initialize
Description
Tip
Most dlnetwork
objects are initialized by default. You only need to
manually initialize a dlnetwork
if it is uninitialized. You can check
if a network is initialized using the Initialized
property of the
dlnetwork
object.
initializes any unset learnable parameters and state values of netUpdated
= initialize(net
)net
based
on the input sizes defined by the network input layers. Any learnable or state parameters
that already contain values remain unchanged.
A network with unset, empty values for learnable and state parameters is
uninitialized. You must initialize an uninitialized
dlnetwork
before you can use it. By default, dlnetwork
objects are constructed with initial parameters and do not need initializing.
initializes any unset learnable parameters and state values of netUpdated
= initialize(net
,X1,...,XN
)net
based
on the example network inputs or network data layout objects X1,...,XN
.
Use this syntax when the network has inputs that are not connected to an input layer.
Examples
Define a simple image classification network as a layer array.
layers = [
imageInputLayer([28 28 1],Normalization="none")
convolution2dLayer(5,20)
batchNormalizationLayer
reluLayer
fullyConnectedLayer(10)
softmaxLayer];
Convert the layer graph to a dlnetwork
object. Create an uninitialized dlnetwork
object by setting the Initialize
option to false
.
net = dlnetwork(layers,Initialize=false);
View the learnable parameters of the network. Because the network is not initialized, the values are empty.
net.Learnables
ans=6×3 table
Layer Parameter Value
___________ _________ ____________
"conv" "Weights" {0×0 double}
"conv" "Bias" {0×0 double}
"batchnorm" "Offset" {0×0 double}
"batchnorm" "Scale" {0×0 double}
"fc" "Weights" {0×0 double}
"fc" "Bias" {0×0 double}
Initialize the learnable parameters of the network using the initialize
function.
net = initialize(net);
View the learnable parameters of the network. Because the network is now initialized, the values are nonempty with sizes inferred using the size of the input layer.
net.Learnables
ans=6×3 table
Layer Parameter Value
___________ _________ ___________________
"conv" "Weights" { 5×5×1×20 dlarray}
"conv" "Bias" { 1×1×20 dlarray}
"batchnorm" "Offset" { 1×1×20 dlarray}
"batchnorm" "Scale" { 1×1×20 dlarray}
"fc" "Weights" {10×11520 dlarray}
"fc" "Bias" {10×1 dlarray}
Define a multi-input image classification network.
numFilters = 24; net = dlnetwork; layersBranch1 = [ convolution2dLayer(3,6*numFilters,Padding="same",Stride=2) groupNormalizationLayer("all-channels") reluLayer convolution2dLayer(3,numFilters,Padding="same") groupNormalizationLayer("channel-wise") additionLayer(2,Name="add") reluLayer fullyConnectedLayer(10) softmaxLayer]; layersBranch2 = [ convolution2dLayer(1,numFilters,Name="conv_branch") groupNormalizationLayer("all-channels",Name="groupnorm_branch")]; net = addLayers(net, layersBranch1); net = addLayers(net,layersBranch2); net = connectLayers(net,"groupnorm_branch","add/in2");
Visualize the layers in a plot.
figure plot(net)
View the learnable parameters of the network. Because the network is not initialized, the values are empty.
net.Learnables
ans=14×3 table
Layer Parameter Value
__________________ _________ ____________
"conv_1" "Weights" {0×0 double}
"conv_1" "Bias" {0×0 double}
"groupnorm_1" "Offset" {0×0 double}
"groupnorm_1" "Scale" {0×0 double}
"conv_2" "Weights" {0×0 double}
"conv_2" "Bias" {0×0 double}
"groupnorm_2" "Offset" {0×0 double}
"groupnorm_2" "Scale" {0×0 double}
"fc" "Weights" {0×0 double}
"fc" "Bias" {0×0 double}
"conv_branch" "Weights" {0×0 double}
"conv_branch" "Bias" {0×0 double}
"groupnorm_branch" "Offset" {0×0 double}
"groupnorm_branch" "Scale" {0×0 double}
View the names of the network inputs.
net.InputNames
ans = 1×2 cell
{'conv_1'} {'conv_branch'}
Create random dlarray
objects representing inputs to the network. Use an example input of size 64-by-64 with 3 channels for the main branch of the network. Use an input of size 64-by-64 with 18 channels for the second branch.
inputSize = [64 64 3]; inputSizeBranch = [32 32 18]; X1 = dlarray(rand(inputSize),"SSCB"); X2 = dlarray(rand(inputSizeBranch),"SSCB");
Initialize the learnable parameters of the network using the initialize
function and specify the example inputs. Specify the inputs with order corresponding to the InputNames
property of the network.
