{"id":25285,"date":"2024-10-26T09:53:41","date_gmt":"2024-10-26T09:53:41","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=25285"},"modified":"2024-11-26T08:02:16","modified_gmt":"2024-11-26T08:02:16","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-%eb%ac%b8%ec%9e%90-%eb%8b%a8%ec%9c%84-rnnchar-rnn","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/25285\/","title":{"rendered":"\ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac, \ubb38\uc790 \ub2e8\uc704 RNN(Char RNN)"},"content":{"rendered":"

<\/p>\n

\ub525 \ub7ec\ub2dd(deep learning) \uae30\uc220\uc740 \ucd5c\uadfc \uba87 \ub144 \ub3d9\uc548 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(natural language processing, NLP) \ubd84\uc57c\uc5d0\uc11c \ud601\uc2e0\uc801\uc778 \ubcc0\ud654\ub97c \uac00\uc838\uc654\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 \ubb38\uc790 \ub2e8\uc704 \uc21c\ud658 \uc2e0\uacbd\ub9dd(character-level recurrent neural network, Char RNN)\uc740 \uac01 \ubb38\uc790\ub97c \uc785\ub825\uc73c\ub85c \uc0ac\uc6a9\ud558\uc5ec \ud14d\uc2a4\ud2b8\ub97c \uc0dd\uc131\ud558\ub294 \ub370 \uc720\uc6a9\ud55c \ubaa8\ub378\uc785\ub2c8\ub2e4. \ubcf8 \ud3ec\uc2a4\ud2b8\uc5d0\uc11c\ub294 Char RNN\uc758 \uac1c\ub150, \uad6c\uc870, \ud65c\uc6a9 \uc0ac\ub840 \ubc0f \uad6c\ud604 \ubc29\ubc95\uc5d0 \ub300\ud574 \uae4a\uc774 \uc788\uac8c \uc0b4\ud3b4\ubcf4\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n

1. \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc640 \ub525 \ub7ec\ub2dd\uc758 \uacb0\ud569<\/h2>\n

\uc790\uc5f0\uc5b4 \ucc98\ub9ac\ub294 \uc778\uac04\uc758 \uc5b8\uc5b4\ub97c \ucef4\ud4e8\ud130\uac00 \uc774\ud574\ud558\uace0 \ucc98\ub9ac\ud558\ub294 \uae30\uc220\uc785\ub2c8\ub2e4. \uc804\ud1b5\uc801\uc73c\ub85c NLP\ub294 \uaddc\uce59 \uae30\ubc18 \uc811\uadfc\ubc95\uc774\ub098 \ud1b5\uacc4\uc801 \ubc29\ubc95\ub860\uc5d0 \uc758\uc874\ud574\uc654\uc2b5\ub2c8\ub2e4. \uadf8\ub7ec\ub098 \ub525 \ub7ec\ub2dd\uc758 \ubc1c\uc804\uacfc \ud568\uaed8, \uc2e0\uacbd\ub9dd \uae30\ubc18\uc758 \ubc29\ubc95\ub860\uc774 \ub300\ub450\ub418\uba70 \uc131\ub2a5 \ud5a5\uc0c1\uc744 \uc774\ub8e8\uc5c8\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 RNN(Recurrent Neural Network)\uc740 \uc2dc\ud000\uc2a4 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\ub294 \ub370 \uac15\ub825\ud55c \uc131\ub2a5\uc744 \ubc1c\ud718\ud569\ub2c8\ub2e4.<\/p>\n

1.1 RNN\uc758 \uae30\ubcf8 \uc6d0\ub9ac<\/h3>\n

RNN\uc740 \uc774\uc804\uc758 \uc815\ubcf4\ub97c \uae30\uc5b5\ud558\ub294 \ub2a5\ub825\uc774 \uc788\uc5b4 \uc2dc\ud000\uc2a4 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\ub294 \ub370 \uc801\ud569\ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc778 \uc778\uacf5 \uc2e0\uacbd\ub9dd\uc740 \uace0\uc815\ub41c \uae38\uc774\uc758 \uc785\ub825\uc744 \ucc98\ub9ac\ud558\ub294 \ubc18\uba74, RNN\uc740 \uac00\ubcc0 \uae38\uc774\uc758 \uc2dc\ud000\uc2a4\ub97c \ucc98\ub9ac\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. RNN\uc740 \uc785\ub825 \uc2dc\uc810\ub9c8\ub2e4 \uc228\uaca8\uc9c4 \uc0c1\ud0dc(hidden state)\ub97c \uac31\uc2e0\ud558\uba70, \uc774\uc804 \uc2dc\uc810\uc758 \uc815\ubcf4\ub97c \ud604\uc7ac \uc2dc\uc810\uc5d0 \uc804\ub2ec\ud569\ub2c8\ub2e4.<\/p>\n

