Get NN working a bit better on the tests

.travis.yml

@@ -11,5 +11,5 @@ install:
 script: python setup.py test
 os:
   - linux
-  
-  
+# Need trusty+ for tensorflow
+dist: trusty

mloop/controllers.py

@@ -675,41 +675,45 @@ def _optimization_routine(self):
         '''
         #Run the training runs using the standard optimization routine.
         self.log.debug('Starting training optimization.')
-        self.log.info('Run:' + str(self.num_in_costs +1))
+        self.log.info('Run:' + str(self.num_in_costs +1) + ' (training)')
         next_params = self._first_params()
         self._put_params_and_out_dict(next_params)
         self.save_archive()
         self._get_cost_and_in_dict()
         
         while (self.num_in_costs < self.num_training_runs) and self.check_end_conditions():
-            self.log.info('Run:' + str(self.num_in_costs +1))
+            self.log.info('Run:' + str(self.num_in_costs +1) + ' (training)')
             next_params = self._next_params()
             self._put_params_and_out_dict(next_params)
             self.save_archive()
             self._get_cost_and_in_dict()
         
         if self.check_end_conditions():
             #Start last training run
-            self.log.info('Run:' + str(self.num_in_costs +1))
+            self.log.info('Run:' + str(self.num_in_costs +1) + ' (training)')
             next_params = self._next_params()
             self._put_params_and_out_dict(next_params)
 
             self.log.debug('Starting ML optimization.')
-            self.new_params_event.set()
-            self.save_archive()
+            # TODO: This is a race. There's no guarantee that this will be available by the time the
+            # event is set.
             self._get_cost_and_in_dict()
+            self.save_archive()
+            self.new_params_event.set()
             self.log.debug('End training runs.')
 
             ml_consec = 0
             ml_count = 0
 
         while self.check_end_conditions():
-            self.log.info('Run:' + str(self.num_in_costs +1))
+            run_num = self.num_in_costs + 1
             if ml_consec==self.generation_num or (self.no_delay and self.ml_learner_params_queue.empty()):
+                self.log.info('Run:' + str(run_num) + ' (trainer)')
                 next_params = self._next_params()
                 self._put_params_and_out_dict(next_params)
                 ml_consec = 0
             else:
+                self.log.info('Run:' + str(run_num) + ' (machine learner)')
                 next_params = self.ml_learner_params_queue.get()
                 super(MachineLearnerController,self)._put_params_and_out_dict(next_params, param_type=self.machine_learner_type)
                 ml_consec += 1