8. \uacb0\ub860<\/a><\/li>\n<\/ul>\n1. \uc784\ubca0\ub529\uc758 \uac1c\ub150<\/h2>\n
\uc784\ubca0\ub529\uc740 \uace0\ucc28\uc6d0\uc758 \ub370\uc774\ud130\ub97c \uc800\ucc28\uc6d0\uc73c\ub85c \ubcc0\ud658\ud558\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \uc774 \uacfc\uc815\uc740 \ub2e8\uc5b4, \ubb38\uc7a5, \uc774\ubbf8\uc9c0 \ub4f1\uc758 \ud2b9\uc131\uc744 \ubca1\ud130 \ud615\ud0dc\ub85c \ud45c\ud604\ud558\uae30 \uc704\ud574 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \ub525\ub7ec\ub2dd \ubaa8\ub378\uc740 \uc784\ubca0\ub529\uc744 \ud1b5\ud574 \uc785\ub825 \ub370\uc774\ud130\ub97c \ubcf4\ub2e4 \uc774\ud574\ud558\uae30 \uc26c\uc6b4 \ud615\ud0dc\ub85c \ud45c\ud604\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\uc784\ubca0\ub529\uc758 \ubaa9\uc801\uc740 \ube44\uc2b7\ud55c \uc758\ubbf8\ub97c \uac00\uc9c4 \ub370\uc774\ud130\uac00 \ube44\uc2b7\ud55c \ubca1\ud130 \uacf5\uac04\uc5d0 \uc704\uce58\ud558\ub3c4\ub85d \ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, ‘\uac15\uc544\uc9c0’\uc640 ‘\uace0\uc591\uc774’\uac00 \ube44\uc2b7\ud55c \uc758\ubbf8\ub97c \uac00\uc9c4\ub2e4\uba74, \uc774 \ub450 \ub2e8\uc5b4\uc758 \uc784\ubca0\ub529 \ubca1\ud130\ub3c4 \uc11c\ub85c \uac00\uae4c\uc6b4 \uc704\uce58\uc5d0 \uc874\uc7ac\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n
2. \uc608\uce21 \uae30\ubc18 \uc784\ubca0\ub529<\/h2>\n
\uc608\uce21 \uae30\ubc18 \uc784\ubca0\ub529\uc740 \uae30\uc874\uc758 \uc784\ubca0\ub529 \uae30\ubc95 \uc911 \ud558\ub098\ub85c, \uc8fc\uc5b4\uc9c4 \uc785\ub825 \ub370\uc774\ud130\ub97c \ubc14\ud0d5\uc73c\ub85c \ub2e4\uc74c \ub2e8\uc5b4\ub97c \uc608\uce21\ud558\ub294 \ubc29\uc2dd\uc73c\ub85c \uc784\ubca0\ub529\uc744 \ud559\uc2b5\ud569\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \ub2e8\uc5b4 \uac04\uc758 \uad00\uacc4\ub97c \ud559\uc2b5\ud558\uace0, \uc758\ubbf8 \uc788\ub294 \ubca1\ud130 \uacf5\uac04\uc744 \ub9cc\ub4e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\uc608\uce21 \uae30\ubc18 \uc784\ubca0\ub529\uc758 \ub300\ud45c\uc801\uc778 \uc608\ub85c\ub294 Word2Vec\uc758 Skip-gram \ubaa8\ub378\uc774 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \ubaa8\ub378\uc740 \uc8fc\uc5b4\uc9c4 \ub2e8\uc5b4\ub97c \uae30\ubc18\uc73c\ub85c \uc8fc\ubcc0 \ub2e8\uc5b4\ub4e4\uc758 \uc874\uc7ac \ud655\ub960\uc744 \uc608\uce21\ud558\ub294 \ubc29\uc2dd\uc73c\ub85c \ub3d9\uc791\ud569\ub2c8\ub2e4.<\/p>\n
3. PyTorch \uae30\ubc18 \uad6c\ud604<\/h2>\n
\uc774\ubc88 \uc139\uc158\uc5d0\uc11c\ub294 PyTorch\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc608\uce21 \uae30\ubc18 \uc784\ubca0\ub529\uc744 \uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. PyTorch\ub294 \ud150\uc11c \uc5f0\uc0b0\uacfc \uc790\ub3d9 \ubbf8\ubd84 \uae30\ub2a5\uc744 \uc81c\uacf5\ud558\uc5ec \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uc27d\uac8c \uad6c\uc131\ud558\uace0 \ud6c8\ub828\ud560 \uc218 \uc788\ub294 \ud504\ub808\uc784\uc6cc\ud06c\uc785\ub2c8\ub2e4.<\/p>\n
