diff --git a/board.py b/board.py
index d32197c..0233cf2 100644
--- a/board.py
+++ b/board.py
@@ -1,3 +1,4 @@
+import quack
 import numpy as np
 import itertools
 
@@ -12,11 +13,7 @@ class Board:
     
     @staticmethod
     def idxs_with_checkers_of_player(board, player):
-        idxs = []
-        for idx, checker_count in enumerate(board):
-            if checker_count * player >= 1:
-                idxs.append(idx)
-        return idxs
+        return quack.idxs_with_checkers_of_player(board, player)
 
 
     # TODO: Write a test for this
@@ -40,18 +37,19 @@ class Board:
     def board_features_quack(board, player):
         board = list(board)
         board += ([1, 0] if np.sign(player) > 0 else [0, 1])
-        return np.array(board).reshape(1, -1)
+        return np.array(board).reshape(1,28)
 
     # quack-fat
     @staticmethod
     def board_features_quack_fat(board, player):
-        board = list(board)
-        positives = [x if x > 0 else 0 for x in board]
-        negatives = [x if x < 0 else 0 for x in board]
-        board.append( 15 - sum(positives))
-        board.append(-15 - sum(negatives))
-        board += ([1, 0] if np.sign(player) > 0 else [0, 1])
-        return np.array(board).reshape(1,-1)
+        return np.array(quack.board_features_quack_fat(board,player)).reshape(1,30)
+        # board = list(board)
+        # positives = [x if x > 0 else 0 for x in board]
+        # negatives = [x if x < 0 else 0 for x in board]
+        # board.append( 15 - sum(positives))
+        # board.append(-15 - sum(negatives))
+        # board += ([1, 0] if np.sign(player) > 0 else [0, 1])
+        # return np.array(board).reshape(1,30)
 
 
     # quack-fatter
@@ -68,7 +66,7 @@ class Board:
         board.append(15 - sum(positives))
         board.append(-15 - sum(negatives))
         board += ([1, 0] if np.sign(player) > 0 else [0, 1])
-        return np.array(board).reshape(1, -1)
+        return np.array(board).reshape(1,30)
 
     # tesauro
     @staticmethod
@@ -124,98 +122,15 @@ class Board:
             # Calculate how many pieces there must be in the home state and divide it by 15
             features.append((15 - sum) / 15)
         features += ([1,0] if np.sign(cur_player) > 0 else [0,1])
-        test = np.array(features).reshape(1,-1)
+        test = np.array(features)
         #print("TEST:",test)
-        return test
+        return test.reshape(1,198)
 
 
 
     @staticmethod
     def is_move_valid(board, player, face_value, move):
-        if face_value == 0:
-            return True
-        else:
-            def sign(a):
-                return (a > 0) - (a < 0)
-
-            from_idx   = move[0]
-            to_idx     = move[1]
-            to_state   = None
-            from_state = board[from_idx]
-            delta      = to_idx - from_idx
-            direction  = sign(delta)
-            bearing_off = None
-
-            # FIXME: Use get instead of array-like indexing
-            if to_idx >= 1 and to_idx <= 24:
-                to_state   = board[to_idx]
-                bearing_off = False
-            else:  # Bearing off
-                to_state   = 0
-                bearing_off = True
-
-            # print("_"*20)
-            # print("board:", board)
-            # print("to_idx:", to_idx, "board[to_idx]:", board[to_idx], "to_state:", to_state)
-            # print("+"*20)
-        
-            def is_forward_move():
-                return direction == player
-
-            def face_value_match_move_length():
-                return abs(delta) == face_value
-
-            def bear_in_if_checker_on_bar():
-                if player == 1:
-                    bar = 0
-                else:
-                    bar = 25
-
-                bar_state = board[bar]
-
-                if bar_state != 0:
-                    return from_idx == bar
-                else:
-                    return True
-
-            def checkers_at_from_idx():
-                return sign(from_state) == player
-
-            def no_block_at_to_idx():
-                if -sign(to_state) == player:
-                    return abs(to_state) == 1
-                else:
-                    return True
-
-            def can_bear_off():
-                checker_idxs = Board.idxs_with_checkers_of_player(board, player)
-                def is_moving_backmost_checker():
-                    if player == 1:
-                        return all([(idx >= from_idx) for idx in checker_idxs])
-                    else:
-                        return all([(idx <= from_idx) for idx in checker_idxs])
-
-                def all_checkers_in_last_quadrant():
-                    if player == 1:
-                        return all([(idx >= 19) for idx in checker_idxs])
-                    else:
-                        return all([(idx <= 6) for idx in checker_idxs])
-
-                return all([ is_moving_backmost_checker(),
-                             all_checkers_in_last_quadrant() ])
-                
-            # TODO: add switch here instead of wonky ternary in all        
-            # print("is_forward:",is_forward_move())
-            # print("face_value:",face_value_match_move_length())
-            # print("Checkes_at_from:",checkers_at_from_idx())
-            # print("no_block:",no_block_at_to_idx())
-        
-            return all([ is_forward_move(),
-                         face_value_match_move_length(),
-                         bear_in_if_checker_on_bar(),
-                         checkers_at_from_idx(),
-                         no_block_at_to_idx(),
-                         can_bear_off() if bearing_off else True ])
+        return quack.is_move_valid(board, player, face_value, move)
 
     @staticmethod
     def any_move_valid(board, player, roll):
@@ -255,12 +170,27 @@ class Board:
 
 
     @staticmethod
-    def apply_moves_to_board(board, player, moves):
-        for move in moves:
-            from_idx, to_idx = move.split("/")
-            board[int(from_idx)] -= int(player)
-            board[int(to_idx)] += int(player)
-        return board
+    def apply_moves_to_board(board, player, move):
+        from_idx = move[0]
+        to_idx = move[1]
+        board = list(board)
+        board[from_idx] -= player
+
+        if (to_idx < 1 or to_idx > 24):
+            return
+
+        if (board[to_idx] * player == -1):
+
+            if (player == 1):
+                board[25] -= player
+            else:
+                board[0] -= player
+
+            board[to_idx] = 0
+
+        board[to_idx] += player
+
+        return tuple(board)
 
     @staticmethod
     def calculate_legal_states(board, player, roll):
@@ -271,24 +201,9 @@ class Board:
         #       turn and then do something with the second die
         
         def calc_moves(board, face_value):
-            idxs_with_checkers = Board.idxs_with_checkers_of_player(board, player)
-            if len(idxs_with_checkers) == 0:
+            if face_value == 0:
                 return [board]
-            boards = [(Board.do_move(board,
-                               player,
-                               (idx, idx + (face_value * player)))
-                       if Board.is_move_valid(board,
-                                              player,
-                                              face_value,
-                                              (idx, idx + (face_value * player)))
-                       else None)
-                      for idx in idxs_with_checkers]
-            # print("pls:",boards)
-            board_list = list(filter(None, boards))  # Remove None-values
-            # if len(board_list) == 0:
-            #     return [board]
-            # print("board list:", board_list)
-            return board_list
+            return quack.calc_moves(board, player, face_value)
 
         # Problem with cal_moves: Method can return empty list (should always contain at least same board).
         #               *Update*: Seems to be fixed.
@@ -302,12 +217,16 @@ class Board:
         if not Board.any_move_valid(board, player, roll):
             return { board }
         dice_permutations = list(itertools.permutations(roll)) if roll[0] != roll[1] else [[roll[0]]*4]
+        #print("Permuts:",dice_permutations)
         # print("Dice permuts:",dice_permutations)
         for roll in dice_permutations:
             # Calculate boards resulting from first move
             #print("initial board: ", board)
             #print("roll:", roll)
+            #print("Rest of roll:",roll[1:])
             boards = calc_moves(board, roll[0])
+            #print("Boards:",boards)
+            #print("Roll:",roll[0])
             #print("boards after first die: ", boards)
 
             for die in roll[1:]:
@@ -347,9 +266,9 @@ class Board:
         return """
   13  14  15  16  17  18               19  20  21  22  23  24
 +--------------------------------------------------------------------------+
-| {13}| {14}| {15}| {16}| {17}| {18}| bar -1: {25} | {19}| {20}| {21}| {22}| {23}| {24}| end -1: TODO|
+| {13}| {14}| {15}| {16}| {17}| {18}| bar -1: {25} | {19}| {20}| {21}| {22}| {23}| {24}| end  1: TODO|
 |---|---|---|---|---|---|------------|---|---|---|---|---|---|             |
-| {12}| {11}| {10}| {9}| {8}| {7}| bar  1: {0} | {6}| {5}| {4}| {3}| {2}| {1}| end  1: TODO|
+| {12}| {11}| {10}| {9}| {8}| {7}| bar  1: {0} | {6}| {5}| {4}| {3}| {2}| {1}| end -1: TODO|
 +--------------------------------------------------------------------------+
   12  11  10   9   8   7                6   5   4   3   2   1 
  """.format(*temp)
@@ -357,42 +276,8 @@ class Board:
     @staticmethod
     def do_move(board, player, move):
         # Implies that move is valid; make sure to check move validity before calling do_move(...)
-        
-        def move_to_bar(board, to_idx):
-            board = list(board)
-            if player == 1:
-                board[25] -= player
-            else:
-                board[0] -= player
-                
-            board[to_idx] = 0
-            return board
+        return quack.do_move(board, player, move)
 
