Chess_RL_bot(3)(3).ipynb

{
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        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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          ]
        }
      ],
      "source": [
        "!pip install tensorflow\n",
        "import tensorflow\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dense,Flatten"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aKJrybOX9_CY"
      },
      "source": [
        "**Importing necessary libraries**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T1iwjsqRRtio"
      },
      "outputs": [],
      "source": [
        "import chess\n",
        "import random\n",
        "import math\n",
        "from stockfish import Stockfish\n",
        "import time\n",
        "counter=0\n",
        "import tensorflow\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dense,Flatten"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xAjoWKoXPjIi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "404061e3-d4cd-4e21-f7fb-3d2e34dc746e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "chess.Board().fen()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MgHsPZV0Z6PL"
      },
      "outputs": [],
      "source": [
        "class TreeNode2():\n",
        "    # class constructor (create tree node class instance)\n",
        "    def __init__(self, board, parent):\n",
        "        # init associated board state which is board.__str__()\n",
        "        self.position = board.__str__()\n",
        "\n",
        "        self.legal_moves = board.legal_moves\n",
        "\n",
        "        self.is_checkmate = board.is_checkmate()\n",
        "\n",
        "        self.fen = board.fen()\n",
        "\n",
        "        self.turn = board.turn\n",
        "\n",
        "        # init is node terminal flag\n",
        "        if board.is_game_over():\n",
        "            # we have a terminal node\n",
        "            self.is_terminal = True\n",
        "            self.winner = board.outcome().winner\n",
        "\n",
        "        # otherwise\n",
        "        else:\n",
        "            # we have a non-terminal node\n",
        "            self.is_terminal = False\n",
        "\n",
        "        # init is fully expanded flag\n",
        "        self.is_fully_expanded = self.is_terminal\n",
        "\n",
        "        # init parent node if available\n",
        "        self.parent = parent\n",
        "\n",
        "        # init the number of node visits\n",
        "        self.visits = 0\n",
        "\n",
        "        # init the total score of the node\n",
        "        self.score = 0\n",
        "\n",
        "        # init current node's children\n",
        "        self.children = {}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AK0KtPo9DZ7C"
      },
      "outputs": [],
      "source": [
        "# MCTS class definition\n",
        "class MCTS2():\n",
        "    def __init__(self):\n",
        "        self.stockfish = Stockfish(path=\"/home/sohaib/Downloads/stockfish-ubuntu-x86-64-vnni512\")\n",
        "        self.stockfish.set_depth(7)\n",
        "    def search_existing_tree(self,node):\n",
        "        for iteration in range(100):\n",
        "            # select a node (selection phase)\n",
        "            # node = self.select(self.root)\n",
        "\n",
        "            # scrore current node (simulation phase)\n",
        "            score, final_node = self.rollout(node)\n",
        "\n",
        "            # backpropagate results\n",
        "            self.backpropagate(final_node, score)\n",
        "\n",
        "        return node\n",
        "\n",
        "    # search for the best move in the current position\n",
        "    def search(self,board):\n",
        "        # create root node\n",
        "        self.root = TreeNode2(board, None)\n",
        "        # walk through 1000 iterations\n",
        "        for iteration in range(200):\n",
        "            # select a node (selection phase)\n",
        "            # node = self.select(self.root)\n",
        "\n",
        "            # scrore current node (simulation phase)\n",
        "            score, final_node = self.rollout(self.root)\n",
        "\n",
        "            # backpropagate results\n",
        "            self.backpropagate(final_node, score)\n",
        "\n",
        "        return self.root\n",
        "\n",
        "    # select most promising node\n",
        "    def select(self, node):\n",
        "        # make sure that we're dealing with non-terminal nodes\n",
        "        while not node.is_terminal:\n",
        "            # case where the node is fully expanded\n",
        "            if node.is_fully_expanded:\n",
        "                node = self.get_best_move(node, 2)\n",
        "\n",
        "            # case where the node is not fully expanded\n",
        "            else:\n",
        "                # otherwise expand the node\n",
        "                return self.expand(node)\n",
        "\n",
        "        # return node\n",
        "        return node\n",
        "\n",
        "    # expand node\n",
        "    def expand(self, node):\n",
        "        # generate legal states (moves) for the given node\n",
        "        self.stockfish.set_fen_position(node.fen)\n",
        "        top_moves = self.stockfish.get_top_moves(5)\n",
        "\n",
        "        # loop over generated states (moves)\n",
        "        for move in top_moves:\n",
        "            move = move['Move']\n",
        "            # make sure that current state (move) is not present in child nodes\n",
        "            if move not in node.children:\n",
        "                # create a new node\n",
        "                new_board = chess.Board(node.fen)\n",
        "                new_board.push_san(move)\n",
        "                new_node = TreeNode2(new_board, node)\n",
        "                del new_board\n",
        "\n",
        "                # add child node to parent's node children list (dict)\n",
        "                node.children[move] = new_node\n",
        "\n",
        "                # case when node is fully expanded\n",
        "                if len(top_moves) == len(node.children):\n",
        "                    node.is_fully_expanded = True\n",
        "\n",
        "                # return newly created node\n",
        "                return new_node\n",
        "\n",
        "        # debugging\n",
        "        print('Should not get here!!!')\n",
        "\n",
        "    # simulate the game via making random moves until reach end of the game\n",
        "    def rollout(self, node):\n",
        "        # make random moves for both sides until terminal state of the game is reached\n",
        "        while not node.is_terminal:\n",
        "            node = self.select(node)\n",
        "\n",
        "        # return score from the player \"x\" perspective\n",
        "        if node.is_checkmate:\n",
        "            if node.winner == True:\n",
        "                return 1, node\n",
        "            else:\n",
        "                return -1, node\n",
        "        else:\n",
        "            return 0, node\n",
        "\n",
        "    # backpropagate the number of visits and score up to the root node\n",
        "    def backpropagate(self, node, score):\n",
        "        # update nodes's up to root node\n",
        "        while node is not None:\n",
        "            # update node's visits\n",
        "            node.visits += 1\n",
        "\n",
        "            # update node's score\n",
        "            node.score += score\n",
        "\n",
        "            # set node to parent\n",
        "            node = node.parent\n",
        "\n",
        "    # select the best node basing on UCB1 formula\n",
        "    def get_best_move(self, node, exploration_constant):\n",
        "        # define best score & best moves\n",
        "\n",
        "        best_moves = []\n",
        "        if node.turn == True:\n",
        "            best_score = float('-inf')\n",
        "        else:\n",
        "            best_score = float('inf')\n",
        "        # loop over child nodes\n",
        "        for child_node in node.children.values():\n",
        "            # define current player\n",
        "            # if child_node.board.player_2 == 'x': current_player = 1\n",
        "            # elif child_node.board.player_2 == 'o': current_player = -1\n",
        "\n",
        "            # get move score using UCT formula\n",
        "            move_score = child_node.score / child_node.visits + exploration_constant * math.sqrt(math.log(node.visits / child_node.visits))\n",
        "\n",
        "            if node.turn == True:\n",
        "                # better move has been found\n",
        "                if move_score > best_score:\n",
        "                    best_score = move_score\n",
        "                    best_moves = [child_node]\n",
        "                # found as good move as already available\n",
        "                elif move_score == best_score:\n",
        "                    best_moves.append(child_node)\n",
        "            else:\n",
        "                # better move has been found\n",
        "                if move_score < best_score:\n",
        "                    best_score = move_score\n",
        "                    best_moves = [child_node]\n",
        "                # found as good move as already available\n",
        "                elif move_score == best_score:\n",
        "                    best_moves.append(child_node)\n",
        "\n",
        "        # return one of the best moves randomly\n",
        "        return random.choice(best_moves)\n",
        "\n",
        "    # def get_matched_child(self,turn,position):\n",
        "    #     if prev_node == None:\n",
        "    #         return self.root\n",
        "    #     for child_node in prev_node.children.values():\n",
        "    #         if child_node.board.position == curr_board.position:\n",
        "    #             return child_node\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "YsRb_SSsD4f_",
        "outputId": "80682b53-415b-467b-e08a-77f8be11edf4"
      },
      "outputs": [
        {
          "ename": "FileNotFoundError",
          "evalue": "[Errno 2] No such file or directory: '/home/sohaib/Downloads/stockfish-ubuntu-x86-64-vnni512'",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-5-208e2ae3cb2d>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mtest_board_2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mchess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBoard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdarakht_2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMCTS2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mtree_2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdarakht_2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msearch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_board_2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-4-61caa574143d>\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mMCTS2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstockfish\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mStockfish\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"/home/sohaib/Downloads/stockfish-ubuntu-x86-64-vnni512\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstockfish\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_depth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0msearch_existing_tree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/stockfish/models.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, path, depth, parameters)\u001b[0m\n\u001b[1;32m     45\u001b[0m         }\n\u001b[1;32m     46\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 47\u001b[0;31m         self._stockfish = subprocess.Popen(\n\u001b[0m\u001b[1;32m     48\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_path\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m             \u001b[0muniversal_newlines\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/subprocess.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, args, bufsize, executable, stdin, stdout, stderr, preexec_fn, close_fds, shell, cwd, env, universal_newlines, startupinfo, creationflags, restore_signals, start_new_session, pass_fds, user, group, extra_groups, encoding, errors, text, umask, pipesize)\u001b[0m\n\u001b[1;32m    969\u001b[0m                             encoding=encoding, errors=errors)\n\u001b[1;32m    970\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 971\u001b[0;31m             self._execute_child(args, executable, preexec_fn, close_fds,\n\u001b[0m\u001b[1;32m    972\u001b[0m                                 \u001b[0mpass_fds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcwd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    973\u001b[0m                                 \u001b[0mstartupinfo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreationflags\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshell\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/subprocess.py\u001b[0m in \u001b[0;36m_execute_child\u001b[0;34m(self, args, executable, preexec_fn, close_fds, pass_fds, cwd, env, startupinfo, creationflags, shell, p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite, restore_signals, gid, gids, uid, umask, start_new_session)\u001b[0m\n\u001b[1;32m   1861\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0merrno_num\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1862\u001b[0m                         \u001b[0merr_msg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrerror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merrno_num\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1863\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mchild_exception_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merrno_num\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr_msg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr_filename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1864\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mchild_exception_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr_msg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1865\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/home/sohaib/Downloads/stockfish-ubuntu-x86-64-vnni512'"
          ]
        }
      ],
      "source": [
        "test_board_2 = chess.Board()\n",
        "darakht_2 = MCTS2()\n",
        "tree_2 = darakht_2.search(test_board_2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h-MEFtTmFTg2",
        "outputId": "45e25d11-1cbe-474e-a7d3-68a834cb9e4a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "r n b q k b n r\n",
            "p p p p p p p p\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . P . . .\n",
            "P P P P . P P P\n",
            "R N B Q K B N R\n",
            "_____________________\n",
            "r n b q k b . r\n",
            "p p p p p p p p\n",
            ". . . . . n . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . P . . .\n",
            "P P P P . P P P\n",
            "R N B Q K B N R\n",
            "_____________________\n",
            "r n b q k b . r\n",
            "p p p p p p p p\n",
            ". . . . . n . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". P . . P . . .\n",
            "P . P P . P P P\n",
            "R N B Q K B N R\n",
            "_____________________\n",
            "r n b q k b . r\n",
            "p p . p p p p p\n",
            ". . p . . n . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". P . . P . . .\n",
            "P . P P . P P P\n",
            "R N B Q K B N R\n",
            "_____________________\n",
            "r n b q k b . r\n",
            "p p . p p p p p\n",
            ". . p . . n . .\n",
            ". . . . . . . .\n",
            ". . . P . . . .\n",
            ". P . . P . . .\n",
            "P . P . . P P P\n",
            "R N B Q K B N R\n",
            "_____________________\n",
            "r n b q k b . r\n",
            "p p . . p p p p\n",
            ". . p . . n . .\n",
            ". . . p . . . .\n",
            ". . . P . . . .\n",
            ". P . . P . . .\n",
            "P . P . . P P P\n",
            "R N B Q K B N R\n",
            "_____________________\n",
            "r n b q k b . r\n",
            "p p . . p p p p\n",
            ". . p . . n . .\n",
            ". . . p . . . .\n",
            ". . . P . . . .\n",
            ". P . B P . . .\n",
            "P . P . . P P P\n",
            "R N B Q K . N R\n",
            "_____________________\n",
            "r n b q k b . r\n",
            "p p . . p p p p\n",
            ". . . . . n . .\n",
            ". . p p . . . .\n",
            ". . . P . . . .\n",
            ". P . B P . . .\n",
            "P . P . . P P P\n",
            "R N B Q K . N R\n",
            "_____________________\n",
            "r n b q k b . r\n",
            "p p . . p p p p\n",
            ". . . . . n . .\n",
            ". . p p . . . .\n",
            ". . . P . . . .\n",
            ". P . B P N . .\n",
            "P . P . . P P P\n",
            "R N B Q K . . R\n",
            "_____________________\n",
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            "p p . . p p p p\n",
            ". . . . . n . .\n",
            ". . p p . . . .\n",
            ". . . P . . b .\n",
            ". P . B P N . .\n",
            "P . P . . P P P\n",
            "R N B Q K . . R\n",
            "_____________________\n",
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            "p p . . p p p p\n",
            ". . . . . n . .\n",
            ". . P p . . . .\n",
            ". . . . . . b .\n",
            ". P . B P N . .\n",
            "P . P . . P P P\n",
            "R N B Q K . . R\n",
            "_____________________\n",
            "r n . q k b . r\n",
            "p p . . . p p p\n",
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            ". . P p p . . .\n",
            ". . . . . . b .\n",
            ". P . B P N . .\n",
            "P . P . . P P P\n",
            "R N B Q K . . R\n",
            "_____________________\n",
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            "p p . . . p p p\n",
            ". . . . . n . .\n",
            ". . P p p . . .\n",
            ". . . . . . b .\n",
            ". P . B P N . .\n",
            "P B P . . P P P\n",
            "R N . Q K . . R\n",
            "_____________________\n",
            "r . . q k b . r\n",
            "p p . . . p p p\n",
            ". . n . . n . .\n",
            ". . P p p . . .\n",
            ". . . . . . b .\n",
            ". P . B P N . .\n",
            "P B P . . P P P\n",
            "R N . Q K . . R\n",
            "_____________________\n",
            "r . . q k b . r\n",
            "p p . . . p p p\n",
            ". . n . . n . .\n",
            ". . P p p . . .\n",
            ". . . . . . b .\n",
            ". P . . P N . .\n",
            "P B P . B P P P\n",
            "R N . Q K . . R\n",
            "_____________________\n",
            "r . . q k b . r\n",
            "p p . . . p p p\n",
            ". . n . . n . .\n",
            ". . P p p . . .\n",
            ". . . . . . . .\n",
            ". P . . P b . .\n",
            "P B P . B P P P\n",
            "R N . Q K . . R\n",
            "_____________________\n",
            "r . . q k b . r\n",
            "p p . . . p p p\n",
            ". . n . . n . .\n",
            ". . P p p . . .\n",
            ". . . . . . . .\n",
            ". P . . P B . .\n",
            "P B P . . P P P\n",
            "R N . Q K . . R\n",
            "_____________________\n",
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            ". . n . . n . .\n",
            ". . b p p . . .\n",
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            ". P . . P B . .\n",
            "P B P . . P P P\n",
            "R N . Q K . . R\n",
            "_____________________\n",
            "r . . q k . . r\n",
            "p p . . . p p p\n",
            ". . n . . n . .\n",
            ". . b p p . . .\n",
            ". . . . . . . .\n",
            ". P . . P B P .\n",
            "P B P . . P . P\n",
            "R N . Q K . . R\n",
            "_____________________\n",
            "r . . . k . . r\n",
            "p p . . q p p p\n",
            ". . n . . n . .\n",
            ". . b p p . . .\n",
            ". . . . . . . .\n",
            ". P . . P B P .\n",
            "P B P . . P . P\n",
            "R N . Q K . . R\n",
            "_____________________\n",
            "r . . . k . . r\n",
            "p p . . q p p p\n",
            ". . n . . n . .\n",
            ". . b B p . . .\n",
            ". . . . . . . .\n",
            ". P . . P . P .\n",
            "P B P . . P . P\n",
            "R N . Q K . . R\n",
            "_____________________\n",
            ". . k r . . . r\n",
            "p p . . q p p p\n",
            ". . n . . n . .\n",
            ". . b B p . . .\n",
            ". . . . . . . .\n",
            ". P . . P . P .\n",
            "P B P . . P . P\n",
            "R N . Q K . . R\n",
            "_____________________\n",
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            "p p . . q p p p\n",
            ". . n . . n . .\n",
            ". . b B p . . .\n",
            ". . P . . . . .\n",
            ". P . . P . P .\n",
            "P B . . . P . P\n",
