\ub2e4\uc591\ud55c \uc9c0\uc6d0:<\/strong> CNN, RNN, LSTM \ub4f1 \ub2e4\uc591\ud55c \ub525 \ub7ec\ub2dd \uc544\ud0a4\ud14d\ucc98\ub97c \uc9c0\uc6d0\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n3. \ucf00\ub77c\uc2a4\ub97c \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac<\/h2>\n
\uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc5d0 \ub525 \ub7ec\ub2dd\uc744 \uc801\uc6a9\ud560 \ub54c, \ucf00\ub77c\uc2a4\ub97c \uc0ac\uc6a9\ud558\uba74 \ubaa8\ub378\ub9c1 \uacfc\uc815\uc774 \ub9e4\uc6b0 \uc6a9\uc774\ud574\uc9d1\ub2c8\ub2e4. \ud14d\uc2a4\ud2b8 \ub370\uc774\ud130\ub97c \uc804\ucc98\ub9ac\ud558\uace0, \uc801\uc808\ud55c \uc2e0\uacbd\ub9dd \uc544\ud0a4\ud14d\ucc98\ub97c \uc124\uacc4\ud55c \ud6c4, \ud6c8\ub828 \ubc0f \ud3c9\uac00\uae4c\uc9c0\uc758 \uc804 \uacfc\uc815\uc744 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
3.1 \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h3>\n
\uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc758 \uccab \ubc88\uc9f8 \ub2e8\uacc4\ub294 \ub370\uc774\ud130\ub97c \uc804\ucc98\ub9ac\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc5d0\uc11c \ud1a0\ud070\ud654(tokenization), \uc815\uc81c(cleaning), \ud328\ub529(padding) \ub4f1\uc758 \uc791\uc5c5\uc744 \uc218\ud589\ud569\ub2c8\ub2e4.<\/p>\n
from keras.preprocessing.text import Tokenizer\nfrom keras.preprocessing.sequence import pad_sequences\nimport numpy as np\n\n# \uc0d8\ud50c \ub370\uc774\ud130\ntexts = [\"\uc774 \uae00\uc740 \ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc5d0 \ub300\ud55c \uac83\uc785\ub2c8\ub2e4.\",\n \"\ub525 \ub7ec\ub2dd\uc740 \ub9e4\uc6b0 \uac15\ub825\ud55c \ub3c4\uad6c\uc785\ub2c8\ub2e4.\",\n \"\uc790\uc5f0\uc5b4 \ucc98\ub9ac\ub294 \uc911\uc694\ud55c \ubd84\uc57c\uc785\ub2c8\ub2e4.\"]\n\n# \ud1a0\ud070\ud654\ntokenizer = Tokenizer()\ntokenizer.fit_on_texts(texts)\nsequences = tokenizer.texts_to_sequences(texts)\n\n# \ud328\ub529\nmax_length = max(len(seq) for seq in sequences)\npadded_sequences = pad_sequences(sequences, maxlen=max_length)\n\nprint(padded_sequences)\n<\/code><\/pre>\n3.2 \ubaa8\ub378 \uc124\uacc4<\/h3>\n
\uc804\ucc98\ub9ac\ub41c \ub370\uc774\ud130\ub97c \uae30\ubc18\uc73c\ub85c \ucf00\ub77c\uc2a4\ub97c \uc774\uc6a9\ud574 \ubaa8\ub378\uc744 \uc124\uacc4\ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, LSTM \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
from keras.models import Sequential\nfrom keras.layers import Embedding, LSTM, Dense\n\n# \ubaa8\ub378 \ucd08\uae30\ud654\nmodel = Sequential()\nmodel.add(Embedding(input_dim=len(tokenizer.word_index)+1, output_dim=64, input_length=max_length))\nmodel.add(LSTM(64))\nmodel.add(Dense(1, activation='sigmoid'))\n\nmodel.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n<\/code><\/pre>\n3.3 \ubaa8\ub378 \ud6c8\ub828<\/h3>\n
\ubaa8\ub378\uc744 \ub370\uc774\ud130\ub85c \ud6c8\ub828\uc2dc\ucf1c\uc57c \ud569\ub2c8\ub2e4. \uc774\ub97c \uc704\ud574 \ub808\uc774\ube14\uc774 \uc788\ub294 \ub370\uc774\ud130\uac00 \ud544\uc694\ud569\ub2c8\ub2e4. \uc544\ub798\ub294 \uc0d8\ud50c \ucf54\ub4dc\uc785\ub2c8\ub2e4.<\/p>\n