-        # TODO: Moving in from bar is handled by the representation
-        # TODONE: Handle bearing off
-
-        from_idx = move[0]
-        #print("from_idx: ", from_idx)
-        to_idx = move[1]
-        #print("to_idx: ", to_idx)
-        # pdb.set_trace()
-        board = list(board) # Make mutable copy of board
-
-        # 'Lift' checker
-        board[from_idx] -= player
-
-        # Handle bearing off
-        if to_idx < 1 or to_idx > 24:
-            return tuple(board)
-        
-        # Handle hitting checkers
-        if board[to_idx] * player == -1:
-            board = move_to_bar(board, to_idx)
-
-        # Put down checker
-        board[to_idx] += player
-
-        return tuple(board)
 
     @staticmethod
     def flip(board):
diff --git a/bot.py b/bot.py
index 297f203..d1d74a6 100644
--- a/bot.py
+++ b/bot.py
@@ -1,24 +1,8 @@
-from cup import Cup
-from network import Network
 from board import Board
 
-import tensorflow as tf
-import numpy as np
-import random
-
 class Bot:
-    def __init__(self, sym, config = None, name = "unnamed"):
-        self.config = config
-        self.cup = Cup()
+    def __init__(self, sym):
         self.sym = sym
-        self.graph = tf.Graph()
-
-        self.network = Network(config, name)
-        self.network.restore_model()
-
-    def restore_model(self):
-        with self.graph.as_default():
-            self.network.restore_model()
 
     def get_session(self):
         return self.session
@@ -26,16 +10,60 @@ class Bot:
     def get_sym(self):
         return self.sym
 
-    def get_network(self):
-        return self.network
 
-    # TODO: DEPRECATE
-    def make_move(self, board, sym, roll):
-        # print(Board.pretty(board))
-        legal_moves = Board.calculate_legal_states(board, sym, roll)
-        moves_and_scores = [ (move, self.network.eval_state(np.array(move).reshape(1,26))) for move in legal_moves ]
-        scores = [ x[1] for x in moves_and_scores ]
-        best_move_pair = moves_and_scores[np.array(scores).argmax()]
-        #print("Found the best state, being:", np.array(move_scores).argmax())
-        return best_move_pair
+    def calc_move_sets(self, from_board, roll, player):
+        board = from_board
+        sets = []
+        total = 0
+        print("board!:",board)
+        for r in roll:
+            # print("Value of r:",r)
+            sets.append([Board.calculate_legal_states(board, player, [r,0]), r])
+            total += r
+        sets.append([Board.calculate_legal_states(board, player, [total,0]), total])
+        return sets
+
+
+    def handle_move(self, from_board, to_board, roll, player):
+
+        # print("Cur board:",board)
+        sets = self.calc_move_sets(from_board, roll, player)
+        for idx, board_set in enumerate(sets):
+            board_set[0] = list(board_set[0])
+            # print("My board_set:",board_set)
+            if to_board in [list(c) for c in board_set[0]]:
+                self.total_moves -= board_set[1]
+                if idx < 2:
+                    # print("Roll object:",self.roll)
+                    self.roll[idx] = 0
+                else:
+                    self.roll = [0,0]
+                break
+        print("Total moves left:",self.total_moves)
+
+
+    def tmp_name(self, from_board, to_board, roll, player, total_moves):
+        sets = self.calc_move_sets(from_board, roll, player)
+        return_board = from_board
+        for idx, board_set in enumerate(sets):
+            board_set = list(board_set[0])
+            if to_board in [list(board) for board in board_set]:
+                total_moves -= board_set[1]
+                # if it's not the sum of the moves
+                if idx < 2:
+                    roll[idx] = 0
+                else:
+                    roll = [0,0]
+                return_board = to_board
+                break
+        return total_moves, roll, return_board
+
+    def make_human_move(self, board, player, roll):
+        total_moves = roll[0] + roll[1]
+        previous_board = board
+        while total_moves != 0:
+            move = input("Pick a move!\n")
+            to_board = Board.apply_moves_to_board(previous_board, player, move)
+            total_moves, roll, board = self.tmp_name(board, to_board, roll, player, total_moves)
+
         
diff --git a/main.py b/main.py
index e2e8988..53b0444 100644
--- a/main.py
+++ b/main.py
@@ -31,19 +31,17 @@ 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')
-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', 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()
 
-if args.model == "baseline_model":
-    print("Model name 'baseline_model' not allowed")
-    exit()
 
 config = {
     'model': args.model,
@@ -59,10 +57,13 @@ config = {
     'model_storage_path': 'models',
     'bench_storage_path': 'bench',
     'board_representation': args.board_rep,
-    'force_creation': args.force_creation,
-    'use_baseline': args.use_baseline
+    'global_step': 0,
+    'verbose': args.verbose,
+    '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'])
@@ -77,6 +78,14 @@ if not os.path.isdir(log_path):
     os.mkdir(log_path)
 
 
+def save_config():
+    import yaml
+    # checkpoint_path = os.path.join(config['model_storage_path'], config['model'])
+    # config_path = os.path.join(checkpoint_path, 'config')
+    # with open(config_path, 'a+') as f:
+    #    print("lol")
+    print(yaml.dump(config))
+
 # Define helper functions
 def log_train_outcome(outcome, diff_in_values, trained_eps = 0, log_path = os.path.join(model_path(), 'logs', "train.log")):
     format_vars = { 'trained_eps': trained_eps,
@@ -125,6 +134,24 @@ 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):
@@ -145,8 +172,25 @@ if __name__ == "__main__":
     # Set up network
     from network import Network
 
+    save_config()
     # 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'])
@@ -161,15 +205,19 @@ if __name__ == "__main__":
             if not config['train_perpetually']:
                 break
 
-            
+    elif args.play:
+        network = Network(config, config['model'])
+        network.play_against_network()
+
     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:
@@ -191,7 +239,7 @@ if __name__ == "__main__":
         episode_counts = [25, 50, 100, 250, 500, 1000, 2500, 5000,
                           10000, 20000]
 
-        def do_eval(sess):
+        def do_eval():
             for eval_method in config['eval_methods']:
                 result_path = os.path.join(config['bench_storage_path'],
                                            eval_method) + "-{}.log".format(int(time.time()))
@@ -199,8 +247,7 @@ if __name__ == "__main__":
                     for i in range(sample_count):
                         start_time = time.time()
                         # Evaluation measure to be benchmarked are described in `config`
-                        outcomes = network.eval(episode_count = n,
-                                                tf_session = sess)
+                        outcomes = network.eval(episode_count = n)
                         time_diff = time.time() - start_time
                         log_bench_eval_outcomes(outcomes,
                                                 time = time_diff,
@@ -210,8 +257,8 @@ if __name__ == "__main__":
 
         # CMM: oh no
         import tensorflow as tf
-        with tf.Session() as session:
-            network.restore_model(session)
-            do_eval(session)
+
+        network.restore_model()
+        do_eval()
         
         
diff --git a/network.py b/network.py
index 84802e3..381197b 100644
--- a/network.py
+++ b/network.py
@@ -8,6 +8,8 @@ import random
 from eval import Eval
 import glob
 from operator import itemgetter
+import tensorflow.contrib.eager as tfe
+from player import Player
 
 class Network:
     # board_features_quack has size 28
@@ -15,21 +17,41 @@ class Network:
     # board_features_tesauro has size 198
 
     board_reps = {
-        'quack-fat' : (30, Board.board_features_quack_fat),
-        'quack'     : (28, Board.board_features_quack),
-        'tesauro'   : (198, Board.board_features_tesauro),
-        'quack-norm': (30, Board.board_features_quack_norm)
+        'quack-fat'   : (30, Board.board_features_quack_fat),
+        'quack'       : (28, Board.board_features_quack),
+        'tesauro'     : (198, Board.board_features_tesauro),
+        'quack-norm'  : (30, Board.board_features_quack_norm),
+        'tesauro-poop': (198, Board.board_features_tesauro_wrong)
     }
 
+
     def custom_tanh(self, x, name=None):
         return tf.scalar_mul(tf.constant(2.00), tf.tanh(x, name))
 
     def __init__(self, config, name):
+        """
+        :param config:
+        :param name:
+        """
+
+        move_options = {
+            '1': self.make_move_1_ply,
+            '0': self.make_move_0_ply
+        }
+
+        tf.enable_eager_execution()
+
+        xavier_init = tf.contrib.layers.xavier_initializer()
+
         self.config = config
         self.checkpoint_path = os.path.join(config['model_storage_path'], config['model'])
 
         self.name = name
 
+        self.make_move = move_options[
+            self.config['ply']
+        ]
+
         # Set board representation from config
         self.input_size, self.board_trans_func = Network.board_reps[
             self.config['board_representation']
@@ -39,16 +61,6 @@ class Network:
         self.max_learning_rate = 0.1
         self.min_learning_rate = 0.001
 
-        self.global_step = tf.Variable(0, trainable=False, name="global_step")
-        self.learning_rate = tf.maximum(self.min_learning_rate,
-                                        tf.train.exponential_decay(self.max_learning_rate,
-                                                                   self.global_step, 50000,
-                                                                   0.96,
-                                                                   staircase=True),
-                                        name="learning_rate")
-
-        
-        
         # Restore trained episode count for model
         episode_count_path = os.path.join(self.checkpoint_path, "episodes_trained")
         if os.path.isfile(episode_count_path):
@@ -57,62 +69,107 @@ class Network:
         else:
             self.episodes_trained = 0
 
