{"id":31381,"date":"2024-10-28T12:01:39","date_gmt":"2024-10-28T12:01:39","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=31381"},"modified":"2024-11-26T06:45:20","modified_gmt":"2024-11-26T06:45:20","slug":"%eb%94%a5%eb%9f%ac%eb%8b%9d%ea%b3%bc-%eb%a8%b8%ec%8b%a0%eb%9f%ac%eb%8b%9d%ec%9d%84-%ed%99%9c%ec%9a%a9%ed%95%9c-%ec%9e%90%eb%8f%99%eb%a7%a4%eb%a7%a4-%eb%94%a5%eb%9f%ac%eb%8b%9d-%eb%aa%a8%eb%8d%b8","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/31381\/","title":{"rendered":"\ub525\ub7ec\ub2dd\uacfc \uba38\uc2e0\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc790\ub3d9\ub9e4\ub9e4, \ub525\ub7ec\ub2dd \ubaa8\ub378\uc758 \uacfc\uc801\ud569 \ubc29\uc9c0 \ub4dc\ub86d\uc544\uc6c3, \uc870\uae30 \uc885\ub8cc \ub4f1 \uacfc\uc801\ud569\uc744 \ubc29\uc9c0\ud558\ub294 \uae30\uc220\ub4e4."},"content":{"rendered":"

<\/p>\n

\n

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

\ucd5c\uadfc \uba87 \ub144\uac04 \ube44\ud2b8\ucf54\uc778\uacfc \uc554\ud638\ud654\ud3d0 \uc2dc\uc7a5\uc740 \uae09\uaca9\ud788 \uc131\uc7a5\ud558\uc600\uc2b5\ub2c8\ub2e4. \uc774\ub85c \uc778\ud574 \uc790\ub3d9\ud654\ub41c \uac70\ub798 \uc2dc\uc2a4\ud15c\uc758 \ud544\uc694\uc131\uc774 \uc99d\uac00\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 \ub525\ub7ec\ub2dd\uacfc \uba38\uc2e0\ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \ube44\ud2b8\ucf54\uc778 \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc758 \uad6c\ucd95 \ubc29\ubc95\uacfc \uac00\uc7a5 \uc911\uc694\ud55c \uc8fc\uc81c \uc911 \ud558\ub098\uc778 \uacfc\uc801\ud569 \ubc29\uc9c0 \uae30\uc220\ub4e4\uc5d0 \ub300\ud574 \uc124\uba85\ud558\uace0\uc790 \ud569\ub2c8\ub2e4.<\/p>\n

2. \ube44\ud2b8\ucf54\uc778 \uc790\ub3d9\ub9e4\ub9e4\uc758 \uae30\ucd08<\/h2>\n

\uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc740 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998\uc744 \ud65c\uc6a9\ud558\uc5ec \uc2dc\uc7a5 \ub370\uc774\ud130\ub97c \ubd84\uc11d\ud558\uace0 \uac70\ub798 \uacb0\uc815\uc744 \uc790\ub3d9\uc73c\ub85c \ub0b4\ub9ac\ub294 \uc2dc\uc2a4\ud15c\uc785\ub2c8\ub2e4. \uc774 \uc2dc\uc2a4\ud15c\uc740 \uc2dc\uc7a5\uc758 \ud328\ud134\uacfc \ud2b8\ub80c\ub4dc\ub97c \ud559\uc2b5\ud558\uc5ec \ucd5c\uc801\uc758 \ub9e4\ub9e4 \uc2dc\uc810\uc744 \ucc3e\uc544\ub0b4\uba70, \uc774\ub97c \ud1b5\ud574 \uc778\uac04 \ud2b8\ub808\uc774\ub354\ubcf4\ub2e4 \ub354 \ube60\ub974\uace0 \uc815\ud655\ud55c \uacb0\uc815\uc744 \ub0b4\ub9b4 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

3. \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc758 \ucc28\uc774\uc810<\/h2>\n