1.2 Char RNN\uc758 \ud544\uc694\uc131<\/h3>\n

\uc804\ud1b5\uc801\uc778 \ub2e8\uc5b4 \uae30\ubc18 \uc811\uadfc\ubc95\uc740 \ub2e8\uc5b4\ub97c \uae30\ubcf8 \ub2e8\uc704\ub85c \uc0ac\uc6a9\ud558\uc5ec \ud14d\uc2a4\ud2b8\ub97c \ucc98\ub9ac\ud569\ub2c8\ub2e4. \uadf8\ub7ec\ub098 \uc774 \ubc29\uc2dd\uc740 OOV(out-of-vocabulary) \ubb38\uc81c\ub97c \uc720\ubc1c\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. Char RNN\uc740 \ubb38\uc790 \ub2e8\uc704\ub85c \ud14d\uc2a4\ud2b8\ub97c \ucc98\ub9ac\ud568\uc73c\ub85c\uc368, \uc0c8\ub85c\uc6b4 \ub2e8\uc5b4\ub098 \ud615\ud0dc\uc18c\uac00 \ub4f1\uc7a5\ud574\ub3c4 \uc720\uc5f0\ud558\uac8c \ub300\ucc98\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

2. Char RNN\uc758 \uad6c\uc870<\/h2>\n

Char RNN\uc740 RNN \uad6c\uc870\ub97c \ubc14\ud0d5\uc73c\ub85c \ud558\uba70, \uc785\ub825\uc73c\ub85c \uac01 \ubb38\uc790\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4. \uc774 \uc139\uc158\uc5d0\uc11c\ub294 Char RNN\uc758 \uae30\ubcf8 \uad6c\uc870\uc640 \ub3d9\uc791 \ubc29\uc2dd\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.<\/p>\n

2.1 \uc785\ub825 \ubc0f \ucd9c\ub825<\/h3>\n

Char RNN\uc758 \uc785\ub825\uc740 \ubb38\uc790 \uc2dc\ud000\uc2a4\ub85c, \uac01 \ubb38\uc790\ub294 \uc6d0-\ud56b \uc778\ucf54\ub529(one-hot encoding) \ud615\ud0dc\ub85c \ud45c\ud604\ub429\ub2c8\ub2e4. \ucd9c\ub825\uc740 \ub2e4\uc74c\uc5d0 \uc62c \ubb38\uc790\uc758 \ud655\ub960 \ubd84\ud3ec\ub97c \ub098\ud0c0\ub0b4\uba70, \uc18c\ud504\ud2b8\ub9e5\uc2a4(softmax) \ud568\uc218\ub97c \ud1b5\ud574 \uacc4\uc0b0\ub429\ub2c8\ub2e4.<\/p>\n

2.2 \uc740\ub2c9 \uc0c1\ud0dc\uc640 \uc7a5\ub2e8\uae30 \uae30\uc5b5 \uc140<\/h3>\n

Char RNN\uc740 \ub274\ub7f0\uc758 \uc740\ub2c9 \uc0c1\ud0dc\ub97c \ud1b5\ud574 \uc774\uc804 \uc785\ub825\uc758 \uc815\ubcf4\ub97c \uae30\uc5b5\ud569\ub2c8\ub2e4. \ub610\ud55c, LSTM(Long Short-Term Memory) \ub610\ub294 GRU(Gated Recurrent Unit)\uc640 \uac19\uc740 \uad6c\uc870\ub97c \ub3c4\uc785\ud558\uc5ec \uc7a5\uae30 \uc758\uc874\uc131\uc744 \ud6a8\uacfc\uc801\uc73c\ub85c \ucc98\ub9ac\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub294 RNN\uc774 \ub354 \uae34 \uc2dc\ud000\uc2a4\ub97c \ucc98\ub9ac\ud558\ub294 \ub370 \uc720\ub9ac\ud558\uac8c \uc791\uc6a9\ud569\ub2c8\ub2e4.<\/p>\n

