{"id":25385,"date":"2024-10-26T09:54:38","date_gmt":"2024-10-26T09:54:38","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=25385"},"modified":"2024-11-26T08:01:56","modified_gmt":"2024-11-26T08:01:56","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-word-level-%eb%b2%88%ec%97%ad%ea%b8%b0-%eb%a7%8c%eb%93%a4%ea%b8%b0-neural-machine-tra","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/25385\/","title":{"rendered":"\ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac, Word-Level \ubc88\uc5ed\uae30 \ub9cc\ub4e4\uae30 (Neural Machine Translation (seq2seq) Tutorial)"},"content":{"rendered":"
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\uc791\uc131\uc790: \uc870\uad11\ud615<\/p>\n

\uc791\uc131\uc77c: 2024\ub144 11\uc6d4 26\uc77c<\/p>\n<\/header>\n

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1. \uc11c\ub860<\/h2>\n

\n \ub525 \ub7ec\ub2dd \uae30\uc220\uc758 \ubc1c\uc804\uc73c\ub85c \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP)\ub294 \uadf8 \uc5b4\ub290 \ub54c\ubcf4\ub2e4\ub3c4 \uc8fc\ubaa9\ubc1b\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 Neural Machine Translation (NMT) \uae30\uc220\uc740 \uba38\uc2e0 \ubc88\uc5ed \ubd84\uc57c\uc5d0\uc11c \ud601\uc2e0\uc744 \uac00\uc838\uc654\uc2b5\ub2c8\ub2e4. \ubcf8 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \uc2dc\ud000\uc2a4 \ud22c \uc2dc\ud000\uc2a4(Seq2Seq) \ubaa8\ub378\uc744 \ud1b5\ud574 \ub2e8\uc5b4 \uc218\uc900\uc758 \ubc88\uc5ed\uae30\ub97c \ub9cc\ub4dc\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. \uc774 \ubc88\uc5ed\uae30\ub294 \uc785\ub825 \ubb38\uc7a5\uc758 \uc758\ubbf8\ub97c \uc774\ud574\ud558\uace0, \uadf8\uc5d0 \uc0c1\uc751\ud558\ub294 \ucd9c\ub825 \uc5b8\uc5b4\ub85c \uc815\ud655\ud558\uac8c \ubc88\uc5ed\ud560 \uc218 \uc788\ub3c4\ub85d \uc124\uacc4\ub418\uc5c8\uc2b5\ub2c8\ub2e4.\n <\/p>\n

\n \uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 TensorFlow\uc640 Keras\ub97c \uc0ac\uc6a9\ud558\uc5ec Seq2Seq \ubaa8\ub378\uc744 \uad6c\ud604\ud558\uace0, \ub370\uc774\ud130 \uc804\ucc98\ub9ac, \ubaa8\ub378 \ud559\uc2b5, \uadf8\ub9ac\uace0 \ud3c9\uac00 \ub2e8\uacc4\uae4c\uc9c0 \ucc28\uadfc\ucc28\uadfc \uc124\uba85\ud560 \uac83\uc785\ub2c8\ub2e4.\n <\/p>\n<\/section>\n

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2. \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP) \uae30\ucd08<\/h2>\n

\n \uc790\uc5f0\uc5b4 \ucc98\ub9ac\ub294 \ucef4\ud4e8\ud130\uac00 \uc790\uc5f0\uc5b4\ub97c \uc774\ud574\ud558\uace0 \ucc98\ub9ac\ud560 \uc218 \uc788\ub3c4\ub85d \ud558\ub294 \uae30\uc220\uc785\ub2c8\ub2e4. \uc774 \ubd84\uc57c\uc5d0\uc11c \ub525 \ub7ec\ub2dd\uc740 \ud2b9\ud788 \ub192\uc740 \uc131\ub2a5\uc744 \ubcf4\uc774\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 \uc2dc\ud000\uc2a4 \ub370\uc774\ud130 \ucc98\ub9ac\uc5d0 \uac15\uc810\uc744 \uac00\uc9c4 RNN(Recurrent Neural Networks)\uc640 LSTM(Long Short-Term Memory) \ub124\ud2b8\uc6cc\ud06c\uac00 \ub9ce\uc774 \uc0ac\uc6a9\ub429\ub2c8\ub2e4.\n <\/p>\n