4. \ub370\uc774\ud130\uc14b \uc900\ube44<\/h2>\n
\uba3c\uc800, \ub370\uc774\ud130\uc14b\uc744 \uc900\ube44\ud574\uc57c \ud569\ub2c8\ub2e4. \uc774\ubc88 \uc608\uc81c\uc5d0\uc11c\ub294 \uac04\ub2e8\ud55c \ubb38\uc7a5 \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc784\ubca0\ub529\uc744 \ud559\uc2b5\ud560 \uac83\uc785\ub2c8\ub2e4. \ubb38\uc7a5 \ub370\uc774\ud130\ub97c \uc544\ub798\uc640 \uac19\uc774 \uc815\uc758\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
sentences = [\n \"\ub525\ub7ec\ub2dd\uc740 \uba38\uc2e0\ub7ec\ub2dd\uc758 \ud55c \ubd84\uc57c\uc785\ub2c8\ub2e4.\",\n \"\uc778\uacf5\uc9c0\ub2a5\uc740 \ubbf8\ub798 \uae30\uc220\ub85c \uc8fc\ubaa9\ubc1b\uace0 \uc788\uc2b5\ub2c8\ub2e4.\",\n \"\ub525\ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc608\uce21 \ubaa8\ub378\uc774 \ub9ce\uc774 \uac1c\ubc1c\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4.\"\n ]<\/code><\/pre>\n\uadf8 \ub2e4\uc74c\uc73c\ub85c \ub370\uc774\ud130 \uc804\ucc98\ub9ac\ub97c \uc218\ud589\ud558\uaca0\uc2b5\ub2c8\ub2e4. \ubb38\uc7a5\uc744 \ub2e8\uc5b4\ub85c \ubd84\ub9ac\ud558\uace0, \uac01 \ub2e8\uc5b4\uc5d0 \uace0\uc720\ud55c \uc778\ub371\uc2a4\ub97c \ubd80\uc5ec\ud569\ub2c8\ub2e4.<\/p>\n
\nfrom collections import Counter\nfrom nltk.tokenize import word_tokenize\n\n# \ubb38\uc7a5 \ub370\uc774\ud130\ub97c \ub2e8\uc5b4\ub85c \ubd84\ub9ac\nwords = [word for sentence in sentences for word in word_tokenize(sentence)]\n\n# \ub2e8\uc5b4 \ube48\ub3c4 \uacc4\uc0b0\nword_counts = Counter(words)\n\n# \ub2e8\uc5b4 \uc778\ub371\uc2a4 \ubd80\uc5ec\nword_to_idx = {word: idx for idx, (word, _) in enumerate(word_counts.items())}\nidx_to_word = {idx: word for word, idx in word_to_idx.items()}\n <\/code><\/pre>\n5. \ubaa8\ub378 \uad6c\uc131<\/h2>\n
\uc774\uc81c \uc784\ubca0\ub529 \ubaa8\ub378\uc744 \uad6c\uc131\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uac04\ub2e8\ud55c \uc2e0\uacbd\ub9dd\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc785\ub825 \ub2e8\uc5b4\ub97c \uc784\ubca0\ub529 \ubca1\ud130\ub85c \ubcc0\ud658\ud558\uace0, \uc8fc\uc5b4\uc9c4 \ub2e8\uc5b4\uc5d0 \ub300\ud55c \uc608\uce21\uc744 \uc218\ud589\ud569\ub2c8\ub2e4.<\/p>\n
\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\n\nclass EmbedModel(nn.Module):\n def __init__(self, vocab_size, embedding_dim):\n super(EmbedModel, self).__init__()\n self.embeddings = nn.Embedding(vocab_size, embedding_dim)\n\n def forward(self, input):\n return self.embeddings(input)\n \n# \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc124\uc815\nembedding_dim = 10\nvocab_size = len(word_to_idx)\n\n# \ubaa8\ub378 \ucd08\uae30\ud654\nmodel = EmbedModel(vocab_size, embedding_dim)\n <\/code><\/pre>\n6. \ubaa8\ub378 \ud6c8\ub828<\/h2>\n
