fix in saved training examples

training.py

@@ -118,23 +118,29 @@ def execute_episode(board_size, neural_network, degree_exploration, num_simulati
 
         policy = mcts.get_policy_action_probabilities(state, policy_temperature)
 
-        if board_view_type == BoardView.ONE_CHANNEL:
-            example = game.board(BoardView.ONE_CHANNEL), policy, game.current_player
-        else:
-            example = state, policy, game.current_player
-        examples.append(example)
-
         #e-greedy
         coin = random.random()
         if coin <= e_greedy:
             action = np.argwhere(policy == policy.max())[0]
         else:
             action = mcts.get_state_actions(state)[np.random.choice(len(mcts.get_state_actions(state)))]
+
+
+        action_choosed = np.zeros((board_size, board_size))
+        action_choosed[action[0]][action[1]] = 1
+
+        #save examples
+        if board_view_type == BoardView.ONE_CHANNEL:
+            example = game.board(BoardView.ONE_CHANNEL), action_choosed, game.current_player
+        else:
+            example = state, action_choosed, game.current_player
+        examples.append(example)
 
         game.play(*action)
 
     logging.info(game.board(BoardView.ONE_CHANNEL))
     winner, winner_points = game.get_winning_player()
+    logging.info(f'The Winner obtained: {winner_points} points.')
 
     return [(state, policy, 1 if winner == player else -1) for state, policy, player in examples]
 
@@ -197,8 +203,8 @@ def training(board_size, num_iterations, num_episodes, num_simulations, degree_e
 
     total_episodes_done = 0
     historic = []
+    training_examples = []
     for i in range(1, num_iterations + 1):
-        training_examples = []
         old_neural_network = neural_network.copy()
 
         logging.info(f'Iteration {i}/{num_iterations}: Starting iteration')
@@ -233,6 +239,7 @@ def training(board_size, num_iterations, num_episodes, num_simulations, degree_e
 
         logging.info(f'Iteration {i}/{num_iterations}: Training model with episodes examples')
 
+        random.shuffle(training_examples)
         history = neural_network.train(training_examples, verbose=training_verbose)
 
 
@@ -276,6 +283,8 @@ def training(board_size, num_iterations, num_episodes, num_simulations, degree_e
             else:
                 neural_network = old_neural_network
 
+        # gambiarra
+        neural_network.save_checkpoint(checkpoint_filepath)
 
         if (i % random_agent_interval) == 0:
             color = [OthelloPlayer.BLACK, OthelloPlayer.WHITE]
@@ -309,6 +318,8 @@ def training(board_size, num_iterations, num_episodes, num_simulations, degree_e
 
             with open(f'historic-last-training-session-{board_size}.txt', 'w') as output:
                 output.write(str(historic))
+            with open(f'examples-{board_size}.txt', 'w') as output:
+                output.write(str(training_examples))