Automatic Hyperparameter Optimization in Keras for the MediaEval 2018 Medico Multimedia Task
Rune Johan Borgli
Overview
We use a work-in-progress system for automatic hyperparameter optimization called Saga.
We use transfer learning with models pre-trained on ImageNet.
We use Bayesian optimization for the hyperparameter optimization.
We make changes to the dataset to try to improve performance.
Dataset observations
Best result for different models
Results
Model
DenseNet169
Optimizer
SGD
LR Block
0.00005
Layer
0
LR TUNE
0.004
Best found hyperparameters
Best result confusion matrix
(A) Ulcerative colitis, (B) Esophagitis, (C) Normal z-line, (D) Dyed lifted polyps, (E) Dyed resection margins, (F) Out of patient, (G) Normal pylorus, (H) Stool inclusions, (I) Stool plenty, (J) Blurry nothing, (K) Polyps, (L) Normal cecum, (M) Colon clear, (N) Retroflex rectum, (O) Retroflex stomach, and (P) Instruments
Summary
Automatic hyperparameter optimization achieves good results.
Creating the optimal model is not the whole picture. We must also improve the dataset.
Future work should include preprocessing and k-fold crossvalidation.
Automatic Hyperparameter Optimization in Keras for the MediaEval 2018 Medico Multimedia Task
By borgli
