Refactored by Sourcery

network.py

@@ -106,10 +106,10 @@ def variables(self):
                              self.scope + '/')
 
   def assign(self, other):
-    copy_ops = []
-    for self_var, other_var in zip(self.variables, other.variables):
-      copy_ops.append(tf.assign(other_var, self_var))
-    return copy_ops
+    return [
+        tf.assign(other_var, self_var)
+        for self_var, other_var in zip(self.variables, other.variables)
+    ]
 
 
 class PolicyNetwork(BaseNetwork):

policy_training.py

@@ -141,8 +141,7 @@ def train_games(self, opponent, games):
 
   def process_results(self, opponent, games, step, summary):
     win_rate = np.mean([game.policy_player_score for game in games])
-    average_moves = sum([len(game.moves)
-                         for game in games]) / self.config.batch_size
+    average_moves = sum(len(game.moves) for game in games) / self.config.batch_size
 
     opponent_summary = tf.Summary()
     opponent_summary.value.add(
@@ -263,10 +262,7 @@ def move(self, move, policy_player_turn=False):
     self.positions.append(self.position)
     if self.position.gameover():
       self.result = self.position.result
-      if self.result:
-        self.policy_player_score = float(policy_player_turn)
-      else:
-        self.policy_player_score = 0.5
+      self.policy_player_score = float(policy_player_turn) if self.result else 0.5
 
 
 def main(_):

util.py

@@ -10,7 +10,7 @@ def find_previous_run(dir):
     if os.path.isdir(dir):
       runs = [child[4:] for child in os.listdir(dir) if child[:4] == 'run_']
       if runs:
-        return max([int(run) for run in runs])
+        return max(int(run) for run in runs)
 
     return 0