PlayStyleTraining-byTingYang.ipynb

{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"executionInfo":{"elapsed":4372,"status":"ok","timestamp":1697731728160,"user":{"displayName":"Ting Yang Lu (アズーサ)","userId":"17219646350901583040"},"user_tz":-480},"id":"bd5077d3"},"outputs":[],"source":["import tensorflow as tf\n","from tensorflow.keras.models import Model, Sequential\n","from tensorflow.keras.layers import Input, Conv2D, ReLU, Flatten, Dense, Softmax, BatchNormalization, Dropout, Add, LSTM, ELU, Dropout, MaxPooling2D, LSTM\n","from tensorflow.keras.optimizers import Adam, SGD, Adagrad, RMSprop\n","from tensorflow.keras import regularizers\n","\n","import numpy as np\n","from sklearn.model_selection import train_test_split"],"id":"bd5077d3"},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":21648,"status":"ok","timestamp":1697731749805,"user":{"displayName":"Ting Yang Lu (アズーサ)","userId":"17219646350901583040"},"user_tz":-480},"id":"dKS4Txkq4HrP","outputId":"be46bb44-7de1-4516-c971-1717618a1b6a"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"id":"dKS4Txkq4HrP"},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":36},"executionInfo":{"elapsed":23,"status":"ok","timestamp":1697731749805,"user":{"displayName":"Ting Yang Lu (アズーサ)","userId":"17219646350901583040"},"user_tz":-480},"id":"4d54d124","outputId":"09d259a6-dc2c-4737-87f4-9e3a6697c62a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["'2.13.0'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":3}],"source":["tf.__version__"],"id":"4d54d124"},{"cell_type":"markdown","metadata":{"id":"5666d92a"},"source":["# Data Pre-Processing\n","\n","Open **play_style_train.csv** file and split the games into a list.\n","Every row of csv: `PSL0000000001,1,B[pd],W[dp],B[qp],W[dc],B[nq],W[nc],B[qf],W[kd],B[ce],W[dg],B[dd],W[cc],B[fd],W[ed],B[ee],W[ec],B[ge],W[gc],B[di]`.\n","\n","Columns are:\n","\n","    1. PSL0000000001: Game ID\n","    2. 1: Game Style\n","    3-... : Moves, the last move represents the play style (B[di] in this case)\n","    \n","We cropped only the moves to game list as:"],"id":"5666d92a"},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":22,"status":"ok","timestamp":1697731749806,"user":{"displayName":"Ting Yang Lu (アズーサ)","userId":"17219646350901583040"},"user_tz":-480},"id":"e4b559ec","outputId":"dcfe2d7e-6e1b-429d-8e4d-cc61b9823ddb"},"outputs":[{"output_type":"stream","name":"stdout","text":["[1, 1, 1, 1, 1, 2, 2, 2, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 3, 1, 1, 1, 1, 1, 1, 2, 2, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 2, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 2, 2, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 1, 1, 1, 1, 2, 2, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 3, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 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1, 2, 2, 2, 3, 3, 3, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3, 3, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3]\n"]}],"source":["df = open('/content/drive/Shareddrives/AICUP圍棋Dataset/play_style_train.csv').read().splitlines()\n","games = [i.split(',',2)[-1] for i in df]\n","game_styles = [int(i.split(',',2)[-2]) for i in df]\n","print(game_styles)"],"id":"e4b559ec"},{"cell_type":"markdown","metadata":{"id":"89f64af3"},"source":["Create a dictionary to convert the coordinates from characters to numbers"],"id":"89f64af3"},{"cell_type":"code","execution_count":5,"metadata":{"executionInfo":{"elapsed":20,"status":"ok","timestamp":1697731749806,"user":{"displayName":"Ting