\uba38\uc2e0\ub7ec\ub2dd\uc740 \ub370\uc774\ud130\ub97c \uae30\ubc18\uc73c\ub85c \ud559\uc2b5\ud558\uc5ec \uc608\uce21 \ubaa8\ub378\uc744 \ub9cc\ub4dc\ub294 \uae30\uc220\uc785\ub2c8\ub2e4. \ubc18\uba74\uc5d0 \ub525\ub7ec\ub2dd\uc740 \uc778\uacf5\uc2e0\uacbd\ub9dd\uc744 \uae30\ubc18\uc73c\ub85c \ud55c \uba38\uc2e0\ub7ec\ub2dd\uc758 \ud55c \ubd84\uc57c\ub85c, \ub354 \uae4a\uace0 \ubcf5\uc7a1\ud55c \uad6c\uc870\ub97c \ud1b5\ud574 \ub354 \ub9ce\uc740 \ub370\uc774\ud130\ub97c \ud6a8\uacfc\uc801\uc73c\ub85c \ucc98\ub9ac\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

4. \uacfc\uc801\ud569\uc774\ub780?<\/h2>\n

\uacfc\uc801\ud569(overfitting)\uc740 \ubaa8\ub378\uc774 \ud559\uc2b5 \ub370\uc774\ud130\uc5d0 \ub108\ubb34 \uc798 \ub9de\ucdb0\uc838 \uc77c\ubc18\ud654 \ub2a5\ub825\uc744 \uc783\ub294 \ud604\uc0c1\uc785\ub2c8\ub2e4. \uc989, \ubaa8\ub378\uc774 \ud559\uc2b5 \ub370\uc774\ud130\uc758 \ub178\uc774\uc988\uae4c\uc9c0 \ud559\uc2b5\ud558\uac8c \ub418\uc5b4 \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\uc5d0 \ub300\ud55c \uc608\uce21 \uc131\ub2a5\uc774 \ub5a8\uc5b4\uc9c4\ub2e4\ub294 \uc758\ubbf8\uc785\ub2c8\ub2e4. \uc774\ub294 \ube44\ud2b8\ucf54\uc778 \uac00\uaca9 \uc608\uce21\uc5d0\uc11c \ub9e4\uc6b0 \uc911\uc694\ud55c \ubb38\uc81c\uc785\ub2c8\ub2e4.<\/p>\n

5. \uacfc\uc801\ud569 \ubc29\uc9c0 \uae30\uc220<\/h2>\n

5.1 \ub4dc\ub86d\uc544\uc6c3(Dropout)<\/h3>\n

\ub4dc\ub86d\uc544\uc6c3\uc740 \ub525\ub7ec\ub2dd \ubaa8\ub378\uc5d0\uc11c \uacfc\uc801\ud569\uc744 \ubc29\uc9c0\ud558\uae30 \uc704\ud574 \uc0ac\uc6a9\ub418\ub294 \uae30\ubc95\uc785\ub2c8\ub2e4. \ub4dc\ub86d\uc544\uc6c3\uc740 \ud559\uc2b5 \uacfc\uc815 \uc911\uc5d0 \uc2e0\uacbd\ub9dd\uc758 \uc77c\ubd80 \ub274\ub7f0\uc744 \uc784\uc758\ub85c “\ub4dc\ub86d”\ud558\uc5ec \ud574\ub2f9 \ub274\ub7f0\uc774 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\uc9c0 \uc54a\ub3c4\ub85d \ud569\ub2c8\ub2e4. \uc774\ub807\uac8c \ud568\uc73c\ub85c\uc368 \ubaa8\ub378\uc774 \ud2b9\uc815 \ub274\ub7f0\uc5d0 \uc9c0\ub098\uce58\uac8c \uc758\uc874\ud558\ub294 \uac83\uc744 \ubc29\uc9c0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

