Teach you how to train Goko Chess Ai using pytorch

 
class Game:
    def __init__(self, h, w):
        # Number of rows         = h
        # Number of columns         = w
        # Chessboard         = [['-' for _ in range(w)] for _ in range(h)]
        # Current player - means empty X first and then O         = 'X'
        # Game winner        self.win_user = None
 
    # Check whether there is any win after this step. y is the row and x is the column. Return True to mean winning.    def check_win(self, y, x):
        directions = [
            # Horizontal, vertical, two diagonal directions            (1, 0), (0, 1), (1, 1), (1, -1)
        ]
        player = [y][x]
        for dy, dx in directions:
            count = 0
            # Check for consecutive identical pieces in four directions            for i in range(-4, 5):  # Check the range of -4 to 4, because the five-piece beads require 5 pieces                ny, nx = y + i * dy, x + i * dx
                if 0 <= ny <  and 0 <= nx <  and [ny][nx] == player:
                    count += 1
                    if count == 5:
                        return True
                else:
                    count = 0
        return False
 
    # Check if you can go down here. Y row x column. Return True to indicate that you can go down.    def check(self, y, x):
        return [y][x] == '-' and self.win_user is None
 
    # Print the board to visualize it    def __str__(self):
        # Determine the width of the row and column numbers        row_width = len(str( - 1))
        col_width = len(str( - 1))
        
        # Generate a chessboard string representation with line and column numbers        result = []
        # Add column number title        (' ' * (row_width + 1) + ' '.join(f'{i:>{col_width}}' for i in range()))
        # Add divider (optional)        (' ' * (row_width + 1) + '-' * (col_width * ))
        # Add a chessboard line        for y, row in enumerate():
            # Add line number            (f'{y:>{row_width}} ' + ' '.join(f'{cell:>{col_width}}' for cell in row))
        return '\n'.join(result)
 
    # One-step chess    def set(self, y, x):
        if self.win_user or not (y, x):
            return False
        [y][x] = 
        if self.check_win(y, x):
            self.win_user = 
            return True
         = 'X' if  == 'O' else 'O'
        return True
    #All-round    def heqi(self):
        for y in range():
            for x in range():
                if [y][x]=='-':
                    return False
        return True
    

#Players go down by themselvesdef run_game01():
    g = Game(15, 15)
    while not g.win_user:
        # Print the current board status        while 1:
            print(g)
            try:
                y,x=input(+':').split(',')
                x=int(x)
                y=int(y)
                if (y,x):
                    break
            except Exception as e:
                print(e)
    print(g)
    print('winner',g.win_user)
 
 
 

import torch
import  as nn
import  as optim
from game import Game

class MyMod():
    def __init__(self, input_channels=1, output_size=15*15):
        super(MyMod, self).__init__()
        
        # Define a convolutional layer for extracting features        self.conv1 = nn.Conv2d(input_channels, 32, kernel_size=3, padding=1)  # Output 32 x 15 x 15        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)  # Output 64 x 15 x 15        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)  # Output 128 x 15 x 15        
        # Define a fully connected layer for final score prediction        self.fc1 = (128 * 15 * 15, 1024)  # After flattening, pass in full connection layer        self.fc2 = (1024, output_size)  # Output 15*15 score prediction
    def forward(self, x):
        # Convolutional layer -> Activation function -> Maximum pooling        x = (self.conv1(x))
        x = (self.conv2(x))
        x = (self.conv3(x))
        
        # Flatten the output of the convolution layer into one dimension        x = ((0), -1)
        
        # Full connection layer        x = (self.fc1(x))
        x = self.fc2(x)
        return x
    # Save model weights    def save(self, path):
        (self.state_dict(), path)

    # Loading model weights    def load(self, path):
        self.load_state_dict((path))

#Improve output The probability of places with chess pieces = 0 to avoid going down these places# Enter the Game object and the MyMod object to get the most likely dropping point (row y, column x)def input_qi(g: Game, m: MyMod):
    # Get the current board status    board_state =   # Use to get the status of the current board (a 2D list of 15x15)    
    # Convert the board state to PyTorch's Tensor and add a dimension (batch_size = 1)    board_tensor = ([[1 if cell == 'X' else -1 if cell == 'O' else 0 for cell in row] for row in board_state], 
                                 dtype=torch.float32).unsqueeze(0).unsqueeze(0)  # The shape becomes (1, 1, 15, 15)    
    # Pass in to get the score for each position    output = m(board_tensor)
    
