13.10.3.인공 신경망 (CNN, RNN, LSTM).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bb64b6bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tensorflow in c:\\users\\angry\\anaconda3\\lib\\site-packages (2.9.1)\n",
      "Requirement already satisfied: libclang>=13.0.0 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from tensorflow) (14.0.1)\n",
      "Requirement already satisfied: opt-einsum>=2.3.2 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from tensorflow) (3.3.0)\n",
      "Requirement already satisfied: h5py>=2.9.0 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from tensorflow) (3.6.0)\n",
      "Requirement already satisfied: astunparse>=1.6.0 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from tensorflow) (1.6.3)\n",
      "Requirement already satisfied: flatbuffers<2,>=1.12 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from tensorflow) (1.12)\n",
      "Requirement already satisfied: packaging in c:\\users\\angry\\anaconda3\\lib\\site-packages (from tensorflow) (21.3)\n",
      "Requirement already satisfied: setuptools in c:\\users\\angry\\anaconda3\\lib\\site-packages (from tensorflow) (61.2.0)\n",
      "Requirement already satisfied: wrapt>=1.11.0 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from tensorflow) (1.12.1)\n",
      "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from tensorflow) (0.26.0)\n",
      "Requirement already satisfied: absl-py>=1.0.0 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from tensorflow) (1.1.0)\n",
      "Requirement already satisfied: typing-extensions>=3.6.6 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from tensorflow) (4.1.1)\n",
      "Requirement already satisfied: tensorboard<2.10,>=2.9 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from tensorflow) (2.9.1)\n",
      "Requirement already satisfied: termcolor>=1.1.0 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from tensorflow) (1.1.0)\n",
      "Requirement already satisfied: gast<=0.4.0,>=0.2.1 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from tensorflow) (0.4.0)\n",
      "Requirement already satisfied: tensorflow-estimator<2.10.0,>=2.9.0rc0 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from tensorflow) (2.9.0)\n",
      "Requirement already satisfied: keras-preprocessing>=1.1.1 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from tensorflow) (1.1.2)\n",
      "Requirement already satisfied: keras<2.10.0,>=2.9.0rc0 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from tensorflow) (2.9.0)\n",
      "Requirement already satisfied: grpcio<2.0,>=1.24.3 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from tensorflow) (1.42.0)\n",
      "Requirement already satisfied: protobuf<3.20,>=3.9.2 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from tensorflow) (3.19.1)\n",
      "Requirement already satisfied: google-pasta>=0.1.1 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from tensorflow) (0.2.0)\n",
      "Requirement already satisfied: six>=1.12.0 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from tensorflow) (1.16.0)\n",
      "Requirement already satisfied: numpy>=1.20 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from tensorflow) (1.21.5)\n",
      "Requirement already satisfied: wheel<1.0,>=0.23.0 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from astunparse>=1.6.0->tensorflow) (0.37.1)\n",
      "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from tensorboard<2.10,>=2.9->tensorflow) (0.6.1)\n",
      "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from tensorboard<2.10,>=2.9->tensorflow) (0.4.6)\n",
      "Requirement already satisfied: werkzeug>=1.0.1 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from tensorboard<2.10,>=2.9->tensorflow) (2.0.3)\n",
      "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from tensorboard<2.10,>=2.9->tensorflow) (1.8.1)\n",
      "Requirement already satisfied: google-auth<3,>=1.6.3 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from tensorboard<2.10,>=2.9->tensorflow) (1.33.0)\n",
      "Requirement already satisfied: requests<3,>=2.21.0 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from tensorboard<2.10,>=2.9->tensorflow) (2.27.1)\n",
      "Requirement already satisfied: markdown>=2.6.8 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from tensorboard<2.10,>=2.9->tensorflow) (3.3.4)\n",
      "Requirement already satisfied: rsa<5,>=3.1.4 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.10,>=2.9->tensorflow) (4.7.2)\n",
      "Requirement already satisfied: cachetools<5.0,>=2.0.0 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.10,>=2.9->tensorflow) (4.2.2)\n",
      "Requirement already satisfied: pyasn1-modules>=0.2.1 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.10,>=2.9->tensorflow) (0.2.8)\n",
      "Requirement already satisfied: requests-oauthlib>=0.7.0 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.10,>=2.9->tensorflow) (1.3.1)\n",