            "R N . Q K . . R\n",
            "_____________________\n",
            ". . k r . . . r\n",
            "p p . . q p p p\n",
            ". . n . . n . .\n",
            ". . b B . . . .\n",
            ". . P . p . . .\n",
            ". P . . P . P .\n",
            "P B . . . P . P\n",
            "R N . Q K . . R\n",
            "_____________________\n",
            ". . k r . . . r\n",
            "p p . . q p p p\n",
            ". . n . . n . .\n",
            ". . b B . . . .\n",
            ". . P . p . . .\n",
            ". P N . P . P .\n",
            "P B . . . P . P\n",
            "R . . Q K . . R\n",
            "_____________________\n",
            ". . k r . . . r\n",
            "p p . . q p p p\n",
            ". . . . . n . .\n",
            ". . b B n . . .\n",
            ". . P . p . . .\n",
            ". P N . P . P .\n",
            "P B . . . P . P\n",
            "R . . Q K . . R\n",
            "_____________________\n",
            ". . k r . . . r\n",
            "p p . . q p p p\n",
            ". . . . . n . .\n",
            ". . b B n . . .\n",
            ". . P . p . . .\n",
            ". P N . P . P .\n",
            "P B . . . P . P\n",
            "R . . Q . R K .\n",
            "_____________________\n",
            ". . k r . . . r\n",
            "p p . . q p p .\n",
            ". . . . . n . .\n",
            ". . b B n . . p\n",
            ". . P . p . . .\n",
            ". P N . P . P .\n",
            "P B . . . P . P\n",
            "R . . Q . R K .\n",
            "_____________________\n",
            ". . k r . . . r\n",
            "p p . . q p p .\n",
            ". . . . . n . .\n",
            ". . b B n . . p\n",
            ". . P . p . . P\n",
            ". P N . P . P .\n",
            "P B . . . P . .\n",
            "R . . Q . R K .\n",
            "_____________________\n",
            ". . k r . . . r\n",
            "p p . . q p . .\n",
            ". . . . . n . .\n",
            ". . b B n . p p\n",
            ". . P . p . . P\n",
            ". P N . P . P .\n",
            "P B . . . P . .\n",
            "R . . Q . R K .\n",
            "_____________________\n",
            ". . k r . . . r\n",
            "p p . . q p . .\n",
            ". . . . . n . .\n",
            ". . b B n . p p\n",
            ". P P . p . . P\n",
            ". . N . P . P .\n",
            "P B . . . P . .\n",
            "R . . Q . R K .\n",
            "_____________________\n",
            ". . k r . . . r\n",
            "p p . . q p . .\n",
            ". . . . . n . .\n",
            ". . . B n . p p\n",
            ". b P . p . . P\n",
            ". . N . P . P .\n",
            "P B . . . P . .\n",
            "R . . Q . R K .\n",
            "_____________________\n",
            ". . k r . . . r\n",
            "p p . . q p . .\n",
            ". . . . . n . .\n",
            ". . . B n . P p\n",
            ". b P . p . . .\n",
            ". . N . P . P .\n",
            "P B . . . P . .\n",
            "R . . Q . R K .\n",
            "_____________________\n",
            ". . k r . . . r\n",
            "p p . . q p . .\n",
            ". . . . . n . .\n",
            ". . . B . . P p\n",
            ". b P . p . . .\n",
            ". . N . P n P .\n",
            "P B . . . P . .\n",
            "R . . Q . R K .\n",
            "_____________________\n",
            ". . k r . . . r\n",
            "p p . . q p . .\n",
            ". . . . . n . .\n",
            ". . . B . . P p\n",
            ". b P . p . . .\n",
            ". . N . P n P .\n",
            "P B . . . P K .\n",
            "R . . Q . R . .\n",
            "_____________________\n",
            ". . k r . . . r\n",
            "p p . . q p . .\n",
            ". . . . . n . .\n",
            ". . . B . . n p\n",
            ". b P . p . . .\n",
            ". . N . P . P .\n",
            "P B . . . P K .\n",
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            "_____________________\n",
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            ". . . . k . . .\n",
            "R . . . . . . .\n",
            ". . . . . K P .\n",
            ". . . . B P . .\n",
            "p . . . . . . .\n",
            "P . . . . . . .\n",
            ". . . . r . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . . . k . . .\n",
            "R . . . . . . .\n",
            ". . . . . K P .\n",
            ". . . . B P . .\n",
            "p . . . r . . .\n",
            "P . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . . . k . . .\n",
            "R . . . . . . .\n",
            ". . . . K . P .\n",
            ". . . . B P . .\n",
            "p . . . r . . .\n",
            "P . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . k . .\n",
            ". . . . . . . .\n",
            "R . . . . . . .\n",
            ". . . . K . P .\n",
            ". . . . B P . .\n",
            "p . . . r . . .\n",
            "P . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . k . .\n",
            ". . . . . . . .\n",
            "R . . . . . . .\n",
            ". . . . K P P .\n",
            ". . . . B . . .\n",
            "p . . . r . . .\n",
            "P . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . . . . k . .\n",
            "R . . . . . . .\n",
            ". . . . K P P .\n",
            ". . . . B . . .\n",
            "p . . . r . . .\n",
            "P . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            "R . . . . . . .\n",
            ". . . . . k . .\n",
            ". . . . . . . .\n",
            ". . . . K P P .\n",
            ". . . . B . . .\n",
            "p . . . r . . .\n",
            "P . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            "R . . . . . . .\n",
            ". . . . . k . .\n",
            ". . . . . . . .\n",
            ". . . . K P P .\n",
            ". . . . B . . .\n",
            "p . . . . . . .\n",
            "P . . . r . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            "R . . . . . . .\n",
            ". . . . . k . .\n",
            ". . . . . P . .\n",
            ". . . . K . P .\n",
            ". . . . B . . .\n",
            "p . . . . . . .\n",
            "P . . . r . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            "R . . . . . . .\n",
            ". . . . . k . .\n",
            ". . . . . P . .\n",
            ". . . . K . P .\n",
            ". . . . r . . .\n",
            "p . . . . . . .\n",
            "P . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            "R . . . . . . .\n",
            ". . . . . k . .\n",
            ". . . . . P . .\n",
            ". . . . . . P .\n",
            ". . . . K . . .\n",
            "p . . . . . . .\n",
            "P . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            "R . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . P k .\n",
            ". . . . . . P .\n",
            ". . . . K . . .\n",
            "p . . . . . . .\n",
            "P . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            "R . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . P k .\n",
            ". . . . . . P .\n",
            ". . . . . K . .\n",
            "p . . . . . . .\n",
            "P . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            "R . . . . . . .\n",
            ". . . . . k . .\n",
            ". . . . . P . .\n",
            ". . . . . . P .\n",
            ". . . . . K . .\n",
            "p . . . . . . .\n",
            "P . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            "R . . . . . . .\n",
            ". . . . . k . .\n",
            ". . . . . P . .\n",
            ". . . . . . P .\n",
            ". . . . . . . .\n",
            "p . . . . . K .\n",
            "P . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            "R . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . k P . .\n",
            ". . . . . . P .\n",
            ". . . . . . . .\n",
            "p . . . . . K .\n",
            "P . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . k P . .\n",
            ". . . . . . P .\n",
            ". . . . . . . .\n",
            "R . . . . . K .\n",
            "P . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . . . . k . .\n",
            ". . . . . P . .\n",
            ". . . . . . P .\n",
            ". . . . . . . .\n",
            "R . . . . . K .\n",
            "P . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . . . . k . .\n",
            ". . . . . P . .\n",
            ". . . . . . P .\n",
            ". . . . . . . K\n",
            "R . . . . . . .\n",
            "P . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . k P . .\n",
            ". . . . . . P .\n",
            ". . . . . . . K\n",
            "R . . . . . . .\n",
            "P . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . k P . .\n",
            ". . . . . . P .\n",
            ". . . . . . . K\n",
            ". . . . . R . .\n",
            "P . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . . . . k . .\n",
            ". . . . . P . .\n",
            ". . . . . . P .\n",
            ". . . . . . . K\n",
            ". . . . . R . .\n",
            "P . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . . . . k . .\n",
            ". . . . . P . .\n",
            ". . . . . . P .\n",
            "P . . . . . . K\n",
            ". . . . . R . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . k . .\n",
            ". . . . . . . .\n",
            ". . . . . P . .\n",
            ". . . . . . P .\n",
            "P . . . . . . K\n",
            ". . . . . R . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . k . .\n",
            ". . . . . . . .\n",
            ". . . . . P . .\n",
            ". . . . . . P .\n",
            "P . . . . . . K\n",
            ". . . . . . . .\n",
            ". . . . . R . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . . . . k . .\n",
            ". . . . . P . .\n",
            ". . . . . . P .\n",
            "P . . . . . . K\n",
            ". . . . . . . .\n",
            ". . . . . R . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . . . . k . .\n",
            ". . . . . P . .\n",
            "P . . . . . P .\n",
            ". . . . . . . K\n",
            ". . . . . . . .\n",
            ". . . . . R . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . k . . .\n",
            ". . . . . . . .\n",
            ". . . . . P . .\n",
            "P . . . . . P .\n",
            ". . . . . . . K\n",
            ". . . . . . . .\n",
            ". . . . . R . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . k . . .\n",
            ". . . . . . . .\n",
            ". . . . . P P .\n",
            "P . . . . . . .\n",
            ". . . . . . . K\n",
            ". . . . . . . .\n",
            ". . . . . R . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . k . .\n",
            ". . . . . . . .\n",
            ". . . . . P P .\n",
            "P . . . . . . .\n",
            ". . . . . . . K\n",
            ". . . . . . . .\n",
            ". . . . . R . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . k . .\n",
            ". . . . . P . .\n",
            ". . . . . . P .\n",
            "P . . . . . . .\n",
            ". . . . . . . K\n",
            ". . . . . . . .\n",
            ". . . . . R . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . . . k P . .\n",
            ". . . . . . P .\n",
            "P . . . . . . .\n",
            ". . . . . . . K\n",
            ". . . . . . . .\n",
            ". . . . . R . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . . . k P . .\n",
            ". . . . . . P .\n",
            "P . . . . . . .\n",
            ". . . . . R . K\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . k . .\n",
            ". . . . . P . .\n",
            ". . . . . . P .\n",
            "P . . . . . . .\n",
            ". . . . . R . K\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . k . .\n",
            ". . . . . P P .\n",
            ". . . . . . . .\n",
            "P . . . . . . .\n",
            ". . . . . R . K\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . . . k P P .\n",
            ". . . . . . . .\n",
            "P . . . . . . .\n",
            ". . . . . R . K\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . Q . .\n",
            ". . . . k . P .\n",
            ". . . . . . . .\n",
            "P . . . . . . .\n",
            ". . . . . R . K\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . Q . .\n",
            ". . . . . . P .\n",
            ". . . . k . . .\n",
            "P . . . . . . .\n",
            ". . . . . R . K\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . Q Q .\n",
            ". . . . . . . .\n",
            ". . . . k . . .\n",
            "P . . . . . . .\n",
            ". . . . . R . K\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . Q Q .\n",
            ". . . k . . . .\n",
            ". . . . . . . .\n",
            "P . . . . . . .\n",
            ". . . . . R . K\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . Q . . Q .\n",
            ". . . k . . . .\n",
            ". . . . . . . .\n",
            "P . . . . . . .\n",
            ". . . . . R . K\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . Q . . Q .\n",
            ". . . . . . . .\n",
            ". . k . . . . .\n",
            "P . . . . . . .\n",
            ". . . . . R . K\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . Q . . . .\n",
            ". . . . . . . .\n",
            ". . k . . . . .\n",
            "P . . Q . . . .\n",
            ". . . . . R . K\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            "True\n",
            "149\n"
          ]
        }
      ],
      "source": [
        "test_node_2 = tree_2\n",
        "count_2 = 0\n",
        "while not test_node_2.is_terminal:\n",
        "    count_2+=1\n",
        "    temp = darakht_2.get_best_move(test_node_2,2)\n",
        "    print(temp.position)\n",
        "    print(\"_____________________\")\n",
        "    test_node_2 = temp\n",
        "print(test_node_2.winner)\n",
        "print(count_2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PFdB6kb2kuNl"
      },
      "outputs": [],
      "source": [
        "darakht_2 = MCTS2()\n",
        "tree_2_extended = darakht_2.search_existing_tree(tree_2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Zzn6lY64kuNm",
        "outputId": "4d0015ab-60b5-4b44-f923-b626aee9cc8a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "r n b q k b n r\n",
            "p p p p p p p p\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . P . . . . .\n",
            ". . . . . . . .\n",
            "P P . P P P P P\n",
            "R N B Q K B N R\n",
            "_____________________\n",
            "r n b q k b n r\n",
            "p p p p . p p p\n",
            ". . . . p . . .\n",
            ". . . . . . . .\n",
            ". . P . . . . .\n",
            ". . . . . . . .\n",
            "P P . P P P P P\n",
            "R N B Q K B N R\n",
            "_____________________\n",
            "r n b q k b n r\n",
            "p p p p . p p p\n",
            ". . . . p . . .\n",
            ". . . . . . . .\n",
            ". . P . . . . .\n",
            ". . N . . . . .\n",
            "P P . P P P P P\n",
            "R . B Q K B N R\n",
            "_____________________\n",
            "r n b q k b n r\n",
            "p p . p . p p p\n",
            ". . . . p . . .\n",
            ". . p . . . . .\n",
            ". . P . . . . .\n",
            ". . N . . . . .\n",
            "P P . P P P P P\n",
            "R . B Q K B N R\n",
            "_____________________\n",
            "r n b q k b n r\n",
            "p p . p . p p p\n",
            ". . . . p . . .\n",
            ". . p . . . . .\n",
            ". . P . . . . .\n",
            ". . N P . . . .\n",
            "P P . . P P P P\n",
            "R . B Q K B N R\n",
            "_____________________\n",
            "r n b q k b n r\n",
            "p . . p . p p p\n",
            ". p . . p . . .\n",
            ". . p . . . . .\n",
            ". . P . . . . .\n",
            ". . N P . . . .\n",
            "P P . . P P P P\n",
            "R . B Q K B N R\n",
            "_____________________\n",
            "r n b q k b n r\n",
            "p . . p . p p p\n",
            ". p . . p . . .\n",
            ". . p . . . . .\n",
            ". . P . . . . .\n",
            ". . N P . N . .\n",
            "P P . . P P P P\n",
            "R . B Q K B . R\n",
            "_____________________\n",
            "r n . q k b n r\n",
            "p b . p . p p p\n",
            ". p . . p . . .\n",
            ". . p . . . . .\n",
            ". . P . . . . .\n",
            ". . N P . N . .\n",
            "P P . . P P P P\n",
            "R . B Q K B . R\n",
            "_____________________\n",
            "r n . q k b n r\n",
            "p b . p . p p p\n",
            ". p . . p . . .\n",
            ". . p . . . . .\n",
            ". . P . P . . .\n",
            ". . N P . N . .\n",
            "P P . . . P P P\n",
            "R . B Q K B . R\n",
            "_____________________\n",
            "r . . q k b n r\n",
            "p b . p . p p p\n",
            ". p n . p . . .\n",
            ". . p . . . . .\n",
            ". . P . P . . .\n",
            ". . N P . N . .\n",
            "P P . . . P P P\n",
            "R . B Q K B . R\n",
            "_____________________\n",
            "r . . q k b n r\n",
            "p b . p . p p p\n",
            ". p n . p . . .\n",
            ". . p . . . . .\n",
            ". . P . P . . .\n",
            ". . N P B N . .\n",
            "P P . . . P P P\n",
            "R . . Q K B . R\n",
            "_____________________\n",
            "r . . q k b . r\n",
            "p b . p . p p p\n",
            ". p n . p n . .\n",
            ". . p . . . . .\n",
            ". . P . P . . .\n",
            ". . N P B N . .\n",
            "P P . . . P P P\n",
            "R . . Q K B . R\n",
            "_____________________\n",
            "r . . q k b . r\n",
            "p b . p . p p p\n",
            ". p n . p n . .\n",
            ". . p . . . . .\n",
            ". . P . P . . .\n",
            ". . N P B N P .\n",
            "P P . . . P . P\n",
            "R . . Q K B . R\n",
            "_____________________\n",
            "r . . q k b . r\n",
            "p b . p . p p p\n",
            ". p n . p . . .\n",
            ". . p . . . . .\n",
            ". . P . P . n .\n",
            ". . N P B N P .\n",
            "P P . . . P . P\n",
            "R . . Q K B . R\n",
            "_____________________\n",
            "r . . q k b . r\n",
            "p b . p . p p p\n",
            ". p n . p . . .\n",
            ". . p . . . . .\n",
            ". . P . P B n .\n",
            ". . N P . N P .\n",
            "P P . . . P . P\n",
            "R . . Q K B . R\n",
            "_____________________\n",
            "r . . q k b . r\n",
            "p b . . . p p p\n",
            ". p n p p . . .\n",
            ". . p . . . . .\n",
            ". . P . P B n .\n",
            ". . N P . N P .\n",
            "P P . . . P . P\n",
            "R . . Q K B . R\n",
            "_____________________\n",
            "r . . q k b . r\n",
            "p b . . . p p p\n",
            ". p n p p . . .\n",
            ". . p . . . . .\n",
            ". . P P P B n .\n",
            ". . N . . N P .\n",
            "P P . . . P . P\n",
            "R . . Q K B . R\n",
            "_____________________\n",
            "r . . q k b . r\n",
            "p b . . . p p p\n",
            ". p . p p . . .\n",
            ". . p . . . . .\n",
            ". . P n P B n .\n",
            ". . N . . N P .\n",
            "P P . . . P . P\n",
            "R . . Q K B . R\n",
            "_____________________\n",
            "r . . q k b . r\n",
            "p b . . . p p p\n",
            ". p . p p . . .\n",
            ". . p . . . . .\n",
            ". . P N P B n .\n",
            ". . N . . . P .\n",
            "P P . . . P . P\n",
            "R . . Q K B . R\n",
            "_____________________\n",
            "r . . q k b . r\n",
            "p b . . . p p p\n",
            ". p . p p . . .\n",
            ". . . . . . . .\n",
            ". . P p P B n .\n",
            ". . N . . . P .\n",
            "P P . . . P . P\n",
            "R . . Q K B . R\n",
            "_____________________\n",
            "r . . q k b . r\n",
            "p b . . . p p p\n",
            ". p . p p . . .\n",
            ". . . . . . . .\n",
            ". . P Q P B n .\n",
            ". . N . . . P .\n",
            "P P . . . P . P\n",
            "R . . . K B . R\n",
            "_____________________\n",
            ". . r q k b . r\n",
            "p b . . . p p p\n",
            ". p . p p . . .\n",
            ". . . . . . . .\n",
            ". . P Q P B n .\n",
            ". . N . . . P .\n",
            "P P . . . P . P\n",
            "R . . . K B . R\n",
            "_____________________\n",
            ". . r q k b . r\n",
            "p b . . . p p p\n",
            ". p . p p . . .\n",
            ". . P . . . . .\n",
            ". . . Q P B n .\n",
            ". . N . . . P .\n",
            "P P . . . P . P\n",
            "R . . . K B . R\n",
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            "p b . . . p p p\n",
            ". p . p p . . .\n",
            ". . r . . . . .\n",
            ". . . Q P B n .\n",
            ". . N . . . P .\n",
            "P P . . . P . P\n",
