What means MinLength in sequenceInputLayer?

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mika
mika 2024-2-8
回答： Pratyush Swain 2024-2-8
Environment
  • MATLAB R2023b
  • Deep Learning Toolbox
Question
I try to use this example program to learn complex-valued neural networks.
https://jp.mathworks.com/help/deeplearning/ug/train-network-with-complex-valued-data.html
At line 38, layers are defined as below and the argments of `sequenceInputLayer` are
  • numChannels
  • SplitComplexInputs
  • MinLength
layers = [ ...
sequenceInputLayer(numChannels,SplitComplexInputs=true,MinLength=minLength)
convolution1dLayer(filterSize,numFilters,Padding="causal")
reluLayer
layerNormalizationLayer
convolution1dLayer(filterSize,2*numFilters,Padding="causal")
reluLayer
layerNormalizationLayer
globalAveragePooling1dLayer
fullyConnectedLayer(numResponses)
regressionLayer];
What means `MinLength` in `sequenceInputLayer`?
I tried `MinLength=1` but this program worked.
If MinLength meant MinLength for down sampling, `MinLength=1` hasn't must work.

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采纳的回答

Pratyush Swain
Pratyush Swain 2024-2-8
Hi mika,
The "MinLength" argument is used to define the smallest length of input sequences that the network can process. When you set "MinLength=1", it means that the network will accept sequences that are at least one time step long. It shows the capability of the network to handle variable-length input sequences.
The "MinLength" argument's default value is 1 itself.When you train or assemble a network, it checks that sequences of length 1 can propagate through the network.
For more information on "sequenceInputLayer" and its arguments, please refer to https://www.mathworks.com/help/deeplearning/ref/nnet.cnn.layer.sequenceinputlayer.html

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