main_Initial_Idea.ipynb

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       "p p p p p p p p\n",
       ". . . . . . . .\n",
       ". . . . . . . .\n",
       ". . . . . . . .\n",
       ". . . . . . . .\n",
       "P P P P P P P P\n",
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/><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(15, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(60, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(105, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(150, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(195, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(240, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(285, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(330, 285)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(15, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(60, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(105, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(150, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(195, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(240, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(285, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(330, 60)\" /><use href=\"#black-rook\" xlink:href=\"#black-rook\" transform=\"translate(15, 15)\" /><use href=\"#black-knight\" xlink:href=\"#black-knight\" transform=\"translate(60, 15)\" /><use href=\"#black-bishop\" xlink:href=\"#black-bishop\" transform=\"translate(105, 15)\" /><use href=\"#black-queen\" xlink:href=\"#black-queen\" transform=\"translate(150, 15)\" /><use href=\"#black-king\" xlink:href=\"#black-king\" transform=\"translate(195, 15)\" /><use href=\"#black-bishop\" xlink:href=\"#black-bishop\" transform=\"translate(240, 15)\" /><use href=\"#black-knight\" xlink:href=\"#black-knight\" transform=\"translate(285, 15)\" /><use href=\"#black-rook\" xlink:href=\"#black-rook\" transform=\"translate(330, 15)\" /></svg>"
      ],
      "text/plain": [
       "Board('rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1')"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pickle\n",
    "from tqdm import tqdm\n",
    "import numpy as np\n",
    "import chess\n",
    "import chess.engine\n",
    "import chess.pgn\n",
    "import chess.svg\n",
    "from IPython.display import *\n",
    "import datetime \n",
    "\n",
    "\n",
    "engine = chess.engine.SimpleEngine.popen_uci(r\"C:\\ThefCraft\\global-env-exe\\stockfish\\stockfish-windows-x86-64-avx2.exe\")\n",
    "board = chess.Board()\n",
    "board"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils.board import board2vec\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "from io import StringIO \n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "import random\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>WhiteElo</th>\n",
       "      <th>BlackElo</th>\n",
       "      <th>Result</th>\n",
       "      <th>transcript</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33733</td>\n",
       "      <td>1554</td>\n",
       "      <td>1522</td>\n",
       "      <td>0-1</td>\n",
       "      <td>1.e4 d6 2.Bc4 Nf6 3.Qf3 g6 4.d3 Bg7 5.c3 O-O 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>83900</td>\n",
       "      <td>1915</td>\n",
       "      <td>2065</td>\n",
       "      <td>1/2-1/2</td>\n",
       "      <td>1.d4 Nf6 2.c4 e6 3.Nc3 b6 4.Nf3 Bb7 5.a3 Ne4 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45565</td>\n",
       "      <td>2101</td>\n",
       "      <td>2114</td>\n",
       "      <td>0-1</td>\n",
       "      <td>1.e4 g6 2.d4 Bg7 3.Nc3 c5 4.Be3 cxd4 5.Bxd4 Nf...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18791</td>\n",
       "      <td>1813</td>\n",
       "      <td>1788</td>\n",
       "      <td>0-1</td>\n",
       "      <td>1.e4 g6 2.f4 Bg7 3.d4 d6 4.c4 Nf6 5.Nc3 O-O 6....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>86451</td>\n",
       "      <td>1823</td>\n",
       "      <td>1927</td>\n",
       "      <td>1-0</td>\n",
       "      <td>1.e4 e5 2.Nf3 Nc6 3.d4 exd4 4.Nxd4 Bc5 5.Nb3 B...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263869</th>\n",
       "      <td>2621868</td>\n",
       "      <td>1664</td>\n",
       "      <td>1689</td>\n",
       "      <td>1-0</td>\n",
       "      <td>1.d4 e6 2.Bf4 c6 3.Nf3 d5 4.e3 Bd6 5.Bxd6 Qxd6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263870</th>\n",
       "      <td>2692262</td>\n",
       "      <td>1728</td>\n",
       "      <td>1784</td>\n",
       "      <td>1-0</td>\n",
       "      <td>1.d4 c5 2.d5 d6 3.c4 e5 4.e4 Na6 5.a3 Bd7 6.Nc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263871</th>\n",
       "      <td>2634235</td>\n",
       "      <td>1711</td>\n",
       "      <td>1707</td>\n",
       "      <td>1-0</td>\n",
       "      <td>1.e4 d6 2.d4 Nc6 3.d5 Ne5 4.Nc3 Nf6 5.h3 e6 6....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263872</th>\n",
       "      <td>2640298</td>\n",
       "      <td>1901</td>\n",
       "      <td>1719</td>\n",
       "      <td>1-0</td>\n",
       "      <td>1.e4 c5 2.Nc3 Nc6 3.Nf3 e6 4.Bb5 d5 5.Bxc6+ bx...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263873</th>\n",
       "      <td>2634918</td>\n",
       "      <td>1810</td>\n",
       "      <td>1819</td>\n",
       "      <td>0-1</td>\n",
       "      <td>1.d4 c6 2.c4 d5 3.Nc3 e6 4.e4 dxe4 5.Bd3 Nd7 6...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>263874 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0  WhiteElo  BlackElo   Result  \\\n",
       "0            33733      1554      1522      0-1   \n",
       "1            83900      1915      2065  1/2-1/2   \n",
       "2            45565      2101      2114      0-1   \n",
       "3            18791      1813      1788      0-1   \n",
       "4            86451      1823      1927      1-0   \n",
       "...            ...       ...       ...      ...   \n",
       "263869     2621868      1664      1689      1-0   \n",
       "263870     2692262      1728      1784      1-0   \n",
       "263871     2634235      1711      1707      1-0   \n",
       "263872     2640298      1901      1719      1-0   \n",
       "263873     2634918      1810      1819      0-1   \n",
       "\n",
       "                                               transcript  \n",
       "0       1.e4 d6 2.Bc4 Nf6 3.Qf3 g6 4.d3 Bg7 5.c3 O-O 6...  \n",
       "1       1.d4 Nf6 2.c4 e6 3.Nc3 b6 4.Nf3 Bb7 5.a3 Ne4 6...  \n",
       "2       1.e4 g6 2.d4 Bg7 3.Nc3 c5 4.Be3 cxd4 5.Bxd4 Nf...  \n",
       "3       1.e4 g6 2.f4 Bg7 3.d4 d6 4.c4 Nf6 5.Nc3 O-O 6....  \n",
       "4       1.e4 e5 2.Nf3 Nc6 3.d4 exd4 4.Nxd4 Bc5 5.Nb3 B...  \n",
       "...                                                   ...  \n",
       "263869  1.d4 e6 2.Bf4 c6 3.Nf3 d5 4.e3 Bd6 5.Bxd6 Qxd6...  \n",
       "263870  1.d4 c5 2.d5 d6 3.c4 e5 4.e4 Na6 5.a3 Bd7 6.Nc...  \n",
       "263871  1.e4 d6 2.d4 Nc6 3.d5 Ne5 4.Nc3 Nf6 5.h3 e6 6....  \n",
       "263872  1.e4 c5 2.Nc3 Nc6 3.Nf3 e6 4.Bb5 d5 5.Bxc6+ bx...  \n",
       "263873  1.d4 c6 2.c4 d5 3.Nc3 e6 4.e4 dxe4 5.Bd3 Nd7 6...  \n",
       "\n",
       "[263874 rows x 5 columns]"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('lichess_100mb/lichess_100mb.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# class CustomDataset(Dataset):\n",
    "#     def __init__(self, size = 100):\n",
    "#         df = pd.read_csv('lichess_100mb/lichess_100mb.csv')\n",
    "#         self.X = []\n",
    "#         self.Y = []\n",
    "#         # Iterate over the DataFrame and analyze each game\n",
    "#         for index, row in tqdm(df.iterrows(), total = df.__len__()):\n",
    "#             if (index>=size): break\n",
    "#             moves = row['transcript']\n",
    "#             board = chess.Board()\n",
    "\n",
    "#             pgn = StringIO(moves) \n",
    "#             game = chess.pgn.read_game(pgn) \n",
    "\n",
    "#             for number, move in enumerate(game.mainline_moves()): \n",
    "#                 self.X.append(board2vec(board))\n",
    "#                 who = F.one_hot(torch.tensor(move.from_square), 64).float()\n",
    "#                 where = F.one_hot(torch.tensor(move.to_square), 64).float()\n",
    "#                 self.Y.append((who, where))\n",
    "#                 board.push(move)\n",
    "#             pgn.close()\n",
    "#         assert(len(self.X)==len(self.Y))\n",
    "        \n",
    "#     def __len__(self): return len(self.X)\n",
    "\n",
    "#     def __getitem__(self, idx): return self.X[idx], self.Y[idx]\n",
    "\n",
    "    \n",
    "# # Create an instance of the custom dataset\n",
    "# custom_dataset = CustomDataset(size=128*128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CustomDataset(Dataset):\n",
    "    def __init__(self, file='chess_dataset_1000.npz'):\n",
