\ucd5c\uadfc \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \uae30\ubc95\ub4e4\uc774 \uc804\ub7b5\uc801\uc73c\ub85c \ud65c\uc6a9\ub418\uba74\uc11c \ud000\ud2b8\ub9e4\ub9e4\uac00 \ub300\ub450\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788, \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\ub97c \ub2e4\ub8f0 \uc218 \uc788\ub294 CNN(Convolutional Neural Network) \ubaa8\ub378\uc740 \uc8fc\uc2dd\uc758 \uc218\uc775\ub960 \uc608\uce21\uc5d0 \ub9e4\uc6b0 \ud6a8\uacfc\uc801\uc778 \ubc29\uc2dd\uc73c\ub85c \uc785\uc99d\ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 CNN\uc744 \ud65c\uc6a9\ud55c \ud2b8\ub808\uc774\ub529 \uc804\ub7b5 \uc124\uacc4 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc2ec\ub3c4 \uae4a\uc740 \ub17c\uc758\ub97c \ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
\uae30\uacc4 \ud559\uc2b5 \ubc0f \uc2ec\uce35 \ud559\uc2b5\uc740 \uae08\uc735 \uc2dc\uc7a5 \ubd84\uc11d \ubc0f \uc608\uce21\uc5d0 \uc911\uc694\ud55c \ub3c4\uad6c\ub85c \uc790\ub9ac \uc7a1\uc558\uc2b5\ub2c8\ub2e4. \uc804\ud1b5\uc801\uc778 \uae30\uc220\uc801 \ubd84\uc11d\uacfc \uae30\ubcf8\uc801 \ubd84\uc11d\uc744 \ub118\uc5b4, \ub370\uc774\ud130 \uae30\ubc18 \uc811\uadfc \ubc29\uc2dd\uc774 \uc810\uc810 \ub354 \uac01\uad11\ubc1b\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 CNN\uc740 \uc774\ubbf8\uc9c0 \ucc98\ub9ac \ubd84\uc57c\uc5d0\uc11c \uac15\ub825\ud55c \uc131\ub2a5\uc744 \ubcf4\uc774\ub294 \ub3d9\uc2dc\uc5d0, \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\uc758 \ud2b9\uc9d5\uc744 \ud30c\uc545\ud558\ub294 \ub370\ub3c4 \uc720\uc6a9\ud558\ub2e4\ub294 \uc810\uc5d0\uc11c \uc8fc\ubaa9\ubc1b\uace0 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\ub294 \uc2dc\uac04\uc758 \ud750\ub984\uc5d0 \ub530\ub77c \uc21c\ucc28\uc801\uc73c\ub85c \uad00\uce21\ub41c \ub370\uc774\ud130\ub97c \ub9d0\ud558\uba70, \uc8fc\uac00, \uac70\ub798\ub7c9, \ud658\uc728 \ub4f1 \ub2e4\uc591\ud55c \uae08\uc735 \ub370\uc774\ud130\uac00 \uc774\uc5d0 \ud574\ub2f9\ud569\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ub370\uc774\ud130\ub294 \ud2b9\uc815 \ud328\ud134\uc774\ub098 \ud2b8\ub80c\ub4dc\ub97c \ubcf4\uc774\uae30 \ub54c\ubb38\uc5d0 \uc608\uce21 \ubaa8\ub378\ub9c1\uc5d0 \uc801\ud569\ud569\ub2c8\ub2e4. \ud2b9\ud788, \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c\ub294 \uc791\uc740 \uc608\uce21 \ucc28\uc774\uac00 \ud070 \uc218\uc775\uc73c\ub85c \uc774\uc5b4\uc9c8 \uc218 \uc788\uae30 \ub54c\ubb38\uc5d0 \uc815\ud655\ud55c \ubaa8\ub378\ub9c1\uc774 \uc911\uc694\ud569\ub2c8\ub2e4.<\/p>\n
CNN\uc740 \uc774\ubbf8\uc9c0 \uc778\uc2dd \ubd84\uc57c\uc5d0\uc11c \uc8fc\ub85c \uc0ac\uc6a9\ub418\uc5b4 \uc654\uc73c\ub098, \uc774\ub294 1D \ub370\uc774\ud130\uc5d0\ub3c4 \uc801\uc6a9 \uac00\ub2a5\ud558\uba70 \uac15\ub825\ud55c \ud328\ud134 \uc778\uc2dd \ub2a5\ub825\uc744 \uac00\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4. CNN\uc758 \uc8fc\uc694 \uad6c\uc131 \uc694\uc18c\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/p>\n
