Cleanup imports.

Till now, most of the modules were importing all of the module objects
(variables, classes, functions, other imports) into module namespace,
which potentially could (and was) cause of unintentional use of class or
methods, which was indirect imported.

With this patch, all the star imports were substituted with top-level
module, which provides desired class or function.

Besides, all imports where sorted (where possible) in a way pep8[1]
suggests - first are imports from standard library, than goes third
party imports (like numpy, tensorflow etc) and finally coach modules.
All of those sections are separated by one empty line.

[1] https://www.python.org/dev/peps/pep-0008/#imports

architectures/neon_components/architecture.py

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2017 Intel Corporation 
+# Copyright (c) 2017 Intel Corporation
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -13,19 +13,16 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
-
-import sys
-import copy
-from ngraph.frontends.neon import *
 import ngraph as ng
-from architectures.architecture import *
 import numpy as np
-from utils import *
+
+from architectures import architecture
+import utils
 
 
-class NeonArchitecture(Architecture):
+class NeonArchitecture(architecture.Architecture):
     def __init__(self, tuning_parameters, name="", global_network=None, network_is_local=True):
-        Architecture.__init__(self, tuning_parameters, name)
+        architecture.Architecture.__init__(self, tuning_parameters, name)
         assert tuning_parameters.agent.neon_support, 'Neon is not supported for this agent'
         self.clip_error = tuning_parameters.clip_gradients
         self.total_loss = None
@@ -113,8 +110,8 @@ class NeonArchitecture(Architecture):
     def accumulate_gradients(self, inputs, targets):
         # Neon doesn't currently allow separating the grads calculation and grad apply operations
         # so this feature is not currently available. instead we do a full training iteration
-        inputs = force_list(inputs)
-        targets = force_list(targets)
+        inputs = utils.force_list(inputs)
+        targets = utils.force_list(targets)
 
         for idx, input in enumerate(inputs):
             inputs[idx] = input.swapaxes(0, -1)