\uc608\uc81c \ucf54\ub4dc: \ub4dc\ub86d\uc544\uc6c3<\/h4>\n
\nimport tensorflow as tf\nfrom tensorflow.keras import layers, models\n\n# \ubaa8\ub378 \uc815\uc758\nmodel = models.Sequential()\nmodel.add(layers.Dense(128, activation='relu', input_shape=(input_shape,)))\nmodel.add(layers.Dropout(0.5))  # 50% \ub4dc\ub86d\uc544\uc6c3\nmodel.add(layers.Dense(64, activation='relu'))\nmodel.add(layers.Dropout(0.5))  # 50% \ub4dc\ub86d\uc544\uc6c3\nmodel.add(layers.Dense(1, activation='sigmoid'))\n\nmodel.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n        <\/code><\/pre>\n
5.2 \uc870\uae30 \uc885\ub8cc(Early Stopping)<\/h3>\n
\uc870\uae30 \uc885\ub8cc\ub294 \ubaa8\ub378\uc774 \ub354 \uc774\uc0c1 \uac1c\uc120\ub418\uc9c0 \uc54a\uc744 \ub54c \ud559\uc2b5\uc744 \uc911\ub2e8\ud558\ub294 \uae30\ubc95\uc785\ub2c8\ub2e4. \uc774 \ubc29\ubc95\uc740 \uacfc\uc801\ud569\uc744 \uc904\uc774\ub294 \ub370 \ud6a8\uacfc\uc801\uc774\uba70, \ud1b5\uc0c1\uc801\uc73c\ub85c \uac80\uc99d \ub370\uc774\ud130\uc758 \uc190\uc2e4\uc774 \uc99d\uac00\ud558\uae30 \uc2dc\uc791\ud560 \ub54c \ud559\uc2b5\uc744 \uc885\ub8cc\ud569\ub2c8\ub2e4.<\/p>\n
\uc608\uc81c \ucf54\ub4dc: \uc870\uae30 \uc885\ub8cc<\/h4>\n
\nfrom tensorflow.keras.callbacks import EarlyStopping\n\nearly_stopping = EarlyStopping(monitor='val_loss', patience=5)\n\n# \ubaa8\ub378 \ud559\uc2b5\nhistory = model.fit(train_data, train_labels, epochs=100, validation_split=0.2, callbacks=[early_stopping])\n        <\/code><\/pre>\n
5.3 L2 \uc815\uaddc\ud654<\/h3>\n
L2 \uc815\uaddc\ud654\ub294 \uac00\uc911\uce58\uc5d0 \ub300\ud55c \ud328\ub110\ud2f0\ub97c \ucd94\uac00\ud558\uc5ec \uacfc\uc801\ud569\uc744 \uc904\uc774\ub294 \uae30\ubc95\uc785\ub2c8\ub2e4. \uc774\ub294 \ubaa8\ub378\uc774 \ub192\uc740 \ubcf5\uc7a1\uc131\uc744 \uac16\uc9c0 \uc54a\ub3c4\ub85d \uc720\ub3c4\ud569\ub2c8\ub2e4.<\/p>\n
\uc608\uc81c \ucf54\ub4dc: L2 \uc815\uaddc\ud654<\/h4>\n
\nfrom tensorflow.keras import regularizers\n\nmodel = models.Sequential()\nmodel.add(layers.Dense(128, activation='relu', kernel_regularizer=regularizers.l2(0.01), input_shape=(input_shape,)))\nmodel.add(layers.Dense(64, activation='relu', kernel_regularizer=regularizers.l2(0.01)))\nmodel.add(layers.Dense(1, activation='sigmoid'))\n\nmodel.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n        <\/code><\/pre>\n
5.4 \ub370\uc774\ud130 \uc99d\uac15(Data Augmentation)<\/h3>\n
\ub370\uc774\ud130 \uc99d\uac15\uc740 \uae30\uc874 \ub370\uc774\ud130\ub97c \ubcc0\ud615\ud558\uc5ec \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. \uc774\ub294 \ubaa8\ub378\uc774 \ub2e4\uc591\ud55c \uc0c1\ud669\uc744 \ud559\uc2b5\ud560 \uc218 \uc788\ub3c4\ub85d \ub3c4\uc640\uc90d\ub2c8\ub2e4.<\/p>\n
