Custom Training Using Automatic Differentiation
If the trainingOptions function does not
provide the training options that you need for your task, or you have a loss
function that the trainnet function does not support, then you can define a custom
training loop. For models that cannot be specified as networks of layers, you
can define the model as a function. To learn more, see Define Custom Training Loops, Loss Functions, and Networks.
Functions
Topics
Custom Training Loops
- 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 adlnetworkobject by looping over mini-batches. - Monitor Custom Training Loop Progress
Track and plot custom training loop progress. - Compare Custom Solvers Using Custom Training Loop
This example shows how to train a deep learning network with different custom solvers and compare their accuracies. - 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. - 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. - Detect Issues During Deep Neural Network Training
This example shows how to automatically detect issues while training a deep neural network. - Speed Up Deep Neural Network Training
Learn how to accelerate deep neural network training.
Automatic Differentiation
- Automatic Differentiation Background
Learn how automatic differentiation works. - Deep Learning Data Formats
Learn about deep learning data formats. - List of Functions with dlarray Support
View the list of functions that supportdlarrayobjects. - Use Automatic Differentiation In Deep Learning Toolbox
How to use automatic differentiation in deep learning.
Generative Adversarial Networks
- Train Generative Adversarial Network (GAN)
This example shows how to train a generative adversarial network to generate images. - Train Conditional Generative Adversarial Network (CGAN)
This example shows how to train a conditional generative adversarial network to generate images. - Train Wasserstein GAN with Gradient Penalty (WGAN-GP)
This example shows how to train a Wasserstein generative adversarial network with a gradient penalty (WGAN-GP) to generate images.
Graph Neural Networks
- Multivariate Time Series Anomaly Detection Using Graph Neural Network
This example shows how to detect anomalies in multivariate time series data using a graph neural network (GNN). - Node Classification Using Graph Convolutional Network
This example shows how to classify nodes in a graph using a graph convolutional network (GCN). - Multilabel Graph Classification Using Graph Attention Networks
This example shows how to classify graphs that have multiple independent labels using graph attention networks (GATs).
Deep Learning Function Acceleration
- 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.
Related Information
Featured Examples
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Train a network to transfer the style of an image to a second image. It is based on the architecture defined in [1].
Train a Twin Network for Dimensionality Reduction
Train a twin neural network with shared weights to compare handwritten digits using dimensionality reduction.
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Train a Bayesian neural network (BNN) for image regression using Bayes by Backpropagation.
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Train a neural network that is robust to adversarial examples using fast gradient sign method (FGSM) adversarial training.
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Train a neural network that is robust to adversarial examples using a Jacobian regularization scheme.
Train a Twin Neural Network to Compare Images
Train a twin neural network with shared weights to identify similar images of handwritten characters.
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Train a network to classify images of objects using a cyclical learning rate schedule and snapshot ensembling for better test accuracy. In the example, you learn how to use a cosine function for the learning rate schedule, take snapshots of the network during training to create a model ensemble, and add L2-norm regularization (weight decay) to the training loss.
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