Yang Lu (アズーサ)","userId":"17219646350901583040"},"user_tz":-480},"id":"3b52349a"},"outputs":[],"source":["mapping_BW = {'B':0,'W':1}\n","chars = 'abcdefghijklmnopqrs'\n","mapping_letter = {k:v for v,k in enumerate(chars)}"],"id":"3b52349a"},{"cell_type":"markdown","metadata":{"id":"e3429687"},"source":["We decided to build a DCNN model in this tutorial. We create data samples by using every move in every game, meaning that the target is to predict the next move by feeding the previous state of the table in every game for every move. Therefore, we can collect much more data samples from games.\n","\n","For the simplicity, we used 2 dimensional feature map to represent the data as below:\n"," 1. Occupied areas: mark them as 1 and the empty places as 0\n"," 2. The last move in the table: mark the position of the last move as 1 and the rest as 0\n","\n","The target is to predict the game style (1, 2 or 3) from the state of the game table. Later this will be one-hot encoded."],"id":"e3429687"},{"cell_type":"code","execution_count":6,"metadata":{"id":"7b28ab61","executionInfo":{"status":"ok","timestamp":1697731749806,"user_tz":-480,"elapsed":20,"user":{"displayName":"Ting Yang Lu (アズーサ)","userId":"17219646350901583040"}}},"outputs":[],"source":["def prepare_input(moves):\n","    x = np.zeros((19, 19, 10))\n","    for move in moves:\n","        color = move.split('[')[0]\n","        x_coord = re.search(r'\\[([^\\]]+)\\]', move).group(1)[0]\n","        y_coord = re.search(r'\\[([^\\]]+)\\]', move).group(1)[1]\n","\n","        bw = mapping_BW[color]\n","        column = mapping_letter[x_coord]\n","        row = mapping_letter[y_coord]\n","        x[row,column,0] = 1\n","    if moves:\n","        last_move_column = mapping_letter[moves[-1][2]]\n","        last_move_row = mapping_letter[moves[-1][3]]\n","        x[row,column,1] = 1\n","    return x"],"id":"7b28ab61"},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":19,"status":"ok","timestamp":1697731749806,"user":{"displayName":"Ting Yang Lu (アズーサ)","userId":"17219646350901583040"},"user_tz":-480},"id":"a1a544a9","outputId":"cdb282d9-4d04-4c8a-f835-dbce4d59ba7b"},"outputs":[{"output_type":"stream","name":"stdout","text":["Total Games: 26615\n"]}],"source":["# Check how many samples can be obtained\n","n_games = 0\n","for game in games:\n","    n_games += 1\n","print(f\"Total Games: {n_games}\")"],"id":"a1a544a9"},{"cell_type":"markdown","metadata":{"id":"fe30773a"},"source":["Since play style training has smaller dataset comparing to kyu or dan training, we can put the complete dataset to memory. Still, it is better to create a data generator."],"id":"fe30773a"},{"cell_type":"code","execution_count":8,"metadata":{"executionInfo":{"elapsed":5387,"status":"ok","timestamp":1697731755184,"user":{"displayName":"Ting Yang Lu (アズーサ)","userId":"17219646350901583040"},"user_tz":-480},"id":"40cce4cb"},"outputs":[],"source":["import re\n","\n","mapping_BW = {'B':0,'W':1}\n","chars = 'abcdefghijklmnopqrs'\n","mapping_letter = {k:v for v,k in enumerate(chars)}\n","\n","x = []\n","for game in games:\n","  train = []\n","  moves = game.split(',')\n","  x.append(prepare_input(moves))\n","\n","x = np.array(x)"],"id":"40cce4cb"},{"cell_type":"code","source":["print(x.