{"id":25333,"date":"2024-10-26T09:54:09","date_gmt":"2024-10-26T09:54:09","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=25333"},"modified":"2024-11-26T08:02:08","modified_gmt":"2024-11-26T08:02:08","slug":"%eb%94%a5-%eb%9f%ac%eb%8b%9d%ec%9d%84-%ec%9d%b4%ec%9a%a9%ed%95%9c-%ec%9e%90%ec%97%b0%ec%96%b4-%ec%b2%98%eb%a6%ac-4","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/25333\/","title":{"rendered":"\ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac"},"content":{"rendered":"

<\/p>\n

RNN\uc744 \uc774\uc6a9\ud55c \ud14d\uc2a4\ud2b8 \ubd84\ub958(Text Classification)<\/h2>\n

\ub525 \ub7ec\ub2dd(Deep Learning) \uae30\uc220\uc740 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(Natural Language Processing, NLP) \ubd84\uc57c\uc5d0\uc11c \ube60\ub974\uac8c \ubc1c\uc804\ud558\uace0 \uc788\uc73c\uba70, \uadf8\uc911\uc21c\ud658 \uc2e0\uacbd\ub9dd(Recurrent Neural Network, RNN)\uc740 \uc2dc\ud000\uc2a4 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\ub294 \ub370 \ud0c1\uc6d4\ud55c \uc131\ub2a5\uc744 \ubcf4\uc774\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 RNN\uc744 \ud65c\uc6a9\ud55c \ud14d\uc2a4\ud2b8 \ubd84\ub958\uc758 \uae30\ubcf8 \uac1c\ub150, \uad6c\uc870, \uad6c\ud604 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n

1. \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc640 \ud14d\uc2a4\ud2b8 \ubd84\ub958<\/h3>\n

\uc790\uc5f0\uc5b4 \ucc98\ub9ac\ub780 \uc778\uac04\uc758 \uc5b8\uc5b4\ub97c \uc774\ud574\ud558\uace0 \ud574\uc11d\ud558\ub294 \ucef4\ud4e8\ud130 \uacfc\ud559\uc758 \ud55c \ubd84\uc57c\ub85c, \ub2e4\uc591\ud55c \uc751\uc6a9 \ud504\ub85c\uadf8\ub7a8\uc5d0\uc11c \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \ud14d\uc2a4\ud2b8 \ubd84\ub958\ub294 \uc8fc\uc5b4\uc9c4 \ud14d\uc2a4\ud2b8 \ub370\uc774\ud130\ub97c \ud2b9\uc815 \uce74\ud14c\uace0\ub9ac\ub85c \ubd84\ub958\ud558\ub294 \uc791\uc5c5\uc73c\ub85c, \uc2a4\ud338 \uba54\uc77c \ud544\ud130\ub9c1, \uac10\uc815 \ubd84\uc11d, \ub274\uc2a4 \uae30\uc0ac \ubd84\ub958 \ub4f1 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \uc0ac\uc6a9\ub429\ub2c8\ub2e4.<\/p>\n

2. RNN\uc758 \uc774\ud574<\/h3>\n

RNN\uc740 \uc21c\ud658 \uad6c\uc870\ub97c \uac00\uc9c4 \uc2e0\uacbd\ub9dd\uc73c\ub85c, \ud2b9\uc815 \uc2dc\uc810\uc758 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\uace0 \ub2e4\uc74c \uc2dc\uc810\uc73c\ub85c \uc804\ub2ec\ud558\ub294 \ubc29\uc2dd\uc73c\ub85c \ub3d9\uc791\ud569\ub2c8\ub2e4. \uc774\ub294 \uc2dc\uac04\uc801 \uc21c\uc11c\ub97c \uac00\uc9c0\uac70\ub098 \uc2dc\ud000\uc2a4 \ud615\ud0dc\uc758 \ub370\uc774\ud130\uc5d0 \uc801\ud569\ud569\ub2c8\ub2e4. RNN\uc758 \uae30\ubcf8\uc801\uc778 \uad6c\uc870\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n

