Board rep can now be inferred from file after being given once.

We can also evaluate multiple times by using the flag "--repeat-eval".
The flag defaults to 1, if not provided.

main.py

@@ -34,14 +34,15 @@ parser.add_argument('--list-models', action='store_true',
 parser.add_argument('--force-creation', action='store_true',
                     help='force model creation if model does not exist')
 parser.add_argument('--board-rep', action='store', dest='board_rep',
-                    default='tesauro',
                     help='name of board representation to use as input to neural network')
 parser.add_argument('--use-baseline', action='store_true',
                     help='use the baseline model, note, has size 28')
 parser.add_argument('--verbose', action='store_true',
                     help='If set, a lot of stuff will be printed')
-parser.add_argument('--ply', action='store', dest='ply',
+parser.add_argument('--ply', action='store', dest='ply', default='0',
                     help='defines the amount of ply used when deciding what move to make')
+parser.add_argument('--repeat-eval', action='store', dest='repeat_eval', default='1',
+                    help='the amount of times the evaluation method should be repeated')
 
 args = parser.parse_args()
 
@@ -67,10 +68,11 @@ config = {
     'use_baseline': args.use_baseline,
     'global_step': 0,
     'verbose': args.verbose,
-    'ply': args.ply
-
+    'ply': args.ply,
+    'repeat_eval': args.repeat_eval
 }
 
+
 # Create models folder
 if not os.path.exists(config['model_storage_path']):
     os.makedirs(config['model_storage_path'])
@@ -133,6 +135,24 @@ def log_bench_eval_outcomes(outcomes, log_path, index, time, trained_eps = 0):
         with open(log_path, 'a+') as f:
             f.write("{method};{count};{index};{time};{sum};{mean}".format(**format_vars) + "\n")
 
+def find_board_rep():
+    checkpoint_path = os.path.join(config['model_storage_path'], config['model'])
+    board_rep_path = os.path.join(checkpoint_path, "board_representation")
+    with open(board_rep_path, 'r') as f:
+        return f.read()
+
+
+def board_rep_file_exists():
+    checkpoint_path = os.path.join(config['model_storage_path'], config['model'])
+    board_rep_path = os.path.join(checkpoint_path, "board_representation")
+    return os.path.isfile(board_rep_path)
+
+def create_board_rep():
+    checkpoint_path = os.path.join(config['model_storage_path'], config['model'])
+    board_rep_path = os.path.join(checkpoint_path, "board_representation")
+    with open(board_rep_path, 'a+') as f:
+        f.write(config['board_representation'])
+
 # Do actions specified by command-line
 if args.list_models:
     def get_eps_trained(folder):
@@ -156,6 +176,22 @@ if __name__ == "__main__":
     # Set up variables
     episode_count = config['episode_count']
 
+    if config['board_representation'] is None:
+        if board_rep_file_exists():
+            config['board_representation'] = find_board_rep()
+        else:
+            sys.stderr.write("Was not given a board_rep and was unable to find a board_rep file\n")
+            exit()
+    else:
+        if not board_rep_file_exists():
+            create_board_rep()
+        else:
+            if config['board_representation'] != find_board_rep():
+                sys.stderr.write("Board representation \"{given}\", does not match one in board_rep file, \"{board_rep}\"\n".
+                                 format(given = config['board_representation'], board_rep = find_board_rep()))
+                exit()
+
+                  
     if args.train:
         network = Network(config, config['model'])
         start_episode = network.episodes_trained
@@ -172,12 +208,13 @@ if __name__ == "__main__":
             
     elif args.eval:
         network = Network(config, config['model'])
-        start_episode = network.episodes_trained
-        # Evaluation measures are described in `config`
-        outcomes = network.eval(config['episode_count'])
-        log_eval_outcomes(outcomes, trained_eps = start_episode)
-        # elif args.play:
-        # g.play(episodes = episode_count)
+        for i in range(int(config['repeat_eval'])):
+            start_episode = network.episodes_trained
+            # Evaluation measures are described in `config`
+            outcomes = network.eval(config['episode_count'])
+            log_eval_outcomes(outcomes, trained_eps = start_episode)
+            # elif args.play:
+            # g.play(episodes = episode_count)
 
         
     elif args.bench_eval_scores:

network.py

@@ -177,6 +177,7 @@ class Network:
         :return: Nothing. It's a side-effect that a model gets restored for the network.
         """
 
+
         if glob.glob(os.path.join(self.checkpoint_path, 'model.ckpt*.index')):
         
             latest_checkpoint = tf.train.latest_checkpoint(self.checkpoint_path)
@@ -235,9 +236,9 @@ class Network:
         :param player:
         :return:
         """
-        start = time.time()
+        # start = time.time()
         best_pair = self.calculate_1_ply(board, roll, player)
-        print(time.time() - start)
+        # print(time.time() - start)
         return best_pair