From 1510d7fa4d5f8eca4ba5545082445fd29f21294e Mon Sep 17 00:00:00 2001 From: Philipp Horstenkamp Date: Wed, 22 Feb 2023 02:14:16 +0100 Subject: [PATCH] Some more bugfixes Added a few more bugfixes --- main.ipynb | 225 +++++++++++++++++++++++++++++++++++++++++------------ 1 file changed, 176 insertions(+), 49 deletions(-) diff --git a/main.ipynb b/main.ipynb index 0927ad4..b98e052 100644 --- a/main.ipynb +++ b/main.ipynb @@ -369,7 +369,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 66, "metadata": {}, "outputs": [ { @@ -385,7 +385,7 @@ ], "source": [ "def plot_othello_board(\n", - " board: np.ndarray,\n", + " board: np.ndarray | torch.Tensor,\n", " action: np.ndarray | None = None,\n", " ax=None,\n", ") -> None:\n", @@ -398,6 +398,8 @@ " board: The bord that should be plotted. Only a single games is allowed. A numpy array of the form 8x8 is expected.\n", " ax: If needed a matplotlib axis object can be defined that is used to place the board as a sublot into a bigger context.\n", " \"\"\"\n", + " if isinstance(board, torch.Tensor):\n", + " board = board.cpu().detach().numpy()\n", " assert board.shape == (8, 8)\n", " plot_all = False\n", " if ax is None:\n", @@ -1274,12 +1276,12 @@ " policy = policies[policy_index]\n", " board_history_stack[turn_index, :, :, :] = current_boards\n", " if policy_index == 0:\n", - " current_boards = current_boards * -1\n", + " current_boards *= -1\n", " current_boards, action_taken = single_turn(current_boards, policy)\n", " action_history_stack[turn_index, :] = action_taken\n", "\n", " if policy_index == 0:\n", - " current_boards = current_boards * -1\n", + " current_boards *= -1\n", "\n", " return board_history_stack, action_history_stack\n", "\n", @@ -2261,7 +2263,7 @@ " )\n", " q_learning_format[:, :, 0, :, :] = board_history\n", " q_learning_format[:, :, 1, :, :] = -1\n", - " \n", + "\n", " game_index = list(range(board_history.shape[1]))\n", " for turn_index in range(SIMULATE_TURNS):\n", " q_learning_format[\n", @@ -2366,7 +2368,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 128, "metadata": {}, "outputs": [], "source": [ @@ -2427,15 +2429,19 @@ " _q_learning_board = q_learning_boards[\n", " poss_turns[range(boards.shape[0]), action_x, action_y]\n", " ].copy()\n", - " _q_learning_board[range(_q_learning_board.shape[0]), 1, action_x, action_y] = 1\n", - " \n", + " _q_learning_board[\n", + " range(_q_learning_board.shape[0]), 1, action_x, action_y\n", + " ] = 1\n", + "\n", " ql_result = self.neural_network.forward(_q_learning_board)\n", " results[poss_turns[:, action_x, action_y], action_x, action_y] = (\n", " ql_result.reshape(-1) + 0.1\n", " )\n", " return results.cpu().detach().numpy()\n", "\n", - " def generate_trainings_data(self, generate_data_size: int) -> tuple[torch.Tensor, torch.Tensor]:\n", + " def generate_trainings_data(\n", + " self, generate_data_size: int\n", + " ) -> tuple[torch.Tensor, torch.Tensor]:\n", " train_boards, train_actions = simulate_game(generate_data_size, (self, self))\n", " action_possible = ~np.all(train_actions[:, :] == -1, axis=2)\n", " q_leaning_formatted_action = build_symetry_action(train_boards, train_actions)\n", @@ -2444,8 +2450,15 @@ " who_won_fraction=self.who_won_fraction,\n", " final_score_fraction=self.final_score_fraction,\n", " )\n", + " q_rewords[::2, :] *= -1\n", + " # print(\"Some line to delete\")\n", + " # print(q_rewords.shape)\n", " if self.symmetry_mode == SymmetryMode.MULTIPLY:\n", - " q_rewords = np.array([q_rewords] * 8)\n", + " print(q_rewords.shape)\n", + " new_q_rewords = np.zeros((2, 2, 2) + q_rewords.shape)\n", + " print(new_q_rewords.shape)\n", + " for i, k, l in ittertools.product((0, 1), (0, 1), (0, 1)):\n", + " new_q_rewords = q_rewords[i, k, j] = q_rewords\n", " action_possible = np.array([action_possible] * 8).reshape(-1)\n", "\n", " elif self.symmetry_mode == SymmetryMode.BREAK_SEQUENCE:\n", @@ -2568,6 +2581,7 @@ " pandas_result.columns = pd.MultiIndex.from_tuples(pandas_result.columns)\n", " return pandas_result\n", "\n", + "\n", "ql_policy1 = QLPolicy(\n", " 0.95,\n", " neural_network=DQLNet(),\n", @@ -2588,7 +2602,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 129, "metadata": { "collapsed": false, "jupyter": { @@ -2602,7 +2616,7 @@ "(70, 10, 8, 8)" ] }, - "execution_count": 51, + "execution_count": 129, "metadata": {}, "output_type": "execute_result" } @@ -2616,14 +2630,14 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 130, "metadata": { "tags": [] }, "outputs": [ { "data": { - "image/png": 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" ] @@ -2638,7 +2652,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 