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

expandir todo

Bucles de entrenamiento personalizados

`dlnetwork`	Redes neuronales de deep learning (desde R2019b)
`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 (desde R2019b)
`dlgradient`	Compute gradients for custom training loops using automatic differentiation (desde R2019b)
`dlfeval`	Evaluate deep learning model for custom training loops (desde R2019b)

Operaciones de pérdida

`crossentropy`	Cross-entropy loss for classification tasks (desde R2019b)
`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 (desde R2019b)
`ctc`	Connectionist temporal classification (CTC) loss for unaligned sequence classification (desde R2021a)

Operaciones de deep learning

`dlconv`	Deep learning convolution (desde R2019b)
`dltranspconv`	Deep learning transposed convolution (desde R2019b)
`lstm`	Memoria de corto-largo plazo (desde R2019b)
`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 (desde R2019b)
`dlode45`	Deep learning solution of nonstiff ordinary differential equation (ODE) (desde R2021b)
`batchnorm`	Normalize data across all observations for each channel independently (desde R2019b)
`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 (desde R2019b)
`maxpool`	Pool data to maximum value (desde R2019b)
`maxunpool`	Unpool the output of a maximum pooling operation (desde R2019b)

Activaciones

`relu`	Aplicar la activación de unidad lineal rectificada (desde R2019b)
`leakyrelu`	Apply leaky rectified linear unit activation (desde R2019b)
`gelu`	Apply Gaussian error linear unit (GELU) activation (desde R2022b)
`softmax`	Apply softmax activation to channel dimension (desde R2019b)
`sigmoid`	Aplicar la activación sigmoide (desde R2019b)

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.

Ejemplos destacados

Solve Partial Differential Equations Using Deep Learning

Solve Burger's equation using deep learning.

Abrir script en vivo

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.

Abrir script en vivo