Deep Learning Custom Layers

Define custom layers for deep learning

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. After defining a custom layer, you can check that the layer is valid, GPU compatible, and outputs correctly defined gradients.

Functions

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Custom Layers

`functionLayer`	Function layer
`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

Network Assembly

`findPlaceholderLayers`	Find placeholder layers in network architecture imported from Keras or ONNX
`replaceLayer`	Replace layer in layer graph
`assembleNetwork`	Assemble deep learning network from pretrained layers
`PlaceholderLayer`	Layer replacing an unsupported Keras or ONNX layer, or unsupported functionality from `functionToLayerGraph`

Topics

Custom Layers Overview

Define Custom Deep Learning Layers

Learn how to define custom deep learning layers.

Define Custom Deep Learning Intermediate Layers

Learn how to define custom deep learning intermediate layers.

Define Custom Deep Learning Output Layers

Learn how to define custom deep learning output layers.

Custom Intermediate Layers

Define 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 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 formatted dlarray inputs.

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 PReLU layer and specify a custom backward function.

Define Custom Deep Learning Layer for Code Generation

This example shows how to define a PReLU layer that supports code generation.

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.

Replace Unsupported Keras Layer with Function Layer

This example shows how to import the layers from a pretrained Keras network, replace the unsupported layers with function layers, and assemble the layers into a network ready for prediction.

Custom Output Layers

Define 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.

Define 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.

Specify Custom Output Layer Backward Loss Function

This example shows how to define a custom classification output layer with sum of squares error (SSE) loss and specify a custom backward loss function.