Work Around for Convolution1DLayer
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I am trying to do code generation of a trained deep learning network however it uses a convolution1dlayer. It seems this is not currently supported by matlab. What are some possible solutions to this problem?
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Aditya
il 7 Ago 2024 alle 20:14
Modificato: Aditya
il 7 Ago 2024 alle 20:14
Hi Kevin,
Please do refer to this MATLAB answer post by MathWorks Support Team on the same issue: Is code generation supported for "convolution1DLayer"? - MATLAB Answers - MATLAB Central (mathworks.com)
Hope this helps!
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Ram Kokku
il 7 Ago 2024 alle 21:07
Modificato: Walter Roberson
il 7 Ago 2024 alle 21:56
Convolution1DLayer supports code generation in the latest (R2024a) version of MATLAB. See the extended capabilities section : here - https://www.mathworks.com/help/deeplearning/ref/nnet.cnn.layer.convolution1dlayer.html
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Steven Lord
il 7 Ago 2024 alle 21:11
Are you certain that you're using release R2024a? It appears from the Release Notes that support for generating code from this layer was added in release R2024a.
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Ram Kokku
il 8 Ago 2024 alle 0:25
@Kevin Monahan, use TargetLibrary=none (https://www.mathworks.com/help/coder/ref/coder.deeplearningconfig.html). Both MATLAB Coder and GPU Coder support TargetLibrary=none. so, you should be able to generate both C/C++ and CUDA. For Better performance, you may need to explicit enable hardware features like instructionset (AVX2, AVX512) and multi-threading. See https://www.mathworks.com/help/coder/ug/optimize-generic-c-cpp-code-performance.html for more details.
森
il 17 Ago 2024 alle 12:15
I meet the same problem when generating coder containg Convolution1DLayer using TargetLibrary=none, and I also use the version 2024a. So this means actualy version 2024a stiil does not support the code generation for Convolution1DLayer?
Matt J
il 7 Ago 2024 alle 21:35
Modificato: Matt J
il 7 Ago 2024 alle 21:36
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Matt J
il 7 Ago 2024 alle 22:15
Modificato: Matt J
il 7 Ago 2024 alle 22:16
Why does that matter? It was given to you, but surely you can use replaceLayer to swap the 1D convolutional layers for 2D ones.
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