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            ". . . q k b . r\n",
            "p b . . . p p p\n",
            ". p . p p . . .\n",
            ". B r . . . . .\n",
            ". . . Q P B n .\n",
            ". . N . . . P .\n",
            "P P . . . P . P\n",
            "R . . . K . . R\n",
            "_____________________\n",
            ". . . q k b . r\n",
            "p b . . . p p p\n",
            ". p . p p . . .\n",
            ". r . . . . . .\n",
            ". . . Q P B n .\n",
            ". . N . . . P .\n",
            "P P . . . P . P\n",
            "R . . . K . . R\n",
            "_____________________\n",
            ". . . q k b . r\n",
            "p b . . . p p p\n",
            ". p . p p . . .\n",
            ". N . . . . . .\n",
            ". . . Q P B n .\n",
            ". . . . . . P .\n",
            "P P . . . P . P\n",
            "R . . . K . . R\n",
            "_____________________\n",
            ". . . q k b . r\n",
            "p b . . . p p p\n",
            ". p . p . . . .\n",
            ". N . . p . . .\n",
            ". . . Q P B n .\n",
            ". . . . . . P .\n",
            "P P . . . P . P\n",
            "R . . . K . . R\n",
            "_____________________\n",
            ". . . q k b . r\n",
            "p b . . . p p p\n",
            ". p . p . . . .\n",
            ". N . . p . . .\n",
            "Q . . . P B n .\n",
            ". . . . . . P .\n",
            "P P . . . P . P\n",
            "R . . . K . . R\n",
            "_____________________\n",
            ". . . . k b . r\n",
            "p b . q . p p p\n",
            ". p . p . . . .\n",
            ". N . . p . . .\n",
            "Q . . . P B n .\n",
            ". . . . . . P .\n",
            "P P . . . P . P\n",
            "R . . . K . . R\n",
            "_____________________\n",
            ". . . . k b . r\n",
            "p b . q . p p p\n",
            ". p . p . . . .\n",
            ". N . . p . . .\n",
            "Q . . . P B n .\n",
            ". . . . . . P .\n",
            "P P . . . P . P\n",
            ". . R . K . . R\n",
            "_____________________\n",
            ". . . . k b . r\n",
            ". b . q . p p p\n",
            "p p . p . . . .\n",
            ". N . . p . . .\n",
            "Q . . . P B n .\n",
            ". . . . . . P .\n",
            "P P . . . P . P\n",
            ". . R . K . . R\n",
            "_____________________\n",
            ". . . . k b . r\n",
            ". b N q . p p p\n",
            "p p . p . . . .\n",
            ". . . . p . . .\n",
            "Q . . . P B n .\n",
            ". . . . . . P .\n",
            "P P . . . P . P\n",
            ". . R . K . . R\n",
            "_____________________\n",
            ". . . . . b . r\n",
            ". b N q k p p p\n",
            "p p . p . . . .\n",
            ". . . . p . . .\n",
            "Q . . . P B n .\n",
            ". . . . . . P .\n",
            "P P . . . P . P\n",
            ". . R . K . . R\n",
            "_____________________\n",
            ". . . . . b . r\n",
            ". b N q k p p p\n",
            "p p . p . . . .\n",
            ". . . . p . . .\n",
            ". . . . P B n .\n",
            ". Q . . . . P .\n",
            "P P . . . P . P\n",
            ". . R . K . . R\n",
            "_____________________\n",
            ". . . . . b . r\n",
            ". b N q k p p p\n",
            "p p . p . . . .\n",
            ". . . . . . . .\n",
            ". . . . P p n .\n",
            ". Q . . . . P .\n",
            "P P . . . P . P\n",
            ". . R . K . . R\n",
            "_____________________\n",
            ". . . . . b . r\n",
            ". b N q k p p p\n",
            "p Q . p . . . .\n",
            ". . . . . . . .\n",
            ". . . . P p n .\n",
            ". . . . . . P .\n",
            "P P . . . P . P\n",
            ". . R . K . . R\n",
            "_____________________\n",
            ". . . . . b . r\n",
            ". . N q k p p p\n",
            "p Q . p . . . .\n",
            ". . . . . . . .\n",
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            ". . . . . . P .\n",
            "P P . . . P . P\n",
            ". . R . K . . R\n",
            "_____________________\n",
            ". . . . . b . r\n",
            ". . N q k p p p\n",
            "p Q . p . . . .\n",
            ". . . . . . . .\n",
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            ". . . . . P P .\n",
            "P P . . . . . P\n",
            ". . R . K . . R\n",
            "_____________________\n",
            ". . . . . b . r\n",
            ". . N q k p p p\n",
            "p Q . p . . . .\n",
            ". . . . . . . .\n",
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            ". . . . . b P .\n",
            "P P . . . . . P\n",
            ". . R . K . . R\n",
            "_____________________\n",
            ". . . . . b . r\n",
            ". . N q k p p p\n",
            "p Q . p . . . .\n",
            ". . . . . . . .\n",
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            ". . . . . b P .\n",
            "P P . . . . . P\n",
            ". . R . . R K .\n",
            "_____________________\n",
            ". . . . . b . r\n",
            ". . N q k p p p\n",
            "p Q . p . . . .\n",
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            ". . . . . b P .\n",
            "P P . . . . . P\n",
            ". . R . . R K .\n",
            "_____________________\n",
            ". . . . . b . r\n",
            ". . N q k p p p\n",
            "p Q . p . . . .\n",
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            "P P . . . . . P\n",
            ". . . . . R K .\n",
            "_____________________\n",
            ". . . . . b . r\n",
            ". . N . k p p p\n",
            "p Q . p . . . .\n",
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            ". . R . . b P .\n",
            "P P . . . . . P\n",
            ". . . . . R K .\n",
            "_____________________\n",
            ". . . . . b . r\n",
            ". . N . k p p p\n",
            "p . . p . . . .\n",
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            ". Q R . . b P .\n",
            "P P . . . . . P\n",
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            "_____________________\n",
            ". . . . . b . r\n",
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            "P P . . . . . P\n",
            ". . . . . R K .\n",
            "_____________________\n",
            ". . . . . b . r\n",
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            ". Q R . . b P .\n",
            "P P . . . . . P\n",
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            "_____________________\n",
            ". . . . . b . r\n",
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            ". Q R . . b P .\n",
            "P P . . . . . P\n",
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            "_____________________\n",
            ". . . . . b . r\n",
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            "P P . . . . . P\n",
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            "_____________________\n",
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            "_____________________\n",
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            "_____________________\n",
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            "_____________________\n",
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            "_____________________\n",
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            "_____________________\n",
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            "_____________________\n",
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            "_____________________\n",
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            "_____________________\n",
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            "_____________________\n",
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            "_____________________\n",
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            "_____________________\n",
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            "_____________________\n",
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            "_____________________\n",
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            "_____________________\n",
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            ". Q . n . . b p\n",
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            ". Q . n . R b p\n",
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            "_____________________\n",
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            "_____________________\n",
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            "_____________________\n",
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            "P P . . . . . P\n",
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            "_____________________\n",
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            "P P . . . . . P\n",
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            "_____________________\n",
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            "P P . . . . . P\n",
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            "_____________________\n",
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            "P P . . . . . P\n",
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            "_____________________\n",
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            ". . . . . . . .\n",
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            "P P . . . . . P\n",
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            "_____________________\n",
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            ". . . . . . . .\n",
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            ". . . . . . P .\n",
            "P P . . . . . P\n",
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            "_____________________\n",
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            "P P . . . . K P\n",
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            "_____________________\n",
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            "_____________________\n",
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            "P P . . . . K P\n",
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            "_____________________\n",
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            "P P . . . . K P\n",
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            "_____________________\n",
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            ". P . . . . . .\n",
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            "P . . . . . K P\n",
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            "_____________________\n",
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            ". P . . . . . .\n",
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            "_____________________\n",
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            "P . . . . . K P\n",
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            "_____________________\n",
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            "_____________________\n",
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            ". P . . . . . .\n",
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            "P . . . . . . P\n",
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            "_____________________\n",
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            ". . . . . . P K\n",
            "P . r . . . . P\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . . n k . . p\n",
            "R . . . . . p .\n",
            ". P . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . P K\n",
            "P . r . . . . P\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . . . k . . p\n",
            "R . . . . . p .\n",
            ". P . . n . . .\n",
            ". . . . . . . .\n",
            ". . . . . . P K\n",
            "P . r . . . . P\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . . . k . . p\n",
            "R . . . . . p .\n",
            ". P . . n . . .\n",
            "P . . . . . . .\n",
            ". . . . . . P K\n",
            ". . r . . . . P\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . . . k . . .\n",
            "R . . . . . p .\n",
            ". P . . n . . p\n",
            "P . . . . . . .\n",
            ". . . . . . P K\n",
            ". . r . . . . P\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . . . k . . .\n",
            "R . . . . . p .\n",
            ". P . . n . . p\n",
            "P . . . . . P .\n",
            ". . . . . . . K\n",
            ". . r . . . . P\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . . . k . . .\n",
            "R . . . . . p .\n",
            ". P . . n . . p\n",
            "P . . . . . P .\n",
            ". . r . . . . K\n",
            ". . . . . . . P\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . . . k . . .\n",
            "R . . . . . p .\n",
            ". P . . n . . p\n",
            "P . . . . . P K\n",
            ". . r . . . . .\n",
            ". . . . . . . P\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . . . k . . .\n",
            "R . . . . . p .\n",
            ". P . . n . . .\n",
            "P . . . . . p K\n",
            ". . r . . . . .\n",
            ". . . . . . . P\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . . . k . . .\n",
            "R . . . . . p .\n",
            "P P . . n . . .\n",
            ". . . . . . p K\n",
            ". . r . . . . .\n",
            ". . . . . . . P\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . . . k . . .\n",
            "R . . . . . p .\n",
            "P P . . n . . .\n",
            ". . . . . . p K\n",
            ". r . . . . . .\n",
            ". . . . . . . P\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . . . k . . .\n",
            ". R . . . . p .\n",
            "P P . . n . . .\n",
            ". . . . . . p K\n",
            ". r . . . . . .\n",
            ". . . . . . . P\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . . . k . . .\n",
            ". R . . . . p .\n",
            "P P . . n . . .\n",
            ". . . . . . p K\n",
            ". . . . . . . r\n",
            ". . . . . . . P\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . . . k . . .\n",
            ". R . . . . p .\n",
            "P P . . n . K .\n",
            ". . . . . . p .\n",
            ". . . . . . . r\n",
            ". . . . . . . P\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . . . k . . .\n",
            ". R . . . . p .\n",
            "P P . . n . K .\n",
            ". . . . . . p .\n",
            ". . . . . . . .\n",
            ". . . . . . . r\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . . . k . . .\n",
            ". R . . . . p .\n",
            "P P . . n . . .\n",
            ". . . . . K p .\n",
            ". . . . . . . .\n",
            ". . . . . . . r\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . . . k . . .\n",
            ". R . . . . p .\n",
            "P P . . n . . r\n",
            ". . . . . K p .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . . . k . . .\n",
            "P R . . . . p .\n",
            ". P . . n . . r\n",
            ". . . . . K p .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . . . k . . .\n",
            "P R . . . . p .\n",
            ". P . . n r . .\n",
            ". . . . . K p .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . . . k . . .\n",
            "P R . . . . p .\n",
            ". P . . n r . .\n",
            ". . . . K . p .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . . n k . . .\n",
            "P R . . . . p .\n",
            ". P . . . r . .\n",
            ". . . . K . p .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . . n k . . .\n",
            "P . . . . . R .\n",
            ". P . . . r . .\n",
            ". . . . K . p .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . . n k . . .\n",
            "P . . . . . R .\n",
            ". r . . . . . .\n",
            ". . . . K . p .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            "P . . n k . . .\n",
            ". . . . . . R .\n",
            ". r . . . . . .\n",
            ". . . . K . p .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            "P . . n k . . .\n",
            ". . . . . . R .\n",
            "r . . . . . . .\n",
            ". . . . K . p .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            "P . . n k . . .\n",
            ". . . . . . . .\n",
            "r . . . . . R .\n",
            ". . . . K . p .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            "P . . . k . . .\n",
            ". . . . . n . .\n",
            "r . . . . . R .\n",
            ". . . . K . p .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            "P . . . k . . .\n",
            ". . . . . n . .\n",
            "r . . . . . R .\n",
            ". . . K . . p .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            "P . . . k . . .\n",
            ". . . . . n . .\n",
            ". . . . . . R .\n",
            ". . . K . . p .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "r . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            "P . . . k . R .\n",
            ". . . . . n . .\n",
            ". . . . . . . .\n",
            ". . . K . . p .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "r . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            "P . . . . . R .\n",
            ". . . k . n . .\n",
            ". . . . . . . .\n",
            ". . . K . . p .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "r . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            "P . . . . . R .\n",
            ". . . k . n . .\n",
            ". . . . . . . .\n",
            ". . K . . . p .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "r . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            "P . . . . . R .\n",
            ". . . . . n . .\n",
            ". . . . k . . .\n",
            ". . K . . . p .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "r . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            "P . . . . . R .\n",
            ". . . . . n . .\n",
            ". K . . k . . .\n",
            ". . . . . . p .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "r . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            "P . . . . . R .\n",
            ". . . . . n . .\n",
            ". K . . . . . .\n",
            ". . . . . k p .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "r . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            "P . . . . . R .\n",
            ". K . . . n . .\n",
            ". . . . . . . .\n",
            ". . . . . k p .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "r . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            "P . . n . . R .\n",
            ". K . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . k p .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "r . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            "P . . R . . . .\n",
            ". K . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . k p .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "r . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            "r . . R . . . .\n",
            ". K . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . k p .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            "R . . . . . . .\n",
            ". K . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . k p .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            "R . . . . . . .\n",
            ". K . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . k . .\n",
            ". . . . . . p .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . . . . R . .\n",
            ". K . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . k . .\n",
            ". . . . . . p .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . . . . R . .\n",
            ". K . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . k . p .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . K . . R . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . k . p .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . K . . R . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . k . . .\n",
            ". . . . . . p .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . K . . . R .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . k . . .\n",
            ". . . . . . p .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . K . . . R .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . k . .\n",
            ". . . . . . p .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . . . . . R .\n",
            ". K . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . k . .\n",
            ". . . . . . p .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". . . . . . R .\n",
            ". K . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . k p .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". K . . . . R .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . k p .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". K . . . . R .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . p .\n",