131, "metadata": { "collapsed": false, "jupyter": { @@ -2652,7 +2666,7 @@ "(70, 10, 2, 8, 8)" ] }, - "execution_count": 53, + "execution_count": 131, "metadata": {}, "output_type": "execute_result" } @@ -2664,7 +2678,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 132, "metadata": { "collapsed": false, "jupyter": { @@ -2674,7 +2688,7 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -2687,27 +2701,6 @@ "plot_othello_boards(q_leaning_formatted_action[:8, 0, 1])" ] }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "ename": "NotImplementedError", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mNotImplementedError\u001B[0m Traceback (most recent call last)", - "Cell \u001B[1;32mIn[55], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mNotImplementedError\u001B[39;00m\n", - "\u001B[1;31mNotImplementedError\u001B[0m: " - ] - } - ], - "source": [ - "raise NotImplementedError" - ] - }, { "cell_type": "code", "execution_count": null, @@ -2731,7 +2724,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 133, "metadata": { "tags": [] }, @@ -2742,7 +2735,7 @@ "'QL-M-G08-WW10-FSF00-DQLNet-MSELoss'" ] }, - "execution_count": 56, + "execution_count": 133, "metadata": {}, "output_type": "execute_result" } @@ -2761,7 +2754,28 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "ename": "NotImplementedError", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNotImplementedError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[55], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m\n", + "\u001b[1;31mNotImplementedError\u001b[0m: " + ] + } + ], + "source": [ + "raise NotImplementedError" + ] + }, + { + "cell_type": "code", + "execution_count": 134, "metadata": { "tags": [] }, @@ -2772,7 +2786,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 126, "metadata": { "tags": [] }, @@ -2780,7 +2794,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1b17c38ecb3a411486de1eb4161333db", + "model_id": "9decb71e42fc4e81ad4071be089486da", "version_major": 2, "version_minor": 0 }, @@ -2794,7 +2808,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f63762051e604180a1a26ba73da6646c", + "model_id": "bc9b419aef164aeca72c4458faad4e5f", "version_major": 2, "version_minor": 0 }, @@ -2804,6 +2818,25 @@ }, "metadata": {}, "output_type": "display_data" + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[126], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mql_policy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m200\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1000\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m100\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mRandomPolicy\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mGreedyPolicy\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n", + "Cell \u001b[1;32mIn[120], line 189\u001b[0m, in \u001b[0;36mQLPolicy.train\u001b[1;34m(self, epochs, batches, batch_size, eval_batch_size, compare_with, save_every_epoch, live_plot)\u001b[0m\n\u001b[0;32m 187\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m tqdm(\u001b[38;5;28mrange\u001b[39m(epochs)):\n\u001b[0;32m 188\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m tqdm(\u001b[38;5;28mrange\u001b[39m(batches)):\n\u001b[1;32m--> 189\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_batch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 190\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining_results\u001b[38;5;241m.\u001b[39mappend(\n\u001b[0;32m 191\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mevaluate_model(compare_with, eval_batch_size)\n\u001b[0;32m 192\u001b[0m )\n\u001b[0;32m 193\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m save_every_epoch:\n", + "Cell \u001b[1;32mIn[120], line 104\u001b[0m, in \u001b[0;36mQLPolicy.train_batch\u001b[1;34m(self, nr_of_games)\u001b[0m\n\u001b[0;32m 103\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mtrain_batch\u001b[39m(\u001b[38;5;28mself\u001b[39m, nr_of_games: \u001b[38;5;28mint\u001b[39m):\n\u001b[1;32m--> 104\u001b[0m x_train, y_train \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate_trainings_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnr_of_games\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 105\u001b[0m y_pred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mneural_network\u001b[38;5;241m.\u001b[39mforward(x_train)\n\u001b[0;32m 106\u001b[0m loss_score \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloss(y_pred, y_train)\n", + "Cell \u001b[1;32mIn[120], line 67\u001b[0m, in \u001b[0;36mQLPolicy.generate_trainings_data\u001b[1;34m(self, generate_data_size)\u001b[0m\n\u001b[0;32m 66\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgenerate_trainings_data\u001b[39m(\u001b[38;5;28mself\u001b[39m, generate_data_size: \u001b[38;5;28mint\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mtuple\u001b[39m[torch\u001b[38;5;241m.