Hyperparameter Optimization Code

examples/hyperopt/driver.py

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