            ". . . . . . k .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". K . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . R .\n",
            ". . . . . . k .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            ". . . . . . . .\n",
            ". K . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . k .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "_____________________\n",
            "None\n",
            "144\n"
          ]
        }
      ],
      "source": [
        "test_node_2_extended = tree_2_extended\n",
        "count_2 = 0\n",
        "while not test_node_2_extended.is_terminal:\n",
        "    count_2+=1\n",
        "    temp = darakht_2.get_best_move(test_node_2_extended,2)\n",
        "    print(temp.position)\n",
        "    print(\"_____________________\")\n",
        "    test_node_2_extended = temp\n",
        "print(test_node_2_extended.winner)\n",
        "print(count_2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "47UR7W2XkuNm"
      },
      "source": [
        "**Some utility functions for getting boards from MCTS and converting it into x and y train**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8uj1mYuKkuNn"
      },
      "outputs": [],
      "source": [
        "def get_move_from_fens(fen1, fen2):\n",
        "  \"\"\"\n",
        "  This function takes two FEN strings representing consecutive chess positions\n",
        "  and returns the move played between them.\n",
        "\n",
        "  Args:\n",
        "      fen1: The FEN string for the starting position.\n",
        "      fen2: The FEN string for the position after the move.\n",
        "\n",
        "  Returns:\n",
        "      A string representing the move played, or None if the FENs are invalid\n",
        "      or the move cannot be determined.\n",
        "  \"\"\"\n",
        "  board1 = chess.Board(fen1)\n",
        "  board2 = chess.Board(fen2)\n",
        "\n",
        "  # Iterate through all legal moves of the first position\n",
        "  for move in board1.legal_moves:\n",
        "    board1.push(move)\n",
        "    if board1.fen() == fen2:\n",
        "      # Found the move that leads to the second position\n",
        "        return str(move)\n",
        "    board1.pop()  # Undo the move\n",
        "\n",
        "  # Move not found, return None\n",
        "  return None"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XUvyf53NkuNo"
      },
      "outputs": [],
      "source": [
        "def storeBoardsFromTree(node, mcts):\n",
        "        list = []\n",
        "        if not chess.Board(node.fen).is_game_over():\n",
        "            for key, value in node.children.items():\n",
        "              if value == mcts.get_best_move(node,2):\n",
        "                move = key\n",
        "                list.append(convert_node_to_eight_boards(node, move))\n",
        "                break  # Exit the loop after finding a match\n",
        "        for child_node in node.children.values():\n",
        "            for x in storeBoardsFromTree(child_node, mcts):\n",
        "                list.append(x)\n",
        "        return list\n",
        "def count_nodes(node):\n",
        "    counter = 0\n",
        "    if node is None:\n",
        "        return 0\n",
        "    elif node.parent is None:\n",
        "        counter = 1\n",
        "    for child_node in node.children.values():\n",
        "        counter = counter + 1 + count_nodes(child_node)\n",
        "    return counter\n",
        "def convert_node_to_eight_boards(node, move):\n",
        "    arr = []\n",
        "    arr.append(chess.Board(node.fen))\n",
        "    count = 7\n",
        "    while len(arr) < 8:\n",
        "        if node.parent == None:\n",
        "            for i in range(count):\n",
        "                arr.append(0)\n",
        "            break\n",
        "        else:\n",
        "            node = node.parent\n",
        "            arr.append(chess.Board(node.fen))\n",
        "            count -= 1\n",
        "    arr.append(move)\n",
        "    return arr"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        },
        "id": "_Q4sy_YskuNo",
        "outputId": "5ff64a38-09fe-4cf3-a56d-bdecc1b56613"
      },
      "outputs": [
        {
          "ename": "NameError",
          "evalue": "name 'tree_2' is not defined",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-5-42c02b5d92c0>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mboards\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstoreBoardsFromTree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtree_2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMCTS2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mboards\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mboards\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcount_nodes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtree_2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mboards\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'tree_2' is not defined"
          ]
        }
      ],
      "source": [
        "boards = storeBoardsFromTree(tree_2, MCTS2())\n",
        "print(len(boards))\n",
        "print(boards[0][8])\n",
        "print(count_nodes(tree_2))\n",
        "boards[1][0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Tx-q6BIUkuNo",
        "outputId": "48dfab90-0c79-4f92-93a7-48ccf3d31f3a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "38637\n",
            "38840\n",
            "f3g5\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "'f1f4'"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print(len(boards))\n",
        "print(count_nodes(tree_2))\n",
        "print(boards[37902][8])\n",
        "boards[500][8]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ACS3yKQNkuNo"
      },
      "source": [
        "**Preparing initial x and y train objects**\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iUBlqs1zkuNo",
        "outputId": "45f9563d-d399-4853-9515-7746c2ba9184"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "38637 38637\n"
          ]
        }
      ],
      "source": [
        "x_train = []\n",
        "y_train = []\n",
        "for x in boards:\n",
        "    x_train.append([x[0],x[1],x[2],x[3],x[4],x[5],x[6],x[7]])\n",
        "    y_train.append(x[8])\n",
        "print(len(x_train),len(y_train))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f4CRinyTkuNp",
        "outputId": "19021a8f-dd64-49e5-c3be-02a6e6278d2b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'str'>\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "[Board('8/1K6/8/8/8/8/8/k3q3 b - - 132 233'),\n",
              " Board('8/8/K7/8/8/8/8/k3q3 w - - 131 233'),\n",
              " Board('8/4q3/K7/8/8/8/8/k7 b - - 130 232'),\n",
              " Board('8/1K2q3/8/8/8/8/8/k7 w - - 129 232'),\n",
              " Board('8/1K6/8/8/8/8/8/k3q3 b - - 128 231'),\n",
              " Board('8/8/K7/8/8/8/8/k3q3 w - - 127 231'),\n",
              " Board('8/4q3/K7/8/8/8/8/k7 b - - 126 230'),\n",
              " Board('8/1K2q3/8/8/8/8/8/k7 w - - 125 230')]"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print(type(y_train[919]))\n",
        "x_train[919]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NWu84SygkuNp"
      },
      "source": [
        "**Handling inputs**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pSSu6ZlwB3St"
      },
      "outputs": [],
      "source": [
        "import chess\n",
        "\n",
        "def create_empty_board_array():\n",
        "    return [[0] * 8 for _ in range(8)]\n",
        "\n",
        "def create_piece_arrays(board):\n",
        "    piece_symbols = [\"P\", \"N\", \"B\", \"R\", \"Q\", \"K\"]\n",
        "    white_pieces = {symbol: create_empty_board_array() for symbol in piece_symbols}\n",
        "    black_pieces = {symbol.lower(): create_empty_board_array() for symbol in piece_symbols}\n",
        "\n",
        "    for square in chess.SQUARES:\n",
        "        piece = board.piece_at(square)\n",
        "        if piece is not None:\n",
        "            piece_name = piece.symbol()\n",
        "            file_idx = square % 8\n",
        "            rank_idx = 7 - square // 8  # Invert rank to match the board orientation\n",
        "            if piece.color == chess.WHITE:\n",
        "                white_pieces[piece_name][rank_idx][file_idx] = 1\n",
        "            else:\n",
        "                black_pieces[piece_name.lower()][rank_idx][file_idx] = 1\n",
        "\n",
        "    return white_pieces, black_pieces\n",
        "\n",
        "def append_piece_arrays(pieces,input_array):\n",
        "    for piece_name, array in pieces.items():\n",
        "        input_array.append(array)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CyFFDsd1kuNp"
      },
      "outputs": [],
      "source": [
        "import chess\n",
        "\n",
        "def create_castling_arrays(board):\n",
        "    white_kingside_castle = [[int(board.has_kingside_castling_rights(chess.WHITE))]*8] * 8\n",
        "    white_queenside_castle = [[int(board.has_queenside_castling_rights(chess.WHITE))]*8] * 8\n",
        "    black_kingside_castle = [[int(board.has_kingside_castling_rights(chess.BLACK))]*8] * 8\n",
        "    black_queenside_castle = [[int(board.has_queenside_castling_rights(chess.BLACK))]*8] * 8\n",
        "\n",
        "    return white_kingside_castle, white_queenside_castle, black_kingside_castle, black_queenside_castle\n",
        "\n",
        "def append_castling_arrays(arrays,input_array):\n",
        "    names = [\"White Kingside\", \"White Queenside\", \"Black Kingside\", \"Black Queenside\"]\n",
        "    for name, array in zip(names, arrays):\n",
        "        input_array.append(array)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cHMU_7GckuNq"
      },
      "outputs": [],
      "source": [
        "import chess\n",
        "\n",
        "def create_turn_array(board):\n",
        "    turn_array = [[int(board.turn)] * 8 for _ in range(8)]\n",
        "    return turn_array\n",
        "\n",
        "def append_turn_array(array,input_array):\n",
        "    input_array.append(array)\n",
        "    # print(\"Turn Array:\")\n",
        "    # for row in array:\n",
        "    #     print(row)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_Ckg_6-ZkuNq"
      },
      "outputs": [],
      "source": [
        "def get_inputs_by_board(board):\n",
        "    if board == 0:\n",
        "        empty_array = [list(list(0 for i in range(8)) for j in range(8))] * 17\n",
        "        return empty_array\n",
        "\n",
        "    input_array=[]\n",
        "    # Get the piece arrays for White and Black players\n",
        "    white_pieces, black_pieces = create_piece_arrays(board)\n",
        "\n",
        "    # Print the piece arrays\n",
        "    append_piece_arrays(white_pieces,input_array)\n",
        "    append_piece_arrays(black_pieces,input_array)\n",
        "\n",
        "    #Get castling arrays\n",
        "    white_kingside, white_queenside, black_kingside, black_queenside = create_castling_arrays(board)\n",
        "\n",
        "    # Print the castling arrays\n",
        "    append_castling_arrays([white_kingside, white_queenside, black_kingside, black_queenside],input_array)\n",
        "\n",
        "    # Get the turn array\n",
        "    turn_array = create_turn_array(board)\n",
        "\n",
        "    # Print the turn array\n",
        "    append_turn_array(turn_array,input_array)\n",
        "\n",
        "    # input_array.append(encode_move_counters(board))\n",
        "\n",
        "    return input_array\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S6mt0WsOkuNq"
      },
      "source": [
        "**Outputs handling**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JDLANl5xkuNq"
      },
      "outputs": [],
      "source": [
        "import chess\n",
        "\n",
        "\n",
        "def get_queen_knight_moves(output_list):\n",
        "    # Create a board\n",
        "    board = chess.Board()\n",
        "\n",
        "    # Dictionary to store all possible moves for each piece\n",
        "    all_possible_moves = {}\n",
        "\n",
        "    # Iterate through each square of the board\n",
        "    for square in chess.SQUARES:\n",
        "        # Iterate through each piece type\n",
        "        for piece_type in [chess.KNIGHT, chess.QUEEN]:\n",
        "            # Create a new board with the current piece at the current square\n",
        "            board.clear()\n",
        "\n",
        "            board.set_piece_at(square, chess.Piece(piece_type, True))  # Assuming it's a white piece for simplicity\n",
        "\n",
        "            # Get all possible moves for the piece on the current square\n",
        "            possible_moves = board.attacks(square)\n",
        "            legal_moves_list = board.legal_moves\n",
        "            for move in legal_moves_list:\n",
        "                output_list.append(move.__str__())\n",
        "\n",
        "            # Store the possible moves in the dictionary\n",
        "            all_possible_moves[(piece_type, square)] = possible_moves\n",
        "\n",
        "    return output_list\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mQvy5b7pkuNr"
      },
      "outputs": [],
      "source": [
        "import chess\n",
        "\n",
        "# Function to find all promotion moves including promotions to other pieces\n",
        "def find_promotion_moves(board, color):\n",
        "    promotion_moves = []\n",
        "    legal_moves_list = board.legal_moves\n",
        "    for move in legal_moves_list:\n",
        "        if move.promotion is not None and board.turn == color:\n",
        "            promotion_moves.append(move)\n",
        "\n",
        "    return promotion_moves\n",
        "\n",
        "def get_pawn_moves(output_list):\n",
        "    # Create a chess board\n",
        "    board = chess.Board(fen='8/8/8/8/8/8/8/8 w - - 0 1')\n",
        "\n",
        "    # Place white pawns on every square of the 7th rank\n",
        "    for file_idx in range(8):\n",
        "        board.set_piece_at(chess.square(file_idx, 6), chess.Piece(chess.PAWN, chess.WHITE))\n",
        "\n",
        "    # Place black pawns on every square of the 2nd rank\n",
        "    for file_idx in range(8):\n",
        "        board.set_piece_at(chess.square(file_idx, 1), chess.Piece(chess.PAWN, chess.BLACK))\n",
        "\n",
        "    # Get all possible promotion moves for white pawns\n",
        "    white_promotion_moves = find_promotion_moves(board, chess.WHITE)\n",
        "    for move in white_promotion_moves:\n",
        "        output_list.append(move.__str__())\n",
        "\n",
        "    # Switch turn to black\n",
        "    board.turn = chess.BLACK\n",
        "\n",
        "    # Get all possible promotion moves for black pawns\n",
        "    black_promotion_moves = find_promotion_moves(board, chess.BLACK)\n",
        "    # output_list.append(move.__str__() for move in black_promotion_moves)\n",
        "    for move in black_promotion_moves:\n",
        "        output_list.append(move.__str__())\n",
        "\n",
        "    # Place white pawns on every square of the 7th rank\n",
        "    for file_idx in range(8):\n",
        "        board.set_piece_at(chess.square(file_idx, 7), chess.Piece(chess.KNIGHT, chess.BLACK))\n",
        "\n",
        "    # Place black pawns on every square of the 2nd rank\n",
        "    for file_idx in range(8):\n",
        "        board.set_piece_at(chess.square(file_idx, 0), chess.Piece(chess.KNIGHT, chess.WHITE))\n",
        "\n",
        "    board.turn = chess.WHITE\n",
        "\n",
        "    # Get all possible promotion moves for white pawns\n",
        "    white_promotion_moves = find_promotion_moves(board, chess.WHITE)\n",
        "    # output_list.append(move.__str__() for move in white_promotion_moves)\n",
        "    for move in white_promotion_moves:\n",
        "        output_list.append(move.__str__())\n",
        "\n",
        "    board.turn = chess.BLACK\n",
        "\n",
        "    # Get all possible promotion moves for black pawns\n",
        "    black_promotion_moves = find_promotion_moves(board, chess.BLACK)\n",
        "    for move in black_promotion_moves:\n",
        "        output_list.append(move.__str__())\n",
        "\n",
        "    return output_list\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_-E9ESltkuNr"
      },
      "outputs": [],
      "source": [
        "import chess\n",
        "\n",
        "def get_outputs():\n",
        "   output_list = []\n",
        "\n",
        "   output_list.append(move for move in get_queen_knight_moves(output_list))\n",
        "\n",
        "   get_pawn_moves(output_list)\n",
        "\n",
        "   return output_list\n",
        "\n",
        "output_list = get_outputs()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ng1rIjlCkuNr"
      },
      "source": [
        "**Making functions to convert initial x and y train to be model compatible**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "69ZdCI0EkuNr"
      },
      "outputs": [],
      "source": [
        "def make_final_y_train(initial_y_train):\n",
        "  final_y_train = []\n",
        "  output_list = get_outputs()\n",
        "\n",
        "  for move in initial_y_train:\n",
        "    try:\n",
        "      index = output_list.index(move)\n",
        "    except:\n",
        "      print(\"Error in training\")\n",
        "    final_y_train.append(index)\n",
        "\n",
        "  return final_y_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b51l2dWHkuNs"
      },
      "outputs": [],
      "source": [
        "def make_final_x_train(initial_x_train):\n",
        "  finalest_x_train = []\n",
        "  for board_array in initial_x_train:\n",
        "    final_x_train = []\n",
        "    for board in board_array:\n",
        "        final_x_train.append(get_inputs_by_board(board))\n",
        "    finalest_x_train.append(final_x_train)\n",
        "\n",
        "  return finalest_x_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_MvxZjFkkuNs"
      },
      "outputs": [],
      "source": [
        "final_x_train = make_final_x_train(x_train)\n",
        "final_y_train = make_final_y_train(y_train)\n",
        "import numpy as np  # Assuming it's a NumPy array\n",
        "\n",
        "# Assuming final_x_train is your array\n",
        "print(\"Shape of final_x_train:\", len(final_x_train))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SqH80iCFkuNs"
      },
      "source": [
        "**Storing x_train and y_train in json files**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mkOYOZ_skuNs",
        "outputId": "7c8e0c21-7cdf-4229-b6e5-3a6fa43fff2e"
      },
      "outputs": [
        {
          "ename": "TypeError",
          "evalue": "Object of type Board is not JSON serializable",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "Cell \u001b[0;32mIn[21], line 16\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[38;5;66;03m# Read the JSON file and load its content into a list\u001b[39;00m\n\u001b[1;32m     15\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx_train.json\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[0;32m---> 16\u001b[0m     \u001b[43mjson\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdump\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfinal_x_train_list\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[0;32m/usr/lib/python3.10/json/__init__.py:179\u001b[0m, in \u001b[0;36mdump\u001b[0;34m(obj, fp, skipkeys, ensure_ascii, check_circular, allow_nan, cls, indent, separators, default, sort_keys, **kw)\u001b[0m\n\u001b[1;32m    173\u001b[0m     iterable \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m(skipkeys\u001b[38;5;241m=\u001b[39mskipkeys, ensure_ascii\u001b[38;5;241m=\u001b[39mensure_ascii,\n\u001b[1;32m    174\u001b[0m         check_circular\u001b[38;5;241m=\u001b[39mcheck_circular, allow_nan\u001b[38;5;241m=\u001b[39mallow_nan, indent\u001b[38;5;241m=\u001b[39mindent,\n\u001b[1;32m    175\u001b[0m         separators\u001b[38;5;241m=\u001b[39mseparators,\n\u001b[1;32m    176\u001b[0m         default\u001b[38;5;241m=\u001b[39mdefault, sort_keys\u001b[38;5;241m=\u001b[39msort_keys, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw)\u001b[38;5;241m.\u001b[39miterencode(obj)\n\u001b[1;32m    177\u001b[0m \u001b[38;5;66;03m# could accelerate with writelines in some versions of Python, at\u001b[39;00m\n\u001b[1;32m    178\u001b[0m \u001b[38;5;66;03m# a debuggability cost\u001b[39;00m\n\u001b[0;32m--> 179\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m chunk \u001b[38;5;129;01min\u001b[39;00m iterable:\n\u001b[1;32m    180\u001b[0m     fp\u001b[38;5;241m.\u001b[39mwrite(chunk)\n",
            "File \u001b[0;32m/usr/lib/python3.10/json/encoder.py:429\u001b[0m, in \u001b[0;36m_make_iterencode.<locals>._iterencode\u001b[0;34m(o, _current_indent_level)\u001b[0m\n\u001b[1;32m    427\u001b[0m     \u001b[38;5;28;01myield\u001b[39;00m _floatstr(o)\n\u001b[1;32m    428\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(o, (\u001b[38;5;28mlist\u001b[39m, \u001b[38;5;28mtuple\u001b[39m)):\n\u001b[0;32m--> 429\u001b[0m     \u001b[38;5;28;01myield from\u001b[39;00m _iterencode_list(o, _current_indent_level)\n\u001b[1;32m    430\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(o, \u001b[38;5;28mdict\u001b[39m):\n\u001b[1;32m    431\u001b[0m     \u001b[38;5;28;01myield from\u001b[39;00m _iterencode_dict(o, _current_indent_level)\n",