\uc608\uc81c \ucf54\ub4dc: \ub370\uc774\ud130 \uc99d\uac15<\/h4>\n
\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\n\ndatagen = ImageDataGenerator(rotation_range=40, width_shift_range=0.2, height_shift_range=0.2,\n                             rescale=1.\/255, shear_range=0.2, zoom_range=0.2,\n                             horizontal_flip=True, fill_mode='nearest')\n\n# \ub370\uc774\ud130 \uc99d\uac15 \uc801\uc6a9\nmodel.fit(datagen.flow(train_data, train_labels, batch_size=32), epochs=50)\n        <\/code><\/pre>\n
6. \ub525\ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \ube44\ud2b8\ucf54\uc778 \uac00\uaca9 \uc608\uce21 \ubaa8\ub378 \uad6c\ud604<\/h2>\n
6.1 \ub370\uc774\ud130 \uc218\uc9d1 \ubc0f \uc900\ube44<\/h3>\n
\ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\uae30 \uc704\ud574\uc11c\ub294 \uc6b0\uc120 \ube44\ud2b8\ucf54\uc778 \uac00\uaca9 \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud558\uace0 \uc804\ucc98\ub9ac\ud574\uc57c \ud569\ub2c8\ub2e4. \uc5ec\ub7ec \uc18c\uc2a4\uc5d0\uc11c \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud560 \uc218 \uc788\uc73c\uba70, \uc8fc\ub85c \uc0ac\uc6a9\ud558\ub294 \uac83\uc740 Binance\uc640 \uac19\uc740 \uac70\ub798\uc18c\uc758 API\uc785\ub2c8\ub2e4.<\/p>\n
\uc608\uc81c \ucf54\ub4dc: \ub370\uc774\ud130 \uc218\uc9d1<\/h4>\n
\nimport pandas as pd\nimport requests\n\ndef get_historical_data(symbol, interval, limit):\n    url = f'https:\/\/api.binance.com\/api\/v3\/klines?symbol={symbol}&interval={interval}&limit={limit}'\n    data = requests.get(url).json()\n    df = pd.DataFrame(data, columns=['Open Time', 'Open', 'High', 'Low', 'Close', 'Volume', 'Close Time', 'Quote Asset Volume', 'Number of Trades', 'Taker Buy Base Asset Volume', 'Taker Buy Quote Asset Volume', 'Ignore'])\n    df['Close'] = df['Close'].astype(float)\n    return df[['Open Time', 'Close']]\n\nbtc_data = get_historical_data('BTCUSDT', '1d', 1000)\n        <\/code><\/pre>\n
6.2 \ubaa8\ub378 \uad6c\ucd95\uacfc \ud6c8\ub828<\/h3>\n
\uc900\ube44\ub41c \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uad6c\ucd95\ud569\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc5d0\uc11c\ub294 RNN, LSTM \ub4f1\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\uc608\uc81c \ucf54\ub4dc: LSTM \ubaa8\ub378 \uad6c\ucd95<\/h4>\n
\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import LSTM, Dense, Dropout\n\nmodel = Sequential()\nmodel.add(LSTM(50, return_sequences=True, input_shape=(timesteps, features)))\nmodel.add(Dropout(0.2))\nmodel.add(LSTM(50))\nmodel.add(Dropout(0.2))\nmodel.add(Dense(1))\n\nmodel.compile(optimizer='adam', loss='mean_squared_error')\nmodel.fit(X_train, y_train, epochs=50, batch_size=32)\n        <\/code><\/pre>\n
7. \uacb0\ub860<\/h2>\n
