clean up

README.org

@@ -42,6 +42,10 @@ The following examples describe commmon operations.
 
 =python3 --train=
 
+*** Train perpetually
+
+=python3 --train --train-perpetually=
+
 *** Train model named =quack=
 
 =python3 --train --model=quack=

game.py

@@ -81,9 +81,20 @@ class Game:
             
 
     def train_model(self, episodes=1000, save_step_size = 100, trained_eps = 0):
+        start_time = time.time()
+        def print_time_estimate(eps_completed):
+            cur_time      = time.time()
+            time_diff     = cur_time - start_time
+            eps_per_sec   = eps_completed / time_diff
+            secs_per_ep   = time_diff / eps_completed
+            eps_remaining = (episodes - eps_completed)
+            sys.stderr.write("[TRAIN] Averaging {per_sec} episodes per second\n".format(per_sec = round(eps_per_sec, 2)))
+            sys.stderr.write("[TRAIN] {eps_remaining} episodes remaining; approx. {time_remaining} seconds remaining\n".format(eps_remaining = eps_remaining, time_remaining = int(eps_remaining * secs_per_ep)))
+
+        
         sys.stderr.write("[TRAIN] Training {} episodes and save_step_size {}\n".format(episodes, save_step_size))
         outcomes = []
-        for episode in range(episodes):
+        for episode in range(1, episodes + 1):
             sys.stderr.write("[TRAIN] Episode {}".format(episode + trained_eps))
             self.board = Board.initial_state
 
@@ -114,6 +125,8 @@ class Game:
                 sys.stderr.write("[TRAIN] Loading model for training opponent...\n")
                 self.p2.restore_model()
 
+            if episode % 50 == 0:
+                print_time_estimate(episode)
 
         sys.stderr.write("[TRAIN] Saving model for final episode...\n")
         self.p1.get_network().save_model(episode+trained_eps)

main.py

@@ -3,11 +3,11 @@ import sys
 import os
 import time
 
-models_storage_path = 'models'
+model_storage_path = 'models'
 
 # Create models folder
-if not os.path.exists(models_storage_path):
+if not os.path.exists(model_storage_path):
-    os.makedirs(models_storage_path)
+    os.makedirs(model_storage_path)
 
 # Define helper functions
 def log_train_outcome(outcome, trained_eps = 0):
@@ -57,18 +57,23 @@ parser.add_argument('--play', action='store_true',
 parser.add_argument('--start-episode', action='store', dest='start_episode',
                     type=int, default=0,
                     help='episode count to start at; purely for display purposes')
+parser.add_argument('--train-perpetually', action='store_true',
+                    help='start new training session as soon as the previous is finished')
+parser.add_argument('--list-models', action='store_true',
+                    help='list all known models')
 
 args = parser.parse_args()
 
 config = {
-    'model_path': os.path.join(models_storage_path, args.model),
+    'model_path': os.path.join(model_storage_path, args.model),
     'episode_count': args.episode_count,
     'eval_methods': args.eval_methods,
     'train': args.train,
     'play': args.play,
     'eval': args.eval,
     'eval_after_train': args.eval_after_train,
-    'start_episode': args.start_episode
+    'start_episode': args.start_episode,
+    'train_perpetually': args.train_perpetually
 }
 
 # Make sure directories exist
@@ -91,7 +96,20 @@ episode_count = config['episode_count']
 
 
 # Do actions specified by command-line
-if args.train:
+if args.list_models:
+    def get_eps_trained(folder):
+        with open(os.path.join(folder, 'episodes_trained'), 'r') as f:
+            return int(f.read())
+    model_folders = [ f.path
+                      for f
+                      in os.scandir(model_storage_path)
+                      if f.is_dir() ]
+    models = [ (folder, get_eps_trained(folder)) for folder in model_folders ]
+    sys.stderr.write("Found {} model(s)\n".format(len(models)))
+    for model in models:
+        sys.stderr.write("  {name}: {eps_trained}\n".format(name = model[0], eps_trained = model[1]))
+                  
+elif args.train:
     eps = config['start_episode']
     while True:
         train_outcome = g.train_model(episodes = episode_count, trained_eps = eps)
@@ -100,6 +118,8 @@ if args.train:
         if config['eval_after_train']:
             eval_outcomes = g.eval(trained_eps = eps)
             log_eval_outcomes(eval_outcomes, trained_eps = eps)
+        if not config['train_perpetually']:
+            break
 elif args.eval:
     eps = config['start_episode']
     outcomes = g.eval()

plot.py

@@ -44,8 +44,7 @@ if __name__ == '__main__':
     plt.show()
 
     while True:
-        df = pd.read_csv('models/c/logs/eval.log', sep=";", names=eval_headers)
+        df = dataframes('default')['eval']
-        df['timestamp'] = df['timestamp'].map(lambda t: datetime.datetime.fromtimestamp(t))
 
         print(df)
         

requirements.txt

@@ -6,7 +6,6 @@ grpcio==1.10.0
 html5lib==0.9999999
 Markdown==2.6.11
 numpy==1.14.1
-pkg-resources==0.0.0
 protobuf==3.5.1
 six==1.11.0
 tensorboard==1.6.0