-        self.x = tf.placeholder('float', [1, self.input_size], name='input')
-        self.value_next = tf.placeholder('float', [1, self.output_size], name="value_next")
-
-        xavier_init = tf.contrib.layers.xavier_initializer()
-
-        W_1 = tf.get_variable("w_1", (self.input_size, self.hidden_size),
-                              initializer=xavier_init)
-        W_2 = tf.get_variable("w_2", (self.hidden_size, self.output_size),
-                              initializer=xavier_init)
-
-        b_1 = tf.get_variable("b_1", (self.hidden_size,),
-                              initializer=tf.zeros_initializer)
-        b_2 = tf.get_variable("b_2", (self.output_size,),
-                              initializer=tf.zeros_initializer)
+        global_step_path = os.path.join(self.checkpoint_path, "global_step")
+        if os.path.isfile(global_step_path):
+            with open(global_step_path, 'r') as f:
+                self.global_step = int(f.read())
+        else:
+            self.global_step = 0
 
 
-        value_after_input = tf.sigmoid(tf.matmul(self.x, W_1) + b_1, name='hidden_layer')
+        self.model = tf.keras.Sequential([
+            tf.keras.layers.Dense(40, activation="sigmoid", kernel_initializer=xavier_init,
+                                  input_shape=(1,self.input_size)),
+            tf.keras.layers.Dense(1, activation="sigmoid", kernel_initializer=xavier_init)
+        ])
 
-        self.value = tf.sigmoid(tf.matmul(value_after_input, W_2) + b_2, name='output_layer')
 
-        # TODO: Alexander thinks that self.value will be computed twice (instead of once)
-        difference_in_values = tf.reshape(tf.subtract(self.value_next, self.value, name='difference_in_values'), [])
+    def exp_decay(self, max_lr, global_step, decay_rate, decay_steps):
+        """
+        Calculates the exponential decay on a learning rate
+        :param max_lr: The learning rate that the network starts at
+        :param global_step: The global step
+        :param decay_rate: The rate at which the learning rate should decay
+        :param decay_steps: The amount of steps between each decay
+        :return: The result of the exponential decay performed on the learning rate
+        """
+        res = max_lr * decay_rate**(global_step // decay_steps)
+        return res
+
+    def do_backprop(self, prev_state, value_next):
+        """
+        Performs the Temporal-difference backpropagation step on the model
+        :param prev_state: The previous state of the game, this has its value recalculated
+        :param value_next: The value of the current move
+        :return: Nothing, the calculation is performed on the model of the network
+        """
+        self.learning_rate = tf.maximum(self.min_learning_rate,
+                                         self.exp_decay(self.max_learning_rate, self.global_step, 0.96, 50000),
+                                         name="learning_rate")
+
+        with tf.GradientTape() as tape:
+            value = self.model(prev_state.reshape(1,-1))
+        grads = tape.gradient(value, self.model.variables)
+
+        difference_in_values = tf.reshape(tf.subtract(value_next, value, name='difference_in_values'), [])
         tf.summary.scalar("difference_in_values", tf.abs(difference_in_values))
 
-        trainable_vars = tf.trainable_variables()
-        gradients = tf.gradients(self.value, trainable_vars)
-
-        apply_gradients = []
-
-        global_step_op = self.global_step.assign_add(1)
-
-
         with tf.variable_scope('apply_gradients'):
-            for gradient, trainable_var in zip(gradients, trainable_vars):
-                backprop_calc = self.learning_rate * difference_in_values * gradient
-                grad_apply = trainable_var.assign_add(backprop_calc)
-                apply_gradients.append(grad_apply)
+            for grad, train_var in zip(grads, self.model.variables):
+                backprop_calc = self.learning_rate * difference_in_values * grad
+                train_var.assign_add(backprop_calc)
 
-        
-        with tf.control_dependencies([global_step_op]):
 
-            self.training_op = tf.group(*apply_gradients, name='training_op')
 
-        self.saver = tf.train.Saver(max_to_keep=1)
+    def print_variables(self):
+        """
+        Prints all the variables of the model
+        :return:
+        """
+        variables = self.model.variables
+        for k in variables:
+            print(k)
 
-    def eval_state(self, sess, state):
-        return sess.run(self.value, feed_dict={self.x: state})
+    def eval_state(self, state):
+        """
+        Evaluates a single state
+        :param state:
+        :return:
+        """
+        return self.model(state.reshape(1,-1))
 
-    def save_model(self, sess, episode_count, global_step):
-        self.saver.save(sess, os.path.join(self.checkpoint_path, 'model.ckpt'), global_step=global_step)
+    def save_model(self, episode_count):
+        """
+        Saves the model of the network, it references global_step as self.global_step
+        :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:
             print("[NETWK] ({name}) Saving model to:".format(name=self.name),
                   os.path.join(self.checkpoint_path, 'model.ckpt'))
             f.write(str(episode_count) + "\n")
 
-    def restore_model(self, sess):
+        with open(os.path.join(self.checkpoint_path, "global_step"), 'w+') as f:
+            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):
+        """
+        Calculate a score of each state in states
+        :param states: A number of states. The states have to be transformed before being given to this function.
+        :return:
+        """
+        values = self.model.predict_on_batch(states)
+        return values
+
+
+    def restore_model(self):
         """
         Restore a model for a session, such that a trained model and either be further trained or
         used for evaluation
@@ -121,47 +178,38 @@ 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)
             print("[NETWK] ({name}) Restoring model from:".format(name=self.name),
                   str(latest_checkpoint))
-            self.saver.restore(sess, latest_checkpoint)
-            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)
+            tfe.Saver(self.model.variables).restore(latest_checkpoint)
+
+            # variables_names = [v.name for v in self.model.variables]
+
 
             # Restore trained episode count for model
             episode_count_path = os.path.join(self.checkpoint_path, "episodes_trained")
             if os.path.isfile(episode_count_path):
                 with open(episode_count_path, 'r') as f:
                     self.config['start_episode'] = int(f.read())
-        elif self.config['use_baseline'] and glob.glob(os.path.join(os.path.join(self.config['model_storage_path'], "baseline_model"), 'model.ckpt*.index')):
-            checkpoint_path = os.path.join(self.config['model_storage_path'], "baseline_model")
-            latest_checkpoint = tf.train.latest_checkpoint(checkpoint_path)
-            print("[NETWK] ({name}) Restoring model from:".format(name=self.name),
-                  str(latest_checkpoint))
-            self.saver.restore(sess, latest_checkpoint)
 
-            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)
-        elif not self.config['force_creation']:
-            print("You need to have baseline_model inside models")
-            exit()
+            global_step_path = os.path.join(self.checkpoint_path, "global_step")
+            if os.path.isfile(global_step_path):
+                with open(global_step_path, 'r') as f:
+                    self.config['global_step'] = int(f.read())
+
+            if self.config['verbose']:
+                self.print_variables()
 
 
-    def make_move(self, sess, board, roll, player):
+
+    def make_move_0_ply(self, board, roll, player):
         """
         Find the best move given a board, roll and a player, by finding all possible states one can go to
-        and then picking the best, by using the network to evaluate each state. The highest score is picked
-        for the 1-player and the max(1-score) is picked for the -1-player.
+        and then picking the best, by using the network to evaluate each state. This is 0-ply, ie. no look-ahead.
+        The highest score is picked for the 1-player and the max(1-score) is picked for the -1-player.
 
         :param sess:
         :param board: Current board
@@ -169,23 +217,37 @@ class Network:
         :param player: Current player
         :return: A pair of the best state to go to, together with the score of that state
         """
-        legal_moves = Board.calculate_legal_states(board, player, roll)
-        moves_and_scores = [(move, self.eval_state(sess, self.board_trans_func(move, player))) for move in legal_moves]
-        scores = [x[1] if np.sign(player) > 0 else 1-x[1] for x in moves_and_scores]
-        best_score_index = np.array(scores).argmax()
-        best_move_pair = moves_and_scores[best_score_index]
-        return best_move_pair
+        legal_moves = list(Board.calculate_legal_states(board, player, roll))
+        legal_states = np.array([self.board_trans_func(move, player)[0] for move in legal_moves])
 
-    def make_move_n_ply(self, sess, board, roll, player, n = 1):
-        best_pair = self.calc_n_ply(n, sess, board, player, roll)
+        scores = self.model.predict_on_batch(legal_states)
+        transformed_scores = [x if np.sign(player) > 0 else 1 - x for x in scores]
+
+        best_score_idx = np.argmax(np.array(transformed_scores))
+        best_move = legal_moves[best_score_idx]
+        best_score = scores[best_score_idx]
+
+        return [best_move, best_score]
+
+    def make_move_1_ply(self, board, roll, player):
+        """
+        Return the best board and best score based on a 1-ply look-ahead.
+        :param board:
+        :param roll:
+        :param player:
+        :return:
+        """
+        # start = time.time()
+        best_pair = self.calculate_1_ply(board, roll, player)
+        # print(time.time() - start)
         return best_pair
 
 
-    def calculate_1_ply(self, sess, board, roll, player):
+    def calculate_1_ply(self, board, roll, player):
         """
-        Find the best move based on a 1-ply look-ahead. First the best move is found for a single ply and then an
-        exhaustive search is performed on the best 15 moves from the single ply.
-
+        Find the best move based on a 1-ply look-ahead. First the x best moves are picked from a 0-ply and then
+        all moves and scores are found for them. The expected score is then calculated for each of the boards from the
+        0-ply.
         :param sess:
         :param board:
         :param roll: The original roll
@@ -197,23 +259,91 @@ class Network:
         # find all legal states from the given board and the given roll
         init_legal_states = Board.calculate_legal_states(board, player, roll)
 
