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Boucles d’apprentissage personnalisées

Personnaliser les boucles d’apprentissage du Deep Learning et les fonctions de perte pour des données séquentielles et tabulaires

Si la fonction trainingOptions ne propose pas les options d’apprentissage dont vous avez besoin pour votre tâche ou si vous avez une fonction de perte non supportée par la fonction trainnet, 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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Boucles d’apprentissage personnalisées

`dlnetwork`	Deep learning neural network
`trainingProgressMonitor`	Monitor and plot training progress for deep learning custom training loops (depuis R2022b)
`minibatchqueue`	Create mini-batches for deep learning (depuis R2020b)
`padsequences`	Pad or truncate sequence data to same length (depuis R2021a)
`dlarray`	Deep learning array for customization
`dlgradient`	Compute gradients for custom training loops using automatic differentiation
`dlfeval`	Evaluate deep learning model for custom training loops

Opérations de perte

`crossentropy`	Cross-entropy loss for classification tasks
`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
`ctc`	Connectionist temporal classification (CTC) loss for unaligned sequence classification (depuis R2021a)

Opérations de Deep Learning

`dlconv`	Deep learning convolution
`dltranspconv`	Deep learning transposed convolution
`lstm`	Long short-term memory
`gru`	Gated recurrent unit (depuis R2020a)
`attention`	Dot-product attention (depuis R2022b)
`embed`	Embed discrete data (depuis R2020b)
`fullyconnect`	Sum all weighted input data and apply a bias
`dlode45`	Deep learning solution of nonstiff ordinary differential equation (ODE) (depuis R2021b)
`batchnorm`	Normalize data across all observations for each channel independently
`crosschannelnorm`	Cross channel square-normalize using local responses (depuis R2020a)
`groupnorm`	Normalize data across grouped subsets of channels for each observation independently (depuis R2020b)
`instancenorm`	Normalize across each channel for each observation independently (depuis R2021a)
`layernorm`	Normalize data across all channels for each observation independently (depuis R2021a)
`avgpool`	Pool data to average values over spatial dimensions
`maxpool`	Pool data to maximum value
`maxunpool`	Unpool the output of a maximum pooling operation

Activations

`relu`	Apply rectified linear unit activation
`leakyrelu`	Apply leaky rectified linear unit activation
`gelu`	Apply Gaussian error linear unit (GELU) activation (depuis R2022b)
`softmax`	Apply softmax activation to channel dimension
`sigmoid`	Appliquer l’activation sigmoïde

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 Sequence Classification Network Using Custom Training Loop
This example shows how to train a network that classifies sequences with a custom learning rate schedule.
Monitor Custom Training Loop Progress
Track and plot custom training loop progress.
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 Neural ODE Network
This example shows how to train an augmented neural ordinary differential equation (ODE) network.
Solve Ordinary Differential Equation Using Neural Network
This example shows how to solve an ordinary differential equation (ODE) using a neural network.
Speed Up Deep Neural Network Training
Learn how to accelerate deep neural network training.

Différenciation automatique

Create Bidirectional LSTM (BiLSTM) Function
This example shows how to create a bidirectional long-short term memory (BiLSTM) function for custom deep learning functions. (depuis R2023b)
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.