# \uc0d8\ud50c \ub808\uc774\ube14 (\uae0d\uc815: 1, \ubd80\uc815: 0)\nlabels = np.array([1, 1, 0])\n\n# \ubaa8\ub378 \ud6c8\ub828\nmodel.fit(padded_sequences, labels, epochs=10, batch_size=2)\n<\/code><\/pre>\n3.4 \ubaa8\ub378 \ud3c9\uac00<\/h3>\n
\ubaa8\ub378 \ud6c8\ub828 \ud6c4, \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\ub97c \uc785\ub825\ud558\uc5ec \uc608\uce21\uc744 \uc218\ud589\ud558\uace0 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud569\ub2c8\ub2e4.<\/p>\n
# \ud3c9\uac00\ntest_texts = [\"\ub525 \ub7ec\ub2dd\uc744 \ud1b5\ud574 \uc790\uc5f0\uc5b4 \ucc98\ub9ac\ub97c \ubc30\uc6b0\ub294 \uac83\uc740 \ud765\ubbf8\ub86d\uc2b5\ub2c8\ub2e4.\"]\ntest_sequences = tokenizer.texts_to_sequences(test_texts)\ntest_padded = pad_sequences(test_sequences, maxlen=max_length)\n\npredictions = model.predict(test_padded)\nprint(predictions)\n<\/code><\/pre>\n4. \uacb0\ub860<\/h2>\n
\ub525 \ub7ec\ub2dd\uc740 \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc758 \uc0c8\ub85c\uc6b4 \uac00\ub2a5\uc131\uc744 \uc5f4\uc5b4\uc8fc\uc5c8\uc2b5\ub2c8\ub2e4. \ucf00\ub77c\uc2a4\uc640 \uac19\uc740 \uace0\uc218\uc900\uc758 API\ub97c \uc0ac\uc6a9\ud558\uba74 \ub354\uc6b1 \uc27d\uac8c \ubcf5\uc7a1\ud55c \ubaa8\ub378\uc744 \uad6c\ud604\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uae30\uc220\uc740 \uc55e\uc73c\ub85c\ub3c4 \uacc4\uc18d \ubc1c\uc804\ud560 \uac83\uc774\uba70, NLP \ubd84\uc57c\uc758 \ud601\uc2e0\uc744 \uc774\ub04c\uc5b4 \ub098\uac08 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
\uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc5d0 \ub300\ud55c \uc774\ud574\uac00 \uae4a\uc5b4\uc9c0\uba74\uc11c, \uc5ec\ub7ec\ubd84\ub3c4 \ub525 \ub7ec\ub2dd\uc744 \ud1b5\ud574 \ub2e4\uc591\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ubb38\uc81c\ub97c \ud574\uacb0\ud560 \uc218 \uc788\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4. \ucf00\ub77c\uc2a4\ub97c \uc0ac\uc6a9\ud558\uc5ec \uac04\ub2e8\ud55c \ud504\ub85c\uc81d\ud2b8\ubd80\ud130 \uc2dc\uc791\ud574\ubcf4\uc138\uc694. \uc5ec\uae30\uc11c\ub294 \uae30\ubcf8\uc801\uc778 \ud750\ub984\uc744 \uc124\uba85\ud588\uc9c0\ub9cc, \uc2e4\uc81c\ub85c\ub294 \uc5ec\ub7ec \uac00\uc9c0 \uae30\uc220\uc801 \uc694\uc18c\uc640 \uc2ec\uce35\uc801\uc778 \ub0b4\uc6a9\uc774 \uc874\uc7ac\ud569\ub2c8\ub2e4.<\/p>\n
\uc774\ubc88 \uac8c\uc2dc\uae00\uc774 \ub525 \ub7ec\ub2dd\uacfc \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc758 \uc138\uacc4\ub85c \ub098\uc544\uac00\ub294 \uccab \uac78\uc74c\uc774 \ub418\uc5c8\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4. \uac10\uc0ac\ud569\ub2c8\ub2e4!