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"mfM26UNq4X54","executionInfo":{"status":"ok","timestamp":1697731755185,"user_tz":-480,"elapsed":16,"user":{"displayName":"Ting Yang Lu (アズーサ)","userId":"17219646350901583040"}},"outputId":"88df2292-3da0-4f2d-87a8-76a414741489"},"id":"mfM26UNq4X54","execution_count":9,"outputs":[{"output_type":"stream","name":"stdout","text":["(26615, 19, 19, 10)\n"]}]},{"cell_type":"code","execution_count":10,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":15,"status":"ok","timestamp":1697731755185,"user":{"displayName":"Ting Yang Lu (アズーサ)","userId":"17219646350901583040"},"user_tz":-480},"id":"0ad8b3fb","outputId":"57e21c10-24b1-4958-e304-dcb00f2d816e"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(26615,)"]},"metadata":{},"execution_count":10}],"source":["y = np.array(game_styles)-1\n","y.shape"],"id":"0ad8b3fb"},{"cell_type":"code","execution_count":11,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":14,"status":"ok","timestamp":1697731755185,"user":{"displayName":"Ting Yang Lu (アズーサ)","userId":"17219646350901583040"},"user_tz":-480},"id":"7f20561a","outputId":"52eaf626-8649-411e-d8f2-78f650de4c4a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([8184, 9403, 9028])"]},"metadata":{},"execution_count":11}],"source":["np.bincount(y)"],"id":"7f20561a"},{"cell_type":"markdown","metadata":{"id":"a1c86e70"},"source":["Target is one-hot encoded and loss is changed to `categorical_crossentropy`"],"id":"a1c86e70"},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2983,"status":"ok","timestamp":1697731758155,"user":{"displayName":"Ting Yang Lu (アズーサ)","userId":"17219646350901583040"},"user_tz":-480},"id":"54f30621","outputId":"024bdcb4-2d89-456d-d00f-bd22ea86aa02"},"outputs":[{"output_type":"stream","name":"stdout","text":["tf.Tensor(\n","[[1. 0. 0.]\n"," [1. 0. 0.]\n"," [1. 0. 0.]\n"," ...\n"," [0. 0. 1.]\n"," [0. 0. 1.]\n"," [0. 0. 1.]], shape=(26615, 3), dtype=float32)\n"]}],"source":["y_hot = tf.one_hot(y, depth=3)\n","print(y_hot)"],"id":"54f30621"},{"cell_type":"markdown","metadata":{"id":"b4ae1a16"},"source":["Dataset splitting: 90% Training, 10% validation"],"id":"b4ae1a16"},{"cell_type":"code","execution_count":13,"metadata":{"executionInfo":{"elapsed":10,"status":"ok","timestamp":1697731758155,"user":{"displayName":"Ting Yang Lu (アズーサ)","userId":"17219646350901583040"},"user_tz":-480},"id":"b80a8a43"},"outputs":[],"source":["x_train, x_val, y_train, y_val = train_test_split(x, y_hot.numpy(), test_size=0.10)"],"id":"b80a8a43"},{"cell_type":"code","source":["x_train"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8lql55di96e_","executionInfo":{"status":"ok","timestamp":1697731758617,"user_tz":-480,"elapsed":470,"user":{"displayName":"Ting Yang Lu (アズーサ)","userId":"17219646350901583040"}},"outputId":"75970b26-aa96-4296-bf13-7f6b96104595"},"id":"8lql55di96e_","execution_count":14,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[[[0., 0., 0., ..., 0., 0., 0.],\n","         [0., 0., 0., ..., 0., 0., 0.],\n","         [0., 0., 0., ..., 0., 0., 0.],\n","         ...,\n","         [0., 0., 0., ..., 0., 0., 0.],\n","         [0., 0., 0., ..., 0., 0., 0.],\n","         [0., 0., 0., ..., 0., 0., 0.]],\n","\n","        [[0., 0., 0., ..., 0., 0., 0.],\n","         [0., 0., 0., ..., 0., 0., 0.],\n","         [0., 0., 0., ..., 0., 0., 0.],\n","         ...,\n","         [0., 0., 0., ..., 0., 0., 0.],\n","         [0., 0., 0., ..., 0., 0., 0.],\n","         [0., 0., 0., ..., 0., 0., 0.]],\n","\n","        [[0., 0., 0., ..., 0., 0., 0.],\n","         [0., 0., 0., ..., 0., 0., 0.],\n","         [0., 0., 0., ..., 0., 0., 0.],\n","         ...,\n","         [0., 0., 0., ..., 0., 0., 0.],\n","         [0., 0., 0., ..., 0., 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0.]]],\n","\n","\n","       [[[0., 0., 0., ..., 0., 0., 0.],\n","         [0., 0., 0., ..., 0., 0., 0.],\n","         [0., 0., 0., ..., 0., 0., 0.],\n","         ...,\n","         [0., 0., 0., ..., 0., 0., 0.],\n","         [0., 0., 0., ..., 0., 0., 0.],\n","         [0., 0., 0., ..., 0., 0., 0.]],\n","\n","        [[0., 0., 0., ..., 0., 0., 0.],\n","         [0., 0., 0., ..., 0., 0., 0.],\n","         [0., 0., 0., ..., 0., 0., 0.],\n","         ...,\n","         [0., 0., 0., ..., 0., 0., 0.],\n","         [0., 0., 0., ..., 0., 0., 0.],\n","         [0., 0., 0., ..., 0., 0., 0.]],\n","\n","        [[0., 0., 0., ..., 0., 0., 0.],\n","         [0., 0., 0., ..., 0., 0., 0.],\n","         [0., 0., 0., ..., 0., 0., 0.],\n","         ...,\n","         [0., 0., 0., ..., 0., 0., 0.],\n","         [0., 0., 0., ..., 0., 0., 0.],\n","         [0., 0., 0., ..., 0., 0., 0.]],\n","\n","        ...,\n","\n","        [[0., 0., 0., ..., 0., 0., 0.],\n","         [0., 0., 0., ..., 0., 0., 0.],\n","         [1., 0., 0., ..., 0., 0., 0.],\n","         ...,\n","         [0., 0., 0., ..., 0., 0., 0.],\n","         [0., 0., 0., ..., 0., 0., 0.],\n","         [0., 0., 0., ..., 0., 0., 0.]],\n","\n","        [[0., 0., 0., ..., 0., 0., 0.],\n","         [0., 0., 0., ..., 0., 0., 0.],\n","         [1., 0., 0., ..., 0., 0., 0.],\n","         ...,\n","         [0., 0., 0., ..., 0., 0., 0.],\n","         [0., 0., 0., ..., 0., 0., 0.],\n","         [0., 0., 0., ..., 0., 0., 0.]],\n","\n","        [[0., 0., 0., ..., 0., 0., 0.],\n","         [0., 0., 0., ..., 0., 0., 0.],\n","         [0., 0., 0., ..., 0., 0., 0.],\n","         ...,\n","         [0., 0., 0., ..., 0., 0., 0.],\n","         [0., 0., 0., ..., 0., 0., 0.],\n","         [0., 0., 0., ..., 0., 0., 0.]]]])"]},"metadata":{},"execution_count":14}]},{"cell_type":"markdown","metadata":{"id":"3a8964d8"},"source":["# Training\n","\n","### Simple DCNN Model:"],"id":"3a8964d8"},{"cell_type":"code","execution_count":28,"metadata":{"executionInfo":{"elapsed":320,"status":"ok","timestamp":1697732200547,"user":{"displayName":"Ting Yang Lu (アズーサ)","userId":"17219646350901583040"},"user_tz":-480},"id":"ad6d4040"},"outputs":[],"source":["def create_model():\n","    model = Sequential()\n","    model.add(Conv2D(16, kernel_size=(3, 3), padding='same', activation='relu', input_shape=(19, 19, 10)))\n","    model.add(BatchNormalization())\n","\n","    for i in range(10):\n","      model.add(Conv2D(16, kernel_size=(3, 3), padding='same', activation='relu', kernel_regularizer = regularizers.l2(0.0001),use_bias=True))\n","      model.add(BatchNormalization())\n","\n","\n","    model.add(MaxPooling2D(pool_size=(3, 3)))\n","    model.add(Flatten())\n","\n","\n","    model.add(Dense(10, activation='relu'))\n","    model.add(Dense(3, activation='softmax'))\n","    model.add(Dropout(0.05))\n","    opt = Adam(learning_rate=0.001)\n","    model.compile(optimizer=opt,\n","                  loss='categorical_crossentropy',\n","                  metrics=['accuracy'])\n","    return model\n"],"id":"ad6d4040"},{"cell_type":"code","execution_count":29,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":680,"status":"ok","timestamp":1697732203120,"user":{"displayName":"Ting Yang Lu (アズーサ)","userId":"17219646350901583040"},"user_tz":-480},"id":"295280dd","outputId":"55d231b6-bf93-4a34-943d-152678cc43fc"},"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"sequential_3\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," conv2d_33 (Conv2D)          (None, 19, 19, 16)        1456      \n","                                                                 \n"," batch_normalization_33 (Ba  (None, 19, 19, 16)        64        \n"," tchNormalization)                                               \n","                                                                 \n"," conv2d_34 (Conv2D)          (None, 19, 19, 16)        2320      \n","                                                                 \n"," batch_normalization_34 (Ba  (None, 19, 19, 16)        64        \n"," tchNormalization)                                               \n","                                                                 \n"," conv2d_35 (Conv2D)          (None, 19, 19, 16)        2320      \n","                                                                 \n"," batch_normalization_35 (Ba  (None, 19, 19, 16)        64        \n"," tchNormalization)                                               \n","                                                                 \n"," conv2d_36 (Conv2D)          (None, 19, 19, 16)        2320      \n","                                                                 \n"," batch_normalization_36 (Ba  (None, 19, 19, 16)        64        \n"," tchNormalization)                                               \n","                                                                 \n"," conv2d_37 (Conv2D)          (None, 19, 19, 16)        2320      \n","                                                                 \n"," batch_normalization_37 (Ba  (None, 19, 19, 16)        64        \n"," tchNormalization)                                               \n","                                                                 \n"," conv2d_38 (Conv2D)          (None, 19, 19, 16)        2320      \n","                                                                 \n"," batch_normalization_38 (Ba  (None, 19, 19, 16)        64        \n"," tchNormalization)                                               \n","                                                                 \n"," conv2d_39 (Conv2D)          (None, 19, 19, 16)        2320      \n","                                                                 \n"," batch_normalization_39 (Ba  (None, 19, 19, 16)        64        \n"," tchNormalization)                                               \n","                                                                 \n"," conv2d_40 (Conv2D)          (None, 19, 19, 16)        2320      \n","                                                                 \n"," batch_normalization_40 (Ba  (None, 19, 19, 16)        64        \n"," tchNormalization)                                               \n","                                                                 \n"," conv2d_41 (Conv2D)          (None, 19, 19, 16)        2320      \n","                                                                 \n"," batch_normalization_41 (Ba  (None, 19, 19, 16)        64        \n"," tchNormalization)                                               \n","                                                                 \n"," conv2d_42 (Conv2D)          (None, 19, 19, 16)        2320      \n","                                                                 \n"," batch_normalization_42 (Ba  (None, 19, 19, 16)        64        \n"," tchNormalization)                                               \n","                                                                 \n"," conv2d_43 (Conv2D)          (None, 19, 19, 16)        2320      \n","                                                                 \n"," batch_normalization_43 (Ba  (None, 19, 19, 16)        64        \n"," tchNormalization)                                               \n","                                                                 \n"," max_pooling2d_3 (MaxPoolin  (None, 6, 6, 16)          0         \n"," g2D)                                                            \n","                                                                 \n"," flatten_3 (Flatten)         (None, 576)               0         \n","                                                                 \n"," dense_11 (Dense)            (None, 10)                5770      \n","                                                                 \n"," dense_12 (Dense)            (None, 3)                 33        \n","                                                                 \n"," dropout_3 (Dropout)         (None, 3)                 0         \n","                                                                 \n","=================================================================\n","Total params: 31163 (121.73 KB)\n","Trainable params: 30811 (120.36 KB)\n","Non-trainable params: 352 (1.38 KB)\n","_________________________________________________________________\n"]}],"source":["model = create_model()\n","model.summary()"],"id":"295280dd"},{"cell_type":"code","source":["model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n","    \"/content/drive/Shareddrives/AICUP圍棋Dataset/save_PS_bestModel/best_model.h5\",\n","    monitor = \"val_loss\",\n","    verbose = 0,\n","    save_best_only = True,\n","    save_weights_only = True,\n","    save_freq=\"epoch\",\n","    options=None,\n","    initial_value_threshold=None,\n",")"],"metadata":{"id":"saY92Kx1_-QH","executionInfo":{"status":"ok","timestamp":1697732204619,"user_tz":-480,"elapsed":448,"user":{"displayName":"Ting Yang Lu (アズーサ)","userId":"17219646350901583040"}}},"id":"saY92Kx1_-QH","execution_count":30,"outputs":[]},{"cell_type":"code","execution_count":31,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4c826c0d","outputId":"4e588f8c-29b6-483f-c443-bf3e90b59069","executionInfo":{"status":"ok","timestamp":1697732351992,"user_tz":-480,"elapsed":146892,"user":{"displayName":"Ting Yang Lu (アズーサ)","userId":"17219646350901583040"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/30\n","188/188 [==============================] - 12s 19ms/step - loss: nan - accuracy: 0.3808 - val_loss: 1.1507 - val_accuracy: 0.3794\n","Epoch 2/30\n","188/188 [==============================] - 3s 16ms/step - loss: nan - accuracy: 0.4233 - val_loss: 1.1609 - val_accuracy: 0.3249\n","Epoch 3/30\n","188/188 [==============================] - 3s 13ms/step - loss: nan - accuracy: 0.4378 - val_loss: 1.0965 - val_accuracy: 0.4020\n","Epoch 4/30\n","188/188 [==============================] - 3s 13ms/step - loss: nan - accuracy: 0.4874 - val_loss: 1.0140 - val_accuracy: 0.5334\n","Epoch 5/30\n","188/188 [==============================] - 2s 13ms/step - loss: nan - accuracy: 0.5454 - val_loss: 0.9612 - val_accuracy: 0.5372\n","Epoch 6/30\n","188/188 [==============================] - 3s 14ms/step - loss: nan - accuracy: 0.5566 - val_loss: 0.9484 - val_accuracy: 0.5451\n","Epoch 7/30\n","188/188 [==============================] - 