      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.10,>=2.9->tensorflow) (0.4.8)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.10,>=2.9->tensorflow) (2021.10.8)\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.10,>=2.9->tensorflow) (2.0.4)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.10,>=2.9->tensorflow) (3.3)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.10,>=2.9->tensorflow) (1.26.9)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.10,>=2.9->tensorflow) (3.2.0)\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in c:\\users\\angry\\anaconda3\\lib\\site-packages (from packaging->tensorflow) (3.0.4)\n"
     ]
    }
   ],
   "source": [
    "# 필요한 패키지 설치\n",
    "\n",
    "!pip install tensorflow\n",
    "import keras\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation, Dropout, Flatten, LSTM\n",
    "from keras.layers.convolutional import Convolution2D, MaxPooling2D\n",
    "import sys\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import random\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bfb0cb95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train shape: (10000, 28, 28, 1)\n",
      "x_test shape: (10000, 28, 28, 1)\n",
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 28, 28, 32)        832       \n",
      "                                                                 \n",
      " activation (Activation)     (None, 28, 28, 32)        0         \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 14, 14, 32)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 14, 14, 64)        8256      \n",
      "                                                                 \n",
      " activation_1 (Activation)   (None, 14, 14, 64)        0         \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 7, 7, 64)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 7, 7, 64)          0         \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 3136)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 512)               1606144   \n",
      "                                                                 \n",
      " activation_2 (Activation)   (None, 512)               0         \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 512)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 10)                5130      \n",
      "                                                                 \n",
      " activation_3 (Activation)   (None, 10)                0         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,620,362\n",
      "Trainable params: 1,620,362\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# CNN 모델 생성\n",
    "\n",
    "# 데이터 불러오기\n",
    "(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\n",
    "\n",
    "#데이터를 10,000개로 제한\n",
    "(x_train, y_train), (x_test, y_test) = (x_train[:10000], y_train[:10000]\n",
    "                                       ), (x_test[:10000], y_test[:10000])\n",
    "\n",
    "input_shape = (28, 28, 1)\n",
    "x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)\n",
    "x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)\n",
    "\n",
    "# 데이터 정규화\n",
    "x_train = x_train.astype('float') / 255.\n",
    "x_test = x_test.astype('float') / 255.\n",
    "\n",
    "# 학습셋, 테스트셋 형태 출력\n",
    "print('x_train shape:', x_train.shape)\n",
    "print('x_test shape:', x_test.shape)\n",
    "\n",
    "# 학습셋, 테스트셋 범주 10 설정\n",
    "y_train = keras.utils.to_categorical(y_train, 10)\n",
    "y_test = keras.utils.to_categorical(y_test, 10)\n",
    "\n",
    "# CNN 모델 구축 -은닉층 생성\n",
    "model = Sequential()\n",
    "model.add(Convolution2D(32, kernel_size=(5, 5), strides=(1, 1), padding='same', input_shape=(28, 28, 1)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))\n",
    "model.add(Convolution2D(64, (2, 2), padding='same'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(512))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(10))\n",
    "model.add(Activation('softmax'))\n",
    "\n",
    "# CNN 컴파일\n",
    "model.compile(loss='binary_crossentropy', \n",
    "              optimizer='rmsprop', metrics=['accuracy'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "42ff559d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "264/264 [==============================] - 11s 39ms/step - loss: 0.0866 - accuracy: 0.8579 - val_loss: 0.0323 - val_accuracy: 0.9508\n",
      "Epoch 2/5\n",
      "264/264 [==============================] - 11s 42ms/step - loss: 0.0279 - accuracy: 0.9598 - val_loss: 0.0165 - val_accuracy: 0.9765\n",
      "Epoch 3/5\n",
      "264/264 [==============================] - 11s 42ms/step - loss: 0.0194 - accuracy: 0.9729 - val_loss: 0.0172 - val_accuracy: 0.9741\n",
      "Epoch 4/5\n",
      "264/264 [==============================] - 11s 40ms/step - loss: 0.0153 - accuracy: 0.9782 - val_loss: 0.0145 - val_accuracy: 0.9789\n",
      "Epoch 5/5\n",
      "264/264 [==============================] - 10s 39ms/step - loss: 0.0132 - accuracy: 0.9821 - val_loss: 0.0116 - val_accuracy: 0.9814\n",
      "Test accuracy: 0.9814000129699707\n",