\n NMT\ub294 \ubb38\uc7a5\uc744 \ub2e8\uc5b4 \ub2e8\uc704\ub85c \uc774\ud574\ud558\uace0 \ubc88\uc5ed\ud558\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uacfc\uc815\uc5d0\uc11c Seq2Seq \ubaa8\ub378\uc774 \uc0ac\uc6a9\ub418\uba70, \uc774 \ubaa8\ub378\uc740 \uc778\ucf54\ub354(Encoder)\uc640 \ub514\ucf54\ub354(Decoder)\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4. \uc778\ucf54\ub354\ub294 \uc785\ub825 \ubb38\uc7a5\uc744 \uc7a0\uc7ac \ubca1\ud130(latent vector)\ub85c \ubcc0\ud658\ud558\uace0, \ub514\ucf54\ub354\ub294 \uc774 \ubca1\ud130\ub97c \uc0ac\uc6a9\ud558\uc5ec \ucd9c\ub825 \ubb38\uc7a5\uc744 \uc0dd\uc131\ud569\ub2c8\ub2e4.\n <\/p>\n<\/section>\n

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3. Seq2Seq \ubaa8\ub378 \uad6c\uc870<\/h2>\n

\n Seq2Seq \ubaa8\ub378\uc740 \uae30\ubcf8\uc801\uc73c\ub85c \uc785\ub825 \uc2dc\ud000\uc2a4\uc640 \ucd9c\ub825 \uc2dc\ud000\uc2a4\ub97c \ucc98\ub9ac\ud558\ub294 \ub450 \uac1c\uc758 RNN\uc73c\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4. \uc778\ucf54\ub354\ub294 \uc785\ub825 \ub370\uc774\ud130\ub97c \uc2dc\ud000\uc2a4\ub85c \ucc98\ub9ac\ud558\uace0, \ub9c8\uc9c0\ub9c9 \uc740\ub2c9 \uc0c1\ud0dc\ub97c \ub514\ucf54\ub354\uc5d0 \uc804\ub2ec\ud558\ub294 \uc5ed\ud560\uc744 \ud569\ub2c8\ub2e4. \ub514\ucf54\ub354\ub294 \uc778\ucf54\ub354\uc758 \ucd9c\ub825 \uacb0\uacfc\ub85c\ubd80\ud130 \ub2e4\uc74c \ub2e8\uc5b4\ub97c \uc608\uce21\ud558\uba70, \uc774\ub7ec\ud55c \uacfc\uc815\uc744 \uc5ec\ub7ec \ubc88 \ubc18\ubcf5\ud569\ub2c8\ub2e4.\n <\/p>\n

