Added a verbosity flag, --verbose, which allows for printing of

variables and such.

main.py

@@ -38,6 +38,8 @@ parser.add_argument('--board-rep', action='store', dest='board_rep',
                     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')
 
 args = parser.parse_args()
 
@@ -61,7 +63,9 @@ config = {
     'board_representation': args.board_rep,
     'force_creation': args.force_creation,
     'use_baseline': args.use_baseline,
-    'global_step': 0
+    'global_step': 0,
+    'verbose': args.verbose
+
 }
 
 # Create models folder

network.py

@@ -98,10 +98,20 @@ class Network:
 
 
 
+    def print_variables(self):
+        variables = self.model.variables
+
+        for k in variables:
+            print(k)
+
     def eval_state(self, state):
         return self.model(state.reshape(1,-1))
 
     def save_model(self, episode_count):
+        """
+        :param episode_count:
+        :return:
+        """
         tfe.Saver(self.model.variables).save(os.path.join(self.checkpoint_path, 'model.ckpt'))
         #self.saver.save(sess, os.path.join(self.checkpoint_path, 'model.ckpt'), global_step=global_step)
         with open(os.path.join(self.checkpoint_path, "episodes_trained"), 'w+') as f:
@@ -113,6 +123,8 @@ class Network:
             print("[NETWK] ({name}) Saving global step to:".format(name=self.name),
                   os.path.join(self.checkpoint_path, 'model.ckpt'))
             f.write(str(self.global_step) + "\n")
+        if self.config['verbose']:
+            self.print_variables()
 
 
     def calc_vals(self, states):
@@ -150,6 +162,8 @@ class Network:
                 with open(global_step_path, 'r') as f:
                     self.config['global_step'] = int(f.read())
 
+            if self.config['verbose']:
+                self.print_variables()
 
 
 
@@ -489,19 +503,13 @@ class Network:
                      in self.config['eval_methods'] ]
         return outcomes
 
+
     def train_model(self, episodes=1000, save_step_size=100, trained_eps=0):
         with tf.Session() as sess:
             difference_in_vals = 0
 
             self.restore_model()
 
-            #variables_names = [v.name for v in tf.trainable_variables()]
-            #values = sess.run(variables_names)
-            #for k, v in zip(variables_names, values):
-            #    print("Variable: ", k)
-            #    print("Shape: ", v.shape)
-            #    print(v)
-
             start_time = time.time()
 
             def print_time_estimate(eps_completed):
@@ -537,9 +545,13 @@ class Network:
 
                     difference_in_vals += abs((cur_board_value - self.eval_state(self.board_trans_func(prev_board, player))))
 
+                    if self.config['verbose']:
+                        print("Difference in values:", difference_in_vals)
+                        print("Current board value :", cur_board_value)
+                        print("Current board is    :\n",cur_board)
+
 
                     # adjust weights
-                    #print(cur_board)
                     if Board.outcome(cur_board) is None:
                         self.do_backprop(self.board_trans_func(prev_board, player), cur_board_value)
                         player *= -1