    "        # Load the dataset\n",
    "        loaded_data = np.load(file)\n",
    "        # Extract the arrays\n",
    "        self.X_position8x8 = loaded_data['X_position8x8'].astype(np.float32)\n",
    "        self.X_attack8x8 = loaded_data['X_attack8x8'].astype(np.float32)\n",
    "        self.X_current_player = loaded_data['X_current_player'].astype(np.float32)\n",
    "        self.X_castling_rights = loaded_data['X_castling_rights'].astype(np.float32)\n",
    "        self.X_en_passant_square = loaded_data['X_en_passant_square'].astype(np.float32)\n",
    "        self.Y_who = loaded_data['Y_who'].astype(np.float32)\n",
    "        self.Y_where = loaded_data['Y_where'].astype(np.float32)\n",
    "        \n",
    "    def __len__(self): return len(self.X_position8x8)\n",
    "    def __getitem__(self, idx): \n",
    "        return (self.X_position8x8[idx], self.X_attack8x8[idx], self.X_current_player[idx], self.X_castling_rights[idx], self.X_en_passant_square[idx],\n",
    "                self.Y_who[idx], self.Y_where[idx])\n",
    "\n",
    "    \n",
    "# Create an instance of the custom dataset\n",
    "custom_dataset = CustomDataset(file='chess_dataset_1000_black.npz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a DataLoader with batch size 64\n",
    "batch_size = 4\n",
    "data_loader = DataLoader(custom_dataset, batch_size=batch_size, shuffle=True)\n",
    "# # Iterate through the DataLoader\n",
    "for batch in data_loader:\n",
    "    position, attacks, current_player, castling_rights, en_passant_square, who, where = batch\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([1, 8, 8]),\n",
       " torch.Size([1, 8, 8]),\n",
       " torch.Size([1, 1]),\n",
       " torch.Size([1, 4]),\n",
       " torch.Size([1, 64]))"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "position, attacks, current_player, castling_rights, en_passant_square = board2vec(board)\n",
    "\n",
    "# (batch_size, channels, height, width)\n",
    "\n",
    "position = torch.from_numpy(position).unsqueeze(0)\n",
    "attacks = torch.from_numpy(attacks).unsqueeze(0)\n",
    "\n",
    "current_player = torch.from_numpy(current_player).unsqueeze(0)\n",
    "castling_rights = torch.from_numpy(castling_rights).unsqueeze(0)\n",
    "en_passant_square = torch.from_numpy(en_passant_square).unsqueeze(0)\n",
    "\n",
    "position.shape, attacks.shape, current_player.shape, castling_rights.shape, en_passant_square.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "en_passant_square"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ChessModel(nn.Module):\n",
    "    def __init__(self, force_learning=.5):\n",
    "        super(ChessModel, self).__init__()\n",
    "        \n",
    "        self.conv_position = nn.Conv2d(1, 16, 3, padding=1)\n",
    "        self.conv_attacks = nn.Conv2d(1, 16, 3, padding=1)\n",
    "        \n",
    "        self.fc_position_attack = nn.Linear(2*16*8*8, 12*12)\n",
    "        \n",
    "        # Max pooling layer\n",
    "        # self.pool = nn.MaxPool2d(kernel_size=2, stride=1, padding=0)\n",
    "        \n",
    "        self.fc_other_info = nn.Linear(69, 144)\n",
    "        self.fc1_main_who = nn.Linear(2*144, 512)\n",
    "        self.fc1_main_where = nn.Linear(2*144, 512)\n",
    "        self.fc2_who = nn.Linear(512, 8*8)\n",
    "        self.fc2_where = nn.Linear(8*8+512, 8*8)\n",
    "        \n",
    "        self.force_learning = force_learning\n",
    "    \n",
    "    def forward(self, position, attacks, current_player, castling_rights, en_passant_square, targetwho=None, targetwhere=None):\n",
    "        N = position.size(0)\n",
    "        position = position.unsqueeze(1)\n",
    "        attacks = attacks.unsqueeze(1)\n",
    "        position = F.relu(self.conv_position(position))\n",
    "        attacks = F.relu(self.conv_attacks(attacks))\n",
    "        \n",
    "        position_attack = F.relu(self.fc_position_attack(torch.concat((position, attacks), dim=1).view(N, -1)))\n",
    "        other_info = F.relu(self.fc_other_info(torch.concat((current_player, castling_rights, en_passant_square), dim=1)))\n",
    "        x=torch.concat((position_attack, other_info), dim=1)\n",
    "        x_who = F.relu(self.fc1_main_who(x))\n",
    "        x_where = F.relu(self.fc1_main_where(x))\n",
    "        who = self.fc2_who(x_who)\n",
    "        \n",
    "        if targetwho is not None:\n",
    "            if random.random()>self.force_learning:\n",
    "                where = self.fc2_where(torch.concat((F.one_hot(torch.argmax(who, dim=1), 64), x_where), dim=1))\n",
    "            else:    \n",
    "                where = self.fc2_where(torch.concat((targetwho, x_where), dim=1))\n",
    "        else:\n",
    "            where = self.fc2_where(torch.concat((F.one_hot(torch.argmax(who, dim=1), 64), x_where), dim=1))\n",
    "        return who, where"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [],
   "source": [
    "for (position, attacks, current_player, castling_rights, en_passant_square, targetwho, targetwhere) in dataloader: break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-3.2687e-02, -2.3239e-02,  6.8189e-02,  ...,  6.9901e-02,\n",
       "         -4.0957e-02,  2.8127e-03],\n",
       "        [-3.4715e-02, -3.3772e-02,  6.5322e-02,  ...,  6.3745e-02,\n",
       "         -3.2770e-02,  6.9300e-03],\n",
       "        [-2.0478e-02, -1.6929e-02,  6.3618e-02,  ...,  5.5279e-02,\n",
       "         -3.8801e-02, -3.1723e-03],\n",
       "        ...,\n",
       "        [-2.5654e-02, -7.7991e-03,  7.3790e-02,  ...,  5.7096e-02,\n",
       "         -4.3084e-02, -4.8411e-04],\n",
       "        [-1.9992e-02, -1.5517e-02,  6.7006e-02,  ...,  6.2181e-02,\n",
       "         -4.1513e-02, -2.7302e-05],\n",
       "        [-3.4034e-02, -3.4189e-02,  6.3581e-02,  ...,  7.3608e-02,\n",
       "         -4.8081e-02,  5.6040e-03]], grad_fn=<AddmmBackward0>)"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ChessModel()(position, attacks, current_player, castling_rights, en_passant_square, targetwho, targetwhere)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [],
   "source": [
    "EPOCH = 1000\n",
    "BATCH_SIZE = 4096*4 # i update model weight after each iteration on batch size so make it larger\n",
    "LR = .4#0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ChessModel(force_learning=.75).to(device)\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=LR) # Adam ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataloader = DataLoader(custom_dataset, batch_size=BATCH_SIZE, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_accuracy(predictions, targets):\n",
    "    with torch.no_grad():\n",
    "        # Assuming predictions and targets are torch tensors\n",
    "        predictions = predictions.argmax(dim=1)\n",
    "        targets = targets.argmax(dim=1)\n",
    "        correct = (predictions == targets).sum().item()\n",
    "        total = targets.size(0)\n",
    "        accuracy = correct / total\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 : Accuracy (From): 17.78% | Accuracy (To): 11.34% | total loss = 1.9561371803283691\n",
      "1 : Accuracy (From): 9.69% | Accuracy (To): 5.41% | total loss = 5.879417419433594\n",
      "2 : Accuracy (From): 18.77% | Accuracy (To): 7.51% | total loss = 3.883880376815796\n",
      "3 : Accuracy (From): 20.88% | Accuracy (To): 8.38% | total loss = 3.6829352378845215\n",
      "4 : Accuracy (From): 24.62% | Accuracy (To): 8.94% | total loss = 3.4889235496520996\n",
      "5 : Accuracy (From): 28.45% | Accuracy (To): 9.36% | total loss = 3.2615060806274414\n",
      "6 : Accuracy (From): 30.02% | Accuracy (To): 9.43% | total loss = 3.0106167793273926\n",
      "7 : Accuracy (From): 23.50% | Accuracy (To): 8.83% | total loss = 2.851510763168335\n",
      "8 : Accuracy (From): 24.22% | Accuracy (To): 9.64% | total loss = 3.383913516998291\n",
      "9 : Accuracy (From): 27.80% | Accuracy (To): 10.23% | total loss = 2.8933167457580566\n",
      "10 : Accuracy (From): 28.57% | Accuracy (To): 10.26% | total loss = 2.7485477924346924\n",
      "11 : Accuracy (From): 28.06% | Accuracy (To): 10.58% | total loss = 2.923768997192383\n",
      "12 : Accuracy (From): 27.53% | Accuracy (To): 10.72% | total loss = 2.7647743225097656\n",
      "13 : Accuracy (From): 24.42% | Accuracy (To): 10.64% | total loss = 2.8184754848480225\n",
      "14 : Accuracy (From): 25.95% | Accuracy (To): 10.77% | total loss = 2.9739744663238525\n",
      "15 : Accuracy (From): 31.34% | Accuracy (To): 11.13% | total loss = 2.741607666015625\n",
      "16 : Accuracy (From): 29.20% | Accuracy (To): 10.74% | total loss = 2.5883021354675293\n",
      "17 : Accuracy (From): 28.76% | Accuracy (To): 10.49% | total loss = 2.8268933296203613\n",
