\ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \uac80\uc99d\ud558\uc5ec \uac15\uac74\uc131\uc744 \ud655\uc778\ud569\ub2c8\ub2e4.<\/li>\n<\/ol>\n3. \uc801\ub300\uc801 \ud6c8\ub828 \ud504\ub85c\uc138\uc2a4 \uc124\uc815<\/h2>\n
\uc774\uc81c \uc801\ub300\uc801 \ud6c8\ub828 \ud504\ub85c\uc138\uc2a4\ub97c \uc9c1\uc811 \uc124\uc815\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc774\ub97c \uc704\ud574 Python\uacfc TensorFlow\ub97c \uc0ac\uc6a9\ud55c \uc608\uc2dc\ub97c \uc791\uc131\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
3.1 \ub370\uc774\ud130 \uc900\ube44<\/h3>\n
\uc801\ub300\uc801 \ud6c8\ub828\uc744 \uc704\ud574\uc11c\ub294 \uba3c\uc800 \uc0ac\uc6a9\ub420 \ud6c8\ub828 \ub370\uc774\ud130\ub97c \uc900\ube44\ud574\uc57c \ud569\ub2c8\ub2e4. \uc8fc\uc2dd \uac00\uaca9 \ub370\uc774\ud130\ub098 \uae30\uc220 \uc9c0\ud45c\ub97c \ud3ec\ud568\ud55c \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
import pandas as pd\n\n# \uac00\uaca9 \ub370\uc774\ud130 \ubd88\ub7ec\uc624\uae30\ndata = pd.read_csv('stock_data.csv')\nfeatures = data[['open', 'high', 'low', 'close', 'volume']]\nlabels = data['target'] # \uc608\uce21\ud560 \ubaa9\ud45c \ubcc0\uc218\n<\/code><\/pre>\n3.2 \ubaa8\ub378 \uc815\uc758<\/h3>\n
\ubaa8\ub378\uc744 \uc815\uc758\ud558\ub294 \ub2e8\uacc4\uc5d0\uc11c\ub294 \uc801\uc808\ud55c \uc2e0\uacbd\ub9dd \uc544\ud0a4\ud14d\ucc98\ub97c \uc120\ud0dd\ud574\uc57c \ud569\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 \uac04\ub2e8\ud55c \ub2e4\uce35 \ud37c\uc149\ud2b8\ub860(MLP)\uc744 \ud1b5\ud574 \uc608\uce21 \ubaa8\ub378\uc744 \ub9cc\ub4e4\uc5b4 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
import tensorflow as tf\nfrom tensorflow.keras import Sequential\nfrom tensorflow.keras.layers import Dense\n\n# \ubaa8\ub378 \uad6c\uc131\nmodel = Sequential()\nmodel.add(Dense(64, activation='relu', input_shape=(features.shape[1],)))\nmodel.add(Dense(64, activation='relu'))\nmodel.add(Dense(1, activation='linear'))\n\n# \ubaa8\ub378 \ucef4\ud30c\uc77c\nmodel.compile(optimizer='adam', loss='mean_squared_error')\n<\/code><\/pre>\n3.3 \uc801\ub300\uc801 \uc0d8\ud50c \uc0dd\uc131<\/h3>\n
\uc801\ub300\uc801 \uc0d8\ud50c\uc744 \uc0dd\uc131\ud558\uae30 \uc704\ud574 \ubaa8\ub378\uc758 \uc608\uce21\uac12\uc744 \uc870\uc791\ud558\ub294 \ud568\uc218\ub97c \uad6c\ud604\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 Fast Gradient Sign Method (FGSM)\ub97c \uc0ac\uc6a9\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
def generate_adversarial_samples(model, x, y, epsilon=0.01):\n x_tensor = tf.convert_to_tensor(x)\n with tf.GradientTape() as tape:\n tape.watch(x_tensor)\n prediction = model(x_tensor)\n loss = tf.keras.losses.mean_squared_error(y, prediction)\n \n gradient = tape.gradient(loss, x_tensor)\n adversarial_sample = x + epsilon * tf.sign(gradient)\n return adversarial_sample.numpy()\n<\/code><\/pre>\n3.4 \ud6c8\ub828 \ud504\ub85c\uc138\uc2a4<\/h3>\n
\uc774\uc81c \uae30\ubcf8 \ubaa8\ub378\uc744 \ud6c8\ub828\ud558\uace0, \uc801\ub300\uc801 \uc0d8\ud50c\uc744 \uc0dd\uc131\ud558\uc5ec \ubaa8\ub378\uc744 \uc7ac\ud6c8\ub828\ud558\ub294 \ub2e8\uacc4\ub85c \ub118\uc5b4\uac00\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
