Table 12 MNIST Results (Batch-Normalized Maxout Network in Network)
From: Effective hyperparameter optimization using Nelder-Mead method in deep learning
Method | Mean loss | Min loss |
|---|---|---|
Random search | 0.045438 (±0.002142) | 0.042694 |
Bayesian optimization | 0.045636 (±0.001197) | 0.044447 |
CMA-ES | 0.045248 (±0.002537) | 0.042250 |
Coordinate-search method | 0.045131 (±0.001088) | 0.043639 |
Nelder-Mead method | 0.044549 (±0.001079) | 0.043238 |
Method | Mean accuracy (%) | Accuracy with min loss (%) |
Random search | 99.56 (±0.02) | 99.58 |
Bayesian optimization | 99.47 (±0.05) | 99.59 |
CMA-ES | 99.49 (±0.14) | 99.59 |
Coordinate-search method | 99.48 (±0.04) | 99.53 |
Nelder-Mead method | 99.53 (±0.00) | 99.54 |