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View Code? Open in Web Editor NEWConText v4: Neural networks for text categorization
Home Page: http://riejohnson.com/cnn_download.html
License: GNU General Public License v3.0
ConText v4: Neural networks for text categorization
Home Page: http://riejohnson.com/cnn_download.html
License: GNU General Public License v3.0
Very nice paper. I am wondering if there is a python implementation for deep pyramid CNN models. I am new to running deep learning on C++. Thanks!
I have an NVIDIA GeForce 940MX GPU on my machine and also have gcc
fully functional, as well as CUDA 9.1 installed. I've followed the README instructions and added the custom CUDA path to the makefile
but I'm getting the following error on running make
/bin/rm -f bin/reNet
/usr/local/cuda-9.1 /bin/nvcc src/com/AzDmat.cpp src/com/AzParam.cpp src/com/AzSmat.cpp src/com/AzStrPool.cpp src/com/AzTextMat.cpp src/com/AzTools.cpp src/com/AzUtil.cpp src/nnet/AzpPatchDflt.cpp src/nnet/AzpReNet.cpp src/nnet/AzpReLayer.cpp src/nnet/AzMultiConn.cpp src/nnet/AzpMain_reNet.cpp src/nnet/AzpLmSgd.cpp src/nnet/AzPmat.cpp src/nnet/AzPmatSpa.cpp src/nnet/AzPmatApp.cpp src/nnet/AzPmat_gpu.cu src/nnet/AzPmatSpa_gpu.cu src/nnet/AzCuda_Pmat.cu src/nnet/AzCuda_PmatSpa.cu src/nnet/AzCuda_PmatApp.cu src/nnet/AzpEv.cpp src/nnet/AzpLossDflt.cpp src/nnet/driv_reNet.cpp -Isrc/com -Isrc/data -Isrc/nnet -D__AZ_SMAT_SINGLE__ -D__AZ_GPU__ -I/usr/local/cuda-9.1 /include -O2 -gencode arch=compute_20,code=sm_20 -gencode arch=compute_20,code=sm_21 -gencode arch=compute_30,code=sm_30 -gencode arch=compute_32,code=sm_32 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_37,code=sm_37 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_52,code=sm_52 -gencode arch=compute_53,code=sm_53 -o bin/reNet -L/usr/local/cuda-9.1 /lib64 -lcudart -lcublas -lcurand -lcusparse
make: execvp: /usr/local/cuda-9.1: Permission denied
makefile:84: recipe for target 'bin/reNet' failed
make: *** [bin/reNet] Error 127
I get the following error after generating the vocabulary with n=3
and then generating regions. If I leave n
to the default value of 1, then this error doesn't show, but does that mean trigrams aren't supported? Is this an architecture-specific error?
!Input error!: (Detected in AzPrepText::gen_regions)NO SUPPORT for n-gram sequential
Thank you very much for releasing the code.
I'm having a hard time reading https://github.com/riejohnson/ConText/blob/master/examples/dpcnn-functions.sh are the weight of the convolution units intended to be shared across the different layers ?
There's no hint about this in the paper so I think it should not be shared, but this random independent implementation: https://github.com/Cheneng/DPCNN/blob/master/model/DPCNN.py made it so (but I found a few other errors within this repo).
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