\n    h_t = f(W_h * h_(t-1) + W_x * x_t + b)\n    <\/code><\/pre>\n
\uc5ec\uae30\uc11c, h_t<\/code>\ub294 \ud604\uc7ac \uc740\ub2c9 \uc0c1\ud0dc, x_t<\/code>\ub294 \ud604\uc7ac \uc785\ub825, W_h<\/code>\ub294 \uc740\ub2c9 \uc0c1\ud0dc\ub97c \uc704\ud55c \uac00\uc911\uce58 \ud589\ub82c, W_x<\/code>\ub294 \uc785\ub825\uc744 \uc704\ud55c \uac00\uc911\uce58 \ud589\ub82c, b<\/code>\ub294 \ud3b8\ud5a5\uc785\ub2c8\ub2e4. RNN\uc758 \ud575\uc2ec\uc740 \uc774\uc804 \uc0c1\ud0dc\ub97c \uae30\uc5b5\ud558\uace0, \uc774\ub97c \uae30\ubc18\uc73c\ub85c \ud604\uc7ac \uc0c1\ud0dc\ub97c \uc5c5\ub370\uc774\ud2b8\ud55c\ub2e4\ub294 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
3. RNN\uc758 \ud55c\uacc4<\/h3>\n
\uc804\ud1b5\uc801\uc778 RNN\uc740 \uc7a5\uae30 \uc758\uc874\uc131(Long-term Dependency) \ubb38\uc81c\ub97c \uacaa\uc2b5\ub2c8\ub2e4. \uc774\ub294 \uc2dc\ud000\uc2a4\uc758 \ucd08\uae30 \uc0c1\ud0dc\uac00 \ud6c4\uc18d \ub2e8\uacc4\uc5d0 \ubbf8\uce58\ub294 \uc601\ud5a5\uc774 \uc810\ucc28 \uac10\uc18c\ud558\uc5ec \uc815\ubcf4 \uc190\uc2e4\uc774 \ubc1c\uc0dd\ud558\ub294 \ud604\uc0c1\uc785\ub2c8\ub2e4. \uc774\ub97c \ud574\uacb0\ud558\uae30 \uc704\ud574 LSTM(Long Short-Term Memory)\uacfc GRU(Gated Recurrent Unit)\uc640 \uac19\uc740 \ubcc0\ud615\uc774 \uac1c\ubc1c\ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uad6c\uc870\ub294 \uac8c\uc774\ud2b8 \uba54\ucee4\ub2c8\uc998\uc744 \ud65c\uc6a9\ud558\uc5ec \uc7a5\uae30\uc801\uc778 \uad00\uc810\uc744 \uc720\uc9c0\ud558\ub294 \ub370 \ub3c4\uc6c0\uc744 \uc90d\ub2c8\ub2e4.<\/p>\n
4. \ud14d\uc2a4\ud2b8 \ubd84\ub958\ub97c \uc704\ud55c \ub370\uc774\ud130 \uc900\ube44<\/h3>\n
\ud14d\uc2a4\ud2b8 \ubd84\ub958\ub97c \uc218\ud589\ud558\uae30 \uc704\ud574\uc11c\ub294 \uc6b0\uc120 \ub370\uc774\ud130\ub97c \uc900\ube44\ud574\uc57c \ud569\ub2c8\ub2e4. \ub2e4\uc74c \ub2e8\uacc4\uc5d0 \ub530\ub77c \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
    \n
  1. \ub370\uc774\ud130 \uc218\uc9d1:<\/strong> \uc6f9 \ud06c\ub864\ub9c1, API, \ub370\uc774\ud130\uc14b \uc11c\ube44\uc2a4 \ub4f1\uc744 \ud1b5\ud574 \ud14d\uc2a4\ud2b8 \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud569\ub2c8\ub2e4.<\/li>\n
  2. \ub370\uc774\ud130 \uc815\uc81c:<\/strong> \ubd88\ud544\uc694\ud55c \uc694\uc18c(HTML \ud0dc\uadf8, \ud2b9\uc218 \ubb38\uc790 \ub4f1)\ub97c \uc81c\uac70\ud558\uace0, \uc18c\ubb38\uc790 \ubcc0\ud658, \uc911\ubcf5 \uc81c\uac70 \ub4f1\uc758 \ucc98\ub9ac\ub97c \uc218\ud589\ud569\ub2c8\ub2e4.<\/li>\n
  3. \ud1a0\ud070\ud654:<\/strong> \ud14d\uc2a4\ud2b8\ub97c \ub2e8\uc5b4, \ubb38\uc7a5 \ub610\ub294 \uae00\uc790\uc758 \uc2dc\ud000\uc2a4\ub85c \ubcc0\ud658\ud569\ub2c8\ub2e4.<\/li>\n
  4. \ub77c\ubca8 \uc778\ucf54\ub529:<\/strong> \ubd84\ub958\ud560 \uce74\ud14c\uace0\ub9ac\ub97c \uc22b\uc790\ud615 \ub370\uc774\ud130\ub85c \ubcc0\ud658\ud569\ub2c8\ub2e4.<\/li>\n
  5. \ud6c8\ub828 \ubc0f \uc2dc\ud5d8 \ub370\uc774\ud130 \ubd84\ub9ac:<\/strong> \uc218\uc9d1\ud55c \ub370\uc774\ud130\ub97c \ud6c8\ub828 \ub370\uc774\ud130\uc640 \uc2dc\ud5d8 \ub370\uc774\ud130\ub85c \ubd84\ub9ac\ud569\ub2c8\ub2e4.<\/li>\n<\/ol>\n
    5. \ud14d\uc2a4\ud2b8 \uc804\ucc98\ub9ac \ubc0f \uc784\ubca0\ub529<\/h3>\n
    \ud14d\uc2a4\ud2b8 \ub370\uc774\ud130\ub97c \uc2e0\uacbd\ub9dd\uc5d0 \uc785\ub825\ud558\uae30 \uc704\ud574\uc11c\ub294 \uc218\uce58\ud615 \ub370\uc774\ud130\ub85c \ubcc0\ud658\ud574\uc57c \ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \uc0ac\uc6a9\ub418\ub294 \ubc29\ubc95\uc740 Word Embedding \uae30\ubc95\uc785\ub2c8\ub2e4. Word2Vec<\/strong>, GloVe<\/strong>, fastText<\/strong>\uc640 \uac19\uc740 \ub2e4\uc591\ud55c \uc784\ubca0\ub529 \uae30\ubc95\uc744 \ud65c\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uc784\ubca0\ub529 \uae30\ubc95\uc740 \uac01 \ub2e8\uc5b4\ub97c \ubc00\uc9d1\ubca1\ud130\ub85c \ubcc0\ud658\ud558\uc5ec, \ub2e8\uc5b4 \uac04\uc758 \uc758\ubbf8\uc801 \uc720\uc0ac\uc131\uc744 \ubc18\uc601\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
    6. RNN \ubaa8\ub378 \uc124\uacc4 \ubc0f \uad6c\ud604<\/h3>\n
    RNN \ubaa8\ub378\uc744 \uc124\uacc4\ud558\uae30 \uc704\ud574\uc11c\ub294 \uc5ec\ub7ec \uad6c\uc131 \uc694\uc18c\uac00 \ud544\uc694\ud569\ub2c8\ub2e4:<\/p>\n
      \n
    1. \uc785\ub825 \ub808\uc774\uc5b4:<\/strong> \ud14d\uc2a4\ud2b8 \ub370\uc774\ud130\uc758 \uc2dc\ud000\uc2a4\ub97c \uc785\ub825\uc73c\ub85c \ubc1b\uc2b5\ub2c8\ub2e4.<\/li>\n
    2. RNN \ub808\uc774\uc5b4:<\/strong> \uc2dc\ud000\uc2a4\ub97c \ucc98\ub9ac\ud558\uace0 \ucd9c\ub825\ud558\ub294 \uc5ed\ud560\uc744 \ud569\ub2c8\ub2e4. \uae30\ubcf8\uc801\uc73c\ub85c \uc5ec\ub7ec \uce35\uc758 RNN\uc744 \uc313\uac70\ub098 LSTM \ub610\ub294 GRU\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n
