How can I cross-validate by Pytorch and Optuna
I want to use cross-validation against the official Optuna and pytorch-based sample code (https://github.com/optuna/optuna/blob/master/examples/pytorch_simple.py).
I thought about splitting the data for cross-validation and trying parameter tuning for each fold, but it seems that the average accuracy of each parameter cannot be obtained because the parameters that can be checked in study.trials_dataframe() are different each time.

I think we need to evaluate all folds and calculate the mean inside an objective function. I create an example notebook, so please take a look.
In the notebook, I slightly modified the objective function to pass the dataset with the arguments and added a wrapper function objective_cv to call the objective function with the split dataset. Then, I optimized the objective_cv instead of the objective function.
def objective(trial, train_loader, valid_loader):

    # Remove the following line.
    # train_loader, valid_loader = get_mnist()

    ...

    return accuracy


def objective_cv(trial):

    # Get the MNIST dataset.
    dataset = datasets.MNIST(DIR, train=True, download=True, transform=transforms.ToTensor())

    fold = KFold(n_splits=3, shuffle=True, random_state=0)
    scores = []
    for fold_idx, (train_idx, valid_idx) in enumerate(fold.split(range(len(dataset)))):
        train_data = torch.utils.data.Subset(dataset, train_idx)
        valid_data = torch.utils.data.Subset(dataset, valid_idx)

        train_loader = torch.utils.data.DataLoader(
            train_data,
            batch_size=BATCHSIZE,
            shuffle=True,
        )
        valid_loader = torch.utils.data.DataLoader(
            valid_data,
            batch_size=BATCHSIZE,
            shuffle=True,
        )

        accuracy = objective(trial, train_loader, valid_loader)
        scores.append(accuracy)
    return np.mean(scores)


study = optuna.create_study(direction=&quot;maximize&quot;)
study.optimize(objective_cv, n_trials=20, timeout=600)

