Switch Python indentation from 2 spaces to 4 spaces. (#726)

* 4 space indentation for actor.py. * 4 space indentation for worker.py. * 4 space indentation for more files. * 4 space indentation for some test files. * Check indentation in Travis. * 4 space indentation for some rl files. * Fix failure test. * Fix multi_node_test. * 4 space indentation for more files. * 4 space indentation for remaining files. * Fixes.
2026-08-12 12:20:11 +08:00 · 2017-07-13 21:53:57 +00:00
parent 310ba82131
commit e0867c8845
100 changed files with 16686 additions and 16189 deletions
@@ -21,80 +21,80 @@ parser.add_argument("--redis-address", default=None, type=str,


 if __name__ == "__main__":
-  args = parser.parse_args()
+    args = parser.parse_args()

-  ray.init(redis_address=args.redis_address)
+    ray.init(redis_address=args.redis_address)

-  # The number of sets of random hyperparameters to try.
-  trials = args.trials
-  # The number of training passes over the dataset to use for network.
-  steps = args.steps
+    # The number of sets of random hyperparameters to try.
+    trials = args.trials
+    # The number of training passes over the dataset to use for network.
+    steps = args.steps

-  # Load the mnist data and turn the data into remote objects.
-  print("Downloading the MNIST dataset. This may take a minute.")
-  mnist = input_data.read_data_sets("MNIST_data", one_hot=True)
-  train_images = ray.put(mnist.train.images)
-  train_labels = ray.put(mnist.train.labels)
-  validation_images = ray.put(mnist.validation.images)
-  validation_labels = ray.put(mnist.validation.labels)
+    # Load the mnist data and turn the data into remote objects.
+    print("Downloading the MNIST dataset. This may take a minute.")
+    mnist = input_data.read_data_sets("MNIST_data", one_hot=True)
+    train_images = ray.put(mnist.train.images)
+    train_labels = ray.put(mnist.train.labels)
+    validation_images = ray.put(mnist.validation.images)
+    validation_labels = ray.put(mnist.validation.labels)

-  # Keep track of the best hyperparameters and the best accuracy.
-  best_hyperparamemeters = None
-  best_accuracy = 0
-  # This list holds the object IDs for all of the experiments that we have
-  # launched and that have not yet been processed.
-  remaining_ids = []
-  # This is a dictionary mapping the object ID of an experiment to the
-  # hyerparameters used for that experiment.
-  hyperparameters_mapping = {}
+    # Keep track of the best hyperparameters and the best accuracy.
+    best_hyperparamemeters = None
+    best_accuracy = 0
+    # This list holds the object IDs for all of the experiments that we have
+    # launched and that have not yet been processed.
+    remaining_ids = []
+    # This is a dictionary mapping the object ID of an experiment to the
+    # hyerparameters used for that experiment.
+    hyperparameters_mapping = {}

-  # A function for generating random hyperparameters.
-  def generate_hyperparameters():
-    return {"learning_rate": 10 ** np.random.uniform(-5, 5),
-            "batch_size": np.random.randint(1, 100),
-            "dropout": np.random.uniform(0, 1),
-            "stddev": 10 ** np.random.uniform(-5, 5)}
+    # A function for generating random hyperparameters.
+    def generate_hyperparameters():
+        return {"learning_rate": 10 ** np.random.uniform(-5, 5),
+                "batch_size": np.random.randint(1, 100),
+                "dropout": np.random.uniform(0, 1),
+                "stddev": 10 ** np.random.uniform(-5, 5)}

-  # Randomly generate some hyperparameters, and launch a task for each set.
-  for i in range(trials):
-    hyperparameters = generate_hyperparameters()
-    accuracy_id = objective.train_cnn_and_compute_accuracy.remote(
-        hyperparameters, steps, train_images, train_labels, validation_images,
-        validation_labels)
-    remaining_ids.append(accuracy_id)
-    # Keep track of which hyperparameters correspond to this experiment.
-    hyperparameters_mapping[accuracy_id] = hyperparameters
+    # Randomly generate some hyperparameters, and launch a task for each set.
+    for i in range(trials):
+        hyperparameters = generate_hyperparameters()
+        accuracy_id = objective.train_cnn_and_compute_accuracy.remote(
+            hyperparameters, steps, train_images, train_labels,
+            validation_images, validation_labels)
+        remaining_ids.append(accuracy_id)
+        # Keep track of which hyperparameters correspond to this experiment.
+        hyperparameters_mapping[accuracy_id] = hyperparameters

-  # Fetch and print the results of the tasks in the order that they complete.
-  for i in range(trials):
-    # Use ray.wait to get the object ID of the first task that completes.
-    ready_ids, remaining_ids = ray.wait(remaining_ids)
-    # Process the output of this task.
-    result_id = ready_ids[0]
-    hyperparameters = hyperparameters_mapping[result_id]
-    accuracy, _ = ray.get(result_id)
-    print("""We achieve accuracy {:.3}% with
-        learning_rate: {:.2}
-        batch_size: {}
-        dropout: {:.2}
-        stddev: {:.2}
-      """.format(100 * accuracy,
-                 hyperparameters["learning_rate"],
-                 hyperparameters["batch_size"],
-                 hyperparameters["dropout"],
-                 hyperparameters["stddev"]))
-    if accuracy > best_accuracy:
-      best_hyperparameters = hyperparameters
-      best_accuracy = accuracy
+    # Fetch and print the results of the tasks in the order that they complete.
+    for i in range(trials):
+        # Use ray.wait to get the object ID of the first task that completes.
+        ready_ids, remaining_ids = ray.wait(remaining_ids)
+        # Process the output of this task.
+        result_id = ready_ids[0]
+        hyperparameters = hyperparameters_mapping[result_id]
+        accuracy, _ = ray.get(result_id)
+        print("""We achieve accuracy {:.3}% with
+            learning_rate: {:.2}
+            batch_size: {}
+            dropout: {:.2}
+            stddev: {:.2}
+          """.format(100 * accuracy,
+                     hyperparameters["learning_rate"],
+                     hyperparameters["batch_size"],
+                     hyperparameters["dropout"],
+                     hyperparameters["stddev"]))
+        if accuracy > best_accuracy:
+            best_hyperparameters = hyperparameters
+            best_accuracy = accuracy

-  # Record the best performing set of hyperparameters.
-  print("""Best accuracy over {} trials was {:.3} with
-        learning_rate: {:.2}
-        batch_size: {}
-        dropout: {:.2}
-        stddev: {:.2}
-    """.format(trials, 100 * best_accuracy,
-               best_hyperparameters["learning_rate"],
-               best_hyperparameters["batch_size"],
-               best_hyperparameters["dropout"],
-               best_hyperparameters["stddev"]))
+    # Record the best performing set of hyperparameters.
+    print("""Best accuracy over {} trials was {:.3} with
+          learning_rate: {:.2}
+          batch_size: {}
+          dropout: {:.2}
+          stddev: {:.2}
+      """.format(trials, 100 * best_accuracy,
+                 best_hyperparameters["learning_rate"],
+                 best_hyperparameters["batch_size"],
+                 best_hyperparameters["dropout"],
+                 best_hyperparameters["stddev"]))