      "18 : Accuracy (From): 32.62% | Accuracy (To): 12.02% | total loss = 2.671024799346924\n",
      "19 : Accuracy (From): 28.84% | Accuracy (To): 11.62% | total loss = 2.469406843185425\n",
      "20 : Accuracy (From): 28.28% | Accuracy (To): 10.64% | total loss = 2.9871363639831543\n",
      "21 : Accuracy (From): 34.22% | Accuracy (To): 11.78% | total loss = 2.553964614868164\n",
      "22 : Accuracy (From): 29.06% | Accuracy (To): 11.63% | total loss = 2.4642860889434814\n",
      "23 : Accuracy (From): 28.45% | Accuracy (To): 11.62% | total loss = 2.8777427673339844\n",
      "24 : Accuracy (From): 33.40% | Accuracy (To): 12.10% | total loss = 2.5256896018981934\n",
      "25 : Accuracy (From): 31.18% | Accuracy (To): 11.72% | total loss = 2.4021897315979004\n",
      "26 : Accuracy (From): 31.37% | Accuracy (To): 11.87% | total loss = 2.64518666267395\n",
      "27 : Accuracy (From): 32.22% | Accuracy (To): 11.15% | total loss = 2.6031272411346436\n",
      "28 : Accuracy (From): 34.46% | Accuracy (To): 11.93% | total loss = 2.424553871154785\n",
      "29 : Accuracy (From): 32.06% | Accuracy (To): 11.40% | total loss = 2.4367995262145996\n",
      "30 : Accuracy (From): 32.64% | Accuracy (To): 11.56% | total loss = 2.549689292907715\n",
      "31 : Accuracy (From): 35.77% | Accuracy (To): 12.15% | total loss = 2.421701431274414\n",
      "32 : Accuracy (From): 32.03% | Accuracy (To): 11.34% | total loss = 2.3804502487182617\n",
      "33 : Accuracy (From): 33.48% | Accuracy (To): 11.61% | total loss = 2.5889852046966553\n",
      "34 : Accuracy (From): 33.90% | Accuracy (To): 11.91% | total loss = 2.393486261367798\n",
      "35 : Accuracy (From): 34.25% | Accuracy (To): 12.02% | total loss = 2.4801290035247803\n",
      "36 : Accuracy (From): 35.05% | Accuracy (To): 12.30% | total loss = 2.341562271118164\n",
      "37 : Accuracy (From): 33.80% | Accuracy (To): 12.44% | total loss = 2.3668618202209473\n",
      "38 : Accuracy (From): 34.86% | Accuracy (To): 12.42% | total loss = 2.399393081665039\n",
      "39 : Accuracy (From): 34.00% | Accuracy (To): 11.72% | total loss = 2.375070810317993\n",
      "40 : Accuracy (From): 34.59% | Accuracy (To): 12.27% | total loss = 2.437718629837036\n",
      "41 : Accuracy (From): 34.19% | Accuracy (To): 12.02% | total loss = 2.271151065826416\n",
      "42 : Accuracy (From): 32.67% | Accuracy (To): 11.56% | total loss = 2.5296273231506348\n",
      "43 : Accuracy (From): 35.92% | Accuracy (To): 12.59% | total loss = 2.3859567642211914\n",
      "44 : Accuracy (From): 34.34% | Accuracy (To): 12.37% | total loss = 2.270674467086792\n",
      "45 : Accuracy (From): 35.28% | Accuracy (To): 11.98% | total loss = 2.4722280502319336\n",
      "46 : Accuracy (From): 34.65% | Accuracy (To): 12.30% | total loss = 2.28985595703125\n",
      "47 : Accuracy (From): 33.53% | Accuracy (To): 11.75% | total loss = 2.4580600261688232\n",
      "48 : Accuracy (From): 35.64% | Accuracy (To): 12.21% | total loss = 2.3446478843688965\n",
      "49 : Accuracy (From): 36.05% | Accuracy (To): 12.01% | total loss = 2.3311872482299805\n",
      "50 : Accuracy (From): 34.36% | Accuracy (To): 11.72% | total loss = 2.3186769485473633\n",
      "51 : Accuracy (From): 33.55% | Accuracy (To): 11.87% | total loss = 2.3560755252838135\n",
      "52 : Accuracy (From): 36.06% | Accuracy (To): 12.38% | total loss = 2.421320915222168\n",
      "53 : Accuracy (From): 36.20% | Accuracy (To): 12.26% | total loss = 2.264946937561035\n",
      "54 : Accuracy (From): 35.02% | Accuracy (To): 11.80% | total loss = 2.366459846496582\n",
      "55 : Accuracy (From): 36.61% | Accuracy (To): 12.25% | total loss = 2.3105740547180176\n",
      "56 : Accuracy (From): 36.36% | Accuracy (To): 11.66% | total loss = 2.3724236488342285\n",
      "57 : Accuracy (From): 37.48% | Accuracy (To): 12.31% | total loss = 2.254270076751709\n",
      "58 : Accuracy (From): 36.62% | Accuracy (To): 11.65% | total loss = 2.2864770889282227\n",
      "59 : Accuracy (From): 37.17% | Accuracy (To): 11.83% | total loss = 2.2517106533050537\n",
      "60 : Accuracy (From): 37.08% | Accuracy (To): 12.08% | total loss = 2.29457426071167\n",
      "61 : Accuracy (From): 35.46% | Accuracy (To): 11.94% | total loss = 2.2630929946899414\n",
      "62 : Accuracy (From): 37.70% | Accuracy (To): 11.66% | total loss = 2.305295944213867\n",
      "63 : Accuracy (From): 36.07% | Accuracy (To): 12.27% | total loss = 2.234287738800049\n",
      "64 : Accuracy (From): 38.60% | Accuracy (To): 12.29% | total loss = 2.3228158950805664\n",
      "65 : Accuracy (From): 36.64% | Accuracy (To): 12.32% | total loss = 2.1394357681274414\n",
      "66 : Accuracy (From): 36.17% | Accuracy (To): 11.71% | total loss = 2.501295328140259\n",
      "67 : Accuracy (From): 37.31% | Accuracy (To): 12.09% | total loss = 2.2637853622436523\n",
      "68 : Accuracy (From): 36.21% | Accuracy (To): 12.02% | total loss = 2.2174997329711914\n",
      "69 : Accuracy (From): 37.59% | Accuracy (To): 12.16% | total loss = 2.3583593368530273\n",
      "70 : Accuracy (From): 38.89% | Accuracy (To): 11.87% | total loss = 2.1943089962005615\n",
      "71 : Accuracy (From): 36.65% | Accuracy (To): 11.33% | total loss = 2.220271587371826\n",
      "72 : Accuracy (From): 37.78% | Accuracy (To): 12.43% | total loss = 2.1978695392608643\n",
      "73 : Accuracy (From): 36.07% | Accuracy (To): 11.82% | total loss = 2.29520320892334\n",
      "74 : Accuracy (From): 37.00% | Accuracy (To): 12.63% | total loss = 2.308772087097168\n",
      "75 : Accuracy (From): 37.13% | Accuracy (To): 12.54% | total loss = 2.2789807319641113\n",
      "76 : Accuracy (From): 38.24% | Accuracy (To): 12.24% | total loss = 2.228384017944336\n",
      "77 : Accuracy (From): 37.40% | Accuracy (To): 11.93% | total loss = 2.170448064804077\n",
      "78 : Accuracy (From): 39.05% | Accuracy (To): 12.23% | total loss = 2.2479591369628906\n",
      "79 : Accuracy (From): 36.96% | Accuracy (To): 12.13% | total loss = 2.1254425048828125\n",
      "80 : Accuracy (From): 36.88% | Accuracy (To): 11.75% | total loss = 2.351306438446045\n",
      "81 : Accuracy (From): 38.79% | Accuracy (To): 12.23% | total loss = 2.168577194213867\n",
      "82 : Accuracy (From): 36.68% | Accuracy (To): 12.13% | total loss = 2.120436429977417\n",
      "83 : Accuracy (From): 38.18% | Accuracy (To): 11.99% | total loss = 2.308745861053467\n",
      "84 : Accuracy (From): 38.67% | Accuracy (To): 12.50% | total loss = 2.0863542556762695\n",
      "85 : Accuracy (From): 37.30% | Accuracy (To): 12.19% | total loss = 2.2611989974975586\n",
      "86 : Accuracy (From): 37.12% | Accuracy (To): 12.09% | total loss = 2.2493343353271484\n",
      "87 : Accuracy (From): 37.99% | Accuracy (To): 12.29% | total loss = 2.203685760498047\n",
      "88 : Accuracy (From): 38.20% | Accuracy (To): 11.88% | total loss = 2.2571945190429688\n",
      "89 : Accuracy (From): 39.42% | Accuracy (To): 12.34% | total loss = 2.1404619216918945\n",
      "90 : Accuracy (From): 38.39% | Accuracy (To): 12.46% | total loss = 2.2053608894348145\n",
      "91 : Accuracy (From): 38.06% | Accuracy (To): 12.47% | total loss = 2.2524852752685547\n",
      "92 : Accuracy (From): 38.18% | Accuracy (To): 12.24% | total loss = 2.2186903953552246\n",
      "93 : Accuracy (From): 40.01% | Accuracy (To): 12.38% | total loss = 2.211430549621582\n",
      "94 : Accuracy (From): 37.20% | Accuracy (To): 11.85% | total loss = 2.124497890472412\n",
      "95 : Accuracy (From): 39.18% | Accuracy (To): 12.43% | total loss = 2.2214152812957764\n",
      "96 : Accuracy (From): 39.89% | Accuracy (To): 12.52% | total loss = 2.1501543521881104\n",
      "97 : Accuracy (From): 38.00% | Accuracy (To): 11.90% | total loss = 2.1490538120269775\n",
      "98 : Accuracy (From): 39.61% | Accuracy (To): 11.89% | total loss = 2.224320650100708\n",
      "99 : Accuracy (From): 38.77% | Accuracy (To): 12.13% | total loss = 2.089298725128174\n",
      "100 : Accuracy (From): 40.30% | Accuracy (To): 12.11% | total loss = 2.0967161655426025\n",
      "101 : Accuracy (From): 39.73% | Accuracy (To): 12.36% | total loss = 2.1220076084136963\n",
      "102 : Accuracy (From): 38.56% | Accuracy (To): 11.76% | total loss = 2.1665842533111572\n",
      "103 : Accuracy (From): 40.36% | Accuracy (To): 12.39% | total loss = 2.184622287750244\n",
      "104 : Accuracy (From): 39.18% | Accuracy (To): 12.08% | total loss = 2.1098451614379883\n",
      "105 : Accuracy (From): 39.03% | Accuracy (To): 12.22% | total loss = 2.2151312828063965\n",
      "106 : Accuracy (From): 39.75% | Accuracy (To): 12.04% | total loss = 2.132467746734619\n",
      "107 : Accuracy (From): 40.21% | Accuracy (To): 12.24% | total loss = 2.081599235534668\n",
      "108 : Accuracy (From): 38.56% | Accuracy (To): 11.90% | total loss = 2.1347415447235107\n",
