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An improvement on regression and data reading-revision #215
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…els, and regression problems are applied. The improved Caffe can handle the standard image multi label regression problem and is very adaptable to non-standard image data. Compared to the support of native Caffe to the regression problem, this modification not only makes the use more simple, but also has a good performance in the slow convergence or non convergence of loss on some regression problems.
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tujie-jiangye
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jgong5
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May 21, 2018
| PreclcRandomNumbers precalculated_rand_numbers; | ||
| this->data_transformer_->GenerateRandNumbers(precalculated_rand_numbers); | ||
| #pragma omp task firstprivate(offset, precalculated_rand_numbers, data, item_id) | ||
| #endif |
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The improvement of this version is mainly aimed at multiple label regression problems. By improving LMDB reading and writing, the Intel caffe LMDB database can directly support multi label regression without the h5py database as an intermediary. Through this improvement, we find that some native Caffe regression loss is not convergent or slow convergence, this improvement has good adaptability, and shows excellent results on similar problems.