-        # find all values for the above boards
-        zero_ply_moves_and_scores = [(move, self.eval_state(sess, self.board_trans_func(move, player))) for move in init_legal_states]
+        legal_states = np.array([self.board_trans_func(state, player)[0] for state in init_legal_states])
 
-        # pythons reverse is in place and I can't call [:15] on it, without applying it to an object like so. Fuck.
-        best_fifteen = sorted(zero_ply_moves_and_scores, key=itemgetter(1), reverse=player==1)
+        scores = self.calc_vals(legal_states)
+        scores = [score.numpy() for score in scores]
 
-        best_fifteen_boards = [x[0] for x in best_fifteen[:10]]
+        moves_and_scores = list(zip(init_legal_states, scores))
 
-        all_rolls_scores = self.do_ply(sess, best_fifteen_boards, player)
+        sorted_moves_and_scores = sorted(moves_and_scores, key=itemgetter(1), reverse=player==1)
+
+        best_boards = [x[0] for x in sorted_moves_and_scores[:10]]
 
 
-        best_score_index = np.array(all_rolls_scores).argmax()
-        best_board = best_fifteen_boards[best_score_index]
 
-        return [best_board, max(all_rolls_scores)]
+        scores, trans_scores = self.do_ply(best_boards, player)
+
+        best_score_idx = np.array(trans_scores).argmax()
+
+        return [best_boards[best_score_idx], scores[best_score_idx]]
+
+    def do_ply(self, boards, player):
+        """
+        Calculates a single extra ply, resulting in a larger search space for our best move.
+        This is somewhat hardcoded to only do a single ply, seeing that it calls max on all scores, rather than
+        allowing the function to search deeper, which could result in an even larger search space. If we wish
+        to have more than 2-ply, this should be fixed, so we could extend this method to allow for 3-ply.
+
+        :param sess:
+        :param boards: The boards to try all rolls on
+        :param player: The player of the previous ply
+        :return: An array of scores where each index describes one of the boards which was given as param
+        to this function.
+        """
+
+        all_rolls = [ (1, 1), (1, 2), (1, 3), (1, 4), (1, 5),
+                      (1, 6), (2, 2), (2, 3), (2, 4), (2, 5),
+                      (2, 6), (3, 3), (3, 4), (3, 5), (3, 6),
+                      (4, 4), (4, 5), (4, 6), (5, 5), (5, 6),
+                      (6, 6) ]
+
+
+        # start = time.time()
+
+        # print("/"*50)
+        length_list = []
+        test_list = []
+        # Prepping of data
+        start= time.time()
+        for board in boards:
+            length = 0
+            for roll in all_rolls:
+                all_states = list(Board.calculate_legal_states(board, player*-1, roll))
+                for state in all_states:
+                    state = np.array(self.board_trans_func(state, player*-1)[0])
+                    test_list.append(state)
+                    length += 1
+            length_list.append(length)
+
+        # print(time.time() - start)
+
+        start = time.time()
+
+        all_scores_legit = self.model.predict_on_batch(np.array(test_list))
+
+        split_scores = []
+        from_idx = 0
+        for length in length_list:
+            split_scores.append(all_scores_legit[from_idx:from_idx+length])
+            from_idx += length
+
+        means_splits = [tf.reduce_mean(scores) for scores in split_scores]
+        transformed_means_splits = [x if player == 1 else (1-x) for x in means_splits]
+        # print(time.time() - start)
+
+        return ([means_splits, transformed_means_splits])
+
 
     def calc_n_ply(self, n_init, sess, board, player, roll):
+        """
+        :param n_init:
+        :param sess:
+        :param board:
+        :param player:
+        :param roll:
+        :return:
+        """
 
         # find all legal states from the given board and the given roll
         init_legal_states = Board.calculate_legal_states(board, player, roll)
@@ -233,6 +363,13 @@ class Network:
 
 
     def n_ply(self, n_init, sess, boards_init, player_init):
+        """
+        :param n_init:
+        :param sess:
+        :param boards_init:
+        :param player_init:
+        :return:
+        """
         def ply(n, boards, player):
             def calculate_possible_states(board):
                 possible_rolls = [ (1, 1), (1, 2), (1, 3), (1, 4), (1, 5),
@@ -324,69 +461,8 @@ class Network:
         best_score_pair = boards_with_scores[np.array(scores).argmax()]
         return best_score_pair
 
-    def do_ply(self, sess, boards, player):
-        """
-        Calculates a single extra ply, resulting in a larger search space for our best move.
-        This is somewhat hardcoded to only do a single ply, seeing that it calls max on all scores, rather than
-        allowing the function to search deeper, which could result in an even larger search space. If we wish
-        to have more than 2-ply, this should be fixed, so we could extend this method to allow for 3-ply.
 
-        :param sess:
-        :param boards: The boards to try all rolls on
-        :param player: The player of the previous ply
-        :return: An array of scores where each index describes one of the boards which was given as param
-        to this function.
-        """
-
-        def gen_21_rolls():
-            """
-            Calculate all possible rolls, [[1,1], [1,2] ..]
-            :return: All possible rolls
-            """
-            a = []
-            for x in range(1, 7):
-                for y in range(1, 7):
-                    if not [x, y] in a and not [y, x] in a:
-                        a.append([x, y])
-
-            return a
-
-        all_rolls = gen_21_rolls()
-
-        all_rolls_scores = []
-        count = 0
-        # loop over boards
-        for a_board in boards:
-            a_board_scores = []
-
-            # loop over all rolls, for each board
-            for roll in all_rolls:
-
-                # find all states we can get to, given the board and roll and the opposite player
-                all_rolls_boards = Board.calculate_legal_states(a_board, player*-1, roll)
-                count += len(all_rolls_boards)
-                # find scores for each board found above
-                spec_roll_scores = [self.eval_state(sess, self.board_trans_func(new_board, player*-1))
-                                    for new_board in all_rolls_boards]
-
-                # if the original player is the -1 player, then we need to find (1-value)
-                spec_roll_scores = [x if player == 1 else (1-x) for x in spec_roll_scores]
-
-                # find the best score
-                best_score = max(spec_roll_scores)
-
-                # append the best score to a_board_scores, where we keep track of the best score for each board
-                a_board_scores.append(best_score)
-
-            # save the expected average of board scores
-            all_rolls_scores.append(sum(a_board_scores)/len(a_board_scores))
-
-        # return all the average scores
-        print(count)
-        return all_rolls_scores
-
-
-    def eval(self, episode_count, trained_eps = 0, tf_session = None):
+    def eval(self, episode_count, trained_eps = 0):
         """
         Used to evaluate a model. Can either use pubeval, a model playing at an intermediate level, or dumbeval
         a model which has been given random weights, so it acts deterministically random.
@@ -397,7 +473,7 @@ class Network:
         :return: outcomes:    The outcomes of the evaluation session
         """
 
-        def do_eval(sess, method, episodes = 1000, trained_eps = 0):
+        def do_eval(method, episodes = 1000, trained_eps = 0):
             """
             Do the actual evaluation
 
@@ -434,7 +510,7 @@ class Network:
                     while Board.outcome(board) is None:
                         roll = (random.randrange(1, 7), random.randrange(1, 7))
 
-                        board = (self.make_move(sess, board, roll, 1))[0]
+                        board = (self.make_move(board, roll, 1))[0]
 
                         roll = (random.randrange(1, 7), random.randrange(1, 7))
 
@@ -457,7 +533,7 @@ class Network:
                     while Board.outcome(board) is None:
                         roll = (random.randrange(1, 7), random.randrange(1, 7))
 
-                        board = (self.make_move(sess, board, roll, 1))[0]
+                        board = (self.make_move(board, roll, 1))[0]
 
                         roll = (random.randrange(1, 7), random.randrange(1, 7))
 
@@ -476,112 +552,122 @@ class Network:
                 sys.stderr.write("[EVAL ] Evaluation method '{}' is not defined\n".format(method))
                 return [0]
             
-        if tf_session == None:
-            with tf.Session() as session:
-                session.run(tf.global_variables_initializer())
-                self.restore_model(session)
-                outcomes = [ (method, do_eval(session,
-                                              method,
-                                              episode_count,
-                                              trained_eps = trained_eps))
-                             for method
-                             in self.config['eval_methods'] ]
-                return outcomes
-        else:
-            outcomes = [ (method, do_eval(tf_session,
-                                          method,
-                                          episode_count,
-                                          trained_eps = trained_eps))
-                         for method
-                         in self.config['eval_methods'] ]
-            return outcomes
+
+        outcomes = [ (method, do_eval(method,
+                                      episode_count,
+                                      trained_eps = trained_eps))
+                     for method
+                     in self.config['eval_methods'] ]
+        return outcomes
+
+
+    def play_against_network(self):
+        """
+        Allows you to play against a supplied model.
+        :return:
+        """
+        self.restore_model()
+        human_player = Player(-1)
+        cur_player = 1
+        player = 1
+        board = Board.initial_state
+        i = 0
+        while Board.outcome(board) is None:
+            print(Board.pretty(board))
+            roll = (random.randrange(1, 7), random.randrange(1, 7))
+            print("Bot rolled:", roll)
+
+            board, _ = self.make_move(board, roll, player)
+            print(Board.pretty(board))
+            roll = (random.randrange(1, 7), random.randrange(1, 7))
+            print("You rolled:", roll)
+            board = human_player.make_human_move(board, roll)
+        print("DONE "*10)
+        print(Board.pretty(board))
+
+
 
     def train_model(self, episodes=1000, save_step_size=100, trained_eps=0):
-        with tf.Session() as sess:
-            difference_in_vals = 0
-            writer = tf.summary.FileWriter("/tmp/log/tf", sess.graph)
+        """
+        Train a model to by self-learning.
+        :param episodes:
+        :param save_step_size:
+        :param trained_eps:
+        :return:
+        """
 
