File size: 6,436 Bytes
9d524af b46b06b 9d524af |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 |
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
import csv
class Architecture(nn.Module):
def __init__(self) -> None:
super(Architecture, self).__init__()
self.input_size = 9
self.hidden_size_1 = 9
self.hidden_size_2 = 9
self.hidden_size_3 = 9
self.hidden_size_4 = 9
self.hidden_size_5 = 9
self.hidden_size_6 = 9
self.hidden_size_7 = 9
self.output_size = 9
self.fc1 = nn.Linear(self.input_size, self.hidden_size_1)
self.fc2 = nn.Linear(self.hidden_size_1, self.hidden_size_2)
self.fc3 = nn.Linear(self.hidden_size_2, self.hidden_size_3)
self.fc4 = nn.Linear(self.hidden_size_3, self.hidden_size_4)
self.fc5 = nn.Linear(self.hidden_size_4, self.hidden_size_5)
self.fc6 = nn.Linear(self.hidden_size_5, self.hidden_size_6)
self.fc7 = nn.Linear(self.hidden_size_6, self.hidden_size_7)
self.fc8 = nn.Linear(self.hidden_size_7, self.output_size)
self.loss = nn.CrossEntropyLoss()
self.relu = nn.ReLU()
def inference(self, x):
with torch.no_grad():
x1 = self.relu(self.fc1(x))
x2 = self.relu(self.fc2(x1))
x3 = self.relu(self.fc3(x2))
x4 = self.relu(self.fc4(x3))
x5 = self.relu(self.fc5(x4))
x6 = self.relu(self.fc6(x5))
x7 = self.relu(self.fc7(x6))
x8 = self.fc8(x7)
return x8
def load_model():
model = Architecture()
model.load_state_dict(torch.load('./model_weights.pth'))
return model
def inference_model(model, input):
return model.inference(input)
def minimax(board, depth, is_maximizing):
if check_winner(board) == 2: # AI wins
return 1
if check_winner(board) == 1: # Human wins
return -1
if check_winner(board) == 3: # Draw
return 0
if is_maximizing:
best_score = -math.inf
for i in range(9):
if board[i] == 0:
board[i] = 2 # AI's move
score = minimax(board, depth + 1, False)
board[i] = 0
best_score = max(score, best_score)
return best_score
else:
best_score = math.inf
for i in range(9):
if board[i] == 0:
board[i] = 1 # Human's move
score = minimax(board, depth + 1, True)
board[i] = 0
best_score = min(score, best_score)
return best_score
def best_move_minimax(board):
best_score = -math.inf
move = None
for i in range(9):
if board[i] == 0:
board[i] = 2 # AI's move
score = minimax(board, 0, False)
board[i] = 0
if score > best_score:
best_score = score
move = i
return move
def oracle(board, position):
output_board = [0] * 9
output_board[position] = 2
# Get minimax prediction
minimax_move = best_move_minimax(board)
# Compare oracle's move with minimax prediction
if minimax_move == position:
return 1 # Predictions match
else:
return -1 # Predictions differ
def print_in_csv(board, position, comparison_result):
output_board = [0] * 9
output_board[position] = 2
with open('game_state.csv', 'a', newline='') as f:
writer = csv.writer(f)
writer.writerow([
','.join(map(str, board)),
','.join(map(str, output_board)),
comparison_result
])
def decode_prediction(prediction, board):
result = torch.argmax(prediction)
position = result.item()
comparison_result = oracle(board, position)
print(f'Oracle comparison result: {comparison_result}')
print_in_csv(board, position, comparison_result)
board[position] = 2
return board
def encode_input(input):
return torch.tensor(input, dtype=torch.float32)
def play():
model = load_model()
model.eval()
print(model.inference(torch.randn(9)))
def print_board(board):
visual_board = [' ' if x == 0 else 'X' if x == 1 else 'O' for x in board]
print(f'{visual_board[0]} | {visual_board[1]} | {visual_board[2]}')
print('---------')
print(f'{visual_board[3]} | {visual_board[4]} | {visual_board[5]}')
print('---------')
print(f'{visual_board[6]} | {visual_board[7]} | {visual_board[8]}')
print('---------')
def check_winner(board):
# Check rows
for i in range(0, 9, 3):
if board[i] == board[i + 1] == board[i + 2] == 1:
return 1
if board[i] == board[i + 1] == board[i + 2] == 2:
return 2
# Check columns
for i in range(3):
if board[i] == board[i + 3] == board[i + 6] == 1:
return 1
if board[i] == board[i + 3] == board[i + 6] == 2:
return 2
# Check diagonals
if board[0] == board[4] == board[8] == 1:
return 1
if board[0] == board[4] == board[8] == 2:
return 2
if board[2] == board[4] == board[6] == 1:
return 1
if board[2] == board[4] == board[6] == 2:
return 2
# Check for a draw
if all(cell != 0 for cell in board):
return 3
return 0
def simulate_game():
board = [0, 0, 0, 0, 0, 0, 0, 0, 0]
end = False
while end != True:
print_board(board)
move = int(input('Enter move: '))
if move <= 9 and move >= 1 and board[move - 1] == 0:
board[move - 1] = 1
print_board(board)
if check_winner(board) == 1:
print('You win')
end = True
break
if check_winner(board) == 3:
print('Draw')
end = True
break
print('Computer is thinking...')
intput = encode_input(board)
model = load_model()
prediction = inference_model(model, intput)
output = decode_prediction(prediction, board)
board = output
if check_winner(board) == 2:
print_board(board)
print('Computer wins')
end = True
break
if check_winner(board) == 3:
print('Draw')
end = True
break
else:
print('Invalid move')
if __name__ == '__main__':
simulate_game()
|