diff --git a/README.md b/README.md
index fdfa94a19..3028b148d 100644
--- a/README.md
+++ b/README.md
@@ -54,6 +54,46 @@ and then execute: ``python train.py``  (To use GPU in PyTorch, set ``use_gpu=Tru
 
 The models (best_policy.model and current_policy.model) will be saved every a few updates (default 50).  
 
+
+With Tensorflow and ResNet30, uncomment the line 
+```
+# from policy_value_net_res_tensorflow import PolicyValueNetRes30 # Tensorflow
+```
+Then execute:
+```
+python train.py -h
+    -h, --help            show this help message and exit
+    --ModelName {baseline,res30}, -m {baseline,res30}
+    --LossFunction {lv,lp,l+,lx}, -l {lv,lp,l+,lx}
+    --EnableForbiddenHands, -fh Enable forbidden hands
+```
+baseline_l+:
+```
+python train.py --ModelName baseline --LossFunction l+ --EnableForbiddenHands True
+```
+
+baseline_lp:
+```
+python train.py --ModelName baseline --LossFunction lp --EnableForbiddenHands True
+```
+
+res30_l+:
+```
+python train.py --ModelName res30 --LossFunction l+ --EnableForbiddenHands True
+```
+
+res30_lp:
+```
+python train.py --ModelName res30 --LossFunction lp --EnableForbiddenHands True
+```
+
+Human play with AI
+
+```
+pip install tensorflow==1.14.0
+python gobang_res30.py
+```
+
 **Note:** the 4 provided models were trained using Theano/Lasagne, to use them with PyTorch, please refer to [issue 5](https://github.com/junxiaosong/AlphaZero_Gomoku/issues/5).
 
 **Tips for training:**
@@ -61,4 +101,4 @@ The models (best_policy.model and current_policy.model) will be saved every a fe
 2. For the case of 8 * 8 board and 5 in a row, it may need 2000~3000 self-play games to get a good model, and it may take about 2 days on a single PC.
 
 ### Further reading
-My article describing some details about the implementation in Chinese: [https://zhuanlan.zhihu.com/p/32089487](https://zhuanlan.zhihu.com/p/32089487) 
+My article describing some details about the implementation in Chinese: [https://zhuanlan.zhihu.com/p/32089487](https://zhuanlan.zhihu.com/p/32089487)
diff --git a/evaluate.py b/evaluate.py
new file mode 100644
index 000000000..bab7017a5
--- /dev/null
+++ b/evaluate.py
@@ -0,0 +1,91 @@
+# -*- coding: utf-8 -*-
+"""
+An implementation of the evaluation pipeline of AlphaZero for Gomoku
+
+@author: Chunlei Wang
+"""
+
+import random
+import numpy as np
+from collections import defaultdict, deque
+from game import Board, Game
+from mcts_alphaZero import MCTSPlayer
+#from policy_value_net import PolicyValueNet  # Theano and Lasagne
+#from policy_value_net_pytorch import PolicyValueNet  # Pytorch
+from policy_value_net_tensorflow import PolicyValueNet # Tensorflow
+#from policy_value_net_keras import PolicyValueNet # Keras
+from policy_value_net_res_tensorflow import PolicyValueNetRes30 # Tensorflow
+from datetime import datetime
+import utils
+import os
+
+OUTPUT_DIR = "evaluation/" + datetime.utcnow().strftime("%Y%m%d%H%M%S")
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+EVALUATION_OUTPUT = OUTPUT_DIR + "/evaluation.txt"
+
+class EvaluationPipeline():
+    def __init__(self, current_model, baseline_model):
+        # params of the board and the game
+        self.board_width = 9
+        self.board_height = 9
+        self.n_in_row = 5
+        self.board = Board(width=self.board_width,
+                           height=self.board_height,
+                           n_in_row=self.n_in_row,
+                           forbidden_hands=True)
+        self.game = Game(self.board)
+        self.n_playout = 400  # num of simulations for each move
+        self.c_puct = 5        
+        
+        self.baseline_policy_value_net = PolicyValueNet(self.board_width,
+                                                   self.board_height,
+                                                   'l+', 
+                                                   model_file=baseline_model)
+
+        self.current_policy_value_net = PolicyValueNetRes30(self.board_width,
+                                                  self.board_height,
+                                                  'l+', 
+                                                   model_file=current_model)
+
+    def policy_evaluate(self, n_games=100):
+        """
+        Evaluate the trained policy by playing against the baseline MCTS player
+        """
+        current_mcts_player = MCTSPlayer(self.current_policy_value_net.policy_value_fn,
+                                      c_puct=self.c_puct,
+                                      n_playout=self.n_playout)
+
+        baseline_mcts_player = MCTSPlayer(self.baseline_policy_value_net.policy_value_fn,
+                                      c_puct=self.c_puct,
+                                      n_playout=self.n_playout)
+
+        win_cnt = defaultdict(int)
+        for i in range(n_games):
+            winner = self.game.start_play(current_mcts_player,
+                                          baseline_mcts_player,
+                                          start_player=i % 2,
+                                          is_shown=1)
+            win_cnt[winner] += 1
+        win_ratio = 1.0*(win_cnt[1] + 0.5*win_cnt[-1]) / n_games
+        
+        output = "Evaluation games: {}, num_playouts: {}, win: {}, lose: {}, tie: {}, win ratio: {}".format(
+                n_games,
+                self.n_playout,
+                win_cnt[1], win_cnt[2], win_cnt[-1], win_ratio)
+
+        utils.log(output, EVALUATION_OUTPUT)
+        
+        return win_ratio
+
+    def run(self):
+        """run the evaluation pipeline"""
+        try:
+            win_ratio = self.policy_evaluate()
+            return win_ratio
+        except KeyboardInterrupt:
+            print('\n\rquit')
+
+
+if __name__ == '__main__':
+    evaluation_pipeline = EvaluationPipeline(current_model='output/current_policy.model', baseline_model='output/baseline_policy.model')
+    evaluation_pipeline.run()
diff --git a/game.py b/game.py
index 7b58ca238..ceb434ac1 100644
--- a/game.py
+++ b/game.py
@@ -6,10 +6,36 @@
 from __future__ import print_function
 import numpy as np
 
+INPUT_STATE_CHANNEL_SIZE = 19
 
 class Board(object):
     """board for the game"""
 
+    """
+    0: blank
+    1: black
+    2: white
+    """
+    forbidden_hands_of_three_patterns = [
+        [0, 1, 1, 1, 0],
+        [0, 1, 0, 1, 1, 0],
+        [0, 1, 1, 0, 1, 0],
+    ]
+
+    forbidden_hands_of_four_patterns = [
+        [0, 1, 1, 1, 1, 0],
+        [0, 1, 1, 1, 0, 1],
+        [0, 1, 0, 1, 1, 1],
+        [1, 1, 1, 0, 1, 0],
+        [1, 0, 1, 1, 1, 0],
+        [2, 1, 1, 1, 1, 0],
+        [2, 1, 1, 1, 0, 1],
+        [2, 1, 0, 1, 1, 1],
+        [0, 1, 1, 1, 1, 2],
+        [1, 1, 1, 0, 1, 2],
+        [1, 0, 1, 1, 1, 2],
+    ]
+
     def __init__(self, **kwargs):
         self.width = int(kwargs.get('width', 8))
         self.height = int(kwargs.get('height', 8))
@@ -19,17 +45,20 @@ def __init__(self, **kwargs):
         self.states = {}
         # need how many pieces in a row to win
         self.n_in_row = int(kwargs.get('n_in_row', 5))
+        self.forbidden_hands = bool(kwargs.get('forbidden_hands', False))
         self.players = [1, 2]  # player1 and player2
 
     def init_board(self, start_player=0):
         if self.width < self.n_in_row or self.height < self.n_in_row:
             raise Exception('board width and height can not be '
                             'less than {}'.format(self.n_in_row))
+        self.start_player = start_player
         self.current_player = self.players[start_player]  # start player
         # keep available moves in a list
         self.availables = list(range(self.width * self.height))
         self.states = {}
         self.last_move = -1
+        self.last_16_move = [0]*(INPUT_STATE_CHANNEL_SIZE-3)
 
     def move_to_location(self, move):
         """
@@ -48,13 +77,41 @@ def location_to_move(self, location):
             return -1
         h = location[0]
         w = location[1]
+        if h < 0 or h >= self.height:
+            return -1
+        if w < 0 or w >= self.width:
+            return -1
+
         move = h * self.width + w
         if move not in range(self.width * self.height):
             return -1
         return move
 
+    def current_last16move_state(self):
+        """return the board state from the perspective of the current res30 player.
+        state shape: INPUT_STATE_CHANNEL_SIZE*width*height
+        """
+
+        square_state = np.zeros((INPUT_STATE_CHANNEL_SIZE, self.width, self.height))
+        if self.states:
+            moves, players = np.array(list(zip(*self.states.items())))
+            move_curr = moves[players == self.current_player]
+            move_oppo = moves[players != self.current_player]
+
+            square_state[0][move_curr // self.width,
+                            move_curr % self.height] = 1.0
+            square_state[1][move_oppo // self.width,
+                            move_oppo % self.height] = 1.0
+            # indicate the last 16 move location
+            for i in range(INPUT_STATE_CHANNEL_SIZE-3):
+                square_state[2+i][np.array(self.last_16_move[i::2]) // self.width,
+                                np.array(self.last_16_move[i::2]) % self.height] = 1.0
+        if len(self.states) % 2 == 0:
+            square_state[INPUT_STATE_CHANNEL_SIZE-1][:, :] = 1.0  # indicate the colour to play
+        return square_state[:, ::-1, :]
+
     def current_state(self):
-        """return the board state from the perspective of the current player.
+        """return the board state from the perspective of the current baseline player.
         state shape: 4*width*height
         """
 
@@ -82,6 +139,8 @@ def do_move(self, move):
             else self.players[1]
         )
         self.last_move = move
+        self.last_16_move.pop(0)
+        self.last_16_move.append(move)
 
     def has_a_winner(self):
         width = self.width
@@ -89,6 +148,9 @@ def has_a_winner(self):
         states = self.states
         n = self.n_in_row
 
+        if self.forbidden_hands and self.states and self.states[self.last_move] == self.players[self.start_player] and self.check_forbidden_hands():
+            return True, self.players[(self.start_player + 1) % 2]
+
         moved = list(set(range(width * height)) - set(self.availables))
         if len(moved) < self.n_in_row *2-1:
             return False, -1
@@ -116,6 +178,71 @@ def has_a_winner(self):
 
         return False, -1
 
+    def check_forbidden_hands(self):
+        directions = [
+            [1, 0],
+            [1, 1],
+            [0, 1],
+            [-1, 1],
+        ]
+        
+        patterns_of_three_matches = [
+            1 if self.check_forbidden_pattern(p, d) else 0
+            for d in directions
+            for p in self.forbidden_hands_of_three_patterns
+        ]
+        
+        patterns_of_four_matches = [
+            1 if self.check_forbidden_pattern(p, d) else 0
+            for d in directions
+            for p in self.forbidden_hands_of_four_patterns
+        ]
+        
+        if sum(patterns_of_three_matches) > 1 or sum(patterns_of_four_matches) > 1:
+            return True
+    
+    def check_forbidden_pattern(self, pattern, direction):
+        for (i, x) in enumerate(pattern):
+            if x == 1:
+                pieces = self.collect_pieces(self.last_move, direction, i, len(pattern))
+                if pieces != [] and Board.list_equal(pieces, pattern):
+                    return True
+        
+        return False
+    
+    def list_equal(list1, list2):
+        if len(list1) != len(list2):
+            return False
+
+        for (a, b) in zip(list1, list2):
+            if a != b:
+                return False
+
+        return True
+
+    def collect_pieces(self, move, direction, look_back, length):
+        cur_location = self.move_to_location(move)
+        start_location = [
+            cur_location[0] - direction[0] * look_back,
+            cur_location[1] - direction[1] * look_back,
+        ]
+
+        pieces = []
+        for i in range(length):
+            location = [
+                start_location[0] + i * direction[0],
+                start_location[1] + i * direction[1],
+            ]
+            move = self.location_to_move(location)
+            if move == -1:
+                return []
+            else:
+                if move in self.states:
+                    pieces.append(1 if self.states[move] == self.players[self.start_player] else 2)
+                else:
+                    pieces.append(0)
+        return pieces
+
     def game_end(self):
         """Check whether the game is ended or not"""
         win, winner = self.has_a_winner()
@@ -180,14 +307,13 @@ def start_play(self, player1, player2, start_player=0, is_shown=1):
                 self.graphic(self.board, player1.player, player2.player)
             end, winner = self.board.game_end()
             if end:
-                if is_shown:
-                    if winner != -1:
+                if winner != -1:
                         print("Game end. Winner is", players[winner])
-                    else:
-                        print("Game end. Tie")
+                else:
+                    print("Game end. Tie")
                 return winner
 
