Deep Learning Custom Training Loops

Customize deep learning training loops and loss functions

If thetrainingOptionsfunction does not provide the training options that you need for your task, or custom output layers do not support the loss functions that you need, then you can define a custom training loop. For networks that cannot be created using layer graphs, you can define custom networks as a function. To learn more, seeDefine Custom Training Loops, Loss Functions, and Networks.

Functions

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Custom Training Loops

`dlnetwork`	深度定制traini的学习网络ng loops
`forward`	Compute deep learning network output for training
`predict`	Compute deep learning network output for inference
`adamupdate`	Update parameters using adaptive moment estimation (Adam)
`rmspropupdate`	Update parameters using root mean squared propagation (RMSProp)
`sgdmupdate`	Update parameters using stochastic gradient descent with momentum (SGDM)
`dlupdate`	Update parameters using custom function
`minibatchqueue`	Create mini-batches for deep learning
`onehotencode`	Encode data labels into one-hot vectors
`onehotdecode`	Decode probability vectors into class labels
`padsequences`	Pad or truncate sequence data to same length
`initialize`	Initialize learnable and state parameters of a`dlnetwork`
`resetState`	Reset state parameters of neural network

Automatic Differentiation

`dlarray`	Deep learning array for custom training loops
`dlgradient`	Compute gradients for custom training loops using automatic differentiation
`dlfeval`	Evaluate deep learning model for custom training loops
`dims`	Dimension labels of`dlarray`
`finddim`	Find dimensions with specified label
`stripdims`	Remove`dlarray`data format
`extractdata`	Extract data from`dlarray`
`isdlarray`	Check if object is`dlarray`
`functionToLayerGraph`	Convert deep learning model function to a layer graph

Deep Learning Operations

`dlconv`	Deep learning convolution
`dltranspconv`	Deep learning transposed convolution
`lstm`	Long short-term memory
`gru`	Gated recurrent unit
`embed`	Embed discrete data
`fullyconnect`	Sum all weighted input data and apply a bias
`dlode45`	Deep learning solution of nonstiff ordinary differential equation (ODE)
`relu`	Apply rectified linear unit activation
`leakyrelu`	Apply leaky rectified linear unit activation
`batchnorm`	Normalize data across all observations for each channel independently
`crosschannelnorm`	Cross channel square-normalize using local responses
`groupnorm`	Normalize data across grouped subsets of channels for each observation independently
`instancenorm`	Normalize across each channel for each observation independently
`layernorm`	Normalize data across all channels for each observation independently
`avgpool`	Pool data to average values over spatial dimensions
`maxpool`	Pool data to maximum value
`maxunpool`	Unpool the output of a maximum pooling operation
`softmax`	Apply softmax activation to channel dimension
`sigmoid`	Apply sigmoid activation

Deep Learning Loss Operations

`sigmoid`	Apply sigmoid activation
`crossentropy`	Cross-entropy loss for classification tasks
`l1loss`	L₁loss for regression tasks
`l2loss`	L₂loss for regression tasks
`huber`	Huber loss for regression tasks
`mse`	Half mean squared error
`ctc`	Connectionist temporal classification (CTC) loss for unaligned sequence classification

Deep Learning Function Acceleration

`dlaccelerate`	Accelerate deep learning function for custom training loops
`AcceleratedFunction`	Accelerated deep learning function
`clearCache`	Clear accelerated deep learning function trace cache

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 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 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
这个例子展示了如何使用作出预测dlnetworkobject by splitting data into mini-batches.
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
这个例子展示了如何训练一个神经网络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
这个例子展示了如何训练一个神经网络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 adlnetwork.
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
这个例子展示了如何使用作出预测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 supportdlarrayobjects.
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 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.

Featured Examples

Train Generative Adversarial Network (GAN)

Train a generative adversarial network to generate images.

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Train Conditional Generative Adversarial Network (CGAN)

Train a conditional generative adversarial network to generate images.

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Train Wasserstein GAN with Gradient Penalty (WGAN-GP)

Train a Wasserstein generative adversarial network with a gradient penalty (WGAN-GP) to generate images.

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Train Fast Style Transfer Network

Train a network to transfer the style of an image to a second image. It is based on the architecture defined in [1].

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Train Network Using Custom Training Loop

Train a network that classifies handwritten digits with a custom learning rate schedule.

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Train Network Using Model Function

Create and train a deep learning network by using functions rather than a layer graph or a dlnetwork. The advantage of using functions is the flexibility to describe a wide variety of networks. The disadvantage is that you must complete more steps and prepare your data carefully. This example uses images of handwritten digits, with the dual objectives of classifying the digits and determining the angle of each digit from the vertical.

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Train a Siamese Network for Dimensionality Reduction

Train a Siamese network to compare handwritten digits using dimensionality reduction.

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Train a Siamese Network to Compare Images

Train a Siamese network to identify similar images of handwritten characters.

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Image Captioning Using Attention

Train a deep learning model for image captioning using attention.

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Multilabel Text Classification Using Deep Learning

Classify text data that has multiple independent labels.

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Sequence-to-Sequence Translation Using Attention

Convert decimal strings to Roman numerals using a recurrent sequence-to-sequence encoder-decoder model with attention.

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Train Network in Parallel with Custom Training Loop

Set up a custom training loop to train a network in parallel. In this example, parallel workers train on portions of the overall mini-batch. If you have a GPU, then training happens on the GPU. During training, a DataQueue object sends training progress information back to the MATLAB client.

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Train Variational Autoencoder (VAE) to Generate Images

Train a deep learning variational autoencoder (VAE) to generate images.

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Train Network Using Cyclical Learning Rate for Snapshot Ensembling

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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Solve Partial Differential Equations Using Deep Learning

Solve Burger's equation using deep learning.

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Solve Partial Differential Equation with LBFGS 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 (LBFGS) algorithm.

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Train Neural ODE Network

Train an augmented neural ordinary differential equation (ODE) network.

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Dynamical System Modeling Using Neural ODE

Train a neural network with neural ordinary differential equations (ODEs) to learn the dynamics of a physical system.

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Node Classification Using Graph Convolutional Network

Classify nodes in a graph using a graph convolutional network (GCN).

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Multilabel Graph Classification Using Graph Attention Networks

Classify graphs that have multiple independent labels using graph attention networks (GATs).

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Train Network Using Federated Learning

Train a network using federated learning. Federated learning is a technique that enables you to train a network in a distributed, decentralized way [1].

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