| # @package _global_ | |
| # example hyperparameter optimization of some experiment with Optuna: | |
| # python train.py -m hparams_search=mnist_optuna experiment=example | |
| defaults: | |
| - override /datamodule: focus150.yaml | |
| - override /model: focusConv_150.yaml | |
| - override /hydra/sweeper: optuna | |
| # choose metric which will be optimized by Optuna | |
| # make sure this is the correct name of some metric logged in lightning module! | |
| optimized_metric: "val/mae_best" | |
| name: "focusConvMSE_150_hyperparameter_search" | |
| # here we define Optuna hyperparameter search | |
| # it optimizes for value returned from function with @hydra.main decorator | |
| # docs: https://hydra.cc/docs/next/plugins/optuna_sweeper | |
| hydra: | |
| sweeper: | |
| _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper | |
| # storage URL to persist optimization results | |
| # for example, you can use SQLite if you set 'sqlite:///example.db' | |
| storage: null | |
| # name of the study to persist optimization results | |
| study_name: focusConvMSE_150_hyperparameter_search | |
| # number of parallel workers | |
| n_jobs: 1 | |
| # 'minimize' or 'maximize' the objective | |
| direction: minimize | |
| # total number of runs that will be executed | |
| n_trials: 20 | |
| # choose Optuna hyperparameter sampler | |
| # docs: https://optuna.readthedocs.io/en/stable/reference/samplers.html | |
| sampler: | |
| _target_: optuna.samplers.TPESampler | |
| seed: 12345 | |
| n_startup_trials: 10 # number of random sampling runs before optimization starts | |
| # define range of hyperparameters | |
| search_space: | |
| datamodule.batch_size: | |
| type: categorical | |
| choices: [64, 128] | |
| model.lr: | |
| type: float | |
| low: 0.0001 | |
| high: 0.01 | |
| model.conv1_size: | |
| type: categorical | |
| choices: [3, 5, 7] | |
| model.conv1_channels: | |
| type: categorical | |
| choices: [3, 6, 9] | |
| model.conv2_size: | |
| type: categorical | |
| choices: [3, 5, 7] | |
| model.conv2_channels: | |
| type: categorical | |
| choices: [6, 11, 16] | |
| model.lin1_size: | |
| type: categorical | |
| choices: [32, 72, 128] | |
| model.lin2_size: | |
| type: categorical | |
| choices: [32, 72, 128] |