diff --git a/network.py b/network.py
index 6358761..6ceefbe 100644
--- a/network.py
+++ b/network.py
@@ -152,8 +152,8 @@ class Network:
         # print("Found the best state, being:", np.array(move_scores).argmax())
         return best_move_pair
 
-    def eval(self, trained_eps=0):
-        def do_eval(sess, method, episodes=1000, trained_eps=trained_eps):
+    def eval(self, episode_count, trained_eps = 0, tf_session = None):
+        def do_eval(sess, method, episodes = 1000, trained_eps = 0):
             start_time = time.time()
 
             def print_time_estimate(eps_completed):
@@ -290,17 +290,26 @@ class Network:
             else:
                 sys.stderr.write("[EVAL ] Evaluation method '{}' is not defined\n".format(method))
                 return [0]
-
-        with tf.Session() as session:
-            session.run(tf.global_variables_initializer())
-            self.restore_model(session)
-            outcomes = [(method, do_eval(session,
-                                         method,
-                                         self.config['episode_count'],
-                                         trained_eps=trained_eps))
-                        for method
-                        in self.config['eval_methods']]
-        return outcomes
+            
+        if tf_session == None:
+            with tf.Session():
+                session.run(tf.global_variables_initializer())
+                self.restore_model(session)
+                outcomes = [ (method, do_eval(session,
+                                              method,
+                                              episode_count,
+                                              trained_eps = trained_eps))
+                             for method
+                             in self.config['eval_methods'] ]
+                return outcomes
+        else:
+            outcomes = [ (method, do_eval(tf_session,
+                                          method,
+                                          episode_count,
+                                          trained_eps = trained_eps))
+                         for method
+                         in self.config['eval_methods'] ]
+            return outcomes
 
     def train_model(self, episodes=1000, save_step_size=100, trained_eps=0):
         with tf.Session() as sess:
@@ -401,43 +410,3 @@ class Network:
                 # save the current state again, so we can continue running backprop based on the "previous" turn.
 
         # NOTE: We need to make a method so that we can take a single turn or at least just pick the next best move, so we know how to evaluate according to TD-learning. Right now, our game just continues in a while loop without nothing to stop it!
-        
-
-
-    def eval(self, episode_count, trained_eps = 0, tf_session = None):
-        def do_eval(sess, method, episodes = 1000, trained_eps = 0):
-            start_time = time.time()
-
-            writer.close()
-
-            return outcomes
-
-            # take turn, which finds the best state and picks it, based on the current network
-            # save current state
-            # run training operation (session.run(self.training_op, {x:x, value_next, value_next})),
-            #  (something which does the backprop, based on the state after having taken a turn,
-            # found before, and the state we saved in the beginning and from now we'll
-            #  save it at the end of the turn
-
-            # save the current state again, so we can continue running backprop based on the "previous" turn.
-
-
-        if tf_session == None:
-            with tf.Session():
-                session.run(tf.global_variables_initializer())
-                self.restore_model(session)
-                outcomes = [ (method, do_eval(session,
-                                              method,
-                                              episode_count,
-                                              trained_eps = trained_eps))
-                             for method
-                             in self.config['eval_methods'] ]
-                return outcomes
-        else:
-            outcomes = [ (method, do_eval(tf_session,
-                                          method,
-                                          episode_count,
-                                          trained_eps = trained_eps))
-                         for method
-                         in self.config['eval_methods'] ]
-            return outcomes