            "File \u001b[0;32m/usr/lib/python3.10/json/encoder.py:325\u001b[0m, in \u001b[0;36m_make_iterencode.<locals>._iterencode_list\u001b[0;34m(lst, _current_indent_level)\u001b[0m\n\u001b[1;32m    323\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    324\u001b[0m             chunks \u001b[38;5;241m=\u001b[39m _iterencode(value, _current_indent_level)\n\u001b[0;32m--> 325\u001b[0m         \u001b[38;5;28;01myield from\u001b[39;00m chunks\n\u001b[1;32m    326\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m newline_indent \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[1;32m    327\u001b[0m     _current_indent_level \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n",
            "File \u001b[0;32m/usr/lib/python3.10/json/encoder.py:325\u001b[0m, in \u001b[0;36m_make_iterencode.<locals>._iterencode_list\u001b[0;34m(lst, _current_indent_level)\u001b[0m\n\u001b[1;32m    323\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    324\u001b[0m             chunks \u001b[38;5;241m=\u001b[39m _iterencode(value, _current_indent_level)\n\u001b[0;32m--> 325\u001b[0m         \u001b[38;5;28;01myield from\u001b[39;00m chunks\n\u001b[1;32m    326\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m newline_indent \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[1;32m    327\u001b[0m     _current_indent_level \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n",
            "File \u001b[0;32m/usr/lib/python3.10/json/encoder.py:438\u001b[0m, in \u001b[0;36m_make_iterencode.<locals>._iterencode\u001b[0;34m(o, _current_indent_level)\u001b[0m\n\u001b[1;32m    436\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCircular reference detected\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    437\u001b[0m     markers[markerid] \u001b[38;5;241m=\u001b[39m o\n\u001b[0;32m--> 438\u001b[0m o \u001b[38;5;241m=\u001b[39m \u001b[43m_default\u001b[49m\u001b[43m(\u001b[49m\u001b[43mo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    439\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m _iterencode(o, _current_indent_level)\n\u001b[1;32m    440\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m markers \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",
            "File \u001b[0;32m/usr/lib/python3.10/json/encoder.py:179\u001b[0m, in \u001b[0;36mJSONEncoder.default\u001b[0;34m(self, o)\u001b[0m\n\u001b[1;32m    160\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdefault\u001b[39m(\u001b[38;5;28mself\u001b[39m, o):\n\u001b[1;32m    161\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Implement this method in a subclass such that it returns\u001b[39;00m\n\u001b[1;32m    162\u001b[0m \u001b[38;5;124;03m    a serializable object for ``o``, or calls the base implementation\u001b[39;00m\n\u001b[1;32m    163\u001b[0m \u001b[38;5;124;03m    (to raise a ``TypeError``).\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    177\u001b[0m \n\u001b[1;32m    178\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 179\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mObject of type \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mo\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    180\u001b[0m                     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mis not JSON serializable\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
            "\u001b[0;31mTypeError\u001b[0m: Object of type Board is not JSON serializable"
          ]
        }
      ],
      "source": [
        "import json\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "# Convert the NumPy array to a Python list\n",
        "final_x_train_list = final_x_train\n",
        "\n",
        "final_y_train_list = final_y_train\n",
        "\n",
        "# Write the list to a JSON file\n",
        "with open('y_train.json', 'w') as f:\n",
        "    json.dump(final_y_train_list, f)\n",
        "\n",
        "# Read the JSON file and load its content into a list\n",
        "with open('x_train.json', 'w') as f:\n",
        "    json.dump(final_x_train_list, f)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-q0XMU9qkuNt"
      },
      "source": [
        "**Loading x_train and y_train from json files**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "1Exos35fkuNt",
        "outputId": "429d129f-398b-4112-edb2-1d3694278082"
      },
      "outputs": [
        {
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-15-eb331b4d8dcf>\u001b[0m in \u001b[0;36m<cell line: 13>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;31m# Read the JSON file and load its content into a list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive/MyDrive/final_x_train.json'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m     \u001b[0mloaded_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;31m# Convert the loaded list back to a NumPy array if needed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/json/__init__.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(fp, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[0m\n\u001b[1;32m    291\u001b[0m     \u001b[0mkwarg\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0motherwise\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mJSONDecoder\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mused\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    292\u001b[0m     \"\"\"\n\u001b[0;32m--> 293\u001b[0;31m     return loads(fp.read(),\n\u001b[0m\u001b[1;32m    294\u001b[0m         \u001b[0mcls\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobject_hook\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobject_hook\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    295\u001b[0m         \u001b[0mparse_float\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparse_float\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparse_int\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparse_int\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/codecs.py\u001b[0m in \u001b[0;36mdecode\u001b[0;34m(self, input, final)\u001b[0m\n\u001b[1;32m    317\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    318\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 319\u001b[0;31m     \u001b[0;32mdef\u001b[0m \u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    320\u001b[0m         \u001b[0;31m# decode input (taking the buffer into account)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    321\u001b[0m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuffer\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "import json\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "# # Convert the NumPy array to a Python list\n",
        "\n",
        "with open('/content/drive/MyDrive/final_x_train.json', 'r') as f:\n",
        "    loaded_list = json.load(f)\n",
        "\n",
        "# Convert the loaded list back to a NumPy array if needed\n",
        "final_x_train = np.array(loaded_list)\n",
        "\n",
        "with open('/content/drive/MyDrive/final_y_train.json', 'r') as f:\n",
        "    loaded_list = json.load(f)\n",
        "\n",
        "# Convert the loaded list back to a NumPy array if needed\n",
        "final_y_train = np.array(loaded_list)\n",
        "final_y_train.shape\n",
        "# # Check if the original array and the loaded array are equal\n",
        "# print(np.array_equal(final_x_train, loaded_array))  # Output: True\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9mCQwGfakuNt",
        "outputId": "58a11b01-d469-4996-db01-288b79ea6ef0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1802\n",
            "1806\n",
            "1808\n",
            "1809\n",
            "1811\n",
            "1812\n",
            "1813\n",
            "1816\n",
            "1817\n",
            "1820\n",
            "1825\n",
            "1829\n",
            "1830\n",
            "1834\n",
            "1835\n",
            "1837\n",
            "1838\n",
            "1840\n",
            "1925\n",
            "1813\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "unique_numbers, counts = np.unique(final_y_train, return_counts=True)\n",
        "count_final=0\n",
        "for num, count in zip(unique_numbers, counts):\n",
        "        if num>1800:\n",
        "           print(num)\n",
        "        count_final+=1\n",
        "print(count_final)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QjSEjb8V-qhu",
        "outputId": "6aaa8497-e51c-40e8-8734-c554b8854619"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "y4pgVRHzkuNt"
      },
      "outputs": [],
      "source": [
        "final_x_train = np.array(final_x_train)\n",
        "final_y_train = np.array(final_y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HNaojPSLkuNt",
        "outputId": "1e9cba37-a33e-4181-9a67-4969e2b606a6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(38637, 8, 17, 8, 8)"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "final_x_train.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4RpOhOO4kuNt"
      },
      "source": [
        "**Training model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cIbp25fgkuNu"
      },
      "outputs": [],
      "source": [
        "import tensorflow\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dense,Flatten\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Flatten(input_shape=(8,17,8,8)))\n",
        "# model.add(Dense(128,activation='relu'))\n",
        "model.add(Dense(32,activation='relu'))\n",
        "model.add(Dense(1969,activation='softmax'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EsCjk2SikuNu"
      },
      "outputs": [],
      "source": [
        "model.compile(loss='sparse_categorical_crossentropy',optimizer='Adam',metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jlSxAtRzkuNu",
        "outputId": "35ac48dc-cf4e-46f9-e4cf-7ac97fbbf8de"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/200\n",
            "966/966 [==============================] - 12s 11ms/step - loss: 6.8854 - accuracy: 0.0115 - val_loss: 6.6779 - val_accuracy: 0.0098\n",
            "Epoch 2/200\n",
            "966/966 [==============================] - 12s 13ms/step - loss: 6.3967 - accuracy: 0.0221 - val_loss: 6.5709 - val_accuracy: 0.0182\n",
            "Epoch 3/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 6.1288 - accuracy: 0.0328 - val_loss: 6.5250 - val_accuracy: 0.0304\n",
            "Epoch 4/200\n",
            "966/966 [==============================] - 10s 10ms/step - loss: 5.8686 - accuracy: 0.0474 - val_loss: 6.4496 - val_accuracy: 0.0348\n",
            "Epoch 5/200\n",
            "966/966 [==============================] - 15s 16ms/step - loss: 5.6300 - accuracy: 0.0601 - val_loss: 6.4809 - val_accuracy: 0.0391\n",
            "Epoch 6/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 5.4214 - accuracy: 0.0753 - val_loss: 6.5483 - val_accuracy: 0.0441\n",
            "Epoch 7/200\n",
            "966/966 [==============================] - 11s 12ms/step - loss: 5.2293 - accuracy: 0.0922 - val_loss: 6.6123 - val_accuracy: 0.0534\n",
            "Epoch 8/200\n",
            "966/966 [==============================] - 11s 12ms/step - loss: 5.0614 - accuracy: 0.1075 - val_loss: 6.6994 - val_accuracy: 0.0577\n",
            "Epoch 9/200\n",
            "966/966 [==============================] - 9s 10ms/step - loss: 4.9103 - accuracy: 0.1214 - val_loss: 6.7887 - val_accuracy: 0.0659\n",
            "Epoch 10/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 4.7759 - accuracy: 0.1357 - val_loss: 6.9332 - val_accuracy: 0.0656\n",
            "Epoch 11/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 4.6611 - accuracy: 0.1466 - val_loss: 7.0622 - val_accuracy: 0.0719\n",
            "Epoch 12/200\n",
            "966/966 [==============================] - 9s 9ms/step - loss: 4.5551 - accuracy: 0.1559 - val_loss: 7.1869 - val_accuracy: 0.0774\n",
            "Epoch 13/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 4.4568 - accuracy: 0.1673 - val_loss: 7.3928 - val_accuracy: 0.0771\n",
            "Epoch 14/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 4.3687 - accuracy: 0.1775 - val_loss: 7.5356 - val_accuracy: 0.0792\n",
            "Epoch 15/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 4.2899 - accuracy: 0.1882 - val_loss: 7.7000 - val_accuracy: 0.0839\n",
            "Epoch 16/200\n",
            "966/966 [==============================] - 9s 10ms/step - loss: 4.2123 - accuracy: 0.1977 - val_loss: 7.9222 - val_accuracy: 0.0824\n",
            "Epoch 17/200\n",
            "966/966 [==============================] - 10s 10ms/step - loss: 4.1459 - accuracy: 0.2055 - val_loss: 7.9483 - val_accuracy: 0.0899\n",
            "Epoch 18/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 4.0758 - accuracy: 0.2147 - val_loss: 8.2427 - val_accuracy: 0.0885\n",
            "Epoch 19/200\n",
            "966/966 [==============================] - 9s 9ms/step - loss: 4.0146 - accuracy: 0.2196 - val_loss: 8.3672 - val_accuracy: 0.0916\n",
            "Epoch 20/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 3.9596 - accuracy: 0.2276 - val_loss: 8.4871 - val_accuracy: 0.0925\n",
            "Epoch 21/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 3.9004 - accuracy: 0.2363 - val_loss: 8.6766 - val_accuracy: 0.0947\n",
            "Epoch 22/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 3.8531 - accuracy: 0.2417 - val_loss: 8.7503 - val_accuracy: 0.0998\n",
            "Epoch 23/200\n",
            "966/966 [==============================] - 9s 10ms/step - loss: 3.7990 - accuracy: 0.2493 - val_loss: 8.9956 - val_accuracy: 0.0998\n",
            "Epoch 24/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 3.7547 - accuracy: 0.2566 - val_loss: 9.0757 - val_accuracy: 0.1052\n",
            "Epoch 25/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 3.7110 - accuracy: 0.2612 - val_loss: 9.2437 - val_accuracy: 0.1026\n",
            "Epoch 26/200\n",
            "966/966 [==============================] - 12s 12ms/step - loss: 3.6675 - accuracy: 0.2683 - val_loss: 9.4009 - val_accuracy: 0.1049\n",
            "Epoch 27/200\n",
            "966/966 [==============================] - 9s 10ms/step - loss: 3.6254 - accuracy: 0.2739 - val_loss: 9.7085 - val_accuracy: 0.1113\n",
            "Epoch 28/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 3.5878 - accuracy: 0.2765 - val_loss: 9.7060 - val_accuracy: 0.1082\n",
            "Epoch 29/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 3.5524 - accuracy: 0.2866 - val_loss: 9.9276 - val_accuracy: 0.1077\n",
            "Epoch 30/200\n",
            "966/966 [==============================] - 9s 10ms/step - loss: 3.5152 - accuracy: 0.2882 - val_loss: 10.0534 - val_accuracy: 0.1140\n",
            "Epoch 31/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 3.4787 - accuracy: 0.2949 - val_loss: 10.1278 - val_accuracy: 0.1100\n",
            "Epoch 32/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 3.4468 - accuracy: 0.2996 - val_loss: 10.2337 - val_accuracy: 0.1124\n",
            "Epoch 33/200\n",
            "966/966 [==============================] - 11s 12ms/step - loss: 3.4212 - accuracy: 0.3045 - val_loss: 10.2997 - val_accuracy: 0.1179\n",
            "Epoch 34/200\n",
            "966/966 [==============================] - 9s 9ms/step - loss: 3.3860 - accuracy: 0.3109 - val_loss: 10.5726 - val_accuracy: 0.1148\n",
            "Epoch 35/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 3.3566 - accuracy: 0.3108 - val_loss: 10.5764 - val_accuracy: 0.1183\n",
            "Epoch 36/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 3.3305 - accuracy: 0.3156 - val_loss: 10.8121 - val_accuracy: 0.1157\n",
            "Epoch 37/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 3.3022 - accuracy: 0.3224 - val_loss: 11.2623 - val_accuracy: 0.1146\n",
            "Epoch 38/200\n",
            "966/966 [==============================] - 9s 10ms/step - loss: 3.2673 - accuracy: 0.3260 - val_loss: 11.1686 - val_accuracy: 0.1215\n",
            "Epoch 39/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 3.2493 - accuracy: 0.3306 - val_loss: 11.1408 - val_accuracy: 0.1206\n",
            "Epoch 40/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 3.2195 - accuracy: 0.3348 - val_loss: 11.3428 - val_accuracy: 0.1194\n",
            "Epoch 41/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 3.1948 - accuracy: 0.3360 - val_loss: 11.2467 - val_accuracy: 0.1224\n",
            "Epoch 42/200\n",
            "966/966 [==============================] - 10s 10ms/step - loss: 3.1703 - accuracy: 0.3431 - val_loss: 11.7673 - val_accuracy: 0.1237\n",
            "Epoch 43/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 3.1455 - accuracy: 0.3458 - val_loss: 11.6864 - val_accuracy: 0.1241\n",
            "Epoch 44/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 3.1250 - accuracy: 0.3515 - val_loss: 11.8765 - val_accuracy: 0.1249\n",
            "Epoch 45/200\n",
            "966/966 [==============================] - 9s 10ms/step - loss: 3.1047 - accuracy: 0.3533 - val_loss: 12.1317 - val_accuracy: 0.1322\n",
            "Epoch 46/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 3.0885 - accuracy: 0.3544 - val_loss: 12.2665 - val_accuracy: 0.1282\n",
            "Epoch 47/200\n",
            "966/966 [==============================] - 12s 13ms/step - loss: 3.0634 - accuracy: 0.3575 - val_loss: 12.2522 - val_accuracy: 0.1259\n",
            "Epoch 48/200\n",
            "966/966 [==============================] - 10s 10ms/step - loss: 3.0406 - accuracy: 0.3654 - val_loss: 12.3312 - val_accuracy: 0.1258\n",
            "Epoch 49/200\n",
            "966/966 [==============================] - 10s 10ms/step - loss: 3.0233 - accuracy: 0.3657 - val_loss: 12.6478 - val_accuracy: 0.1303\n",
            "Epoch 50/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 3.0012 - accuracy: 0.3741 - val_loss: 12.6909 - val_accuracy: 0.1298\n",
            "Epoch 51/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 2.9851 - accuracy: 0.3764 - val_loss: 12.8846 - val_accuracy: 0.1290\n",
            "Epoch 52/200\n",
            "966/966 [==============================] - 9s 9ms/step - loss: 2.9653 - accuracy: 0.3768 - val_loss: 12.9382 - val_accuracy: 0.1359\n",
            "Epoch 53/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 2.9478 - accuracy: 0.3804 - val_loss: 13.0579 - val_accuracy: 0.1307\n",
            "Epoch 54/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 2.9312 - accuracy: 0.3821 - val_loss: 13.3103 - val_accuracy: 0.1350\n",
            "Epoch 55/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 2.9221 - accuracy: 0.3837 - val_loss: 13.2891 - val_accuracy: 0.1378\n",
            "Epoch 56/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 2.8985 - accuracy: 0.3891 - val_loss: 13.4477 - val_accuracy: 0.1364\n",
            "Epoch 57/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 2.8801 - accuracy: 0.3911 - val_loss: 13.6018 - val_accuracy: 0.1360\n",
            "Epoch 58/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 2.8658 - accuracy: 0.3909 - val_loss: 14.1484 - val_accuracy: 0.1351\n",
            "Epoch 59/200\n",
            "966/966 [==============================] - 9s 9ms/step - loss: 2.8474 - accuracy: 0.3964 - val_loss: 13.8140 - val_accuracy: 0.1427\n",
            "Epoch 60/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 2.8388 - accuracy: 0.3988 - val_loss: 14.1188 - val_accuracy: 0.1403\n",
            "Epoch 61/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 2.8190 - accuracy: 0.4012 - val_loss: 13.9549 - val_accuracy: 0.1452\n",
            "Epoch 62/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 2.8080 - accuracy: 0.4050 - val_loss: 14.3122 - val_accuracy: 0.1421\n",
            "Epoch 63/200\n",
            "966/966 [==============================] - 10s 10ms/step - loss: 2.7907 - accuracy: 0.4078 - val_loss: 14.3047 - val_accuracy: 0.1445\n",
            "Epoch 64/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 2.7700 - accuracy: 0.4109 - val_loss: 14.6313 - val_accuracy: 0.1405\n",
            "Epoch 65/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 2.7686 - accuracy: 0.4126 - val_loss: 14.7101 - val_accuracy: 0.1449\n",
            "Epoch 66/200\n",
            "966/966 [==============================] - 9s 9ms/step - loss: 2.7438 - accuracy: 0.4156 - val_loss: 15.0653 - val_accuracy: 0.1462\n",
            "Epoch 67/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 2.7353 - accuracy: 0.4146 - val_loss: 15.0648 - val_accuracy: 0.1448\n",
            "Epoch 68/200\n",
            "966/966 [==============================] - 12s 12ms/step - loss: 2.7194 - accuracy: 0.4211 - val_loss: 15.1976 - val_accuracy: 0.1435\n",
            "Epoch 69/200\n",
            "966/966 [==============================] - 10s 10ms/step - loss: 2.7051 - accuracy: 0.4244 - val_loss: 15.2957 - val_accuracy: 0.1457\n",
            "Epoch 70/200\n",
            "966/966 [==============================] - 9s 10ms/step - loss: 2.6921 - accuracy: 0.4256 - val_loss: 15.4281 - val_accuracy: 0.1408\n",
            "Epoch 71/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 2.6866 - accuracy: 0.4254 - val_loss: 15.3734 - val_accuracy: 0.1471\n",
            "Epoch 72/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 2.6691 - accuracy: 0.4279 - val_loss: 15.7469 - val_accuracy: 0.1484\n",
            "Epoch 73/200\n",
            "966/966 [==============================] - 9s 9ms/step - loss: 2.6569 - accuracy: 0.4329 - val_loss: 15.6570 - val_accuracy: 0.1467\n",