      "109 : Accuracy (From): 40.94% | Accuracy (To): 12.51% | total loss = 2.2242050170898438\n",
      "110 : Accuracy (From): 39.06% | Accuracy (To): 11.83% | total loss = 2.046380043029785\n",
      "111 : Accuracy (From): 39.94% | Accuracy (To): 11.91% | total loss = 2.220536708831787\n",
      "112 : Accuracy (From): 38.36% | Accuracy (To): 11.98% | total loss = 2.1271591186523438\n",
      "113 : Accuracy (From): 39.97% | Accuracy (To): 12.17% | total loss = 2.1786413192749023\n",
      "114 : Accuracy (From): 40.69% | Accuracy (To): 12.34% | total loss = 2.0423755645751953\n",
      "115 : Accuracy (From): 38.07% | Accuracy (To): 12.65% | total loss = 2.172450542449951\n",
      "116 : Accuracy (From): 41.97% | Accuracy (To): 12.52% | total loss = 2.107093334197998\n",
      "117 : Accuracy (From): 39.11% | Accuracy (To): 11.98% | total loss = 2.1021385192871094\n",
      "118 : Accuracy (From): 41.41% | Accuracy (To): 12.47% | total loss = 2.189116954803467\n",
      "119 : Accuracy (From): 40.04% | Accuracy (To): 11.94% | total loss = 2.0722718238830566\n",
      "120 : Accuracy (From): 39.92% | Accuracy (To): 11.83% | total loss = 2.1492929458618164\n",
      "121 : Accuracy (From): 35.66% | Accuracy (To): 11.29% | total loss = 2.1506576538085938\n",
      "122 : Accuracy (From): 38.70% | Accuracy (To): 12.28% | total loss = 2.369549036026001\n",
      "123 : Accuracy (From): 39.65% | Accuracy (To): 12.03% | total loss = 2.0698163509368896\n",
      "124 : Accuracy (From): 38.78% | Accuracy (To): 12.07% | total loss = 2.1480724811553955\n",
      "125 : Accuracy (From): 41.09% | Accuracy (To): 12.02% | total loss = 2.161402940750122\n",
      "126 : Accuracy (From): 41.38% | Accuracy (To): 12.54% | total loss = 2.0416133403778076\n",
      "127 : Accuracy (From): 38.87% | Accuracy (To): 11.66% | total loss = 2.1779143810272217\n",
      "128 : Accuracy (From): 42.21% | Accuracy (To): 12.37% | total loss = 2.0963797569274902\n",
      "129 : Accuracy (From): 40.41% | Accuracy (To): 12.42% | total loss = 2.0883736610412598\n",
      "130 : Accuracy (From): 40.07% | Accuracy (To): 12.02% | total loss = 2.036688804626465\n",
      "131 : Accuracy (From): 38.44% | Accuracy (To): 12.10% | total loss = 2.1903128623962402\n",
      "132 : Accuracy (From): 41.55% | Accuracy (To): 12.26% | total loss = 2.165626049041748\n",
      "133 : Accuracy (From): 41.54% | Accuracy (To): 12.52% | total loss = 1.9932329654693604\n",
      "134 : Accuracy (From): 40.88% | Accuracy (To): 12.55% | total loss = 2.081449031829834\n",
      "135 : Accuracy (From): 40.67% | Accuracy (To): 12.04% | total loss = 2.0328316688537598\n",
      "136 : Accuracy (From): 40.74% | Accuracy (To): 12.61% | total loss = 2.1531577110290527\n",
      "137 : Accuracy (From): 40.30% | Accuracy (To): 12.29% | total loss = 2.1260173320770264\n",
      "138 : Accuracy (From): 42.25% | Accuracy (To): 12.41% | total loss = 2.1203956604003906\n",
      "139 : Accuracy (From): 40.77% | Accuracy (To): 12.28% | total loss = 2.063530445098877\n",
      "140 : Accuracy (From): 42.52% | Accuracy (To): 12.12% | total loss = 2.08740234375\n",
      "141 : Accuracy (From): 40.04% | Accuracy (To): 11.95% | total loss = 2.0883960723876953\n",
      "142 : Accuracy (From): 42.92% | Accuracy (To): 12.37% | total loss = 2.066781759262085\n",
      "143 : Accuracy (From): 40.89% | Accuracy (To): 12.48% | total loss = 2.0055315494537354\n",
      "144 : Accuracy (From): 42.96% | Accuracy (To): 12.32% | total loss = 2.0986924171447754\n",
      "145 : Accuracy (From): 41.34% | Accuracy (To): 12.36% | total loss = 2.0233662128448486\n",
      "146 : Accuracy (From): 41.82% | Accuracy (To): 12.29% | total loss = 2.086414098739624\n",
      "147 : Accuracy (From): 41.31% | Accuracy (To): 12.00% | total loss = 2.050719738006592\n",
      "148 : Accuracy (From): 42.87% | Accuracy (To): 12.15% | total loss = 2.070582628250122\n",
      "149 : Accuracy (From): 42.03% | Accuracy (To): 12.44% | total loss = 1.9579838514328003\n",
      "150 : Accuracy (From): 40.67% | Accuracy (To): 12.05% | total loss = 2.2071614265441895\n",
      "151 : Accuracy (From): 42.05% | Accuracy (To): 12.38% | total loss = 2.04182505607605\n",
      "152 : Accuracy (From): 40.17% | Accuracy (To): 12.16% | total loss = 2.0393309593200684\n",
      "153 : Accuracy (From): 43.01% | Accuracy (To): 12.06% | total loss = 2.1743083000183105\n",
      "154 : Accuracy (From): 42.60% | Accuracy (To): 12.02% | total loss = 1.967261791229248\n",
      "155 : Accuracy (From): 41.67% | Accuracy (To): 12.03% | total loss = 2.094888925552368\n",
      "156 : Accuracy (From): 42.53% | Accuracy (To): 12.18% | total loss = 2.042814254760742\n",
      "157 : Accuracy (From): 42.67% | Accuracy (To): 12.15% | total loss = 1.9959115982055664\n",
      "158 : Accuracy (From): 40.86% | Accuracy (To): 11.34% | total loss = 2.099318504333496\n",
      "159 : Accuracy (From): 40.29% | Accuracy (To): 11.60% | total loss = 2.0377769470214844\n",
      "160 : Accuracy (From): 43.05% | Accuracy (To): 12.38% | total loss = 2.1146726608276367\n",
      "161 : Accuracy (From): 41.60% | Accuracy (To): 12.02% | total loss = 1.961188554763794\n",
      "162 : Accuracy (From): 42.65% | Accuracy (To): 12.62% | total loss = 2.094526767730713\n",
      "163 : Accuracy (From): 41.80% | Accuracy (To): 12.48% | total loss = 1.968802571296692\n",
      "164 : Accuracy (From): 40.33% | Accuracy (To): 11.98% | total loss = 2.0986995697021484\n",
      "165 : Accuracy (From): 43.91% | Accuracy (To): 12.51% | total loss = 2.035869836807251\n",
      "166 : Accuracy (From): 41.52% | Accuracy (To): 12.63% | total loss = 1.9363903999328613\n",
      "167 : Accuracy (From): 42.14% | Accuracy (To): 12.40% | total loss = 2.144801616668701\n",
      "168 : Accuracy (From): 42.54% | Accuracy (To): 12.18% | total loss = 1.9936909675598145\n",
      "169 : Accuracy (From): 42.13% | Accuracy (To): 12.23% | total loss = 2.0560343265533447\n",
      "170 : Accuracy (From): 42.24% | Accuracy (To): 12.38% | total loss = 1.9966504573822021\n",
      "171 : Accuracy (From): 42.66% | Accuracy (To): 12.52% | total loss = 2.047503709793091\n",
      "172 : Accuracy (From): 42.55% | Accuracy (To): 12.01% | total loss = 2.0618937015533447\n",
      "173 : Accuracy (From): 43.50% | Accuracy (To): 11.87% | total loss = 2.024944305419922\n",
      "174 : Accuracy (From): 44.27% | Accuracy (To): 12.38% | total loss = 1.924257516860962\n",
      "175 : Accuracy (From): 42.22% | Accuracy (To): 12.34% | total loss = 1.9488389492034912\n",
      "176 : Accuracy (From): 43.63% | Accuracy (To): 11.99% | total loss = 2.091709613800049\n",
      "177 : Accuracy (From): 44.32% | Accuracy (To): 12.27% | total loss = 1.8948981761932373\n",
      "178 : Accuracy (From): 41.63% | Accuracy (To): 12.01% | total loss = 2.0318689346313477\n",
      "179 : Accuracy (From): 42.90% | Accuracy (To): 12.30% | total loss = 2.0249063968658447\n",
      "180 : Accuracy (From): 42.24% | Accuracy (To): 12.12% | total loss = 1.9911683797836304\n",
      "181 : Accuracy (From): 44.90% | Accuracy (To): 12.50% | total loss = 1.9740040302276611\n",
      "182 : Accuracy (From): 42.29% | Accuracy (To): 12.14% | total loss = 1.918158769607544\n",
      "183 : Accuracy (From): 44.00% | Accuracy (To): 12.50% | total loss = 2.0613532066345215\n",
      "184 : Accuracy (From): 43.08% | Accuracy (To): 12.04% | total loss = 1.9912216663360596\n",
      "185 : Accuracy (From): 43.68% | Accuracy (To): 12.07% | total loss = 2.1295242309570312\n",
      "186 : Accuracy (From): 42.66% | Accuracy (To): 12.24% | total loss = 1.9178276062011719\n",
      "187 : Accuracy (From): 44.20% | Accuracy (To): 12.19% | total loss = 2.0180656909942627\n",
      "188 : Accuracy (From): 42.85% | Accuracy (To): 12.13% | total loss = 1.9563955068588257\n",
      "189 : Accuracy (From): 42.91% | Accuracy (To): 11.69% | total loss = 2.0813815593719482\n",
      "190 : Accuracy (From): 44.59% | Accuracy (To): 12.41% | total loss = 1.9281682968139648\n",
      "191 : Accuracy (From): 43.01% | Accuracy (To): 12.45% | total loss = 2.0540127754211426\n",
      "192 : Accuracy (From): 44.93% | Accuracy (To): 12.36% | total loss = 1.9662892818450928\n",
      "193 : Accuracy (From): 44.51% | Accuracy (To): 12.88% | total loss = 1.9249401092529297\n",
      "194 : Accuracy (From): 42.47% | Accuracy (To): 12.05% | total loss = 1.9764361381530762\n",
      "195 : Accuracy (From): 42.21% | Accuracy (To): 11.86% | total loss = 2.0341873168945312\n",
      "196 : Accuracy (From): 42.55% | Accuracy (To): 11.94% | total loss = 2.0405778884887695\n",
      "197 : Accuracy (From): 46.24% | Accuracy (To): 12.41% | total loss = 1.947725772857666\n",
      "198 : Accuracy (From): 43.41% | Accuracy (To): 12.33% | total loss = 1.8861596584320068\n",
