8b3f79f3079834f00c7ec62d5decd6bc026c1bc8,python/ray/tune/examples/tune_mnist_keras.py,,,#,47

Before Change

        max_t=400,
        grace_period=20)

    tune.run(
        train_mnist,
        name="exp",
        scheduler=sched,
        stop={
            "mean_accuracy": 0.99,
            "training_iteration": 5 if args.smoke_test else 300
        },
        num_samples=10,
        resources_per_trial={
            "cpu": 2,
            "gpu": 0
        },
        config={
            "threads": 2,
            "lr": tune.sample_from(lambda spec: np.random.uniform(0.001, 0.1)),
            "momentum": tune.sample_from(
                lambda spec: np.random.uniform(0.1, 0.9)),
            "hidden": tune.sample_from(
                lambda spec: np.random.randint(32, 512)),
        })

After Change

    sched = AsyncHyperBandScheduler(
        time_attr="training_iteration", max_t=400, grace_period=20)

    analysis = tune.run(
        train_mnist,
        name="exp",
        scheduler=sched,
        metric="mean_accuracy",
        mode="max",
        stop={
            "mean_accuracy": 0.99,
            "training_iteration": 5 if args.smoke_test else 300
        },
        num_samples=10,
        resources_per_trial={
            "cpu": 2,
            "gpu": 0
        },
        config={
            "threads": 2,
            "lr": tune.uniform(0.001, 0.1),
            "momentum": tune.uniform(0.1, 0.9),
            "hidden": tune.randint(32, 512),
        })
    print("Best hyperparameters found were: ", analysis.best_config)

Instances