3. Char RNN \ud559\uc2b5 \uacfc\uc815<\/h2>\n

Char RNN\uc740 \uc8fc\uc5b4\uc9c4 \ud14d\uc2a4\ud2b8 \ub370\uc774\ud130\uc5d0 \ub300\ud574 \ud559\uc2b5\uc744 \uc9c4\ud589\ud569\ub2c8\ub2e4. \ud559\uc2b5 \uacfc\uc815\uc740 \uc8fc\ub85c \ub2e4\uc74c \ub2e8\uacc4\ub85c \uc774\ub8e8\uc5b4\uc9d1\ub2c8\ub2e4.<\/p>\n

3.1 \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h3>\n

\ud14d\uc2a4\ud2b8 \ub370\uc774\ud130\ub294 \uc804\ucc98\ub9ac\ub97c \ud1b5\ud574 \ubb38\uc790 \uc9d1\ud569(character set)\uc744 \uad6c\uc131\ud558\uace0, \uac01 \ubb38\uc790\ub97c \uc6d0-\ud56b \uc778\ucf54\ub529 \ud615\uc2dd\uc73c\ub85c \ubcc0\ud658\ud569\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc5d0\uc11c \ud2b9\uc218 \ubb38\uc790\ub098 \uacf5\ubc31 \ucc98\ub9ac\ub3c4 \uace0\ub824\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n

3.2 \uc190\uc2e4 \ud568\uc218 \ubc0f \ucd5c\uc801\ud654<\/h3>\n

\ubaa8\ub378 \ud559\uc2b5\uc758 \ubaa9\ud45c\ub294 \uc2e4\uc81c \ub2e4\uc74c \ubb38\uc790\uc758 \ud655\ub960 \ubd84\ud3ec\uc640 \ubaa8\ub378 \uc608\uce21 \uacb0\uacfc \uac04\uc758 \ucc28\uc774\ub97c \ucd5c\uc18c\ud654\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \ud06c\ub85c\uc2a4 \uc5d4\ud2b8\ub85c\ud53c \uc190\uc2e4(cross-entropy loss)\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc190\uc2e4\uc744 \uacc4\uc0b0\ud558\uace0, \ucd5c\uc801\ud654 \uc54c\uace0\ub9ac\uc998(\uc608: Adam, RMSprop)\uc744 \ud1b5\ud574 \uac00\uc911\uce58\ub97c \uc5c5\ub370\uc774\ud2b8\ud569\ub2c8\ub2e4.<\/p>\n

3.3 \uc0dd\uc131 \uacfc\uc815<\/h3>\n

\ud559\uc2b5\uc774 \uc644\ub8cc\ub41c Char RNN \ubaa8\ub378\uc740 \uc0c8\ub85c\uc6b4 \ud14d\uc2a4\ud2b8\ub97c \uc0dd\uc131\ud558\ub294 \ub370 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc8fc\uc5b4\uc9c4 \uc785\ub825 \uc2dc\ud000\uc2a4\ub97c \uae30\ubc18\uc73c\ub85c \ub2e4\uc74c \ubb38\uc790\ub97c \uc608\uce21\ud558\uace0, \uc774\ub97c \ubc18\ubcf5\ud558\uc5ec \uc0c8\ub85c\uc6b4 \uc2dc\ud000\uc2a4\ub97c \ub9cc\ub4e4\uc5b4\ub0c5\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc5d0\uc11c \ud0d0\uc0c9 \uae30\ubc95(\uc608: \uc0d8\ud50c\ub9c1, \ube54 \uc11c\uce58)\uc744 \uc801\uc6a9\ud558\uc5ec \ub2e4\uc591\ud55c \uc0dd\uc131 \uacb0\uacfc\ub97c \uc5bb\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

4. Char RNN\uc758 \ud65c\uc6a9 \uc0ac\ub840<\/h2>\n

Char RNN\uc740 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \ud65c\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c\uc740 \uba87 \uac00\uc9c0 \uc0ac\ub840\uc785\ub2c8\ub2e4.<\/p>\n