-    def start_self_play(self, player, is_shown=0, temp=1e-3):
+    def start_self_play(self, player, model_name, is_shown=0, temp=1e-3):
         """ start a self-play game using a MCTS player, reuse the search tree,
         and store the self-play data: (state, mcts_probs, z) for training
         """
@@ -199,7 +325,7 @@ def start_self_play(self, player, is_shown=0, temp=1e-3):
                                                  temp=temp,
                                                  return_prob=1)
             # store the data
-            states.append(self.board.current_state())
+            states.append(self.board.current_state() if model_name == 'baseline' else self.board.current_last16move_state())
             mcts_probs.append(move_probs)
             current_players.append(self.board.current_player)
             # perform a move
@@ -220,4 +346,4 @@ def start_self_play(self, player, is_shown=0, temp=1e-3):
                         print("Game end. Winner is player:", winner)
                     else:
                         print("Game end. Tie")
-                return winner, zip(states, mcts_probs, winners_z)
+                return winner, zip(states, mcts_probs, winners_z)
\ No newline at end of file
diff --git a/gobang.py b/gobang.py
new file mode 100644
index 000000000..2de364450
--- /dev/null
+++ b/gobang.py
@@ -0,0 +1,208 @@
+from tkinter import *
+import math 
+
+#定义棋盘类
+class chessBoard() :
+    def __init__(self) :
+        self.window = Tk()
+        self.window.title("五子棋游戏")
+        self.window.geometry("660x500")
+        self.window.resizable(0,0)
+        self.canvas=Canvas(self.window , bg="#EEE8AC" , width=500, height=500)
+        self.paint_board()
+        self.canvas.grid(row=0, column=0)
+
+    def paint_board(self) :
+        for row in range(0, 10):
+            if row == 0 or row == 9:
+                self.canvas.create_line(25, 25+row*50, 25+9*50, 25+row*50, width=2)
+            else :
+                self.canvas.create_line(25, 25+row*50, 25+9*50, 25+row*50, width=1)
+        
+        for column in range(0, 10):
+            if column == 0 or column == 9:
+                self.canvas.create_line(25+column*50, 25, 25+column*50, 25+9*50, width=2)
+            else :
+                self.canvas.create_line(25+column*50, 25, 25+column*50, 25+9*50, width=1)
+            
+        self.canvas.create_oval(122, 122, 128, 128, fill="black")
+        self.canvas.create_oval(372, 122, 378, 128, fill="black")
+        self.canvas.create_oval(122, 372, 128, 378, fill="black")
+        self.canvas.create_oval(372, 372, 378, 378, fill="black")
+
+
+#定义五子棋游戏类
+#0为黑子 ， 1为白子 ， 2为空位
+class Gobang() :
+    #初始化
+    def __init__(self) :
+        self.board = chessBoard()
+        self.game_print = StringVar()
+        self.game_print.set("")
+        #16*16的二维列表，保证不会out of index
+        self.db = [([2] * 9) for i in range(9)]
+        #悔棋用的顺序列表
+        self.order = []
+        #棋子颜色
+        self.color_count = 0 
+        self.color = 'black'
+        #清空与赢的初始化，已赢为1，已清空为1
+        self.flag_win = 1
+        self.flag_empty = 1
+        self.options()
+        
+     
+    #黑白互换
+    def change_color(self) :
+        self.color_count = (self.color_count + 1 ) % 2
+        if self.color_count == 0 :
+            self.color = "black"
+        elif self.color_count ==1 :
+            self.color = "white"
+    
+    
+    #落子
+    def chess_moving(self ,event) :
+        #不点击“开始”与“清空”无法再次开始落子
+        if self.flag_win ==1 or self.flag_empty ==0:
+            return
+        #坐标转化为下标
+        x,y = event.x-25 , event.y-25
+        x = round(x/50)
+        y = round(y/50)
+        #点击位置没用落子，且没有在棋盘线外，可以落子
+        while self.db[y][x] == 2 and self.limit_boarder(y,x):
+            self.db[y][x] = self.color_count
+            self.order.append(x+15*y)   
+            self.board.canvas.create_oval(25+50*x-15 , 25+50*y-15 , 25+50*x+15 , 25+50*y+15 , fill = self.color,tags = "chessman")
+            if self.game_win(y,x,self.color_count) :
+                print(self.color,"获胜")
+                self.game_print.set(self.color+"获胜")
+            else :
+                self.change_color()
+                self.game_print.set("请"+self.color+"落子")
+    
+
+    #保证棋子落在棋盘上
+    def limit_boarder(self , y , x) :
+        if x<0 or x>8 or y<0 or y>8 :
+            return False
+        else :
+            return True
+
+
+    #计算连子的数目,并返回最大连子数目
+    def chessman_count(self , y , x , color_count ) :
+        count1,count2,count3,count4 = 1,1,1,1
+        #横计算
+        for i in range(-1 , -5 , -1) :
+            if self.db[y][x+i] == color_count  :
+                count1 += 1
+            else:
+                break 
+        for i in  range(1 , 5 ,1 ) :
+            if self.db[y][x+i] == color_count  :
+                count1 += 1
+            else:
+                break 
+        #竖计算
+        for i in range(-1 , -5 , -1) :
+            if self.db[y+i][x] == color_count  :
+                count2 += 1
+            else:
+                break 
+        for i in  range(1 , 5 ,1 ) :
+            if self.db[y+i][x] == color_count  :
+                count2 += 1
+            else:
+                break 
+        #/计算
+        for i in range(-1 , -5 , -1) :
+            if self.db[y+i][x+i] == color_count  :
+                count3 += 1
+            else:
+                break 
+        for i in  range(1 , 5 ,1 ) :
+            if self.db[y+i][x+i] == color_count  :
+                count3 += 1
+            else:
+                break 
+        #\计算
+        for i in range(-1 , -5 , -1) :
+            if self.db[y+i][x-i] == color_count :
+                count4 += 1
+            else:
+                break 
+        for i in  range(1 , 5 ,1 ) :
+            if self.db[y+i][x-i] == color_count :
+                count4 += 1
+            else:
+                break 
+            
+        return max(count1 , count2 , count3 , count4)
+
+
+    #判断输赢
+    def game_win(self , y , x , color_count ) :
+        if self.chessman_count(y,x,color_count) >= 5 :
+            self.flag_win = 1
+            self.flag_empty = 0
+            return True
+        else :
+            return False
+        
+
+    #悔棋,清空棋盘，再画剩下的n-1个棋子
+    def withdraw(self ) :
+        if len(self.order)==0 or self.flag_win == 1:
+            return
+        self.board.canvas.delete("chessman")
+        z = self.order.pop()
+        x = z%15
+        y = z//15
+        self.db[y][x] = 2
+        self.color_count = 1
+        for i in self.order :
+            ix = i%15
+            iy = i//15
+            self.change_color()
+            self.board.canvas.create_oval(25+50*ix-15 , 25+50*iy-15 , 25+50*ix+15 , 25+50*iy+15 , fill = self.color,tags = "chessman")
+        self.change_color()
+        self.game_print.set("请"+self.color+"落子")
+    
+
+    #清空
+    def empty_all(self) :
+        self.board.canvas.delete("chessman")
+        #还原初始化
+        self.db = [([2] * 16) for i in range(16)]
+        self.order = []
+        self.color_count = 0 
+        self.color = 'black'
+        self.flag_win = 1
+        self.flag_empty = 1
+        self.game_print.set("")
+
+
+
+    #将self.flag_win置0才能在棋盘上落子
+    def game_start(self) :
+        #没有清空棋子不能置0开始
+        if self.flag_empty == 0:
+            return
+        self.flag_win = 0
+        self.game_print.set("请"+self.color+"落子")
+
+
+    def options(self) :
+        self.board.canvas.bind("<Button-1>",self.chess_moving)
+        Label(self.board.window , textvariable = self.game_print , font = ("Arial", 20) ).place(relx = 0, rely = 0 ,x = 505 , y = 200)
+        Button(self.board.window , text= "开始游戏" ,command = self.game_start,width = 13, font = ("Verdana", 12)).place(relx=0, rely=0, x=505, y=15)
+        Button(self.board.window , text= "我要悔棋" ,command = self.withdraw,width = 13, font = ("Verdana", 12)).place(relx=0, rely=0, x=505, y=60)
+        Button(self.board.window , text= "清空棋局" ,command = self.empty_all,width = 13, font = ("Verdana", 12)).place(relx=0, rely=0, x=505, y=105)
+        Button(self.board.window , text= "结束游戏" ,command = self.board.window.destroy,width = 13, font = ("Verdana", 12)).place(relx=0, rely=0, x=505, y=420)
+        self.board.window.mainloop()
+
+    
+if __name__ == "__main__":
+    game = Gobang()
\ No newline at end of file
diff --git a/gobang_res30.py b/gobang_res30.py
new file mode 100644
index 000000000..f43983037
--- /dev/null
+++ b/gobang_res30.py
@@ -0,0 +1,316 @@
+from tkinter import *
+import math
+import pickle
+from game import Board, Game
+from mcts_alphaZero import MCTSPlayer
+from policy_value_net_tensorflow import PolicyValueNet # Tensorflow
+from policy_value_net_res_tensorflow import PolicyValueNetRes30 # Tensorflow
+from human_play import Human
+
+#定义棋盘类
+class chessBoard() :
+    def __init__(self, **kwargs) :
+        self.width = int(kwargs.get('width', 9))
+        self.height = int(kwargs.get('height', 9))
+        # need how many pieces in a row to win
+        self.n_in_row = int(kwargs.get('n_in_row', 5))
+        self.row = self.height - 1
+        self.column = self.width - 1
+
+        self.window = Tk()
+        self.window.title("五子棋游戏")
+        self.window.geometry("660x450")
+        self.window.resizable(0,0)
+        self.canvas=Canvas(self.window , bg="#EEE8AC" , width=self.column*50+50, height=self.row*50+50)
+        self.paint_board()
+        self.canvas.grid(row=0, column=0)
+
+    def paint_board(self) :
+        for row in range(0, self.height):
+            if row == 0 or row == self.row:
+                self.canvas.create_line(25, 25+row*50, 25+self.row*50, 25+row*50, width=2)
+            else :
+                self.canvas.create_line(25, 25+row*50, 25+self.row*50, 25+row*50, width=1)
+        
+        for column in range(0, self.width):
+            if column == 0 or column == self.column:
+                self.canvas.create_line(25+column*50, 25, 25+column*50, 25+self.column*50, width=2)
+            else :
+                self.canvas.create_line(25+column*50, 25, 25+column*50, 25+self.column*50, width=1)
+        column = self.column // 4
+        row = self.row // 4
+        x = 25+column*50
+        y = 25+row*50
+        self.canvas.create_oval(x-3, y-3, x+3, y+3, fill="black")
+        x = 25+(self.column-column)*50
+        self.canvas.create_oval(x-3, y-3, x+3, y+3, fill="black")
+        y = 25+(self.row-row)*50
+        self.canvas.create_oval(x-3, y-3, x+3, y+3, fill="black")
+        x = 25+column*50
+        self.canvas.create_oval(x-3, y-3, x+3, y+3, fill="black")
+        x = 25+(self.column//2)*50
+        y = 25+(self.row//2)*50
+        self.canvas.create_oval(x-3, y-3, x+3, y+3, fill="black")
+
+
+#定义五子棋游戏类
+#0为黑子 ， 1为白子 ， 2为空位
+class Gobang() :
+    #初始化
+    def __init__(self) :
+        self.board = chessBoard()
+        self.game_print = StringVar()
+        self.game_print.set("")
+        #16*16的二维列表，保证不会out of index
+        self.db = [([2] * 9) for i in range(9)]
+        #悔棋用的顺序列表
+        self.order = []
+        #棋子颜色
+        self.color_count = 0 
+        self.color = 'black'
+        #清空与赢的初始化，已赢为1，已清空为1
+        self.flag_win = 1
+        self.flag_empty = 1
+
+        self.start_player = 0
+        width, height, n_in_row = 9, 9, 5
+        model_file = 'output/best_policy.model'
+        baseline_file = 'output/baseline_policy.model'
+        board = Board(width=width, height=height, n_in_row=n_in_row, forbidden_hands=False)
+        self.game = Game(board)
+        self.game.board.init_board(self.start_player)
+        self.best_policy = PolicyValueNetRes30(width, height, 'l+', model_file=model_file)
+        self.baseline_policy = PolicyValueNet(width, height, 'l+', model_file=baseline_file)
+        self.mcts_player = MCTSPlayer(self.best_policy.policy_value_fn,
+                                 c_puct=5,
+                                 n_playout=500)  # set larger n_playout for better performance
+        self.mcts_baseline_player = MCTSPlayer(self.baseline_policy.policy_value_fn,
+                                 c_puct=5,
+                                 n_playout=500)  # set larger n_playout for better performance
+        self.human_player = Human()
+        self.human_player.set_player_ind(1)
+        #self.mcts_baseline_player.set_player_ind(1)
+        self.mcts_player.set_player_ind(2)
+        self.players = {1:self.human_player, 2:self.mcts_player}
+        #self.players = {1:self.mcts_baseline_player, 2:self.mcts_player}
+
+        self.options()
+        
+
+    #黑白互换
+    def change_color(self) :
+        self.color_count = (self.color_count + 1 ) % 2
+        if self.color_count == 0 :
+            self.color = "black"
+        elif self.color_count ==1 :
+            self.color = "white"
+    
+    
+    #落子
+    def chess_moving(self ,event) :
+        #不点击“开始”与“清空”无法再次开始落子
+        if self.flag_win ==1 or self.flag_empty ==0:
+            return
+        #坐标转化为下标
+        x,y = event.x-25 , event.y-25
+        x = round(x/50)
+        y = round(y/50)
+        #点击位置没用落子，且没有在棋盘线外，可以落子
+        while self.db[y][x] == 2 and self.limit_boarder(y,x):
+            if len(self.order) > 0:
+                last_move = self.order[-1]
+                last_y = last_move//9
+                last_x = last_move%9
+                self.change_color()
+                self.board.canvas.delete("chessman_new")
+                self.board.canvas.create_oval(25+50*last_x-15 , 25+50*last_y-15 , 25+50*last_x+15 , 25+50*last_y+15 , fill = self.color,tags = "chessman")
+                self.change_color()
+
+            self.db[y][x] = self.color_count
+            current_move = x+9*y
+            self.order.append(current_move)
+            self.board.canvas.create_oval(25+50*x-18 , 25+50*y-18 , 25+50*x+18 , 25+50*y+18 , fill = self.color,tags = "chessman_new")
+            player_in_turn = self.get_current_player()
+            print(self.color, player_in_turn, f"{x}, {y}")
+            self.game.board.do_move(current_move)
+            end, winner = self.game.board.game_end()
+            if end:
+                self.flag_win = 1
+                self.flag_empty = 0
+                print(self.color, player_in_turn, "win!!!")
+                self.game_print.set(self.color+"-"+str(player_in_turn)+"获胜")
+            else:
+                self.change_color()
+                player_in_turn = self.get_current_player()
+                self.game_print.set("请"+self.color+"-"+str(player_in_turn)+"落子")
+                if player_in_turn is self.human_player:
+                    return
+                self.board.window.update()
+                move = player_in_turn.get_action(self.game.board)
+                x = move%9
+                y = move//9
+    
+
+    #保证棋子落在棋盘上
+    def limit_boarder(self , y , x) :
+        if x<0 or x>8 or y<0 or y>8 :
+            return False
+        else :
+            return True
+
+
+    #计算连子的数目,并返回最大连子数目
+    def chessman_count(self , y , x , color_count ) :
+        count1,count2,count3,count4 = 1,1,1,1
+        #横计算
+        for i in range(-1 , -5 , -1) :
+            if self.db[y][x+i] == color_count  :
+                count1 += 1
+            else:
+                break 
+        for i in  range(1 , 5 ,1 ) :
+            if self.db[y][x+i] == color_count  :
+                count1 += 1
+            else:
+                break 
+        #竖计算
+        for i in range(-1 , -5 , -1) :
+            if self.db[y+i][x] == color_count  :
+                count2 += 1
+            else:
+                break 
+        for i in  range(1 , 5 ,1 ) :
+            if self.db[y+i][x] == color_count  :
+                count2 += 1
+            else:
+                break 
+        #/计算
+        for i in range(-1 , -5 , -1) :
+            if self.db[y+i][x+i] == color_count  :
+                count3 += 1
+            else:
+                break 
+        for i in  range(1 , 5 ,1 ) :
+            if self.db[y+i][x+i] == color_count  :
+                count3 += 1
+            else:
+                break 
+        #\计算
+        for i in range(-1 , -5 , -1) :
+            if self.db[y+i][x-i] == color_count :
+                count4 += 1
+            else:
+                break 
+        for i in  range(1 , 5 ,1 ) :
+            if self.db[y+i][x-i] == color_count :
+                count4 += 1
+            else:
+                break 
+            
+        return max(count1 , count2 , count3 , count4)
+
+
+    #判断输赢
+    def game_win(self , y , x , color_count ) :
+        if self.chessman_count(y,x,color_count) >= 5 :
+            self.flag_win = 1
+            self.flag_empty = 0
+            return True
+        else :
+            return False
+        
+
+    #悔棋,清空棋盘，再画剩下的n-1个棋子
+    def withdraw(self ) :
+        if len(self.order)==0 or self.flag_win == 1:
+            return
+        self.board.canvas.delete("chessman")
+        z = self.order.pop()
+        x = z%9
+        y = z//9
+        self.db[y][x] = 2
+        self.color_count = 1
+        for i in self.order :
+            ix = i%9
+            iy = i//9
+            self.change_color()
+            self.board.canvas.create_oval(25+50*ix-15 , 25+50*iy-15 , 25+50*ix+15 , 25+50*iy+15 , fill = self.color,tags = "chessman")
+        self.change_color()
+        self.game_print.set("请"+self.color+"落子")
+    
+
+    #清空
+    def empty_all(self) :
+        self.board.canvas.delete("chessman_new")
+        self.board.canvas.delete("chessman")
+        print(" Empty all!!!")
+        #还原初始化
+        self.db = [([2] * 9) for i in range(9)]
+        self.order = []
+        self.color_count = 0 
+        self.color = 'black'
+        self.flag_win = 1
+        self.flag_empty = 1
+        self.game_print.set("")
+        self.start_player = (self.start_player+1)%2
+        self.game.board.init_board(self.start_player)
+
+    def get_current_player(self):
+        current_player = self.game.board.get_current_player()
+        player_in_turn = self.players[current_player]
+        return player_in_turn
+
+    #将self.flag_win置0才能在棋盘上落子
+    def game_start(self) :
+        #没有清空棋子不能置0开始
+        if self.flag_empty == 0:
+            return
+        self.flag_win = 0
+        print(" New game start...")
+
+        while True:
+            player_in_turn = self.get_current_player()
+            self.game_print.set("请"+self.color+"-"+str(player_in_turn)+"落子")
+            self.board.window.update()
+            if player_in_turn is self.human_player:
+                return
+
+            if len(self.order) > 0:
+                last_move = self.order[-1]
+                last_y = last_move//9
+                last_x = last_move%9
+                self.change_color()
+                self.board.canvas.delete("chessman_new")
+                self.board.canvas.create_oval(25+50*last_x-15 , 25+50*last_y-15 , 25+50*last_x+15 , 25+50*last_y+15 , fill = self.color,tags = "chessman")
+                self.change_color()
+
+            move = player_in_turn.get_action(self.game.board)
+            x = move%9
+            y = move//9    
+            self.db[y][x] = self.color_count
+            self.order.append(move)
+            self.board.canvas.create_oval(25+50*x-18 , 25+50*y-18 , 25+50*x+18 , 25+50*y+18 , fill = self.color,tags = "chessman_new")
+            print(self.color, player_in_turn, f"{x}, {y}")
+            self.game.board.do_move(move)
+            end, winner = self.game.board.game_end()
+            if end:
+                self.flag_win = 1
+                self.flag_empty = 0
+                print(self.color, player_in_turn, "win!!!")
+                self.game_print.set(self.color+"-"+str(player_in_turn)+"获胜")
+                return
+            else:
+                self.change_color()
+
+    def options(self) :
+        self.board.canvas.bind("<Button-1>",self.chess_moving)
+        Label(self.board.window , textvariable = self.game_print , font = ("Arial", 12) ).place(relx = 0, rely = 0 ,x = 475 , y = 200)
+        Button(self.board.window , text= "开始游戏" ,command = self.game_start,width = 13, font = ("Verdana", 12)).place(relx=0, rely=0, x=475, y=15)
+        #Button(self.board.window , text= "我要悔棋" ,command = self.withdraw,width = 13, font = ("Verdana", 12)).place(relx=0, rely=0, x=475, y=60)
+        Button(self.board.window , text= "清空棋局" ,command = self.empty_all,width = 13, font = ("Verdana", 12)).place(relx=0, rely=0, x=475, y=105)
+        Button(self.board.window , text= "结束游戏" ,command = self.board.window.destroy,width = 13, font = ("Verdana", 12)).place(relx=0, rely=0, x=475, y=400)
+        self.board.window.mainloop()
+
+    
+if __name__ == "__main__":
+    game = Gobang()
\ No newline at end of file
diff --git a/human_play.py b/human_play.py
index 9e80c6701..393ad8b28 100644
--- a/human_play.py
+++ b/human_play.py
@@ -11,10 +11,10 @@
 from game import Board, Game
 from mcts_pure import MCTSPlayer as MCTS_Pure
 from mcts_alphaZero import MCTSPlayer
-from policy_value_net_numpy import PolicyValueNetNumpy
+# from policy_value_net_numpy import PolicyValueNetNumpy
 # from policy_value_net import PolicyValueNet  # Theano and Lasagne
 # from policy_value_net_pytorch import PolicyValueNet  # Pytorch
-# from policy_value_net_tensorflow import PolicyValueNet # Tensorflow
+from policy_value_net_res_tensorflow import PolicyValueNetRes30 # Tensorflow
 # from policy_value_net_keras import PolicyValueNet  # Keras
 
 
@@ -48,10 +48,10 @@ def __str__(self):
 
 def run():
     n = 5
-    width, height = 8, 8
-    model_file = 'best_policy_8_8_5.model'
+    width, height = 9, 9
+    model_file = 'output/best_policy.model'
     try:
-        board = Board(width=width, height=height, n_in_row=n)
+        board = Board(width=width, height=height, n_in_row=n, forbidden_hands=True)
         game = Game(board)
 
         # ############### human VS AI ###################
@@ -61,12 +61,7 @@ def run():
         # mcts_player = MCTSPlayer(best_policy.policy_value_fn, c_puct=5, n_playout=400)
 
