Error using dnnfpga.compiler.codegenfpga. Layer 'block2b_add' has multiple inputs.
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Hello,
I have a pretrained NN imported from TensorFlow. I have imported and replaced the only one placeholder layer and the DAGNetwork seems to be ok. However, when I try to compile the NN with Deep Learning HDL Toolbox, I get the following error:
Error using dnnfpga.compiler.codegenfpga
Layer 'block2b_add' has multiple inputs. Specify which input of layer 'block2b_add' to use.
Error in dnnfpga.apis.Workflow/compileNetwork
Error in dnnfpga.apis.Workflow/compile
Error in generate_HDL_from_NN (line 18)
hW.compile;
block2b_add has two inputs and connections are defined.
Could anyone help me to understand the issue?
Thank you in advance
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Software version:
Deep Learning HDL Toolbox Support Package For Intel FPGA And SoC Devices 21.2.4
Matlab R2021b Update 6 (9.11.0.2207237)
Ubuntu 20.04
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3 comentarios
Aravind Devarakonda
el 12 de Mayo de 2023
Hi Ruben
Thank you for replacing the network originally attached.
Regarding your inquiry, I have observed that the network given has a few layers that are not yet supported in the Deep Learning HDL Toolbox and are planned to be integrated in future updates.
These layers include :
1) Scaling Layer
2) Layer Normalization Layer
3) Swish Layer and,
4) FlattenCStyleLayer immediately followed by Convolution2DLayer is also not supported.
Currently, a flatten layer must be followed by either fully connected layer(s) or a join layer (eg. depth concatenation, addition) followed by fully connected layer(s).
As a workaround, I would suggest implementing the Scaling Layer as a custom layer by following the steps available on this documentation link -
https://in.mathworks.com/help/deep-learning-hdl/ug/create-deep-learning-processor-configuration-for-custom-layers.html
Regarding FlattenCStyleLayer immediately followed by Convolution2DLayer, I suggest replacing it with a fully connected layer.
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