configs/hparams_search/focusResNetMSE_150.yaml

# @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: focusResNet_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"

datamodule:
 batch_size: 64
 augmentation: True

name: "focusResNet_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: focusResNet_150_hyperparameter

 # 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:
 model.pretrained:
 type: categorical
 choices: [true, false]
 model.lr:
 type: float
 low: 0.0001
 high: 0.01
 model.resnet_type:
 type: categorical
 choices: [
 "ResNet", 
 "resnet18",
 "resnet34",
 "resnet50",
 "resnet101",
 "resnext50_32x4d",
 "wide_resnet50_2",
 ]