      "313/313 [==============================] - 2s 5ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 3600x2400 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CNN 모델 결과 출력\n",
    "\n",
    "# 모델 적합 (반복횟수 5)\n",
    "model_fit = model.fit(x_train, y_train, batch_size=38, epochs=5, verbose=1\n",
    "                      , validation_data=(x_test, y_test))\n",
    "score = model.evaluate(x_test, y_test, verbose=0)\n",
    "\n",
    "# 모델 정확도 출력\n",
    "print('Test accuracy:', score[1])\n",
    "\n",
    "# 샘플 결과 확인\n",
    "pred_test = model.predict(x_test)\n",
    "pred_test_y = np.argmax(pred_test, axis=1)\n",
    "test_y = np.argmax(y_test, axis=1)\n",
    "\n",
    "count = 0\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "for n in range(16):\n",
    "    count += 1\n",
    "    plt.subplot(4, 4, count)\n",
    "    plt.imshow(x_test[n].reshape(28, 28), cmap='binary', interpolation='nearest')\n",
    "    sample_result = \"Label:\" + str(test_y[n]) + \", Pred:\" + str(pred_test_y[n])\n",
    "    plt.title(sample_result)\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6d0bce6",
   "metadata": {},
   "outputs": [],
   "source": [
    "############ RNN과 LSTM ############"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c2b72b3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/3/2012</td>\n",
       "      <td>325.25</td>\n",
       "      <td>332.83</td>\n",
       "      <td>324.97</td>\n",
       "      <td>663.59</td>\n",
       "      <td>7,380,500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/4/2012</td>\n",
       "      <td>331.27</td>\n",
       "      <td>333.87</td>\n",
       "      <td>329.08</td>\n",
       "      <td>666.45</td>\n",
       "      <td>5,749,400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/5/2012</td>\n",
       "      <td>329.83</td>\n",
       "      <td>330.75</td>\n",
       "      <td>326.89</td>\n",
       "      <td>657.21</td>\n",
       "      <td>6,590,300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/6/2012</td>\n",
       "      <td>328.34</td>\n",
       "      <td>328.77</td>\n",
       "      <td>323.68</td>\n",
       "      <td>648.24</td>\n",
       "      <td>5,405,900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/9/2012</td>\n",
       "      <td>322.04</td>\n",
       "      <td>322.29</td>\n",
       "      <td>309.46</td>\n",
       "      <td>620.76</td>\n",
       "      <td>11,688,800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Date    Open    High     Low   Close      Volume\n",
       "0  1/3/2012  325.25  332.83  324.97  663.59   7,380,500\n",
       "1  1/4/2012  331.27  333.87  329.08  666.45   5,749,400\n",
       "2  1/5/2012  329.83  330.75  326.89  657.21   6,590,300\n",
       "3  1/6/2012  328.34  328.77  323.68  648.24   5,405,900\n",
       "4  1/9/2012  322.04  322.29  309.46  620.76  11,688,800"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 데이터 불러오기\n",
    "# https://www.kaggle.com/datasets/medharawat/google-stock-price\n",
    "df_train = pd.read_csv(\"datasets/Google_Stock_Price_Train.csv\")\n",
    "df_test = pd.read_csv(\"datasets/Google_Stock_Price_Test.csv\")\n",
    "# 데이터 샘플 확인\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "479e5a5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1198, 60, 1)\n"
     ]
    }
   ],
   "source": [
    "# 데이터 정규화 및 numpy 배열 변환\n",
    "\n",
    "df_train_array = df_train.iloc[:, 1:2].values\n",
    "scaler = MinMaxScaler(feature_range = (0,1))\n",
    "df_train_array_scaled = scaler.fit_transform(df_train_array)\n",
    "\n",
    "x_train = []\n",
    "y_train = []\n",
    "for i in range(60, 1258):\n",
    "# 독립변수와 종속변수 생성 - 60일 시간차\n",
    "    x_train.append(df_train_array_scaled[i-60:i, 0])\n",
    "    y_train.append(df_train_array_scaled[i, 0])\n",
    "    \n",
    "# numpy 배열로 변환\n",
    "x_train, y_train = np.array(x_train), np.array(y_train)\n",
    "\n",
    "# 새로운 차원 추가\n",
    "x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))\n",
    "print(x_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "55a6a6e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "43/43 [==============================] - 8s 56ms/step - loss: 0.0376\n",
      "Epoch 2/100\n",
      "43/43 [==============================] - 2s 53ms/step - loss: 0.0060\n",
      "Epoch 3/100\n",
      "43/43 [==============================] - 2s 53ms/step - loss: 0.0058\n",
      "Epoch 4/100\n",
      "43/43 [==============================] - 2s 53ms/step - loss: 0.0046\n",
      "Epoch 5/100\n",
      "43/43 [==============================] - 2s 53ms/step - loss: 0.0049\n",
      "Epoch 6/100\n",
      "43/43 [==============================] - 2s 53ms/step - loss: 0.0048\n",
      "Epoch 7/100\n",
      "43/43 [==============================] - 2s 53ms/step - loss: 0.0052\n",
      "Epoch 8/100\n",
      "43/43 [==============================] - 2s 54ms/step - loss: 0.0051\n",
      "Epoch 9/100\n",
      "43/43 [==============================] - 2s 53ms/step - loss: 0.0049\n",
      "Epoch 10/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0053\n",
