def train_and_score(network, x_train, y_train, x_test, y_test):
    '\n\n    :param network dict: dictionary with network parameters\n    :param x_train array: numpy array with features for traning\n    :param y_train array: numpy array with labels for traning\n    :param x_test array: numpy array with labels for test\n    :param y_test array: numpy array with labels for test\n    :return float: score\n    '
    model = compile_model(network)
    model.fit(x_train, y_train)
    y_pred = model.predict(np.array(x_test))
    true = y_test
    pred = y_pred
    print(' R2 = ', r2_score(true, pred))
    return (r2_score(true, pred), model)
:param network dict: dictionary with network parameters
:param x_train array: numpy array with features for traning
:param y_train array: numpy array with labels for traning
:param x_test array: numpy array with labels for test
:param y_test array: numpy array with labels for test
:return float: score
def train_and_score(network, x_train, y_train, x_test, y_test):
    '\n\n    :param network dict: dictionary with network parameters\n    :param x_train array: numpy array with features for traning\n    :param y_train array: numpy array with labels for traning\n    :param x_test array: numpy array with labels for test\n    :param y_test array: numpy array with labels for test\n    :return float: score\n    '
    model = compile_model(network)
    model.fit(x_train, y_train)
    y_pred = model.predict(np.array(x_test))
    true = y_test
    pred = y_pred
    print(' R2 = ', r2_score(true, pred))
    return (r2_score(true, pred), model)