-            sess.run(tf.global_variables_initializer())
-            self.restore_model(sess)
+        difference_in_vals = 0
 
-            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)
+        self.restore_model()
 
-            start_time = time.time()
+        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)))
+        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(1, episodes + 1):
+        sys.stderr.write("[TRAIN] Training {} episodes and save_step_size {}\n".format(episodes, save_step_size))
+        outcomes = []
+        for episode in range(1, episodes + 1):
 
-                sys.stderr.write("[TRAIN] Episode {}".format(episode + trained_eps))
-                # TODO decide which player should be here
+            sys.stderr.write("[TRAIN] Episode {}".format(episode + trained_eps))
+            # TODO decide which player should be here
 
-                player = 1
-                prev_board = Board.initial_state
-                i = 0
-                while Board.outcome(prev_board) is None:
-                    i += 1
-
-                    cur_board, cur_board_value = self.make_move(sess,
-                                                                prev_board,
-                                                                (random.randrange(1, 7), random.randrange(1, 7)),
-                                                                player)
-
-                    difference_in_vals += abs((cur_board_value - self.eval_state(sess, self.board_trans_func(prev_board, player))))
+            player = 1
+            prev_board = Board.initial_state
+            i = 0
+            while Board.outcome(prev_board) is None:
+                i += 1
+                self.global_step += 1
 
 
-                    # adjust weights
-                    sess.run(self.training_op,
-                             feed_dict={self.x: self.board_trans_func(prev_board, player),
-                                        self.value_next: cur_board_value})
+                cur_board, cur_board_value = self.make_move(prev_board,
+                                                            (random.randrange(1, 7), random.randrange(1, 7)),
+                                                            player)
 
+                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
+                if Board.outcome(cur_board) is None:
+                    self.do_backprop(self.board_trans_func(prev_board, player), cur_board_value)
                     player *= -1
 
-                    prev_board = cur_board
+                prev_board = cur_board
 
-                final_board = prev_board
-                sys.stderr.write("\t outcome {}\t turns {}".format(Board.outcome(final_board)[1], i))
-                outcomes.append(Board.outcome(final_board)[1])
-                final_score = np.array([Board.outcome(final_board)[1]])
-                scaled_final_score = ((final_score + 2) / 4)
+            final_board = prev_board
+            sys.stderr.write("\t outcome {}\t turns {}".format(Board.outcome(final_board)[1], i))
+            outcomes.append(Board.outcome(final_board)[1])
+            final_score = np.array([Board.outcome(final_board)[1]])
+            scaled_final_score = ((final_score + 2) / 4)
 
-                with tf.name_scope("final"):
-                    merged = tf.summary.merge_all()
-                    global_step, summary, _ = sess.run([self.global_step, merged, self.training_op],
-                                          feed_dict={self.x: self.board_trans_func(prev_board, player),
-                                                     self.value_next: scaled_final_score.reshape((1, 1))})
-                    writer.add_summary(summary, episode + trained_eps)
+            self.do_backprop(self.board_trans_func(prev_board, player), scaled_final_score.reshape(1,1))
 
-                sys.stderr.write("\n")
+            sys.stderr.write("\n")
 
-                if episode % min(save_step_size, episodes) == 0:
-                    sys.stderr.write("[TRAIN] Saving model...\n")
-                    self.save_model(sess, episode + trained_eps, global_step)
+            if episode % min(save_step_size, episodes) == 0:
+                sys.stderr.write("[TRAIN] Saving model...\n")
+                self.save_model(episode + trained_eps)
 
-                if episode % 50 == 0:
-                    print_time_estimate(episode)
+            if episode % 50 == 0:
+                print_time_estimate(episode)
 
-            sys.stderr.write("[TRAIN] Saving model for final episode...\n")
-            self.save_model(sess, episode+trained_eps, global_step)
-            
-            writer.close()
-        
-            return outcomes, difference_in_vals[0][0]
+        sys.stderr.write("[TRAIN] Saving model for final episode...\n")
+        self.save_model(episode+trained_eps)
+
+        return outcomes, difference_in_vals[0][0]
 
 
diff --git a/network_test.py b/network_test.py
index a514dfc..1bcb878 100644
--- a/network_test.py
+++ b/network_test.py
@@ -9,14 +9,12 @@ from board import Board
 import main
 
 config = main.config.copy()
-config['model'] = "tesauro_blah"
-config['force_creation'] = True
+config['model'] = "player_testings"
+config['ply'] = "1"
+config['board_representation'] = 'quack-fat'
 network = Network(config, config['model'])
 
-session = tf.Session()
-
-session.run(tf.global_variables_initializer())
-network.restore_model(session)
+network.restore_model()
 initial_state = Board.initial_state
 
 initial_state_1 = ( 0,
@@ -38,65 +36,25 @@ boards = {initial_state,
           initial_state_2 }
 
 
-def gen_21_rolls():
-    """
-    Calculate all possible rolls, [[1,1], [1,2] ..]
-    :return: All possible rolls
-    """
-    a = []
-    for x in range(1, 7):
-        for y in range(1, 7):
-            if not [x, y] in a and not [y, x] in a:
-                a.append([x, y])
-
-    return a
-
-def calc_all_scores(board, player):
-    scores = []
-    trans_board = network.board_trans_func(board, player)
-    rolls = gen_21_rolls()
-    for roll in rolls:
-        score = network.eval_state(session, trans_board)
-        scores.append(score)
-    return scores
-
-
-def calculate_possible_states(board):
-    possible_rolls = [(1, 1), (1, 2), (1, 3), (1, 4), (1, 5),
-                      (1, 6), (2, 2), (2, 3), (2, 4), (2, 5),
-                      (2, 6), (3, 3), (3, 4), (3, 5), (3, 6),
-                      (4, 4), (4, 5), (4, 6), (5, 5), (5, 6),
-                      (6, 6)]
-
-    for roll in possible_rolls:
-        meh = Board.calculate_legal_states(board, -1, roll)
-        print(len(meh))
-    return [Board.calculate_legal_states(board, -1, roll)
-            for roll
-            in possible_rolls]
 
 
 
-#for board in boards:
-#    calculate_possible_states(board)
 
-#print("-"*30)
-#print(network.calculate_1_ply(session, Board.initial_state, [2,4], 1))
+# board = network.board_trans_func(Board.initial_state, 1)
 
-#print(" "*10 + "network_test")
-print(" "*20 + "Depth 1")
-print(network.calc_n_ply(2, session, Board.initial_state, 1, [2, 4]))
 
-#print(scores)
+# pair = network.make_move(Board.initial_state, [3,2], 1)
 
-#print(" "*20 + "Depth 2")
-#print(network.n_ply(2, session, boards, 1))
+# print(pair[1])
 
-# #print(x.shape)
-# with graph_lol.as_default():
-#     session_2 = tf.Session(graph = graph_lol)
-#     network_2 = Network(session_2)
-#     network_2.restore_model()
-#     print(network_2.eval_state(initial_state))
-    
-# print(network.eval_state(initial_state))
+# network.do_backprop(board, 0.9)
+
+
+# network.print_variables()
+
+
+# network.save_model(2)
+
+# print(network.calculate_1_ply(Board.initial_state, [3,2], 1))
+
+network.play_against_network()
\ No newline at end of file
diff --git a/player.py b/player.py
index 596449f..4208cdd 100644
--- a/player.py
+++ b/player.py
@@ -11,19 +11,55 @@ class Player:
     def get_sym(self):
         return self.sym
     
-    def make_move(self, board, sym, roll):
-        print(Board.pretty(board))
-        legal_moves = Board.calculate_legal_states(board, sym, roll)
-        if roll[0] == roll[1]:
-            print("Example of move: 4/6,6/8,12/14,13/15")
-        else:
-            print("Example of move: 4/6,13/17")
+    def calc_move_sets(self, from_board, roll, player):
+        board = from_board
+        sets = []
+        total = 0
+        for r in roll:
+            # print("Value of r:",r)
+            sets.append([Board.calculate_legal_states(board, player, [r,0]), r])
+            total += r
+        sets.append([Board.calculate_legal_states(board, player, [total,0]), total])
+        return sets
 
-        user_moves = input("Enter your move: ").strip().split(",")
-        board = Board.apply_moves_to_board(board, sym, user_moves)
-        while board not in legal_moves:
-            print("Move is invalid, please enter a new move")
-            user_moves = input("Enter your move: ").strip().split(",")
-            board = Board.apply_moves_to_board(board, sym, user_moves)
 