      "199 : Accuracy (From): 45.31% | Accuracy (To): 12.34% | total loss = 2.0827596187591553\n",
      "200 : Accuracy (From): 44.13% | Accuracy (To): 12.02% | total loss = 1.8971669673919678\n",
      "201 : Accuracy (From): 43.40% | Accuracy (To): 12.17% | total loss = 1.9904236793518066\n",
      "202 : Accuracy (From): 44.77% | Accuracy (To): 12.51% | total loss = 1.9787867069244385\n",
      "203 : Accuracy (From): 44.40% | Accuracy (To): 12.44% | total loss = 1.9132449626922607\n",
      "204 : Accuracy (From): 44.64% | Accuracy (To): 12.74% | total loss = 1.9919154644012451\n",
      "205 : Accuracy (From): 43.25% | Accuracy (To): 12.05% | total loss = 1.8557839393615723\n",
      "206 : Accuracy (From): 42.63% | Accuracy (To): 11.51% | total loss = 2.004422426223755\n",
      "207 : Accuracy (From): 45.35% | Accuracy (To): 12.24% | total loss = 1.9068756103515625\n",
      "208 : Accuracy (From): 43.90% | Accuracy (To): 12.34% | total loss = 1.9625651836395264\n",
      "209 : Accuracy (From): 44.96% | Accuracy (To): 12.55% | total loss = 2.066774606704712\n",
      "210 : Accuracy (From): 43.71% | Accuracy (To): 12.34% | total loss = 1.8758001327514648\n",
      "211 : Accuracy (From): 44.59% | Accuracy (To): 12.29% | total loss = 2.016465902328491\n",
      "212 : Accuracy (From): 45.39% | Accuracy (To): 12.34% | total loss = 1.8482791185379028\n",
      "213 : Accuracy (From): 45.29% | Accuracy (To): 12.66% | total loss = 1.8917287588119507\n",
      "214 : Accuracy (From): 42.16% | Accuracy (To): 12.07% | total loss = 1.9744899272918701\n",
      "215 : Accuracy (From): 43.24% | Accuracy (To): 12.67% | total loss = 2.076853036880493\n",
      "216 : Accuracy (From): 44.82% | Accuracy (To): 13.02% | total loss = 1.9932796955108643\n",
      "217 : Accuracy (From): 44.98% | Accuracy (To): 12.43% | total loss = 1.9792335033416748\n",
      "218 : Accuracy (From): 44.27% | Accuracy (To): 12.32% | total loss = 1.8546315431594849\n",
      "219 : Accuracy (From): 45.10% | Accuracy (To): 12.56% | total loss = 1.88285231590271\n",
      "220 : Accuracy (From): 43.91% | Accuracy (To): 12.36% | total loss = 1.9252254962921143\n",
      "221 : Accuracy (From): 45.22% | Accuracy (To): 11.79% | total loss = 2.0308451652526855\n",
      "222 : Accuracy (From): 42.79% | Accuracy (To): 11.77% | total loss = 1.9079880714416504\n",
      "223 : Accuracy (From): 42.21% | Accuracy (To): 11.90% | total loss = 2.072885274887085\n",
      "224 : Accuracy (From): 45.21% | Accuracy (To): 12.32% | total loss = 2.038316011428833\n",
      "225 : Accuracy (From): 43.86% | Accuracy (To): 12.07% | total loss = 1.8768142461776733\n",
      "226 : Accuracy (From): 44.66% | Accuracy (To): 12.23% | total loss = 1.959909200668335\n",
      "227 : Accuracy (From): 45.50% | Accuracy (To): 12.21% | total loss = 1.8600242137908936\n",
      "228 : Accuracy (From): 45.38% | Accuracy (To): 12.54% | total loss = 1.866818904876709\n",
      "229 : Accuracy (From): 44.58% | Accuracy (To): 12.08% | total loss = 1.9400252103805542\n",
      "230 : Accuracy (From): 42.99% | Accuracy (To): 12.48% | total loss = 1.9479936361312866\n",
      "231 : Accuracy (From): 46.20% | Accuracy (To): 12.65% | total loss = 1.9264638423919678\n",
      "232 : Accuracy (From): 45.28% | Accuracy (To): 12.40% | total loss = 1.822326421737671\n",
      "233 : Accuracy (From): 45.83% | Accuracy (To): 12.39% | total loss = 1.9165892601013184\n",
      "234 : Accuracy (From): 44.01% | Accuracy (To): 11.93% | total loss = 1.9200694561004639\n",
      "235 : Accuracy (From): 45.83% | Accuracy (To): 12.46% | total loss = 1.8511075973510742\n",
      "236 : Accuracy (From): 43.96% | Accuracy (To): 12.41% | total loss = 1.9229152202606201\n",
      "237 : Accuracy (From): 47.39% | Accuracy (To): 12.54% | total loss = 1.9450501203536987\n",
      "238 : Accuracy (From): 44.11% | Accuracy (To): 12.40% | total loss = 1.8496252298355103\n",
      "239 : Accuracy (From): 46.36% | Accuracy (To): 12.35% | total loss = 2.0206592082977295\n",
      "240 : Accuracy (From): 45.02% | Accuracy (To): 12.56% | total loss = 1.7757415771484375\n",
      "241 : Accuracy (From): 46.37% | Accuracy (To): 12.18% | total loss = 1.9408729076385498\n",
      "242 : Accuracy (From): 45.31% | Accuracy (To): 12.02% | total loss = 1.8588807582855225\n",
      "243 : Accuracy (From): 45.38% | Accuracy (To): 11.98% | total loss = 1.8705766201019287\n",
      "244 : Accuracy (From): 46.59% | Accuracy (To): 12.30% | total loss = 1.8645691871643066\n",
      "245 : Accuracy (From): 44.98% | Accuracy (To): 11.82% | total loss = 1.814160704612732\n",
      "246 : Accuracy (From): 46.68% | Accuracy (To): 12.43% | total loss = 1.9688513278961182\n",
      "247 : Accuracy (From): 42.08% | Accuracy (To): 11.74% | total loss = 1.8711414337158203\n",
      "248 : Accuracy (From): 45.59% | Accuracy (To): 12.55% | total loss = 2.0589652061462402\n",
      "249 : Accuracy (From): 45.06% | Accuracy (To): 12.13% | total loss = 1.8811216354370117\n",
      "250 : Accuracy (From): 46.62% | Accuracy (To): 12.26% | total loss = 1.9521722793579102\n",
      "251 : Accuracy (From): 46.59% | Accuracy (To): 12.53% | total loss = 1.8368148803710938\n",
      "252 : Accuracy (From): 44.70% | Accuracy (To): 12.00% | total loss = 1.8600717782974243\n",
      "253 : Accuracy (From): 45.28% | Accuracy (To): 12.02% | total loss = 1.8764019012451172\n",
      "254 : Accuracy (From): 45.58% | Accuracy (To): 12.62% | total loss = 1.9973299503326416\n",
      "255 : Accuracy (From): 45.48% | Accuracy (To): 12.48% | total loss = 1.895789623260498\n",
      "256 : Accuracy (From): 47.31% | Accuracy (To): 12.16% | total loss = 1.8008272647857666\n",
      "257 : Accuracy (From): 46.43% | Accuracy (To): 12.30% | total loss = 1.8123767375946045\n",
      "258 : Accuracy (From): 48.99% | Accuracy (To): 12.59% | total loss = 1.8192799091339111\n",
      "259 : Accuracy (From): 46.16% | Accuracy (To): 12.30% | total loss = 1.7625792026519775\n",
      "260 : Accuracy (From): 46.80% | Accuracy (To): 12.21% | total loss = 1.8673484325408936\n",
      "261 : Accuracy (From): 46.14% | Accuracy (To): 12.75% | total loss = 1.8191242218017578\n",
      "262 : Accuracy (From): 46.36% | Accuracy (To): 12.04% | total loss = 1.95768404006958\n",
      "263 : Accuracy (From): 47.13% | Accuracy (To): 12.04% | total loss = 1.790822148323059\n",
      "264 : Accuracy (From): 44.37% | Accuracy (To): 11.86% | total loss = 1.8950679302215576\n",
      "265 : Accuracy (From): 46.35% | Accuracy (To): 12.32% | total loss = 1.9826364517211914\n",
      "266 : Accuracy (From): 47.13% | Accuracy (To): 12.09% | total loss = 1.815005898475647\n",
      "267 : Accuracy (From): 46.71% | Accuracy (To): 12.21% | total loss = 1.8668171167373657\n",
      "268 : Accuracy (From): 44.85% | Accuracy (To): 12.55% | total loss = 1.8812607526779175\n",
      "269 : Accuracy (From): 48.26% | Accuracy (To): 12.21% | total loss = 1.9187495708465576\n",
      "270 : Accuracy (From): 48.26% | Accuracy (To): 12.12% | total loss = 1.839824914932251\n",
      "271 : Accuracy (From): 46.64% | Accuracy (To): 12.37% | total loss = 1.7678539752960205\n",
      "272 : Accuracy (From): 47.22% | Accuracy (To): 12.27% | total loss = 1.829911470413208\n",
      "273 : Accuracy (From): 44.93% | Accuracy (To): 11.87% | total loss = 1.8020639419555664\n",
      "274 : Accuracy (From): 49.10% | Accuracy (To): 12.32% | total loss = 2.0195627212524414\n",
      "275 : Accuracy (From): 45.80% | Accuracy (To): 12.02% | total loss = 1.8096907138824463\n",
      "276 : Accuracy (From): 47.52% | Accuracy (To): 12.15% | total loss = 1.8599395751953125\n",
      "277 : Accuracy (From): 46.39% | Accuracy (To): 12.51% | total loss = 1.8480777740478516\n",
      "278 : Accuracy (From): 47.34% | Accuracy (To): 12.51% | total loss = 1.835434079170227\n",
      "279 : Accuracy (From): 48.69% | Accuracy (To): 12.34% | total loss = 1.745500087738037\n",
      "280 : Accuracy (From): 46.39% | Accuracy (To): 11.94% | total loss = 1.8032753467559814\n",
      "281 : Accuracy (From): 48.77% | Accuracy (To): 11.89% | total loss = 1.8158488273620605\n",
      "282 : Accuracy (From): 47.33% | Accuracy (To): 12.08% | total loss = 1.8125336170196533\n",
      "283 : Accuracy (From): 46.00% | Accuracy (To): 11.98% | total loss = 1.831718921661377\n",
      "284 : Accuracy (From): 48.14% | Accuracy (To): 12.43% | total loss = 1.8486100435256958\n",
      "285 : Accuracy (From): 47.38% | Accuracy (To): 12.44% | total loss = 1.7578299045562744\n",
      "286 : Accuracy (From): 50.09% | Accuracy (To): 12.60% | total loss = 1.8997408151626587\n",
      "287 : Accuracy (From): 47.38% | Accuracy (To): 12.07% | total loss = 1.754084825515747\n",
      "288 : Accuracy (From): 48.99% | Accuracy (To): 12.10% | total loss = 1.8436834812164307\n",
