Initialize Learnable Parameters for Model Function
When you train a network using layers, layer graphs, or dlnetwork
objects, the software automatically initializes the learnable parameters according to the
layer initialization properties. When you define a deep learning model as a function, you
must initialize the learnable parameters manually.
How you initialize learnable parameters (for example, weights and biases) can have a big impact on how quickly a deep learning model converges.
Tip
This topic explains how to initialize learnable parameters for a deep learning model
defined a function in a custom training loop. To learn how to specify the learnable
parameter initialization for a deep learning layer, use the corresponding layer
property. For example, to set the weights initializer of a convolution2dLayer
object, use the WeightsInitializer
property.
Default Layer Initializations
This table shows the default initializations for the learnable parameters for each layer, and provides links that show how to initialize learnable parameters for model functions by using the same initialization.
Layer  Learnable Parameter  Default Initialization 

convolution2dLayer  Weights  Glorot Initialization 
Bias  Zeros Initialization  
convolution3dLayer  Weights  Glorot Initialization 
Bias  Zeros Initialization  
groupedConvolution2dLayer  Weights  Glorot Initialization 
Bias  Zeros Initialization  
transposedConv2dLayer  Weights  Glorot Initialization 
Bias  Zeros Initialization  
transposedConv3dLayer  Weights  Glorot Initialization 
Bias  Zeros Initialization  
fullyConnectedLayer  Weights  Glorot Initialization 
Bias  Zeros Initialization  
batchNormalizationLayer  Offset  Zeros Initialization 
Scale  Ones Initialization  
lstmLayer  Input weights  Glorot Initialization 
Recurrent weights  Orthogonal Initialization  
Bias  Unit Forget Gate Initialization  
gruLayer  Input weights  Glorot Initialization 
Recurrent weights  Orthogonal Initialization  
Bias  Zeros Initialization  
wordEmbeddingLayer  Weights  Gaussian Initialization, with mean 0 and standard deviation 0.01 
Learnable Parameter Sizes
When initializing learnable parameters for model functions, you must specify parameters of the correct size. The size of the learnable parameters depends on the type of deep learning operation.
Operation  Learnable Parameter  Size 

batchnorm  Offset 

Scale 
 
dlconv  Weights 

Bias  One of the following:
 
dlconv (grouped)  Weights 

Bias  One of the following:
 
dltranspconv  Weights 

Bias  One of the following:
 
dltranspconv (grouped)  Weights 

Bias  One of the following:
 
fullyconnect  Weights 

Bias 
 
gru  Input weights 

Recurrent weights 
 
Bias 
 
lstm  Input weights 

Recurrent weights 
 
Bias 

Glorot Initialization
The Glorot (also known as Xavier) initializer [1] samples weights from the uniform distribution with bounds $$\left[\sqrt{\frac{6}{{N}_{o}+{N}_{i}}},\sqrt{\frac{6}{{N}_{o}+{N}_{i}}}\right]$$, where the values of N_{o} and N_{i} depend on the type of deep learning operation.
Operation  Learnable Parameter  N_{o}  N_{i} 

dlconv  Weights 


dlconv (grouped)  Weights 


dltranspconv  Weights 


dltranspconv (grouped)  Weights 


fullyconnect  Weights  Number of output channels of the operation  Number of input channels of the operation 
gru  Input weights  3*numHiddenUnits , where
numHiddenUnits is the number of hidden units
of the operation  Number of input channels of the operation 
Recurrent weights  3*numHiddenUnits , where
numHiddenUnits is the number of hidden units
of the operation  Number of hidden units of the operation  
lstm  Input weights  4*numHiddenUnits , where
numHiddenUnits is the number of hidden units
of the operation  Number of input channels of the operation 
Recurrent weights  4*numHiddenUnits , where
numHiddenUnits is the number of hidden units
of the operation  Number of hidden units of the operation 
To initialize learnable parameters using the Glorot initializer easily, you can define
a custom function. The function initializeGlorot
takes as input the
size of the learnable parameters sz
and the values
N_{o} and
N_{i} (numOut
and
numIn
, respectively), and returns the sampled weights as a
dlarray
object with underlying type
'single'
.
function weights = initializeGlorot(sz,numOut,numIn) Z = 2*rand(sz,'single')  1; bound = sqrt(6 / (numIn + numOut)); weights = bound * Z; weights = dlarray(weights); end
Example
Initialize the weights for a convolutional operation with 128 filters of size 5by5 and 3 input channels.
filterSize = [5 5]; numChannels = 3; numFilters = 128; sz = [filterSize numChannels numFilters]; numOut = prod(filterSize) * numFilters; numIn = prod(filterSize) * numChannels; parameters.conv.Weights = initializeGlorot(sz,numOut,numIn);
He Initialization
The He initializer [2] samples weights from the normal distribution with zero mean and variance $$\frac{2}{{N}_{i}}$$, where the value N_{i} depends on the type of deep learning operation.
Operation  Learnable Parameter  N_{i} 

