How to create a transformer network for sequence to sequence classification task?

Question

veritas 2024-9-6

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2150569-how-to-create-a-transformer-network-for-sequence-to-sequence-classification-task

回答： Prasanna 2024-9-9

I am currently trying to use MATLAB to complete a task of classifying time series using a transformer network. The following is my code, but I cannot solve the error after compiling.

lgraph = [ ...

sequenceInputLayer(InputSize,Name="input")

positionEmbeddingLayer(InputSize,maxPosition,Name="pos-emb");

additionLayer(2, Name="embed_add");

selfAttentionLayer(numHeads,numKeyChannels) % self attention

additionLayer(2,Name="attention_add") % residual connection around attention

layerNormalizationLayer(Name="attention_norm") % layer norm

fullyConnectedLayer(feedforwardHiddenSize) % feedforward part 1

reluLayer % nonlinear activation

fullyConnectedLayer(attentionHiddenSize) % feedforward part 2

additionLayer(2,Name="feedforward_add") % residual connection around feedforward

layerNormalizationLayer() % layer norm

% selfAttentionLayer(numHeads,numKeyChannels,'AttentionMask','causal');

% selfAttentionLayer(numHeads,numKeyChannels);

indexing1dLayer("last")

fullyConnectedLayer(NumClass)

softmaxLayer

classificationLayer];

% Layers = layerGraph(lgraph);

% Layers = connectLayers(Layers,"input","add/in2");

net = dlnetwork(lgraph,Initialize=false);

net = connectLayers(net,"embed_add","attention_add/in2");

net = connectLayers(net,"pos-emb","embed_add/in2");

net = connectLayers(net,"attention_norm","feedforward_add/in2");

% net = connectLayers(net,"encoder1_out","attention2_add/in2");

% net = connectLayers(net,"attention2_norm","feedforward2_add/in2");

net = initialize(net);

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Answer 1

Prasanna 2024-9-9

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此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2150569-how-to-create-a-transformer-network-for-sequence-to-sequence-classification-task#answer_1513239

Hi veritas,

The error you're encountering is due to the use of the ‘classificationLayer’, which is not supported in the context of a ‘dlnetwork‘ object because ‘dlnetwork’ is designed for custom training loops and does not require an explicit output layer like ‘classificationLayer’. Instead, you should handle the loss calculation separately during training.

Here's how you can modify your setup to avoid using classificationLayer:

Remove the ‘classificationLayer’ from your layer graph definition.
With ‘dlnetwork’, you typically use a custom training loop where you manually compute the loss and update the model parameters.
Use a loss function such as cross-entropy directly in your training loop.

To perform the above, you can use the ‘trainnet’ function instead of train ‘dlnetwork’ objects and set the loss function to ‘crossentropy’ instead. For more references on the functions, refer the following documentation: