notebooks/awesome-spacer-train-colab.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"awesome-spacer-train-colab.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","metadata":{"id":"P9D3Nl--A51h","colab_type":"text"},"source":["#Project setting\n"]},{"cell_type":"code","metadata":{"id":"agYzuW8Os_Sj","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":146},"outputId":"0663d4e3-1225-4a30-8cfc-9a3eae2f492a","executionInfo":{"status":"ok","timestamp":1576279124857,"user_tz":-540,"elapsed":5137,"user":{"displayName":"정연준","photoUrl":"","userId":"04915506338685687945"}}},"source":["!git clone https://github.com/fuzzythecat/awesome-spacer"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Cloning into 'awesome-spacer'...\n","remote: Enumerating objects: 73, done.\u001b[K\n","remote: Counting objects:   1% (1/73)\u001b[K\rremote: Counting objects:   2% 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done.\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"giAG9L3y55SF","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":54},"outputId":"6f6bb44e-5deb-4fd9-a1c9-650bfb1b27d3","executionInfo":{"status":"ok","timestamp":1576279130377,"user_tz":-540,"elapsed":3674,"user":{"displayName":"정연준","photoUrl":"","userId":"04915506338685687945"}}},"source":["%cd awesome-spacer\n","!ls"],"execution_count":2,"outputs":[{"output_type":"stream","text":["/content/awesome-spacer\n","data  images  LICENSE  models  notebooks  README.md  train.py\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"nnf9lh5R702E","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":515},"outputId":"6f650f61-bad0-438a-bd76-aa60919eb7b5","executionInfo":{"status":"ok","timestamp":1576279146146,"user_tz":-540,"elapsed":13691,"user":{"displayName":"정연준","photoUrl":"","userId":"04915506338685687945"}}},"source":["%cd data\n","!wget 'https://www.dropbox.com/s/3ocgkkr3ueklv19/sejong_corpus.zip?dl=0'\n","!unzip -qq 'sejong_corpus.zip?dl=0' -d sejong_corpus\n","%cd .."],"execution_count":3,"outputs":[{"output_type":"stream","text":["/content/awesome-spacer/data\n","--2019-12-13 23:18:54--  https://www.dropbox.com/s/3ocgkkr3ueklv19/sejong_corpus.zip?dl=0\n","Resolving www.dropbox.com (www.dropbox.com)... 162.125.8.1, 2620:100:601b:1::a27d:801\n","Connecting to www.dropbox.com (www.dropbox.com)|162.125.8.1|:443... connected.\n","HTTP request sent, awaiting response... 301 Moved Permanently\n","Location: /s/raw/3ocgkkr3ueklv19/sejong_corpus.zip [following]\n","--2019-12-13 23:18:54--  https://www.dropbox.com/s/raw/3ocgkkr3ueklv19/sejong_corpus.zip\n","Reusing existing connection to www.dropbox.com:443.\n","HTTP request sent, awaiting response... 302 Found\n","Location: 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/cd/0/inline2/AuN_ns1fwyKVE_b7-kVZoE5vj54OEnG1iJ11gS7DoJbyXuVQOyKTWxdl02xiqkHWYE5a4EPU43N95WzrpzZG8KdBT6ot4HCHiGna737vGyUdq-Tqhj2BpE6ML9X54pSOnR9vyAqQFIUEgTmgPbOSlXANWES74h3lEIWAQOWvBqe_c-KG8BCjZcoYXSAAEkkOIyapsQG5odUUJ89UKT0gjJb6Kk_5k_9JCHHPYay6JgvMI1ARl4I1ftQnl1EfwYUXmAZMVYr_VyyhEV1V2M4tf8uFZbrtxW-dU3_gqyAoKgPivioCafdo0hQA8F4U_nghCRilXoRLEk-pBzjs2bnv1K1Zf70k78W53OisWXMTtIy7mw/file [following]\n","--2019-12-13 23:18:54--  https://ucd3a9db2c1ac8cf79f9f5c81d28.dl.dropboxusercontent.com/cd/0/inline2/AuN_ns1fwyKVE_b7-kVZoE5vj54OEnG1iJ11gS7DoJbyXuVQOyKTWxdl02xiqkHWYE5a4EPU43N95WzrpzZG8KdBT6ot4HCHiGna737vGyUdq-Tqhj2BpE6ML9X54pSOnR9vyAqQFIUEgTmgPbOSlXANWES74h3lEIWAQOWvBqe_c-KG8BCjZcoYXSAAEkkOIyapsQG5odUUJ89UKT0gjJb6Kk_5k_9JCHHPYay6JgvMI1ARl4I1ftQnl1EfwYUXmAZMVYr_VyyhEV1V2M4tf8uFZbrtxW-dU3_gqyAoKgPivioCafdo0hQA8F4U_nghCRilXoRLEk-pBzjs2bnv1K1Zf70k78W53OisWXMTtIy7mw/file\n","Reusing existing connection to ucd3a9db2c1ac8cf79f9f5c81d28.dl.dropboxusercontent.com:443.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 201241801 (192M) [application/zip]\n","Saving to: ‘sejong_corpus.zip?dl=0’\n","\n","sejong_corpus.zip?d 100%[===================>] 191.92M  43.8MB/s    in 4.4s    \n","\n","2019-12-13 23:18:59 (43.8 MB/s) - ‘sejong_corpus.zip?dl=0’ saved [201241801/201241801]\n","\n","/content/awesome-spacer\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"upWm-bAQDBMU","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":1000},"outputId":"4c586f34-6250-4bb3-e191-1efe4ed49b6c","executionInfo":{"status":"ok","timestamp":1576279190829,"user_tz":-540,"elapsed":47392,"user":{"displayName":"정연준","photoUrl":"","userId":"04915506338685687945"}}},"source":["!pip install tensorflow-gpu==2.0.0"],"execution_count":4,"outputs":[{"output_type":"stream","text":["Collecting tensorflow-gpu==2.0.0\n","\u001b[?25l  Downloading 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tensorflow 1.15.0 has requirement tensorboard<1.16.0,>=1.15.0, but you'll have tensorboard 2.0.2 which is incompatible.\u001b[0m\n","\u001b[31mERROR: tensorflow 1.15.0 has requirement tensorflow-estimator==1.15.1, but you'll have tensorflow-estimator 2.0.1 which is incompatible.\u001b[0m\n","\u001b[31mERROR: tensorboard 2.0.2 has requirement grpcio>=1.24.3, but you'll have grpcio 1.15.0 which is incompatible.\u001b[0m\n","\u001b[31mERROR: google-colab 1.0.0 has requirement google-auth~=1.4.0, but you'll have google-auth 1.8.2 which is incompatible.\u001b[0m\n","Installing collected packages: google-auth, tensorboard, tensorflow-estimator, tensorflow-gpu\n","  Found existing installation: google-auth 1.4.2\n","    Uninstalling google-auth-1.4.2:\n","      Successfully uninstalled google-auth-1.4.2\n","  Found existing installation: tensorboard 1.15.0\n","    Uninstalling tensorboard-1.15.0:\n","      Successfully uninstalled tensorboard-1.15.0\n","  Found existing installation: tensorflow-estimator 1.15.1\n","    Uninstalling tensorflow-estimator-1.15.1:\n","      Successfully uninstalled tensorflow-estimator-1.15.1\n","Successfully installed google-auth-1.8.2 tensorboard-2.0.2 tensorflow-estimator-2.0.1 tensorflow-gpu-2.0.0\n"],"name":"stdout"},{"output_type":"display_data","data":{"application/vnd.colab-display-data+json":{"pip_warning":{"packages":["google"]}}},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"jolzdSdRDS9l","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":35},"outputId":"c4adf337-72bf-472e-eea3-bf09aa5feea0","executionInfo":{"status":"ok","timestamp":1576279236212,"user_tz":-540,"elapsed":1732,"user":{"displayName":"정연준","photoUrl":"","userId":"04915506338685687945"}}},"source":["# Check that TensorFlow 2.0 is installed correctly.\n","import tensorflow as tf\n","tf.