and then in the eras directory (unfortunatly not linked from the previous web page) you see examples on using the Keras interface.
See then these in GitHub:
Dear Moneta,
TMVA DNN is quite new right? if I look here (1) the current release is "TMVA version 4.2.0 " made in 2013, and I wonder if it include DNN? It is the right place to get the last TMVA? Maybe should I work with a special root_TMVA?
Regards
I ran (1) with root -l ./TMVAClassification.C. In that code I set only “Use[ “DNN_GPU”] = 1”. So all the other method are set to 0. When I run I got the error message (2). I have root "6.10/04 ". Should I install more things on my side?
Regards
(2)
: Transformation, Variable selection :
: Input : variable ‘myvar1’ <—> Output : variable ‘myvar1’
: Input : variable ‘myvar2’ <—> Output : variable ‘myvar2’
: Input : variable ‘var3’ <—> Output : variable ‘var3’
: Input : variable ‘var4’ <—> Output : variable ‘var4’
: CUDA backend not enabled. Please make sure you have CUDA installed and it was successfully detected by CMAKE.
: CUDA backend not enabled. Please make sure you have CUDA installed and it was successfully detected by CMAKE.
***> abort program execution
terminate called after throwing an instance of ‘std::runtime_error’
what(): FATAL error
When I use “Use[“DNN_CPU”] = 1” I have the error message (1)?
Regards
(1)
: Transformation, Variable selection :
: Input : variable ‘myvar1’ <—> Output : variable ‘myvar1’
: Input : variable ‘myvar2’ <—> Output : variable ‘myvar2’
: Input : variable ‘var3’ <—> Output : variable ‘var3’
: Input : variable ‘var4’ <—> Output : variable ‘var4’
: Multi-core CPU backend not enabled. Please make sure you have a BLAS implementation and it was successfully detected by CMake as well that the imt CMake flag is set.
: Multi-core CPU backend not enabled. Please make sure you have a BLAS implementation and it was successfully detected by CMake as well that the imt CMake flag is set.
***> abort program execution
terminate called after throwing an instance of ‘std::runtime_error’
what(): FATAL error
I can see from the manual that the"standard backend" can be use on any platform but when I use Architecture=STANDARD, as option, it says that it is not available (1)!
Regards
(1)
: Transformation, Variable selection :
: Input : variable ‘myvar1’ <—> Output : variable ‘myvar1’
: Input : variable ‘myvar2’ <—> Output : variable ‘myvar2’
: Input : variable ‘var3’ <—> Output : variable ‘var3’
: Input : variable ‘var4’ <—> Output : variable ‘var4’
: The STANDARD architecture has been deprecated. Please use Architecture=CPU or Architecture=CPU.See the TMVA Users’ Guide for instructions if you encounter problems.
: The STANDARD architecture has been deprecated. Please use Architecture=CPU or Architecture=CPU.See the TMVA Users’ Guide for instructions if you encounter problems.
***> abort program execution
terminate called after throwing an instance of ‘std::runtime_error’
what(): FATAL error
Dear experts,
when I used “DNN_GPU”, it trains and take many times (1), what puzzle me is that i do not see the progress bar despite my request (2). Is it implemented for DNN? if yes is there something wrong?
Regards
(1)
TFHandler_DNN_GPU : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: Mll01: -0.85850 0.11802 [ -1.0000 1.0000 ]
: DRll01: -0.29341 0.35139 [ -1.0000 1.0000 ]
: Ptll01: -0.80687 0.12644 [ -1.0000 1.0000 ]
: SumPtJet: -0.77872 0.15662 [ -1.0000 1.0000 ]
: -----------------------------------------------------------
: Start of neural network training on GPU.
:
: Training phase 1 of 1:
: Epoch | Train Err. Test Err. GFLOP/s Conv. Steps
: --------------------------------------------------------------
HERE I KEEP WAITING BUT I DO NOT KNOW IF SOMETHING IS WRONG AS I DO NOT SEE THE PROGRESS BAR?!
(2)
TMVA::Factory *factory = new TMVA::Factory( “TMVAClassification”, outputFile,
“!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=Classification” );
Thanks for reporting this. On my machine it takes quite some time for the network to be constructed and data loaded. It then output erroneous results. I will make a bug report.
Two questions: What OS/Cuda versions are you running? If you are not running cuda-7.5, do you have the possibility to test with this cuda version? (I have so far only been able to test with cuda 8.0 and 9.0).