    # Convert the output to a probability value (can be normalized using softmax)    probabilities = (output, dim=-1).view(, ).detach().numpy()  # becomes (15, 15) size    
    # Set the probability of the position of the existing chess piece to -inf to avoid selecting these positions    for y in range():
        for x in range():
            if board_state[y][x] != '-':
                probabilities[y, x] = -float('inf')  # Set the probability of a place where there are already chess pieces to -inf    
    # Find the most likely place    max_prob_pos = divmod((), )  # Get the row and column coordinates of the maximum probability    
    # Make sure that the returned legal location    y, x = max_prob_pos
    
    return (y, x), output  # Return coordinates and model output

import os
import torch
import  as optim
import  as F
from mod import MyMod, input_qi, Game

# Two weight files represent X and O respectivelyMX = 'MX'
MO = 'MO'

# Load the model, initialize if the file does not existdef load_model(model, path):
    if (path):
        (path)
        print(f"Loaded model from {path}")
    else:
        print(f"{path} not found, initializing new model.")
        # Here you can add some code to initialize the model, for example:        # (init_weights) If initialization of weights is required
# Initialize the modelmodx = MyMod()
load_model(modx, MX)

modo = MyMod()
load_model(modo, MO)

# Define an optimizerlr=0.001
optimizer_x = ((), lr=lr)
optimizer_o = ((), lr=lr)

# Loss function: adjust the loss according to the game resultsdef compute_loss(winner: int, player: str, model_output):
    # Convert the target value to the corresponding tensor    if player == "X":
        if winner == 1:  # X Win            target = (1.0, dtype=torch.float32)
        elif winner == 0:  #Grading game            target = (0.5, dtype=torch.float32)
        else:  # X Lost            target = (0.0, dtype=torch.float32)
    else:
        if winner == -1:  # O Win            target = (1.0, dtype=torch.float32)
        elif winner == 0:  #Grading game            target = (0.5, dtype=torch.float32)
        else:  # O Lose            target = (0.0, dtype=torch.float32)

    # Ensure that the shape of the target value is consistent with model_output, assuming that model_output is a single value    target = (0).unsqueeze(0)  # The shape becomes (1, 1)
    # Calculate using mean square error loss    return F.mse_loss(model_output, target)



# The process of training the modeldef train_game():
    ()
    ()

    # Create a new game instance    game = Game(15, 15)  # The default is 15x15 chessboard    # Backpropagation and optimization    optimizer_x.zero_grad()
    optimizer_o.zero_grad()

    while not game.win_user:  # The game is not over        # X Fang Luozi        x_move, x_output = input_qi(game, modx)  # Get the drop position and model output (x_output is the output of the model)        (x_move[0], x_move[1])  # X Play Chess        
        if game.win_user:
            break
        
        # O Fang Luozi        
        o_move, o_output = input_qi(game, modo)  # Get the drop position and model output (o_output is the output of the model)        #print(o_move,game)
        (o_move[0], o_move[1])  # O Play Chess       

    # Get the results of the match    winner = 0 if () else (1 if game.win_user == 'X' else -1)  # 1 is X win, -1 is O win, 0 is tie
    # Calculate the loss    loss_x = compute_loss(winner, "X", x_output)  # Pass the model output to the calculation loss function    loss_o = compute_loss(winner, "O", o_output)  # Pass the model output to the calculation loss function
    # Calculate the loss and perform backpropagation    loss_x.backward()
    loss_o.backward()

    # Update weight    optimizer_x.step()
    optimizer_o.step()
    print(game)
    return loss_x.item(), loss_o.item()


# Train multiple roundsdef train(num_epochs,n):
    k=0
    for epoch in range(num_epochs):
        loss_x, loss_o = train_game()
        print(f"Epoch [{epoch+1}/{num_epochs}], Loss X: {loss_x}, Loss O: {loss_o}")
        k+=1
        if k==n:
            ('MO')
            ('MX')
            print('saved')
            k=0

# Start trainingtrain(50000,1000)

from game import Game
from mod import MyMod,input_qi


#Players down X ai down Odef playX():
    m=MyMod()
    ('MO')
    g=Game(15,15)
    while 1:
        print(g)
        if () or g.win_user:
            break
        while 1:
            try:
                r=input('X:')
                y,x=(',')
                y=int(y)
                x=int(x)
                if (y,x):
                    break
            except Exception as e:
                print(e)
        if () or g.win_user:
            break
        while 1:
            (y,x),_=input_qi(g,m)
            if (y,x):
                break
    print(g)
    print('winner',g.win_user)


#Players go down O ai go down Xdef playO():
    m=MyMod()
    ('MX')
    g=Game(15,15)
    while 1:
        
        if () or g.win_user:
            break
        while 1:
            (y,x),_=input_qi(g,m)
            if (y,x):
                break
        if () or g.win_user:
            break
        print(g)
        while 1:
            try:
                r=input('O:')
                y,x=(',')
                y=int(y)
                x=int(x)
                if (y,x):
                    break
            except Exception as e:
                print(e)
    print(g)
    print('winner',g.win_user)

playX()