CNN\uc740 \ub370\uc774\ud130\uc5d0\uc11c \ub85c\uceec \ud328\ud134\uc744 \uac10\uc9c0\ud558\uace0, \uc774\ub97c \ud1b5\ud574 \uc810\ucc28\uc801\uc73c\ub85c \ub370\uc774\ud130\uc758 \ucd94\uc0c1\uc801 \ud45c\ud604\uc744 \ud559\uc2b5\ud558\uac8c \ub429\ub2c8\ub2e4. \uc8fc\uc2dd \uac00\uaca9 \ub370\uc774\ud130\uc5d0\uc11c\ub294 \ud2b9\uc815 \ud328\ud134\uc774 \ubc18\ubcf5\uc801\uc73c\ub85c \ubc1c\uc0dd\ud560 \uc218 \uc788\uc73c\uba70, \uc774\ub7ec\ud55c \ud328\ud134\uc744 \ud559\uc2b5\ud558\ub3c4\ub85d \uc124\uacc4\ub41c CNN\uc740 \uadf8 \uc608\uce21 \ub2a5\ub825\uc774 \uacb0\uacfc\uc801\uc73c\ub85c \ud5a5\uc0c1\ub429\ub2c8\ub2e4.<\/p>\n
\ub2e4\uc74c \ub2e8\uacc4\ub294 \uae08\uc735 \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\ub85c\uc11c \uc8fc\uac00 \uc608\uce21 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\uae30 \uc704\ud574 CNN\uc744 \ud65c\uc6a9\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\ub294 \uac83\uc785\ub2c8\ub2e4. \uc544\ub798 \ub2e8\uacc4\ubcc4 \uacfc\uc815\uc744 \ud1b5\ud574 CNN \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
\ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0a4\uae30 \uc704\ud574 \ud544\uc694\ud55c \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud574\uc57c \ud569\ub2c8\ub2e4. \uc8fc\uac00 \ub370\uc774\ud130\ub294 Yahoo Finance, Alpha Vantage\uc640 \uac19\uc740 \ub2e4\uc591\ud55c \uc18c\uc2a4\ub97c \ud1b5\ud574 \uc218\uc9d1\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc8fc\uac00 \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud55c \ud6c4, \uc544\ub798\uc640 \uac19\uc740 \uc804\ucc98\ub9ac \ub2e8\uacc4\ub97c \uc218\ud589\ud569\ub2c8\ub2e4.<\/p>\n
\uc774\uc81c Keras\uc640 TensorFlow\ub97c \uc774\uc6a9\ud558\uc5ec CNN \ubaa8\ub378\uc744 \uad6c\ucd95\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc544\ub798\ub294 \uae30\ubcf8\uc801\uc778 CNN \uad6c\uc870\ub97c \uac16\ucd98 \ubaa8\ub378\uc758 \uc608\uc2dc \ucf54\ub4dc\uc785\ub2c8\ub2e4.<\/p>\n
\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom keras.models import Sequential\nfrom keras.layers import Conv1D, MaxPooling1D, Flatten, Dense\nfrom keras.optimizers import Adam\n\n# \ub370\uc774\ud130 \ub85c\ub4dc \ubc0f \uc804\ucc98\ub9ac\ndata = pd.read_csv('stock_data.csv')\n# \ud544\uc694\ud55c \uc804\ucc98\ub9ac \ucf54\ub4dc\ub97c \ucd94\uac00\ud569\ub2c8\ub2e4...\n\n# \ud0c0\uc784 \uc708\ub3c4\uc6b0 \uc0dd\uc131\ndef create_dataset(data, window_size):\n X, y = [], []\n for i in range(len(data) - window_size - 1):\n X.append(data[i:(i + window_size), :])\n y.append(data[i + window_size, 0]) # \uc608\uce21\ud558\ub824\ub294 \uac12\n return np.array(X), np.array(y)\n\nX, y = create_dataset(data.values, window_size=60)\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)\n\n# CNN \ubaa8\ub378 \uad6c\ucd95\nmodel = Sequential()\nmodel.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(X_train.shape[1], X_train.shape[2])))\nmodel.add(MaxPooling1D(pool_size=2))\nmodel.add(Flatten())\nmodel.add(Dense(50, activation='relu'))\nmodel.add(Dense(1))\n\n# \ubaa8\ub378 \ucef4\ud30c\uc77c\nmodel.compile(optimizer=Adam(lr=0.001), loss='mean_squared_error')\n\n# \ubaa8\ub378 \ud559\uc2b5\nmodel.fit(X_train, y_train, epochs=50, batch_size=32, validation_data=(X_test, y_test))\n<\/code><\/pre>\n3.3. \ubaa8\ub378 \ud3c9\uac00 \ubc0f \uc608\uce21<\/h3>\n