3s 17ms/step - loss: nan - accuracy: 0.5621 - val_loss: 0.9482 - val_accuracy: 0.5518\n","Epoch 8/30\n","188/188 [==============================] - 2s 13ms/step - loss: nan - accuracy: 0.5716 - val_loss: 0.9433 - val_accuracy: 0.5605\n","Epoch 9/30\n","188/188 [==============================] - 2s 13ms/step - loss: nan - accuracy: 0.5739 - val_loss: 0.9441 - val_accuracy: 0.5563\n","Epoch 10/30\n","188/188 [==============================] - 2s 13ms/step - loss: nan - accuracy: 0.5783 - val_loss: 0.9385 - val_accuracy: 0.5744\n","Epoch 11/30\n","188/188 [==============================] - 2s 13ms/step - loss: nan - accuracy: 0.5878 - val_loss: 0.9580 - val_accuracy: 0.5594\n","Epoch 12/30\n","188/188 [==============================] - 3s 16ms/step - loss: 1.5901 - accuracy: 0.5926 - val_loss: 0.9419 - val_accuracy: 0.5714\n","Epoch 13/30\n","188/188 [==============================] - 3s 13ms/step - loss: 1.5717 - accuracy: 0.5930 - val_loss: 0.9465 - val_accuracy: 0.5639\n","Epoch 14/30\n","188/188 [==============================] - 2s 13ms/step - loss: 1.5856 - accuracy: 0.6036 - val_loss: 0.9799 - val_accuracy: 0.5665\n","Epoch 15/30\n","188/188 [==============================] - 2s 13ms/step - loss: nan - accuracy: 0.6049 - val_loss: 0.9550 - val_accuracy: 0.5579\n","Epoch 16/30\n","188/188 [==============================] - 2s 13ms/step - loss: nan - accuracy: 0.6130 - val_loss: 1.0339 - val_accuracy: 0.5500\n","Epoch 17/30\n","188/188 [==============================] - 3s 16ms/step - loss: 1.5601 - accuracy: 0.6188 - val_loss: 0.9790 - val_accuracy: 0.5597\n","Epoch 18/30\n","188/188 [==============================] - 3s 14ms/step - loss: nan - accuracy: 0.6247 - val_loss: 1.0927 - val_accuracy: 0.5349\n","Epoch 19/30\n","188/188 [==============================] - 2s 13ms/step - loss: nan - accuracy: 0.6336 - val_loss: 1.0009 - val_accuracy: 0.5443\n","Epoch 20/30\n","188/188 [==============================] - 2s 13ms/step - loss: nan - accuracy: 0.6362 - val_loss: 0.9581 - val_accuracy: 0.5413\n","Epoch 21/30\n","188/188 [==============================] - 2s 13ms/step - loss: nan - accuracy: 0.6459 - val_loss: 0.9745 - val_accuracy: 0.5496\n","Epoch 22/30\n","188/188 [==============================] - 3s 14ms/step - loss: nan - accuracy: 0.6508 - val_loss: 0.9855 - val_accuracy: 0.5594\n","Epoch 23/30\n","188/188 [==============================] - 3s 15ms/step - loss: nan - accuracy: 0.6586 - val_loss: 1.0304 - val_accuracy: 0.5676\n","Epoch 24/30\n","188/188 [==============================] - 2s 13ms/step - loss: 1.4615 - accuracy: 0.6673 - val_loss: 1.0361 - val_accuracy: 0.5624\n","Epoch 25/30\n","188/188 [==============================] - 3s 13ms/step - loss: nan - accuracy: 0.6719 - val_loss: 1.0154 - val_accuracy: 0.5684\n","Epoch 26/30\n","188/188 [==============================] - 2s 13ms/step - loss: nan - accuracy: 0.6744 - val_loss: 1.1041 - val_accuracy: 0.5590\n","Epoch 27/30\n","188/188 [==============================] - 3s 14ms/step - loss: nan - accuracy: 0.6798 - val_loss: 1.0716 - val_accuracy: 0.5702\n","Epoch 28/30\n","188/188 [==============================] - 3s 16ms/step - loss: nan - accuracy: 0.6838 - val_loss: 1.0877 - val_accuracy: 0.5631\n","Epoch 29/30\n","188/188 [==============================] - 2s 13ms/step - loss: nan - accuracy: 0.6963 - val_loss: 1.0669 - val_accuracy: 0.5635\n","Epoch 30/30\n","188/188 [==============================] - 2s 13ms/step - loss: nan - accuracy: 0.6952 - val_loss: 