-        return board
+    def tmp_name(self, from_board, to_board, roll, player, total_moves):
+        sets = self.calc_move_sets(from_board, roll, player)
+        return_board = from_board
+        for idx, board_set in enumerate(sets):
+
+            board_set[0] = list(board_set[0])
+            print(to_board)
+            print(board_set)
+            if to_board in board_set[0]:
+                total_moves -= board_set[1]
+                # if it's not the sum of the moves
+                if idx < 2:
+                    roll[idx] = 0
+                else:
+                    roll = [0,0]
+                return_board = to_board
+                break
+        return total_moves, roll, return_board
+
+    def make_human_move(self, board, roll):
+        total_moves = roll[0] + roll[1] if roll[0] != roll[1] else int(roll[0])*4
+        move = ""
+        while total_moves != 0:
+            while True:
+                print("You have {roll} left!".format(roll=total_moves))
+                move = input("Pick a move!\n")
+                pot_move = move.split("/")
+                if len(pot_move) == 2:
+                    try:
+                        pot_move[0] = int(pot_move[0])
+                        pot_move[1] = int(pot_move[1])
+                        move = pot_move
+                        break;
+                    except TypeError:
+                        print("The correct syntax is: 2/5 for a move from index 2 to 5.")
+
+            to_board = Board.apply_moves_to_board(board, self.get_sym(), move)
+            total_moves, roll, board = self.tmp_name(board, to_board, list(roll), self.get_sym(), total_moves)
+            print(Board.pretty(board))
+        return board
\ No newline at end of file
diff --git a/quack/quack.c b/quack/quack.c
new file mode 100644
index 0000000..213c49c
--- /dev/null
+++ b/quack/quack.c
@@ -0,0 +1,484 @@
+#include <Python.h>
+
+static PyObject* QuackError;
+
+typedef struct board_list board_list;
+struct board_list {
+  int size;
+  PyObject* list[16];
+};
+
+/* Utility functions */
+int sign(int x) {
+    return (x > 0) - (x < 0);
+}
+
+int abs(int x) {
+  if (x >= 0) return x;
+  else        return -x;
+}
+/* end utility functions */
+
+/* Helper functions */
+
+int *idxs_with_checkers_of_player(int board[], int player) {
+  int idxs_tmp[26];
+  int ctr = 0;
+  
+  for (int i = 0; i < 26; i++) {
+    if (board[i] * player >= 1) {
+      idxs_tmp[ctr] = i;
+      ctr++;
+    }
+  }
+
+  int *idxs = malloc((1 + ctr) * sizeof(int));
+  if (idxs == NULL) {
+    PyErr_NoMemory();
+    abort();
+  }
+
+  idxs[0] = ctr;
+  for (int i = 0; i < ctr; i++) {
+    idxs[i+1] = idxs_tmp[i];
+  }
+
+  return idxs;
+}
+
+int is_forward_move(int direction, int player) {
+  return direction == player;
+}
+
+int face_value_match_move_length(int delta, int face_value) {
+  return abs(delta) == face_value;
+}
+
+int bear_in_if_checker_on_bar(int board[], int player, int from_idx) {
+  int bar;
+    
+  if (player == 1) bar = 0;
+  else             bar = 25;
+
+  if (board[bar] != 0) return from_idx == bar;
+  else                 return 1;
+}
+
+int checkers_at_from_idx(int from_state, int player) {
+  return sign(from_state) == player;
+}
+
+int no_block_at_to_idx(int to_state, int player) {
+  if (-sign(to_state) == player) return abs(to_state) == 1;
+  else                           return 1;
+}
+
+
+int can_bear_off(int board[], int player, int from_idx, int to_idx) {
+  int* checker_idxs = idxs_with_checkers_of_player(board, player);
+
+  int moving_backmost_checker = 1;
+  int bearing_directly_off = 0;
+  int all_checkers_in_last_quadrant = 1;
+
+  /* Check if bearing directly off */
+  if      (player ==  1 && to_idx == 25) bearing_directly_off = 1;
+  else if (player == -1 && to_idx == 0)  bearing_directly_off = 1;
+  
+  for (int i = 1; i <= checker_idxs[0]; i++) {
+    if (player == 1 ) {
+      /* Check if all checkers are in last quardrant */
+      if (checker_idxs[i] < 19) {
+	all_checkers_in_last_quadrant = 0;
+	break;
+      }
+      
+      /* Check if moving backmost checker */
+      if (checker_idxs[i] < from_idx) {
+	moving_backmost_checker = 0;
+	if (!bearing_directly_off) break;
+      }
+    } else {
+      if (checker_idxs[i] > 6) {
+	all_checkers_in_last_quadrant = 0;
+	break;
+      }
+
+      if (checker_idxs[i] > from_idx) {
+	moving_backmost_checker = 0;
+	if (!bearing_directly_off) break;
+      }
+    }
+  }
+
+  free(checker_idxs);
+  
+  if (all_checkers_in_last_quadrant &&
+      (bearing_directly_off || moving_backmost_checker))  return 1;
+  else                                                    return 0;
+}
+
+
+
+/* end helper functions */
+
+int is_move_valid(int board[], int player, int face_value, int move[]) {
+  int from_idx = move[0];
+  int to_idx = move[1];
+  int to_state;
+  int from_state = board[from_idx];
+  int delta = to_idx - from_idx;
+  int direction = sign(delta);
+  int bearing_off;
+
+  if (to_idx >= 1 && to_idx <= 24) {
+    to_state = board[to_idx];
+    bearing_off = 0;
+  } else {
+    to_state = 0;
+    bearing_off = 1;
+  }
+  
+  return is_forward_move(direction, player)
+    && face_value_match_move_length(delta, face_value)
+    && bear_in_if_checker_on_bar(board, player, from_idx)
+    && checkers_at_from_idx(from_state, player)
+    && no_block_at_to_idx(to_state, player)
+    && (!bearing_off || can_bear_off(board, player, from_idx, to_idx))
+    ;
+}
+
+void do_move(int board[], int player, int move[]) {
+  int from_idx = move[0];
+  int to_idx   = move[1];
+
+  /* "lift" checker */
+  board[from_idx] -= player;
+
+  /* Return early if bearing off */
+  if (to_idx < 1 || to_idx > 24) return;
+
+  /* Hit opponent checker */
+  if (board[to_idx] * player == -1) {
+    /* Move checker to bar */
+    if (player == 1) board[25] -= player;
+    else             board[0]  -= player;
+
+    board[to_idx] = 0;
+  }
+
+  /* Put down checker */
+  board[to_idx] += player;
+
+  return;
+}
+
+int* do_move_clone(int board[], int player, int move[]) {
+  int* new_board = malloc(sizeof(int) * 26);
+  if (new_board == NULL) {
+    PyErr_NoMemory();
+    abort();
+  }
+  
+  for (int i = 0; i < 26; i++) {
+    new_board[i] = board[i];
+  }
+
+  do_move(new_board, player, move);
+  return new_board;
+}
+
+PyObject* store_board_to_pytuple(int board[], int size) {
+  PyObject* board_tuple = PyTuple_New(size);
+  for (int i = 0; i < size; i++) {
+    PyTuple_SetItem(board_tuple, i, Py_BuildValue("i", board[i]));
+  }
+  return board_tuple;
+}
+
+board_list calc_moves(int board[], int player, int face_value) {
+  int* checker_idxs = idxs_with_checkers_of_player(board, player);
+  board_list boards = { .size = 0 };
+  
+  if (checker_idxs[0] == 0) { 
+    boards.size = 1;
+    PyObject* board_tuple = store_board_to_pytuple(board, 26);
+    boards.list[0] = board_tuple;
+    free(checker_idxs);
+    return boards;
+  }
+
+  int ctr = 0;
+  for (int i = 1; i <= checker_idxs[0]; i++) {
+    int move[2];
+    move[0] = checker_idxs[i];
+    move[1] = checker_idxs[i] + (face_value * player);
+
+    if (is_move_valid(board, player, face_value, move)) {
+      int* new_board = do_move_clone(board, player, move);
+      PyObject* board_tuple = store_board_to_pytuple(new_board, 26);
+
+      // segfault maybe :'(
+      free(new_board);
+
+      boards.list[ctr] = board_tuple;
+      ctr++;
+    }
+  }
+
+  free(checker_idxs);
+  
+  boards.size = ctr;
+  return boards;
+}
+
+int* board_features_quack_fat(int board[], int player) {
+  int* new_board = malloc(sizeof(int) * 30);
+  if (new_board == NULL) {
+    PyErr_NoMemory();
+    abort();
+  }
+
+  int pos_sum = 0;
+  int neg_sum = 0;
+  for (int i = 0; i < 26; i++) {
+    new_board[i] = board[i];
+    if (sign(new_board[i] > 0)) pos_sum += new_board[i];
+    else                        neg_sum += new_board[i]; 
+  }
+
+  new_board[26] = 15 - pos_sum;
+  new_board[27] = -15 - neg_sum;
+  if (player == 1) {
+    new_board[28] = 1;
+    new_board[29] = 0;
+  } else {
+    new_board[28] = 0;
+    new_board[29] = 1;
+  }
+  
+  return new_board;
+}
+
+/* Meta definitions */
+int extract_board(int *board, PyObject* board_tuple_obj) {
+  long numValuesBoard;
+  numValuesBoard = PyTuple_Size(board_tuple_obj);
+  if (numValuesBoard != 26) {
+    PyErr_SetString(QuackError, "Board tuple must have 26 entries");
+    return 1;
+  }
+  
+  PyObject* board_val_obj;
+  // Iterate over tuple to retreive positions
+  for (int i=0; i<numValuesBoard; i++) {
+    board_val_obj = PyTuple_GetItem(board_tuple_obj, i);
