52 lines
2.1 KiB
Markdown
52 lines
2.1 KiB
Markdown
---
|
|
title: Custom Layers
|
|
short_title: Custom Layers
|
|
description: Extend DL4J functionality for custom layers.
|
|
category: Models
|
|
weight: 10
|
|
---
|
|
|
|
## 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](https://github.com/deeplearning4j/dl4j-examples/tree/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/misc/customlayers).
|
|
|
|
## API
|
|
|
|
{{autogenerated}} |