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: 0.001
        high: 0.01
      model.resnet_type:
        type: categorical
        choices: [ 
          "resnet18",
          "resnet34",
          "resnet50",
          "resnet101",
          "resnext50_32x4d",
          "wide_resnet50_2",
        ]