Pull NNI construction into create_neural_net

mloop/learners.py

@@ -1670,6 +1670,13 @@ def __init__(self,
         #Remove logger so gaussian process can be safely picked for multiprocessing on Windows
         self.log = None
 
+    def create_neural_net(self):
+        '''
+        Creates the neural net. Must be called from the same process as fit_neural_net, predict_cost and predict_costs_from_param_array.
+        '''
+        import mloop.nnlearner as mlnn
+        self.neural_net_impl = mlnn.NeuralNetImpl(self.num_params)
+
     def fit_neural_net(self):
         '''
         Determine the appropriate number of layers for the NN given the data.
@@ -1871,8 +1878,7 @@ def run(self):
         self.log = mp.log_to_stderr(logging.WARNING)
 
         # The network needs to be created in the same process in which it runs
-        import mloop.nnlearner as mlnn
-        self.neural_net_impl = mlnn.NeuralNetImpl(self.num_params)
+        self.create_neural_net()
 
         try:
             while not self.end_event.is_set():