            "Epoch 74/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 2.6501 - accuracy: 0.4308 - val_loss: 15.8048 - val_accuracy: 0.1498\n",
            "Epoch 75/200\n",
            "966/966 [==============================] - 10s 10ms/step - loss: 2.6349 - accuracy: 0.4350 - val_loss: 16.0616 - val_accuracy: 0.1486\n",
            "Epoch 76/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 2.6253 - accuracy: 0.4377 - val_loss: 16.1259 - val_accuracy: 0.1443\n",
            "Epoch 77/200\n",
            "966/966 [==============================] - 10s 10ms/step - loss: 2.6067 - accuracy: 0.4419 - val_loss: 16.2435 - val_accuracy: 0.1462\n",
            "Epoch 78/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 2.6020 - accuracy: 0.4424 - val_loss: 16.3324 - val_accuracy: 0.1474\n",
            "Epoch 79/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 2.6174 - accuracy: 0.4407 - val_loss: 16.5366 - val_accuracy: 0.1443\n",
            "Epoch 80/200\n",
            "966/966 [==============================] - 9s 9ms/step - loss: 2.5809 - accuracy: 0.4453 - val_loss: 16.4351 - val_accuracy: 0.1501\n",
            "Epoch 81/200\n",
            "966/966 [==============================] - 12s 13ms/step - loss: 2.5754 - accuracy: 0.4474 - val_loss: 16.6201 - val_accuracy: 0.1526\n",
            "Epoch 82/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 2.5579 - accuracy: 0.4489 - val_loss: 16.9021 - val_accuracy: 0.1506\n",
            "Epoch 83/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 2.5547 - accuracy: 0.4512 - val_loss: 17.3009 - val_accuracy: 0.1497\n",
            "Epoch 84/200\n",
            "966/966 [==============================] - 9s 9ms/step - loss: 2.5317 - accuracy: 0.4548 - val_loss: 17.2845 - val_accuracy: 0.1440\n",
            "Epoch 85/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 2.5309 - accuracy: 0.4539 - val_loss: 17.5462 - val_accuracy: 0.1537\n",
            "Epoch 86/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 2.5257 - accuracy: 0.4563 - val_loss: 17.3726 - val_accuracy: 0.1492\n",
            "Epoch 87/200\n",
            "966/966 [==============================] - 9s 9ms/step - loss: 2.5124 - accuracy: 0.4616 - val_loss: 17.6318 - val_accuracy: 0.1555\n",
            "Epoch 88/200\n",
            "966/966 [==============================] - 12s 12ms/step - loss: 2.4904 - accuracy: 0.4614 - val_loss: 17.9182 - val_accuracy: 0.1523\n",
            "Epoch 89/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 2.4893 - accuracy: 0.4661 - val_loss: 17.7926 - val_accuracy: 0.1537\n",
            "Epoch 90/200\n",
            "966/966 [==============================] - 11s 12ms/step - loss: 2.4824 - accuracy: 0.4643 - val_loss: 17.6509 - val_accuracy: 0.1540\n",
            "Epoch 91/200\n",
            "966/966 [==============================] - 9s 9ms/step - loss: 2.4758 - accuracy: 0.4681 - val_loss: 18.1870 - val_accuracy: 0.1497\n",
            "Epoch 92/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 2.4639 - accuracy: 0.4699 - val_loss: 18.0849 - val_accuracy: 0.1567\n",
            "Epoch 93/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 2.4422 - accuracy: 0.4745 - val_loss: 18.3671 - val_accuracy: 0.1548\n",
            "Epoch 94/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 2.4416 - accuracy: 0.4730 - val_loss: 18.3245 - val_accuracy: 0.1518\n",
            "Epoch 95/200\n",
            "966/966 [==============================] - 9s 9ms/step - loss: 2.4424 - accuracy: 0.4730 - val_loss: 18.4257 - val_accuracy: 0.1527\n",
            "Epoch 96/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 2.4337 - accuracy: 0.4758 - val_loss: 18.3260 - val_accuracy: 0.1546\n",
            "Epoch 97/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 2.4210 - accuracy: 0.4761 - val_loss: 18.5903 - val_accuracy: 0.1544\n",
            "Epoch 98/200\n",
            "966/966 [==============================] - 9s 9ms/step - loss: 2.4114 - accuracy: 0.4785 - val_loss: 19.0942 - val_accuracy: 0.1610\n",
            "Epoch 99/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 2.4003 - accuracy: 0.4844 - val_loss: 18.9779 - val_accuracy: 0.1573\n",
            "Epoch 100/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 2.3929 - accuracy: 0.4857 - val_loss: 18.8716 - val_accuracy: 0.1576\n",
            "Epoch 101/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 2.3921 - accuracy: 0.4863 - val_loss: 19.0037 - val_accuracy: 0.1610\n",
            "Epoch 102/200\n",
            "966/966 [==============================] - 9s 10ms/step - loss: 2.3799 - accuracy: 0.4847 - val_loss: 19.6798 - val_accuracy: 0.1567\n",
            "Epoch 103/200\n",
            "966/966 [==============================] - 12s 12ms/step - loss: 2.3624 - accuracy: 0.4883 - val_loss: 19.4717 - val_accuracy: 0.1583\n",
            "Epoch 104/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 2.3639 - accuracy: 0.4907 - val_loss: 19.0339 - val_accuracy: 0.1552\n",
            "Epoch 105/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 2.3568 - accuracy: 0.4894 - val_loss: 19.7968 - val_accuracy: 0.1611\n",
            "Epoch 106/200\n",
            "966/966 [==============================] - 9s 10ms/step - loss: 2.3460 - accuracy: 0.4929 - val_loss: 20.0687 - val_accuracy: 0.1593\n",
            "Epoch 107/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 2.3416 - accuracy: 0.4919 - val_loss: 19.7586 - val_accuracy: 0.1615\n",
            "Epoch 108/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 2.3313 - accuracy: 0.4966 - val_loss: 19.9006 - val_accuracy: 0.1586\n",
            "Epoch 109/200\n",
            "966/966 [==============================] - 12s 13ms/step - loss: 2.3182 - accuracy: 0.4979 - val_loss: 20.6517 - val_accuracy: 0.1645\n",
            "Epoch 110/200\n",
            "966/966 [==============================] - 9s 10ms/step - loss: 2.3095 - accuracy: 0.4991 - val_loss: 20.1269 - val_accuracy: 0.1592\n",
            "Epoch 111/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 2.3132 - accuracy: 0.4999 - val_loss: 20.5506 - val_accuracy: 0.1616\n",
            "Epoch 112/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 2.3013 - accuracy: 0.5019 - val_loss: 20.4715 - val_accuracy: 0.1623\n",
            "Epoch 113/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 2.2922 - accuracy: 0.5032 - val_loss: 20.7215 - val_accuracy: 0.1654\n",
            "Epoch 114/200\n",
            "966/966 [==============================] - 9s 10ms/step - loss: 2.2897 - accuracy: 0.5037 - val_loss: 20.7744 - val_accuracy: 0.1576\n",
            "Epoch 115/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 2.2901 - accuracy: 0.5030 - val_loss: 21.2184 - val_accuracy: 0.1593\n",
            "Epoch 116/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 2.2799 - accuracy: 0.5066 - val_loss: 20.7251 - val_accuracy: 0.1634\n",
            "Epoch 117/200\n",
            "966/966 [==============================] - 9s 9ms/step - loss: 2.2710 - accuracy: 0.5078 - val_loss: 21.1554 - val_accuracy: 0.1637\n",
            "Epoch 118/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 2.2884 - accuracy: 0.5056 - val_loss: 20.9080 - val_accuracy: 0.1602\n",
            "Epoch 119/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 2.2727 - accuracy: 0.5075 - val_loss: 21.4397 - val_accuracy: 0.1617\n",
            "Epoch 120/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 2.2519 - accuracy: 0.5123 - val_loss: 21.3543 - val_accuracy: 0.1676\n",
            "Epoch 121/200\n",
            "966/966 [==============================] - 9s 9ms/step - loss: 2.2587 - accuracy: 0.5093 - val_loss: 21.7260 - val_accuracy: 0.1667\n",
            "Epoch 122/200\n",
            "966/966 [==============================] - 11s 12ms/step - loss: 2.2352 - accuracy: 0.5135 - val_loss: 21.7650 - val_accuracy: 0.1646\n",
            "Epoch 123/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 2.2272 - accuracy: 0.5176 - val_loss: 21.9526 - val_accuracy: 0.1643\n",
            "Epoch 124/200\n",
            "966/966 [==============================] - 14s 14ms/step - loss: 2.2267 - accuracy: 0.5162 - val_loss: 22.2124 - val_accuracy: 0.1628\n",
            "Epoch 125/200\n",
            "966/966 [==============================] - 9s 9ms/step - loss: 2.2137 - accuracy: 0.5199 - val_loss: 22.2668 - val_accuracy: 0.1630\n",
            "Epoch 126/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 2.2161 - accuracy: 0.5190 - val_loss: 22.2216 - val_accuracy: 0.1625\n",
            "Epoch 127/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 2.2037 - accuracy: 0.5220 - val_loss: 22.5698 - val_accuracy: 0.1663\n",
            "Epoch 128/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 2.1997 - accuracy: 0.5238 - val_loss: 22.2597 - val_accuracy: 0.1647\n",
            "Epoch 129/200\n",
            "966/966 [==============================] - 11s 12ms/step - loss: 2.1987 - accuracy: 0.5222 - val_loss: 22.7758 - val_accuracy: 0.1685\n",
            "Epoch 130/200\n",
            "966/966 [==============================] - 10s 10ms/step - loss: 2.1895 - accuracy: 0.5253 - val_loss: 23.0687 - val_accuracy: 0.1669\n",
            "Epoch 131/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 2.1883 - accuracy: 0.5248 - val_loss: 23.5800 - val_accuracy: 0.1676\n",
            "Epoch 132/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 2.1823 - accuracy: 0.5280 - val_loss: 23.1394 - val_accuracy: 0.1601\n",
            "Epoch 133/200\n",
            "966/966 [==============================] - 9s 9ms/step - loss: 2.1726 - accuracy: 0.5265 - val_loss: 22.9028 - val_accuracy: 0.1678\n",
            "Epoch 134/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 2.1696 - accuracy: 0.5293 - val_loss: 23.1072 - val_accuracy: 0.1645\n",
            "Epoch 135/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 2.1641 - accuracy: 0.5310 - val_loss: 23.0109 - val_accuracy: 0.1650\n",
            "Epoch 136/200\n",
            "966/966 [==============================] - 10s 10ms/step - loss: 2.1538 - accuracy: 0.5302 - val_loss: 23.6021 - val_accuracy: 0.1669\n",
            "Epoch 137/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 2.1427 - accuracy: 0.5331 - val_loss: 23.3832 - val_accuracy: 0.1700\n",
            "Epoch 138/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 2.1548 - accuracy: 0.5302 - val_loss: 23.6798 - val_accuracy: 0.1655\n",
            "Epoch 139/200\n",
            "966/966 [==============================] - 11s 12ms/step - loss: 2.1419 - accuracy: 0.5337 - val_loss: 24.0859 - val_accuracy: 0.1681\n",
            "Epoch 140/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 2.1418 - accuracy: 0.5354 - val_loss: 23.9145 - val_accuracy: 0.1671\n",
            "Epoch 141/200\n",
            "966/966 [==============================] - 9s 10ms/step - loss: 2.1264 - accuracy: 0.5373 - val_loss: 24.2084 - val_accuracy: 0.1641\n",
            "Epoch 142/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 2.1264 - accuracy: 0.5402 - val_loss: 24.3851 - val_accuracy: 0.1654\n",
            "Epoch 143/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 2.1300 - accuracy: 0.5356 - val_loss: 23.9771 - val_accuracy: 0.1686\n",
            "Epoch 144/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 2.1126 - accuracy: 0.5389 - val_loss: 24.6365 - val_accuracy: 0.1711\n",
            "Epoch 145/200\n",
            "966/966 [==============================] - 10s 10ms/step - loss: 2.1018 - accuracy: 0.5416 - val_loss: 24.4001 - val_accuracy: 0.1704\n",
            "Epoch 146/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 2.1081 - accuracy: 0.5408 - val_loss: 24.6738 - val_accuracy: 0.1712\n",
            "Epoch 147/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 2.1044 - accuracy: 0.5399 - val_loss: 24.5614 - val_accuracy: 0.1707\n",
            "Epoch 148/200\n",
            "966/966 [==============================] - 9s 9ms/step - loss: 2.0906 - accuracy: 0.5442 - val_loss: 24.9732 - val_accuracy: 0.1727\n",
            "Epoch 149/200\n",
            "966/966 [==============================] - 12s 13ms/step - loss: 2.0894 - accuracy: 0.5440 - val_loss: 24.9181 - val_accuracy: 0.1733\n",
            "Epoch 150/200\n",
            "966/966 [==============================] - 12s 12ms/step - loss: 2.0767 - accuracy: 0.5464 - val_loss: 25.2885 - val_accuracy: 0.1700\n",
            "Epoch 151/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 2.0759 - accuracy: 0.5482 - val_loss: 25.1642 - val_accuracy: 0.1682\n",
            "Epoch 152/200\n",
            "966/966 [==============================] - 10s 10ms/step - loss: 2.0847 - accuracy: 0.5480 - val_loss: 25.1109 - val_accuracy: 0.1713\n",
            "Epoch 153/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 2.0759 - accuracy: 0.5463 - val_loss: 25.3341 - val_accuracy: 0.1726\n",
            "Epoch 154/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 2.0655 - accuracy: 0.5490 - val_loss: 25.4521 - val_accuracy: 0.1717\n",
            "Epoch 155/200\n",
            "966/966 [==============================] - 9s 10ms/step - loss: 2.0636 - accuracy: 0.5500 - val_loss: 25.3667 - val_accuracy: 0.1712\n",
            "Epoch 156/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 2.0588 - accuracy: 0.5507 - val_loss: 25.4654 - val_accuracy: 0.1721\n",
            "Epoch 157/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 2.0593 - accuracy: 0.5513 - val_loss: 25.6733 - val_accuracy: 0.1658\n",
            "Epoch 158/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 2.0590 - accuracy: 0.5513 - val_loss: 26.0386 - val_accuracy: 0.1721\n",
            "Epoch 159/200\n",
            "966/966 [==============================] - 10s 10ms/step - loss: 2.0389 - accuracy: 0.5532 - val_loss: 26.1204 - val_accuracy: 0.1729\n",
            "Epoch 160/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 2.0424 - accuracy: 0.5546 - val_loss: 25.7754 - val_accuracy: 0.1724\n",
            "Epoch 161/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 2.0388 - accuracy: 0.5557 - val_loss: 26.1172 - val_accuracy: 0.1672\n",
            "Epoch 162/200\n",
            "966/966 [==============================] - 9s 10ms/step - loss: 2.0434 - accuracy: 0.5561 - val_loss: 26.5106 - val_accuracy: 0.1709\n",
            "Epoch 163/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 2.0363 - accuracy: 0.5548 - val_loss: 26.8548 - val_accuracy: 0.1761\n",
            "Epoch 164/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 2.0287 - accuracy: 0.5588 - val_loss: 27.0141 - val_accuracy: 0.1726\n",
            "Epoch 165/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 2.0132 - accuracy: 0.5604 - val_loss: 26.6894 - val_accuracy: 0.1726\n",
            "Epoch 166/200\n",
            "966/966 [==============================] - 9s 9ms/step - loss: 2.0169 - accuracy: 0.5577 - val_loss: 27.0406 - val_accuracy: 0.1751\n",
            "Epoch 167/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 2.0085 - accuracy: 0.5612 - val_loss: 26.7102 - val_accuracy: 0.1691\n",
            "Epoch 168/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 2.0045 - accuracy: 0.5634 - val_loss: 27.2392 - val_accuracy: 0.1738\n",
            "Epoch 169/200\n",
            "966/966 [==============================] - 12s 12ms/step - loss: 2.0078 - accuracy: 0.5615 - val_loss: 27.2244 - val_accuracy: 0.1765\n",
            "Epoch 170/200\n",
            "966/966 [==============================] - 10s 10ms/step - loss: 1.9947 - accuracy: 0.5616 - val_loss: 27.0916 - val_accuracy: 0.1708\n",
            "Epoch 171/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 1.9824 - accuracy: 0.5664 - val_loss: 27.3932 - val_accuracy: 0.1734\n",
            "Epoch 172/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 1.9884 - accuracy: 0.5667 - val_loss: 27.4581 - val_accuracy: 0.1756\n",
            "Epoch 173/200\n",
            "966/966 [==============================] - 9s 10ms/step - loss: 1.9854 - accuracy: 0.5674 - val_loss: 27.4652 - val_accuracy: 0.1724\n",
            "Epoch 174/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 1.9862 - accuracy: 0.5668 - val_loss: 28.2264 - val_accuracy: 0.1764\n",
            "Epoch 175/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 1.9791 - accuracy: 0.5668 - val_loss: 27.9374 - val_accuracy: 0.1777\n",
            "Epoch 176/200\n",
            "966/966 [==============================] - 11s 12ms/step - loss: 1.9690 - accuracy: 0.5699 - val_loss: 28.0514 - val_accuracy: 0.1760\n",
            "Epoch 177/200\n",
            "966/966 [==============================] - 10s 10ms/step - loss: 1.9741 - accuracy: 0.5691 - val_loss: 28.3087 - val_accuracy: 0.1768\n",
            "Epoch 178/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 1.9666 - accuracy: 0.5689 - val_loss: 28.1469 - val_accuracy: 0.1731\n",
            "Epoch 179/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 1.9629 - accuracy: 0.5702 - val_loss: 28.2464 - val_accuracy: 0.1683\n",
            "Epoch 180/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 1.9585 - accuracy: 0.5733 - val_loss: 27.7226 - val_accuracy: 0.1727\n",
            "Epoch 181/200\n",
            "966/966 [==============================] - 9s 10ms/step - loss: 1.9463 - accuracy: 0.5741 - val_loss: 28.5153 - val_accuracy: 0.1762\n",
            "Epoch 182/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 1.9507 - accuracy: 0.5755 - val_loss: 28.6275 - val_accuracy: 0.1752\n",
            "Epoch 183/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 1.9486 - accuracy: 0.5697 - val_loss: 28.8841 - val_accuracy: 0.1716\n",
            "Epoch 184/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 1.9499 - accuracy: 0.5744 - val_loss: 28.7980 - val_accuracy: 0.1793\n",
            "Epoch 185/200\n",
            "966/966 [==============================] - 10s 10ms/step - loss: 1.9389 - accuracy: 0.5752 - val_loss: 28.9284 - val_accuracy: 0.1795\n",
            "Epoch 186/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 1.9387 - accuracy: 0.5758 - val_loss: 29.3473 - val_accuracy: 0.1761\n",
            "Epoch 187/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 1.9371 - accuracy: 0.5760 - val_loss: 29.2511 - val_accuracy: 0.1743\n",
            "Epoch 188/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 1.9286 - accuracy: 0.5776 - val_loss: 29.2473 - val_accuracy: 0.1742\n",
            "Epoch 189/200\n",
            "966/966 [==============================] - 11s 12ms/step - loss: 1.9276 - accuracy: 0.5762 - val_loss: 29.1915 - val_accuracy: 0.1775\n",
            "Epoch 190/200\n",
            "966/966 [==============================] - 13s 13ms/step - loss: 1.9201 - accuracy: 0.5815 - val_loss: 29.7306 - val_accuracy: 0.1764\n",
            "Epoch 191/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 1.9131 - accuracy: 0.5847 - val_loss: 29.5136 - val_accuracy: 0.1792\n",
            "Epoch 192/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 1.9316 - accuracy: 0.5816 - val_loss: 29.7920 - val_accuracy: 0.1781\n",
            "Epoch 193/200\n",
            "966/966 [==============================] - 9s 10ms/step - loss: 1.9136 - accuracy: 0.5810 - val_loss: 29.6334 - val_accuracy: 0.1771\n",
            "Epoch 194/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 1.9114 - accuracy: 0.5820 - val_loss: 30.0766 - val_accuracy: 0.1821\n",
            "Epoch 195/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 1.9007 - accuracy: 0.5857 - val_loss: 30.1380 - val_accuracy: 0.1721\n",
            "Epoch 196/200\n",
            "966/966 [==============================] - 12s 12ms/step - loss: 1.9050 - accuracy: 0.5827 - val_loss: 30.0841 - val_accuracy: 0.1796\n",
            "Epoch 197/200\n",
            "966/966 [==============================] - 10s 10ms/step - loss: 1.9037 - accuracy: 0.5849 - val_loss: 30.3381 - val_accuracy: 0.1760\n",
            "Epoch 198/200\n",
            "966/966 [==============================] - 12s 12ms/step - loss: 1.8880 - accuracy: 0.5853 - val_loss: 30.5466 - val_accuracy: 0.1749\n",
            "Epoch 199/200\n",
            "966/966 [==============================] - 10s 11ms/step - loss: 1.8981 - accuracy: 0.5845 - val_loss: 30.3443 - val_accuracy: 0.1797\n",
            "Epoch 200/200\n",
            "966/966 [==============================] - 11s 11ms/step - loss: 1.8918 - accuracy: 0.5854 - val_loss: 30.6820 - val_accuracy: 0.1821\n"
          ]
        }
      ],
      "source": [
        "history = model.fit(final_x_train,final_y_train,epochs=200,validation_split=0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "N9ldYXVcAdNG"
      },
      "outputs": [],
      "source": [
        "def create_model():\n",
        "  model = Sequential()\n",
        "\n",
        "  model.add(Flatten(input_shape=(8,17,8,8)))\n",
        "  # model.add(Dense(128,activation='relu'))\n",
        "  model.add(Dense(32,activation='relu'))\n",
        "  model.add(Dense(1969,activation='softmax'))\n",
        "\n",
        "  model.compile(loss='sparse_categorical_crossentropy',optimizer='Adam',metrics=['accuracy'])\n",
        "  return model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6iXh5l-AkuNu"
      },
      "source": [
        "**Testing model on samples**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Dg2z295AE0J",
        "outputId": "157a7afc-8dae-468e-cb9e-bcd2fee08de5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " flatten (Flatten)           (None, 8704)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 32)                278560    \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1969)              64977     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 343537 (1.31 MB)\n",
