steffen/Mel_Vision_Transformer_ComParE-21
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optuna tune

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This commit is contained in:
Steffen 2021-04-03 18:39:29 +02:00
parent cec3a07d60
commit 3955f5ccd0
3 changed files with 95 additions and 2530 deletions
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113
notebooks/Dataset Analysis.ipynb
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2498
notebooks/Study Plots.ipynb
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14
optuna_tune.py
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@ -26,7 +26,7 @@ class ContiniousSavingCallback:
f'best_{self.study.best_trial.number}_' \ f'best_{self.study.best_trial.number}_' \
f'score_{self.study.best_value}.pkl' f'score_{self.study.best_value}.pkl'
def __init__(self, root:Union[str, Path], study: optuna.Study): def __init__(self, root: Union[str, Path], study: optuna.Study):
self._study = study self._study = study
self.root = Path(root) self.root = Path(root)
pass pass
@ -49,7 +49,7 @@ class ContiniousSavingCallback:
temp_study_file.unlink(missing_ok=True) temp_study_file.unlink(missing_ok=True)
def __call__(self, study: optuna.study.Study, trial: optuna.trial.FrozenTrial) -> None: def __call__(self, study: optuna.study.Study, trial: optuna.trial.FrozenTrial) -> None:
self._write_to_disk(study, self.tmp_study_path(trial.number)) self._write_to_disk(study, self.tmp_study_path)
def __enter__(self): def __enter__(self):
return self return self
@ -70,10 +70,10 @@ def optimize(trial: optuna.Trial):
lr_scheduler_parameter = None lr_scheduler_parameter = None
optuna_suggestions = dict( optuna_suggestions = dict(
model_name='VisualTransformer', model_name='CNNBaseline',
data_name='CCSLibrosaDatamodule', data_name='PrimatesLibrosaDatamodule',
batch_size=trial.suggest_int('batch_size', 5, 50, step=5), batch_size=trial.suggest_int('batch_size', 5, 50, step=5),
max_epochs=200, max_epochs=400,
target_mel_length_in_seconds=trial.suggest_float('target_mel_length_in_seconds', 0.2, 1.5, step=0.1), target_mel_length_in_seconds=trial.suggest_float('target_mel_length_in_seconds', 0.2, 1.5, step=0.1),
random_apply_chance=trial.suggest_float('random_apply_chance', 0.1, 0.5, step=0.1), random_apply_chance=trial.suggest_float('random_apply_chance', 0.1, 0.5, step=0.1),
loudness_ratio=trial.suggest_float('loudness_ratio', 0.0, 0.5, step=0.1), loudness_ratio=trial.suggest_float('loudness_ratio', 0.0, 0.5, step=0.1),
@ -99,8 +99,8 @@ def optimize(trial: optuna.Trial):
transformer_dict = dict( transformer_dict = dict(
mlp_dim=2 ** trial.suggest_int('mlp_dim', 1, 5, step=1), mlp_dim=2 ** trial.suggest_int('mlp_dim', 1, 5, step=1),
head_dim=2 ** trial.suggest_int('head_dim', 1, 5, step=1), head_dim=2 ** trial.suggest_int('head_dim', 1, 5, step=1),
patch_size=trial.suggest_int('patch_size', 6, 12, step=3), patch_size=trial.suggest_int('patch_size', 6, 20, step=3),
attn_depth=trial.suggest_int('attn_depth', 2, 14, step=4), attn_depth=trial.suggest_int('attn_depth', 2, 20, step=4),
heads=trial.suggest_int('heads', 2, 16, step=2), heads=trial.suggest_int('heads', 2, 16, step=2),
embedding_size=trial.suggest_int('embedding_size', 12, 64, step=12) embedding_size=trial.suggest_int('embedding_size', 12, 64, step=12)
) )