\u001b[39mTensor, torch\u001b[38;5;241m.\u001b[39mTensor]:\n\u001b[1;32m---> 67\u001b[0m train_boards, train_actions \u001b[38;5;241m=\u001b[39m \u001b[43msimulate_game\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgenerate_data_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 68\u001b[0m action_possible \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m~\u001b[39mnp\u001b[38;5;241m.\u001b[39mall(train_actions[:, :] \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m)\n\u001b[0;32m 69\u001b[0m q_leaning_formatted_action \u001b[38;5;241m=\u001b[39m build_symetry_action(train_boards, train_actions)\n", + "Cell \u001b[1;32mIn[23], line 25\u001b[0m, in \u001b[0;36msimulate_game\u001b[1;34m(nr_of_games, policies, tqdm_on)\u001b[0m\n\u001b[0;32m 23\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m policy_index \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m 24\u001b[0m current_boards \u001b[38;5;241m=\u001b[39m current_boards \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m\n\u001b[1;32m---> 25\u001b[0m current_boards, action_taken \u001b[38;5;241m=\u001b[39m \u001b[43msingle_turn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcurrent_boards\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpolicy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 26\u001b[0m action_history_stack[turn_index, :] \u001b[38;5;241m=\u001b[39m action_taken\n\u001b[0;32m 28\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m policy_index \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n", + "Cell \u001b[1;32mIn[22], line 15\u001b[0m, in \u001b[0;36msingle_turn\u001b[1;34m(current_boards, policy)\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msingle_turn\u001b[39m(\n\u001b[0;32m 2\u001b[0m current_boards: np, policy: GamePolicy\n\u001b[0;32m 3\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mtuple\u001b[39m[np\u001b[38;5;241m.\u001b[39mndarray, np\u001b[38;5;241m.\u001b[39mndarray]:\n\u001b[0;32m 4\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Execute a single turn on a board.\u001b[39;00m\n\u001b[0;32m 5\u001b[0m \n\u001b[0;32m 6\u001b[0m \u001b[38;5;124;03m Places a new stone on the board. Turns captured enemy stones.\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[38;5;124;03m The new game board and the policy vector containing the index of the action used.\u001b[39;00m\n\u001b[0;32m 14\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m---> 15\u001b[0m policy_results \u001b[38;5;241m=\u001b[39m \u001b[43mpolicy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_policy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcurrent_boards\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 17\u001b[0m \u001b[38;5;66;03m# if the constant VERIFY_POLICY is set to true the policy is verified. Should be good though.\u001b[39;00m\n\u001b[0;32m 18\u001b[0m \u001b[38;5;66;03m# todo deactivate the policy verification after some testing.\u001b[39;00m\n\u001b[0;32m 19\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m VERIFY_POLICY:\n", + "Cell \u001b[1;32mIn[19], line 65\u001b[0m, in \u001b[0;36mGamePolicy.get_policy\u001b[1;34m(self, boards)\u001b[0m\n\u001b[0;32m 58\u001b[0m policies \u001b[38;5;241m=\u001b[39m policies \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mepsilon \u001b[38;5;241m+\u001b[39m np\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mrand(\u001b[38;5;241m*\u001b[39mboards\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;241m*\u001b[39m (\n\u001b[0;32m 59\u001b[0m \u001b[38;5;241m1\u001b[39m \u001b[38;5;241m-\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mepsilon\n\u001b[0;32m 60\u001b[0m )\n\u001b[0;32m 62\u001b[0m \u001b[38;5;66;03m# todo talk to team about backpropagation of score and epsilon for greedy factor\u001b[39;00m\n\u001b[0;32m 63\u001b[0m \n\u001b[0;32m 64\u001b[0m \u001b[38;5;66;03m# todo possibly change this function to only validate the purpose turn and not all turns\u001b[39;00m\n\u001b[1;32m---> 65\u001b[0m possible_turns \u001b[38;5;241m=\u001b[39m \u001b[43mget_possible_turns\u001b[49m\u001b[43m(\u001b[49m\u001b[43mboards\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 66\u001b[0m policies[possible_turns \u001b[38;5;241m==\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1.0\u001b[39m\n\u001b[0;32m 67\u001b[0m max_indices \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m 68\u001b[0m np\u001b[38;5;241m.