for unaligned sequence classification (depuis R2021a)

Accélération de fonction

`dlaccelerate`	Accelerate deep learning function for custom training loops (depuis R2021a)
`AcceleratedFunction`	Accelerated deep learning function (depuis R2021a)
`clearCache`	Clear accelerated deep learning function trace cache (depuis R2021a)

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 a dlnetwork object 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.
Classify Videos Using Deep Learning with Custom Training Loop
This example shows how to create a network for video classification by combining a pretrained image classification model and a sequence classification network.
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 Ordinary Differential Equation Using Neural Network
This example shows how to solve an ordinary differential equation (ODE) using a neural network.
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 support dlarray objects.
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

Opérations

Exemples présentés

Train Generative Adversarial Network (GAN)

Train Generative Adversarial Network (GAN)

Train a generative adversarial network to generate images.

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Train Conditional Generative Adversarial Network (CGAN)

Train Conditional Generative Adversarial Network (CGAN)

Train a conditional generative adversarial network to generate images.

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Train Wasserstein GAN with Gradient Penalty (WGAN-GP)

Train Wasserstein GAN with Gradient Penalty (WGAN-GP)

Train a Wasserstein generative adversarial network with a gradient penalty (WGAN-GP) to generate images.

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Generate Images Using Diffusion

Generate Images Using Diffusion

Generate new images using a diffusion model.

Depuis R2023b

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Train Fast Style Transfer Network

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].

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Train a Twin Network for Dimensionality Reduction

Train a Twin Network for Dimensionality Reduction

Train a twin neural network with shared weights to compare handwritten digits using dimensionality reduction.

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Train a Twin Neural Network to Compare Images

Train a Twin Neural Network to Compare Images

Train a twin neural network with shared weights to identify similar images of handwritten characters.

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Image Captioning Using Attention

Image Captioning Using Attention

Train a deep learning model for image captioning using attention.

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Sequence-to-Sequence Translation 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.

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Train Network in Parallel with Custom Training Loop

Train Network in Parallel with Custom Training Loop

Set up a custom training loop to train a network in parallel.

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Train Variational Autoencoder (VAE) to Generate Images

Train Variational Autoencoder (VAE) to Generate Images

Train a deep learning variational autoencoder (VAE) to generate images.

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Train Network Using Cyclical Learning Rate for Snapshot Ensembling

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.

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Solve Partial Differential Equations Using Deep Learning

Solve Partial Differential Equations Using Deep Learning

Solve Burger's equation using deep learning.

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Solve Partial Differential Equation with L-BFGS Method and Deep Learning

Solve Partial Differential Equation with L-BFGS Method and Deep Learning

Train a physics informed neural network (PINN) to numerically compute the solution of the Burger's equation by using the limited-memory BFGS (L-BFGS) algorithm.

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Dynamical System Modeling Using Neural ODE

Dynamical System Modeling Using Neural ODE

Train a neural network with neural ordinary differential equations (ODEs) to learn the dynamics of a physical system.

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Reduced Order Modeling Using Continuous-Time Echo State Network

Reduced Order Modeling Using Continuous-Time Echo State Network

Train a continuous-time echo state network (CTESN) model to solve Robertson's equation.

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Node Classification Using Graph Convolutional Network

Node Classification Using Graph Convolutional Network

Classify nodes in a graph using a graph convolutional network (GCN).

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Multilabel Graph Classification Using Graph Attention Networks

Multilabel Graph Classification Using Graph Attention Networks

Classify graphs that have multiple independent labels using graph attention networks (GATs).

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Train Network Using Federated Learning

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].

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