4.1 \ud14d\uc2a4\ud2b8 \uc790\ub3d9 \uc0dd\uc131<\/h3>\n

Char RNN\uc744 \uc774\uc6a9\ud558\uc5ec \uc18c\uc124, \uc2dc\ub098\ub9ac\uc624, \ub610\ub294 \uc74c\uc545\uc758 \uac00\uc0ac\uc640 \uac19\uc740 \ud14d\uc2a4\ud2b8\ub97c \uc790\ub3d9\uc73c\ub85c \uc0dd\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc740 \uae30\uc874 \ud14d\uc2a4\ud2b8\ub97c \ud559\uc2b5\ud558\uace0, \uc774\ub97c \ubc14\ud0d5\uc73c\ub85c \uc0c8\ub85c\uc6b4 \ubb38\uc7a5\uc744 \uad6c\uc131\ud558\ub294 \uac83\uc774\uba70, \ucc3d\uc758\uc801\uc778 \uc791\uc5c5\uc5d0 \ub9ce\uc740 \ub3c4\uc6c0\uc774 \ub429\ub2c8\ub2e4.<\/p>\n

4.2 \uc5b8\uc5b4 \ubaa8\ub378\ub9c1<\/h3>\n

Char RNN\uc740 \uc5b8\uc5b4 \ubaa8\ub378\ub85c \ud65c\uc6a9\ub418\uc5b4 \ub2e4\uc74c \ub2e8\uc5b4 \uc608\uce21, \ud14d\uc2a4\ud2b8 \ubd84\ub958 \ubc0f \uac10\uc815 \ubd84\uc11d \ub4f1 \ub2e4\uc591\ud55c NLP \ud0dc\uc2a4\ud06c\uc5d0 \ud65c\uc6a9\ub429\ub2c8\ub2e4. \ubb38\uc790 \ub2e8\uc704\ub85c \ucc98\ub9ac\ub428\uc73c\ub85c\uc368 \ub354\uc6b1 \uc815\uad50\ud55c \ubaa8\ub378\uc744 \uad6c\ucd95\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

5. \uad6c\ud604 \uc608\uc81c<\/h2>\n

\ub2e4\uc74c\uc740 Python\uacfc TensorFlow\ub97c \uc0ac\uc6a9\ud558\uc5ec Char RNN\uc744 \uad6c\ud604\ud558\ub294 \uac04\ub2e8\ud55c \uc608\uc81c\uc785\ub2c8\ub2e4. \uc774 \ucf54\ub4dc \uc608\uc2dc\ub294 \uae30\ubcf8\uc801\uc778 \uad6c\uc870\ub97c \uc124\uba85\ud558\uba70, \uc2e4\uc81c \ud65c\uc6a9\uc744 \uc704\ud574\uc11c\ub294 \ucd94\uac00\uc801\uc778 \ubaa8\ub4c8\uacfc \uc124\uc815\uc774 \ud544\uc694\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