+    board[i] = PyLong_AsLong(board_val_obj);
+  }
+  
+  return 0;
+}
+
+int extract_move(int *move, PyObject* move_tuple_obj) {
+  long numValuesMove;
+  numValuesMove = PyTuple_Size(move_tuple_obj);
+  if (numValuesMove != 2) {
+    PyErr_SetString(QuackError, "Move tuple must have exactly 2 entries");
+    return 1;
+  }
+  PyObject* move_val_obj;
+  for (int i=0; i<numValuesMove; i++) {
+    move_val_obj = PyTuple_GetItem(move_tuple_obj, i);
+    move[i] = PyLong_AsLong(move_val_obj);
+  }
+  
+  return 0;
+}
+
+static PyObject*
+quack_is_move_valid(PyObject *self, PyObject *args) {
+  int board[26];
+  int player;
+  int face_value;
+  int move[2];
+  
+  PyObject* board_tuple_obj;
+  PyObject* move_tuple_obj;
+  
+  if (! PyArg_ParseTuple(args, "O!iiO!",
+			 &PyTuple_Type, &board_tuple_obj,
+			 &player,
+			 &face_value,
+			 &PyTuple_Type, &move_tuple_obj))
+    return NULL;
+
+ if (extract_board(board, board_tuple_obj)) return NULL;
+ if (extract_move(move, move_tuple_obj))    return NULL;
+ 
+ if (is_move_valid(board, player, face_value, move)) Py_RETURN_TRUE;
+ else                                                Py_RETURN_FALSE;
+}
+
+static PyObject*
+quack_idxs_with_checkers_of_player(PyObject *self, PyObject *args) {
+
+  int board[26];
+  int player;
+  
+  int* idxs;
+  
+  PyObject* board_tuple_obj;
+  
+  if (! PyArg_ParseTuple(args, "O!i",
+			 &PyTuple_Type, &board_tuple_obj,
+			 &player))
+    return NULL;
+
+  if (extract_board(board, board_tuple_obj)) return NULL;
+  
+  idxs = idxs_with_checkers_of_player(board, player);
+  PyObject* idxs_list = PyList_New(idxs[0]);
+  
+  for (int i = 0; i < idxs[0]; i++) {
+    PyList_SetItem(idxs_list, i, Py_BuildValue("i", idxs[i+1]));
+  }
+  free(idxs);
+
+  PyObject *result = Py_BuildValue("O", idxs_list);
+  Py_DECREF(idxs_list);
+  
+  return result;
+}
+
+static PyObject*
+quack_do_move(PyObject *self, PyObject *args) {
+  int board[26];
+  int player;
+  int move[2];
+  
+  PyObject* board_tuple_obj;
+  PyObject* move_tuple_obj;
+  
+  if (! PyArg_ParseTuple(args, "O!iO!",
+			 &PyTuple_Type, &board_tuple_obj,
+			 &player,
+			 &PyTuple_Type, &move_tuple_obj))
+    return NULL;
+
+  if (extract_board(board, board_tuple_obj)) return NULL;
+  if (extract_move(move, move_tuple_obj))    return NULL;
+
+  do_move(board, player, move);
+  PyObject* board_tuple = store_board_to_pytuple(board, 26);
+
+  // This is shaky
+  Py_DECREF(board);
+
+  PyObject *result = Py_BuildValue("O", board_tuple);
+  Py_DECREF(board_tuple);
+  
+  return result;
+}
+
+static PyObject*
+quack_calc_moves(PyObject *self, PyObject *args) {
+  int board[26];
+  int player;
+  int face_value;
+  
+  PyObject* board_tuple_obj;
+  
+  if (! PyArg_ParseTuple(args, "O!ii",
+			 &PyTuple_Type, &board_tuple_obj,
+			 &player,
+			 &face_value))
+    return NULL;
+
+  if (extract_board(board, board_tuple_obj)) return NULL;
+
+  board_list boards = calc_moves(board, player, face_value);
+  PyObject* boards_list = PyList_New(boards.size);
+  
+  for (int i = 0; i < boards.size; i++) {
+    if (PyList_SetItem(boards_list, i, boards.list[i])) {
+      printf("list insertion failed at index %i\n",i);
+      abort();
+    }
+  }
+  
+  PyObject *result = Py_BuildValue("O", boards_list);
+  Py_DECREF(boards_list);
+  
+  return result;
+}
+
+static PyObject*
+quack_board_features_quack_fat(PyObject *self, PyObject *args) {
+  int board[26];
+  int player;
+  
+  PyObject* board_tuple_obj;
+  
+  if (! PyArg_ParseTuple(args, "O!i",
+			 &PyTuple_Type, &board_tuple_obj,
+			 &player))
+    return NULL;
+
+  if (extract_board(board, board_tuple_obj)) return NULL;
+
+  int* new_board = board_features_quack_fat(board, player);
+  PyObject* board_tuple = store_board_to_pytuple(new_board, 30);
+  free(new_board);
+
+  PyObject *result = Py_BuildValue("O", board_tuple);
+  Py_DECREF(board_tuple);
+  
+  return result;
+}
+
+
+static PyMethodDef quack_methods[] = {
+  {
+    "is_move_valid", quack_is_move_valid, METH_VARARGS,
+    "Evaluates the validity of the proposed move."
+  },
+  {
+    "idxs_with_checkers_of_player", quack_idxs_with_checkers_of_player, METH_VARARGS,
+    "Returns a list of indexes with checkers of the specified player"
+  },
+  {
+    "do_move", quack_do_move, METH_VARARGS,
+    "Returns the board after doing the specified move"
+  },
+  {
+    "calc_moves", quack_calc_moves, METH_VARARGS,
+    "Calculates all legal moves from board with specified face value"
+  },
+  {
+    "board_features_quack_fat", quack_board_features_quack_fat, METH_VARARGS,
+    "Transforms a board to the quack-fat board representation"
+  },
+  {NULL, NULL, 0, NULL}
+};
+
+static struct PyModuleDef quack_definition = {
+  PyModuleDef_HEAD_INIT,
+  "quack",
+  "A Python module that provides various useful Backgammon-related functions.",
+  -1,
+  quack_methods
+};
+
+PyMODINIT_FUNC PyInit_quack(void) {
+  PyObject* module;
+
+  module = PyModule_Create(&quack_definition);
+  if (module == NULL)
+    return NULL;
+    
+  QuackError = PyErr_NewException("quack.error", NULL, NULL);
+  Py_INCREF(QuackError);
+  PyModule_AddObject(module, "error", QuackError);
+  
+  return module;
+}
diff --git a/quack/setup.py b/quack/setup.py
new file mode 100644
index 0000000..d0ba3b5
--- /dev/null
+++ b/quack/setup.py
@@ -0,0 +1,9 @@
+from distutils.core import setup, Extension
+
+quack = Extension('quack',
+                  sources = ['quack.c'])
+
+setup (name = 'quack',
+       version = '0.1',
+       description = 'Quack Backgammon Tools',
+       ext_modules = [quack])
diff --git a/report_docs.txt b/report_docs.txt
new file mode 100644
index 0000000..e9d68ca
--- /dev/null
+++ b/report_docs.txt
@@ -0,0 +1,28 @@
+<christoffer> Alexander og jeg skrev noget af vores bachelorprojekt om til C her i fredags.
+<christoffer> Man skal virkelig passe på sine hukommelsesallokeringer.
+<Jmaa> Ja, helt klart.
+<christoffer> Jeg fandt et memory leak, der lækkede 100 MiB hukommelse i sekundet.
+<Jmaa> Hvilken del blev C-ificeret?
+<Jmaa> Damned
+<christoffer> Årsagen var at vi gav et objekt med tilbage til Python uden at dekrementere dets ref-count, så fortolkeren stadig troede at nogen havde brug for det.
+<christoffer> Den del af spillogikken, der tjekker om træk er gyldige.
+<christoffer> Det bliver kaldt ret mange tusinde gange pr. spil, så vi tænkte at der måske kunne være lidt optimering at hente i at omskrive det til C.
+<Jmaa> Ok, så I har ikke selv brugt alloc og free. Det er alligevel noget.
+<christoffer> Metoden selv blev 7 gange hurtigere!
+<Jmaa> Wow!
+<christoffer> Jo. Det endte vi også med at gøre.
+<christoffer> Vi havde brug for lister af variabel størrelse. Det endte med en struct med et "size" felt og et "list" felt.
+<Jmaa> Inkluderer det speedup, frem og tilbagen mellem C og python?
+<christoffer> Det burde det gøre, ja!
+<Jmaa> Gjorde det nogen stor effekt for hvor hurtigt I kan evaluere?
+<christoffer> Jeg tror ikke at der er særligt meget "frem og tilbage"-stads. Det ser ud til at det kode man skriver bliver kastet ret direkte ind i fortolkeren.
+<christoffer> Det gjorde en stor forskel for når vi laver 1-ply.
+<christoffer> "ply" er hvor mange træk man kigger fremad.
+<christoffer> Så kun at kigge på det umiddelbart næste træk er 0-ply, hvilket er det vi har gjort indtil nu
+<christoffer> 1-ply var for langsomt. Det tog ca. 6-7 sekunder at evaluere ét træk.
+<christoffer> Alexander lavede lidt omskrivninger, så TensorFlow udregnede det hurtigere og fik det ned på ca. 3-4 sekunder *pr. spil*.
+<christoffer> Så skrev vi noget af det om til C, og nu er vi så på ca. 2 sekunder pr. spil med 1-ply, hvilket er ret vildt.
+<christoffer> Det er så godt at Python-fortolkeren kan udvides med C!
+<christoffer> caspervk, kan I optimere jeres bachelorprojekt med et par C-moduler?
+<Jmaa> Det er en hel lille sektion til rapporten det der.
+<christoffer> Yeah. Kopierer bare det her verbatim ind.
diff --git a/requirements.txt b/requirements.txt
index e7ac94a..2738d5d 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -16,8 +16,8 @@ pyparsing==2.2.0
 python-dateutil==2.7.2
 pytz==2018.3
 six==1.11.0
-tensorboard==1.6.0
-tensorflow==1.6.0
+tensorboard==1.8.0
+tensorflow==1.8.0
 termcolor==1.1.0
 Werkzeug==0.14.1
 pygame==1.9.3
diff --git a/tensorflow_impl_tests/eager_main.py b/tensorflow_impl_tests/eager_main.py
index 1b58abc..0cce81f 100644
--- a/tensorflow_impl_tests/eager_main.py
+++ b/tensorflow_impl_tests/eager_main.py
@@ -1,41 +1,94 @@
 import time
 import numpy as np
 import tensorflow as tf
+from board import Board
+import tensorflow.contrib.eager as tfe
+
 