\uc774\uc81c \ubaa8\ub378\uc744 \ud6c8\ub828\uc2dc\ucf1c\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc190\uc2e4 \ud568\uc218\ub97c \uc124\uc815\ud558\uace0, \uc635\ud2f0\ub9c8\uc774\uc800\ub97c \uc0ac\uc6a9\ud558\uc5ec \uac00\uc911\uce58\ub97c \uc5c5\ub370\uc774\ud2b8\ud569\ub2c8\ub2e4. \uc8fc\uc5b4\uc9c4 \ub2e8\uc5b4\ub97c \uae30\ubc18\uc73c\ub85c \ub2e4\uc74c \ub2e8\uc5b4\ub97c \uc608\uce21\ud558\ub294 \uc791\uc5c5\uc744 \uc218\ud589\ud569\ub2c8\ub2e4.<\/p>\n
\n# \uc190\uc2e4 \ud568\uc218 \ubc0f \uc635\ud2f0\ub9c8\uc774\uc800 \uc124\uc815\nloss_function = nn.CrossEntropyLoss()\noptimizer = optim.SGD(model.parameters(), lr=0.01)\n\n# \ud6c8\ub828 \ub370\uc774\ud130 \uc900\ube44\ntrain_data = [(word_to_idx[words[i]], word_to_idx[words[i + 1]]) for i in range(len(words) - 1)]\n\n# \ubaa8\ub378 \ud6c8\ub828\nfor epoch in range(100): # \uc5d0\ud3ec\ud06c \uc218\n total_loss = 0\n for input_word, target_word in train_data:\n model.zero_grad() # \uae30\uc6b8\uae30 \ucd08\uae30\ud654\n input_tensor = torch.tensor([input_word], dtype=torch.long)\n target_tensor = torch.tensor([target_word], dtype=torch.long)\n\n # \ubaa8\ub378 \ucd9c\ub825 \uacc4\uc0b0\n output = model(input_tensor)\n\n # \uc190\uc2e4 \uacc4\uc0b0\n loss = loss_function(output, target_tensor)\n total_loss += loss.item()\n\n # \uc5ed\uc804\ud30c \ubc0f \uac00\uc911\uce58 \uc5c5\ub370\uc774\ud2b8\n loss.backward()\n optimizer.step()\n\n print(f\"Epoch {epoch + 1}, Loss: {total_loss:.4f}\")\n <\/code><\/pre>\n7. \uacb0\uacfc \ubd84\uc11d<\/h2>\n
\ud6c8\ub828\uc774 \uc644\ub8cc\ub41c \ud6c4, \uac01 \ub2e8\uc5b4\uc5d0 \ub300\ud55c \uc784\ubca0\ub529 \ubca1\ud130\ub97c \ubd84\uc11d\ud558\uc5ec \ub2e8\uc5b4 \uac04\uc758 \uad00\uacc4\ub97c \uc2dc\uac01\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \uc608\uce21 \uae30\ubc18 \uc784\ubca0\ub529\uc758 \ud6a8\uacfc\ub97c \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\n# \ub2e8\uc5b4 \uc784\ubca0\ub529 \ubca1\ud130 \ucd94\ucd9c\nwith torch.no_grad():\n word_embeddings = model.embeddings.weight.numpy()\n\n# \uacb0\uacfc \ucd9c\ub825\nfor word, idx in word_to_idx.items():\n print(f\"{word}: {word_embeddings[idx]}\")\n <\/code><\/pre>\n8. \uacb0\ub860<\/h2>\n
\uc774\ubc88 \uae00\uc5d0\uc11c\ub294 \ub525\ub7ec\ub2dd\uc758 \uc608\uce21 \uae30\ubc18 \uc784\ubca0\ub529 \uac1c\ub150\uc5d0 \ub300\ud574 \uc0b4\ud3b4\ubcf4\uace0, PyTorch\ub97c \ud65c\uc6a9\ud558\uc5ec \uc774\ub97c \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc744 \ubc30\uc6cc\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \uc784\ubca0\ub529\uc740 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \ud65c\uc6a9\ud560 \uc218 \uc788\uc73c\uba70, \uc608\uce21 \uae30\ubc18 \uc784\ubca0\ub529\uc740 \ub2e8\uc5b4 \uac04\uc758 \uad00\uacc4\ub97c \ud6a8\uacfc\uc801\uc73c\ub85c \ud45c\ud604\ud560 \uc218 \uc788\ub294 \uc720\uc6a9\ud55c \uae30\ubc95\uc785\ub2c8\ub2e4. \uc55e\uc73c\ub85c \ub354 \ub9ce\uc740 \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud558\uace0, \ub2e4\uc591\ud55c \ubaa8\ub378\uc744 \uc2e4\ud5d8\ud574\ubcf4\uba70 \uc784\ubca0\ub529\uc758 \uac00\ub2a5\uc131\uc744 \ud0d0\uad6c\ud574 \ubcf4\uae38 \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n
