connectLayers
Connect layers in neural network
Description
connects the source layer netUpdated
= connectLayers(net
,s
,d
)s
to the destination layer
d
in the dlnetwork
object
net
. The updated network, netUpdated
,
contains the same layers as net
and includes the new
connection.
Examples
Create and Connect Addition Layer
Create an empty neural network dlnetwork
object and add an addition layer with two inputs and the name 'add'
.
net = dlnetwork; layer = additionLayer(2,'Name','add'); net = addLayers(net,layer);
Add two ReLU layers to the neural network and connect them to the addition layer. The addition layer outputs the sum of the outputs from the ReLU layers.
layer = reluLayer('Name','relu1'); net = addLayers(net,layer); net = connectLayers(net,'relu1','add/in1'); layer = reluLayer('Name','relu2'); net = addLayers(net,layer); net = connectLayers(net,'relu2','add/in2');
Visualize the updated network in a plot.
plot(net)
Create Neural Network from Scratch
Define a two-output neural network that predicts both categorical labels and numeric values given 2-D images as input.
Specify the number of classes and responses.
numClasses = 10; numResponses = 1;
Create an empty neural network.
net = dlnetwork;
Define the layers of the main branch of the network and the softmax output.
layers = [ imageInputLayer([28 28 1],Normalization="none") convolution2dLayer(5,16,Padding="same") batchNormalizationLayer reluLayer(Name="relu_1") convolution2dLayer(3,32,Padding="same",Stride=2) batchNormalizationLayer reluLayer convolution2dLayer(3,32,Padding="same") batchNormalizationLayer reluLayer additionLayer(2,Name="add") fullyConnectedLayer(numClasses) softmaxLayer(Name="softmax")]; net = addLayers(net,layers);
Add the skip connection.
layers = [ convolution2dLayer(1,32,Stride=2,Name="conv_skip") batchNormalizationLayer reluLayer(Name="relu_skip")]; net = addLayers(net,layers); net = connectLayers(net,"relu_1","conv_skip"); net = connectLayers(net,"relu_skip","add/in2");
Add the fully connected layer for the regression output.
layers = fullyConnectedLayer(numResponses,Name="fc_2"); net = addLayers(net,layers); net = connectLayers(net,"add","fc_2");
View the neural network in a plot.
figure plot(net)
Input Arguments
net
— Neural network
dlnetwork
object
Neural network, specified as a dlnetwork
object.
s
— Connection source
string scalar | character vector
Connection source, specified as a character vector or a string scalar.
If the source layer has a single output, then
s
is the name of the layer.If the source layer has multiple outputs, then
s
is the layer name followed by the"/"
character and the name of the layer output:"layerName/outputName"
.
Example: "conv"
Example: "mpool/indices"
d
— Connection destination
string scalar | character vector
Connection destination, specified as a string scalar or a character vector.
If the destination layer has a single input, then
d
is the name of the layer.If the destination layer has multiple inputs, then
d
is the layer name followed by the"/"
character and the name of the layer input:"layerName/inputName"
.
Example: "fc"
Example: "add/in1"
Output Arguments
netUpdated
— Updated network
dlnetwork
object
Updated network, returned as an uninitialized dlnetwork
object.
To initialize the learnable parameters of a dlnetwork
object, use the initialize
function.
The connectLayers
function does not preserve
quantization information. If the input network is a quantized network, then the output network
does not contain quantization information.
Version History
Introduced in R2017bR2024a: LayerGraph
objects are not recommended
Starting in R2024a, LayerGraph
objects are not recommended. Use
dlnetwork
objects instead. This
recommendation means that this syntax is not recommended for
LayerGraph
input:
lgraphUpdated = connectLayers(lgraph,s,d)
Most functions that support LayerGraph
objects also support
dlnetwork
objects. This table shows some typical usages of
LayerGraph
objects and how to update your code to use
dlnetwork
object functions instead.
Not Recommended | Recommended |
---|---|
lgraph = layerGraph; | net = dlnetwork; |
lgraph = layerGraph(layers); | net = dlnetwork(layers,Initialize=false); |
lgraph = layerGraph(net); | net = dag2dlnetwork(net); |
lgraph = addLayers(lgraph,layers); | net = addLayers(net,layers); |
lgraph = removeLayers(lgraph,layerNames); | net = removeLayers(net,layerNames); |
lgraph =
replaceLayer(lgraph,layerName,layers); | net = replaceLayer(net,layerName,layers); |
lgraph = connectLayers(lgraph,s,d); | net = connectLayers(net,s,d); |
lgraph = disconnectLayers(lgraph,s,d); | net = disconnectLayers(net,s,d); |
plot(lgraph); | plot(net); |
To train a neural network specified as a dlnetwork
object,
use the trainnet
function.
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