\ub514\ucf54\ub354(Decoder)<\/strong>: \uc7a0\uc7ac \uacf5\uac04\uc5d0\uc11c \ub2e4\uc2dc \uc6d0\ub798\uc758 \uc785\ub825 \ub370\uc774\ud130\ub97c \ubcf5\uc6d0\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\nAutoencoder\uc758 \ubaa9\uc801\uc740 \uc785\ub825 \ub370\uc774\ud130\uc640 \ucd9c\ub825 \ub370\uc774\ud130\ub97c \ucd5c\ub300\ud55c \uc720\uc0ac\ud558\uac8c \ub9cc\ub4dc\ub294 \uac83\uc785\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \uc190\uc2e4 \ud568\uc218\ub85c\ub294 \ud3c9\uade0 \uc81c\uacf1 \uc624\ucc28(Mean Squared Error)\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/p>\n
2. Autoencoder\uc758 \uad6c\uc870<\/h2>\n
Autoencoder\uc758 \uae30\ubcf8 \uad6c\uc870\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n
\nclass Autoencoder(nn.Module):\n def __init__(self):\n super(Autoencoder, self).__init__()\n self.encoder = nn.Sequential(\n nn.Linear(3, 2),\n nn.ReLU(True)\n )\n self.decoder = nn.Sequential(\n nn.Linear(2, 3),\n nn.Sigmoid()\n )\n\n def forward(self, x):\n x = self.encoder(x)\n x = self.decoder(x)\n return x\n<\/code><\/pre>\n3. \ube44\ud2b8\ucf54\uc778 \uac00\uaca9 \ub370\uc774\ud130\uc640 \uc774\uc0c1\uce58 \ud0d0\uc9c0<\/h2>\n
\ube44\ud2b8\ucf54\uc778\uc758 \uac00\uaca9 \ub370\uc774\ud130\ub294 \ub2e4\uc591\ud55c \uc694\uc778\uc5d0 \uc758\ud574 \uc601\ud5a5\uc744 \ubc1b\uc73c\uba70, \uc774\ub85c \uc778\ud574 \ube44\uc815\uc0c1\uc801\uc778 \uac00\uaca9 \ubcc0\ub3d9\uc774 \ubc1c\uc0dd\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. Autoencoder\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc8fc\uc5b4\uc9c4 \ub370\uc774\ud130\uc5d0\uc11c \uc774\uc0c1\uce58\ub97c \ud0d0\uc9c0\ud558\ub294 \uacfc\uc815\uc740 \ud06c\uac8c \uc138 \ub2e8\uacc4\ub85c \ub098\ub269\ub2c8\ub2e4:<\/p>\n
\n- \uac00\uaca9 \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/li>\n
- Autoencoder \ubaa8\ub378 \ud559\uc2b5<\/li>\n
- \uc774\uc0c1\uce58 \ud0d0\uc9c0<\/li>\n<\/ol>\n
3.1 \uac00\uaca9 \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h3>\n
\ube44\ud2b8\ucf54\uc778 \uac00\uaca9 \ub370\uc774\ud130\ub97c \ubd88\ub7ec\uc640\uc11c \uc804\ucc98\ub9ac\ud558\ub294 \uacfc\uc815\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/p>\n
\nimport pandas as pd\n\n# \ub370\uc774\ud130 \ubd88\ub7ec\uc624\uae30\ndata = pd.read_csv('bitcoin_price.csv')\ndata['Date'] = pd.to_datetime(data['Date'])\ndata.set_index('Date', inplace=True)\n\n# \ud544\uc694\ud55c \uceec\ub7fc \uc120\ud0dd\nprice_data = data['Close'].values.reshape(-1, 1)\n\n# \uc815\uaddc\ud654\nfrom sklearn.preprocessing import MinMaxScaler\nscaler = MinMaxScaler()\nnormalized_data = scaler.fit_transform(price_data)\n<\/code><\/pre>\n3.2 Autoencoder \ubaa8\ub378 \ud559\uc2b5<\/h3>\n
\ub370\uc774\ud130\ub97c \uc900\ube44\ud55c \ud6c4, Autoencoder \ubaa8\ub378\uc744 \uc0dd\uc131\ud558\uace0 \ud559\uc2b5\uc2dc\ud0b5\ub2c8\ub2e4.<\/p>\n