      "Epoch 11/100\n",
      "43/43 [==============================] - 2s 53ms/step - loss: 0.0042\n",
      "Epoch 12/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0040\n",
      "Epoch 13/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0040\n",
      "Epoch 14/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0040\n",
      "Epoch 15/100\n",
      "43/43 [==============================] - 2s 53ms/step - loss: 0.0037\n",
      "Epoch 16/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0037\n",
      "Epoch 17/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0035\n",
      "Epoch 18/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0038\n",
      "Epoch 19/100\n",
      "43/43 [==============================] - 2s 53ms/step - loss: 0.0037\n",
      "Epoch 20/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0030\n",
      "Epoch 21/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0039\n",
      "Epoch 22/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0035\n",
      "Epoch 23/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0029\n",
      "Epoch 24/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0030\n",
      "Epoch 25/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0029\n",
      "Epoch 26/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0030\n",
      "Epoch 27/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0035\n",
      "Epoch 28/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0034\n",
      "Epoch 29/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0034\n",
      "Epoch 30/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0031\n",
      "Epoch 31/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0033\n",
      "Epoch 32/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0029\n",
      "Epoch 33/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0027\n",
      "Epoch 34/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0026\n",
      "Epoch 35/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0026\n",
      "Epoch 36/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0026\n",
      "Epoch 37/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0027\n",
      "Epoch 38/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0025\n",
      "Epoch 39/100\n",
      "43/43 [==============================] - 2s 53ms/step - loss: 0.0029\n",
      "Epoch 40/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0026\n",
      "Epoch 41/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0026\n",
      "Epoch 42/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0023\n",
      "Epoch 43/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0023\n",
      "Epoch 44/100\n",
      "43/43 [==============================] - 2s 53ms/step - loss: 0.0023\n",
      "Epoch 45/100\n",
      "43/43 [==============================] - 2s 53ms/step - loss: 0.0022\n",
      "Epoch 46/100\n",
      "43/43 [==============================] - 2s 53ms/step - loss: 0.0026\n",
      "Epoch 47/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0022\n",
      "Epoch 48/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0023\n",
      "Epoch 49/100\n",
      "43/43 [==============================] - 2s 53ms/step - loss: 0.0027\n",
      "Epoch 50/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0027\n",
      "Epoch 51/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0023\n",
      "Epoch 52/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0021\n",
      "Epoch 53/100\n",
      "43/43 [==============================] - 2s 53ms/step - loss: 0.0022\n",
      "Epoch 54/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0021\n",
      "Epoch 55/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0024\n",
      "Epoch 56/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0022\n",
      "Epoch 57/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0019\n",
      "Epoch 58/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0021\n",
      "Epoch 59/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0023\n",
      "Epoch 60/100\n",
      "43/43 [==============================] - 2s 53ms/step - loss: 0.0019\n",
      "Epoch 61/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0020\n",
      "Epoch 62/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0019\n",
      "Epoch 63/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0020\n",
      "Epoch 64/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0020\n",
      "Epoch 65/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0019\n",
      "Epoch 66/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0019\n",
      "Epoch 67/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0019\n",
      "Epoch 68/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0016\n",
      "Epoch 69/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0017\n",
      "Epoch 70/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0017\n",
      "Epoch 71/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0017\n",
