\ud2b8\ub79c\uc2a4\ud3ec\uba38(Transformer)<\/strong>: \uc5b4\ud150\uc158 \uba54\ucee4\ub2c8\uc998\uc744 \ud65c\uc6a9\ud558\uc5ec \uc2dc\ud000\uc2a4 \ub370\uc774\ud130\ub97c \ud6a8\uacfc\uc801\uc73c\ub85c \ucc98\ub9ac\ud558\ub294 \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc785\ub2c8\ub2e4.<\/li>\n<\/ul>\n\ud30c\uc774\ud1a0\uce58 \uae30\ubcf8 \uac1c\ub150<\/h2>\n
\ud30c\uc774\ud1a0\uce58(PyTorch)\ub294 Facebook\uc5d0\uc11c \uac1c\ubc1c\ud55c \ub525\ub7ec\ub2dd \ub77c\uc774\ube0c\ub7ec\ub9ac\ub85c, \ub3d9\uc801 \uadf8\ub798\ud504 \uad6c\uc131 \ubc0f GPU \uac00\uc18d\uc744 \uc9c0\uc6d0\ud569\ub2c8\ub2e4. \ud30c\uc774\ud1a0\uce58\ub294 Tensor\ub77c\ub294 \uae30\ubcf8 \ub370\uc774\ud130 \uad6c\uc870\ub97c \uae30\ubc18\uc73c\ub85c \ud558\uba70, \uc774 Tensor\ub294 NumPy\uc758 \ubc30\uc5f4\uc5d0\uc11c \uc601\uac10\uc744 \ubc1b\uc558\uc2b5\ub2c8\ub2e4. PyTorch\uc758 \uc7a5\uc810 \uc911 \ud558\ub098\ub294 \uc9c1\uad00\uc801\uc778 API\uc640 \uc720\uc5f0\ud55c \uac1c\ubc1c\ud658\uacbd\uc785\ub2c8\ub2e4.<\/p>\n
\ud30c\uc774\ud1a0\uce58 \uc124\uce58<\/h3>\n
\ud30c\uc774\ud1a0\uce58\ub294 pip\ub098 Conda\ub97c \uc0ac\uc6a9\ud574 \uc27d\uac8c \uc124\uce58\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
pip install torch torchvision torchaudio<\/code><\/pre>\n\ud30c\uc774\ud1a0\uce58\ub85c \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ubaa8\ub378 \uad6c\ud604<\/h2>\n
\uc774\uc81c \ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ubaa8\ub378\uc744 \uac04\ub2e8\ud788 \uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
\ub370\uc774\ud130 \uc900\ube44<\/h3>\n
\uba3c\uc800, \ub370\uc774\ud130\ub97c \uc900\ube44\ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uac04\ub2e8\ud55c \uc601\ud654 \ub9ac\ubdf0 \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\nimport pandas as pd\n\n# \ub370\uc774\ud130 \uc0dd\uc131 \uc608\uc2dc\ndata = {\n 'review': ['\uac00\uc7a5 \uc88b\uc558\ub358 \uc601\ud654', '\uc644\uc804 \uc9c0\ub8e8\ud55c \uc601\ud654', '\uc815\ub9d0 \uc7ac\ubc0c\uc5b4\uc694', '\uc2dc\uac04 \ub0ad\ube44\uc785\ub2c8\ub2e4'],\n 'label': [1, 0, 1, 0]\n}\ndf = pd.DataFrame(data)\n\n# \ub370\uc774\ud130 \ud655\uc778\nprint(df)\n <\/code><\/pre>\n\ud1a0\ud070\ud654 \ubc0f \ubca1\ud130\ud654<\/h3>\n
\ud14d\uc2a4\ud2b8 \ub370\uc774\ud130\ub97c \uc22b\uc790\ub85c \ubcc0\ud658\ud558\uae30 \uc704\ud574 \ud1a0\ud070\ud654 \ubc0f \ubca1\ud130\ud654\ub97c \uc9c4\ud589\ud569\ub2c8\ub2e4.<\/p>\n
\nfrom torchtext.data import Field, TabularDataset, BucketIterator\n\n# \ud544\ub4dc \uc815\uc758\nTEXT = Field(sequential=True, tokenize='basic_english', lower=True)\nLABEL = Field(sequential=False, use_vocab=False)\n\n# \ub370\uc774\ud130\uc14b \ub85c\ub4dc\nfields = {'review': ('text', TEXT), 'label': ('label', LABEL)}\ntrain_data, valid_data = TabularDataset.splits(\n path='', train='train.csv', validation='valid.csv', format='csv', fields=fields)\n\n# \ub2e8\uc5b4 \ubaa9\ub85d \uad6c\ucd95\nTEXT.build_vocab(train_data, max_size=10000)\n <\/code><\/pre>\n\uc2e0\uacbd\ub9dd \ubaa8\ub378 \uc815\uc758<\/h3>\n
\ub2e4\uc74c\uc73c\ub85c, RNN \uad6c\uc870\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc744 \uc815\uc758\ud569\ub2c8\ub2e4.<\/p>\n
\nimport torch.nn as nn\n\nclass RNNModel(nn.Module):\n def __init__(self, input_dim, emb_dim, hidden_dim, output_dim):\n super().__init__()\n self.embedding = nn.Embedding(input_dim, emb_dim)\n self.rnn = nn.RNN(emb_dim, hidden_dim)\n self.fc = nn.Linear(hidden_dim, output_dim)\n \n def forward(self, text):\n embedded = self.embedding(text)\n output, hidden = self.rnn(embedded)\n return self.fc(hidden)\n \n# \ubaa8\ub378 \uc778\uc2a4\ud134\uc2a4\ud654\ninput_dim = len(TEXT.vocab)\nemb_dim = 100\nhidden_dim = 256\noutput_dim = 1\n\nmodel = RNNModel(input_dim, emb_dim, hidden_dim, output_dim)\n <\/code><\/pre>\n\ubaa8\ub378 \ud559\uc2b5<\/h3>\n
