Custom Layers

Writing Your Custom Layer

There are two components to adding a custom layer:

  1. Adding the layer configuration class: extends org.deeplearning4j.nn.conf.layers.Layer
  2. Adding the layer implementation class: implements org.deeplearning4j.nn.api.Layer

The configuration layer ((1) above) class handles the settings. It’s the one you would use when constructing a MultiLayerNetwork or ComputationGraph. You can add custom settings here, and use them in your layer.

The implementation layer ((2) above) class has parameters, and handles network forward pass, backpropagation, etc. It is created from the org.deeplearning4j.nn.conf.layers.Layer.instantiate(…) method. In other words: the instantiate method is how we go from the configuration to the implementation; MultiLayerNetwork or ComputationGraph will call this method when initializing the

An example of these are CustomLayer (the configuration class) and CustomLayerImpl (the implementation class). Both of these classes have extensive comments regarding their methods.

You’ll note that in Deeplearning4j there are two DenseLayer clases, two GravesLSTM classes, etc: the reason is because one is for the configuration, one is for the implementation. We have not followed this “same name” pattern here to hopefully avoid confusion.

Testing Your Custom Layer

Once you have added a custom layer, it is necessary to run some tests to ensure it is correct.

These tests should at a minimum include the following:

  1. Tests to ensure that the JSON configuration (to/from JSON) works correctly This is necessary for networks with your custom layer to function with both model serialization (saving) and Spark training.
  2. Gradient checks to ensure that the implementation is correct.

Example

A full custom layer example is available in our examples repository.

API

API Reference

API Reference

Detailed API docs for all libraries including DL4J, ND4J, DataVec, and Arbiter.

Examples

Examples

Explore sample projects and demos for DL4J, ND4J, and DataVec in multiple languages including Java and Kotlin.

Tutorials

Tutorials

Step-by-step tutorials for learning concepts in deep learning while using the DL4J API.

Guide

Guide

In-depth documentation on different scenarios including import, distributed training, early stopping, and GPU setup.

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