dlconv  Weights 

dltranspconv  Weights 

fullyconnect  Weights  Number of input channels of the operation 
gru  Input weights  Number of input channels of the operation 
Recurrent weights  Number of hidden units of the operation.  
lstm  Input weights  Number of input channels of the operation 
Recurrent weights  Number of hidden units of the operation. 
To initialize learnable parameters using the He initializer easily, you can define a
custom function. The function initializeHe
takes as input the size of
the learnable parameters sz
, and the value
N_{i}, and returns the sampled weights as
a dlarray
object with underlying type
'single'
.
function weights = initializeHe(sz,numIn) weights = randn(sz,'single') * sqrt(2/numIn); weights = dlarray(weights); end
Example
Initialize the weights for a convolutional operation with 128 filters of size 5by5 and 3 input channels.
filterSize = [5 5]; numChannels = 3; numFilters = 128; sz = [filterSize numChannels numFilters]; numIn = prod(filterSize) * numChannels; parameters.conv.Weights = initializeHe(sz,numIn);
Gaussian Initialization
The Gaussian initializer samples weights from a normal distribution.
To initialize learnable parameters using the Gaussian initializer easily, you can
define a custom function. The function initializeGaussian
takes as
input the size of the learnable parameters sz
, the distribution mean
mu
, and the distribution standard deviation
sigma
, and returns the sampled weights as a
dlarray
object with underlying type
'single'
.
function weights = initializeGaussian(sz,mu,sigma) weights = randn(sz,'single')*sigma + mu; weights = dlarray(weights); end
Example
Initialize the weights for an embedding operation with a dimension of 300 and vocabulary size of 5000 using the Gaussian initializer with mean 0 and standard deviation 0.01.
embeddingDimension = 300; vocabularySize = 5000; mu = 0; sigma = 0.01; sz = [embeddingDimension vocabularySize]; parameters.emb.Weights = initializeGaussian(sz,mu,sigma);
Uniform Initialization
The uniform initializer samples weights from a uniform distribution.
To initialize learnable parameters using the uniform initializer easily, you can
define a custom function. The function initializeUniform
takes as
input the size of the learnable parameters sz
, and the distribution
bound bound
, and returns the sampled weights as a
dlarray
object with underlying type
'single'
.
function parameter = initializeUniform(sz,bound) Z = 2*rand(sz,'single')  1; parameter = bound * Z; parameter = dlarray(parameter); end
Example
Initialize the weights for an attention mechanism with size 100by100 and bound 0.1 using the uniform initializer.
sz = [100 100]; bound = 0.1; parameters.attentionn.Weights = initializeUniform(sz,bound);
Orthogonal Initialization
The orthogonal initializer returns the orthogonal matrix Q given by the QR decomposition of Z = QR, where Z is sampled from a unit normal distribution and the size of Z matches the size of the learnable parameter.
To initialize learnable parameters using the orthogonal initializer easily, you can
define a custom function. The function initializeOrthogonal
takes as
input the size of the learnable parameters sz
, and returns the
orthogonal matrix as a dlarray
object with underlying type
'single'
.
function parameter = initializeOrthogonal(sz) Z = randn(sz,'single'); [Q,R] = qr(Z,0); D = diag(R); Q = Q * diag(D ./ abs(D)); parameter = dlarray(Q); end
Example
Initialize the recurrent weights for an LSTM operation with 100 hidden units using the orthogonal initializer.
numHiddenUnits = 100; sz = [4*numHiddenUnits numHiddenUnits]; parameters.lstm.RecurrentWeights = initializeOrthogonal(sz);
Unit Forget Gate Initialization
The unit forget gate initializer initializes the bias for an LSTM operation such that the forget gate component of the biases are ones and the remaining entries are zeros.
To initialize learnable parameters using the orthogonal initializer easily, you can
define a custom function. The function initializeUnitForgetGate
takes
as input the number of hidden units in the LSTM operation, and returns the bias as a