\uc774\uc81c \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0a4\uae30 \uc704\ud574 \ud559\uc2b5\ub960\uacfc \uc190\uc2e4 \ud568\uc218\ub97c \uc815\uc758\ud569\ub2c8\ub2e4. \uc774\uc5b4\uc11c \uc5d0\ud3ec\ud06c \ub3d9\uc548 \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0b5\ub2c8\ub2e4.<\/p>\n
\nimport torch.optim as optim\n\noptimizer = optim.Adam(model.parameters())\ncriterion = nn.BCEWithLogitsLoss()\n\n# \ubaa8\ub378 \ud559\uc2b5\nmodel.train()\nfor epoch in range(10):\n for batch in BucketIterator(train_data, batch_size=32):\n optimizer.zero_grad()\n predictions = model(batch.text).squeeze()\n loss = criterion(predictions, batch.label.float())\n loss.backward()\n optimizer.step()\n print(f'Epoch {epoch+1}, Loss: {loss.item()}')\n <\/code><\/pre>\n\uc815\ub9ac<\/h2>\n
\ubcf8 \uae00\uc5d0\uc11c\ub294 \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc758 \uae30\ubcf8 \uc6a9\uc5b4\uc640 \ud30c\uc774\ud1a0\uce58\ub97c \uc774\uc6a9\ud55c \uae30\ubcf8\uc801\uc778 \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ubaa8\ub378 \uad6c\ucd95 \uacfc\uc815\uc744 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \uc2e4\uc81c \uc5c5\ubb34\uc5d0\uc11c\ub294 \ub354 \ub2e4\uc591\ud55c \ub370\uc774\ud130 \uc804\ucc98\ub9ac \ubc0f \ubaa8\ub378 \ud29c\ub2dd\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \uc55e\uc73c\ub85c \ub354 \uae4a\uc774 \uc788\ub294 \ub525\ub7ec\ub2dd \uacf5\ubd80\uac00 \ud544\uc694\ud558\uba70, \ub2e4\uc591\ud55c \ud328\ud0a4\uc9c0\uc640 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud65c\uc6a9\ud574\ubcf4\ub294 \uac83\uc744 \ucd94\ucc9c\ub4dc\ub9bd\ub2c8\ub2e4.<\/p>\n
\n
\ucc38\uace0 \uc790\ub8cc:<\/strong><\/p>\n\n- Deep Learning for Natural Language Processing by Palash Goyal<\/li>\n
- PyTorch Documentation<\/li>\n
- Natural Language Processing with PyTorch by Delip Rao and Greg Diamos<\/li>\n<\/ul>\n<\/div>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\ub525\ub7ec\ub2dd\uc740 \ub300\ub7c9\uc758 \ub370\uc774\ud130\ub85c\ubd80\ud130 \ud328\ud134\uc774\ub098 \uaddc\uce59\uc744 \ud559\uc2b5\ud558\ub294 \uac15\ub825\ud55c \uae30\uacc4\ud559\uc2b5 \uae30\ubc95\uc785\ub2c8\ub2e4. \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP)\ub294 \uc5b8\uc5b4\uc640 \uad00\ub828\ub41c \ub370\uc774\ud130\uc758 \uc774\ud574, \ud574\uc11d, \uc0dd\uc131 \ub4f1\uc744 \ucef4\ud4e8\ud130\uac00 \uac00\ub2a5\ud558\ub3c4\ub85d \ud558\ub294 \ub525\ub7ec\ub2dd\uc758 \ud55c \ud2b9\uc815 \ubd84\uc57c\uc785\ub2c8\ub2e4. \ud30c\uc774\ud1a0\uce58\ub294 \uc2e0\uacbd\ub9dd\uc744 \uc27d\uac8c \uc815\uc758\ud558\uace0 \ud559\uc2b5\ud560 \uc218 \uc788\ub294 \ud504\ub808\uc784\uc6cc\ud06c\ub85c, \ub9ce\uc740 \uc5f0\uad6c\uc790 \ubc0f \uc2e4\ubb34\uc790\ub4e4\uc774 \uc0ac\uc6a9\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc758 \uae30\ubcf8 \uc6a9\uc5b4 \ud1a0\ud070\ud654(Tokenization): \ubb38\uc7a5\uc744 \ub2e8\uc5b4 \ub610\ub294 \ubb38\uc7a5 \ub2e8\uc704\ub85c \ub098\ub204\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4. \uc5b4\ud718(Vocabulary): \ubaa8\ub378\uc774 \uc774\ud574\ud560 \uc218 … \ub354 \ubcf4\uae30 “\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \uc790\uc5f0\uc5b4 \ucc98\ub9ac \uc6a9\uc5b4 \ubc0f \uacfc\uc815”<\/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-30088","post","type-post","status-publish","format-standard","hentry","category-33"],"yoast_head":"\n\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \uc790\uc5f0\uc5b4 \ucc98\ub9ac \uc6a9\uc5b4 \ubc0f \uacfc\uc815 - 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