embeddingConcatenationLayer
Description
An embedding concatenation layer combines its input and an embedding vector by concatenation.
Creation
Description
creates
an embedding concatenation layer.layer
= embeddingConcatenationLayer
creates an embedding concatenation layer and sets the Parameters and Initialization and layer
= embeddingConcatenationLayer(Name=Value
)Name
properties using one or more name-value arguments.
Properties
Parameters and Initialization
WeightsInitializer
— Function to initialize weights
"narrow-normal"
(default) | "glorot"
| "he"
| "zeros"
| "ones"
| function handle
Function to initialize the weights, specified as one of these values:
"narrow-normal"
— Initialize the weights by independently sampling from a normal distribution with zero mean and a standard deviation of 0.01."glorot"
— Initialize the weights with the Glorot initializer [1] (also known as Xavier initializer). The Glorot initializer independently samples from a uniform distribution with zero mean and a variance of2/(numIn + numOut)
, wherenumIn
andnumOut
are the number of channels in the layer input, respectively."he"
— Initialize the weights with the He initializer [2]. The He initializer samples from a normal distribution with zero mean and a variance of2/numIn
, wherenumIn
is the number of channels in the layer input."zeros"
— Initialize the weights with zeros."ones"
— Initialize the weights with ones.Function handle – Initialize the weights with a custom function. If you specify a function handle, then the function must have the form
weights = func(sz)
, wheresz
is the size of the weights.
The layer initializes the weights only when the Weights
property is empty.
Weights
— Learnable weights
[]
(default) | column vector
Learnable weights, specified as a numeric column vector of length numChannels
or []
.
The layer weights are learnable parameters. You can specify the initial value of the weights
directly using the Weights
property of the layer. When
you train a network, if the Weights
property of the layer
is nonempty, then the trainnet
function uses the Weights
property as the initial value.
If the Weights
property is empty, then the software uses
the initializer specified by the WeightsInitializer
property of the layer.
Data Types: single
| double
Layer
Name
— Layer name
""
(default) | character vector | string scalar
NumInputs
— Number of inputs
1
(default)
This property is read-only.
Number of inputs to the layer, returned as 1
. This layer accepts a
single input only.
Data Types: double
InputNames
— Input names
{'in'}
(default)
This property is read-only.
Input names, returned as {'in'}
. This layer accepts a single input
only.
Data Types: cell
NumOutputs
— Number of outputs
1
(default)
This property is read-only.
Number of outputs from the layer, returned as 1
. This layer has a
single output only.
Data Types: double
OutputNames
— Output names
{'out'}
(default)
This property is read-only.
Output names, returned as {'out'}
. This layer has a single output
only.
Data Types: cell
Examples
Create Embedding Concatenation Layer
Create an embedding concatenation layer.
layer = embeddingConcatenationLayer
layer = EmbeddingConcatenationLayer with properties: Name: '' InputSize: 'auto' WeightsInitializer: 'narrow-normal' WeightLearnRateFactor: 1 WeightL2Factor: 1 Learnable Parameters Weights: [] State Parameters No properties. Use properties method to see a list of all properties.
Include an embedding concatenation layer in a neural network.
net = dlnetwork; numChannels = 1; embeddingOutputSize = 64; numWords = 128; maxSequenceLength = 100; maxPosition = maxSequenceLength+1; numHeads = 4; numKeyChannels = 4*embeddingOutputSize; layers = [ sequenceInputLayer(numChannels) wordEmbeddingLayer(embeddingOutputSize,numWords,Name="word-emb") embeddingConcatenationLayer(Name="emb-cat") positionEmbeddingLayer(embeddingOutputSize,maxPosition,Name="pos-emb"); additionLayer(2,Name="add") selfAttentionLayer(numHeads,numKeyChannels,AttentionMask="causal") fullyConnectedLayer(numWords) softmaxLayer]; net = addLayers(net,layers); net = connectLayers(net,"emb-cat","add/in2");
View the neural network architecture.
plot(net) axis off box off
Algorithms
Embedding Concatenation Layer
An embedding concatenation layer combines its input and an embedding vector by concatenation.
The output of the layer has the same number of dimensions as the input. In the output,
each vector in the first position over the channel dimension is the learnable embedding
weights vector Weights
.
For example:
For sequence data
X
represented by anumChannels
-by-numObservations
-by-numTimeSteps
array, wherenumChannels
,numObservations
, andnumTimeSteps
are the numbers of channels, observations, and time steps of the input, respectively, the output is anOutputSize
-by-numObservations
-by-(numTimeSteps+1)
arrayY
, whereY(:,:,1)
isWeights
andY(:,:,2:end)
isX
.For 1-D image data
X
represented by aheight
-by-numChannels
-by-numObservations
array, whereheight
,numChannels
, andnumObservations
are the height, number of channels, and the number of observations of the input images, respectively, the output is a(height+1)
-by-OutputSize
-by-numObservations
arrayY
, whereY(1,:,:)
isWeights
andY(2:end,:,:)
isX
.
Layer Input and Output Formats
Layers in a layer array or layer graph pass data to subsequent layers as formatted dlarray
objects.
The format of a dlarray
object is a string of characters in which each
character describes the corresponding dimension of the data. The formats consist of one or
more of these characters:
"S"
— Spatial"C"
— Channel"B"
— Batch"T"
— Time"U"
— Unspecified
For example, you can describe 2-D image data that is represented as a 4-D array, where the
first two dimensions correspond to the spatial dimensions of the images, the third
dimension corresponds to the channels of the images, and the fourth dimension
corresponds to the batch dimension, as having the format "SSCB"
(spatial, spatial, channel, batch).
You can interact with these dlarray
objects in automatic differentiation
workflows, such as those for developing a custom layer, using a functionLayer
object, or using the forward
and predict
functions with
dlnetwork
objects.
This table shows the supported input formats of EmbeddingConcatenationLayer
objects and the
corresponding output format. If the software passes the output of the layer to a custom
layer that does not inherit from the nnet.layer.Formattable
class, or a
FunctionLayer
object with the Formattable
property
set to 0
(false
), then the layer receives an
unformatted dlarray
object with dimensions ordered according to the formats
in this table. The formats listed here are only a subset. The layer may support additional
formats such as formats with additional "S"
(spatial) or
"U"
(unspecified) dimensions.
Input Format | Output Format |
---|---|
"SCB" (spatial, channel, batch) | "SCB" (spatial, channel, batch) |
"CBT" (channel, batch, time) | "CBT" (channel, batch, time) |
"SC" (spatial, channel) | "SC" (spatial, channel) |
In dlnetwork
objects, EmbeddingConcatenationLayer
objects also support
these input and output format combinations.
Input Format | Output Format |
---|---|
"CT" (channel, time) | "CT" (channel, time) |
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. https://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf
[2] He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification." In 2015 IEEE International Conference on Computer Vision (ICCV), 1026–34. Santiago, Chile: IEEE, 2015. https://doi.org/10.1109/ICCV.2015.123
Extended Capabilities
C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.
Usage notes and limitations:
You can generate generic C/C++ code that does not depend on third-party libraries and deploy the generated code to hardware platforms.
GPU Code Generation
Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.
Usage notes and limitations:
You can generate CUDA code that is independent of deep learning libraries and deploy the generated code to platforms that use NVIDIA® GPU processors.
Version History
Introduced in R2023b
See Also
selfAttentionLayer
| attentionLayer
| positionEmbeddingLayer
| indexing1dLayer
| trainnet
| trainingOptions
| dlnetwork
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