net = initialize(net,X1,X2);
View the learnable parameters of the network. Because the network is now initialized, the values are nonempty with sizes inferred using the size of the input data.
net.Learnables
ans=14×3 table
Layer Parameter Value
__________________ _________ _____________________
"conv_1" "Weights" { 3×3×3×144 dlarray}
"conv_1" "Bias" { 1×1×144 dlarray}
"groupnorm_1" "Offset" { 1×1×144 dlarray}
"groupnorm_1" "Scale" { 1×1×144 dlarray}
"conv_2" "Weights" { 3×3×144×24 dlarray}
"conv_2" "Bias" { 1×1×24 dlarray}
"groupnorm_2" "Offset" { 1×1×24 dlarray}
"groupnorm_2" "Scale" { 1×1×24 dlarray}
"conv_branch" "Weights" { 1×1×18×24 dlarray}
"conv_branch" "Bias" { 1×1×24 dlarray}
"groupnorm_branch" "Offset" { 1×1×24 dlarray}
"groupnorm_branch" "Scale" { 1×1×24 dlarray}
"fc" "Weights" {10×24576 dlarray}
"fc" "Bias" {10×1 dlarray}
Create an uninitialized dlnetwork
object that has two unconnected inputs.
layers = [ convolution2dLayer(5,16,Name="conv") batchNormalizationLayer reluLayer fullyConnectedLayer(50) flattenLayer concatenationLayer(1,2,Name="cat") fullyConnectedLayer(10) softmaxLayer]; net = dlnetwork(layers,Initialize=false);
View the input names of the network.
net.InputNames
ans = 1×2 cell
{'conv'} {'cat/in2'}
Create network data layout objects that represent input data for the inputs. For the first input, specify a batch of 28-by-28 grayscale images. For the second input specify a batch of single-channel feature data.
layout1 = networkDataLayout([28 28 1 NaN],"SSCB"); layout2 = networkDataLayout([1 NaN],"CB");
Initialize the network using the network data layout objects.
net = initialize(net,layout1,layout2)
net = dlnetwork with properties: Layers: [8×1 nnet.cnn.layer.Layer] Connections: [7×2 table] Learnables: [8×3 table] State: [2×3 table] InputNames: {'conv' 'cat/in2'} OutputNames: {'softmax'} Initialized: 1 View summary with summary.
Input Arguments
Uninitialized network, specified as a dlnetwork
object.
Example network inputs or data layouts to use to determine the size and formats of learnable and state parameters, each specified as one of these values:
Formatted
dlarray
objectFormatted
networkDataLayout
objectUnformatted
dlarray
object (since R2025a)Unformatted
networkDataLayout
object (since R2025a)
The software propagates X1,...XN
through the network to
determine the appropriate sizes and formats of the learnable and state parameters of the
dlnetwork
object and initializes any unset learnable or state
parameters.
To create a neural network that receives unformatted
data, use an inputLayer
object
and do not specify a format. (since R2025a)
Before R2025a: X1,...XN
must be formatted
dlarray
or networkDataLayout
objects.
Provide example inputs in the same order as the order specified by the
InputNames
property of the input network.
Note
Automatic initialization uses only the size and format information of the input data. For initialization that depends on the values on the input data, you must initialize the learnable parameters manually.
Output Arguments
Initialized network, returned as an initialized dlnetwork
object.
The initialize
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 R2021aInitialize neural networks with unformatted data by specifying the inputs X1,...,XN
as unformatted dlarray
or unformatted networkDataLayout
objects.
Input layers such as imageInputLayer
and sequenceInputLayer
contain properties that networks use for data
normalization. These properties are Mean
,
StandardDeviation
, Min
, and
Max
. The software uses these properties to apply the data
normalization method defined by the Normalization
property of the
layer.
Starting in R2023b, when you initialize a network by creating an initialized dlnetwork
or by using the initialize
function, the software initializes the Mean
,
StandardDeviation
, Min
, and
Max
properties of input layers if you do not set them when you
create the layer and if the normalization method requires them. For normalization methods
that use two properties, for example, zscore
, the software initializes
those properties only if you do not set either property when you create the layer.
For
zerocenter
normalization,Mean
is initialized to0
.For
zscore
normalization,Mean
is initialized to0
andStandardDeviation
is initialized to1
.For
rescale-symmetric
normalization,Min
is initialized to-1
andMax
is initialized to1
.For
rescale-zero-one
normalization,Min
is initialized to0
andMax
is initialized to1
.
By default, the software automatically calculates the normalization statistics during
training. To customize the normalization, set the Mean
,
StandardDeviation
, Min
, and
Max
properties of input layers manually.
In previous releases, the software errors when you initialize a network containing an input layer that uses a normalization method requiring properties that you do not specify when you create the layer.
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