__version__"],"execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'2.0.0'"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"markdown","metadata":{"id":"7YF9JH8tEfs1","colab_type":"text"},"source":["# Model configuration"]},{"cell_type":"code","metadata":{"id":"RWXYsJxGD6pD","colab_type":"code","colab":{}},"source":["import os\n","import argparse\n","import sys\n","from random import shuffle\n","from glob import glob\n","\n","import seaborn as sns; sns.set()\n","import matplotlib.pyplot as plt\n","from tensorflow.keras import optimizers\n","from tensorflow.keras.callbacks import ModelCheckpoint\n","from tensorflow.keras.callbacks import TensorBoard\n","from tensorflow.keras.preprocessing.sequence import pad_sequences\n","\n","from models.config import Config\n","from models.utils import build_dataset\n","from models.utils import encode_string\n","from models.spacer import Spacer"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"_oWCcqhjEEDH","colab_type":"code","colab":{}},"source":["# In Colab / Jupyter Notebook / etc. it is hard to change the contents\n","# of config.py. Override configuration settings for custom training settings. \n","class TrainingConfig(Config):\n","    BATCH_SIZE = 256\n","    # Determines N in N-grams.\n","    # Each element in the list must have a corresponding \n","    # filter configuration in `filter_nums`.\n","    FILTER_SIZES = [4, 6, 8, 10]\n","    # Determines the number of N-grams features that \n","    # are learned. Each element in the list must have a\n","    # corresponding `N` parameter in `filter_sizes`.\n","    FILTER_NUMS = [256, 256, 256, 256]\n","    EPOCHS = 3\n","\n","config = TrainingConfig()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"BxTMfBUhGIz2","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":734},"outputId":"1c54d978-aefe-4ae8-d3f5-ff390f2434a9","executionInfo":{"status":"ok","timestamp":1576279246700,"user_tz":-540,"elapsed":3668,"user":{"displayName":"정연준","photoUrl":"","userId":"04915506338685687945"}}},"source":["# Single GPU training.\n","model = Spacer(None, config) \n","optimizer = optimizers.Adam(config.LEARNING_RATE)\n","model.compile(loss='binary_crossentropy',\n","              optimizer=optimizer,\n","              metrics=['accuracy',\n","                       tf.metrics.Precision(),\n","                       tf.metrics.Recall(),\n","                       tf.metrics.AUC()])\n","model.summary()"],"execution_count":8,"outputs":[{"output_type":"stream","text":["Model: \"model\"\n","__________________________________________________________________________________________________\n","Layer (type)                    Output Shape         Param #     Connected to                     \n","==================================================================================================\n","input_1 (InputLayer)            [(None, 200)]        0                                            \n","__________________________________________________________________________________________________\n","embedding (Embedding)           (None, 200, 64)      721216      input_1[0][0]                    \n","__________________________________________________________________________________________________\n","conv1d (Conv1D)                 (None, 200, 256)     65792       embedding[0][0]                  \n","__________________________________________________________________________________________________\n","conv1d_1 (Conv1D)               (None, 200, 256)     98560       embedding[0][0]                  \n","__________________________________________________________________________________________________\n","conv1d_2 (Conv1D)               (None, 200, 256)     131328      embedding[0][0]                  \n","__________________________________________________________________________________________________\n","conv1d_3 (Conv1D)               (None, 200, 256)     164096      embedding[0][0]                  \n","__________________________________________________________________________________________________\n","concatenate (Concatenate)       (None, 200, 1024)    0           conv1d[0][0]                     \n","                                                                 conv1d_1[0][0]                   \n","                                                                 conv1d_2[0][0]                   \n","                                                                 conv1d_3[0][0]                   \n","__________________________________________________________________________________________________\n","bidirectional (Bidirectional)   (None, 200, 200)     900000      concatenate[0][0]                \n","__________________________________________________________________________________________________\n","lstm_1 (LSTM)                   (None, 200, 50)      50200       bidirectional[0][0]              \n","__________________________________________________________________________________________________\n","time_distributed (TimeDistribut (None, 200, 300)     15300       lstm_1[0][0]                     \n","__________________________________________________________________________________________________\n","dropout (Dropout)               (None, 200, 300)     0           time_distributed[0][0]           \n","__________________________________________________________________________________________________\n","time_distributed_1 (TimeDistrib (None, 200, 150)     45150       dropout[0][0]                    \n","__________________________________________________________________________________________________\n","time_distributed_2 (TimeDistrib (None, 200, 1)       151         time_distributed_1[0][0]         \n","__________________________________________________________________________________________________\n","reshape (Reshape)               (None, 200)          0           time_distributed_2[0][0]         \n","==================================================================================================\n","Total params: 2,191,793\n","Trainable params: 2,191,793\n","Non-trainable params: 0\n","__________________________________________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"TbKEE-FKJPHc","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":54},"outputId":"ba664d54-7b6f-4137-bc57-d9c8ce6c10cf","executionInfo":{"status":"ok","timestamp":1576279289773,"user_tz":-540,"elapsed":562,"user":{"displayName":"정연준","photoUrl":"","userId":"04915506338685687945"}}},"source":["# Data preparation\n","filenames = glob(os.path.join('data/sejong_corpus', '*.txt'))\n","shuffle(filenames)\n","filenames = filenames[:100]\n","\n","validation_split = int(len(filenames) * 0.2)\n","val_filenames = filenames[:validation_split]\n","train_filenames =  filenames[validation_split:]\n","\n","print('Found {} files.'.format(len(filenames)))\n","print('Using {} files for training.'.format(len(train_filenames)))"],"execution_count":12,"outputs":[{"output_type":"stream","text":["Found 100 files.\n","Using 80 files for training.