import numpy as np\nimport tensorflow as tf\n\n# \ub370\uc774\ud130 \uc804\ucc98\ub9ac \ud568\uc218\ndef preprocess_text(text):\n    # \ubb38\uc790 \uc9d1\ud569 \uc0dd\uc131\n    chars = sorted(list(set(text)))\n    char_to_idx = {c: i for i, c in enumerate(chars)}\n    idx_to_char = {i: c for i, c in enumerate(chars)}\n    \n    # \ubb38\uc790\ub97c \uc6d0-\ud56b \uc778\ucf54\ub529 \ud615\ud0dc\ub85c \ubcc0\ud658\n    encoded = [char_to_idx[c] for c in text]\n    return encoded, char_to_idx, idx_to_char\n\n# RNN \ubaa8\ub378 \uc815\uc758\ndef create_model(vocab_size, seq_length):\n    model = tf.keras.Sequential()\n    model.add(tf.keras.layers.Embedding(vocab_size, 256, input_length=seq_length))\n    model.add(tf.keras.layers.LSTM(256, return_sequences=True))\n    model.add(tf.keras.layers.LSTM(256))\n    model.add(tf.keras.layers.Dense(vocab_size, activation='softmax'))\n    return model\n\ntext = \"\uc5ec\ub7ec\ubd84, \ub525 \ub7ec\ub2dd\uc740 \ud765\ubbf8\uc9c4\uc9c4\ud55c \ubd84\uc57c\uc785\ub2c8\ub2e4.\"\n\nencoded_text, char_to_idx, idx_to_char = preprocess_text(text)\nvocab_size = len(char_to_idx)\nseq_length = 10\n\nmodel = create_model(vocab_size, seq_length)\nmodel.compile(loss='sparse_categorical_crossentropy', optimizer='adam')\n\n# \ubaa8\ub378 \ud559\uc2b5 (dummy labels\uacfc epochs \uc124\uc815 \ud544\uc694)\n# model.fit(X_train, y_train, epochs=100)\n<\/code><\/pre>\n
6. \uacb0\ub860<\/h2>\n
Char RNN\uc740 \ub525 \ub7ec\ub2dd \uae30\uc220\uc744 \ud65c\uc6a9\ud558\uc5ec \uc790\uc5f0\uc5b4 \ucc98\ub9ac\ub97c \uc218\ud589\ud558\ub294 \ud6a8\uacfc\uc801\uc778 \ubc29\ubc95 \uc911 \ud558\ub098\uc785\ub2c8\ub2e4. \ubb38\uc790 \ub2e8\uc704\ub85c \ucc98\ub9ac\ub418\uae30 \ub54c\ubb38\uc5d0 \ub192\uc740 \uc720\uc5f0\uc131\uc744 \uac00\uc9c0\uace0 \uc788\uc73c\uba70, \ucc3d\uc758\uc801\uc774\uace0 \uc608\uc220\uc801\uc778 \uc791\uc5c5\uc5d0\ub3c4 \ud65c\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8 \ud3ec\uc2a4\ud2b8\ub97c \ud1b5\ud574 Char RNN\uc758 \uae30\ubcf8 \uac1c\ub150\uacfc \uad6c\uc870, \ud559\uc2b5 \ubc0f \uad6c\ud604 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc774\ud574\ud558\uc600\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4. \uc55e\uc73c\ub85c\uc758 NLP \ubc1c\uc804\uc5d0 \ub300\ud55c \uae30\ub300\uc640 \ud568\uaed8, Char RNN\uc744 \ud65c\uc6a9\ud558\uc5ec \ub2e4\uc591\ud55c \uc751\uc6a9 \ud504\ub85c\uadf8\ub7a8\uc744 \uac1c\ubc1c\ud574\ubcf4\uc138\uc694!<\/p>\n
\uac10\uc0ac\ud569\ub2c8\ub2e4!<\/p>\n
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\ub525 \ub7ec\ub2dd(deep learning) \uae30\uc220\uc740 \ucd5c\uadfc \uba87 \ub144 \ub3d9\uc548 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(natural language processing, NLP) \ubd84\uc57c\uc5d0\uc11c \ud601\uc2e0\uc801\uc778 \ubcc0\ud654\ub97c \uac00\uc838\uc654\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 \ubb38\uc790 \ub2e8\uc704 \uc21c\ud658 \uc2e0\uacbd\ub9dd(character-level recurrent neural network, Char RNN)\uc740 \uac01 \ubb38\uc790\ub97c \uc785\ub825\uc73c\ub85c \uc0ac\uc6a9\ud558\uc5ec \ud14d\uc2a4\ud2b8\ub97c \uc0dd\uc131\ud558\ub294 \ub370 \uc720\uc6a9\ud55c \ubaa8\ub378\uc785\ub2c8\ub2e4. \ubcf8 \ud3ec\uc2a4\ud2b8\uc5d0\uc11c\ub294 Char RNN\uc758 \uac1c\ub150, \uad6c\uc870, \ud65c\uc6a9 \uc0ac\ub840 \ubc0f \uad6c\ud604 \ubc29\ubc95\uc5d0 \ub300\ud574 \uae4a\uc774 \uc788\uac8c \uc0b4\ud3b4\ubcf4\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4. 1. \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc640 … \ub354 \ubcf4\uae30 “\ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac, \ubb38\uc790 \ub2e8\uc704 RNN(Char RNN)”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[16],"tags":[],"class_list":["post-25285","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"\n\ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac, \ubb38\uc790 \ub2e8\uc704 RNN(Char RNN) - 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