Comments (2)
Is the activation function the root cause? The neural net in current DAAL library seems using ReLU as the activation function.
Is it possible that the "delta rule" (induced from chain rule) based on ReLU function generates these BIG derivatives (gradient) from my training sample data? Gradient blewn-up (explosion)???
How can i change the sample code to use Sigmoid or Tanh instead of ReLU? Any pointer will be deeply appreciated.
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After reducing some hyperparameters, the problem seems gone.
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Related Issues (20)
- Feature request: Add support for ClangCl on MSVC HOT 4
- Looking forward to kd-tree implementation.. HOT 1
- Support for accelerating NGBoost HOT 1
- Compile Failure: "No kernel name provided without -fsycl-unnamed-lambda enabled!" HOT 2
- Support for RISC-V architecture HOT 1
- Error in running daal c++ examples HOT 4
- Datasets used for producing speedup benchmarks in scikit-learn intelex HOT 1
- Remote branch develop not found in upstream origin HOT 2
- `xsyrk` vs `xxsyrk` vs `dsyrk` (in netlib LAPACK) HOT 4
- PCA tests fail periodically.
- Not able to build daal/cpp examples HOT 3
- How to validate results when adding a new backend (i.e., OpenBLAS, Lapack, etc) HOT 1
- Differences betwen SPBLAS - xcsrmultd & xcsrmm ? HOT 1
- How to perform LightGBM inference in online mode using c++? HOT 7
- Fatal error: stdlib.h: No such file or directory from #include_next <stdlib.h> HOT 2
- Add single precision support for roc_auc_score HOT 2
- Improvement: Allow to pass arguments to error message functions
- LinuxMakeGNU_OpenBLAS_x86 and LinuxMakeLLVM_OpenBLAS_rv64 Public CI failing HOT 3
- Feature Request: Add Support for Linear Trees in LightGBM HOT 3
- How to convert a trained LGBM model to DAAL model by CPP interfaces? HOT 6
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