Deep Learning Network Composition
To create a custom layer that itself defines a layer graph, you can declare a
dlnetwork object as a learnable parameter in the
(Learnable) section of the layer definition. This method is known as
network composition. You can use network composition to:
Create a single custom layer that represents a block of learnable layers, for example, a residual block.
Create a network with control flow, for example, a network with a section that can dynamically change depending on the input data.
Create a network with loops, for example, a network with sections that feed the output back into itself.
For nested networks that have both learnable and state parameters, for example, networks
with batch normalization or LSTM layers, declare the network in the
(Learnable, State) section of the layer definition.
For an example showing how to define a custom layer containing a learnable
dlnetwork object, see Define Nested Deep Learning Layer.
For an example showing how to train a network with nested layers, see Train Deep Learning Network with Nested Layers.
Automatically Initialize Learnable
dlnetwork Objects for Training
You can create a custom layer and allow the software to automatically initialize the
learnable parameters of any nested
dlnetwork objects after the parent
network is fully constructed. Automatic initialization of the nested network means that
you do not need to keep track of the size and shape of the inputs passed to each custom
layer containing a nested
To use the
forward functions for
dlnetwork objects, the input data must be formatted
dlarray objects. To ensure that the software passes formatted
dlarray objects to the layer functions, include the
Formattable mixin in the class definition.
classdef myLayer < nnet.layer.Layer & nnet.layer.Formattable ... end
To take advantage of automatic initialization, you must specify that the constructor
function creates an uninitialized
dlnetwork object. To create an
dlnetwork object, set the
name-value option to false. You do not need to specify an input layer, so you do not
need to specify an input size for the
function layer = myLayer % Initialize layer properties. ... % Define network. layers = [ % Network layers go here. ]; layer.Network = dlnetwork(lgraph,Initialize=false); end
When the parent network is initialized, the learnable parameters of any nested
dlnetwork objects are initialized at the same time. The size of
the learnable parameters depends on the size of the input data of the custom layer. The
software propagates the data through the nested network and automatically initializes
the parameters according to the propagated sizes and the initialization properties of
the layers of the nested network.
If the parent network is trained using the
then any nested
dlnetwork objects are initialized when you call
trainNetwork. If the parent network is a
dlnetwork, then any nested
dlnetwork objects are
initialized when the parent network is constructed (if the parent
dlnetwork is initialized at construction) or when you use the
function with the parent network (if the parent
dlnetwork is not
initialized at construction).
If you do not want to make use of automatic initialization, you can construct the
custom layer with the nested network already initialized. In this case, the nested
network is initialized before the parent network. To initialize the nested network at
construction, you must manually specify the size of any inputs to the nested network.
This requires manually specifying the size of any inputs to the nested network. You can
do so either by using input layers or by providing example inputs to the
dlnetwork constructor function. Because you must specify the sizes
of any inputs to the
dlnetwork object, you might need to specify input
sizes when you create the layer. For help determining the size of the inputs to the
layer, you can use the
analyzeNetwork function and check the size of the activations of the
Predict and Forward Functions
Some layers behave differently during training and during prediction. For example, a
dropout layer performs dropout only during training and has no effect during prediction. A
layer uses one of two functions to perform a forward pass:
forward. If the forward pass is at prediction time, then the layer
predict function. If the forward pass is at training time, then
the layer uses the
forward function. If you do not require two different
functions for prediction time and training time, then you can omit the
forward function. When you do so, the layer uses
predict at training time.
When implementing the
predict and the
functions of the custom layer, to ensure that the layers in the
dlnetwork object behave in the correct way, use the
forward functions for
dlnetwork objects, respectively.
Custom layers with learnable
dlnetwork objects do not support custom backward functions.
This example code shows how to use the
forward functions with
function Z = predict(layer,X) % Predict using network. net = layer.Network; Z = predict(net,X); end function Z = forward(layer,X) % Forward pass using network. net = layer.Network; Z = forward(net,X); end
dlnetwork object does not behave differently during training
and prediction, then you can omit the forward function. In this case, the software uses
predict function during training.
If the layer forward functions fully support
dlarray objects, then the layer
is GPU compatible. Otherwise, to be GPU compatible, the layer functions must support inputs
and return outputs of type
gpuArray (Parallel Computing Toolbox).
Many MATLAB® built-in functions support
gpuArray (Parallel Computing Toolbox) and
dlarray input arguments. For a list of
functions that support
dlarray objects, see List of Functions with dlarray Support. For a list of functions
that execute on a GPU, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).
To use a GPU for deep
learning, you must also have a supported GPU device. For information on supported devices, see
GPU Support by Release (Parallel Computing Toolbox). For more information on working with GPUs in MATLAB, see GPU Computing in MATLAB (Parallel Computing Toolbox).
- Train Deep Learning Network with Nested Layers
- Define Custom Deep Learning Intermediate Layers
- Define Custom Deep Learning Output Layers
- Define Custom Deep Learning Layer with Learnable Parameters
- Define Custom Deep Learning Layer with Multiple Inputs
- Define Custom Deep Learning Layer with Formatted Inputs
- Define Custom Recurrent Deep Learning Layer
- Define Custom Deep Learning Layer for Code Generation
- Define Nested Deep Learning Layer
- Check Custom Layer Validity