      "289 : Accuracy (From): 47.80% | Accuracy (To): 12.18% | total loss = 1.7728652954101562\n",
      "290 : Accuracy (From): 49.30% | Accuracy (To): 12.48% | total loss = 1.8526986837387085\n",
      "291 : Accuracy (From): 47.88% | Accuracy (To): 12.38% | total loss = 1.6599887609481812\n",
      "292 : Accuracy (From): 46.70% | Accuracy (To): 11.52% | total loss = 1.8662129640579224\n",
      "293 : Accuracy (From): 46.25% | Accuracy (To): 12.06% | total loss = 1.7732555866241455\n",
      "294 : Accuracy (From): 48.97% | Accuracy (To): 12.21% | total loss = 1.8547823429107666\n",
      "295 : Accuracy (From): 46.84% | Accuracy (To): 12.27% | total loss = 1.8152644634246826\n",
      "296 : Accuracy (From): 49.13% | Accuracy (To): 11.99% | total loss = 1.8359237909317017\n",
      "297 : Accuracy (From): 46.89% | Accuracy (To): 12.11% | total loss = 1.7509405612945557\n",
      "298 : Accuracy (From): 47.44% | Accuracy (To): 12.04% | total loss = 1.9170105457305908\n",
      "299 : Accuracy (From): 48.72% | Accuracy (To): 12.03% | total loss = 1.7602894306182861\n",
      "300 : Accuracy (From): 48.57% | Accuracy (To): 12.35% | total loss = 1.8080120086669922\n",
      "301 : Accuracy (From): 47.81% | Accuracy (To): 12.23% | total loss = 1.7505916357040405\n",
      "302 : Accuracy (From): 49.75% | Accuracy (To): 12.41% | total loss = 1.889996886253357\n",
      "303 : Accuracy (From): 48.85% | Accuracy (To): 12.33% | total loss = 1.7144954204559326\n",
      "304 : Accuracy (From): 45.73% | Accuracy (To): 11.86% | total loss = 1.7781484127044678\n",
      "305 : Accuracy (From): 49.21% | Accuracy (To): 12.49% | total loss = 1.9427443742752075\n",
      "306 : Accuracy (From): 50.08% | Accuracy (To): 12.47% | total loss = 1.6990203857421875\n",
      "307 : Accuracy (From): 46.78% | Accuracy (To): 11.82% | total loss = 1.805506706237793\n",
      "308 : Accuracy (From): 47.95% | Accuracy (To): 12.02% | total loss = 1.7111244201660156\n",
      "309 : Accuracy (From): 47.00% | Accuracy (To): 12.25% | total loss = 1.834484577178955\n",
      "310 : Accuracy (From): 48.46% | Accuracy (To): 12.04% | total loss = 1.8437751531600952\n",
      "311 : Accuracy (From): 50.43% | Accuracy (To): 12.16% | total loss = 1.698448657989502\n",
      "312 : Accuracy (From): 45.75% | Accuracy (To): 11.91% | total loss = 1.792895793914795\n",
      "313 : Accuracy (From): 48.50% | Accuracy (To): 12.33% | total loss = 1.854621171951294\n",
      "314 : Accuracy (From): 49.04% | Accuracy (To): 12.78% | total loss = 1.7168560028076172\n",
      "315 : Accuracy (From): 49.38% | Accuracy (To): 12.88% | total loss = 1.7440475225448608\n",
      "316 : Accuracy (From): 47.20% | Accuracy (To): 12.18% | total loss = 1.7095458507537842\n",
      "317 : Accuracy (From): 48.88% | Accuracy (To): 12.07% | total loss = 1.791785478591919\n",
      "318 : Accuracy (From): 50.39% | Accuracy (To): 12.41% | total loss = 1.6688429117202759\n",
      "319 : Accuracy (From): 45.86% | Accuracy (To): 11.82% | total loss = 1.7850594520568848\n",
      "320 : Accuracy (From): 48.41% | Accuracy (To): 12.24% | total loss = 1.8414700031280518\n",
      "321 : Accuracy (From): 49.15% | Accuracy (To): 11.78% | total loss = 1.7980859279632568\n",
      "322 : Accuracy (From): 47.04% | Accuracy (To): 12.45% | total loss = 1.7860734462738037\n",
      "323 : Accuracy (From): 48.05% | Accuracy (To): 12.26% | total loss = 1.7131403684616089\n",
      "324 : Accuracy (From): 48.77% | Accuracy (To): 12.65% | total loss = 1.7911372184753418\n",
      "325 : Accuracy (From): 48.56% | Accuracy (To): 12.83% | total loss = 1.8377424478530884\n",
      "326 : Accuracy (From): 48.21% | Accuracy (To): 11.76% | total loss = 1.7640941143035889\n",
      "327 : Accuracy (From): 50.29% | Accuracy (To): 12.26% | total loss = 1.6728750467300415\n",
      "328 : Accuracy (From): 49.26% | Accuracy (To): 12.55% | total loss = 1.7066166400909424\n",
      "329 : Accuracy (From): 50.84% | Accuracy (To): 12.81% | total loss = 1.7439274787902832\n",
      "330 : Accuracy (From): 46.15% | Accuracy (To): 11.97% | total loss = 1.8365315198898315\n",
      "331 : Accuracy (From): 50.73% | Accuracy (To): 12.79% | total loss = 1.7905218601226807\n",
      "332 : Accuracy (From): 49.40% | Accuracy (To): 12.43% | total loss = 1.6829439401626587\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[286], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m model\u001b[38;5;241m.\u001b[39mtrain()\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m epoch \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(EPOCH):\n\u001b[1;32m----> 3\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m idx, (position, attacks, current_player, castling_rights, en_passant_square, targetwho, targetwhere) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(dataloader):\n\u001b[0;32m      4\u001b[0m         optimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[0;32m      5\u001b[0m         position, attacks, current_player, castling_rights, en_passant_square \u001b[38;5;241m=\u001b[39m position\u001b[38;5;241m.\u001b[39mto(device), attacks\u001b[38;5;241m.\u001b[39mto(device), current_player\u001b[38;5;241m.\u001b[39mto(device), castling_rights\u001b[38;5;241m.\u001b[39mto(device), en_passant_square\u001b[38;5;241m.\u001b[39mto(device)\n",
      "File \u001b[1;32mc:\\Users\\laksh\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\utils\\data\\dataloader.py:633\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    630\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    631\u001b[0m     \u001b[38;5;66;03m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[0;32m    632\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset()  \u001b[38;5;66;03m# type: ignore[call-arg]\u001b[39;00m\n\u001b[1;32m--> 633\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_next_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    634\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m    635\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[0;32m    636\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[0;32m    637\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called:\n",
      "File \u001b[1;32mc:\\Users\\laksh\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\utils\\data\\dataloader.py:677\u001b[0m, in \u001b[0;36m_SingleProcessDataLoaderIter._next_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    675\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_next_data\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m    676\u001b[0m     index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_next_index()  \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[1;32m--> 677\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dataset_fetcher\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfetch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[0;32m    678\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory:\n\u001b[0;32m    679\u001b[0m         data \u001b[38;5;241m=\u001b[39m _utils\u001b[38;5;241m.\u001b[39mpin_memory\u001b[38;5;241m.\u001b[39mpin_memory(data, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory_device)\n",
      "File \u001b[1;32mc:\\Users\\laksh\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py:54\u001b[0m, in \u001b[0;36m_MapDatasetFetcher.fetch\u001b[1;34m(self, possibly_batched_index)\u001b[0m\n\u001b[0;32m     52\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m     53\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset[possibly_batched_index]\n\u001b[1;32m---> 54\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcollate_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\laksh\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\utils\\data\\_utils\\collate.py:265\u001b[0m, in \u001b[0;36mdefault_collate\u001b[1;34m(batch)\u001b[0m\n\u001b[0;32m    204\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdefault_collate\u001b[39m(batch):\n\u001b[0;32m    205\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    206\u001b[0m \u001b[38;5;124;03m        Function that takes in a batch of data and puts the elements within the batch\u001b[39;00m\n\u001b[0;32m    207\u001b[0m \u001b[38;5;124;03m        into a tensor with an additional outer dimension - batch size. The exact output type can be\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    263\u001b[0m \u001b[38;5;124;03m            >>> default_collate(batch)  # Handle `CustomType` automatically\u001b[39;00m\n\u001b[0;32m    264\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 265\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcollate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcollate_fn_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdefault_collate_fn_map\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\laksh\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\utils\\data\\_utils\\collate.py:142\u001b[0m, in \u001b[0;36mcollate\u001b[1;34m(batch, collate_fn_map)\u001b[0m\n\u001b[0;32m    139\u001b[0m transposed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mzip\u001b[39m(\u001b[38;5;241m*\u001b[39mbatch))  \u001b[38;5;66;03m# It may be accessed twice, so we use a list.\u001b[39;00m\n\u001b[0;32m    141\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(elem, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[1;32m--> 142\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m [collate(samples, collate_fn_map\u001b[38;5;241m=\u001b[39mcollate_fn_map) \u001b[38;5;28;01mfor\u001b[39;00m samples \u001b[38;5;129;01min\u001b[39;00m transposed]  \u001b[38;5;66;03m# Backwards compatibility.\u001b[39;00m\n\u001b[0;32m    143\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    144\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n",
