Deep Learning Import, Export, and Customization

Import, export, and customize deep learning networks, and customize layers, training loops, and loss functions

Import networks and network architectures from TensorFlow™-Keras, Caffe, and the ONNX™ (Open Neural Network Exchange) model format. You can also export a trained Deep Learning Toolbox™ network to the ONNX model format.

You can define your own custom deep learning layer for your problem. You can specify a custom loss function using a custom output layers and define custom layers with or without learnable parameters. For example, you can use a custom weighted classification layer with weighted cross entropy loss for classification problems with an imbalanced distribution of classes. After defining a custom layer, you can check that the layer is valid, GPU compatible, and outputs correctly defined gradients.

If the trainingOptions function 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, see Define Custom Training Loops, Loss Functions, and Networks.

Featured Examples

Train Generative Adversarial Network (GAN)

Train Generative Adversarial Network (GAN)

Train a generative adversarial network (GAN) to generate images.

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Train Network Using Custom Training Loop

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

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 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 Compare Images

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

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Image Captioning Using Attention

Image Captioning Using Attention

Train a deep learning model for image captioning using attention.

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Multilabel Text Classification Using Deep Learning

Multilabel Text Classification Using Deep Learning

Classify text data that has multiple independent labels.

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Sequence-to-Sequence Translation Using Attention

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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Sequence-to-Sequence Classification Using 1-D Convolutions

Sequence-to-Sequence Classification Using 1-D Convolutions

Classify each time step of sequence data using a generic temporal convolutional network (TCN).

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Train Network in Parallel with Custom Training Loop

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 Variational Autoencoder (VAE) to Generate Images

Create a variational autoencoder (VAE) in MATLAB to generate digit images. The VAE generates hand-drawn digits in the style of the MNIST data set.

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Train Network Using Cyclical Learning Rate for Snapshot Ensembling

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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