def __init__(self):
self.someoneWin = False
self.humanChessed = False
self.IsStart = False
self.player = 0
self.playmethod = 0
self.whi_chessed = []
self.bla_chessed = []
self.board = self.init_board()
self.window = Tk()
self.var = IntVar()
self.var.set(0)
self.var1 = IntVar()
self.var1.set(0)
self.window.title("myGoBang")
self.window.geometry("600x470+80+80")
self.window.resizable(0, 0)
self.can = Canvas(self.window, bg="#EEE8AC", width=470, height=470)
self.draw_Board()
self.can.grid(row=0, column=0)
self.net_board = self.get_net_board()
self.robot = Robot(self.board)
self.sgf = SGFflie()
self.cnn = myCNN()
self.cnn.restore_save()
def __init__(self):
"""
初始化：
someoneWin:标识是否有人赢了
humanChessed:人类玩家是否下了
IsStart:是否开始游戏了
player:玩家是哪一方
hardLevel:基本规则的强度
CnnMode:神经网络是否被基本规则限制
bla_start_pos:黑棋开局时下在正中间的位置
bla_chessed:保存黑棋已经下过的棋子
whi_chessed:保存白棋已经下过的棋子
board:棋盘
window:窗口
var:用于标记选择玩家颜色的一个变量
var1:用于标记选择神经网络模式的一个变量
var2:用于标记选择基本规则强度的一个变量
can:画布，用于绘出棋盘
net_board:棋盘的点信息
robot:机器人
sgf:处理棋谱
cnn:cnn神经网络
"""
self.someoneWin = False
self.humanChessed = False
self.IsStart = False
self.player = 0
self.playmethod = 0
self.hardLevel = 0
self.CnnMode = 0
self.bla_start_pos = [235, 235]
self.whi_chessed = []
self.bla_chessed = []
self.board = self.init_board()
self.window = Tk()
self.var = IntVar()
self.var.set(0)
self.var1 = IntVar()
self.var1.set(0)
self.var2 = IntVar()
self.var2.set(0)
self.window.title("Dept5 AI GoBang")
self.window.geometry("675x500+80+80")
self.window.resizable(0, 0)
self.can = Canvas(self.window, bg="#C1FFC1", width=470, height=470)
self.draw_board()
self.can.grid(row=0, column=0)
self.net_board = self.get_net_board()
self.robot = Robot(self.board)
self.sgf = SGFflie()
self.cnn = myCNN()
self.cnn.restore_save()
sys.setrecursionlimit(15 * 15)
def __init__(self):
"""
初始化：
someoneWin:标识是否有人赢了
humanChessed:人类玩家是否下了
IsStart:是否开始游戏了
player:玩家是哪一方
playmethod:模式，和robot下棋，还是和ai下棋
bla_start_pos:黑棋开局时下在正中间的位置
bla_chessed:保存黑棋已经下过的棋子
whi_chessed:保存白棋已经下过的棋子
board:棋盘
window:窗口
var:用于标记选择玩家颜色的一个变量
var1:用于标记选择robot或者ai的一个变量
can:画布，用于绘出棋盘
net_board:棋盘的点信息
robot:机器人
sgf:处理棋谱
cnn:cnnc神经网络
"""
self.someoneWin = False
self.humanChessed = False
self.IsStart = False
self.player = 0
self.playmethod = 0
self.bla_start_pos = [235, 235]
self.whi_chessed = []
self.bla_chessed = []
self.board = self.init_board()
self.window = Tk()
self.var = IntVar()
self.var.set(0)
self.var1 = IntVar()
self.var1.set(0)
self.window.title("myGoBang")
self.window.geometry("600x470+80+80")
self.window.resizable(0, 0)
self.can = Canvas(self.window, bg="#EEE8AC", width=470, height=470)
self.draw_board()
self.can.grid(row=0, column=0)
self.net_board = self.get_net_board()
self.robot = Robot(self.board)
self.sgf = SGFflie()
self.cnn = myCNN()
self.cnn.restore_save()
def reTrain(self):
"""再训练方法"""
self.cnn.sess.close()
sgf = SGFflie()
_cnn = myCNN()
path = sgf.allFileFromDir('sgf\\')
_x, _y = sgf.createTraindataFromqipu(path[0])
step = 0
_path = path[:2000]
for filepath in path:
x, y = sgf.createTraindataFromqipu(filepath)
for i in range(1):
_cnn.sess.run(_cnn.train_step,
feed_dict={
_cnn.x: x,
_cnn.y: y,
_cnn.keep_prob: 0.5
})
print(step)
step += 1
_cnn.restore_save(method=0)
_cnn.restore_save(method=1)
print(
_cnn.sess.run(tf.argmax(_cnn.y_conv, 1),
feed_dict={
_cnn.x: _x[0:10],
_cnn.keep_prob: 1.0
}))
self.cnn = _cnn
self.cnn.restore_save()
self.resetButton()
label = Label(self.window,
text="训练完成!",
background='#EEEEEE',
font=("宋体", 15, "bold"))
label.place(relx=0, rely=0, x=490, y=15)
validate_data.shape[0], num_channel,
int(validate_data.shape[1] / num_channel), validate_data.shape[2])
test_data = test_data.reshape(test_data.shape[0], num_channel,
int(test_data.shape[1] / num_channel),
test_data.shape[2])
# ===============================================
# Rearange tensor"s dimension
training_data = np.moveaxis(training_data, 1, -1)
validate_data = np.moveaxis(validate_data, 1, -1)
test_data = np.moveaxis(test_data, 1, -1)
# ===============================================
# Train CNN
training_history = myCNN(training_data, training_label_1, training_label_2,
validate_data, validate_label_2, classes,
CNN_architecture_package, CNN_training_package,
CNN_callbacks_package)
# ===============================================
# Load Trained CNN
fold_best_CNN = load_model(saved_model_name)
# ===============================================
# save the plot of validation loss
plt.plot(training_history.history[callbacks_monitor])
plt.savefig(val_loss_plot_name + str(fold_index + 1) + ".png")
plt.clf()
# ===============================================
# First we release the results directly from the CNN
fold_result_package = evaluateCNN(fold_best_CNN, test_combo, test_data,