    3. \ucd9c\ub825 \ub808\uc774\uc5b4:<\/strong> \ud074\ub798\uc2a4\uc5d0 \ub300\ud55c \ud655\ub960 \ubd84\ud3ec\ub97c \ucd9c\ub825\ud569\ub2c8\ub2e4. \uc774\ub294 \ubcf4\ud1b5 \uc18c\ud504\ud2b8\ub9e5\uc2a4(Softmax) \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \uad6c\ud604\ub429\ub2c8\ub2e4.<\/li>\n<\/ol>\n
      6.1. Keras\ub97c \uc774\uc6a9\ud55c RNN \ubaa8\ub378 \uc608\uc81c<\/h4>\n
      Keras\ub294 \uc0ac\uc6a9\uc790 \uce5c\ud654\uc801\uc778 \ub525 \ub7ec\ub2dd API\ub85c, \ud14d\uc2a4\ud2b8 \ubd84\ub958\ub97c \uc704\ud55c RNN \ubaa8\ub378\uc744 \uc27d\uac8c \uad6c\ud604\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c\uc740 \uac04\ub2e8\ud55c LSTM \uae30\ubc18\uc758 \ud14d\uc2a4\ud2b8 \ubd84\ub958 \ubaa8\ub378 \uc608\uc81c\uc785\ub2c8\ub2e4:<\/p>\n
      \n    from keras.models import Sequential\n    from keras.layers import Embedding, LSTM, Dense, Dropout\n\n    model = Sequential()\n    model.add(Embedding(input_dim=vocab_size, output_dim=embedding_dim, input_length=max_length))\n    model.add(LSTM(units=128, return_sequences=True))\n    model.add(Dropout(0.5))\n    model.add(LSTM(units=64))\n    model.add(Dense(units=num_classes, activation='softmax'))\n\n    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n    <\/code><\/pre>\n
      7. \ubaa8\ub378 \ud6c8\ub828 \ubc0f \ud3c9\uac00<\/h3>\n
      \ubaa8\ub378\uc744 \ud6c8\ub828\uc2dc\ud0a4\uae30 \uc704\ud574\uc11c\ub294 \uc900\ube44\ub41c \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uc5ec \ud559\uc2b5\uc744 \uc9c4\ud589\ud569\ub2c8\ub2e4. \ub2e4\uc74c\uacfc \uac19\uc740 \ubc29\ubc95\uc73c\ub85c \ubaa8\ub378\uc744 \ud6c8\ub828\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
      \n    model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_val, y_val))\n    <\/code><\/pre>\n
      \ud6c8\ub828\uc774 \uc644\ub8cc\ub41c \ud6c4, \uc2dc\ud5d8 \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \uc815\ud655\ub3c4(accuracy), \uc815\ubc00\ub3c4(precision), \uc7ac\ud604\uc728(recall) \ub4f1\uc758 \uc9c0\ud45c\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud3c9\uac00\ud569\ub2c8\ub2e4.<\/p>\n
      8. \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc870\uc815<\/h3>\n
      \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \uadf9\ub300\ud654\ud558\uae30 \uc704\ud574 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc870\uc815\uc774 \ud544\uc694\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \uc870\uc815\ud560 \uc218 \uc788\ub294 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n