python optuna-trial.py

Posted 2021-05-09

import optuna
from keras import models
from keras import optimizers
from keras import layers
from keras.datasets import boston_housing

# データ読み込み
(train_data, train_targets), (test_data, test_targets) = boston_housing.load_data()

mean = train_data.mean(axis=0)
std = train_data.std(axis=0)

# 正規化
train_data -= mean
train_data /= std
test_data -= mean
test_data /= std


# モデル作成
def create_model(trial):
    n_layers = trial.suggest_int('n_layers', 1, 10)
    lr = trial.suggest_loguniform('learning_rate', 1e-5, 1e-2)
    model = models.Sequential()

    for i in range(n_layers):
        n_units = trial.suggest_int("n_units{}".format(i), 20, 100)
        if i == 0:
            model.add(layers.Dense(activation='relu', units=n_units, input_shape=(train_data.shape[1],)))
        else:
            model.add(layers.Dense(activation='relu', units=n_units))

    model.add(layers.Dense(1))
    model.compile(optimizers.rmsprop(lr=lr), loss='mse', metrics=['mae'])
    return model


# 最適化対象
def objective(trial):
    model = create_model(trial)
    batch_size = trial.suggest_int('batch_size', 32, 128)
    epochs = trial.suggest_int('epochs', 50, 200)

    model.fit(train_data, train_targets, batch_size=batch_size, epochs=epochs, verbose=False)
    result = model.evaluate(test_data, test_targets, verbose=False)
    return result[0]


if __name__ == '__main__':
    study = optuna.create_study()
    study.optimize(objective, n_trials=100)

    print(study.best_value)
    print(study.best_params)