\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\n\n# Hyperparameters\nnum_epochs = 100\nlearning_rate = 0.001\n\n# \ub370\uc774\ud130\uc14b \uc900\ube44\ntensor_data = torch.FloatTensor(normalized_data)\n\n# \ubaa8\ub378 \ucd08\uae30\ud654\nmodel = Autoencoder()\ncriterion = nn.MSELoss()\noptimizer = optim.Adam(model.parameters(), lr=learning_rate)\n\n# \ud559\uc2b5\nfor epoch in range(num_epochs):\n model.train()\n optimizer.zero_grad()\n output = model(tensor_data)\n loss = criterion(output, tensor_data)\n loss.backward()\n optimizer.step()\n\n if epoch % 10 == 0:\n print(f'Epoch [{epoch}\/{num_epochs}], Loss: {loss.item():.4f}')\n<\/code><\/pre>\n3.3 \uc774\uc0c1\uce58 \ud0d0\uc9c0<\/h3>\n
\ud6c8\ub828\ub41c \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc785\ub825 \ub370\uc774\ud130\uc758 \uc7ac\uad6c\uc131 \uc624\ucc28\ub97c \uacc4\uc0b0\ud558\uace0, \ud2b9\uc815 \uc784\uacc4\uce58 \uc774\uc0c1\uc778 \ub370\uc774\ud130\ub97c \uc774\uc0c1\uce58\ub85c \uac10\uc9c0\ud569\ub2c8\ub2e4.<\/p>\n
\n# \ubaa8\ub378 \ud3c9\uac00\nmodel.eval()\nwith torch.no_grad():\n reconstructed = model(tensor_data)\n reconstruction_loss = criterion(reconstructed, tensor_data)\n\n# \uc774\uc0c1\uce58 \ud0d0\uc9c0\nreconstruction_loss_values = torch.sum((tensor_data - reconstructed) ** 2, axis=1).numpy()\nthreshold = 0.1 # \uc608\uc2dc \uc784\uacc4\uce58\nanomalies = reconstruction_loss_values > threshold\n\n# \uc774\uc0c1\uce58 \uc778\ub371\uc2a4\nanomaly_indices = [i for i, x in enumerate(anomalies) if x]\nprint(f'\uc774\uc0c1\uce58 \uc778\ub371\uc2a4: {anomaly_indices}')\n<\/code><\/pre>\n4. \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc5d0 \ud1b5\ud569<\/h2>\n
\uc774\uc0c1\uce58 \ud0d0\uc9c0\ub97c \ud1b5\ud574 \ud2b9\uc815 \uc2dc\uc810\uc5d0\uc11c \ube44\uc815\uc0c1\uc801\uc778 \uc6c0\uc9c1\uc784\uc774 \uac10\uc9c0\ub418\uba74, \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc5d0\uc11c \ub9e4\ub3c4 \ub610\ub294 \ub9e4\uc218 \uc2e0\ud638\ub97c \uc0dd\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub97c \uc704\ud574 \ud0d0\uc9c0\ub41c \uc774\uc0c1\uce58\uc640 \ud568\uaed8 \uadf8\uc5d0 \ub530\ub978 \uac70\ub798 \uc804\ub7b5\uc744 \uc815\uc758\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n
4.1 \uac70\ub798 \uc804\ub7b5 \uc608\uc81c<\/h3>\n
\uc774\uc0c1\uce58\uac00 \ud0d0\uc9c0\ub420 \uacbd\uc6b0 \ub9e4\ub3c4 \ud3ec\uc9c0\uc158\uc744 \ucde8\ud558\ub294 \ub2e8\uc21c\ud55c \uc804\ub7b5\uc744 \uc608\ub85c \ub4e4\uc5b4 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4:<\/p>\n
\n# \uac70\ub798 \uc804\ub7b5\nfor index in anomaly_indices:\n price = price_data[index][0]\n # \ube44\uc815\uc0c1\uc801\uc778 \uac00\uaca9 \ubcc0\ub3d9\uc5d0 \ub300\ud574 \ub9e4\ub3c4\n print(f'\uc774\uc0c1\uce58 \ud0d0\uc9c0 - \ub9e4\ub3c4: \uac00\uaca9 {price} at index {index}')\n<\/code><\/pre>\n5. \uacb0\ub860<\/h2>\n
\ub525\ub7ec\ub2dd\uacfc \uba38\uc2e0\ub7ec\ub2dd \uae30\ubc95, \ud2b9\ud788 Autoencoder\ub97c \ud65c\uc6a9\ud55c \uc774\uc0c1\uce58 \ud0d0\uc9c0\ub294 \ube44\ud2b8\ucf54\uc778\uacfc \uac19\uc740 \ubcc0\ub3d9\uc131\uc774 \ud070 \uc790\uc0b0\uc758 \ub9ac\uc2a4\ud06c \uad00\ub9ac\ub97c \uc704\ud55c \ud6a8\uacfc\uc801\uc778 \ub3c4\uad6c\uc785\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 Python\uc744 \uc0ac\uc6a9\ud558\uc5ec Autoencoder\ub97c \uad6c\ud604\ud558\uace0 \uc774\uc0c1\uce58\ub97c \ud0d0\uc9c0\ud55c \ud6c4, \uc774\ub97c \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc5d0 \ud1b5\ud569\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud558\uc600\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uc2dc\uc2a4\ud15c\uc744 \ud1b5\ud574 \ud22c\uc790\uc790\ub294 \ubcf4\ub2e4 \ub370\uc774\ud130 \uae30\ubc18\uc758 \uacb0\uc815\uc744 \ub0b4\ub9b4 \uc218 \uc788\uc73c\uba70, \ubd88\ud655\uc2e4\uc131\uc744 \uac10\uc18c\uc2dc\ud0a4\ub294 \ub370 \uae30\uc5ec\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\ucd94\ud6c4 \ubc1c\uc804\ud560 \uc218 \uc788\ub294 \ubd80\ubd84\uc73c\ub85c\ub294 \ub2e4\uc591\ud55c \uc54c\uace0\ub9ac\uc998 \uc2e4\ud5d8, \ub354 \ub9ce\uc740 \uc785\ub825 \ubcc0\uc218 \ucd94\uac00, \uac70\ub798 \uc804\ub7b5 \ucd5c\uc801\ud654\ub97c \ud1b5\ud574 \uc131\ub2a5\uc744 \uac1c\uc120\ud560 \uc218 \uc788\ub294 \uac00\ub2a5\uc131\uc774 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \ub354\uc6b1 \uc2a4\ub9c8\ud2b8\ud558\uace0 \ud6a8\uacfc\uc801\uc778 \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc774 \uad6c\ucd95\ub420 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\ube44\ud2b8\ucf54\uc778\uacfc \uac19\uc740 \uc554\ud638\ud654\ud3d0 \uc2dc\uc7a5\uc740 \ub192\uc740 \ubcc0\ub3d9\uc131\uacfc \ubd88\ud655\uc2e4\uc131\uc73c\ub85c \uc778\ud574 \ud22c\uc790\uc790\uc5d0\uac8c \ud070 \ub9ac\uc2a4\ud06c\ub97c \uc548\uaca8\uc90d\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ub9ac\uc2a4\ud06c\ub97c \uad00\ub9ac\ud558\uae30 \uc704\ud574 \ub525\ub7ec\ub2dd\uacfc \uba38\uc2e0\ub7ec\ub2dd \uae30\ubc95\uc744 \ud65c\uc6a9\ud55c \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc774 \uc810\uc810 \uc8fc\ubaa9\ubc1b\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788, Autoencoder\ub294 \ub370\uc774\ud130\uc758 \ube44\uc815\uc0c1\uc801\uc778 \uc6c0\uc9c1\uc784\uc744 \ud0d0\uc9c0\ud558\uc5ec \ub9ac\uc2a4\ud06c \uad00\ub9ac\uc5d0 \uc720\uc6a9\ud55c \ub3c4\uad6c\ub85c \uc790\ub9ac \uc7a1\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 Autoencoder\uc758 \uac1c\ub150, \uc774\ub860\uc801 \ubc30\uacbd, \ube44\ud2b8\ucf54\uc778 \uac00\uaca9 \ub370\uc774\ud130\uc758 \uc774\uc0c1\uce58 \ud0d0\uc9c0 \uc801\uc6a9 \uc608\uc81c\uc640 \ud568\uaed8 \uc5b4\ub5bb\uac8c \uc774\ub97c \uc790\ub3d9\ub9e4\ub9e4 … \ub354 \ubcf4\uae30 “\ub525\ub7ec\ub2dd\uacfc \uba38\uc2e0\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc790\ub3d9\ub9e4\ub9e4, Autoencoder\ub97c \uc774\uc6a9\ud55c \uc774\uc0c1\uce58 \ud0d0\uc9c0 \uac00\uaca9 \ub370\uc774\ud130\uc5d0\uc11c \ube44\uc815\uc0c1\uc801 \uc6c0\uc9c1\uc784\uc744 \ud0d0\uc9c0\ud558\uc5ec \ub9ac\uc2a4\ud06c \uad00\ub9ac.”<\/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":[29],"tags":[],"class_list":["post-31347","post","type-post","status-publish","format-standard","hentry","category-29"],"yoast_head":"\n\ub525\ub7ec\ub2dd\uacfc \uba38\uc2e0\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc790\ub3d9\ub9e4\ub9e4, Autoencoder\ub97c \uc774\uc6a9\ud55c \uc774\uc0c1\uce58 \ud0d0\uc9c0 \uac00\uaca9 \ub370\uc774\ud130\uc5d0\uc11c \ube44\uc815\uc0c1\uc801 \uc6c0\uc9c1\uc784\uc744 \ud0d0\uc9c0\ud558\uc5ec \ub9ac\uc2a4\ud06c \uad00\ub9ac. - 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