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.
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.