            "Trainable params: 343537 (1.31 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n",
            "Restored model:  None\n"
          ]
        }
      ],
      "source": [
        "# Save the weights\n",
        "\n",
        "# model.save_weights('/content/drive/MyDrive/checkpoints/my_checkpoint')\n",
        "\n",
        "# Create a new model instance\n",
        "model = create_model()\n",
        "\n",
        "# Restore the weights\n",
        "model.load_weights('/content/drive/MyDrive/checkpoints/my_checkpoint')\n",
        "\n",
        "# Continue training the model\n",
        "# history = model.fit(final_x_train,final_y_train,epochs=1,validation_split=0.2)\n",
        "\n",
        "\n",
        "print(\"Restored model: \" , model.summary())\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_YBytgcpkaq1"
      },
      "source": [
        "TESTING CELLS START(DELETE LATER)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ssfjp0B_ijrY"
      },
      "outputs": [],
      "source": [
        "model.save('test_save.h5')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "joYskGLFiz4v",
        "outputId": "b062db3e-9197-454a-d249-ac8bec7407b2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " flatten_1 (Flatten)         (None, 8704)              0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 32)                278560    \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 1969)              64977     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 343537 (1.31 MB)\n",
            "Trainable params: 343537 (1.31 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model= keras.models.load_model('test_save.h5')\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "teWwW2R-kzYw",
        "outputId": "16cf63f4-1279-4cd3-e81d-ffdb7e64ea89"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  adding: test_save/ (stored 0%)\n",
            "  adding: test_save/assets/ (stored 0%)\n",
            "  adding: test_save/fingerprint.pb (stored 0%)\n",
            "  adding: test_save/variables/ (stored 0%)\n",
            "  adding: test_save/variables/variables.index (deflated 49%)\n",
            "  adding: test_save/variables/variables.data-00000-of-00001 (deflated 6%)\n",
            "  adding: test_save/saved_model.pb (deflated 85%)\n",
            "  adding: test_save/keras_metadata.pb (deflated 84%)\n"
          ]
        }
      ],
      "source": [
        "!zip -r hello/file2.zip test_save/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "idkAXL-rkfWT"
      },
      "source": [
        "TESTING CELLS END"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3tRuNXXGkuNu"
      },
      "outputs": [],
      "source": [
        "board = chess.Board()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "me3IsDL0kuNv",
        "outputId": "91209465-a6a0-4aaa-d312-8e7ad5520376"
      },
      "outputs": [
        {
          "ename": "ValueError",
          "evalue": "in user code:\n\n    File \"/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py\", line 2440, in predict_function  *\n        return step_function(self, iterator)\n    File \"/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py\", line 2425, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py\", line 2413, in run_step  **\n        outputs = model.predict_step(data)\n    File \"/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py\", line 2381, in predict_step\n        return self(x, training=False)\n    File \"/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\", line 70, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"/usr/local/lib/python3.10/dist-packages/keras/src/engine/input_spec.py\", line 298, in assert_input_compatibility\n        raise ValueError(\n\n    ValueError: Input 0 of layer \"sequential_1\" is incompatible with the layer: expected shape=(None, 8, 17, 8, 8), found shape=(None, 17, 8, 8)\n",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-28-48be24d31fcb>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mboard\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_game_over\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mx_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mget_inputs_by_board\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mboard\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0my_prob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     68\u001b[0m             \u001b[0;31m# To get the full stack trace, call:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m             \u001b[0;31m# `tf.debugging.disable_traceback_filtering()`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     71\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m             \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py\u001b[0m in \u001b[0;36mtf__predict_function\u001b[0;34m(iterator)\u001b[0m\n\u001b[1;32m     13\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m                     \u001b[0mdo_return\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m                     \u001b[0mretval_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mag__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconverted_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mag__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mld\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep_function\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mag__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mld\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mag__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mld\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfscope\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m                 \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m                     \u001b[0mdo_return\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: in user code:\n\n    File \"/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py\", line 2440, in predict_function  *\n        return step_function(self, iterator)\n    File \"/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py\", line 2425, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py\", line 2413, in run_step  **\n        outputs = model.predict_step(data)\n    File \"/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py\", line 2381, in predict_step\n        return self(x, training=False)\n    File \"/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\", line 70, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"/usr/local/lib/python3.10/dist-packages/keras/src/engine/input_spec.py\", line 298, in assert_input_compatibility\n        raise ValueError(\n\n    ValueError: Input 0 of layer \"sequential_1\" is incompatible with the layer: expected shape=(None, 8, 17, 8, 8), found shape=(None, 17, 8, 8)\n"
          ]
        }
      ],
      "source": [
        "while not board.is_game_over():\n",
        "    x_test = np.array([get_inputs_by_board(board)])\n",
        "    y_prob = model.predict(x_test)\n",
        "    while True:\n",
        "        try:\n",
        "            y_pred = y_prob.argmax(axis=1)\n",
        "            move = get_outputs()[y_pred[0]]\n",
        "            board.push_san(move)\n",
        "            break\n",
        "        except:\n",
        "            y_prob[0][y_prob.argmax(axis=1)[0]] = 0\n",
        "    print(board)\n",
        "board"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "id": "5UGpcK-UkuNv",
        "outputId": "ef2f1a95-df8a-44cd-8d30-ba603d996710"
      },
      "outputs": [
        {
          "ename": "NameError",
          "evalue": "name 'y_prob' is not defined",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-29-442668b225c3>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0my_prob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'y_prob' is not defined"
          ]
        }
      ],
      "source": [
        "y_prob.argmax(axis=1)[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bMYp7rkxkuNv",
        "outputId": "40f20b3f-978d-46ff-cf7e-322eea440388"
      },
      "outputs": [
        {
          "ename": "IllegalMoveError",
          "evalue": "no matching legal move for c6d4 (c6 -> d4) in rnbqkb1r/ppp1pppp/5n2/3p4/3P1B2/2N5/PPP1PPPP/R2QKBNR b KQkq - 1 3",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mIllegalMoveError\u001b[0m                          Traceback (most recent call last)",
            "Cell \u001b[0;32mIn[59], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mboard\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpush_san\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mc6d4\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      2\u001b[0m board \n",
            "File \u001b[0;32m~/.local/lib/python3.10/site-packages/chess/__init__.py:3105\u001b[0m, in \u001b[0;36mBoard.push_san\u001b[0;34m(self, san)\u001b[0m\n\u001b[1;32m   3091\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpush_san\u001b[39m(\u001b[38;5;28mself\u001b[39m, san: \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Move:\n\u001b[1;32m   3092\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   3093\u001b[0m \u001b[38;5;124;03m    Parses a move in standard algebraic notation, makes the move and puts\u001b[39;00m\n\u001b[1;32m   3094\u001b[0m \u001b[38;5;124;03m    it onto the move stack.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   3103\u001b[0m \u001b[38;5;124;03m        - :exc:`AmbiguousMoveError` if the SAN is ambiguous.\u001b[39;00m\n\u001b[1;32m   3104\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3105\u001b[0m     move \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse_san\u001b[49m\u001b[43m(\u001b[49m\u001b[43msan\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3106\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpush(move)\n\u001b[1;32m   3107\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m move\n",
            "File \u001b[0;32m~/.local/lib/python3.10/site-packages/chess/__init__.py:3063\u001b[0m, in \u001b[0;36mBoard.parse_san\u001b[0;34m(self, san)\u001b[0m\n\u001b[1;32m   3059\u001b[0m     from_mask \u001b[38;5;241m&\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpieces_mask(piece_type, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mturn)\n\u001b[1;32m   3060\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m match\u001b[38;5;241m.\u001b[39mgroup(\u001b[38;5;241m2\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m match\u001b[38;5;241m.\u001b[39mgroup(\u001b[38;5;241m3\u001b[39m):\n\u001b[1;32m   3061\u001b[0m     \u001b[38;5;66;03m# Allow fully specified moves, even if they are not pawn moves,\u001b[39;00m\n\u001b[1;32m   3062\u001b[0m     \u001b[38;5;66;03m# including castling moves.\u001b[39;00m\n\u001b[0;32m-> 3063\u001b[0m     move \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfind_move\u001b[49m\u001b[43m(\u001b[49m\u001b[43msquare\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfrom_file\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrom_rank\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mto_square\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpromotion\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3064\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m move\u001b[38;5;241m.\u001b[39mpromotion \u001b[38;5;241m==\u001b[39m promotion:\n\u001b[1;32m   3065\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m move\n",
            "File \u001b[0;32m~/.local/lib/python3.10/site-packages/chess/__init__.py:2357\u001b[0m, in \u001b[0;36mBoard.find_move\u001b[0;34m(self, from_square, to_square, promotion)\u001b[0m\n\u001b[1;32m   2355\u001b[0m move \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_from_chess960(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mchess960, from_square, to_square, promotion)\n\u001b[1;32m   2356\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mis_legal(move):\n\u001b[0;32m-> 2357\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m IllegalMoveError(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mno matching legal move for \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmove\u001b[38;5;241m.\u001b[39muci()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mSQUARE_NAMES[from_square]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m -> \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mSQUARE_NAMES[to_square]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m) in \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfen()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   2359\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m move\n",
            "\u001b[0;31mIllegalMoveError\u001b[0m: no matching legal move for c6d4 (c6 -> d4) in rnbqkb1r/ppp1pppp/5n2/3p4/3P1B2/2N5/PPP1PPPP/R2QKBNR b KQkq - 1 3"
          ]
        }
      ],
      "source": [
        "board.push_san('c6d4')\n",
        "board"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SfQOANpB5Our"
      },
      "source": [
        "**Code to test model on test data**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3ZueOfpnkuNv"
      },
      "outputs": [],
      "source": [
        "class TestNode():\n",
        "   def __init__(self,board,parent):\n",
        "      self.board = board\n",
        "      self.parent = parent\n",
        "      self.fen = board.fen()\n",
        "\n",
        "\n",
        "\n",
        "board = chess.Board()\n",
        "node = TestNode(board,None)\n",
        "\n",
        "def execute_logic(node,move):\n",
        "  new_board = chess.Board(node.fen)\n",
        "  new_board.push_san(move)\n",
        "  # if new_board.is_game_over():\n",
        "  #    return\n",
        "  new_node = TestNode(new_board, node)\n",
        "  # execute_logic(new_node)\n",
        "  return new_node\n",
        "\n",
        "def convert_test_node_to_eight_boards(node):\n",
        "    arr = []\n",
        "    arr.append(chess.Board(node.fen))\n",
        "    while len(arr) < 8:\n",
        "        if node.parent == None:\n",
        "            arr += [0] * (8 - len(arr))\n",
        "            break\n",
        "        else:\n",
        "            node = node.parent\n",
        "            arr.append(chess.Board(node.fen))\n",
        "    return arr\n",
        "\n",
        "board = chess.Board()\n",
        "node = TestNode(board,None)\n",
        "\n",
        "# while True:\n",
        "#     boards_of_node = convert_test_node_to_eight_boards(node)\n",
        "#     x_test = []\n",
        "#     for board in boards_of_node:\n",
        "#         x_test.append(get_inputs_by_board(board))\n",
        "#     y_prob = model.predict([x_test])\n",
        "#     while True:\n",
        "#         try:\n",
        "#             y_pred = y_prob.argmax(axis=1)\n",
        "#             move = get_outputs()[y_pred[0]]\n",
        "#             node = execute_logic(node,move)\n",
        "#             break\n",
        "#         except:\n",
        "#             y_prob[0][y_prob.argmax(axis=1)[0]] = 0\n",
        "#     if node.board.is_game_over():\n",
        "#        break\n",
        "\n",
        "# node.board"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "o7EkkJMuzELU",
        "outputId": "c68df50f-0bee-412d-b0c9-1590054922ed"
      },
      "outputs": [
        {
          "data": {
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/><use href=\"#black-bishop\" xlink:href=\"#black-bishop\" transform=\"translate(285, 195)\" /><use href=\"#black-rook\" xlink:href=\"#black-rook\" transform=\"translate(105, 150)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(330, 150)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(150, 105)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(330, 105)\" /><use href=\"#black-bishop\" xlink:href=\"#black-bishop\" transform=\"translate(285, 60)\" /><use href=\"#black-king\" xlink:href=\"#black-king\" transform=\"translate(330, 60)\" /><use href=\"#white-rook\" xlink:href=\"#white-rook\" transform=\"translate(105, 15)\" /></svg>",
            "text/plain": [
              "Board('2R5/6bk/3P3p/2r4P/5Kb1/p7/2q3R1/r7 b - - 3 95')"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "node.parent.parent.parent.parent.parent.board"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OiL3ILxNZ595"
      },
      "source": [
        "**Play Against Human**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "UgC2_AgfW0RE",
        "outputId": "8a1d529f-73af-4fd7-fa27-55f9863d4322"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "r n b q k b n r\n",
            "p p p p p p p p\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "P P P P P P P P\n",
            "R N B Q K B N R\n",
            "1/1 [==============================] - 0s 44ms/step\n",
            "r n b q k b n r\n",
            "p p p p p p . p\n",
            ". . . . . . p .\n",
            ". . . . . . . .\n",
            ". . . . P . . .\n",
            ". . . . . . . .\n",
            "P P P P . P P P\n",
            "R N B Q K B N R\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "r . b q k b n r\n",
            "p p p p p p . p\n",
            ". . n . . . p .\n",
            ". . . . P . . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "P P P P . P P P\n",
            "R N B Q K B N R\n",
            "1/1 [==============================] - 0s 44ms/step\n",
            "r . b q k b . r\n",
            "p p p p p p . p\n",
            ". . n . P n p .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "P P P P . P P P\n",
            "R N B Q K B N R\n",
            "Illegal Move, try again.\n",
            "r . b q k b . r\n",
            "p p p p p p . p\n",
            ". . n . P n p .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "P P P P . P P P\n",
            "R N B Q K B N R\n",
            "1/1 [==============================] - 0s 40ms/step\n",
            "r . b q . b . r\n",
            "p p p p p k . p\n",
            ". . n . . n p .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "P P P P . P P P\n",
            "R N B Q K B N R\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "r . b q . b . r\n",
            "p p p . p k . p\n",
            ". . n . . n p .\n",
            ". . . p . . . .\n",
            ". . B . . . . .\n",
            ". . . . . . . .\n",
            "P P P P . P P P\n",
            "R N B Q K . N R\n",
            "1/1 [==============================] - 0s 43ms/step\n",
            "r . b . . b . r\n",
            "p p p . p k . p\n",
            ". . n . . n p .\n",
            ". . . q . . . .\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "P P P P . P P P\n",
            "R N B Q K . N R\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "r . b . . b r .\n",
            "p p p . p k . p\n",
            ". . n . . n p .\n",
            ". . . q . . . Q\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            "P P P P . P P P\n",
            "R N B . K . N R\n",
            "1/1 [==============================] - 0s 51ms/step\n",
            "r . b . . b r .\n",
            "p p p . p k . p\n",
            ". . . . . n p Q\n",
            ". . . q . . . .\n",
            ". n . . . . . .\n",
            ". . . . . . . .\n",
            "P P P P . P P P\n",
            "R N B . K . N R\n",
            "1/1 [==============================] - 0s 39ms/step\n",
            "r . b . . b r .\n",
            "p p p . p k . n\n",
            ". . . . . . p .\n",
            ". . . q . . . .\n",
            ". n . . . . . .\n",
            ". . . . . . . .\n",
            "P P P P . P P P\n",
            "R N B . K . N R\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "r . b . . b r .\n",
            "p p p . p k . n\n",
            ". . . . . . p .\n",
            ". . . q . . . .\n",
            ". . . . . . . .\n",
            ". . N . . . . .\n",
            "P P n P . P P P\n",
            "R . B . K . N R\n",
            "1/1 [==============================] - 0s 61ms/step\n",
            "r . b . . b r .\n",
            "p p p . p k . n\n",
            ". . . . . . p .\n",
            ". . . . . q . .\n",
            ". . . . . . . .\n",
            ". . N . . . . .\n",
            "P P n P . P P P\n",
            "R . B K . . N R\n"
          ]
        }
      ],
      "source": [
        "pve_board = chess.Board()\n",
        "pve_node = TestNode(board,None)\n",
        "\n",
        "while True:\n",
        "  if pve_node.board.is_game_over():\n",
        "       break\n",
        "  if pve_node.board.turn:\n",
        "    while True:\n",
        "        try:\n",
        "            print(pve_node.board)\n",
        "            move = input(\"Enter your move: \")\n",
        "            pve_node = execute_logic(pve_node,move)\n",
        "            break\n",
        "        except:\n",
        "            print(\"Illegal Move, try again.\")\n",
        "  else:\n",
        "    boards_of_node = convert_test_node_to_eight_boards(pve_node)\n",
        "    x_test = []\n",
        "    for board in boards_of_node:\n",
        "          x_test.append(get_inputs_by_board(board))\n",
        "    y_prob = model.predict([x_test])\n",
        "    while True:\n",
        "        try:\n",
        "            y_pred = y_prob.argmax(axis=1)\n",
        "            move = get_outputs()[y_pred[0]]\n",
        "            pve_node = execute_logic(pve_node,move)\n",
        "            break\n",
        "        except:\n",
        "            y_prob[0][y_prob.argmax(axis=1)[0]] = 0\n",
        "\n",
        "pve_node.board"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sqd2DU4ZdYvd"
      },
      "source": [
