optuna-model-definition.py

# data: fastai's DataBunch

def objective(trial: optuna.Trial):
    num_layers = trial.suggest_int('n_layers', 1, 5)  # `num_layers` is 1, 2, 3, 4, or 5.
    layers, ps = [], []  # define the number of unit of each layer / the ratio of dropout of each layer
    for i in range(n_layers - 1):  # `TabularModel` automatically adds the last layer.
        num_units = trial.suggest_categorical(f'num_units_layer_{i}', [800, 900, 1000, 1100, 1200])
        p = trial.suggest_discrete_uniform(f'dropout_p_layer_{i}', 0, 1, 0.05)
        layers.append(num_units); ps.append(p)
        
    emb_drop = trial.suggest_discrete_uniform('emb_drop', 0, 1, 0.05)
    learn = tabular_learner(data, layers=layers, ps=ps, emb_drop=emb_drop, y_range=y_range, metrics=exp_rmspe)
    
    learn.fit_one_cycle(5, 1e-3, wd=0.2)
    return learn.validate()[-1].item()  # Of course you can use the last record of `learn.recorder`.
    
study = optuna.create_study()
study.optimize(objective)
best_trial = study.best_trial