         # load the provided model (trained in Theano/Lasagne) into a MCTS player written in pure numpy
-        try:
-            policy_param = pickle.load(open(model_file, 'rb'))
-        except:
-            policy_param = pickle.load(open(model_file, 'rb'),
-                                       encoding='bytes')  # To support python3
-        best_policy = PolicyValueNetNumpy(width, height, policy_param)
+        best_policy = PolicyValueNetRes30(width, height, 'l+', model_file=model_file)
         mcts_player = MCTSPlayer(best_policy.policy_value_fn,
                                  c_puct=5,
                                  n_playout=400)  # set larger n_playout for better performance
diff --git a/intermediate_preprocess.ipynb b/intermediate_preprocess.ipynb
new file mode 100644
index 000000000..efe0cedf6
--- /dev/null
+++ b/intermediate_preprocess.ipynb
@@ -0,0 +1,81 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "2999\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "def preprocess(intermediate_result_file, new_file):\n",
+    "    new_lines = []\n",
+    "    with open(intermediate_result_file,'r') as f:\n",
+    "        for line in f:\n",
+    "            if \"episode_len\" not in line and \"lr_multiplier\" not in line:\n",
+    "                continue\n",
+    "            \n",
+    "            if \"episode_len\" in line:\n",
+    "                new_line = line.strip()\n",
+    "            elif new_line:\n",
+    "                new_line += ', '\n",
+    "                new_line += line\n",
+    "                new_lines.append(new_line)\n",
+    "                new_line = ''\n",
+    "    print(len(new_lines))\n",
+    "    with open(new_file,'w') as f:\n",
+    "        f.writelines(new_lines)\n",
+    "    \n",
+    "\n",
+    "preprocess('output/res30_l+_console.txt', \"output/res30_l+_intermediate_result_tmp.txt\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "57baa5815c940fdaff4d14510622de9616cae602444507ba5d0b6727c008cbd6"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.5 64-bit"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.5"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "57baa5815c940fdaff4d14510622de9616cae602444507ba5d0b6727c008cbd6"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/loss_plot.ipynb b/loss_plot.ipynb
new file mode 100644
index 000000000..b2f86224e
--- /dev/null
+++ b/loss_plot.ipynb
@@ -0,0 +1,228 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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125.857044 \r\nL 95.752942 128.021284 \r\nL 95.854465 126.598833 \r\nL 96.057509 130.879298 \r\nL 96.159031 127.867091 \r\nL 96.260553 128.463713 \r\nL 96.362076 130.012121 \r\nL 96.463598 127.937976 \r\nL 96.56512 129.098191 \r\nL 96.666642 127.400224 \r\nL 96.768165 128.861807 \r\nL 97.072731 126.296422 \r\nL 97.377298 130.142423 \r\nL 97.47882 124.615793 \r\nL 97.681865 130.697312 \r\nL 97.783387 126.194596 \r\nL 97.986431 129.279479 \r\nL 98.189476 131.141323 \r\nL 98.290998 130.377764 \r\nL 98.39252 128.750144 \r\nL 98.494042 132.506942 \r\nL 98.697087 130.262816 \r\nL 98.798609 125.319438 \r\nL 98.900131 130.990295 \r\nL 99.001654 127.589671 \r\nL 99.103176 128.019983 \r\nL 99.204698 125.941484 \r\nL 99.509265 130.089021 \r\nL 99.610787 127.705871 \r\nL 99.712309 128.014522 \r\nL 99.813831 127.815592 \r\nL 99.915354 128.115304 \r\nL 100.016876 128.008363 \r\nL 100.118398 128.820298 \r\nL 100.21992 126.403452 \r\nL 100.321443 128.782852 \r\nL 100.422965 128.726695 \r\nL 100.524487 129.394033 \r\nL 100.626009 129.129751 \r\nL 100.829054 126.861453 \r\nL 100.930576 128.602472 \r\nL 101.032098 127.147891 \r\nL 101.13362 127.168747 \r\nL 101.235143 128.319173 \r\nL 101.336665 126.73083 \r\nL 101.438187 128.799475 \r\nL 101.641231 125.51408 \r\nL 101.742754 124.794206 \r\nL 101.844276 128.806469 \r\nL 101.945798 125.467712 \r\nL 102.148843 128.861317 \r\nL 102.250365 128.800992 \r\nL 102.351887 127.163085 \r\nL 102.453409 127.759515 \r\nL 102.554932 125.035738 \r\nL 102.656454 130.185973 \r\nL 102.757976 128.855077 \r\nL 102.859498 130.254777 \r\nL 102.96102 126.277824 \r\nL 103.062543 128.157898 \r\nL 103.164065 124.510634 \r\nL 103.265587 126.872776 \r\nL 103.367109 126.216303 \r\nL 103.468632 126.265931 \r\nL 103.570154 129.176914 \r\nL 103.671676 127.602536 \r\nL 103.87472 129.870464 \r\nL 103.976243 131.100384 \r\nL 104.179287 128.071843 \r\nL 104.280809 127.349464 \r\nL 104.483854 127.791669 \r\nL 104.585376 125.483435 \r\nL 104.686898 130.143339 \r\nL 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114.33151 124.051477 \r\nL 114.433032 120.645987 \r\nL 114.534554 123.436417 \r\nL 114.636077 122.67256 \r\nL 114.737599 118.173659 \r\nL 114.839121 124.202666 \r\nL 114.940643 120.596969 \r\nL 115.042165 124.27698 \r\nL 115.143688 123.722815 \r\nL 115.346732 119.9501 \r\nL 115.549777 124.919144 \r\nL 115.651299 122.775623 \r\nL 115.752821 125.423601 \r\nL 115.854343 125.244208 \r\nL 115.955865 122.799562 \r\nL 116.15891 129.587198 \r\nL 116.260432 128.967424 \r\nL 116.361954 125.002154 \r\nL 116.463477 128.180094 \r\nL 116.564999 125.35449 \r\nL 116.768043 129.836672 \r\nL 116.971088 126.923673 \r\nL 117.07261 128.925778 \r\nL 117.174132 127.061636 \r\nL 117.275654 127.123479 \r\nL 117.377177 127.42279 \r\nL 117.478699 128.217861 \r\nL 117.580221 130.267578 \r\nL 117.681743 129.69895 \r\nL 117.783266 131.036992 \r\nL 117.884788 128.257138 \r\nL 117.98631 130.162789 \r\nL 118.087832 126.822746 \r\nL 118.392399 133.482915 \r\nL 118.493921 126.95618 \r\nL 118.595443 128.985235 \r\nL 118.696966 126.38169 \r\nL 118.798488 127.657649 \r\nL 118.90001 127.229682 \r\nL 119.001532 127.770581 \r\nL 119.103054 125.619054 \r\nL 119.204577 128.252031 \r\nL 119.407621 122.527594 \r\nL 119.610666 126.51098 \r\nL 119.712188 124.28281 \r\nL 119.81371 124.667654 \r\nL 120.016755 127.082491 \r\nL 120.219799 126.257676 \r\nL 120.321321 122.409402 \r\nL 120.422843 126.272452 \r\nL 120.524366 121.300557 \r\nL 120.625888 124.173436 \r\nL 120.72741 123.392554 \r\nL 120.930455 125.776772 \r\nL 121.133499 121.559523 \r\nL 121.235021 124.530245 \r\nL 121.336543 123.061812 \r\nL 121.438066 123.534075 \r\nL 121.539588 129.16107 \r\nL 121.64111 125.381882 \r\nL 121.742632 125.945114 \r\nL 121.844155 124.193568 \r\nL 122.047199 126.728822 \r\nL 122.148721 120.640245 \r\nL 122.453288 127.465118 \r\nL 122.55481 126.340213 \r\nL 122.656332 123.770235 \r\nL 122.757855 126.436144 \r\nL 122.859377 124.877987 \r\nL 122.960899 126.498942 \r\nL 123.062421 125.172866 \r\nL 123.163944 128.600504 \r\nL 123.265466 120.289708 \r\nL 123.46851 126.964765 \r\nL 123.570032 121.853261 \r\nL 123.671555 122.465977 \r\nL 123.773077 124.542684 \r\nL 123.874599 124.056994 \r\nL 124.179166 128.952639 \r\nL 124.280688 125.031682 \r\nL 124.483733 127.470194 \r\nL 124.585255 127.691401 \r\nL 124.686777 124.375225 \r\nL 124.788299 126.967929 \r\nL 124.889821 124.757354 \r\nL 125.092866 128.751726 \r\nL 125.194388 127.361943 \r\nL 125.397433 129.286746 \r\nL 125.498955 126.022608 \r\nL 125.600477 129.430283 \r\nL 125.701999 129.382277 \r\nL 125.803521 129.902586 \r\nL 125.905044 127.960461 \r\nL 126.006566 130.6068 \r\nL 126.108088 127.327388 \r\nL 126.20961 132.014732 \r\nL 126.412655 127.293484 \r\nL 126.717222 134.470982 \r\nL 126.818744 127.873901 \r\nL 127.021788 133.207374 \r\nL 127.12331 129.915346 \r\nL 127.224833 130.762736 \r\nL 127.326355 130.573257 \r\nL 127.427877 135.176683 \r\nL 127.529399 135.158959 \r\nL 127.630922 134.111756 \r\nL 127.833966 134.615249 \r\nL 127.935488 138.833574 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141.945556 129.098818 \r\nL 142.1486 129.602424 \r\nL 142.250122 129.406818 \r\nL 142.351644 125.095765 \r\nL 142.453167 130.593751 \r\nL 142.656211 126.103194 \r\nL 142.757733 129.587102 \r\nL 142.859256 125.852194 \r\nL 142.960778 128.319591 \r\nL 143.0623 127.714392 \r\nL 143.265345 132.338889 \r\nL 143.366867 129.042171 \r\nL 143.468389 130.052619 \r\nL 143.772956 127.669895 \r\nL 143.874478 128.563765 \r\nL 143.976 131.713839 \r\nL 144.179045 128.493996 \r\nL 144.382089 129.400393 \r\nL 144.483611 126.544202 \r\nL 144.585133 130.732196 \r\nL 144.788178 126.649979 \r\nL 144.8897 127.134272 \r\nL 144.991222 126.286617 \r\nL 145.092745 124.389953 \r\nL 145.194267 129.983372 \r\nL 145.397311 124.744272 \r\nL 145.498834 130.346196 \r\nL 145.8034 124.494389 \r\nL 145.904922 124.835715 \r\nL 146.107967 127.208553 \r\nL 146.209489 126.822441 \r\nL 146.311011 127.257322 \r\nL 146.412534 123.382299 \r\nL 146.514056 127.543174 \r\nL 146.615578 124.170858 \r\nL 146.7171 127.078243 \r\nL 146.818622 124.721169 \r\nL 146.920145 126.095822 \r\nL 147.021667 124.44623 \r\nL 147.224711 126.486897 \r\nL 147.326234 125.980304 \r\nL 147.427756 124.389559 \r\nL 147.529278 128.818651 \r\nL 147.6308 126.554409 \r\nL 147.732322 128.736042 \r\nL 148.036889 123.968643 \r\nL 148.138411 123.851013 \r\nL 148.239934 121.582265 \r\nL 148.442978 125.944648 \r\nL 148.5445 124.645345 \r\nL 148.646023 127.797362 \r\nL 148.747545 123.523105 \r\nL 148.849067 126.11271 \r\nL 148.950589 124.211524 \r\nL 149.052111 126.12478 \r\nL 149.255156 123.938392 \r\nL 149.4582 130.411604 \r\nL 149.559723 124.962034 \r\nL 149.661245 127.173107 \r\nL 149.762767 124.032429 \r\nL 149.965811 127.422886 \r\nL 150.067334 123.208577 \r\nL 150.168856 128.201896 \r\nL 150.270378 126.309464 \r\nL 150.473423 129.935358 \r\nL 150.574945 127.081142 \r\nL 150.676467 128.274548 \r\nL 150.777989 132.678055 \r\nL 150.981034 129.209269 \r\nL 151.082556 132.741038 \r\nL 151.184078 129.649073 \r\nL 151.2856 134.42243 \r\nL 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160.82869 135.456576 \r\nL 160.930212 134.378239 \r\nL 161.031734 137.843339 \r\nL 161.234779 132.895423 \r\nL 161.336301 132.46997 \r\nL 161.437823 132.810156 \r\nL 161.640868 139.05503 \r\nL 161.74239 136.58879 \r\nL 161.843912 138.246966 \r\nL 161.945434 135.879476 \r\nL 162.046956 139.291262 \r\nL 162.148479 138.380295 \r\nL 162.250001 134.655257 \r\nL 162.351523 135.464518 \r\nL 162.554568 134.695626 \r\nL 162.859134 140.234728 \r\nL 162.960657 137.443286 \r\nL 163.062179 141.111284 \r\nL 163.163701 140.920802 \r\nL 163.366745 138.655571 \r\nL 163.468268 140.376224 \r\nL 163.56979 139.02237 \r\nL 163.671312 145.156304 \r\nL 163.772834 139.751383 \r\nL 163.874357 143.01781 \r\nL 164.077401 141.439456 \r\nL 164.178923 142.416192 \r\nL 164.280445 146.629394 \r\nL 164.48349 142.474911 \r\nL 164.585012 142.670147 \r\nL 164.686534 142.657122 \r\nL 164.788057 143.446612 \r\nL 164.889579 141.778759 \r\nL 164.991101 134.103975 \r\nL 165.092623 139.047698 \r\nL 165.194146 136.130178 \r\nL 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145.171947 \r\nL 203.366503 149.562485 \r\nL 203.468025 145.68808 \r\nL 203.569547 149.106765 \r\nL 203.67107 145.346376 \r\nL 203.772592 145.449889 \r\nL 203.874114 144.396061 \r\nL 203.975636 149.76735 \r\nL 204.178681 145.378667 \r\nL 204.280203 145.888866 \r\nL 204.381725 145.466432 \r\nL 204.483247 144.392005 \r\nL 204.686292 148.168398 \r\nL 204.787814 147.344096 \r\nL 204.990858 146.573213 \r\nL 205.092381 148.91576 \r\nL 205.295425 145.444541 \r\nL 205.49847 146.30192 \r\nL 205.599992 141.012782 \r\nL 205.803036 145.6173 \r\nL 205.904559 147.9991 \r\nL 206.006081 145.614272 \r\nL 206.107603 147.24003 \r\nL 206.310647 145.43635 \r\nL 206.513692 150.165315 \r\nL 206.716736 146.733284 \r\nL 206.818259 148.607054 \r\nL 206.919781 148.03482 \r\nL 207.021303 145.927171 \r\nL 207.224347 146.51076 \r\nL 207.32587 150.633884 \r\nL 207.528914 147.503035 \r\nL 207.630436 145.984637 \r\nL 207.731959 149.267591 \r\nL 207.833481 145.825032 \r\nL 207.935003 148.963598 \r\nL 208.036525 142.09499 \r\nL 208.23957 147.895146 \r\nL 208.341092 147.474254 \r\nL 208.442614 146.883879 \r\nL 208.544136 147.828292 \r\nL 208.645659 146.866814 \r\nL 208.747181 149.842129 \r\nL 208.848703 146.470937 \r\nL 208.950225 148.023914 \r\nL 209.051748 142.778189 \r\nL 209.356314 148.937033 \r\nL 209.457836 147.503364 \r\nL 209.559359 149.39305 \r\nL 209.660881 149.186547 \r\nL 209.762403 151.797561 \r\nL 209.863925 151.053531 \r\nL 209.965448 152.147697 \r\nL 210.06697 150.874404 \r\nL 210.168492 153.57796 \r\nL 210.371537 150.927349 \r\nL 210.473059 152.096189 \r\nL 210.574581 148.2554 \r\nL 210.676103 148.556036 \r\nL 210.777625 152.121108 \r\nL 210.879148 150.840965 \r\nL 210.98067 151.069552 \r\nL 211.183714 148.507677 \r\nL 211.386759 150.046737 \r\nL 211.589803 149.911167 \r\nL 211.691325 150.198312 \r\nL 211.792848 149.89566 \r\nL 211.89437 150.297488 \r\nL 211.995892 148.007918 \r\nL 212.097414 151.248189 \r\nL 212.198937 149.867763 \r\nL 212.300459 151.346314 \r\nL 212.401981 150.393821 \r\nL 212.503503 153.791707 \r\nL 212.605026 149.588319 \r\nL 212.706548 150.665267 \r\nL 212.80807 153.53613 \r\nL 212.909592 150.502811 \r\nL 213.011114 153.711146 \r\nL 213.214159 149.946606 \r\nL 213.417203 156.52651 \r\nL 213.518726 147.384008 \r\nL 213.620248 148.067728 \r\nL 213.72177 150.453905 \r\nL 213.823292 150.132503 \r\nL 214.026337 152.697461 \r\nL 214.330903 144.092951 \r\nL 214.432426 148.873777 \r\nL 214.533948 145.582054 \r\nL 214.63547 149.938302 \r\nL 214.838515 147.284615 \r\nL 214.940037 151.684444 \r\nL 215.041559 148.547781 \r\nL 215.244603 151.113109 \r\nL 215.346126 151.038137 \r\nL 215.54917 146.547363 \r\nL 215.752215 151.870807 \r\nL 215.853737 152.220846 \r\nL 216.056781 148.277725 \r\nL 216.158303 150.508159 \r\nL 216.259826 150.32488 \r\nL 216.361348 149.86553 \r\nL 216.564392 150.167796 \r\nL 216.665915 153.295449 \r\nL 216.767437 148.330822 \r\nL 216.970481 153.961166 \r\nL 217.072003 152.605826 \r\nL 217.173526 152.819332 \r\nL 217.275048 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144.571566 \r\nL 270.168128 144.952531 \r\nL 270.371173 150.480631 \r\nL 270.574217 146.289152 \r\nL 270.675739 146.802434 \r\nL 270.777262 144.009161 \r\nL 270.878784 148.843807 \r\nL 270.980306 144.758258 \r\nL 271.18335 148.896471 \r\nL 271.386395 146.67475 \r\nL 271.487917 146.403939 \r\nL 271.589439 144.751874 \r\nL 271.690962 147.236328 \r\nL 271.792484 142.159748 \r\nL 271.894006 148.972664 \r\nL 271.995528 144.362068 \r\nL 272.09705 152.482872 \r\nL 272.198573 147.340402 \r\nL 272.300095 150.47056 \r\nL 272.401617 147.322623 \r\nL 272.503139 148.121227 \r\nL 272.604662 145.48222 \r\nL 272.706184 148.170205 \r\nL 272.807706 147.611093 \r\nL 272.909228 149.503614 \r\nL 273.112273 148.61624 \r\nL 273.213795 148.60972 \r\nL 273.315317 150.381519 \r\nL 273.518362 147.757456 \r\nL 273.619884 149.259327 \r\nL 273.721406 145.340233 \r\nL 273.822928 150.287996 \r\nL 273.924451 146.616625 \r\nL 274.025973 153.373449 \r\nL 274.127495 149.404871 \r\nL 274.229017 151.544096 \r\nL 274.330539 150.648387 \r\nL 274.432062 148.469275 \r\nL 274.635106 151.062606 \r\nL 274.736628 150.888112 \r\nL 274.838151 146.543187 \r\nL 275.041195 150.191607 \r\nL 275.142717 145.132533 \r\nL 275.345762 149.033663 \r\nL 275.447284 145.508736 \r\nL 275.751851 150.672639 \r\nL 275.853373 148.225214 \r\nL 275.954895 152.283652 \r\nL 276.056417 147.602508 \r\nL 276.15794 150.730932 \r\nL 276.259462 147.356206 \r\nL 276.360984 149.409914 \r\nL 276.462506 147.006464 \r\nL 276.564028 153.361556 \r\nL 276.868595 145.305959 \r\nL 277.07164 150.56161 \r\nL 277.173162 149.38816 \r\nL 277.274684 155.291009 \r\nL 277.477729 148.637505 \r\nL 277.782295 156.280369 \r\nL 277.883817 156.214246 \r\nL 277.98534 149.875897 \r\nL 278.188384 156.269335 \r\nL 278.289906 153.055668 \r\nL 278.391429 154.697141 \r\nL 278.492951 151.655454 \r\nL 278.594473 154.021394 \r\nL 278.695995 153.403298 \r\nL 278.797517 154.626673 \r\nL 278.89904 151.325185 \r\nL 279.102084 155.185143 \r\nL 279.203606 153.147159 \r\nL 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235.346004 220.143919 \r\nL 235.447527 220.700423 \r\nL 235.650571 218.775786 \r\nL 235.752093 219.761663 \r\nL 235.853615 217.587871 \r\nL 235.955138 218.769022 \r\nL 236.05666 214.884385 \r\nL 236.158182 215.335941 \r\nL 236.259704 214.700329 \r\nL 236.462749 219.01209 \r\nL 236.564271 214.337547 \r\nL 236.665793 219.620009 \r\nL 236.767316 219.178077 \r\nL 236.868838 219.411572 \r\nL 237.071882 217.395359 \r\nL 237.173404 217.185052 \r\nL 237.274927 219.105085 \r\nL 237.376449 217.529947 \r\nL 237.477971 220.761844 \r\nL 237.681016 215.080139 \r\nL 237.782538 217.684885 \r\nL 237.88406 216.699983 \r\nL 238.087104 219.631901 \r\nL 238.188627 214.873381 \r\nL 238.493193 220.365157 \r\nL 238.696238 216.135926 \r\nL 238.79776 219.876048 \r\nL 238.899282 216.865319 \r\nL 239.000805 220.635906 \r\nL 239.203849 218.336597 \r\nL 239.305371 218.69764 \r\nL 239.406893 217.865139 \r\nL 239.508416 219.13099 \r\nL 239.71146 218.296972 \r\nL 239.812982 215.240843 \r\nL 240.117549 219.511797 \r\nL 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146.759303 \r\nL 97.275776 145.745129 \r\nL 97.377298 148.494338 \r\nL 97.47882 143.060741 \r\nL 97.681865 147.926842 \r\nL 97.783387 144.941537 \r\nL 97.884909 146.959486 \r\nL 97.986431 145.818278 \r\nL 98.087954 147.053546 \r\nL 98.189476 145.542721 \r\nL 98.494042 148.602107 \r\nL 98.798609 143.003018 \r\nL 98.900131 145.819635 \r\nL 99.103176 144.910363 \r\nL 99.204698 141.627128 \r\nL 99.407742 145.876531 \r\nL 99.509265 145.918048 \r\nL 99.712309 144.542383 \r\nL 99.915354 144.945038 \r\nL 100.118398 144.877237 \r\nL 100.21992 144.033437 \r\nL 100.524487 146.590061 \r\nL 100.727531 144.522372 \r\nL 100.930576 145.843518 \r\nL 101.032098 145.586431 \r\nL 101.13362 145.686434 \r\nL 101.235143 147.070378 \r\nL 101.336665 143.636179 \r\nL 101.438187 146.170654 \r\nL 101.742754 144.246405 \r\nL 101.844276 147.445609 \r\nL 102.04732 144.404605 \r\nL 102.148843 146.335102 \r\nL 102.351887 144.709216 \r\nL 102.453409 145.484307 \r\nL 102.554932 143.7967 \r\nL 102.656454 146.776721 \r\nL 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140.151211 \r\nL 112.301065 138.317915 \r\nL 112.402588 139.561872 \r\nL 112.50411 138.270061 \r\nL 112.605632 140.868836 \r\nL 112.808676 138.425346 \r\nL 113.011721 141.309114 \r\nL 113.214765 138.310824 \r\nL 113.519332 142.669425 \r\nL 113.620854 139.66985 \r\nL 113.722376 142.343774 \r\nL 113.823899 140.146682 \r\nL 113.925421 140.399641 \r\nL 114.026943 143.167851 \r\nL 114.229988 141.56062 \r\nL 114.33151 143.588534 \r\nL 114.433032 141.044888 \r\nL 114.534554 143.677287 \r\nL 114.636077 143.610216 \r\nL 114.737599 138.876144 \r\nL 114.839121 142.783128 \r\nL 114.940643 142.122912 \r\nL 115.042165 143.754484 \r\nL 115.143688 143.389202 \r\nL 115.346732 140.722313 \r\nL 115.448254 144.572988 \r\nL 115.549777 144.218772 \r\nL 115.651299 143.787232 \r\nL 115.752821 145.299752 \r\nL 115.854343 143.782976 \r\nL 115.955865 144.754251 \r\nL 116.057388 143.963299 \r\nL 116.15891 148.710133 \r\nL 116.361954 145.205763 \r\nL 116.463477 146.78496 \r\nL 116.564999 144.324148 \r\nL 116.666521 145.045853 \r\nL 116.768043 147.007548 \r\nL 116.869566 145.862341 \r\nL 117.07261 146.508793 \r\nL 117.174132 145.235861 \r\nL 117.275654 145.722001 \r\nL 117.377177 145.492169 \r\nL 117.580221 147.746984 \r\nL 117.681743 145.98752 \r\nL 117.783266 146.652249 \r\nL 117.884788 144.968006 \r\nL 117.98631 145.13051 \r\nL 118.087832 144.506311 \r\nL 118.392399 148.335544 \r\nL 118.493921 145.617139 \r\nL 118.595443 145.862044 \r\nL 118.696966 144.505797 \r\nL 118.798488 145.876788 \r\nL 118.90001 144.444886 \r\nL 119.001532 144.945014 \r\nL 119.103054 142.988547 \r\nL 119.204577 144.30094 \r\nL 119.407621 141.751183 \r\nL 119.610666 144.501629 \r\nL 119.81371 142.27423 \r\nL 120.016755 145.092807 \r\nL 120.118277 145.351299 \r\nL 120.321321 142.333053 \r\nL 120.422843 142.530112 \r\nL 120.524366 142.4668 \r\nL 120.625888 143.16492 \r\nL 120.72741 140.698029 \r\nL 120.930455 143.744879 \r\nL 121.031977 142.287095 \r\nL 121.235021 143.634131 \r\nL 121.336543 143.171055 \r\nL 121.539588 145.85468 \r\nL 121.64111 145.49604 \r\nL 121.742632 144.566435 \r\nL 121.844155 141.951326 \r\nL 122.047199 144.958594 \r\nL 122.148721 142.068875 \r\nL 122.351766 143.620624 \r\nL 122.453288 144.479714 \r\nL 122.55481 143.206381 \r\nL 122.757855 145.074072 \r\nL 122.859377 142.341124 \r\nL 123.163944 145.973466 \r\nL 123.265466 142.487993 \r\nL 123.46851 146.602765 \r\nL 123.570032 142.327207 \r\nL 123.671555 143.954916 \r\nL 123.874599 142.580479 \r\nL 123.976121 146.375864 \r\nL 124.077644 145.9622 \r\nL 124.179166 147.34783 \r\nL 124.280688 145.450716 \r\nL 124.38221 146.029937 \r\nL 124.483733 145.200752 \r\nL 124.585255 145.389508 \r\nL 124.686777 143.267035 \r\nL 124.788299 144.41588 \r\nL 124.889821 144.051996 \r\nL 125.092866 148.145206 \r\nL 125.194388 146.512141 \r\nL 125.29591 146.810938 \r\nL 125.397433 147.539132 \r\nL 125.498955 143.828067 \r\nL 125.600477 146.84975 \r\nL 125.701999 146.781467 \r\nL 125.803521 146.355531 \r\nL 125.905044 146.642773 \r\nL 126.006566 147.600774 \r\nL 126.108088 145.567647 \r\nL 126.20961 147.763872 \r\nL 126.412655 144.520998 \r\nL 126.514177 146.700231 \r\nL 126.615699 146.47594 \r\nL 126.717222 150.514214 \r\nL 126.818744 145.246879 \r\nL 127.021788 149.191044 \r\nL 127.12331 146.800306 \r\nL 127.224833 147.202005 \r\nL 127.529399 150.435475 \r\nL 127.630922 148.142669 \r\nL 127.833966 150.198754 \r\nL 127.935488 154.811936 \r\nL 128.03701 149.418989 \r\nL 128.138533 153.146316 \r\nL 128.240055 146.207964 \r\nL 128.544622 151.464891 \r\nL 128.646144 153.285869 \r\nL 128.747666 152.793024 \r\nL 128.849188 151.371385 \r\nL 128.950711 152.995994 \r\nL 129.052233 152.962868 \r\nL 129.153755 149.906855 \r\nL 129.255277 154.160474 \r\nL 129.559844 151.677377 \r\nL 129.661366 152.661895 \r\nL 129.762888 149.585982 \r\nL 129.864411 149.863627 \r\nL 129.965933 151.413874 \r\nL 130.067455 149.153646 \r\nL 130.168977 149.236641 \r\nL 130.270499 149.953729 \r\nL 130.372022 149.575687 \r\nL 130.473544 150.253136 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162.671558 \r\nL 178.087468 162.995153 \r\nL 178.188991 165.587238 \r\nL 178.392035 161.973197 \r\nL 178.493557 163.409444 \r\nL 178.696602 160.924805 \r\nL 178.798124 164.336592 \r\nL 179.102691 161.151561 \r\nL 179.204213 166.042654 \r\nL 179.305735 162.719226 \r\nL 179.407257 164.980317 \r\nL 179.50878 162.667189 \r\nL 179.610302 163.671172 \r\nL 179.711824 163.009238 \r\nL 179.813346 163.472306 \r\nL 179.914868 160.873539 \r\nL 180.117913 162.793877 \r\nL 180.219435 163.214505 \r\nL 180.320957 160.135034 \r\nL 180.727046 164.409581 \r\nL 181.031613 