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This repository has been archived by the owner on Aug 5, 2022. It is now read-only.
Running examples/cifar10/train_full_sigmoid_bn.sh results in a SEGFAULT. See test instructions. The test was done on the CPU instead of GPU (see Why train on a GPU? in the instructions).
Final part of the output:
I1115 10:17:46.048431 16368 net.cpp:345] Network initialization done.
I1115 10:17:46.048583 16368 solver.cpp:104] Solver scaffolding done.
*** Aborted at 1479205066 (unix time) try "date -d @1479205066" if you are using GNU date ***
PC: @ 0x7f4d4ab46869 caffe::Blob<>::Reshape()
*** SIGSEGV (@0x28) received by PID 16368 (TID 0x7f4d4b998a40) from PID 40; stack trace: ***
@ 0x7f4d46a71330 (unknown)
@ 0x7f4d4ab46869 caffe::Blob<>::Reshape()
@ 0x7f4d4ab474f5 caffe::Blob<>::Blob()
@ 0x7f4d4aa9930d caffe::Creator_SGDSolver<>()
@ 0x7f4d4b7bf707 caffe::SolverRegistry<>::CreateSolver()
@ 0x7f4d4b7aeaab train()
@ 0x7f4d4b7ad166 main
@ 0x7f4d466bdf45 (unknown)
@ 0x7f4d4b7acf69 (unknown)
@ 0x0 (unknown)
Segmentation fault
If I modify layer_factory.cpp and force the use of the Caffe implementation of the BatchNorm layer, it works:
Running
examples/cifar10/train_full_sigmoid_bn.sh
results in a SEGFAULT. See test instructions. The test was done on the CPU instead of GPU (see Why train on a GPU? in the instructions).Final part of the output:
If I modify
layer_factory.cpp
and force the use of the Caffe implementation of theBatchNorm
layer, it works:Relevant
Makefile.config
vars:The text was updated successfully, but these errors were encountered: