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Bucles de entrenamiento personalizados

Personalice bucles de entrenamiento y funciones de pérdida de deep learning para datos de secuencia y en tablas.

Si la función trainingOptions no proporciona las opciones de entrenamiento que necesita para la tarea o tiene una función de pérdida que la función trainnet no admite, puede definir un bucle de entrenamiento personalizado. Para los modelos que no se pueden especificar como redes de capas, puede definir el modelo como una función. Para obtener más información, consulte Define Custom Training Loops, Loss Functions, and Networks.

Funciones

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Bucles de entrenamiento personalizados

`dlnetwork`	Redes neuronales de deep learning
`trainingProgressMonitor`	Monitor and plot training progress for deep learning custom training loops (desde R2022b)
`minibatchqueue`	Create mini-batches for deep learning (desde R2020b)
`padsequences`	Pad or truncate sequence data to same length (desde R2021a)
`dlarray`	Arreglo de deep learning para personalización
`dlgradient`	Compute gradients for custom training loops using automatic differentiation
`dlfeval`	Evaluate deep learning model for custom training loops

Operaciones de pérdida

`crossentropy`	Cross-entropy loss for classification tasks
`l1loss`	L₁ loss for regression tasks (desde R2021b)
`l2loss`	L₂ loss for regression tasks (desde R2021b)
`huber`	Huber loss for regression tasks (desde R2021a)
`mse`	Error cuadrático medio dividido
`ctc`	Connectionist temporal classification (CTC) loss for unaligned sequence classification (desde R2021a)

Operaciones de deep learning

`dlconv`	Deep learning convolution
`dltranspconv`	Deep learning transposed convolution
`lstm`	Memoria de corto-largo plazo
`gru`	Gated recurrent unit (desde R2020a)
`attention`	Dot-product attention (desde R2022b)
`embed`	Embed discrete data (desde R2020b)
`fullyconnect`	Sum all weighted input data and apply a bias
`dlode45`	Deep learning solution of nonstiff ordinary differential equation (ODE) (desde R2021b)
`batchnorm`	Normalize data across all observations for each channel independently
`crosschannelnorm`	Cross channel square-normalize using local responses (desde R2020a)
`groupnorm`	Normalize data across grouped subsets of channels for each observation independently (desde R2020b)
`instancenorm`	Normalize across each channel for each observation independently (desde R2021a)
`layernorm`	Normalize data across all channels for each observation independently (desde 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

Activaciones

`relu`	Aplicar la activación de unidad lineal rectificada
`leakyrelu`	Apply leaky rectified linear unit activation
`gelu`	Apply Gaussian error linear unit (GELU) activation (desde R2022b)
`softmax`	Apply softmax activation to channel dimension
`sigmoid`	Aplicar la activación sigmoide

Temas

Bucles de entrenamiento personalizados

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.

Diferenciación automática

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