162.602158 \r\nL 181.133135 167.289008 \r\nL 181.234657 162.550683 \r\nL 181.33618 164.021328 \r\nL 181.437702 162.323124 \r\nL 181.539224 164.263013 \r\nL 181.640746 163.56354 \r\nL 181.742269 166.752923 \r\nL 181.843791 166.510066 \r\nL 181.945313 162.251292 \r\nL 182.046835 168.396408 \r\nL 182.148357 163.806112 \r\nL 182.24988 166.16132 \r\nL 182.351402 164.104311 \r\nL 182.452924 164.498389 \r\nL 182.554446 165.457579 \r\nL 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187.325991 168.424815 \r\nL 187.529035 163.261161 \r\nL 187.630558 162.893174 \r\nL 187.73208 167.13229 \r\nL 187.833602 165.52905 \r\nL 188.036647 166.748077 \r\nL 188.138169 165.49217 \r\nL 188.239691 167.85589 \r\nL 188.341213 167.418885 \r\nL 188.442735 168.14497 \r\nL 188.544258 167.421113 \r\nL 188.64578 171.215245 \r\nL 188.848824 166.09767 \r\nL 188.950347 169.379235 \r\nL 189.051869 166.285069 \r\nL 189.153391 167.092077 \r\nL 189.254913 166.183451 \r\nL 189.356436 164.184118 \r\nL 189.457958 164.345779 \r\nL 189.661002 166.869907 \r\nL 189.762524 166.995238 \r\nL 189.864047 166.25173 \r\nL 189.965569 166.65257 \r\nL 190.067091 170.023835 \r\nL 190.168613 164.676151 \r\nL 190.371658 167.935937 \r\nL 190.777747 164.921189 \r\nL 190.879269 165.25477 \r\nL 190.980791 168.644858 \r\nL 191.183836 165.545813 \r\nL 191.285358 166.91458 \r\nL 191.38688 164.111502 \r\nL 191.589925 167.480763 \r\nL 191.792969 164.210365 \r\nL 191.894491 164.856792 \r\nL 191.996013 164.358539 \r\nL 192.097536 167.025573 \r\nL 192.199058 166.454993 \r\nL 192.30058 167.932862 \r\nL 192.402102 167.223496 \r\nL 192.503625 168.03095 \r\nL 192.605147 164.206699 \r\nL 192.808191 167.694799 \r\nL 192.909713 166.447617 \r\nL 193.011236 167.16727 \r\nL 193.21428 165.127029 \r\nL 193.620369 167.316709 \r\nL 193.721891 165.778476 \r\nL 193.823414 167.860732 \r\nL 193.924936 166.861382 \r\nL 194.026458 167.097514 \r\nL 194.12798 170.81581 \r\nL 194.331025 164.884012 \r\nL 194.432547 167.177593 \r\nL 194.534069 165.487195 \r\nL 194.635591 166.440795 \r\nL 194.737114 165.436575 \r\nL 194.838636 167.051776 \r\nL 195.143202 162.631534 \r\nL 195.346247 166.041565 \r\nL 195.447769 167.235284 \r\nL 195.549291 165.820411 \r\nL 195.650814 167.11769 \r\nL 195.853858 160.987989 \r\nL 196.056903 167.278026 \r\nL 196.158425 162.861879 \r\nL 196.259947 166.53215 \r\nL 196.361469 165.075699 \r\nL 196.462991 167.545726 \r\nL 196.564514 162.124684 \r\nL 196.666036 167.036185 \r\nL 196.767558 164.530126 \r\nL 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168.730559 \r\nL 225.701393 172.475472 \r\nL 225.802915 170.093375 \r\nL 225.904437 171.491135 \r\nL 226.005959 171.2359 \r\nL 226.209004 173.31231 \r\nL 226.310526 173.982451 \r\nL 226.412048 172.107926 \r\nL 226.513571 173.228616 \r\nL 226.818137 168.853244 \r\nL 226.91966 172.450465 \r\nL 227.122704 169.260432 \r\nL 227.224226 172.10086 \r\nL 227.325748 171.94757 \r\nL 227.427271 171.527301 \r\nL 227.528793 168.11867 \r\nL 227.731837 170.91116 \r\nL 227.83336 170.940804 \r\nL 227.934882 170.052375 \r\nL 228.036404 171.103807 \r\nL 228.137926 170.623144 \r\nL 228.239448 171.334827 \r\nL 228.340971 169.581004 \r\nL 228.442493 169.888811 \r\nL 228.544015 169.401901 \r\nL 228.645537 167.345418 \r\nL 228.74706 169.923695 \r\nL 228.848582 166.45929 \r\nL 228.950104 167.296958 \r\nL 229.051626 166.105721 \r\nL 229.153149 167.940093 \r\nL 229.254671 167.775662 \r\nL 229.356193 164.933054 \r\nL 229.457715 166.444192 \r\nL 229.559237 165.64034 \r\nL 229.762282 168.452472 \r\nL 229.965326 165.432291 \r\nL 230.066849 164.46689 \r\nL 230.168371 166.989669 \r\nL 230.371415 163.559645 \r\nL 230.57446 165.294793 \r\nL 230.675982 163.938297 \r\nL 230.777504 165.338929 \r\nL 230.879026 163.487468 \r\nL 230.980549 163.657665 \r\nL 231.082071 167.126423 \r\nL 231.183593 162.223555 \r\nL 231.589682 167.659308 \r\nL 231.792726 164.119593 \r\nL 231.894249 164.306084 \r\nL 232.097293 163.410311 \r\nL 232.198815 161.653425 \r\nL 232.300338 166.847606 \r\nL 232.503382 160.965118 \r\nL 232.706426 163.102632 \r\nL 232.807949 160.193022 \r\nL 233.010993 162.917868 \r\nL 233.112515 164.52611 \r\nL 233.31556 161.911499 \r\nL 233.417082 161.157721 \r\nL 233.620126 165.728647 \r\nL 233.721649 161.928452 \r\nL 233.823171 163.750746 \r\nL 234.026215 160.404296 \r\nL 234.22926 164.129017 \r\nL 234.330782 161.041006 \r\nL 234.432304 161.53895 \r\nL 234.533827 163.401148 \r\nL 234.736871 160.431125 \r\nL 234.838393 162.360835 \r\nL 234.939915 160.908014 \r\nL 235.041438 163.84347 \r\nL 235.14296 159.881762 \r\nL 235.244482 162.717572 \r\nL 235.346004 160.070895 \r\nL 235.549049 163.576525 \r\nL 235.752093 160.304879 \r\nL 235.955138 160.132449 \r\nL 236.05666 159.060247 \r\nL 236.158182 162.561621 \r\nL 236.259704 160.191481 \r\nL 236.564271 162.889368 \r\nL 236.665793 162.218118 \r\nL 236.767316 163.283334 \r\nL 236.868838 161.159014 \r\nL 236.97036 163.033145 \r\nL 237.071882 161.12478 \r\nL 237.173404 162.603764 \r\nL 237.274927 162.111401 \r\nL 237.376449 159.232725 \r\nL 237.579493 165.346453 \r\nL 237.782538 163.042364 \r\nL 237.88406 165.219239 \r\nL 238.087104 163.497064 \r\nL 238.188627 160.417875 \r\nL 238.391671 163.533571 \r\nL 238.493193 162.209365 \r\nL 238.594716 163.079256 \r\nL 238.696238 162.835877 \r\nL 238.79776 163.885758 \r\nL 238.899282 160.690557 \r\nL 239.102327 162.50601 \r\nL 239.203849 162.913917 \r\nL 239.406893 165.112687 \r\nL 239.508416 163.527845 \r\nL 239.609938 167.226985 \r\nL 239.71146 163.621207 \r\nL 239.812982 166.470753 \r\nL 239.914505 163.63147 \r\nL 240.016027 164.832597 \r\nL 240.117549 168.088612 \r\nL 240.219071 166.004312 \r\nL 240.320593 166.713012 \r\nL 240.422116 165.360149 \r\nL 240.523638 165.896668 \r\nL 240.62516 162.725121 \r\nL 240.726682 167.724335 \r\nL 240.828205 164.999124 \r\nL 240.929727 166.369573 \r\nL 241.031249 164.19948 \r\nL 241.335816 169.820404 \r\nL 241.53886 166.200075 \r\nL 241.741905 168.806274 \r\nL 241.843427 167.666748 \r\nL 242.147994 171.032079 \r\nL 242.249516 168.462321 \r\nL 242.351038 169.094158 \r\nL 242.45256 168.060779 \r\nL 242.554082 168.890718 \r\nL 242.655605 168.083858 \r\nL 242.858649 171.527847 \r\nL 243.061694 169.344615 \r\nL 243.163216 169.161124 \r\nL 243.264738 166.910778 \r\nL 243.36626 171.863363 \r\nL 243.467782 170.152728 \r\nL 243.569305 172.342046 \r\nL 243.670827 171.158434 \r\nL 243.873871 167.450007 \r\nL 244.076916 172.208211 \r\nL 244.27996 167.090576 \r\nL 244.381483 170.180284 \r\nL 244.584527 167.822869 \r\nL 244.686049 168.997146 \r\nL 244.787571 167.952737 \r\nL 244.889094 169.668391 \r\nL 244.990616 168.933978 \r\nL 245.19366 171.186645 \r\nL 245.396705 168.873565 \r\nL 245.701271 173.095873 \r\nL 245.802794 170.208564 \r\nL 245.904316 170.514596 \r\nL 246.10736 168.609383 \r\nL 246.310405 173.467916 \r\nL 246.411927 170.836389 \r\nL 246.513449 170.902262 \r\nL 246.614972 172.230503 \r\nL 246.716494 167.974178 \r\nL 246.818016 170.250085 \r\nL 246.919538 169.474709 \r\nL 247.02106 170.649002 \r\nL 247.122583 168.530721 \r\nL 247.325627 170.813759 \r\nL 247.427149 170.335589 \r\nL 247.528672 168.706503 \r\nL 247.630194 172.571079 \r\nL 247.731716 171.075431 \r\nL 247.833238 171.100807 \r\nL 247.93476 172.098535 \r\nL 248.036283 170.205661 \r\nL 248.137805 172.519551 \r\nL 248.442372 168.97819 \r\nL 248.543894 171.250186 \r\nL 248.645416 168.184528 \r\nL 248.746938 168.947252 \r\nL 248.848461 171.052825 \r\nL 248.949983 169.641987 \r\nL 249.051505 172.795044 \r\nL 249.457594 165.095417 \r\nL 249.559116 168.185415 \r\nL 249.660638 165.29481 \r\nL 249.863683 170.640722 \r\nL 250.066727 166.008524 \r\nL 250.168249 169.670419 \r\nL 250.269772 168.68124 \r\nL 250.371294 168.919772 \r\nL 250.472816 168.691161 \r\nL 250.574338 169.662176 \r\nL 250.675861 165.404702 \r\nL 250.777383 167.4262 \r\nL 250.878905 166.294344 \r\nL 250.980427 170.777112 \r\nL 251.183472 168.913537 \r\nL 251.284994 168.066512 \r\nL 251.386516 170.587469 \r\nL 251.488038 167.961583 \r\nL 251.589561 169.722376 \r\nL 251.792605 167.753815 \r\nL 251.99565 167.2078 \r\nL 252.097172 165.654349 \r\nL 252.198694 168.04958 \r\nL 252.401738 166.570351 \r\nL 252.604783 163.996538 \r\nL 252.706305 163.498317 \r\nL 252.807827 166.318315 \r\nL 252.90935 166.095586 \r\nL 253.010872 165.101914 \r\nL 253.213916 165.515382 \r\nL 253.315439 162.702226 \r\nL 253.518483 164.670803 \r\nL 253.620005 163.27911 \r\nL 253.82305 165.062673 \r\nL 253.924572 164.475884 \r\nL 254.026094 165.791276 \r\nL 254.127616 164.310469 \r\nL 254.229139 165.311569 \r\nL 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259.102205 161.42748 \r\nL 259.203728 163.026809 \r\nL 259.508294 157.643185 \r\nL 259.609817 162.286995 \r\nL 259.812861 160.274628 \r\nL 259.914383 160.052626 \r\nL 260.015905 160.197985 \r\nL 260.117428 161.999513 \r\nL 260.21895 160.740362 \r\nL 260.320472 162.212424 \r\nL 260.421994 158.489474 \r\nL 260.625039 161.574236 \r\nL 260.726561 158.993369 \r\nL 260.828083 161.336198 \r\nL 260.929606 161.182703 \r\nL 261.031128 161.475357 \r\nL 261.13265 161.188365 \r\nL 261.234172 157.468016 \r\nL 261.437217 161.910536 \r\nL 261.538739 161.251629 \r\nL 261.640261 163.54144 \r\nL 261.741783 159.367179 \r\nL 261.944828 160.546362 \r\nL 262.04635 158.92095 \r\nL 262.147872 161.631991 \r\nL 262.350917 159.193721 \r\nL 262.452439 162.392042 \r\nL 262.553961 162.050354 \r\nL 262.655483 163.807738 \r\nL 262.757006 160.789974 \r\nL 262.858528 164.078806 \r\nL 262.96005 160.923183 \r\nL 263.061572 161.22019 \r\nL 263.264617 160.108044 \r\nL 263.366139 161.468973 \r\nL 263.467661 159.817799 \r\nL 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164.455563 \r\nL 272.503139 164.772384 \r\nL 272.604662 163.338422 \r\nL 272.706184 163.427006 \r\nL 272.807706 163.655995 \r\nL 272.909228 167.056956 \r\nL 273.010751 163.253139 \r\nL 273.112273 163.564592 \r\nL 273.315317 166.744082 \r\nL 273.518362 164.666166 \r\nL 273.619884 165.340936 \r\nL 273.721406 163.171309 \r\nL 273.822928 165.087776 \r\nL 273.924451 163.462164 \r\nL 274.025973 167.215975 \r\nL 274.127495 164.946717 \r\nL 274.229017 165.878981 \r\nL 274.432062 163.162821 \r\nL 274.635106 169.592515 \r\nL 274.838151 164.602095 \r\nL 275.041195 167.122063 \r\nL 275.24424 165.726222 \r\nL 275.345762 167.213775 \r\nL 275.548806 163.820551 \r\nL 275.751851 167.42883 \r\nL 275.954895 166.704411 \r\nL 276.15794 166.576133 \r\nL 276.259462 165.492302 \r\nL 276.564028 169.403583 \r\nL 276.665551 166.816327 \r\nL 276.767073 167.909212 \r\nL 276.868595 163.520585 \r\nL 277.07164 166.62823 \r\nL 277.173162 166.318347 \r\nL 277.274684 168.780998 \r\nL 277.477729 165.606098 \r\nL 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282.046229 169.028581 \r\nL 282.249273 166.090174 \r\nL 282.350795 165.873484 \r\nL 282.452318 164.026086 \r\nL 282.55384 166.775854 \r\nL 282.858407 163.558079 \r\nL 282.959929 165.150655 \r\nL 283.162973 161.80035 \r\nL 283.264495 165.974045 \r\nL 283.569062 164.236717 \r\nL 283.670584 163.755123 \r\nL 283.772107 161.411459 \r\nL 283.873629 164.824667 \r\nL 283.975151 162.18708 \r\nL 284.076673 167.412737 \r\nL 284.279718 161.439092 \r\nL 284.38124 164.878824 \r\nL 284.482762 162.678994 \r\nL 284.584284 162.886798 \r\nL 284.685807 166.036326 \r\nL 284.787329 162.274034 \r\nL 284.888851 162.754488 \r\nL 284.990373 160.788207 \r\nL 285.091896 163.494486 \r\nL 285.193418 161.80626 \r\nL 285.29494 163.898743 \r\nL 285.396462 160.180222 \r\nL 285.497984 162.731826 \r\nL 285.599507 162.238307 \r\nL 285.701029 163.669655 \r\nL 286.107118 159.132344 \r\nL 286.20864 161.085158 \r\nL 286.310162 159.476433 \r\nL 286.411684 161.663896 \r\nL 286.513207 161.216086 \r\nL 286.614729 159.724389 \r\nL 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138.816858 \r\nL 176.145024 135.606085 \r\nL 176.778455 140.333718 \r\nL 177.09517 137.389337 \r\nL 177.411886 136.523022 \r\nL 177.728602 137.012034 \r\nL 178.045317 140.48703 \r\nL 178.362033 137.006039 \r\nL 178.678748 135.62818 \r\nL 178.995464 132.954352 \r\nL 179.312179 136.729962 \r\nL 179.628895 138.12404 \r\nL 179.94561 137.672967 \r\nL 180.262326 138.099941 \r\nL 180.579041 133.259735 \r\nL 180.895757 136.46906 \r\nL 181.212473 135.63224 \r\nL 181.845904 132.167545 \r\nL 182.162619 131.850207 \r\nL 182.479335 128.841896 \r\nL 182.79605 133.629031 \r\nL 183.112766 134.079676 \r\nL 183.429481 133.145355 \r\nL 183.746197 136.949328 \r\nL 184.379628 132.05731 \r\nL 184.696343 136.184179 \r\nL 185.013059 135.385503 \r\nL 185.329775 136.33941 \r\nL 185.64649 138.380232 \r\nL 185.963206 130.892797 \r\nL 186.596637 136.950133 \r\nL 186.913352 138.273473 \r\nL 187.230068 133.242059 \r\nL 187.546783 134.655946 \r\nL 187.863499 137.307987 \r\nL 188.180214 135.722538 \r\nL 188.49693 131.296255 \r\nL 189.130361 136.738235 \r\nL 189.447077 135.175394 \r\nL 189.763792 138.540087 \r\nL 190.080508 134.143332 \r\nL 190.397223 133.890977 \r\nL 190.713939 130.161476 \r\nL 191.030654 138.378973 \r\nL 191.34737 131.072889 \r\nL 191.664085 136.670622 \r\nL 191.980801 130.3768 \r\nL 192.614232 138.285694 \r\nL 192.930948 136.541554 \r\nL 193.247663 133.943706 \r\nL 193.564379 135.892336 \r\nL 193.881094 136.064421 \r\nL 194.19781 139.77616 \r\nL 194.514525 133.231003 \r\nL 195.147956 142.13563 \r\nL 195.464672 133.306811 \r\nL 195.781387 137.445277 \r\nL 196.098103 132.471207 \r\nL 196.414819 139.041457 \r\nL 196.731534 136.507007 \r\nL 197.04825 139.243729 \r\nL 197.364965 135.115438 \r\nL 197.681681 135.893604 \r\nL 197.998396 139.77109 \r\nL 198.631827 133.214226 \r\nL 198.948543 133.743137 \r\nL 199.265258 137.189916 \r\nL 199.89869 131.76777 \r\nL 200.532121 139.24426 \r\nL 200.848836 135.715002 \r\nL 201.165552 136.759627 \r\nL 201.482267 138.53882 \r\nL 201.798983 138.076253 \r\nL 202.115698 139.886558 \r\nL 202.432414 140.281957 \r\nL 202.749129 139.134693 \r\nL 203.065845 135.689284 \r\nL 203.38256 135.232687 \r\nL 203.699276 136.13313 \r\nL 204.332707 139.326962 \r\nL 204.649423 138.301649 \r\nL 204.966138 136.67326 \r\nL 205.282854 138.429184 \r\nL 205.599569 137.463783 \r\nL 205.916285 138.340992 \r\nL 206.233 140.497975 \r\nL 206.549716 135.044408 \r\nL 206.866431 140.108323 \r\nL 207.499863 142.215207 \r\nL 207.816578 137.540568 \r\nL 208.133294 138.477981 \r\nL 208.450009 142.031176 \r\nL 208.766725 141.473669 \r\nL 209.08344 141.141327 \r\nL 209.400156 142.556858 \r\nL 209.716871 140.666232 \r\nL 210.033587 140.887217 \r\nL 210.350302 142.947144 \r\nL 210.667018 141.866876 \r\nL 210.983734 143.128136 \r\nL 211.300449 141.587544 \r\nL 211.617165 141.105624 \r\nL 211.93388 140.946745 \r\nL 212.250596 139.933472 \r\nL 212.567311 144.460031 \r\nL 212.884027 144.940659 \r\nL 213.517458 140.752933 \r\nL 213.834173 140.024618 \r\nL 214.150889 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141.863647 \r\nL 226.819511 143.071879 \r\nL 227.136226 142.086096 \r\nL 227.452942 143.076015 \r\nL 227.769657 140.030039 \r\nL 228.086373 143.539549 \r\nL 228.403088 139.135695 \r\nL 229.036519 145.124202 \r\nL 229.353235 142.805727 \r\nL 229.669951 142.223822 \r\nL 229.986666 142.6039 \r\nL 230.303382 140.186101 \r\nL 230.620097 140.881924 \r\nL 230.936813 142.900308 \r\nL 231.253528 139.084893 \r\nL 231.570244 146.962751 \r\nL 231.886959 147.591398 \r\nL 232.203675 144.385781 \r\nL 232.52039 144.738643 \r\nL 232.837106 139.877241 \r\nL 233.153821 143.928217 \r\nL 233.470537 144.782174 \r\nL 233.787253 141.334059 \r\nL 234.103968 141.991396 \r\nL 234.420684 143.140682 \r\nL 235.054115 141.660869 \r\nL 235.37083 145.759486 \r\nL 235.687546 142.760621 \r\nL 236.004261 142.746747 \r\nL 236.320977 140.814919 \r\nL 236.637692 143.690027 \r\nL 236.954408 143.083226 \r\nL 237.271124 141.462605 \r\nL 237.587839 147.771619 \r\nL 237.904555 141.793988 \r\nL 238.22127 144.872978 \r\nL 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263.558514 140.75057 \r\nL 263.875229 138.791431 \r\nL 264.191945 137.706058 \r\nL 264.825376 140.8077 \r\nL 265.142091 143.969692 \r\nL 265.458807 136.115231 \r\nL 265.775522 141.341767 \r\nL 266.092238 140.98709 \r\nL 266.408953 140.034578 \r\nL 266.725669 140.517938 \r\nL 267.3591 140.699297 \r\nL 267.675816 141.143023 \r\nL 267.992531 138.249965 \r\nL 268.309247 142.253419 \r\nL 268.625962 140.292568 \r\nL 268.942678 139.536232 \r\nL 269.259393 139.552418 \r\nL 269.576109 143.533991 \r\nL 269.892824 143.227401 \r\nL 270.20954 141.526809 \r\nL 270.526255 139.156145 \r\nL 270.842971 139.088233 \r\nL 271.159687 144.033579 \r\nL 271.793118 144.082257 \r\nL 272.109833 139.512921 \r\nL 272.426549 140.305046 \r\nL 272.743264 142.203928 \r\nL 273.376695 138.561942 \r\nL 273.693411 142.436517 \r\nL 274.010126 139.754742 \r\nL 274.326842 141.444432 \r\nL 274.643558 141.362945 \r\nL 274.960273 143.342381 \r\nL 275.276989 142.190354 \r\nL 275.593704 141.727797 \r\nL 275.91042 143.273483 \r\nL 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138.795902 194.719593 \r\nL 138.897424 194.082916 \r\nL 138.998947 194.142692 \r\nL 139.201991 188.865156 \r\nL 139.303513 190.499743 \r\nL 139.405036 187.289215 \r\nL 139.506558 191.951582 \r\nL 139.60808 186.815045 \r\nL 139.811124 192.262908 \r\nL 139.912647 186.27983 \r\nL 140.014169 192.293008 \r\nL 140.115691 185.329489 \r\nL 140.318736 191.409318 \r\nL 140.420258 189.510935 \r\nL 140.52178 197.47884 \r\nL 140.623302 189.983488 \r\nL 140.724825 190.938045 \r\nL 140.826347 194.909826 \r\nL 140.927869 193.829724 \r\nL 141.029391 190.37692 \r\nL 141.130913 192.403746 \r\nL 141.232436 189.055911 \r\nL 141.333958 190.45557 \r\nL 141.43548 186.430527 \r\nL 141.537002 187.192899 \r\nL 141.638525 190.928011 \r\nL 141.740047 188.841609 \r\nL 141.841569 192.486522 \r\nL 141.943091 191.422907 \r\nL 142.146136 188.256104 \r\nL 142.34918 187.109015 \r\nL 142.450702 181.762602 \r\nL 142.653747 188.12346 \r\nL 142.755269 187.682361 \r\nL 142.856791 175.943263 \r\nL 142.958313 178.219674 \r\nL 143.059836 188.436415 \r\nL 143.161358 187.918321 \r\nL 143.364402 182.337303 \r\nL 143.465925 185.778594 \r\nL 143.668969 175.333949 \r\nL 143.770491 175.460191 \r\nL 143.973536 182.391721 \r\nL 144.075058 182.067315 \r\nL 144.17658 189.564905 \r\nL 144.481147 178.796961 \r\nL 144.582669 181.507782 \r\nL 144.684191 176.889054 \r\nL 144.785714 181.460277 \r\nL 144.887236 180.60272 \r\nL 145.09028 183.559522 \r\nL 145.191802 179.127297 \r\nL 145.293325 180.236093 \r\nL 145.394847 174.25151 \r\nL 145.496369 176.113503 \r\nL 145.597891 174.694673 \r\nL 145.699414 167.047394 \r\nL 145.902458 179.748297 \r\nL 146.00398 180.027647 \r\nL 146.207025 188.338956 \r\nL 146.308547 176.432762 \r\nL 146.410069 187.476078 \r\nL 146.511591 180.643636 \r\nL 146.613114 180.935145 \r\nL 146.714636 180.624254 \r\nL 146.816158 182.13787 \r\nL 146.91768 174.911325 \r\nL 147.019203 182.928054 \r\nL 147.120725 181.364098 \r\nL 147.222247 182.505132 \r\nL 147.323769 174.670231 \r\nL 147.628336 181.71174 \r\nL 147.729858 182.600963 \r\nL 147.83138 178.794176 \r\nL 148.034425 186.080099 \r\nL 148.237469 175.941273 \r\nL 148.338992 176.109624 \r\nL 148.542036 179.39501 \r\nL 148.643558 179.514612 \r\nL 148.74508 176.101145 \r\nL 148.846603 181.936635 \r\nL 148.948125 179.467915 \r\nL 149.049647 186.454708 \r\nL 149.151169 182.501464 \r\nL 149.252692 184.580084 \r\nL 149.455736 179.32515 \r\nL 149.65878 187.310102 \r\nL 149.760303 179.050475 \r\nL 149.963347 184.687129 \r\nL 150.064869 180.705688 \r\nL 150.267914 183.506074 \r\nL 150.369436 185.375029 \r\nL 150.674003 179.234367 \r\nL 150.775525 183.921624 \r\nL 150.877047 179.781691 \r\nL 151.080092 186.160265 \r\nL 151.181614 179.346087 \r\nL 151.283136 180.073386 \r\nL 151.384658 186.475396 \r\nL 151.486181 182.216967 \r\nL 151.689225 182.996645 \r\nL 151.790747 182.69381 \r\nL 151.892269 176.019475 \r\nL 151.993792 184.53141 \r\nL 152.196836 177.579167 \r\nL 152.298358 182.972924 \r\nL 152.399881 177.190571 \r\nL 152.501403 181.010574 \r\nL 152.602925 180.07361 \r\nL 152.80597 187.233218 \r\nL 152.907492 182.129267 \r\nL 153.009014 183.693634 \r\nL 153.313581 180.004721 \r\nL 153.415103 181.388602 \r\nL 153.516625 186.26552 \r\nL 153.71967 181.280587 \r\nL 153.922714 182.683864 \r\nL 154.024236 178.261933 \r\nL 154.125758 184.745749 \r\nL 154.328803 178.425696 \r\nL 154.430325 179.175473 \r\nL 154.531847 177.863142 \r\nL 154.63337 174.926742 \r\nL 154.734892 183.586427 \r\nL 154.937936 175.475296 \r\nL 155.039459 184.148148 \r\nL 155.344025 175.088429 \r\nL 155.445547 175.616867 \r\nL 155.648592 179.290476 \r\nL 155.750114 178.6927 \r\nL 155.851636 179.365979 \r\nL 155.953159 173.366701 \r\nL 156.054681 179.808533 \r\nL 156.156203 174.587541 \r\nL 156.257725 179.088731 \r\nL 156.359247 175.439528 \r\nL 156.46077 177.567754 \r\nL 156.562292 175.01387 \r\nL 156.765336 178.173213 \r\nL 156.866859 177.38891 \r\nL 156.968381 174.926133 \r\nL 157.069903 181.783216 \r\nL 157.171425 178.277734 \r\nL 157.272947 181.655321 \r\nL 157.577514 174.274474 \r\nL 157.679036 174.09236 \r\nL 157.780559 170.579904 \r\nL 157.983603 177.333709 \r\nL 158.085125 175.322138 \r\nL 158.186648 180.202065 \r\nL 158.28817 173.584696 \r\nL 158.389692 177.5939 \r\nL 158.491214 174.6505 \r\nL 158.592736 177.612586 \r\nL 158.795781 174.22764 \r\nL 158.998825 184.249412 \r\nL 159.100348 175.812433 \r\nL 159.20187 179.235598 \r\nL 159.303392 174.373226 \r\nL 159.506436 179.622304 \r\nL 159.607959 173.097745 \r\nL 159.709481 180.828361 \r\nL 159.811003 177.898513 \r\nL 160.014048 183.512092 \r\nL 160.11557 179.093219 \r\nL 160.217092 180.940839 \r\nL 160.318614 187.758312 \r\nL 160.521659 182.387966 \r\nL 160.623181 187.855822 \r\nL 160.724703 183.068867 \r\nL 160.826225 190.458939 \r\nL 160.927748 180.950562 \r\nL 161.232314 187.168493 \r\nL 161.333837 185.362659 \r\nL 161.435359 185.696302 \r\nL 161.536881 183.805329 \r\nL 161.638403 190.946524 \r\nL 161.739925 182.094107 \r\nL 161.841448 185.548801 \r\nL 161.94297 183.707496 \r\nL 162.044492 185.918201 \r\nL 162.146014 179.586063 \r\nL 162.247537 188.873387 \r\nL 162.349059 180.639695 \r\nL 162.653626 193.729082 \r\nL 162.755148 193.00831 \r\nL 163.059714 179.06946 \r\nL 163.262759 186.988505 \r\nL 163.364281 187.672589 \r\nL 163.567326 182.140829 \r\nL 163.668848 185.931578 \r\nL 163.871892 183.26825 \r\nL 163.973414 186.829143 \r\nL 164.074937 184.163527 \r\nL 164.176459 187.532634 \r\nL 164.277981 186.682438 \r\nL 164.379503 187.696696 \r\nL 164.481026 195.850346 \r\nL 164.582548 186.253423 \r\nL 164.68407 190.337944 \r\nL 164.785592 185.791972 \r\nL 164.887115 188.463046 \r\nL 164.988637 186.72308 \r\nL 