Personalizzazione dei loop di addestramento
Personalizzare i loop di addestramento Deep Learning e le funzioni di perdita
Se la funzione trainingOptions
non fornisce le opzioni di addestramento necessarie per l’attività, o se i livelli di output non supportano le funzioni di perdita necessarie, è possibile definire un loop di addestramento personalizzato. Per i modelli che non possono essere specificati come reti di livelli, è possibile definire il modello come funzione. Per saperne di più, vedere Define Custom Training Loops, Loss Functions, and Networks.
Funzioni
Argomenti
Personalizzazione dei loop di addestramento
- 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 adlnetwork
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.
Differenziazione automatica
- Deep Learning Data Formats
Learn about deep learning data formats. - List of Functions with dlarray Support
View the list of functions that supportdlarray
objects. - Automatic Differentiation Background
Learn how automatic differentiation works. - Use Automatic Differentiation In Deep Learning Toolbox
How to use automatic differentiation in deep learning.
Accelerazione della funzione di 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.