Custom Layers - MATLAB & Simulink (original) (raw)
Define custom layers for deep learning
For most tasks, you can use built-in layers. If there is not a built-in layer that you need for your task, then you can define your own custom layer. You can define custom layers with learnable and state parameters. After defining a custom layer, you can check that the layer is valid, GPU compatible, and outputs correctly defined gradients. For a list of supported layers, see List of Deep Learning Layers.
Apps
| Deep Network Designer | Design and visualize deep learning networks |
|---|
Functions
| functionLayer | Function layer (Since R2021b) |
|---|---|
| checkLayer | Check validity of custom or function layer |
| setLearnRateFactor | Set learn rate factor of layer learnable parameter |
| setL2Factor | Set L2 regularization factor of layer learnable parameter |
| getLearnRateFactor | Get learn rate factor of layer learnable parameter |
| getL2Factor | Get L2 regularization factor of layer learnable parameter |
| networkDataLayout | Deep learning network data layout for learnable parameter initialization (Since R2022b) |
| dlnetwork | Deep learning neural network |
|---|---|
| findPlaceholderLayers | Find placeholder layers in network architecture imported from Keras orONNX |
| replaceLayer | Replace layer in neural network |
| PlaceholderLayer | Layer replacing an unsupported Keras or ONNX layer |
Topics
Custom Layers Overview
- Define Custom Deep Learning Layers
Learn how to define custom deep learning layers. - Check Custom Layer Validity
Learn how to check the validity of custom deep learning layers. - View Autogenerated Custom Layers Using Deep Network Designer
This example shows how to import a pretrained TensorFlow™ network and view the autogenerated layers in Deep Network Designer.
Define Custom Layers
- Define Custom Deep Learning Layer with Learnable Parameters
This example shows how to define a SReLU layer and use it in a convolutional neural network. - Define Custom Deep Learning Layer with Multiple Inputs
This example shows how to define a custom weighted addition layer and use it in a convolutional neural network. - Define Custom Deep Learning Layer with Formatted Inputs
This example shows how to define a custom layer with formatteddlarrayinputs. - Define Custom Recurrent Deep Learning Layer
This example shows how to define a peephole LSTM layer and use it in a neural network. - Specify Custom Layer Backward Function
This example shows how to define a SReLU layer and specify a custom backward function. - Custom Layer Function Acceleration
Accelerate custom layer forward and predict functions by caching and reusing traces. - Define Custom Deep Learning Layer for Code Generation
This example shows how to define a SReLU layer that supports code generation.
Network Composition and Nested Layers
- Deep Learning Network Composition
Define custom layers that contain neural networks. - Define Nested Deep Learning Layer Using Network Composition
This example shows how to define a nested custom deep learning layer. - Train Network with Custom Nested Layers
This example shows how to create and train a network with nested layers defined using network composition. - Weight Tying Using Nested Layer
This example shows how to implement weight tying using a nested layer.
Featured Examples
Convert Convolutional Network to Spiking Neural Network
Convert a conventional convolutional neural network (CNN) to a spiking neural network (SNN).
- Since R2023b
- Open Live Script
Train Bayesian Neural Network
Train a Bayesian neural network (BNN) for image regression using Bayes by Backpropagation.
Train Variational Autoencoder (VAE) to Generate Images
Train a deep learning variational autoencoder (VAE) to generate images.