EmbeddingConcatenationLayer - Embedding concatenation layer - MATLAB (original) (raw)

Embedding concatenation layer

Since R2023b

Description

An embedding concatenation layer combines its input and an embedding vector by concatenation.

Creation

Syntax

Description

`layer` = embeddingConcatenationLayer creates an embedding concatenation layer.

example

Properties

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Parameters and Initialization

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 of 2/(numIn + numOut), where numIn 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, where numIn 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), where sz is the size of the weights.

The layer initializes the weights only when the Weights property is empty.

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

Data Types: char | string

This property is read-only.

Number of inputs to the layer, stored as 1. This layer accepts a single input only.

Data Types: double

This property is read-only.

Input names, stored as {'in'}. This layer accepts a single input only.

Data Types: cell

This property is read-only.

Number of outputs from the layer, stored as 1. This layer has a single output only.

Data Types: double

This property is read-only.

Output names, stored as {'out'}. This layer has a single output only.

Data Types: cell

Examples

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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.

Show 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

Figure contains an axes object. The hidden axes object contains an object of type graphplot.

Algorithms

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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, where numChannels, numObservations, andnumTimeSteps are the numbers of channels, observations, and time steps of the input, respectively, the output is anOutputSize-by-numObservations-by-(numTimeSteps+1) array Y, where Y(:,:,1) isWeights and Y(:,:,2:end) isX.
For 1-D image data X represented by aheight-by-numChannels-by-numObservations array, where height, 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 array Y, where Y(1,:,:) isWeights and Y(2:end,:,:) isX.

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 format consists 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 withdlnetwork 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 aFunctionLayer 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

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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.

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