\uc774 \uae00\uc774 \uc5ec\ub7ec\ubd84\uc5d0\uac8c \ub3c4\uc6c0\uc774 \ub418\uc5c8\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4. \uc5ec\ub7ec\ubd84\uc758 \ub525\ub7ec\ub2dd \uc5ec\uc815\uc5d0 \ud589\uc6b4\uc774 \uac00\ub4dd\ud558\uae38 \uae30\uc6d0\ud569\ub2c8\ub2e4!<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\ub525\ub7ec\ub2dd\uc758 \uc138\uacc4\ub294 \uacc4\uc18d\ud574\uc11c \uc9c4\ud654\ud558\uace0 \uc788\uc73c\uba70, \uc778\uacf5 \uc2e0\uacbd\ub9dd\uc740 \ub2e4\uc591\ud55c \uc751\uc6a9 \ubd84\uc57c\uc5d0\uc11c \uc7a0\uc7ac\ub825\uc744 \ubcf4\uc5ec\uc8fc\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uadf8 \uc911 \ud558\ub098\uac00 \ubc14\ub85c ‘\uc784\ubca0\ub529’\uc785\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \uc608\uce21 \uae30\ubc18 \uc784\ubca0\ub529\uc758 \uac1c\ub150\uc744 \uc774\ud574\ud558\uace0, \uc774\ub97c PyTorch\ub97c \uc0ac\uc6a9\ud558\uc5ec \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc744 \ubc30\uc6cc\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \ubaa9\ucc28 1. \uc784\ubca0\ub529\uc758 \uac1c\ub150 2. \uc608\uce21 \uae30\ubc18 \uc784\ubca0\ub529 3. PyTorch \uae30\ubc18 \uad6c\ud604 4. \ub370\uc774\ud130\uc14b \uc900\ube44 5. \ubaa8\ub378 \uad6c\uc131 6. \ubaa8\ub378 \ud6c8\ub828 7. \uacb0\uacfc \ubd84\uc11d … \ub354 \ubcf4\uae30 “\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \uc608\uce21 \uae30\ubc18 \uc784\ubca0\ub529”<\/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":[33],"tags":[],"class_list":["post-30082","post","type-post","status-publish","format-standard","hentry","category-33"],"yoast_head":"\n\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \uc608\uce21 \uae30\ubc18 \uc784\ubca0\ub529 - \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\/30082\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \uc608\uce21 \uae30\ubc18 \uc784\ubca0\ub529 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"\ub525\ub7ec\ub2dd\uc758 \uc138\uacc4\ub294 \uacc4\uc18d\ud574\uc11c \uc9c4\ud654\ud558\uace0 \uc788\uc73c\uba70, \uc778\uacf5 \uc2e0\uacbd\ub9dd\uc740 \ub2e4\uc591\ud55c \uc751\uc6a9 \ubd84\uc57c\uc5d0\uc11c \uc7a0\uc7ac\ub825\uc744 \ubcf4\uc5ec\uc8fc\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uadf8 \uc911 \ud558\ub098\uac00 \ubc14\ub85c ‘\uc784\ubca0\ub529’\uc785\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \uc608\uce21 \uae30\ubc18 \uc784\ubca0\ub529\uc758 \uac1c\ub150\uc744 \uc774\ud574\ud558\uace0, \uc774\ub97c PyTorch\ub97c \uc0ac\uc6a9\ud558\uc5ec \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc744 \ubc30\uc6cc\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \ubaa9\ucc28 1. \uc784\ubca0\ub529\uc758 \uac1c\ub150 2. \uc608\uce21 \uae30\ubc18 \uc784\ubca0\ub529 3. 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