mirror of
https://github.com/vale981/ray
synced 2025-03-08 19:41:38 -05:00
40 lines
1.2 KiB
Python
40 lines
1.2 KiB
Python
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import numpy as np
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import ray
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import ray.services as services
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import os
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import functions
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num_workers = 3
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samples = 50
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epochs = 100
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worker_dir = os.path.dirname(os.path.abspath(__file__))
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worker_path = os.path.join(worker_dir, "worker.py")
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services.start_singlenode_cluster(return_drivers=False, num_objstores=1, num_workers_per_objstore=num_workers, worker_path=worker_path)
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best_params = None
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best_accuracy = 0
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results = []
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for i in range(samples):
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learning_rate = 10 ** np.random.uniform(-6, 1)
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batch_size = np.random.randint(30, 100)
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dropout = np.random.uniform(0, 1)
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stddev = 10 ** np.random.uniform(-3, 1)
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randparams = {"learning_rate": learning_rate, "batch_size": batch_size, "dropout": dropout, "stddev": stddev}
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results.append((randparams, functions.train_cnn(randparams, epochs)))
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for i in range(samples):
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params, ref = results[i]
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accuracy = ray.get(ref)
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print "With hyperparameters {}, we achieve an accuracy of {:.4}%.".format(params, 100 * accuracy)
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if accuracy > best_accuracy:
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best_params = params
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best_accuracy = accuracy
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print "Best parameters are now {}.".format(params)
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print "Best parameters over {} samples was {}, with an accuracy of {:.4}%.".format(samples, best_params, 100 * best_accuracy)
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services.cleanup()
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