\n            \n                class Encoder(tf.keras.Model):\n                    def __init__(self, vocab_size, embedding_dim, units):\n                        super(Encoder, self).__init__()\n                        self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)\n                        self.rnn = tf.keras.layers.LSTM(units, return_sequences=True, return_state=True)\n\n                    def call(self, x):\n                        x = self.embedding(x)\n                        output, state = self.rnn(x)\n                        return output, state\n\n                class Decoder(tf.keras.Model):\n                    def __init__(self, vocab_size, embedding_dim, units):\n                        super(Decoder, self).__init__()\n                        self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)\n                        self.rnn = tf.keras.layers.LSTM(units, return_sequences=True, return_state=True)\n                        self.fc = tf.keras.layers.Dense(vocab_size)\n\n                    def call(self, x, state):\n                        x = self.embedding(x)\n                        output, state = self.rnn(x, initial_state=state)\n                        x = self.fc(output)\n                        return x, state\n            <\/code>\n        <\/pre>\n<\/section>\n
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4. \ub370\uc774\ud130 \uc900\ube44<\/h2>\n
\n            Seq2Seq \ubaa8\ub378\uc744 \ud6c8\ub828\uc2dc\ud0a4\uae30 \uc704\ud574\uc11c\ub294 \ub300\ub7c9\uc758 \ud3c9\ud589 \ucf54\ud37c\uc2a4(parallel corpus)\uac00 \ud544\uc694\ud569\ub2c8\ub2e4. \uc774 \ub370\uc774\ud130\ub294 \ubc88\uc5ed\ud558\uace0\uc790 \ud558\ub294 \uc6d0\ubb38\uacfc \uadf8\uc5d0 \uc0c1\uc751\ud558\ub294 \ubc88\uc5ed\ubb38\uc73c\ub85c \uad6c\uc131\uc774 \ub418\uc5b4\uc57c \ud569\ub2c8\ub2e4. \ub370\uc774\ud130 \uc900\ube44 \uacfc\uc815\uc740 \ub2e4\uc74c\uacfc \uac19\uc740 \uacfc\uc815\uc744 \ud3ec\ud568\ud569\ub2c8\ub2e4:\n        <\/p>\n
    \n
  1. \ub370\uc774\ud130 \uc218\uc9d1: OSI (Open Subtitles) \ub370\uc774\ud130\uc14b\uacfc \uac19\uc740 \uacf5\uac1c \ubc88\uc5ed \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n
  2. \ub370\uc774\ud130 \uc815\uc81c: \ubb38\uc7a5\uc744 \uc18c\ubb38\uc790\ub85c \ubcc0\ud658\ud558\uace0, \ubd88\ud544\uc694\ud55c \uae30\ud638\ub97c \uc81c\uac70\ud569\ub2c8\ub2e4.<\/li>\n
  3. \ub2e8\uc5b4 \ubd84\ub9ac: \ubb38\uc7a5\uc744 \ub2e8\uc5b4 \ub2e8\uc704\ub85c \ubd84\ub9ac\ud558\uace0, \uac01 \ub2e8\uc5b4\uc5d0 \uc778\ub371\uc2a4\ub97c \ubd80\uc5ec\ud569\ub2c8\ub2e4.<\/li>\n<\/ol>\n
    \n            \ub2e4\uc74c\uc740 \ub370\uc774\ud130\ub97c \uc804\ucc98\ub9ac\ud558\ub294 \ucf54\ub4dc \uc608\uc81c\uc785\ub2c8\ub2e4.\n        <\/p>\n
    \n            \n                def preprocess_data(sentences):\n                    # \uc18c\ubb38\uc790\ud654 \ubc0f \uae30\ud638 \uc81c\uac70\n                    sentences = [s.lower() for s in sentences]\n                    sentences = [re.sub(r\"[^\\w\\s]\", \"\", s) for s in sentences]\n                    return sentences\n\n                # \uc0d8\ud50c \ub370\uc774\ud130\n                original = [\"Hello, how are you?\", \"I am learning deep learning.\"]\n                translated = [\"\uc548\ub155\ud558\uc138\uc694, \uc5b4\ub5bb\uac8c \uc9c0\ub0b4\uc138\uc694?\", \"\uc800\ub294 \ub525 \ub7ec\ub2dd\uc744 \ubc30\uc6b0\uace0 \uc788\uc2b5\ub2c8\ub2e4.\"]\n\n                # \ub370\uc774\ud130 \uc804\ucc98\ub9ac\n                original = preprocess_data(original)\n                translated = preprocess_data(translated)\n            <\/code>\n        <\/pre>\n<\/section>\n
    \n
    5. \ubaa8\ub378 \ud559\uc2b5<\/h2>\n
    \n            \ub370\uc774\ud130 \uc900\ube44\uac00 \ub05d\ub098\uba74, \ubaa8\ub378 \ud559\uc2b5\uc744 \uc9c4\ud589\ud569\ub2c8\ub2e4. Seq2Seq \ubaa8\ub378\uc758 \ud559\uc2b5\uc740 \uc8fc\ub85c teacher forcing \uae30\ubc95\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4. \uc774\ub294 \ub514\ucf54\ub354\uac00 \uc774\uc804\uc758 \uc608\uce21 \uacb0\uacfc \ub300\uc2e0 \uc2e4\uc81c \uac12\uc744 \uc785\ub825\uc73c\ub85c \uc0ac\uc6a9\ud558\uc5ec \ud559\uc2b5\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4.\n        <\/p>\n
    \n            \n                optimizer = tf.keras.optimizers.Adam()\n                loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n\n                def train_step(input_tensor, target_tensor):\n                    with tf.GradientTape() as tape:\n                        enc_output, enc_state = encoder(input_tensor)\n                        dec_state = enc_state\n                        predictions, _ = decoder(target_tensor, dec_state)\n                        loss = loss_object(target_tensor[:, 1:], predictions)\n\n                    gradients = tape.gradient(loss, encoder.trainable_variables + decoder.trainable_variables)\n                    optimizer.apply_gradients(zip(gradients, encoder.trainable_variables + decoder.trainable_variables))\n                    return loss\n            <\/code>\n        <\/pre>\n<\/section>\n