\u001b[39munravel_index(policy\u001b[38;5;241m.\u001b[39margmax(), policy\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;28;01mfor\u001b[39;00m policy \u001b[38;5;129;01min\u001b[39;00m policies\n\u001b[0;32m 69\u001b[0m ]\n", + "Cell \u001b[1;32mIn[13], line 60\u001b[0m, in \u001b[0;36mget_possible_turns\u001b[1;34m(boards, tqdm_on)\u001b[0m\n\u001b[0;32m 58\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m poss_turns[game, idx, idy]:\n\u001b[0;32m 59\u001b[0m position \u001b[38;5;241m=\u001b[39m idx, idy\n\u001b[1;32m---> 60\u001b[0m poss_turns[game, idx, idy] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43many\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[0;32m 61\u001b[0m \u001b[43m \u001b[49m\u001b[43m_recursive_steps\u001b[49m\u001b[43m(\u001b[49m\u001b[43mboards\u001b[49m\u001b[43m[\u001b[49m\u001b[43mgame\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdirection\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mposition\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m>\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\n\u001b[0;32m 62\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdirection\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mDIRECTIONS\u001b[49m\n\u001b[0;32m 63\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 64\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m poss_turns\n", + "Cell \u001b[1;32mIn[13], line 60\u001b[0m, in \u001b[0;36m\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m 58\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m poss_turns[game, idx, idy]:\n\u001b[0;32m 59\u001b[0m position \u001b[38;5;241m=\u001b[39m idx, idy\n\u001b[1;32m---> 60\u001b[0m poss_turns[game, idx, idy] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28many\u001b[39m(\n\u001b[0;32m 61\u001b[0m _recursive_steps(boards[game, :, :], direction, position) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m 62\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m direction \u001b[38;5;129;01min\u001b[39;00m DIRECTIONS\n\u001b[0;32m 63\u001b[0m )\n\u001b[0;32m 64\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m poss_turns\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + ] } ], "source": [ @@ -2812,10 +2845,104 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] + "execution_count": 127, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(70, 1)\n", + "(2, 2, 2, 70, 1)\n" + ] + }, + { + "ename": "IndexError", + "evalue": "too many indices for array: array is 2-dimensional, but 3 were indexed", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[127], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m boards_and_actions, score \u001b[38;5;241m=\u001b[39m \u001b[43mql_policy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate_trainings_data\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(boards_and_actions\u001b[38;5;241m.\u001b[39mshape)\n\u001b[0;32m 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(score\u001b[38;5;241m.\u001b[39mshape)\n", + "Cell \u001b[1;32mIn[120], line 82\u001b[0m, in \u001b[0;36mQLPolicy.generate_trainings_data\u001b[1;34m(self, generate_data_size)\u001b[0m\n\u001b[0;32m 80\u001b[0m new_q_rewords \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mzeros((\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m2\u001b[39m) \u001b[38;5;241m+\u001b[39m q_rewords\u001b[38;5;241m.\u001b[39mshape)\n\u001b[0;32m 81\u001b[0m \u001b[38;5;28mprint\u001b[39m(new_q_rewords\u001b[38;5;241m.\u001b[39mshape)\n\u001b[1;32m---> 82\u001b[0m new_q_rewords \u001b[38;5;241m=\u001b[39m \u001b[43mq_rewords\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;241m=\u001b[39m q_rewords\n\u001b[0;32m 83\u001b[0m action_possible \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([action_possible] \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m8\u001b[39m)\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m 85\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msymmetry_mode \u001b[38;5;241m==\u001b[39m SymmetryMode\u001b[38;5;241m.\u001b[39mBREAK_SEQUENCE:\n", + "\u001b[1;31mIndexError\u001b[0m: too many indices for array: array is 2-dimensional, but 3 were indexed" + ] + } + ], + "source": [ + "boards_and_actions, score = ql_policy.generate_trainings_data(1)\n", + "print(boards_and_actions.shape)\n", + "print(score.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([480, 2, 8, 8])" + ] + }, + "execution_count": 140, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "boards_and_actions.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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