\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"edxhSPCfJS3n","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":127},"outputId":"e61a49fe-2b8d-445b-ad3b-2a83092beee3","executionInfo":{"status":"ok","timestamp":1576279297639,"user_tz":-540,"elapsed":5273,"user":{"displayName":"정연준","photoUrl":"","userId":"04915506338685687945"}}},"source":["X_train, y_train = build_dataset(train_filenames, config) \n","X_val, y_val = build_dataset(val_filenames, config)\n"," \n","train_dataset = tf.data.Dataset.from_tensor_slices((X_train, y_train))\n","train_dataset = train_dataset.batch(config.BATCH_SIZE)\n","train_dataset = train_dataset.repeat()\n","\n","valid_dataset = tf.data.Dataset.from_tensor_slices((X_val, y_val))\n","valid_dataset = valid_dataset.batch(config.BATCH_SIZE)\n","valid_dataset = valid_dataset.repeat()\n","\n","print('X_train.shape: ', X_train.shape)\n","print('y_train.shape: ', y_train.shape)\n","print('X_val.shape: ', X_val.shape)\n","print('y_val.shape: ', y_val.shape)"],"execution_count":13,"outputs":[{"output_type":"stream","text":["100%|██████████| 80/80 [00:02<00:00, 32.95it/s]\n","100%|██████████| 20/20 [00:00<00:00, 12.98it/s]\n"],"name":"stderr"},{"output_type":"stream","text":["X_train.shape:  (38371, 200)\n","y_train.shape:  (38371, 200)\n","X_val.shape:  (12096, 200)\n","y_val.shape:  (12096, 200)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"o95CS7PeJoBG","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":166},"outputId":"d92712ba-af66-495e-8b36-01b637a6dc7b","executionInfo":{"status":"ok","timestamp":1576279397381,"user_tz":-540,"elapsed":93535,"user":{"displayName":"정연준","photoUrl":"","userId":"04915506338685687945"}}},"source":["history = model.fit(train_dataset,\n","                    validation_data=valid_dataset,\n","                    epochs=config.EPOCHS,\n","                    steps_per_epoch=len(X_train)//config.BATCH_SIZE,\n","                    validation_steps=len(X_val)//config.BATCH_SIZE,\n","                    verbose=1)"],"execution_count":14,"outputs":[{"output_type":"stream","text":["Train for 149 steps, validate for 47 steps\n","Epoch 1/3\n","149/149 [==============================] - 38s 253ms/step - loss: 0.3332 - accuracy: 0.8429 - precision: 0.8356 - recall: 0.3463 - auc: 0.8644 - val_loss: 0.1607 - val_accuracy: 0.9354 - val_precision: 0.8624 - val_recall: 0.8499 - val_auc: 0.9767\n","Epoch 2/3\n","149/149 [==============================] - 28s 186ms/step - loss: 0.1348 - accuracy: 0.9456 - precision: 0.8952 - recall: 0.8488 - auc: 0.9824 - val_loss: 0.1374 - val_accuracy: 0.9439 - val_precision: 0.9192 - val_recall: 0.8240 - val_auc: 0.9827\n","Epoch 3/3\n","149/149 [==============================] - 27s 184ms/step - loss: 0.1151 - accuracy: 0.9535 - precision: 0.9116 - recall: 0.8702 - auc: 0.9870 - val_loss: 0.1188 - val_accuracy: 0.9513 - val_precision: 0.9271 - val_recall: 0.8512 - val_auc: 0.9868\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"SIZaHxS9lo9g","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":468},"outputId":"2455599c-ffa2-4188-f2e8-9d5e88919a6e","executionInfo":{"status":"ok","timestamp":1576279645544,"user_tz":-540,"elapsed":1647,"user":{"displayName":"정연준","photoUrl":"","userId":"04915506338685687945"}}},"source":["hist = history.history\n","\n","plt.figure(figsize=(21,7))\n","plt.subplot(1, 3, 1)\n","plt.plot(range(config.EPOCHS), hist['loss'], label='loss')\n","plt.plot(range(config.EPOCHS), hist['val_loss'], label='val_loss')\n","plt.xlabel('Epochs')\n","plt.ylabel('Loss')\n","plt.legend(loc='best')\n","\n","plt.subplot(1, 3, 