clean up imports (#230)

examples/hyperopt/driver.py

@@ -1,6 +1,5 @@
 import numpy as np
 import ray
-import ray.services as services
 import os
 
 import functions
@@ -11,7 +10,7 @@ epochs = 100
 
 worker_dir = os.path.dirname(os.path.abspath(__file__))
 worker_path = os.path.join(worker_dir, "worker.py")
-services.start_ray_local(num_workers=num_workers, worker_path=worker_path)
+ray.services.start_ray_local(num_workers=num_workers, worker_path=worker_path)
 
 best_params = None
 best_accuracy = 0

examples/hyperopt/functions.py

@@ -38,12 +38,12 @@ def train_cnn(params, epochs):
       sess.run(train_step, feed_dict={x: batch[0], y: batch[1], keep_prob: keep})
       if i % 100 == 0: # checks if accuracy is low enough to stop early every set number of epochs
         train_ac = accuracy.eval(feed_dict={x: batch[0], y: batch[1], keep_prob: 1.0})
-        if train_ac < 0.25: # Accuracy threshold is on a application to application basis. 
+        if train_ac < 0.25: # Accuracy threshold is on a application to application basis.
           totalacc = accuracy.eval(feed_dict={x: mnist.validation.images, y: mnist.validation.labels, keep_prob: 1.0})
           return totalacc
     totalacc = accuracy.eval(feed_dict={x: mnist.validation.images, y: mnist.validation.labels, keep_prob: 1.0})
-  return totalacc
-  
+  return totalacc.astype("float64")
+
 def cnn_setup(x, y, keep_prob, lr, stddev):
   first_hidden = 32
   second_hidden = 64

examples/hyperopt/worker.py

@@ -1,6 +1,5 @@
 import argparse
 import ray
-import ray.worker as worker
 
 import functions
 
@@ -13,4 +12,4 @@ if __name__ == "__main__":
   args = parser.parse_args()
   ray.connect(args.scheduler_address, args.objstore_address, args.worker_address)
   ray.register_module(functions)
-  worker.main_loop()
+  ray.worker.main_loop()