      "File \u001b[1;32mc:\\Users\\laksh\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\utils\\data\\_utils\\collate.py:142\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    139\u001b[0m transposed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mzip\u001b[39m(\u001b[38;5;241m*\u001b[39mbatch))  \u001b[38;5;66;03m# It may be accessed twice, so we use a list.\u001b[39;00m\n\u001b[0;32m    141\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(elem, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[1;32m--> 142\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m [\u001b[43mcollate\u001b[49m\u001b[43m(\u001b[49m\u001b[43msamples\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcollate_fn_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcollate_fn_map\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m samples \u001b[38;5;129;01min\u001b[39;00m transposed]  \u001b[38;5;66;03m# Backwards compatibility.\u001b[39;00m\n\u001b[0;32m    143\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    144\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n",
      "File \u001b[1;32mc:\\Users\\laksh\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\utils\\data\\_utils\\collate.py:119\u001b[0m, in \u001b[0;36mcollate\u001b[1;34m(batch, collate_fn_map)\u001b[0m\n\u001b[0;32m    117\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m collate_fn_map \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    118\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m elem_type \u001b[38;5;129;01min\u001b[39;00m collate_fn_map:\n\u001b[1;32m--> 119\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcollate_fn_map\u001b[49m\u001b[43m[\u001b[49m\u001b[43melem_type\u001b[49m\u001b[43m]\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcollate_fn_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcollate_fn_map\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    121\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m collate_type \u001b[38;5;129;01min\u001b[39;00m collate_fn_map:\n\u001b[0;32m    122\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(elem, collate_type):\n",
      "File \u001b[1;32mc:\\Users\\laksh\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\utils\\data\\_utils\\collate.py:171\u001b[0m, in \u001b[0;36mcollate_numpy_array_fn\u001b[1;34m(batch, collate_fn_map)\u001b[0m\n\u001b[0;32m    168\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m np_str_obj_array_pattern\u001b[38;5;241m.\u001b[39msearch(elem\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mstr) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    169\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(default_collate_err_msg_format\u001b[38;5;241m.\u001b[39mformat(elem\u001b[38;5;241m.\u001b[39mdtype))\n\u001b[1;32m--> 171\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m collate([torch\u001b[38;5;241m.\u001b[39mas_tensor(b) \u001b[38;5;28;01mfor\u001b[39;00m b \u001b[38;5;129;01min\u001b[39;00m batch], collate_fn_map\u001b[38;5;241m=\u001b[39mcollate_fn_map)\n",
      "File \u001b[1;32mc:\\Users\\laksh\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\utils\\data\\_utils\\collate.py:171\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    168\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m np_str_obj_array_pattern\u001b[38;5;241m.\u001b[39msearch(elem\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mstr) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    169\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(default_collate_err_msg_format\u001b[38;5;241m.\u001b[39mformat(elem\u001b[38;5;241m.\u001b[39mdtype))\n\u001b[1;32m--> 171\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m collate([\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mas_tensor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m b \u001b[38;5;129;01min\u001b[39;00m batch], collate_fn_map\u001b[38;5;241m=\u001b[39mcollate_fn_map)\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model.train()\n",
    "for epoch in range(EPOCH):\n",
    "    for idx, (position, attacks, current_player, castling_rights, en_passant_square, targetwho, targetwhere) in enumerate(dataloader):\n",
    "        optimizer.zero_grad()\n",
    "        position, attacks, current_player, castling_rights, en_passant_square = position.to(device), attacks.to(device), current_player.to(device), castling_rights.to(device), en_passant_square.to(device)\n",
    "        targetwho, targetwhere = targetwho.to(device), targetwhere.to(device)\n",
    "        who, where = model(position, attacks, current_player, castling_rights, en_passant_square)#, targetwho, targetwhere)\n",
    "        loss1 = criterion(who, targetwho)  # Targets1 are the ground truth labels for output1\n",
    "        loss2 = criterion(where, targetwhere)  # Targets2 are the ground truth labels for output2\n",
    "        total_loss = loss1# + loss2\n",
    "        total_loss.backward()\n",
    "        optimizer.step()\n",
    "        if idx%50 == 0:\n",
    "            # Calculate accuracy\n",
    "            with torch.no_grad():\n",
    "                who, where = model(position, attacks, current_player, castling_rights, en_passant_square)\n",
    "            accuracy_who = calculate_accuracy(who, targetwho)\n",
    "            accuracy_where = calculate_accuracy(where, targetwhere)\n",
    "            print(f\"{epoch} : Accuracy (From): {accuracy_who * 100:.2f}% | Accuracy (To): {accuracy_where * 100:.2f}% | total loss = {total_loss}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Move.from_uci('e7f6')"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def predict(model, board:chess.Board, forceRight=True)->chess.Move:\n",
    "    position, attacks, current_player, castling_rights, en_passant_square = board2vec(board)\n",
    "    position = torch.from_numpy(position).unsqueeze(0).to(device)\n",
    "    attacks = torch.from_numpy(attacks).unsqueeze(0).to(device)\n",
    "\n",
    "    current_player = torch.from_numpy(current_player).unsqueeze(0).to(device)\n",
    "    castling_rights = torch.from_numpy(castling_rights).unsqueeze(0).to(device)\n",
    "    en_passant_square = torch.from_numpy(en_passant_square).unsqueeze(0).to(device)\n",
    "    with torch.no_grad():\n",
    "        who, where = model(position, attacks, current_player, castling_rights, en_passant_square)\n",
    "    who = who.squeeze().cpu().numpy()\n",
    "    where = where.squeeze().cpu().numpy()\n",
    "\n",
    "    if forceRight:\n",
    "        who_possible = np.zeros(64, dtype=np.float32)\n",
    "        who_possible[list(set([i.from_square for i in board.legal_moves]))] = 1\n",
    "        who = who*who_possible\n",
    "        who = who.argmax().item()\n",
    "        \n",
    "        where_possible = np.zeros(64, dtype=np.float32)\n",
    "        where_possible[[i.to_square for i in board.legal_moves if i.from_square==who]] = 1\n",
    "        where = where*where_possible\n",
    "        where = where.argmax().item()\n",
    "    else:\n",
    "        who,where = who.argmax().item(), where.argmax().item()\n",
    "    \n",
    "    f,t = chess.square_name(who), chess.square_name(where)\n",
    "    return chess.Move.from_uci(f + t)\n",
    "predict(model, board, forceRight=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
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       "p r p p b p p p\n",
       ". p . P . . . .\n",
       ". . . . P . . .\n",
       ". . . . . . . .\n",
       ". . P B . N . .\n",
       "P P . N . P P P\n",