      "Epoch 72/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0019\n",
      "Epoch 73/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0018\n",
      "Epoch 74/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0017\n",
      "Epoch 75/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0019\n",
      "Epoch 76/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0016\n",
      "Epoch 77/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0016\n",
      "Epoch 78/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0016\n",
      "Epoch 79/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0018\n",
      "Epoch 80/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0016\n",
      "Epoch 81/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0016\n",
      "Epoch 82/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0018\n",
      "Epoch 83/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0014\n",
      "Epoch 84/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0016\n",
      "Epoch 85/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0017\n",
      "Epoch 86/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0016\n",
      "Epoch 87/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0017\n",
      "Epoch 88/100\n",
      "43/43 [==============================] - 2s 53ms/step - loss: 0.0015\n",
      "Epoch 89/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0014\n",
      "Epoch 90/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0015\n",
      "Epoch 91/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0014\n",
      "Epoch 92/100\n",
      "43/43 [==============================] - 2s 54ms/step - loss: 0.0014\n",
      "Epoch 93/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0014\n",
      "Epoch 94/100\n",
      "43/43 [==============================] - 2s 53ms/step - loss: 0.0014\n",
      "Epoch 95/100\n",
      "43/43 [==============================] - 2s 53ms/step - loss: 0.0012\n",
      "Epoch 96/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0015\n",
      "Epoch 97/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0015\n",
      "Epoch 98/100\n",
      "43/43 [==============================] - 2s 53ms/step - loss: 0.0013\n",
      "Epoch 99/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0015\n",
      "Epoch 100/100\n",
      "43/43 [==============================] - 2s 52ms/step - loss: 0.0014\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x193112f69a0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# RNN 모델에 LSTM 레이어 추가하여 모델 생성 및 학습\n",
    "\n",
    "# 모델 설정\n",
    "RL_model = Sequential()\n",
    "epochs = 100\n",
    "batch_size = 28\n",
    "\n",
    "# 첫 번째 LSTM 레이어 및 일부 Dropout 정규화 추가\n",
    "RL_model.add(LSTM( units = 50, \n",
    "                  return_sequences = True, input_shape = (x_train.shape[1], 1) ))\n",
    "# Ignore 20% of the neurons\n",
    "RL_model.add(Dropout(0.2))\n",
    "\n",
    "# 두 번째 LSTM 레이어 및 일부 Dropout 정규화 추가\n",
    "RL_model.add(LSTM(units = 50, return_sequences = True))\n",
    "RL_model.add(Dropout(0.2))\n",
    "\n",
    "# 세 번째 LSTM 레이어 및 일부 Dropout 정규화 추가\n",
    "RL_model.add(LSTM(units = 50, return_sequences = True))\n",
    "RL_model.add(Dropout(0.2))\n",
    "\n",
    "# 네 번째 LSTM 레이어 및 일부 Dropout 정규화 추가\n",
    "RL_model.add(LSTM(units = 50))\n",
    "RL_model.add(Dropout(0.2))\n",
    "\n",
    "# 출력 레이어 추가\n",
    "RL_model.add(Dense(units = 1))\n",
    "\n",
    "# RNN 컴파일\n",
    "RL_model.compile(optimizer = 'adam', loss = 'mean_squared_error')\n",
    "\n",
    "# 학습셋에 RNN(LSTM) 모델 적용\n",
    "RL_model.fit(x_train, y_train, epochs = epochs, batch_size = batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fcd52910",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 1s 1s/step\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 예측 및 결과 시각화\n",
    "\n",
    "df_test_array = df_test.iloc[:, 1:2].values\n",
    "# 학습셋과 테스트셋 결합\n",
    "dataset_total = pd.concat( (df_train['Open'], df_test['Open']), axis = 0 )\n",
    "\n",
    "# 마지막 60일 기간 분리\n",
    "y_test = dataset_total[len(dataset_total) - len(df_test) - 60:].values\n",
    "\n",
    "# 데이터를 하나의 열로 가공\n",
    "y_test = y_test.reshape(-1, 1)\n",
    "\n",
    "# 테스트셋 정규화 적용\n",
    "y_test = scaler.transform(y_test)\n",
    "\n",
    "# 60일 데이터 구조 생성\n",
    "x_test = []\n",
    "for i in range(60, 80):\n",
    "    x_test.append(y_test[i-60:i, 0])\n",
    "x_test = np.array(x_test)\n",
    "# 새로운 차원 추가\n",
    "x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))\n",
    "\n",
    "# 예측값 산출\n",
    "predicted_stock_price = RL_model.predict(x_test)\n",
    "\n",
    "# 정규화된 값을 기존 값으로 재 변환\n",
    "predicted_stock_price = scaler.inverse_transform(predicted_stock_price)\n",
    "\n",
    "# 결과값 시각화\n",
    "plt.plot(df_test_array, color = 'm', label = 'real price')\n",
    "plt.plot(predicted_stock_price, color = 'c', label = 'predicted price')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Google stock price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}