165.090159 190.155445 \r\nL 165.293203 189.372857 \r\nL 165.394726 184.413312 \r\nL 165.59777 197.154907 \r\nL 165.800815 183.573994 \r\nL 166.003859 189.549563 \r\nL 166.105381 188.01547 \r\nL 166.206903 182.671207 \r\nL 166.308426 192.084151 \r\nL 166.409948 184.898945 \r\nL 166.51147 192.435822 \r\nL 166.714515 187.261564 \r\nL 166.816037 186.376084 \r\nL 167.120604 192.998861 \r\nL 167.222126 186.722235 \r\nL 167.323648 188.642673 \r\nL 167.42517 183.92288 \r\nL 167.526692 185.510869 \r\nL 167.628215 183.251677 \r\nL 167.729737 184.168351 \r\nL 168.034304 193.196889 \r\nL 168.237348 186.38879 \r\nL 168.440392 190.639921 \r\nL 168.541915 188.835106 \r\nL 168.643437 189.453508 \r\nL 168.744959 191.308787 \r\nL 168.846481 190.888277 \r\nL 168.948004 193.961735 \r\nL 169.049526 188.304529 \r\nL 169.151048 190.849076 \r\nL 169.25257 188.926126 \r\nL 169.354093 191.306425 \r\nL 169.557137 185.955064 \r\nL 169.658659 191.754325 \r\nL 169.760181 189.897168 \r\nL 169.861704 191.086292 \r\nL 169.963226 190.054939 \r\nL 170.064748 193.888158 \r\nL 170.16627 193.533354 \r\nL 170.267793 189.225605 \r\nL 170.369315 192.059995 \r\nL 170.470837 190.390522 \r\nL 170.572359 195.755161 \r\nL 170.775404 188.09484 \r\nL 170.876926 187.436156 \r\nL 170.978448 187.96283 \r\nL 171.181493 197.631091 \r\nL 171.283015 193.812878 \r\nL 171.384537 196.380053 \r\nL 171.486059 192.714724 \r\nL 171.587581 197.996823 \r\nL 171.689104 196.586472 \r\nL 171.790626 190.8194 \r\nL 171.892148 192.07229 \r\nL 172.095193 190.881899 \r\nL 172.399759 199.457489 \r\nL 172.501282 195.135802 \r\nL 172.602804 200.814566 \r\nL 172.704326 200.519663 \r\nL 172.90737 197.012652 \r\nL 173.008893 199.676553 \r\nL 173.110415 197.580527 \r\nL 173.211937 207.077031 \r\nL 173.313459 198.709178 \r\nL 173.414982 203.766233 \r\nL 173.618026 201.32264 \r\nL 173.719548 202.834814 \r\nL 173.82107 209.357657 \r\nL 174.024115 202.925722 \r\nL 174.125637 203.227985 \r\nL 174.227159 203.207819 \r\nL 174.328682 204.430101 \r\nL 174.430204 201.847945 \r\nL 174.531726 189.965908 \r\nL 174.633248 197.61974 \r\nL 174.734771 193.10286 \r\nL 174.836293 197.863495 \r\nL 174.937815 196.043139 \r\nL 175.242382 204.809037 \r\nL 175.445426 196.760517 \r\nL 175.546948 200.921847 \r\nL 175.648471 195.913081 \r\nL 175.749993 200.833003 \r\nL 175.851515 199.5155 \r\nL 175.953037 206.405194 \r\nL 176.359126 197.059349 \r\nL 176.460648 203.357994 \r\nL 176.562171 198.021141 \r\nL 176.663693 201.746781 \r\nL 176.866737 196.266405 \r\nL 177.069782 203.448417 \r\nL 177.171304 203.027222 \r\nL 177.272826 194.804534 \r\nL 177.475871 208.492181 \r\nL 177.678915 201.55201 \r\nL 177.780437 197.764245 \r\nL 177.983482 205.117131 \r\nL 178.085004 201.297215 \r\nL 178.288048 204.579865 \r\nL 178.389571 202.981284 \r\nL 178.491093 207.385038 \r\nL 178.592615 202.167689 \r\nL 178.694137 203.157989 \r\nL 178.897182 201.731774 \r\nL 178.998704 206.255044 \r\nL 179.100226 199.768082 \r\nL 179.201749 205.858181 \r\nL 179.404793 200.601059 \r\nL 179.506315 208.557701 \r\nL 179.70936 202.529306 \r\nL 179.810882 204.1255 \r\nL 180.216971 200.65493 \r\nL 180.420015 207.896718 \r\nL 180.62306 202.91268 \r\nL 180.724582 203.879134 \r\nL 180.826104 199.496652 \r\nL 180.927626 201.061889 \r\nL 181.029149 204.363151 \r\nL 181.130671 204.274369 \r\nL 181.232193 202.604275 \r\nL 181.333715 203.686838 \r\nL 181.435238 198.965366 \r\nL 181.53676 202.319728 \r\nL 181.638282 198.973808 \r\nL 181.739804 202.679505 \r\nL 181.841326 197.61468 \r\nL 181.942849 201.379469 \r\nL 182.044371 200.891101 \r\nL 182.145893 201.224061 \r\nL 182.348938 203.639482 \r\nL 182.45046 197.233332 \r\nL 182.653504 202.438174 \r\nL 182.755026 198.874794 \r\nL 182.856549 209.306484 \r\nL 183.059593 203.608239 \r\nL 183.161115 211.370893 \r\nL 183.262638 204.83885 \r\nL 183.36416 204.986911 \r\nL 183.465682 206.279537 \r\nL 183.668726 198.527947 \r\nL 183.770249 204.982013 \r\nL 183.871771 200.989842 \r\nL 183.973293 205.213074 \r\nL 184.074815 204.036209 \r\nL 184.176338 199.164339 \r\nL 184.379382 206.886712 \r\nL 184.582427 200.140702 \r\nL 184.785471 213.611076 \r\nL 184.886993 207.20382 \r\nL 184.988515 207.782138 \r\nL 185.090038 207.536667 \r\nL 185.19156 209.316903 \r\nL 185.394604 204.622037 \r\nL 185.597649 210.819852 \r\nL 185.800693 207.587554 \r\nL 186.003738 209.730749 \r\nL 186.10526 209.642055 \r\nL 186.206782 207.452834 \r\nL 186.409827 214.637767 \r\nL 186.511349 213.075551 \r\nL 186.612871 209.000541 \r\nL 186.714393 216.095287 \r\nL 186.917438 212.436349 \r\nL 187.01896 208.803122 \r\nL 187.222004 213.324403 \r\nL 187.323527 205.348603 \r\nL 187.526571 210.204845 \r\nL 187.628093 210.565865 \r\nL 187.729616 216.791331 \r\nL 187.831138 207.99046 \r\nL 187.93266 209.543588 \r\nL 188.034182 213.513604 \r\nL 188.237227 209.193446 \r\nL 188.338749 212.726279 \r\nL 188.643316 205.773166 \r\nL 188.744838 213.93159 \r\nL 188.84636 208.773023 \r\nL 188.947882 212.201583 \r\nL 189.049405 207.896444 \r\nL 189.150927 209.659224 \r\nL 189.252449 209.036757 \r\nL 189.353971 210.69565 \r\nL 189.455493 204.177494 \r\nL 189.658538 212.990325 \r\nL 189.76006 211.51474 \r\nL 189.963105 202.581374 \r\nL 190.064627 213.644122 \r\nL 190.166149 212.994664 \r\nL 190.267671 211.77733 \r\nL 190.369193 214.183862 \r\nL 190.572238 208.769442 \r\nL 190.67376 218.17387 \r\nL 190.775282 210.201476 \r\nL 190.876805 217.01262 \r\nL 190.978327 210.570341 \r\nL 191.079849 216.692056 \r\nL 191.181371 209.02149 \r\nL 191.282894 219.22366 \r\nL 191.384416 219.039072 \r\nL 191.485938 211.109372 \r\nL 191.58746 223.125943 \r\nL 191.688982 215.470023 \r\nL 191.790505 220.3774 \r\nL 191.892027 214.665492 \r\nL 191.993549 214.716801 \r\nL 192.196594 217.054033 \r\nL 192.298116 216.963548 \r\nL 192.399638 212.871195 \r\nL 192.50116 214.662371 \r\nL 192.602682 212.322205 \r\nL 192.704205 215.64142 \r\nL 192.907249 208.725008 \r\nL 193.008771 216.935674 \r\nL 193.211816 213.023831 \r\nL 193.313338 217.860069 \r\nL 193.41486 211.749294 \r\nL 193.617905 216.83763 \r\nL 193.719427 208.388182 \r\nL 193.922471 213.673364 \r\nL 194.023994 212.907287 \r\nL 194.125516 211.026943 \r\nL 194.227038 217.319608 \r\nL 194.32856 214.552976 \r\nL 194.430083 216.268959 \r\nL 194.531605 213.08945 \r\nL 194.633127 217.582484 \r\nL 194.734649 216.227558 \r\nL 194.836171 216.909553 \r\nL 194.937694 210.012822 \r\nL 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220.208627 \r\nL 227.627851 213.86929 \r\nL 227.830895 215.498481 \r\nL 227.932417 213.950351 \r\nL 228.03394 207.571839 \r\nL 228.135462 220.186994 \r\nL 228.236984 217.620876 \r\nL 228.338506 222.541855 \r\nL 228.541551 213.177822 \r\nL 228.846117 225.153551 \r\nL 228.94764 219.18681 \r\nL 229.150684 221.19373 \r\nL 229.252206 216.95032 \r\nL 229.353729 223.605061 \r\nL 229.455251 214.870482 \r\nL 229.658295 221.445007 \r\nL 229.759818 216.249701 \r\nL 229.86134 217.741163 \r\nL 229.962862 222.123819 \r\nL 230.165906 214.444003 \r\nL 230.267429 224.650699 \r\nL 230.368951 219.598655 \r\nL 230.470473 222.406414 \r\nL 230.571995 216.521753 \r\nL 230.77504 223.397298 \r\nL 230.876562 216.73792 \r\nL 230.978084 223.309101 \r\nL 231.079606 221.835779 \r\nL 231.181129 218.729337 \r\nL 231.282651 223.811294 \r\nL 231.384173 222.469609 \r\nL 231.485695 223.821489 \r\nL 231.587218 219.84512 \r\nL 231.68874 221.847279 \r\nL 231.891784 219.051119 \r\nL 231.993307 225.065739 \r\nL 232.094829 215.736791 \r\nL 232.60244 226.644031 \r\nL 232.805484 216.514629 \r\nL 232.907007 215.694035 \r\nL 233.008529 219.72047 \r\nL 233.110051 218.882333 \r\nL 233.313095 214.130762 \r\nL 233.51614 220.26665 \r\nL 233.617662 216.860332 \r\nL 233.719184 223.413312 \r\nL 233.922229 218.773585 \r\nL 234.023751 220.392892 \r\nL 234.125273 224.929701 \r\nL 234.328318 217.371439 \r\nL 234.42984 217.63454 \r\nL 234.531362 210.745082 \r\nL 234.734407 219.726313 \r\nL 234.835929 217.659231 \r\nL 234.937451 212.850445 \r\nL 235.140496 217.786952 \r\nL 235.34354 216.197819 \r\nL 235.546584 215.582451 \r\nL 235.648107 216.938633 \r\nL 235.749629 216.466701 \r\nL 235.851151 222.170255 \r\nL 235.952673 219.191236 \r\nL 236.054196 219.771494 \r\nL 236.155718 215.80946 \r\nL 236.25724 218.826983 \r\nL 236.358762 215.406255 \r\nL 236.460285 216.530294 \r\nL 236.561807 215.246893 \r\nL 236.663329 211.971093 \r\nL 236.764851 213.105464 \r\nL 236.866373 220.363774 \r\nL 237.069418 211.470392 \r\nL 237.17094 216.375992 \r\nL 237.272462 211.717206 \r\nL 237.373985 212.808497 \r\nL 237.475507 212.512673 \r\nL 237.678551 214.584679 \r\nL 237.780073 221.328339 \r\nL 237.881596 214.311061 \r\nL 237.983118 216.003447 \r\nL 238.08464 215.364817 \r\nL 238.186162 208.937843 \r\nL 238.287685 214.678073 \r\nL 238.490729 211.326459 \r\nL 238.592251 210.823047 \r\nL 238.693774 213.852979 \r\nL 238.795296 212.4247 \r\nL 238.896818 213.969846 \r\nL 238.99834 207.034275 \r\nL 239.099862 208.667432 \r\nL 239.302907 218.130989 \r\nL 239.404429 212.930213 \r\nL 239.505951 213.539799 \r\nL 239.607474 210.718874 \r\nL 239.708996 215.645783 \r\nL 240.216607 210.519317 \r\nL 240.318129 216.878645 \r\nL 240.419651 215.788672 \r\nL 240.521174 209.770124 \r\nL 240.622696 216.233252 \r\nL 240.724218 208.10867 \r\nL 240.927262 218.160467 \r\nL 241.028785 210.392441 \r\nL 241.130307 219.394299 \r\nL 241.333351 208.437701 \r\nL 241.434874 211.966742 \r\nL 241.536396 207.229829 \r\nL 241.637918 209.078481 \r\nL 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251.384052 217.1201 \r\nL 251.587096 219.398153 \r\nL 251.688619 223.583751 \r\nL 251.790141 219.091836 \r\nL 251.891663 222.297466 \r\nL 251.993185 217.041812 \r\nL 252.094707 220.075995 \r\nL 252.19623 217.410652 \r\nL 252.399274 225.47368 \r\nL 252.500796 225.305528 \r\nL 252.602319 219.564539 \r\nL 252.703841 225.556072 \r\nL 252.805363 218.109667 \r\nL 252.906885 229.874489 \r\nL 253.008407 223.532218 \r\nL 253.10993 228.700234 \r\nL 253.312974 220.924588 \r\nL 253.414496 217.484776 \r\nL 253.617541 223.764361 \r\nL 253.820585 215.009914 \r\nL 253.922108 221.823259 \r\nL 254.328196 216.726469 \r\nL 254.531241 221.883309 \r\nL 254.632763 219.758502 \r\nL 254.734285 220.769913 \r\nL 254.93733 217.592592 \r\nL 255.241896 224.463115 \r\nL 255.343419 220.559502 \r\nL 255.444941 220.623406 \r\nL 255.546463 219.015127 \r\nL 255.647985 214.960221 \r\nL 255.85103 229.003766 \r\nL 256.054074 219.852629 \r\nL 256.155597 221.651825 \r\nL 256.257119 215.596301 \r\nL 256.358641 219.807797 \r\nL 256.460163 216.289262 \r\nL 256.561685 220.153811 \r\nL 256.663208 215.346479 \r\nL 256.76473 220.016169 \r\nL 256.866252 219.515542 \r\nL 257.069297 220.20486 \r\nL 257.170819 225.9318 \r\nL 257.373863 220.936299 \r\nL 257.475385 223.67676 \r\nL 257.576908 220.8276 \r\nL 257.67843 225.248102 \r\nL 257.881474 220.113305 \r\nL 257.982997 219.762654 \r\nL 258.084519 220.968425 \r\nL 258.287563 217.79343 \r\nL 258.389086 221.398447 \r\nL 258.490608 218.765056 \r\nL 258.59213 222.596522 \r\nL 258.896697 210.015868 \r\nL 258.998219 209.204561 \r\nL 259.099741 211.687032 \r\nL 259.201263 210.624473 \r\nL 259.404308 216.402262 \r\nL 259.50583 214.450307 \r\nL 259.607352 215.001957 \r\nL 259.708874 218.879984 \r\nL 259.810397 218.519908 \r\nL 259.911919 220.583335 \r\nL 260.013441 215.883173 \r\nL 260.114963 218.149191 \r\nL 260.216486 206.32606 \r\nL 260.318008 218.019679 \r\nL 260.41953 215.395837 \r\nL 260.521052 221.848124 \r\nL 260.622575 219.893831 \r\nL 260.724097 220.703696 \r\nL 260.825619 217.475352 \r\nL 260.927141 222.946067 \r\nL 261.33323 218.307309 \r\nL 261.434752 220.188362 \r\nL 261.637797 213.811242 \r\nL 261.739319 222.113463 \r\nL 262.24693 208.945837 \r\nL 262.449975 216.994692 \r\nL 262.551497 211.897094 \r\nL 262.653019 213.610666 \r\nL 262.856064 207.23045 \r\nL 262.957586 212.399723 \r\nL 263.059108 212.03731 \r\nL 263.16063 210.776388 \r\nL 263.262152 211.958972 \r\nL 263.363675 209.87799 \r\nL 263.465197 210.328712 \r\nL 263.566719 215.291937 \r\nL 263.668241 210.407759 \r\nL 263.769764 214.714464 \r\nL 263.871286 211.945047 \r\nL 263.972808 212.117861 \r\nL 264.07433 211.807939 \r\nL 264.175852 210.709798 \r\nL 264.277375 218.22595 \r\nL 264.480419 211.557247 \r\nL 264.581941 212.209963 \r\nL 264.683464 206.075255 \r\nL 264.98803 216.741911 \r\nL 265.089553 208.12758 \r\nL 265.191075 211.485063 \r\nL 265.292597 211.091668 \r\nL 265.394119 208.583375 \r\nL 265.495641 209.149136 \r\nL 265.698686 211.626982 \r\nL 265.90173 195.736413 \r\nL 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201.42915 \r\nL 303.667999 202.273031 \r\nL 303.769521 201.527344 \r\nL 303.871043 203.626751 \r\nL 303.972565 202.463624 \r\nL 304.074088 211.201361 \r\nL 304.17561 209.644889 \r\nL 304.378654 202.479463 \r\nL 304.480177 201.482176 \r\nL 304.581699 202.456687 \r\nL 304.683221 205.705246 \r\nL 304.784743 204.94965 \r\nL 304.886265 210.857598 \r\nL 304.987788 203.233356 \r\nL 305.08931 203.268031 \r\nL 305.190832 203.60247 \r\nL 305.292354 204.500843 \r\nL 305.393877 198.760264 \r\nL 305.495399 212.630013 \r\nL 305.799966 201.741024 \r\nL 306.00301 204.7235 \r\nL 306.104532 205.254935 \r\nL 306.206054 206.523951 \r\nL 306.307577 200.549165 \r\nL 306.409099 204.797338 \r\nL 306.612143 203.585785 \r\nL 306.713666 206.297987 \r\nL 306.815188 201.993432 \r\nL 306.91671 204.791071 \r\nL 307.018232 203.73867 \r\nL 307.119754 207.926407 \r\nL 307.322799 199.583333 \r\nL 307.424321 205.89855 \r\nL 307.627366 204.008298 \r\nL 307.728888 204.748614 \r\nL 307.83041 208.770499 \r\nL 308.033454 201.613092 \r\nL 308.134977 209.604135 \r\nL 308.236499 207.946548 \r\nL 308.338021 212.387551 \r\nL 308.541066 203.508367 \r\nL 308.74411 208.38475 \r\nL 308.947155 202.678423 \r\nL 309.048677 205.892495 \r\nL 309.150199 197.319926 \r\nL 309.353243 203.855015 \r\nL 309.454766 208.482247 \r\nL 309.65781 201.999897 \r\nL 309.860855 209.814061 \r\nL 309.962377 208.125554 \r\nL 310.063899 204.070187 \r\nL 310.165421 205.25956 \r\nL 310.266943 208.403909 \r\nL 310.469988 202.358879 \r\nL 310.57151 208.927747 \r\nL 310.673032 206.110303 \r\nL 310.876077 197.184248 \r\nL 310.977599 200.777864 \r\nL 311.079121 208.907855 \r\nL 311.180644 200.622344 \r\nL 311.383688 203.779549 \r\nL 311.48521 200.96801 \r\nL 311.586732 202.00073 \r\nL 311.688255 200.999999 \r\nL 311.789777 201.529184 \r\nL 311.891299 205.97 \r\nL 312.094344 199.45412 \r\nL 312.297388 208.009532 \r\nL 312.39891 197.622239 \r\nL 312.703477 211.31343 \r\nL 312.804999 210.207406 \r\nL 312.906521 204.29497 \r\nL 313.109566 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124.278224 204.669467 \r\nL 124.379746 203.582441 \r\nL 124.481268 204.48795 \r\nL 124.58279 208.783541 \r\nL 124.785835 204.936633 \r\nL 124.887357 198.403558 \r\nL 125.090402 210.316615 \r\nL 125.293446 201.128541 \r\nL 125.49649 206.809592 \r\nL 125.598013 207.016969 \r\nL 125.699535 204.597694 \r\nL 125.801057 211.81622 \r\nL 126.004102 203.698587 \r\nL 126.105624 203.934584 \r\nL 126.207146 204.798233 \r\nL 126.308668 209.239373 \r\nL 126.410191 205.877464 \r\nL 126.613235 212.826835 \r\nL 126.816279 205.506721 \r\nL 126.917802 208.141853 \r\nL 127.019324 202.94866 \r\nL 127.120846 209.938126 \r\nL 127.323891 202.530114 \r\nL 127.425413 207.193985 \r\nL 127.628457 204.452903 \r\nL 127.729979 208.784921 \r\nL 127.933024 204.365078 \r\nL 128.237591 213.458253 \r\nL 128.339113 202.055608 \r\nL 128.440635 209.643186 \r\nL 128.542157 209.128287 \r\nL 128.643679 207.745487 \r\nL 128.846724 208.599923 \r\nL 128.948246 208.710486 \r\nL 129.049768 209.354661 \r\nL 129.151291 205.15469 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134.735013 208.564129 \r\nL 134.836535 208.869475 \r\nL 134.938058 207.053819 \r\nL 135.03958 210.388873 \r\nL 135.141102 209.476675 \r\nL 135.242624 203.960183 \r\nL 135.344146 208.593507 \r\nL 135.445669 207.072779 \r\nL 135.547191 217.796873 \r\nL 135.750235 208.153826 \r\nL 135.851758 208.81456 \r\nL 136.054802 212.031504 \r\nL 136.156324 210.253643 \r\nL 136.257847 212.08438 \r\nL 136.359369 207.265908 \r\nL 136.663935 214.171131 \r\nL 136.968502 210.836325 \r\nL 137.070024 214.849744 \r\nL 137.273069 209.254354 \r\nL 137.374591 214.840419 \r\nL 137.476113 207.67104 \r\nL 137.577635 216.352059 \r\nL 137.679158 213.225638 \r\nL 137.78068 213.415572 \r\nL 138.085247 217.134323 \r\nL 138.186769 215.593728 \r\nL 138.288291 217.021373 \r\nL 138.389813 221.004604 \r\nL 138.592858 214.648782 \r\nL 138.69438 222.283492 \r\nL 138.795902 215.61868 \r\nL 138.998947 218.759038 \r\nL 139.100469 217.378464 \r\nL 139.201991 218.046049 \r\nL 139.303513 222.602937 \r\nL 139.506558 216.14544 \r\nL 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "def loss_plot(intermediate_result_file, model_name):\n",
+    "    batch = []\n",
+    "    loss = []\n",
+    "    value_loss = []\n",
+    "    policy_loss = []\n",
+    "    entropy = []\n",
+    "    with open(intermediate_result_file,'r') as f:\n",
+    "        for line in f:\n",
+    "            line = line.strip().split(',')\n",
+    "            batch.append(line[0].split(':')[-1])\n",
+    "            loss.append(line[4].split(':')[-1])\n",
+    "            value_loss.append(line[5].split(':')[-1])\n",
+    "            policy_loss.append(line[6].split(':')[-1])\n",
+    "            entropy.append(line[7].split(':')[-1])\n",
+    "\n",
+    "    batch = np.array(batch).astype(int)\n",
+    "    loss = np.array(loss).astype(float)\n",
+    "    value_loss = np.array(value_loss).astype(float)\n",
+    "    policy_loss = np.array(policy_loss).astype(float)\n",
+    "    entropy = np.array(entropy).astype(float)\n",
+    "\n",
+    "    plt.plot(batch, loss, label='loss')\n",
+    "    plt.plot(batch, value_loss, label='value loss')\n",
+    "    plt.plot(batch, policy_loss, label='policy loss')\n",
+    "    plt.legend()\n",
+    "    plt.title(model_name + ' loss')\n",
+    "    plt.xlabel('batch num')\n",
+    "    plt.ylabel('loss')\n",
+    "    plt.grid(axis='y')\n",
+    "    plt.show()\n",
+    "    return pd.DataFrame(data=[batch, loss]).T\n",
+    "\n",
+    "def win_ratio_plot(scores_result_file_1, model_name_1, scores_result_file_2, model_name_2):\n",
+    "    batch_50_1 = []\n",
+    "    win_ratio_1 = []\n",
+    "    with open(scores_result_file_1,'r') as f:\n",
+    "        for line in f:\n",
+    "            line = line.strip().split(',')\n",
+    "            batch_50_1.append(line[0].split(':')[-1])\n",
+    "            win_ratio_1.append(line[-1].split(':')[-1])\n",
+    "\n",
+    "    batch_50_1 = np.array(batch_50_1).astype(int)\n",
+    "    win_ratio_1 = np.array(win_ratio_1).astype(float)\n",
+    "\n",
+    "    batch_50_2 = []\n",
+    "    win_ratio_2 = []\n",
+    "    with open(scores_result_file_2,'r') as f:\n",
+    "        for line in f:\n",
+    "            line = line.strip().split(',')\n",
+    "            batch_50_2.append(line[0].split(':')[-1])\n",
+    "            win_ratio_2.append(line[-1].split(':')[-1])\n",
+    "\n",
+    "    batch_50_2 = np.array(batch_50_2).astype(int)\n",
+    "    win_ratio_2 = np.array(win_ratio_2).astype(float)\n",
+    "\n",
+    "    plt.plot(batch_50_1, win_ratio_1, label=model_name_1 + ' win ratio')\n",
+    "    plt.plot(batch_50_2, win_ratio_2, label=model_name_2 + ' win ratio')\n",
+    "    plt.legend()\n",
+    "    plt.title('win ratio')\n",
+    "    plt.xlabel('batch num')\n",
+    "    plt.ylabel('win ratio')\n",
+    "    plt.grid(axis='y')\n",
+    "    plt.show()\n",
+    "\n",
+    "win_ratio_plot('output/scores.txt', 'baseline', 'output/res30_l+_scores.txt', 'res30')\n",
+    "\n",
+    "df_baseline = loss_plot('output/intermediate_result.txt', 'baseline')\n",
+    "df_baseline.columns = ['batch_num', 'loss_baseline']\n",
+    "df_res30 = loss_plot('output/res30_l+_intermediate_result.txt', 'res30')\n",
+    "df_res30.columns = ['batch_num', 'loss_res30']\n",
+    "df_merged = pd.merge(df_baseline, df_res30, how='left', on='batch_num')\n",
+    "plt.plot(df_merged['batch_num'], df_merged['loss_baseline'], label='loss baseline')\n",
+    "plt.plot(df_merged['batch_num'], df_merged['loss_res30'], label='loss res30')\n",
+    "plt.legend()\n",
+    "plt.title('Loss Comparison')\n",
+    "plt.xlabel('batch num')\n",
+    "plt.ylabel('loss')\n",
+    "plt.grid(axis='y')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rescue_code(function):\n",
+    "    import inspect\n",
+    "    get_ipython().set_next_input(\"\".join(inspect.getsourcelines(function)[0]))\n",
+    "\n",
+    "rescue_code(loss_plot)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def loss_plot(intermediate_result_file):\n",
+    "    batch = []\n",
+    "    loss = []\n",
+    "    value_loss = []\n",
+    "    policy_loss = []\n",
+    "    entropy = []\n",
+    "    with open(intermediate_result_file,'r') as f:\n",
+    "        for line in f:\n",
+    "            line = line.strip().split(',')\n",
+    "            batch.append(line[0].split(':')[-1])\n",
+    "            loss.append(line[4].split(':')[-1])\n",
+    "            value_loss.append(line[5].split(':')[-1])\n",
+    "            policy_loss.append(line[6].split(':')[-1])\n",
+    "            entropy.append(line[7].split(':')[-1])\n",
+    "\n",
+    "    batch = np.array(batch).astype(float)\n",
+    "    loss = np.array(loss).astype(float)\n",
+    "    value_loss = np.array(value_loss).astype(float)\n",
+    "    policy_loss = np.array(policy_loss).astype(float)\n",
+    "    entropy = np.array(entropy).astype(float)\n",
+    "\n",
+    "    plt.plot(batch, loss, label='loss')\n",
+    "    plt.plot(batch, value_loss, label='value loss')\n",
+    "    plt.plot(batch, policy_loss, label='policy loss')\n",
+    "    plt.legend()\n",
+    "    plt.title('loss')\n",
+    "    plt.xlabel('batch num')\n",
+    "    plt.ylabel('loss')\n",
+    "    plt.grid(axis='y')\n",
+    "    plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "57baa5815c940fdaff4d14510622de9616cae602444507ba5d0b6727c008cbd6"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.5 64-bit"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.5"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "57baa5815c940fdaff4d14510622de9616cae602444507ba5d0b6727c008cbd6"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/mcts_alphaZero.py b/mcts_alphaZero.py
index 214e0ed9b..096ef7ce1 100644
--- a/mcts_alphaZero.py
+++ b/mcts_alphaZero.py
@@ -215,4 +215,4 @@ def get_action(self, board, temp=1e-3, return_prob=0):
             print("WARNING: the board is full")
 