<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\ub525 \ub7ec\ub2dd\uc758 \ubc1c\uc804\uc73c\ub85c \uc6b0\ub9ac\ub294 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(Natural Language Processing, NLP)\uc5d0 \ub300\ud574 \uc0c8\ub85c\uc6b4 \uc811\uadfc \ubc29\ubc95\uc744 \uac16\uac8c \ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uc624\ub298\ub0a0\uc758 \ub525 \ub7ec\ub2dd \uae30\ubc18 \ubc29\ubc95\uc740 \uc774\uc804\uc758 \uc804\ud1b5\uc801\uc778 \ubc29\ubc95\ub4e4\ubcf4\ub2e4 \ub354 \ub6f0\uc5b4\ub09c \uc131\ub2a5\uc744 \uc790\ub791\ud558\uba70, \ud2b9\ud788 \ucf00\ub77c\uc2a4(Keras)\uc640 \uac19\uc740 \uace0\uc218\uc900 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud1b5\ud574 \uad6c\ud604\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ubc88 \uae00\uc5d0\uc11c\ub294 \ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc758 \uae30\ubcf8 \uac1c\ub150, \ucf00\ub77c\uc2a4\uc758 \ud2b9\uc9d5, \uadf8\ub9ac\uace0 \uc2e4\uc81c \uad6c\ud604 \uc0ac\ub840\ub97c \uc790\uc138\ud788 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. \uc790\uc5f0\uc5b4 … \ub354 \ubcf4\uae30 “07-08 \ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac, \ucf00\ub77c\uc2a4(Keras) \ud6d1\uc5b4\ubcf4\uae30”<\/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-25261","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"\n07-08 \ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac, \ucf00\ub77c\uc2a4(Keras) \ud6d1\uc5b4\ubcf4\uae30 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/atmokpo.com\/w\/25261\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"07-08 \ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac, \ucf00\ub77c\uc2a4(Keras) \ud6d1\uc5b4\ubcf4\uae30 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"\ub525 \ub7ec\ub2dd\uc758 \ubc1c\uc804\uc73c\ub85c \uc6b0\ub9ac\ub294 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(Natural Language Processing, NLP)\uc5d0 \ub300\ud574 \uc0c8\ub85c\uc6b4 \uc811\uadfc \ubc29\ubc95\uc744 \uac16\uac8c \ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uc624\ub298\ub0a0\uc758 \ub525 \ub7ec\ub2dd \uae30\ubc18 \ubc29\ubc95\uc740 \uc774\uc804\uc758 \uc804\ud1b5\uc801\uc778 \ubc29\ubc95\ub4e4\ubcf4\ub2e4 \ub354 \ub6f0\uc5b4\ub09c \uc131\ub2a5\uc744 \uc790\ub791\ud558\uba70, \ud2b9\ud788 \ucf00\ub77c\uc2a4(Keras)\uc640 \uac19\uc740 \uace0\uc218\uc900 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud1b5\ud574 \uad6c\ud604\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ubc88 \uae00\uc5d0\uc11c\ub294 \ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc758 \uae30\ubcf8 \uac1c\ub150, \ucf00\ub77c\uc2a4\uc758 \ud2b9\uc9d5, \uadf8\ub9ac\uace0 \uc2e4\uc81c \uad6c\ud604 \uc0ac\ub840\ub97c \uc790\uc138\ud788 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. \uc790\uc5f0\uc5b4 … \ub354 \ubcf4\uae30 "07-08 \ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac, \ucf00\ub77c\uc2a4(Keras) \ud6d1\uc5b4\ubcf4\uae30"\" \/>\n<meta property=\"og:url\" content=\"https:\/\/atmokpo.com\/w\/25261\/\" \/>\n<meta property=\"og:site_name\" content=\"\ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"article:published_time\" content=\"2024-10-26T09:53:25+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-11-26T08:02:21+00:00\" \/>\n<meta name=\"author\" content=\"root\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@bebubo4\" \/>\n<meta name=\"twitter:site\" 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