diff --git a/board.py b/board.py
index c341a7a..c32340d 100644
--- a/board.py
+++ b/board.py
@@ -268,23 +268,13 @@ class Board:
         # print("Dice permuts:",dice_permutations)
         for roll in dice_permutations:
             # Calculate boards resulting from first move
-            #print("initial board: ", board)
-            #print("roll:", roll)
-            #print("Rest of roll:",roll[1:])
             boards = calc_moves(board, roll[0])
-            #print("Boards:",boards)
-            #print("Roll:",roll[0])
-            #print("boards after first die: ", boards)
 
             for die in roll[1:]:
                 # Calculate boards resulting from second move
                 nested_boards = [calc_moves(board, die) for board in boards]
-                #print("nested boards: ", nested_boards)
                 boards = [board for boards in nested_boards for board in boards]
-                # What the fuck
-                #for board in boards:
-                #    print(board)
-                #    print("type__:",type(board))
+
                 # Add resulting unique boards to set of legal boards resulting from roll
 
                 #print("printing boards from calculate_legal_states: ", boards)
diff --git a/network.py b/network.py
index 1dbbbc1..a1b5149 100644
--- a/network.py
+++ b/network.py
@@ -114,15 +114,14 @@ class Network:
 
         with tf.GradientTape() as tape:
             value = self.model(prev_state.reshape(1,-1))
+
         grads = tape.gradient(value, self.model.variables)
 
         difference_in_values = tf.reshape(tf.subtract(value_next, value, name='difference_in_values'), [])
-        tf.summary.scalar("difference_in_values", tf.abs(difference_in_values))
 
-        with tf.variable_scope('apply_gradients'):
-            for grad, train_var in zip(grads, self.model.variables):
-                backprop_calc = self.learning_rate * difference_in_values * grad
-                train_var.assign_add(backprop_calc)
+        for grad, train_var in zip(grads, self.model.variables):
+            backprop_calc = self.learning_rate * difference_in_values * grad
+            train_var.assign_add(backprop_calc)
 
 
 
@@ -299,7 +298,7 @@ class Network:
         length_list = []
         test_list = []
         # Prepping of data
-        start = time.time()
+        # start = time.time()
         for board in boards:
             length = 0
             for roll in all_rolls:
@@ -478,7 +477,6 @@ class Network:
         for episode in range(1, episodes + 1):
 
             sys.stderr.write("[TRAIN] Episode {}".format(episode + trained_eps))
-            # TODO decide which player should be here
 
             # player = 1
             player = random.choice([-1,1])