1.0671 - val_accuracy: 0.5530\n"]}],"source":["history = model.fit(\n","    x = x_train,\n","    y = y_train,\n","    batch_size = 128,\n","    epochs = 30,\n","    callbacks=[model_checkpoint_callback],\n","    validation_data=(x_val, y_val),\n",")"],"id":"4c826c0d"},{"cell_type":"code","execution_count":32,"metadata":{"executionInfo":{"elapsed":328,"status":"ok","timestamp":1697732360140,"user":{"displayName":"Ting Yang Lu (アズーサ)","userId":"17219646350901583040"},"user_tz":-480},"id":"6ed0f073"},"outputs":[],"source":["model.load_weights('/content/drive/Shareddrives/AICUP圍棋Dataset/save_PS_bestModel/best_model.h5')"],"id":"6ed0f073"},{"cell_type":"markdown","metadata":{"id":"496855bf"},"source":["# Testing\n","\n","**PublicUpload.csv** must be in the following form:\n","```\n","PSTPU0000000001_79,1\n","PSTPU0000000001_7,1\n","PSTPU0000000001_153,1\n","PSTPU0000000001_115,1\n","PSTPU0000000001_131,1\n","PSTPU0000000001_99,1\n","PSTPU0000000001_21,2\n","```\n","\n","- Column 1: Game ID\n","- Column 2: Predicted Game Style\n","\n","The code block below is to use **play_style_test_public.csv** to predict and save the results in required form."],"id":"496855bf"},{"cell_type":"code","execution_count":33,"metadata":{"executionInfo":{"elapsed":4433,"status":"ok","timestamp":1697732366422,"user":{"displayName":"Ting Yang Lu (アズーサ)","userId":"17219646350901583040"},"user_tz":-480},"id":"56c11247","colab":{"base_uri":"https://localhost:8080/"},"outputId":"dcd56111-e409-4f78-a6c0-c2360b56df3c"},"outputs":[{"output_type":"stream","name":"stdout","text":["313/313 [==============================] - 1s 3ms/step\n","[1 0 0 ... 2 1 2]\n"]}],"source":["df = open('/content/drive/Shareddrives/AICUP圍棋Dataset/play_style_test_public.csv').read().splitlines()\n","games_id = [i.split(',',2)[0] for i in df]\n","games = [i.split(',',2)[-1] for i in df]\n","\n","x_testing = []\n","\n","for game in games:\n","    moves_list = game.split(',')\n","    x_testing.append(prepare_input(moves_list))\n","\n","x_testing = np.array(x_testing)\n","predictions = model.predict(x_testing)\n","prediction_numbers = np.argmax(predictions, axis=1)\n","print(prediction_numbers)"],"id":"56c11247"},{"cell_type":"markdown","metadata":{"id":"bfae7078"},"source":["Save results to **PublicUpload.csv**."],"id":"bfae7078"},{"cell_type":"code","execution_count":34,"metadata":{"id":"d7053c79","scrolled":true,"executionInfo":{"status":"ok","timestamp":1697732369351,"user_tz":-480,"elapsed":326,"user":{"displayName":"Ting Yang Lu (アズーサ)","userId":"17219646350901583040"}}},"outputs":[],"source":["with open('/content/drive/Shareddrives/AICUP圍棋Dataset/PublicUpload.csv','a') as f:\n","    for index, number in enumerate(prediction_numbers):\n","        answer_row = games_id[index] + ',' + str(number+1) + '\\n'\n","        f.write(answer_row)"],"id":"d7053c79"},{"cell_type":"markdown","metadata":{"id":"9436139c"},"source":["# End of Tutorial\n","\n","You are free to use more modern NN architectures, a better pre-processing, feature extraction methods to achieve much better accuracy!"],"id":"9436139c"},{"cell_type":"code","execution_count":20,"metadata":{"id":"fc41e067","executionInfo":{"status":"ok","timestamp":1697718883588,"user_tz":-480,"elapsed":18,"user":{"displayName":"Ting Yang Lu (アズーサ)","userId":"17219646350901583040"}}},"outputs":[],"source":[],"id":"fc41e067"}],"metadata":{"accelerator":"GPU","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":"3.9.12"}},"nbformat":4,"nbformat_minor":5}