       "R . B Q R . 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transform=\"translate(150, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(240, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(285, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(330, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(105, 240)\" /><use href=\"#white-bishop\" xlink:href=\"#white-bishop\" transform=\"translate(150, 240)\" /><use href=\"#white-knight\" xlink:href=\"#white-knight\" transform=\"translate(240, 240)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(195, 150)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(60, 105)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(150, 105)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(15, 60)\" /><use href=\"#black-rook\" xlink:href=\"#black-rook\" transform=\"translate(60, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(105, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(150, 60)\" /><use href=\"#black-bishop\" xlink:href=\"#black-bishop\" transform=\"translate(195, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(240, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(285, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(330, 60)\" /><use href=\"#black-bishop\" xlink:href=\"#black-bishop\" transform=\"translate(105, 15)\" /><use href=\"#black-queen\" xlink:href=\"#black-queen\" transform=\"translate(150, 15)\" /><use href=\"#black-king\" xlink:href=\"#black-king\" transform=\"translate(195, 15)\" /><use href=\"#black-knight\" xlink:href=\"#black-knight\" transform=\"translate(285, 15)\" /><use href=\"#black-rook\" xlink:href=\"#black-rook\" transform=\"translate(330, 15)\" /></svg>"
      ],
      "text/plain": [
       "Board('2bqk1nr/prppbppp/1p1P4/4P3/8/2PB1N2/PP1N1PPP/R1BQR1K1 b k - 0 11')"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "board"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" version=\"1.2\" baseProfile=\"tiny\" viewBox=\"0 0 390 390\" width=\"390\" height=\"390\"><desc><pre>r . b . k . . r\n",
       "p p P p . p Q p\n",
       ". . . . p . . .\n",
       ". . . . . . . .\n",
       ". . . . . . . .\n",
       ". . . . . . . .\n",
       "P P P . . P P P\n",
       "R . B . K B N R</pre></desc><defs><g id=\"white-pawn\" class=\"white pawn\"><path d=\"M22.5 9c-2.21 0-4 1.79-4 4 0 .89.29 1.71.78 2.38C17.33 16.5 16 18.59 16 21c0 2.03.94 3.84 2.41 5.03-3 1.06-7.41 5.55-7.41 13.47h23c0-7.92-4.41-12.41-7.41-13.47 1.47-1.19 2.41-3 2.41-5.03 0-2.41-1.33-4.5-3.28-5.62.49-.67.78-1.49.78-2.38 0-2.21-1.79-4-4-4z\" fill=\"#fff\" stroke=\"#000\" stroke-width=\"1.5\" stroke-linecap=\"round\" /></g><g id=\"white-knight\" class=\"white knight\" fill=\"none\" fill-rule=\"evenodd\" stroke=\"#000\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M 22,10 C 32.5,11 38.5,18 38,39 L 15,39 C 15,30 25,32.5 23,18\" style=\"fill:#ffffff; stroke:#000000;\" /><path d=\"M 24,18 C 24.38,20.91 18.45,25.37 16,27 C 13,29 13.18,31.34 11,31 C 9.958,30.06 12.41,27.96 11,28 C 10,28 11.19,29.23 10,30 C 9,30 5.997,31 6,26 C 6,24 12,14 12,14 C 12,14 13.89,12.1 14,10.5 C 13.27,9.506 13.5,8.5 13.5,7.5 C 14.5,6.5 16.5,10 16.5,10 L 18.5,10 C 18.5,10 19.28,8.008 21,7 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xlink:href=\"#white-rook\" transform=\"translate(15, 330)\" /><use href=\"#white-bishop\" xlink:href=\"#white-bishop\" transform=\"translate(105, 330)\" /><use href=\"#white-king\" xlink:href=\"#white-king\" transform=\"translate(195, 330)\" /><use href=\"#white-bishop\" xlink:href=\"#white-bishop\" transform=\"translate(240, 330)\" /><use href=\"#white-knight\" xlink:href=\"#white-knight\" transform=\"translate(285, 330)\" /><use href=\"#white-rook\" xlink:href=\"#white-rook\" transform=\"translate(330, 330)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(15, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(60, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(105, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(240, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(285, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(330, 285)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(195, 105)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(15, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(60, 60)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(105, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(150, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(240, 60)\" /><use href=\"#white-queen\" xlink:href=\"#white-queen\" transform=\"translate(285, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(330, 60)\" /><use href=\"#black-rook\" xlink:href=\"#black-rook\" transform=\"translate(15, 15)\" /><use href=\"#black-bishop\" xlink:href=\"#black-bishop\" transform=\"translate(105, 15)\" /><use href=\"#black-king\" xlink:href=\"#black-king\" transform=\"translate(195, 15)\" /><use href=\"#black-rook\" xlink:href=\"#black-rook\" transform=\"translate(330, 15)\" /></svg>"
      ],
      "text/plain": [
       "Board('r1b1k2r/ppPp1pQp/4p3/8/8/8/PPP2PPP/R1B1KBNR b KQq - 0 10')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[301], line 19\u001b[0m\n\u001b[0;32m     17\u001b[0m clear_output(wait\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m     18\u001b[0m display(board)\n\u001b[1;32m---> 19\u001b[0m \u001b[43mtime\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msleep\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m.5\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m     20\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m board\u001b[38;5;241m.\u001b[39mturn:\n\u001b[0;32m     21\u001b[0m     move \u001b[38;5;241m=\u001b[39m engine\u001b[38;5;241m.\u001b[39mplay(board, chess\u001b[38;5;241m.\u001b[39mengine\u001b[38;5;241m.\u001b[39mLimit(time\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m.000000001\u001b[39m))\u001b[38;5;241m.\u001b[39mmove\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "matches = 0\n",
    "while True:\n",
    "    matches+=1\n",
    "    movehistory =[]\n",
    "    board.reset()\n",
    "    game = chess.pgn.Game()\n",
    "    game.headers[\"Event\"] = \"MakeDataset\"\n",
    "    game.headers[\"Site\"] = \"ThefCraft\"\n",
    "    game.headers[\"Date\"] = str(datetime.datetime.now().date())\n",
    "    game.headers[\"Round\"] = matches\n",
    "    game.headers[\"White\"] = \"Stockfish-1\"\n",
    "    game.headers[\"Black\"] = \"Stockfish-2\"\n",
    "    \n",
    "    print('*'*50, 'Matches : ', matches, '*'*50)\n",
    "\n",
    "    while not board.is_game_over():\n",
    "        clear_output(wait=True)\n",
    "        display(board)\n",
    "        time.sleep(.5)\n",
    "        if board.turn:\n",
    "            move = engine.play(board, chess.engine.Limit(time=.000000001)).move\n",
    "            movehistory.append(move)\n",
    "            board.push(move)\n",
    "        else:\n",
    "            move = predict(model, board, forceRight=True)\n",
    "            movehistory.append(move)\n",
    "            board.push(move)\n",
    "\n",
    "    break\n",
    "\n",
    "game.add_line(movehistory)\n",
    "game.headers[\"Result\"] = str(board.result())\n",
    "print(game)\n",
    "print(game, file=open(\"test.pgn\", \"w\"), end=\"\\n\\n\")\n",
    "SVG(chess.svg.board(board=board,size=400))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Move.from_uci('e5e4')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "move = predict(model, board, forceRight=True)\n",
    "move"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" version=\"1.2\" baseProfile=\"tiny\" viewBox=\"0 0 390 390\" width=\"390\" height=\"390\"><desc><pre>r n b q k b n r\n",
       "p p p . . p p p\n",
       ". . . . . . . .\n",
       ". . . P . . . .\n",
       ". . . P p . . .\n",
       ". . . . . . . .\n",
       "P P P . . P P P\n",
       "R N B Q K B N R</pre></desc><defs><g id=\"white-pawn\" class=\"white pawn\"><path d=\"M22.5 9c-2.21 0-4 1.79-4 4 0 .89.29 1.71.78 2.38C17.33 16.5 16 18.59 16 21c0 2.03.94 3.84 2.41 5.03-3 1.06-7.41 5.55-7.41 13.47h23c0-7.92-4.41-12.41-7.41-13.47 1.47-1.19 2.41-3 2.41-5.03 0-2.41-1.33-4.5-3.28-5.62.49-.67.78-1.49.78-2.38 0-2.21-1.79-4-4-4z\" fill=\"#fff\" stroke=\"#000\" stroke-width=\"1.5\" stroke-linecap=\"round\" /></g><g id=\"white-knight\" class=\"white knight\" fill=\"none\" fill-rule=\"evenodd\" stroke=\"#000\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M 22,10 C 32.5,11 38.5,18 38,39 L 15,39 C 15,30 25,32.5 23,18\" style=\"fill:#ffffff; stroke:#000000;\" /><path d=\"M 24,18 C 24.38,20.91 18.45,25.37 16,27 C 13,29 13.18,31.34 11,31 C 9.958,30.06 12.41,27.96 11,28 C 10,28 11.19,29.23 10,30 C 9,30 5.997,31 6,26 C 6,24 12,14 12,14 C 12,14 13.89,12.1 14,10.5 C 13.27,9.506 13.5,8.5 13.5,7.5 C 14.5,6.5 16.5,10 16.5,10 L 18.5,10 C 18.5,10 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      ],
      "text/plain": [
       "Board('rnbqkbnr/ppp2ppp/8/3P4/3Pp3/8/PPP2PPP/RNBQKBNR w KQkq - 0 4')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "board.push(move)\n",
    "board"
   ]
  }
 ],
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