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75d08b0632
This PR renames the `suggest` package to `search` and alters the layout slightly. In the new package, the higher-level abstractions are on the top level and the search algorithms have their own subdirectories. In a future refactor, we can turn algorithms such as PBT into actual `SearchAlgorithm` classes and move them into the `search` package. The main reason to keep algorithms and searchers in the same directory is to avoid user confusion - for a user, `Bayesopt` is as much a search algorithm as e.g. `PBT`, so it doesn't make sense to split them up.
31 lines
904 B
Python
31 lines
904 B
Python
# flake8: noqa
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accuracy = 42
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# __keras_hyperopt_start__
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from ray import tune
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from ray.tune.search.hyperopt import HyperOptSearch
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import keras
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# 1. Wrap a Keras model in an objective function.
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def objective(config):
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model = keras.models.Sequential()
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model.add(keras.layers.Dense(784, activation=config["activation"]))
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model.add(keras.layers.Dense(10, activation="softmax"))
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model.compile(loss="binary_crossentropy", optimizer="adam", metrics=["accuracy"])
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# model.fit(...)
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# loss, accuracy = model.evaluate(...)
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return {"accuracy": accuracy}
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# 2. Define a search space and initialize the search algorithm.
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search_space = {"activation": tune.choice(["relu", "tanh"])}
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algo = HyperOptSearch()
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# 3. Start a Tune run that maximizes accuracy.
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analysis = tune.run(
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objective, search_alg=algo, config=search_space, metric="accuracy", mode="max"
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)
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# __keras_hyperopt_end__
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