    \n
    6. \ubaa8\ub378 \ud3c9\uac00<\/h2>\n
    \n            \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\uae30 \uc704\ud574 BLEU \uc810\uc218\uc640 \uac19\uc740 \uc9c0\ud45c\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. BLEU\ub294 \uae30\uacc4 \ubc88\uc5ed\uc758 \ud488\uc9c8\uc744 \ud3c9\uac00\ud558\ub294\ub370 \ub110\ub9ac \uc0ac\uc6a9\ub418\ub294 \ubc29\ubc95\uc774\uba70, \uc608\uc0c1 \ucd9c\ub825\uacfc\uc758 \uc720\uc0ac\uc131\uc744 \uce21\uc815\ud569\ub2c8\ub2e4.\n        <\/p>\n
    \n            \n                from nltk.translate.bleu_score import sentence_bleu\n\n                def evaluate_model(input_sentence):\n                    # \uc778\ucf54\ub529\n                    input_tensor = encode_sentence(input_sentence)\n                    enc_output, enc_state = encoder(input_tensor)\n                    dec_state = enc_state\n\n                    # \ub514\ucf54\ub529\n                    output_sentence = []\n                    for _ in range(max_length):\n                        predictions, dec_state = decoder(dec_input, dec_state)\n\n                        predicted_id = tf.argmax(predictions[:, -1, :], axis=-1).numpy()\n                        output_sentence.append(predicted_id)\n\n                        if predicted_id == end_token:\n                            break\n\n                    return output_sentence\n            <\/code>\n        <\/pre>\n<\/section>\n
    \n
    7. \uacb0\ub860<\/h2>\n
    \n            \ubcf8 \ud29c\ud1a0\ub9ac\uc5bc\uc744 \ud1b5\ud574 \ub525 \ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c Word-Level \ubc88\uc5ed\uae30\uc758 \uae30\ubcf8\uc801\uc778 \uad6c\uc870\uc640 \uad6c\ud604 \ubc29\ubc95\uc744 \uc54c\uc544\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c \ub2e4\ub8ec \ub0b4\uc6a9\uc744 \ubc14\ud0d5\uc73c\ub85c \uc5ec\ub7ec\ubd84\uc774 \ub354\uc6b1 \ubc1c\uc804\ub41c \uc790\uc5f0\uc5b4 \ucc98\ub9ac \uc2dc\uc2a4\ud15c\uc744 \uac1c\ubc1c\ud558\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4. \ucd94\uac00\uc801\uc778 \uae30\uc220\uacfc \ubc29\ubc95\uc744 \ud65c\uc6a9\ud574 \uc131\ub2a5\uc744 \ub354\uc6b1 \uac1c\uc120\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n        <\/p>\n
    \n            \ub354 \ub9ce\uc740 \uc815\ubcf4\uc640 \uc790\ub8cc\ub294 \uad00\ub828 \uc5f0\uad6c \ub17c\ubb38\uc774\ub098 GitHub \uc800\uc7a5\uc18c\uc5d0\uc11c \ud655\uc778\ud560 \uc218 \uc788\uc73c\uba70, \uac01\uc885 \ud504\ub808\uc784\uc6cc\ud06c\uc758 \ubb38\uc11c\ub97c \ud1b5\ud574 \ub354 \ub9ce\uc740 \uad6c\ud604 \uae30\ubc95\uc744 \ubc30\uc6b8 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc5ec\ub7ec\ubd84\uc758 \ubc88\uc5ed\uae30 \uac1c\ubc1c \uc5ec\uc815\uc744 \uc751\uc6d0\ud569\ub2c8\ub2e4!\n        <\/p>\n<\/section>\n<\/article>\n","protected":false},"excerpt":{"rendered":"
    \uc791\uc131\uc790: \uc870\uad11\ud615 \uc791\uc131\uc77c: 2024\ub144 11\uc6d4 26\uc77c 1. \uc11c\ub860 \ub525 \ub7ec\ub2dd \uae30\uc220\uc758 \ubc1c\uc804\uc73c\ub85c \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP)\ub294 \uadf8 \uc5b4\ub290 \ub54c\ubcf4\ub2e4\ub3c4 \uc8fc\ubaa9\ubc1b\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 Neural Machine Translation (NMT) \uae30\uc220\uc740 \uba38\uc2e0 \ubc88\uc5ed \ubd84\uc57c\uc5d0\uc11c \ud601\uc2e0\uc744 \uac00\uc838\uc654\uc2b5\ub2c8\ub2e4. \ubcf8 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \uc2dc\ud000\uc2a4 \ud22c \uc2dc\ud000\uc2a4(Seq2Seq) \ubaa8\ub378\uc744 \ud1b5\ud574 \ub2e8\uc5b4 \uc218\uc900\uc758 \ubc88\uc5ed\uae30\ub97c \ub9cc\ub4dc\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. \uc774 \ubc88\uc5ed\uae30\ub294 \uc785\ub825 \ubb38\uc7a5\uc758 \uc758\ubbf8\ub97c \uc774\ud574\ud558\uace0, \uadf8\uc5d0 \uc0c1\uc751\ud558\ub294 \ucd9c\ub825 \uc5b8\uc5b4\ub85c \uc815\ud655\ud558\uac8c \ubc88\uc5ed\ud560 … \ub354 \ubcf4\uae30 “\ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac, Word-Level \ubc88\uc5ed\uae30 \ub9cc\ub4e4\uae30 (Neural Machine Translation (seq2seq) Tutorial)”<\/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-25385","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, Word-Level \ubc88\uc5ed\uae30 \ub9cc\ub4e4\uae30 (Neural Machine Translation (seq2seq) Tutorial) - 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