\ubaa8\ub378\uc774 \ud559\uc2b5\ub41c \ud6c4\uc5d0\ub294 \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\ub97c \uc774\uc6a9\ud558\uc5ec \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud569\ub2c8\ub2e4. \ud3c9\uac00 \uc9c0\ud45c\ub85c\ub294 RMSE(Root Mean Squared Error)\uc640 MAE(Mean Absolute Error)\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\nfrom sklearn.metrics import mean_squared_error, mean_absolute_error\n\n# \uc608\uce21 \uc218\ud589\npredicted = model.predict(X_test)\n\n# \ud3c9\uac00\nrmse = np.sqrt(mean_squared_error(y_test, predicted))\nmae = mean_absolute_error(y_test, predicted)\nprint(f\"RMSE: {rmse}, MAE: {mae}\")\n<\/code><\/pre>\n4. \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \ud29c\ub2dd<\/h2>\n
\ubaa8\ub378 \uc131\ub2a5\uc744 \ucd5c\uc801\ud654\ud558\uae30 \uc704\ud574 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \ud29c\ub2dd\uc744 \uc218\ud589\ud574\uc57c \ud569\ub2c8\ub2e4. \uc774\ub97c \uc704\ud574 Grid Search \ub610\ub294 Random Search\uc640 \uac19\uc740 \uae30\ubc95\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
4.1. \uc8fc\uc694 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130<\/h3>\n\n- \ubc30\uce58 \ud06c\uae30(batch size)<\/strong>: \ud559\uc2b5 \uc2dc \uc0ac\uc6a9\ub418\ub294 \ub370\uc774\ud130\uc758 \uac1c\uc218\uc785\ub2c8\ub2e4.<\/li>\n
- \uc5d0\ud3ec\ud06c(epoch)<\/strong>: \uc804\uccb4 \ub370\uc774\ud130\uac00 \ubaa8\ub378\uc744 \ud1b5\ud574 \uba87 \ubc88 \ubc18\ubcf5\ub418\ub294\uc9c0\ub97c \ub098\ud0c0\ub0c5\ub2c8\ub2e4.<\/li>\n
- \ud544\ud130\uc758 \uc218 \ubc0f \ud06c\uae30<\/strong>: Conv1D \ub808\uc774\uc5b4\uc5d0\uc11c \uc0ac\uc6a9\ud558\ub294 \ud544\ud130\uc758 \uac1c\uc218\uc640 \ud06c\uae30\ub97c \uc870\uc808\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n
4.2. \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \ucd5c\uc801\ud654 \ucf54\ub4dc \uc608\uc2dc<\/h3>\n
\ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \ucd5c\uc801\ud654\ub97c \uc704\ud574 Keras Tuner\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc544\ub798\ub294 \uc608\uc2dc \ucf54\ub4dc\uc785\ub2c8\ub2e4.<\/p>\n
\nfrom keras_tuner import RandomSearch\n\ndef build_model(hp):\n model = Sequential()\n model.add(Conv1D(filters=hp.Int('filters', min_value=32, max_value=128, step=32), \n kernel_size=hp.Int('kernel_size', 2, 5),\n activation='relu', input_shape=(X_train.shape[1], X_train.shape[2])))\n model.add(MaxPooling1D(pool_size=2))\n model.add(Flatten())\n model.add(Dense(50, activation='relu'))\n model.add(Dense(1))\n model.compile(optimizer=Adam(lr=hp.Float('lr', 1e-4, 1e-2, sampling='log')), loss='mean_squared_error')\n return model\n\ntuner = RandomSearch(build_model, objective='val_loss', max_trials=5)\ntuner.search(X_train, y_train, epochs=50, validation_data=(X_test, y_test))\n<\/code><\/pre>\n5. \ubaa8\ub378 \ubc30\ud3ec \ubc0f \uc2e4\uc804 \uc801\uc6a9<\/h2>\n
