\uc644\uc804 \uc5f0\uacb0 \uce35(Fully Connected Layer):<\/strong> \ubd84\ub958 \uc791\uc5c5\uc744 \uc218\ud589\ud558\uae30 \uc704\ud574 \ub9c8\uc9c0\ub9c9\uc5d0 \uc704\uce58\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n3.1 CNN\uc758 \uc791\ub3d9 \ubc29\uc2dd<\/h3>\n
CNN\uc740 \uc785\ub825\uce35\uc5d0\uc11c \uc2dc\uc791\ud558\uc5ec \uc5ec\ub7ec \uac1c\uc758 \uc911\uac04 \uce35\uc744 \ud1b5\ud574 \uc815\ubcf4\ub97c \uc810\uc9c4\uc801\uc73c\ub85c \uac00\uacf5\ud55c \ud6c4, \ucd9c\ub825\uce35\uc5d0 \ub3c4\ub2ec\ud558\ub294 \uad6c\uc870\uc785\ub2c8\ub2e4. \uac01 \ucee8\ubcfc\ub8e8\uc158 \uce35\uc5d0\uc11c\ub294 \ub2e4\uc218\uc758 \ud544\ud130\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc785\ub825 \ub370\uc774\ud130\uc5d0\uc11c \ud2b9\uc9d5\uc744 \ucd94\ucd9c\ud569\ub2c8\ub2e4. \uc774\ub294 \uc774\ubbf8\uc9c0\uc758 \uacbd\uc6b0 \uc0c9\uc0c1\uc774\ub098 \ud615\ud0dc\uc758 \ud328\ud134\uc744 \uac10\uc9c0\ud558\ub294 \uac83\uacfc \uc720\uc0ac\ud558\uac8c, \uae08\uc735 \ub370\uc774\ud130\uc5d0\uc11c\ub294 \uac00\uaca9\uc758 \ubcc0\ub3d9 \ud328\ud134\uc774\ub098 \ud2b8\ub80c\ub4dc\ub97c \uc778\uc2dd\ud558\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4.<\/p>\n
4. CNN\uc744 \ud65c\uc6a9\ud55c \uadf8\ub9ac\ub4dc \ub370\uc774\ud130 \ubaa8\ub378\ub9c1<\/h2>\n
\uadf8\ub9ac\ub4dc \ub370\uc774\ud130\ub294 \uc2dc\uac04\uc5d0 \ub530\ub77c \ubc30\uc5f4\ub41c \ub370\uc774\ud130\ub85c, \uc608\ub97c \ub4e4\uc5b4 \uc8fc\uc2dd \uac00\uaca9\uc774 \uc77c\uc815\ud55c \uc2dc\uac04 \uac04\uaca9\uc73c\ub85c \uae30\ub85d\ub41c \uacbd\uc6b0\uac00 \uc774\uc5d0 \ud574\ub2f9\ud569\ub2c8\ub2e4. CNN\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc774\ub7ec\ud55c \ub370\uc774\ud130\ub97c \ubaa8\ub378\ub9c1\ud558\ub294 \uacfc\uc815\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n
4.1 \ub370\uc774\ud130 \uc900\ube44<\/h3>\n
\uccab \ubc88\uc9f8 \ub2e8\uacc4\ub294 \ub370\uc774\ud130\uc14b\uc744 \uc900\ube44\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud558\uc5ec \uadf8\ub9ac\ub4dc \ud615\ud0dc\ub85c \ubcc0\ud658\ud55c \ud6c4, CNN\uc5d0 \uc785\ub825\ud560 \uc218 \uc788\ub3c4\ub85d \uac00\uacf5\ud574\uc57c \ud569\ub2c8\ub2e4. \uc774\ub97c \uc704\ud574 pandas\uc640 numpy\uc640 \uac19\uc740 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\nimport pandas as pd\nimport numpy as np\n\n# \ub370\uc774\ud130 \ubd88\ub7ec\uc624\uae30\ndata = pd.read_csv('stock_data.csv')\n# \ud544\uc694\ud55c \uc5f4 \uc120\ud0dd\ndata = data[['Date', 'Open', 'High', 'Low', 'Close', 'Volume']]\n# \ub370\uc774\ud130 \uc815\ub82c\ndata['Date'] = pd.to_datetime(data['Date'])\ndata.set_index('Date', inplace=True)\n# \uacb0\uce21\uce58 \ucc98\ub9ac\ndata.fillna(method='ffill', inplace=True)\n# \uc815\uaddc\ud654\ndata = (data - data.mean()) \/ data.std()\n<\/code><\/pre>\n4.2 CNN \ubaa8\ub378 \uad6c\ucd95<\/h3>\n
\ub370\uc774\ud130 \uc900\ube44\uac00 \ub05d\ub0ac\ub2e4\uba74, \uc774\uc81c CNN \ubaa8\ub378\uc744 \uad6c\ucd95\ud569\ub2c8\ub2e4. Keras \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud65c\uc6a9\ud558\uc5ec \ub2e4\uc74c\uacfc \uac19\uc740 \uad6c\uc870\uc758 \ubaa8\ub378\uc744 \uc815\uc758\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
\nfrom keras.models import Sequential\nfrom keras.layers import Conv1D, MaxPooling1D, Flatten, Dense\n\nmodel = Sequential()\nmodel.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(timesteps, features)))\nmodel.add(MaxPooling1D(pool_size=2))\nmodel.add(Flatten())\nmodel.add(Dense(units=1, activation='sigmoid'))\nmodel.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n<\/code><\/pre>\n4.3 \ubaa8\ub378 \ud559\uc2b5<\/h3>\n
\ubaa8\ub378\uc774 \uc815\uc758\ub418\uba74, \uc2e4\uc81c \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud559\uc2b5\uc744 \uc9c4\ud589\ud569\ub2c8\ub2e4. \ud559\uc2b5\uc774 \ub05d\ub09c \ud6c4, \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud569\ub2c8\ub2e4:<\/p>\n
