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 networks using automatic differentiation.

Train Network Using Custom Training Loop

This example shows how to train a network that classifies handwritten digits 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 Gradients Function for Custom Training Loop

Learn how to define a model gradients 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 a dlnetwork object by splitting data into mini-batches.

Train Network on Image and Feature Data

This example shows how to train a network that classifies handwritten digits using both image and feature input data.

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 Neural ODE Network

This example shows how to train an augmented neural ordinary differential equation (ODE) network.

Train Robust Deep Learning Network with Jacobian Regularization

This example shows how to train a neural network that is robust to adversarial examples using a Jacobian regularization scheme [1].

Solve Ordinary Differential Equation Using Neural Network

This example shows how to solve an ordinary differential equation (ODE) using a neural network.

Assemble Multiple-Output Network for Prediction

This example shows how to assemble a multiple output network for prediction.

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.

Model Functions

Train Network Using Model Function

This example shows how to create and train a deep learning network by using functions rather than a layer graph or a dlnetwork.

Update Batch Normalization Statistics Using Model Function

This example shows how to update the network state in a network defined as a function.

Make Predictions Using Model Function

This example shows how to make predictions using a model function by splitting data into mini-batches.

Initialize Learnable Parameters for Model Function

Learn how to initialize learnable parameters for custom training loops using a model function.

Automatic Differentiation

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.

Deep Learning Function Acceleration

Deep Learning Function Acceleration for Custom Training Loops

Accelerate model functions and model gradients 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.