     def __str__(self):
-        return "MCTS {}".format(self.player)
+        return "AI {}".format(self.player)
diff --git a/mcts_pure.py b/mcts_pure.py
index 92a67484c..734eed757 100644
--- a/mcts_pure.py
+++ b/mcts_pure.py
@@ -133,6 +133,7 @@ def _playout(self, state):
         # Evaluate the leaf node by random rollout
         leaf_value = self._evaluate_rollout(state)
         # Update value and visit count of nodes in this traversal.
+        # This leaf value is used for parent, so if evaluation value is 1, then current player win, and this node's value should be -1.
         node.update_recursive(-leaf_value)
 
     def _evaluate_rollout(self, state, limit=1000):
diff --git a/output/baseline_policy.model.data-00000-of-00001 b/output/baseline_policy.model.data-00000-of-00001
new file mode 100644
index 000000000..ce068a20a
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diff --git a/output/baseline_policy.model.index b/output/baseline_policy.model.index
new file mode 100644
index 000000000..48afa340a
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diff --git a/output/best_policy.model.data-00000-of-00001 b/output/best_policy.model.data-00000-of-00001
new file mode 100644
index 000000000..1b82e2f29
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diff --git a/output/best_policy.model.index b/output/best_policy.model.index
new file mode 100644
index 000000000..3a7014476
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diff --git a/policy_value_net_res_tensorflow.py b/policy_value_net_res_tensorflow.py
new file mode 100644
index 000000000..b4b75989b
--- /dev/null
+++ b/policy_value_net_res_tensorflow.py
@@ -0,0 +1,191 @@
+# -*- coding: utf-8 -*-
+"""
+An implementation of the policyValueNet in Tensorflow
+Tested in Tensorflow 1.4 and 1.5
+
+@author: Chunlei Wang
+"""
+
+import numpy as np
+import tensorflow as tf
+from tensorflow.contrib.layers.python.layers import batch_norm as batch_norm
+from game import INPUT_STATE_CHANNEL_SIZE
+
+class PolicyValueNetRes30():
+    def __init__(self, board_width, board_height, loss_function, model_file=None):
+        self.board_width = board_width
+        self.board_height = board_height
+
+        self.graph = tf.Graph()              # create graph for each instance individuly
+        with self.graph.as_default():        
+            self.is_training = tf.placeholder(tf.bool)
+            # Define the tensorflow neural network
+            # 1. Input:
+            self.input_states = tf.placeholder(
+                    tf.float32, shape=[None, INPUT_STATE_CHANNEL_SIZE, board_height, board_width])
+            self.input_state = tf.transpose(self.input_states, [0, 2, 3, 1])
+            
+            # 2. Common Networks Layers
+            self.block1 = self._block(self.input_state, 32, 3, is_training=self.is_training, scope="block1")
+            self.block2 = self._block(self.block1, 64, 3, is_training=self.is_training, scope="block2")
+            self.block3 = self._block(self.block2, 128, 3, is_training=self.is_training, scope="block3")
+            
+            # 3-1 Action Networks
+            self.action_conv = tf.layers.conv2d(inputs=self.block3, filters=4,
+                                                kernel_size=[1, 1], padding="same",
+                                                data_format="channels_last",
+                                                activation=tf.nn.relu)
+            # Flatten the tensor
+            self.action_conv_flat = tf.reshape(
+                    self.action_conv, [-1, 4 * board_height * board_width])
+            # 3-2 Full connected layer, the output is the log probability of moves
+            # on each slot on the board
+            self.action_fc = tf.layers.dense(inputs=self.action_conv_flat,
+                                            units=board_height * board_width,
+                                            activation=tf.nn.log_softmax)
+            # 4 Evaluation Networks
+            self.evaluation_conv = tf.layers.conv2d(inputs=self.block3, filters=2,
+                                                    kernel_size=[1, 1],
+                                                    padding="same",
+                                                    data_format="channels_last",
+                                                    activation=tf.nn.relu)
+            self.evaluation_conv_flat = tf.reshape(
+                    self.evaluation_conv, [-1, 2 * board_height * board_width])
+            self.evaluation_fc1 = tf.layers.dense(inputs=self.evaluation_conv_flat,
+                                                units=64, activation=tf.nn.relu)
+            # output the score of evaluation on current state
+            self.evaluation_fc2 = tf.layers.dense(inputs=self.evaluation_fc1,
+                                                units=1, activation=tf.nn.tanh)
+
+            # Define the Loss function
+            # 1. Label: the array containing if the game wins or not for each state
+            self.labels = tf.placeholder(tf.float32, shape=[None, 1])
+            # 2. Predictions: the array containing the evaluation score of each state
+            # which is self.evaluation_fc2
+            # 3-1. Value Loss function
+            self.value_loss = tf.losses.mean_squared_error(self.labels,
+                                                        self.evaluation_fc2)
+            # 3-2. Policy Loss function
+            self.mcts_probs = tf.placeholder(
+                    tf.float32, shape=[None, board_height * board_width])
+            self.policy_loss = tf.negative(tf.reduce_mean(
+                    tf.reduce_sum(tf.multiply(self.mcts_probs, self.action_fc), 1)))
+            # 3-3. L2 penalty (regularization)
+            l2_penalty_beta = 1e-4
+            vars = tf.trainable_variables()
+            l2_penalty = l2_penalty_beta * tf.add_n(
+                [tf.nn.l2_loss(v) for v in vars if 'bias' not in v.name.lower()])
+            # 3-4 Add up to be the Loss function
+            if loss_function == 'lv':
+                self.loss = self.value_loss + l2_penalty
+            elif loss_function == 'lp':
+                self.loss = self.policy_loss + l2_penalty
+            elif loss_function == 'l+':
+                self.loss = self.value_loss + self.policy_loss + l2_penalty
+            elif loss_function == 'lx':
+                self.loss = self.value_loss * self.policy_loss + l2_penalty
+
+            # Define the optimizer we use for training
+            self.learning_rate = tf.placeholder(tf.float32)
+            
+            self.adam_optimizer = tf.train.AdamOptimizer(
+                    learning_rate=self.learning_rate).minimize(self.loss)
+
+            # Make a session
+            self.session = tf.Session(graph=self.graph)
+
+            # calc policy entropy, for monitoring only
+            self.entropy = tf.negative(tf.reduce_mean(
+                    tf.reduce_sum(tf.exp(self.action_fc) * self.action_fc, 1)))
+
+            # Initialize variables
+            init = tf.global_variables_initializer()
+            self.session.run(init)
+
+            # For saving and restoring
+            self.saver = tf.train.Saver()
+            if model_file is not None:
+                self.restore_model(model_file)
+
+            '''
+            self.mom_optimizer = tf.train.MomentumOptimizer(
+                    learning_rate=self.learning_rate,momentum=0.9).minimize(self.loss)
+            var_list = [var for var in tf.global_variables() if 'Momentum' in var.name]
+            self.session.run(tf.variables_initializer(var_list))
+            '''
+    def _batch_norm(self, x, is_training, scope="bn"):
+        z = tf.cond(is_training, lambda: batch_norm(x, decay=0.9, center=True, scale=True, updates_collections=None,is_training=True, reuse=None, trainable=True, scope=scope), 
+                                lambda: batch_norm(x, decay=0.9, center=True, scale=True, updates_collections=None,is_training=False, reuse=True, trainable=False, scope=scope))
+        return z
+
+    def _block(self, x, n_out, n, is_training, scope="block"):
+        with tf.variable_scope(scope):
+            out = self._bottleneck(x, n_out, is_training, scope="bottleneck1")
+            for i in range(1, n):
+                out = self._bottleneck(out, n_out, is_training, scope=("bottleneck%s" % (i + 1)))
+            return out
+
+    def _bottleneck(self, x, n_out, is_training, scope="bottleneck"):
+        """ A residual bottleneck unit"""
+        n_in = x.get_shape()[-1]
+
+        with tf.variable_scope(scope):
+            h = tf.layers.conv2d(inputs=x, filters=n_out, kernel_size=[3, 3], padding="same", data_format="channels_last", activation=None)
+            h = self._batch_norm(h, is_training, scope="bn_1")
+            h = tf.nn.relu(h)
+            h = tf.layers.conv2d(inputs=h, filters=n_out, kernel_size=[3, 3], padding="same", data_format="channels_last", activation=None)
+            h = self._batch_norm(h, is_training, scope="bn_2")
+            h = tf.nn.relu(h)
+            h = tf.layers.conv2d(inputs=h, filters=n_out, kernel_size=[3, 3], padding="same", data_format="channels_last", activation=None)
+            h = self._batch_norm(h, is_training, scope="bn_3")
+
+            if n_in != n_out:
+                shortcut = tf.layers.conv2d(inputs=x, filters=n_out, kernel_size=[1, 1], padding="same", data_format="channels_last", activation=None)
+                shortcut = self._batch_norm(shortcut, is_training, scope="bn_4")
+            else:
+                shortcut = self._batch_norm(x, is_training, scope="bn_4")
+            return tf.nn.relu(self._batch_norm(shortcut + h, is_training, scope="bn_5"))
+            
+    def policy_value(self, state_batch):
+        """
+        input: a batch of states
+        output: a batch of action probabilities and state values
+        """
+        log_act_probs, value = self.session.run(
+                [self.action_fc, self.evaluation_fc2],
+                feed_dict={self.input_states: state_batch,
+                            self.is_training: False}
+                )
+        act_probs = np.exp(log_act_probs)
+        return act_probs, value
+
+    def policy_value_fn(self, board):
+        """
+        input: board
+        output: a list of (action, probability) tuples for each available
+        action and the score of the board state
+        """
+        legal_positions = board.availables
+        current_state = np.ascontiguousarray(board.current_last16move_state().reshape(
+                -1, INPUT_STATE_CHANNEL_SIZE, self.board_width, self.board_height))
+        act_probs, value = self.policy_value(current_state)
+        act_probs = zip(legal_positions, act_probs[0][legal_positions])
+        return act_probs, value
+
+    def train_step(self, state_batch, mcts_probs, winner_batch, lr):
+        """perform a training step"""
+        winner_batch = np.reshape(winner_batch, (-1, 1))
+        loss, value_loss, policy_loss, entropy, _ = self.session.run(
+                [self.loss, self.value_loss, self.policy_loss, self.entropy, self.adam_optimizer],
+                feed_dict={self.input_states: state_batch,
+                           self.mcts_probs: mcts_probs,
+                           self.labels: winner_batch,
+                           self.learning_rate: lr,
+                           self.is_training: True})
+        return loss, value_loss, policy_loss, entropy
+
+    def save_model(self, model_path):
+        self.saver.save(self.session, model_path)
+
+    def restore_model(self, model_path):
+        self.saver.restore(self.session, model_path)
diff --git a/policy_value_net_tensorflow.py b/policy_value_net_tensorflow.py
index 589110708..e5b1b0d7f 100644
--- a/policy_value_net_tensorflow.py
+++ b/policy_value_net_tensorflow.py
@@ -11,96 +11,105 @@
 