# \uae30\ubcf8 \ubaa8\ub378 \ud6c8\ub828\nmodel.fit(features, labels, epochs=50, batch_size=32)\n\n# \uc801\ub300\uc801 \uc0d8\ud50c \uc0dd\uc131\nadversarial_samples = generate_adversarial_samples(model, features.values, labels.values)\n\n# \uc801\ub300\uc801 \uc0d8\ud50c \ucd94\uac00 \ud6c8\ub828\nmodel.fit(adversarial_samples, labels, epochs=50, batch_size=32)\n<\/code><\/pre>\n3.5 \uac80\uc99d \ubc0f \ud3c9\uac00<\/h3>\n
\ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \uac80\uc99d\ud558\uae30 \uc704\ud574\uc11c\ub294 \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc77c\ubc18\ud654 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\uace0, \uc801\ub300\uc801 \ud6c8\ub828\uc744 \ud1b5\ud574 \uc5bc\ub9c8\ub098 \uac15\uac74\ud558\uac8c \ubcf4\ud638\ub418\ub294\uc9c0\ub97c \ud655\uc778\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n
test_data = pd.read_csv('test_stock_data.csv')\ntest_features = test_data[['open', 'high', 'low', 'close', 'volume']]\ntest_labels = test_data['target']\n\n# \uc131\ub2a5 \ud3c9\uac00\nevaluation = model.evaluate(test_features, test_labels)\nprint(f'Test Loss: {evaluation}')\n<\/code><\/pre>\n4. \uace0\uae09 \uae30\ubc95\uacfc \ucd94\uac00 \uace0\ub824\uc0ac\ud56d<\/h2>\n
\uc801\ub300\uc801 \ud6c8\ub828 \uc678\uc5d0\ub3c4 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc744 \uc704\ud55c \uace0\uae09 \uae30\ubc95 \ubc0f \uace0\ub824\uc0ac\ud56d\uc774 \uc874\uc7ac\ud569\ub2c8\ub2e4. \uc544\ub798\uc5d0\uc11c\ub294 \uba87 \uac00\uc9c0\ub97c \uc18c\uac1c\ud569\ub2c8\ub2e4.<\/p>\n
4.1 \ub2e4\uc591\ud55c \uc2e0\uacbd\ub9dd \uad6c\uc870<\/h3>\n
\ubcf5\uc7a1\ud55c \ub370\uc774\ud130 \ud328\ud134\uc744 \ud559\uc2b5\ud558\uae30 \uc704\ud574\uc11c\ub294 \ub2e4\uc591\ud55c \ud615\ud0dc\uc758 \uc2e0\uacbd\ub9dd\uc744 \uace0\ub824\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4 LSTM(Long Short-Term Memory)\uc740 \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\ub294 \ub370 \uc720\ub9ac\ud558\uba70, CNN(Convolutional Neural Network)\uc740 \uc774\ubbf8\uc9c0 \ub370\uc774\ud130\uc5d0 \uc801\ud569\ud569\ub2c8\ub2e4.<\/p>\n
4.2 \uc815\uaddc\ud654 \uae30\ubc95<\/h3>\n
\ubaa8\ub378\uc758 \uacfc\uc801\ud569\uc744 \ubc29\uc9c0\ud558\uae30 \uc704\ud574 \uc815\uaddc\ud654 \uae30\ubc95\uc744 \uc0ac\uc6a9\ud574\uc57c \ud569\ub2c8\ub2e4. \ub4dc\ub86d\uc544\uc6c3(Dropout), L2 \uc815\uaddc\ud654 \ub4f1\uc758 \uae30\ubc95\uc744 \ud1b5\ud574 \ubaa8\ub378\uc758 \uc77c\ubc18\ud654\ub97c \uac1c\uc120\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
4.3 \ubc31\ud14c\uc2a4\ud305<\/h3>\n
\ubaa8\ub378\uc774 \uc2e4\uc81c \uac70\ub798\uc5d0 \uc0ac\uc6a9\ub418\uae30 \uc804\uc5d0 \ubc31\ud14c\uc2a4\ud305\uc744 \ud1b5\ud574 \uc804\ub7b5\uc758 \uc720\ud6a8\uc131\uc744 \uac80\uc99d\ud574\uc57c \ud569\ub2c8\ub2e4. \uacfc\uac70 \ub370\uc774\ud130\ub97c \uae30\ubc18\uc73c\ub85c \ubaa8\ub378\uc758 \uc131\uacfc\ub97c \uc2dc\ubbac\ub808\uc774\uc158\ud558\uc5ec \ub9ac\uc2a4\ud06c\ub97c \ud3c9\uac00\ud558\ub294 \uacfc\uc815\uc774 \ud3ec\ud568\ub429\ub2c8\ub2e4.<\/p>\n