\nmodel.fit(X_train, y_train, epochs=50, batch_size=32, validation_data=(X_val, y_val))\nloss, accuracy = model.evaluate(X_test, y_test)\nprint(f\"Test Accuracy: {accuracy:.2f}\")\n<\/code><\/pre>\n5. \uacb0\uacfc \ubd84\uc11d \ubc0f \uc2dc\uac01\ud654<\/h2>\n
\ubaa8\ub378\uc758 \uc131\ub2a5\uc774 \ub9cc\uc871\uc2a4\ub7ec\uc6b0\uba74, \uc2e4\uc81c \uac70\ub798\uc5d0 \uc801\uc6a9\ud558\uae30 \uc804\uc5d0 \uacb0\uacfc\ub97c \ubd84\uc11d\ud558\uace0 \uc2dc\uac01\ud654\ud558\uc5ec \uc778\uc0ac\uc774\ud2b8\ub97c \ub3c4\ucd9c\ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uc608\uce21 \uacb0\uacfc\uc640 \uc2e4\uc81c \uac00\uaca9\uc744 \ube44\uad50\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
\nimport matplotlib.pyplot as plt\n\nplt.figure(figsize=(14, 7))\nplt.plot(y_test, label='Actual Prices', color='blue')\nplt.plot(predictions, label='Predicted Prices', color='red')\nplt.title('Actual vs Predicted Prices')\nplt.legend()\nplt.show()\n<\/code><\/pre>\n6. \uacb0\ub860<\/h2>\n
CNN\uc744 \uc0ac\uc6a9\ud55c \uadf8\ub9ac\ub4dc \ub370\uc774\ud130 \ubaa8\ub378\ub9c1\uc740 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc5d0\uc11c \ub9e4\uc6b0 \uc720\uc6a9\ud55c \uc811\uadfc \ubc29\uc2dd\uc785\ub2c8\ub2e4. \uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 CNN\uc758 \uae30\ubcf8 \uac1c\ub150\ubd80\ud130 \uc2dc\uc791\ud558\uc5ec, \uadf8\ub9ac\ub4dc \ub370\uc774\ud130\ub97c \uc900\ube44\ud558\uace0 CNN \ubaa8\ub378\uc744 \uad6c\ucd95 \ubc0f \ud559\uc2b5\uc2dc\ud0a4\ub294 \uc804 \uacfc\uc815\uc744 \uc18c\uac1c\ud588\uc2b5\ub2c8\ub2e4. \uc774 \uc9c0\uc2dd\uc744 \ubc14\ud0d5\uc73c\ub85c \uc5ec\ub7ec\ubd84\uc758 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc744 \ud55c \ub2e8\uacc4 \ubc1c\uc804\uc2dc\ud0a4\uc2dc\uae38 \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n
\ub2e4\uc74c \uac15\uc88c\uc5d0\uc11c\ub294 \uc131\ub2a5\uc744 \ub354\uc6b1 \ud5a5\uc0c1\uc2dc\ud0ac \uc218 \uc788\ub294 \ub2e4\uc591\ud55c \uae30\ubc95\ub4e4\uc744 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc758 \uc138\uacc4\uc5d0 \uae4a\uc774 \ub4e4\uc5b4\uac00, \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c\uc758 \uc131\uacf5\uc744 \uac70\ub450\uc2dc\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4!<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\ucd5c\uadfc \uba87 \ub144 \ub3d9\uc548 \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998\uc740 \ub180\ub77c\uc6b4 \ubc1c\uc804\uc744 \uc774\ub8e8\uc5b4\uc654\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529 \ubd84\uc57c\uc5d0\uc11c \uc774\ub7ec\ud55c \uae30\uc220\uc740 \uc804\ud1b5\uc801\uc778 \ub370\uc774\ud130 \ubd84\uc11d \ubc29\ubc95\uc744 \ub2a5\uac00\ud558\ub294 \uc131\uacfc\ub97c \uac70\ub450\uba70, \ub2e4\uc591\ud55c \ud22c\uc790 \uc804\ub7b5\uc758 \uae30\ubc18\uc774 \ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 CNN(Convolutional Neural Network)\uc774 \uadf8\ub9ac\ub4dc\uc640 \uac19\uc740 \ub370\uc774\ud130\ub97c \uc5b4\ub5bb\uac8c \ubaa8\ub378\ub9c1\ud558\ub294\uc9c0\uc5d0 \ub300\ud574 \uc2ec\uce35\uc801\uc73c\ub85c \ub2e4\ub8e8\uace0\uc790 \ud569\ub2c8\ub2e4. \uc0c1\uc138\ud55c \uc774\ub860\uc801 \ubc30\uacbd\uacfc \ud568\uaed8 \uc2e4\uc81c \uad6c\ud604 \uc608\uc2dc\ub97c \ud1b5\ud574 \uc774\ud574\ub97c \ub3d5\uaca0\uc2b5\ub2c8\ub2e4. … \ub354 \ubcf4\uae30 “\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, CNN\uc774 \uadf8\ub9ac\ub4dc \uac19\uc740 \ub370\uc774\ud130\ub97c \ubaa8\ub378\ub9c1\ud558\ub294 \ubc29\ubc95”<\/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-28650","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, CNN\uc774 \uadf8\ub9ac\ub4dc \uac19\uc740 \ub370\uc774\ud130\ub97c \ubaa8\ub378\ub9c1\ud558\ub294 \ubc29\ubc95 - 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