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Deep Learning Import, Export, and Customization
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 define custom output layers and 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. You can check layers for validity, GPU compatibility, and correctly defined gradients.
For full flexibility in preprocessing image and sequence data, build your own datastore for training deep learning networks. You can optionally add support for functionality such as shuffling during training, parallel and multi-GPU training, and background dispatch.
Functions
Classes
Topics
Define Custom Deep Learning Layers
Learn how to define custom deep learning layers
Define a Custom Deep Learning Layer with Learnable Parameters
This example shows how to define a PReLU layer and use it in a convolutional neural network.
Define Custom Weighted Classification Layer
This example shows how to define and create a custom weighted classification output layer with weighted cross entropy loss.
Define a Custom Regression Output Layer
This example shows how to define a custom regression output layer with mean absolute error (MAE) loss and use it in a convolutional neural network.
Define a Custom Classification Output Layer
This example shows how to define a custom classification output layer with sum of squares error (SSE) loss and use it in a convolutional neural network.
Learn how to check the validity of custom deep learning layers
Develop Custom Mini-Batch Datastore
Create a fully customized mini-batch datastore that contains training and test data sets for network training, prediction, and classification.
Train Network Using Out-of-Memory Sequence Data
This example shows how to train a deep learning network on out-of-memory sequence data using a custom mini-batch datastore.
Assemble Network from Pretrained Keras Layers
This example shows how to import the layers from a pretrained Keras network, replace the unsupported layers with custom layers, and assemble the layers into a network ready for prediction.
Learn how to improve the accuracy of deep learning networks.