dlarray
object with underlying type
'single'
.
function bias = initializeUnitForgetGate(numHiddenUnits) bias = zeros(4*numHiddenUnits,1,'single'); idx = numHiddenUnits+1:2*numHiddenUnits; bias(idx) = 1; bias = dlarray(bias); end
Example
Initialize the bias of an LSTM operation with 100 hidden units using the unit forget gate initializer.
numHiddenUnits = 100;
parameters.lstm.Bias = initializeUnitForgetGate(numHiddenUnits,'single');
Ones Initialization
To initialize learnable parameters with ones easily, you can define a custom function.
The function initializeOnes
takes as input the size of the learnable
parameters sz
, and returns the parameters as a
dlarray
object with underlying type
'single'
.
function parameter = initializeOnes(sz) parameter = ones(sz,'single'); parameter = dlarray(parameter); end
Example
Initialize the scale for a batch normalization operation with 128 input channels with ones.
numChannels = 128; sz = [numChannels 1]; parameters.bn.Scale = initializeOnes(sz);
Zeros Initialization
To initialize learnable parameters with zeros easily, you can define a custom
function. The function initializeZeros
takes as input the size of the
learnable parameters sz
, and returns the parameters as a
dlarray
object with underlying type
'single'
.
function parameter = initializeZeros(sz) parameter = zeros(sz,'single'); parameter = dlarray(parameter); end
Example
Initialize the offset for a batch normalization operation with 128 input channels with zeros.
numChannels = 128; sz = [numChannels 1]; parameters.bn.Offset = initializeZeros(sz);
Storing Learnable Parameters
It is recommended to store the learnable parameters for a given model function in a single object, such as a structure, table, or cell array. For an example showing how to initialize learnable parameters as a struct, see Train Network Using Model Function.
Storing Parameters on GPU
If you train your model using a GPU, then the software converts the learnable
parameters of the model function to gpuArray
objects which are
stored on the GPU.
To make it easier to load learnable parameters on machines without a GPU, it is
recommended practice to gather all the parameters to the local workspace before
saving them. To gather learnable parameters stored as a structure, table, or cell
array of dlarray
objects, use the dlupdate
function with the gather
function. For
example, if you have network learnable parameters stored on the GPU in the
structure, table, or cell array parameters
, you can transfer the
parameters to the local workspace by using the following
code:
parameters = dlupdate(@gather,parameters);
If you load learnable parameters that are not on the GPU, you can move the
parameters onto the GPU using the dlupdate
function with the
gpuArray
function. Doing so ensures that your network
executes on the GPU for training and inference, regardless of where the input data
is stored. For example, to move the parameters stored in the structure, table, or
cell array parameters
, you can transfer the parameters to the GPU
by using the following
code:
parameters = dlupdate(@gpuArray,parameters);
References
[1] Glorot, Xavier, and Yoshua Bengio. "Understanding the Difficulty of Training Deep Feedforward Neural Networks." In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 249–356. Sardinia, Italy: AISTATS, 2010.
[2] He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. "Delving Deep into Rectifiers: Surpassing HumanLevel Performance on ImageNet Classification." In Proceedings of the 2015 IEEE International Conference on Computer Vision, 1026–1034. Washington, DC: IEEE Computer Vision Society, 2015.
See Also
dlarray
 dlgradient
 dlfeval
 dlnetwork
Related Topics
 Train Network Using Model Function
 Define Custom Training Loops, Loss Functions, and Networks
 Define Model Loss Function for Custom Training Loop
 Update Batch Normalization Statistics Using Model Function
 Make Predictions Using Model Function
 Train Network Using Custom Training Loop
 Specify Training Options in Custom Training Loop
 List of Functions with dlarray Support