\ubaa8\ub378\uc774 \uc131\uacf5\uc801\uc73c\ub85c \ud559\uc2b5\ub41c \uc774\ud6c4\uc5d0\ub294 \uc2e4\uc804 \ud22c\uc785\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \uc2e4\uc2dc\uac04\uc73c\ub85c \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud558\uace0, \uc608\uce21\uc744 \uc218\ud589\ud558\uc5ec \uc790\ub3d9\uc73c\ub85c \ub9e4\ub9e4 \uc8fc\ubb38\uc744 \uc0dd\uc131\ud558\ub294 \uc2dc\uc2a4\ud15c\uc73c\ub85c \ud655\uc7a5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
5.1. \uc2e4\uc2dc\uac04 \ub370\uc774\ud130 \ucc98\ub9ac<\/h3>\n
\ubc1b\uc544\ub4e4\uc778 \ub370\uc774\ud130\ub294 \uc8fc\uae30\uc801\uc73c\ub85c \uc5c5\ub370\uc774\ud2b8\ub418\uba70, \uc774\ub97c \uc804\ucc98\ub9ac\ud558\uc5ec \ubaa8\ub378\uc5d0 \uc785\ub825\ud558\ub294 \uacfc\uc815\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \uc774\ub54c, \uc801\uc808\ud55c API\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc2e4\uc2dc\uac04 \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud558\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4.<\/p>\n
5.2. \ubc30\ud3ec \ubc0f \ubaa8\ub2c8\ud130\ub9c1<\/h3>\n
\ud6c8\ub828\ub41c \ubaa8\ub378\uc740 Flask \ub610\ub294 Django\uc640 \uac19\uc740 \uc6f9 \ud504\ub808\uc784\uc6cc\ud06c\ub97c \ud1b5\ud574 REST API\ub97c \uad6c\ucd95\ud558\uc5ec \ubc30\ud3ec\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub610\ud55c, \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \uc9c0\uc18d\uc801\uc73c\ub85c \ubaa8\ub2c8\ud130\ub9c1\ud558\uc5ec \ud544\uc694 \uc2dc \uc7ac\ud559\uc2b5\uc744 \uc218\ud589\ud558\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4.<\/p>\n
6. \uacb0\ub860<\/h2>\n
\uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 CNN\uc744 \uc0ac\uc6a9\ud55c \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\uc758 \uc8fc\uc2dd \uc218\uc775\ub960 \uc608\uce21\uc5d0 \ub300\ud574 \uc2ec\uce35\uc801\uc73c\ub85c \ub2e4\ub8e8\uc5c8\uc2b5\ub2c8\ub2e4. CNN\uc758 \uc774\ud574\uc5d0\uc11c\ubd80\ud130 \ub370\uc774\ud130 \uc900\ube44, \ubaa8\ub378 \uad6c\ucd95 \ubc0f \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \ud29c\ub2dd\uae4c\uc9c0, \uc804\uccb4\uc801\uc778 \ud504\ub85c\uc138\uc2a4\ub97c \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \uae08\uc735 \uc2dc\uc7a5\uc740 \uc2e4\uc81c\ub85c \ubb34\uc218\ud55c \ubcc0\uc218\ub4e4\uc774 \uc5bd\ud600 \uc788\ub294 \ubcf5\uc7a1\ud55c \ud658\uacbd\uc774\ubbc0\ub85c, \ub2e4\uc591\ud55c \uc2dc\ub3c4\uc640 \uc9c0\uc18d\uc801\uc778 \uac1c\uc120\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\ub3d9 \ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc758 \uad6c\ucd95\uc740 \ub2e4\uc591\ud55c \uae30\ud68c\ub97c \uc81c\uacf5\ud560 \uac83\uc774\uba70, \uc9c0\uc18d\uc801\uc778 \ubc1c\uc804\uc774 \uc694\uad6c\ub429\ub2c8\ub2e4.<\/p>\n
6.1. \ucc38\uace0 \ubb38\ud5cc \ubc0f \uc790\ub8cc<\/h3>\n\n- TensorFlow \uacf5\uc2dd \ubb38\uc11c<\/a><\/li>\n
- Keras \uacf5\uc2dd \ubb38\uc11c<\/a><\/li>\n
- Towards Data Science<\/a><\/li>\n<\/ul>\n
6.2. \uc9c8\uc758\uc751\ub2f5<\/h3>\n
\uc774 \uac15\uc88c\uc5d0 \ub300\ud55c \uc9c8\ubb38\uc774\ub098 \ucd94\uac00\uc801\uc778 \uc815\ubcf4\uac00 \ud544\uc694\ud558\uc2dc\uba74 \ub313\uae00\uc744 \ub0a8\uaca8\uc8fc\uc138\uc694. \ucd5c\ub300\ud55c \ube60\ub974\uac8c \ub2f5\ubcc0\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"