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.
|Import a pretrained Keras network and weights|
|Import layers from Keras network|
|Import pretrained convolutional neural network models from Caffe|
|Import convolutional neural network layers from Caffe|
|Import pretrained ONNX network|
|Import layers from ONNX network|
|Export network to ONNX model format|
|Find placeholder layers in network architecture imported from Keras or ONNX|
|Replace layer in layer graph|
|Assemble deep learning network from pretrained layers|
|Layer replacing an unsupported Keras or ONNX layer|
|Set learn rate factor of layer learnable parameter|
|Set L2 regularization factor of layer learnable parameter|
|Get learn rate factor of layer learnable parameter|
|Get L2 regularization factor of layer learnable parameter|
|Check validity of custom layer|
Learn how to define custom deep learning layers
This example shows how to define a PReLU layer and use it in a convolutional neural network.
This example shows how to define and create a custom weighted classification output layer with weighted cross entropy loss.
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.
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
Create a fully customized mini-batch datastore that contains training and test data sets for network training, prediction, and classification.
This example shows how to train a deep learning network on out-of-memory sequence data using a custom mini-batch datastore.
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.