        "**Code for model to mcts**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Nn7_5AslBghE"
      },
      "outputs": [],
      "source": [
        "class TreeNode3():\n",
        "    # class constructor (create tree node class instance)\n",
        "    def __init__(self, board, parent):\n",
        "        # init associated board state which is board.__str__()\n",
        "        self.position = board.__str__()\n",
        "\n",
        "        self.legal_moves = list(x.__str__() for x in board.legal_moves)\n",
        "\n",
        "        self.is_checkmate = board.is_checkmate()\n",
        "\n",
        "        self.fen = board.fen()\n",
        "\n",
        "        self.turn = board.turn\n",
        "\n",
        "        if board.is_game_over():\n",
        "            # we have a terminal node\n",
        "            self.is_terminal = True\n",
        "            self.winner = board.outcome().winner\n",
        "\n",
        "        # otherwise\n",
        "        else:\n",
        "            # we have a non-terminal node\n",
        "            self.is_terminal = False\n",
        "\n",
        "        # init is fully expanded flag\n",
        "        self.is_fully_expanded = self.is_terminal\n",
        "\n",
        "        # init parent node if available\n",
        "        self.parent = parent\n",
        "\n",
        "        # init the number of node visits\n",
        "        self.visits = 0\n",
        "\n",
        "        # init the total score of the node\n",
        "        self.score = 0\n",
        "\n",
        "        # init current node's children\n",
        "        self.children = {}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MPoQfgOsHoIk"
      },
      "outputs": [],
      "source": [
        "class MCTS3():\n",
        "    def __init__(self, model):\n",
        "      self.model = model\n",
        "    def convert_node_to_input(self, node):\n",
        "      boards_of_node = convert_test_node_to_eight_boards(node)\n",
        "      x_test = []\n",
        "      for board in boards_of_node:\n",
        "        x_test.append(get_inputs_by_board(board))\n",
        "      return x_test\n",
        "\n",
        "    def get_moves_from_input(self, node, input, model):\n",
        "      y_prob = model.predict([input])\n",
        "      move_probs = {}\n",
        "      while len(move_probs)<len(node.legal_moves):\n",
        "          try:\n",
        "            # problem seems to be in this block as always highest probable legal move will get into move_probs\n",
        "              y_pred = y_prob.argmax(axis=1)\n",
        "              move = get_outputs()[y_pred[0]]\n",
        "              chess.Board(node.fen).push_san(move)\n",
        "              move_probs.update({move : y_pred})\n",
        "          except:\n",
        "              y_prob[0][y_prob.argmax(axis=1)[0]] = 0\n",
        "      return move_probs\n",
        "\n",
        "    def get_best_move_from_input(self, node, input, model):\n",
        "      y_prob = model.predict([input])\n",
        "      while True:\n",
        "          try:\n",
        "              y_pred = y_prob.argmax(axis=1)\n",
        "              move = get_outputs()[y_pred[0]]\n",
        "              chess.Board(node.fen).push_san(move)\n",
        "              return move\n",
        "          except:\n",
        "              y_prob[0][y_prob.argmax(axis=1)[0]] = 0\n",
        "\n",
        "    def get_moves_from_node(self, node, model):\n",
        "      input = self.convert_node_to_input(node)\n",
        "      move_probs = self.get_moves_from_input(node, input, model)\n",
        "      return move_probs\n",
        "\n",
        "    def get_best_move_from_node(self, node, model):\n",
        "      input = self.convert_node_to_input(node)\n",
        "      move = self.get_best_move_from_input(node, input, model)\n",
        "      return move\n",
        "\n",
        "    def search_existing_tree(self,node):\n",
        "        for iteration in range(100):\n",
        "            # select a node (selection phase)\n",
        "            node = self.select(node)\n",
        "\n",
        "            # scrore current node (simulation phase)\n",
        "            score, final_node = self.rollout(node)\n",
        "\n",
        "            # backpropagate results\n",
        "            self.backpropagate(final_node, score)\n",
        "\n",
        "        return node\n",
        "\n",
        "    # search for the best move in the current position\n",
        "    def search(self,board):\n",
        "        # create root node\n",
        "        self.root = TreeNode3(board, None)\n",
        "\n",
        "        # walk through 1000 iterations\n",
        "        for iteration in range(1):\n",
        "            # select a node (selection phase)\n",
        "            #problem, should call rollout only\n",
        "            node = self.select(self.root, self.model)\n",
        "\n",
        "            print(\"select done\")\n",
        "\n",
        "            # scrore current node (simulation phase)\n",
        "            score, final_node = self.rollout(self.root, self.model)\n",
        "\n",
        "            print(\"rollout done\")\n",
        "\n",
        "            # backpropagate results\n",
        "            self.backpropagate(final_node, score)\n",
        "\n",
        "            print(\"DONEE\")\n",
        "\n",
        "        return self.root\n",
        "\n",
        "    # select most promising node\n",
        "    def select(self, node, model):\n",
        "        # make sure that we're dealing with non-terminal nodes\n",
        "        while len(node.children) != 0:\n",
        "            ucb_max = float('-inf')\n",
        "            ucb = 0\n",
        "            next_node = None\n",
        "            for key, value in self.get_moves_from_node(node, model): # problem here as this is index not value\n",
        "                try:\n",
        "                  child_node = node.children[key]\n",
        "                  if node.turn:\n",
        "                    ucb = self.average_reward(child_node) + self.bonus_reward(child_node, value)\n",
        "                  else:\n",
        "                    ucb = ((-1)*self.average_reward(child_node)) + self.bonus_reward(child_node, value)\n",
        "                  if ucb > ucb_max:\n",
        "                    ucb_max = ucb\n",
        "                    next_node = child_node\n",
        "                except:\n",
        "                  ucb = self.bonus_reward(None, value)\n",
        "                  if ucb > ucb_max:\n",
        "                    ucb_max = ucb\n",
        "                    next_node = key\n",
        "\n",
        "            if isinstance(next_node, str):\n",
        "              new_board = chess.Board(node.fen)\n",
        "              new_board.push_san(next_node)\n",
        "              new_node = TreeNode3(new_board, node)\n",
        "              del new_board\n",
        "\n",
        "              # add child node to parent's node children list (dict)\n",
        "              node.children[next_node] = new_node\n",
        "              node = new_node\n",
        "            else:\n",
        "              node = next_node\n",
        "        # return node\n",
        "        return node\n",
        "\n",
        "    # expand node\n",
        "    def expand(self, node, model):\n",
        "        move = self.get_best_move_from_node(node, model)\n",
        "        new_board = chess.Board(node.fen)\n",
        "        new_board.push_san(move)\n",
        "        new_node = TreeNode3(new_board, node)\n",
        "        del new_board\n",
        "\n",
        "        # add child node to parent's node children list (dict)\n",
        "        node.children[move] = new_node\n",
        "        return new_node\n",
        "\n",
        "    # simulate the game via making random moves until reach end of the game\n",
        "    def rollout(self, node, model):\n",
        "        # make random moves for both sides until terminal state of the game is reached\n",
        "        while not node.is_terminal:\n",
        "            node = self.expand(node, model) # problem, call get_best_move here\n",
        "\n",
        "        # return score from the player \"x\" perspective\n",
        "        if node.is_checkmate:\n",
        "            if node.winner == True:\n",
        "                return 0.5, node\n",
        "            else:\n",
        "                return -0.5, node\n",
        "        else:\n",
        "            return 0, node\n",
        "\n",
        "    # backpropagate the number of visits and score up to the root node\n",
        "    def backpropagate(self, node, score):\n",
        "        # update nodes's up to root node\n",
        "        while node is not None:\n",
        "            # update node's visits\n",
        "            node.visits += 1\n",
        "\n",
        "            # update node's score\n",
        "            node.score += score\n",
        "\n",
        "            # set node to parent\n",
        "            node = node.parent\n",
        "\n",
        "    # select the best node basing on UCB1 formula\n",
        "    def get_best_move(self, node, exploration_constant):#problem in its implementation\n",
        "        # define best score & best moves\n",
        "\n",
        "        best_moves = []\n",
        "\n",
        "        best_score = float('-inf')\n",
        "        # loop over child nodes\n",
        "        for child_node in node.children.values():# problem here as possibly we don't need children here\n",
        "            # as we are getting them from get_moves_from_node function\n",
        "\n",
        "            # define current player\n",
        "            # if child_node.board.player_2 == 'x': current_player = 1\n",
        "            # elif child_node.board.player_2 == 'o': current_player = -1\n",
        "\n",
        "            # get move score using UCT formula\n",
        "\n",
        "            if node.turn == True:\n",
        "                move_score = self.average_reward(child_node) + self.bonus_reward(child_node)\n",
        "\n",
        "            else:\n",
        "                move_score = ((-1) * self.average_reward(child_node)) + self.bonus_reward(child_node)\n",
        "\n",
        "            # better move has been found\n",
        "            if move_score > best_score:\n",
        "                best_score = move_score\n",
        "                best_moves = [child_node]\n",
        "            # found as good move as already available\n",
        "            elif move_score == best_score:\n",
        "                best_moves.append(child_node)\n",
        "        # return one of the best moves randomly\n",
        "        return random.choice(best_moves)\n",
        "\n",
        "def average_reward(self, node):\n",
        "    return node.score/node.visits\n",
        "\n",
        "def bonus_reward(self, node, move_prob):\n",
        "    if node == None:\n",
        "      return move_prob\n",
        "    return move_prob/(1+node.visits)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M2hhYLVM2nge"
      },
      "source": [
        "**Code by Sohaib**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KxDD07I013uB"
      },
      "outputs": [],
      "source": [
        "import math\n",
        "import time\n",
        "import numpy as np\n",
        "\n",
        "total_moves = get_outputs()\n",
        "\n",
        "class MCTS3():\n",
        "\n",
        "    def __init__(self, model):\n",
        "      self.model = model\n",
        "\n",
        "    def convert_node_to_input(self, node):\n",
        "      boards_of_node = convert_test_node_to_eight_boards(node)\n",
        "      x_test = []\n",
        "      for board in boards_of_node:\n",
        "        x_test.append(get_inputs_by_board(board))\n",
        "      return x_test\n",
        "\n",
        "    def get_moves_from_input(self, node, input, model):\n",
        "      legal_moves = node.legal_moves\n",
        "      y_prob = model.predict([input])\n",
        "      # print(type(y_prob))\n",
        "      y_prob = np.array([[element + 2 for element in row] for row in y_prob])\n",
        "      # print(type(y_prob))\n",
        "      move_probs = {}\n",
        "      # print(len(node.legal_moves))\n",
        "      # print(len(y_prob[0]))\n",
        "      for move in total_moves:\n",
        "          if move in legal_moves:\n",
        "             move_probs[move] = y_prob[0][total_moves.index(move)]\n",
        "      # print(move_probs)\n",
        "      # while len(move_probs)<len(node.legal_moves):\n",
        "      #     try:\n",
        "      #         y_pred = y_prob.argmax(axis=1)\n",
        "      #         move = get_outputs()[y_pred[0]]\n",
        "      #         chess.Board(node.fen).push_san(move)\n",
        "      #         #newly added lines\n",
        "      #         # if len(move_probs)>15:\n",
        "      #           # print(y_prob[0][y_prob.argmax(axis=1)[0]])\n",
        "      #         move_probs[move] = round(y_prob[0][y_prob.argmax(axis=1)[0]]+2,2)\n",
        "      #         y_prob[0][y_prob.argmax(axis=1)[0]] = 0\n",
        "      #     except:\n",
        "      #         y_prob[0][y_prob.argmax(axis=1)[0]] = 0\n",
        "      # print(\"yahan ni aa paraha\")\n",
        "      return move_probs\n",
        "\n",
        "    # def get_best_move_from_input(self, node, input, model):\n",
        "    #   y_prob = model.predict([input])\n",
        "    #   while True:\n",
        "    #       try:\n",
        "    #           y_pred = y_prob.argmax(axis=1)\n",
        "    #           move = get_outputs()[y_pred[0]]\n",
        "    #           chess.Board(node.fen).push_san(move)\n",
        "    #           return move\n",
        "    #       except:\n",
        "    #           y_prob[0][y_prob.argmax(axis=1)[0]] = 0\n",
        "\n",
        "    def get_moves_from_node(self, node, model):\n",
        "      # start_time = time.time()\n",
        "      input = self.convert_node_to_input(node)\n",
        "      # end_time = time.time()\n",
        "      # time_difference = end_time - start_time\n",
        "      # print(\"Time difference:\", time_difference, \"seconds\")\n",
        "      # start_time = time.time()\n",
        "      move_probs = self.get_moves_from_input(node, input, model)\n",
        "      # end_time = time.time()\n",
        "      # time_difference = end_time - start_time\n",
        "      # print(\"Time difference after:\", time_difference, \"seconds\")\n",
        "      return move_probs\n",
        "\n",
        "    # def get_best_move_from_node(self, node, model):\n",
        "    #   input = self.convert_node_to_input(node)\n",
        "    #   move = self.get_best_move_from_input(node, input, model)\n",
        "    #   return move\n",
        "\n",
        "    def search_existing_tree(self,node):\n",
        "        for iteration in range(100):\n",
        "            # select a node (selection phase)\n",
        "            node = self.select(node)\n",
        "\n",
        "            # scrore current node (simulation phase)\n",
        "            score, final_node = self.rollout(node)\n",
        "\n",
        "            # backpropagate results\n",
        "            self.backpropagate(final_node, score)\n",
        "\n",
        "        return node\n",
        "\n",
        "    # search for the best move in the current position\n",
        "    def search(self,board):\n",
        "        # create root node\n",
        "        self.root = TreeNode3(board, None)\n",
        "\n",
        "        # walk through 1000 iterations\n",
        "        for iteration in range(1):\n",
        "            # scrore current node (simulation phase)\n",
        "            score, final_node = self.rollout(self.root, self.model)\n",
        "\n",
        "            # backpropagate results\n",
        "            self.backpropagate(final_node, score)\n",
        "\n",
        "        return self.root\n",
        "\n",
        "    # simulate the game via making random moves until reach end of the game\n",
        "    def rollout(self, node, model):\n",
        "        count = 0\n",
        "        # make random moves for both sides until terminal state of the game is reached\n",
        "        while not node.is_terminal:\n",
        "            # print(count)\n",
        "            # count+=1\n",
        "            node = self.get_best_move(node) # problem, call get_best_move here\n",
        "\n",
        "        # return score from the player \"x\" perspective\n",
        "        if node.is_checkmate:\n",
        "            if node.winner == True:\n",
        "                return 0.5, node\n",
        "            else:\n",
        "                return -0.5, node\n",
        "        else:\n",
        "            return 0, node\n",
        "\n",
        "    # backpropagate the number of visits and score up to the root node\n",
        "    def backpropagate(self, node, score):\n",
        "        # update nodes's up to root node\n",
        "        while node is not None:\n",
        "            # update node's visits\n",
        "            node.visits += 1\n",
        "\n",
        "            # update node's score\n",
        "            node.score += score\n",
        "\n",
        "            # set node to parent\n",
        "            node = node.parent\n",
        "\n",
        "    # select the best node basing on UCB1 formula\n",
        "    def get_best_move(self, node):#problem in its implementation\n",
        "        # define best score & best moves\n",
        "        # print(\"came here\")\n",
        "        ucb_max = float('-inf')\n",
        "        ucb = 0\n",
        "        next_node = None\n",
        "        # loop over child nodes\n",
        "        for key, value in self.get_moves_from_node(node, model).items():\n",
        "                try:\n",
        "                  # print(\"in try\")\n",
        "                  child_node = node.children[key]\n",
        "                  if node.turn:\n",
        "                    ucb = self.average_reward(child_node) + self.bonus_reward(child_node, value)\n",
        "                  else:\n",
        "                    ucb = ((-1)*self.average_reward(child_node)) + self.bonus_reward(child_node, value)\n",
        "                  if ucb > ucb_max:\n",
        "                    ucb_max = ucb\n",
        "                    next_node = child_node\n",
        "                except: #enters here on divided by zero exception when node.visits == 0\n",
        "                  ucb = self.bonus_reward(None, value)\n",
        "                  # print(\"getting here\")\n",
        "                  if ucb > ucb_max:\n",
        "                    ucb_max = ucb\n",
        "                    next_node = key\n",
        "\n",
        "        if isinstance(next_node, str):\n",
        "              new_board = chess.Board(node.fen)\n",
        "              new_board.push_san(next_node)\n",
        "              new_node = TreeNode3(new_board, node)\n",
        "              del new_board\n",
        "\n",
        "              # add child node to parent's node children list (dict)\n",
        "              node.children[next_node] = new_node\n",
        "              node = new_node\n",
        "        else:\n",
        "              node = next_node\n",
        "\n",
        "            # get move score using UCT formula\n",
        "        # print(\"why not this\")\n",
        "        return node\n",
        "\n",
        "    def average_reward(self, node):\n",
        "        return node.score/node.visits\n",
        "\n",
        "    def bonus_reward(self, node, move_prob):\n",
        "        if node == None:\n",
        "          return move_prob\n",
        "        return move_prob/(1+node.visits)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NxejMh0v7G3Y",
        "outputId": "ef651cc0-40a0-4884-c99c-a97481db4c73"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0\n",
            "1/1 [==============================] - 0s 48ms/step\n",
            "1\n",
            "1/1 [==============================] - 0s 50ms/step\n",
            "2\n",
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            "rollout done\n",
            "backpropogate done\n"
          ]
        }
      ],
      "source": [
        "darakht_3 = MCTS3(model)\n",
        "tree_3 = darakht_3.search(chess.Board())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6EMzixhC7T_7"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "col5F4gDWONL"
      },
      "outputs": [],
      "source": [
        "list(x.__str__() for x in chess.Board().legal_moves)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yfphCK07XfXy",
        "outputId": "a35d1a2f-c17b-47fe-9e00-9be0e9f4da0f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "r n b q k b n r\n",
            "p p p p p p p p\n",
            ". . . . . . . .\n",
            ". . . . . . . .\n",
            ". . P . . . . .\n",
            ". . . . . . . .\n",
            "P P . P P P P P\n",
            "R N B Q K B N R\n"
          ]
        }
      ],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PhxBZ2nCf-fa"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.12"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}