2)\n","plt.plot(range(config.EPOCHS), hist['accuracy'], label='accuracy')\n","plt.plot(range(config.EPOCHS), hist['val_accuracy'], label='val_accuracy')\n","plt.xlabel('Epochs')\n","plt.ylabel('Accuracy')\n","plt.legend(loc='best')\n","\n","plt.subplot(1, 3, 3)\n","plt.plot(range(config.EPOCHS), hist['auc'], label='auc')\n","plt.plot(range(config.EPOCHS), hist['val_auc'], label='val_auc')\n","plt.xlabel('Epochs')\n","plt.ylabel('AUC')\n","plt.legend(loc='best')\n","\n","plt.show()"],"execution_count":15,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABNUAAAGvCAYAAAB8V+MSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmBMSYDHFId2og17HruNwY1HtPGgkEUsKh+KVbV/g89IG5A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bt22G1do2HWbJkCR599FGM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\nNvwyQVthWQ07j5xsGbQlelcdPT9HW1ZyNKEhlwnaTBu2+HRs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size 1512x504 with 3 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"V8d0F7Fglxgf","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":258},"outputId":"e4f0c50f-e567-4bb3-c6de-76bd9ecd03e9","executionInfo":{"status":"ok","timestamp":1576279681143,"user_tz":-540,"elapsed":1978,"user":{"displayName":"정연준","photoUrl":"","userId":"04915506338685687945"}}},"source":["\n","tests = [ '''아무튼 동아시아에도 상투를 자르기 전에 내 목을 먼저 쳐라, \n","예의의 나라가 어떻게 양이의 법도를 따르겠냐라며 엄청난 저항이 있었지만, \n","결국 서구문명을 완전히 받아들이고 흡수했습니다.''',\n","    \n","    '''내가그린기린그림은긴기린그린그림이고, \n","네가그린기린그림은길지않은기린그린그림이다.''',\n","\n","    '''병원에 응급 환자가 실려 왔다. 클리블랜드에게 3쿼터 막판~4쿼터 초반 구간 0-24 런(RUN) 일방적인 \n","폭행을 당했던 휴스턴이다. 러셀 웨스트브룩, 클린트 카펠라 등 수련생들은 어찌할 바를 몰라\n","발을 동동 굴렀다. 여기서 전문의 털보 선생님 등장. \"모두 물러나라. 내가 치료하겠다.\" \n","환자는 '털보네이터'의 자상한 개인 전술 기반 연속 13득점 시술을 받은 후 기적적으로 소생했다. \n","털보는 팀이 4쿼터 마지막 8분 구간에서 기록한 28득점 중 23득점을 생산한 후 유유히 퇴근한다.\n","(본인 20득점+AST 기반 동료 3점) 3점슛 10개 포함 55득점(FG 20/34)은 치료비다. ''',\n","\n","    '''경기후 컨퍼런스 무리뉴 : 난 그런 기분이 들었어, 예전에 알렉스 퍼거슨 경이 \n","호랑이 얘기 한거 나는 기억하거든? 아마 이런 건 문화적인 것이겠지. 지성 팍과 \n","손, 둘다 감독으로서 지도하기 정말 좋은 선수들이야. 손흥민은 판타스틱해. \n","나는 기뻐, 정말로! - 국뽕코인 탑승하는 무버지''']\n","\n","# Remove whitespaces\n","tests = [''.join(t.split()) for t in tests]\n","encoded = [encode_string(t, config.CHR_TO_IDX) for t in tests]\n","encoded = pad_sequences(encoded, maxlen=config.MAX_TEXT_LEN, \n","                        padding='post', truncating='post',\n","                        value=config.CHR_TO_IDX['<PAD>'])\n","\n","probs = model.predict([encoded])\n","\n","for i, t in enumerate(tests):\n","    print('#{}: '.format(i+1))\n","    for j, c in enumerate(t[:config.MAX_TEXT_LEN]):\n","        print(c, end='')\n","        if probs[i][j] >= 0.5:\n","            print(' ', end='')\n","    print('\\n')"],"execution_count":16,"outputs":[{"output_type":"stream","text":["#1: \n","아무튼 동아시아에도 상투를 자르기 전에 내목을 먼저 쳐라, 예의의 나라가 어떻게 양이의 법도를 따르겠냐라며 엄청난 저항이 있었지만, 결국서구 문명을 완전히 받아들이고 흡수했습니다.\n","\n","#2: \n","내가 그린 기린 그림은 긴기린 그린 그림이고, 네가 그린 기린 그림은 길지 않은 기린 그린 그림이다.\n","\n","#3: \n","병원에 응급환자가 실려왔다. 클리블 랜드에게 3쿼터 막판~ 4쿼터 초반구간0-24런(RUN) 일방적인 폭행을 당했던 휴스턴이다. 러셀웨스트브룩, 클린트카펠라 등 수련생들은 어찌할 바를 몰라발을 동동굴렀다. 여기서 전문의 털보 선생님 등장.\" 모두 물러나라. 내가 치료하겠다.\" 환자는 '털보네이터'의 자상한 개인전술기반연속 13득점 시술을 받은 후기적적으로 소생했다. 털보는 팀이 4쿼터 마지막 8분구간에서 기록한 28득점중 23득점을 생산한 \n","\n","#4: \n","경기 후컨퍼런 스무리뉴 :난 그런 기분이 들었어, 예전에 알렉스퍼 거슨 경이 호랑이 얘기한 거나는 기억하거든? 아마이런 건 문화적인 것이겠지.지 성팍과 손, 둘다 감독으로서지도 하기 정말 좋은 선수들이야. 손흥민은 판타스틱해. 나는 기뻐, 정말로! -국뽕코인 탑승하는 무버지\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Git-hrgxDtFG","colab_type":"text"},"source":[""]}]}