 tf.enable_eager_execution()
+xavier_init = tf.contrib.layers.xavier_initializer()
 
 
 
+opt = tf.train.MomentumOptimizer(learning_rate=0.1, momentum=1)
+
 output_size = 1
 hidden_size = 40
 input_size = 30
 
 
-
 model = tf.keras.Sequential([
-    tf.keras.layers.Dense(40, activation="sigmoid", input_shape=(1,30)),
-    tf.keras.layers.Dense(1, activation="sigmoid")
+    tf.keras.layers.Dense(40, activation="sigmoid", kernel_initializer=tf.constant_initializer(-2), input_shape=(1,input_size)),
+    tf.keras.layers.Dense(1, activation="sigmoid", kernel_initializer=tf.constant_initializer(0.2))
 ])
 
-input = [0, 2, 0, 0, 0, 0, -5, 0, -3, 0, 0, 0, 5, -5, 0, 0, 0, 3, 0, 5, 0, 0, 0, 0, -2, 0, 0, 0, 1, 0]
 
-all_input = np.array([input for _ in range(8500)])
+# tfe.Saver(model.variables).restore(tf.train.latest_checkpoint("./"))
 
-single_in = np.array(input).reshape(1,-1)
+input = [0, 2, 0, 0, 0, 0, -5, 0, -3, 0, 0, 0, 5, -5, 0, 0, 0, 3, 0, 5, 0, 0, 0, 0, -2, 0]
+
+
+
+all_input = np.array([Board.board_features_quack_fat(input, 1) for _ in range(20)])
+
+
+single_in = Board.board_features_quack_fat(input, 1)
 
 
 start = time.time()
 
 all_predictions = model.predict_on_batch(all_input)
 
-print(all_predictions)
-print(time.time() - start)
+
+learning_rate = 0.1
+
+with tf.GradientTape() as tape:
+    value = model(single_in)
 
 
+print("Before:", value)
 
-start = time.time()
-all_predictions = [model(single_in) for _ in range(8500)]
+grads = tape.gradient(value, model.variables)
+print("/"*40,"model_variables","/"*40)
+print(model.variables)
+print("/"*40,"grads","/"*40)
+print(grads)
 
-print(all_predictions[:10])
-print(time.time() - start)
+difference_in_values = tf.reshape(tf.subtract(0.9, value, name='difference_in_values'), [])
 
+for grad, train_var in zip(grads, model.variables):
+    backprop_calc = 0.1 * difference_in_values * grad
+    train_var.assign_add(backprop_calc)
+
+value = model(single_in)
+print("/"*40,"model_variables","/"*40)
+print(model.variables)
+print("After:", value)
+
+
+# # grads = [0.1*val-np.random.uniform(-1,1)+grad for grad, trainable_var in zip(grads, model.variables)]
+#
+# # print(model.variables[0][0])
+# weights_before = model.weights[0]
+#
+# start = time.time()
+# #[trainable_var.assign_add(0.1*val-0.3+grad) for grad, trainable_var in zip(grads, model.variables)]
+#
+# start = time.time()
+# for gradient, trainable_var in zip(grads, model.variables):
+#     backprop_calc = 0.1 * (0.9 - val) * gradient
+#     trainable_var.assign_add(backprop_calc)
+#
+# # opt.apply_gradients(zip(grads, model.variables))
+#
+# print(time.time() - start)
+#
+# print(model(single_in))
+#
+# vals = model.predict_on_batch(all_input)
+# vals = list(vals)
+# vals[3] = 4
+# print(vals)
+# print(np.argmax(np.array(vals)))
+
+# tfe.Saver(model.variables).save("./tmp_ckpt")
diff --git a/tensorflow_impl_tests/normal_main.py b/tensorflow_impl_tests/normal_main.py
index acfc044..a8b106c 100644
--- a/tensorflow_impl_tests/normal_main.py
+++ b/tensorflow_impl_tests/normal_main.py
@@ -16,9 +16,9 @@ class Everything:
 
 
         W_1 = tf.get_variable("w_1", (self.input_size, self.hidden_size),
-                              initializer=xavier_init)
+                              initializer=tf.constant_initializer(-2))
         W_2 = tf.get_variable("w_2", (self.hidden_size, self.output_size),
-                              initializer=xavier_init)
+                              initializer=tf.constant_initializer(0.2))
 
         b_1 = tf.get_variable("b_1", (self.hidden_size,),
                               initializer=tf.zeros_initializer)
@@ -29,16 +29,37 @@ class Everything:
 
         self.value = tf.sigmoid(tf.matmul(value_after_input, W_2) + b_2, name='output_layer')
 
+        apply_gradients = []
+
+
+        trainable_vars = tf.trainable_variables()
+        gradients = tf.gradients(self.value, trainable_vars)
+
+        difference_in_values = tf.reshape(tf.subtract(0.9, self.value, name='difference_in_values'), [])
+
+        with tf.variable_scope('apply_gradients'):
+            for gradient, trainable_var in zip(gradients, trainable_vars):
+                backprop_calc = 0.1 * difference_in_values * gradient
+                grad_apply = trainable_var.assign_add(backprop_calc)
+                apply_gradients.append(grad_apply)
+
+
+        self.training_op = tf.group(*apply_gradients, name='training_op')
+
+
+
     def eval(self):
         input = np.array([0, 2, 0, 0, 0, 0, -5, 0, -3, 0, 0, 0, 5, -5, 0, 0, 0, 3, 0, 5, 0, 0, 0, 0, -2, 0, 0, 0, 1, 0])
         start = time.time()
         sess = tf.Session()
         sess.run(tf.global_variables_initializer())
-        for i in range(8500):
+        for i in range(20):
             val = sess.run(self.value, feed_dict={self.input: input.reshape(1,-1)})
         print(time.time() - start)
         print(val)
-
+        sess.run(self.training_op, feed_dict={self.input: input.reshape(1,-1)})
+        val = sess.run(self.value, feed_dict={self.input: input.reshape(1, -1)})
+        print(val)
 
 everything = Everything()
 everything.eval()
diff --git a/test.py b/test.py
index 6c9c130..90cea23 100644
--- a/test.py
+++ b/test.py
@@ -141,6 +141,56 @@ class TestIsMoveValid(unittest.TestCase):
     # TODO: More tests for bearing off are needed
 
 
+    def test_bear_off_non_backmost(self):
+        board = ( 0,
+                  0, 0, 0, 0, 0, 0,
+                  0, 0, 0, 0, 0, 0,
+                  0, 0, 0, 0, 0, 0,
+                  0, 0, 0, 0, 1, 1,
+                  0 )
+        self.assertEqual(Board.is_move_valid(board, 1, 2, (23, 25)), True)
+        self.assertEqual(Board.is_move_valid(board, 1, 1, (24, 25)), True)
+        self.assertEqual(Board.is_move_valid(board, 1, 2, (24, 26)), False)
+
+    def test_bear_off_quadrant_limits_white(self):
+        board = ( 0,
+                  0, 0, 0, 0, 0, 0,
+                  0, 0, 0, 0, 0, 0,
+                  0, 0, 0, 0, 0, 1,
+                  1, 1, 1, 1, 1, 1,
+                  0 )
+        self.assertEqual(Board.is_move_valid(board, 1, 2, (23, 25)), False)
+        self.assertEqual(Board.is_move_valid(board, 1, 1, (24, 25)), False)
+
+    def test_bear_off_quadrant_limits_black(self):
+        board = ( 0,
+                  -1, -1, -1, -1, -1, -1,
+                  -1, 0, 0, 0, 0, 0,
+                  0, 0, 0, 0, 0, 0,
+                  0, 0, 0, 0, 0, 0,
+                  0 )
+        self.assertEqual(Board.is_move_valid(board, -1, 2, (2, 0)), False)
+        self.assertEqual(Board.is_move_valid(board, -1, 1, (1, 0)), False)
+
+    def test_bear_off_quadrant_limits_white_2(self):
+        board = ( 0,
+                  0, 0, 0, 0, 0, 0,
+                  0, 0, 0, 0, 0, 0,
+                  0, 0, 0, 0, 0, 0,
+                  1, 0, 0, 0, 0, 1,
+                  0 )
+        self.assertEqual(Board.is_move_valid(board, 1, 1, (24, 25)), True)
+
+    def test_bear_off_quadrant_limits_black_2(self):
+        board = ( 0,
+                  -1, 0, 0, 0, 0, -1,
+                  0, 0, 0, 0, 0, 0,
+                  0, 0, 0, 0, 0, 0,
+                  0, 0, 0, 0, 0, 0,
+                  0 )
+        self.assertEqual(Board.is_move_valid(board, -1, 1, (1, 0)), True)
+        
+        
 class TestNumOfChecker(unittest.TestCase):
     def test_simple_1(self):
         board = ( 0,