\ub525\ub7ec\ub2dd \ubc0f \uba38\uc2e0\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \ube44\ud2b8\ucf54\uc778 \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c \uad6c\ucd95\uc740 \uc2e4\uc6a9\uc801\uc774\uace0 \ud6a8\uacfc\uc801\uc778 \uc811\uadfc\ubc29\ubc95\uc785\ub2c8\ub2e4. \uadf8\ub7ec\ub098 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \uadf9\ub300\ud654\ud558\uae30 \uc704\ud574\uc11c\ub294 \uacfc\uc801\ud569\uc744 \ubc29\uc9c0\ud558\ub294 \uc5ec\ub7ec \uae30\ubc95\ub4e4\uc744 \uc801\uc808\ud788 \uc0ac\uc6a9\ud574\uc57c \ud569\ub2c8\ub2e4. \ub4dc\ub86d\uc544\uc6c3, \uc870\uae30 \uc885\ub8cc, L2 \uc815\uaddc\ud654 \ubc0f \ub370\uc774\ud130 \uc99d\uac15 \ub4f1 \ub2e4\uc591\ud55c \ubc29\ubc95\uc744 \ud1b5\ud574 \ub354 \ub098\uc740 \uc608\uce21 \uc131\ub2a5\uc744 \uc774\ub04c\uc5b4\ub0bc \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/article>\n
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1. \uc11c\ub860 \ucd5c\uadfc \uba87 \ub144\uac04 \ube44\ud2b8\ucf54\uc778\uacfc \uc554\ud638\ud654\ud3d0 \uc2dc\uc7a5\uc740 \uae09\uaca9\ud788 \uc131\uc7a5\ud558\uc600\uc2b5\ub2c8\ub2e4. \uc774\ub85c \uc778\ud574 \uc790\ub3d9\ud654\ub41c \uac70\ub798 \uc2dc\uc2a4\ud15c\uc758 \ud544\uc694\uc131\uc774 \uc99d\uac00\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 \ub525\ub7ec\ub2dd\uacfc \uba38\uc2e0\ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \ube44\ud2b8\ucf54\uc778 \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc758 \uad6c\ucd95 \ubc29\ubc95\uacfc \uac00\uc7a5 \uc911\uc694\ud55c \uc8fc\uc81c \uc911 \ud558\ub098\uc778 \uacfc\uc801\ud569 \ubc29\uc9c0 \uae30\uc220\ub4e4\uc5d0 \ub300\ud574 \uc124\uba85\ud558\uace0\uc790 \ud569\ub2c8\ub2e4. 2. \ube44\ud2b8\ucf54\uc778 \uc790\ub3d9\ub9e4\ub9e4\uc758 \uae30\ucd08 \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc740 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998\uc744 \ud65c\uc6a9\ud558\uc5ec \uc2dc\uc7a5 \ub370\uc774\ud130\ub97c \ubd84\uc11d\ud558\uace0 \uac70\ub798 \uacb0\uc815\uc744 \uc790\ub3d9\uc73c\ub85c … \ub354 \ubcf4\uae30 “\ub525\ub7ec\ub2dd\uacfc \uba38\uc2e0\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc790\ub3d9\ub9e4\ub9e4, \ub525\ub7ec\ub2dd \ubaa8\ub378\uc758 \uacfc\uc801\ud569 \ubc29\uc9c0  \ub4dc\ub86d\uc544\uc6c3, \uc870\uae30 \uc885\ub8cc \ub4f1 \uacfc\uc801\ud569\uc744 \ubc29\uc9c0\ud558\ub294 \uae30\uc220\ub4e4.”<\/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-31381","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, \ub525\ub7ec\ub2dd \ubaa8\ub378\uc758 \uacfc\uc801\ud569 \ubc29\uc9c0 \ub4dc\ub86d\uc544\uc6c3, \uc870\uae30 \uc885\ub8cc \ub4f1 \uacfc\uc801\ud569\uc744 \ubc29\uc9c0\ud558\ub294 \uae30\uc220\ub4e4. - 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