Bucles de entrenamiento personalizados

Entrene redes de deep learning con bucles de entrenamiento personalizados

Si la función trainingOptions no proporciona las opciones de entrenamiento que necesita para la tarea, o si las capas de salida personalizadas no son compatibles con las funciones de pérdida que necesita, puede definir un bucle de entrenamiento personalizado. En el caso de las redes que no se pueden crear mediante gráficas de capa, puede definir redes personalizadas 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`	Deep learning network for custom training loops
`trainingProgressMonitor`	Monitor and plot training progress for deep learning custom training loops
`minibatchqueue`	Create mini-batches for deep learning
`padsequences`	Pad or truncate sequence data to same length
`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
`l2loss`	L₂ loss for regression tasks
`huber`	Huber loss for regression tasks
`mse`	Half mean squared error
`ctc`	Connectionist temporal classification (CTC) loss for unaligned sequence classification

Temas

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.
Define Custom Training Loops, Loss Functions, and Networks
Learn how to define and customize deep learning training loops, loss functions, and networks using automatic differentiation.
Specify Training Options in Custom Training Loop
Learn how to specify common training options in a custom training loop.
Monitor Custom Training Loop Progress
Track and plot custom training loop progress.
Detect Issues During Deep Neural Network Training
This example shows how to automatically detect issues while training a deep neural network.