 
 class PolicyValueNet():
-    def __init__(self, board_width, board_height, model_file=None):
+    def __init__(self, board_width, board_height, loss_function, model_file=None):
         self.board_width = board_width
         self.board_height = board_height
 
-        # Define the tensorflow neural network
-        # 1. Input:
-        self.input_states = tf.placeholder(
-                tf.float32, shape=[None, 4, board_height, board_width])
-        self.input_state = tf.transpose(self.input_states, [0, 2, 3, 1])
-        # 2. Common Networks Layers
-        self.conv1 = tf.layers.conv2d(inputs=self.input_state,
-                                      filters=32, kernel_size=[3, 3],
-                                      padding="same", data_format="channels_last",
-                                      activation=tf.nn.relu)
-        self.conv2 = tf.layers.conv2d(inputs=self.conv1, filters=64,
-                                      kernel_size=[3, 3], padding="same",
-                                      data_format="channels_last",
-                                      activation=tf.nn.relu)
-        self.conv3 = tf.layers.conv2d(inputs=self.conv2, filters=128,
-                                      kernel_size=[3, 3], padding="same",
-                                      data_format="channels_last",
-                                      activation=tf.nn.relu)
-        # 3-1 Action Networks
-        self.action_conv = tf.layers.conv2d(inputs=self.conv3, filters=4,
+        self.graph = tf.Graph()              # create graph for each instance individuly
+        with self.graph.as_default():
+            # Define the tensorflow neural network
+            # 1. Input:
+            self.input_states = tf.placeholder(
+                    tf.float32, shape=[None, 4, board_height, board_width])
+            self.input_state = tf.transpose(self.input_states, [0, 2, 3, 1])
+            # 2. Common Networks Layers
+            self.conv1 = tf.layers.conv2d(inputs=self.input_state,
+                                    filters=32, kernel_size=[3, 3],
+                                    padding="same", data_format="channels_last",
+                                    activation=tf.nn.relu)
+            self.conv2 = tf.layers.conv2d(inputs=self.conv1, filters=64,
+                                    kernel_size=[3, 3], padding="same",
+                                    data_format="channels_last",
+                                    activation=tf.nn.relu)
+            self.conv3 = tf.layers.conv2d(inputs=self.conv2, filters=128,
+                                    kernel_size=[3, 3], padding="same",
+                                    data_format="channels_last",
+                                    activation=tf.nn.relu)
+            # 3-1 Action Networks
+            self.action_conv = tf.layers.conv2d(inputs=self.conv3, filters=4,
                                             kernel_size=[1, 1], padding="same",
                                             data_format="channels_last",
                                             activation=tf.nn.relu)
-        # Flatten the tensor
-        self.action_conv_flat = tf.reshape(
-                self.action_conv, [-1, 4 * board_height * board_width])
-        # 3-2 Full connected layer, the output is the log probability of moves
-        # on each slot on the board
-        self.action_fc = tf.layers.dense(inputs=self.action_conv_flat,
-                                         units=board_height * board_width,
-                                         activation=tf.nn.log_softmax)
-        # 4 Evaluation Networks
-        self.evaluation_conv = tf.layers.conv2d(inputs=self.conv3, filters=2,
-                                                kernel_size=[1, 1],
-                                                padding="same",
-                                                data_format="channels_last",
-                                                activation=tf.nn.relu)
-        self.evaluation_conv_flat = tf.reshape(
-                self.evaluation_conv, [-1, 2 * board_height * board_width])
-        self.evaluation_fc1 = tf.layers.dense(inputs=self.evaluation_conv_flat,
-                                              units=64, activation=tf.nn.relu)
-        # output the score of evaluation on current state
-        self.evaluation_fc2 = tf.layers.dense(inputs=self.evaluation_fc1,
-                                              units=1, activation=tf.nn.tanh)
-
-        # Define the Loss function
-        # 1. Label: the array containing if the game wins or not for each state
-        self.labels = tf.placeholder(tf.float32, shape=[None, 1])
-        # 2. Predictions: the array containing the evaluation score of each state
-        # which is self.evaluation_fc2
-        # 3-1. Value Loss function
-        self.value_loss = tf.losses.mean_squared_error(self.labels,
-                                                       self.evaluation_fc2)
-        # 3-2. Policy Loss function
-        self.mcts_probs = tf.placeholder(
-                tf.float32, shape=[None, board_height * board_width])
-        self.policy_loss = tf.negative(tf.reduce_mean(
-                tf.reduce_sum(tf.multiply(self.mcts_probs, self.action_fc), 1)))
-        # 3-3. L2 penalty (regularization)
-        l2_penalty_beta = 1e-4
-        vars = tf.trainable_variables()
-        l2_penalty = l2_penalty_beta * tf.add_n(
+            # Flatten the tensor
+            self.action_conv_flat = tf.reshape(
+                    self.action_conv, [-1, 4 * board_height * board_width])
+            # 3-2 Full connected layer, the output is the log probability of moves
+            # on each slot on the board
+            self.action_fc = tf.layers.dense(inputs=self.action_conv_flat,
+                                            units=board_height * board_width,
+                                            activation=tf.nn.log_softmax)
+            # 4 Evaluation Networks
+            self.evaluation_conv = tf.layers.conv2d(inputs=self.conv3, filters=2,
+                                                    kernel_size=[1, 1],
+                                                    padding="same",
+                                                    data_format="channels_last",
+                                                    activation=tf.nn.relu)
+            self.evaluation_conv_flat = tf.reshape(
+                    self.evaluation_conv, [-1, 2 * board_height * board_width])
+            self.evaluation_fc1 = tf.layers.dense(inputs=self.evaluation_conv_flat,
+                                            units=64, activation=tf.nn.relu)
+            # output the score of evaluation on current state
+            self.evaluation_fc2 = tf.layers.dense(inputs=self.evaluation_fc1,
+                                            units=1, activation=tf.nn.tanh)
+
+            # Define the Loss function
+            # 1. Label: the array containing if the game wins or not for each state
+            self.labels = tf.placeholder(tf.float32, shape=[None, 1])
+            # 2. Predictions: the array containing the evaluation score of each state
+            # which is self.evaluation_fc2
+            # 3-1. Value Loss function
+            self.value_loss = tf.losses.mean_squared_error(self.labels,
+                                                    self.evaluation_fc2)
+            # 3-2. Policy Loss function
+            self.mcts_probs = tf.placeholder(
+                    tf.float32, shape=[None, board_height * board_width])
+            self.policy_loss = tf.negative(tf.reduce_mean(
+                    tf.reduce_sum(tf.multiply(self.mcts_probs, self.action_fc), 1)))
+            # 3-3. L2 penalty (regularization)
+            l2_penalty_beta = 1e-4
+            vars = tf.trainable_variables()
+            l2_penalty = l2_penalty_beta * tf.add_n(
             [tf.nn.l2_loss(v) for v in vars if 'bias' not in v.name.lower()])
-        # 3-4 Add up to be the Loss function
-        self.loss = self.value_loss + self.policy_loss + l2_penalty
-
-        # Define the optimizer we use for training
-        self.learning_rate = tf.placeholder(tf.float32)
-        self.optimizer = tf.train.AdamOptimizer(
-                learning_rate=self.learning_rate).minimize(self.loss)
-
-        # Make a session
-        self.session = tf.Session()
-
-        # calc policy entropy, for monitoring only
-        self.entropy = tf.negative(tf.reduce_mean(
-                tf.reduce_sum(tf.exp(self.action_fc) * self.action_fc, 1)))
-
-        # Initialize variables
-        init = tf.global_variables_initializer()
-        self.session.run(init)
-
-        # For saving and restoring
-        self.saver = tf.train.Saver()
-        if model_file is not None:
-            self.restore_model(model_file)
+            # 3-4 Add up to be the Loss function
+            if loss_function == 'lv':
+                self.loss = self.value_loss + l2_penalty
+            elif loss_function == 'lp':
+                self.loss = self.policy_loss + l2_penalty
+            elif loss_function == 'l+':
+                self.loss = self.value_loss + self.policy_loss + l2_penalty
+            elif loss_function == 'lx':
+                self.loss = self.value_loss * self.policy_loss + l2_penalty
+
+            # Define the optimizer we use for training
+            self.learning_rate = tf.placeholder(tf.float32)
+            self.optimizer = tf.train.AdamOptimizer(
+                    learning_rate=self.learning_rate).minimize(self.loss)            
+
+            # calc policy entropy, for monitoring only
+            self.entropy = tf.negative(tf.reduce_mean(
+                    tf.reduce_sum(tf.exp(self.action_fc) * self.action_fc, 1)))
+
+            # Make a session
+            self.session = tf.Session(graph=self.graph)
+
+            # Initialize variables
+            init = tf.global_variables_initializer()
+            self.session.run(init)
+
+            # For saving and restoring
+            self.saver = tf.train.Saver()
+            if model_file is not None:
+                self.restore_model(model_file)
 