5. \uacb0\ub860<\/h2>\n
\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \uae30\uc874\uc758 \ud2b8\ub808\uc774\ub529 \ubc29\uc2dd\uc5d0 \ube44\ud574 \ud6e8\uc52c \ub354 \uc815\uad50\ud558\uace0 \uc2e0\ub8b0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc801\ub300\uc801 \ud6c8\ub828 \uc5ed\uc2dc \uc774\ub7ec\ud55c \uc2dc\uc2a4\ud15c\uc758 \uac15\uac74\uc131\uc744 \ub192\uc774\ub294 \ub370 \uc911\uc694\ud55c \uc5ed\ud560\uc744 \ud558\uba70, \uc2e4\uc81c \uc2dc\uc7a5\uc758 \ubd88\ud655\uc2e4\uc131\uc744 \ub354 \uc798 \ucc98\ub9ac\ud560 \uc218 \uc788\ub3c4\ub85d \ub9cc\ub4ed\ub2c8\ub2e4. \uadf8\ub7ec\ub098 \ubaa8\ub4e0 \ubaa8\ub378\uc740 \ub2e4\uc18c\uc758 \ub9ac\uc2a4\ud06c\ub97c \ub0b4\ud3ec\ud558\ubbc0\ub85c, \ud56d\uc0c1 \uac80\uc99d\uacfc \ud3c9\uac00 \uacfc\uc815\uc744 \uac70\uccd0\uc57c \ud569\ub2c8\ub2e4.<\/p>\n
\uc774 \uac15\uc758\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc758 \uae30\ucd08\ubd80\ud130, \uc801\ub300\uc801 \ud6c8\ub828 \ud504\ub85c\uc138\uc2a4\uc758 \uc124\uc815\uae4c\uc9c0 \ub2e4\uc591\ud55c \uc8fc\uc81c\ub97c \ub2e4\ub8e8\uc5c8\uc2b5\ub2c8\ub2e4. \uacc4\uc18d\ud574\uc11c \ubc1c\uc804\ud558\ub294 \uc774 \ubd84\uc57c\uc5d0\uc11c \ub354 \ub9ce\uc740 \uc5f0\uad6c\uc640 \uc2e4\ud5d8\uc744 \ud1b5\ud574 \ub354\uc6b1 \ud5a5\uc0c1\ub41c \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc744 \uac1c\ubc1c\ud574 \ub098\uac00\uae38 \uae30\ub300\ud569\ub2c8\ub2e4.<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd\uc740 \ud604\ub300\uc758 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc5d0\uc11c \ud575\uc2ec\uc801\uc778 \uc5ed\ud560\uc744 \ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \uc774\ub7ec\ud55c \uae30\uc220\ub4e4\uc744 \ud65c\uc6a9\ud55c \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc758 \uad6c\uc131 \uc694\uc18c\uc640 \uc801\ub300\uc801 \ud6c8\ub828(adversarial training) \ud504\ub85c\uc138\uc2a4\ub97c \uc124\uc815\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uae4a\uc774 \uc788\uac8c \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. \uc801\ub300\uc801 \ud6c8\ub828\uc740 \ubaa8\ub378\uc758 \uac15\uac74\ud568\uc744 \ub192\uc774\uace0, \uc608\uae30\uce58 \ubabb\ud55c \uc0c1\ud669\uc5d0\uc11c\ub3c4 \uc548\uc815\uc801\uc778 \uc131\ub2a5\uc744 \uc81c\uacf5\ud558\ub294 \ub370 \ub3c4\uc6c0\uc774 \ub429\ub2c8\ub2e4. 1. \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uae30\ucd08 \uba38\uc2e0\ub7ec\ub2dd\uc740 \ub370\uc774\ud130\ub97c \ubd84\uc11d\ud558\uc5ec \uc608\uce21 \ubaa8\ub378\uc744 \ub9cc\ub4dc\ub294 \uae30\uc220\ub85c, … \ub354 \ubcf4\uae30 “\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, \uc801\ub300\uc801 \ud6c8\ub828 \ud504\ub85c\uc138\uc2a4 \uc124\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":[29],"tags":[],"class_list":["post-29281","post","type-post","status-publish","format-standard","hentry","category-29"],"yoast_head":"\n\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, \uc801\ub300\uc801 \ud6c8\ub828 \ud504\ub85c\uc138\uc2a4 \uc124\uc815 - 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