dd8e4a102fa5997dbbffd0d48d65e8bf2acc6fd9,examples/chainer_mnist.py,,objective,#Any#,52

Before Change

    // Optimizer --- We optimize the choice of optimizers as well as their learning rates.
    optimizer_name = client.sample_categorical("optimizer", ["Adam", "MomentumSGD"])
    if optimizer_name == "Adam":
        adam_alpha = client.sample_loguniform("adam_apha", 1e-5, 1e-1)
        optimizer = chainer.optimizers.Adam(alpha=adam_alpha)
    else:
        momentum_sgd_lr = client.sample_loguniform("momentum_sgd_lr", 1e-5, 1e-1)
        optimizer = chainer.optimizers.MomentumSGD(lr=momentum_sgd_lr)
    optimizer.setup(model)

    // Dataset
    rng = np.random.RandomState(0)

After Change

def objective(client):
    // Model and optimizer
    model = L.Classifier(create_model(client))
    optimizer = create_optimizer(client, model)

    // Dataset
    rng = np.random.RandomState(0)
    train, test = chainer.datasets.get_mnist()

Instances