     def policy_value(self, state_batch):
         """
@@ -130,13 +139,13 @@ def policy_value_fn(self, board):
     def train_step(self, state_batch, mcts_probs, winner_batch, lr):
         """perform a training step"""
         winner_batch = np.reshape(winner_batch, (-1, 1))
-        loss, entropy, _ = self.session.run(
-                [self.loss, self.entropy, self.optimizer],
+        loss, value_loss, policy_loss, entropy, _ = self.session.run(
+                [self.loss, self.value_loss, self.policy_loss, self.entropy, self.optimizer],
                 feed_dict={self.input_states: state_batch,
                            self.mcts_probs: mcts_probs,
                            self.labels: winner_batch,
                            self.learning_rate: lr})
-        return loss, entropy
+        return loss, value_loss, policy_loss, entropy
 
     def save_model(self, model_path):
         self.saver.save(self.session, model_path)
diff --git a/train.py b/train.py
index d33a20879..d461f9248 100644
--- a/train.py
+++ b/train.py
@@ -12,27 +12,32 @@
 from game import Board, Game
 from mcts_pure import MCTSPlayer as MCTS_Pure
 from mcts_alphaZero import MCTSPlayer
-from policy_value_net import PolicyValueNet  # Theano and Lasagne
-# from policy_value_net_pytorch import PolicyValueNet  # Pytorch
-# from policy_value_net_tensorflow import PolicyValueNet # Tensorflow
-# from policy_value_net_keras import PolicyValueNet # Keras
-
+#from policy_value_net import PolicyValueNet  # Theano and Lasagne
+#from policy_value_net_pytorch import PolicyValueNet  # Pytorch
+from policy_value_net_tensorflow import PolicyValueNet # Tensorflow
+#from policy_value_net_keras import PolicyValueNet # Keras
+from policy_value_net_res_tensorflow import PolicyValueNetRes30 # Tensorflow
+from datetime import datetime
+import utils
+import os
+import argparse
 
 class TrainPipeline():
-    def __init__(self, init_model=None):
+    def __init__(self, model_name, loss_function, forbidden_hands, init_model=None):
         # params of the board and the game
-        self.board_width = 6
-        self.board_height = 6
-        self.n_in_row = 4
+        self.board_width = 9
+        self.board_height = 9
+        self.n_in_row = 5
         self.board = Board(width=self.board_width,
                            height=self.board_height,
-                           n_in_row=self.n_in_row)
+                           n_in_row=self.n_in_row,
+                           forbidden_hands=forbidden_hands)
         self.game = Game(self.board)
         # training params
         self.learn_rate = 2e-3
         self.lr_multiplier = 1.0  # adaptively adjust the learning rate based on KL
         self.temp = 1.0  # the temperature param
-        self.n_playout = 400  # num of simulations for each move
+        self.n_playout = 1000  # num of simulations for each move
         self.c_puct = 5
         self.buffer_size = 10000
         self.batch_size = 512  # mini-batch size for training
@@ -41,20 +46,35 @@ def __init__(self, init_model=None):
         self.epochs = 5  # num of train_steps for each update
         self.kl_targ = 0.02
         self.check_freq = 50
-        self.game_batch_num = 1500
+        self.game_batch_num = 3000
         self.best_win_ratio = 0.0
         # num of simulations used for the pure mcts, which is used as
         # the opponent to evaluate the trained policy
         self.pure_mcts_playout_num = 1000
+        self.model_name = model_name
         if init_model:
             # start training from an initial policy-value net
-            self.policy_value_net = PolicyValueNet(self.board_width,
-                                                   self.board_height,
-                                                   model_file=init_model)
+            if self.model_name == 'baseline':
+                self.policy_value_net = PolicyValueNet(self.board_width,
+                                                    self.board_height,
+                                                    loss_function,
+                                                    model_file=init_model)
+            else:
+                self.policy_value_net = PolicyValueNetRes30(self.board_width,
+                                                            self.board_height,
+                                                            loss_function,
+                                                            model_file=init_model)
         else:
             # start training from a new policy-value net
-            self.policy_value_net = PolicyValueNet(self.board_width,
-                                                   self.board_height)
+            if self.model_name == 'baseline':
+                self.policy_value_net = PolicyValueNet(self.board_width,
+                                                    self.board_height,
+                                                    loss_function)
+            else:
+                self.policy_value_net = PolicyValueNetRes30(self.board_width,
+                                                    self.board_height,
+                                                    loss_function)
+
         self.mcts_player = MCTSPlayer(self.policy_value_net.policy_value_fn,
                                       c_puct=self.c_puct,
                                       n_playout=self.n_playout,
@@ -86,14 +106,15 @@ def collect_selfplay_data(self, n_games=1):
         """collect self-play data for training"""
         for i in range(n_games):
             winner, play_data = self.game.start_self_play(self.mcts_player,
-                                                          temp=self.temp)
+                                                        self.model_name,
+                                                        temp=self.temp)
             play_data = list(play_data)[:]
             self.episode_len = len(play_data)
             # augment the data
             play_data = self.get_equi_data(play_data)
             self.data_buffer.extend(play_data)
 
-    def policy_update(self):
+    def policy_update(self, batch_num, episode_len):
         """update the policy-value net"""
         mini_batch = random.sample(self.data_buffer, self.batch_size)
         state_batch = [data[0] for data in mini_batch]
@@ -101,7 +122,7 @@ def policy_update(self):
         winner_batch = [data[2] for data in mini_batch]
         old_probs, old_v = self.policy_value_net.policy_value(state_batch)
         for i in range(self.epochs):
-            loss, entropy = self.policy_value_net.train_step(
+            loss, value_loss, policy_loss, entropy = self.policy_value_net.train_step(
                     state_batch,
                     mcts_probs_batch,
                     winner_batch,
@@ -125,21 +146,30 @@ def policy_update(self):
         explained_var_new = (1 -
                              np.var(np.array(winner_batch) - new_v.flatten()) /
                              np.var(np.array(winner_batch)))
-        print(("kl:{:.5f},"
-               "lr_multiplier:{:.3f},"
-               "loss:{},"
-               "entropy:{},"
-               "explained_var_old:{:.3f},"
-               "explained_var_new:{:.3f}"
-               ).format(kl,
+
+        utils.log(("batch:{},"
+                "episode_len:{},"
+                "kl:{:.5f},"
+                "lr_multiplier:{:.3f},"
+                "loss:{},"
+                "value_loss:{},"
+                "policy_loss:{},"
+                "entropy:{},"
+                "explained_var_old:{:.3f},"
+                "explained_var_new:{:.3f}"
+                ).format(batch_num,
+                        episode_len,
+                        kl,
                         self.lr_multiplier,
                         loss,
+                        value_loss,
+                        policy_loss,
                         entropy,
                         explained_var_old,
-                        explained_var_new))
+                        explained_var_new), INTERMEDIATE_RESULT)
         return loss, entropy
 
-    def policy_evaluate(self, n_games=10):
+    def policy_evaluate(self, current_batch, n_games=10):
         """
         Evaluate the trained policy by playing against the pure MCTS player
         Note: this is only for monitoring the progress of training
@@ -157,9 +187,14 @@ def policy_evaluate(self, n_games=10):
                                           is_shown=0)
             win_cnt[winner] += 1
         win_ratio = 1.0*(win_cnt[1] + 0.5*win_cnt[-1]) / n_games
-        print("num_playouts:{}, win: {}, lose: {}, tie:{}".format(
+        
+        output = "current_batch:{},num_playouts:{},win:{},lose:{},tie:{},win_ratio:{}".format(
+                current_batch,
                 self.pure_mcts_playout_num,
-                win_cnt[1], win_cnt[2], win_cnt[-1]))
+                win_cnt[1], win_cnt[2], win_cnt[-1], win_ratio)
+
+        utils.log(output, SCORE_OUTPUT)
+        
         return win_ratio
 
     def run(self):
@@ -167,21 +202,19 @@ def run(self):
         try:
             for i in range(self.game_batch_num):
                 self.collect_selfplay_data(self.play_batch_size)
-                print("batch i:{}, episode_len:{}".format(
-                        i+1, self.episode_len))
                 if len(self.data_buffer) > self.batch_size:
-                    loss, entropy = self.policy_update()
+                    loss, entropy = self.policy_update(i+1, self.episode_len)
                 # check the performance of the current model,
                 # and save the model params
                 if (i+1) % self.check_freq == 0:
-                    print("current self-play batch: {}".format(i+1))
-                    win_ratio = self.policy_evaluate()
-                    self.policy_value_net.save_model('./current_policy.model')
-                    if win_ratio > self.best_win_ratio:
-                        print("New best policy!!!!!!!!")
+                    utils.log("current self-play batch: {}".format(i+1), CONSOLE_OUTPUT)
+                    win_ratio = self.policy_evaluate(current_batch=i+1)
+                    self.policy_value_net.save_model(OUTPUT_DIR+'/current_policy.model')
+                    if win_ratio >= self.best_win_ratio:
+                        utils.log("New best policy!!!!!!!!", CONSOLE_OUTPUT)
                         self.best_win_ratio = win_ratio
                         # update the best_policy
-                        self.policy_value_net.save_model('./best_policy.model')
+                        self.policy_value_net.save_model(OUTPUT_DIR+'/best_policy.model')
                         if (self.best_win_ratio == 1.0 and
                                 self.pure_mcts_playout_num < 5000):
                             self.pure_mcts_playout_num += 1000
@@ -191,5 +224,37 @@ def run(self):
 
 
 if __name__ == '__main__':
-    training_pipeline = TrainPipeline()
+    parser = argparse.ArgumentParser(prog='train.py')
+    parser.add_argument('--ModelName', '-m', dest='ModelName', required=True, choices=['baseline', 'res30'])
+    parser.add_argument('--LossFunction', '-l', dest='LossFunction', required=True, choices=['lv', 'lp', 'l+', 'lx'])
+    parser.add_argument('--EnableForbiddenHands', '-fh', dest='EnableForbiddenHands', action='store_false', help=r'Enable forbidden hands')
+
+    args = parser.parse_args()
+    model_name = args.ModelName
+    loss_function = args.LossFunction
+    forbidden_hands = args.EnableForbiddenHands
+    
+    OUTPUT_DIR = "output/"
+    OUTPUT_DIR += model_name
+    OUTPUT_DIR += "_" + loss_function
+    OUTPUT_DIR += "_forbiddenhands/" if forbidden_hands else "/"
+    init_model = OUTPUT_DIR + "current_policy.model"
+    if not os.path.exists(init_model):
+        init_model = None
+    OUTPUT_DIR += datetime.utcnow().strftime("%Y%m%d%H%M%S")
+    os.makedirs(OUTPUT_DIR, exist_ok=True)
+    INTERMEDIATE_RESULT = OUTPUT_DIR + "/intermediate_result.txt"
+    SCORE_OUTPUT = OUTPUT_DIR + "/scores.txt"
+    CONSOLE_OUTPUT = OUTPUT_DIR + "/console.txt"
+
+    print("**************************************************************")
+    print("Start new training process...")
+    print(f"ModelName: {model_name}, LossFunction: {loss_function}, EnableForbiddenHands: {forbidden_hands}")
+    print(f"init model : {init_model}")
+    print(f"intermediate result : {INTERMEDIATE_RESULT}")
+    print(f"score output : {SCORE_OUTPUT}")
+    print(f"console output : {CONSOLE_OUTPUT}")
+    print("**************************************************************")
+
+    training_pipeline = TrainPipeline(model_name, loss_function, forbidden_hands, init_model)
     training_pipeline.run()
diff --git a/utils.py b/utils.py
new file mode 100644
index 000000000..43a19ed71
--- /dev/null
+++ b/utils.py
@@ -0,0 +1,10 @@
+from datetime import datetime
+import os
+
+def log(message, logpath):
+    timestamp = datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S ")
+    message = timestamp + message
+    print(message)
+    os.makedirs(os.path.dirname(logpath), exist_ok=True)
+    with open(logpath,'a+') as fs:
+        fs.write(message+'\n')